Reviewerly/exHarmony huggingface dataset:
Detection of Phishing Attacks on Online Collaboration Tools Using Logistic Regression

Phishing is a process and activity related to the security of the internet, and it does not target only the software but to data that is vulnerable to day-to-day activity in human life. This can also be narrated as striking or pouncing on innocent online users to steal very sensitive information eventually causing financial losses and damaging the credentials of individuals. Phishing is a very commonly used means of threat on the internet which resulted into the growth of the World Wide Web in volume significantly in recent times. In general, Phishing criminals use the latest and highly sophisticated methods to fool online users i.e. zero-day. Therefore, it is very essential that the anti-phishing system be real fast, real time and holds from the intelligent phishing detection solution. The need of the hour is that we establish a detection system that will adjustably meet the transposing environment and phishing websites. As the approach proposed extracts various kinds of perceptive aspects from the source-code of webpages and URLs, it is completely a tailor-made solution from the clientele side and it does not require any third-party service. In this paper, the system is intelligent; it provides a brilliant system for detecting or to finding out phishing websites. The entire system is a machine learning based, significantly monitor learning in particular. As the best presentation in categorization the Logistic Regression Technique has been selected.
Distributed Ledger Technology: Use Cases, Design, and Implementation Issues

Distributed Ledger Technology (DLT) based network provides an increasing application trends for various use cases. Distributed Ledger Technology (DLT) and Blockchain (BC) are used interchangeably in the current trend. DLT or Blockchain is an emerging decentralized and distributed computing paradigm that underpins the bitcoin cryptocurrency. The technology trend is shifting toward a trustless network, meaning there is no need to trust any of the components of the network, which is distributed in the form of a connected network. We discuss the issues arising in use cases based on business modeling while designing and implementing a DLT-based system. However, the problem of designing the system for implementation of DLT is competitive as compared to non-blockchain-based systems in terms of transaction throughput and scalability. In this chapter, we analyze the shortcomings of designing DLT-based network and its implementation as well as its underlying blockchain technology. We also present the framework and simulation results as well as network benchmarking using different tools and techniques considering two different DLT-based networks such as Ethereum and Hyperledger fabric.
An Analysis on Prayer Room Design in Shopping Complexes: Significance and Function of Ablution Area

Wudhu' (ablution) area is a space to perform ablution by clean certain body parts with water for Muslims before praying. There are several design guidelines for praying facilities in Malaysia, but they lack criteria and specification guidelines for ablution space; they are still inadequate and not user-friendly. This paper is to identify the level of ablution areas practicality based on the design and satisfaction criteria; space planning, ergonomic, the relationship between prayer hall, and users satisfaction. It focuses on commercial buildings interrogative shopping complexes that do not fully apply and are not Islamic compliant. An observation and on-site measurement manage by a selection of shopping complexes in the Klang Valley and Johor Bahru. It enhanced the standard guideline for prayer areas in shopping complexes based on the local authority's essential for the respective parties: local authorities and architects, besides regulating the prominence of users' comfort.
Augmentation of Artificial Intelligence and Maqasid Shariah in Fiqh Robotics for Therapy and Community Activities

Fiqh Robotics (FR) is a collection of systematic rulings from Islamic Jurisprudence concerning humanoids used for therapy and community service. Through various technological inventions, artificial intelligence (AI) has taken over society. Humanoids are built with special characters to perform tasks that humans can do. Humanoids are designed with defined abilities to operate tasks that humans can perform. These humanoids are regarded as merciful gifts to humanity, despite elevating moralistic issues concerning virtue and theological argumentation. This FR contributes to a set of modules that address those contentious issues by incorporating the latest technologies that are compatible with Islamic Law.
Model Checking for Entanglement Swapping

Entanglement swapping is a basic primitive in long distance quantum communications. The stochastic nature of various operations like entanglement generation and BSMs makes the entanglement swapping primitive failure prone. It is difficult to predict whether or not an entanglement swapping operation will succeed within a stipulated time. In this paper, we use Probabilistic Timed Automata (PTA) to model the experiment and analyze it through model checking. We report a proof-of-concept mechanism, opening way for the analysis of large scale quantum networks through formal methods. We also report supporting results on a quantum simulator.
Adaptive Multi-Modal Ensemble Network for Video Memorability Prediction

Video memorability prediction aims to quantify the credibility of being remembered according to the video content, which provides significant value in advertising design, social media recommendation, and other applications. However, the main attributes that affect the memorability prediction have not been determined so that making the design of the prediction model more challenging. Therefore, in this study, we analyze and experimentally verify how to select the most impact factors to predict video memorability. Furthermore, we design a new framework, Adaptive Multi-modal Ensemble Network, based on the chosen vital impact factors to predict video memorability efficiently. Specifically, we first conduct three main impact factors that affect video memorability, i.e., temporal 3D information, spatial information and semantics derived from video, image and caption, respectively. Then, the Adaptive Multi-modal Ensemble Network integrates the three individual base learners (i.e., ResNet3D, Deep Random Forest and Multi-Layer Perception) into a weighted ensemble framework to score the video memorability. In addition, we also design an adaptive learning strategy to update the weights based on the importance of memorability, which is predicted by the base learners rather than assigning weights manually. Finally, the experiments on the public VideoMem dataset demonstrate that the proposed method provides competitive results and high efficiency for video memorability prediction.
Additive Orthant Loss for Deep Face Recognition

In this paper, we propose a novel loss function for deep face recognition, called the additive orthant loss (Orthant loss), which can be combined for softmax-based loss functions to improve the feature-discriminative capability. The Orthant loss makes features away from the origin using the rescaled softplus function and an additive margin. Additionally, the Orthant loss compresses feature spaces by mapping features to an orthant of each class using element-wise operation and 1-bit quantization. As a consequence, the Orthant loss improves the inter-class separabilty and the intra-class compactness. We empirically show that the ArcFace combined with the Orthant loss further compresses and moves the feature spaces farther away from the origin compared to the original ArcFace. Experimental results show that the new combined loss has the most improved accuracy on CFP-FP, AgeDB-30, and MegaFace testing datasets, among some of the latest loss functions.
A landmark-free approach for automatic, dense and robust correspondence of 3D faces

Global dense registration of 3D faces commonly prioritizes correspondences of facial landmarks which are fiducial points for the anatomical structures. However, it is not always easy to pre-annotate the landmarks accurately in raw scans of 3D faces. Contrary to the current state-of-the-art in dense 3D face correspondence, we propose a general framework without pre-annotated landmarks, which promotes its robustness and allows the meshes to deform in a uniform manner. The proposed framework includes two stages: first the correspondences are established using a template face; and then we select some well-reconstructed samples to build a prior model and leverage it into the correspondence process of other samples. In both stages, the dense registration is revisited in two perspectives: semantic and topological correspondence. In the latter stage, we further incorporate shape and normal statistics of 3D faces to regularize the correspondence process for more robust results. This provides a feasible way to handle data with noises and occlusions, as well as large deformation caused by facial expressions. Our basic idea is to gradually refine the correspondence of individual points in a way global-to-local. At the same time, we solve the local-to-global deformation based on the refined correspondences. The two processes are alternated, and aided by some confidence checks for each individual points. In the experiments, the proposed method is evaluated both qualitatively and quantitatively on three datasets including two publicly available ones: FRGC v2.0 and BU-3DFE datasets, demonstrating its effectiveness.
Cyberspace's endogenous safety and security problem and the countermeasures

In this article, we present the general concept and characteristics of the endogenous safety issue in cyberspace along with the thinking perspective, methodology, and practice norms of the endogenous security &amp;safety defense paradigm. Studies have shown that the endogenous safety problem and its intrinsic functional entities are contradictory expressions of the interdependent and mutually exclusive relationships between the opposites within things, which are indivisible and can only be transformed or reconciled but not eliminated. Next, the cyberspace endogenous security &amp;safety problem concept and the related instances are put forward. The common problems of endogenous security &amp;safety in cyberspace are defined with the basic characteristics and related instances are listed, which emphasize that the current cyberspace security defense paradigm cannot fundamentally solve the endogenous security &amp;safety common problems. The paper compares the differences between the cyberspace endogenous security defense paradigm and the existing paradigm in terms of thinking perspective, methodology, and practice norms.
Learning to Generate Synthetic Training Data Using Gradient Matching and Implicit Differentiation

Using huge training datasets can be costly and inconvenient. This article explores various data distillation techniques that can reduce the amount of data required to successfully train deep networks. Inspired by recent ideas, we suggest new data distillation techniques based on generative teaching networks, gradient matching, and the Implicit Function Theorem. Experiments with the MNIST image classification problem show that the new methods are computationally more efficient than previous ones and allow to increase the performance of models trained on distilled data.
Strategic Road Safety Dashboard: Visualizing Results of Accident Data Mining

Road safety is a major concern, as accidents kill on average 3,600 people per day. In order to reduce the number of road accidents, the police or local authorities jointly implement actions and measures to increase road safety. Therefore, it is necessary to analyze and predict the different circumstances of accidents comprehensively. Only with the knowledge, e.g., about the temporal pattern, locations, or road conditions, meaningful actions can be derived and implemented. A framework to support strategic planning of road safety measures is designed that consists of several consecutive data mining stages, i.e., frequent itemset mining, time series clustering, forecasting, and scoring. An informative and comprehensible presentation of the results is necessary to make them usable for the planning of measures. With a strategic road safety dashboard, we enable police managers to identify accident blackspots and especially their temporal pattern for different feature combinations.
Language-Free Regular Expression Search of Document’s References

In this paper, we address the problem of robust multilingual references section extraction not only from scientific papers, but a broader range of scientific documents. Although several publishers provide document’s metadata including references separately, there are still many cases when it is extracted by hand. All existing methods of automation of this task work only with English language. It is motivated by the fact that international scientific literature is often written in English. However, there are many sources of scientific documents in national languages that are to be processed, for example, master or PhD theses. At the current time it is done mostly manually. We describe a simple yet efficient regular expression based method, which (1) takes as an input neither layout nor formatting features, but only plain text; (2) does not require any annotated multilingual data and performs in transfer learning manner; (3) achieves quality comparable to state-of-the-art multilingual BERT-based models with a substantial gain in terms of inference time.
Analysis of Thresholding Methods for the Segmentation of Brain Vessels

In this paper, the problem of segmentation of the vascular network is solved based on the results of rotational angiography. This task is considered as one of the stages of data pre-processing in the construction of a computer-aided diagnostic system for the recognition of cerebral aneurysms. A statistical analysis of the effectiveness of using threshold segmentation methods to solve the problem under consideration was carried out and the choice of one of them was justified. The following methods are considered in this study: Otsu’s method, Yen’s thresholding method, triangle method, and Sauvola method. As a result of the experiments, it was determined that, among the considered methods, the triangle method, followed by the selection of the largest connected area, is the most suitable for solving the problem of vascular segmentation because this method proved to be the most sensitive to the isolation of low-contrast vessels of small size, allowing to get a complete picture of the vascular network structure.
Routing in Reinforcement Learning Markov Chains

With computers beating human players in challenging games like Chess, Go, and StarCraft, Reinforcement Learning has gained much attention recently. The growing field of this data-driven approach to control theory has produced various promising algorithms that combine simulation for data generation, optimization, and often bootstrapping. However, underneath each of those lies the assumption that the problem can be cast as a Markov Decision Process, which extends the usual Markov Chain by assigning controls and resulting rewards to each potential transition. This assumption implies that the underlying Markov Chain and the reward, the data equivalent of an inverse cost function, form a weighted network. Consequently, the optimization problem in Reinforcement Learning can be translated to a routing problem in such possibly immense and largely unknown networks. This paper analyzes this novel interpretation and provides some first approaches to its solution.
Neural Architecture Search with Structure Complexity Control

The paper investigates the problem of deep learning model selection. We propose a method of a neural architecture search with respect to the desired model complexity called DARTS-CC. An amount of parameters in the model is considered as a model complexity. The proposed method is based on a differential architecture search algorithm (DARTS). Instead of optimizing structural parameters of the architecture, we consider them as a function depending on the complexity parameter. It enables us to obtain multiple architectures at one optimization procedure and select the architecture based on our computation budget. To evaluate the performance of the proposed algorithm, we conduct experiments on the Fashion-MNIST and CIFAR-10 datasets and compare the resulting architecture with architectures obtained by other neural architecture search methods.
Context-Based Text-Graph Embeddings in Word-Sense Induction Tasks

Word Sense Induction (WSI) is the process of automatically discovering multiple senses or meanings of a word. WSI task can be described as grouping contexts of a given word by its senses which are not provided beforehand. Modern WSI systems are given small text fragments only and should cluster them into some unidentified number of clusters. In the present work contextualized word embeddings, calculated by BERT, are applied in conjunction with clustering techniques to the WSI task for the Russian language. We hypothesize that novel language model embeddings, already viable for sense induction, may be enhanced by graph-based post-processing. We evaluate that proposition on 3 datasets from the Russian language WSI competition task. Fusion of graph algorithms and vector representations allowed us to beat one of the tasks’ baseline (wiki-wiki, ARI = 0.7513) and demonstrate viability of further research. This work provides insight into how vector sentence representations can be organized for more efficient sense extraction.
Research on the application of mixed reality technology in the field of art design

Aim: summarize the application of mixed reality technology in the field of art design and analyse its shortcomings and trends. Method: by searching the Web of science as the main source of information to explain the basic concept and development of MR and state of art in product design, display design and interactive design. Discuss the pros and cons of mixed reality technology in human-computer interaction, hardware, and user experience. Result: Mixed reality technology is widely used in art, design, especially in product design, display design, and interactive design. The future trend of mixed reality technology is summarized the combining status of market and industry. Conclusion: The application of mixed reality technology in the field of art design is already drawing more and more attention from industry, but it still lacks enough attention from academia community. This paper systematically introduces the state-of-the-art applications and trends of MR in the field of art design which helps readers to get an insight on this fascinating research area.
A Deep Learning-Based Robust Automatic Modulation Classification Scheme for Next-Generation Networks

Due to stochastic wireless environment, the process of modulation classification has become a challenging task. Because of its powerful feature extraction ability and promising performance over the conventional schemes, deep learning (DL) models are employed to automatic modulation classification (AMC) problems. Most of the conventional models proposed are tested for the limited set of modulation schemes transmitted over additive white Gaussian noise (AWGN) channels without considering the effect of multipath fading and Doppler shift. The next-generation networks use adaptive and higher-order quadrature amplitude modulation (QAM) schemes for higher spectral efficiency. The classification accuracy of conventional DL-based AMC schemes drastically reduces, when different order QAM modulation schemes are accommodated. In this work, different scaling factors are selected for the generation of [Formula: see text]-QAM frames. The combination of scaling factors, which maximize the classification accuracy is chosen. A convolutional neural network (CNN) with six stages is employed for AMC. The simulation results show that the classification accuracy of proposed scheme is higher than the conventional DL-based schemes under various signal-to-noise ratio (SNR) conditions. The proposed scheme shows at least 4% improvement in classification accuracy over the other DL-based schemes.
OptiNET—Automatic Network Topology Optimization

The recent boom of artificial Neural Networks (NN) has shown that NN can provide viable solutions to a variety of problems. However, their complexity and the lack of efficient interpretation of NN architectures (commonly considered black box techniques) has adverse effects on the optimization of each NN architecture. One cannot simply use a generic topology and have the best performance in every application field, since the network topology is commonly fine-tuned to the problem/dataset in question. In this paper, we introduce a novel method of computationally assessing the complexity of the dataset. The NN is treated as an information channel, and thus information theory is used to estimate the optimal number of neurons for each layer, reducing the memory and computational load, while achieving the same, if not greater, accuracy. Experiments using common datasets confirm the theoretical findings, and the derived algorithm seems to improve the performance of the original architecture.
Implications of Freire’s Education Theory and Corresponding Action Plans for Secondary School Home Economics Education

The purpose of this study was to explore the implications of Freire’s education theory for secondary school Home Economics Education (HEedu). We conducted a review of books written by Freire and classified the characteristics of his education theory into 9 categories. Based on these categories, we concretely explored the forms in which Freire’s education theory could be applied in this area of education through interviews with Home Economics (HE) teachers. We also searched for activities that can be carried out by HEedu agents and the support that can be given by social institutions. The implications of Freire’s education theory for HEedu are that education should exhibit love and respect for human beings, be ethically and democratically responsible, enable people to dream as well as hope, support consciousness and praxis by reflection, stimulate epistemological curiosity in learners’ lives and experiences, promote subjectivity, autonomy and self-reliance, engage in dialogue to bring about a change in relationships rather than viewing dialogue as a skill, and stimulate political engagement. Freire‘s education theory therefore has multiple implications for the purpose, mission, and content of education, teaching methods and perspectives, and directions regarding the role of HE teachers. The results are expected to provide practical assistance in developing a persuasive curriculum for HEedu that can help learners restore subjectivity and love for human beings and their society by expanding the horizons of HEedu philosophy from the critical science of Habermas to Freire’s education theory.