Reviewerly/exHarmony huggingface dataset:
Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation

The aim of session-based recommendation is to predict the users' next clicked item, which is a challenging task due to the inherent uncertainty in user behaviors and anonymous implicit feedback information. A powerful session-based recommender can typically explore the users' evolving interests (i.e., a combination of his/her long-term and short-term interests). Recent advances in attention mechanisms have led to state-of-the-art methods for solving this task. However, there are two main drawbacks. First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively. Second, current studies typically think long-term and short-term interests as equally important, but the importance of them should be user-specific. Therefore, we propose a novel Parallel Attention Network model (PAN) for Session-based Recommendation. Specifically, we propose a novel time-aware attention mechanism to learn user's short-term interest by taking into account the contextual information and temporal signals simultaneously. Besides, we introduce a gated fusion method that adaptively integrates the user's long-term and short-term preferences to generate the hybrid interest representation. Experiments on the three real-world datasets show that PAN achieves obvious improvements than the state-of-the-art methods.
An Inversion-Based Neuro-Controller for Robot Manipulators

Neuro-controllers that are applicable to a wider range of dynamical systems are the focus of this paper. The proposed controllers are inversion-based wherein the inverse of the dynamical system is modelled using a feedforward neural network with a single hidden layer and trained with the online sequential learning algorithm. We demonstrate that the proposed controller is applicable to both single- and twolink robot manipulators, and is able to handle flexible joints and unmodelled dynamics.
Adiabatic quantum computation with spin ensembles

In the standard approach to adiabatic quantum computing (AQC), quantum information stored on qubits are adia- batically evolved to find the lowest energy state of a problem Hamiltonian. Here we investigate a variation of AQC where spin ensembles are used in place of qubits. The use of ensembles duplicates the quantum information, and allows errors to be suppressed during the adiabatic evolution. We show that there are two distinct types of problem Hamiltonians under this mapping, where the first excited state is a single particle perturbation on the ground state (Type I); or a macroscopically distinct fully polarized state (Type II). For Type I problems, we find that the minimum gap for large ensembles is well predicted by mean-field theory and the AQC performance improves with ensemble size, realizing error-suppression. For Type II problems, the performance of the AQC is mixed, and the gap can increase or decrease with ensemble size depending on the problem instance. The incidences of the Type II problems are greatly suppressed with ensemble size, and consequently for randomly chosen problem instances the success probability of the scheme improves with the ensemble size. Our approach shows that it is possible to perform AQC without the necessity of controlling individual qubits, which allows for an alternative route towards implementing AQC.
Distance Invariant RGB-D Object Recognition Using DSMS System

In computer vision, object recognition has gained a lot of attention due to its numerous practical usage. For real-world applications, it is necessary to consider conditions like object images are captured from multiple viewpoints, change in illumination and different distance locations of objects from the camera for better recognition. In this work, a new CVPR34K RGB-D dataset is proposed consisting of RGB-D images which are acquired from different distance location from the camera. A distance invariant RGB-D object recognition system is introduced using Depth Estimation, Scale data with Unit Depth and Multimodal Convolutional neural network with SVM (DSMS). The proposed DSMS system is divided into three parts. First, the Depth Estimation is introduced to detect distance location of acquired RGB-D object image. The second stage consists of several preprocessing operation to normalize input RGB-D data with respect to a reference distance. The final stage is to learn features from normalized RGB and depth images and performed RGB-D object recognition. The experimental results show that the DSMS method achieves comparable performance to state-of-the-art methods on the RGB-D object dataset. Effectiveness of our method is clearly observed for the cases when distance location RGB-D object image is changed in proposed CVPR34K Dataset.
Трансформация системы образования в Республике Беларусь

At the state level the strategic direction of the education system’ development is associated with ensuring the quality of the educational process in the context of the information society’ formation in Belarus. In this regard, one of the most sustainable trends is the active information and communication technologies’ implementation in the educational sphere. The author identifies three key areas of digitalization of education in the Republic of Belarus: the formation of digital competence of subjects, the creation of an electronic information and educational environment and the deployment of distance learning in education institutions of various types and levels. The COVID-19 pandemic has demonstrated the degree of demand for these areas in all developed countries of the world. In the face of an unfavorable epidemiological situation, many higher education institutions in Belarus were able to quickly organize the distance work of teachers and students. However, the rest of the educational institutions of the republic were not ready for an adequate response to the situation caused by the COVID-19 epidemic in the country.
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator

The number of processing elements (PEs) in a fixed-sized systolic accelerator is well matched for large and compute-bound DNNs; whereas, memory-bound DNNs suffer from PE underutilization and fail to achieve peak performance and energy efficiency. To mitigate this, specialized dataflow and/or micro-architectural techniques have been proposed. However, due to the longer development cycle and the rapid pace of evolution in the deep learning fields, these hardware-based solutions can be obsolete and ineffective in dealing with PE underutilization for state-of-the-art DNNs. In this work, we address the challenge of PE underutilization at the algorithm front and propose data reuse aware co-optimization (DRACO). This improves the PE utilization of memory-bound DNNs without any additional need for dataflow/micro-architecture modifications. Furthermore, unlike the previous co-optimization methods, DRACO not only maximizes performance and energy efficiency but also improves the predictive performance of DNNs. To the best of our knowledge, DRACO is the first work that resolves the resource underutilization challenge at the algorithm level and demonstrates a trade-off between computational efficiency, PE utilization, and predictive performance of DNN. Compared to the state-of-the-art row stationary dataflow, DRACO achieves 41.8% and 42.6% improvement in average PE utilization and inference latency (respectively) with negligible loss in predictive performance in MobileNetV1 on a $64\times64$ systolic array. DRACO provides seminal insights for utilization-aware DNN design methodologies that can fully leverage the computation power of systolic array-based hardware accelerators.
Uncovering Social Media Users’ Emotions Towards Companies Using Semantic Web Technologies

In the last few years, online social media networks have witnessed an amazing growth in their worldwide usage, with millions of users constantly publishing messages containing opinions on virtually any imaginable topic, including opinions about companies. Accurately understanding these opinions could provide an almost real-time overview of how the company and its actions are perceived by the general public. While existing approaches used for analyzing the opinions expressed in social media messages commonly limit themselves in discovering the polarity of the messages, expressed as a positive, negative, or neutral value, in the present paper, we use semantic web technologies and natural language processing in order to uncover actual feelings, such as happiness, surprise, or disappointment. The emotions are structured in a hierarchy using an ontology, thus offering the possibility to analyze the overall opinion regarding the company at different levels of granularity. The proposed approach is validated by performing an analysis of the public perception towards four well-known technology companies.
Performance Improvement of a Deep Learning-based Object Recognition using Imitated Red-green Color Blindness of Camouflaged Soldier Images

The camouflage pattern was difficult to distinguish from the surrounding background, so it was difficult to classify the object and the background image when the color image is used as the training data of deep-learning.In this paper, we proposed a red-green color blindness image transformation method using the principle that people of red-green blindness distinguish green color better than ordinary people.Experimental results show that the camouflage soldier's recognition performance improved by proposed a deep learning model of the ensemble technique using the imitated red-green-blind image data and the original color image data.
Everett Library: Library 101: Evaluating Sources

This page provides videos and definitions regarding scholarly vs popular publications, website searching, and evaluation of other sources.
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning

Deep learning highly relies on the amount of annotated data. However, annotating medical images is extremely laborious and expensive. To this end, self-supervised learning (SSL), as a potential solution for deficient annotated data, attracts increasing attentions from the community. However, SSL approaches often design a proxy task that is not necessarily related to target task. In this paper, we propose a novel SSL approach for 3D medical image classification, namely Task-related Contrastive Prediction Coding (TCPC), which embeds task knowledge into training 3D neural networks. The proposed TCPC first locates the initial candidate lesions via supervoxel estimation using simple linear iterative clustering. Then, we extract features from the sub-volume cropped around potential lesion areas, and construct a calibrated contrastive predictive coding scheme for self-supervised learning. Extensive experiments are conducted on public and private datasets. The experimental results demonstrate the effectiveness of embedding lesion-related prior-knowledge into neural networks for 3D medical image classification.
Joint Translation and Unit Conversion for End-to-end Localization

A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions.In this paper, we take unit conversions as an example and propose a data augmentation technique which lead to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.
THAI CUSTOM INFORMATION SHARING ON THE INTERNET BY LINKED DATA TECHNIQUES

This paper propose a set of preparation for Linked Data creation and publication the Thai custom information on the Internet based totally on ontology construction, collectively with one pilot mannequin example in the area of Thai custom in northeast of Thailand. By presenting distinctive descriptions of every task in the generation and e-book processes, these training advantage each private and public agencies that work with data about custom and lifestyle related different area in Thailand in generating Linked Data formal ready-existing information and in launching the generated information in accordance to the contemporary standards. This instance assistances the audience from one-of-a-kind businesses to reap better understanding into the procedures of Linked Data creation and publication, as a consequence confirming the easiest exceptional of the outputs of these practises.
Improving First-Order Optimization Algorithms (Student Abstract)

This paper presents a simple and intuitive technique to accelerate the convergence of first-order optimization algorithms. The proposed solution modifies the update rule, based on the variation of the direction of the gradient and the previous step taken during training. Results after tests show that the technique has the potential to significantly improve the performance of existing first-order optimization algorithms.
The Impact of Social Indicators of Economic Freedom and Poverty on Greece's GDP Index

Socio-economic indicators are usually complex indicators, i.e. they are derived from the composition of individual indicators using a method of weighting. The index of economic freedom shows us how open and friendly our country is towards 'business'. The poverty index is an indication of the standard of living of our country and it is generally considered that it can effectively reflect the various shortcomings of the population. GDP, respectively, measures, in units of money, all finished products produced annually in an economy and traded through the market. Annual GDP growth over a given period is a measure of growth. This economic growth is currently one of the most dynamic growing branches of economic science.&#x0D; In the research section of this paper, we examine the degree of correlation between the social indicators of Economic Freedom and Poverty with the Gross Domestic Product (GDP) financial indicator, and finally, the conclusions drawn from the processing of the correlational data are recorded for future study.
Designing for Cognitive DisabilityHow FlexiChat Affects the Accessibility of Instant Messaging

In this thesis an Instant Messaging application named FlexiChat, suited for adolescents in special needs school, has been developed and evaluated. Communication is based on symbols instead of text ...
Issues in adapting web content to be displayed on devices with various screen shapes using a JavaScript built screen shape simulator

This paper is devoted to an upcoming issue in web design: Styling web content to be displayed correctly on devices with various screen shapes. This issue is a consequence of the rapid development of LED displays with a wide variety of screen shapes. Many of them can be connected to a computer which means that one can display web content on them. Such displays are used as indoor and outdoor advertising panels, which makes them very important for business. Also, various smart devices are developing rapidly and they are often web browsing enabled. They come with various screen shapes, which is an imperative of modern design. Hence the need to style web pages in such a way that their content is displayed correctly on all those devices. By now, responsive web design has a solution only for styling web pages to be displayed on rectangular screens with various dimensions by using media queries and flexible grids. The W3C organization is currently working on media queries for round shaped displays, but all new suggested CSS rules are still in the working draft. It is obviously that the development of LED displays is running faster than the development of web standards. For these reasons, a responsive web design testing tool with a various screen shape simulator is built in JavaScript and is available on GitHub. Its main features are described herein. An example of styling a web site for various screen shapes using that testing tool is given in this paper in order to highlight the problems that arise during that process.
DEVELOPMENT OF STUDENTS’ COGNITIVE ACTIVITY

It has been investigated different approaches to the definition of the concepts of “cognitive activity”, “cognitive functioning” and “cognitive autonomy”. Their comparisons have also been made. As a result we have found that cognitive function as a form of mental activity is aimed at cognition, perception and thinking of the learning subject. Cognitive activity is characterized as a personality trait that manifests itself in its relation to the process of cognition, readiness and desire for cognitive activity. It is proved that cognitive autonomy is a quality of the personality, which is inherent in the desire and ability without the help (independently) to acquire the necessary knowledge and skills, to solve tasks.&#x0D; The means, methods and conditions for improvement of students’ cognitive activity are determined i.e. a game as a way of activating cognitive functioning, project method, cognitive tasks, modeling, independent work, vocabulary work, web quest technology, project activity. It is concluded that all of them are focused on the students’ creative work, on the avoiding of the standard form of conducting classes and changing of thinking and participation of both the student and the teacher. It is noted the essential role of the teacher in the process of using such activities while working with students. The contemplative and guiding functions of the teacher in the learning process are distinguished. The importance of creating a work atmosphere comfortable for all participants is emphasized.&#x0D; Emphasis is placed on the fact that cognitive activity begins with activity, independence, initiative, desire to know more, go beyond what is already known. It is noted that the relationship between the student and the teacher has moved to the subject-subjective level, which enables the student to independently plan his/her activity, predict the results, be responsible for the final result of learning i.e. knowledge acquisition, comprehensive development and ability to work.
MILITARY LEADERSHIP AND COMPETENCE-ORIENTED MENTORING IN THE PROFESSIONAL TRAINING OF FUTURE RESERVE OFFICERS

The collective monograph describes the theoretical and practical aspects of development of the European research area. The general issues of the history of pedagogy, theory and methods of teaching, correctional pedagogy, theory and methods of vocational education and educational management, the current state of philological research, etc. are considered. The publication is intended for scholars, teachers, postgraduate students, and students of pedagogical specialties, as well as a wide readership interested in pedagogy and philological. Theoretical and practical aspects of the development of the European Research Area: collective monograph / edited by authors. – 2nd ed. – Riga, Latvia : “Baltija Publishing”, 2020. – 334 p.
A new approach to the application of conflict redistribution rule in Satellite Image Classification

Nowadays solution of different scientific problems using satellite images, generally includes a classification procedure. Classification is one of the most important procedures used in remote sensing, because it involves a lot of mathematical operations and data preprocessing. The processing of information and combining of conflicting data is a very difficult problem in classification tasks. Nowadays many classification methods are applied in remote sensing. Classification of conflicting data has been a key problem, both from a theoretical and practical point of view. But a lot of known classification methods can not deal with highly conflicted data and uncertainty. The main purpose of this article is to apply proportional conflict redistribution rule (PRC5) for satellite image classification in conditions of uncertainty, when conflicting sources of evidence give incomplete and vague information. This rule can process conflicting data and combine conflicting bodies of evidence (spectral bands). Proportional conflict redistribution rule can redistribute the partial conflicting mass proportionally on non-empty sets involved in the conflict. It was noticed, that this rule can provide a construction of aggregated estimate under conflict. It calculates all partial conflicting masses separately. It was also shown, that proportional conflict redistribution rule is the most mathematically exact redistribution of conflicting mass to non-empty set. But this rule consists of difficult calculation procedures. The more hypotheses and more masses are involved in the fusion, the more difficult is to implement proportional conflict redistribution rule, therefore special computer software should be used. It was considered an example of practical use of the proposed conflict redistribution rule. It also was noticed, that this new approach to the application of conflict redistribution rule in satellite image classification can be applied for analysis of satellite images, solving practical and ecological tasks, assessment of agricultural lands, classification of forests, in searching for oil and gas.
Bayesian Sampling Bias Correction: Training with the Right Loss Function

We derive a family of loss functions to train models in the presence of sampling bias. Examples are when the prevalence of a pathology differs from its sampling rate in the training dataset, or when a machine learning practioner rebalances their training dataset. Sampling bias causes large discrepancies between model performance in the lab and in more realistic settings. It is omnipresent in medical imaging applications, yet is often overlooked at training time or addressed on an ad-hoc basis. Our approach is based on Bayesian risk minimization. For arbitrary likelihood models we derive the associated bias corrected loss for training, exhibiting a direct connection to information gain. The approach integrates seamlessly in the current paradigm of (deep) learning using stochastic backpropagation and naturally with Bayesian models. We illustrate the methodology on case studies of lung nodule malignancy grading.
Geochemical Approach and Industrial Utilization of Some Marble Bodies from Ubo River Area and Environs, Southwestern Nigeria

Marble bodies occur in Ubo River Area as lenses of rocks. Seven (07) marble samples were obtained, analyzed and subjected to X-Ray Flourescence (XRF) analysis to determine both the major and trace elemental composition of the samples. The analysis revealed high CaO values (52.23-58.10 wt. %) and moderate values for SiO2 (7.50-10.43wt. %), fairly low values for Al2O3 (2.30 – 4.21 wt. %), Fe2O3 (2.62 – 5.15 wt. %), MgO (0.42 – 0.7 wt. %), MnO (0.01 – 0.06 wt. %), K2O (0.32 – 1.55 wt. %) and Na2O (0.01 – 0.03 wt. %), respectively. Variation plots involving Na2O/Al2O3 vs K2O/Al2O3 reveal sedimentary progenitors for the rocks. The fairly high SiO2 and low K2O content is an indication of the environment of deposition and may have been in a shallow marine environment with fair input of classic sediments into the system. The relatively high CaO content, moderate SiO2content and fairly low Al2O3 and Fe2O3 values indicates a fair degree of purity of the parent rock, hence its suitability as a raw material in the industrial sector.&#x0D; Keywords: Clastic, Deposition, Environment, Progenitor, Marble, Chemical