Reviewerly/exHarmony huggingface dataset:
Text Document Clustering Using K-means Algorithm with Dimension Reduction Techniques

With growing technologies commercial sites, social media, organizations generate lots of data. However, this huge amount of information needs to be organized properly. For this, the text mining process used as extracting relevant features and knowledge of the given unstructured text documents. The document clustering method in text mining is used for retrieving interesting features. In which similar documents organize into different groups called clusters. The large dimensions of data become a barrier in the extraction of useful information, so using dimensionality reduction (DR) technique for reducing the dimensions of the data matrices. Further data divide into groups using the k-means clustering algorithm. This study implements TF-IDF, singular value decomposition (SVD), non-negative matrix factorization (NMF), and k-means clustering. Finally, the results of the comparison of scores of kmeans, SVD with kmeans, and NMF with k-means are shown by graphical representation. The system uses 20 newsgroup datasets for simulating results.
Hub-based subspace clustering

Data often exists in subspaces embedded within a high-dimensional space. Subspace clustering seeks to group data according to the dimensions relevant to each subspace. This requires the estimation of subspaces as well as the clustering of data. Subspace clustering becomes increasingly challenging in high dimensional spaces due to the curse of dimensionality which affects reliable estimations of distances and density. Recently, another aspect of high-dimensional spaces has been observed, known as the hubness phenomenon, whereby few data points appear frequently as nearest neighbors of the rest of the data. The distribution of neighbor occurrences becomes skewed with increasing intrinsic dimensionality of the data, and few points with high neighbor occurrences emerge as hubs. Hubs exhibit useful geometric properties and have been leveraged for clustering data in the full-dimensional space. In this paper, we study hubs in the context of subspace clustering. We present new characterizations of hubs in relation to subspaces, and design graph-based meta-features to identify a subset of hubs which are well fit to serve as seeds for the discovery of local latent subspaces and clusters. We propose and evaluate a hubness-driven algorithm to find subspace clusters, and show that our approach is superior to the baselines, and is competitive against state-of-the-art subspace clustering methods. We also identify the data characteristics that make hubs suitable for subspace clustering. Such characterization gives valuable guidelines to data mining practitioners.
Development of an Augmented Reality System Achieving in CNC Machine Operation Simulations in Furniture Trial Teaching Course

In schools, due to narrow processing spaces and limited machines, students can only understand the operation and processing procedures of computer numerical control (CNC) machines through the unilateral operation and explanation of technicians or by group operation or observations in turn. Under the existing teaching restrictions, students can only obtain limited operating experience through the partial processing steps of group machine operation. Students are therefore not familiar with the operation of the machines and it is difficult to complete consistent operation procedures, resulting in fear and uncertainty in the operation of the machines. Therefore, this research used augmented reality (AR) technology to simulate the complete operation flow and processing information of CNC processing machines, and combined the practical teaching courses to deepen the students’ understanding of CNC machine operation. In this study, a total of ten participants were recruited to conduct AR system operation experiments. The follow-up verification was conducted through real CNC machine operation and by filling in the System Usability Scale (SUS) to evaluate the experience feedback after using the AR-CNC training system. Finally, the results of this study showed that the AR-CNC training system could not only solve the problem of machining operation interruption caused by students’ rotating use of the CNC machine but also solve the hardware limitations of the actual machining space and the machine itself, so that every student could learn how to operate the CNC machine as if they were operating it in person.
Research on Fault Diagnosis of Electric Vehicle Power Battery Based on Attribute Recognition

Along with the popularization of electric vehicles, electric vehicle power battery in the process of spontaneous combustion, a fire problem has occurred frequently, in order to reduce the electric car driving potential safety hazard in the process, in this paper, by using attribute recognition theory, this paper analyzes the reason of the electric vehicle power battery failure, using attribute recognition model of new energy in the process of the electric vehicle charging power battery fault, achieve rapid positioning of fault source, the guarantee of power battery in the process of the electric car driving safety.
A Systematic Review of Usability Evaluation Methods and Tools for ATM Interfaces

In this work, we present the results of a systematic review about methods for evaluate the usability on ATM interfaces and tools utilized. Following a Systematic Review Process, a total of 132 studies were found and 96 discounting duplicates, where 12 works were finally selected for the review. We also found the most used tools for usability evaluations and the frequency of them in the selected studies. Finally, we found that there exist some usability evaluation methods and process for some systems, but in the case of ATM, there is not studies was found. This work is intended to give ideas for usability evaluations on ATM interfaces, giving information of the current status of the Systematic Review and potential studies that can help to perform usability evaluations for ATM, especially, quantitative type researches. Based on this information, we intend to propose a usability evaluation process oriented to ATM applications.
Analysis of appropriate standards to solve cybersecurity problems in public organizations

The development of ICTs in the globalized world has forced countries to develop problems or implement affected problems because new vulnerabilities have always been presented every day, so not having implemented a standard security standard brings problems such as data theft, leaks, malicious software among others. The objective of this investigative work is to analyze the cybersecurity analyzes that are used in public organizations in the Republic of Ecuador, for this purpose, a search was made of the different national and international security controls in order to compare them and by their similarities determine the security guidelines that are used in public organizations and any that apply. The deductive and exploratory method was used for the development of this research, it resulted in the analysis of EGSI data and after analyzing the percentage of initial and final compliance, it was concluded that the cybersecurity sensors in public organizations were low and are currently can be considered as means, since even the assessment does not incorporate all the international security parameters.
Cyberbullying in Polish Debate on the Białowieża National Forest

Abstract Social media platforms have conquered almost all fields of human life; their impact as opinion creating tools is undisputable. They not only offer a place for people to exchange experiences, but are also a virtual space where people fight with words in defence of their beliefs. This second function has made social media a rich source for linguistic analysis, providing material for the most current social, political, and economic issues. The main aim of this paper is to contribute to reducing the identified gap in the literature on hate speech and consequential cyberbullying from the linguistic perspective and provide conclusions on elements of hate speech through the analysis of statements relating to the cut-out of the Białowieża National Forest. The examples were excerpted from the Polish social media websites of activists representing two opponent groups. This paper consists of three parts. The first part provides an overview of the literature related to hate speech, cyberbullying, their definitions, roles, and the possibilities of analysis. In this part, the background of the discussed polemic is also highlighted (the geographic location of the Białowieża National Forest, arguments used by both sides of the conflict etc.). The second part of the paper presents and discusses the results of the conducted research. After having examined some of the social media platforms used by the groups representing different attitudes to the described conflict (including Facebook, Twitter etc.), we have identified linguistic patterns within aggressive and vulgar statements expressed both directly and indirectly. Therefore, our analysis concentrates on categorisation of characteristic elements of hate statements. In the third part of the paper, we present conclusions referring to the results of the analysis.
ReCoFused partial reconfiguration for secure moving-target countermeasures on FPGAs

Partial reconfiguration is a versatile technique to modify the functionality of field programmable gate arrays (FPGAs) at run time. When performing partial reconfiguration a dedicated intellectual property (IP) component of the FPGA vendor, i.e., the partial reconfiguration controller (PRC), among a wide range of IP components has to be used. While ensuring the functional safety of FPGA designs is well understood, ensuring hardware security still remains challenging. This applies in particular to reconfiguration-based countermeasures which are intensively used to create a moving target for an attacker. Reconfiguration-based countermeasures against side-channel attacks or differential power analysis (DPA) attacks were implemented. However, from the system security perspective, the above mentioned PRC is a critical component as was noticed by many papers before. In this work, we extend a previously proposed safety mechanism which creates a container around an IP, to encapsulate and thereby to protect and observe the PRC of a FPGA. The proposed encapsulation scheme results in an architecture comprising so-called ReCoFuses (RCFs), each capturing a specific protective goal which have to be fulfilled at any time during PRC operation. The terminology follows the classical electric installation including a fuse box. In our scheme we employ formal verification to guarantee the correctness in detecting a security violation. Only after successful verification, the RCFs are integrated into the ReCoFuse Container. Experimental results demonstrate the advantage of our approach by preventing attacks on the PRC of a system secured by partial reconfiguration.
Mediation as Calibration

Emerging communication technologies remediate and redefine relations between reader and author, but a comprehensive progressive framework for assessing this dynamic during the process of preparation, transmission, reception, and consumption of media remains elusive. Such a framework is of consequence for hypertext (and first generation electronic literature in particular). Speculative claims for its utility and equally reductive rejections of the reading experience it offers call for a model which assesses the calibration of the reader/author relationship from within the medium itself. This paper presents a first framework for assessing these dynamics both at the stage of authoring and reading. Within this analysis framework we identify eleven remediating factors conceived as scales between opposing tensions, and implement this model with reference to first generation electronic literature.
Social Engineering and Prototype Awareness Enhancing During International Coronavirus Outbreak

This paper reports on a prototype awareness enhancing and monitoring device which will assist its users in order to reduce the spreading of Coronavirus (COVID-19).An awareness network between humans and sensors was used to build a human-sensor cognitive system that projects human unawareness of habitual behavior into self-correctness.The collaborative action was based on the ability of the sensor to collect information (face touching), communicate information to a human through visual (sound) warning and affect his/her decisions through fostering behavioral change, The prototype has two additional functions: a vibrating and sound awareness functions that are activated when a person places their hands in a location associated with the face.To some extend this approach coincides with the features of social engineering, and with the approach of harnessing the social and artificial intelligence for the social benefit.
A Comparative Analysis of Genetic Programming and Genetic Algorithm on Multi-Criteria Recommender Systems

Recommender systems (RSs) are software tools that work as guides by suggesting products to users from a vast catalogue of products. Various approaches and techniques have been developed to provide effective recommendations to users. Classical collaborative filtering (CF) based RSs helps users by providing suggestions based on their overall assessment of items. However, providing suggestions based on their overall assessment is not an efficient way. So, multi-criteria recommender systems (MCRS) came into existence as an extended approach for suggesting products to users based on multiple features of products, and adding these multiple features can enhance the performance of the system. However, aggregation of these feature assessment i.e. feedback provided to multiple criteria is a key issue in MCRS. In this paper, we present a comparative analysis of genetic algorithm (GA) and genetic programming (GP) approaches to aggregate criteria ratings for predicting user preferences in MCRS. These two algorithms are bio-inspired and have great potential to solve optimization problems. In this research, GP and GA are used to solve the aggregation problem in MCRS by estimating weights for each criterion in a system. We compared the results of genetic programming and genetic algorithm approaches to show their effectiveness in multi-criteria rating systems.
Eye Landmarks Detection Technology for Facial Micro-Expressions Analysis

In the paper the problem of the facial micro-expressions analysis in the image sequence is considered. One of the important tasks of such analysis is the facial landmarks detection. The distinctive characteristics of the main categories of the modern methods to solve this task are indicated. The selection of the Spontaneous Actions and Micro-Movements (SAMM) dataset for experiments is proved. In this dataset the Facial Action Coding System (FACS) is used. The results of the landmarks detection in the areas of eyes obtained in two ways are represented, i.e. using the known Active Shape Model (ASM) and using the technology developed by the authors. The proposed technology makes it possible to detect the eye landmarks by the simple tools. The main stages of this technology are described. Its advantages in comparison with the ASM method are highlighted.
Fuzzy Hough transformation in aiding computer tomography based liver diagnosis

In the liver many types of roundish lesions can appear, as well as near the liver. Finding the contour of such objects can improve both the segmentation of the liver from its environment, and the segmentation of the lesions within the liver. However, classical Hough transform, which is one of the main methods for finding objects described by a predefined parameterized formula, usually fails to identify these object as they possess not perfectly round or elliptic contours. A fuzzification of the Hough transform is described and suggested for using in image preprocessing for liver diagnosis based on CT images in this paper. Fuzzifying the Hough transform improves the detection of roundish contours.
User Request Emotion Prediction Approach in a Crowdsourcing Platform

Sentiment analysis manages to recognize and arranging opinions or conclusions communicated in the source content. The far-reaching of the World Wide Web has brought another method for communicating the assumptions of people. Web-based social networking is creating an immense measure of estimation rich information as tweets, status updates, blog entries, and so on. Sentiment analysis of this user-generated information is extremely helpful in knowing the opinion of the group. Twitter sentiment analysis is troublesome contrasted with general sentiment analysis because of the existence of slang words and incorrect spellings. It is additionally a medium with a tremendous measure of data where clients can see the opinion of different clients that are categorized into various sentiment classes and are progressively developing as a key factor in dynamic. In this paper, the Emotion Prediction approach has been established by collecting the real-time twitter data from the Twitter API, pre-processing the data and to predict the emotions using various Machine Learning algorithms. The proposed user emotion prediction approach can be used to predict the emotions of the user and the user emotion approach has been compared with the machine learning and sentiment classification algorithms. With the comparative study of the algorithm, the best accuracy can be analyzed for the user emotion analysis. The crowdsourcing system can be used for the Business systems to predict the popularity of their brands and to make changes in their brand according to the feedback received. This proposed Emotion Prediction approach is mainly focusing on working with a real-time Twitter dataset. With the evaluated accuracy measure the end-user can make a quick prediction in the usage of the crowdsourcing system.
A knowledge-based approach to hierarchical classification: A voting metaphor

The paper proposes a new approach to hierarchical classification based on condition-action rules that represent expert knowledge in a given domain. The approach adopts a voting metaphor: each rule is regarded as a voter that expresses a preference for a given category to be assigned to an item to be classified; the category that receives more votes wins. Novel performance measures of hierarchical classifiers are also introduced that aim at overcoming the limitations of the current concepts of precision and recall. The proposed approach can be applied to any hierarchical classification task, for which expert knowledge is available. The viability of the approach and its performance are shown through a real-size application concerning the e-mail dispatching task inside a large public administration. The results obtained demonstrate that the proposed knowledge-based approach to hierarchical classification can reach a performance level comparable to that of human experts, if not even better.
Boolean negation and non-conservativity I: Relevant modal logics

Abstract Many relevant logics can be conservatively extended by Boolean negation. Mares showed, however, that E is a notable exception. Mares’ proof is by and large a rather involved model-theoretic one. This paper presents a much easier proof-theoretic proof which not only covers E but also generalizes so as to also cover relevant logics with a primitive modal operator added. It is shown that from even very weak relevant logics augmented by a weak K-ish modal operator, and up to the strong relevant logic R with a S5 modal operator, all fail to be conservatively extended by Boolean negation. The proof, therefore, also covers Meyer and Mares’ proof that NR—R with a primitive S4-modality added—also fails to be conservatively extended by Boolean negation.
Effect of Inadequate Self-Organized Teams in Agile Project Management

Building self-organizing teams in agile projects is considered an important job for project leaders. However, the reality is that building self-organized teams lacks focus as many go back to managing tasks because it is more concrete and tangible. While there are an excessive number of studies proving that developing self-organized teams has a positive contribution to project success, there is a lack of knowledge about the consequences of not doing it. This study, therefore, explores the impact inadequate self-organizing teams has on agile project success. Results have identified five failure areas in a self-organizing team that have a negative impact on three success factors in agile projects. Due to a weak direct link between success factors and success criteria, conclusions are limited to a universally applicable impact on success factors. Further research is recommended to generate a universal checklist for success criteria in agile projects that can have a direct link to the identified success factors.
ProFL: a fault localization framework for Prolog

Prolog is a declarative, first-order logic that has been used in a variety of domains to implement heavily rules-based systems. However, it is challenging to write a Prolog program correctly. Fortunately, the SWI-Prolog environment supports a unit testing framework, plunit, which enables developers to systematically check for correctness. However, knowing a program is faulty is just the first step. The developer then needs to fix the program which means the developer needs to determine what part of the program is faulty. ProFL is a fault localization tool that adapts imperative-based fault localization techniques to Prolog’s declarative environment. ProFL takes as input a faulty Prolog program and a plunit test suite. Then, ProFL performs fault localization and returns a list of suspicious program clauses to the user. Our toolset encompasses two different techniques: ProFLs, a spectrum-based technique, and ProFLm, a mutation-based technique. This paper describes our Python implementation of ProFL, which is a command-line tool, released as an open-source project on GitHub (https://github.com/geoorge1d127/ProFL). Our experimental results show ProFL is accurate at localizing faults in our benchmark programs.
Vehicle and Driver Detection on Highway Based on Cascade R-CNN

Abstract Vehicle and driver detection in the highway scene has been a research hotspot in the field of object detection in recent years, and it is still a challenging problem in the research of traffic order and road safety. In this paper, we propose a novel end-to-end vehicle and driver detection method named VDDNet which is based on Cascade R-CNN and SENet. By introducing FPN structure and SENet attention mechanism in the backbone, the ability of the model to learn effective features is enhanced. It can improve the accuracy of detection in difficult scenes such as weak light, partial occlusion, and low picture resolution. The test results based on the database of highway traffic vehicle and drivers constructed by the Jiangsu Provincial Public Security Department. It shows that the detection method has the AP rate of 91.3% and the Recall rate of 92.4%, which demonstrates the effectiveness of the proposed method in complex highway environments.
Reflections on Persuasive and Digital Nudging Methods for Cultural Heritage

In this short position paper we review two methodologies: persuasion and digital nudging and their potential implication for Cultural Heritage (CH) use. As they both involve personalization as a key component, so it seems appropriate for discussion in this workshop. Both seem to open new opportunities for better serving visitors to CH sites while at the same time pose ethical dilemmas.