Reviewerly/exHarmony huggingface dataset:
Future of University in Conditions of Uncertainty and Complexity

The article focuses on the analysis of the role and place of the university in the concepts of restructuring the basic foundations of society on the principles of extensive use of information and communication technologies. It has been established that in the domestic system of training highly qualified personnel for the new economy, the contradictions between its elements are aggravated. On the one hand, universities have a high intellectual potential of teachers and scientists representing generations X and Y; they train the creative part of generation Z. An optimistic future scenario, as a product of university discussions, is presented in the mission of the university. On the other hand, the state and business community define the contours of the future in terms of simplicity, certainty, linear development, and high controllability. This approach is contrary to the global fundamental vectors that indicate the ability to work in conditions of complexity and uncertainty. The methodological basis of the study are systemic, constructivist and transdisciplinary approaches, applying the theoretical foundations of complex systems to social processes, and, above all, to the system of university education in Russia. The main conclusions are the following: 1) uncertainty and complexity of the future are not an obstacle to scenario forecasting in the sphere of higher education as a ground for the knowledge society; 2) this movement will be successful if the state and business community go to a higher level of strategic planning; 3) academically free university, humanistically oriented state reforms and programs of society modernization, socially oriented business (investments for a person) become the targets of the future. The results of the study can be used in the practice of Russian and international university education
Using classical bit-flip correction for error mitigation in quantum computations including 2-qubit correlations

We present an error mitigation scheme which corrects readout errors on Noisy Intermediate-Scale Quantum (NISQ) computers [1,2].After a short review of applying the method to one qubit, we proceed to discuss the case when correlations between different qubits occur.We demonstrate how the readout error can be mitigated in this case.By performing experiments on IBMQ hardware, we show that such correlations do not have a strong effect on the results, justifying to neglect them.
ASSESSMENT OF COAL INDUSTRY FORMAT IN THAILAND USING SATELLITE REMOTE SENSING DATA

the article presents the results of studying the activities of a coal mining enterprise in the fuel and energy sector of thailand.the remote monitoring and analytical calculations revealed the number of mining and transport machines operating in the coal strip mine.Satellite observations over a multi-year period reveal a stable trend in the extraction and consumption of the mined coal.
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations

Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy, still far from its upper limit. To address the problem, we propose a decoupled automatic diagnostic framework DxFormer, which divides the diagnosis process into two steps: symptom inquiry and disease diagnosis, where the transition from symptom inquiry to disease diagnosis is explicitly determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model respectively. We use the inverted version of Transformer, i.e., the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross entropy loss. Extensive experiments on three public real-world datasets prove that our proposed model can effectively learn doctors' clinical experience and achieve the state-of-the-art results in terms of symptom recall and diagnostic accuracy.
A Literature Survey of Complexity Metrics for Object-Oriented Programs

Software complexity refers to the factors that determine the complexity level of a software project. High complexity is caused by the many attributes used in the system and the complex logic relationships among these attributes and features. The increased complexity of software is undesirable and affects maintenance. Over the years, Software Engineering scholars recommended several metrics like Halstead metric, cyclomatic complexity, and line of code metrics to deal with the complexity. With the complexity increasing as time goes by, there is a need for better metrics that can evaluate software more effectively. This research aims to develop a metrics model to determine the features that cause high complexity in software design architectures and to implement the multi-language complexity evaluation model for software architectures. Although this is the case, the literature on complexity metrics that implement diagram-centric complexity measures are inadequate. This study presents the outcomes obtained from our survey on metrics utilized in object-oriented environments. The survey comprises a small set of the most common and frequently implemented software metrics, which could be adopted to a group of object-oriented metrics and object-oriented programming. After reviewing the literature, Findings indicate that metrics that employ diagram-centric complexity measures are lacking.
Transport and localization in quantum walks on a random hierarchy of barriers

We study transport within a spatially heterogeneous one-dimensional quantum walk with a combination of hierarchical and random barriers. Recent renormalization group calculations for a spatially disordered quantum walk with a regular hierarchy of barriers alone have shown a gradual decrease in transport but no localization for increasing (but finite) barrier sizes. In turn, it is well-known that extensive random disorder in the spatial barriers is sufficient to localize a quantum walk on the line. Here we show that adding only a sparse (sub-extensive) amount of randomness to a hierarchy of barriers is sufficient to induce localization such that transport ceases. Our numerical results suggest the existence of a localization transition for a combination of both, the strength of the regular barrier hierarchy at large enough randomness as well as the increasing randomness at sufficiently strong barriers in the hierarchy.
MAIN COMPONENTS AND STRUCTURE OF SOCIOCULTURAL COMPETENCE

The article deals with the main components and structure of sociocultural competence. The author gives a definition of the competence
Privacy-Preserving Distributed Machine Learning Made Faster

With the development of machine learning, it is difficult for a single server to process all the data. So machine learning tasks need to be spread across multiple servers, turning the centralized machine learning into a distributed one. However, privacy remains an unsolved problem in distributed machine learning. Multi-key homomorphic encryption is one of the suitable candidates to solve the problem. However, the most recent result of the Multi-key homomorphic encryption scheme (MKTFHE) only supports the NAND gate. Although it is Turing complete, it requires efficient encapsulation of the NAND gate to further support mathematical calculation. This paper designs and implements a series of operations on positive and negative integers accurately. First, we design basic bootstrapped gates with the same efficiency as that of the NAND gate. Second, we construct practical $k$-bit complement mathematical operators based on our basic binary bootstrapped gates. The constructed created can perform addition, subtraction, multiplication, and division on both positive and negative integers. Finally, we demonstrated the generality of the designed operators by achieving a distributed privacy-preserving machine learning algorithm, i.e. linear regression with two different solutions. Experiments show that the operators we designed are practical and efficient.
Hand Sign Language Detection Using Machine Learning

Millions of people around the world suffer from hearing disability. This large number demonstrates the importance of developing a sign language recognition system converting sign language to text for sign language to become clearer to understand without a translator. CNN Algorithm is proposed based on Sign Language. Sign Language may be a language within which we tend to create use of hand movements and gestures to communicate with other people who are chiefly deaf and dumb.
Making National Cancer Institute–Designated Comprehensive Cancer Center Knowledge Accessible to Community Oncologists via an Online Tumor Board: Longitudinal Observational Study

Expert knowledge is often shared among multidisciplinary academic teams at tumor boards (TBs) across the country, but these conversations exist in silos and do not reach the wider oncology community.Using an oncologist-only question and answer (Q&A) website, we sought to document expert insights from TBs at National Cancer Institute-designated Comprehensive Cancer Centers (NCI-CCCs) to provide educational benefits to the oncology community.We designed a process with the NCI-CCCs to document and share discussions from the TBs focused on areas of practice variation on theMednet, an interactive Q&A website of over 13,000 US oncologists. The faculty translated the TB discussions into concise, non-case-based Q&As on theMednet. Answers were peer reviewed and disseminated in email newsletters to registered oncologists. Reach and engagement were measured. Following each Q&A, a survey question asked how the TB Q&As impacted the readers' practice.A total of 23 breast, thoracic, gastrointestinal, and genitourinary programs from 16 NCI-CCC sites participated. Between December 2016 and July 2021, the faculty highlighted 368 questions from their TBs. Q&As were viewed 147,661 times by 7381 oncologists at 3515 institutions from all 50 states. A total of 277 (75%) Q&As were viewed every month. Of the 1063 responses to a survey question on how the Q&A affected clinicians' practices, 646 (61%) reported that it confirmed their current practice, 163 (20%) indicated that a Q&A would change their future practice, and 214 (15%) reported learning something new.Through an online Q&A platform, academics at the NCI-CCCs share knowledge outside the walls of academia with oncologists across the United States. Access to up-to-date expert knowledge can reassure clinicians' practices, significantly impact patient care in community practices, and be a source of new knowledge and education.
Dual-Level Template for Enhancing Resolution of Quantum Images

Quantum information science is an emerging research field devoted to the use of quantum mechanical systems to devise and implement information processing tasks faster than that possible with classical computers. In this study, two quantum image resolution enhancement (QIRE-I and QIRE-II) schemes are proposed based on quantum wavelet transform and quantum interpolation. Using these, the resolutions of low-resolution (LR) images are enhanced by decomposing them into four frequency sub-bands using a single-level one-dimensional (1-D) quantum Haar wavelet transform (QHWT). Subsequently, to preserve the edges and obtain sharper high-resolution (HR) images, quantum interpolation was applied to three of the high-frequency sub-bands. A few simulation-based demonstrations are presented to illustrate the feasibility and effectiveness of the proposed schemes. The visual and quantitative results demonstrate the superiority of the proposed schemes over those that use only quantum interpolation.
AI Based False Positive Analysis of Software Vulnerabilities

Abstract: Programming measurements and shortcoming information having a place with a past programming variant are utilized to assemble the product issue expectation model for the following arrival of the product. Notwithstanding, there are sure situations when past issue information are absent. As such foreseeing the shortcoming inclination of program modules when the issue marks for modules are inaccessible is a difficult assignment oftentimes arised in the product business There is need to foster a few strategies to assemble the product issue forecast model in light of unaided realizing which can assist with anticipating the shortcoming inclination of a program modules when shortcoming names for modules are absent. One of the strategies is utilization of grouping methods. Solo methods like grouping might be utilized for issue expectation in programming modules, all the more so in those situations where shortcoming names are not accessible. In this review, we propose a Machine Learning grouping based programming shortcoming forecast approach for this difficult issue
Examination of model by Bayesian Approach for Cognitive Impairment and Alzheimer Disease

Alzheimer and cognitive impairment are very dangerous disorders in the world. While cognitive decline is connected with Alzheimer. More researches have been carried out in recent days. Alzheimer is a kind of disease which is very much less in exposure. This paper gives a detailed explanation about the cognitive decline and its association with Alzheimer Disease and also some approaches. A conceptual framework for screening and therapy has been developed, as well as potential research requirements. This paper computes the model which gives better performance, worse, or nearly equal to another within the Bayesian technique. GridSearch revealed that CV ‘rbf <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ’ have 7.8% possibility to be worst condition than ‘linear’, and have 2.8 percent to be worse than ‘3 poly <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ’. Both have a percent chance of nearly comparable, but ‘rbf <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ’ and ‘3 poly <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ’ only have a 12% chance.
INTEGRATIVE FUNCTION OF THE UNIVERSITY LIBRARY

Not only the traditional functions of the library are considered, but also the integrative function associated with the formation of a digital microenvironment in the information and educational environment of the university
Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks

Along with the recent advance in wireless networking and data processing technologies, demands for low latency communication (LLC) are increasing in a wide variety of future-driven autonomous applications such as a smart factory, self-driving cars, and so on. The fifth generation of cellular mobile communications (5G) will cover this need as one of three key capacities in their usage scenarios: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable low-latency communications (URLLC). The 5G systems are composed of mobile devices and various internet of things (IoT) devices for sensing, acting, and information services; they configure diverse networking topologies such as direct mobile-to-mobile, also known as device-to-device (D2D). In the 5G D2D network systems, the network topologies are easily broken because of the mobile devices such as smartphones, IoT devices, and so on. Thus, for the highly flexible and extensible 5G D2D network systems, mobility support for the mobile devices is necessary. In this paper, we first explore the mobility issues in beyond 5G D2D. Since there are static and mobile elements in the 5G application domains such as the smart factory, overall mobility would lead to highly frequent topology reconfiguration or connectivity reconstruction. Thus, latency-related problems derived from topology changes and connectivity failures due to the mobility are addressed. To handle the problems, a fast connectivity construction scheme, denoted by LMK, is proposed with a deep neural network dealing with learning on radio signal information in order to achieve the LLC. Evaluation results demonstrate that the proposed framework can provide reliable connectivity for the MAC layer link with a low latency data transmission.
Research on "i-Home Yunju" Intelligent Home Improvement System based on Virtual Reality Technology

With the rapid development of economy and culture, people's home decoration not only stays practical, but also emphasizes aesthetics, creativity and environmental protection, and the home improvement industry has ushered in rapid development. The traditional decoration industry also has obvious drawbacks such as long construction period, opaque price, and unnecessary construction. With the introduction of supply-side reforms, the home improvement industry also urgently needs to improve efficiency, thereby reducing the home improvement burden that modern residents are increasingly concerned about. In response to this problem, the "i-Home Yunju" smart APP was first developed. By exploring the relevant functions of the APP and carrying out innovative design, at the same time, the feasibility of AR and VR technology was analyzed to explore the importance of virtual reality technology to smart home decoration.
Design and Implementation of Mine Information Management System Based on Wireless Network

With the increasing demand for mineral resources in China, how to manage mineral resources scientifically, efficiently and intelligently has become a practical problem faced by relevant governments, enterprises and academia. As the most advanced technology of GIS in recent years, Web GIS can collect, store, process, analyze and visualize heterogeneous, multi-source and massive spatial geographic data. Therefore, introducing Web GIS technology into mine information management and building an information system for massive, multi-source and heterogeneous mine information management is of great significance for the government and enterprises to improve the management efficiency of mineral resources and realize the accurate and scientific planning and management of mineral resources.
Basis of Selecting Research Topic: An Analytical Study

Between Scientific study and successful solution of the problem is a perfect research process. The present article expresses the importance of how to identify the problem, the source, features, standard and delimitation. The necessity is the mother of invention or research. The researcher acquires knowledge in the field chosen for research and have to conducts meaningful research for results prove to be very useful in the society. In any sector or field there are many types of research topics have different sources are available for different research topics. Any research is done to clarify theoretical issues but sometimes carry out research to understand or add a theory or idea in a practical way or to examine its flaws; sometimes research is being conducted to find out the reasons for the discrepancy; Research to correct improper use of faulty statistical methods; Research carried out to correct a faulty research method; Research to solve real questions or problems. Research conducted in a scientific manner becomes very specific and important.
Correction: STASiamRPN: visual tracking based on spatiotemporal and attention

During online tracking, we use 5 frames as a video sequence, which includes the first 4 frames and the last frame. We use the last frame as the tracking frame. If the video sequence was less than 5 frames, we repeated the first frame.
Content Based Image Retrieval Using Machine Learning

With the headway of media innovation, the quickly expanding utilization of an enormous computerized site is conceivable. To additionally oversee and recover it, Content Based Image Retrieval (CBIR) is a powerful technique. This paper exhibits the advantages of a substance based picture obtaining program, too as fundamental advancements. Contrasted with the weaknesses that only one component is utilized in the norm framework, this paper presents a technique that joins tone, surface and shape to accomplish the picture also, show its advantages. Then this paper centers around include evacuation and portrayal, a couple usually utilized calculations and picture matching methods. The elements of an internet based web index are turning out to be progressively perplexing. Not just by utilizing watchwords, search data can now likewise be done by embedding pictures with the picture highlight. Returns results connected with the inquiry picture, its size, furthermore, the destinations that transfer the picture as question.
Factorization of the Degree of Sphenic Polynomials Over the Galois Fields of Arbitrary Characteristics

By sphenic polynomials, we mean polynomials formed by the product of three (not necessarily different) irreducible polynomials with a priori unknown degree. The study's main goal is to develop an effective algorithm for factorizing degrees of sphenic polynomials with minimal computational complexity. Different solutions to the problem of factorization degrees of sphenic polynomials depending on the ratio degree of the cycle period of these polynomials consider. The sphenic polynomial cycle period defines as a parameter equal to the number of non-repeating subtractions computed on the linear-logarithmic scale of the group formed by the sphenic polynomial. The proposed algorithm is invariant to the characteristics of the Galois fields generated by the multipliers of sphenic polynomials. Numerous numerical examples confirm the correctness of the results. Directions for further research outlines.
Single-Shot Optical Neural Network

As deep neural networks (DNNs) grow to solve increasingly complex problems, they are becoming limited by the latency and power consumption of existing digital processors. For improved speed and energy efficiency, specialized analog optical and electronic hardware has been proposed, however, with limited scalability (input vector length $K$ of hundreds of elements). Here, we present a scalable, single-shot-per-layer analog optical processor that uses free-space optics to reconfigurably distribute an input vector and integrated optoelectronics for static, updatable weighting and the nonlinearity -- with $K \approx 1,000$ and beyond. We experimentally test classification accuracy of the MNIST handwritten digit dataset, achieving 94.7% (ground truth 96.3%) without data preprocessing or retraining on the hardware. We also determine the fundamental upper bound on throughput ($\sim$0.9 exaMAC/s), set by the maximum optical bandwidth before significant increase in error. Our combination of wide spectral and spatial bandwidths in a CMOS-compatible system enables highly efficient computing for next-generation DNNs.
Image encryption based on enhanced product trigonometric chaotic sequences

Image encryption based on chaotic sequence is an important hot research issue in the field of application at present. The quality of chaotic sequence, such as ergodicity, initial value sensitivity, dense periodicity and so on, is of great significance. In view of the good chaotic characteristics of trigonometric function system, this paper constructs a system of summation after product of two sinusoidal functions, and a system in which a linear function term is added after the product of trigonometric functions. The experimental analysis proves that the system has strong chaotic characteristics. Some equivalent forms of the system are discussed, and the chaotic sequences of the system are used for image encryption, and good encryption effect is achieved.