Reviewerly/exHarmony huggingface dataset:
MODELLING THE WAYS TO INCREASE THE CREATIVITY OF PSYCHOLOGY STUDENTS AS A BASIC FACTOR IN PROFESSIONAL DEVELOPMENT

One of the important tasks of the modern system of psychology in higher educational institutions is not only the development of student’s professional potential but also the comprehensive development of their personality. The purpose of the article is to systematize the process of increasing the creativity of psychology students with the help of mechanisms and elements of the functional model of IDEF0. The novelty of this study lies in the fact that in the field of education, in particular, increasing such a subjective indicator as creative abilities, there has not yet been a methodology that allows forming an understandable and simple, but at the same time flexible system of actions. The reason for choosing this model was that it is the most visual and easy to understand for representatives of higher educational institutions. Also, this model is universal in terms of adding separate sublevels and subtasks, if for a particular educational institution it turns out to be incomplete or insufficiently detailed. Thus, it is the IDEF0 model that can reveal to the greatest extent such a subjective process as an increase in creativity among psychology students. The steps for increasing the creativity of psychology students are presented, which are the result of scrupulous analysis and many years of research by the authors in this area. The scientific contribution of this article can be considered the formation of a mechanism for increasing the creativity of psychology students, not in the context of creating a clear methodology, but in the context of forming a universal algorithm with poly-levels for detailing the goal.
Cognitive biases in developing biased Artificial Intelligence recruitment system

Artificial Intelligence (AI) in a business context is designed to provide organizations with valuable insight into decision-making and planning.Although AI can help managers make decisions, it may pose unprecedented issues, such as datasets and implicit biases built into algorithms.To assist managers with making unbiased effective decisions, AI needs to be unbiased too.Therefore, it is important to identify biases that may arise in the design and use of AI.One of the areas where AI is increasingly used is the Human Resources recruitment process.This article reports on the preliminary findings of an empirical study answering the question: how do cognitive biases arise in AI?We propose a model to determine people's role in developing AI recruitment systems.Identifying the sources of cognitive biases can provide insight into how to develop unbiased AI.The academic and practical implications of the study are discussed.
An Accurate and Lightweight Method for Human Body Image Super-Resolution

In this paper, we propose a new method to super-resolve low resolution human body images by learning efficient multi-scale features and exploiting useful human body prior. Specifically, we propose a lightweight multi-scale block (LMSB) as basic module of a coherent framework, which contains an image reconstruction branch and a prior estimation branch. In the image reconstruction branch, the LMSB aggregates features of multiple receptive fields so as to gather rich context information for low-to-high resolution mapping. In the prior estimation branch, we adopt the human parsing maps and nonsubsampled shearlet transform (NSST) sub-bands to represent the human body prior, which is expected to enhance the details of reconstructed human body images. When evaluated on the newly collected HumanSR dataset, our method outperforms state-of-the-art image super-resolution methods with ~8× fewer parameters; moreover, our method significantly improves the performance of human image analysis tasks (e.g. human parsing and pose estimation) for low-resolution inputs.
AI-Assisted and Explainable Hate Speech Detection for Social Media Moderators – A Design Science Approach

To date, the detection of hate speech is still primarily carried out by humans, yet there is great potential for combining human expertise with automated approaches.However, identified challenges include low levels of agreement between humans and machines due to the algorithms' missing expertise of, e.g., cultural, and social structures.In this work, a design science approach is used to derive design knowledge and develop an artifact, through which humans are integrated in the process of detecting and evaluating hate speech.For this purpose, explainable artificial intelligence (XAI) is utilized: the artifact will provide explanative information, why the deep learning model predicted whether a text contains hate.Results show that the instantiated design knowledge in form of a dashboard is perceived as valuable and that XAI features increase the perception of the artifact's usefulness, ease of use, trustworthiness as well as the intention to use it.
Improvement in the Convolutional Neural Network for Computed Tomography Images

Background and purpose. This study evaluated a modified specialized convolutional neural network (CNN) to improve the accuracy of medical images. Materials and Methods. We defined computed tomography (CT) images as belonging to one of the following 10 classes: head, neck, chest, abdomen, and pelvis with and without contrast media, with 10,000 images per class. We modified the CNN based on the AlexNet with an input size of 512 × 512. We resized the filter sizes of the convolution layer and max pooling. Using these modified CNNs, various models were created and evaluated. The improved CNN was evaluated to classify the presence or absence of the pancreas in the CT images. We compared the overall accuracy, which was calculated from images not used for training, to that of the ResNet. Results. The overall accuracies of the most improved CNN and ResNet in the 10 classes were 94.8% and 89.3%, respectively. The filter sizes of the improved CNN for the convolution layer were (13, 13), (7, 7), (5, 5), (5, 5), and (5, 5) in order from the first layer, and that of max-pooling was (7, 7). The calculation times of the most improved CNN and ResNet were 56 and 120 min, respectively. Regarding the classification of the pancreas, the overall accuracies of the most improved CNN and ResNet were 75.75% and 58.25%, respectively. The calculation times of the most improved CNN and ResNet were 36 and 55 min, respectively. Conclusion. By optimizing the filter size of the convolution layer and max-pooling of 512 × 512 images, we quickly obtained a highly accurate medical image classification model. This improved CNN can be useful for classifying lesions and anatomies for related diagnostic aid applications.
Thermal Infrared Video Stabilization for Aerial Monitoring of Active Wildfires

Measuring wildland fire behavior is essential for fire science and fire management. Aerial thermal infrared (TIR) imaging provides outstanding opportunities to acquire such information remotely. Variables such as fire rate of spread (ROS), fire radiative power (FRP), and fireline intensity may be measured explicitly both in time and space, providing the necessary data to study the response of fire behavior to weather, vegetation, topography, and firefighting efforts. However, raw TIR imagery acquired by unmanned aerial vehicles (UAVs) requires stabilization and georeferencing before any other processing can be performed. Aerial video usually suffers from instabilities produced by sensor movement. This problem is especially acute near an active wildfire due to fire-generated turbulence. Furthermore, the nature of fire TIR video presents some specific challenges that hinder robust interframe registration. Therefore, this article presents a software-based video stabilization algorithm specifically designed for TIR imagery of forest fires. After a comparative analysis of existing image registration algorithms, the KAZE feature-matching method was selected and accompanied by pre- and postprocessing modules. These included foreground histogram equalization and a multireference framework designed to increase the algorithm's robustness in the presence of missing or faulty frames. The performance of the proposed algorithm was validated in a total of nine video sequences acquired during field fire experiments. The proposed algorithm yielded a registration accuracy between 10 and 1000× higher than other tested methods, returned 10× more meaningful feature matches, and proved robust in the presence of faulty video frames. The ability to automatically cancel camera movement for every frame in a video sequence solves a key limitation in data processing pipelines and opens the door to a number of systematic fire behavior experimental analyses. Moreover, a completely automated process supports the development of decision support tools that can operate in real time during an emergency.
Generalized 2-step phase-shifting algorithm for fringe projection

Fringe projection profilometry using the phase-shifting technique enables high-precision, high-speed and flexible measurements. Among them, the two-step phase-shifting method has become an important approach to coordinate the accuracy and measurement speed; methods especially based on the amount of phase-shift of π /2, π or 3 π /2 can improve time efficiency by reducing steps in unique way. In this paper, a phase iterative technique is proposed and established for two-step phase-shifting with arbitrary phase shift. The general mathematical scheme and parameters linear and nonlinear optimization of the proposed method are described in detail. The simulational and experimental results show that the proposed technique is an effective and precise phase detection method for two-step phase-shifting profilometry.
Extreme Learning Machine and AdaBoost-Based Localization Using CSI and RSSI

This letter develops an extreme learning machine (ELM) and AdaBoost technique for indoor localization using channel state information (CSI) and received signal strength indicators (RSSI), aiming to (a) resolve an issue with ELM in which weight parameters are generated randomly that leads to significant performance variations, (b) to maximize localization performance given the same set of measurement data, and (c) reduce the data storage and computational needs of existing schemes. To this end, we first form the fingerprint training dataset that consists of both CSI and RSSI, and fully extract from it the statistical information such as quartile, kurtosis, skewness, entropy, etc., in addition to the commonly used channel mean and std. The random weight issue of ELM is solved by optimizing the AdaBoost algorithm to combine multiple weak ELM classifiers and generate a stronger classifier for offline training. We provide experimental results to assess the effectiveness of the proposed scheme in terms of localization accuracy and storage/complexity.
A port-based forwarding load-balancing scheduling approach for cloud datacenter networks

Abstract Today’s datacenter networks (DCNs) scale is rapidly increasing because of the wide deployment of cloud services and the rapid rise of edge computing. The bandwidth consumption and cost of a DCN are growing sharply with the extensions of network size. Thus, how to keep the traffic balanced is a key and challenging issue. However, the traditional load balancing algorithms such as Equal-Cost Multi-Path routing (ECMP) are not suitable for high dynamic traffic in cloud DCNs. In this paper, we propose a port-based forwarding load balancing scheduling (PFLBS) approach for Fat-tree based DCNs with some new features which can overcome the disadvantages of the existing load balancing methods in the following aspects. Firstly, we define a port-based source-routing addressing scheme, which decreases the switch complexity and makes the table-lookup operation unnecessary. Secondly, based on this addressing scheme, we proposed an effective routing mechanism which can obtain multiple available paths for flow scheduling based in Fat-tree. All the path information is saved in servers and each server only needs to maintain its own path information. Thirdly, we propose an efficient algorithm to implement large flows scheduling dynamically in terms of current link utilization ratio. This method is suitable for cloud DCNs and edge computing, which can reduce the complexity of the switches and the power consumption of the whole network. The experiment results indicate that the PFLBS approach has better performance compared with the ECMP, Hedera and MPTCP approaches, which decreases the flow completion time and improves the average throughput significantly. PFLBS is simple and can be implemented with a few signaling overheads.
Investigating teachers’ attitude toward digital literacy in EFL classroom

Digital literacy has been a major concern for people involved in education sectors including students, teachers and policy makers. Thus, to make the education in this era more effective to help reach the goals, English teachers should be digitally more literate. The aims of this study are; 1) to find out how the digital literacy was implemented; 2) to determine teachers’ attitude in implementing the digital literacy; 3) to scrutinize the challenges; and 4) the actions they took to cope with them. Employing a qualitative research method, this study used a semi-structured interview and classroom observation to collect the data from two EFL teachers at a junior high school in Temanggung, Central Java, Indonesia as the subjects. The data were then identified, categorized, organized, coded, described and reported following the thematic analysis. The findings showed that they used computer and smartphone to search for digital information. The teachers exhibited positive attitudes in the use of digital literacy for EFL teaching. They used syllabus as a major consideration, understood their position as teachers, developed effective teaching, implemented multiple literacy and improved the four language skills. However, issues related to lack of technology, students’ background, lack of time and limited budget were acknowledged as hindrances in digital literacy implementation. As a strategy, teachers have therefore developed an early planning and support plan to deal with these challenges. Finally, this study suggests the need to develop teachers' technical facilities, technological pedagogy, and policy makers to give digital literacy more attention.
Private and rateless adaptive coded matrix-vector multiplication

Abstract Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones.
Systematic Analysis of Virtual Reality &amp; Augmented Reality

Nowadays, users are moving from old 2D screens to modern devices such as 3D screens and virtual reality devices to enjoy videos and games like real-world experience, and this demand increased further development.Virtual Reality (VR) is based on the creation of a simulated environment of real-world with computer creation, and Augmented Reality (AR) is based on the addition of simulation components (environment) in the real-world scene.In this paper, systematic analysis of relationships and features both VR and AR varies by outline, arrangement, administrations, and devices for associations and clients.This paper provides a difference between AR and VR, advantages, future, and open research issues.
Cryptocurrency Open Innovation Payment System: Comparative Analysis of Existing Cryptocurrencies

This study was conducted to evaluate the implementation of an open innovation cryptocurrency financial system using a statistical approach. The data array reflects the actual speed of the cryptocurrency system, expressed in transactions per second (TPS), taken as the average annual speed. The article offers a comprehensive approach for choosing the optimal cryptocurrency financial system. The final analysis shows that the reasons for the adoption of the cryptocurrency financial system are practicality and convenience, as well as efficient transaction time, faster payment, and simplicity of the payment process. The impact of social factors, expected efforts, and conditions of assistance on the attitude to the cryptocurrency financial system were evaluated. In addition, social factors that have a significant impact on the implementation of the cryptocurrency financial system were identified.
Few-Shot Ship Classification in Optical Remote Sensing Images Using Nearest Neighbor Prototype Representation

With the development of ship detection in optical remote sensing images, it is convenient to obtain accurate detection results and ship images. Owing to the superior performance of convolutional neural networks (CNNs), one way to acquire the category of ship is to train a classifier using numerous ship images. However, the classification performance of CNN may degrade in the case of a small number of training samples. To solve this problem, we propose a metric-based few-shot method to generate novel concept (class) representation using nearest neighbor prototype. Different from image-to-image measure in common few-shot methods, we use an image-to-feature measure. We map small number of samples to the feature space through CNN, and generate prototypes by computing nearest neighbor value on each dimension of the feature separately. Our method is validated on patch-level ship image dataset, a reproduced ship classification dataset based on HRSC2016. The experimental results demonstrate the accuracy and robustness of our method for ship classification with a small amount of labeled data.
Unsupervised Extractive Summarization using Pointwise Mutual Information

Unsupervised approaches to extractive summarization usually rely on a notion of sentence importance defined by the semantic similarity between a sentence and the document.We propose new metrics of relevance and redundancy using pointwise mutual information (PMI) between sentences, which can be easily computed by a pre-trained language model.Intuitively, a relevant sentence allows readers to infer the document content (high PMI with the document), and a redundant sentence can be inferred from the summary (high PMI with the summary).We then develop a greedy sentence selection algorithm to maximize relevance and minimize redundancy of extracted sentences.We show that our method outperforms similarity-based methods on datasets in a range of domains including news, medical journal articles, and personal anecdotes.
Using Dynamic Virtual Microscopy to Train Pathology Residents During the Pandemic: Perspectives on Pathology Education in the Age of COVID-19

The COVID-19 pandemic has forced educational programs, including pathology residency, to move to a physically distanced learning environment. Tandem microscopic review (also known as “double-scoping”) of pathology slides is a traditional cornerstone of pathology education. However, this requires the use of a double- or multi-headed optical light microscope which is unfortunately not amenable to physical distancing. The loss of double-scoping has forced educational innovation in order to continue teaching microscopy. Digital pathology options such as whole slide imaging could be considered; however, financial constraints felt by many departments often render this option cost-prohibitive. Alternatively, a shift toward teaching via dynamic virtual microscopy offers a readily available, physically distanced, and cost-conscious alternative for pathology education. Required elements include a standard light microscope, a mounted digital camera, computers, and videoconferencing software to share a slide image with the learner(s). Through survey data, we show immediate benefits include maintaining the essence of the traditional light microscope teaching experience, and additional gains were discovered such as the ability for educators and learners to annotate images in real time, among others. Existing technology may not be initially optimized for a dynamic virtual experience, resulting in lag time with image movement, problems focusing, image quality issues, and a narrower field of view; however, these technological barriers can be overcome through hardware and software optimization. Herein, we share the experience of establishing a dynamic virtual microscopy educational system in response to the COVID-19 pandemic, utilizing readily available technology in the pathology department of a major academic medical center.
PURA: A Product-and-User Oriented Approach for Requirement Analysis From Online Reviews

Users often show different and personalized product requirements with respect to their gender, age, location, and other attributes. The requirement analysis of various users is thus vitally important for product evolution and customization. Accordingly, we propose a product-and-user oriented approach for requirement analysis, which can automatically elicit, classify, and rank product requirements from online reviews, and identify requirement differences among users. More specifically, the product features are first extracted from online reviews in a machine-learning manner. Next, a hierarchical clustering model is applied for product feature classification, and a weighted function of customer sentiment and attention is put forward for requirement ranking. Then, statistical methods are used for requirement preference identification. Finally, the effectiveness and feasibility of the proposed approach is examined on 2198 online reviews with 148 527 subsentences about new energy vehicles.
Target tracking algorithms for multi-UAVs formation cooperative detection*

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