Reviewerly/exHarmony huggingface dataset:
Subjective Assessment of the Effective Contract Impact in Universities on Improving the Indicators of Lectures` Research Work

Article shows the results of a sociological survey aimed at studying the opinion of the teaching staff on the impact of an effective contract on improving the research work indicators in universities in Russia and Belarus. The survey analyzes foreign and Russian evaluation of academic staff performance. A cross-sectional (single-point) study was the design. The focus of research was the academic staff of educational institutions that went over to an effective contract (Northern State Medical University, Russia) and without the implementation of an effective contract (Tobolsk Pedagogical Institute named after D. I. Mendeleev (branch) the University of Tyumen, Russia; National Institute for Higher Education and Belarusian State Pedagogical University named after Maxim Tank, Belarus), the overall number of respondents is 395. A semi-structured questionnaire created by the National Research University Higher School of Economics was used as a tool for sociological research. The study showed that both study groups are aware of an effective contract purpose and criteria evaluation frequency. Respondents from universities without an effective contract receive information from the Internet and have overestimated expectations of the future income level. Respondents of both groups underlined the importance of determining the greatest weight in calculating incentive payments for publication and grant activity, participation in scientific conferences. When implementing an effective contract, not all lecturers are ready to fulfill the criteria for research work, but they note the positive effect of its implementation on the indicators of scientific activity. In the group of respondents without an effective contract, one third expressed doubts about the significant impact of the implementation of an effective contract on improving the performance of research work. This article will be useful to the management of universities when calculating weighting coefficients according to indicators and criteria for evaluating the activities of the teaching staff for the appointment of incentive payments. An interesting study in the future may be a comparison of subjective assessments obtained in universities without an effective contract, and after its implementation in the same universities.
Augmenting Human Memory through Cognition-Aware AI-based Technologies

Memory is a cognitive ability used in our everyday lives. However, our memory capabilities eventually decline and the adaptive nature of these capabilities results in memory troubles. With the growth of the ageing population, new requirements and opportunities arise. In this talk, I introduce how we can augment memory in complex everyday situations through systems that are aware of our cognitive contexts and interfaces that integrate with our perceptions and behaviours. These systems sense cognitive contexts, such as cognitive load and visual recognition processes, using physiological signals (biosignals) and provide support by enhancing our memory through memory training and assisting with memory tasks. I will also describe our work to encourage user receptivity to these interventions, for example, using familiar AI-generated voices for reminders. This talk offers new perspectives and design insights on supporting our cognitive abilities and experiences.
Towards distributed bitruss decomposition on bipartite graphs

Mining cohesive subgraphs on bipartite graphs is an important task. The k -bitruss is one of many popular cohesive subgraph models, which is the maximal subgraph where each edge is contained in at least k butterflies. The bitruss decomposition problem is to find all k -bitrusses for k ≥ 0. Dealing with large graphs is often beyond the capability of a single machine due to its limited memory and computational power, leading to a need for efficiently processing large graphs in a distributed environment. However, all current solutions are for a single machine and a centralized environment, where processors can access the graph or auxiliary indexes randomly and globally. It is difficult to directly deploy such algorithms on a shared-nothing model. In this paper, we propose distributed algorithms for bitruss decomposition. We first propose SC-HBD as the baseline, which uses H -function to define bitruss numbers and computes them iteratively to a fix point in parallel. We then introduce a subgraph-centric peeling method SC-PBD, which peels edges in batches over different butterfly complete subgraphs. We then introduce local indexes on each fragment, study the butterfly-aware edge partition problem including its hardness, and propose an effective partitioner. Finally we present the bitruss butterfly-complete subgraph concept, and divide and conquer DC-BD method with optimization strategies. Extensive experiments show the proposed methods solve graphs with 30 trillion butterflies in 2.5 hours, while existing parallel methods under shared-memory model fail to scale to such large graphs.
On construction of quantum codes with dual-containing quasi-cyclic codes

One of the main objectives of quantum error-correction theory is to construct quantum codes with optimal parameters and properties. In this paper, we propose a class of 2-generator quasi-cyclic codes and study their applications in the construction of quantum codes over small fields. Firstly, some sufficient conditions for these 2-generator quasi-cyclic codes to be dual-containing concerning Hermitian inner product are determined. Then, we utilize these Hermitian dual-containing quasi-cyclic codes to produce quantum codes via the famous Hermitian construction. Moreover, we present a lower bound on the minimum distance of these quasi-cyclic codes, which is helpful to construct quantum codes with larger lengths and dimensions. As the computational results, many new quantum codes that exceed the quantum Gilbert-Varshamov bound are constructed over $$F_q$$ , where q is 2, 3, 4, 5. In particular, 16 binary quantum codes raise the lower bound on the minimum distance in Grassl's table (Grassl in bounds on the minimum distance of linear codes and quantum codes. http://www.codetables.de ). In nonbinary cases, many quantum codes are new or have better parameters than those in the literature.
A Critical Literature Review of Historic Scientific Analog Data: Uses, Successes, and Challenges

For years scientists in fields from climate change to biodiversity to hydrology have used older data to address contemporary issues. Since the 1960s researchers, recognizing the value of this data, have expressed concern about its management and potential for loss. No widespread solutions have emerged to address the myriad issues around its storage, access, and findability. This paper summarizes observations and concerns of researchers in various disciplines who have articulated problems associated with analog data and highlights examples of projects that have used historical data. The authors also examined selected papers to discover how researchers located historical data and how they used it. While many researchers are not producing huge amounts of analog data today, there are still large volumes of it that are at risk. To address this concern, the authors recommend the development of best practices for managing historic data. This will take communication across disciplines and the involvement of researchers, departments, institutions, and associations in the process.
A High-Precision Recognition Method of Circular Marks Based on CMNet Within Complex Scenes

Accurate recognition of circular marks is crucial for calibration, object tracking, and three-dimensional reconstruction in videogrammetry. However, most existing studies were designed under single or relatively simple scenes. When the existing algorithms are applied to more complex scenarios, it will result in higher false detection and miss-detection rate. In this article, we present a high-precision recognition method based on a novel deep learning model, Circular-MarkNet (CMNet) to solve this problem. The proposed network consists of three main steps: first, circular marks are detected using the improved YOLOv4 model to narrow the search region of the circular contour; the contour of the circular marks is then extracted based on the saliency object detection model BASNet; and finally, least square fitting (LSF) is used to calculate the central pixel coordinate of the identified contour on the saliency map. The proposed method was tested under three complex scenarios with different characteristics and disturbances. The experimental results demonstrated that: (1) the proposed CMNet can effectively recognize of circular marks within complex scenes, which reveals the superiority and generalization ability of the proposed method; (2) the improved YOLOv4 can significantly enhance the detection accuracy of circular marks, which is crucial to the subsequent saliency courter detection and circle center identification; (3) Circular-MarkNet achieved the best performance, with an RMSE of 0.0713 pixel, compared to the state-of-the-art methods.
A Discriminative Hierarchical PLDA-Based Model for Spoken Language Recognition

Spoken language recognition (SLR) refers to the automatic process used to determine the language present in a speech sample. SLR is an important task in its own right, for example, as a tool to analyze or categorize large amounts of multi-lingual data. Further, it is also an essential tool for selecting downstream applications in a work flow, for example, to chose appropriate speech recognition or machine translation models. SLR systems are usually composed of two stages, one where an embedding representing the audio sample is extracted and a second one which computes the final scores for each language. In this work, we approach the SLR task as a detection problem and implement the second stage as a probabilistic linear discriminant analysis (PLDA) model. We show that discriminative training of the PLDA parameters gives large gains with respect to the usual generative training. Further, we propose a novel hierarchical approach where two PLDA models are trained, one to generate scores for clusters of highly-related languages and a second one to generate scores conditional to each cluster. The final language detection scores are computed as a combination of these two sets of scores. The complete model is trained discriminatively to optimize a cross-entropy objective. We show that this hierarchical approach consistently outperforms the non-hierarchical one for detection of highly related languages, in many cases by large margins. We train our systems on a collection of datasets including over 100 languages, and test them both on matched and mismatched conditions, showing that the gains are robust to condition mismatch.
Research on Robot Path Planning Based on Improved Genetic Algorithm

In order to solve the problems of slow convergence speed and easy to fall into local optimum in solving the robot path planning problem, this paper improves the basic genetic algorithm. This paper introduces the artificial potential field method to initialize the population, and proposes an adaptive selection method based on the evaluation of the degree of population diversity. The adaptive crossover probability and mutation probability are designed to improve the algorithm solution quality, and multiple simulations are carried out in the grid environment to further prove the feasibility and effectiveness of the algorithm.
An Enhanced Method for Encrypting Image and Text Data Simultaneously using AES Algorithm and LSB-Based Steganography

Secure data transmission in the virtual world is becoming increasingly difficult due to the activities of hackers on confidential and sensitive information. To avoid hacking at various levels, there exists a significant number of techniques for data transmission. Data security issues have led to developing a stronger algorithm in cryptography and steganography which have also led us to develop a secure data transmission model by concealing both image and text data simultaneously through the Advanced Encryption Standard (AES) algorithm and LSB-based image steganography. To develop a robust and effective model we have followed four steps in both sender and receiver end for enhancing the data security. On the sender side, first, the targeted image is encrypted using AES then the targeted text data is concealed within the AES encrypted image based on the LSB algorithm. The data security is increased further by masking this blended text image with a cover image using LSB-based image steganography. Finally, the Stego image is split and indexed for secure data transmission from the sender end. Since decryption is the reverse mathematical process of encryption, at the receiving end, inverse steps are performed sequentially to retrieve the image and text of interest. Histogram analysis at a different stage has shown a better realization of the effect of encryption and decryption whereas various comparison indexes such as SSIM, PSNR, MS-SSIM, MSE between the original and retrieved image provide acceptable data integrity after decryption of the input data.
A colorization-based anisotropic variational model for vector-valued image compression

Image compression is an important technology in digital image processing. In this paper, a novel colorization-based codec for vector-valued images is proposed. In compression, we first define the concept of 'structure image', which contains rich geometric structure information of the vector-valued image. Then, to extract representative pixels from the original vector-valued image, a 'one-iteration method' is proposed. It can tremendously improve compression efficiency. In decompression, starting from colorizing the structure image, an anisotropic variational model is proposed. The existence and uniqueness of minimizers for the proposed variational model are established. Besides, we develop a fast and efficient algorithm for solving the model numerically by employing the scaled form of the alternating direction method of multipliers (ADMM). Numerical experiments on natural color images demonstrate that the proposed method outperforms the state-of-art colorization-based image compression method. Compared with the transform-based approaches, experiments on satellite multispectral images illustrate that the proposed method is superior to the JPEG and JPEG2000 standards.
Using Natural Language Relations between Answer Choices for Machine Comprehension

Rajkumar Pujari, Dan Goldwasser. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Scalable Blockchain Storage Model Based on DHT and IPFS

Blockchain is a distributed ledger that combines technologies such as cryptography, consensus mechanism, peer-to-peer transmission, and time stamping.The rapid development of blockchain has attracted attention from all walks of life, but storage scalability issues have hindered the application of blockchain.In this paper, a scalable blockchain storage model based on Distributed Hash Table (DHT) and the InterPlanetary File System (IPFS) was proposed.This paper introduces the current research status of the scalable blockchain storage model, as well as the basic principles of DHT and the InterPlanetary File System.The model construction and workflow are explained in detail.At the same time, the DHT network construction mechanism, block heat identification mechanism, new node initialization mechanism, and block data read and write mechanism in the model are described in detail.Experimental results show that this model can reduce the storage burden of nodes, and at the same time, the blockchain network can accommodate more local blocks under the same block height.
Energy Efficient VM Consolidation Technique in Cloud Computing Using Cat Swarm Optimization

Effective consolidation of VMS is necessary in cloud computing to pack and keep the VMs in the corresponding physical machines. It is a challenging task as incoming tasks are variable and changes with respect to time. To handle this scenario, a consolidation mechanism is needed to bundle the VMs in physical machines automatically without human intervention. Earlier authors proposed various consolidation mechanisms by using different heuristics and addressed metrics such as makespan, throughput, and execution time but authors have not addressed metrics named as energy consumption as it is one of the important metric in cloud computing. Minimization of energy consumption in cloud computing helpful in perspectives of both cloud user and provider. In the proposed approach, we have used cat swarm optimization algorithm to address metrics named as makespan and energy consumption and it is simulated on Cloudsim. The simulation results were compared against algorithms named as PSO and CS, and results revealed that proposed approach is showing significant impact over existing algorithms for the mentioned metrics named as makespan and energy consumption.
Towards Fair and Decentralized Federated Learning System for Gradient Boosting Decision Trees

At present, gradient boosting decision trees (GBDTs) has become a popular machine learning algorithm and has shined in many data mining competitions and real-world applications for its salient results on classification, ranking, prediction, etc. Federated learning which aims to mitigate privacy risks and costs, enables many entities to keep data locally and train a model collaboratively under an orchestration service. However, most of the existing systems often fail to make an excellent trade-off between accuracy and communication. In addition, they overlook an important aspect: fairness such as performance gains from different parties’ datasets. In this paper, we propose a novel federated GBDT scheme based on the blockchain which can achieve constant communication overhead and good model performance and quantify the contribution of each party. Specifically, we replace the tree-based communication scheme with the pure gradient-based scheme and compress the intermediate gradient information to a limit to achieve good model performance and constant communication overhead in skewed datasets. On the other hand, we introduce a novel contribution allocation scheme named split Shapley value, which can quantify the contribution of each party with a limited gradient update and provide a basis for monetary reward. Finally, we combine the quantification mechanism with blockchain organically and implement a closed-loop federated GBDT system FGBDT-Chain in a permissioned blockchain environment and conduct a comprehensive experiment on public datasets. The experimental results show that FGBDT-Chain achieves a good trade-off between accuracy, communication overhead, fairness, and security under large-scale skewed datasets.
About this title - Recent Advances in Understanding Gold Deposits: from Orogeny to Alluvium

Gold occurs in many settings, but the dynamic nature of Earth's crust means overlapping and overprinting deposit styles are common. Characterization of mineralization from an early stage becomes important, particularly where the mineralization is complex, in order to maximize exploration and project development success and mining productivity. Various techniques are used at different stages of a project to characterize gold deposits. This Special Publication offers a cross-section of some specific techniques used to investigate a variety of gold deposit types. The papers highlight both the breadth of the available techniques and their utility in deposit characterization, but also the many significant remaining questions and problems related to the exploration and research of gold deposits. Several papers include suggestions of avenues for fruitful further research, including a paper discussing a new approach to classifying orogenic gold deposits, and a paper describing archaeological applications of natural gold analyses.
Сhallenges for scientific and pedagogical staff of universities after pandemia 2019

Nowadays situation at the universities in Ukraine and all other countries round the world are faceted with different challenges almost every new quarter of the year. The very vivid example of it was the pandemic period, which determined transforming of forms of education and different approaches to education. Moreover, the Pandemia provoke the huge wave of challenges for scientific and pedagogical staff of universities. A great number of changes took place at the system of educational process organization: pandemic has exacerbated the need for digital, technology-enabled education experiences, new types of online-classes appared and new ways to scale them etc. But after pandemic period and all these transmitions in universities their appared the need for analyses of challenges which we are going to face with, when we come back to ordinary style of teaching? what problems are we going to solve? To answer these questions, we created a Questionnaire for teaching staff and students of different Universities in Ukraine in different regions. We asked them to give their feedbacks, opinions and feelings of the quarantine restrictions of COVID-19, what difficulties they had durining the next few months. The investigation helped to distinguish challenges to scientific and pedagogical staff of higher educational institutions, the nature of which is the peculiarities of the professional activities of teachers and challenges to the scientific and pedagogical staff of higher educational institutions, the nature of which are the features of the educational activities of students.
Soft Skills formation in professional-oriented foreign language education at higher education institutions

The article aims to analyze the constituent characteristics of soft skills and evaluate students' existing skills and their readiness to receive professionally-oriented foreign language training. General scientific methods of cognition are used for the study, including analysis of foreign investigations, periodicals, and surveys conducted by experts and scientists. During the study, the requirements of employers to the personnel for the qualities of soft skills in students are determined. The results show that in foreign language teaching, insufficient attention is paid to the education of these qualities. The study results are of great importance for the higher education teachers forming methodological developments in fostering these soft skills among students.
U.S. Geological Survey Core Research Center: a gateway to subsurface discovery for geoscience research

Abstract The U.S. Geological Survey (USGS) operates the Core Research Center (CRC) in Denver, Colorado, USA, a public access repository of rock cores from over 9800 wells and drill cuttings from over 53 000 wells, primarily from states in or adjacent to the Rocky Mountain Region. Annually, approximately 1400 visitors use the collection for traditional and innovative research. The CRC has an online, searchable database which includes downloadable core photos, analytical data, and thin-section images. When visitors sample for analyses, the results must be returned to the CRC for public dissemination providing immediate, free access to users while sparing the finite, irreplaceable collection from redundant testing. A representative quantity of every core depth is preserved in perpetuity. Studies on CRC materials, paired with new extraction methods, have unlocked new productive deposits. Materials drilled and curated decades ago remain in high demand while materials receiving little attention today may be crucial for future research. The collection provides immediate, inexpensive access to subsurface materials at a fraction of the cost of new drilling, sparing money, time and environmental impacts.