Reviewerly/exHarmony huggingface dataset:
Signature Verification Time Reduction for GOST Digital Signature Algorithm

Although many digital signature algorithms are available nowadays, the speed of signing and/or verifying a digital signature is crucial for different applications. Some algorithms are fast for signing but slow for verification, but others are the inverse. Research efforts for an algorithm being fast in both signing and verification is essential. The traditional GOST algorithm has the shortest signing time but longest verification time compared with other DSA algorithms. Hence an improvement in its signature verification time is sought in this work. A modified GOST digital signature algorithm variant is developed improve the signature verification speed by reducing the computation complexity as well as benefiting from its efficient signing speed. The obtained signature verification execution speed for this variant was 1.5 time faster than that for the original algorithm. Obviously, all parameters' values used, such as public and private key, random numbers, etc. for both signing and verification processes were the same. Hence, this algorithm variant will prove suitable for applications that require short time for both, signing and verification processes. Keywords— Discrete Algorithms, Authentication, Digital Signature Algorithms DSA, GOST, Data Integrity
Sentiment Analysis on Students’ Satisfaction Towards University

The arrival of social media platforms has changed the way people express themselves. They share their opinions or perceptions towards products and services online and in real-time. With the abundance of publicly available data, it could be used to perform sentiment analysis that has the capability to classify each data into positive, negative and neutral sentiment. Developing a sentiment analysis model to analyse how people talk about UTP brands could enhance their marketing strategy. Issues related to facilities and services such as poor facilities, expensive annual fees, expensive cafeteria food would be a key highlight to this project. Several scopes has been identified for this project development. Besides, this paper reports on methodology that would be implemented for developing sentiment analysis model and it would adopt waterfall model. Sentiment analysis requires dataset to undergo data pre�processing and data classification. Due to its capability to outperform other classifier, Naive Bayes classifier has been chosen to perform the classification process. In the end, data would be presented in a form of smart chart.
MiniNihongo: Location Based Japanese Minorlearning Application

Mobile language learning applications are changing the way we learn. However, users are facing one problem: motivation. Self learning via application is easy to deceiving, so we propose a new approach for language learning application that provide users words that are relevant to daily activities in a ”suitable” time determined by the application: It is a location and activity based learning application, which can detect users’ position and physical activities to find a proper time for suggesting new Japanese words that are related that geological location. However, the suggested lessons are required to be short and effective enough so that users can learn them in their small ”suitable” time zone.
On weak filters and ultrafilters: Set theory from (and for) knowledge representation

Abstract Weak filters were introduced by K. Schlechta in the ’90s with the aim of interpreting defaults via a generalized ‘most’ quantifier in first-order logic. They arguably represent the largest class of structures that qualify as a ‘collection of large subsets’ of a given index set $I$, in the sense that it is difficult to think of a weaker, but still plausible, definition of the concept. The notion of weak ultrafilter naturally emerges and has been used in epistemic logic and other knowledge representation (KR) applications. We provide a comprehensive exposition of weak filters and ultrafilters, comparing them with their classical counterparts that have found very important applications in logic, set theory and topology. Weak (ultra)filters capture the ‘majorities’ of social choice theory (which coincide with the commonsense understanding of a ‘large subset’) and in that respect, they outperform classical (ultra)filters in their role as ‘collections of large subsets’. Yet, they lack some of the elegant properties of classical (ultra)filters that make them an almost perfect match for logical theories. We investigate the extent to which some important classical results carry through in this new setting, and we focus on genuinely weak filters and genuinely weak ultrafilters. For weak ultrafilters, we proceed to provide concrete examples, answer questions of existence and characterize their construction. The class of weak (ultra)filters represents a genuine contribution of KR to set theory that might be of some interest to set theorists too and we initiate this study by providing a glimpse on natural set-theoretic questions.
On the application of matrix congruence to QUBO formulations for systems of linear equations

Recent studies on quantum computing algorithms focus on excavating features of quantum computers which have potential for contributing to computational model enhancements. Among various approaches, quantum annealing methods effectively parallelize quadratic unconstrained binary optimization (QUBO) formulations of systems of linear equations. In this paper, we simplify these formulations by exploiting congruence of real symmetric matrices to diagonal matrices. We further exhibit computational merits of the proposed QUBO models, which can outperform classical algorithms such as QR and SVD decomposition.
ARNet: Accurate and Real-Time Network for Crowd Counting

Taking into account the problem of the redundant structure and excessive parameters in the current crowd counting network, we propose an end-to-end encoder-decoder architecture called Accurate and Real-time Network (ARNet) for high-accuracy and real-time crowd counting. The encoder adopts lightweight SqueezeNet as the backbone network to extract multi-level features, the decoder can integrate contextual information to enhance the semantic representation capabilities of low-level features. In addition, we design the Parameter-Sharing Context-Aware Module (PSCAM) and the Mask Density Generator (MDG). Without adding excessive parameters, the PSCAM can capture the global context by applying multiple dilated convolutional layers with the same convolution parameters and different dilation rates. The MDG based on multi-task learning can generate accurate density maps by introducing semantic segmentation to suppress background interference. Extensive experiments on four benchmark crowd datasets, which indicate our ARNet can achieve the optimal trade-off between counting accuracy and computation efficiency.
Dual Attention Based Network with Hierarchical ConvLSTM for Video Object Segmentation

Semi-supervised Video object segmentation is one of the most basic tasks in the field of computer vision, especially in the multi-object case. It aims to segment masks of multiple foreground objects in given video sequence with annotation mask of the first frame as prior knowledge. In this paper, we propose a novel multi-object video segmentation model. We use the U-Net architecture to obtain multi-scale spatial features. In the encoder part, the spatial attention mechanism and channel attention is used to enhance the spatial features simultaneously. We use the recurrent ConvLSTM module in the decoder to segment different object instances in one stage and keep the segmentation object consistent over time. In addition, we use three loss functions for joint training to improve the model training effect. We test our network on the popular video object segmentation dataset DAVIS2017. The experiment results demonstrate that our model achieves state-of-art performance. Moreover, our model achieves faster inference runtimes than other methods.
Spelling Features of SMS Communication in Russian and Croatian Languages

In this article, we define the range of problems associated with spelling rules used in mobile communication, with a focus on SMS communication. SMS messages are one of the most common methods of communication today. The subject of our research is the language of private SMS, which is difficult to describe. This is explained by the liberties users take with the norms of the literary language, as well as the appearance of numerous regionalisms, vulgarisms, dialectisms, neologisms, etc. Although we are talking about colloquial vocabulary, not literary, and SMS messages that do not observe spelling rules, we note that some patterns exist and are used in both Russian and Croatian languages. We can say that with the advent of virtual communication, in contrast to the traditional (written) genres of spoken language, new modern genres and styles of written colloquial speech have appeared; that is, new rules are established and created that differ from the rules of traditional spelling. Drawing on the latest research on this topic, we analyse SMS messages at the spelling level. The corpus of the studied texts includes several dozen SMS messages of different styles of speech, which reflect some of the features of SMS communication in the Russian and Croatian languages.
Interdisciplinary Design Teams Translating Ethnographic Field Data Into Design Models

Translating ethnographic field data to engineering requirements and design models suitable for implementing socio-technical systems is problematic. Ethnographic field data is often “messy” and unstructured, while requirements models are organized and systematic. Cooperation and communication within an interdisciplinary design team makes the process even more complicated. A shared understanding between ethnographers, interaction designers, and software engineers is vital to ensure that complex and subtle social interactions present in the data are considered in the final system design. One solution for supporting team conversations uses the quality goal construct as a container for complex and ambiguous interaction attributes. Quality goals in system modelling promote shared understandings and collaborative design solutions by retaining a high level of abstraction for as long as possible during the design process. This chapter illustrates the effectiveness of abstract goals for conveying complex and ambiguous information in the design of a socio-technical system supporting social interaction between couples.
Pagination And Its Efficient Methods For RESTful Web Services

In this paper on the basis of analyzed resources and accumulated IT-industry ideas we discuss the problem of the overhead of the common approaches to pagination in RESTful web services, as well as the ways of overcoming it by using a method that fuses usual approaches while covering their weaknesses.
Improving Performance of Machine Learning on Prediction of Breast Cancer Over a Small Sample Dataset

The application of machine learning (ML) algorithms aim to develop prognostic tools that could be trained on data that is routinely collected. In a typical scenario, the ML algorithm-based prognostic tool is utilized to search through large volumes of data to look for complex relationships in the training data. However, not much attention has been devoted to scenarios where small sample datasets are a widespread occurrence in research areas involving human participants such as clinical trials, genetics, and neuroimaging. In this research, we have studied the impact of the size of the sample dataset on the model performance of different ML algorithms. We compare the model fitting and model prediction performance on the original small dataset and the augmented dataset. Our research has discovered that the model fitted on a small dataset exhibits severe overfitting during the testing stage, which reduces when the model is trained on the augmented dataset. However, to different ML algorithms, the improvement of the model performance due to trained by the augmented dataset may vary.
3D Scene Angles using UL Decomposition of Planar Homography

Proctoring during online exams often requires students to be under surveillance from a side pose and there is a strong need to estimate the side camera’s relative position with respect to student’s computer screen. This work uses edge and line detectors to extract the computer screen’s boundaries and estimates homography with respect to rectangular shape with corresponding aspect ratio as in a normal view. A novel Upper-Lower Decomposition of Homography (ULDH) algorithm is proposed that calculates the polar and azimuthal angles with less than 5° mean errors and can help distinguish bad camera placements from good ones with good precision. A purpose-built dataset is created and validated for this purpose and the software for key parts of image processing pipeline is made available for remote proctoring purposes.
A Visual Analysis Method of Knowledge Graph Based on the Elements and Structure

As Knowledge Graphs (KG) are widely used in various industries, it is crucial to improve KG's readability and acceptance by the public. To assist the public to understand KG more comprehensively, we proposed a visual analysis method of KG and verified, evaluated, and applied the method. First, we extracted the visualization analysis dimensions of KG from its essential elements. Secondly, supported by the Beijing cultural KG data, we verified the algorithm of each analysis dimension. Finally, we evaluated the usability of this method through user experiments and verified the feasibility of this method by implementing a visual analysis application of KG. The results show that the method can effectively assist users in understanding KG from multiple angles.
Variational local gradient threshold driven convex optimization for single image reflection suppression

To better suppress the reflection layer image by shooting through the glass, we propose a reflection suppression model to highlight the main information of the reflected image. We combine the local linear model of a guided filter with the gradient threshold to enhance the boundary contour of the image to achieve the effect of suppressing reflections and effectively solve the established partial differential equations by using discrete cosine transform. Experiments on images taken in different scenes prove the superiority of this method is the problem of single- image reflection suppression.
Termination Analysis for the $$\pi $$-Calculus by Reduction to Sequential Program Termination

We propose an automated method for proving termination of $$\pi $$ -calculus processes, based on a reduction to termination of sequential programs: we translate a $$\pi $$ -calculus process to a sequential program, so that the termination of the latter implies that of the former. We can then use an off-the-shelf termination verification tool to check termination of the sequential program. Our approach has been partially inspired by Deng and Sangiorgi’s termination analysis for the $$\pi $$ -calculus, and checks that there is no infinite chain of communications on replicated input channels, by converting such a chain of communications to a chain of recursive function calls in the target sequential program. We have implemented an automated tool based on the proposed method and confirmed its effectiveness.
Traditional Knowledge and the Inclusive Subordination of African Customary Law in Kenya: Lessons from Personal Law

Abstract Traditional knowledge forms the fabric of indigenous communities’ social and economic life. Its attempted protection through intellectual property law has been dismal. There is now wide consensus that sui generis regimes should be employed for this purpose, and that customary laws are conceivable as an integral part of such protection. This article finds that the expressed legislative intent to protect traditional knowledge through customary law in Kenya is ill-fated. Sustained inclusive subordination of the latter will obstruct any meaningful efforts to protect the former. This finding is reached by an examination of the historical application of African customary law in personal law regimes that have it as the defining legal regimen. This history is one of subtle subordination, and such subtlety remains embedded even in Kenya's law on traditional knowledge. The unpleasant effects of this phenomenon as observed in personal law regimes are likely to recur for traditional knowledge.
How Requirements Engineering is Performed in Small Businesses?

Despite that small businesses dominate the land-scape of the software industry firms, they have been understudied by the requirements engineering community. So, motivated by understanding how RE is conducted by small business firms, we extracted and analyzed sample data belonging to seven small businesses from a recent online survey we conducted in 2020 on the RE state of practice. In this paper, we present a panoramic overview of the findings from the analyzed sample.
NED

Explainable machine learning methods have attracted increased interest in recent years. In this work, we pose and study the niche detection problem, which imposes an explainable lens on the classical problem of co-clustering interactions across two modes. In the niche detection problem, our goal is to identify niches, or co-clusters with node-attribute oriented explanations. Niche detection is applicable to many social content consumption scenarios, where an end goal is to describe and distill high-level insights about user-content associations: not only that certain users like certain types of content, but rather the types of users and content, explained via node attributes. Some examples are an e-commerce platform with who-buys-what interactions and user and product attributes, or a mobile call platform with who-calls-whom interactions and user attributes. Discovering and characterizing niches has powerful implications for user behavior understanding, as well as marketing and targeted content production. Unlike prior works, ours focuses on the intersection of explainable methods and co-clustering. First, we formalize the niche detection problem and discuss preliminaries. Next, we design an end-to-end framework, NED, which operates in two steps: discovering co-clusters of user behaviors based on interaction densities, and explaining them using attributes of involved nodes. Finally, we show experimental results on several public datasets, as well as a large-scale industrial dataset from Snapchat, demonstrating that NED improves in both co-clustering (20% accuracy) and explanation-related objectives (12% average precision) compared to state-of-the-art methods.
ALGNN: Auto-Designed Lightweight Graph Neural Network

Graph neural networks (GNNs) are widely used on graph-structured data, and its research has made substantial progress in recent years. However, given the various number of choices and combinations of components such as aggregator and activation function, designing GNNs for specific tasks is very heavy manual work. Recently, neural architecture search (NAS) was proposed with the aim of automating the GNN design process and generating task-dependent architectures. While existing approaches have achieved competitive performance, they are not well suited to practical application scenarios where the computational budget is limited. In this paper, we propose an auto-designed lightweight graph neural network (ALGNN) method to automatically design lightweight, task-dependent GNN architectures. ALGNN uses multi-objective optimization to optimize the architecture constrained by the computation cost and complexity of the model. We define, for the first time, an evaluation standard for consumption cost with the analysis of the message passing process in GNNs. Experiments on real-world datasets demonstrate that ALGNN can generate a lightweight GNN model that has much fewer parameters and GPU hours, meanwhile has comparable performance with state-of-the-art approaches.
SaDes

Before the publication of particular datasets, in order to protect the private information while preserving the usability as much as possible, desensitization is required. Automatic identification and evaluation of sensitive attributes are prerequisites for targeted desensitization of datasets, sensitivity can also reflect the effect of desensitization in turn. However, existing desensitization systems all rely on predefined desensitization model with respect to manually given sensitivity levels, which is subjective and unable to be applied end-to-end. Besides, there is no way for the user to tell whether the desensitization is performed enough or superfluous. In this demonstration, we present an interactive system for sensitivity-aware desensitization towards tabular data (SaDes). It automatically evaluates the risks of re-identification for arbitrary columns according to record-linkage attack, and performs desensitization accordingly. The risks of re-identification for the desensitized data can be immediately evaluated such that the user can iteratively execute desensitization in order to achieve a better balance between the usability and privacy. To the best of our knowledge, SaDes is the first system that provides automatic sensitivity evaluation and interactive desensitization in a back-to-back manner.
Advanced Training Set Generator for Use in Self-Organizing Neural Networks

Success in training of artificial neural networks (ANNs) depends on properly selected training data set. The problem is not trivial, especially in situations in which the structure of data to be classified by the neural network (NN) after completing the learning phase is not known. To cope with this problem, one of the solutions is to conduct comprehensive tests of the ANN with training it with data sets that differ in the distribution of the learning patterns in the input data space. In order to prepare such sets, appropriate tools are required that introduce the ability of a broad parameterization. In the proposed work, an advanced data generator has been developed, which based on a specific variability of particular predefined parameters, generates a collection of learning data sets. The obtained sets are then used to optimize the learning process of selected algorithms of self-organizing ANNs. The NN then automatically retrieves subsequent data sets from specific location, and as a response generates reports with an assessment of the quality of the learning process. The aim of the proposed investigations is to optimize the structure of selected ANNs, so as to obtain their minimum complexity, important in case of their hardware implementation.
Research on Key Technologies and Function Analysis of Live Interactive Classroom in AI+ Era

Intelligent live interactive classroom in the era of AI+ is a quality assessment method of precision teaching based on deep learning and big data behavior analysis. It has gradually developed into an important teaching approach and method. On the issues of teaching standard missing in adult open education online live class, education quality supervision is not in place and lack of interactivity, this paper explores the key technologies of live interactive teaching platform for open education, and puts forward the design method of AI+ live classroom framework and functions from the aspects of teaching management and evaluation, so as to realize learner-centered comprehensive intelligence. This method plays an important role in promoting the individualized teaching and accurate service of open education.