Reviewerly/exHarmony huggingface dataset:
A Bayesian Hierarchical Network Model for Daily Streamflow Forecasting

Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]A Bayesian Hierarchical Network Model for Daily Streamflow ForecastingAuthorsÁlvaroOssandóniDBalajiRajagopalaniDUpmanuLalliDVimalMishraNandithaJ SiDSee all authors Álvaro OssandóniDCorresponding Author• Submitting AuthorUniversity of Colorado BoulderiDhttps://orcid.org/0000-0003-2165-8736view email addressThe email was not providedcopy email addressBalaji RajagopalaniDUniversity of Colorado at BoulderiDhttps://orcid.org/0000-0002-6883-7240view email addressThe email was not providedcopy email addressUpmanu LalliDColumbia UniversityiDhttps://orcid.org/0000-0003-0529-8128view email addressThe email was not providedcopy email addressVimal MishraIndian Institute of Technology Gandhinagarview email addressThe email was not providedcopy email addressNanditha J SiDIndian Institute of Technology, GandhinagariDhttps://orcid.org/0000-0003-3474-2590view email addressThe email was not providedcopy email address
Semantics-Aligned Representation Learning for Person Re-identification

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID.
Multi-GPU Server Deign Parameters Selection based on Empirical Observation of HPL Behavior

Design parameters of a multi-GPU based server that affect the performance of HPL are evaluated and suggested for optimized GPU server to achieve optimal HPL performance. GPUs are widely used in High-performance Computing and AI Computing. To obtain more performance, multi-GPU servers are generally used in various configurations. The configurations in a Multi-GPU server affect the overall performance of a system. The major parameters for a multi-GPU server configurations are the number of cores per GPU and number of PCI Express per GPU in HPL running. In this paper, the two parameters are tested, measured, and analyzed to figure out its impact on HPL performance. The experimental results show that the optimal number of cores has to be more than three per GPU, and the optimal number of GPUs per PCIe is three in a multi-GPU server. The empirical observation reveals that there exists the optimal number of GPUs in a multi-server for best HPL performance.
Actionable Pattern-Driven Analytics and Prediction

Pattern-driven analytics and mining has received a lot of attention in the last two decades, because information discovered in data can be used to support decision and strategy making [...]
Review on Machine Learning Technique to Detect Breast Cancer

The breast cancer is a malignant breast tumor, which is characterized by uncontrolled cell growth in the tissue of the breast. For the survival of patients, detect breast cancer in the early stage is very helpful. To identify breast cancer using imaging techniques are mammography and ultrasound. Radiologists’ technique is used to read the mammograms to detect the sign of breast cancer. Still, the radiologists may miss up to 30% of breast cancer depending upon the density of the breast. To improve the accuracy, different machine learning techniques are used. In this paper, we presented a review of the latest machine learning techniques on breast cancer. After review analyzes, the higher the accuracy achieved by SVM (Support Vector Machine) and ANN (Artificial Neural Network).
28. Computer misuse (additional chapter)

The impact of computer technology on society has been profound and this chapter examines the offences in the Computer Misuse Act 1990 as amended in the Police and Justice Act 2006 and in the Serious Crime Act 2015. The focus is on offences related to computer misuse, including unauthorized access to computer material, unauthorized access with intent to commit or facilitate further offences, unauthorized acts with intent to impair or recklessness as to impairment of a computer, impairing a computer such as to cause serious damage and making, supplying or obtaining articles for use in such offences.
Historiae, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case

The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative framework for detecting semantic change in multilingual historical corpora and generating diachronic ontologies as linguistic linked open data (LLOD). Initiated as a use case (UC4.2.1) within the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, the study will explore emerging trends in knowledge extraction, analysis and representation from linguistic data science, and apply the devised methodology to datasets in the humanities to trace the evolution of concepts from the domain of socio-cultural transformation. The paper will describe the main elements of the methodological framework and preliminary planning of the intended workflow.
Teachers' self-efficiency from the perspective of scientific thinking development in students

The article addresses the issue of teachers' self-efficiency, their need for permanent care for their both personal and professional efficiency, through the research of theoretical specialists, but also of those who have developed and applied strategies to increase personal efficiency with reflection on career.
Blended Methods of Teaching the English Language of Profession

In the information society, which focuses on self-education, the forms of presentation of educational material and work on it have changed. Due to the use of information and communicative technologies, blended learning, which is a relatively new educational form, has firmly entered the educational environment. In this regard, it is important that both the teacher and the student have skills that combine distant and traditional learning methods.The article examines the techniques of the flipped classroom and case study in teaching the English language of advertising and public relations which change the sequence of work and make the educational process more creative and attractive compared to traditional teaching methods.The flipped classroom and case study methods are fully consistent with the current stage of education development, which puts creativity at the center of the learning process; makes self-study the leading educational form; aims at lifelong learning; changes the role of the teacher, turning him/her from the only source of knowledge into a consultant and assistant in solving specific problems; individualizes the learning process, taking into account students’ interests and capabilities.
Quantum photonics for computer security and other applications

The precise quantum control of single photons, together with the intrinsic advantage of being mobile make optical quantum system ideally suited for delegated quantum information tasks, reaching from well-established quantum cryptography to quantum clouds and quantum computer networks. Here I present that the exploit of quantum photonics allows for a variety of quantum-enhanced data security for quantum and classical computers. The latter is based on feasible hybrid classical-quantum technology, which shows promising new applications of readily available quantum photonics technology for complex data processing. As outlook I will discuss technological challenges for the scale up of photonic quantum computers, and our group’s current work for addressing some of those.
Computational Complexity of Jumping Block Puzzles

In combinatorial reconfiguration, the reconfiguration problems on a vertex subset (e.g., an independent set) are well investigated. In these problems, some tokens are placed on a subset of vertices of the graph, and there are three natural reconfiguration rules called ``token sliding,'' ``token jumping,'' and ``token addition and removal''. In the context of computational complexity of puzzles, the sliding block puzzles play an important role. Depending on the rules and set of pieces, the sliding block puzzles characterize the computational complexity classes including P, NP, and PSPACE. The sliding block puzzles correspond to the token sliding model in the context of combinatorial reconfiguration. On the other hand, a relatively new notion of jumping block puzzles is proposed in puzzle society. This is the counterpart to the token jumping model of the combinatorial reconfiguration problems in the context of block puzzles. We investigate several variants of jumping block puzzles and determine their computational complexities.
Factors Influencing the Preference of Distance Learners to Study through Online during COVID-19 Pandemic.

The teaching-learning system in India changed significantly during the COVID 19 pandemic situation. Educational institutions adopted blended learning approaches for effective teaching and used digital technologies to deliver academic programs. Online learning during pandemic situations helps learners learn the necessary skills to meet the need for future jobs conveniently and quickly. Graduates of vocational programs will have to play a crucial role in economic recovery. The study's objectives were to examine to what extent the open and distance learning students of IGNOU had prior digital skills, preferred, used digital platforms for study through online mode during the lockdown. A survey of vocational students studying through distance mode from Indira Gandhi National Open University (IGNOU), India was conducted to identify the reasons for their preferences to study through online mode rather than traditional distance learning mode during the lockdown. Researchers identified nine reasons based on the literature review for students' preferences to learn through online mode. The responses to nine sub-questions were on a five-point scale, i.e., Strongly agree (1), agree (2), can't say (3), disagree (4), and strongly disagree (5). The study was limited to vocational distance learners. Descriptive statistical methods like ANOVA, Chi-square, and t-test were applied to test the hypothesis. The results of ANOVA and Chi-square revealed that they value all the reasons equally, thereby rejecting the null hypothesis. The implication of the study is to adopt a blended learning approach for offering future distance learning programs
What Do Concurrency Developers Ask About? A Large-Scale Study on Stack Overflow

Context: Software developers are increasingly facing the challenges of writing code that is not only concurrent but also correct. Objective: To help these developers, it is necessary to understand concurrency topics they are interested in, their difficulty in finding answers for questions in these topics, their sentiment for these topics, and how they use concurrency tools and techniques to guarantee correctness. Method: We conduct a large-scale study on the entirety of Stack Overflow to understand interests, difficulties, sentiment, and tool usages of concurrency developers. We discuss the implications of our findings for the practice, research, and education of concurrent software development, and investigate the relation of our findings with the findings of the previous work. Results: A few findings of our study are: (1) questions that concurrency developers ask can be grouped into a hierarchy with 27 concurrency topics under 8 major categories, (2) thread safety is among the most popular concurrency topics and client-server concurrency is among the least popular, (3) irreproducible behavior is among the most difficult topics and memory consistency is among the least difficult, (4) data scraping is among the most positive concurrency topics and irreproducible behavior is among the most negative, (5) root cause identification has the most number of questions for usage of data race tools and alternative use has the least. Conclusion: The results of our study can not only help concurrency developers but also concurrency educators and researchers to better decide where to focus their efforts, by trading off one concurrency topic against another.
Building Research Data Services at University Libraries in South Africa: An offering of resources towards our communities of practice

Presentations given to libraries staff at University of Johannesburg on 15th and 16th of October 2019, to assist with developing Research Data Services, in view of strengthening inter-university communities of practice, and with a particular focus on the use of Figshare for Institutions.
Augmented Reality (AR) in Physics Learning: Opportunities to Improve Teacher and Student Interaction in Online Learning

This study aims to describe the views of teachers and students in Islamic schools regarding Augmented Reality (AR) assisted learning to facilitate student involvement in learning, as well as increase teacher-student interactions in the learning process during the Covid-19 pandemic. This study uses a mixed method with a qualitative-quantitative explanatory design. The data collection technique used a questionnaire involving 31 respondents, 3 teachers, and 3 students as resource persons in three Islamic high schools in Lampung Province. The results of the study show that AR-assisted methods have the potential to increase teacher-student interaction by implementing a Learning Management System (LMS) that suits their needs and learning facilities. The results also show that AR provides an effective learning experience, because it displays 3D images so that it is easily accepted by students and makes students interested in learning during online learning. Therefore, the use of AR in learning has the opportunity to increase learning interactions between teachers and students.
Keynote Lecture : Learning Representations: Opportunities for Parallel and Distributed Computing

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Learning representations is critical for machine learning and very computation-intensive process. There are many opportunities to introduce efficiencies through parallel and distributed computing. The success of machine learning algorithms depends on data representation. Different representations can expose or hide different features of the data. As we think about the future of learning representations and its impact on machine learning it is important to consider the state of the art of machine learning today, the challenges and opportunities for addressing the computation issues around representation learning, and how to get to deeper understanding and more capabilities in machine learned models. In this talk I will describe 4 ideas related to computational issues in representation learning: reducing uncertainty, developing compact representations, debasing the training data, and developing privacy-preserving representations.
Towards a Trustful Digital World : Exploring Self-Sovereign Identity Ecosystems

In the current global situation-burdened by, among others, a vast number of people without formal identification, digital leap, the need for health passports and contact tracking applications-providing private and secure digital identity for individuals, organizations and other entities is crucial. The emerging self-sovereign identity (SSI) solutions rely on distributed ledger technologies and verifiable credentials and have the potential to enable trustful digital interactions. In this human-centric paradigm, trust among actors can be established in a decentralized manner while the identity holders are able to own and control their confidential data. In this paper, we build on observations gathered in a field study to identify the building blocks, antecedents and possible outcomes of SSI ecosystems. We also showcase opportunities for researchers and practitioners to investigate this phenomenon from a wide range of domains and theories, such as the digital innovation ecosystems, value co-creation, surveillance theory, or entrepreneurship theories.
Overview of Prioritization Model for National Critical Sectors Protection

The national critical sectors are an important sector that should be paramount in maintaining the state security when cybersecurity incident occurs. The national critical sectors aim to secure facilities, networks, information and physical assets. Protection against national criticality involves protection of both physical and cyber components, where cyber protection plan must be included in the national defense strategy. This article aims to propose a design of prioritizing model as early detection of cyber incidents as part of managing the incident and protecting the national critical sector.
Opinion Mining in Tamil Tweets using Dense Neural Network (DNN) Architecture

Tamil is one of the Indian languages which still requires the state of the art model for Opining mining or Sentiment Analysis(SA). It aims to extract the opinions from tweets, reviews and comments for development of NLP(Natural Language Processing) in Tamil Language. Therefore, this article proposed a Neural Network model to analyze the Tamil tweets into two polarities as Positive and Negative. The work comprises different phases such as its own Tamil corpus creation with 1015 Tamil tweets, Word Vectorization and Classification. A Deep learning based Dense Neural Network (DNN) architecture was trained with word vectors for finding the positive and negative comments. The proposed model works well in the manually annotated corpus without removing the stop words and sentimental symbols like emojis’. It is observed that the proposed model gives better accuracy of 86% when compared with the Machine Learning algorithm.
Out of a Hundred Trials, How Many Errors Does Your Speaker Verifier Make?

Out of a hundred trials, how many errors does your speaker verifier make? For the user this is an important, practical question, but researchers and vendors typically sidestep it and supply instead the conditional error-rates that are given by the ROC/DET curve. We posit that the user's question is answered by the Bayes error-rate. We present a tutorial to show how to compute the error-rate that results when making Bayes decisions with calibrated likelihood ratios, supplied by the verifier, and an hypothesis prior, supplied by the user. For perfect calibration, the Bayes error-rate is upper bounded by min(EER,P,1-P), where EER is the equal-error-rate and P, 1-P are the prior probabilities of the competing hypotheses. The EER represents the accuracy of the verifier, while min(P,1-P) represents the hardness of the classification problem. We further show how the Bayes error-rate can be computed also for non-perfect calibration and how to generalize from error-rate to expected cost. We offer some criticism of decisions made by direct score thresholding. Finally, we demonstrate by analyzing error-rates of the recently published DCA-PLDA speaker verifier.
The Myth of Signing Avatars.

Development of automatic translation between signed and spoken languages has lagged behind the development of automatic translation between spoken languages, but it is a common misperception that extending machine translation techniques to include signed languages should be a straightforward process. A contributing factor is the lack of an acceptable method for displaying sign language apart from interpreters on video. This position paper examines the challenges of displaying a signed language as a target in automatic translation, analyses the underlying causes and suggests strategies to develop display technologies that are acceptable to sign language communities.
App Service and Containers in Azure Compute

In the previous chapter of this book, we have learned more about, still the most popular, the IaaS cloud model. This chapter covers services within the second cloud model – PaaS:
Viktor Leis Speaks Out on Concurrency and Parallelism on Multicore CPUs

Welcome to ACM SIGMOD Record's series of interviews with distinguished members of the database community. I'm Marianne Winslett, and today I have here with me Viktor Leis who won the 2018 ACM SIGMOD Jim Gray Dissertation Award for his thesis entitled Query Processing and Optimization in Modern Database Systems. Viktor is now at the University of University of Erlangen-Nuremberg and his Ph.D. is from the Technical University of Munich, where he worked with Thomas Neumann and Alfons Kemper. So, Viktor, welcome.
Detection of Fall Events and Rehabilitation Exercises for Elders Using Deep Learning Algorithm – A Brief Survey

Gradual increase of elderly population occurs everywhere throughout the world. Fall in elders is a main problem which causes severe injuries and sometimes even to mortality. They can be rescued from this disasters by our immediate attention. The quick detection of fall events in elders prevent them from fall injuries. Computer vision based instruments such as single RGB camera, 3D – based multiple camera, 3D – depth camera can be used to detect fall occurrence in elders. This instruments are very cheap to buy. The use of computer vision methods track human skeleton joints and thereby suitable rehabilitation exercises are given in homely environment. Every year, in western countries more than 2 million of elderly population are given treatment for fall injuries in emergency wards. At present, researchers pay more attention towards finding several systems for fall detection and prevention in elders. In this paper, we are going to survey a brief study of art of fall detection in elderly people and also the use of deep learning algorithms for detection of fall in elderly people.
A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild.

In this paper, we describe the current main approaches to sign language translation which use deep neural networks with videos as input and text as output. We highlight that, under our point of view, their main weakness is the lack of generalization in daily life contexts. Our goal is to build a state-of-the-art system for the automatic interpretation of sign language in unpredictable video framing conditions. Our main contribution is the shift from image features to landmark positions in order to diminish the size of the input data and facilitate the combination of data augmentation techniques for landmarks. We describe the set of hypotheses to build such a system and the list of experiments that will lead us to their verification.