Reviewerly/exHarmony huggingface dataset:
A model to collaborate on R&D projects based on TRL

Regarding the importance of CoPS in providing the organization with technological capability and the differences existing between these systems and mass products, it is essential to develop a model to collaborate on CoPS R&D. Exploitation of the capabilities of the network of collaborators is a desirable option due to inability of the organization in providing the resources comprising finance, human, facilities and expertise. Reviewing the literature on CoPS and technological collaboration incorporating the kind and motivations of collaborators and organizational forms of collaboration and interviewing 18 experts of the organization, a model was developed based on TRL approach and fuzzy cognitive mapping tool. Considering the model developed, the steps of project is classified into 3 categories: Green(able to be collaborated in network), Yellow (able to be collaborated on condition) and red (unable to be collaborated). The role of the members of the network and the strategy of collaborating with them is specified as follows: R&D contract with universities and consulting agents, Educational acquisition with faculty members, outsourcing the projects intended as military service compensation, and R&D contract or research funding with knowledge-based institutions. On the other hand, the position of technology developing and designing centers is specified employing the national innovation system as an upper hand document. Validity of model is proven by checklist of good theories.
Segmentation-Grounded Scene Graph Generation

Scene graph generation has emerged as an important problem in computer vision. While scene graphs provide a grounded representation of objects, their locations and relations in an image, they do so only at the granularity of proposal bounding boxes. In this work, we propose the first, to our knowledge, framework for pixel-level segmentation-grounded scene graph generation. Our framework is agnostic to the underlying scene graph generation method and address the lack of segmentation annotations in target scene graph datasets (e.g., Visual Genome) through transfer and multi-task learning from, and with, an auxiliary dataset (e.g., MS COCO). Specifically, each target object being detected is endowed with a segmentation mask, which is expressed as a lingual-similarity weighted linear combination over categories that have annotations present in an auxiliary dataset. These inferred masks, along with a novel Gaussian attention mechanism which grounds the relations at a pixel-level within the image, allow for improved relation prediction. The entire framework is end-to-end trainable and is learned in a multi-task manner with both target and auxiliary datasets.
Optically-addressed spatial light modulator for the Ising machine implementation

Many problems in mathematics, statistical mechanics, and computer science are hard to tackle by conventional computational methods, but can be reduced to the combinatorial problem of finding the ground state of the Ising model. However, the computational cost associated with a combinatorial optimization problem increases exponentially with the number of variables. This explains growing research interest towards physical and artificial spin-networks (schematically shown in Fig. 1 (a) ) that evolve according to an Ising Hamiltonian, which provides the combinatorial solution through the ground state observed in the experiment [1] . The implementation of analog autonomous spin-networks is currently considered a highly attractive prospect due to a potentially significant increase in energy efficiency and processing speed.
PRACTICE AND DEVELOPMENT OF SPORTS SOMATIC SCIENCE IN SOCIAL PHYSICAL EDUCATION TEACHING USING FRACTAL THEORY

In view of the recognition of human motion function in physical education, a multidimensional evaluation index of motion function based on fatigue, muscle strength and muscle contraction, and relaxation ability is proposed, and the mapping rules among fatigue characteristic value, muscle strength, fractal dimension characteristic change, and motion function state are studied. Based on the characteristics of surface electromyography (sEMG) signal and its generating principle, the collected signal is preprocessed by median filtering to obtain the filtered signal. The effectiveness of the filtering method is proved by comparing the signals before and after filtering. The current muscle strength calculation method based on sEMG signal is analyzed and studied. The muscle strength calculation method of Hill three-element model is used to establish the muscle model through the muscle activity and force–length curve calculated by sEMG signals. The muscle strength is obtained by muscle model, and the feasibility of the experimental method is verified by comparing the calculated actual muscle strength with the theoretical muscle strength. In the experiment, the distribution of different subjects in the multidimensional evaluation index space is studied. Moreover, the mapping relationship between sEMG signal characteristics and human motion function is discussed comprehensively based on theory and data.
How Next-Generation Endpoint Security can Protect Remote Workers

Government agencies can protect employees working remotely by investing in cybersecurity tools that shield users endpoints more thoroughly than before.
Motivation And Keeping Of Faculty Members Of Federal Universities

The teaching staff of universities should be considered as a key element of the higher education system, ensuring the quality of the educational process. The changes which are taking place in the development of higher education and its main productive force, urgently require an adequate transformation of the methods of management. The article describes the features of motivation and keeping of teachers at Federal Universities. A comparison of the salaries of teachers of Russian and foreign universities is carried out; a significant gap is noticeable between the salaries of teachers of higher education in Russia and in developed countries, despite the trend towards an increase of salaries in Russia in the last decades. The mechanisms of keeping of teachers at federal universities at various stages of career management are investigated: recruitment, adaptation, motivation, creation of a personnel reserve, assessment, training and development. A survey of 99 teachers of the Kazan (Volga region) Federal University conducted by the authors showed that 30% of teachers think about changing their place of work in the upcoming year, they are mostly dissatisfied with the motivation system and the amount of remuneration. The costs of federal universities for the motivation and keeping of teachers and for their replacement are compared, which showed that the costs of replacement currently exceed the costs of motivation and retention. It was offered an algorithm to upgrade hold program of Federal Universities teachers.
The Development of Methodical Approaches to the Implementation of the Propedeutic Course of Informatics in Primary School by Means оf Kodu Game Lab

The article describes the problem of teaching programming at an early school age. The main objective of the article is to demonstrate the results of research on the problems of early learning. The authors analyzed teaching programming in Russian schools, its past, present and prospects. The local (subject) tasks and the global impact of early learning in programming on learning in primary school have been determined. The authors, as the results of their research, highlight several problems that arise in early learning to program. And they establish the reasons, as well as possible ways to overcome these problems.
EFISIENSI SDM DENGAN CUSTOMER RELATIONSHIP MANAGEMENT BERBASIS WEB DAN SMS GATEWAY

This study aims to develop an application for improving HR efficiency by applying the concept of web-based and SMS Gateway Customer Relationship Management (CRM). The study is used as a tool to help in providing information to the general public and patients, especially those with Tuberculosis in the Community Centre for Lung Health (BKPM) of Klaten Regency.&#x0D; The CRM system in BKPM Klaten is used to efficiently manage patients and all health information. This application will provide information including medical check-up timing and manage appointments with patients more effectively to improve medical staffs’ schedule efficiency. The patients’ notification method is using SMS Gateway (Short Message Service).&#x0D; This research implemented a survey and an interview to the agency and analyze the system that was running in BKPM Klaten as its research method. The development of this system uses both Data Flow Diagrams (DAD) and Relationship Between Tables methods.&#x0D; The application is expected to improve the efficiency of human resources management in BKPM Klaten in doing its job of providing information to the public, arranging required reports, and specifically for the patients, impacting on increasing the customers’ satisfaction and loyalty. This system can also make it easier for BKPM to interact directly with patients using an SMS gateway.
Capturing Structural Locality in Non-parametric Language Models.

Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies. Some examples include topical clusters in text or project hierarchies in source code repositories. In this paper, we explore utilizing this structural locality within non-parametric language models, which generate sequences that reference retrieved examples from an external source. We propose a simple yet effective approach for adding locality information into such models by adding learned parameters that improve the likelihood of retrieving examples from local neighborhoods. Experiments on two different domains, Java source code and Wikipedia text, demonstrate that locality features improve model efficacy over models without access to these features, with interesting differences. We also perform an analysis of how and where locality features contribute to improved performance and why the traditionally used contextual similarity metrics alone are not enough to grasp the locality structure.
Glowworm Attack: Optical TEMPEST Sound Recovery via a Device's Power Indicator LED.

Two main classes of optical TEMPEST attacks against the confidentiality of information processed/delivered by devices have been demonstrated in the past two decades; the first class includes methods for recovering content from monitors, and the second class includes methods for recovering keystrokes from physical and virtual keyboards. In this paper, we identify a new class of optical TEMPEST attacks: recovering sound by analyzing optical emanations from a device's power indicator LED. We analyze the response of the power indicator LED of various devices to sound and show that there is an optical correlation between the sound that is played by connected speakers and the intensity of their power indicator LED due to the facts that: (1) the power indicator LED of various devices is connected directly to the power line, (2) the intensity of a device's power indicator LED is correlative to the power consumption, and (3) many devices lack a dedicated means of countering this phenomenon. Based on our findings, we present the Glowworm attack, an optical TEMPEST attack that can be used by eavesdroppers to recover sound by analyzing optical measurements obtained via an electro-optical sensor directed at the power indicator LED of various devices (e.g., speakers, USB hub splitters, and microcontrollers). We propose an optical-audio transformation (OAT) to recover sound in which we isolate the speech from optical measurements obtained by directing an electro-optical sensor at a device's power indicator LED. Finally, we test the performance of the Glowworm attack in various experimental setups and show that an eavesdropper can apply the attack to recover speech from speakers' power LED indicator with good intelligibility from a distance of 15 meters and with fair intelligibility from 35 meters.
Demo: Automatically Retrainable Self Improving Model for the Automated Classification of Software Incidents into Multiple Classes

Developers across most of the organizations face the issue of manually dealing with the classification of the software bug reports. Software bug reports often contain text and other useful information that are common for a particular type of bug. This information can be extracted using the techniques of Natural Language Processing and combined with the manual classification done by the developers until now to create a properly labelled data set for training a supervised learning model for automatically classifying the bug reports into their respective categories. Previous studies have only focused on binary classification of software incident reports as bug and non-bug. Our novel approach achieves an accuracy of 76.94% for a 10-factor classification problem on the bug repository created by Microsoft Dynamics 365 team. In addition, we propose a novel method for automatically retraining the model and updating it with developer feedback in case of misclassification that will significantly reduce the maintenance cost and effort.
APPLICATION OF NONLINEAR DYNAMIC SYSTEM AND TECHNOLOGY IN MOVING TARGET DETECTION DURING SPORTS TRAINING

The aim of this paper is to improve the accurate analysis of the sports training project, research its video data profoundly, and discover deficiencies and summarize experience from the existing videos, thereby promoting China’s sports industry. According to previous works, the existing sports training target detection system has the problem of nonlinear video and low target detection accuracy, which can easily cause target loss or tracking failure. Therefore, the Kalman Filter (KF) is studied in-depth and applied to detect the targets in sports videos. Combined with the Multi-Innovation (MI) theory, an MI-Extended KF (EKF) algorithm model based on nonlinear dynamic technology is proposed. This model can effectively solve the filtering accuracy problem under the strong nonlinear system. Finally, the performance analysis through different datasets has verified the effectiveness of the model proposed further. Results demonstrate that the model proposed can effectively improve the filtering accuracy of nonlinear systems. As the angle module changes, the accuracy of sports recognition also changes. The final accuracy of the model proposed can reach above 96%. The simulation results and convergence are better than other algorithms, which also proves the effectiveness of the model proposed. The results can provide a theoretical basis for the research related to the detection of training sports targets in sports videos.
SecFL: Confidential Federated Learning using TEEs.

Federated Learning (FL) is an emerging machine learning paradigm that enables multiple clients to jointly train a model to take benefits from diverse datasets from the clients without sharing their local training datasets. FL helps reduce data privacy risks. Unfortunately, FL still exist several issues regarding privacy and security. First, it is possible to leak sensitive information from the shared training parameters. Second, malicious clients can collude with each other to steal data, models from regular clients or corrupt the global training model. To tackle these challenges, we propose SecFL - a confidential federated learning framework that leverages Trusted Execution Environments (TEEs). SecFL performs the global and local training inside TEE enclaves to ensure the confidentiality and integrity of the computations against powerful adversaries with privileged access. SecFL provides a transparent remote attestation mechanism, relying on the remote attestation provided by TEEs, to allow clients to attest the global training computation as well as the local training computation of each other. Thus, all malicious clients can be detected using the remote attestation mechanisms.
SPATIOTEMPORAL AND GENETIC RELATIONSHIPS OF GOLD ORE AND MERCURY-ANTIMONY MINERALIZATION AT THE HG-SB-GOLD-BEARINGCHAUVAI DEPOSIT (KIRGHIZIA): GEOLOGY, MINERALOGY OF ORES AND FEATURES OF HYDROTHERMAL-METASOMATIC PROCESSES

The Chauvai Hg-Sb deposit is a striking example of combining two contrasting types of mineralization in space: mercury-antimony and gold ones. The article studies the spatial-temporal and genetic relationships of goldore and mercury-antimony mineralization based on a complex of both traditional geological and mineralogicalgeochemical methods, as well as modern instrumental methods for analyzing the mineral composition. Two types of ores with clear structural confinedness have been found at the deposit: a) mercury-antimonic (cinnabarantimonite) ores, associated with jasperoid breccias and manifested exclusively along the tectonic contact of limestone of the Alai section and terrigenous rocks of the Tolubai Formation, and b) gold- sulphide (arsenopyritepyritic) ores, localized in slightly modified carbonate-terrigenous rocks of the Tolubai Formation, overlying the plane of tectonic contact. Ore formation occurred during the following stages: in the late diagenetic, without interruption passing into the catagenetic-hydrothermal, characterized by the formation of gold mineralization, and then in the later hydrothermal-telethermal, characterized by the development of Hg-Sb mineralization. It is established that the main carrying agent of invisible gold (“invisible gold”) in ores is framboidal and idiomorphic pyrite and, especially, its high-arsenic varieties. A set of conducted studies has shown that the gold ore and mercury-antimony mineralization is broken in time and is genetically associated with various hydrothermalmetasomatic processes, and the Chauvai deposit can be classified as a Carlin-like type.
Learning Canonical 3D Object Representation for Fine-Grained Recognition

We propose a novel framework for fine-grained object recognition that learns to recover object variation in 3D space from a single image, trained on an image collection without using any ground-truth 3D annotation. We accomplish this by representing an object as a composition of 3D shape and its appearance, while eliminating the effect of camera viewpoint, in a canonical configuration. Unlike conventional methods modeling spatial variation in 2D images only, our method is capable of reconfiguring the appearance feature in a canonical 3D space, thus enabling the subsequent object classifier to be invariant under 3D geometric variation. Our representation also allows us to go beyond existing methods, by incorporating 3D shape variation as an additional cue for object recognition. To learn the model without ground-truth 3D annotation, we deploy a differentiable renderer in an analysis-by-synthesis framework. By incorporating 3D shape and appearance jointly in a deep representation, our method learns the discriminative representation of the object and achieves competitive performance on fine-grained image recognition and vehicle re-identification. We also demonstrate that the performance of 3D shape reconstruction is improved by learning fine-grained shape deformation in a boosting manner.
Debts, Money, Intellectual Property, Data and the Concept of Dematerialised Property

Debts, (electronic) money, intellectual property, and, in principle, data and digitised objects (if ownership rights are to be recognised for these), can be conceptualised as versions of the general principle of dematerialised property. This article discusses first the concept of dematerialised property and its application to debts, money and intellectual property. Then it deals with the idea of ownership of data within traditional property concepts. While data ownership can theoretically be accommodated fairly easily within the framework of dematerialised property, there are several reasons, both theoretical and from a legal policy perspective, which make the introduction of data ownership modelled upon conventional (intellectual) property rights problematic.
Interval Probabilities In Lpm For Risk Assessment In Socio-Economic Systems

In the article the disadvantages of the logical and probabilistic method (LPM) for assessment risk and safety in socio-economic systems are described, namely, the assumption of the independence of events, "conjunction error", the subjectivity of experts in event tree construction. Proceed from properties of socio-economic systems, the author proposes to use interval estimations for probabilities of initiating events instead of point ones. Interval estimations allow avoid significant limitations in applying LPM for risk management in socio-economic systems which are caused by subjectivity of experts and “conjunction error”. The semantics of the formula for calculation the probabilities of events is determined by the rules of addition and multiplication of probabilities, and operations are performed according to the rules of interval mathematics. Examples of calculations of interval probabilities of events are given using numerical examples. The scientific foundation of the interval estimations of probabilities based on the axiomatics of the probability space and the space of events by A.N. Kolmogorov and sigma-algebra by E. Borel are stated.
Development Of Students Research Potential: "From Cultural Acceptance To Cultural Creation"

The object of the study is theoretically and experimentally foundation the organization of high school student’s advancement within the cultural genesis principle which is being implemented in the Russian education. The complex of complementary methods includes general theoretical methods such as logical and deductive analysis of philosophical, psychological and pedagogical sources, structural and functional analysis, generalization, systematization, classification, theoretical modeling; empirical methods such as experiment (establishing, modeling, forming), observation, expert evaluation, computer testing; methods of mathematical statistics such as correlation analysis (analysis of the total number of correlations, calculation of the average statistical weight of one relationship. It is revealed an essential contradiction between the need to prepare students for culturally creative cognitive activity and the lack of didactic support for this process. The establishing experiment resulted in the following: 6.3% of high school students have got adaptive level of research capability; 61.6% of high school students under test have got reproductive level, heuristic level is for 31.6%. As for creative level it is peculiar only for 0.5% of high school students under test. The problems connected with the organization of high school students advancement through learning situations from cultural appropriation to cultural creativity are as following: (1) how to use texts in organization of high school students research; (2) how to overcome the domination of the organization of high school students cognitive activity in the form of appropriation; (3) how high school students are capable to master research methods;(4) how to use digital technologies within the established goals.
Rejoinder to the Critique of an Article on Machine Learning in the Detection of Accounting Fraud

This is a rejoinder to the reply by Yang Bao, Bin Ke, Bin Li, Y. Julia Yu, and Jie Zhang to my article “Critique of an Article on Machine Learning in the Detection of Accounting Fraud,” published in Econ Journal Watch in March 2021. Here I explain why the authors’ reply did not address the fundamental issue raised in my critique, which asked for a reasonable justification as to why fraud identifiers were changed for select observations in the sample—a choice that was undisclosed in the original publication, and one that contradicted the logic presented in their paper. That change was critical. Without it, their publication failed to improve upon prior literature in the detection of accounting fraud.