Reviewerly/exHarmony huggingface dataset:
An Improved Transformer-Based Neural Machine Translation Strategy: Interacting-Head Attention

Transformer-based models have gained significant advances in neural machine translation (NMT). The main component of the transformer is the multihead attention layer. In theory, more heads enhance the expressive power of the NMT model. But this is not always the case in practice. On the one hand, the computations of each head attention are conducted in the same subspace, without considering the different subspaces of all the tokens. On the other hand, the low-rank bottleneck may occur, when the number of heads surpasses a threshold. To address the low-rank bottleneck, the two mainstream methods make the head size equal to the sequence length and complicate the distribution of self-attention heads. However, these methods are challenged by the variable sequence length in the corpus and the sheer number of parameters to be learned. Therefore, this paper proposes the interacting-head attention mechanism, which induces deeper and wider interactions across the attention heads by low-dimension computations in different subspaces of all the tokens, and chooses the appropriate number of heads to avoid low-rank bottleneck. The proposed model was tested on machine translation tasks of IWSLT2016 DE-EN, WMT17 EN-DE, and WMT17 EN-CS. Compared to the original multihead attention, our model improved the performance by 2.78 BLEU/0.85 WER/2.90 METEOR/2.65 ROUGE_L/0.29 CIDEr/2.97 YiSi and 2.43 BLEU/1.38 WER/3.05 METEOR/2.70 ROUGE_L/0.30 CIDEr/3.59 YiSi on the evaluation set and the test set, respectively, for IWSLT2016 DE-EN, 2.31 BLEU/5.94 WER/1.46 METEOR/1.35 ROUGE_L/0.07 CIDEr/0.33 YiSi and 1.62 BLEU/6.04 WER/1.39 METEOR/0.11 CIDEr/0.87 YiSi on the evaluation set and newstest2014, respectively, for WMT17 EN-DE, and 3.87 BLEU/3.05 WER/9.22 METEOR/3.81 ROUGE_L/0.36 CIDEr/4.14 YiSi and 4.62 BLEU/2.41 WER/9.82 METEOR/4.82 ROUGE_L/0.44 CIDEr/5.25 YiSi on the evaluation set and newstest2014, respectively, for WMT17 EN-CS.
Designing an Additional Professional Program of Psychological and Pedagogical Assistance to Families with Young Children on the Basis of a Digital Information and Educational Resource

The article presents conceptual approaches to the design of an additional professional program “Psychological and pedagogical assistance to families with young children” on the basis of a digital information and educational resource of the I. S. Turgenev Oryol State University. Special attention is paid to the substantiation of the list of professional competencies necessary for the implementation of psychological and pedagogical assistance to families with young children.
Simulation of a Small-Scale Chemical Reservoir Computer for Pattern Recognition

Reservoir computing is a framework within which an arbitrary non-trainable reservoir, e.g. a network with a fixed structure, is used for computing, and the output of the reservoir is supplied with a simple classification or regression algorithm, which is trained to yield required results. In the current study, we investigate the possibility of a simple spiking neural network (SNN) based on the extended Brusselator model to serve as the reservoir for binary pattern recognition. We train various classifiers on data obtained from the four-neuron SNN and show that when there are two and three digits in a binary pattern three and six unique patterns can be distinguished, respectively, with the positive predictive value not less than 89 %. The obtained results can be used for developing a fast-training chemical computer taking advantage of its hardware structure.
Recognizing irrelevant faces in short-form videos based on feature fusion and active learning

In recent years, short-form videos spread rapidly around the world and became a popular way of entertainment for people to share their daily lives. However, many videos record behaviors of other people without their awareness and are uploaded onto the short-form video platforms. Such behavior severely invades personal privacy and can even bring risks of personal information leakage. At present, few studies focus on detecting privacy violations in short-form videos. Meanwhile, due to the difficulty in transferring existing models to the scenario of short-form videos and the lack of reliable datasets, it is very challenging to recognize irrelevant faces in short-form videos. To deal with this problem, we constructed and published an irrelevant faces dataset (IF-Dataset) with 43,965 irrelevant face images and 89,924 relevant face images based on the videos collected from Douyin (the Chinese version of TikTok). In addition, we constructed a framework that implemented our proposed deep learning model M ulti-features M ulti-head F usion Net work (MMFNet) to recognize irrelevant faces from short-form videos. The experimental results show that the F1 score of the MMFNet can reach 87.03%. We also proposed a novel loss function as well as an active learning system to improve the generalization ability of models, which can reach the Relative Error Reduction (RER) up to 29.58%. Our work provides both theoretical and practical support for face protection in short-form videos.
Expression retargeting from images to three‐dimensional face models represented in texture space

Abstract Facial expressions play a very important role in creating a vivid and realistic virtual character. This paper proposes a convolutional neural network‐based facial expression retargeting framework named EXPUV‐Net, which can extract expression information from an image and transfer it to a specified 3D face. With the help of the nonlinear generator, the proposed framework can generate rich facial expressions and does not rely on the blendshape model. A data augmentation approach is adopted in this paper, which can improve the adaptability of the proposed framework. The framework can realize expression retargeting for face models with different topologies. This paper also demonstrates the effectiveness of the proposed framework through some experiments and compares it with some linear face model‐based methods for facial component extraction and face reconstruction. The results show that the proposed framework has better performance than the linear methods. The proposed framework is very helpful for facial expression generation for virtual characters.
Phishing URL classification using Extra-Tree and DNN

Machine Learning (ML) and Deep Learning (DL) methods have become indispensable in cybersecurity. Recently, they are often used to detect and classify phishing websites. Phishing websites are a major problem that has a negative impact on organization and of societies. Statistics report that the number of phishing website is continuously increasing and it is becoming more difficult to detect them. Various works have shown that ML and DL can be efficient to solve this problem. In this work, we adopted lexical analysis and Tiny URL approaches for URL features extraction. The accuracy metric obtained surpasses 98% for Extra Tree algorithm and can achieve 99% for Deep Neural Network model.
The Need for Emergency-Based Access Control in AAL Systems

AAL applications are designed for elderly people and collecting personally identifiable information (PII), e.g. health data. During normal operations, these data should be kept private. But during emergency situations, the information is critical for helpers and emergency doctors. This paper discusses the results of a survey conducted for PII in AAL and proofs the requirement of special access control rules for systems in situations of emergency.
Histogram memory reduction in FPGA gradient edge detectors

Gradient edge detectors create an edge map in two steps. First, the gradient image is calculated using appropriate convolution masks. Then, a binary edge map is obtained by thresholding the gradient image. The threshold value significantly affects the final edge map. The most used threshold determination algorithms, Otsu's and Rosin's methods, are based on a gradient image histogram. In some FPGAs, the size of the histogram memory can be a deciding factor in whether an edge detector can be implemented. Based on the analysis of the images of the three databases, we proposed the concept of gradient histogram memory, which reduces the memory size by thirty percent.
Code Mutation as a mean against ROP Attacks for Embedded Systems

This paper presents a program-code mutation technique that is applied in-field to embedded systems in order to create diversity in a population of systems that are identical at the time of their deployment. With this diversity, it becomes more difficult for attackers to carry out the very popular Return-Oriented-Programming (ROP) attack in a large scale, since the gadgets in different systems are located at different program addresses after code permutation. In order to prevent the system from a system crash after a failed ROP attack, we further propose the combination of the code mutation with a return address checking. We will report the overhead in time and memory along with a security analysis.
Context similarity based hybrid approach for extracting hindi multiword expressions

In natural language processing, multiword expressions (MWEs) play a significant role in understanding the context and meaning of a sentence. A MWE comprises two or more words that are handled as if they were one. MWE has the property that the constituent words are consistent and are often used in related contexts. Hindi is used as a case study. We employed three properties: linguistic or syntactical pattern, a relationship between constituent words, and context similarity and proposed a three-phase hybrid approach to extract MWE from unstructured Hindi text. Experimental analysis and comparison of results on the TDIL dataset show the superiority of the proposed hybrid method over the context-based method and association-based methods.
Characterizing phenotypes for Mental Health Disorders with Wrapper-typed Feature selection techniques:

Random Forest is a popular feature selection method suitable for handling “small n, large p” problem but lacking capability of dealing collinearity. To compensate the gap of removing highly correlated features, wrapper-typed feature selection methods: Fuzzy Forest (FF) and Random Forest- Recursive Feature Elimination (RF-RFE) have been developed. These two methods are similar in multiple ways but implement different strategies to deal with features. Meanwhile, the field of clinical psychiatry is changing in the way it characterizes mental health disorders. Thus, the aims of our paper are to compare the impact of FF and RF-RFE and to study phenotypic features relevant to three mental health disorders: schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder (ADHD). We specify the classification problem as “one versus rest” (OVR) and implement phenotype data from Consortium for Neuropsychiatric Phenomics. FF and RF-RFE are applied to select the optimal feature subsets separately, which are then evaluated by Support Vector Machines (SVM) and Extreme Learning Machines (ELM) classifiers respectively. The evaluation criteria include precision, recall, accuracy, F-measure and Area under the curve. As a result, RF-RFE showed superior feature selection performance over FF. Also, we found that the features of the original data are informative in diseases from most to least: schizophrenia, ADHD and bipolar disorder.
The Analytical Evaluation and Study of the Developed Web Application “Space Information Systems and Technologies” for Learning English

This document presents an information system for teaching and monitoring students in English in the training program “Information science and computer engineering”. The article gives an analytical evaluation and study of the developed web application “Space Information Systems and Technologies” for learning English, the comparison with other modern E-platforms. The advantages and disadvantages of the developed web-application is revealed.
An approach to error label discrimination based on joint clustering

Abstract Inaccurate multi-label learning aims at dealing with multi-label data with wrong labels. Error labels in data sets usually result in cognitive bias for objects. To discriminate and correct wrong labels is a significant issue in multi-label learning. In this paper, a joint discrimination model based on fuzzy C-means (FCM) and possible C-means (PCM) is proposed to find wrong labels in data sets. In this model, the connection between samples and their labels is analyzed based on the assumption of consistence between samples and their labels. Samples and labels are clustered by considering this connection in the joint FCM-PCM clustering model. An inconsistence measure between a sample and its label is established to recognize wrong labels. A series of simulated experiments are comparatively implemented on several real multi-label data sets and experimental results show superior performance of the proposed model in comparison with two state of the art methods of mislabeling correction.
Towards an Automatic Generation of UML Class Diagrams from Textual Requirements using Case-based Reasoning Approach

Nowadays automatic information extraction plays a major roles in software development life cycle. It allows producing automated tools that help humans in routine tasks. The automatic extraction of Unified Modeling Language (UML) class diagrams from textual requirements can help in reducing the design time and cost. This paper proposes a novel case-based reasoning model to facilitate the process of generating the UML class diagrams from textual requirements. The result of this work can help on minimizing the manual work, and can be employed to extract other type of information from documents that we cope with during software development life cycle.
Lads: Deep Survival Analysis for Churn Prediction Analysis in the Contract User Domain

Survival analysis methods are currently used in the fields of medicine, economics, biology and engineering, and focus on the relationship between covariates and the timing of events. Survival analysis makes such use of survival time that it is better suited to the user domain than other machine learning models, In order to mine information about the user's time-series characteristics, improve the ability of neural networks to process information and the efficiency of network training with large data sets. Based on deep survival analysis, the Lads (LSTM Attention Deep Survival) model was designed to combine Long Short-Term Memory networks and Attention mechanisms to predict the occurrence of events of interest in the contract user domain. The LSTM acts as a feature extractor and performs pre-processing of time-series characteristics information, while the Attention mechanism mainly enhances the interpretability of the model. The final experimental results show that the Lads survival analysis model is a better predictor in the contract user domain than survival analysis methods such as CPH (Cox Proportional Hazard Model) and DeepSurv.
Lightweight hybrid signature scheme for Internet of Thing based on bilinear mapping

Abstract Most of the existing IoT communication encryption schemes have the following two problems: the sensor side needs to perform complex bilinear mapping calculations; most schemes separate key agreement and data encryption, which increases the user’s computational burden and management difficulty. This paper proposes a lightweight IoT hybrid signature scheme based on bilinear mapping. In terms of transmission efficiency, the calculation process of the bilinear map is transferred to the initialization phase of the system. The sensor side only needs low-cost operations such as hash mapping and exponential operation, which reduces the overall computing cost of the solution. In terms of security, the mathematical difficulty caused by the bilinear mapping calculation in the initialization phase is used to ensure the security of data transmission. In solving the problem of key management, the scheme uses the semi-trusted key generation center (KGC) and sensor ID to generate user session keys and data keys, which solves the public key authentication and key escrow problems of massive sensors in the Internet of Things.
Neighborhood Extended Dynamic Graph Neural Network

Representation learning on dynamic graphs has drawn much attention due to its ability to learn hidden relationships as well as capture temporal patterns in graphs. It can be applied to represent a broad spectrum of graph-based data like social networks and further use the learned representations to solve the downstream tasks including link prediction and edge classification. Although some approaches have been proposed for dynamic graphs in recent years, most of them paid little attention to the evolution of the entire graph topology, leading to the lack of global information of what happened in nodes’ neighborhoods during their update. We propose NEDGNN, a novel Neighborhood Extended Dynamic Graph Neural Network on dynamic graphs represented as sequences of time-stamped events. We introduce a temporal attention propagation module to generate messages for n-hop neighbors through a self-attention mechanism, which can help disseminate information among nodes’ n-hop neighbors. Besides, a FIFO message box module is also applied to gain some time efficiency. Due to the introduction of these modules, NEDGNN outperforms many state-of-the-art baselines in several tasks. We also perform a detailed ablation study to test the effectiveness and time cost of each module.
A Peer-to-Peer Distributed Bisecting K-means

Distributed machine learning over peer-to-peer network has become popular in the past few years due to the growing demand for privacy protection. Recent peer-to-peer distributed K-means algorithm can achieve the same performance as centralized K-means, but they also has high sensitivity to initialization as centralized K-means, which worsens its performance for clustering. In this paper, we first proposes a distributed bisecting K-means algorithm over a peer-to-peer network to alleviate this drawback by combining bisecting K-means with Metropolis algorithm, since the previous works showed that bisecting K-means is much less sensitive to initialization than traditional K-means. It is shown by extensive simulations that our algorithm has the same performance with centralized bisecting K-means and outperforms the existing peer-to-peer distributed K-means.
A compact and robust perceptual hashing function through self-supervised learning

Thanks to the Internet, it is easy to share and redis-tribute multimedia items for different purposes. However, to avoid malicious usage of sensitive data, it is desirable to have efficient ways to track and identify relevant items. A hash-based content identification system uses a robust perceptual hashing function to generate a short binary string to match query multimedia items with a set of relevant assets in a database. In low consumption systems, such as in IoT applications, the properties of this function are crucial to reducing the network bandwidth consumption to communicate with a server and efficiently compute the binary identifier with limited computing power. Moreover, it still requires to be adaptive to new scenarios. In this work, we propose to exploit the feature learning capabilities of convolutional neural networks to learn automatically a perceptual hashing function invariant against a set of expected manipulations. To achieve this, we define a compact and efficient architecture and a self-supervised learning pretext task that enforces a perceptual hashing algorithm's robustness and discrimination properties. We evaluate this approach against 15 manipulations with a comprehensive set of intensities in different datasets, achieving high robustness and identification performance.
YouTube Trending Video Analysis in Sri Lanka

YouTube, owned by Google LLC, is the most widespread video platform. It has gained much popularity in Sri Lanka within the last couple of years. Youth has an interest of becoming full-time content creators, but lacks technical and marketing knowledge. In this paper, we present a computer aided analysis to provide insights of trending YouTube videos in Sri Lanka. The system gets YouTube trending video data of one and a half months in year 2021 as input and analyze them to report information on video title, description and thumbnail which are the main parameters of YouTube algorithm when deciding on a trending video. Particularly, the system uses YOLO and RetinaNet for detecting objects in thumbnails. Moreover, Sri Lankan video content mainly include Sinhala language which is the mostly used language in the country. We used Sinhala language processing techniques for reporting various information. In summary, using the provided information, a YouTuber can decide on the: (1) colors and objects to include in the thumbnail, (2) title and the description by considering word count and symbols, (3) number of tags, words and phrases to include in a tag and (4) date and time to publish the video. The results indicate that the proposed approach can significantly support an accurate decision of YouTube video upload with a little computational effort.
A Transmittance Optimization-based Framework for Image Dehazing on Multi-rotor Drones Imaging

The imaging quality of images collected by multi-rotor drones determines its practical application effects. However, current image enhancement dehazing methods have problems such as being affected by depth information, high computational complexity, and artefacts in the restoration results. In this paper, based on the dark channel prior model, a tolerance mechanism is introduced in the transmittance estimation part. The total variation (TV) model constrained by the ℓ1-norm is used to refine the transmittance estimation. In addition, to reduce our algorithm’s calculation, we use down-sampling technology to reduce the original image to obtain the transmittance part. Then we calculate the transmittance of the small resolution image. In the end, we can generate the transmittance of the original image by interpolation. The experimental data processing results verify the effectiveness of our algorithm.
A Context-Aware Vocabulary Management and Reading Assistance System using Machine Learning and Natural Language Processing

Through the increase in the popularity of online reading, many people rely on online dictionaries to further understand the text [1]. However, looking up a word manually is a great inconvenience as well as a form of distraction [2]. This paper develops a chrome extension to automatically detect the difficult words for each user, and provide the words’ associated definition with a mouse hover. The chrome extension can be customized by adding and removing personal difficult words and personal easy words [3]. Also, the chrome extension offers a deeper level of analytic, including the system analyzing part of speech of the world, to further understand the definition of a selected word or sentence. The chrome extension is applied to a school/work setting in order to improve the working efficiency by providing a simple model to analyze the word definition; it is also useful for casual reading, especially to those that aren’t fluent in English. Following the strict SDLC model, the end of the testing stage reflects that most of the users gave positive feedback to the chrome extension with most of the comments centered around convenience and accuracy [4]. Through alpha testing and a small sample of beta testing, the Chrome extension presents productivity improvement on difficult texts.