Ethnoscience Study of the Application and Delivery Procession of Adat Melayu Jambi as Science Learning Resources

Article history: Received: 21 June 2019 Received in revised form: 21 October 2019 Accepted: 10 December 2019 Available online: 31 December 2019

Introduction
Today, ethnoscience based learning into one research trend in several developing countries, including Indonesia. Research on ethnoscience based learning has been done in some countries with the object of local wisdom different. Implementation ethnoscience can enhance science process skills in high school biology students in Nigeria (Ibe & Nwosu, 2017). The ethnoscience approach significantly influences student achievement and interest in the State of Benue, Nigeria (Okwara & Upu, 2017). Ethnoscience based learning effectively to improve learning outcomes, the entrepreneurial character (Sudarmin et al., 2017) and the cultural awareness of students . Science module-based ethnoscience can improve scientific literacy (Nisa et al., 2015), Chemistry learning module that oriented ethnoscience could improve the students achievement (Lia et al., 2016), critical thinking skills (Fitriani & Setiawan, 2017), cognitive ability (Rosyidah et al., 2013), and effective to develop the spirit of conservation of students (Rahayu & Sudarmin, 2015).
The integration of local wisdom in learning can be done on some subjects, for example, science and mathematics subjects for junior level. Learning that integrates local knowledge gives the impression of more meaningful learning so that students more easily understand the material being studied. Learning local-oriented knowledge can enhance learning motivation, improve creative thinking, increase understanding of science concepts, and student learning outcomes. Novitasari et al., (2017) ethnoscience is an activity of transforming between the original science of society and scientific science. Parmin et al., (2015), the use of local wisdom in learning also ensures that science learning is not only to understand the concept but also strengthen Indonesia with a variety of cultural identities.
The integration of local knowledge in learning should consider the suitability of the material being studied. Therefore, teachers should be able to identify the potential of local knowledge in the area that can be integrated into the learning of science and mathematics.  state that indigenous communities in an area that has the values of science can be used as a source of learning science in junior high school. Phiri (2008) explains the success of teachers in applying local wisdom based learning dipenaruhi by the design of the curriculum, teachers' academic background, pedagogical knowledge, and knowledge of the local culture. Based on interviews with some of the science teachers at SMP N 7 Muaro Jambi revealed that teachers are still experiencing difficulties in integrating local knowledge with learning. This is due to the lack of knowledge about the local wisdom Jambi teachers, who have the potential to be used as a learning resource. Teachers also still have difficulties in analyzing the science and math concepts contained in objects Jambi local wisdom.
Jambi is a province rich in local knowledge. One form of local wisdom, namely Jambi Malay wedding ceremonies. The traditional ceremony of Jambi wedding consists of premarital stage namely introduction (berusik sirih bergurau pinang), application (tegak batuik duduk batanyo) and delivery customs (ulur antar serah terimo adat dan lembago) and stage a wedding that the akad nikah dan ulur antar serah terimo pengantin (Gafar, 2012). Tools and materials on the procession customary application and delivery is a requirement that must be taken and used in traditional ceremonies. Custom application and the delivery procession is a tradition and culture that developed from the knowledge society. Public knowledge about the procession of custom application and delivery can be reconstructed into science related to science (ethnoscience).
The purpose of this study was to determine the meaning of the procession of the application and delivery of Jambi Malay traditional and reconstruction of indigenous society in procession application and delivery into scientific knowledge. Science concepts reconstruction results can be mapped in accordance with the basic competencies contained in the national curriculum for junior high school. The results of this study are expected to bridge the difficulties of teachers in analyzing the science content contained in your application and delivery procession Jambi Malay customs. Teachers are expected to integrate and develop local knowledge-based learning so as to improve the skills, attitudes, and knowledge of the students as well as insight into the local culture.

Methods
This type of research was qualitative research through the study of ethnoscience (Battiste, 2005). This study was conducted to analyze the knowledge of indigenous people in the procession of application and delivery Jambi Malay customs and reconstruction that knowledge into scientific knowledge in the field of science. The reconstruction process focused on activities, tools, and materials that were taken during the procession of application and delivery Jambi Malay customs. These research subjects, including traditional leaders Jambi (3) and education experts. The criterion of this research subject is a person who understands the procession of custom application and conductivity Jambi wither and experts in the field of science. Data were collected through interviews, observation, and documentation. Data were analyzed using the descriptive analysis model of Miles and Huberman (data collection, reduction, display, and verification) (Sugiyono, 2013). The process of data analysis is done from the beginning to the end of the study. Data obtained verification and reconstruction into a science. Interpretation of the data is made through discussions with experts who are competent in ethnoscience. Test the validity of the data used are the credibility test. Credibility test data is done by means of triangulation. Triangulation of data is done through observation, interviews, and documentation on the same object.

Application and a delivery procession of Adat Melayu Jambi
Jambi community to live a public life always adhered to a philosophy of indigenous jointed Adat Bersendi Syara', Syara' Bersedikan Kitabullah. Deeds or work should always be given the customary and religious rules, should not contradict with each other. One of the traditions of the people who always practice the philosophy that Jambi Malay wedding ceremonies.
Series of activities carried out before the wedding the procession of custom application and delivery. Based on interviews with some of the traditional leaders (Datuk) at the Institute of Traditional Malay, Jambi City obtained information about the procession of custom application and delivery. Process custom application starts from the introduction (berusik sirih bergurau pinang), application (tegak batuik duduk bertanyo), and ulur antar serah terimo adat dan lembago. Community knowledge about the procession of application and delivery of customs is knowledge gained from experience sourced from traditions that have been passed down for generations.

Introductions (Berusik Sirih Bergurau Pinang)
The introductory stage of the prospective bridegroom and the bride called berusik sirih bergurau pinang. The introductory stage is done by the prospective bridegroom that happened to visit the female candidates. By the time came for a visit, the prospective bridegroom should not come alone but must be with a companion as well as the bride must be accompanied by mamak or mother. Must know him through prantara should not be home by home or face to face. When it comes to house women, men need to take food such as bread, tea, and so forth.
The goal is to help ease the woman. After the introduction will be given a sign called bemudon namely gloves or long cloth.

Application (Tegak Batuik Duduk Betanyo)
Tegak batuik duduk betanyo means asking if the girl there who are applying or already have a bond with other men. The man was sent a messenger or Menti to ask the woman, and handed bungo nan berangkai, buah nan betumpuk, in the form of betel leaves to the parents of the girl. If it turns out the girl is already spoken for others or became engaged to someone else, then it should not be continued applying. When the girl no one has applied for or laying tanyo, then she continued on the process of applying bias. Male party elders sent a messenger to carry sirih tanyo pinang tanyo as a binding command. Tando in the form of betel nut, rattan bladed ring, sepululusan clothing, and cash is fitting. The decision about whether the application is accepted or rejected must discuss advance by the girl's parents with relatives, tengganai-uncle, and grandmother in the family. The next stage-uncle grandmother both sides held talks to discuss the customary level to be filled and to be poured lumbago, lumbago pours the filling customary, the ceremony, determine the dowry, and the back and forth between the handover of the bride or the anchoring lek.

Ulur antar serah terimo adat dan lembago
Ulur antar serah terimo adat dan lumbago means a series of activities to fill and pour lembago customs. The prospective groom carrying tools and materials into the terms and agreement at the time of application. Adat dan lumbago should be brought to Jambi Malay traditional wedding procession has three levels, including the following: At this level it means a great party but not so much custom adat lembago to be completed are as follows: Adat: Gold, gold scales, cash, clothing sepululusan Lembago: A goat, coconut rope 10, selepak sweet as acidic as segaram (chili, salt, sugar, spices), rice 10 bushels.

c. Level III (Lek Nenek Mamak)
At this level quite family alone during the wedding procession ceremony to be filled is lembago only. Lembago must bring two male chicken, two bushels of rice, coconut two ropes, selemak sweet as acidic as segaram. When the man could not also meet the customs and institutions at the third level even though it can be argued that the men have not been able to get married.

Ethnoscience analysis of application and delivery procession of Adat Melayu Jambi
Public knowledge about the procession of custom application and delivery can be reconstructed into science related to science (ethnoscience). The reconstruction process focused on activities, tools, and materials that were taken during the procession of application and delivery Jambi Malay customs. Based on interviews with some traditional leaders (Datuk) at the Institute of Traditional Malay, Jambi City revealed the community's original knowledge about the activities, tools, and materials contained in procession customary application and delivery. Results reconstruction genuine knowledge society of the procession customary application and delivery of scientific knowledge can be seen in Table. 1. Table 1. Results of the reconstruction of indigenous knowledge into a scientific knowledge society

Tepak sirih
Tepak sirih, according to the Jambi tradition called kapala basoh (the beginning of negotiations). Tepak sirih containing betel, areca nut, lime, gambier, and tobacco. Tepak sirih must be taken when talking, discussing, and talk customs from both parties are coming and waiteating betel done first before starting negotiations.

Picture 1. Tepak sirih
Betel (Piper betle L), including the family Piperaceae (betelsirihan). Betel leaves contain saponins, tannins, eugenol, and various types of essential oils. The contents in the betel leaf make the benefits to human health. Betel leaves are known to be antiseptic, anti-inflammatory, cooling the skin, anti-bacterial, anticavity and anti-diabetic.
Areca nut (Areca catechu L.) is a plant of monocots and including Palmaceae family. The content contained in betel nuts are alkaloids, such as arecoline, guvakolin, guvasine, phenolic compounds, gallic acid, arekolidine, arekain, gum, lignin, the oil evaporates and does not evaporate, and salt. Betel nut has many benefits such as to increase appetite, treat indigestion, cope with worms, protect teeth, overcome bad breath, and others.
Lime is sediment resulting from the process of soaking the rock has a bitter taste and distinctive bitterness. Lime containing compounds Calcium HydroxideCa(OH) 2 . Lime has several benefits, namely as an antimicrobial agent to kill bacteria on the teeth and protects teeth from infection.
Gambir (Uncaria gambier Roxb.) It is a kind of dried sap derived from extracts squeeze the leaves and twigs of plants of the same name. Gambir contains flavonoids, catechins, tanning substances, alkaloids, d-catechin, and astrigen katekunat acid. Gambir has many benefits, including reducing the secretion of a drug mixture burns, migraine headaches, diarrhea, dysentery medicine, mouthwash, mouth ulcer drug, skin pain medication, anti-allergic, anti-oxidants, hepatitis, and injury to the liver.
Tobacco is one of the raw materials for the manufacture of cigarettes. Tobacco contains some compounds, which are carbon monoxide, nicotine, tar, hydrogen cyanide, benzene, formaldehyde, arsenic, cadmium, ammonia. Tobacco has several benefits, among which prevents cartilage, joint damage, stimulant, appetite suppressant.
Rattan (Calamus sp.) is typical tropical plant that grow in wet tropical forests heterogeneous. Rattan is a plant that grows climbing

Antar Tando
Antar tando interpreted as laying the mark application. At the time of application, the men are carrying signs such as the rattan blade ring. In ancient times the cane is a plant that many live in Jambi Province and the meaning of rattan blades circular rings symbolize the two sides are still innocent and holy. Preferred types are rattan cane sego (the smallest).

Adat and Lembago (Level 1)
The bride and groom in the delivery process must fill the adat and lembago. Adat was brought in the form of gold, gold scales, selarih rifles and spears. Gold brought at 3.5 tail (22-28 parts) however, this amount is adjusted again with the outcome of negotiations between the families based on economic conditions. Gold scales are an allegory. Where this scale to measure the weight of one of the bride and groom with crops. Rifles and spears menage meaningful when both parties have been provided techniques and how to make money by hunting, trade, and farming.  Unit conversion means changing the value of a system of units to another unit values. Unit conversions do not change the value of a quantity. In Jambi gold known as tribal units and tahil. But the national unit used is gram. 1 Unit conversion rate and equivalent to 6.7 grams and 1 tahil equivalent to 37.7994 grams. Scales are tools used measuring the mass of an object. Gold scales included in the category of digital scales. Gold scales are the type of scales that have high akuransi to measure the mass of gold (precious metals). According KBBI rifle is a firearm. Meanwhile, according to KBBI spear issharp weapon and pointed, double-edged, long-stemmed, to stab home from close range or far.
Buffalo is the mammalian animal farm animals. Taxonomy from buffalo as follows: Kingdom: Animalia Phylum: Chordata Class: Mammalia Order: Artiodactyla Family: Bovidae Subfamily: Bovinae Genus: Bubalus Species: B. bubalis The buffalo has very many uses for humans is extremely tough worker, has the job of being the forklift, towing carts as well as plow the fields that are still widely used in Indonesia. Meat and milk consumption for most people, its horns be decorated home to various customary in Indonesia and other countries, and their droppings which can be dried and used as fertilizer for plants.
Coconut including plant parts Aricaceae a nut-proposal. Coconut have a seed pieces, fibrous roots and classified family

Objects of local wisdom
Science concepts rajo used when inviting 500 guests or more. So that buffalo are used. Coconut is a fruit that used to be used as an additional ingredient when cooking foods such as rendang. According to the former, as much as 100 coconut rope (200) serves to make the food taste like rendang is much more enjoyable. In addition, also to balance the amount of rice, about 100 bushels. Rice is a staple food used to entertain the public and invited guests when executing an event.
In conducting this process, the men carrying rice 100 bushels. Gantang is a national measure when measuring rice. Coconut fruit contains 30% vegetable oil. Coconut fruit has many health manffat for them to meet the body fluid intake, high blood pressure, treat and prevent gingivitis, burn belly fat, and prevent kidney stones.
In Jambi in bringing better known as unit coconut rope. But the national unit used is the fruit. Unit conversion means changing the value of a system of units to another unit values. Unit conversions do not change the value of a quantity. Conversion unit 1 is equivalent to 2 pieces of rope.
Rice is part of rice grains (grain) which has been separated from the chaff. Husk is anatomy called the palea and lemma. In bioloi, Baian adala rice grains consisting of aleurone. Endosperm and embryo. While rice is a plant belonging Oryza genius l. which includes about 25 species and is spread in the tropical and subtropics such as in Asia, Africa, America, and Australia. Taxonomy of the rice plant itself is as follows: Kingdom: According Indonesia Dictionary bushels is defined as a unit of measure or the contents of 3,125 kg dose, usually to measure or rice swine, beans, and so on. 1 bushel = 3.125 kg Kilogram (kg) is the unit of International Standards for the masses.
Red onion (Allium cepa L) is an annual plant that forms clumps and grow upright with a height reaches 15-40cm.
Kingdom: Plantae Subkingdom: Tracheobionta Superdivisio: Spermatophyta Division: Magnoliophyta Class: Liliopsida Sub-Class: Liliidae Order: liliales Familia: Allium Genus: Allium Species: Allium cepa L. var. Aggregatum Garlic can prevent diseases related to the heart, such as high blood pressure, atherosclerosis and cholesterol. Salt or sodium chloride (NaCl) is an ionic compound consisting of positive ions and negative ions, thereby forming a neutral compound (without charged). Salts formed from the reaction of acids and bases.

Objects of local wisdom Science concepts
Peppers (Capsicum annum L)., Is a plant of the order solanales and and family solanaceae. The chili is a fruit that has a spicy flavor and is so popular among the people of Southeast Asia.
Classification  Applying the concept of measurement of various magnitudes using standard and non-standard units relating to equipment and materials brought in procession application and conductivity Jambi Malay customs.

Malay custom conduction Jambi
At the time of conduction usually carry betel slap containing betel, areca nut, lime, gambier, and tobacco. Eating betel done first before starting negotiations.
The custom was brought in the form of gold, gold scales, selarih rifles and spears.
3.2 Classifying living things and objects based on the observed characteristics.
Classification of plants animals, and objects in the environment that is used in the application and conductivity procession Jambi Malay customs.
3.3 Explain the concept of mix and single substances (elements and compounds), physical and chemical properties, physical and chemical changes in everyday life.
Explaining the concept of the mix, the physical and chemical properties on the activity of eating betel nut when the procession of application and conductivity Jambi Malay customs.
Explaining the characteristics of the substance (gold) used to charge customs and lembago.
Public knowledge about the procession of custom application and conductivity can be reconstructed into a science that deals with science. Science concepts were identified that magnitude, units and measurement, classification of living things, elements, compounds and mixtures. The results of reconstruction of public knowledge about the procession of custom application and conductivity can be integrated Jambi wither science lessons. The results are consistent with research conducted  that the traditional fishing gear (tangkul fish) can be utilized as a source of learning science, namely by making tangkul fish as a context to explore the science content. Students can learn about the concept of a simple plane through direct experience; the best applications are simple in tangkul fish. Basuki et al., (2019) explain that local knowledge Senamat Desa Ulu as independent village electricity can be integrated into the learning of science in materials environmental conservation, environmentally friendly technologies, changes in energy, and electric energy sources. Sumarni et al., (2017) explain proses palm sugar production, which is a legacy of knowledge ancestors, there are many science communities that can be reconstructed into a science that can be a source of science learning for students. Found there is 16 original science that can be explained by scientific knowledge, which is divided into 48 concepts of science.  conducted an analysis of nginang cultural science in the context of an integrated STEM with ethnoscience. Culture nginang able to strengthen teeth because the betel contains phenyl propanoid volatile oil and tannin. Mixing betel leaf with some chemicals containing chemical concepts mixture, acid-base reactions, and changes in substance. Nuroso and Sudarmin et al., (2018) identifying the original science in the process of making bricks through studies Penggaron ethnoscience village, Semarang, and in the village Welahan, Jepara. The results showed that the original science in the process of making the bricks that cover the materials composing, printing, drying, burning, and brick quality testing. The process of making bricks can be integrated into the Environmental Physics.

Conclusions
The custom procession and delivery procession is part of a series of traditional Malay marriage process. The procession starts from the introduction (berusik sirih bergurau pinang), applying (tegak batuik duduk betanyo) , and ulur antar serah terimo adat dan lembago. In this customary procession, the bridegroom usually carries a siri / kepalo baso (containing betel, betel, gambier, areca nut, tobacco), rattan bladed rings, graduation clothes and surrenders the adat (gold, gold scales, selilh, and spears) and lembago (kerbau, kelapa 100 rope, selemak sweet as acidic as segaram, and rice 100 bushels ). Procession application and carrying this custom is a tradition and culture that developed from the knowledge society.
Tools and materials are brought in procession application, and conductivity of the custom has its own meaning and art with symbols of life. Public knowledge about the procession of custom applications and delivery can be reconstructed into scientific knowledge related to science. The identified scientific concepts are quantity, unit and measurement, classification of living things, elements, compounds, and mixtures. The results of the reconstruction of public knowledge about the procession of Malay custom application and conductivity can Jambi integrated by science learning. The next researcher is expected to be able to conduct research on the development of teaching materials and learning media based on the local wisdom of the application procession and the traditional delivery of Malay Jambi.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy:
Computer Science
Biology

text:
Classification of adaptor proteins using recurrent neural networks and PSSM profiles

Background Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-signaling molecules. Many studies determined that the adaptor proteins had been implicated in a variety of human diseases. Therefore, creating a precise model to predict the function of adaptor proteins is one of the vital tasks in bioinformatics and computational biology. Few computational biology studies have been conducted to predict the protein functions, and in most of those studies, position specific scoring matrix (PSSM) profiles had been used as the features to be fed into the neural networks. However, the neural networks could not reach the optimal result because the sequential information in PSSMs has been lost. This study proposes an innovative approach by incorporating recurrent neural networks (RNNs) and PSSM profiles to resolve this problem. Results Compared to other state-of-the-art methods which had been applied successfully in other problems, our method achieves enhancement in all of the common measurement metrics. The area under the receiver operating characteristic curve (AUC) metric in prediction of adaptor proteins in the cross-validation and independent datasets are 0.893 and 0.853, respectively. Conclusions This study opens a research path that can promote the use of RNNs and PSSM profiles in bioinformatics and computational biology. Our approach is reproducible by scientists that aim to improve the performance results of different protein function prediction problems. Our source code and datasets are available at https://github.com/ngphubinh/adaptors.

Background
Protein function prediction is a technique that assigns biological or biochemical roles to proteins with regards to their genome sequences. The essential of understanding the protein function has drawn researchers' attentions on enhancing the predictive performance of protein functions. Numerous solutions have been proposed in the past decades for this purpose. Two most effective solutions are finding strong feature sets and adopting powerful neural *Correspondence: susantorahardja@ieee.org; binh.p.nguyen@vuw.ac.nz † Nguyen Quoc Khanh Le and Quang H. Nguyen contributed equally to this work. 4 School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an 710072, China Full list of author information is available at the end of the article network models. Previous studies have revealed that using strong feature sets alone, for example, position specific scoring matrix (PSSM) [1], biochemical properties (AAindex) [2], and PseAAC [3], can achieve satisfactory prediction results. With the popularity of deep learning, many researchers in the field of bioinformatics attempted to apply the technique to protein function prediction. Some of the recent works like [4,5] have demonstrated some successes. Motivated by these two observations, we intend to take the advantages of strong feature sets and deep neural network to further improve the performance by deriving a novel approach for protein function prediction.
In this work, we put special focus to the prediction of adaptor protein, which is one of the most vital molecule function in signal transduction.
Signal transduction, so-called cell signaling, is the transmission from a cell's outside to inside of molecular signals. Received signals must be transported viably into cells to guarantee a proper reaction. This progression is started by cell-surface receptors. One of the primary objectives of researchers who conduct their experiments on signal transduction is to decide the mechanisms that regulate cross-talk between signaling cascades and to decide the accomplishment of signaling. A rising class of proteins that much contributes to the signal transduction process are adaptor (or adapter) proteins. In adaptor proteins, there are numerious protein-binding modules linking protein-binding partners together. In addition, they are able to facilitate the signaling complexes creation [6]. They are vital in intermolecular interactions and play a role in the control of signal transduction started by commitment of surface receptors on all cell types.
In detail, adaptor proteins have been shown to be associated with a lot of human diseases. For instance, Gab adaptor proteins play an important role as therapeutic targets for hematologic disease [7]. XB130, a specific adaptor protein, plays an important role in cancer [8]. Likewise, Src-like adaptor proteins (SLAP-1 and SLAP-2) are important in the pathogenesis of osteoporosis, type I hypersensitivity, and numerous malignant diseases [9]. In [10], adaptor protein is also noted to be a therapeutic target in chronic kidney disease. Moreover, a review paper from [11] showed the association of adapter proteins with the regulation of heart diseases. Further, the involvement of adaptor protein complex 4 in hypersensitive cell death induced by avirulent bacteria has been shown in [12].
Given the significance of adaptor proteins to the functions and structures of signal transduction, elucidating the molecular mechanisms of adaptor proteins is therefore a very important research area which has recently gained rapid advancement. However, it is costly and timeconsuming with these experimental techniques. Therefore, it is highly desired to develop automated prediction methods for quick and accurate identification of adaptor proteins.
PSSM is one of the most strong feature sets in biology to decode the evolutionary information of a protein sequence. Many computational studies have investigated the protein function prediction using PSSM profiles such as protein fold recognition [13], phosphoglycerylation prediction [14], succinylation prediction [15], and protein subcellular localization prediction [16]. However, among the existing approaches, none of them has found a solution to prevent the loss of amino acid sequence information in PSSM profiles. Here, to address this problem, we present an innovative approach via the use of a Recurrent Neural Network (RNN) architecture.
Standard neural network typically assumes independent relationship between input signals, but this is usually not the case in real world. Likewise, utilizing the corelationship between genome sequences can help in protein function prediction.
We thus present a novel deep learning framework which utilizes RNNs and PSSM profiles to classify adaptor proteins. RNNs have been recently demonstrated to extract sequential information from sequences to predict various properties of protein sequences in several studies [17][18][19]. However, how to apply it on PSSM profiles to address the ordering information of them is still an open research question. The main contributions of this paper include (1) introducing a first sequence-based model for distinguishing adaptor proteins from general proteins, (2) proposing an efficient deep learning architecture constructed from RNNs and PSSM profiles for protein function prediction, (3) presenting a benchmark dataset and newly discovered data for adaptor proteins, and (4) providing valuable information to biologists and researchers for better understanding the adaptor protein structures.

Experiment setup
Given an unknown sequence, the objective is to determine if the sequence is an adaptor protein and thus this can be treated as a supervised learning classification. As a representation, we defined adaptor protein as positive data with label "Positive", and otherwise, non-adaptor protein as negative data with label "Negative". We applied 5-fold cross-validation method in our training dataset with hyper-parameter optimization techniques. Finally, the independent dataset was used to evaluate the correctness as well as overfitting in our model. Our proposed RNN model was implemented using PyTorch library with a Titan Xp GPU. We trained the RNN model from scratch using Adam optimizer for 30 epochs. The learning rate was fixed to 1 × 10 −4 in the entire training process. Due to the significant imbalance in the sample numbers of adaptor proteins and non-adaptor proteins in the dataset, we adopted weighted binary cross-entropy loss in the training process. The weighting factors were the inverse class frequency.
Sensitivity, specificity, accuracy, and MCC (Matthew's correlation coefficient) were used to measure the prediction performance. TP, FP, TN, FN are true positives, false positives, true negatives, and false negatives, respectively.
In addition, we also utilized Receiver Operating Characteristic (ROC) curves to examine the predictive performance of our model. In the ROC curve, the Area Under the Curve (AUC) metric is a floating point value ranging from 0 to 1 in which higher value represents better model. ROC curve and AUC are reliable metrics to compare the performance results among different models.
We first investigated the composition of amino acid in adaptor proteins and non-adaptor proteins to understand how we could better utilize the dataset for protein function prediction. We also studied how different hyper-parameters affected the performance of the RNN model. Besides, comparison between the proposed model and existing methods was based on the provided PSSM profiles.

Comparison between adaptor proteins and non-adaptor proteins
We computed the amino acid frequency of adaptor and non-adaptor proteins in the whole dataset to analyze the differences between the two types. It can be seen from Fig. 1 that there are differences in amino acid composition surrounding adaptor and non-adaptor proteins. For example, the amino acid E, F, G, or V had higher variations to separate between two classes. The significant differences show that our model can distinguish adaptor proteins from general proteins according to some amino acid distributions.

Study on selection of hyper-parameters
In this section, the selection of hyper-parameters is studied. Specifically, we have examined our model with different hyper-parameters, i.e., number of convolution filters, fully connected layer size, kernel size, and so on. We performed 5-fold cross-validation and varied the number of filters of the fully connected layer from 32 to 1,024 to find the optimal number. Our model has been selected based on the optimal performance results on validation dataset at a specific random seed value (i.e., random_seed = 7 in this study).
In our experiments, among different tested sizes, the fully connected layer size of 512 reached the maximum performance when discriminating the adaptor proteins in different validation settings. When testing our model in the independent dataset, the performance results were also consistent with the 5-fold cross-validation. It means that our model did not suffer from the over-fitting problem and can be applied in most of unseen data. A reason to explain this point is that we applied dropout, which is the regulation technique to prevent over-fitting in deep learning models.
The next important hyper-parameter that needs to be examined is the gated recurrent unit (GRU) hidden layer size. After several steps, we observed that the GRU with 256 hidden layer sizes was superior. Finally, these optimal parameters were used on our best model.

Comparison between the current method and state-of-the-art techniques using pSSM profiles
After tuning up the hyper-parameters, we identified 512 filters and GRU size of 256 as the best performing architecture. We then used our optimized model to compare with the previous state-of-the-art methods. To use PSSM profiles, most recent techniques summed up all the same amino acids to produce a 400-dimensional vector and then fed to neural networks. A number of bioinformatics researchers have used this technique in their applications and obtained promising results [2,5]. We also conducted experiments according to widely used machine learning algorithms including k-NN [20], the Random Forests (RF) [21] and the Support Vector Machines (SVM) [22]. Besides, we also compared our proposed method with a two-dimensional convolutional neural network (2-D CNN), which is a method treating PSSM profiles as images and successfully applied in sequence analysis [5].
Overall, the comparison between our proposed method and the other methods is shown in Table 1. Note that we used grid search cross-validation to find the optimal parameters of all algorithms. This ensures that our comparison is fair and reliable among these methods. The optimal results were: k = 10 nearest neighbors in k-NN, 500 trees in RF, c = 8 and g = 0.5 in SVM, and 128 filters with each filter size of 3×3 in 2-D CNN. We easily observe that our RNN also exhibited the higher performance than the other techniques at the same level comparison. This was also supported by our preliminary work when testing with other classification algorithms including kernel dictionary learning [23][24][25] and an enhanced k-NN method [26]. It can be concluded that the sequential information of PSSM plays a vital role in predicting the adaptor protein as well as the other protein functions in general. Using 1D-CNN in our method helps to prevent the loss of sequential information compared to other embedding methods (e.g., 2D-CNN).
Specifically, our sensitivity was significantly higher than that of the other methods. This is a very important point because our model aims to predict as much as adaptor proteins as possible. Via this high sensitivity, a large number of adaptor proteins could be discovered with a high accuracy. It provides a lot of information for biologists as well as researchers to understand and conduct their works on adaptor proteins.
The results in Table 1 are based on the default decision threshold value of each algorithm and this is not sufficiently significant. Hence, we show the ROC Curve and AUC to evaluate the performance results at different threshold levels. They are the most important evaluation metrics for checking the performance of most supervised learning classification. The ROC curve is plotted from True Positive Rate and False Positive Rate. As the value of AUC approaches to unity, the corresponding model is regarded to have shown optimal performance. As shown in Fig. 2, our model could predict the adaptor proteins with AUC of 0.893 and this is a significant level to show that our model performed well in this kind of dataset. It also determines that our results did not only perform well in a specific point but also at different levels. We can use this model to predict adaptor proteins with high performance and superior to the previous techniques (Table 1).

Conclusions
In this study, we proposed an innovative method using RNN and PSSM profiles for distinguishing the adaptor proteins using sequence information only. It is also the first computational model that applies this combination to adaptor protein prediction. Via this method, we can conserve all the PSSM information in training process and to prevent the missing information as much as possible. The performance using 5-fold cross validation and independent testing dataset (including 245 adaptor proteins and 2,202 non-adaptor proteins) is evaluated. The proposed method could predict adaptor proteins with a 5-fold cross validation accuracy and MCC of 80.4% and 44.5%, respectively. To evaluate the correctness of our model, we applied an independent dataset testing and its accuracy and MCC achieved 75.7% and 37.3%, respectively. Our performance results are superior to the state-of-the-art methods in term of accuracy, MCC, as well as the other metrics.
This study discussed a powerful model for discovering new proteins that belong to adaptor proteins or not. This study opens a research path that can promote the use of RNN and PSSM profiles in bioinformatics and computational biology. Our approach is able to be reproduced by scientists that aim to improve the performance results of different protein function prediction problems. Finally, physicochemical properties had been successfully used in a number of bioinformatics applications with high performance [27][28][29]. Therefore, it is possible to combine PSSM profiles and physicochemical proteins into a set of hybrid features. Subsequently, these hybrid features could be fed directly into our proposed architecture. We hope that the future studies will consider these hybrid features to help improving the performance results of protein function prediction. Figure 3 illustrates the flowchart of the study. A detailed description on the construction of the benchmark dataset is provided as follows.

Benchmark dataset
Because our study is the first computational study to classify adaptor proteins, therefore, we manually created a dataset from well-known protein data sources. We collected data from UniProt [30] and Gene Ontology (GO) [31], which provide high quality resources for research on gene products. We collected all the proteins from UniProt with GO molecular function annotations related to adaptor proteins. An important selection criteria is that we had to select the reviewed sequences, which means they had been published in scientific papers. Thus, the full query for collecting data was: "keyword:"adaptor" OR goa:("adaptor")) AND reviewed:yes" After this step, we received 4,049 adaptor proteins in all species.
We solved the proposed problem as a binary classification problem, thus we collected a set of general proteins as negative samples. Actually, our classifier aimed to classify between adaptor proteins and non-adaptor proteins. So we needed a real set of adaptors and non-adaptors to train the model. However, in practice, if we collect all non-adaptor proteins as negative data, the number of negative dataset will reach hundred thousands of data. This will result in serious data imbalance and affect the model's performance. Therefore, in most of the related problems in bioinformatics, scientists can only select a subset of negative data and treat them as general proteins. In this study, we chose membrane protein, which is a general protein including a big enough number of sequences and functions. Briefly, we extracted all of the membrane proteins in UniProt and excluded the adaptor proteins. Similar to the previous step, only reviewed proteins were retained.
Subsequently, BLAST [32] was applied to all the collected data to remove redundant sequences with sequence identity level of more than 30%. This was an important step to prevent over-fitting in training model. The remaining sequences were regarded as valid for the benchmark dataset and were naturally divided into 1,224 adaptor proteins and 11,078 non-adaptor proteins. For fair comparison, we held up one-fifths of both the adaptor proteins and the non-adaptor proteins as the test set to evaluate model performance. The rest of the valid sequences were used as a cross-validation (Train-Val) set for model training. Table 2 lists the statistics of the benchmark dataset.

RNN model
In this study, we propose an RNN model for distinguishing adaptor proteins from non-adaptor proteins. An overview of the proposed RNN model is shown in Fig. 4. The RNN where the spatial context within the entire PSSM profile is explored and utilized for final prediction. The input sequence has a length of N. After going through two layers of 1-D CNN and 1-D Max-Pool, the length became N/9. Subsequently, this N/9 vector was fed into GRU, taking the output of GRU (256 features) to the input of the last vector for which the characteristic of the sequence was formed. Finally, our model took this output through a Fully Connected (FC) layer (512 nodes), and passed to a Sigmoid layer to produce a prediction probability value.

Preventing information missing by preserving ordering of pSSM profiles
A PSSM profile for a query protein is an N × 20 matrix (N is the length of the query sequence), in which a score P ij is assigned for the j th amino acid in the i th position of the query sequence with a large value and a small value indicating a highly conservative position and a weakly conservative position, respectively. PSSM was first proposed by [1] and applied to various bioinformatics applications with promising improvements. The acquired protein sequences in the benchmark dataset are in FASTA format. From these FASTA sequences, we used PSI-BLAST [32] to generate PSSM profiles by searching them in the non-redundant (NR) database with two iterations.
Some studies attempted to predict the protein functions by summing up all of the same amino acids [2]. It helped to convert PSSM profiles with 20 × N matrix to 20×20 matrix and all of the sequences had the same input length that can be easily used in supervised classification learning. However, important information could be lost since the ordering of PSSM profiles would be discarded. Therefore, an RNN architecture was presented to not only input PSSM profiles but also preserve the ordering. As the proposed RNN network accepts PSSM sequences with different lengths, we were thus able to well utilize their spatial context for better protein function prediction.

Feature extraction via CNN
The proposed RNN model first extracts convolutional features maps from PSSM profiles via an 1-D CNN. The CNN contains two 1-D convolution layers, each followed by a Rectified Linear Unit (ReLU) as non-linear activation, and two average pooling layers to reduce the dimension of the feature maps as well as enlarge the receptive field of the CNN network. The extracted feature maps are then fed forward to the RNN module for exploring the spatial relationship within the entire PSSM profile before final prediction.

Learning and classification using RNN
RNN is a neural network which had been shown to perform very well in various fields such as time series prediction [33], speech recognition [34], and language model [35]. Since RNN can memorize parts of sequential data, we used GRU which is an advanced architecture of RNN in this study.
After using the aforementioned CNN to create feature maps, we applied a multi-layer GRU to the extracted features. The standard RNN has a major drawback called the gradient vanishing problem, leading to that the network fails in memorizing information which is far away from the sequence and it makes predictions based on the most recent information only. Therefore, more powerful recurrent units, like GRU and Long Short-Term Memory (LSTM), were explored and introduced.
GRU is an advanced version of the standard RNN, in which the gradient vanishing problem is resolved by the introduction of an update gate and a reset gate for determining what information should be passed or discarded. GRU enables the possibility of long dependencies between the current input and far away information.
Basically, the structure of GRU is similar to LSTM. However, the fact that GRU requires less parameters than LSTM so it is more suitable for small datasets. This eases the training procedure and motivates us to adopt GRU as the basic unit in our RNN module. In the RNN module, a GRU layer consists of two gates: (1) Update gate decides what information to throw away and what new information to add. To calculate the update gate z t , we used the following formula: where t is the time step, σ represents the sigmoid function, W represents weight, x t represents the input at time t, h (t−1) represents the hidden state of the previous layer at time t − 1 or the initial hidden state at time 0, and b represents bias.
(2) Reset gate is applied in the model to determine how much past information to forget. The following formula is used: Moreover, to save the past information from the reset gate, GRU uses a current memory content. It can be calculated using the following equation: n t = tanh W in x t + b in + r t • W hn h (t−1) + b hn . (7) Finally, the last step is final memory, to determine what to collect from the current memory content and the previous steps at the last step. To perform this step, GRU calculates vector h t as follows: The final output from the RNN module is then mapped to the prediction with a fully connected layer and the sigmoid function. The output from the RNN model is a scalar in [ 0, 1] representing the probability that the PSSM profile belongs to the adaptor protein category (close to 1) or the non-adaptor protein category (close to 0).


extfieldsofstudy:
Mathematics
Computer Science

text:
SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

I. INTRODUCTION
S EMANTIC segmentation of remote sensing images is a fundamental task that classifies each pixel in an image into a specified category. It plays an important role in many fields such as change detection, element extraction, and military target recognition.
Image semantic segmentation methods can be divided into two categories: traditional methods and deep learning based ones. Traditional methods use the color, texture, shape, and spatial position relationships of an object to extract features and then use clustering, classification, and threshold algorithms to segment an image [1] [2]. However, these methods depend heavily on artificial design features and show some bottlenecks. Recently, deep learning based methods have been regarded as a promising approach to solve image semantic segmentation problems [3][4] [5]. For example, methods based on fully convolutional network (FCN) [4] have achieved stateof-the-art segmentation results on many natural image datasets, such as PASCAL VOC [6] and Cityscapes [7].
However, remote sensing images are different from natural images and often viewed from a high-altitude angle. Thus, the range of imaging is wide, and the background is complex and diverse. Especially in HRRSIs, the difference of ground objects becomes further notable. To segment HRRSIs effectively, many advanced methods have been proposed. For example, weighted focal loss [8] and multi-class loss combined by Dice loss and Binary Cross Entropy loss [9] have been proposed to solve the class imbalance problem in remote sensing images. Multi-modal datas such as digital surface models (DSMs) [10] [11] have been used to improve semantic segmentation performance. In addition, spatial relation module [12] and spatial information inference structure [13] have been designed to model more effective contextual spatial relations. These methods have achieved satisfactory results for remote sensing images segmentation.
Recently, attention mechanisms have been successfully applied to semantic segmentation. These methods can be divided into two categories. One uses attention mechanism to select meaningful features at channel dimension. For example, PAN [14] uses a global attention module as a context prior to select precise features channel-wisely. Attention U-net [15] uses a channel attention module to control the fusion of high-level and low-level features at channel dimension. However, these method do not consider to enhance the feature representation at spatial dimension. Another one is called the self attention mechanism, which calculates the feature representation in each position by weighted sum the features of all other positions [16] [17]. Thus, it can model the longrange context information of semantic segmentation task. For example, DANet [16] uses two self-attention modules to model long-range context information at spatial and channel dimension respectively. However, these methods are complexity in the size of model, thus yield inefficient computation which will be great challenge to process massive remote sensing images. In our work, two lightweight attention modules [18] which contains spatial attention and channel attention are adopted for the semantic segmentation of HRRSIs. The spatial attention module models the spatial features of HRRSIs, and the channel attention module captures what to enhance. Integrating these two attention modules can effectively improve the accuracy of semantic segmentation. The contributions of this study primarily include the following three points: (1) We propose a new semantic segmentation Network with Spatial and Channel Attention (SCAttNet) to improve the semantic segmentation accuracy of HRRSIs.
(2) We visualize the representations learned by spatial and channel attention modules to explain why does our method work.
(3) Experiments on Vaihingen and Potsdam datasets demonstrate competitive results by learning representations for HRRSIs via spatial and channel attention modules, and show arXiv:1912.09121v2 [cs.CV] 7 May 2020

A. Overview of SCAttNet
The proposed semantic segmentation network is shown in Fig. 1. It consists of two parts: a backbone for feature extraction and an attention module. The attention module is composed of a channel attention and a spatial attention in cascade. For an input remote sensing image, we first use the backbone network for feature extraction. Then we feed the extracted feature map into the channel attention module to refine the features in channels. Afterward, we feed the refined channel feature map into the spatial attention module for refinement at spatial axis. Lastly, we can obtain the semantic segmentation results via convolution and SoftMax operations. Specific network details and design ideas are shown in the following sections.

B. Backbone for feature extraction
In this study, we use two representative backbones for feature extraction: SegNet and ResNet50 [19]. On this basis, we proposed two networks: SCAttNet V1 with SegNet as backbone and SCAttNet V2 with ResNet50 as backbone. For SegNet network, it has been widely used as the baseline model of remote sensing images semantic segmentation and achieved sound semantic segmentation results. For example, Audebert et al. [11] based on SegNet network, combined with multimodal data, achieved state-of-the-art semantic segmentation results on the ISPRS Vaihingen dataset. And ResNet50 is also a common backbone for semantic segmentation tasks because it can build a deep-layer model with a wide receptive field. We use eight times downsampling for ResNet50 in our work. Unlike the SegNet and ResNet50 networks, we do not directly use the features of the last layer for semantic inference, instead, we feed the feature map of the last layer into the attention module for feature refinement and then make semantic inference, which is conducive to learning better feature expression.

C. Attention Module
Channel Attention: Given a high-resolution remote sensing image, it will produce a multichannel feature map F ∈ R C×H×W (where C, H, and W denote the number of channels, the height, the width of the feature map, respectively) after passing through several convolutional layers. The information expressed in the feature map of each channel is different. Channel attention aims to use the relationships between each channel of the feature map to learn a 1D weight W c ∈ R C×1×1 , and then multiply it to the corresponding channel. In this manner, it can pay more attention to the meaningful semantic information for the current task. To learn effective weight representation, we first aggregate spatial dimension information through global average pooling and global max pooling to generate two feature descriptors for each channel. Then we feed the two feature descriptors into a shared multilayer perceptron with one hidden layer (where the number of the hidden layer units is C/8) to generate more representative feature vectors. Afterward, we merge the output feature vectors through an element-wise summation operation. Finally, using a sigmoid function, we can obtain the final channel attention map. The flow chart is illustrated in the channel attention module of Fig. 1. The formula for calculating channel attention is shown in Formula 1: Spatial Attention: For the spatial attention, it focuses on where are valuable for current tasks. In HRRSIs, the ground objects are exhibited in various sizes and the distribution is complicated. Therefore, using spatial attention is useful for aggregating spatial information, especially for small ground objects. Spatial attention utilizes the relationships between different spatial positions to learn a 2D spatial weight map W s and then multiplies it to the corresponding spatial position to learn more representative features. To learn the spatial weight relationships effectively, we first generate two feature descriptors for each spatial position through global average pooling and global max pooling operations. Then we concatenate two feature descriptors together and generate a spatial attention map through a 7 × 7 convolution operation. Lastly, we use a sigmoid function to scale the spatial attention map to 0 ∼ 1. The flow chart is illustrated in the spatial attention module of Fig. 1. The spatial attention calculation formula is shown in Formula 2.
where f 7×7 represents a convolution operation with 7 × 7 kernel size.
We can easily compute that the number of parameters of the attention modules are so small that they can be neglected for both our SCAttNet V1 and SCAttNet V2, which have tens of millions of parameters. Specifically, the number of parameters of the channel attention module is C × C/4(C is the number of channels) and the and the number of parameters of the spatial attention module is 98. In addition, we only add the attention module at the end layer of the backbone network; thus, it seldom brings computation complexity to our proposed network.
In this study, we follow the method of Woo [18]to integrate the two attention modules. First, we use channel attention to select significant feature maps in each layer, then use spatial attention to choose considerable neuron's activity in each feature maps, which is conducive to extracting more valuable feature expression.

III. EXPERIMENT
In this section, we evaluate the performance of our network on the ISPRS Vaihingen and ISPRS Potsdam datasets 1 .

A. Datasets and evaluation metrics
The ISPRS Vaihingen dataset contains 33 orthophoto maps and related DSMs. Sixteen of them are labeled. The average size of the images is 2494 × 2064 and the resolution is 9 cm. Each image contains three bands: near infrared, red and green bands. Moreover, it includes six categories: impervious surfaces, buildings, low vegetation, trees, cars and clutter/background. The ISPRS Potsdam dataset contains 38 orthophoto maps and related normalized DSMs. Twenty-four of them are labeled. The size of each image is 6000 × 6000 and the resolution is 5 cm. Each image contains four bands: near infrared, red, green and blue bands. It has the similar categories as the ISPRS Vaihingen dataset.
To evaluate our proposed model, we use three evaluation metrics including mean inter-section over union (MIoU), average F1-score (AF) and overall accuracy (OA) to evaluate semantic segmentation performance.

B. Implementation details
Considering that many remote sensing datasets in practice do not have DSMs, we do not use such datasets in this experiment for wide application value. For the ISPRS Vaihingen dataset, we divide the labeled dataset into two parts, in which (ID 30, 32, 34, 37) are for evaluating the performance of the network, and the remaining 12 images are for training. The number of Vaihingen dataset is small; thus, to prevent overfitting, we first crop the training dataset randomly into a 256 × 256 size and then expand the data through rotation and translation operations, and finally obtain 12,000 patches for training. The Potsdam dataset is also divided into two parts, in which (ID 2 12, 3 12, 4 12, 5 12, 6 12, 7 12) are for testing, and the remaining 18 images are for training. Then, we crop it randomly and obtain 27,000 256 × 256 patches for training.
We trained all the model from the scratch without bells and whistles. The models including FCN-32s, FCN-8s, Unet, SegNet and G-FRNet all use VGG-16 as backbone. As for RefineNet and DeepLabv3+, we adopt the ResNet50 as backbone with 32 times downsampling for RefineNet and eight times downsampling for DeepLabv3+ as the original paper. We also adopt ResNet50 as backbone with eight times downsampling for CBAM. To train the proposed SCAttNet V1, we set the learning rate of SCAttNet V1 on the Vaihingen and Potsdam datasets to 1e-3 and 1e-4, respectively. To train SCAttNet V2, we set the learning rate of SCAttNet V2 on both datasets to 1e-3. The proposed models all adopt Adam as the optimizer and cross-entropy as the loss function. And the training epochs is set to 50. Considering limited computing resources, the batch size is set to 16. To test all the above models, we only use a sliding window without overlap to crop the images and then stitch together. We conduct all our experiments in Tensorflow platform with a NVIDIA 1080Ti GPU. Table I reports the semantic segmentation of the ISPRS Vaihingen dataset. We adopt the practice of [23][24] and do not report the accuracy of the clutter/background class because the Vaihingen dataset has less clutter/background. From the experimental results in Table 1, the MIoU/AF/OA increased by 1.4%/1.25%/0.56% with the channel attention module, compared with original SegNet. This increase shows the effectiveness of the channel attention module to capture meaningful semantic information; With the spatial attention module, the IoU/F1-score for small objects such as cars increased by 3.08%/3.21%. This increase shows that the spatial attention module can aggregate more location information even if the small objects in original images are 32 times downsampling in SegNet. Though the accuracy of impervious surfaces, buildings, and trees classes slightly decreased, we can still increase the MIoU/AF by 2.9%/2.59% compared with original SegNet after inputting the channel-refined features to the spatial attention module. Moreover, in SCAttNet V2, the MIoU/AF/OA increased by 1.21%/0.83%/0.90% compared with ResNet50 which as a baseline model. In addition, we find that in FCN-32s, the car category shows a low accuracy, but the car category in FCN-8s has improved much compared with FCN-32s, which shows the importance of low-level features for segmenting small objects.

C. Results of Vaihingen dataset
To compare the semantic segmentation results, we visualize the semantic segmentation results of ID32. The visualization results are shown in the first row of Fig. 2. We can see that our SCAttNet V1 achieves more coherent results compared with the original SegNet model. In addition, our model infers more accurately in small object areas such as cars in the right middle of the image. We also test the accuracy of the ID32 area. The MIoU/AF/OA of SegNet is 60.57%/74.01%/82.78%, whereas that of our SCAttNet V1 is 64.92%/77.74%/85.49%. On this basis, our network can improve semantic segmentation accuracy by incorporating the attention module effectively.  Table II reports the semantic segmentation results of the IS-PRS Potsdam dataset. From the semantic segmentation results, the proposed SCAttNet V2 model is superior to the comparative models. Moreover, it achieves 1.12%/1.08%/1.03% higher in MIoU/AF/OA metrics compared with ResNet50 which as a baseline model. In SCAttNet V1, The MIoU/AF/OA has increased by 2.08%, 1.74% and 1.35%, respectively compared with SegNet which as a baseline model. Thus, we further confirm the effectiveness of the attention module.

D. Results of Potsdam dataset
The second row of Fig. 2 shows the visualization results of the ID 5 12 area of the ISPRS Potsdam dataset. Compared with the comparative model, our proposed network achieves improved segmentation results in the building area, especially in terms of avoiding background interference. We also test the semantic segmentation performance in this area. The MIoU/AF/OA of the SegNet network is 57.19%/68.87%/83.81%, whereas that of SCAttNet V1 network is 59.50%/71.16%/85.04%. We find that our network achieves higher accuracy in all three metrics compared with the comparative model.

E. Quantitative analysis of improved interpretability
To analyze the enhancement of the attention module to the network, we visualize the feature expression of the SegNet network and our SCAttNet V1 network on the ISPRS Vaihingen dataset. We can simply overlay the heatmap of the network before SoftMax operation with an original image, and the relevant areas can be highlighted for a specific category. The visualization results are shown in Fig. 3. After incorporating the attention module, our network can focus on the areas of target and suppress the influence of other categories better than before. As shown in the first column of Fig.3, after incorporating the attention module, our network only has a strong response in the car category and other areas are cold toned, which means that the relevance in other areas is very low. However, in the original SegNet network, the impervious area also shows a certain response that may cause interference for the car category. In addition, as shown in the building category in the third column of Fig.3 , the impervious surface area is suppressed after incorporating the attention module. Thus, the impervious surface area avoids classifying into Fig. 3. Visualization analysis results of Vaihingen dataset. From left to right: the first two columns represent car categories, the second two columns represent building category, the third two columns represent impervious surfaces category, the fourth two columns represent low vegetation category, the fifth two columns represent tree category. buildings.

IV. CONCLUSION
In this study, we propose a new semantic segmentation network that can adaptively refine features based on the attention module. Experiments on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of our method. However, some shortcomings remain. The study only uses two common attention modules. Thus, how to design the attention module effectively and capture more discriminative features for semantic inference remains a promising direction for future work.