Reviewerly/exHarmony huggingface dataset:
Two Feature Selection Methods Comparison Chi-square and Relief-F for Facial Expression Recognition

Abstract Feature selection metho represents one of the main keys that has direct influence on classification accuracy. During the last two decades, researchers have given a lot of attention in feature selection approaches due to their importance. This paper provides a comparative approach between the two feature selection methods: Chi-Square and Relief-F. The two methods rank the features according to their score. The first highest six emotion features from the both methods are selected. The six features are used to compare the accuracy ratio among the four classifiers: Support Vector Machine, K-Nearest, Decision Tree, and Radial Base Function. These classifiers are used for the mission of expression recognition and to compare their proportional performance. The ultimate aim of the provided approach is to use minimum number of features from the both methods in order to distinguish the performance accuracy of the four classifiers. The provided approach has been applied on CK+ facial expression recognition dataset. The result of the experiment illustrates that K-Nearest Neighbor is the most accurate classifier on the both feature selection methods according to the employed dataset. The K-Nearest Neighbor accuracy average rate for Chi-square is 94.18% and for Relief-F is 94.93%.
ESS‐IBAA: Efficient, short, and secure ID‐based authentication algorithm for wireless sensor network

Summary Designing an efficient, short, and secure authentication algorithm for resource‐constrained sensor nodes of wireless sensor networks (WSNs) is a challenging task. Authentication in WSNs is mainly performed by the digital signature algorithm. In this paper, we propose an efficient, short, and secure pairing computation‐free ID‐based authentication algorithm (signature scheme) ESS‐IBAA for WSNs which completely follows the rule of identity‐based cryptosystem. Identity‐based schemes, unlike traditional public‐key infrastructure (PKI)‐based schemes, remove the need for public‐key certificates for public‐key validation. It also removes extra costs associated with the public‐key certificate and traffic management. Further, due to the requirement of low power and fast authentication, bilinear pairing computation‐free identity‐based signature schemes are applicable in WSNs. Keeping this in mind, ESS‐IBAA scheme is proposed, which is pairing computation free and uses a general cryptographic hash function in the place of a costly map‐to‐point hash function. The proposed scheme ESS‐IBAA is secure against existential forgery on adaptive chosen message and ID attack in the random oracle model under the hardness of the elliptic curve discrete logarithm problem (ECDLP). Moreover, comparative performance analysis shows that the proposed scheme ESS‐IBAA is much more efficient in both communication cost and computation cost from the existing related schemes.
A Hybrid Deep Model for Learning to Rank Data Tables

We address the problem of ad hoc table retrieval via a new neural architecture that incorporates both semantic and relevance matching. Understanding the connection between the structured form of a table and query tokens is an important yet neglected problem in information retrieval. We use a learning- to-rank approach to train a system to capture semantic and relevance signals within interactions between the structured form of candidate tables and query tokens. Convolutional filters that extract contextual features from query/table interactions are combined with a feature vector based on the distributions of term similarity between queries and tables. We propose using row and column summaries to incorporate table content into our new neural model. We evaluate our approach using two datasets, and we demonstrate substantial improvements in terms of retrieval metrics over state-of-the-art methods in table retrieval and document retrieval, and neural architectures from sentence, document, and table type classification adapted to the table retrieval task. Our ablation study supports the importance of both semantic and relevance matching in the table retrieval.
Twisted cubic and plane-line incidence matrix in $$\mathrm {PG}(3,q)$$

The point-plane, the point-line, and the plane-line incidence matrices of $$\mathrm {PG}(3,q)$$ are of interest in combinatorics, finite geometry, graph theory and group theory. Some of the properties of these matrices and their submatrices are related with the interplay of orbits of points, lines and planes under the action of subgroups of $$\mathrm {PGL}(4,q)$$ . A remarkable particular case is the subgroup $$G\cong \mathrm {PGL}(2,q)$$ , viewed as the stabilizer group of the twisted cubic $$\mathscr {C}$$ . For this case, the study of the point-plane incidence matrix, initiated by D. Bartoli and the present authors, has attracted attention as being related to submatrices with useful applications in coding theory for the construction of multiple covering codes. In this paper, we extend our investigation to the plane-line incidence matrix apart from just one class of the line orbits, named $$\mathcal {O}_6$$ in the literature. For all $$q\ge 2$$ , in each submatrix, the numbers of lines in any plane and planes through any line are obtained. As a tool for the present investigation, we use submatrices of incidences arising from orbits of planes and unions of line orbits, including $$\mathcal {O}_6$$ . For each such submatrix we determine the total number of lines from the union in any plane and the average number of planes from the orbit through any line of the union.
MuRNet: A deep recursive network for super resolution of bicubically interpolated images

In many real-world cases such as printer devices and in-camera interpolation, only the interpolated versions of the low-resolution images are available. In this paper, a new low-complexity high-performance image super resolution network is proposed that starting from the bicubic interpolated version of the low resolution image produces a high quality super resolved image. The main idea in the proposed scheme is the development of a feature generating block that is capable of producing features using multiple local spatial ranges and multiple resolution levels, fusing them in order to provide a rich set of feature maps, and using them in a recursive framework. The objective in designing such a recursive block is not simply to provide a light-weight network, as is traditionally done in the design of such a network, but also to provide a low count on the number of multiply-accumulate operations with high performance. The experimental results are provided to show that the proposed network outperforms other recursive super resolution networks when their super resolution capability, the number of parameters and number of multiply-accumulate operations are simultaneously taken into consideration.
Offline Signature Authentication Algorithm Based on the Fuzzy Set

There exists a problem that it is difficult to identify the authenticity of offline signatures. Firstly, a segmentation model is established based on the theory of fuzzy sets to extract signatures completely. Secondly, statistical shape model (SSM) and variance distance discretization of intraclass signatures are introduced for stability analysis and quantification. Finally, multilayer classifiers are constructed to realize signature authentication. The algorithm has low false detection rate and short authentication time.
Analyzing Web Search Behavior for Software Engineering Tasks

Web search plays an integral role in software engineering (SE) to help with various tasks such as finding documentation, debugging, installation, etc. In this work, we present the first large-scale analysis of web search behavior for SE tasks using the search query logs from Bing, a commercial web search engine. First, we use distant supervision techniques to build a machine learning classifier to extract the SE search queries with an F1 score of 93%. We then perform an analysis on one million search sessions to understand how software engineering related queries and sessions differ from other queries and sessions. Subsequently, we propose a taxonomy of intents to identify the various contexts in which web search is used in software engineering. Lastly, we analyze millions of SE queries to understand the distribution, search metrics and trends across these SE search intents. Our analysis shows that SE related queries form a significant portion of the overall web search traffic. Additionally, we found that there are six major intent categories for which web search is used in software engineering. The techniques and insights can not only help improve existing tools but can also inspire the development of new tools that aid in finding information for SE related tasks.
Customizing Contextualized Language Models for Legal Document Reviews

Inspired by the inductive transfer learning on computer vision, many efforts have been made to train contextualized language models that boost the performance of natural language processing tasks. These models are mostly trained on large general-domain corpora such as news, books, or Wikipedia. Although these pre-trained generic language models well perceive the semantic and syntactic essence of a language structure, exploiting them in a real-world domain-specific scenario still needs some practical considerations to be taken into account such as token distribution shifts, inference time, memory, and their simultaneous proficiency in multiple tasks. In this paper, we focus on the legal domain and present how different language models trained on general-domain corpora can be best customized for multiple legal document reviewing tasks. We compare their efficiencies with respect to task performances and present practical considerations.
Designing for a socioenactive experience: A case study in an educational workshop on deep time

The new scenarios brought by ubiquitous and pervasive technologies have drawn attention to design considerations towards integrating physical artefacts and digital processes. As for the human element in these scenarios, literature has emphasized individual bodies sensed by or acting with technology as social parties who participate in the environment. In this work, we draw on the enactivism-inspired perspective to cognition, to make explicit the social–physical–digital tripartite articulation into design considerations. The article proposes to clarify the tripartite coupling in the concept we are naming ‘socioenactive’. A case study carried out in the context of an educational workshop in an exploratory museum, with the use of installations designed on the deep time theme, illustrates some aspects of this tripartite relation. Data from the evaluation of the experience by children participating in the workshop, and from observations of intersubjective aspects of the experience made from recorded video, are presented and discussed. Results of the study clarify the main ideas behind an experience we are naming socioenactive, in its intersubjective aspects, and suggest lines to be weaved in design towards (smart) contemporary technology-based environments.
Measurement and Analysis of Electromagnetic Information Leakage From Printed Circuit Board Power Delivery Network of Cryptographic Devices

This article presents a novel measurement and analysis of electromagnetic (EM) information leakage from printed circuit board (PCB) power delivery network (PDN) of cryptographic devices. We propose an accurate EM information leakage analysis method based on a correlation electromagnetic analysis (CEMA) considering advanced encryption standard (AES) operation cycles and clock frequency. We measure field distribution on the PCB level AES core PDN and conduct the proposed analysis method to derive the information leakage maps. For the first time, we verified that the EM information leakage depends on the intensity of dominant field distribution on the PCB PDN using the proposed method. We validated that the whole secret key information can be extracted from locations in the PCB PDN distant from the cryptographic integrated circuit where a specific field is dominant due to the physical structure of the PCB PDN. Based on the measurement and analysis results, we discuss an efficient EM information leakage evaluation method based on the dominant field radiation. We evaluate the EM information leakage from the decoupling capacitor in the backside of the PCB. Finally, we propose a design methodology to suppress the EM information leakage from the PCB PDN.
Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions

During the last decade, distributed ledger solutions such as blockchain have gained significant attention due to their decentralized, immutable, and verifiable features. However, the public availability of data stored on the blockchain and its link to users may raise privacy and security issues. In some cases, addressing these issues requires blockchain data to be secured with mechanisms that allow on-demand (as opposed to full) disclosure. In this paper, we give a comprehensive overview of blockchain privacy and security requirements, and detail how existing mechanisms answer them. We provide a taxonomy of current attacks together with related countermeasures. We present a thorough comparative analysis based on various parameters of state-of the-art privacy and security mechanisms, we provide recommendations to design secure and privacy-aware blockchain, and we suggest guidelines for future research.
A high payload EMD-based steganographic method using two extraction functions

In recent years, the EMD-based steganographic method has become more and more popular because of its easy implementation and high security. However, it had two shortcomings: One is the secret data must convert into non-binary numeral system, and the other is low payload. In order to overcome these shortcomings, some studies used the hexagon (or octagon)-shaped reference matrix instead of the extraction function. In this study, a high payload EMD-based steganographic method is proposed, a combined mapping matrix is generated by two extraction functions in the embedding and extracting procedure. With the diversity of the definition of these two extraction functions, the proposed method achieves high security. The experimental results show that the proposed method achieves high payload and the quality of stego-image satisfies the human vision system sensitivity.
Salt and Pepper Noise Removal Method Based on a Detail-Aware Filter

The median-type filter is an effective technique to remove salt and pepper (SAP) noise; however, such a mechanism cannot always effectively remove noise and preserve details due to the local diversity singularity and local non-stationarity. In this paper, a two-step SAP removal method was proposed based on the analysis of the median-type filter errors. In the first step, a median-type filter was used to process the image corrupted by SAP noise. Then, in the second step, a novel-designed adaptive nonlocal bilateral filter is used to weaken the error of the median-type filter. By building histograms of median-type filter errors, we found that the error almost obeys Gaussian–Laplacian mixture distribution statistically. Following this, an improved bilateral filter was proposed to utilize the nonlocal feature and bilateral filter to weaken the median-type filter errors. In the proposed filter, (1) the nonlocal strategy is introduced to improve the bilateral filter, and the intensity similarity is measured between image patches instead pixels; (2) a novel norm based on half-quadratic estimation is used to measure the image patch- spatial proximity and intensity similarity, instead of fixed L1 and L2 norms; (3) besides, the scale parameters, which were used to control the behavior of the half-quadratic norm, were updated based on the local image feature. Experimental results showed that the proposed method performed better compared with the state-of-the-art methods.
Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks

Abstract Computer vision has the challenge to detect the facial emotions of humans. Recently, in computer vision and machine learning, it’s possible to detect emotion from video or image accurate. In our research will propose to classify facial emotion using Haar-Cascade Classifier and Convolutional Neural Networks. The experiment uses the FER2013 dataset which was collected for the facial expression recognition dataset, and we proposed seven classified facial expression. The CNN model gain MSE and accuracy value based on epoch variety. The results showed that with the increase in the epoch value, the smaller MSE value would be obtained, likewise, the accuracy value would be increased. Thus, the proposed algorithm of CNN is proven to be effective for facial emotion detection.
RL-VAEGAN: Adversarial defense for reinforcement learning agents via style transfer

Reinforcement learning (RL) agents parameterized by deep neural networks have achieved great success in many domains. However, deep RL policies have been shown to be vulnerable to adversarial attacks, i.e., inputs with slight perturbations should result in a substantial agent failure. Inspired by recent advances in deep generative networks that have greatly facilitated the development of adversarial attacks, in this paper, we investigate the adversarial robustness of RL agents and propose a novel defense framework for RL based on the idea of style transfer. More precisely, our defense framework containing variational autoencoders (VAEs) and generative adversarial networks (GANs), called RL-VAEGAN, learns the distribution of the styles of the original and adversarial states, respectively, and naturally eliminates the threat of adversarial attacks for RL agents by transferring adversarial states to unperturbed legitimate one under the shared-content latent space assumption. We empirically show that our methods are effective against the state-of-the-art methods in white-box and black-box scenarios with diverse magnitudes of perturbations.
Convergence of Finite Memory Q Learning for POMDPs and Near Optimality of Learned Policies Under Filter Stability

In this paper, for partially observed Markov decision problems (POMDPs), we provide the convergence of a Q learning algorithm for control policies using a finite history of past observations and control actions, and consequentially, we establish near optimality of such limit Q functions under explicit filter stability conditions. We present explicit error bounds relating the approximation error to the length of the finite history window. We establish the convergence of such Q learning iterations under mild ergodicity assumptions on the state process during the exploration phase. We further show that the limit fixed point equation gives an optimal solution for an approximate belief Markov decision problem (MDP). We then provide bounds on the performance of the policy obtained using the limit Q values compared with the performance of the optimal policy for the POMDP, in which we also present explicit conditions using recent results on filter stability in controlled POMDPs. Whereas there exist many experimental results, (i) the rigorous asymptotic convergence (to an approximate MDP value function) for such finite memory Q learning algorithms and (ii) the near optimality with an explicit rate of convergence (in the memory size) under filter stability are results that are new to the literature to our knowledge. Funding: This research was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
Exploiting Papers’ Reference’s Section for Multi-Label Computer Science Research Papers’ Classification

The profusion of documents production at an exponential rate over the web has made it difficult for the scientific community to retrieve most relevant information against the query. The research community is busy in proposing innovative mechanisms to ensure the document retrieval in a flexible manner. The document classification is a core concept of information retrieval that classifies the documents into predefined categories. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as information retrieval, finding experts, recommender systems, etc. In Computer Science, the Association for Computing Machinery (ACM) categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In this paper, we exploit the reference section of a research paper to predict the topics of the paper. We have proposed a framework called Category-Based Category Identification (CBCI) for multi-label research papers classification. The proposed approach extracted references from training dataset and grouped them in a Topic-Reference (TR) pair such as TR {Topic, Reference}. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The approach collects the corresponding list of topics matched with the references in the said pair. We have evaluated our technique for two datasets that is Journal of Universal Computer Science (JUCS) and ACM. The proposed approach is able to predict the first node in the ACM topic (topic A to K) with 74% accuracy for both JUCS and ACM dataset for multi-label classification.
Impact of Artificial Intelligence on E-Commerce Development

Artificial Intelligence (AI) in the E-trade industry is being utilized by online retailers for giving visit bot administrations, dismembering customer comments, and for offering redid sorts of help to online clients. In all honesty, a 2019 Impartial assessment found that 1 in every 5 clients are anxious to purchase products or organizations from a discussion bot, while 40% of the online clients are scanning for unfathomable offers and shopping deals from visit bots. While the overall Web-based business bargains are foreseen to contact $4.8 billion continually in 2021, Gartner predicts that around 80% of all customer affiliations will be regulated by PC based insight headways (with no human expert) ceaselessly 2020. Taking everything into account, how has Man-made mental aptitude in Online business changing or changing the shopping contribution with 2019? Through this part, we are going to highlight a segment of the noteworthy employments of man-made mental aptitude in Web-based business nearby a segment of the certifiable plans of action.
Outlier Prediction Using Random Forest Classifier

Random forest is an ensemble learning method for classification, regression and other tasks that operate by constructing a multiple decision trees using training data and majority of the class will be consider as output. Out-of-Bag (OOB) takes the samples from the training set with replacement. In random forests, if you choose oob to true then there is no need for a separate test set to validate the model. It is estimated internally when the forest is built on training data, and each tree is tested on one-third of the samples not used in building that tree. Out of bag estimate an internal estimate of a random forest as it is being constructed. In this paper we propose two approach to implement outlier prediction by applying random forest classifier and LSTM model with experiments.