Reviewerly/exHarmony huggingface dataset:
Deep Learning Approaches to Predict Future Frames in Videos

Deep neural networks are becoming central in several areas of computer vision. While there have been a lot of studies regarding the classification of images and videos, future frame prediction is still a rarely investigated approach, and even some applications could make good use of the knowledge regarding the next frame of an image sequence in pixel-space. Examples include video compression and autonomous agents in robotics that have to act in natural environments. Learning how to forecast the future of an image sequence requires the system to understand and efficiently encode the content and dynamics for a certain period. It is viewed as a promising avenue from which even supervised tasks could benefit since labeled video data is limited and hard to obtain. Therefore, this work gives an overview of scientific advances covering future frame prediction and proposes a recurrent network model which utilizes recent techniques from deep learning research. The presented architecture is based on the recurrent decoder-encoder framework with convolutional cells, which allows the preservation of Spatio-temporal data correlations. Driven by perceptual-motivated objective functions and a modern recurrent learning strategy, it can outperform existing approaches concerning future frame generation in several video content types. All this can be achieved with fewer training iterations and model parameters.
The importance of the mutual learning system in the history of Russian education

Educational systems based solely on the information paradigm do not meet the requirements of modern society. The resolution of this contradiction in education lies in changing the educational paradigm: new pedagogical systems modeled on the basis of historical experience and rethinking the values of education become in demand. The article discusses the problem of the influence of social processes in the field of education on the formation of a value-oriented sphere of the younger generation using the example of the Lancaster system of mutual learning in Russia. The methodology of mutual learning through the prism of an axiological approach is substantiated. The features of the mutual learning methodology are revealed, which are considered by the author as a historical continuity in educational practice. The author characterizes the considered historical methodology of primary education, arguing the presence of its components such as the goals and objectives of training, content, tools and learning outcomes. The article emphasizes the importance of the mutual learning system for the development of the political and economic situation in the state.
Pedagogical functions of team management

The article discusses the pedagogical functions of the head of the internal affairs body, carried out in the process of managing the service team. The authors give a detailed pedagogical description of the legal, diagnostic, analytical and evaluative, stimulating and research functions. Special attention is paid to pedagogical stimulation, presented as a purposeful process of applying external stimuli that interact with the motives and internal attitudes of the individual. The authors analyze the results of a study conducted in the form of a questionnaire of employees of internal affairs bodies to identify the features of applying types of incentives and incentive methods. Among the types of incentives, the heads of the body, organization, division of the Ministry of Internal Affairs of Russia most often use approval, praise and gratitude, awarding prizes, diplomas, medals, gifts. The most common incentive measures include the provision of an additional day of rest, public recognition of merit, transfer to a more prestigious unit. In conclusion, a reasonable conclusion is made about the importance and necessity of the implementation of pedagogical functions by the head of the body, organization, department of internal affairs.
Poster

Multi-layer ceramic capacitors (MLCCs), commonly used in electronics products, can generate acoustic noise driven by electrical deviation. Such acoustic noise has been exploited to attack systems' security. In particular, CapSpeaker silently delivers voice commands to voice-controllable systems using inaudible acoustic noise and intermodulation distortion (IMD). Silicon capacitor is an emerging technology that achieves a larger capacitance with a smaller footprint fabricated with semiconductor manufacturing processes using different structures and materials. Do silicon capacitors still generate acoustic noise despite the differences? We positively answer the question by experimentally showing that an off-the-shelf silicon capacitor generates audible acoustic noise through IMD; CapSpeaker is still feasible with the new capacitor
Wizard Gauntlets Adventure: Appraising the Cognitive Load in Gesture-based Environment

Wizard Gauntlets Adventure is a fantasy RPG game with unique glove shaped controllers. Our adventure begins when a small town is terrorised by an evil dragon. The player discovers magical wizard gauntlets that grant them special powers. These give the player the option to join either the light or dark side based on ethical decisions they make in the game. On their adventure, they face multiple enemies they must defeat in order to progress. The player must use fast and precise gestures to strike these foes. Two core mechanics of many videogames, especially ones that use tangible interaction, are immersion and cognitive load. This is because when playing videogames, the player wants to escape reality for a bit without being too overburdened that it takes them out of this world. With our game, we investigate the consequences different hand gestures have on these two mechanics. It is our goal to see whether these specific gestures add to the immersion and if some of these would not be too complex for the user’s cognitive capacity.
Survey Paper on Multi-view Object Detection: Challenges and Techniques

Object localization and detection is the emerging area of computer vision and image processing that finds out the location of the object in the video frames or in the digital images. Object localization and detection have many challenges like occlusion, scale variations, intraclass similarities, illumination conditions, pose variations, etc. Multi-view object detection focuses on different views of objects like the top, bottom, side view, etc. We aim to discuss the different Object detection models based on Machine learning and Deep neural network approaches in a multi-view environment. We also discuss different datasets used for object detections and their applications. We apply some object detection models like SSD and Faster R-CNN to specific object categories of Open Image Dataset V6 and compare the results based on mAP (Mean Average Precision).
Web-based Online Application for Information System of Student Report Card at SLBN Cinta Asih Soreang

Monitoring learning outcomes in the form of report cards of competencies from students is a routine agenda of teachers to students in each semester. SLBN Cinta Asih Soreang in collaboration with the Politeknik Negeri Bandung Community Service prgram since 2021 has developed a student report card desktop application.This application has been run and successfully implemented, but not all devices are compatible to run the application. In addition, filling out reports can only be done at school because the desktop application storage can only be stored in a local area. In order to overcome these problems, it is necessary to develop an online-based system for digitizing student achievement reports. The proposed solution is the development of a Web-based online application for an information system for student progress report cards at SLBN Cinta Asih Soreang. The result reveals that more than 90% of participants feel this application is useful and can be utilized in the future.
Perubahan Kepribadian Tokoh Utama dalam Novel Catatan Juang Karya Fiersa Besari

This thesis examines the changes in this personality of the main character in Fiersa Besari novel Catatan Juang, which tells the story of Suar struggle as the main character in achiecing his dream as filmmaker. In Suar journey to become a filmmaker, the various things he has gone through havemade him experience changes in his development period. This study analyzes the structure of the novel and the changes that occur to the main characte. To obtain the desire result, the theories used are Stantont Structural amda Hurlock develompent psychology. The method use to collect data is literature study with reading, listening, and note-taking techniques. Then to analyze the data used descriptive analytical method by describing the result of data analysis that has been done. Structural theory is use to analyze plot, characters, and setting. The flow analysis uses three stages, initial stages, middle, and final stages. So it was found if the groove used was a conventional flashback groove. Suar characterization as the main character, as a developing character. This cause changes in the nature and character of Suar to develop from beginning of its appearance to the end story. Then, the setting that dominates this novel is Jakarta, Bandung, North Village, and Karst Mountain. The time setting ranges from 2015 to 2016, other time setting are also visible such as morning, afternoon, evening, and night. Bacground the atmosphere that appears, among others, surrender, sad, happy. The personality changes that occur are Suar seccessful adjusment to adulthood, the achievements he shows and the satisfaction that Suar gets in his work, making him a pleasant person, tough, and unyielding.
Pengukuran Percepatan Gravitasi Di Kota Metro

Metro City is one of the cities in Lampung, Metro City has a height of 30-60 meters above sea level, but the value of the acceleration of gravity in Metro City is not yet known. Therefore, a study was conducted on measuring the acceleration of gravity in Metro City. The purpose of this study is to determine the value of the acceleration of gravity in the city of Metro, and find out how the difference in the acceleration of gravity in the city of Metro is calculated using the mathematical pendulum method and the acceleration of gravity in the CPU-Z application on Android. This study uses quantitative research with experimental methods in which there are numerical calculations and calculation of error/error values. The data collection instrument in this study used an observation sheet and a questionnaire sheet. Data collection was carried out in each sub-district for 5 trials. After that, look for the average size in each sub-district, then do a weighted calculation to find out the magnitude of the acceleration of gravity in the city of Metro. From the results of the study, the magnitude of the acceleration of gravity in Metro City with a mathematical pendulum is 9.86m/s2 with an error value of 0.011m/s2 and with CPU-Z application of 9.800 m/s2 with an error value of 0.003m/s2. The research results are made in the form of modules and validated. The results of the validation stated that it was very feasible with a score of 83.03 %.
Quantum Sensor Network Algorithms for Transmitter Localization

A quantum sensor (QS) is able to measure various physical phenomena with extreme sensitivity. QSs have been used in several applications such as atomic interferometers, but few applications of a quantum sensor network (QSN) have been proposed or developed. We look at a natural application of QSN -- localization of an event (in particular, of a wireless signal transmitter). In this paper, we develop effective quantum-based techniques for the localization of a transmitter using a QSN. Our approaches pose the localization problem as a well-studied quantum state discrimination (QSD) problem and address the challenges in its application to the localization problem. In particular, a quantum state discrimination solution can suffer from a high probability of error, especially when the number of states (i.e., the number of potential transmitter locations in our case) can be high. We address this challenge by developing a two-level localization approach, which localizes the transmitter at a coarser granularity in the first level, and then, in a finer granularity in the second level. We address the additional challenge of the impracticality of general measurements by developing new schemes that replace the QSD's measurement operator with a trained parameterized hybrid quantum-classical circuit. Our evaluation results using a custom-built simulator show that our best scheme is able to achieve meter-level (1-5m) localization accuracy; in the case of discrete locations, it achieves near-perfect (99-100\%) classification accuracy.
The Effect of Using Spaced Learning Repetition Method on Iraqi Islamic School Students' Achievement in English Language

The current study aims to investigate the effect of using spaced learning repetition method on Iraqi Islamic school students' achievement in English language. The aim of the study is supposed to be achieved through verifying its hypothesis. The sample of the study consists of twenty EFL Islamic school students who are spread into both groups, the experimental groups and control .The experimental group has been taught the English reading comprehension(The Prophet of Allah unit seven and eight) by using spaced learning repetition method ,while the control group has been taught the same material by using their current method. A posttest has been applied and conducted to the both groups. The required data are collected and analysed statistically. The gained findings show that the mean scores of students' achievement of the experimental group in the posttest are better than that of the control group. This means that the investigated method has an efficient effect on Iraqi Islamic school learners' achievement language of English.
DeFi '22

Powered by blockchains, Decentralized Finance (DeFi) has grown to a significant economy covering exchanges, borrowing/lending, margin trading, derivatives, and more. While DeFi systems are gaining significant traction (e.g., they already manage tens of billions of dollars worth of assets), making them secure has proven exceptionally challenging---a staggering $1.9 billion was stolen in various hacks in the first seven months of 2022 alone.[4]
Improving IDE code inspections with tree automata

Integrated development environments (IDEs) are equipped with code inspections to warn developers of malformed or incorrect code by analyzing the code's data flow. However, the data flow analysis performed by the IDE code inspections may issue warnings in code where warnings are irrelevant. Existing methods to prevent any bogus warnings are either too conservative --- suppressing the same type of warnings in the entire codebase --- or are not sustainable as they require adding a set of heuristics to the data flow analysis. We propose a programming-by-example (PBE) framework that synthesizes tree automata (TAs) to suppress bogus warnings in all the code containing the same patterns as the user-provided examples. Experiments with a prototype of the framework demonstrate that several real-world patterns heuristically suppressed in production IDE can be mechanically translated to a TA in a systematic way. Furthermore, we briefly discuss how TA-based solution can be useful in other IDE features such as code refactoring.
Matproofs

We present Matproofs, a matrix commitment scheme that allows one to commit to any matrix and then open any subset of the matrix entries. If we encode any vector as a matrix, by committing to the matrix Matproofs may function well as a vector commitment (VC) scheme. We show that Matproofs are simultaneously concise, aggregatable, easily updatable and maintainable. With these promising features, Matproofs give solutions to payment-only stateless cryptocurrencies with lower bandwidth and computational complexity. Compared with Hyperproofs, the only existing VC scheme that is simultaneously aggregatable, easily updatable and maintainable, Matproofs achieve the additional property of conciseness. Furthermore, in the worst case, the proof aggregation and verification in Matproofs are 700x and 10x faster than Hyperproofs, respectively.
The Digital Empowerment for Cares Cloud Computing

This paper brief describes the necessity of the promotion and introduction of digital empowerment in cares and its significance. It facilitates the main composition and functions of the cares cloud computing. It highlights some development trends and thus concludes also some practical knowhow.
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection

Reference-based image super-resolution (RefSR) is a promising SR branch and has shown great potential in overcoming the limitations of single image super-resolution. While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as. Therefore, in this work, we propose a reciprocal learning framework that can appropriately leverage such a fact to reinforce the learning of a RefSR network. Besides, we deliberately design a progressive feature alignment and selection module for further improving the RefSR task. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection in a progressive manner, thus more precise reference features can be transferred into the input features and the network capability is enhanced. Our reciprocal learning paradigm is model-agnostic and it can be applied to arbitrary RefSR models. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm. Furthermore, our proposed model together with the reciprocal learning strategy sets new state-of-the-art performances on multiple benchmarks.
BER: Balanced Error Rate For Speaker Diarization

DER is the primary metric to evaluate diarization performance while facing a dilemma: the errors in short utterances or segments tend to be overwhelmed by longer ones. Short segments, e.g., `yes' or `no,' still have semantic information. Besides, DER overlooks errors in less-talked speakers. Although JER balances speaker errors, it still suffers from the same dilemma. Considering all those aspects, duration error, segment error, and speaker-weighted error constituting a complete diarization evaluation, we propose a Balanced Error Rate (BER) to evaluate speaker diarization. First, we propose a segment-level error rate (SER) via connected sub-graphs and adaptive IoU threshold to get accurate segment matching. Second, to evaluate diarization in a unified way, we adopt a speaker-specific harmonic mean between duration and segment, followed by a speaker-weighted average. Third, we analyze our metric via the modularized system, EEND, and the multi-modal method on real datasets. SER and BER are publicly available at https://github.com/X-LANCE/BER.
Design of computer English vocabulary management system based on Struts

With the rapid development of computer technology, more and more computer software programs have entered the English vocabulary management system. The design system based on Struts framework provides technical support for vocabulary management software. Struts is mainly composed of data storage design, computer software system and struts model framework. The data storage design uses the third paradigm technology, combined with the system function structure design, and uses the modeling technology to reorganize and classify the system. Because the data analysis speed of the struts framework system has been effectively improved, the development efficiency of the vocabulary management system has been steadily improved. The use of the links between various plates makes the vocabulary management system more convenient and fast.
The application of computer technology in tourism English teaching

Computer technology is in a state of rapid development, and its application scope is constantly expanding. Nowadays, many colleges and universities introduce computer technology into tourism English teaching to improve teaching efficiency and quality and promote students' practical ability. Tourism English requires higher students' practical ability. Under the traditional teaching mode, students' learning efficiency is not ideal. Therefore, more and more teachers began to introduce computer technology into teaching work to improve teaching quality and efficiency. Therefore, this paper introduces the characteristics of computer-assisted instruction, analyzes the application of computer technology in tourism English teaching, and discusses the specific application of computer technology in tourism English teaching from two aspects: establishing the computer index system of tourism English professional training mode and reasonably applying computer translation software, hoping to provide corresponding reference for tourism English teaching in colleges and universities.
Research on natural image classification method based on deep learning

The research on natural image classification method based on deep learning is an attempt to use the ability of artificial neural network (ANN) to classify images. Deep learning is a machine learning technology, which uses neural networks to learn complex functions. It has been used in the fields of computer vision, speech recognition and natural language processing for many years. The main idea behind deep learning is to use multi-layer nodes, which have different types of connections. This enables us to create artificial neural networks that can learn from data more effectively than traditional methods such as linear regression or support vector machine. This technology has been used for many years and has become very popular recently. The main idea behind them is to use the data set not seen in the training, so that they can be used to identify the new data set.
Misestimate of the performance in VCSEL-based reservoir computing systems with optical information injection by high surface reflectivity

In reservoir computing (RC) systems based on semiconductor lasers (SLs), the information that must be processed usually enters the reservoir through optical injection. Part of the injection information directly reflected by the front facet of the SLs is inevitably hybridized into the output of the SLs and contributes to the state of virtual nodes. For an RC system based on vertical-cavity surface-emitting lasers (VCSELs), the proportion of the reflected information coupled to the laser output is relatively huge due to the high surface reflectivity. Thus the influence of the directly reflected information will be much more obvious. Using a Santa Fe chaotic time series prediction task and waveform recognition task, we theoretically investigate the influence of high front facet reflectivity on the evaluation of the performance of a VCSEL-based RC system with optical information injection. The simulation results demonstrate that, after taking the directly reflected information into account, a lower error rate is obtained for each benchmark task. The physical mechanism to misestimate the RC performance has been studied through memory correlation and a statistical histogram of virtual node states.