Reviewerly/exHarmony huggingface dataset:
Crowd behavior detection in videos using statistical physics

Anomaly in the crowds can create a threat to public security. Manual detection of crowd behavior in video surveillance by human operators is a difficult and time-consuming task. There is an urgent need for automated detection of abnormal event in videos. In this process, the calculation of descriptors which expressed as measurements with accurate interpretation is a challenging problem. In this paper, we defined a novel descriptor of crowd behavior which is analogized between human crowds and statistical physics principles. We build a new crowd motion systems and the theory of entropy and internal energy are introduced for evaluating the state of the crowd. Specifically, entropy is calculated with the concept of position density map and pixels in the image are treated as particles. The internal energy of a crowd is obtained with optical flow field. Finally, the entropy and internal energy are collectively as energy flow, and LDA method is used to train the normal crowd behavior. A benchmark with state-of-the-art abnormal detection methods confirms that the effectiveness of the proposed method. Comparison of available techniques for detecting abnormal crowd behavior in surveillance videos has also been done in this paper.
The higher education and the sphere of labor in digital economy: a new mechanism of interaction

The article addresses a new mechanism of interaction of Russia’s higher education and the sphere of labor. New challenges facing modern higher education are predetermined by digital transformation in the sectors of the economy and changes in the structure of the labor market resulted from the development of digital technologies. The author shows how the application of modern digital technologies affects the transition to a new digital format of training, radically changing the paradigm of university development, scientific and educational activities, and also organizing the work to attract and retain young talents. Finally, the author provides valuable conclusions concerning specific development of digital educational environment in the sphere of higher education.
Intelligent Violence Video Detection System

Due to the busy and stressful lifestyle, humans tend to feel frustrated frequently. This harmful emotional behavior results in violations of several rules, regulations and legislation. Violence is one of the serious issues which emerges due to this situation. It also results in uncontrollable human behavior. This behavior can either be verbal arguments or even physical conflicts. A trend of recording and publishing videos related to these kinds of violations in various platforms can be observed widely at present. Therefore, the terms and conditions of these platforms are subjected to frequent changes. Difficulty in identifying and controlling of violent events will result in an increase of such cases. Due to these reasons, the demand for violence detection systems will be significantly increased. Efficient violent detection systems are lacking currently. But, the usage of artificial intelligence in these systems are further limited. Four major components have been used to achieve this goal. They are video-based, embedded audio-based, abused text-based and thumbnail-based violence detection. The machine learning and image processing techniques are used along with these components to improve the clarity of violence detection.
Shuffling Countermeasure against Power Side-Channel Attack for MLP with Software Implementation

In recent years, several attack methods for artificial intelligence (AI) have been reported. Therefore, the study of countermeasure against illegal attacks for AI is very important. This study proposes a new countermeasure method against power side-channel based model extraction attacks for AI. The proposed method improves the resistance against power side-channel based model extraction attacks by randomizing the calculation order with shuffling method. Experiments using an actual device indicated that the proposed countermeasure could improve the resistance against power side-channel based model extraction attacks.
USING QUIZIZZ GAME TO IMPROVE STUDENT’S VOCABULARY MASTERY IN GRADE VIII C AT MTs IBNU ROSYAD

The Covid-19 pandemic situation made the government announce the use of e-learning. With this policy, teachers are expected to apply interesting media to be studied. This research aimed to find out whether or not Quizizz Game could improve students’ vocabulary mastery and the supporting and inhibiting factors of the success of Quizizz Game implementation. It was a collaborative Classroom Action Research (CAR). The subject of this research was grade VIII-C of MTs Ibnu Rosyad in the academic year of 2020/2021 which consisted of 20 students. This research was conducted in two cycles which each consists of planning, acting, observing, and reflecting. The data were gathered through qualitative and quantitative data. The results of this research showed that there was improvement from cycle 1 to cycle 2. It can be seen from the mean score of pre-test were 53, the mean score of post-test 1 was 69 and the mean score of post-test 2 was 84. The students' attention and motivation also helped use of Quizizz. But, limited school facilities and wifi become the obstruction
Crime Rate Detection Based on Text Mining on Social Media Using Logistic Regression Algorithm

Social media recently are very populer in Indonesia and in the world. Luckly, this platform may express their opinion and emotion even other party especially reseacher mostly use this opportunity to find any solution for any case likely competitive business, decisions maker, and possible analitics and predictive support system. In this case our analysis is content on Twitter and Facebook which user often posted information about the crime which matters need police attention. Therefore our purpose is detection of crime rate on social media to find pattern trend of tweet number crime. This work used text mining approach for classification of tweet and post content text into 10 class of crime. The Algorithm used for classifier are Logistic Reggression, Naive Buyes, Support Vector Machine (SVM) and Decission Tree, from all the algorithm used, Logistic regression give the best accuracy of 90%
Secure Implementation of a Quantum-Future GAKE Protocol

Incorrect cryptographic protocol implementation and malware attacks targeting its runtime may lead to insecure execution even if the protocol design has been proven safe. This research focuses on adapting a runtime-verification-centric trusted execution environment (RV-TEE) solution to a quantum-future cryptographic protocol deployment. We aim to show that our approach is practical through an instantiation of a trusted execution environment supported by runtime verification and any hardware security module compatible with commodity hardware. In particular, we provide: (i) A group chat application case study which uses the quantum-future group key establishment protocol from González Vasco et al., (ii) An implementation of the protocol from González Vasco et al. employing a resource-constrained hardware security module, (iii) The runtime verification setup tailored for the protocol’s properties, (iv) An empirical evaluation of the setup focusing on the user experience of the chat application.
Are duplicates really harmful? An empirical study on bug report summarization techniques

Abstract Recent research works have proven that duplicate bug reports can provide helpful information to assist developers in software tasks such as fault localization and program fixing, while thoroughly reading duplicate bug reports is time‐consuming and inefficient. Summarization is a possible solution for gaining essential information quickly. However, there are many challenges when applying existing summarizing techniques on duplicate bug reports. Duplicate bug reports describe the same problem from different views and vary in quality, content, and writing style. Moreover, the code snippet understanding and the semantic gap between natural and programming languages make the summary generation even more difficult. Thus, in this paper, we want to investigate whether the state‐of‐the‐art summarization approaches can overcome the resistance and generate an effective summary for duplicate bug reports. We collected more than 8,000 groups of duplicate reports from GitHub and labeled 60 groups with 149 reports manually for the evaluation. Results showed that although the existing summarization approaches can work on duplicate bug reports, there are significant differences between them when it comes to code snippet summarization. Moreover, several methods can be very sluggish for summarizing long bug reports. Our study provides insights and guidelines for choosing proper summarization approaches in different scenarios.
AEFNet: Adaptive Scale Feature Based on Elastic-and-Funnel Neural Network for Healthcare Representation

Healthcare Representation learning has been a key element to achieving state-of-the-art performance on healthcare prediction. Recent advances based Electronic Healthcare Records(EHRs) are mostly devoted to extracting temporal progression patterns with temporal model and their variants. Although these works have shown excellent performances in healthcare prediction, the unified temporal pattern may not be suitable for individuals in all healthcare conditions. Moreover, some studies ususally introduce complex Deep Neural Networks models and medical prior knowledge to get compact representation, causing great computational burden. In this paper, we propose a general health care representation model, named AEFNet. We only leverage three simple convolution operations and a set of up and down sampling to ensure performance and model complexity equally, which achieves adaptively extract distinct individual key feature in a light manner. AEFNet can shrink and refine highly suitable scale information adaptively and comletely. Breaking traditional fixed convolution scale or multi-scale, AEFNet achieves scale adaptively to extract the most significant information and context relationship. Finally, We validate our method on the public dataset MIMIC-III, and the evaluation results indicate that our method can significantly outperform other remarkable baseline models.
Computational Assessment of Semantic Novelty in Texts

The process by which novel ideas are flagged from a large set of creative ideas is slow and error prone. Experts first read thousands of ideas and then assign a novelty to each idea based on its relative perceived frequency. This chapter investigates computational methods to detect novel ideas that could either augment or replace expert assessment. Our goal is to find a computational method that assesses novelty of ideas in text form in a similar way and with similar results as human experts. A review of text embedding approaches using vector spaces representations, probabilistic language models, word and sentence embeddings shows that semantic similarity especially in short text is currently difficult to capture. The cognitive process of novelty detection is similar to that of memory recognition or recall as well as decision making. A short overview of recognition and recall experiments, as well as decision making and recognition models identifies the most important aspects needed in a computational model for novelty detection. Our cognitive model for novelty detection has three components: (a) a perceptual surprise of an idea which combines the individual surprise of each word with the surprise of its context versus the dataset of all ideas as well as a domain specific corpus, (b) a relevance of each idea accumulated from the relevance of individual words in comparison with the vocabulary of the domain specific corpus, and (c) a conceptual familiarity evaluated as similarity between holistic embeddings of individual ideas. These three features are then combined in a logistic classifier and trained to assess novel ideas. Results on a collection of ideas from a group experiment are very promising with F1-scores over 0.60. We discuss the results of the current model and possible extensions to a model that evaluates novelty in real time and which could provide immediate feedback during ideation sessions.
Applications across Co-located Devices: User Interface Distribution, State Management and Collaboration

We live surrounded by computing devices, but we are not yet combining them into a natural unified user experience. Therefore, we have designed and implemented a framework to generalize and support the development of multi-device applications, which distribute their User Interfaces (UI) across co-located devices. The framework is composed of: authentication and authorization services; a broker to sync information across application instances; background services to gather the capabilities of the devices; an integration module with an indoor positioning system to determine when to associate devices. It provides a library to determine the components to be displayed based on UI requirements and the capabilities of the available devices. It also includes elements to manage the distribution of UI components and application states that can be shared to enable collaboration. We present an application prototype and a user study which assesses how multi-device applications are perceived by users. The results are very positive, indicating that we should keep working on our research since the users received very well the prototype and the concept, which also validates the support provided by the framework.
New Way to Enumerate Large Quadratic Residue Codes Based on Hash and Automorphism Group

Quadratic Residue (QR) codes are cyclic binary linear block codes. They are known for their remarkable high error correction capacity. However, it is very hard to enumerate and analyze them. A very interesting method called Hash_MIM_Weights_Enumerators (HMWE) was developed previously to determine the weights enumerators of QR codes having lengths in the form 8m-1. Nevertheless, these enumerators were found just for lengths less than or equal to 223. In this paper, we propose an important improvement to HMWE method by new exploitation of Automorphism Group. Instead of the list of codewords collected by cyclic permutations, a very large list of all codewords obtained by using Automorphism group will be generated. This important modification on HMWE has significantly reduced both the run time complexity and the spatial memory complexity. Consequently, the likelihood weights enumerator of the QR (239, 120) is determined for the weights 31, 32, 34 and 35. For the weights 39 and 40, approximated values are computed with confidence interval.
Time-energy uncertainty relation for noisy quantum metrology

We derive a fundamental trade-off relating the accuracy loss of a quantum clock due to a noise channel to the information about the energy of the clock that leaks to the environment.
A Homotopy Optimization Method for Orthogonal Non-Negative Matrix Factorization

Data clustering is a key problem in data science and machine learning. In this paper, we consider orthogonal nonnegative matrix factorization (ONMF) for scaled data clustering. The non-convex orthogonality constraint of ONMF raises a great challenge from an optimization viewpoint. We study a convex-constrained transformation of ONMF that allows us to control the approximation accuracy and problem difficulty through a parameter. We then apply a homotopy strategy in which we trace the solution path of a sequence of the aforementioned transformed problems, gradually moving from easy problems to near-ONMF problems. Intuitively, doing so may allow us to avoid local minima. Numerical results show that our homotopy method yields competitive clustering performance in synthetic data experiments and in a real-data hyperspectral clustering experiment.
GPU-Intensive Fast Entropy Coding Framework for Neural Image Compression

Neural compression has benefited from technological advances such as convolutional neural networks (CNNs) to achieve advanced bitrates, especially in image compression. In neural image compression, an encoder and a decoder can run in parallel on a GPU, so the speed is relatively fast. However, the conventional entropy coding for neural image compression requires serialized iterations in which the probability distribution is estimated by multi-layer CNNs and entropy coding is processed on a CPU. Therefore, the total compression and decompression speed is slow. We propose a fast, practical, GPU-intensive entropy coding framework that consistently executes entropy coding on a GPU through highly parallelized tensor operations, as well as an encoder, decoder, and entropy estimator with an improved network architecture. We experimentally evaluated the speed and rate-distortion performance of the proposed framework and found that we could significantly increase the speed while maintaining the bitrate advantage of neural image compression.
An Insider’s Take on Data Curation: Context, Quality, and Efficiency

This commentary describes how context, quality, and efficiency guide data curation at the University of Michigan's Inter-university Consortium for Political and Social Research (ICPSR). These three principals manifest from necessity. A primary purpose of this work is to facilitate secondary data analysis but in order to so, the context of data must be documented. Since a mistake in this work would render any results published from the data inaccurate, quality is paramount. However, optimizing data quality can be time consuming, so automative curation practices are necessary for efficiency. The implementation of these principles (context, quality, and efficiency) is demonstrated by a recent case study with a high-profile dataset. As the nature of data work changes, these principles will continue to guide the practice of curation and establish valuable skills for future curators to cultivate.
Video Games as a Media for Tourism Experience

Abstract Virtual reality technology in recent year has been a hot topic in tourism and hospitality field, both for research and practical purposes. Virtual technology has some limitation, such as costs and time, which can be addressed by a different form of virtual technology, which is video games. The purpose of this study is to analyze the effects of mental imagery, sense of presence and addition of storytelling towards tourism experience by using video games as a medium. Previous studies have analyzed effects of mental imagery and presence towards the tourism experience, but there is lack of research in using storytelling or narratives to add to the tourism experience. Storytelling has been studied in marketing field, and has been found to be a great marketing tool, because people love to hear stories. Video games can be a better tool for delivering storytelling, while providing solid mental imagery and a sense of presence to the location. The methodology proposed for this study is a quantitative approach, using survey questionnaire as a tool. Two sampling method is proposed. The first is to use players of location-based games as the population, and randomly take the sample. The second method is to uses tourists or potential tourists of a destination, have the respondents try out the game, and answer the questionnaire. The proposed research could provide the importance of storytelling to be added for virtual reality and video games usage in tourism and hospitality field.
Self-adaptive Graph Neural Networks for Personalized Sequential Recommendation

Sequential recommendation systems have attracted much attention for the practical applications, and various methods have been proposed. Existing methods based on graph neural networks (GNNs) mostly capture the sequential dependencies on an item graph by the historical interactions. However, due to the pre-defined item graph, there are some unsuitable edges connecting the items that may be weakly relevant or even irrelevant to each other, which will limit the ability of hidden representation in GNNs and reduce the recommendation performance. To address this limitation, we design a new method called Self-Adaptive Graph Neural Networks (SA-GNN). In particular, we employ a self-adaptive adjacency matrix to improve the flexibility of learning by adjusting the weights of the edges in the item graph, so as to weaken the effect of unsuitable connections. Empirical studies on three real-world datasets demonstrate the effectiveness of our proposed method.
Joint learning adaptive metric and optimal classification hyperplane

Metric learning has attracted a lot of interest in classification tasks due to its efficient performance. Most traditional metric learning methods are based on k-nearest neighbors (kNN) classifiers to make decisions, while the choice k affects the generalization. In this work, we propose an end-to-end metric learning framework. Specifically, a new linear metric learning (LMML) is first proposed to jointly learn adaptive metrics and the optimal classification hyperplanes, where dissimilar samples are separated by maximizing classification margin. Then a nonlinear metric learning model (called RLMML) is developed based on a bound nonlinear kernel function to extend LMML. The non-convexity of the proposed models makes them difficult to optimize. The half-quadratic optimization algorithms are developed to solve iteratively the problems, by which the optimal classification hyperplane and adaptive metric are alternatively optimized. Moreover, the resulting algorithms are proved to be convergent theoretically. Numerical experiments on different types of data sets show the effectiveness of the proposed algorithms. Finally, the Wilcoxon test shows also the feasibility and effectiveness of the proposed models.
Degree Matters: Assessing the Generalization of Graph Neural Network

Graph neural network (GNN) is a general framework for using deep neural networks on graph data. The defining feature of a GNN is that it uses a form of neural message passing where vector messages are exchanged between nodes and updated using neural networks. The message passing operation that underlies GNNs has recently been applied to develop neural approximate inference algorithms, but little work has been done on understanding under what conditions GNNs can be used as a core module for building general inference models. To study this question, we consider the task of out-of-distribution generalization where training and test data have different distributions, by systematically investigating how the graph size and structural properties affect the inferential performance of GNNs. We find that (1) the average unique node degree is one of the key features in predicting whether GNNs can generalize to unseen graphs; (2) the graph size is not a fundamental limiting factor of the generalization in GNNs when the average node degree remains invariant across training and test distributions; (3) despite the size-invariant generalization, training GNNs on graphs of high degree (and of large size consequently) is not trivial (4) neural inference by GNNs outperforms algorithmic inferences especially when the pairwise potentials are strong, which naturally makes the inference problem harder.