Reviewerly/exHarmony huggingface dataset:
Typical and atypical solutions in nonconvex neural networks with discrete and continuous weights

We study the binary and continuous negative-margin perceptrons as simple nonconvex neural network models learning random rules and associations. We analyze the geometry of the landscape of solutions in both models and find important similarities and differences. Both models exhibit subdominant minimizers which are extremely flat and wide. These minimizers coexist with a background of dominant solutions which are composed by an exponential number of algorithmically inaccessible small clusters for the binary case (the frozen 1-RSB phase) or a hierarchical structure of clusters of different sizes for the spherical case (the full RSB phase). In both cases, when a certain threshold in constraint density is crossed, the local entropy of the wide flat minima becomes nonmonotonic, indicating a breakup of the space of robust solutions into disconnected components. This has a strong impact on the behavior of algorithms in binary models, which cannot access the remaining isolated clusters. For the spherical case the behavior is different, since even beyond the disappearance of the wide flat minima the remaining solutions are shown to always be surrounded by a large number of other solutions at any distance, up to capacity. Indeed, we exhibit numerical evidence that algorithms seem to find solutions up to the SAT/UNSAT transition, that we compute here using an 1RSB approximation. For both models, the generalization performance as a learning device is shown to be greatly improved by the existence of wide flat minimizers even when trained in the highly underconstrained regime of very negative margins.
Augmented reality applications and the future library

Purpose This paper aims to investigate how augmented reality (AR) technology is adopted in the library to transform it into an immersive and interactive platform and highlights the future possibilities of the application of AR in libraries. Design/methodology/approach The study examines the current applications of AR in the library by consulting related literature and analyzing different library websites covering diverse geographical areas. Findings The paper with examples summarizes the current library practices of using AR technology and focuses on the AR-supported major library initiatives – right from document tracking, inventory management, wayfinding, supporting innovation and research, developing information literacy skills, etc. Originality/value The paper explores the possibility of improving user experience in the library by introducing AR applications and engaging users in an interactive and captivating environment.
The Smallness of Large Language Models

There is so much more to language and human beings than large language models can possibly master.
10 Ethical Dimensions of Research Information Management: A New Challenge for Information Professionals

The evaluation and monitoring of research performance are major chal­lenges of research management. Research information management (RIM) systems are designed to assess this performance and to contribute to the steady improve­ment of research. Based on former studies on RIM systems, a recent exploratory survey on ethical aspects of current research information systems and expert interviews with members of euroCRIS, the International Organisation for Research Information, this chapter provides an overview of ethical dimensions of RIM, together with original empirical evidence on how RIM systems represent ethical aspects of research projects and how their development, implementation and func­tioning should comply with usual ethical standards of scientific research. Special attention is paid to the role of academic librarians in RIM, in particular regarding the quality of bibliographic records, the standards of data and the management of institutional repositories, data repositories and other open archives.
Determining the Number of Ants in Ant Colony Optimization

The goal of this contribution article is to investigate the effect of the numbers of ants on the Ant Colony Optimization (ACO) metaheuristic's obtained solution while addressing the Traveling Salesman Problem. Within a restricted number of iterations, the purpose was to see how the duration of the calculated tours varied for various numbers of ants. Three well-known ACO algorithms: Elitist Ant System (EAS), Ranked Ant System (RAS), and Min-Max Ant System (MMAS), were developed and tested in this paper. The findings revealed comparable patterns across several test instances. EAS and RAS, both of which use specialized ants, demonstrated that the number of specialists had a significant impact on the duration of solutions. Normal ants, on the other hand, had no effect on the solutions. The response differed somewhat between EAS and MMAS, with a smaller number of ants being more preferred. When working with five specialists and ants, which are the same to the smart cities, however, RAS outperformed by a considerable margin.
A Tool Kit for Relation Induction in Text Analysis

Distances derived from word embeddings can measure a range of gradational relations— similarity, hierarchy, entailment, and stereotype—and can be used at the document- and author-level in ways that overcome some of the limitations of weighted dictionary methods. We provide a comprehensive introduction to using word embeddings for relation induction, and demonstrate how such techniques can complement dictionary methods as unsupervised, deductive methods.
RACFIS: A New Rapid Adaptive Complex Fuzzy Inference System for Regression Modelling

The theory of complex fuzzy sets has made great breakthroughs in recent times. Complex fuzzy theory (CFT) allows a fuzzy set to include more information with the help of its two-dimensional rule-base, which is of great potential for improving the related fuzzy system performance while managing the size of the associated rule-bases. In this paper, a new rapid adaptive complex fuzzy inference system (RACFIS) is proposed by redesigning the optimization policy of the earlier complex neuro-fuzzy system (CNFS) algorithm. Improvements include a new three-parameter Quasi-hyperbolic momentum (QHM) optimization method to replace the original particle swarm optimization (PSO), and unsupervised learning is introduced, for the first time, into the complex neuro-fuzzy model to pre-train the antecedent parameters for a better global optimum as well as a faster convergence. Experimental results show that RACFIS performs very well on all data sets, obtaining excellent prediction accuracies with on average 10 times lower epoch numbers (as compared with all benchmark models) and a reduction in the size of the rule-bases by nearly 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 30\%$</tex-math></inline-formula> (as compared with non-complex fuzzy models). RACFIS also possesses a strong generalization capability that outperforms all the benchmark algorithms employed in the simulation experiments. In addition, a mean impact value (MIV) algorithm based on radial basis function (RBF) network is also employed to select variables with higher relevance in order to mitigate the drawbacks caused by the higher dimensionality of the data.
Universal Domain Adaptation via Compressive Attention Matching

Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in how to determine whether the target samples belong to common categories. The mainstream methods make judgments based on the sample features, which overemphasizes global information while ignoring the most crucial local objects in the image, resulting in limited accuracy. To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information. The proposed framework introduces a novel Compressive Attention Matching (CAM) approach to explore the core information by compressively representing attentions. Furthermore, CAM incorporates a residual-based measurement to determine the sample commonness. By utilizing the measurement, UniAM achieves domain-wise and category-wise Common Feature Alignment (CFA) and Target Class Separation (TCS). Notably, UniAM is the first method utilizing the attention in vision transformer directly to perform classification tasks. Extensive experiments show that UniAM outperforms the current state-of-the-art methods on various benchmark datasets.
Unraveling Emotions: Contemporary Approaches in Sentiment Analysis

Sentiment analysis, an essential component of natural language processing, plays a pivotal role in deciphering public opinion and emotional cues within the vast sea of user-generated content on social media platforms. This paper presents a focused analysis on sentiment analysis leveraging two different techniques, namely VADER (Bag of Words approach) and the RoBERTa model, an extension of the BERT architecture, known for its outstanding performance in a wide range of Natural Language Processing (NLP) tasks. Model A achieved a 47% accuracy in sentiment classification, while Model B demonstrated a higher accuracy of 73%. The findings not only highlight the performance disparities between the two models but also offer insights into the factors contributing to their varying degrees of accuracy. This research underscores the significance of model selection in sentiment analysis tasks and contributes to a better understanding of their applicability in real-world scenarios
PENERAPAN LOGIKA FUZZY UNTUK SISTEM REKOMENDASI BERBASIS M-COMMERCE

Smartphones and tablets at this time became a popular gadgets and much liked by the community. Two of these gadgets have a wide range of brands and specifications. Many people who choose and buy two of these gadgets from the gadget specification and not a few of them who do not understand about the specifications owned two of these gadgets. Recommendation system is a system that can allow shoppers to assist in selecting a gadget based on the specifications of the gadgets owned. This system also can help buyers who do not understand about the gadget specification for the system made using the model Tahani Fuzzy Logic, Fuzzy Logic is a method that mimics human thinking by using the concept of vagueness nature of a value, so with the method of this system is expected to facilitate and can  well  understood  by  the  buyer.  This  recommendation  is  made  in  the  system  in  M - Commerce websites in order to facilitate buyers to access the system anywhere, anytime.
Mineral Chemistry of Silicate and Oxide Phases From Fertile Peridotite Equilibrated With A C-O-H Fluid Phase - a Low FO2 Data Set for the Evaluation of Mineral Barometers, Thermometers and Oxygen Sensors

To determine which particular mineral thermometer and barometer formulations provide the most accurate calibrations for natural spinel or garnet peridotite assemblages, one method is to test them against independent experimental data from multicomponent (natural-system) compositions (e.g.Carswell and Gibb, 1987a).Oxygen sensor formulations, involving ilmenite-or spinel-olivine-orthopyroxene equilibria, may be
Data on security requirements in open-source software projects

The datasets presented in this article are related the research paper entitled “A Linear Classifier Approach for Identifying Security Requirements in Open Source Software Development” Wang et al. (2018) [1]. This article describes requirements collected from three open-source software (OSS) projects and labels of security requirements. The datasets are made available to support automated security requirements analyzing tools development as well as tools’ evaluation.
Research on the Construction of Core Literacy of Information Technology Curriculum in the Age of Wisdom

With the rapid development of social economy, many kinds of science and technology, such as information technology, internet technology, cloud computing technology and so on, have been produced.These technologies are widely used in production and life of people at this stage.The remarkable application effect has made working efficiency and life quality of people greatly improved.Therefore, it is still necessary to apply advanced technology in the future intelligent city development to realize the pluralistic development of the society.At present, the market economy of our country presents a prosperous and competitive development situation.Therefore, if many enterprises want to stand firm in the fierce market competition, they need to absorb innovative information technology talents with core literacy.Only in this way can the strength of talents be brought into play and promote the enterprises to be stable and healthy development.However, by analyzing the teaching situation of the information technology courses offered by the school under the background of the wisdom age, it is found that teachers have many shortcomings and defects in the construction of the core literacy of the curriculum, which need to be improved urgently.Therefore, based on the development requirements of the wisdom era, this paper analyzes the basic situation of the core literacy construction of the current school information technology curriculum, and probes into the specific content of the core literacy construction, that is helpful for more schools to cultivate innovative talents and meet the needs of creative talents in the market.
Integrated Geophysical Research on Archeological Sites Without Relief Features (Ust-Tartas Mounds and Tartas-1)

Summary Comprehensive geophysical studies were conducted on two archaeological sites - Ust-Tartas barrows and Tartas-1. Magnetic gradient survey has been done here before, but its effectiveness has been reduced due to littering of the area and low contrast of magnetic properties. The paper shows that electrical and electromagnetic methods significantly complement the results of magnetic surveys. Electromagnetic profiling was used to solve search problems. A detailed study of the structure of the objects found was carried out by additional methods: electrotomography and multiple-height magnetic survey.
On the Use of Solar Power Sources in the Method of Partial Extraction of Metals

Summary The paper presents a new technique for deep-lying ore deposits prospecting based on a modification of the method of partial extraction of metals (in Russian-CHIM). Movable forms of elements are involved in the movement under the action of electric current in the vicinity of the partial extraction of metals station and accumulate in the elemental cell, thus achieving the selectivity of the analysis of the moving, migrating form of elements. The authors have developed a functioning model for the partial extraction of metals method, using a renewable resource - solar energy - as a source of electrical current. An assessment of the proposed method effectiveness for chemical elements extracting from the fixed (mineral) form was made experimentally in laboratory and field conditions, which showed the possibility of using the proposed modification at the current level of development of chemical analytical technologies. The use of solar cells gives to the method environmental and technological advantages over the classical implementation of the partial extraction of metals. Different geoelectrochemical methods of prospecting were compared and features of new technique were investigated.
AI platform in mammography to screening breast cancer: A sole-center experience.

e18052 {\rtf1\ansi\ansicpg936\cocoartf1347\cocoasubrtf570 {\fonttbl\f0\froman\fcharset0 Times-Roman;\f1\fnil\fcharset0 Calibri;\f2\fswiss\fcharset0 Helvetica; \f3\fnil\fcharset134 STHeitiSC-Light;\f4\froman\fcharset0 TimesNewRomanPSMT;} {\colortbl;\red255\green255\blue255;\red0\green0\blue0;} \paperw11900\paperh16840\margl1440\margr1440\vieww25400\viewh13600\viewkind0 \deftab720 \pard\pardeftab720 \f0\fs24 \cf2 \expnd0\expndtw0\kerning0 \outl0\strokewidth0 \strokec2 AI platform in mammagraphy to screening breast cancer: a sole center experence. \f1\fs21 \cf0 \kerning1\expnd0\expndtw0 \outl0\strokewidth0 \ \pard\pardeftab720\ri714\qj \cf0 Background: The aim of this study was to use deep convolutional neural network (DCNN) to relieve radiologists ' burden and to minimize misses and interpretation errors of suspicious lesions at digital mammography. Methods: We developed and trained the DCNN model on the training set, then validated it in an internal validation set. The labeled data were used for training the DCNN model, and the mass detection, calcification detection and benign and malignant diagnosis were interpreted respectively. After verifying the feasibility of the DCNN model in the diagnosis of breast cancer, we gradually increased the number of cases in the training set and established the verification set to test the training results. The latest training sets include 21100 atlas of our hospital between April 2010 and October 2017, among which the malignant case atlas is 1774. Each atlas contains 2 ipsilateral mammography images. The verification set includes 1307 atlas, among which the malignant case atlas is 248. Results: The DCNN model achieved high performance in identifying breast cancer patients in the validation sets tested, with sensitivity of 98.02%, specificity of 91.86% \f3 \'a3\'ac \f2 and area under the curve values of 0.9813. As for mass detection, we reached a recall rate of 89.6% on the premise of lowing the false positive to 0.168%. For the latest clinical data between October 2018 and November 2018 including 1043 atlas from 576 patients, the sensitivity was 78.6% (95%CI 62.8-89.1) versus 90.5% (95%CI 76.5 \f4 - \f2 96.9; p=0.021) and specificity was 93.4% (95%CI 91.6 \f4 - \f2 94.8) versus 82.7% (95%CI 80.2 \f4 - \f2 85 \f4 . \f2 0; p&lt;0.0001). Conclusions: The DCNN model showed improved specificity in diagnosis breast cancer compared with a group of skilled radiologists. As the number of cases in the training set increases, the capacity of DCNN model will be further improved.
Electrical Properties of Metal Oxides At. № ≤ 20 As Petrophysical, Raw Markers of Quality of Solid Mineral Resources (on the Example of the Ural Deposits of the Upland Type)

Summary Summary: The article Reveals why the development of mineral bases of solid minerals in the current situation with a tendency to mineral depletion is extremely important operational evaluation of commercial raw materials of high accuracy, the only one able to effectively solve the problem of energy and resource conservation in mining, to exclude economic harm from gross errors in the definition of varieties. Taking into account the conditions of the obvious effect of the General spectral nature of the petrophysical methods (measurements and signals), the factors in favor of the key, binding role of electrical conductivity observations (areal Electrometry to a small depth of mountain blocks) to confirm the actual, field data predicted deep inversion of metals properties. The additivity of the sum of oxides: K(+), Ca(2+), Na(+); and, on the contrary, the individual synergy of oxides: Mg(2+), Al(3+), we found, but not found in the known models of semiconductors: synthesis and minerals. On the examples of chemical types of poor raw materials and contacts changing in the deposits of various minerals the prospects of complex measurement algorithms with possibly implemented in the interpretation of approaches to the identification of metals (oxides) as markers of quality and enrichment are assumed. Application variants of use and results are briefly given. Keywords: losses of solid minerals, re-grading, raw materials assessment, semiconductor markers in electrical conductivity, electrometry, exploration and calculations of enrichment, energy- and mineral-saving.
Perspectives of Gpr Application in the Geophysical Complex for Prospecting and Exploration of Alluvial Gold Deposits

Summary One of the ways to improve the mining and geological work at placers is a wider application of geophysical research, the role of which is currently clearly insufficient. We propose the complex of geophysical investigations to solve problems arising from prospecting gold deposits of various morphogenetic types. Depending on the tasks, it may include georadar sensing, electric profiling, electric sounding and magnetometric survey. In the course of the work, it was found that the most effective (in terms of productivity and costs) type of electrical exploration, especially when solving problems related to the evaluation of loose sediment thickness and determining the morphology of a hard base relief, is GPR profiling. Geophysical work was carried out at several test sites in the Magadan region, the Northern Urals and the Republic of Tanzania where industrial mining of placer gold is planned. This complex is quite efficient and cheap and can be recommended before specifying the specific boundaries of landfill mining sites.
Results of Frequency Electromagnetic Soundings on the Chernorud-2 Area in the Valley (or the Plot of Land) Burun-Khal

Summary The purpose of this work is to test the Nemfis electromagnetic scanner on the archaeological survey of Priolkhonia (western Baikal region). Frequency electromagnetic sounding method, implemented in Nemfis scanner, intended for research positional relation electrical resistivity to a depth of 10 m. Relevance of the work is to evaluate the applicability of frequency electromagnetic sounding for mapping the near-surface section for solving archaeological problems in conditions of significant conductivity contrast of the earth material, lying in the foundation. In the summer of 2017, work was field trial work the purpose of which was to the effect extraction of a thin near-surface layer of the purpose of which was to isolate the effect of a thin near-surface layer of quaternary deposits on the background of significant anomalies caused by contrasting conduction primary rocks. Researches were conducted in the plot of land Bul-Durun near the student practice base of INRTU "Chernorud" (Shara-Togot village, Olkhon district, Irkutsk region).Detailed exploratory survey with a step of one 1 m. per 1 m. was performed on a reference area of 100 m. per 40 m. (Fig. 1). Areal survey data were processed using the program GeliosSMI-64 [ 1 ]. In progress of processing, using regression analysis was minimized background effect from rocks with contrasting conduction and near-surface anomalies were isolated, including from archaeological objects. As a result of experimental work has been developed processing technology of areal data frequency scan to detect weakly contrasting archaeological objects in the background of significant influence of bedrock.
Research on the Cultivation of Innovative and Entrepreneurial Talents in Application-oriented Colleges under the Background of “Supply-side” Reform: A Case Study of Wuhan Business University

In the era of "innovation and entrepreneurship", studies on its development have been increasing under the background of supply-side reform.Therefore, the paper takes the research on innovation and entrepreneurship development in Wuhan Business University as a breakthrough point, summarizing problems of the talents cultivation in application-oriented colleges and universities through a questionnaire.Furthermore, under the background of supply-side reform, it can effectively contribute to the supply of talents to meet social needs by revising the cultivation mode of innovative and entrepreneurial talents, optimizing the educational teaching system, and improving the quality of talents.