Reviewerly/exHarmony huggingface dataset:
Blockchain for committing peer-to-peer transactions using distributed ledger technologies

Blockchain consists of networks of successive blocks that are interconnected to each other by references to their former block. These form a chain. Blockchain technology creates a database like support by creation of digital ledgers, in order to support distributed transactions. The adoption of blockchain in real-world applications poses many challenges. This study aims to understand the method, its characteristics as well as the implementation concepts of transactional systems in terms of distributed transactions over web resources. The study also examines the current trends and issues in the use of blockchain in many large-scale public utility applications in e-commerce.
Domain-specific Genetic Algorithm for Multi-tenant DNNAccelerator Scheduling.

As Deep Learning continues to drive a variety of applications in datacenters and HPC, there is a growing trend towards building large accelerators with several sub-accelerator cores/chiplets. This work looks at the problem of supporting multi-tenancy on such accelerators. In particular, we focus on the problem of mapping layers from several DNNs simultaneously on an accelerator. Given the extremely large search space, we formulate the search as an optimization problem and develop a specialized genetic algorithm called G# withcustom operators to enable structured sample-efficient exploration. We quantitatively compare G# with several common heuristics, state-of-the-art optimization methods, and reinforcement learning methods across different accelerator set-tings (large/small accelerators) and different sub-accelerator configurations (homogeneous/heterogeneous), and observeG# can consistently find better solutions. Further, to enable real-time scheduling, we also demonstrate a method to generalize the learnt schedules and transfer them to the next batch of jobs, reducing schedule compute time to near zero.
Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation

Quality of Experience (QoE) monitoring of video streaming traffic is crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.
Design, Development, and Evaluation of CHEMBOND: An Educational Mobile Application for the Mastery of Binary Ionic Bonding Topic in Chemistry

The integration of information and communication tools in education has been a common trend in the last decade which resulted in increased student engagement, motivation, and even achievement and learning. Despite these improvements, science teachers still report challenges with its use in science education. Several abstract topics in chemistry are still not This research aims to develop, design, evaluate a mobile application that can be used to teach and master binary ionic bonding topic in chemistry. The research utilized a design and development research design for the app development while a quantitative-descriptive research design was used to evaluate the app. A researcher-made rubric (Krippendorf’s alpha = 0.82) was used to evaluate the app and it was found out that the application is interactive and attractive. Intrinsic motivation was also determined using an adopted instrument and was modified to fit the objectives of this research. The respondents agreed that, in terms of their intrinsic motivation, the application had some value in it and that it increased their concentration towards learning. This implies that the developed mobile application developed can be used to teach and master binary ionic bonding topic in chemistry.
Iris Movement and Eye Gaze Tracking

Iris Movement and gaze tracking has been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human-computer interaction. The goal of this paper is to present a study on the existing literature on Iris Movement and Gaze Tracking and Develop an Efficient Technique that can revolutionize the field of Computer Vision. With the uptrend of systems based on eye Tracking in many different areas of life in recent years, this subject has gained much more attention by in the academic and industrial area.
DISDi: Discontinuous Intervals in Subgroup Discovery

The subgroup discovery problem aims to identify, from data, a subset of objects which exhibit interesting characteristics according to a quality measure defined on a target attribute. Main approaches in this area make the implicit assumption that optimal subgroups emerge from continuous intervals. In this paper, we propose a new approach, called DISDi, for extracting subgroups in numerical data whose originality consists of searching for subgroups on discontinuous attribute intervals. The intuition behind this approach is that disjoint intervals allow refining the definition of subgroups and therefore the quality of the subgroups identified. Thus unlike the main algorithms in the field, the novelty of our proposal lies in the way it breaks down the intervals of the attributes during the subgroup research process. The algorithm also limits the exploration of the search space by exploiting the closure property and combining some branches. The efficiency of the proposal is demonstrated by comparing the results with two algorithms that are references in the field of several benchmark datasets.
SURVEY OF E-MAIL CLASSIFICATION: REVIEW AND OPEN ISSUES

Email is an economical facet of communication, the importance of which is increasing in spite of access to other approaches, such as electronic messaging, social networks, and phone applications. The business arena depends largely on the use of email, which urges the proper management of emails due to disruptive factors such as spams, phishing emails, and multi-folder categorization. The present study aimed to review the studies regarding emails, which were published during 2016-2020, based on the problem description analysis in terms of datasets, applications areas, classification techniques, and feature sets. In addition, other areas involving email classifications were identified and comprehensively reviewed. The results indicated four email application areas, while the open issues and research directions of email classifications were implicated for further investigation.
Schema-Guided Paradigm for Zero-Shot Dialog

Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog policies from the training data. We posit that this implicit memorization has precluded zero-shot transfer learning. To this end, we leverage the schema-guided paradigm, wherein the task-specific dialog policy is explicitly provided to the model. We introduce the Schema Attention Model (SAM) and improved schema representations for the STAR corpus. SAM obtains significant improvement in zero-shot settings, with a +22 F1 score improvement over prior work. These results validate the feasibility of zero-shot generalizability in dialog. Ablation experiments are also presented to demonstrate the efficacy of SAM.
Active contour model for image segmentation based on salient fitting energy

Although segmentation of image is very important in disease diagnosis, there still exist some difficult problems to precise segmentation, such as noise and intensity inhomogeneity. Aiming at these issues, a novel level set algorithm based on salient fitting energy is presented. We firstly transform original image into a new modality which utilises greyscale change characteristics of a local area. Secondly, a data term of salient fitting energy can be constructed by solving the deviation between new modality and input images in a neighbourhood. In addition, distance regularised term is introduced in the proposed method to remove the re-initialisation process. The experiment on a lot of medical and synthetic images demonstrate that the proposed method has good segmentation ability on the part of visual perception.
Learning from Multiple Modalities of Imaging Data for Cancer Detection/Diagnosis

Methods of automatic analysis and visualization of multispectral medical images are considered. In particular, the chapter includes method for multispectral images visualization. Proposed method supposes the synthesis of image based on an image obtained in white light and image based on a fluorescent image in the NIR channel. A feature of the method is the presentation of fluorescence data in the form of a special level map that takes into account the properties of human vision. Metric CIEDE2000 is used for estimation of visualization quality. Special attention is paid to preprocessing and enhancement image obtained in white light. Methods of medical multispectral images analysis for cancer changes differential diagnosis based on traditional machine learning technologies, deep learning technologies, as well as the combined approach based on both technologies are shown. The combined approach allows to effectively use the advantages of deep learning technology in conditions of limited volume of verified database for training. All the proposed methods and algorithms are illustrated by implementation in real medical applications.
Recommendation Systems: The Different Filtering Techniques, Challenges and Review Ways to Measure the Recommender System

A Recommender System (RS) is a computer program that helps a user to discover products or contents by predicting the user’s ratings of each item and showing them the items they would rate highly. Recommendation Systems are everywhere. RS plays an important role in helping users finding products or contents they care about. These systems are powered by recommender function. Recommender function takes information from the user and predicts the rating the user is giving the product. If we predict the product before even the user sees it, it is very powerful. So the users are shown only the products that they liked the best. The intelligence aspect is what makes a suggestion more interesting and useful. Intelligence is one of the main routes of personalization to know the interests of the user, anticipate the unknown favorites of the user, and eventually provide suggestions by matching the question and the content beyond a basic search. This analysis has resulted in many important results, which will allow current and the next generation researchers of RS to evaluate and set the roadmap of their research in this field.
Criticality-based Varying Step-number Algorithm for Reinforcement Learning

In the context of reinforcement learning we introduce the concept of criticality of a state, which indicates the extent to which the choice of action in that particular state influences the expected return. That is, a state in which the choice of action is more likely to influence the final outcome is considered as more critical than a state in which it is less likely to influence the final outcome. We formulate a criticality-based varying step number algorithm (CVS) — a flexible step number algorithm that utilizes the criticality function provided by a human, or learned directly from the environment. We test it in three different domains including the Atari Pong environment, Road-Tree environment, and Shooter environment. We demonstrate that CVS is able to outperform popular learning algorithms such as Deep Q-Learning and Monte Carlo.
SongMASS: Automatic Song Writing with Pre-training and Alignment  Constraint

Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry. In automatic song writing, lyric-to-melody generation and melody-to-lyric generation are two important tasks, both of which usually suffer from the following challenges: 1) the paired lyric and melody data are limited, which affects the generation quality of the two tasks, considering a lot of paired training data are needed due to the weak correlation between lyric and melody; 2) Strict alignments are required between lyric and melody, which relies on specific alignment modeling. In this paper, we propose SongMASS to address the above challenges, which leverages masked sequence to sequence (MASS) pre-training and attention based alignment modeling for lyric-to-melody and melody-to-lyric generation. Specifically, 1) we extend the original sentence-level MASS pre-training to song level to better capture long contextual information in music, and use a separate encoder and decoder for each modality (lyric or melody); 2) we leverage sentence-level attention mask and token-level attention constraint during training to enhance the alignment between lyric and melody. During inference, we use a dynamic programming strategy to obtain the alignment between each word/syllable in lyric and note in melody. We pre-train SongMASS on unpaired lyric and melody datasets, and both objective and subjective evaluations demonstrate that SongMASS generates lyric and melody with significantly better quality than the baseline method without pre-training or alignment constraint.
ALGORITHMS FOR SYNTHESIS OF DISCRETE CONTROLLERS IN NONLINEAR CONTROL SYSTEMS WITH DELAY

Algorithms for the synthesis of discrete controllers in nonlinear control systems, taking into account the delay, have been proposed. The synthesized vector controller provides a solution to the control problem, for example, the translation of the image point of a closed system from arbitrary initial conditions to the origin of the phase space coordinates. Built on the basis of a series-parallel set of invariant manifolds, the dynamic discrete controller ensures the fulfillment of the specified technological requirements, the asymptotic stability of a closed discrete-continuous system, and has the property of predicting the behavior of the system after sampling. These algorithms have made it possible to effectively solve the problems of synthesis of discrete controllers, taking into account the delay in process control systems.
MOBILE TECHNOLOGIES IN THE HIGHER EDUCATION SYSTEM

The article examines aspects of the use of mobile technologies in the higher education system. The advantages and disadvantages of mobile learning are presented. Directions and examples of the application of various mobile services and technologies in educational activities are proposed. It is noted that in the modern educational environment there is still a limited use of mobile technologies, their use as a way to disseminate knowledge, and not as cognitive tools. Today, the most common use of mobile devices is still storing and retrieving information, teaching materials, and scheduling. Several directions of using mobile devices in the learning process have been proposed. The conclusion was made about the novelty of the application and introduction of mobile technologies in the educational environment, the prospects and potential of mobile education in education.
Exploring Evolution of Augmented and Virtual Reality Education Space in 2020 Through Systematic Literature Review

Research is increasingly being conducted to identify the benefits provided by the latest developments in the AR/VR domain, which has seen an increase in interest as a result of the stay-at-home phenomena in 2020. Of particular interest is the application of AR/VR to education, a discipline that has seen a rapid shift to online modules in 2020. To better understand the advancements in AR/VR enabled education, we conducted a systematic literature review consisting of N=61 papers published in the year 2020 that focused on AR/VR in the education sector. We particularly focused on papers where studies have evaluated user perceptions in different countries, academic fields, and at varied educational levels. We found that while most papers conducted user studies and evaluated the technical applications of AR/VR, user perceptions, impact, and awareness were not explored in detail. Our findings highlight trends that can drive critically needed innovations through AR/VR especially to help a globalized digital evolution in the education sector.
DISBURSEMENT AND UTILIZATION OF MAINTENANCE AND OTHER OPERATING EXPENSES (MOOE)PRACTICES OF SELECTED SCHOOLS IN THE DIVISION OF PALAWAN, PHILIPPINES

This study aimed to correlate the disbursement and utilization of Maintenance and Other Operating Expenses (MOOE’s) among the administrators in selected schools in the Division of Palawan. The descriptive correlational and comparative research design was employed to compare the difference on the observation between teachers and administrators, the perception and observation of the teachers and school administrators on the practices of school administrators in utilizing and disbursing MOOE funds, and the relationship of the observations to the administrator’s practices.Data were obtained through the use of self-formulated survey questionnaires. Frequency, percentage, mean, Spearman Rho and Cramer’s V were applied in the treatment of raw data.Result of the study as to the practices of the School Administrators in disbursing and utilizing the MOOE funds of the school as observed by the teachers all indicators were rated with much extent except for the indicator related to learner’s development stating that fund is provided to finance the graduation rites which was rated with very much extent. There is no significant difference on the observation of the teachers and the practices of administrators in disbursing and utilizing the MOOE funds of the school when grouped according to age, sex, civil status, highest degree obtained, and number of years of experience. There is no significant difference on the observation of the teachers over the actual practice of the School Administrators in disbursing and utilizing the MOOE Fund.
Random singlet phase of cold atoms coupled to a photonic crystal waveguide

Systems consisting of cold atoms trapped near photonic crystal waveguides have recently emerged as an exciting platform for quantum atom-light interfaces. Such a system enables realization of tunable long-range interactions between internal states of atoms (spins), mediated by guided photons. Currently, experimental platforms are still limited by low filling fractions, where the atom number is much smaller than the number of sites at which atoms can potentially be trapped. Here, we show that this regime in fact enables interesting many-body quantum phenomena, which are typically associated with short-range disordered systems. As an example, we show how the system can realize the so-called ``random singlet phase'' (RSP), in which all atoms pair into entangled singlets, but the pairing occurs over a distribution of ranges as opposed to nearest neighbors. We use a renormalization group method to obtain the distribution of spin entanglement in the RSP, and show how this state can be approximately reached via adiabatic evolution from the ground state of a noninteracting Hamiltonian. We also discuss how experimentally this RSP can be observed. We anticipate that this work will accelerate the route toward the exploration of strongly correlated matter in atom-nanophotonics interfaces, by avoiding the requirement of perfectly filled lattices.
Towards a Single Culture in Cross-Cultural Communication: Digital Culture

Culture is a multifaceted, complex process which consists of knowledge, art, morals, customs, skills and habits. Based on this point of view of Tylor, we can say that the culture is the human in the society, his learning styles and the technical or artistic products that originate from these learning styles, in other words, the content. In antropology it is argued that when the concept of culture is considered as a component in a social system, the combination of the social and cultural areas form the socio-cultural system. Approaches that handle culture within the socio-cultural system are functionalism (Malinowski), structural-functionalism (Radliffe-Brown), historical-extensionist (Kluckhohn, Krober), environmental adaptive (White), while the approaches that treat culture as a system of thought are cognitive (Goodenough), structural (Levi Strauss) and symbolic (Geertz) approaches. In addition to these approaches that evaluate cultures specific to communities, another definition is made according to the learning time: Margeret Mead, Cofigurative Culture. In order to evaluate today’s societies in terms of culture, we are observing a new culture which has cofigurative features under the influence of convergent technologies (mobile, cloud technology, robots, virtual reality): Digital Culture. This study aims to discuss the characteristics of the digital culture, which is observed after the theoretic approaches that define different cultures in cross-cultural communication (Hofstede’s Cultural Dimension and Cofigurative Culture) and called as network society by Manual Castells and accelerated during the Covid19 pandemic, in other words the common communication culture. Common cultural features will be studied through methods of semiology and text analysis upon digital contents which are starting to take hold of cross-cultural communication, a comparison between cross-cultural communication and communicative ecology will be made, the alteration in the cultural features of the society will be examined via visual and written findings obtained.
Communication design and co-creation of information solutions for sustainable social change at the margins

Information poverty and the digital divide cause discursive disparities as well as erosion of cultural expressions and local knowledge in underserved communities in the global South. In this chapter, I describe several communication for development and social change (CDSC) research projects conducted in geographically remote parts of India with the objective of creating access to information and preserving local cultural resources and espousing participatory, bottom-up and co-creative visual and sensory approaches. The communication design used in this research adopted a reflexive and empathetic approach by situating the cultural participants at the center, thus legitimizing local knowledge, resources and agencies. Use of local languages and visuals, harnessing indigenous wisdom, designing in-situ and intercultural competences were some of the key aspects of the communication design initiatives employed in these research studies.These research initiatives envision indigenous participants as active co-researchers and leaders while ensuring dependency reduction, capacity building and sustained transformation.