Reviewerly/exHarmony huggingface dataset:
ROLE OF IOT IN BIG DATA MANAGEMENT OVER CLOUD PLATFORM

At this point of time, demands of two technologies are at peak. These two technologies are IOT and clouds increasing day to day. When, it was tried to put big data over cloud various problems were raised. A technology which was made by the integration of previous and latest technology is known in the form. In other words we can say that it is not an individual technology. This technology provides support in the favor of companies. With the help of this support companies achieve what they want. It shows that this technology is highly capable and it can manage large amount of unrelated data. The most important thing is that this management has done with an accurate speed, and inside the right time frame. As a result, it allows real-time analysis and reaction. The two major issues in management of big data about its size and security. This paper has focused on these two issues. The research paper has considered different security threats to data along with compression mechanism to manage the big data during remote transmission.
HIGHER EDUCATION CONTRIBUTION TO ESTABLISHING THE KNOWLEDGE ECONOMY ALGERIAN CASE 2011-2018 WITH REFERENCE TO ARAB COUNTRIES

Algeria initiated reforms in various fields, including higher education in order to facilitate the transition to knowledge economy. This study showed the reality of higher education in Algeria and investigated its role in the production of knowledge by assessing the number of patents, scientific researches, university rankings; and it compared the Algerian higher education performance through Global Innovation Index and global competitiveness index. Using the collected data, the study clarified the relationship between expenditure on higher education and the number of patents issued, the result showed a strong positive relationship, and on the other hand it tried to explain the regression between Algeria's ranking on the global innovation index and the sub-indicator tertiary education. This study concluded by proposing the most important possible solutions to upgrade the Algerian performance. KEY WORDS: higher education, knowledge economy, government expenditure, university rankings, Innovation, patents, scientific research, human capital, GII, global competitiveness index, Algeria, Arab countries.
Machine Learning Interpretability Techniques for Outage Prediction: A Comparative Study

Interpretable machine learning has recently attracted a lot of interest in the community. Currently, it mainly focuses on models trained on non-time series data. LIME and SHAP are well-known examples and provide visual-explanations of feature contributions to model decisions on an instance basis. Other post-hoc approaches, such as attribute-wise interpretations, also focus on tabular data only. Little research has been done so far on the interpretability of predictive models trained on time series data. Therefore, this work focuses on explaining decisions made by black-box models such as Deep Neural Networks trained on sensor data. In this paper, we present the results of a qualitative study, in which we systematically compare the types of explanations and the properties (e.g., method, computational complexity) of existing interpretability approaches for models trained on the PHM08-CMAPSS dataset. We compare shallow models such as regression trees (with limited depth) and black-box models such as Long-Short Term Memories (LSTMs) and Support Vector Regression (SVR). We train models on processed sensor data and explain their output using LIME, SHAP, and attribute-wise methods. Throughout our experiments, we point out the advantages and disadvantages of using these approaches for interpreting models trained on time series data. Our investigation results can serve as a guideline for selecting a suitable explainability method for black-box predictive models trained on time-series data.
Autism Spectrum Disorder: Guidelines for management during the period of Social Isolation by Coronavirus (COVID-19)

Autism Spectrum Disorder (ASD) is a condition of neurodevelopment characterized by the presence of alterations in social communication, restricted interests and repetitive behaviors As a result, many of the children who have ASD require different types of intervention (behavioral, language, social, occupational, pharmacological or others), so they attend specialized public or private care centers to receive their therapies After the presence of COVID-19 was identified in December 2019 and is currently considered a pandemic, several countries have established multiple measures to prevent contagion and its spread Within this, Peru has considered social isolation mandatory;therefore, children can no longer carry out their therapies, staying in their homes any longer This could trigger alterations in it, leading to difficulties and overload in parents and/or caregivers In this regard, this document gathers some suggestions that families or caregivers may apply during this period in the management of children with ASD status
Development of scientific and information knowledge bases in education

The article describes the approach to solving the problem of formation of scientific and information bases in education. The basis for the creation of a knowledge system is theoretical typology and the construction of models of scientific information. Models of scientific methods of research, which reveal the principles of their objective choice, the field of effective use and adequate presentation, form the knowledge base. A more technological presentation of a variety of scientific methods increases the effectiveness of research and education activities.
CPU AND MEMORY REQUIREMENT BASED TASK CONSOLIDATION FORREDUCING ENERGY CONSUMPTION IN CLOUD COMPUTING

With the rise in popularity of Cloud Computing for high-performance computing, the number of Data Centers (DC)has increased exponentially. That has not only lead to new challenges of managing dynamic load of incoming requests butalso to ensure the effective utilization of available resources in DC. With that, high Energy consumption in DC is anothercritical situation of IT industry. Ineffective utilization of resources and overburden of communication is the main reasonbehind wasting a huge amount of energy. So, the objective should be optimizing the utility of available resources in the datacenters with less coupling among resources. This work proposed a task scheduling policy that overcomes the issue bycategorizing the incoming tasks as per their requirement of CPU and Memory (Ram). And, accordingly, categorize the VMsand identify the VMs with high CPU capacity and VMs with high ram availability. This assignment of tasks according to thesuitability of requirement scales the effective and managed utilization of resources, which not only results in finishing thetasks early but also helps in reducing the energy consumption by activating minimum number of hosts at a time. Experiment isperformed using CloudSim using the dynamic workload from PlanetLab. Results are proving the effectiveness of the proposedapproach for reducing energy consumption in DC.
Unlocking the Black Box of AI Listening Machines: Assemblages for Art, Technology and Innovation

The black box of innovation in the realm of connected AI technologies renders not only their technicalities opaque but also, and more importantly, the social effects and relations that constitute their creation and mediation. This presents an opportunity for creative interventions by artists and researchers, to unveil the networked relations that are part of AI technologies, and speculate on their ontological effects. This article presents such an unpacking around an AI listening machine present today in ubiquitous devices like voice assistants and smart speakers, and incorporates computational models of machine audition. By tracing the scientific research, technical expertise, and social relations that led to our cultural adoption of AI listening machines, the article presents a socio-technical assemblage within which these machines operate. At the same time, the article reveals various contexts for artists as well as innovation researchers to engage with the socio-technical complexity of AI listening machines, by sharing some instances of creative and artistic interventions that have attempted to unveil the nature of their assemblages.
WhiteMech: White-Box Self-Programming Mechanisms

Dynamic systems that operate autonomously in nondeterministic (uncertain) environments are becoming a reality. These include intelligent robots, self-driving cars, but also manufacturing systems (Industry 4.0), smart objects and spaces (IoT), advanced business process management systems (BPM), and many others. These systems are currently being revolutionized by advancements in sensing (vision, language understanding) and actuation components (autonomous mobile manipulators, automated storage and retrieval systems). However, despite of these advances, their core logic is still mainly based on hard-wired rules either designed or possibly obtained through a learning process. On the other hand, we can envision systems that are able to deliberate by themselves about their course of action when un-anticipated circumstances arise, new goals are submitted, new safety conditions are required, and new regulations and conventions are imposed. Crucially, empowering dynamic systems with deliberating capabilities carries significant risks and therefore we must be able to balance such power with trust. For this reason, it is of interest to make these systems queryable, analyzable and explainable in human terms, so as to be guarded by human oversight. Recent scientific discoveries in Knowledge Representation and Planning combined with insights from Verification and Synthesis in Formal Methods, Data-Aware Processes in Databases, as well as other areas of AI, chart a novel path for realizing what we may call White-Box Self-Programming Mechanisms, that is, systems with a multifaceted model of the world that can be exploited to deliberate on their course of action and answer queries about their behavior.
Tiny device harvests energy from the tap of a finger

By utilizing natural human interactions as an energy source, the device can be used to introduce smart functionalities to everyday objects.
A Lecture Improvement Consulting Case Study for Lecture Undervalued Professors

이 연구는 강의평가가 낮은 교수자를 대상으로 교수학습지원센터에서 실시한 강의개선 컨설팅 사례를 분석하고 시사점을 도출하는 데 있다. 강의평가의 목적은 수업과 강의의 질을 향상시키는 것으로 오늘날은 강의평가 개념에 대한 확장성이 요구된다. 교수업적평가 등의 사후관리보다는 강의역량 향상을 위한 강의개선, 환류체계 등의 사전평가가 강조되고 있다. 이를 위해 다수의 대학들은 강의평가 문항의 타당성과 신뢰성 확보에 노력하고 있다. 특히 학령인구의 감소에 따른 입학자원의 감소와 재정확충의 어려움은 대학의 위기를 가져왔으며, 강의 평가가 낮은 교수자의 강의역량은 선택아 아닌 필수가 되었다. K대학교의 강의개선 컨설팅 사업은 2017년 2학기 시작 전부터 논의되어 41명의 교원을 선정하였다. 강의개선 컨설팅 절차는 대상자 선정, 강의개선 컨설팅, 교수자의 강의개선과 성찰, 평가 및 사후관리 단계로 걸쳐 진행되었다. 연구 결과는 첫째, K대학교의 강의개선 컨설팅의 특징이 확인되었다. 둘째, 강의개선 컨설팅의 통계적 효과가 검증되었다. 셋째, 사전조사 강의평가 점수보다 사후조사 강의평가 점수가 상당히 향상된 (최)우선지원교원의 성공사례가 확인되었다. 이 연구결과를 바탕으로 논의 및 이론적, 실천적 시사점과 연구의 한계점을 제시하였다.The purpose of this study is to analyze the case of lecture improvement consulting conducted by the Center for Teaching and Learning for professors with poor feedback, namely poor evaluation scores regarding their lectures and to draw implications. The purpose of lecture evaluation is to improve the quality of instruction and lectures. Nowadays extensive exploration and further research into the concept of lecture evaluation is required. Pre-evaluations, such as the improvement of lecture competency, are emphasized rather than post-management such as the evaluation of a professor’s achievements. To this end, many universities are trying to secure the validity and reliability of the lecture evaluation questions. In particular, the decrease in admission resources and the difficulty of financial expansion due to the decrease in the school age population has about a crisis in universities, and the teaching ability of professors with low lecture evaluations became essential rather than optional. The lecture improvement consulting project of K University was discussed before the start of the second semester in 2017 and 41 professors were selected. The lecture improvement consulting process was carried out through a selection of subjects, lecture improvement consulting, each professor’s lecture improvement and reflection, and evaluation coupled with follow-up management. The research results are as follows. First, the characteristics of K University s lecture improvement consulting were confirmed. Second, the statistical effect of lecture improvement consulting was verified. Third, the success cases of the (first) priority support professors were significantly improved, compared to the pre-test lecture evaluation scores. Based on the results of this study, discussions and theoretical and practical implications and limitations of the study were presented.
HUBUNGAN PENGETAHUAN, SIKAP, DAN DUKUNGAN PENGELOLA TERHADAP KESIAPSIAGAAN PEDAGANG DALAM UPAYA PENCEGAHAN KEBAKARAN DI PASAR SEMAWIS KOTA SEMARANG

Fire is a situation that can cause extraordinary losses. So, fire prevention is needed especially preparedness in fire prevention efforts. Semawis Market is a tourist attraction that has become an icon of Semarang City that needs to be preserved and sustainable. But in its activities, Semawis Market has a high risk of fire seen from the activities in Semawis Market, the layout and the density of visitors who visit it. The purpose of this study is to provide an overview of Semawis Market and find the relationship between knowledge, attitudes, and support of management to the preparedness of traders in efforts to prevent fire in the Semarang City Semawis Market. The research used analytic research using quantitative methods with cross sectional approach. The population in this study were 130 traders in the Semawis Market. The sample in this study amounted to 70 people who were selected based on the purposive sampling method which was then analyzed by chi-square statistical analysis. Questionnaire was used as instrument in this study. The results.of the univariate analysis of this study were 77.1% respondents have good knowledge, 82.9% respondents have good trade, respondents agreed that management support is well was 52.9% and 61.4% respondents have good preparedness. Bivariate analysis results in this study indicate that there is a relationship between knowledge (p = 0.025) and attitude (p = 0.028) with preparedness and there is no relationship between management support (p = 0.264) with preparedness. Traders need to prepare themselves and equipment better to prevent fires as well as the need for more tangible management support in preparedness actions.
Kadazan-Dusun Youth Consumerist Culture: Social Media, Clothing and Fashion

Development at times brings to fore conflict between tradition and the need to modernise so as to keep abreast with current trends and approaches. In some rapid developing economies such as Malaysia, the populace is still generally conservative in terms of how they portray themselves particularly in style and fashion. Malaysians, particularly the older generation, are inclined to be selective and prefer fashion and attire forms that are modest and not exposing body shape or skin mainly due to religious and cultural norms and sensitivities. This traditional perspective is prevalent in Asian communities where preserving „face‟ is paramount – so much so that members of that community would go to great lengths to avoid actions and behaviour that could be construed as „detrimental‟ to dignity and self-worth. The fashion and attire choice of the younger generation often become issues of conflict between the young and their elders with the fashion sense of youths perceived to be dictated more by the fulfilment of social-media-induced wants rather than actual needs. This paper is an attempt to examine the fashion and attire choices of young Kadazan-Dusun milennials in Sabah, Malaysia. The study involved four Focus Group discussions with six participants in each group consisting undergraduates of a local university in Kota Kinabalu, Sabah, Malaysia. The findings showed that while the youths on the wholewere inclined towards following current fashion trends, they would only indulge in this fashion preference at the right place and time to blend in with their circle of „cool modern global citizens‟. Some participants however, especially from the rural areas, demonstrated displeasure towards extreme and „inappropriate‟ fashion and attire. The outcome of this study also indicated Instagram as contributory to the fashion and attire choices of Kadazan-Dusun youths.
Towards Measuring and Understanding Performance in Infrastructure- and Function-as-a-Service Clouds

Context. Cloud computing has become the de facto standard for deploying modern software systems, which makes its performance crucial to the efficient functioning of many applications. However, the unabated growth of established cloud services, such as Infrastructure-as-a-Service (IaaS), and the emergence of new services, such as Function-as-a-Service (FaaS), has led to an unprecedented diversity of cloud services with different performance characteristics. Objective. The goal of this licentiate thesis is to measure and understand performance in IaaS and FaaS clouds. My PhD thesis will extend and leverage this understanding to propose solutions for building performance-optimized FaaS cloud applications. Method. To achieve this goal, quantitative and qualitative research methods are used, including experimental research, artifact analysis, and literature review. Findings. The thesis proposes a cloud benchmarking methodology to estimate application performance in IaaS clouds, characterizes typical FaaS applications, identifies gaps in literature on FaaS performance evaluations, and examines the reproducibility of reported FaaS performance experiments. The evaluation of the benchmarking methodology yielded promising results for benchmark-based application performance estimation under selected conditions. Characterizing 89 FaaS applications revealed that they are most commonly used for short-running tasks with low data volume and bursty workloads. The review of 112 FaaS performance studies from academic and industrial sources found a strong focus on a single cloud platform using artificial micro-benchmarks and discovered that the majority of studies do not follow reproducibility principles on cloud experimentation. Future Work. Future work will propose a suite of application performance benchmarks for FaaS, which is instrumental for evaluating candidate solutions towards building performance-optimized FaaS applications.
Biometric Mirror: Exploring Values and Attitudes towards Facial Analysis and Automated Decision-Making

Facial analysis applications are increasingly being applied to inform decision-making processes. However, as global reports of unfairness emerge, governments, academia and industry have recognized the ethical limitations and societal implications of this technology. Alongside initiatives that aim to formulate ethical frameworks, we believe that the public should be invited to participate in the debate. In this paper, we discuss Biometric Mirror, a case study that explored opinions about the ethics of an emerging technology. The interactive application distinguished demographic and psychometric information from people’s facial photos and presented speculative scenarios with potential consequences based on their results. We analyzed the interactions with Biometric Mirror and media reports covering the study. Our findings demonstrate the nature of public opinion about the technology’s possibilities, reliability, and privacy implications. Our study indicates an opportunity for case study-based digital ethics research, and we provide practical guidelines for designing future studies.
PROMUOVERE L’INCLUSIONE DI MIGRANTI E RIFUGIATI ATTRAVERSO RISORSE E-LEARNING E ATTIVITÀ IN PRESENZA: UN’ESPERIENZA SUL CAMPO IN ITALIA

The ReGap project (Reducing the Education Gap) is in line with the EC’s Erasmus+ objectives for equity and inclusion in its aim to produce open educational resources for the promotion of social belonging of immigrants and refugees through the development of new country linguistic ability, especially that used in key areas such as employment, health, and education. The innovative feature of the ReGap project lies in the use of a blended learning approach, combining student-centered, classroom-based activities with e-learning activities. This article presents the language learning resources created by the LUMSA research unit on the topic of employment. It also describes the research tools adopted for trailing the resources in language courses, which involved 84 participants. Trial results highlighted both the key role of knowledge of the new country’s language for social inclusion and the educational potential of the adopted blended approach that emphasizes interactive and communication-oriented learning experiences.
RECOMMENDATION SYSTEM WITH CONTENT-BASED FILTERING METHOD FOR CULINARY TOURISM IN MANGAN APPLICATION

Culinary tourism has become one of the current trends in the culinary world. The amount of information circulating does not necessarily make culinary tourists easier to determine the desired menu choices. The use of search engines alone is still not enough, so we need a recommendation system that can provide advice according to user needs. Content-based filtering method is able to produce recommendations that are user independence, making it suitable for use in developing culinary information providers such as the case studies of MANGAN applications where the number of users is still small and culinary data will always increase. This method recommends several objects based on the similarity of the selected object to the recommended object. Object similarity is calculated using the cosine similarity function based on profile items formed from a restaurant's content features. Tests carried out on the results of recommendations with three different thresholds, obtained an average precision value of  0,8915 and an average accuracy value of 0,5118. The low value of accuracy is due to systematic errors. The results of this study are the content-based filtering method can be used to assist users in choosing restaurants based on the similarity of the item profile of a restaurant.
Catalysts that Cause Individuals’ Change: A Taxonomy of Cycles

The evidence revealing just how much individuals’ affect, cognition, and motivation vary over time is astounding. Yet, so much about why this fluctuation occurs remains unclear. This theoretical manuscript elucidates the effects of cycles, a classifiable series of distinct yet related factors (i.e., circadian cycles, human life cycles, organizational life cycles, macro business cycles, work and recovery cycles, tenure cycles, orbital and rotational cycles of the earth, family life cycles, etc.) that unfold in stages while creating forces that lead to changes in individuals’ affect, cognition, and motivation. Using sensemaking and conservation of resources theories, I explain why cycles are a causal agent of change for individuals. By developing a taxonomy and classifying the different kinds of cycles in it, I reveal the distinctive features of cycles while also linking cycle-types that have not previously been connected. By providing a structure that highlights the multitude of factors upon which a given cycle can be categorized, the manuscript can be used in future research to prompt nuanced expectations about when, how, and why individuals experience fluctuation and change. Through the new connections established between disparate cycle-types, I hope the manuscript facilitates the transfer of knowledge across domains.