Reviewerly/exHarmony huggingface dataset:
TEACHING SPEAKING IN THE REALITY OF COVID-19 IMPACT

The article is aimed at defining the challenges and problems that lecturers face in the process of distance education. In the reality of Covid-19, this aspect is increasingly important and subjected to discussion in order to find the ways and solutions that would help to facilitate the process of integrating both lecturers and students into educating. The main issues the article looks into are teaching speaking distantly and getting involved into communicative process by means of available online tools. Speaking is one of the most common producing processes for students learning foreign languages in communicatively oriented classrooms. Many things were usual routine for lecturers and students but now they can cause the emotional ups and downs for both sides. Lecturers have to do some action research in order to become more informed and efficient in the learning web-based environment. Therefore, a research into the most effective online instruments that assist in teaching online was conducted on students of different departments of two universities in Kyiv. As a result, a language teaching framework that draws from this research is presented in the article. New technology is changing the future of work with unprecedented speed and intensity. This drives the reinvention of our lives, workplace, and economy. The speed and advancement of technology in the future workplace will require a lot of new skills and capabilities, but the most valuable is the ability to embrace change. The skills needed by teachers and lecturers at universities, new tools, advancements and methods which work in educational process, and results gained from practical application of the framework areincluded into the article.The framework has proved to be successful: the lecturers are driven by learning outcomes and end goals of the students; learning outcomes give us the opportunity to say that the students are eager for the classes. They gain a lot from discussing their thoughts/ideas and opinions.
A Volumetric Measurement Algorithm of Defects in 3D CT Image Based on Spatial Intuitionistic Fuzzy C-means

Defect volume measurement of 3D computed tomography (CT) image is of great importance in defect analysis. Currently, there are few volumetric measurement algorithms for defects, and it is difficult to meet the requirements of practical applications. To address this issue, an automatic volumetric measurement algorithm is proposed to obtain the defect volume in a 3D CT image in a more precise way. Spatial intuitionistic fuzzy C-means (SIFCM) is used to segment the defects in each slice image. Compared with fuzzy C-means (FCM) and OTSU algorithms, defect segmentation can achieve better results and accuracy. Subsequently, the defect volume of the connected component can be calculated using the 3D region growing in the segmented binary images. The experimental results demonstrate the feasibility and effectiveness of the proposed algorithm, especially for large defects.
Opposition-based Grey Wolf Optimization Approaches for Feature Selection

Feature selection is an approach to select the important and key feature subset from a large number of redundant features, which can effectively reduce the computational cost and storage cost in the data processing process. Therefore, feature selection is widely used in data mining and pattern recognition. The grey Wolf optimization algorithm can solve the problem of feature selection by simulating the predation behavior of gray wolves to obtain the optimal feature subset step by step. In this paper, we propose an opposition based learning grey Wolf optimization algorithm(OGWO), which uses opposition-based learning to initialize and update the population to improve the exploration ability of the population. In order to avoid the local optimum, a nonlinear parameter adjustment strategy is used to update the important parameters of the grey Wolf optimization algorithm. Experiments on 12 datasets demonstrate the effectiveness of the proposed method. Experimental results show that OGWO has better classification performance compared with other swarm intelligence algorithms.
Adversarial representation learning for synthetic replacement of private attributes

Data privacy is an increasingly important aspect of many real-world analytics tasks. Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output. Furthermore, finding the right balance between privacy and utility is often a tricky trade-off. In this work, we propose a novel approach for data privatization, which involves two steps: in the first step, it removes the sensitive information, and in the second step, it replaces this information with an independent random sample. Our method builds on adversarial representation learning which ensures strong privacy by training the model to fool an increasingly strong adversary. While previous methods only aim at obfuscating the sensitive information, we find that adding new random information in its place strengthens the provided privacy and provides better utility at any given level of privacy. The result is an approach that can provide stronger privatization on image data, and yet be preserving both the domain and the utility of the inputs, entirely independent of the downstream task.
Studies in aspects of peer review

The process of peer-review is considered as the sentinel of science in scholarly communications. However, with the deluge of research articles and the overload of scholarly information, it is increasingly becoming difficult for humans to keep up with the pace of the latest research. This thesis uses Machine Learning (ML) and Natural Language Processing (NLP) to investigate critical issues relevant to scholarly communications and peer review. Detecting whether a document contains sufficient new information to be deemed as novel is of immense significance in this age of data duplication. We investigate if we can automatically identify the newness in a document based on its information content against a set of relevant documents. We create two benchmark datasets: TAP-DLND 1.0 and TAP-DLND 2.0, and devise several methods for novelty detection at the document level. One particular challenge in peer review is that the journal editors and conference program chairs are overwhelmed with the ever-increasing rise in article submissions. Studies show that many submissions are not well-informed and do not fit within the scope of the intended journal or conference. We investigate how an AI could assist the editors and program chairs in identifying potential out-of-scope submissions based on the past accepted papers of the particular journal conference. We experiment with several methods, including feature-based machine learning, deep multimodal architectures, to multiview clustering. Again, the recent unprecedented growth in paper submissions in major Machine Learning and AI conferences is a grand challenge to the community to maintain high-quality reviewing practices while ensuring fair evaluation of the manuscripts. We investigate if we can identify which reviews were significant enough to be considered in the decision-making. We design an end-to-end deep network that leverages on exhaustiveness and strength of the peer review in a multi-task fashion to comprehend it's informativeness. Our DeepSentiPeer model is one of the first attempts to predict decisions in peer review automatically. Finally, finding the relevant literature or identifying the works that have inspired the current work under scrutiny is central to an evaluation in peer review. We analyze the context of citations within paper full-text to identify a given research lineage. The idea is to leverage citation significance classification to build a research lineage via a significant citation graph. We argue that finding the lineage of given research would help identify the true academic impact of a paper beyond quantitative citation counts. Awarded by : Indian Institute of Technology Patna, India on 28 August, 2021. Supervised by : Asif Ekbal and Pushpak Bhattacharyya. Available at : https://www.iitp.ac.in/~ai-nlp-ml/resources/dissertations/PhD/Tirthankar_Ghosal_PhD_Thesis_IIT_Patna.pdf.
An SG-CIM Model Table Classification Method Based on Multi Feature Semantic Recognition Technology

In this paper, based on the SG-CIM model Knowledge Graph, we introduce the semantic recognition technology in Natural Language Processing to model the information contained in the graph and mine the semantic information with multiple features according to the characteristics of the data itself. For the problem of graph domain name-related attribute complementation, this paper adopts a multi-feature semantic recognition approach to classify the given SG-CIM model table by domain name. We propose an ATT-ALE-TextRNN model for the descriptive features of the table, adding N times second-level domain name embeddings to the basic TextRNN and calculating the attention score together to capture the tendency of different contextual information for a given category. In this paper, with reference to the multidimensional discrete feature classification problem of the recommender system, an improved DeepFM model is proposed for the table discrete, class-forming features. It facilitates the discovery of semantic dependencies between class features, makes the feature distribution more diverse, and avoids the problems of low repetition between multidimensional features and low performance of combined computation. By combining the above two models, this paper achieves more accurate mining of multi-feature semantics and accurate classification of topic domains.
The Challenges and Solutions of Cybersecurity Among Malaysian Companies

The objective of this chapter is to analyze the challenges faced by Malaysian companies in cybersecurity and to determine solution for Malaysian companies to overcome challenges in cybersecurity. The data were collected from the expert people in cybersecurity fields using interview sessions. The finding confirmed that the awareness and budget are very important in other to implement the element of cybersecurity in the company. Cybersecurity is good and desired as a protection for an organization in developing strategic planning to gain more profitability and increase the productivity of goods and services. This research will be beneficial for the organization because it will provide the solution for the company to overcome the cybersecurity issues. From this research, an organization can have potential to enhance competitiveness and understand the problem occur, then do the improvement by implementing cybersecurity.
An Emotion and Gender Detection Using Hybridized Convolutional 2D and Batch Norm Residual Network Learning

The deep learning algorithm called convolutional neural network (CNN) particularly with Residual Network (ResNet) receiving much attention from the research community in facial recognition recently. Unfortunately, the complexity of optimization problems in overfitting and vanishing gradient cause huge obstacles. More specifically, once the gradient is backpropagated in initial layers, repeated multiplication among layers constructs gradient infinitely small and causes the layers of the network to become deeper and degrade the performance. Moreover, the skip connection that comprises the residual network (ResNet) is not enough to solve the above-mentioned limitations, and this could downgrade the optimization of used layers and potentially further downgrade the accuracy. Therefore, a deep residual network (ResNet) with hybridized function i.e., convolutional-2D and Batch Norm is proposed as this could allow direct signal propagation from the initial to the final layer of the network for every single residual block deeply. Initially, the convolutional-2D and Batch Norm were constructed to overcome bias in-depth nets and propagate the gradients directly from the loss layers to any previous layers, while skipping intermediate weight layers deeply that have the potential to trigger vanishing or deterioration of the gradient signal. The proposed learning model has improved the degradation of accuracy drawback by decreasing the number of layers needed more in low level as compared to existing work for each block using batch normalization and convolutional-2D function.
Privacy Knowledge Base for Supporting Decision-Making in Software Development

Integrating security and privacy requirements at every stage of the software development cycle is critical to guarantee the confidentiality, integrity and availability of the system and consequently of the data. Developers need to be supported in this challenge, as many different skills are required to respond effectively to the growing number of cyber-attacks. In such a context, this research study endeavors to define the key elements that support decision-making in privacy oriented software development. A Privacy Knowledge Base (PKB) is defined to support developers’ decisions in all software development phases, and a prototype (PKB-Tool) is developed to operationally integrate privacy and security requirements into the development of new systems and the re-engineering of legacy systems. An ongoing experimentation in the context of an industrial project is presented to validate the efficacy of the 5 key elements in supporting developers in integrating privacy and security requirements in the software life cycle.
Supervised Attention Network for Arbitrary-Shaped Text Detection in Edge-Fainted Noisy Scene Images

Text mining in noisy situations, like poor contrast and fainted edges, is one of the challenging areas of research in the domain of social networks and computer vision. Scene text detection is a complicated task as text regionhaving varying span in term of size, orientation, aspect ratio, color, font, and script. Furthermore, the contrast of a scene image varies drastically in noisy situations due to poor illumination and image filtering. This faints the text edges and make the task of detection more challenging. In this article, we bring forward a semantic edge supervised spatial-channel attention network, known as SESANet, for detecting arbitrary-shaped text instances in noisy scene images with faint text edges. Our network learns multiscale (MS) supervised edge semantic, pixel-wise spatial structure information, and interchannel dependencies for precisely localizing the text masks in scene images with poor contrast and illumination. Our network is efficient, precise, and fast in nature. SESANet captures rich, dense, discriminative, and MS semantic information. The experimental results show the success of the proposed network. It shows a superior performance with regard to recall on the publicly available benchmark datasets. A new dataset scene images, named as Edge-fainted Noisy Arbitrary-shaped Scene Text (EFNAST) dataset, having varying noise density, poor contrast, low illumination, and faint edges is created.
Visual Domain Adaptation in the Deep Learning Era

Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and
Multimodal Retrieval in E-Commerce

E-commerce provides rich multimodal data that is barely leveraged in practice. The majority of e-commerce search mechanisms are uni-modal, which are cumbersome and often fail to grasp the customer's needs. For the Ph.D. we conduct research aimed at combining information across multiple modalities to improve search and recommendations in e-commerce. The research plans are organized along the two principal lines. First, motivated by the mismatch between a textual and a visual representation of a given product category, we propose the task of category-to-image retrieval, i.e., the problem of retrieval of an image of a category expressed as a textual query. Besides, we propose a model for the task. The model leverages information from multiple modalities to create product representations. We explore how adding information from multiple modalities impacts the model's performance and compare our approach with state-of-the-art models. Second, we consider fine-grained text-image retrieval in e-commerce. We start off by considering the task in the context of reproducibility. Moreover, we address the problem of attribute granularity in e-commerce. We select two state-of the-art (SOTA) models with distinct architectures, a CNN-RNN model and a Transformer-based model, and consider their performance on various e-commerce categories as well as on object-centric data from general domain. Next, based on the lessons learned from the reproducibility study, we propose the model for the fine-grained text-image retrieval.
Algebraic Representation of Topologies on a Finite Set

Since the 1930s, topological counting on finite sets has been an interesting work so as to enumerate the number of corresponding order relations on the sets. Starting from the semi-tensor product (STP), we give the expression of the relationship between subsets of finite sets from the perspective of algebra. Firstly, using the STP of matrices, we present the algebraic representation of the subset and complement of finite sets and corresponding structure matrices. Then, we investigate respectively the relationship between the intersection and union and intersection and minus of structure matrices. Finally, we provide an algorithm to enumerate the numbers of topologies on a finite set based on the above theorems.
Quantifying Uncertainties of Residuals in Symbolic Regression via Kriging

Genetic programming (GP) based symbolic regression is a powerful technique for white-box modelling. However, the prediction uncertainties of the symbolic regression are still unknown. This paper proposes to use Kriging to model the residual of a symbolic expression. The residual model follows a normal distribution with parameters of a mean value and a standard deviation, where the mean value can be used to regulate the prediction and the standard deviation represents the uncertainties of residuals in a specific symbolic expression. The proposed algorithms are compared with a canonical GP-based symbolic regression and Kriging regression on three benchmarks in symbolic regression field. The results show that the proposed algorithm significantly outperforms the other two algorithms on the three benchmarks w.r.t. mean squared error in the test dataset with a small generation budget.
Multi-criteria Evaluation of Students’ Performance Based on Hybrid AHP-Entropy Approach with TOPSIS, MOORA and WPM

Worldwide pandemics and long periods of social containment require further improvement of Intelligent Education System (IES) with reliable communication networks and computing systems for providing accessible and effective teaching and learning. One of the main challenges in distance learning is to ensure adequate monitoring of the education process and evaluation of interim and final results. The paper presents a hybrid approach for multi-criteria evaluation of students’ performance in a flexible framework of three preference scenarios where the theoretical learning, practical skills, and final exams participate with different weights. The systematization of criteria in blocks and their weighting according to preferences allow obtaining more objective macroscopic results. The Multiple Assessment integration (MAI) of the evaluation values in TOPSIS, MOORA and WPM allows to explore their behavior with different data sets and contributes to consolidate the final results for obtaining a better holistic and personalized view of the education process for each individual student.
TNRAS: Tor Nodes Reliability Analysis Scheme

With the rapid Internet development, the security attacks become more and more noteworthy that people have paid more concentration on personal privacy. The Onion Router (Tor), a distributed anonymous communication network, has gradually come into our field of vision for secure communicating on the Internet. However, there are still several security issues like the Sybil attack in Tor, which pose a non-negligible threat to both the network and its users. This paper proposes an ideal method Tor nodes reliability analysis scheme (TNRAS), Tor nodes reliability analysis scheme, against the Sybil attack. We explore an effective mean to reveal the Sybil or unreliable nodes in Tor. Through analyzing the general characteristics of nodes from six aspects, we can predict the unreliable nodes. We filter them and assess the remaining high reliable nodes for practical application afterwards. After that, the Tor reliable nodes analysis scheme application is implemented and analyzed. Moreover, this paper points out the definition of the Sybil nodes, high reliable nodes, and the reliability, then further assessing the nodes’ reliability. The research reveals the Sybil nodes characteristics through our six aspects detection method, and the predict model achieves to 0.928 training correlation and 0.715 validation correlation both with more than 0.8 ROC, which is of great significance for Tor with its increasing number of users and the worsening network context.
Collecting Spatial Data Under Local Differential Privacy

By adding noise to real data locally and providing quantitative privacy protection that can be rigorously mathematically proven, Local differential privacy is the suitable technology for the private collection of two dimensional location data. Most current solutions discretize the location information into grids, and then apply LDP-based frequency oracle to obtain distribution information of all users for spatial range query. However, the discretization step of gridding will result in a more or less loss of accuracy, while eliminating the inherent correlation between adjacent grids. Thus leading to a large overall error. Drawing on the idea of continuous perturbation on finite intervals, we propose a two-dimensional continuous density estimation method, called LTD-EM. It takes advantage of numerical nature of the map domain and uses the near-neighbor perturbation and EM algorithm. We also optimize the algorithm considering the irregular shape of the geography map. The experimental results show that the accuracy of the spatial range query provided by LTD-EM is significantly better than that of existing solutions.
Cross-Domain Attentive Sequential Recommendations based on General and Current User Preferences (CD-ASR)

Sequential Recommendations (SR) have become increasingly important because of their accuracy and consistency with real world scenarios, where a user interacts with a sequence of items over time. SR systems have the capability of modeling temporal information to extract user’s current preferences. However, data sparsity is a real challenge for SR models, causing them to sometimes function poorly and generate inaccurate recommendations. A practical solution to this problem is to transfer information from multiple source domains to tackle the sparsity in the target domain, an approach known as Cross-Domain Recommendations (CDR). Extracting users’ preferences from different domains in a sequential manner can help generate an effective CDR for sequential models. In this paper, we propose a Cross-Domain Attentive Sequential Recommendation model based on general and current user preferences (CD-ASR). We assume the user information from the source domains to be a user’s general information, and apply a general attention model to aggregate user source representational vectors. At the same time, we apply a self-attention sequential model to obtain user’s current preferences in the target domain. Implicitly, we utilize the aggregated user’s source vectors to transfer knowledge to produce more precise recommendation in the target domain. Our model shows superiority over other state-of-the-art SR models using three datasets extracted from Amazon dataset.