Reviewerly/exHarmony huggingface dataset:
Forward Warping-Based Video Frame Interpolation Using a Motion Selective Network

Recently, deep neural networks have shown surprising results in solving most of the traditional image processing problems. However, the video frame interpolation field does not show relatively good performance because the receptive field requires a vast spatio-temporal range. To reduce the computational complexity, in most frame interpolation studies, motion is first calculated with the optical flow, then interpolated frames are generated through backward warping. However, while the backward warping process is simple to implement, the interpolated image contains mixed motion and ghosting defects. Therefore, we propose a new network that does not use the backward warping method through the proposed max-min warping. Since max-min warping generates a clear warping image in advance according to the size of the motion and the network is configured to select the warping result according to the warped layer, using the proposed method, it is possible to optimize the computational complexity while selecting a contextually appropriate image. The video interpolation method using the proposed method showed 34.847 PSNR in the Vimeo90k dataset and 0.13 PSNR improvement compared to the Quadratic Video Interpolation method, showing that it is an efficient frame interpolation self-supervised learning.
A Multi-Domain Embedding Framework for Robust Reversible Data Hiding Scheme in Encrypted Videos

For easier cloud management, reversible data hiding is performed in an encrypted domain to embed label information. However, the existing schemes are not robust and may cause the loss of label information during transmission. Enhancing robustness while maintaining reversibility in data hiding is a challenge. In this paper, a multi-domain embedding framework in encrypted videos is proposed to achieve both robustness and reversibility. In the framework, the multi-domain characteristic of encrypted video is fully used. The element for robust embedding is encrypted through Logistic chaotic scrambling, which is marked as element-I. To further improve robustness, the label information will be encoded with the Bose–Chaudhuri–Hocquenghem code. Then, the label information will be robustly embedded into element-I by modulating the amplitude of element-I, in which the auxiliary information is generated for lossless recovery of the element-I. The element for reversible embedding is marked as element-II, the sign of which will be encrypted by stream cipher. The auxiliary information will be reversibly embedded into element-Ⅱ through traditional histogram shifting. To verity the feasibility of the framework, an anti-recompression RDH-EV based on the framework is proposed. The experimental results show that the proposed scheme outperforms the current representative ones in terms of robustness, while achieving reversibility. In the proposed scheme, video encryption and data hiding are commutative and the original video bitstream can be recovered fully. These demonstrate the feasibility of the multi-domain embedding framework in encrypted videos.
Relational sensitivity and participatory practice

Poise – self-awareness of how one is and acts, and punctuation – attunement to relational dynamics, describe integral relational sensitivities for practitioners in Participatory Design (PD). These concepts describe important personal and embodied “practices of readiness” for participating in contingent and relational processes of designing with others. However, more attention on these “practices of readiness” is required in design research and design training to better comprehend and develop relevant relational sensitivity and competences. Through an empirical study, this paper explores what relational sensitivity i.e., poise and punctuation, could be in a participatory practice and what it can do for the process. The paper also explores ways in which relational sensitivity could be nurtured through participatory practices. This research contributes to PD literature by connecting concepts related to relational sensitivity with an empirical study, elaborating on the role of relational sensitivity in PD practice.
Foraging Tangibles for Participatory Design

The sustainability of materials used in Participatory Design processes—be they tangibles, or other—typically provided by the designer; is not commonly foregrounded. We focus on the social and environmental impact of tangibles by considering two cases. The first concerns the conception of a Forest-Library. A steering committee gathered to map stakeholders across a municipality, using foraged elements from a barn. The second case brings together organisations concerned with waste activism, to collectively compare and negotiate their stakeholder interrelations. The foraged tangibles are environmentally sustainable by virtue of a) being foraged rather than designed, and b) their ability to be returned to use or to the nutrition cycle once their usefulness to the PD process has ended. Following Liboiron's conceptualisation of pollution as colonialism we consider if their connection to place might assist in troubling the ways that these mapping processes might be considered socially, as well as environmentally sustainable.
An Impact Study on the Use of Telework during Covid-19 Pandemics

In the context of the pandemic caused by Covid-19, an important aspect that was affected and changed was the way people communicate. Communication efficiency can be improved by using modern technologies, for example: mobile devices, digital audio and video platforms, text platforms, social media and more. For further economic success, digital skills are needed not only for executives, but also for managers, in order to be able to lead teams and avoid situations of stress and tension in the virtual environment. This scientific paper includes a primary research of how the pandemic affected the communication at the workplace. For this a qualitative analysis was first performed, in which the interview was used as a working tool. Next, on the basis of a questionnaire, a hypothesis was formulated and validated afterwards through multiple linear regression. This research highlighted the particularly important role of communication and, in this context, the role of the remote team leader. In order to keep the team motivated and interested in the virtual environment, the manager must use modern tools to motivate, support and express his appreciation when the team, respectively the members of his team, achieve their goals successfully.
Debiased Large Language Models Still Associate Muslims with Uniquely Violent Acts

Recent work demonstrates a bias in the GPT-3 model towards generating violent text completions when prompted about Muslims, compared with Christians and Hindus. Two pre-registered replication attempts, one exact and one approximate, found only the weakest bias in the more recent Instruct Series version of GPT-3, fine-tuned to eliminate biased and toxic outputs. Few violent completions were observed. Additional pre-registered experiments, however, showed that using common names associated with the religions in prompts yields a highly significant increase in violent completions, also revealing a stronger second-order bias against Muslims. Names of Muslim celebrities from non-violent domains resulted in relatively fewer violent completions, suggesting that access to individualized information can steer the model away from using stereotypes. Nonetheless, content analysis revealed religion-specific violent themes containing highly offensive ideas regardless of prompt format. Our results show the need for additional debiasing of large language models to address higher-order schemas and associations.
Implementation of IMRRT Path Planning Algorithm for Mobile Robot

Among many robot path planning algorithms, the Rapidly-exploring Random Tree (RRT) algorithm is widely used due to its strong search ability and easy implementation. However, the RRT algorithm has strong randomness and low search efficiency, and the path generated by the expansion has a certain degree of optimization. In response to this situation, this paper proposes a performance-optimized IMRRT algorithm. Based on the basic RRT algorithm, the expansion weight of the target node is introduced, so that the generation of new nodes in the expanded random tree is biased in the direction of the target node. At the same time, adjust the planned path according to the space environment to improve the problems in the RRT algorithm. The results of experimental simulation show that compared with the basic RRT algorithm, the RRT<sup>*</sup> algorithm, and the Informed RRT<sup>*</sup> algorithm, the running time of the IMRRT algorithm is reduced by an average of 25.99%, 56.91%, and 53.31%; And the path planning length is also effectively reduced, an average decrease of 4.2%. The IMRRT algorithm improves search efficiency and realizes the optimization of the planned path.
TMsDP: two-stage density peak clustering based on multi-strategy optimization

Purpose The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP. Design/methodology/approach First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results. Findings The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms. Originality/value The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.
Empirical Study of Cyber Security Threats in Moldovan Higher Education Institutions

Higher Education Institutions (HEIs) have always been the target of cyber attacks due to the information assets they hold. The move to online study as a result of restrictions imposed in the spring of 2020 has led to increased cyber security threats to academia due to the vulnerabilities of online learning platforms and video conferencing applications. The hypothesis of this paper was that Moldovan Higher Education Institutions had to face cyber security challenges similar to international higher education institutions, through the empirical study, the use of the questionnaire-based survey to collect quantitative data from stakeholders of the institutions. The results of the survey conducted between September and November 2020, showed that HEIs in Moldova are targeted by cyber attacks as well as international ones and the nature of threats is mostly the same, namely: malware, phishing and DoS attacks.
Application Research on Face Image Evaluation Algorithm of Deep Learning Mobile Terminal for Student Check-In Management

With the increase in the number of data, the traditional shallow image features cannot meet the needs of image representation. As an important means of image research, deep learning network has been paid attention to. In the field of face image evaluation, deep learning algorithm has been introduced, and the recognition technology has gradually matured. Based on this, this paper studies the application of face image evaluation algorithm of deep learning mobile terminal for student check-in management. A face image detection model for student check-in management is constructed, and a deep learning network is used to realize face detection. A face detection algorithm based on candidate region joint deep learning network is designed, and a face key point detection method based on cascaded convolution network is proposed. Aiming at the low efficiency of face recognition and detection, the existing loss function is optimized, the extraction algorithm of face binary features is proposed, and experiments are designed to analyze the performance of the algorithm. The simulation results show that the face detection based on the improved deep learning network can shorten the retrieval time and improve the accuracy of face image classification.
The Impact of Dynamic Capabilities on Strategic Supremacy through Organizational Immunity at Jordanian Telecommunication Companies

This study aimed at analyzing the impact of dynamic capabilities on strategic supremacy through organizational immunity at Jordanian telecommunication companies. The study population consisted of all managers and department heads, who represented the sampling unit to collect data from the researched companies. A survey sample of the study population was adopted. The descriptive analytical approach was used and the questionnaire was adopted as a main tool for data collection. A number of statistical methods were used, including means, multiple regression and structural equation The study concluded that there is an effect of dynamic capabilities with its dimensions on strategic supremacy, that there is an effect of dynamic capabilities on organizational immunity and that there is an effect of organizational immunity on strategic supremacy with an indirect effect of organizational immunity as a mediating variable between dynamic capabilities and strategic supremacy. The study recommended the need to pay attention to learning, integration, building organizational memory, applying benchmarking practices and managing strategic alliances in line with the main objectives.
Effectiveness of the Implementation of Education Budget Arrangements in the APBN/APBD Based on the 1945 Constitution Article 31 paragraph (4) and Law No. 20 of 2003 concerning the National Education System Article 49 paragraph (1) related to the Covid 19 Pandemic

The mandate of Law number 20 of 2003 concerning the National Education System ("Sisdiknas)" article 49 paragraph 1 regulates education funds which are allocated at least 20 percent of the APBD and APBN.This is in accordance with the mandate of the 1945 Constitution article 31 paragraph (4).The implementation of the mandate of the 1945 Constitution has been stated consistently since 2009.This means that the policy of the education budget in APBN and APBD has been launched for more than a decade.Various studies have been carried out highlighting the implementation problems, but have not obtained satisfactory results.Meanwhile, the 2021 Education Budget has been increased 5 times to IDR 550 trillion, as stated by the Minister of Finance, Sri Mulyani.This significant increase was carried out to support the education sector during the COVID-19 pandemic.However, it is necessary to study the extent to which the effectiveness of the implementation of the policy by the Central Government and Regional Governments are regulated in the relevant laws and regulations if these are not implemented proper.This paper also discusses the role of existing state institutions in the seriousness of their respective commitments in realizing the mandate of the 1945 Constitution in the field of education.
Face Mask Detection and Classification System Using Deep Learning

Abstract: COVID-19 pandemic caused by novel corona virus is unendingly spreading up to now everywhere the planet. The impact of COVID-19 has been fallen on the majority sectors of development. The health care system goes through a crisis. several preventative measures are taken to cut back the unfold of this sickness wherever carrying a mask is most significant one in all them. In this paper, we have a tendency to propose a system that restricts the expansion of COVID-19 by looking for people that aren't carrying any facial mask in a very good town network wherever all the general public places area unit monitored with television system (CCTV) cameras. While someone while not a mask is detected, the corresponding authority is educated through the town network. A deep learning design is trained on a dataset that consists of pictures of individuals with and while not masks collected from numerous sources. The trained design achieved ninety-eight accuracies on characteristic folks with and while not a facial mask for antecedently unseen check knowledge. It's hoped that our study would be a useful gizmo to cut back the unfold of this disease for several countries within the world. A mask detection dataset consists of with mask and while not mask pictures, we have a tendency to area unit planning to use OpenCV to try to period of time face detection from a live stream via our digital camera We will use the dataset to create a COVID-19 mask detector with pc vision exploitation Python, OpenCV, and Tensor Flow and Kera’s. Our goal is to spot whether or not the person on image/video stream carrying a mask or not with the assistance of pc vision and deep learning
Proposed New Product Feature Development of Video Communication Platform in PT. XYZ Using Six Sigma DMADV

Over time, technology has developed quite rapidly. The development of technology is also motivated by the Covid19 pandemic. One of the things affected by the pandemic is the face-to-face habit which is starting to be replaced by using video communication platforms. As a company engaged in the digital industry in Indonesia, PT. XYZ needs to be adaptive in accordance with the values held within the organization. This is done by providing a video communication platform that can help the Indonesian people. Not only providing, but organizations also need to adjust whether the products offered are in accordance with the wishes and needs of users. find out what factors influence the number of clients who register and what product features are desired by the user. Problem analysis is carried out using the Six Sigma DMADV method, which is a method in developing new products or processes within an organization. After identifying the problem and analyzing the root cause of the problem, it was found that so far there has been no clear document guide related to the products offered, causing confusion for all teams, especially the developer team and design team. There must be some guides or documents, and new products that are in accordance with the wishes and needs of stakeholders. It was found that customer desire can be answered by developing new products, such as: Automization Quality Assurance and Dashboard Admin. In developing the product, the organization needs to ensure 3 things for this plan to be successful. 3 things that need to be considered are: cycle time, resource capabilities, and the costs required to develop the product.
LBS user location privacy protection scheme based on trajectory similarity

During the data set input or output, or the data set itself adds noise to enable data distortion to effectively reduce the risk of user privacy leakage. However, in the conventional method, the added noise may cause data distortion, thereby appealed against it. However, the amount of noise is too small and cannot meet the effect of privacy protection. Therefore, we propose a LBS user location privacy protection scheme based on trajectory similarity (DPTS). With double privacy protection without reducing the efficiency of algorithms, it does not cause data distortion to provide more reliable privacy protection. The main contributions of this article include: (1) In the process of collecting and publishing the location data, introduce into the privacy protection method, (2) The differential privacy algorithm based on the trajectory prefix tree is superimposed on the basis of the false position replacement algorithm based on the trajectory similarity, (3) Propose LBS-based Difference Privacy Protection Algorithm. In the algorithm, We reach the purpose of protecting user personal privacy by replace the original trajectory into a fake track trace that is the lowest degree of similarity in the interval. Then establish a prefix tree and add noise to the positional frequency. It is in order to further protect the sensitive location information, double protection in the trajectory data set, and the degree of privacy protection is improved. Simulation experiment results show that the proposed algorithm is effective. The algorithm can suppress the distortion rate of data while improving the amount of noise, and in improving the algorithm operation efficiency, it reduces the risk of leakage of sensitive position information.
Violence Recognition on Videos Using Two-stream 3D CNN with Custom Spatiotemporal Crop

Abstract Violence may happen anywhere. One of the ways to know and overseethe violence in some places is by installing Closed-circuit Television(CCTV). The recorded video captured by CCTV can be used as proofin a law court. Violence video classification is also one of the topicsbeing discussed in deep learning. The latest violence video dataset isRWF-2000. That dataset contains violent and non-violent videos, 5 secondsduration, 30 frames per second, with the amount of 2000 videos.That publication also has the best accuracy of 87.25% by their proposedmethod. In this study, we will use a Residual Network known tohave the advantage of solving the vanishing gradient problem. Besidethat, we also implement transfer learning from Kinetics and Kinetics+ Moments in Time pre-trained data. We also test the numberof frames and the location of the sampling frame range. RGB andoptical flow inputs are separately trained with different configurations.The RGB input best accuracy is 89.25% with pre-trained Kinetics +Moments in Time, using frame location 49-149. The optical flow inputbest accuracy is 88.5% with pre-trained Kinetics, using 74 frames. Wealso try to sum the output of both inputs making accuracy of 90.5%.
ULDGNN: A Fragmented UI Layer Detector Based on Graph Neural Networks

While some work attempt to generate front-end code intelligently from UI screenshots, it may be more convenient to utilize UI design drafts in Sketch which is a popular UI design software, because we can access multimodal UI information directly such as layers type, position, size, and visual images. However, fragmented layers could degrade the code quality without being merged into a whole part if all of them are involved in the code generation. In this paper, we propose a pipeline to merge fragmented layers automatically. We first construct a graph representation for the layer tree of a UI draft and detect all fragmented layers based on the visual features and graph neural networks. Then a rule-based algorithm is designed to merge fragmented layers. Through experiments on a newly constructed dataset, our approach can retrieve most fragmented layers in UI design drafts, and achieve 87% accuracy in the detection task, and the post-processing algorithm is developed to cluster associative layers under simple and general circumstances.