Reviewerly/exHarmony huggingface dataset:
Be Relevant, Non-Redundant, and Timely: Deep Reinforcement Learning for Real-Time Event Summarization

Real-time event summarization is an essential task in natural language processing and information retrieval areas. Despite the progress of previous work, generating relevant, non-redundant, and timely event summaries remains challenging in practice. In this paper, we propose a Deep Reinforcement learning framework for real-time Event Summarization (DRES), which shows promising performance for resolving all three challenges (i.e., relevance, non-redundancy, timeliness) in a unified framework. Specifically, we (i) devise a hierarchical cross-attention network with intra- and inter-document attentions to integrate important semantic features within and between the query and input document for better text matching. In addition, relevance prediction is leveraged as an auxiliary task to strengthen the document modeling and help to extract relevant documents; (ii) propose a multi-topic dynamic memory network to capture the sequential patterns of different topics belonging to the event of interest and temporally memorize the input facts from the evolving document stream, avoiding extracting redundant information at each time step; (iii) consider both historical dependencies and future uncertainty of the document stream for generating relevant and timely summaries by exploiting the reinforcement learning technique. Experimental results on two real-world datasets have demonstrated the advantages of DRES model with significant improvement in generating relevant, non-redundant, and timely event summaries against the state-of-the-arts.
Robust Speech Hashing for Digital Audio Forensics

The verification of the integrity and authenticity of multimedia content is an essential task in the forensic field, in order to make digital evidence admissible. The main objective is to establish whether the multimedia content has been manipulated with significant changes to its content, such as the removal of noise (e.g., a gunshot) that could clarify the facts of a crime. In this project we propose a method to generate a summary value for audio recordings, known as hash. Our method is robust, which means that if the audio has been modified slightly (without changing its significant content) with perceptual manipulations such as MPEG-4 AAC, the hash value of the new audio is very similar to that of the original audio; on the contrary, if the audio is altered and its content changes, for example with a low pass filter, the new hash value moves away from the original value. The method starts with the application of MFCC (Mel-frequency cepstrum coefficients) and the reduction of dimensions through the analysis of main components (principal component analysis, PCA). The reduced data is encrypted using as inputs two values from a particular binarization system using Collatz conjecture as the basis. Finally, a robust 96-bit code is obtained, which varies little when perceptual modifications are made to the signal such as compression or amplitude modification. According to experimental tests, the BER (bit error rate) between the hash value of the original audio recording and the manipulated audio recording is low for perceptual manipulations, i.e., 0% for FLAC and re-quantization, 1% in average for volume (−6 dB gain), less than 5% in average for MPEG-4 and resampling (using the FIR anti-aliasing filter); but more than 25% for non-perceptual manipulations such as low pass filtering (3 kHz, fifth order), additive noise, cutting and copy-move.
Maxout Networks for Visual Recognition

This study investigates the effectiveness of multiple maxout activation variants on image classification, facial identification and verification tasks using convolutional neural networks. A network with maxout activation has a higher number of trainable parameters compared to networks with traditional activation functions. However, it is not clear if the activation function itself or the increase in the number of trainable parameters is responsible for yielding the best performance on different entity recognition tasks. This article investigates if an increase in the number of convolutional filters on the rectified linear unit activation performs equal-to or better-than maxout networks. Our experiments compare rectified linear unit, leaky rectified linear unit, scaled exponential linear unit, and hyperbolic tangent to four maxout variants. Throughout the experiments, we found that on average, across all datasets, the rectified linear unit networks perform better than any maxout activation when the number of convolutional filters is increased six times.
Utilization Driven Model for Server Consolidation in Cloud Data Centers

The application of cloud computing has diversified with the adoption of Internet of Things (IoTs) and edge computing. However, it has increased the uncertainty of workload demand; thus, the efficient utilization of cloud computing resources become more challenging. Traditionally, dynamic consolidation of workload inside cloud data centers relies on identifying overload and under-load hosts using either static or dynamic threshold value. In this paper, we propose a Utilization Driven Model (UDM) model to estimate the number of under-utilized and over-utilized processing machines through percentile ranks of low and high utilization from mean value of resource utilization of hosts and the value of mean absolute deviation of resource demand. UDM swiftly reacts to any change in workload demand and adapts the system to the current demand of resource utilization. The UDM approach not only impacts the energy consumption and quality of service but also increases the elastic nature of cloud by robustly managing the sudden changes in workload. Experiment results show that UDM is an efficient server consolidation technique, improving 30% energy, 40% quality of service compared to contemporary techniques. Thus, the UDM is more robust to support stochastic resource demand compared to traditional techniques.
Features of Pedagogical Workers Preparation for the Organization and Holding of All-Russian School Olympiads in Municipal Education System

The article discusses the features of training teachers to organize and hold the All-Russian School Olympiad in the municipal education system. The authors substantiate the necessity and relevance of teacher training organization for the organization and holding of the School Olympiad in the municipal education system. Specific attention is paid to the normative documents regulating the procedure of the Olympiad. Experience of teacher preparation for the organization and holding of the School Olympiad in municipal education system is considered. The programme of the training seminar “Organization and Holding of the All-Russian Olympiad in the Municipal Education System” is presented.
Methodological Readiness of Musical Art Master’s Degree Students: A Theoretical Research

Modern trends of social development, processes of science modernization along with information and technologies integrations in education, make the search of ways to improve the scientific training of musical art masters timely relevant, which involves a thorough mastery of the scientific basics of musical art and more to the point – methods of their knowledge in the institution of higher music-pedagogical education. The scientific training of master’s degree students of musical art includes methodological training, which is based not only on knowledge of the theory and methodology of musical art but also on scientific methods of their study. Master’s degree students should know the scientific innovations of music performance and music pedagogics, to analyze, synthesize, generalize, systematize, structure, classify, conduct experimental research, interpret, evaluate, use them in their work with students, etc. The methodological readiness of master’s degree students of musical art should be regarded from the standpoint of personally-oriented, activity and competence approaches, i.e., it is a professional-personal entity based on the motivation to such a kind of activity, a complex of methodological knowledge, skills, and psychological capacities to be applied into practice. In order to carry out the process effectively, we have defined the structure of methodological readiness of master’s degree students of musical art which includes: motivation-and-need, informational-cognitive, science-and-activity, reflexive-evaluative and self-improving components; certain criteria, factors and levels of methodological readiness formation of musical art masters have been considered. For the sake of master’s degree students’ methodological training improvement, there were developed and theoretically substantiated the content, forms and methods of such training in the process of instrumental and vocal training during the Master’s degree course.
Why Do the Students Do Disruptive Behavior in English Classroom? A Case Study On Senior High School Students in One of the Small Town in Indonesia

A conducive learning situation is essential in learning English. However, one problem that usually disturbs the learning situation is the appearance of disruptive behavior. Students' disruptive behavior is an inappropriate behavior conducted by students during the learning process which turns the classroom into unconducive. Exactly, there are several factors contribute to the occurrence of disruptive behavior in the English lesson, especially on senior high school students. Thus, the current study was a case study aimed to investigate several causes of students' disruptive behavior in English teaching-learning process in the classroom. The subjects of this study consisted of an English teacher and a class of twelfth-grade students in a small town in Indonesia. The data were collected using observation and interview. The result of this study showed that students' disruptive behavior in the English classroom was caused by internal factor and external factor. Feeling boredom, feeling anxiety, and seeking attention were internal factor that became the occurrence of disruptive behavior. While fatigue was the external factor that causes the emergence of students' disruptive behavior in the English teaching-learning process in the classroom. &#x0D; &#x0D; Keywords&#x0D; Disruptive Behavior, English Classroom, Senior High School Students, Teachers’ Management
Knowledge Construction by Immersion in Virtual Reality Environments

The objective of this work is to analyze the potential use of Immersive Virtual Reality technologies as a teaching/learning tool to enrich the organization of the learning environments of educational programs.The study and analysis of human cognition is theoretically based, also considering the Biology of Cognition and various approaches proposed by the theoreticians and researchers of Education Sciences.In the work, the state of the art of immersive technologies is established and their contributions in the construction of the knowledge of cognitive subjects are analyzed as a means for the development of teaching/learning activities, with the support of emerging immersive technologies.The methodology used is that of the bibliographic review of the classic works of printed literature in relation to the Biology of Cognition, and the search in diverse databases of theses and diverse works in universities and digital repositories.The main weakness of the research lies in the fact that the search was limited to documents using English, Spanish, and Portuguese language.To finish, conclusions and recommendations for future work have been established.
How many ways can we teach data literacy?

Academic Libraries are ideally positioned to teach data literacy. What is ‘data literacy’ in the first place? Is it the new information literacy? Will the ways we teach information literacy limit imaginative ways to teach data literacy?&#x0D; With those questions in mind, the Library of New York University Shanghai has explored multiple ways to teach data literacy to undergraduate students through university events, ‘for-class’ instruction and workshops, and online casebooks. (1) We initiated the yearlong series of events titled ‘Lying with Data’, inviting faculty across disciplines to each address one core data literacy question that students of data science may elude. (2) We offered workshops and in-class instruction that are up-to-date with the latest technology and that fit with the curriculum. (3) We created online casebooks on various topics in the data lifecycle, tackling user needs at different levels. Essential to our teaching activities are two core values: ‘let the quality speak for itself’, and ‘outreach by teaching’.
Aplikasi Teknik Fuzzy Delphi Terhadap Keperluan Elemen Teknologi Sebagai Wadah dalam Pembelajaran Berasaskan Pemikiran Reka Bentuk

As technology becomes ubiquitous in society, there is increasing momentum to incorporate it into education.Therefore, the purpose of this study is to find out the elements needed in integrating technology as a tool for design thinking-based learning.This study is a quantitative study with Fuzzy Delphi method.The data was gathered using questionnaire.Verification is done by 18 experts in the field of education, educational technology and design thinking.The study found that expert consensus on all element is more than 75% with threshold value (d) ≤ 0.2.This suggests that these eight elements are needed in integrating technology as a tool for design thinking-based learning.
Publication trends in forensic science research: Friction ridge discipline

Sciences report, "Strengthening Forensic Science in the United States: A Path Forward," and one valuable way to measure progress in forensic science is through published research.The purpose of this study is to examine where the friction ridge discipline stands with respect to published research.Two time periods were selected (2005e2009 and 2010e2014) and two different methods were used to describe research growth
Multimodal Attribute and Feature Embedding for Activity Recognition

Human Activity Recognition (HAR) automatically recognizes human activities such as daily life and work based on digital records, which is of great significance to medical and health fields. Egocentric video and human acceleration data comprehensively describe human activity patterns from different aspects, which have laid a foundation for activity recognition based on multimodal behavior data. However, on the one hand, the low-level multimodal signal structures differ greatly and the mapping to high-level activities is complicated. On the other hand, the activity labeling based on multimodal behavior data has high cost and limited data amount, which limits the technical development in this field. In this paper, an activity recognition model MAFE based on multimodal attribute feature embedding is proposed. Before the activity recognition, the middle-level attribute features are extracted from the low-level signals of different modes. On the one hand, the mapping complexity from the low-level signals to the high-level activities is reduced, and on the other hand, a large number of middle-level attribute labeling data can be used to reduce the dependency on the activity labeling data. We conducted experiments on Stanford-ECM datasets to verify the effectiveness of the proposed MAFE method.
Geometric Properties Estimation from Line Point Clouds Using Gaussian-Weighted Discrete Derivatives

A line point cloud is a series of sequential discrete points acquired by a laser ranging sensor. Geometric properties estimation from line point clouds plays a crucial role in many applications, such as reverse engineering, industrial inspection, and autonomous navigation. This article proposes a new method for estimating the geometric properties, such as tangent, normal, curvature, and torsion, from line point clouds based on derivative estimation. According to the geometric meaning of the derivative, the derivative of a discrete function at a point is defined by using the Gaussian-weighted least squares, which is called the Gaussian-weighted discrete derivative (GDD) for short. By using the GDDs, classical differential geometry is discretized, and the geometric properties are estimated from a line point cloud based on its parametrized expression. Moreover, the noise intensity of the line point cloud is computed by using the Gaussian process regression for adaptive neighborhood adjustment to improve the estimation accuracy. The proposed method introduces the Gaussian weight into derivative estimation and estimates the geometric properties directly from line point clouds, which makes the estimation process reliable and understandable. The experimental results show that the proposed method is accurate, robust to noise, and suitable for different shapes.
Student Educating Experience in the New Digital Education

Introduction. The paper is devoted to a review of the main issues that digitalization poses for higher education sociologists. Readers are also offered the results of an empirical study aimed at obtaining answers to some of these questions by analyzing the direct experience of students who have completed online courses and express their views on its functional characteristics and performance. Methodology and sources. As a methodological framework for the study, the authors used a poly-paradigmatic approach, including the provisions of the economy of higher education, the principles and methods of the institutional tradition of the sociology of education, the ideas of theoretical concepts of social representations and communicative action. Results and discussion . The results of two sociological studies carried out in the form of questionnaires are presented. The target audience of the surveys were first-year undergraduates in technical areas of training who did not have direct experience of smart education at the university, and undergraduate students who took various online courses as part of their educational process. According to the survey, undergraduates have a clear interest in smart education and are independently looking for open online courses. However, they do not clearly assess some of the advantages of digital technology, for example, the ability to repeat the explanation. Undergraduate students, regardless of the course, direction and form of study, prefer the hybrid format over online courses and traditional classroom instruction. The most important aspects of online learning for students were identified: the clarity and consistency of the presentation of educational material, the usefulness of the course for the specialty, the entertaining course. At the same time, the image side of the presentation of materials by the teacher is of much less interest to them than the developers of the courses. As a result of the research, the most interesting topics for future discussions about the functional characteristics and effectiveness of smart education, as a new educational paradigm, are the following paradoxes: weak interest (or even lack thereof) of students in the possibility of a digital learning format such as individual and collective feedback, the possibility of organizing forums and conducting group discussions of the issues under study without/with the participation of a teacher; the contradictions between the targets of teachers-developers of online courses and the educational needs of students-users of online courses. Conclusion. The authors of the paper revealed a generally positive attitude of students to smart education. Most noted that it is more interesting to study online, however, classes based on the traditional form provide more solid and clear knowledge. The most effective and functional version of teaching practices seems to be hybrid courses, which allow combining the advantages of the "classical" and the new digital education paradigms.
A Probe Survey of Bitcoin Transactions Through Analysis of Advertising in an On-Line Discussion Forum

Cryptocurrencies have become a major phenomenon in recent years. For IT, a breakthrough is both the cryptocurrency itself as a commodity and the technology that cryptocurrency development has brought. The article focuses on the bitcoin cryptocurrency as the most important cryptocurrency. A relatively unexplored topic is what goods or services are purchased for bitcoins. To track what bitcoins are spent on, it is necessary to look for places that are dedicated to trading cryptocurrencies. The bitcointalk.org forum was chosen as a source for our data mining. The aim of the article is to find an answer to the research question: What are bitcoins on the discussion forum bitcointalk.org planned to be spent on? As part of the research, an application was developed using a PHP script to gather information from the discussion forum (bitcointalk.org). There is some evidence which suggests what types of products or services people spend cryptocurrencies on. This research has proven that cryptocurrencies are used to buy and sell goods or services in the electronics and computer world segments. Today, these segments are widespread, which may speed up the integration of cryptocurrencies into everyday life. This applies, of course, only if the risks associated with cryptocurrencies do not increase.
An Experimental Study on The Unfairness in Adaptive Streaming with HTTP/2 Server Push

Adaptive streaming over HTTP/2 has recently caught attention in the development of multimedia delivery. The server push feature of HTTP/2 enables the streaming server to send multiple video segments to the clients within a single request, therefore reducing unnecessary overheads, RTTs and energy consumption on portable devices, also empowering the use of short segment duration to improve network adaptability. However, recent researches only investigate the performance of the HTTP/2 server push under a single-client scenario. In fact, when multiple clients share a limited bottleneck bandwidth, it is highly possible that the unfairness in bitrate selection will happen due to the mismatch of the segment download state among the clients. In this paper, we hypothetically discuss various cases that a client decides its bitrate higher or lower than one another. Then, an experiment with the DASH.js player under several scenarios is provided to confirm the reliability of our hypothesis.
Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction

This study evaluates the use of the Artificial Bee Colony (ABC) algorithm to optimize the Recurrent Neural Network (RNN) that is used to analyze traffic volume. Related studies have shown that Deep Neural Networks are superseding the Shallow Neural Networks especially in terms of performance. Here we show that using the ABC algorithm in training the Recurrent Neural Network yields better results, compared to several other algorithms that are based on statistical or heuristic techniques that were preferred in earlier studies. The ABC algorithm is an example of swarm intelligence algorithms which are inspired by nature. Therefore, this study evaluates the performance of the RNN trained using the ABC algorithm for the purpose of forecasting. The performance metric used in this study is the Mean Squared Error (MSE) and ultimately, the outcome of the study may be generalized and extended to suit other domains.
3D Facial Emotion Recognition Using Deep Learning Technique

In this paper, a 3D facial emotion recognition model using deep learning technique is proposed.In the deep learning architecture, two convolution layers and a pooling layer is used.Pooling is performed after convolution operation.The sigmoid activation function is used to obtain the probabilities for different classes of human faces.In order to validate the performance of deep learning based face recognition model, Kaggle dataset is used.The accuracy of the model is approximately 65% which is less than the other techniques used for facial emotion recognition.Despite dramatic improvements in representation precision attributable to the non-linearity of profound image representations.