Reviewerly/exHarmony huggingface dataset:
Combating Misinformation in Bangladesh: Roles and Responsibilities as Perceived by Journalists, Fact-checkers, and Users

There has been a growing interest within CSCW community in understanding the characteristics of misinformation propagated through computational media, and the devising techniques to address the associated challenges. However, most work in this area has been concentrated on the cases in the western world leaving a major portion of this problem unaddressed that is situated in the Global South. This paper aims to broaden the scope of this discourse by focusing on this problem in the context of Bangladesh, a country in the Global South. The spread of misinformation on Facebook in Bangladesh, a country with a population of over 163 million, has resulted in chaos, hate attacks, and killings. By interviewing journalists, fact-checkers, in addition to surveying the general public, we analyzed the current state of verifying misinformation in Bangladesh. Our findings show that most people in the 'news audience' want the news media to verify the authenticity of online information that they see online. However, the newspaper journalists say that fact-checking online information is not a part of their job, and it is also beyond their capacity given the amount of information being published online every day. We further find that the voluntary fact-checkers in Bangladesh are not equipped with sufficient infrastructural support to fill in this gap. We show how our findings are connected to some of the core concerns of CSCW community around social media, collaboration, infrastructural politics, and information inequality. From our analysis, we also suggest several pathways to increase the impact of fact-checking efforts through collaboration, technology design, and infrastructure development.
Using Network Cooperation Opportunities in E-Learning for Future Technical Specialists

Features of network cooperation in e-learning for technical specialists defined by their major, developed competences, future professional activity are revealed in this paper. Interdepartmental and intradepartmental forms of network cooperation of students are characterized: practical-oriented (dual) training in the conditions of cluster cooperation with enterprises-social partners of the educational organization; organization of work between teachers from different departments based on the interdisciplinary approach to the educational process. A specific electronic means of network cooperation is developed: an electronic educational and methodical complex created on the basis of training and production facilities together with employers, which suggests completing an electronic interdisciplinary task. An electronic information and education environment is developed which functions on the basis of several synchronized information resources of an educational organization using cloud based technologies. The efficiency of electronic network training is assessed according to the revealed criteria of interdepartmental electronic cooperation in the network training of students.
Exploratory Search Behaviors in Linear List Visualization

Presenting hyperlinks in the form of linear lists is the mainstream visualization method of semantic information space. Users need to click hyperlinks and jump to multiple pages to forage information and perceive the relation between pages and the connectivity within information space. The jumping process would interfere with the learning and understanding of the semantic information space, increase cognitive load of information retrieval and degrade user experience. In this paper, an experiment is conducted to investigate the exploratory search behaviors in linear list visualization. We create two exploratory search tasks, and use process analysis and questionnaire surveys to study users' disorientation, their search behaviors, and search preferences. Results confirm the learning characteristic of exploratory search, which is specifically manifested in their continuous searching through multiple interactions with the searching system, and adjusting search strategies and methods until the search task is completed. Two search preferences based on retrieval keywords and literatures are observed in the search. Through observations and surveys, we find it is difficult for users to build the mental map between hyperlinks and the information space using a linear list visualization. They will get lost with deeper information searching. This paper can inspire the information retrieval and multimedia system designers to be more considerate of users' learning abilities and their perceptual bottleneck.
Game Architecture based Obstacle Avoidance Control of Electric Surface Vessel

This paper presents an obstacle avoidance simulation of the autonomous vessel using the Unity3D game engine. The concern for the autonomous vessel is the obstacle avoidance capability. Ensuring the obstacle avoidance is essential to prevent damage to the autonomous vessel during real operations. The results show that using Unity3D game engine is a viable solution to simulate the obstacle avoidance of the autonomous vessel.
From Requirements to Verifiable Executable Models Using Rebeca

Software systems are complicated, and the scientific and engineering methodologies for software development are relatively young. We need robust methods for handling the ever-increasing complexity of software systems that are now in every corner of our lives. In this paper we focus on asynchronous event-based reactive systems and show how we start from the requirements, move to actor-based Rebeca models, and formally verify the models for correctness. The Rebeca models include the details of the signals and messages that are passed at the network level including the timing, and can be mapped to the executable code. We show how we can use the architecture design and structured requirements to build the behavioral models, including Rebeca models, and use the state diagrams to write the properties of interest, and then use model checking to check the properties. The formally verified models can then be used to develop the executable code. The natural mappings among the models for requirements, the formal models, and the executable code improve the effectiveness and efficiency of the approach. It also helps in runtime monitoring and adaptation.
Adaptive regularized parameter selection based on random projection

Arbitrary projection is a well-known AI calculation, which can be executed by neural organizations and prepared in an exceptionally productive way. Notwithstanding, the quantity of highlights should be huge enough when applied to a fairly enormous scope informational collection, which brings about moderate speed in testing technique and more extra room under certain conditions. Besides, a portion of the highlights are repetitive and even uproarious since they are arbitrarily created, so the presentation might be influenced by these highlights. In the proposed framework is to actualize Adaptive Regularization Parameter Selection. It will regularize the highlights which are significant and select a most appropriate component for an explicit issue. The primary objective is to diminish the computational expense of both characterization and relapse task by utilizing Randomization Algorithm. It gives the most ideal outcome to the high-dimensional improvement. It utilizes Multilayer neural organization for performing direct and non-straight capacity .
Serendipity Recommender System for Academic Disciplines

This paper proposes a system that encourages users to discover disciplines such as mathematics and biology, which they have not explored before. The recommendation of unexpected information is highly demanded in current recommender systems, which are used in various situations. To meet this need, the present study considers the serendipitous discovery of unexpected disciplines related to the information of interest to users. Serendipity means the ability to see what others do not, and to discover useful things with your own wisdom. Our system encourages users to select a photograph of interest, and then ponder the relevance of another photograph appearing on the display. When the two photos are perceived as relevant, the system recommends a discipline that relates to the photos, but which surprises the user. Whether users gain serendipity from the system was evaluated in an experiment. The results confirmed that the system induced serendipity by providing unexpected information through photographs linked to disciplines.
Multi-level Gate Feature Aggregation with Spatially Adaptive Batch-Instance Normalization for Semantic Image Synthesis

In this paper, we focus on the task of generating realistic images given an input semantic layout, which is also called semantic image synthesis. Most of previous methods are based on conditional generative adversarial networks mechanism, which is stacks of convolution, normalization, and non-linearity layers. However, these methods easily generate blurred regions and distorted structures. There are two limits existing: their normalization layers are unable to make a good balance between keeping semantic layout information and geometric changes; and cannot effectively aggregated multi-level feature. To address the above problems, we propose a novel method which incorporates multi-level gate feature aggregation mechanism (GFA) and spatially adaptive batch-instance normalization (SPAda-BIN) for semantic image synthesis. Experiments on several challenging datasets demonstrate the advantage of the proposed method over existing approaches, in terms of both visual fidelity and quantitative metrics.
Quantifiable Fine-Grain Occlusion Removal Assistance for Efficient VR Exploration

This article presents an occlusion management approach that handles fine-grain occlusions, and that quantifies and localizes occlusions as a user explores a virtual environment (VE). Fine-grain occlusions are handled by finding the VE region where they occur, and by constructing a multiperspective visualization that lets the user explore the region from the current location, with intuitive head motions, without first having to walk to the region. VE geometry close to the user is rendered conventionally, from the user's viewpoint, to anchor the user, avoiding disorientation and simulator sickness. Given a viewpoint, residual occlusions are quantified and localized as VE voxels that cannot be seen from the given viewpoint but that can be seen from nearby viewpoints. This residual occlusion quantification and localization helps the user ascertain that a VE region has been explored exhaustively. The occlusion management approach was tested in three controlled studies, which confirmed the exploration efficiency benefit of the approach, and in perceptual experiments, which confirmed that exploration efficiency does not come at the cost of reducing spatial awareness and sense of presence, or of increasing simulator sickness.
Sensitivity analysis of latency to data size in Spark environment

The explosion of quantity of data coming from the Internet of Things (IoT) and the generation of big data in social networks and E-commerce websites, becomes a difficult task for researchers because of the 5 V characterizing this type of data (volume, variety, variability, veracity and velocity). Many platforms have been proposed to deal with big data in the WSN domain. This article focuses on studying the performance of the Big data environment platform, namely Spark. We start by describing the Big Data ecosystem, then we will attack the components of the Spark platform, namely the Hadoop Distributed file system for storage, and MapReduce with YARN (Yet Another Resource Negotiator) for parallel processing, thus, we will describe the collection of libraries that uses resilient distributed data to overcome computational complexity. The last part of this article is devoted to an experiment which we will apply a MapReduce job which will calculate the number of replication of a word in files of different sizes with the Spark environment and we will analyze the results in speed of execution of complex calculations (in our case browsing a file of more than 6 million lines) by job MapReduce, we obtained results which favor the veracity of big data according to the size of data received by the cluster.
Performance Analysis and Enhancement to Biometric based Attendance System

The proposed research work defined for the acknowledgement on biometric based attendance system for institution faculty, as well as its significance. We mainly emphasize the problem the faculty face due to the lack of any existing system which does not use any alert or notification mechanism like sending an SMS to the faculty's mobile phone to prompt them to mark their attendance. The essential purpose of this system is to create a automated notifications based on the biometric fingerprint authentication for attendance. The system avoids attendance oriented discrepancies and overheads on a central repository.
A Brief Review of Conditions, Circumstances and Applicability of Sampling Techniques in Computer Science Domain

It is tricky to examine the whole population. To save time and money researchers select a small group called sample for best representation of the whole population. Sampling technique is used for choosing sample in the population. Sampling techniques has two types': probability and non-probability. In research the sampling techniques play an important role, for decision making and analysis of data in different fields. In this paper, we have presented a review of sampling techniques from different perspectives, to enable researchers to select a best sampling technique while doing research. After study the review they will come to know which sampling technique is best suited according to the resources (time, money and effort etc.) available in the domain.
Signal Detection and Noise Reduction Method of Massage Chair Based on EMD

A signal noise reduction method of motor detection for massage chair is proposed in this paper. Based on EMD, signal is separated to the noise signal and the normal signal, and interval soft threshold denoising technology is used to maintain the correlation of the normal signal and isolate the noise. The S-G smooth filtering, wavelet noise reduction and EMD soft threshold noise reduction are compared to prove the EMD soft threshold noise reduction which have higher signal-to-noise ratio and retain more useful signal.
Use Case of HPVT in Counting Stars: A Vision-Based Approach of Monitoring Natural Events

Counting stars could reveal the level of a cloudy sky at night, which provides important climate information. The monitoring of natural events, such as counting stars, can pose as a highly demanding task which requires an immense amount of time and dedication to be completed by a human observer. By using rich visual data from a video transmission system, namely HpVT, as the input, a specialized monitoring program utilizing computer vision techniques was developed to count stars under various night sky conditions. We present the design of a star counting framework and describe the counting process employed. We perform testing on the star counting framework using our own data set. The data set consists of 154 video footages (of 24-h period each) contain footages of the night sky which were recorded from various locations in different countries. This work shows a proof of concept that the number of stars observed can correlate well with climate information. We also discuss some of the issues faced and make recommendations for future projects.
The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19

Stroke patients under the background of the new crown epidemic need to be home-based care. However, traditional nursing methods cannot take care of the patients' lives in all aspects. Based on this, based on machine learning algorithms, our work combines regression models and SVM to build a smart wearable device system and builds a system prediction module to predict patient care needs. The node is used to collect human body motion and physiological parameter information and transmit data wirelessly. The software is used to quickly process and analyze the various motion and physiological parameters of the patient and save the analysis and processing structure in the database. By comparing the results of nursing intervention experiments, we can see that the smart wearable device designed in this paper has a certain effect in stroke care.
The Tower of Babel Problem: Making Data Make Sense with Basic Formal Ontology

Purpose – Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications. Design/methodology/approach – The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing a contextual review of the widely used Basic Formal Ontology (BFO) through digital methods and descriptions, and presents historical research alongside unstructured interview data with leading experts in BFO development. Findings – The author found that ACOs are products of communal deliberation and decision-making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data. Originality/value – The paper presents a new approach for studying ACOs, the unintended social effects of ACO work, and describes methods that may be used to produce further applied ontology studies.
Pólya urn models in the presence of additional information

Pólya urn models are of the well-known probabilistic concepts and stochastic processes. The proportion of the colors is the question of the most interest in these models. Always, the structure (The number of all the balls in the urn and the proportion of the colors) of the urn at the beginning is given. Also, in the studies where the outcome of certain withdrawals is known, these withdrawals are connected to the first structure, and there is no gap between the known structure and the additional information about the color of the withdrawn balls. The existing gap between the first structure and any relevant information motivates this paper. In the present article, we consider the Pólya-Eggenberger urn model. We prove that an urn with an initial known structure after some unknown withdrawals – conditioned to meet an assumption – behaves like an urn with a known structure. By the results of the present paper, we overcome the complexity and ambiguity caused by the unknown withdrawals, and in spite of the lack of information concerning some withdrawals, we deal with the urn as a process that behaves like a completely determined structure. To illustrate the truth of the obtained results, we simulate several urns and survey the variation in the probabilities of the colors.
Managing Big Data using Model Driven Engineering: From Big Data Meta-model to Cloudera PSM meta-model

In the era of Big Data, all our actions generate digital traces. A huge amount of data is generated daily. They are extremely related to personal life and they can be exploited in different fields. There is also internally generated data known as offline data that is created by the operations of organizations. The processing of this big data plays a key role in decision making. After the definition of generic meta-model in previous work for the processing layer. This paper presents a shift from the generic processing layer meta-model within Big Data (PIM) to a processing layer meta-model for the Cloudera distribution (PSM) through the use of the ATL transformation language. The result of the ATL transformation represents the PSM (Platform-Specific Models) according to the architecture of the MDA.
The Impact of CSR Interventions on Female Education Development in the Rural Niger Delta Region of Nigeria

The objective of this investigation was to assess the impact of multinational oil companies’ (MOCs) corporate social responsibility (CSR) interventions in female education programmes in the Niger Delta region of Nigeria. A total of 800 rural women were sampled across the region. The results from the logit model showed that rural women depended on CSR interventions of MOCs to address some of the logistical and cultural challenges associated with women’s access to post-secondary education in local communities. However, despite the significant success in supporting education initiatives generally, none of the scholarships target females specifically, and compared to men, the low level of human capital in rural women has persisted. This implies that if CSR interventions are not tailored to enhance gender diversity and promote economic opportunities for women alongside education, they may perpetuate the obstruction of women’s participation in economic, political and social development. By extension, this could delay the reduction of poverty and attainment of Sustainable Development Goals in the Niger Delta region.
Semantic-Based Automatic Generation of Reconfigurable Distributed Mobile Applications in Pervasive Environments

Nowadays, mobile applications are used in various smart domains. These applications are available through interconnected smart connected objects. Several existing research works suffer from a lack of distributed semantic-based agile strategy that improve accuracy and increase the system’s efficiency. To address this problem, this paper comes up with a new flexible, modular and hierarchical loosely coupled framework for efficiently generate context-aware applications based on user’s location and his situation. The classification of user’s situations reveals new insight on identifying efficiently hierarchical composite situations in order to meet the quality of user’s constraints. It ensures minimum execution time for context-aware of distributed mobile applications using parallel and distributed strategy. Firstly, the framework filters the contextual user’s constraints of different smart-domains into domain-specific user’s context. Then, it detects parallel incoming events captured by sensors that are able for identification of factorized composite situations. Based on these identifications, we automatically generate the application reconfiguration as a collection of adapted services that deployed on available distributed devices. We compare the situation identification performance of the proposed reasoning approach to efficient map-reduce implementations for healthcare system. Experimental results show the effectiveness, reusability and scalability of the proposed approach.
Technological Resources to Stimulate Multiple Intelligences: Verbal-Linguistic and Logical-Mathematical

Multiple intelligences encompass the different types of intelligences that a human being possesses; which are the verbal-linguistic and logical-mathematical intelligences. These two types of intelligence represent, in a significant part, the axis of the human cognitive procedure and are activated when there is a problem that needs to be solved. This paper aims to provide a summary of technological resources which allow stimulation in various contexts.