Reviewerly/exHarmony huggingface dataset:
Influence of audiovisual feedback on player behavior and performance in response to video game failure

Audiovisual feedback is one of the most essential elements in video games. Although most feedback does not affect game mechanics, it is believed to have an impact on a player’s experience. One of the concepts in audiovisual feedback is “juiciness,” or providing excessive positive feedback. A few empirical studies have analyzed how juiciness affects the performance and behavior of players. Yet there are few studies on how the kind of feedback and its timing affect player behavior and performance in video games. This paper focuses on the audiovisual feedbacks given to players when they fail in a game. If players experience many failures during gameplay, their behavior may change depending on the audiovisual feedback they receive at the time of failure. We hypothesized that if the feedback to failures was “feelgood,” a player would be motivated to replay the game and try to improve performance. In the first phase of the experiment, five different feedback patterns were prepared and measured by quantifying impressions using the semantic differential method. An A/B test was then conducted using a simple web action game. Players were presented with different audiovisual feedback patterns for each of five groups when they failed in the game. The analysis in multiple comparisons revealed no significant differences in spontaneous replay behavior. The test did, however, result in differences in players’ average scores, suggesting that audiovisual feedback on failure could affect player performance.
On nonparametric regression for bivariate circular long-memory time series

Abstract We consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.
A study of a rally scene estimation method in a table tennis video

In this paper, we propose an estimation method of a rally scene from a table tennis video for broadcasting. The video is constructed of entry, rally, replay, and break scenes, the scenes are detected by comparison of adjacent frames. The rally scene shows a ping-pong table, and the table is extracted in the detected scene. In addition, an appearance of the rally scene is higher than the others, and the top number group of the scenes is defined as the rally scene. As a result of an evaluation experiment, the estimation accuracy of the proposed method is 96.8 % from 711 rally scenes of ten videos.
PanGEA: The Panoramic Graph Environment Annotation Toolkit

PanGEA, the Panoramic Graph Environment Annotation toolkit, is a lightweight toolkit for collecting speech and text annotations in photo-realistic 3D environments. PanGEA immerses annotators in a web-based simulation and allows them to move around easily as they speak and/or listen. It includes database and cloud storage integration, plus utilities for automatically aligning recorded speech with manual transcriptions and the virtual pose of the annotators. Out of the box, PanGEA supports two tasks -- collecting navigation instructions and navigation instruction following -- and it could be easily adapted for annotating walking tours, finding and labeling landmarks or objects, and similar tasks. We share best practices learned from using PanGEA in a 20,000 hour annotation effort to collect the Room-Across-Room dataset. We hope that our open-source annotation toolkit and insights will both expedite future data collection efforts and spur innovation on the kinds of grounded language tasks such environments can support.
Fast gradient methods for uniformly convex and weakly smooth problems

In this paper, acceleration of gradient methods for convex optimization problems with weak levels of convexity and smoothness is considered. Starting from the universal fast gradient method which was designed to be an optimal method for weakly smooth problems whose gradients are H\"older continuous, its momentum is modified appropriately so that it can also accommodate uniformly convex and weakly smooth problems. Different from the existing works, fast gradient methods proposed in this paper do not use the restarting technique but use momentums that are suitably designed to reflect both the uniform convexity and the weak smoothness information of the target energy function. Both theoretical and numerical results that support the superiority of proposed methods are presented.
Evaluation of College Students' Employment Quality Based on Analytic Hierarchy Process

In order to improve the level of College Students' employment management, this paper puts forward an evaluation model of College Students' Employment Quality Based on AHP. First of all, this paper analyzes the evaluation index system of College Students' employment quality, and uses AHP to evaluate the employment quality of college students. The empirical data analysis results show that the accurate evaluation of the employment quality of college students can be achieved by using the analytic hierarchy process to evaluate the employment quality of College Students.
Relationship between learning motivation and learning attitude of middle school students according to the type of feedback

The purpose of this study is to analyze the relationship between middle school students" learning motivation and learning attitude according to the type of feedback to provide basic data to help students" teaching methods. To achieve the purpose of this study, 854 middle school students enrolled in middle schools located in the Jeonbuk region were analyzed to analyze the relationship between the middle school students" learning motivation and learning attitude according to the type of feedback.<BR> Data processing was analyzed using SPSS 25.0 Program.<BR> First, it was found that there was a significant difference in the learning motivation of middle school students according to the type of feedback.<BR> Second, it was found that the learning attitudes of middle school students according to the type of feedback were partially significantly different.<BR> Third, it was shown that the learning attitudes of middle school students according to their learning motivation differed partially.<BR> Therefore, through feedback, teachers need to use the effects of feedback more efficiently to create plans to guide students" lessons more efficiently and use them as a tool for students.
PROJECT MANAGEMENT FOR SECURITY AS A TOOL FOR INTERNATIONALIZATION, KNOWLEDGE TRANSFER AND ENHANCEMENT OF COMPETENCIES IN SECURITY AND DEFENSE EDUCATION

Information about the paper titled "PROJECT MANAGEMENT FOR SECURITY AS A TOOL FOR INTERNATIONALIZATION, KNOWLEDGE TRANSFER AND ENHANCEMENT OF COMPETENCIES IN SECURITY AND DEFENSE EDUCATION" at IATED Digital Library
Evaluating Model Robustness and Stability to Dataset Shift

As the use of machine learning in high impact domains becomes widespread, the importance of evaluating safety has increased. An important aspect of this is evaluating how robust a model is to changes in setting or population, which typically requires applying the model to multiple, independent datasets. Since the cost of collecting such datasets is often prohibitive, in this paper, we propose a framework for analyzing this type of stability using the available data. We use the original evaluation data to determine distributions under which the algorithm performs poorly, and estimate the algorithm's performance on the "worst-case" distribution. We consider shifts in user defined conditional distributions, allowing some distributions to shift while keeping other portions of the data distribution fixed. For example, in a healthcare context, this allows us to consider shifts in clinical practice while keeping the patient population fixed. To address the challenges associated with estimation in complex, high-dimensional distributions, we derive a "debiased" estimator which maintains $\sqrt{N}$-consistency even when machine learning methods with slower convergence rates are used to estimate the nuisance parameters. In experiments on a real medical risk prediction task, we show this estimator can be used to analyze stability and accounts for realistic shifts that could not previously be expressed. The proposed framework allows practitioners to proactively evaluate the safety of their models without requiring additional data collection.
Peruvian Postgraduate Students' Perception Towards the Use of Digital Technologies

This paper examines how postgraduate students use and perceive digital technologies in Tacna, a border region at the south of Peru. The study was positioned in the interpretive paradigm with qualitative research design. Semi-structured interview with open-ended questions was selected as the means of data. Thematic analysis was employed to analyse the verbatim transcription as outlined by Braun and Clarke. The digital technologies that participants mostly use are the Smartphone, laptop, Smart TV, WhatsApp, email, the university's institutional Intranet and social networks (Instagram, Facebook and Twitter). The students are aware of the benefits of using digital technologies for academic and social purposes such as communication, closeness, access to information, entertainment, among others.
ONLINE LEARNING DURING COVID-19 LOCKDOWN: A STUDY REGARDING PORTUGUESE PUBLIC HIGHER EDUCATION STUDENTS

ONLINE LEARNING DURING COVID-19 LOCKDOWN: A STUDY REGARDING PORTUGUESE PUBLIC HIGHER EDUCATION STUDENTS
UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution

The recent success of NeRF and other related implicit neural representation methods has opened a new path for continuous image representation, where pixel values no longer need to be looked up from stored discrete 2D arrays but can be inferred from neural network models on a continuous spatial domain. Although the recent work LIIF has demonstrated that such novel approaches can achieve good performance on the arbitrary-scale super-resolution task, their upscaled images frequently show structural distortion due to the inaccurate prediction of high-frequency textures. In this work, we propose UltraSR, a simple yet effective new network design based on implicit image functions in which we deeply integrated spatial coordinates and periodic encoding with the implicit neural representation. Through extensive experiments and ablation studies, we show that spatial encoding is a missing key toward the next-stage high-performing implicit image function. Our UltraSR sets new state-of-the-art performance on the DIV2K benchmark under all super-resolution scales compared to previous state-of-the-art methods. UltraSR also achieves superior performance on other standard benchmark datasets in which it outperforms prior works in almost all experiments.
UNDERSTANDING THE MEANING OF DIGITALLY COMPETENT LEADERSHIP IN SCHOOLS: A REVIEW OF RESEARCH

The digitalization of schools is a multi-dimensional process, spanning many different areas from infrastructure and administrative routines to pedagogical issues, competence development, and student learning. Recently, digital leadership has been emphasized as crucial to reaching a fruitful digitalization in schools [1, 2]. However, there is a lack of systematic knowledge of what competencies are needed to lead such strategic work [3]. This study is a systematic review of research about digital leadership in schools in studies published from 2009 to 2019. The search was conducted using three databases to cover a board spectrum of relevant publication outlets including; ‘Education Resources Information Center’ (ERIC), Academic Search Premier, and Business Source Premier. Broad search terms were used to get a comprehensive search result, focused on locating results from different perspectives in the educational research field. The findings stress the importance of concretizing policies, having a coherent educational vision, and organizing for school development as key factors for cultivating the digitalization of schools. However, research that addresses what competence school leaders need to lead this strategic work remains limited. The contribution of the paper includes theorization about the meaning of digitally competent leadership in a school context, and future directions for research are discussed.References:[1] A. Bonifaz and A. Zucker, "Lessons learned about providing laptops for all students," Newton, MA: Education Development Center. Retrieved October, vol. 15, p. 2007, 2004.[2] M. S. Islam and Å. Grönlund, "An international literature review of 1: 1 computing in schools," Journal of educational change, vol. 17, no. 2, pp. 191-222, 2016.[3] F. Pettersson, "On the issues of digital competence in educational contexts–a review of literature," Education and Information Technologies, vol. 23, no. 3, pp. 1005-1021, 2018.
Cross-domain recommendation based on multilayer graph analysis using subgraph representation

This paper presents cross-domain recommendation based on multilayer graph analysis using subgraph representation. The proposed method constructs two graphs in source and target domains utilizing user-item embedding and trains link relationships between the users’ embedding features on each above graph via graph convolutional networks considering subgraph representation. Thus, the proposed method can obtain features with high representation ability, and this is the main contribution of this paper. Then the proposed method can estimate the user’s embedding features in the target domain from those in the source domain and recommend items to users by using the estimated features. Experiments on real-world e-commerce datasets verify the effectiveness of the proposed method.
APPLSS: Adaptive privacy preserved location sharing scheme based on attribute-based encryption

Unauthorized access to location information in location-based service is one of the most critical security threats to mobile Internet. In order to solve the problem of quality of location sharing while keeping privacy preserved, adaptive privacy preserved location sharing scheme called APPLSS is proposed, which is based on a new hierarchical ciphertext-policy attribute-based encryption algorithm. In the algorithm, attribute authority sets the attribute vector according to the attribute tags of registration from the location service providers. Then the attribute vector can be adaptively transformed into an access structure to control the encryption and decryption. The APPLSS offers a natural hierarchical mechanism in protecting location information when partially sharing it in mobile networks. It allows service providers access to end user's sensitive location more flexibly, and satisfies a sufficient-but-no-more strategy. For end-users, the quality of service is obtained while no extra location privacy is leaked. To improve service response performance, outsourced decryption is deployed to avoid the bottlenecks of the service providers and location information providers. The performance analysis and experiments show that APPLSS is an efficient and practical location sharing scheme.
A Comparative Study on Lower Limb Muscle Activity during Planing depending on Skill Level among Windsurfers

This study aimed to identify the lower limb muscle activity during windsurfing planing depending on the their skill level among windsurfers and to provide the useful information on the development of training programs for enhancing windsurfers’ performance. Seven elite and 6 amateur windsurfers who had no neurological or musculoskeletal impairment in the past two years in lower extremities participated in this study. All participants used footstraps located in metatarsophalangeal joint, and the muscle activation of their tibialis anterior, peroneus longus, medial gastrocnemius, and biceps femoris in the front and rear legs were measured during the starboard and port tacks of windsurfing planing. The mean value of each muscle in 15s was calculated and analyzed. The conclusions are as follows. Tibialis anterior in elite windsurfers showed higher activity than in amateurs, but both medial gastrocnemius and biceps femoris in amateurs showed higher activation than in elites, regardless of planing directions. These results can explain that amateur windsurfers used the larger muscles such as medial gastrocnemius and biceps femoris in the front leg for controlling their board and maintaining the balance on board, whereas elites used the tibialis anterior more strongly compared to amateurs for maintaining board control with an ankle strategy.
Balancing Biases and Preserving Privacy on Balanced Faces in the Wild

There are demographic biases in current models used for facial recognition (FR). Our Balanced Faces In the Wild (BFW) dataset serves as a proxy to measure bias across ethnicity and gender subgroups, allowing one to characterize FR performances per subgroup. We show performances are non-optimal when a single score threshold is used to determine whether sample pairs are genuine or imposter. Across subgroups, performance ratings vary from the reported across the entire dataset. Thus, claims of specific error rates only hold true for populations matching that of the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial features extracted using state-of-the-art. Not only does this technique balance performance, but it also boosts the overall performance. A benefit of the proposed is to preserve identity information in facial features while removing demographic knowledge in the lower dimensional features. The removal of demographic knowledge prevents future potential biases from being injected into decision-making. This removal satisfies privacy concerns. We explore why this works qualitatively; we also show quantitatively that subgroup classifiers can no longer learn from the features mapped by the proposed.
Disigning A Novel Architecture to Reduce Stand by and Dynamic Power Dissipation for Sleepy Keeper Cmos Logic Circuits

Scaling down of transistors will directly affects the decreasing in threshold voltage.Impact of decreasing threshold voltage is increasing in leakage current.In this paper many power gating circuits are discussed for their advantages and disadvantages, based on this literature survey a novel low power state retention technique is proposed in this paper.The proposed technique with its novel architecture reduces the leakage power (static power) and dynamic power dissipations.It also gives the state retained output.Performance of the proposed circuit is evaluated on some basic circuits in terms of their total and dynamic power dissipations.Extensive SPICE simulations were carried out and the results are compared with some existing techniques.Simulated results shows that proposed technique gives the efficient power minimization and state retained standby mode output.
A summary of research progress of single image to remove rain and fog based on deep learning

With the rapid development of artificial intelligence, computer vision systems based on image recognition, object detection, target tracking and other technologies are widely used in aviation, military industry, agriculture and other fields. However, due to bad weather conditions, such as haze and rain, the quality of the images collected by this type of system is impaired, which directly causes its performance to decline, causing serious losses to related fields. Therefore, the research on removing rain and fog on images has attracted the attention of many scholars. In recent years, deep learning has shined in the field of computer vision. Hence, many scholars have combined deep learning methods to remove rain and fog on degraded images and have achieved certain results. In order to gain a deeper understanding of the research progress of the single image rain and fog removal algorithm based on deep learning, this paper collates and analyzes some related literature, introduces and summarizes the algorithm research of the two application scenarios of rain and fog removal in detail. Finally, this paper will briefly summarize these rain and fog removal algorithms and puts forward a prospect for the research of deep learning in single image rain and fog removal.
Group-aware Label Transfer for Domain Adaptive Person Re-identification

Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any further annotations. Most successful UDA-ReID approaches combine clustering-based pseudo-label prediction with representation learning and perform the two steps in an alternating fashion. However, offline interaction between these two steps may allow noisy pseudo labels to substantially hinder the capability of the model. In this paper, we propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning. Specifically, a label transfer algorithm simultaneously uses pseudo labels to train the data while refining the pseudo labels as an online clustering algorithm. It treats the online label refinery problem as an optimal transport problem, which explores the minimum cost for assigning M samples to N pseudo labels. More importantly, we introduce a group-aware strategy to assign implicit attribute group IDs to samples. The combination of the online label refining algorithm and the group-aware strategy can better correct the noisy pseudo label in an online fashion and narrow down the search space of the target identity. The effectiveness of the proposed GLT is demonstrated by the experimental results (Rank-1 accuracy) for Market1501$\to$DukeMTMC (82.0\%) and DukeMTMC$\to$Market1501 (92.2\%), remarkably closing the gap between unsupervised and supervised performance on person re-identification.
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning

Ethereum smart contracts are automated decentralized applications on the blockchain that describe the terms of the agreement between buyers and sellers, reducing the need for trusted intermediaries and arbitration. However, the deployment of smart contracts introduces new attack vectors into the cryptocurrency systems. In particular, programming flaws in smart contracts can be and have already been exploited to gain enormous financial profits. It is thus an emerging yet crucial issue to detect vulnerabilities of different classes in contracts in an efficient manner. Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable, or train individual classifiers for each specific vulnerability, or demonstrate multi-class vulnerability detection without extensibility consideration. To overcome the scalability and generalization limitations of existing works, we propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for Ethereum smart contracts that support lightweight transfer learning on unseen security vulnerabilities, thus is extensible and generalizable. ESCORT leverages a multi-output NN architecture that consists of two parts: (i) A common feature extractor that learns the semantics of the input contract; (ii) Multiple branch structures where each branch learns a specific vulnerability type based on features obtained from the feature extractor. Experimental results show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract. When extended to new vulnerability types, ESCORT yields an average F1-score of 93%. To the best of our knowledge, ESCORT is the first framework that enables transfer learning on new vulnerability types with minimal modification of the DNN model architecture and re-training overhead.