Reviewerly/exHarmony huggingface dataset:
Advanced energy efficient scheduling of servers in cloud

Cloud computing is one of the most important technologies for providing applications to people all around the world. Cloud computing provides a range of services to both users and consumers, including Platform as a Service, Infrastructure as a Service, and Software as a Service. The core system in cloud storage is data centers, which are made up of networked computers. In order to achieve energy efficiency in cloud computing, a variety of techniques and algorithms have been proposed, Virtualization, DVFS (Dynamic Voltage Frequency Scaling) are few among them. To meet the demand request, the cloud data centers are wasting a huge amount of energy. In cloud data centers, inactive computers use a large amount of energy. To minimize data center power usage, we could reduce the number of idle servers by pushing them into sleep mode and then getting them back to active mode as required. During the transition from sleep to active state, there are gaps in extra power usage. Cloud Computing has a major impact on the atmosphere and environment causing pollution and global warming. To reduce the impact of global warming, Green computing is the only way to go.
Composite mixture of log-linear models with application to psychiatric studies

Psychiatric studies of suicide provide fundamental insights on the evolution of severe psychopathologies, and contribute to the development of early treatment interventions. Our focus is on modelling different traits of psychosis and their interconnections, focusing on a case study on suicide attempt survivors. Such aspects are recorded via multivariate categorical data, involving a large numbers of items for multiple subjects. Current methods for multivariate categorical data-such as penalized log-linear models and latent structure analysis-are either limited to low-dimensional settings or include parameters with difficult interpretation. Motivated by this application, this article proposes a new class of approaches, which we refer to as Mixture of Log Linear models (mills). Combining latent class analysis and log-linear models, mills defines a novel Bayesian approach to model complex multivariate categorical data with flexibility and interpretability, providing interesting insights on the relationship between psychotic diseases and psychological aspects in suicide attempt survivors.
A Three-step Method for Multi-Hop Inference Explanation Regeneration

Multi-hop inference for explanation generation is to combine two or more facts to make an inference.The task focuses on generating explanations for elementary science questions.In the task, the relevance between the explanations and the QA pairs is of vital importance.To address the task, a three-step framework is proposed.Firstly, vector distance between two texts is utilized to recall the top-K relevant explanations for each question, reducing the calculation consumption.Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts.Thirdly, we adopt a re-ranking module to re-rank the retrieved candidate explanations with relevance between each fact and the QA pairs.Experimental results illustrate the effectiveness of the proposed framework with an improvement of 39.78% in NDCG over the official baseline.
Task-Optimal Exploration in Linear Dynamical Systems

Exploration in unknown environments is a fundamental problem in reinforcement learning and control. In this work, we study task-guided exploration and determine what precisely an agent must learn about their environment in order to complete a particular task. Formally, we study a broad class of decision-making problems in the setting of linear dynamical systems, a class that includes the linear quadratic regulator problem. We provide instance- and task-dependent lower bounds which explicitly quantify the difficulty of completing a task of interest. Motivated by our lower bound, we propose a computationally efficient experiment-design based exploration algorithm. We show that it optimally explores the environment, collecting precisely the information needed to complete the task, and provide finite-time bounds guaranteeing that it achieves the instance- and task-optimal sample complexity, up to constant factors. Through several examples of the LQR problem, we show that performing task-guided exploration provably improves on exploration schemes which do not take into account the task of interest. Along the way, we establish that certainty equivalence decision making is instance- and task-optimal, and obtain the first algorithm for the linear quadratic regulator problem which is instance-optimal. We conclude with several experiments illustrating the effectiveness of our approach in practice.
A Novel Digital Image Processing based Mechanism for Liver Tumor Diagnosis

The liver is the most significant internal organ in the human body's abdomen. A person cannot survive without a healthy liver. Liver cancer is a life-threatening illness, difficult to detect by biomedical engineering technicians. Hepatocellular carcinoma (HCC) is the most common type of liver cancer which makes up 75% of cases. Plenty of people with liver tumors have lost their lives because of poor and late detection. Hence it is far essential to discover the tumor at an early stage. So, the principal intention is to detect liver cancer at an earlier stage using the image processing technique. Here the tumors are detected from Magnetic Resonance Imaging images. The image undergoes image pre-processing and is segmented by a hybrid method consisting of the edge and mask method, which is simple and easy to use. Detected tumors are further categorized into the cyst, adenoma, hemangioma, and malignant tumor based on statistical features. The scope of this propounded technique is to highlight and categorized the tumor region present in the Magnetic Resonance Imaging images.
TO STUDY THE PREVALENCE OF ANAEMIA IN INDOOR PATIENTS OF TERTIARY CARE CENTRE

Introduction: Anaemia is a global health problem, prevalent among the individuals of all age groups. It has great impact on the mental as well as physical growth and development. Material And Method: The present study is prospective in nature conducted in the Post graduate department of Pathology, GMC Jammu which included 1600 indoor patients. History, physical examination and primary blood investigation were done in all the cases. Results: Moderate anaemia was prevalent in female patients. Majority of the female patients were in the age group of 61-70 years. Mild anaemia was prevalent in male patients and 51-60 years was the most common age group. Conclusion: Study of anaemia is important to reach the underlying etiological factor in order to direct the early management of anaemia.
Exploring Active Supermassive Black Holes at 100 Micro-arcsecond Resolution

Super-high spatial resolution observations in the infrared are now enabling major advances in our understanding of supermassive black hole systems at the centers of galaxies. We describe the discovery potential of the current and future long-baseline interferometry in the infrared in this field.
Recommendation system using Geo-location based on Sentiment Analysis of Covid-19 Tweets

In the year 2020, we all faced a big challenge of covid-19. The WHO announced it as pandemic and we were all forced to shut down and stay indoors. While the frontline workers like doctors, scientists, army officials, police were fighting to stop the spread of this deadly virus. Today, after almost a year we are being threatened by a second wave of covid-19 which is believed to be much more dangerous than the previous virus. In this difficult situation where we are forced to stay at our home, work from home, it has affected our mental health in many ways. Some people find it amusing that we get to stay home but others find it hard, lonely and tiresome. For the mental well-being of people we have proposed this project wherein we analyze the mental state of people in an area. Based on this analysis we give recommendation to the user as to what measures to take in order to stay mentally and emotionally healthy.
Storing four elements in the two query bitprobe model

In this paper, we look into the adaptive bitprobe model that stores subsets of size at most four from a universe of size m, and answers membership queries using two bitprobes. We propose a scheme that stores arbitrary subsets of size four using O(m4/5) amount of space. This improves upon the O(m5/6)-scheme by the authors Baig et al. (2019). The proposed scheme also answers an open problem posed by Nicholson (2019) in the affirmative.
Challenges of Online Education in a Lockdown Environment

Lockdowns in Europe, the United States and Russia have forced universities to abandon the traditional format of education and move to online. The article analyzes the statistics of a sharp decline in foreign students studying at universities in the United States, Great Britain and Russia, and examines the attempts of world universities to reduce financial losses. Comparison of the difficulties faced by the teachers of Russian and Western universities revealed similar problems, the main ones of which are devaluation of co-presence, the growth of academic cheating and the lack of control over the student's independent work. The only possible way to objectively assess the student's knowledge during an online session is to change the format of the exam, namely the introduction of the Open Book Exam. Exam questions within this format are formed in the form of a solution to a specific "case", which makes it possible to assess not the student's ability to memorize theoretical material, but his ability for critical and analytical thinking, the skills of applying the knowledge gained in practice.
Working with Databases in Android

Files are not the only way to store information from your application onto an Android device. Android provides two more major approaches to information management: a fully fledged relational database option based on SQLite and the Android Content Provider framework. In this chapter, we will explore the SQLite database – for those of you interested in Content Providers, you can learn more on the book's website at www.beginningandroid.org.
Meet the Robot Nurse of the Future

A NURSE TAKES your blood, helps you up on to the freshly laundered bed, and holds your shaking hand. They're a clinician, carer and comforter. The vast majority of us have, at some point in our lives, been looked after by a ward sister, matron or community nurse. Most of us were delivered into this world by a midwife. Nurses are an essential part of our healthcare from birth to our final days.
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification

We propose unsupervised embedding adaptation for the downstream few-shot classification task. Based on findings that deep neural networks learn to generalize before memorizing, we develop Early-Stage Feature Reconstruction (ESFR) -- a novel adaptation scheme with feature reconstruction and dimensionality-driven early stopping that finds generalizable features. Incorporating ESFR consistently improves the performance of baseline methods on all standard settings, including the recently proposed transductive method. ESFR used in conjunction with the transductive method further achieves state-of-the-art performance on mini-ImageNet, tiered-ImageNet, and CUB; especially with 1.2%~2.0% improvements in accuracy over the previous best performing method on 1-shot setting.
Which transformer architecture fits my data? A vocabulary bottleneck in self-attention

After their successful debut in natural language processing, Transformer architectures are now becoming the de-facto standard in many domains. An obstacle for their deployment over new modalities is the architectural configuration: the optimal depth-to-width ratio has been shown to dramatically vary across data types (e.g., $10$x larger over images than over language). We theoretically predict the existence of an embedding rank bottleneck that limits the contribution of self-attention width to the Transformer expressivity. We thus directly tie the input vocabulary size and rank to the optimal depth-to-width ratio, since a small vocabulary size or rank dictates an added advantage of depth over width. We empirically demonstrate the existence of this bottleneck and its implications on the depth-to-width interplay of Transformer architectures, linking the architecture variability across domains to the often glossed-over usage of different vocabulary sizes or embedding ranks in different domains. As an additional benefit, our rank bottlenecking framework allows us to identify size redundancies of $25\%-50\%$ in leading NLP models such as ALBERT and T5.
Reverse-engineering a digital twin of a classical guitar using the finite element method and experimental modal analysis

The classical guitar is not only delighting musicians and audiences worldwide but also scientists are interested in the interplay of the coupled oscillatory systems that constitute the instrument. The bridge couples the plucked strings with the guitar body, which acts as an amplifier to radiate the guitar's sound while being, additionally, coupled strongly with the enclosed air volume. In this contribution, a numerical finite element model of a classical guitar body is presented and compared to experimental data. The guitar's geometry is reverse-engineered from computed tomography scans to a very high level of detail, and care is taken for including all necessary physical influences. Five different sorts of wood used in the guitar are modeled with their corresponding orthotropic material properties, and the fluid–structure interaction between the guitar body and the enclosed air is taken into account in the coupled finite element model. To substantiate the quality of the numerical model, the numerically calculated modal parameters are compared to experimentally identified ones. For this reason, an experimental modal analysis setup employing a scanning laser Doppler vibrometer is proposed, and modal parameters are identified utilizing the complex mode indicator function and enhanced frequency response functions.
Social Features and Cryptocurrency Returns

Similarly, to private corporations, the activity of cryptocurrencies has externalities that affect stakeholders. While environmental concerns of the mining protocol and governance issues related to market regulation have been heavily expressed, social dimensions are also crucial for users and miners. This study examines the predictive relationship between social sustainability and cryptocurrency returns. We break down the social pillar into three metrics: (1) miners' incentive, (2) community inclusion and (3) user privacy. Using a sample of twenty major cryptocurrencies between 2015 and 2020, we rank cryptocurrencies on their social features and form portfolios. The derived returns are analyzed using a extended version of the simple three-factor pricing model for cryptocurrencies. The results show that privacy is penalized by the market whereas inclusion and miners' wage premiums are irrelevant to coins' valuation or unaccounted for by market participants.
Monitoring of scientific activity of the university chair as a condition for implementing educational programs

In the context of integration and globalization of the world economy, the strategic goal of the higher vocational school is to ensure the advanced development of the scientific and technological potential of society. However, there is a lack of understanding of the role of scientific activities of the university in the implementation of educational programs. The purpose of the present article is to identify the essence of the scientific work of the chair as an integrative component of the scientific activity of the university and to justify the method of its objective assessment.
Rancang Bangun Game Edukasi Berbasis Android Pembantu Sistem Kegiatan Belajar Mengajar (KBM) Mata Pelajaran Komputer Untuk Sekolah

Abstract In the activities of Computer subjects especially the material "Computer Hardware and Software" subject teachers are less than optimal in utilizing and empowering learning resources. Students are less able to develop themselves and tend to be forced to memorize or remember the material presented. The ability of students who are still low in identifying the types and functions of computer hardware, the habit of playing games after school, so that learning outcomes in computer subjects the material "Knowing Computer Hardware and Software" has not been satisfactory. The system development method uses the System Development Life Cycle (SDLC) method and the system design uses UML. The implementation of this system uses Construct 2, as well as system testing using the Black Box. The results achieved are Android-based Educational Game applications to facilitate students in understanding the learning process at school. Keywords Game, System Development Life Cycle, UML.
Transforming the Latent Space of Stylegan for Real Face Editing

Despite recent advances in semantic manipulation using StyleGAN, semantic editing of real faces remains challenging. The gap between the W space and the W+ space demands an undesirable trade-off between reconstruction quality and editing quality. To solve this problem, we propose to expand the latent space by replacing fully-connected layers in the StyleGAN's mapping network with attention-based transformers. This simple and effective technique integrates the aforementioned two spaces and transforms them into one new latent space called W++. Our modified StyleGAN maintains the state-of-the-art generation quality of the original StyleGAN with moderately better diversity. But more importantly, the proposed W++ space achieves superior performance in both reconstruction quality and editing quality. Besides these significant advantages, our W++ space supports existing inversion algorithms and editing methods with only negligible modifications thanks to its structural similarity with the W/W+ space. Extensive experiments on the FFHQ dataset prove that our proposed W++ space is evidently more preferable than the previous W/W+ space for real face editing. The code is publicly available for research purposes at https://github.com/AnonSubm2021/TransStyleGAN .
Specifics of Developing Professional Thinking in Law Students

The article examines the essence, role and significance of professional thinking within lawyers’ professional development, making a special emphasis on the specifics of evolving law students’ professional thinking. The research purpose is a theoretical and empirical study of the developmental specifics of law students’ professional thinking. Besides, there is a focus on the structure and features of lawyers’ professional thinking as a special professionally important quality. The authors conduct a comprehensive analysis of the developmental specifics of law students’ professional thinking, propose their own definition of this concept and consider its structure and structural components. The analysis of relevant literature contributed to studying the validity of the chosen topic. The article also reflects the methodology of research and its algorithm that include conducting a complex psychological and pedagogical experiment, assessing the declared parameters and fulfilling the statistical analysis of the results based on methods such as variance analysis, correlation analysis using Marsch’s I coefficient, Friedman’s R coefficient and Kruskal – Wallis H-criterion. The research base was the International Law Institute (Moscow, Russia). The experimental and control groups of subjects were formed, numbering 60 people representative in gender, etc. The results of the study demonstrate the effectiveness of combining trainings, special courses and the evolvement of formal-logical reasoning within the development of law students’ professional thinking.
AN ANIMATED MODELS DEVELOPMENT OF CARDIOVASCULAR SYSTEM IN PHARMACOLOGY LEARNING MODULE FOR MEDICAL STUDENTS

Hypertension is the most known common cardiovascular disease. Based on the Indonesian Competency Standard of Medical Doctors (StandarKompetensiDokter Indonesia or SKDI), the management of essential hypertension is at 4A or the highest level. Therefore, doctors should be able to establish a complete and independent diagnosis and management. The student passing rate in the cardiovascular system at the undergraduate level of medical education of Faculty of Medicine Widya Mandala Catholic University (FK UKWMS) was around 60-70%. This study is an evaluation to develop learning methods that would improve student learning outcomes. The development of this animation model is aimed to increase the engagement process in Pharmacology learning, especially in the cardiovascular system. 98 active students participated in this study, divided into 74 undergraduate students and 24 clinical internship students. The evaluation was carried out using a pre-test and a post-test related to pharmacological animation video exposure. Using the Wilcoxon different test, there was a significant difference between the pre-test and post-test results, especially in the clinical internship program’s students. This animated model was expected to help students to determine the rational determination of hypertension therapy.
Let’s Create Real-World Objects

In the previous chapter, we looked at True automation with functions. We looked at saving time, space, and lines of code with functions, defining functions, calling them, sending arguments to our functions, using default arguments, returning values back to our calling statement, and accepting arbitrary arguments and lists as arguments. b
The Power of Adaptivity for Stochastic Submodular Cover

In the stochastic submodular cover problem, the goal is to select a subset of stochastic items of minimum expected cost to cover a submodular function. Solutions in this setting correspond to sequential decision processes that select items one by one adaptively (depending on prior observations). While such adaptive solutions achieve the best objective, the inherently sequential nature makes them undesirable in many applications. We ask: how well can solutions with only a few adaptive rounds approximate fully-adaptive solutions? We give nearly tight answers for both independent and correlated settings, proving smooth tradeoffs between the number of adaptive rounds and the solution quality, relative to fully adaptive solutions. Experiments on synthetic and real datasets show qualitative improvements in the solutions as we allow more rounds of adaptivity; in practice, solutions with a few rounds of adaptivity are nearly as good as fully adaptive solutions.
Blockchain Technology for Supply Chain Management

The aim of the research is to discuss what challenges and problems Blockchain can solve in Supply Chain, and what is the future to come for the Supply Chain Management. Blockchain can be defined as a chain of the block that contains information. The technique is intended to timestamp digital documents so that it's not possible to backdate them or temper them. The blockchain is used for the secure transfer of items like money, property, contracts, etc. without requiring a third-party intermediary like bank or government. Once data is recorded in a blockchain, it is very difficult to change it. The implementation of Blockchain Technology can help organizations reduce order quantities, lower selling prices and reduce the target-inventory levels.
An investigation of sound radiation from the double bass using acoustical holography

Studying radiation from musical instruments helps increase understanding and improve applications in auralizations, room acoustics, and microphone placement techniques. This work reviews a string bass's directivity measurements and demonstrates how numerical techniques such as spherical near-field acoustical holography enhance understanding of both the instrument's near-field and far-field radiation. The technique also allows calculation of the sound intensity field around the instrument, providing additional insights.