Reviewerly/exHarmony huggingface dataset:
Nursing Care Teaching System Using Mixed Reality Technology

We propose a novel system for teaching nursing care movements using mixed reality technology and human pose estimation technology. This paper proposes a teaching system that guides a patient from a lying posture to a sitting posture. This nursing care operation is not easy to learn because it requires several steps to guide the patient to an appropriate posture. In this method, a mixed reality headset is attached to the caregiver, and “how-to guide the patient” is projected onto the real world to teach the caregiving operation intuitively. The system uses a camera mounted above the patient’s bed to estimate the patient’s pose and then uses a mixed reality headset mounted on the caregiver to project nursing care instructions based on the patient’s pose.
A Framework for integrating responsible AI into social media platforms

The development of social media has led to calls for increased data oversight when moderating online content to reduce harms to potentially vulnerable users and groups. An analysis of the drivers of data governance and social harm was undertaken to highlight the issues faced by social media companies when moderating content, including human interpretation; mediated protections; and machine learning content classification, to determine how protection can be implemented by those who make commercial social media enterprises their business model. This work outlines a framework for the governance of social media websites that incorporates a socio-technical model of content moderation. A high-level system architecture for the moderation of content is proposed that accounts for the contextual nature of social harms and behaviours, which may demand human interpretation, combined with a proposed ML system that can trawl through the massive amounts of information such networks produce. The framework allows the maintenance and creation of policy goals to minimise the risk of harm whilst ensuring that the privacy and protection needs of vulnerable stakeholders are maintained.
Effect of a Body Part Action on Body Perception of the Other Inactive Body Part

The rubber hand illusion (RHI) is an illusory experience in which a fake rubber hand is felt as if it were one’s own hand when the visible fake and invisible real hands are stimulated synchronously. Although several studies have suggested contributions of action to body ownership using the RHI paradigm, the relationship between body ownership and agency has not yet been fully revealed. To better understand this relationship, the present study investigated the transfer of body ownership between body parts induced by a body part action. Using an RHI paradigm involving virtual reality, we tested whether a simple finger action of the dominant (active) hand can induce the embodiment of a virtual hand corresponding to the non-dominant (inactive) hand. We evaluated the illusory experience, perceptual changes, and physiological changes during the experiment with a subjective questionnaire, crossmodal congruency effect measurement, and skin temperature measurement, respectively. The results demonstrated that the finger action induced the embodiment of both virtual hands, causing a significant increase in agency of the inactive hand. This suggests that the illusory experience induced by an active body part contributes to an increase in agency as well as body ownership over the virtual body of the other inactive body part.
Characterization of cellular mobile operating systems: Android, Symbian, iphone and Windows phone

The present theme refers to the characterization of cellular mobile operating systems: Android, Symbian, iPhone, Windows Phone. To present which of the cellular mobile operating systems, is the most secure and the most susceptible to computer viruses, the qualitative methodology based on the bibliographic reference, data collected in books, technical manuals, manufacturer information and on internet sites; to the analysis of the documentary data, done in tables. However, it is concluded that not all types of computer viruses infect cell phones, it depends on the kernel of the operating system. It was possible to know that Symbian is the operating system most prone to contamination of computer viruses, this operating system is made of a C ++ programming language coming from the C language one of the most popular and has many developers. Android is a mobile operating system, not so secure, based on the kernel of Linux, being free software allows more number of developers of the technology. Windows phone is the least susceptible to virtual pests. And Microsoft has invested heavily in their security system, restricted access to the app store to prevent the user from downloading programs out of the market, since every day are placed numerous applications. Bluetooth technology represents a major form of virus transmission.
Efektifitas Youtube sebagai Media Pembelajaran Digital terhadap Kemampuan Berbicara Siswa di Desa Inggris Singosari

&#x0D; This study aimed at investigating the effectiveness of YouTube as digital learning media to improve students’ speaking proficiency at Desa Inggris Singosari. The participants of this study were 15 trainees of Desa Inggris Singosari. This study used a quantitative method with pre-experimental design. Data were collected using speaking tests and scoring rubric. The speaking tests were analyzed using IBM SPSS Version 22.0. Meanwhile, the scoring rubric the researcher adapted from Brown (2004). After applying YouTube in teaching speaking, it was found that the value of significance (two-tailed) it showed 0.000 was lower than the significant level of 0.05 (0.000 &lt; 0.05). It presented that Ha was accepted and H0 was rejected, it means that YouTube as digital learning media was effective to improve students’ speaking proficiency. &#x0D; &#x0D; Keywords: YouTube, digital learning media, speaking proficiency&#x0D; &#x0D;
GN-GCN: Combining Geographical Neighbor Concept with Graph Convolution Network for POI Recommendation

Point-of-interest (POI) recommendation helps users filter information and discover their interests. In recent years, graph convolution network (GCN)–based methods have become state-of-the-art algorithms for improving recommendation performance. Especially integrating GCN with multiple information, such as geographical information, is a promising way to achieve better performance; however, it tends to increase the number of trainable parameters, resulting in the difficulty of model training to reduce the performance. In this study, we mine users’ active areas and extend the definition of neighbors in GCN, called active area neighbors. Our study is the first attempt to integrate geographic information into a GCN POI recommendation system without increasing the number of trainable parameters and maintaining the ease of training. The experimental evaluation confirms that compared with the state-of-the-art lightweight GCN models, our method improves $$Recall@10$$ from 0.0562 to 0.0590 (4.98%) on Yelp dataset and from 0.0865 to 0.0898 (3.82%) on Gowalla dataset.
Towards Integrating Human-Centric Characteristics into the Goal-Oriented Requirements Language

In Requirements Engineering (RE), goal-oriented techniques have captured significant attention due to their ability to bridge the gap between stakeholders’ goals and the means by which these goals can be achieved. However, current goal-oriented modeling frameworks suffer from the lack of an in-depth and thorough understanding of human-centric requirements during the design and modelling of the system. Human-centric characteristics of users are mainly related to user’s gender, culture, language, age, personality traits, emotions, and any special requirements stemmed from physical and/or mental impairments. These aspects are vital, and they play an essential role in the acceptance and usage of the developed systems. Hence, neglecting or oversighting such characteristics while designing and modeling a system will lead to ineffective and hard to use systems for some end users. This paper proposes a vision for integrating human-centric characteristics into goal modeling, with the latter being one of the most important early activities in requirements engineering. We aim to characterize the different aspects of human-centric characteristics (which we call them user profiles), and to provide a framework for the systematic integration of user profiles with goal modeling. This in turn will help identify and prioritize critical human-centric characteristics of end users, which will affect the design, modelling, and development of systems. We thus propose a long-term research agenda and urge community contributions in this research direction to achieve enhanced human-centric modelling.
Extracting Symbolic Models of Collective Behaviors with Graph Neural Networks and Macro-Micro Evolution

Collective behaviors are typically hard to model. The scale of the swarm, the large number of interactions, and the richness and complexity of the behaviors are factors that make it difficult to distill a collective behavior into simple symbolic expressions. In this paper, we propose a novel approach to symbolic regression designed to facilitate such modeling. Using raw and post-processed data as an input, our approach produces viable symbolic expressions that closely model the target behavior. Our approach is composed of two phases. In the first, a graph neural network (GNN) is trained to extract an approximation of the target behavior. In the second phase, the GNN is used to produce data for a nested evolutionary algorithm called macro-micro evolution (MME). The macro layer of this algorithm selects candidate symbolic expressions, while the micro layer tunes its parameters. Preliminary experimental evaluation shows that our approach outperforms competing solutions for symbolic regression, making it possible to extract compact expressions for complex swarm behaviors.
Multimedia Retrieval and Analysis with Cottontail DB

Analysis and retrieval of media collections get more and more challenging the larger the collections become. Keeping everything in the main memory becomes less feasible, and more and more time and effort have to be spent to deal with the data management. However, traditional relational databases do not support primitives often used in multimedia workloads, such as the nearest-neighbour search on vectors. In this column, we introduce Cottontail DB, an open-source database management system for multimedia features. Cottontail DB supports traditional relational database operations and text retrieval based on Lucene and, most importantly, efficient vector-space retrieval operations for large datasets. Cottontail DB is the new data storage system powering the vitrivr multimedia retrieval stack, which was also previously featured in the SIGMM Records [7]. Just like the other components of vitrivr, Cottontail DB is released under the permissive MIT license. It is written in Kotlin, runs on all major operating systems, and comes with a flexible and easy-to-use gRPC API, which makes it usable in many applications, independent of the programming languages used. Cottontail DB's clean and modular architecture enables the easy extension of its functionalities and also makes it useful in an educational context. In the following, we will give a brief introduction on how Cottontail DB works, what we are using it for, and, most importantly, how it can help you manage your data. To learn more about Cottontail DB, including performance evaluations, we kindly refer readers to our Open Source Software Track Contribution at ACM MM 2020 [1], where Cottontail DB was honored with that year's Best Open Source Award.
Students' attitude and difficulties in practicing speaking through autonomous learning on podcast

This research aims to examine the students' attitude on producing podcasts as a media to practice speaking for autonomous learning and the difficulties faced by students in producing podcast of speaking. This research was descriptive qualitative research and the data is collected using questionnaire and semi-structured interview. As many 28 students of English Education Department of one of public Islamic institution in Central Java in various semesters participated in the questionnaire and 4 students were chosen to be interviewed to validate the information from the questionnaire. The research found that the majority of the students have positive attitude towards producing podcasts activity as their media to practice their speaking. In the affective aspect, students are excited with the process and behaviorally they are encouraged to prepare the material well. In the cognitive aspect, students consider the activity of producing podcast is a great activity for beginner students. They are assisted to autonomously practice speaking without partner which allows them to evaluate their own quality of speaking particularly in pronunciation. Students faced many difficulties such as speaking anxiety, complex preparation, and the inconvenience of doing repeated recording.
Decimal Versus Binary Representation of Numbers in Computers

The dichotomy created by the advent of computers and brought up by the title of this column was a major quandary in the early days of the computer revolution, causing major controversy in both the academic and commercial communities involved in the development of modern computer architectures. Even though the controversy was eventually decided (in favor of binary representation; all commercially available computers use a binary internal architecture), echoes of that controversy still affect computer usage today by creating errors when data is transferred between computers, especially in the chemometric world. A close examination of the consequences reveals a previously unexpected error source.
Deep Twin Support Vector Networks

Twin support vector machine (TSVM) is a successful improvement for traditional support vector machine (SVM) for binary classification. However, it is still a shallow model and has many limitations on prediction performance and computational efficiency. In this paper, we propose deep twin support vector networks (DTSVN) which could enhance its performances in all aspects. Specifically, we put forward two version of DTSVN, for binary classification and multi classification, respectively. DTSVN improves the abilities of feature extraction and classification performance with neural networks instead of a manually selected kernel function. Besides, in order to break the bottleneck that the original model cannot directly handle multi classification tasks, multiclass deep twin support vector networks (MDTSVN) is further raised, which could avoid the inefficient one-vs-rest or one-vs-one strategy. In the numerical experiments, our proposed DTSVN and MDTSVN are compared with the other four methods on MNIST, FASHION MNIST and CIFAR10 datasets. The results demonstrate that our DTSVN achieves the best prediction accuracy for the binary problem, and our MDTSVN significantly outperforms other existing shallow and deep methods for the multi classification problem.
Multimodal Interaction Fusion Network Based on Transformer for Video Captioning

Learning to generate the description for a video is essentially a challenging task as it involves an understanding of vision and language. Existing methods are mainly based on Recurrent Neural Networks (RNN). Nevertheless, there are some limitations, such as feeble representation power and sequential nature. The transformer-based architecture was proposed to address such issues, and it is widely used in the domain of image captioning. Although it has achieved success in existing methods, the applicability to video captioning is still largely under-explored. To fully explore its significance in video captioning, this paper proposes a novel network by utilizing the transformer for video captioning named Multimodal Interaction Fusion Network (MIFN). To effectively learn the relationship between multiple features, a cross-attention module is introduced within the encoder, which provides a better representation. Moreover, in the decoder, we use a gated mechanism for filtering the essential information to produce the next word. Moreover, we evaluate the proposed approach by using the benchmark MSR-VTT and MSVD video captioning datasets to illustrate its quantitative and qualitative effectiveness and employ extensive ablation experiments to fully understand the significance of each component of MIFN. The extensive experimental results demonstrate that MIFN obtains performance comparable to the state-of-the-art methods.
Analysis of Ciphertext Behaviour Using the Example of the AES Block Cipher in ECB, CBC, OFB and CFB Modes of Operation, Using Multiple Encryption

This paper explores the Advance Encryption Standard (AES) block cipher in Electronic Code Book (ECB), Cipher Block Chaining (CBC), Output Feedback (OFB) and Cipher Feedback (CFB) modes of operation to compare the characteristic properties of ciphertext, and to compare the block complexity level of building ciphertext schemes using the methodology of periodic regularities. This paper investigates the features of four block modes of operation, which includes two analytical principles: the first principle, which defines periodicity with respect to the ciphertext; and the second, which includes the principle of repeated cipher iterations, to react the characteristic manifestations of the ciphertext, under certain control input data. In accordance with the above principles, the results of analysis of the regularities of ciphertext with respect to blocks and with respect to encryption iterations were shown in tables and respectively in obtained formulae. The package Matplotlib of the Python programming language was used for graphical visualization ciphertexts of first iteration of encryption on all investigated modes of operation under different key sizes. The implementation of AES algorithm and obtaining encryption results were performed using the package Crypto.
Using Fuzzy Logic to Assess Studentsʼ Mathematical Knowledge

Assessing students’ mathematical knowledge informs students, educators, and parents about students’ mathematical competencies. In Italy, some students receive both written and oral grades in mathematics at the end of the first semester, which are then averaged for a final grade. The possibility of applying fuzzy logic, which has been widely used to deal with uncertain or verbal descriptions, to this process has not yet been explored extensively. In the present contribution, we consider a sample of N = 47 Italian high school students, and analyze two fuzzy combinations of their mathematics grades. Students’ hypothetical grades produced with the center-of-gravity defuzzification method are lower than students’ grades in their report cards, while the mean-of-maxima defuzzification method produced grades that are statistically higher than the students’ original grades. Implications are discussed, leading to suggestions for assessment research.
Michael Saylor, MicroStrategy, and the Bitcoin Clientele Effect

Michael Saylor’s decision to position MicroStrategy as the largest corporate holder of bitcoin (BTC) turned MicroStrategy stock into a partial BTC derivative and, in the eyes of some commentators, a quasi BTC exchange-traded-fund (ETF). The decision certainly shook MicroStrategy stock out of a long slumber, and Michael Saylor has since risen to prominence as a bitcoin advocate. We interpret Saylor’s decision to embrace BTC as a twist on the old “dividend clientele” idea. The move to acquire bitcoin as a treasury reserve asset changed the information structure around MicroStrategy, attracted the attention of new investors, and ensured that MicroStrategy stock would be considered by portfolio managers forming new crypto-oriented portfolios to take advantage of investor appetite in this emerging tech-fin space. We add arbitrage pricing theory (APT) and the prospect theory concept of diminishing sensitivity to our usual information economics to highlight some of the key features of the “bitcoin clientele decision”.
Formulation and development of a novel framework to overcome communication challenges in agile distributed teams

Nowadays, many organisations have combined agile methodology with global software development due to the numerous benefits they offer such as faster time to market, cheaper and skilled labour, 24-hour development and better project control. However, agile methods prefer team members to be co-located to promote effective communication between team members and this lack of face-to-face communication in a distributed setting result in various communication challenges due to distance, time and cultural differences. This research proposes a framework to overcome communication challenges such as language barriers, time-zone differences and trust issues. A mixed-method research choice combining both qualitative and quantitative design was opted for this research where for the initial stage of the study, a systematic literature review (SLR) was performed. The analysis of the survey data collected demonstrated that the majority of respondents have rated the framework components as effective, hence making the latter viable for adoption by distributed agile projects.
Camouflage Target Segmentation Algorithm Using Multi-Scale Feature Extraction and Multi-Level Attention Mechanism

There are two problems when semantic segmentation algorithm is used in the battlefield environment: The camouflage target segmentation boundary is not ideal, and the small target segmentation accuracy is low. So a camouflage target semantic segmentation algorithm CSS-Net is proposed, which combines multi-scale feature extraction method and multi-level attention mechanism. The algorithm consistes of an encoder-decoder structure. In the encoder part, a lightweight and deep separable convolution with residual structure is used to construct the feature encoder to extract features from the reconnaissance images. In the decoder part, a strategy-selected multi-scale feature fusion module and a multi-level attention feature enhancement module are designed to obtain the multi-scale representation information and the channel information of the images. It furtherly enhances the semantic decoding process while gradually refining the segmentation results. The experiment results show that the CSS-Net algorithm can effectively complete the segmentation and recognition of camouflage targets in complex battlefield environment. The overall mIoU value reaches 91.98%, and the segmentation boundary is improved. Compared with DeepLab v3+ algorithm, the mIoU value of CSS-Net on camouflage small targets is increased by 3.71 percents, and the mIoU value on multi-scale targets exceeds 85%. The segmentation effects are improved significantly.
Cultural Algorithms (CAs)

CAs are EAs modelled based on the biocultural evolutionary theory in real societies. The idea of using multiple sources of knowledge during the search process has emerged with the development of CAs. They model a dual inheritance system observed from the human cultural evolution, in which the belief space represents the macro-evolutionary level and the population space performs the micro-evolutionary level. The belief space includes a network of knowledge components obtained from the evolution process of individuals in the population space, in which each knowledge category represents a collection of problem-specific information. This chapter presents the basic definitions, framework, operators, and formulations of CAs.