Reviewerly/exHarmony huggingface dataset:
A Brief General Industrial Virtual Reality Applications Overview Including a Simple Factory VR Simulation

Today, all enterprises are trying to shift their management and manufacturing processes to the new innovations according to the Revolution Industry 4.0. Among those are simulation and digitation which lead to digital twinning. For digital twinning, virtual, augmented, mixed and extended reality play the key role with the advancements in haptic devices and VR glasses. Those operations are used for many purposes and areas, for example by training or manufacturing organisation. Accordingly, a simple factory planning simulation is created in a VR environment with the physics engine Unity 3D. It can be accepted as a start point to reach to the importance conclusions of VR usage in industrial applications. So, it is aimed to emphasize the powerful and effective sides of VR today in technical applications through a simple instance for reaching the future conclusions and suggestions.
Implementasi Metode One Time Password pada Sistem Pemesanan Online

The most commonly used security system in the authentication method is the password. The ease of implementation is a major factor in the use of password-based systems and the use of insecure networks is still a threat for some applications, for example on this online ordering website based application. Where the seller must register in advance to be able to place an order. Therefore, it is necessary to have a mechanism to prevent the negative impact of various security attacks, one of which is by implementing a two factor authentication system, it can be built using a combination of username and password and validated ownership with dynamic passwords one time password. One method for generating One Time Password (OTP) is Time-based One Time Password (TOTP), this method generates a dynamic password that changes following a specified time lag. Where the password is generated through the Secure Hash Algorithm 256 (SHA-256) encryption process with the help of a pseudo random number generator that produces a 6-digit hexadecimal value. The results of the system testing at the beginning to the end of the system testing are the application of scenarios that obtain test results in the form of outputs and assessments with a range of values around 95% - 100%. The average results achieved are successful and appropriate based on the design carried out.
Intellectual Property at a Crossroad

This chapter explores the legal protection awarded to algorithms and argues that in the coming decade, with changes in coding methods, awarding IP protection for algorithms might not prevail. Even today, machines controlled by algorithms are outsmarting humans in many areas. For example, advanced algorithms influence markets and affect finance, commerce, human resources, health, and transportation.
Hyperparameter Transfer Across Developer Adjustments

After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as developer adjustments can change which hyperparameter settings perform well, or even the hyperparameter search space itself. While many approaches exist that leverage knowledge obtained on previous tasks, so far, knowledge from previous development steps remains entirely untapped. In this work, we remedy this situation and propose a new research framework: hyperparameter transfer across adjustments (HT-AA). To lay a solid foundation for this research framework, we provide four simple HT-AA baseline algorithms and eight benchmarks changing various aspects of ML algorithms, their hyperparameter search spaces, and the neural architectures used. The best baseline, on average and depending on the budgets for the old and new HPO, reaches a given performance 1.2-3.6x faster than a prominent HPO algorithm without transfer. As HPO is a crucial step in ML development but requires extensive computational resources, this speedup would lead to faster development cycles, lower costs, and reduced environmental impacts. To make these benefits available to ML developers off-the-shelf and to facilitate future research on HT-AA, we provide python packages for our baselines and benchmarks.
PLFG: A Privacy Attack Method Based on Gradients for Federated Learning

Privacy of machine learning becomes increasingly crucial, abundant emerging technologies have been spawned to solve privacy problem and federated learning (FL) is one of them. FL can replace data transmission through transmission gradient to prevent the leakage of data privacy. Recent researches indicated that privacy can be revealed through gradients and a little auxiliary information. To further verify the safety of gradient transmission mechanism, we propose a novel method called Privacy-leaks From Gradients (PLFG) to infer sensitive information through gradients only. To our knowledge, the weak assumption of this level is currently unique. PLFG uses the gradients obtained from victims in each iteration to build a special model, then updates initial noise through the model to fit victims’ privacy data. Experimental results demonstrate that even if only gradients are leveraged, users’ privacy can be disclosed, and current popular defense (gradient noise addition and gradient compression) cannot defend effectively. Furthermore, we discuss the limitations and feasible improvements of PLFG. We hope our attack can provide different ideas for future defense attempts to protect sensitive privacy.
Catholic News Archive

The Catholic News Archive (CNA) is a freely available unique digital collection of American Catholic Newspapers. With an impressive range of advanced search options and search filters, and a friendly mobile layout, it encourages serious research as well as easy browsing. Every newspaper page is scanned with optimal character recognition (OCR). The lack of export options is a serious hindrance to heavy-duty research, which is unfortunate given how extensive and interdisciplinary the content is. The uncorrected OCR is counterbalanced by powerful search options, diverse result display options, and user-corrected OCR.
Peningkatan Hasil Belajar Mahasiswa Melalui Model Pembelajaran Tutor Sebaya

This research aims to describe the learning by applying peer tutor learning model as an effort to improve the learning outcomes in the linear program of fourth semester students in Mathematics education study program of FKIP UM Mataram. The research method used a class action research. The research population was fourth semester students of Mathematics education study program of FKIP UM Mataram, with as many as four students taken as samples. The instruments of the study were the observation results of the learning implementation and the final test result of students study. Analysis of research data on students test results was obtained from the final test result of the learning cycle. The test was said to succeed if 85% of the total students got a value of ≥ 65. Based on the results of the study, the application of peer tutor learning models could improve learning outcomes with the observation result of lecturers and student learning activities were in good criteria. And, the final test score of cycle I had achieved a classical learning standard with a percentage achievement reached 75%.
A Lightweight Customized Build Chain Visualization Approach Applied in Industry

To visualize the progress of the software development and to foster the awareness of software quality, visualizations of the software build chain have been established in recent years and can be applied in the software service domain easily. Other domains like mechanical engineering are lacking a comprehensive view of the software build chain, since existing of-the-shelf solutions do not support the integration of software quality assurance activities outside the responsibility of the used CI tool adequately. In this paper we present a prototype of a tool that supports the requirement of an extended build chain visualization. In close cooperation with an industrial partner company, we implemented a flexible, adaptable, web based visualization system called DevOpsView and we discuss our findings about the future of customized build chain visualization systems.
Convergence of Applied Intelligent Systems with Cognitive Component

This paper considers knowledge convergation problems for different types of intelligence systems with cognitive-based decision-making. The basis is semantic interoperability of knowledge bases, all of which are managed by knowledge metabase of subject domain or several adjacent domains. The concept of using an information system with various intelligent decision support systems provides for the formation of knowledge base rules based on synonyms. To make and to justify decisions in convergent intelligent systems, it is proposed to use two and 3-simplex prisms. 2-simplex prism is more appropriate if analyzed indicators are changing dynamically.
Machine learning in healthcare toward early risk prediction: A case study of liver transplantation

Healthcare paradigms have always focused into the domain of early prediction of diseases. Especially in field of chronic diseases, the spotlight is always on the aspect of early detection and prevention by controlling the key risk factors in a comprehensive and integrated manner. In this endeavor the colossal power of health data comes into consideration; clubbed with the advanced techniques of computational intelligence to harvest the health data in the best possible way, the aim lies at the prediction of risks or deciphering interesting patterns and early signs of the diseases hidden in the health data. The output obtained from the intelligent analysis of the health data provides seminal insights to the design of more efficient treatment strategies. This work highlights some aspects of artificial intelligence in healthcare, illustrating through a case study of liver transplantation program, where the patient cohort could be interestingly separated into contrasting groups in a pretransplant scenario using machine learning, evincing a relationship with their respective posttransplant risks. In addition to relating the risk groups before liver transplantation with cardiometabolic risks through vascular age, this study accentuates the foundation of Clinical Decision Support System in transplantations, an assistive tool for the medical personnel to computationally analyze and visualize the comprehensive health situation of patients from the perspective of risks.
Penerapan Algoritma K-Means Untuk Pengelompokkan Penyakit Kronis pada Warga Lansia (Studi Kasus Pada: Posyandu Lansia RW 07)

Health is very valuable for all humans, anyone can experience health problems, especially for the elderly. Posyandu elderly RW 07 Pulogebang sub-district is one of the health services available for elderly residents. One of the government's efforts to deal with health problems is by establishing posyandu for elderly residents, considering how elderly people are vulnerable to health problems. At this time, health problems have the potential to attack people who are elderly, and have a history of chronic disease and a weak immune system, more likely to develop disease. In order to provide proper treatment, the elderly posyandu officers classify elderly people who have a history of chronic disease so that they can provide appropriate education and treatment. The data collection and counseling methods carried out by the elderly posyandu are still random and take turns with elderly residents in RW 07, Pulogebang sub-district. However, this method has the risk of being less accurate with the resulting data, because each resident has a different history of disease. Therefore we need an analysis of the health data of the elderly, so that it can be seen the distribution of people who have a history of chronic disease. One solution is to use data mining. So that in this study the clustering technique was used using the K-Means algorithm to classify patients with chronic disease in the elderly residents of RW 07, Pulogebang Village.
News - Comment. Money &amp; Markets. Cryptocurrencies - Market noise reflects coronavirus concerns but logic will prevail

While traders react emotionally to the spread of the virus, causing price volatility, Bitcoin is the new digital equivalent of gold.
DMix: decentralized mixer for unlinkability

We present a protocol that lets participants operate a decentralized mixer to exchange coins in the Bitcoin blockchain. DMix does not need the election of any leader and respects both the unlinkability and the atomicity properties, so that there is no possibility to correlate addresses or lose funds using the protocol. We leverage the MuSig aggregate signatures. This aggregation scheme is based on the Schnorr signature scheme, a recent proposal for a ECDSA alternative, the current Bitcoin signature scheme. We also present an analysis of the method and mitigation of attacks.
Classification of Disaster-Related Tweets Using Supervised Learning: A Case Study on Cyclonic Storm FANI

With unprecedented growth in the ICT sector in India, use of social media platforms like Facebook, Twitter by Government Agencies has increased multifold to connect millions of people in the shortest time span. Even common people use it as an effective media to raise their concerns to the authorities. During disaster situations, people share ground conditions, ask for evacuation, seek relief and medical help, etc., through platforms like Twitter. Those tweets can be an important source of information for disaster managers to plan relief and rescue operations. Distinguishing and classification of millions of tweets in real time is tedious and time-consuming job through manual process. Thus, an automated solution is required which can classify the tweets dynamically in relevant classes. In this paper, different supervised machine learning models have been compared based upon its use case to address the challenge of accurate classification of tweets into actionable classes. The paper presents a case study on categorization of real-time streamed tweets on Cyclonic Storm ‘FANI’ into different actionable classes using the trained classifiers.
Scientometric portrait of Professor CNR Rao using bibliometrix R package

The study reveals a long research experiences of Professor CNR Rao. In his 85 years of life and research experiences, he published 1648 articles, which are indexed in Scopus database during 1956-2019 with average citation per paper is 52.01. But, he might have published a few hundred more papers, which are not indexed in Scopus are not considered in this study for analysis. A collaboration co-authorship and co-occurrences networks of the author were built using Bibliometrix R Package. Author co-citation network, co-authorship and authors’ coupling were built using R Package. The major findings of the study show that the highest collaboration happened with the USA and UK, France, Canada and Japan. The result shows that he collaborated with 1156 unique authors from various countries with co-authors per document is 3.81 and collaborative index is 0.73. Rao is having 1,19,169 citations from Google Scholar with h-index (156) and i10-index (1325). The result shows that CNR Rao preferred to communicate most of his papers to the Chemical Physics Letters, Journals of Solid State Chemistry, Solid State Communications, Journal of Materials Chemistry etc.
A Personalized Preservation Mechanism Satisfying Local Differential Privacy in Location-Based Services

With the wide application of location-based services, there is a huge amount of users’ spatial data generated by mobile devices every day. However, the data is left from mobile users and faced with leakage risk from adversaries or untrusted data receivers. Therefore, spatial data should be perturbed to satisfy local differential privacy (LDP), which is a strong privacy metric in the local setting. In this paper, we study the problem of designing a personalized mechanism satisfying LDP for spatial data. We first construct attack and defense for privacy of spatial data and give a novel privacy definition with LDP and users’ personalized requirements. We propose a personalized location privacy preservation mechanism for spatial data satisfying LDP. We demonstrate the optimal utility and privacy guarantee of our mechanism. We analyze the impact of the key parameters on data utility via the experiments over the real dataset.
Gaze-music Media Transformation by Similarity of Impression Words

In this paper, we present gaze-music transformation realization method by realizing gaze emotion detection system and interconnecting the detection system and an automatic music media creation system. This method automatically creates a music media contents by a detected emotion from human gaze. When it is possible to realize a system that acquires human gaze data from a camera device, detects emotions from the data, and creates media content in response to those emotions, it will contribute to creative activities corresponding to human emotion. Our method connects a gaze emotion detection system and an automatic music creation system by the similarity of words that represent emotions and impressions. It is possible to compare emotions by comparing the emotions expressed by each different vocabulary using the similarity of words.
When Do Algorithmic Tortfeasors that Caused Damage Warrant Unique Legal Treatment?

From a historical context, the design and use of algorithms pre-dates the current proliferation of algorithms throughout society. For example, culminating in the Industrial Revolution, automated machines and tools have for centuries been assisting humans in performing physical tasks. Drills, engravers, weaving machines, and the like employed machines’ physical advantages to free human beings from repetitive physical labor. In a similar manner, algorithms are now taking on the decision-making and human supervisory control of systems which require human cognitive skills.
An Optical-to-SAR Transformation Method for SAR Ship Image Augmentation

For ship detection in remote sensing, the labeled synthetic aperture radar (SAR) ship images are far less than optical ship images, which limited the development of SAR ship detection and classification. In this paper, an optical-to-SAR transformation method is proposed to transforming the well labeled optical ship images to SAR ship images. Based on electromagnetic theory, the backscattering and reflection mechanism is analyzed and modeled into the transformation. A background iteration method based on probability theory is also proposed to learn the distribution of SAR ship images. The result shows that the proposed method can transform the optical ship images to SAR ship images with the confidence coefficient of 84.643%.
Fundamental limitations on quantum channel manipulation

We establish universal limitations on the manipulation of quantum channel resources under the most general, adaptive transformation protocols. Focusing in particular on the class of distillation tasks -- which can be understood either as the purification of noisy channels into unitary ones, or the extraction of state-based resources from channels -- we develop fundamental restrictions on the error necessarily incurred in such transformations, applicable to many different settings in general resource theories. We introduce comprehensive lower bounds for the overhead of any physical distillation protocol in terms of required channel uses, imposing strong limitations on the practical efficiency and cost of channel manipulation protocols. In the asymptotic setting, our results yield broadly applicable strong converse bounds for the rates of distillation. As a special case, our methods apply to the manipulation of quantum states, in which case they significantly improve on and extend the recent no-go theorems for resource purification in [Fang and Liu, Phys. Rev. Lett. 125, 060405 (2020)]. We demonstrate our results through explicit applications to quantum communication, where we recover in particular a number of strong converse bounds for the quantum capacity of channels assisted by various classes of operations, as well as to stabilizer quantum computation, where we study the precise quantitative links between magic state distillation and quantum gate synthesis, and provide improved bounds for both tasks.
Early Detection Of Skin Basal Cell Carcinoma Nodular Using Extraction Methods ABCD Feature Comparison With Dermatofibroma Data

Cancer is one of the biggest causes of death now, cancer is also a disease that is very difficult to cure and still relatively difficult to treat, the skin can also be a medium that can cause cancer. Prevention and detection of skin cancer early is one of the best ways to reduce the greater risk that can be caused by the disease, in this paper will explain the making of applications to detection cancer skin Carcinoma Basal Cell type nodular using Image Processing with tested by comparing the feature dermatofibroma and Basal Cell Carcinoma features. In this paper the system is made based on Android to support input features by comparing the 4 criteria of the feature, that is Asymmetry, Border, Color, and Diameter. The extraction method used in the feature xtraction is the ABCD feature with classification using K-Nearest Neighbor.
Salami Science in the Age of Open Data: Déjà lu and Accountability in Management and Business Research

by-region-country-or-economy. (a) The countries
Hyperparameter determination in multivariate macromodeling based on radial basis functions

This paper introduces a simple and effective algorithm for the automated selection of Radial Basis Function hyperparameters in the context of high-dimensional multivariate macromodeling. Numerical results show an average speedup of at least one order of magnitude with respect to direct hyperparameter optimization.
Design and Implementation of Crypt Analysis of Cloud Data Intrusion Management System

Cloud computing is the method of employing a set-up of isolated servers to be hosted on the web to accumulate and supervise information instead of an area server or a private laptop. Storage of data in cloud sometimes creates security issues in the data stored so, security in provided for the stored cloud data. In order to provide secured cloud data transaction, our proposed method initially verifies the authentication of the user followed by splitting the information of the user using pattern-matching technique. The blowfish computation is used to encrypt the alienated data. After encryption, resorting to the selection of the optimal position of a data center by means of the cross grey wolf optimization and firefly technique is done. Finally, the encrypted data are stored at an optimal location in the cloud. Then the data split column wise and separated at an optimal location in the cloud, this method is highly secured since the user cannot retrieve the file without authentication verification
Leaning from the Human: Virginia Woolf, Olaf Stapledon, and the Challenge of Behaviorism

This essay argues that Virginia Woolf's Flush (1933) and Olaf Stapledon's Sirius (1944)—novels that focus on the representation of canine consciousness—can be read as a riposte to the reductive materialism of John B. Watson's behaviorism. Following in the wake of Watson's ridicule of anthropomorphism and the "stream of consciousness" (indeed, any consciousness at all), Woolf and Stapledon embrace these techniques as they work to affirm the sexuality, spirituality, and rich mental experience of nonhuman animals. Behaviorism is revealed as a productive antagonist that played a vital role in the development of early twentieth-century literature and literary criticism.