Reviewerly/exHarmony huggingface dataset:
Culturally Aware Intelligent Learning Environments for Resource-Poor Countries

This paper presents current work being done on the development of a speech and language technology (SLT) based intelligent tutoring system for coaching young learners from a Caribbean context in developing literacy skills. The system uses speech recognition, socio-cultural modelling and educational technology techniques that aim to enable two-way, dynamic communication between a young reader and the synthetic SLT tutor. Two goals of the research are to firstly model a reader computationally from instructional and socio-cultural perspectives, and secondly to create speech models that capture the unique linguistic variations and pronunciations expressed by young readers in Trinidad and Jamaica in particular.
Urban Monthly Water Consumption Forecasting Based on Signal Decomposition and Optimized Extreme Learning Machine

With the development of information technologies, the smart water is getting to be popular. Water consumption forecasting plays a significant role in the smart water system. This paper proposes a PSO-ELM (Extreme Learning Machine Optimized by Particle Swarm Optimization) coupled with SDEMD (Secondary Decomposition of Empirical Mode Decomposition by Variational Mode Decomposition) for monthly water demand forecasting. Firstly, we use the EMD (Empirical Mode Decomposition) to decompose the water consumption data and the VMD (Variational Mode Decomposition) to process the first sub-time series. Then, PSO-ELM is employed to forecast the sub-time series separately, the forecasting results of every sub-time series are combined together to form the final forecasting result. The experimental results reveal that the SDEMD-PSO-ELM model can effectively forecast the monthly water consumption for strong nonlinearity and non-stationary data and obtains better performance than ELM (Extreme Learning Machine), RBFNN (Radial Basis Function Neural Network) and VMD-ELM models.
Grounds for Searching the Best Solution for Controlling the Pressurized Water Reactor in Dynamic Modes when Changing the Controlled Parameters

The article focused on the development of information technology for the optimization of control over complex dynamic systems at the stage of their design that should realize possibilities of modeling of linear and nonlinear dynamic systems, the analysis and synthesis of such systems, their optimization on various quality criteria. The purpose of this article is to develop the structure and elements of information technology to optimize the control of complex dynamic systems, including automated control systems. The general structure and functional model of information technology of the analysis and synthesis of control systems includes modeling of dynamic systems in the form of systems of differential equations and transfer functions, integration of systems of differential equations, calculation of system quality criteria, methods for solving various optimization tasks on solutions of optimization problems of dynamic systems is offered.
Operational effects of using restaurant management system: An assessment according to business features

This research was conducted to determine the positive and negative aspects of using Restaurant Management System (RMS) in restaurants based on the perspectives of restaurant staff. The operational effect of RMS usage was examined in terms of business characteristics, and the differences were tried to be revealed. 385 personnel actively working in a restaurant were included in the study. Data were collected using a structured questionnaire. While the perspectives of the participants working in restaurants where RMS is used are that RMS facilitated the operations management, increased sales and provided production/service standard, the staff working in restaurants where RMS is not used had higher scores in the expressions regarding the difficulty of using the system (p < 0.05). In addition, RMS has a more positive effect in terms of increasing sales and providing production/service standard according to the personnel working in chain restaurants (p < 0.05). Again, the lowest scores regarding the positive effects of using RMS in terms of operations management and sales increase belonged to the employees of restaurants with 10 or less staff. Therefore, there is a relationship between business size and the need for RMS usage.
Logics of dependence and independence: The local variants

Abstract Modern logics of dependence and independence are based on team semantics, which means that formulae are evaluated not on a single assignment of values to variables, but on a set of such assignments, called a team. This leads to high expressive power, on the level of existential second-order logic. As an alternative, Baltag and van Benthem have proposed a local variant of dependence logic, called logic of functional dependence ($ {\textsf {LFD}}$). While its semantics is also based on a team, the formulae are evaluated locally on just one of its assignments, and the team just serves as the supply of the possible assignments that are taken into account in the evaluation process. This logic thus relies on the modal perspective of generalized assignments semantics and can be seen as a fragment of first-order logic. For the variant of $ {\textsf {LFD}}$ without equality, the satisfiability problem is decidable. We extend the idea of localizing logics of dependence and independence in a systematic way, taking into account local variants of standard atomic dependency properties: besides dependence and independence, also inclusion, exclusion and anonymity. We study model-theoretic and algorithmic questions of the localized logics and also resolve some of the questions that had been left open by Baltag and van Benthem. In particular, we study decidability issues of the local logics and prove that satisfiability of $ {\textsf {LFD}}$ with equality is undecidable. Further, we establish characterization theorems via appropriate notions of bisimulation and study the complexity of model checking problems for these logics.
Accelerated action recognition and segmentation in videos

This work deals with human action recognition in video streams when the sequence is not temporally segmented. The problem of simultaneous temporal segmentation and action recognition is often tackled according to the concept of spatiotemporal proposals. More precisely, we focus on a joint action recognition and temporal segmentation that learns sparse dictionaries to represent the temporal proposals and selects the most appropriate ones to recover temporal segments containing actions. Our contribution has firstly consisted in studying the influence of the different parameters according to the target context. Secondly, we have proposed some solutions to decrease the complexity. Our preliminary experimental results indicate an important decrease of the computational load at a price of slight degradation of the precision.
Research Data Sharing and Reuse Through Open Data

Research data management is considered a critical step in the research process among researchers. Researchers are required to submit RDM plans with details about data storage, data sharing, and reuse procedures when submitting research proposals for grants. This chapter presents findings of an investigation into the perceptions and practices of ZARI researchers towards research data management. Mixed methods research using a self-administered questionnaire was adopted for data collection. Fifty-one researchers were sampled and recruited for participation into the study. The study established that the majority of the researchers were not depositing their research data in central repositories; data was kept on individual's devices and was therefore not readily available for sharing. The major challenges being faced by researchers included lack of a policy, lack of a repository, and inadequate knowledge in RDM. The study concludes that research data at ZARI was not being professionally managed. The study recommends for formulation of policies, establishment of repository and staff training.
The Implementation of Hardware Security Based Zymkey 4i in HDVA

Technology advancement has made the life of electronics users much easier. During the pandemic, the usage of a smart assistant is found to be useful and convenient in everyday task or reminder. The electronics consumers who want their everyday life to be more convenient, they sacrifice the needs of a basic cybersecurity in their everyday electronic usage. Home Digital Voice Assistant (HDVA) that comes without a monitor can pose a threatening privacy issue towards the user. This research is to provide a countermeasure to the malicious attack towards the HDVA. The result of this implementation shows that Zymkey 4i is feasible in protecting the Raspberry Pi from being tampered by using the device ‘binding’ method from Zymkey 4i.
Kripke Semantics for Intersection Formulas

We propose a notion of the Kripke-style model for intersection logic. Using a game interpretation, we prove soundness and completeness of the proposed semantics. In other words, a formula is provable (a type is inhabited) if and only if it is forced in every model. As a by-product, we obtain another proof of normalization for the Barendregt–Coppo–Dezani intersection type assignment system.
A Boolean operation based off-line full coverage path planning strategy for industry robot

This research aims to develop a robot trajectory rapid generation strategy for industry robots. On the basis of Boolean operation, this work is implemented in an off-line programming environment name Robotstudio (product of ABB, Swiss), the basic principle of this method is to generated a series of cutting curves by the Boolean operation between coating surface and an orthogonal surface, the cutting curve is then discrete and analyzed to obtain the robot trajectory points. As shown by our simulation results, this presented strategy is to determine series of appropriate path curves that can not only minimize the total length of subsequent curves but also ensure the distance consistency among offset curves, thus it is suitable for industry robot application.
Book culture: The dialectic of the static and the dynamic

The integrity of book culture is examined from the viewpoint of dialectical unity of the dynamic and the static: what ensures reader interest and long life of printed books in the historical time and what makes the mobile superstructure adapting the book culture to the changing environment and expanding its life. The author emphasizes that the book culture subject-object and subject categories, i.e. the humans and books/documents, take up the more stable and static positions. The book culture stability is manifested through its autonomy and persistence within the changing historical and cultural landscape (homeostasis, autopoiesis, and self-referencing). The dynamics is manifested in the processes of organization, disorganization and self-reorganization. The author substantiates that the World Wide Web enforces the fundamentals of the printed books and printed word; the functionality of libraries is to change in the future as they will be functioning both offline and online. These factors will influence the book culture and give rise to its new forms and contents. The technological era challenges the human ecosystem. The deep processes have been changing the book culture. However, the inevitability of the process must not be overestimated. Being analyzed and interpreted, the process can be managed.
Paired Training Framework for Time-Constrained Learning

This paper presents a design framework for machine learning applications that operate in systems such as cyber-physical systems where time is a scarce resource. We manage the tradeoff between processing time and solution quality by performing as much preprocessing of data as time will allow. This approach leads us to a design framework in which there are two separate learning networks: one for preprocessing and one for the core application functionality. We show how these networks can be trained together and how they can operate in an anytime fashion to optimize performance.
Use of ICT and Digital Technology to Conserve India's Cultural Heritage

The heritage of any place is required to be conserved, documented, and protected to the extent possible for the benefit of future generations to retain its culture, pride, and traditions. Advancements in information, communication, and digital technologies have considerably extended supports in identifying, preserving, and managing cultural heritage items. The use of web-based applications and digital tools plays a significant role in preserving and sharing cultural assets. This chapter proposes the importance of building a comprehensive cultural heritage information system for preserving and conserving the cultural heritages pertaining to one of the UNESCO's world listed cultural heritages in India named Agra Fort. The researcher highlights the opportunities and the need for conserving the knowledge content of the heritage items associated with Agra Fort.
Method of Ensuring Structural Secrecy of the Signal

A method for providing energy and structural secrecy of a signal is presented, which is based on the method of pseudo-random restructuring of the spreading sequence. This method complicates the implementation of the accumulation mode, and therefore the detection of the signal-code structure of the signal in a third-party receiver, due to the use of nested pseudo-random sequences (PRS) and their restructuring. And since the receiver-detector is similar to the receiver of the communication system, it is necessary to ensure optimal signal processing to implement an acceptable level of structural secrecy.
Key Challenges to Data Management in the Fourth Industrial Revolution

This chapter explores the numerous challenges impacting data management in the fourth industrial revolution. It highlights the importance of data management in the era of technological and industrial development in the 21st century business world. It draws attention to the value of countries enacting policies and reforms to curtail the rate of cyber-crimes and hacking into sensitive information of organisations whose operations occur significantly in the cyber realms. Much focus is given to the literature on legal policies enacted in many Western nations to prevent and minimize the consequences of data breaches. It draws attention to the benefits of effective data management in organisations and the key reasons considerable focus should be given to it from national governments across the globe.
Japanese and English Students Perspective on Social Media

Social media has created new possibilities for digitally native students to engage, interact and collaborate in learning tasks that foster learning processes and the overall learning experience. By using both qualitative and quantitative data, this article discusses the perspectives of Japanese and English students towards social media. As we know, foreign language learning is facilitated by social media and especially for a new generation of students. This paper contributes to an understanding of how language learners’ perspectives on social media. 52 English students and 18 Japanese students participate in the research as the sample. We found that most of the students aware of the benefit of social media for learning languages. Furthermore, the study showed that both English and Japanese students preferred to use English or Indonesian languages to do social media activities such as posting, writing a caption, or commenting than local language or Japanese language. Therefore, Japanese and English students should improve their awareness of social media for language learners.
Grover’s Algorithm and Walsh Spectrum

Grover’s algorithm provides a square root improvement over its classical counterpart in searching an unsorted array. The most important part of this algorithm is that, it can amplify the amplitude of a subset of states and reduce that of the others. First, we show how the satisfying inputs of a Boolean function can be efficiently discovered by the Grover’s strategy. Further, one can consider the state out of the Deutsch-Jozsa algorithm that is related to Walsh spectrum of a Boolean function, and then try to amplify certain places through Grover to understand the Walsh spectrum distribution with further accuracy. In particular, we provide detailed understanding of how the nonlinearity and resiliency of a Boolean function can be studied in this direction. Examples and Qiskit codes are presented here to explain the idea with complete details.
A Four-Stage dynamic approach for Software Defect Prediction

Defects are a major constraint in the software development process. Defects can cause a lot of trouble if not found in earlier stages of software development. They can result in cost overruns and can even lead to a complete system failure. Many supervised machine learning algorithms have been used over the past few decades for software defect prediction. However, the huge variety of software projects causes a problem of varying performance. No machine learning algorithm can deliver the best results consistently for all datasets. Also, a given dataset can have a variety of tuples having different similarities and dissimilarities. Therefore, there is a need to address these problems while predicting defects in any project. This paper introduces an approach for defect prediction which takes into consideration the variety of software projects available. This approach is based on dividing the training dataset into multiple subsets and applying multiple supervised learning algorithms on each such subset to determine the best algorithms for each such subset and then using this information to predict software defect in future projects by making use of similarities in datasets. The given dataset is divided into four parts based on whether the results of applying any random machine learning algorithm on that dataset were true positive, true negative, false positive, or false negative. Experimental results on the PROMISE data repository show an improvement in performance over the existing machine learning algorithms.
Accessing LinkedIn and Google E-mail Databases Using Kali Linux and TheHarvester

In this scientific paper, a test is made on how reliable and secure databases are. The simulation shows how easily hackers can access a database and use its contents maliciously. It is performed on a virtual machine Kali Linux using software program - TheHarvester. People's personal email addresses have been used and shown for educational purposes only. The aim is to analyse the accessibility of data collections.
Empathy Map Instrument for Analyzing Human-Computer Interaction in Using Web Search UI by Elderly Users

Since elderly users lack intellectual ability, which becomes less due to age factor their interactions with computer interfaces exhibit limitations essentially arising from problems inherent to their age. This is due to the minimal cognitive flow, physical impairments, and lack of knowledge about computers and technologies. All these factors are often analyzed by employing qualitative and quantitative research methods. The empathy map tool was used as the qualitative analysis method in this research study. To address this study objective, the study target group was elderly people 60 years and above, and the group consists of 15 volunteers, with seven females and eight males. Age is the only factor in this study. The test used in this study is the usability test to do seven search tasks by using Google engine and seven different search tasks with Bing engine individually by each volunteer. The observation method and note-taking are used in this study to collect data. Also, a SUS questionnaire was used to be a validation method to prove the results obtained by the empathy map instrument. The result of the empathy map tool enables to explore, understand, and empathize, from the perspective of the elderly user and their interaction with search engines; more specifically, with the Google and Bing interface. The results of the empathy mapping method show that the elderly are dissatisfied with the usability of search interfaces, which is consistent with the findings of the SUS questionnaire.
Co-immersive-Creation: A Hybrid Approach for Using Virtual Reality and Telepresence Systems in Co-design Research

The Covid-19 pandemic is changing the way design researchers conduct research activities with safety regulations restricting much of in-person methods of research. Co-design (or participatory design) utilizes creative toolkits that enable stakeholders from non-design backgrounds to contribute and collaborate in the design process, from front-end to tail-end. The pandemic has forced co-design researchers to reconceptualize and adapt their research methods and toolkits into remote digital formats. This presents new challenges in conducting co-design research such as the varying levels of technological literacy of parties involved and being able to facilitate the same level of engagement and hands-on involvement as in-person co-design activities. This paper examines the exploration of a novel hybrid approach, named Co-Immersive-Creation (CIC), that combines virtual reality (VR) and telepresence systems such as video conferencing applications for remote co-design research. It presents a use case of the co-design of the interior spaces in intensive care unit (ICU) hospital rooms to reduce sleep deprivation and delirium among the patients. An outline of the technical setup and the considerations surrounding this hybrid approach is presented to encourage its continued exploration. The outcome of the exploration highlights the growing potential of CIC and the possibilities offered by integrating technology such as VR for conducting remote co-design research between designers and non-design stakeholders.
Mystery functions

This article documents how to make the concepts of specification, properties of specifications, unit tests, soundness of unit tests, implementation, and satisfaction coexist correctly, harmoniously, and effectively in the mind of undergraduate students, using an off-the-shelf proof assistant. The concepts are instilled through a family of puzzles: given the specification of a mystery function (i.e., conditions this function should satisfy), which functions---if any---satisfy this specification? Each puzzle is solved using a combination of informal induction (as in machine learning), structural recursion (to implement the function), and formal induction (to prove satisfaction), and in a consolidating way such that each concept has a place that makes sense, instead of being perceived as vague, alien, arbitrary, and irreproducible. The eureka moment that concludes each informal induction and the subsequent formal induction that confirms that the student was right make mystery functions rewarding, and repetition makes them addictive. Mystery functions can also be used as a vector for expansion as well as for reflection.
The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios

Abstract The exchange of ideas between computer science and statistical physics has advanced the understanding of machine learning and inference significantly. This interdisciplinary approach is currently regaining momentum due to the revived interest in neural networks and deep learning. Methods borrowed from statistical mechanics complement other approaches to the theory of computational and statistical learning. In this brief review, we outline and illustrate some of the basic concepts. We exemplify the role of the statistical physics approach in terms of a particularly important contribution: the computation of typical learning curves in student teacher scenarios of supervised learning. Two, by now classical examples from the literature illustrate the approach: the learning of a linearly separable rule by a perceptron with continuous and with discrete weights, respectively. We address these prototypical problems in terms of the simplifying limit of stochastic training at high formal temperature and obtain the corresponding learning curves.