Reviewerly/exHarmony huggingface dataset:
Detecting Phishing Websites Using an Efficient Feature-based Machine Learning Framework

Phishing is a form of digital crime where spam messages and spam sites attract users to exploit sensitive information on fishermen. Sensitive information obtained is used to take notes or to access money. To combat the crime of identity theft, Microsoft's cloud-based program attempts to use logical testing to determine how you can build trust with the characters. The purpose of this paper is to create a molded channel using a variety of machine learning methods. Separation is a method of machine learning that can be used effectively to identify fish, assemble and test models, use different mixing settings, and look at different mechanical learning processes, and measure the accuracy of the modified model and show multiple measurement measurements. The current study compares predictive accuracy, f1 scores, guessing and remembering multiple machine learning methods including Naïve Bayes (NB) and Random forest (RF) to detect criminal messages to steal sensitive information and improve the process by selecting highlighting strategies and improving crime classification accuracy. to steal sensitive information.
Quantum blockchain system

Blockchain technology represented by Bitcoin and Ethereum has been deeply developed and widely used due to its broad application prospects such as digital currency and IoT. However, the security of the existing blockchain technologies built on the classical cryptography depends on the computational complexity problem. With the enhancement of the attackers’ computing power, especially the upcoming quantum computers, this kind of security is seriously threatened. Based on quantum hash, quantum SWAP test and quantum teleportation, a quantum blockchain system is proposed with quantum secure communication. In classical cryptographic theory sense, the security of this system is unconditional since it has nothing to do with the attackers’ computing power and computing resources.
Incorporating Item Response Theory into Knowledge Tracing

The popularity of artificial neural networks has brought high predictive power to many difficult machine learning problems. Knowledge tracing (KT), the task of tracking students’ understanding of various concepts over time, is included in this category. But the deep learning methods which have performed best in knowledge tracing are hard to explain in a statistical sense.In this work, we leverage the psychological theory from Item Response Theory (IRT) to build interpretable neural networks for knowledge tracing which are competitive with other deep learning methods. This presents a trade-off between a small loss in predictive power and an increase in interpretability. The advantage of IRT-inspired knowledge tracing is that it transforms the high-dimensional student ability representation from deep learning models into an explainable IRT representation at each timestep. Further, the item parameters from IRT models can be directly recovered from the trained neural network weights.
Linear Cryptanalyses of Three AEADs with GIFT-128 as Underlying Primitives

This paper considers the linear cryptanalyses of Authenticated Encryptions with Associated Data (AEADs) GIFT-COFB, SUNDAE-GIFT, and HyENA. All of these proposals take GIFT-128 as underlying primitives. The automatic search with the Boolean satisfiability problem (SAT) method is implemented to search for linear approximations that match the attack settings concerning these primitives. With the newly identified approximations, we launch key-recovery attacks on GIFT-COFB, SUNDAE-GIFT, and HyENA when the underlying primitives are replaced with 16-round, 17-round, and 16-round versions of GIFT-128. The resistance of GIFT-128 against linear cryptanalysis is also evaluated. We present a 24-round key-recovery attack on GIFT-128 with a newly obtained 19-round linear approximation. We note that the attack results in this paper are far from threatening the security of GIFT-COFB, SUNDAE-GIFT, HyENA, and GIFT-128.
Optimization and Simulation of Virtual Experiment System of Human Sports Science Based on VR

Virtual reality technology is an emerging technology developed on the basis of information technology. It is widely used in military, medical, mining, entertainment, and other fields. Therefore, many countries have been vigorously conducting research in recent years. As one of the important components of the virtual reality system, the three-dimensional human motion tracking system is of great significance to the research of practical virtual reality systems. It introduces the measurement principle of the spatial three-dimensional coordinate dynamic measurement device and discusses in detail the ultrasonic transmission, reception, amplification, filtering, comparison, shaping circuit, and single-chip interface circuit. This paper introduces the working principle and characteristics of the virtual experiment system and gives the structure diagram, hardware schematic diagram, and software flow diagram of the system. We mainly study the method of tracking human motion by measuring the three-dimensional coordinates of the space point, which lays a good foundation for the research of the actual three-dimensional motion tracking system. At the same time, the three-dimensional human body modeling is discussed, and the interactive movement policy of the human arm is briefly introduced. It has a certain effect on the actual virtual reality human-computer interaction system.
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts

Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs.One challenge to these tasks is the lack of medical domain datasets with highquality annotations, especially medical-related tags that reveal the characteristics of Chinese EMRs.In this paper, we collected a Chinese EMR corpus, namely, ACEMR, with human annotations for Chinese word segmentation and EMR-related tags.On the ACEMR corpus, we run well-known models (i.e., BiLSTM, BERT, and ZEN) and existing state-of-the-art systems (e.g., WMSeg and TwASP) for CWS and medical concept recognition.Experimental results demonstrate the necessity of building a dedicated medical dataset and show that models that leverage extra resources achieve the best performance for both tasks, which provides certain guidance for future studies on model selection in the medical domain. 1
Cyberbullying Detection in Social Networks: A Survey

The rate of innovation is increasing rapidly. This growth in innovation gives another dimension of communication. But in spite of the fact that innovation inspires us in various parts of life, it goes with different effects that influence people in a hard or the other way. Cyberbullying is one of them. Social media gives a platform to communicate with masses, but it also becomes a place for negative comments and cyber-bullying. Cyberbullying is bullying executed through digital equipment like computer laptops and mobile devices. Cyberbullying occurs on social media, chat rooms, and gaming platforms where people can view and share their own content. Cyberbullying includes sending hurtful messages posting sensitive photos or video on social media and extending mean rumors online. Cyberbullying is a huge problem. So as to prevent such a problem cyberbullying detection method based on deep neural networks is developed called Convolution Neural Network (CNN). CNN is used for better outcomes when contrasted with the current systems.
Continuous Self-Attention Models with Neural ODE Networks

Stacked self-attention models receive widespread attention, due to its ability of capturing global dependency among words. However, the stacking of many layers and components generates huge parameters, leading to low parameter efficiency. In response to this issue, we propose a lightweight architecture named Continuous Self-Attention models with neural ODE networks (CSAODE). In CSAODE, continuous dynamical models (i.e., neural ODEs) are coupled with our proposed self-attention block to form a self-attention ODE solver. This solver continuously calculates and optimizes the hidden states via only one layer of parameters to improve the parameter efficiency. In addition, we design a novel accelerated continuous dynamical model to reduce computing costs, and integrate it in CSAODE. Moreover, since the original self-attention ignores local information, CSAODE makes use of N-gram convolution to encode local representations, and a fusion layer with only two trainable scalars are designed for generating sentence vectors. We perform a series of experiments on text classification, neural language inference (NLI) and text matching tasks. With fewer parameters, CSAODE outperforms state-of-the-art models on text classification tasks (e.g., 1.3% accuracy improved on SUBJ task), and has competitive performances for NLI and text matching tasks as well.
Revisión sistemática sobre aprendizaje autónomo universitario a través de la virtualidad

Autonomous university learning is the capacity that students achieve outside the established conditions, achieving self-regulated learning.Virtual teaching has strengthened the autonomous learning of students in all modalities and levels.The objective was to systematize the scientific evidence on the autonomy of university students, using the PRISMA Flowchart.The Scopus, Scielo, Eric, and Ebsco Host databases were analyzed.Through the Boolean operators "AND" and "OR", and search operations in Spanish, English, and Portuguese.Inclusion and exclusion criteria were used, including the years from 2011 to 2021, publications in the final stage, Ibero-American countries, and topics in education and social sciences.Through systematization, 11,271,889 articles were obtained, but 21 articles were analyzed, which responded to the research question "What are the strategies used by university students to achieve autonomy in learning?",including instruments, approaches, and countries with the most evidence in publications on the subject.The results indicate that the expertise, knowledge, and disposition of the teaching staff facilitate autonomous learning.Concluding that concentration and planning are key to achieve learning autonomy.
On-the-Fly Attention Modulation for Neural Generation

Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, selfcontradictory, and often lacks commonsense.Our analyses on sentence-level attention patterns in LMs reveal that neural degeneration may be associated with insufficient learning of task-specific characteristics by the attention mechanism.This finding motivates onthe-fly attention modulation 1 -a simple but effective method that enables the injection of priors into attention computation during inference.Automatic and human evaluation results on three text generation benchmarks demonstrate that attention modulation helps LMs generate text with enhanced fluency, creativity, and commonsense reasoning, in addition to significantly reduce sentence-level repetition.
Counter-Argument Generation by Attacking Weak Premises

Text generation has received a lot of attention in computational argumentation research as of recently.A particularly challenging task is the generation of counter-arguments.So far, approaches primarily focus on rebutting a given conclusion, yet other ways to counter an argument exist.In this work, we go beyond previous research by exploring argument undermining, that is, countering an argument by attacking one of its premises.We hypothesize that identifying the argument's weak premises is key to effective countering.Accordingly, we propose a pipeline approach that first assesses the premises' strength and then generates a counter-argument undermining the weakest among them.On one hand, both manual and automatic evaluation underline the importance of identifying weak premises in counterargument generation.On the other hand, when considering correctness and content richness, human annotators favored our approach over state-of-the-art counter-argument baselines.
Data and Cloud Polymorphic Application Modelling in Multi-clouds and Fog Environments

Multi-cloud management prevents vendor lock-in as well as improves the provisioning of cloud applications. However, the optimal deployment of such applications is still impossible, not only due to the dynamicity of the cloud and hybrid environments that host these applications but also due to the use of potentially unsuitable forms or configurations of application components. As such, the real cloud application optimisation can only be achieved by considering all possible component forms and selecting the best one, based on both application requirements and the current context. This gives rise to the era of polymorphic applications which can change form at runtime based on their context. A major pre-requisite for the management of such applications is their proper modelling. Therefore, this paper presents extensions on two well-integrated cloud modelling solutions to support the complete specification of polymorphic applications and it presents an illustrative example of their use.
BanditMTL: Bandit-based Multi-task Learning for Text Classification

Task variance regularization, which can be used to improve the generalization of Multitask Learning (MTL) models, remains unexplored in multi-task text classification.Accordingly, to fill this gap, this paper investigates how the task might be effectively regularized, and consequently proposes a multi-task learning method based on adversarial multiarmed bandit.The proposed method, named BanditMTL, regularizes the task variance by means of a mirror gradient ascent-descent algorithm.Adopting BanditMTL in the multitask text classification context is found to achieve state-of-the-art performance.The results of extensive experiments back up our theoretical analysis and validate the superiority of our proposals.
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations

The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and nonlinear transformations along deep hierarchies. In this paper, we propose a new attribution method, Relative Sectional Propagation (RSP), for fully decomposing the output predictions with the characteristics of class-discriminative attributions and clear objectness. We carefully revisit some shortcomings of backpropagation-based attribution methods, which are trade-off relations in decomposing DNNs. We define hostile factor as an element that interferes with finding the attributions of the target and propagate it in a distinguishable way to overcome the non-suppressed nature of activated neurons. As a result, it is possible to assign the bi-polar relevance scores of the target (positive) and hostile (negative) attributions while maintaining each attribution aligned with the importance. We also present the purging techniques to prevent the decrement of the gap between the relevance scores of the target and hostile attributions during backward propagation by eliminating the conflicting units to channel attribution map. Therefore, our method makes it possible to decompose the predictions of DNNs with clearer class-discriminativeness and detailed elucidations of activation neurons compared to the conventional attribution methods. In a verified experimental environment, we report the results of the assessments: (i) Pointing Game, (ii) mIoU, and (iii) Model Sensitivity with PASCAL VOC 2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our method outperforms existing backward decomposition methods, including distinctive and intuitive visualizations.
Computer Vision Mobile System for Education Using Augmented Reality Technology

This article analyzes the algorithms of computer vision, the features of the application of augmented reality technology and existing software modules, frameworks and libraries. The result is a computer vision mobile system (application) using augmented reality technology, which allows users (for example, students) to obtain additional virtual information about the research object and to be able to interact with it. The functional model of the system is formed, the process of application development using the Vuforia library is described and the results of the work are given. The result is an Android application, which using augmented reality tools, allows the user to obtain a virtual environment object in a real-world. This computer vision mobile system is intended for educational purposes, in particular for use in schools and universities for more effective interaction between users and educational material.
FastSeq: Make Sequence Generation Faster

Transformer-based models have made tremendous impacts in natural language generation.However the inference speed is a bottleneck due to large model size and intensive computing involved in auto-regressive decoding process.We develop FastSeq framework to accelerate sequence generation without accuracy loss.The proposed optimization techniques include an attention cache optimization, an efficient algorithm for detecting repeated n-grams, and an asynchronous generation pipeline with parallel I/O.These optimizations are general enough to be applicable to Transformer-based models (e.g., T5, GPT2, and UniLM).Our benchmark results on a set of widely used and diverse models demonstrate 4-9x inference speed gain.Additionally, FastSeq is easy to use with a simple one-line code change.
Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images

Depth adjustment aims to enhance the visual experience of stereoscopic 3D (S3D) images, which accompanied with improving visual comfort and depth perception. For a human expert, the depth adjustment procedure is a sequence of iterative decision making. The human expert iteratively adjusted the depth until he is satisfied with the both levels of visual comfort and the perceived depth. In this work, we present a novel deep reinforcement learning (DRL)-based approach for depth adjustment named VCA-RL (Visual Comfort Aware Reinforcement Learning) to explicitly model human sequential decision making in depth editing operations. We formulate the depth adjustment process as a Markov decision process where actions are defined as camera movement operations to control the distance between the left and right cameras. Our agent is trained based on the guidance of an objective visual comfort assessment metric to learn the optimal sequence of camera movement actions in terms of perceptual aspects in stereoscopic viewing. With extensive experiments and user studies, we show the effectiveness of our VCA-RL model on three different S3D databases.
IndoCollex: A Testbed for Morphological Transformation of Indonesian Word Colloquialism

Indonesian language is heavily riddled with colloquialism whether in written or spoken forms.In this paper, we identify a class of Indonesian colloquial words that have undergone morphological transformations from their standard forms, categorize their word formations, and propose a benchmark dataset of Indonesian Colloquial Lexicons (IndoCollex) consisting of informal words on Twitter expertly annotated with their standard forms and their word formation types/tags.We evaluate several models for character-level transduction to perform morphological word normalization on this testbed to understand their failure cases and provide baselines for future work.As IndoCollex catalogues word formation phenomena that are also present in the non-standard text of other languages, it can also provide an attractive testbed for methods tailored for cross-lingual word normalization and non-standard word formation.
Evaluating binary defocusing quantitatively in real-time for fringe projection profilometry

In fringe projection profilometry (FPP), non-sinusoidal fringes due to the gamma effect of a projector will cause measurement errors. To solve this problem, a binary defocusing technique has been introduced in recent years, yet the appropriate defocusing is hard to evaluate quantitatively. An innovative approach to quantify binary defocusing in real-time is proposed. No matter how the projector and camera are arranged in FPP system, the fringe period in a captured defocusing fringe image is strictly varying; therefore, unlike previous methods, the proposed method uses a limited window for defocusing evaluation. This not only improves the accuracy of defocusing evaluation, but also greatly improves the evaluation speed, thus in turn enabling real-time evaluation. In addition, numerical differentiation is introduced on the window using a modified five-interval-point algorithm to effectively improve the sensitivity to improper (inadequate or excessive) defocusing. The difference between the numerical differentiation and its fundamental harmonic extracted by Levenberg–Marquardt iterative is taken as an evaluation value of binary defocusing. The smaller the difference, the more appropriate is the defocusing. By minimizing this difference, the most appropriate defocusing can be obtained quantitatively. Both numerical simulations and experiments validate the high sensitivity and high speed of the proposed method.
Reusable Abstractions and Patterns for Recognising Compositional Conversational Flows

Task-oriented conversational bots allow users to access services and perform tasks through natural language conversations. However, integrating these bots and software-enabled services has not kept pace with our ability to deploy individual devices and services. The main drawbacks of current bots and services integration techniques stem from the inherent development and maintenance cost. In addition, existing Natural Language Processing (NLP) techniques automate various tasks but the synthesis of API calls to support broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose three types of reusable patterns for recognising compositional conversational flows and therefore automatically support increased complexity and expressivity during the conversation.
Task allocation and coordinated motion planning for autonomous multi-robot optical inspection systems

Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dynamic environments and applied to different fields. However, these approaches may not work for the autonomous multi-robot optical inspection system due to fast computation requirements of inline optimization, unique characteristics on robotic end-effector orientations, and complex large-scale free-form product surfaces. This paper proposes a novel task allocation methodology for coordinated motion planning of multi-robot inspection. Specifically, (1) a local robust inspection task allocation is proposed to achieve efficient and well-balanced measurement assignment among robots; (2) collision-free path planning and coordinated motion planning are developed via dynamic searching in robotic coordinate space and perturbation of probe poses or local paths in the conflicting robots. A case study shows that the proposed approach can mitigate the risk of collisions between robots and environments, resolve conflicts among robots, and reduce the inspection cycle time significantly and consistently.