Reviewerly/exHarmony huggingface dataset:
Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes

Air leakage in braking pipes is a commonly encountered mechanical defect on trains. A severe air leakage will lead to braking issues and therefore decrease the reliability and cause train delays or stranding. However, air leakage is difficult to be detected via visual inspection and therefore most air leakage defects are run to fail. In this research we present a framework that not only can detect air leakages but also predicts the severity of air leakages so that action plans can be determined based on the severity level. The proposed contextual anomaly detection method detects air leakages based on the on/off logs of a compressor. Air leakage causes failure in the context when the compressor idle time is short than the compressor run time, that is, the speed of air consumption is faster than air generation. In our method the logistic regression classifier is adopted to model two different classes of compressor behavior for each train separately. The logistic regression classifier defines the boundary separating the two classes under normal situations and models the distribution of the compressor idle time and run time separately using logistic functions. The air leakage anomaly is further detected in the context that when a compressor idle time is erroneously classified as a compressor run time. To distinguish anomalies from outliers and detect anomalies based on the severity degree, a density-based clustering method with a dynamic density threshold is developed for anomaly detection. The results have demonstrated that most air leakages can be detected one to four weeks before the braking failure and therefore can be prevented in time. Most importantly, the contextual anomaly detection method can pre-filter anomaly candidates and therefore avoid generating false alarms. To facilitate the decisionmaking process, the logistic function built on the compressor run time is further used together with the duration of an air leakage to model the severity of the air leakage. By building the prediction model on the severity, the remaining useful life of the air braking pipe until it reaches a certain level of severity can be estimated.
The Role of Blockchain Technology in Ensuring Digital Transformation for Businesses: Advantages, Challenges and Application Steps

In recent years, the concept of digital transformation has emerged as an area where businesses have been working intensively. Digital transformation refers to the holistic transformation of businesses in human and business processes and technology elements in order to provide more effective and efficient services. Therefore, blockchain, which is called a disruptive technology, has great importance in ensuring the digital transformation of businesses. In this study, some determinations have been made about the adoption of blockchain technology and situation analysis while performing digital transformation in businesses. In this context, semi-structured interviews were held with experts in the blockchain field.
ArCycleGAN: Improved CycleGAN for Style Transferring of Fruit Images

CycleGAN can realize image translation and style transferring among unpaired images. However, it will easily generate inappropriate image results when the number and shapes of objects in the style offering image and the source image are greatly different. The paper proposed an improved network, named arCycleGAN, which introduced the mechanism of attribute registration into CycleGAN to solve the problem. The arCycleGAN can transfer the freshness styles from the style offering images to the unpaired input source images. The generated target images will have the freshness attributes of the style offering images, while maintaining the shapes and key features of the input source images. The realization of mechanism of attribute registration consists of three modules. The first module is attribute recognition module, which can identify and label the attributes of objects in images. The second module is image pre-screening module, which selects appropriate image subset as screened training set from raw image set according to the attributes of the input source images. The third module is similarity matching module, which matches the images in screened training set based on the similarity. The generator and discriminator in the new network are similar to that in the CycleGAN network. Experimental results demonstrate the effectiveness and better performance of the arCycleGAN. Compared with the CycleGAN, the new network can generate more convincing images. It can generate the target images of similar quality based on a smaller training set and less training time than the original CycleGAN. For generating images of similar quality, the number of images in the required training set can be reduced by 50%, while training time is reduced by 5.8%.
Tf-GCZSL: Task-free generalized continual zero-shot learning

Learning continually from a stream of training data or tasks with an ability to learn the unseen classes using a zero-shot learning framework is gaining attention in the literature. It is referred to as continual zero-shot learning (CZSL). Existing CZSL requires clear task-boundary information during training which is not practically feasible. This paper proposes a task-free generalized CZSL (Tf-GCZSL) method with short-term/long-term memory to overcome the requirement of task-boundary in training. A variational autoencoder (VAE) handles the fundamental ZSL tasks. The short-term and long-term memory help to overcome the condition of the task boundary in the CZSL framework. Further, the proposed Tf-GCZSL method combines the concept of experience replay with dark knowledge distillation and regularization to overcome the catastrophic forgetting issues in a continual learning framework. Finally, the Tf-GCZSL uses a fully connected classifier developed using the synthetic features generated at the latent space of the VAE. The performance of the proposed Tf-GCZSL is evaluated in the existing task-agnostic prediction setting and the proposed task-free setting for the generalized CZSL over the five ZSL benchmark datasets. The results clearly indicate that the proposed Tf-GCZSL improves the prediction at least by 12%, 1%, 3%, 4%, and 3% over existing state-of-the-art and baseline methods for CUB, aPY, AWA1, AWA2, and SUN datasets, respectively in both settings (task-agnostic prediction and task-free learning). The source code is available at https://github.com/Chandan-IITI/Tf-GCZSL.
Use of Modern Educational Technologies in Remote Learning in Higher Education During a Pandemic: the Case of COVID-19 in Cameroon

Abstract: In March 2020, the first case of COVID-19 was discovered in Cameroon. This led to the Higher Education Institutions resorting to remote learning to ensure continued teaching and learning. The researcher thus set out to bring to the limelight some recommendations for the use of educational technologies for remote learning in Cameroon and propose some suggestions to the government, the policy makers, the stakeholders and the teachers for more effective implementation of E-learning in Higher Education Institutions in Cameroon.&#x0D;
Optimize Machine Learning Programming Algorithms for Sentiment Analysis in Social Media

Sentiment analysis (SA) is used to evaluate the polarity of reviews.In SA, the feature selection phase is essential, as the best way to solve all optimization problems not happen yet so this paper proposes a hybrid approach that combines hybrid algorithms [ (GWO and (PSO)], its name (GWOPS).To reduce the search space filter features selection.(GWOPS) used for training neural networks (NN) classifiers to select the best features.Data was collected from Twitter on different topics used in our experimental.The proposed algorithm is compared with three well-known optimization algorithms the experiments and comparisons result to evaluate the quality and effectiveness of the (GWOPS)
Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs

Abstract In this work we have proposed a model for Citizen Profiling. It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries. Previously, most of the work lacks representation of Citizen Profile and have used surveillance for data acquisition. Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge Graphs. Our proposed solution is storage efficient as we have only stored data logs for Citizen Profiling instead of storing images, audio, and video for profiling purposes. Our proposed system can be extended to Smart City, Smart Traffic Management, Workplace profiling etc. Agent based mechanism can be used for data acquisition where each Citizen has its own agent. Another improvement can be to incorporate a decentralized version of database for maintaining Citizen profile.
Use of time management methods by students for self study at the university

The concept of time management and its importance for students during independent study at the university is considered in the article. The existing methods of time management are analyzed and their essence is revealed. Each student can develop their own method of time management based on general rules and principles, taking into account their style and rhythm of life, priorities, personal qualities, strengths and weaknesses. The analysis of use of methods of time management by students is made. Practical recommendations for improving the efficiency of time management methods are offered. Time management is aimed at organizing their own and working time. The student at the university must be aware of the correct distribution of time in accordance with personal interests and interests of work.During self-study, you need to use the time with the maximum number of tasks, divided into intermediate tasks with small breaks, which will not lead to fatigue and overload. To do this, you need to have time for rest and healthy sleep. The effective implementation of the methods of "time management" depends on a person's consciousness and his desire to work rationally for himself. To solve the problems of time management among students, you can build a wheel of life, which will help determine the main priorities of life, optimize the use of time and direct it to productive self-study. With the help of the life wheel of balance, you can look at your life from the side and find out what is missing and what is in excess. It will help to understand and solve some problems.Periodic performance of self-audit and analysis of the managerial personality improves the rational use of time and its allocation for self-study
Point of Sales System in InHome Café Website using Agile Methodology

The Point of Sales (POS) system is a system that supports sales transactions where POS is currently evolving because it can record sales, record inventory, print invoices, calculate profits and improve services for businesspeople and entrepreneurs. InHome Café is one of the growing cafe in Subang, Bandung. Ease of access to purchase products through online services led to an increase in transactions. At InHome Café Subang, data management, data processing, and sales transaction processing used to rely on a manual system or paper-based recording, which open to risks in data management and security. Therefore, to solve the problem, a website-based Point of Sales System was developed to record sales, collect inventory, print invoices, calculate profits using the PHP programming language with the CodeIgniter framework, and MySQL. The system was developed using the Agile development system with a reasoning that a short-term system development that emphasizes client satisfaction was required. The system functionality testing resulted in a success rate of 96.15%. &#x0D; Keywords: agile, café, point of sales, scrum, website
Exercise? I thought you said 'Extra Fries’: Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis

Today's Internet is awash in memes as they are humorous, satirical, or ironic which make people laugh. According to a survey, 33% of social media users in age bracket [13-35] send memes every day, whereas more than 50% send every week. Some of these memes spread rapidly within a very short time-frame, and their virality depends on the novelty of their (textual and visual) content. A few of them convey positive messages, such as funny or motivational quotes; while others are meant to mock/hurt someone's feelings through sarcastic or offensive messages. Despite the appealing nature of memes and their rapid emergence on social media, effective analysis of memes has not been adequately attempted to the extent it deserves. Recently, in SemEval'20, a pioneering attempt has been made in this direction by organizing a shared task on `Memotion Analysis' (meme emotion analysis). As expected, the competition attracted more than 500 participants with the final submission of [23-32] systems across three sub-tasks. In this paper, we attempt to solve the same set of tasks suggested in the SemEval'20 - Memotion Analysis competition. We propose a multi-hop attention-based deep neural network framework, called MHA-Meme, whose prime objective is to leverage the spatial-domain correspondence between the visual modality (an image) and various textual segments to extract fine-grained feature representations for classification. We evaluate MHA-Meme on the `Memotion Analysis' dataset for all three sub-tasks - sentiment classification, affect classification, and affect class quantification. Our comparative study shows state-of-the-art performances of MHA-Meme for all three tasks compared to the top systems that participated in the competition. Unlike all the baselines which perform inconsistently across all three tasks, MHA-Meme outperforms baselines in all the tasks on average. Moreover, we validate the generalization of MHA-Meme on another set of manually annotated test samples and observe it to be consistent. Finally, we establish the interpretability of MHA-Meme.
Using interactive scribe-presentations when teaching Ukrainian

Abstract The article is devoted to the urgent topic of using new modern information technologies in lessons in general and in the Ukrainian language lessons in particular. The authors of the article prove that multimedia presentations have become one of the most popular ways of visual accompaniment in the presentation of theoretical material. In the article the authors analyze the results of the external independent evaluation (EIE) of the Ukrainian language and literature from 2015 to 2018 as well as online testing results of students from the Australian, Canadian and Polish diasporas on the level of Ukrainian language proficiency. According to this analysis it is determined that the level of knowledge on this subject deteriorates every year. Relying on these statistics, scientists have proved the need to improve the quality of knowledge using multimedia presentations, which provide more effective perception of educational information by helping students to visualize it. The researchers have proved the effectiveness of using the PowToon service in teaching philological disciplines in the educational process. This article is a practical step-by-step assistant for teachers and academic staff in creating a scribe-presentation. The authors have analyzed in detail the peculiarities of the methodological approaches that are worth using for effective implementation of PowToon and PowerPoint in the educational process (competence (the ability to actualize available knowledge, skills, experience to solve the difficult tasks in professional activities), systemic (forms the relationship in the study of philology disciplines, systematizes and structures complex information, using them in teaching), informative (use of information and communication technologies in the study of philology disciplines). The effectiveness of using online (PowToon) and offline (PowerPoint) services to create multimedia presentations has been compared. The key stages of the scribe and the advantages of the scribe-presentation have been considered. Taking into account that information is absorbed and reproduced better when it is visualized, that is, through visual perception, therefore, it is proved that use of PowToon service is accompanied by positive emotions and high indicators in the students’ learning outcomes. It is confirmed by a reflection questionnaire at the end of the lesson. The questionnaire consists of five questions and is evaluated on a 10-point scale. After analyzing the responses to the questionnaire the authors have come to the conclusion that the use of the script-presentations has a better influence on the perception of new visual information; increases the motivation for learning, interest in a subject that prompts subjects of the educational process to develop their creative projects and use their knowledge in everyday life; saves teachers’ time while preparing for a lesson; inspires teachers and academic staff to give unusual lessons and provide new interesting teaching ideas, as well as create integrated lessons.
Blockchain-Based Efficient Public Integrity Auditing for Cloud Storage Against Malicious Auditors

Public integrity auditing enables a user to delegate a third-party auditor (TPA) to periodically audit the integrity of the outsourced data. Whereas, the security of public auditing schemes relies on the trustworthiness of TPA: once TPA misbehaves, the data integrity auditing would be invalidated. In this paper, we propose a blockchain-based efficient public integrity auditing scheme to resist misbehaved TPA, where the user is required to check the behaviors of TPA in a much longer period compared with that of the data integrity auditing performed by TPA. To free users from heavy computation costs during the checking, our scheme uses two key techniques. We first design a smart contract to (1) enable TPA to record each auditing entry (which records the information about each auditing task) into the blockchain and (2) ensure the validity of each recorded entry. Then, we propose an auditing record chain built on the Ethereum blockchain to link all auditing entries corresponding to the same data in the chronological order. By doing so, the user only needs to check the last auditing entry generated by TPA to verify the trustworthiness of TPA (i.e., whether TPA has correctly performed the prescribed auditing tasks). Compared with existing schemes, where the user has to check multiple entries one by one, our scheme achieves the same security guarantee with constant and low costs in terms of communication and computation.
Survey of Recent Video Watermarking Techniques

With the development of the Internet coupled with expanding the accessibility of multimedia, various copyright issues have resulted.Several researchers have been working on watermarks to provide the security, durability, and a perception of multimedia.This paper a review of some recent works is presented related to video watermarking techniques, this study focuses on the pros and cons of recent video watermarking, areas of application, and the different types and attacks that are standing against these watermarking techniques.The results obtained from this study showed that watermark techniques based on the transfer domain, are more popular than those of the spatial domain.
Studying Moral-based Differences in the Framing of Political Tweets

In this paper, we study the moral framing of political content on Twitter. Specifically, we examine differences in moral framing in two datasets: (i) tweets from US-based politicians annotated with political affiliation and (ii) COVID-19 related tweets in German from followers of the leaders of the five major Austrian political parties. Our research is based on recent work that introduces an unsupervised approach to extract framing bias and intensity in news using a dictionary of moral virtues and vices. In this paper, we use a more extensive dictionary and adapt it to German-language tweets. Overall, in both datasets, we observe a moral framing that is congruent with the public perception of the political parties. In the US dataset, democrats have a tendency to frame tweets in terms of care, while loyalty is a characteristic frame for republicans. In the Austrian dataset, we find that the followers of the governing conservative party emphasize care, which is a key message and moral frame in the party's COVID-19 campaign slogan. Our work complements existing studies on moral framing in social media. Also, our empirical findings provide novel insights into moral-based framing on COVID-19 in Austria.
Strongly convex optimization for joint fractal feature estimation and texture segmentation

The present work investigates the segmentation of textures by formulating it as a strongly convex optimization problem, aiming to favor piecewise constancy of fractal features (local variance and local regularity) widely used to model real-world textures in numerous applications very different in nature. Two objective functions combining these two features are compared, referred to as joint and coupled, promoting either independent or co-localized changes in local variance and regularity. To solve the resulting convex nonsmooth optimization problems, because the processing of large size images and databases are targeted, two categories of proximal algorithms (dual forward-backward and primal-dual), are devised and compared. An in-depth study of the objective functions, notably of their strong convexity, memory and computational costs, permits to propose significantly accelerated algorithms. A class of synthetic models of piecewise fractal texture is constructed and studied. They enable, by means of large-scale Monte-Carlo simulations, to quantify the benefits in texture segmentation of combining local regularity and local variance (as opposed to regularity only) while using strong-convexity accelerated primal-dual algorithms. Achieved results also permit to discuss the gains/costs in imposing co-localizations of changes in local regularity and local variance in the problem formulation. Finally, the potential of the proposed approaches is illustrated on real-world textures taken from a publicly available and documented database.
Quantum case-based reasoning (qCBR)

Case-Based Reasoning (CBR) is an artificial intelligence approach to problem-solving with a good record of success. This article proposes using Quantum Computing to improve some of the key processes of CBR, such that a quantum case-based reasoning (qCBR) paradigm can be defined. The focus is set on designing and implementing a qCBR based on the variational principle that improves its classical counterpart in terms of average accuracy, scalability and tolerance to overlapping. A comparative study of the proposed qCBR with a classic CBR is performed for the case of the social workers’ problem as a sample of a combinatorial optimization problem with overlapping. The algorithm’s quantum feasibility is modelled with docplex and tested on IBMQ computers, and experimented on the Qibo framework.