Reviewerly/exHarmony huggingface dataset:
Cloud Computing – Data Center – Access from Anywhere

Now days we are attached with our electronic gadgets either offline or online in such a way as we breathe, as without breathing we can’t alive, same like these electronics have become a vital part of our daily life. As of now by growing demand of these gadgets either in office work, or in research work or in various technical fields. As the use of computers increases as in day in and day out, the relevant resources that we need also go up. For companies like Oracle, IBM and Microsoft, utilizing the resources and establishing a huge infrastructure or network is not a big issue for these big giants. But for the new startup and small enterprises But when it comes to smaller enterprises, expensiveness becomes a huge factor. At the user or developer end we are facing a lot of issues like hardware disruption, various bugs in software, network errors. So this was such a big hassle for the large community of computer science in all over the world. Cloud Computing offers a quick fix to this situation. This technology has been completely transformed the way in which computing is moved up from PCs and as well as for the single user to enterprise via various servers to a ‘cloud’ of computers e.g: web servers, application servers, database servers and so on. A cloud is a virtualized server grid which can provide the different computing resources through IAAS (Infrastructure as a service),PAAS(platform as a service),SAAS(Software as a service) of their clients. The underlying details of how it is implemented which is not direct to end user. The data and the services provided reside in extensively scalable data centers so anyone can use or access it from any part of the globe. Various big giants like Google, Microsoft, Yahoo, IBM and Amazon have started providing cloud computing services from the individual user either paid or free upto some extent or to other small enterprises, big businesses through various models like B2B,B2C,C2C etc. Amazon, salesforce is the trailblazer in the field of cloud computing. Every big and small enterprise, companies which are doing their businesses online using cloud services for the storing information so that later on they will extract some meaning full insights for the growth of businesses. Cloud Computing is finding use in various areas like web hosting, parallel batch processing, graphics rendering, financial modeling, web crawling, genomics analysis, etc.
Physics Informed Self Supervised Learning For Fault Diagnostics and Prognostics in the Context of Sparse and Noisy Data

Sparse &amp; noisy monitoring data leads to numerous challenges in prognostic and health management (PHM). Big data volume but poor quality with scarce healthy states information limits the performance of training machine learning (ML) and physics-based failure modeling. To address these challenges, this thesis aims to develop a new hybrid PHM framework with the ability to autonomously discover and exploit incomplete implicit physics knowledge in sparse &amp; noisy monitoring data, providing a solution for deep physics knowledge-ML fusion by physics-informed machine learning algorithms. In addition, the developed hybrid framework also applies the self-supervised learning paradigm to significantly improve the learning performance under uncertain, sparse, and noisy data with lower requirements for specialist area knowledge. The performance of the developed algorithms will be investigated on the sparse and noise data generated by simulation data sets, public benchmark data sets, and the PHM platform to demonstrate its applicability.
Robust Rule Learning for Reliable and Interpretable Insight into Expertise Transfer Opportunities

Intensive care in hospitals is distributed to different units that care for patient populations reflecting specific comorbidities, treatments, and outcomes. Unit expertise can be shared to potentially improve the quality of methods and outcomes for patients across units. We propose an algorithmic rule pruning approach for use in building short lists of human-interpretable rules that reliably identify patient beneficiaries of expertise transfers in the form of machine learning risk models. Our experimental results, obtained with two intensive care monitoring datasets, demonstrate the potential utility of the proposed method in practice.
Students’ Perceptions on Zoom Digital Media in Learning Speaking at SMP IT Fathona Palembang

The aim of this study was to find out how students perceived the use of Zoom application in learning speaking. This study used qualitative study as its method. Five eighth-grade students from SMP IT Fathona Palembang took part in this study. They were chosen purposively. The data from the interview was evaluated using thematic analysis. There were 4 categories of students’ perception on Zoom digital media in learning speaking: 1) helpful in learning speaking; 2) costly application; 3) difficulty in internet connection; and 4) aspects of learning speaking. Finally, the findings revealed that the majority of students enjoy and are more engaged in learning speaking taught by using Zoom application.
Research work of masters’ students in the context of a digitalized dialogue of cultures

Introduction. The relevance of the research is determined by the need to identify the possibilities of mutually enriching dialogue of philological and mathematical knowledge in the digital space in the implementation of research work of undergraduates. The purpose of the article is to find ways to improve organizational and methodological tools for the development of research skills of future philologists. In this regard, the authors propose their own concept and specific ways to solve the problem, which consist in supplementing the list of professional competencies in the field of research and development. Materials and methods. Assessment of the formation of research competencies of undergraduates in the process of solving interdisciplinary tasks in a network format was carried out on the control (n1=20) and experimental (n2=20) Diagnostics was carried out on the basis of monitoring the actual results of the activity of research activities of undergraduates. The empirical results of the study were analyzed using a set of mathematical and statistical methods (methods of descriptive statistics φ*-test Fisher). The results of the study. The organization of a communication space for program participants is carried out through the creation of web sites where managers pose professional problems to undergraduates in the humanities, and students of other profiles provide technical or mathematical assistance in solving them with further critical discussion and evaluation of the results obtained. The study illustrates interdisciplinary tasks for philology students based on computer modeling methods and textual analysis. Diagnostics of the formation of research competencies proved the statistical significance of the proposed content and forms of training (φ emp* =2,58 in terms of academic performance of research and φemp* =2,353 in terms of research activity). Conclusion. The innovative methodology of organizing research of undergraduates in a network format, implemented at Bunin Yelets State University, is effective for various humanitarian areas of training. The actions of undergraduates associated with the formulation, modeling and interpretation of data demonstrate the formation of competencies that reflect key types of research activities in solving professional problems based on the dialogue of cultures.
Understanding Enthymemes in Deductive Argumentation Using Semantic Distance Measures

An argument can be regarded as some premises and a claim following from those premises. Normally, arguments exchanged by human agents are enthymemes, which generally means that some premises are implicit. So when an enthymeme is presented, the presenter expects that the recipient can identify the missing premises. An important kind of implicitness arises when a presenter assumes that two symbols denote the same, or nearly the same, concept (e.g. dad and father), and uses the symbols interchangeably. To model this process, we propose the use of semantic distance measures (e.g. based on a vector representation of word embeddings or a semantic network representation of words) to determine whether one symbol can be substituted by another. We present a theoretical framework for using substitutions, together with abduction of default knowledge, for understanding enthymemes based on deductive argumentation, and investigate how this could be used in practice.
Identification of key Predicting Factors Affecting Classified Information Assurance in Institutions of Higher Learning

The recent escalation in leakages of classified information (CI) has attracted sustained interest from information security scholars and practitioners alike.CI is sensitive information that must be protected from being accessed by unauthorised persons.Thus, the purpose of this research is to identify the key factors that influence CI leakages in Institutions of Higher Learning (IHL).In doing this, we conducted a literature survey with a meta-analysis of 19 articles to identify the Key Predicting Factors (KPFs) that influences CI assurance in IHL.The factors found are categorised to organisational (communication structures), regulatory (enforceability), human (social norms, self-efficacy, training, and awareness of being monitored), and technological (internet of data, access control and storage control).These factors were validated via Delphi method to ascertain its consistency by information security experts.This research contributed to the knowledge by identifying KPFs influencing CI violation in IHL.In view of all factors that have been mentioned so far, there is no single information security theory/model that covers all identified KPFs.Therefore, we suggested for the development of a security violation prevention model to safeguard CI in IHL using KPFs.
Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification. However, previous works usually address one of two aspects: i) how to extract accurate rationales for explainability while being beneficial to prediction; ii) how to make the predictive model robust to different types of adversarial attacks. Intuitively, a model that produces helpful explanations should be more robust against adversarial attacks, because we cannot trust the model that outputs explanations but changes its prediction under small perturbations. To this end, we propose a joint classification and rationale extraction model named AT-BMC. It includes two key mechanisms: mixed Adversarial Training (AT) is designed to use various perturbations in discrete and embedding space to improve the model’s robustness, and Boundary Match Constraint (BMC) helps to locate rationales more precisely with the guidance of boundary information. Performances on benchmark datasets demonstrate that the proposed AT-BMC outperforms baselines on both classification and rationale extraction by a large margin. Robustness analysis shows that the proposed AT-BMC decreases the attack success rate effectively by up to 69%. The results indicate that there are connections between robust models and better explanations.
Floods in Bojonegoro, 1920–1940

During the 1920s to 1940s, Bojonegoro was often hit by floods, which had an impact on its people.Floods were detrimental to the socioeconomic sector of the Bojonegoro community.In writing about the flood events in Bojonegoro from 1920 to 1940, the author uses historical research methods that have several stages, such as heuristics and interpretation.Bojonegoro is an area close to the Bengawan Solo River.The Bojonegoro area is prone to flooding caused by rainfall, which causes the overflow of water from the Bengawan Solo River to the Bojonegoro community settlements.The flood had an impact on the activities of the residents of Bojonegoro, as in 1931 the flood disrupted the road traffic and caused congestion.In addition, in 1933 the flood caused the temporary shutdown of Bumiputra women's school.Actions were taken during the Dutch colonial period by repairing and building dams in Bojonegoro; this has been done since the enactment of the era of ethical politics.
Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks

It has been crucial to leverage the rich information of multiple modalities in many tasks. Existing works have tried to design multi-modal networks with descent multi-modal fusion modules. Instead, we focus on improving generalization capability of multi-modal networks, especially the fusion module. Viewing the multi-modal data as different projections of information, we first observe that bad projection can cause poor generalization behaviors of multi-modal networks. Then, motivated by well-generalized network's low sensitivity to perturbation, we propose a novel multi-modal training method, multi-head modularization (MHM). We modularize a multi-modal network as a series of uni-modal embedding, multi-modal embedding, and task-specific head modules. Also, for training, we exploit multiple head modules learned with different datasets, swapping each other. From this, we can make the multi-modal embedding module robust to all the heads with different generalization behaviors. In testing phase, we select one of the head modules not to increase the computational cost. Owing to the perturbation of head modules, though including one selected head, the deployed network is more well-generalized compared to the simply end-to-end learned. We verify the effectiveness of MHM on various multi-modal tasks. We use the state-of-the-art methods as baselines, and show notable performance gain for all the baselines.
Deep Learning Representation Pre-training for Industry 4.0

Deep learning (DL) approaches have multiple potential advantages that have been explored in various fields, but for prognostic and health management (PHM) applications, this is not the case due to the lack of data in particular applications and also due of the absence of multiple DL-oriented benchmarks as in other fields, which limits the research in this area even though these types of applications will have a strong impact on the industrial world. To introduce the benefits of DL in this area, we should be able to develop models even when we have small data sets, to verify whether or not this is possible, in this thesis we explore the research direction of few shot learning in the context of equipment PHM.
Natural Black-Box Adversarial Examples against Deep Reinforcement Learning

Black-box attacks in deep reinforcement learning usually retrain substitute policies to mimic behaviors of target policies as well as craft adversarial examples, and attack the target policies with these transferable adversarial examples. However, the transferability of adversarial examples is not always guaranteed. Moreover, current methods of crafting adversarial examples only utilize simple pixel space metrics which neglect semantics in the whole images, and thus generate unnatural adversarial examples. To address these problems, we propose an advRL-GAN framework to directly generate semantically natural adversarial examples in the black-box setting, bypassing the transferability requirement of adversarial examples. It formalizes the black-box attack as a reinforcement learning (RL) agent, which explores natural and aggressive adversarial examples with generative adversarial networks and the feedback of target agents. To the best of our knowledge, it is the first RL-based adversarial attack on a deep RL agent. Experimental results on multiple environments demonstrate the effectiveness of advRL-GAN in terms of reward reductions and magnitudes of perturbations, and validate the sparse and targeted property of adversarial perturbations through visualization.
Preliminary Study of the Integration of Big Data to Answer the Challenges of Islamic Education in the Technological Age

Along with the rapid development of technology, individuals today are required to align every aspect of their lives with the technological developments of the industrial revolution 4.0, consisting of artificial intelligence, the internet of things, and big data presented in society. Notably, it was related to education, including Islamic education, which frequently stereotyped about delays in responding to globalization's challenges. This preliminary study aims to encourage empirical research that is still lacking by exploring the role of big data in Islamic education and combining data from general education that has similar core. The study focused on using the scoping review method as a part of a literature review. As a result of this study, there are four impacted factors for strengthening the usage of big data: the performance and behavior factors of learning; the storage of education data; the update in the education system; and the use of big data in the education curriculum. Future studies should begin empirical research to elaborate more on these four impacted factors practically.
CrowdFL: A Marketplace for Crowdsourced Federated Learning

Amid data privacy concerns, Federated Learning (FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists a need for a platform that matches data owners (supply) with model requesters (demand). In this paper, we present CrowdFL, a platform to facilitate the crowdsourcing of FL model training. It coordinates client selection, model training, and reputation management, which are essential steps for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency. To the best of our knowledge, it is the first platform to support crowdsourcing-based FL on edge devices.
Incorporating Item Frequency for Differentially Private Set Union

We study the problem of releasing the set union of users' items subject to differential privacy. Previous approaches consider only the set of items for each user as the input. We propose incorporating the item frequency, which is typically available in set union problems, to boost the utility of private mechanisms. However, using the global item frequency over all users would largely increase privacy loss. We propose to use the local item frequency of each user to approximate the global item frequency without incurring additional privacy loss. Local item frequency allows us to design greedy set union mechanisms that are differentially private, which is impossible for previous greedy proposals. Moreover, while all previous works have to use uniform sampling to limit the number of items each user would contribute to, our construction eliminates the sampling step completely and allows our mechanisms to consider all of the users' items. Finally, we propose to transfer the knowledge of the global item frequency from a public dataset into our mechanism, which further boosts utility even when the public and private datasets are from different domains. We evaluate the proposed methods on multiple real-life datasets.
Extended Goal Recognition Design with First-Order Computation Tree Logic

Goal recognition design (GRD) is the task of modifying environments for aiding observers to recognize the objectives of agents during online observations. The worst case distinctiveness (WCD), a widely used performance measure in GRD research, can fail to provide useful guidance to the redesign process when some goals are too hard to be distinguished. Moreover, the existing WCD-based approaches do not work when an agent aims for a sequence of goals instead of just one goal. The paper presents a new GRD framework called extended goal recognition design (EGRD) for goal recognition that involves multiple goals. The objective of EGRD is to modify an environment to minimize the worst case distinctiveness of a goal condition that describes how an agent can reach a set of goals. A goal condition can be formally expressed in first-order computation tree logic (FO-CTL) that can be evaluated by model checking. We introduce a novel graphical representation of FO-CTL sentences that is suitable for extended goal recognition. Moreover, we present a search algorithm for EGRD with a novel caching mechanism. Our experimental results show that the caching mechanism can greatly speed up our EGRD search algorithm by reusing the previous evaluation of FO-CTL sentences.
Is There a Strongest Die in a Set of Dice with the Same Mean Pips?

Jan-ken, a.k.a. rock-paper-scissors, is a cerebrated example of a non-transitive game with three (pure) strategies, rock, paper and scissors. Interestingly, any Jan-ken generalized to four strategies contains at least one useless strategy unless it allows a tie between distinct pure strategies. Non-transitive dice could be a stochastic analogue of Jan-ken: the stochastic transitivity does not hold on some sets of dice, e.g., Efron's dice. Including the non-transitive dice, this paper is interested in dice sets which do not contain a useless die. In particular, we are concerned with the existence of a strongest (or weakest, symmetrically) die in a dice set under the two conditions that (1) any number appears on at most one die and at most one side, i.e., no tie break between two distinct dice, and (2) the mean pips of dice are the same. We firstly prove that a strongest die never exist if a set of n dice of m-sided is given as a partition of the set of numbers {1,…,mn}. Next, we show some sufficient conditions that a strongest die exists in a dice set which is not a partition of a set of numbers. We also give some algorithms to find a strongest die in a dice set which includes given dice.
Stochastic Goal Recognition Design Problems with Suboptimal Agents

Goal Recognition Design (GRD) problems identify the minimum number of environmental modifications aiming to force an interacting agent to reveal its goal as early as possible. Researchers proposed several extensions to the original model, some of them handling stochastic agent action outcomes. While this generalization is useful, it assumes optimal acting agents, which limits its applicability to more realistic scenarios. This paper presents the Suboptimal Stochastic GRD model, where we consider boundedly rational agents that, due to limited resources, might follow a suboptimal policy. Inspired by theories on human behavior asserting that humans are (close to) optimal when making perceptual decisions, we assume the chosen policy has at most m suboptimal actions. Our contribution includes (I) Extending the stochastic goal recognition design framework by supporting suboptimal agents in cases where an observer has either full or partial observability; (ii) Presenting methods to evaluate the ambiguity of the model under these assumptions; and (iii) Evaluating our approach on a range of benchmark applications.