Reviewerly/exHarmony huggingface dataset:
PI(t)D(t) Control and Motion Profiling for Omnidirectional Mobile Robots

Recently, a trend is emerging toward human-servicing autonomous mobile robots, with diverse applications including delivery of supplies in hospitals, hotels, or labs where personnel are scarce, or reacting to indoor emergencies. However, existing autonomous mobile robot (AMR) motion is slow and inefficient, a foundational barrier to proliferation of human-servicing applications. This research has developed a motion control architecture that demonstrates the potential of several algorithms for increasing speed and efficiency. These include a novel PI(t)D(t) controller that sets integral and derivative gains as functions of time, and motion-profiling applied for holonomic motion. Resulting performance indicates potential for faster, more efficient AMRs, that maintain high levels of accuracy and repeatability. The hope is that this research can serve as a proof of concept for faster motion-control, to remove a key barrier to further use of human-servicing mobile robots.
Career Guidance and Counseling and Personal Career Management – New Competencies for Pedagogical Specialists

The article analyzes main documents from the legislation of secondary education. The new function of pedagogical specialists „career guidance and counseling“ has been highlighted. Emphasis is placed on the professional portfolio as a tool for career development. The problem of the formation of career guidance and counseling competences and career management competencies is outlined. Good practices in basic university training of pedagogues in relation to the formation of these competences are presented. These include updating curricula and programs, introducing new courses. Recommendations and conclusions are formulated.
Usage analysis of SVD, DWT and JPEG compression methods for image compression

Through image compression, can save bandwidth usage on telecommunication networks, accelerate image file sending time and can save memory in image file storage. Technique to reduce image size through compression techniques is needed. Image compression is one of the image processing techniques performed on digital images with the aim of reducing the redundancy of the data contained in the image so that it can be stored or transmitted efficiently. This research analyzed the results of image compression and measure the error level of the image compression results. The analysis to be carried out is in the form of an analysis of JPEG compression techniques with various types of images. The method of measuring the compression results uses the MSE and PSNR methods. Meanwhile, to determine the percentage level of compression using the compression ratio calculation. The average ratio for JPEG compression was 0.08605, the compression rate was 91.39%. The average compression ratio for the DWT method was 0.133090833, the compression rate was 86.69%. The average compression ratio of the SVD method was 0.101938833 and the compression rate was 89.80%.
Recommendation System Using User-Based Collaborative Filtering and Spectral Clustering

Recommendation systems are tools that can solve this problem by classifying people using User-Based Collaborative Filtering and Spectral Clustering approaches, resulting in more accurate recommendations.Preprocessing the data is the first step in the recommendation process, after which the data is grouped using the Spectral Clustering method.In the process of creating rating predictions and film recommendations based on similarity derived using the Pearson Correlation and Cosine Similarity algorithms, the clustering results are used to determine which users will be neighbors.Based on system experiments that have been conducted using variations in the number of clusters 3, 5, and 7, variations in the number of neighbors 1, 2, 3, 5, and 10, and comparing the results of MAE calculations of rating prediction results using a combination of spectral clustering methods with Pearson correlation, spectral Clustering with cosine similarity, with Pearson correlation and with cosine similarity, get results where the combination of methods with cosine similarity using 3 clusters and two neighbors becomes the method that has the best accuracy in making movie recommendations, namely with an MAE value of 0.3114.This is because the combination of methods has the smallest MAE calculation value.In other words, it has a minimal recommendation error rate.Meanwhile, the recommendation system with spectral Clustering only gets an MAE value of 0.3611, namely by a combination of spectral clustering methods with cosine similarity using 5 clusters and two neighbors.The result of the accuracy of the combination of spectral clustering method with Pearson correlation gets the lowest MAE value of 0.4109 with an average MAE value of 1,159, the combination of spectral clustering method with cosine similarity gets the lowest MAE value of 0.3611 with an average MAE value of 1,190, the combination of a method with Pearson correlation gets the lowest MAE value of 0.4711 with an average MAE value of 0.911 and the combination of methods with cosine similarity get the lowest MAE value of 0.3114 with an average MAE value of 0.807.
Making Document-Level Information Extraction Right for the Right Reasons

Document-level models for information extraction tasks like slot-filling are flexible: they can be applied to settings where information is not necessarily localized in a single sentence. For example, key features of a diagnosis in a radiology report may not be explicitly stated in one place, but nevertheless can be inferred from parts of the report's text. However, these models can easily learn spurious correlations between labels and irrelevant information. This work studies how to ensure that these models make correct inferences from complex text and make those inferences in an auditable way: beyond just being right, are these models "right for the right reasons?" We experiment with post-hoc evidence extraction in a predict-select-verify framework using feature attribution techniques. We show that regularization with small amounts of evidence supervision during training can substantially improve the quality of extracted evidence. We evaluate on two domains: a small-scale labeled dataset of brain MRI reports and a large-scale modified version of DocRED (Yao et al., 2019) and show that models' plausibility can be improved with no loss in accuracy.
Assessment of Ecology-Economic Efficiency in Providing Thermal Stabilization of Biogas Installations

Formalization and hierarchical classification of quantity and quality factors were performed in order to build an expert modeling system of multifactor analysis of the factors, which influence the ecological and economic indicators of the mechanisms that enable thermal stabilization in biogas installations. The quantity and quality factors are important and significantly help determine the innovative appeal of a bioconversion project variant. They allow constructing a tree of inference of influencing factors as linguistic variables on ecological and economic assessment of the project variant. The proposed system of fuzzy logical equations corresponds to linguistic variables and estimation terms according to the tree of inference. The system characterizes the knowledge base by means of expert statements about the links between fuzzy terms of input and output variables, which allow assessing an ecology-economic efficiency.
Stabilizing Dynamical Systems via Policy Gradient Methods

Stabilizing an unknown control system is one of the most fundamental problems in control systems engineering. In this paper, we provide a simple, model-free algorithm for stabilizing fully observed dynamical systems. While model-free methods have become increasingly popular in practice due to their simplicity and flexibility, stabilization via direct policy search has received surprisingly little attention. Our algorithm proceeds by solving a series of discounted LQR problems, where the discount factor is gradually increased. We prove that this method efficiently recovers a stabilizing controller for linear systems, and for smooth, nonlinear systems within a neighborhood of their equilibria. Our approach overcomes a significant limitation of prior work, namely the need for a pre-given stabilizing control policy. We empirically evaluate the effectiveness of our approach on common control benchmarks.
Rethinking Person Re-Identification via Semantic-Based Pretraining

Pretraining is a dominant paradigm in computer vision. Generally, supervised ImageNet pretraining is commonly used to initialize the backbones of person re-identification (Re-ID) models. However, recent works show a surprising result that CNN-based pretraining on ImageNet has limited impacts on Re-ID system due to the large domain gap between ImageNet and person Re-ID data. To seek an alternative to traditional pretraining, here we investigate semantic-based pretraining as another method to utilize additional textual data against ImageNet pretraining. Specifically, we manually construct a diversified FineGPR-C caption dataset for the first time on person Re-ID events. Based on it, a pure semantic-based pretraining approach named VTBR is proposed to adopt dense captions to learn visual representations with fewer images. We train convolutional neural networks from scratch on the captions of FineGPR-C dataset, and then transfer them to downstream Re-ID tasks. Comprehensive experiments conducted on benchmark datasets show that our VTBR can achieve competitive performance compared with ImageNet pretraining - despite using up to 1.4x fewer images, revealing its potential in Re-ID pretraining.
The Effect of ICT Utilization Competency on the Psychological Well-being of the Elderly in the Post-Corona Era: Focusing on the Self-Esteem of the Elderly

The study assesses how levels of competency in using smart devices and actual use of social media services were associated with elderly people’s self-esteem. Given that the Covid-19 pandemic remains an ongoing phenomenon, social interactions using smart devices appears to be more relevant to the psychological well-being of the elderly than ever before. The study utilized the Korean Media Panel Survey collected by Korea Information Society Development Institute (KISDI) in 2018 and 2020. The study found that elderly people who were able to use smart devices tended to use more internet media services and reported higher levels of self-esteem than those who did not. In particular, respondents who experienced increasing competency in their use of smart devices reported high levels of self-esteem. In addition, older adults living alone were less likely to access social media and more vulnerable in terms of psychological well-being. The study suggests that social services that improve competency in the use of smart devices and encourage social media use can enhance psychological well-being among the elderly, who may have difficulties in maintaining social interactions.
SQptr a Proposed Set of Guidelines for Setting Up Proceduring for Enabling Solution Development

We propose a set of guidelines under the name SQptr (pronounced as ‘Skepter’) that enable proceduring that is a planning stage just before actual development of an application. The guidelines are independent of the underlying language used for development. Universal testing of guidelines is a work in progress We demonstrate the fundamental idea through examples.
Features of the social situation of first-grader development in the context of humanistic education

The article presents a scientific analysis of the problems of the social situation of the development of students in the context of the humanistic education paradigm, characterizes the features of the social situation of the development of a first grader in the context of humanistic education. During the study, the authors carried out a theoretical analysis of the literature on the research problem, which allowed to identify: age and psychological characteristics of the social situation of the development of a first grader; the main directions of diagnosing the features of the social situation of the development of first-grade students. The results obtained contribute to the creation of an effective social situation for the development of a first-grader within the framework of the organization of the educational process in the humanistic education paradigm.
A Vision-Based Real-Time Driver Identity Recognition and Attention Monitoring System

In the current decade, many automobile crashes are occurred due to the non-professional drivers and the negligence of legal drivers during driving. To reduce the automobile crashes, illegal driver recognition and legal driver’s attention estimation requires significant research attention in the field of computer vision. In this paper, a driver recognition and assistance system is proposed to monitor the driver’s attention and drowsiness based on face and eyes tracking. For that, initially, the driver is recognized before starting the automobile through SVM, where features are extracted by using uniform LBP and Gabor filters. After recognize the driver, the driver is allowed to drive. Furthermore, from the recognized face, the eye’s pupils are detected and tracked to estimate the attention of the driver. Here, color feature is utilized to track the face and eyes through the mean shift algorithm. The system generates the alarm for the illegal driver and awareness alarm of the driver in the case of driver face angle and eye’s fatigue. The effectiveness of the proposed system is demonstrated through the real-time experiment. The proposed system is evaluated in the different lighting conditions and presented outcomes demonstrate the adequacy.
Few-Shot Bot: Prompt-Based Learning for Dialogue Systems

Learning to converse using only a few examples is a great challenge in conversational AI. The current best conversational models, which are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL), are language models (LMs) fine-tuned on large conversational datasets. Training these models is expensive, both in terms of computational resources and time, and it is hard to keep them up to date with new conversational skills. A simple yet unexplored solution is prompt-based few-shot learning (Brown et al. 2020) which does not require gradient-based fine-tuning but instead uses a few examples in the LM context as the only source of learning. In this paper, we explore prompt-based few-shot learning in dialogue tasks. We benchmark LMs of different sizes in nine response generation tasks, which include four knowledge-grounded tasks, a task-oriented generations task, three open-chat tasks, and controlled stylistic generation, and five conversational parsing tasks, which include dialogue state tracking, graph path generation, persona information extraction, document retrieval, and internet query generation. The current largest released LM (GPT-J-6B) using prompt-based few-shot learning, and thus requiring no training, achieves competitive performance to fully trained state-of-the-art models. Moreover, we propose a novel prompt-based few-shot classifier, that also does not require any fine-tuning, to select the most appropriate prompt given a dialogue history. Finally, by combining the power of prompt-based few-shot learning and a Skill Selector, we create an end-to-end chatbot named the Few-Shot Bot (FSB), which automatically selects the most appropriate conversational skill, queries different knowledge bases or the internet, and uses the retrieved knowledge to generate a human-like response, all using only few dialogue examples per skill.
Distance education as a part of the educational process during the quarantine at universities

The study aims to assess the effectiveness of the current distance learning process in the higher education institutions, to identify the types of distance learning, negative and positive aspects of its implementation, to describe the prospects and approaches to solving the problems of distance education at universities. The study was based on the analysis of the questionnaire survey of lecturers throughout Ukraine and the Kyrgyz Republic results, which were received in the period from March to April 2020. The novelty of the research conducted in the article was to find solutions to the emerging difficulties in the pedagogical staff of Ukraine and the Kyrgyz Republic during the first periods of the pandemic outbreak. The proposed methods can be used to achieve long-term practical results in providing support to higher education institutions, their leaders, lecturers and students in organizing distance learning, as well as to overcome barriers to lecturers' access to information on the usage of digital tools for distance learning.
Ethnopsychological Characteristics of Motivation of Business Activity

Entrepreneurial psychology basically does not have its own purely specific methods. This article analyzes the results of research conducted using common methods, as in other branches of entrepreneurial psychology.
Domain Generalization via Domain-based Covariance Minimization

Researchers have been facing a difficult problem that data generation mechanisms could be influenced by internal or external factors leading to the training and test data with quite different distributions, consequently traditional classification or regression from the training set is unable to achieve satisfying results on test data. In this paper, we address this nontrivial domain generalization problem by finding a central subspace in which domain-based covariance is minimized while the functional relationship is simultaneously maximally preserved. We propose a novel variance measurement for multiple domains so as to minimize the difference between conditional distributions across domains with solid theoretical demonstration and supports, meanwhile, the algorithm preserves the functional relationship via maximizing the variance of conditional expectations given output. Furthermore, we also provide a fast implementation that requires much less computation and smaller memory for large-scale matrix operations, suitable for not only domain generalization but also other kernel-based eigenvalue decompositions. To show the practicality of the proposed method, we compare our methods against some well-known dimension reduction and domain generalization techniques on both synthetic data and real-world applications. We show that for small-scale datasets, we are able to achieve better quantitative results indicating better generalization performance over unseen test datasets. For large-scale problems, the proposed fast implementation maintains the quantitative performance but at a substantially lower computational cost.
Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach

Distance-based outlier detection is widely adopted in many fields, e.g., data mining and machine learning, because it is unsupervised, can be employed in a generic metric space, and does not have any assumptions of data distributions. Data mining and machine learning applications face a challenge of dealing with large datasets, which requires efficient distance-based outlier detection algorithms. Due to the popularization of computational environments with large memory, it is possible to build a main-memory index and detect outliers based on it, which is a promising solution for fast distance-based outlier detection. Motivated by this observation, we propose a novel approach that exploits a proximity graph. Our approach can employ an arbitrary proximity graph and obtains a significant speed-up against state-of-the-art. However, designing an effective proximity graph raises a challenge, because existing proximity graphs do not consider efficient traversal for distance-based outlier detection. To overcome this challenge, we propose a novel proximity graph, MRPG. Our empirical study using real datasets demonstrates that MRPG detects outliers significantly faster than the state-of-the-art algorithms.
The Privacy-preserving Padding Problem: Non-negative Mechanisms for Conservative Answers with Differential Privacy

Differentially private noise mechanisms commonly use symmetric noise distributions. This is attractive both for achieving the differential privacy definition, and for unbiased expectations in the noised answers. However, there are contexts in which a noisy answer only has utility if it is conservative, that is, has known-signed error, which we call a padded answer. Seemingly, it is paradoxical to satisfy the DP definition with one-sided error, but we show how it is possible to bury the paradox into approximate DP's delta parameter. We develop a few mechanisms for one-sided padding mechanisms that always give conservative answers, but still achieve approximate differential privacy. We show how these mechanisms can be applied in a few select areas including making the cardinalities of set intersections and unions revealed in Private Set Intersection protocols differential private and enabling multiparty computation protocols to compute on sparse data which has its exact sizes made differential private rather than performing a fully oblivious more expensive computation.
一种适用于多用户MIMO系统的低复杂度S-GMI-THP预编码算法及硬件实现

区块对角化(Block Diagonalization, BD)预编码技术广泛应用于多用户MIMO(Multiple-Input Multiple-Output), 其算法的计算复杂性主要来源于两次奇异值分解操作.S-GMI(Simplified Generalized MMSE Channel Inversion)算法提出以一次QR分解代替BD算法中一次奇异值分解.本文提出S-GMI-THP算法, 使用THP(Tomlinson-Harashima Precoding)代替S-GMI算法中的奇异值分解, 极大降低了复杂度.分析与仿真结果表明, 所提出的S-GMI-THP算法相较于THP与MMSE算法可以实现明显的BER性能增益.文章还给出了S-GMI-THP算法整体框图以及8×8规模矩阵求逆模块的硬件实现架构, 该结构在virtex-7平台以较低的资源实现了23.8 M Matrix/s的吞吐.
Social media as a factor in personal injury underwriting: risk, rate and regulation

In this study, the authors analyze the rise of social media claims and how such consumer usage of social media and other types of data might be analyzed to ascertain predictive and appropriate rate factors. Rates must be based on approved rate factors, which must correlate with the risk, be predictive of losses, and not be unfairly discriminatory.
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers

Motivated by extreme multi-label classification applications, we consider training deep learning models over sparse data in multi-GPU servers. The variance in the number of non-zero features across training batches and the intrinsic GPU heterogeneity combine to limit accuracy and increase the time to convergence. We address these challenges with Adaptive SGD, an adaptive elastic model averaging stochastic gradient descent algorithm for heterogeneous multi-GPUs that is characterized by dynamic scheduling, adaptive batch size scaling, and normalized model merging. Instead of statically partitioning batches to GPUs, batches are routed based on the relative processing speed. Batch size scaling assigns larger batches to the faster GPUs and smaller batches to the slower ones, with the goal to arrive at a steady state in which all the GPUs perform the same number of model updates. Normalized model merging computes optimal weights for every GPU based on the assigned batches such that the combined model achieves better accuracy. We show experimentally that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy and is scalable with the number of GPUs.