Reviewerly/exHarmony huggingface dataset:
Investigating the Sketchplan: A Novel Way of Identifying Tactical Behavior in Massive Soccer Datasets

Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards; or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interaction. We bridge the gap between these approaches by contributing a light-weight, simplified interaction and visualization system, which we conceptualized in an iterative design study with the coaching team of a European first league soccer team. Our approach is walk-up usable by all domain stakeholders, and at the same time, can leverage advanced data retrieval and analysis techniques: a virtual magnetic tactic-board. Users place and move digital magnets on a virtual tactic-board, and these interactions get translated to spatio-temporal queries, used to retrieve relevant situations from massive team movement data. Despite such seemingly imprecise query input, our approach is highly usable, supports quick user exploration, and retrieval of relevant results via query relaxation. Appropriate simplified result visualization supports in-depth analyses to explore team behavior, such as formation detection, movement analysis, and what-if analysis. We evaluated our approach with several experts from European first league soccer clubs. The results show that our approach makes the complex analytical processes needed for the identification of tactical behavior directly accessible to domain experts for the first time, demonstrating our support of coaches in preparation for future encounters.
CALIBRATION OF SMARTPHONE’S REAR DUAL CAMERA SYSTEM

This paper aims to calibrate smartphone’s rear dual camera system which is composed of two lenses, namely; wide-angle lens and telephoto lens. The proposed approach handles large sized images. Calibration was done by capturing 13 photos for a chessboard pattern from different exposure positions. First, photos were captured in dual camera mode. Then, for both wide-angle and telephoto lenses, image coordinates for node points of the chessboard were extracted. Afterwards, intrinsic, extrinsic, and lens distortion parameters for each lens were calculated. In order to enhance the accuracy of the calibration model, a constrained least-squares solution was applied. The applied constraint was that the relative extrinsic parameters of both wide-angle and telephoto lenses were set as constant regardless of the exposure position. Moreover, photos were rectified in order to eliminate the effect of lens distortion. For results evaluation, two oriented photos were chosen to perform a stereo-pair intersection. Then, the node points of the chessboard pattern were used as check points.
Software Defect Prediction Based on Hybrid Swarm Intelligence and Deep Learning

In order to improve software quality and testing efficiency, this paper implements the prediction of software defects based on deep learning. According to the respective advantages and disadvantages of the particle swarm algorithm and the wolf swarm algorithm, the two algorithms are mixed to realize the complementary advantages of the algorithms. At the same time, the hybrid algorithm is used in the search of model hyperparameter optimization, the loss function of the model is used as the fitness function, and the collaborative search ability of the swarm intelligence population is used to find the global optimal solution in multiple local solution spaces. Through the analysis of the experimental results of six data sets, compared with the traditional hyperparameter optimization method and a single swarm intelligence algorithm, the model using the hybrid algorithm has higher and better indicators. And, under the processing of the autoencoder, the performance of the model has been further improved.
Compensated differential Zernike fitting method for wavefront aberration metrology based on grating lateral shearing

Lateral shearing based on the grating is one of the classical configurations when measuring the wavefront aberration of optical systems such as the lithographic projection lens. Because the wavefront under test is spherical, but a detector surface is a plane, the coordinate of the wavefront surface will be distorted on the detector surface. As the numerical aperture (NA) of the optics under test increases, the shear ratios at different positions within the shearing region are significantly different due to the coordinate distortion. Therefore, the reconstructed wavefront from the traditional lateral-shearing reconstruction method designed for a fixed shearing ratio will contain a non-negligible error. In this work, we use the ray-tracing method to calculate the shearing ratio distribution in the shearing region and propose a compensated differential Zernike fitting method to solve the coordinate distortion and shearing ratio variation problem. The relative error of the uncompensated result will increase as the NA increases. This error is around 1% for a 0.1 NA, 10% for a 0.3 NA, and over 100% for an NA above 0.7. Compensation for the shearing ratio variation is necessary when the NA is larger than 0.3. The proposed method has been validated by simulations and experiments.
Augmenting Social Science Research with Multimodal Data Collection: The EZ-MMLA Toolkit

While the majority of social scientists still rely on traditional research instruments (e.g., surveys, self-reports, qualitative observations), multimodal sensing is becoming an emerging methodology for capturing human behaviors. Sensing technology has the potential to complement and enrich traditional measures by providing high frequency data on people's behavior, cognition and affects. However, there is currently no easy-to-use toolkit for recording multimodal data streams. Existing methodologies rely on the use of physical sensors and custom-written code for accessing sensor data. In this paper, we present the EZ-MMLA toolkit. This toolkit was implemented as a website and provides easy access to multimodal data collection algorithms. One can collect a variety of data modalities: data on users' attention (eye-tracking), physiological states (heart rate), body posture (skeletal data), gestures (from hand motion), emotions (from facial expressions and speech) and lower-level computer vision algorithms (e.g., fiducial/color tracking). This toolkit can run from any browser and does not require dedicated hardware or programming experience. We compare this toolkit with traditional methods and describe a case study where the EZ-MMLA toolkit was used by aspiring educational researchers in a classroom context. We conclude by discussing future work and other applications of this toolkit, potential limitations and implications.
Digital pathomorphology: creation of an automated microscopy system

Introduction. Nowadays digital pathology is an actively developing area. The basis for changing specialty to digital pathology is a Whole slide imaging method which allows to transfer histological slides into digital form. Significant workload on pathologists and insufficient equipment of pathological states require to change the way pathologists work and to implement new technologies which would allow to optimize and facilitate a work with pathological materials. The aim of this paper is to review solutions for scanning pathomorphological materials available on international and Russian markets and scopes of application of these solutions. Materials and methods. The search was conducted on Pubmed database. The search of information about microscopes-scanners was made on sites of developers including open sources in these sites and FDA site. Results. The application of slide scanning and microscopes for WSI was overviewed, the scopes of application and limitations of digital technologies in medicine were described. Based on literature review a classification of devices used for microslides scanning was made. The first type of devices is microscopes-scanners. They are closed systems for microscopy where cameras and lenses are located inside the device and slides scanning is carried out on high speed with capacity for 400 slides in one load. These microscopes are the most widespread ones nowadays. The second type is microscopes that are similar in shape and size to usual light microscopes. Openness of such system provides using different types of microscopy but the capacity of slides is less than in previous mentioned devices. Nevertheless, these specifications allow to use such microscopes in small laboratories including using in scientific research. Among compact microscopes with the sizes similar to a smartphone some models have a high availability due to printing their parts via 3D printers. But these microscopes are used only for visualization of structures where high magnification isn’t needed. This can limit application of this microscopes type in pathology. The fourth type is an optical microscope with attached details that can transform it to a microscope-scanner. Attached details can be printed via 3D printers and thus their using has a great potential in digital pathology since it significantly reduces the price of the device. In addition, based on this type of microscopes the devices scanning all types of slides can be made and therefore they can be used in all laboratories. Conclusion. Our literature review has shown ample opportunities for application of WSI in pathomorphological microslides scanning. Telepathology is an evolving area of telemedicine which creates a need for developing of new technologies.
Filipino Sign Language Recognition using Deep Learning

The Filipino deaf community continues to lag behind the fast-paced and technology-driven society in the Philippines. The use of Filipino Sign Language (FSL) has contributed to the improvement of communication of deaf people, however, the majority of the population in the Philippines do not understand FSL. This project utilized computer vision in obtaining the images and Convolutional Neural Network (CNN) ResNet architecture in building the automated FSL recognition model, with the goal of bridging the communication gap between the deaf community and the hearing majorities. In the experimentation, the dataset used are static images generated from a signer which gestured Filipino number signs which range from (0-9). Based on experimentation, the best-achieved performance is on fine-tuned ResNet-50 model which obtained a validation accuracy as high as 86.7% when the epoch value equals 15. For future work, real-time FSL recognition will be implemented and more data will be collected to enable recognition of Filipino alphabets, basic phrases, and common greetings.
Design of Speech Emotion Recognition Algorithm Based on Deep Learning

As a key technology of human-computer interaction, speech emotion recognition has wide application value and practical significance in various fields including criminal investigation. This paper firstly describes the research background, then introduces the technical basis of speech emotion recognition, including speech emotion description, speech signal preprocessing and short-time energy and derived parameters extraction, then, this paper proposes a deep learning-based speech emotion recognition algorithm, builds a speech emotion recognition model based on convolutional neural network, bi-directional long- and short-term memory network and multi-headed attention mechanism, which improves the human-computer interaction The efficiency and capability of speech emotion recognition in human-computer interaction devices are improved.
Rphx: Result Pattern Hiding Conjunctive Query Over Private Compressed Index Using Intel SGX

Deploying data storage and query service in an untrusted cloud server raises critical privacy and security concerns. This paper focuses on the fundamental problem of processing conjunctive keyword queries over an untrusted cloud in a privacy-preserving manner. Previous tree-based searchable symmetric encryption (SSE) schemes, such as <i>IBTree</i> and <i>VBTree</i>, can process conjunctive keyword queries in a secure and efficient way. However, these schemes cannot address &#x201C;Result Pattern (<i>RP</i>)&#x201D; leakage, which can be used to recover the keywords contained in a conjunctive keyword query. To combat this challenging problem, we propose a result pattern hiding conjunctive query scheme named <i>Rphx</i> using Intel SGX. In particular, we first propose a new &#x201C;SGX-aware&#x201D; compressed index named <i>VIBT</i> by combining variable-length bloom filter tree, matryoshka filter and online cipher. To achieve <i>RP</i> hiding, we then introduce a new tree-based SSE scheme named <i>Rphx</i> by deploying <i>VIBT</i> to Intel SGX. Security analysis shows that <i>Rphx</i> can enhance the security requirements by hiding <i>RP</i> leakage under the IND-CKA2 security model. Experimental results show that <i>VIBT</i> gains at least <inline-formula> <tex-math notation="LaTeX">$30\times $ </tex-math></inline-formula> improvement in storage efficiency and <i>Rphx</i> can achieve comparable search efficiency comparing with previous works.
Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection

Network anomaly detection is a very relevant research area nowadays, especially due to its multiple applications in the field of network security. The boost of new models based on variational autoencoders and generative adversarial networks has motivated a reevaluation of traditional techniques for anomaly detection. It is, however, essential to be able to understand these new models from the perspective of the experience attained from years of evaluating network security data for anomaly detection. In this paper, we revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view, and contribute a mathematical model that relates them. Specifically, we start with the probabilistic PCA model and explain its connection to the Multivariate Statistical Network Monitoring (MSNM) framework. MSNM was recently successfully proposed as a means of incorporating industrial process anomaly detection experience into the field of networking. We have evaluated the mathematical model using two different datasets. The first, a synthetic dataset created to better understand the analysis proposed, and the second, UGR’16, is a specifically designed real-traffic dataset for network security anomaly detection. We have drawn conclusions that we consider to be useful when applying generative models to network security detection.
COC-LBP: Complete Orthogonally Combined Local Binary Pattern for Face Recognition

Literature reports several local descriptors where discriminativity is enhanced by updating or modifying the previous techniques. This work suggests such novel descriptor (in light variations) by updating the methodology of OC-LBP. In OC-LBP, the difference among orthogonal neighbors and center pixel (of respective group) is thresholded to 1 or 0 according to thresholding function, which leads to 2 OLBP codes by putting binomial weights and adding values. Then both codes are concatenated to form OC-LBP size. The major drawback of OC-LBP is the thresholding of only Sign (S) component. This limits discriminativity of OC-LBP. To build more productive descriptor so called Complete (C) OC-LBP (COC-LBP), the Magnitude (M) details are incorporated with the S component of OC-LBP. The M based OC-LBP is called as MOC-LBP and S based is called as SOC-LBP. In MOC-LBP, the absolute of differences among orthogonal neighbors and center pixel (of respective group) is thresholded to 1 or 0 according to thresholding function. In thresholding function this time mean is utilized for comparison rather than center pixel, which is used in SOC-LBP. The two evolved MOLBP codes are concatenated to form the MOC-LBP size. Finally SOC-LBP and MOC-LBP codes are concatenated to build the COC-LBP size. Moving constantly in all positions builds the SOC-LBP and MOC-LBP images. The region feature concatenation of SOC-LBP and MOC-LBP makes COC-LBP size. The huge feature size is compressed by FLDA and matching is assisted from SVMs. On 2 benchmark datasets YB and EYB, COC-LBP marks its influence by defeating the results of many descriptors.
Dialogue Systems for Language Learning

Dialogue systems allow a user to interact, orally or in writing, with an automated interlocutor, whether it is referred to as a chatbot, a robot, a conversational agent or an intelligent personal assistant. We discuss the different typologies of dialogue-based computer-assisted language learning (CALL), the natural language processing (NLP) technology operating those systems, and the issues of their instructional design. We review the scientific findings on the cognitive, behavioral, and emotional effects of dialogue systems. Finally, we provide recommendations for the use of dialogue-based CALL in foreign language learning and teaching, as well as for the development of new conversational applications.
Review of Generative Adversarial Networks in Image Generation

Generative adversarial network (GAN) model generates and discriminates images using an adversarial competitive strategy to generate high-quality images. The implementation of GAN in different fields is helpful for generating samples that are not easy to obtain. Image generation can help machine learning to balance data and improve the accuracy of the classifier. This paper introduces the principles of the GAN model and analyzes the advantages and disadvantages of improving GANs. The applications of GANs in image generation are analyzed. Finally, the problems of GANs in image generation are summarized.
The College Students Depressive Symptoms Associated with the Levels of Physical and Social Activities during the COVID-19 Pandemic in South Korea: A Web-based Cross-Sectional Survey

Purpose: This study aimed to examine the depressive symptoms of college students associated with the limited physical and social activities during the coronavirus disease 2019 (COVID-19) pandemic. Methods: A web-based survey was conducted with a standardized e-questionnaire generated using the Google form. The web-link was distributed to college students through the social media platform, Facebook, to obtain data on the levels of physical and social activities and depressive symptoms owing to strict social distancing measures adopted for coronavirus prevention. Physical and social activities and depressive symptoms were assessed by the Korean version of the International Physical Activity Questionnaire-Short Form, measured on a 0-to-100 numerical rating scale, and Center for Epidemiology Studies Short Depression Scale, respectively. Results: A total of 261 university students participated in this survey. The median score of depressive symptoms was 3.0 (IQ1,3=2.0, 5.0), with 61.3% experiencing depression using the cut-off point≥3. The levels of physical activity were classified as level 1(inactive group), 27.2%, level 2 (minimally active group), 42% to level 3 (health-enhancing group), 29.9%. Depressive symptoms were not significantly different by physical activity levels (H=5.01, p =.081). The median score of perceived restriction in social activity was 70.0 (50.0, 80.0), suggesting that increased perceived social activity restriction was associated with increased likelihood of depression (OR=1.012, CI=1.001, 1.023). Conclusion: Owing to social distancing during the COVID-19 pandemic, depressive symptoms in college students were substantial. Notably, depressive symptoms increased with more perceived restrictions in social activity, suggesting the urgent need to address college students mental health during this global crisis of the COVID-19.
Posthumanism and corpus linguistics

Apps, chatbots, virtual reality, digital assistants, facial recognition, ubiquitous computing, augmented reality, the internet of things, fourth industrial revolution, online multi-player video games, etc. We are entangled with the digital, and our thinking and behaviour are affected by this entanglement. The more entangled we are, the more we operate as a different kind of human – “a posthuman”. Scholarly reflection on the posthuman condition is “posthumanism”. This chapter situates corpus linguistics within posthumanism. Section 2 outlines some key posthumanist perspectives. Section 3 highlights salient ways in which corpus linguistics has been implicitly posthuman for some time. In contrast, Section 4 demonstrates an explicitly posthuman use of corpus linguistics. The final Section 5 encourages a more explicit engagement with posthumanism, and the philosophy of technology more generally, to open new possibilities for corpus linguistics.
API-GNN: attribute preserving oriented interactive graph neural network

Abstract Attributed graph embedding aims to learn node representation based on the graph topology and node attributes. The current mainstream GNN-based methods learn the representation of the target node by aggregating the attributes of its neighbor nodes. These methods still face two challenges: (1) In the neighborhood aggregation procedure, the attributes of each node would be propagated to its neighborhoods which may cause disturbance to the original attributes of the target node and cause over-smoothing in GNN iteration. (2) Because the representation of the target node is derived from the attributes and topology of its neighbors, the attributes and topological information of each neighbor have different effects on the representation of the target node. However, this different contribution has not been considered by the existing GNN-based methods. In this paper, we propose a novel GNN model named API-GNN ( A ttribute P reserving Oriented I nteractive G raph N eural N etwork). API-GNN can not only reduce the disturbance of neighborhood aggregation to the original attribute of target node, but also explicitly model the different impacts of attribute and topology on node representation. We conduct experiments on six public real-world datasets to validate API-GNN on node classification and link prediction. Experimental results show that our model outperforms several strong baselines over various graph datasets on multiple graph analysis tasks.
Non-participation in online Sacred Harp singing during the COVID-19 pandemic

During the COVID-19 pandemic, a large number of Sacred Harp singers took their activities online, adopting and adapting various platforms for the purpose of participatory music-making. While many singers found online activity to be meaningful, others did not, and an additional group lacked access altogether. This study, which was conducted by means of an online questionnaire, surveys the experiences of Sacred Harp singers who were unable or unwilling to participate in online singing. It documents the practical concerns and negative experiences that contributed to non-participation and considers the impacts of non-participation on the Sacred Harp community. Although technological barriers denied access to some singers, dissatisfaction with the online singing experience was the most significant factor in non-participation. Even with the improvement of online platforms, however, many singers will remain unable to participate in virtual singing due to lack of access to a private domestic space.
Missing the MARC: Utilization of MARC Fields in the Search Process

Utah State University Cataloging and Metadata Services (CMS) unit analyzed MARC record discoverability within the libraries’ discovery layer, Encore, using web analytics, a web-scrapping tool, and a relational database to examine MARC records listed in users’ search results. MARC records were identified, isolated, and coded to pinpoint where search terms appeared, determine whether they were present in full or in part, and ascertain prominent fields not present in records. Analysis of results showed that notes and summaries were important for record retrieval and that users interacted with authorized name fields more frequently than authorized subject fields.