Reviewerly/exHarmony huggingface dataset:
Baseline Isolated Printed Text Image Database for Pashto Script Recognition

The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages. Moreover, the absence of a standard publicly available dataset for several low-resource languages, including the Pashto language remained a hurdle in the advancement of language processing. Realizing that, a clean dataset is the fundamental and core requirement of character recognition, this research begins with dataset generation and aims at a system capable of complete language understanding. Keeping in view the complete and full autonomous recognition of the cursive Pashto script. The first achievement of this research is a clean and standard dataset for the isolated characters of the Pashto script. In this paper, a database of isolated Pashto characters for forty four alphabets using various font styles has been introduced. In order to overcome the font style shortage, the graphical software Inkscape has been used to generate sufficient image data samples for each character. The dataset has been pre-processed and reduced in dimensions to 32 × 32 pixels, and further converted into the binary format with a black background and white text so that it resembles the Modified National Institute of Standards and Technology (MNIST) database. The benchmark database is publicly available for further research on the standard GitHub and Kaggle database servers both in pixel and Comma Separated Values (CSV) formats.
FSA-Net: A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization

The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images. In this study, the Face Swapping Attention Network (FSA-Net) is proposed to generate photorealistic face swapping. The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint (cheek, mouth, eye, nose, etc.), which causes artifacts and makes the generated face silhouette non-realistic. To address this problem, a novel reinforced multi-aware attention module, referred to as RMAA, is proposed for handling facial fusion and expression occlusion flaws. The framework includes two stages. In the first stage, a novel attribute encoder is proposed to extract multiple levels of target face attributes and integrate identities and attributes when synthesizing swapped faces. In the second stage, a novel Stochastic Error Refinement (SRE) module is designed to solve the problem of facial occlusion, which is used to repair occlusion regions in a semi-supervised way without any post-processing. The proposed method is then compared with the current state-of-the-art methods. The obtained results demonstrate the qualitative and quantitative outperformance of the proposed method. More details are provided at the footnote link and at .
When Memory Corruption Met Concurrency: Vulnerabilities in Concurrent Programs

Concurrent programs are widespread in modern systems. They make better use of processor resources but inevitably introduce a new set of problems in terms of reliability and security. Concurrency bugs usually lead to program crashes and unexpected behavior, and are an active research topic. From a security perspective, concurrency vulnerabilities are those that exhibit harmful behavior exclusively in concurrent executions. They can take place in a diverse range of environments, such as in operating system kernels, file system operations, or general-purpose multithreaded programs. A particular characteristic of concurrency is that it not only introduces new problems, but also enables traditional vulnerabilities to be triggered in concurrent-specific ways. Those that lead to dangerous security vulnerabilities usually cause memory corruption, a strong and flexible primitive for exploitation, and are known as concurrency memory corruption vulnerabilities. In this paper, we systematically analyze concurrency vulnerabilities in C and C++ programs, their exploitation and their detection, focusing on concurrency memory corruption vulnerabilities. We organize previous work on concurrency bug characteristics and detection, and highlight the differences in relation to vulnerabilities. Then, we examine the existence of concurrency vulnerabilities in real-world programs by searching the CVE database and point out a growing trend. Further, we analyze and compare existing detection approaches towards concurrency memory corruption.
Toward Effective Evaluation of Cyber Defense: Threat Based Adversary Emulation Approach

Attackers compromise organizations with increasingly sophisticated ways, such as Advanced Persistent Threat (APT) attackers.Usually, such attacks have the intention to exploit endpoints to gain access to critical data.For security controls and defense evaluation, organizations may employ offensive security activities.The most important one is penetration testing and red teaming, but such operations are usually resource exhaustive and extend over a longer period of time.Furthermore, traditional Venerability Assessment and Penetration Testing (VAPT) works effectively in the mitigation of known attacks but did not prove to be effective against stealthy attacks.VAPT considers the whole offsec as an acting problem but in reality, an attacker has to deal with uncertainty while conducting real-world attacks.In this paper, we are presenting an adversary emulation approach based on MITRE ATT&CK adversary emulation plan with consideration of planning as a major part of each attack phase.The approach utilizes stealthy attack vectors and paths to emulate adversary for defense evaluation.For effective defense evaluation, we picked more than 40 techniques from ATT&CK, deployed their mitigation on target machines, and then launched attacks against all those techniques.We show that attack paths and payloads generated using our approach are strong enough to evade security controls at endpoints.This approach provides a special environment for cyber defenders to think like adversary, and create new attack vectors and paths to evaluate organizational security preparedness.This process constructs a special environment to expand the attack landscape view and defense evaluation with minimal resources for the organization.
TDN: Triplet Distributor Network for Knowledge Graph Completion

Conventional Knowledge Graph Completion (KGC) methods typically map entities and relations to a unified space through the shared mapping matrix, and then interact with entities and relations to infer the missing items in the knowledge graph. Although this shared mapping matrix considers the suitability of all triplets, it neglects the specificity of each triplet. To solve this problem, we dynamically learn one information distributor for each triplet to exchange its specific information. In this paper, we propose a novel Triplet Distributor Network (TDN) for the knowledge graph completion task. Specifically, we adaptively learn one Triplet Distributor (TD) for each triplet to assist the interaction between the entity and relation. Furthermore, on the basis of TD, we creatively design the information exchange layer to dynamically propagate the information of the entity and relation, thus mutually enhancing entity and relation representations. Except for several commonly-used knowledge graph datasets, we still implement the link prediction task on the social-relational and medical datasets to test the proposed method. Experimental results demonstrate that the proposed method performs better than existing state-of-the-art KGC methods. The source codes of this paper are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TDN</uri> for Knowledge Graph Completion.git.
Disturbed Augmentation Invariance for Unsupervised Visual Representation Learning

Contrastive learning has gained great prominence recently, which achieves excellent performance by simple augmentation invariance. However, the simple contrastive pairs suffer from lacking of diversity due to the mechanical augmentation strategies. In this paper, we propose Disturbed Augmentation Invariance (DAI for abbreviation), which constructs disturbed contrastive pairs by generating appropriate disturbed views for each augmented view in the feature space to increase the diversity. In practice, we establish a multivariate normal distribution for each augmented view, whose mean is corresponding augmented view and covariance matrix is estimated from its nearest neighbors in the dataset. Then we sample random vectors from this distribution as the disturbed views to construct disturbed contrastive pairs. In order to avoid extra computational cost with the increase of disturbed contrastive pairs, we utilize an upper bound of the trivial disturbed augmentation invariance loss to construct the DAI loss. In addition, we propose Bottleneck version of Disturbed Augmentation Invariance (BDAI for abbreviation) inspired by the Information Bottleneck principle, which further refines the extracted information and learns a compact representation by additionally increasing the variance of the original contrastive pair. In order to make BDAI work effectively, we design a statistical strategy to control the balance between the amount of the information shared by all disturbed contrastive pairs and the compactness of the representation. Our approach gets a consistent improvement over the popular contrastive learning methods on a variety of downstream tasks, e.g. image classification, object detection and instance segmentation.
Blockchain-Backed Searchable Proxy Signcryption for Cloud Personal Health Records

Patient-centered data management and sharing of personal health records (PHRs) are difficult to be realized as data is controlled by doctors/hospitals. In addition, security and privacy, oppressive costs, search and tracing unreliability, and complicated access authorization caused by traditional encryption severely hinder the widespread adoption of PHRs. To overcome these challenges, we propose a blockchain-backed data sharing framework for PHRs, where the blockchain achieves reliable search and tracing. Furthermore, we design a hybrid <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</b> lock <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> hain-backed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> earchable <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> roxy <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> ign <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> ryption scheme, named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BC-SPSC</b> . Specifically, an identity-based proxy signature (IBPS) is utilized to perform the authorization from patients to doctors to achieve authentic patient-centricity, therefore the blockchain can relate data with associated patients and doctors during data tracing. Moreover, BC-SPSC supports two search modes. The first mode adopts attribute-based encryption with keyword-based search (SABE), where all legitimate users can implement searches, but only users whose attributes satisfy the access structure can successfully decrypt. By contrast, the second mode utilizes attribute-based searchable encryption (ABSE) to accomplish fine-grained authorization in both search and data access/decryption, that is, who can search is also constrained by data owners. Adequate performance comparisons and simulation experiments indicate significant advantages of the BC-SPSC scheme in storage and computation overheads.
Voice-Based Intelligent Virtual Assistant for Windows

An average individual can pronounce about 140 words per minute but only can type 40. Voice communication is the most convenient and efficient way to interact. The majority of individuals prefer speech-to-text-based communication. Voice assistant for windows is a software program or application that any windows user can make use of with the help of their voice just by saying what the user wants. In most cases, personal assistants will respond to questions and carry out tasks utilizing a natural language user interface. Our voice is the input key here. It is a digital assistant that employs voice recognition, speech synthesis, natural language processing, and classification algorithms to give exceptional service via an interface. The existing system is incapable of performing or executing custom commands. As a result, we attempted to address this flaw in our project. The main job of an intelligent assistant is to classify the input into a task. In this paper, we have used the Naive Bayes classification algorithm for task classification which gave the best accuracy among KNN, logistic regression, random forest, decision tree, and SVM. The assistant will simplify the user's work by allowing them to perform a task with just one voice command.
Real-Time Object Detection and Tracking Design Using Deep Learning with Spatial–Temporal Mechanism for Video Surveillance Applications

We propose a CNN-based framework for “real-time object detection and tracking using deep learning” in this paper, which includes a spatial–temporal mechanism. The impact of efficient data on performance benchmarks in terms of accuracy has changed. The data processing is handled by industry buzzwords: deep learning (DL) and computer vision (CV). The CNN-based framework uses the single object tracker value to match arrival models and find targets in the next frame. Simply applying single object tracking to multiple object tracking will encounter problems in computational efficiency and results due to occlusion. In this paper, we introduce a “spatial attention mechanism (STAM)” to manage occlusion bias and target interaction. Object tracking is a sensational technology in image processing with great future implications. Multiple object tracking (MOT) has seen an extensive boom in the last few years due to machine learning, deep learning, computer vision, and more. This paper aims to provide an object tracking software solution. Using YOLO’s “You Only Look Once” technology with the help of Tensor flow, the system is geared toward object detection, tracking, and counting. Proven, effective detection and tracking on various dataset. Algorithms that offer real-time, accurate, and precise identifications appropriate for real-time applications.
EXISTING PROBLEMS OF IMPLEMENTING THE DISTANCE FORM OF PEDAGOGICAL EMPLOYEES QUALIFICATION IMPROVEMENT

The current situation in the country has a decisive impact on the field of education, which leads to a more significant introduction and ubiquitous implementation of distance learning not only for teaching students, but also for training of teachers. Distance learning is characterized as an individualized process of acquiring knowledge, skills, abilities and means of human cognitive activity, which basically occurs through indirect interaction of distant participants of the educational process in a specialized environment based on modern psychological, pedagogical, and information and communication technologies. It is shown in the article that many scientific and methodological works analyze positive aspects of distance learning (e.g., mobility, constant access to materials, blended and / or flipped learning, the ability to involve large numbers of participants, efficiency, new teacher role, work in synchronous and / or asynchronous mode, etc.). The presented research revealed the insufficient coverage of existing problems in distance learning, especially distance learning of teachers by modern scientific works (low level of information and communication competence, lack of Internet, outdated equipment, poor self-organization of students, lack of «live» communication, the problem of personalization, copyright violations, etc.). In the paper, the main stages of the history of distance learning beginning from the XVIII century are described. The effectiveness of introducing distance learning of teachers in the postgraduate system of education is substantiated. We explore and analyze pedagogical, methodological and scientific works devoted to the problems of continuous professional education of teachers, professional development of teachers and their qualification improvement. The problems of implementing the distance form of qualification improvement of pedagogical workers in the system of postgraduate pedagogical education are investigated and described in the article.
Digital Teaching Skills to Design Virtual Learning Classrooms with the 4PADAFE Methodology

The main purpose of this research is to apply the 4PADAFE Methodology so that teachers can develop their digital skills. Educators have to develop 21st century digital skills to improve their didactic practice and innovate their classes in all kind of modalities of study. Educational institutions, faced with the new demands of the knowledge and information society, have to head towards the transformation of new teaching and learning models. The theoretical foundations of the methodology proposed are behaviourism, cognitivism, constructivism, and connectivism as a transversal axis in a flipped classroom and student-based learning context. The importance of the methodology lies in the fact that, being a comprehensive methodology, it encompasses pedagogical, methodological and technological components as well as didactic strategies, focusing on developing digital skills. In order to reach the objectives established in this research project, surveys were applied on teachers from different educational institutions and, consequently, a quantitative analysis of the digital skills proposed by this methodology was carried out. As main findings, it is evident that the 4PADAFE Methodology contributed to developing digital skills in teachers through the accompaniment in the systematic process that guided the development and construction of a massive virtual classroom.
Augmented Reality for Self-management of Learning in Manufacturing Laboratories (AR-ManufacturingLab)

This proposal focuses on applying augmented reality tools to promote self-management of learning, specifically, the specialized machinery housed in the manufacturing laboratories of the campus. The machines involved include industrial robots, lathe, milling machine, and modeling CNCs (Computer Numerical Control), 3D printers, laser and plasma cutters, electronic circuit printers, and PLCs (Programmable Logic Controllers), among others. Traditionally, an instructor is needed as a guide to train the students using the machinery. This work proposes that the students use their mobile devices to receive the instructions for operating each machine. Because students learn at different rates, we seek to reduce students’ deficits in disciplinary skills because, under traditional learning, there is no heterogeneity in understanding the contents. Many students ask for personal guidance on how to use the laboratory equipment, which causes teachers to invest more time than in the classroom. With this proposed learning methodology, students can resort to practice with the augmented reality tool as many times as necessary.
Energy-Efficient Data Center Network Infrastructure With Network Switch Refresh Model

Data centers and their underline transmissions are required to guarantee critical services in a 24/7/365 framework.The extensive energy consumption of these data centers and their transmission networks impose a threat to globally available scarce energy resources.Furthermore, addressing performance and energy requirements trade-offs is also challenging.This article has been specifically focused to assess the inside-energy-view of typical data center networks and assess how network infrastructure replacements or ''refresh'' can lead to a better energy-efficient data center network (DCN) design without compromising performance or service level requirements.Server refresh techniques are found in the literature.However, considering network infrastructure refresh to attain energy efficiency is the first of its kind.A model has been proposed in this article that works on algorithms to recommend network switch replacements.The algorithms perform parametric analysis for replacement.The analysis considers parameters impacting the performance and energy consumption of the switches.In addition to technical parameters, the proposed model has also evaluated the cost impacts of the replacement.The replacement is only recommended if it is energy efficient and performance effective and validated when replacement is also cost-effective.The proposed model has been evaluated through two replacement options, one with the same manufacturer and the other with a different manufacturer.Replacement with two different options has been proposed to evaluate the impact of various manufacturers in designing a better energy-efficient data center meeting all service level requirements and performance guarantees.The results obtained from the proposed reflect the attainment of the desired objectives.
Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors

Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied and developed methods to address these objectives separately. However, no approach currently performs both tasks simultaneously. In this paper, we propose a fully Bayesian functional concurrent regression mixture model that simultaneously performs functional variable selection and clustering for subject-specific trajectories. Our approach introduces a novel spiked Ewens-Pitman attraction prior that identifies and clusters subjects’ trajectories marginally for each functional covariate while using similarities in subjects’ auxiliary covariate patterns to inform clustering allocation. Using simulated data, we evaluate the clustering, variable selection, and parameter estimation performance of our approach and compare its performance with alternative spiked processes. We then apply our method to functional data collected in a novel, smartphone-based smoking cessation intervention study to investigate individual-level dynamic relations between smoking behaviors and potential risk factors.