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Afzal-Houshmand, Sam, Homayoun, Sajad, Giannetsos, Thanassis.  2021.  A Perfect Match: Deep Learning Towards Enhanced Data Trustworthiness in Crowd-Sensing Systems. 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). :258—264.
The advent of IoT edge devices has enabled the collection of rich datasets, as part of Mobile Crowd Sensing (MCS), which has emerged as a key enabler for a wide gamut of safety-critical applications ranging from traffic control, environmental monitoring to assistive healthcare. Despite the clear advantages that such unprecedented quantity of data brings forth, it is also subject to inherent data trustworthiness challenges due to factors such as malevolent input and faulty sensors. Compounding this issue, there has been a plethora of proposed solutions, based on the use of traditional machine learning algorithms, towards assessing and sifting faulty data without any assumption on the trustworthiness of their source. However, there are still a number of open issues: how to cope with the presence of strong, colluding adversaries while at the same time efficiently managing this high influx of incoming user data. In this work, we meet these challenges by proposing the hybrid use of Deep Learning schemes (i.e., LSTMs) and conventional Machine Learning classifiers (i.e. One-Class Classifiers) for detecting and filtering out false data points. We provide a prototype implementation coupled with a detailed performance evaluation under various (attack) scenarios, employing both real and synthetic datasets. Our results showcase how the proposed solution outperforms various existing resilient aggregation and outlier detection schemes.
Sutton, Sara, Siasi, Nazli.  2021.  Decoy VNF for Enhanced Security in Fog Computing. 2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). :75—81.
Fog computing extends cloud resources to the edge of the network, thus enabling network providers to support real-time applications at low latencies. These applications further demand high security against malicious attacks that target distributed fog servers. One effective defense mechanism here against cyber attacks is the use of honeypots. The latter acts as a potential target for attackers by diverting malicious traffic away from the servers that are dedicated to legitimate users. However, one main limitation of honeypots is the lack of real traffic and network activities. Therefore, it is important to implement a solution that simulates the behavior of the real system to lure attackers without the risk of being exposed. Hence this paper proposes a practical approach to generate network traffic by introducing decoy virtual network functions (VNF) embedded on fog servers, which make the network traffic on honeypots resemble a legitimate, vulnerable fog system to attract cyber attackers. The use of virtualization allows for robust scalability and modification of network functions based on incoming attacks, without the need for dedicated hardware. Moreover, deep learning is leveraged here to build fingerprints for each real VNF, which is subsequently used to support its decoy counterpart against active probes. The proposed framework is evaluated based on CPU utilization, memory usage, disk input/output access, and network latency.
Raafat, Maryam A., El-Wakil, Rania Abdel-Fattah, Atia, Ayman.  2021.  Comparative study for Stylometric analysis techniques for authorship attribution. 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). :176—181.
A text is a meaningful source of information. Capturing the right patterns in written text gives metrics to measure and infer to what extent this text belongs or is relevant to a specific author. This research aims to introduce a new feature that goes more in deep in the language structure. The feature introduced is based on an attempt to differentiate stylistic changes among authors according to the different sentence structure each author uses. The study showed the effect of introducing this new feature to machine learning models to enhance their performance. It was found that the prediction of authors was enhanced by adding sentence structure as an additional feature as the f1\_scores increased by 0.3% and when normalizing the data and adding the feature it increased by 5%.
White, Riley, Sprague, Nathan.  2021.  Deep Metric Learning for Code Authorship Attribution and Verification. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). :1089—1093.
Code authorship identification can assist in identifying creators of malware, identifying plagiarism, and giving insights in copyright infringement cases. Taking inspiration from facial recognition work, we apply recent advances in metric learning to the problem of authorship identification and verification. The metric learning approach makes it possible to measure similarity in the learned embedding space. Access to a discriminative similarity measure allows for the estimation of probability distributions that facilitate open-set classification and verification. We extend our analysis to verification based on sets of files, a previously unexplored problem domain in large-scale author identification. On closed-set tasks we achieve competitive accuracies, but do not improve on the state of the art.
Frankel, Sophia F., Ghosh, Krishnendu.  2021.  Machine Learning Approaches for Authorship Attribution using Source Code Stylometry. 2021 IEEE International Conference on Big Data (Big Data). :3298—3304.
Identification of source code authorship is vital for attribution. In this work, a machine learning framework is described to identify source code authorship. The framework integrates the features extracted using natural language processing based approaches and abstract syntax tree of the code. We evaluate the methodology on Google Code Jam dataset. We present the performance measures of the logistic regression and deep learning on the dataset.
Liu, Pengcheng, Han, Zhen, Shi, Zhixin, Liu, Meichen.  2021.  Recognition of Overlapped Frequency Hopping Signals Based on Fully Convolutional Networks. 2021 28th International Conference on Telecommunications (ICT). :1—5.
Previous research on frequency hopping (FH) signal recognition utilizing deep learning only focuses on single-label signal, but can not deal with overlapped FH signal which has multi-labels. To solve this problem, we propose a new FH signal recognition method based on fully convolutional networks (FCN). Firstly, we perform the short-time Fourier transform (STFT) on the collected FH signal to obtain a two-dimensional time-frequency pattern with time, frequency, and intensity information. Then, the pattern will be put into an improved FCN model, named FH-FCN, to make a pixel-level prediction. Finally, through the statistics of the output pixels, we can get the final classification results. We also design an algorithm that can automatically generate dataset for model training. The experimental results show that, for an overlapped FH signal, which contains up to four different types of signals, our method can recognize them correctly. In addition, the separation of multiple FH signals can be achieved by a slight improvement of our method.
Razack, Aquib Junaid, Ajith, Vysyakh, Gupta, Rajiv.  2021.  A Deep Reinforcement Learning Approach to Traffic Signal Control. 2021 IEEE Conference on Technologies for Sustainability (SusTech). :1–7.
Traffic Signal Control using Reinforcement Learning has been proved to have potential in alleviating traffic congestion in urban areas. Although research has been conducted in this field, it is still an open challenge to find an effective but low-cost solution to this problem. This paper presents multiple deep reinforcement learning-based traffic signal control systems that can help regulate the flow of traffic at intersections and then compares the results. The proposed systems are coupled with SUMO (Simulation of Urban MObility), an agent-based simulator that provides a realistic environment to explore the outcomes of the models.
Chawla, Kushal, Clever, Rene, Ramirez, Jaysa, Lucas, Gale, Gratch, Jonathan.  2021.  Towards Emotion-Aware Agents For Negotiation Dialogues. 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII). :1–8.
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. Specifically, we analyze the extent to which emotion attributes extracted from the negotiation help in the prediction, above and beyond the individual difference variables. We focus on a recent dataset in chat-based negotiations, grounded in a realistic camping scenario. We study three degrees of emotion dimensions – emoticons, lexical, and contextual by leveraging affective lexicons and a state-of-the-art deep learning architecture. Our insights will be helpful in designing adaptive negotiation agents that interact through realistic communication interfaces.
Rajamalli Keerthana, R, Fathima, G, Florence, Lilly.  2021.  Evaluating the Performance of Various Deep Reinforcement Learning Algorithms for a Conversational Chatbot. 2021 2nd International Conference for Emerging Technology (INCET). :1–8.
Conversational agents are the most popular AI technology in IT trends. Domain specific chatbots are now used by almost every industry in order to upgrade their customer service. The Proposed paper shows the modelling and performance of one such conversational agent created using deep learning. The proposed model utilizes NMT (Neural Machine Translation) from the TensorFlow software libraries. A BiRNN (Bidirectional Recurrent Neural Network) is used in order to process input sentences that contain large number of tokens (20-40 words). In order to understand the context of the input sentence attention model is used along with BiRNN. The conversational models usually have one drawback, that is, they sometimes provide irrelevant answer to the input. This happens quite often in conversational chatbots as the chatbot doesn't realize that it is answering without context. This drawback is solved in the proposed system using Deep Reinforcement Learning technique. Deep reinforcement Learning follows a reward system that enables the bot to differentiate between right and wrong answers. Deep Reinforcement Learning techniques allows the chatbot to understand the sentiment of the query and reply accordingly. The Deep Reinforcement Learning algorithms used in the proposed system is Q-Learning, Deep Q Neural Network (DQN) and Distributional Reinforcement Learning with Quantile Regression (QR-DQN). The performance of each algorithm is evaluated and compared in this paper in order to find the best DRL algorithm. The dataset used in the proposed system is Cornell Movie-dialogs corpus and CoQA (A Conversational Question Answering Challenge). CoQA is a large dataset that contains data collected from 8000+ conversations in the form of questions and answers. The main goal of the proposed work is to increase the relevancy of the chatbot responses and to increase the perplexity of the conversational chatbot.
Scotti, Vincenzo, Tedesco, Roberto, Sbattella, Licia.  2021.  A Modular Data-Driven Architecture for Empathetic Conversational Agents. 2021 IEEE International Conference on Big Data and Smart Computing (BigComp). :365–368.
Empathy is a fundamental mechanism of human interactions. As such, it should be an integral part of Human-Computer Interaction systems to make them more relatable. With this work, we focused on conversational scenarios where integrating empathy is crucial to perceive the computer like a human. As a result, we derived the high-level architecture of an Empathetic Conversational Agent we are willing to implement. We relied on theories about artificial empathy to derive the function approximating this mechanism and selected the conversational aspects to control for an empathetic interaction. In particular, we designed a core empathetic controller manages the empathetic responses, predicting, at each turn, the high-level content of the response. The derived architecture integrates empathy in a task-agnostic manner; hence we can employ it in multiple scenarios by changing the objective of the controller.
Bendre, Nihar, Desai, Kevin, Najafirad, Peyman.  2021.  Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention. 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :3006–3012.
Visual Question Answering (VQA) models have achieved significant success in recent times. Despite the success of VQA models, they are mostly black-box models providing no reasoning about the predicted answer, thus raising questions for their applicability in safety-critical such as autonomous systems and cyber-security. Current state of the art fail to better complex questions and thus are unable to exploit compositionality. To minimize the black-box effect of these models and also to make them better exploit compositionality, we propose a Dynamic Neural Network (DMN), which can understand a particular question and then dynamically assemble various relatively shallow deep learning modules from a pool of modules to form a network. We incorporate compositional temporal attention to these deep learning based modules to increase compositionality exploitation. This results in achieving better understanding of complex questions and also provides reasoning as to why the module predicts a particular answer. Experimental analysis on the two benchmark datasets, VQA2.0 and CLEVR, depicts that our model outperforms the previous approaches for Visual Question Answering task as well as provides better reasoning, thus making it reliable for mission critical applications like safety and security.
Gepperth, Alexander, Pfülb, Benedikt.  2021.  Image Modeling with Deep Convolutional Gaussian Mixture Models. 2021 International Joint Conference on Neural Networks (IJCNN). :1–9.
In this conceptual work, we present Deep Convolutional Gaussian Mixture Models (DCGMMs): a new formulation of deep hierarchical Gaussian Mixture Models (GMMs) that is particularly suitable for describing and generating images. Vanilla (i.e., flat) GMMs require a very large number of components to describe images well, leading to long training times and memory issues. DCGMMs avoid this by a stacked architecture of multiple GMM layers, linked by convolution and pooling operations. This allows to exploit the compositionality of images in a similar way as deep CNNs do. DCGMMs can be trained end-to-end by Stochastic Gradient Descent. This sets them apart from vanilla GMMs which are trained by Expectation-Maximization, requiring a prior k-means initialization which is infeasible in a layered structure. For generating sharp images with DCGMMs, we introduce a new gradient-based technique for sampling through non-invertible operations like convolution and pooling. Based on the MNIST and FashionMNIST datasets, we validate the DCGMMs model by demonstrating its superiority over flat GMMs for clustering, sampling and outlier detection.
Karthikeyan, P., Anandaraj, S.P., Vignesh, R., Poornima, S..  2021.  Review on Trustworthy Analysis in binary code. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). 1:1386—1389.
The software industry is dominating many are like health care, finance, agriculture and entertainment. Software security has become an essential issue-outsider libraries, which assume a significant part in programming. The finding weaknesses in the binary code is a significant issue that presently cannot seem to be handled, as showed by numerous weaknesses wrote about an everyday schedule. Software seller sells the software to the client if the client wants to check the software's vulnerability it is a cumbersome task. Presently many deep learning-based methods also introduced to find the security weakness in the binary code. This paper present the merits and demerits of binary code analysis used by a different method.
Zhang, KunSan, Chen, Chen, Lin, Nan, Zeng, Zhen, Fu, ShiChen.  2021.  Automatic patch installation method of operating system based on deep learning. 2021 IEEE 5th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC). 5:1072—1075.
In order to improve the security and reliability of information system and reduce the risk of vulnerability intrusion and attack, an automatic patch installation method of operating systems based on deep learning is proposed, If the installation is successful, the basic information of the system will be returned to the visualization server. If the installation fails, it is recommended to upgrading manually and display it on the patch detection visualization server. Through the practical application of statistical analysis, the statistical results show that the proposed method is significantly better than the original and traditional installation methods, which can effectively avoid the problem of client repeated download, and greatly improve the success rate of patch automatic upgrades. It effectively saves the upgrade cost and ensures the security and reliability of the information system.
Qian, Tiantian, Yang, Shengchun, Wang, Shenghe, Pan, Dong, Geng, Jian, Wang, Ke.  2021.  Static Security Analysis of Source-Side High Uncertainty Power Grid Based on Deep Learning. 2021 China International Conference on Electricity Distribution (CICED). :973—975.
As a large amount of renewable energy is injected into the power grid, the source side of the power grid becomes extremely uncertain. Traditional static safety analysis methods based on pure physical models can no longer quickly and reliably give analysis results. Therefore, this paper proposes a deep learning-based static security analytical method. First, the static security assessment index of the power grid under the N-1 principle is proposed. Secondly, a neural network model and its input and output data for static safety analysis problems are designed. Finally, the validity of the proposed method was verified by IEEE grid data. Experiments show that the proposed method can quickly and accurately give the static security analysis results of the source-side high uncertainty grid.
Zhang, Dayin, Chen, Xiaojun, Shi, Jinqiao, Wang, Dakui, Zeng, Shuai.  2021.  A Differential Privacy Collaborative Deep Learning Algorithm in Pervasive Edge Computing Environment. 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :347—354.
With the development of 5G technology and intelligent terminals, the future direction of the Industrial Internet of Things (IIoT) evolution is Pervasive Edge Computing (PEC). In the pervasive edge computing environment, intelligent terminals can perform calculations and data processing. By migrating part of the original cloud computing model's calculations to intelligent terminals, the intelligent terminal can complete model training without uploading local data to a remote server. Pervasive edge computing solves the problem of data islands and is also successfully applied in scenarios such as vehicle interconnection and video surveillance. However, pervasive edge computing is facing great security problems. Suppose the remote server is honest but curious. In that case, it can still design algorithms for the intelligent terminal to execute and infer sensitive content such as their identity data and private pictures through the information returned by the intelligent terminal. In this paper, we research the problem of honest but curious remote servers infringing intelligent terminal privacy and propose a differential privacy collaborative deep learning algorithm in the pervasive edge computing environment. We use a Gaussian mechanism that meets the differential privacy guarantee to add noise on the first layer of the neural network to protect the data of the intelligent terminal and use analytical moments accountant technology to track the cumulative privacy loss. Experiments show that with the Gaussian mechanism, the training data of intelligent terminals can be protected reduction inaccuracy.
Fan, Wenqi, Derr, Tyler, Zhao, Xiangyu, Ma, Yao, Liu, Hui, Wang, Jianping, Tang, Jiliang, Li, Qing.  2021.  Attacking Black-box Recommendations via Copying Cross-domain User Profiles. 2021 IEEE 37th International Conference on Data Engineering (ICDE). :1583—1594.
Recommender systems, which aim to suggest personalized lists of items for users, have drawn a lot of attention. In fact, many of these state-of-the-art recommender systems have been built on deep neural networks (DNNs). Recent studies have shown that these deep neural networks are vulnerable to attacks, such as data poisoning, which generate fake users to promote a selected set of items. Correspondingly, effective defense strategies have been developed to detect these generated users with fake profiles. Thus, new strategies of creating more ‘realistic’ user profiles to promote a set of items should be investigated to further understand the vulnerability of DNNs based recommender systems. In this work, we present a novel framework CopyAttack. It is a reinforcement learning based black-box attacking method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items. CopyAttack is constructed to both efficiently and effectively learn policy gradient networks that first select, then further refine/craft user profiles from the source domain, and ultimately copy them into the target domain. CopyAttack’s goal is to maximize the hit ratio of the targeted items in the Top-k recommendation list of the users in the target domain. We conducted experiments on two real-world datasets and empirically verified the effectiveness of the proposed framework. The implementation of CopyAttack is available at
N, Praveena., Vivekanandan, K..  2021.  A Study on Shilling Attack Identification in SAN using Collaborative Filtering Method based Recommender Systems. 2021 International Conference on Computer Communication and Informatics (ICCCI). :1—5.
In Social Aware Network (SAN) model, the elementary actions focus on investigating the attributes and behaviors of the customer. This analysis of customer attributes facilitate in the design of highly active and improved protocols. In specific, the recommender systems are highly vulnerable to the shilling attack. The recommender system provides the solution to solve the issues like information overload. Collaborative filtering based recommender systems are susceptible to shilling attack known as profile injection attacks. In the shilling attack, the malicious users bias the output of the system's recommendations by adding the fake profiles. The attacker exploits the customer reviews, customer ratings and fake data for the processing of recommendation level. It is essential to detect the shilling attack in the network for sustaining the reliability and fairness of the recommender systems. This article reviews the most prominent issues and challenges of shilling attack. This paper presents the literature survey which is contributed in focusing of shilling attack and also describes the merits and demerits with its evaluation metrics like attack detection accuracy, precision and recall along with different datasets used for identifying the shilling attack in SAN network.
Farrukh, Yasir Ali, Ahmad, Zeeshan, Khan, Irfan, Elavarasan, Rajvikram Madurai.  2021.  A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System. 2021 North American Power Symposium (NAPS). :1—6.
Modern smart grid systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyber-attacks. The occurrence of a cyber-attack has increased in recent years resulting in substantial damage to power systems. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. Automated detection of cyberattacks with high accuracy is a challenge. To address this, we propose a two-layer hierarchical machine learning model having an accuracy of 95.44 % to improve the detection of cyberattacks. The first layer of the model is used to distinguish between the two modes of operation - normal state or cyberattack. The second layer is used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we compared its performance against other recent cyber attack detection models proposed in the literature.
Akowuah, Francis, Kong, Fanxin.  2021.  Real-Time Adaptive Sensor Attack Detection in Autonomous Cyber-Physical Systems. 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS). :237—250.
Cyber-Physical Systems (CPS) tightly couple information technology with physical processes, which rises new vulnerabilities such as physical attacks that are beyond conventional cyber attacks. Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This issue is even emphasized with the increasing autonomy in CPS. While this fact has motivated many defense mechanisms against sensor attacks, a clear vision on the timing and usability (or the false alarm rate) of attack detection still remains elusive. Existing works tend to pursue an unachievable goal of minimizing the detection delay and false alarm rate at the same time, while there is a clear trade-off between the two metrics. Instead, we argue that attack detection should bias different metrics when a system sits in different states. For example, if the system is close to unsafe states, reducing the detection delay is preferable to lowering the false alarm rate, and vice versa. To achieve this, we make the following contributions. In this paper, we propose a real-time adaptive sensor attack detection framework. The framework can dynamically adapt the detection delay and false alarm rate so as to meet a detection deadline and improve the usability according to different system status. The core component of this framework is an attack detector that identifies anomalies based on a CUSUM algorithm through monitoring the cumulative sum of difference (or residuals) between the nominal (predicted) and observed sensor values. We augment this algorithm with a drift parameter that can govern the detection delay and false alarm. The second component is a behavior predictor that estimates nominal sensor values fed to the core component for calculating the residuals. The predictor uses a deep learning model that is offline extracted from sensor data through leveraging convolutional neural network (CNN) and recurrent neural network (RNN). The model relies on little knowledge of the system (e.g., dynamics), but uncovers and exploits both the local and complex long-term dependencies in multivariate sequential sensor measurements. The third component is a drift adaptor that estimates a detection deadline and then determines the drift parameter fed to the detector component for adjusting the detection delay and false alarms. Finally, we implement the proposed framework and validate it using realistic sensor data of automotive CPS to demonstrate its efficiency and efficacy.
ERÇİN, Mehmet Serhan, YOLAÇAN, Esra Nergis.  2021.  A system for redicting SQLi and XSS Attacks. 2021 International Conference on Information Security and Cryptology (ISCTURKEY). :155—160.
In this study, it is aimed to reduce False-Alarm levels and increase the correct detection rate in order to reduce this uncertainty. Within the scope of the study, 13157 SQLi and XSS type malicious and 10000 normal HTTP Requests were used. All HTTP requests were received from the same web server, and it was observed that normal requests and malicious requests were close to each other. In this study, a novel approach is presented via both digitization and expressing the data with words in the data preprocessing stages. LSTM, MLP, CNN, GNB, SVM, KNN, DT, RF algorithms were used for classification and the results were evaluated with accuracy, precision, recall and F1-score metrics. As a contribution of this study, we can clearly express the following inferences. Each payload even if it seems different which has the same impact maybe that we can clearly view after the preprocessing phase. After preprocessing we are calculating euclidean distances which brings and gives us the relativity between expressions. When we put this relativity as an entry data to machine learning and/or deep learning models, perhaps we can understand the benign request or the attack vector difference.
Özdemir, Durmuş, Çelik, Dilek.  2021.  Analysis of Encrypted Image Data with Deep Learning Models. 2021 International Conference on Information Security and Cryptology (ISCTURKEY). :121—126.
While various encryption algorithms ensure data security, it is essential to determine the accuracy and loss values and performance status in the analyzes made to determine encrypted data by deep learning. In this research, the analysis steps made by applying deep learning methods to encrypted cifar10 picture data are presented practically. The data was tried to be estimated by training with VGG16, VGG19, ResNet50 deep learning models. During this period, the network’s performance was tried to be measured, and the accuracy and loss values in these calculations were shown graphically.
Schoneveld, Liam, Othmani, Alice.  2021.  Towards a General Deep Feature Extractor for Facial Expression Recognition. 2021 IEEE International Conference on Image Processing (ICIP). :2339—2342.
The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for verbalisation. Visual emotion recognition has been extensively studied. Recently several end-to-end trained deep neural networks have been proposed for this task. However, such models often lack generalisation ability across datasets. In this paper, we propose the Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any other facial emotion recognition task or dataset. DeepFEVER outperforms state-of-the-art results on the AffectNet and Google Facial Expression Comparison datasets. DeepFEVER’s extracted features also generalise extremely well to other datasets – even those unseen during training – namely, the Real-World Affective Faces (RAF) dataset.
Wang, Caixia, Wang, Zhihui, Cui, Dong.  2021.  Facial Expression Recognition with Attention Mechanism. 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). :1—6.
With the development of artificial intelligence, facial expression recognition (FER) has greatly improved performance in deep learning, but there is still a lot of room for improvement in the study of combining attention to focus the network on key parts of the face. For facial expression recognition, this paper designs a network model, which use spatial transformer network to transform the input image firstly, and then adding channel attention and spatial attention to the convolutional network. In addition, in this paper, the GELU activation function is used in the convolutional network, which improves the recognition rate of facial expressions to a certain extent.
Siyaka, Hassan Opotu, Owolabi, Olumide, Bisallah, I. Hashim.  2021.  A New Facial Image Deviation Estimation and Image Selection Algorithm (Fide-Isa) for Facial Image Recognition Systems: The Mathematical Models. 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS). :1—7.
Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Reducing the training sample sizes of deep learning models is still an open problem. This paper proposes the reduction of the number of samples required by the convolutional neutral network used in training a facial recognition system using a new Facial Image Deviation Estimation and Image Selection Algorithm (FIDE-ISA). The algorithm was used to select appropriate facial image training samples incrementally based on their facial deviation. This will reduce the need for huge dataset in training deep learning models. Preliminary results indicated a 100% accuracy for models trained with 54 images (at least 3 images per individual) and above.