Reviewerly/exHarmony huggingface dataset:
A Methodological Perspective on Lawful Internet Surveillance

In this paper we discuss research issues, practical matters and legal constraints driving the design and development of lawful tools and frameworks for a real-time, automated analysis of Internet traffic, supporting detection and recovery of hidden traffic and botnets, as well as preserving the integrity and confidentiality of evidences. We propose a methodological perspective and lines of development that in our vision are the basic pillars of such a framework, to address a multi-stakeholder, multinational scenario.
Intermodal Sentiment Analysis for Images with Text Captions Using the VGGNET Technique

More individuals actively express their opinions and attitudes in social media through advanced improvements such as visual content and text captions. Sentiment analysis for visuals such as images, video, and GIFs has become an emerging research trend in understanding social involvement and opinion prediction. Numerous individual researchers have obtained good progress in outcomes for text sentiment analysis and image sentiment analysis. The combination of image sentiment analysis with text caption analysis needs more research. This article presents a VGG Network-based Intermodal Sentiment Analysis Model (VGGNET-ISAM) for transferring the connection between texts to images. A mapping process is developed using the VGG Network for gathering the opinion information as numerical vectors. The Active Deep Learning (ADL) classifier is used for opinion prediction from the obtained information vectors. Simulation experiments are carried out to evaluate the proposed approach. The findings show that the model outperforms and gives better solutions with high accuracy, precision with low delay, and low error rate.
Machine Learning in Error Types of Machine Translation

This studio is a performance of Javanese in Singapore and Chinese, but expected to be a hot situation, found no small review of the performance of Javanese. The qualitative analysis method is adopted to analyze the translation problems and errors, so as to make the research more practical.
Multi-scale Feature Mergence Reinforced Network for Person Re-Identification

Person Re-Identification (Re-ID) is a challenging task due to large different appearances of people across disjoint views heavily influenced by many factors such as illumination variations, person pose variations, and view variations. Therefore, it is imperative to learn more discriminative feature representation for Re-ID. Existing deep learning models focus on developing feature representation with different scales to characterize person better, which tend to incur interferential and redundant feature output. In this paper, we develop a dynamical and adaptive selective feature fusion module (SFFM) for feature learning, which depends on the input images entirely and facilitates to extract more discriminative features for Re-ID. Moreover, we further improve the discriminative capability of the proposed deep network by incorporating the island loss (IL) with the triplet loss and the softmax loss. The newly proposed loss function is beneficial to increase the distance between those samples belonging to different classes. Validation experiments performed on two standard benchmarks called Market-ISOI and DukeMTMC-reID datasets demonstrate the effectiveness of the proposed method.
The use of a tilted diffractive optical element with an increased depth of field in the problem of accounting and control of railway rolling stock

The paper proposes a video surveillance scheme for compact placement of a system for railway rolling stock accounting. This design is based on the use of a tilted diffractive optical element and a tilted lens. Such an optical design makes it possible to significantly increase the depth of focus of the imaging system. This work considers the influence of the tilt of a diffractive lens on the shape and size of the focused area. Analytical relations describing the geometry of the focused region for various spectral channels are given. The possibility of increasing by several times the size of the zone of accurate image classification using a neural network has been demonstrated. The proposed approach has been tested on real-world dataset of images of house number plates.
Finding Balance, Fostering Productivity, and Avoiding Burnout

The Department ChairVolume 32, Issue 1 p. 26-26 Articles Finding Balance, Fostering Productivity, and Avoiding Burnout Laura G. McGee, Laura G. McGeeSearch for more papers by this author Laura G. McGee, Laura G. McGeeSearch for more papers by this author First published: 28 June 2021 https://doi.org/10.1002/dch.30401Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume32, Issue1Summer 2021Pages 26-26 RelatedInformation
On Feature Expansion with Finite Normal Mixture Models in Machine Learning

This paper is devoted to the research of quality improvement of medium-term data forecasting with neural networks within introduction of statistical models for observations. The main goal is to study the efficiency of nontrivial expansion of the feature space based on the characteristics of finite mixtures models. Such probabilistic models are successfully used as convenient approximations of plasma turbulence processes. In the paper, expectation, variance, skewness and kurtosis of the mixture models are introduced as additional features for machine learning algorithms. Comparison of medium-term forecasts with and without additional features is carried out, and various neural network architectures are investigated. The proposed methods are tested on the unique turbulent plasma ensembles obtained from the L-2M stellarator. It is demonstrated that the usage of the above-mentioned statistical characteristics can increase the accuracy of neural network forecasts in terms of such standard metrics as root-mean-square error and mean absolute errors. Hybrid high-performance computing cluster is used in order to increase the learning rate.
Issue of Resolutiveness in Ontognoseological Essentialism

The study aims to actualise apodictically reliable criteria for essential unity of being and thinking in a resolutive way at the content level of the categorical structure of reality. The article provides consideration of the issue of resolutiveness from Leibniz-Wolff metaphysics to M. A. Lifshitz’s ontognoseology. The researcher has determined how productive theoretical provisions of ontognoseological essentialism are. Scientific novelty of the study lies in identifying the normative function of resolutions in combinatorial disposition of the classical categorical complex, as well as in correlation of different degrees of completeness of the resolutive complex with three content arrays at the level of one-to-one correspondence. The attained results have shown relevance of resolutions within the concept of ontognoseological essentialism.
Performance Optimization of Quantum Computing Applications using D Wave Two Quantum Computer

Quantum computing shows optimized performance in storing, managing and analyzing huge volumes of data. It also plays a key role in many applications areas like health, finance, marketing and drug development. In this research paper, the overview of quantum D wave one and D wave two computers are analyzed based on mathematical computation, circuit building, physical properties and the type of execution. The study also shows the efficiency of D wave two computers in various applications. It also proves that the D wave two quantum computers shows remarkable performance.
Content Based Image and Video Retrieval: A Compressive Review

Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy data and inaccuracy, which often result in the showing of irrelevant images or videos. The authors of the proposed study integrate image and visual data to improve the precision of the retrieved results for both photographs and videos. In response to a user's query, this study investigates alternative ways for fetching high-quality photos and related videos.
Application of improved adaboost iteration in small sample for prediction of oral performance

Adaboost as a kind of iterative algorithm, is a kind of important integrated learning technology, can be randomly surmised with slightly higher prediction accuracy than usingweak learning enhancement study for high prediction precision of apparatus, it's is very difficult in strongly constructing learning, it provides an effective new way of thinking and new methods for the design of the learning algorithm. As a kind of algorithm framework, Boosting can be applied to almost all the popular machine learning algorithm to further improve forecasting precision of the original algorithm, and its application is very extensive and has a great influence. Based on Adaboost, 90 students' oral English at a university as weak classifier samples, via the iterative calculation, get a high reliability of strong classifier, and it's concluded that the prediction result with student can evaluate the prediction accuracy of the algorithm for results compared with the actual results.
Computer Application Scenarios of Stochastic Optimization Model: From Data Complexity Perspectives

Computer application scenarios of the stochastic optimization model from data complexity perspectives is studied in this paper. The implementation design of the computer architecture changes with the improvement of chip integration. Whenever the feature size is reduced to a certain level, not only can more functional blocks be integrated on the chip, but there will also be some new physical constraints to consider go to chip design. Hence, the data complexity optimization should be then considered. The use of virtual machines or virtual networks can be expressed as a type of mapping process. We consider the core applications of the fuzzy model to organize the optimization steps for the complexity estimation. The exepriment is conducted on the public database and the performance is verified.
End-to-End Motion Planning of Mobile Robot based on CNN with Object Detection and Depth Images

For autonomous navigation of mobile robots, obstacle avoidance in consideration of the destination is an essential capability. In this paper, we focus on a mobile robot equipped with RGB-D camera and LiDAR sensors, and propose an end-to-end motion planner based on a convolutional neural network, CNN, through imitation learning. In order for the robot to avoid various obstacles, we generate novel object detection images from the original RGB images. The object detection and depth images are then fed as inputs to the CNN. In a fully connected layer, moreover, a direction angle to the destination is inputted. In the navigation experiments, we show that the robot based on the proposed motion planner is able to move toward the goal destination while avoiding collisions with various obstacles.
Quantization Strategy for Pareto-optimally Low-cost and Accurate CNN

Quantization is an effective technique to reduce memory and computational costs for inference of convolutional neural networks (CNNs). However, it has not been clarified which model can achieve higher recognition accuracy with lower memory and computational costs: a fat model (large number of parameters) quantized to an extremely low bit width (e.g., 1 or 2 bits) or a slim model (small number of parameters) quantized to moderately low bit width (e.g., 4 or 5 bits). To answer this question, we define a metric that combines the number of parameters and computations with bit widths of quantized weight parameters. Using this metric, we demonstrate that Pareto-optimal performance, where the best accuracy is obtained at a given memory or computational cost, is achieved when a slim model is moderately quantized rather than when a fat model is extremely quantized. Moreover, employing a strategy based on this finding, we empirically show that the Pareto frontier is improved by 4.3× under a post-training quantization scenario on the ImageNet dataset.
A Bio-Inspired Motion Detection Circuit Model for the Computation of Optical Flow: The Spatial-Temporal Filtering Reichardt Model

Optical flow is the pattern of apparent motion in a visual scene produced by the relative movement between objects and an observer. Optical flow is used in many engineering applications such as optical odometry. A variety of optical-flow algorithms has been proposed in the past few decades; however, most of these algorithms involve complex computation, making them difficult to be implemented in neuromorphic systems that operate based on neural networks. Interestingly, studies have shown that insect visual systems are able to perform complex optical flow algorithms. Inspired by the classic Reichardt motion detection model proposed for insects, we designed a spatial-temporal filtering Reichardt (STR) model. This model computes optical flow based on simple filters in the spatial and temporal domains. The STR model is hardware friendly: it does not require time-consuming iteration processes nor computationally intensive multi-layer convolutional networks, which are typical in other optical flow algorithms. We systematically investigate the performance of the STR model with different parameters including: object size, speed, luminance, and filter forms. We also compare the performance of the STR model to the classical Farneback algorithm, and we demonstrate that the STR model is comparable to the classical algorithms while requiring much less computational power.
Investigating Automated Mechanisms for Multi-Modal Prediction of User Online-Video Commenting Behaviour

Online-video commenting is attracting increasing attention among young people, particularly in the form of "danmu" comments in Asia. These provide a channel for engagement enabling users to share time-synchronous comments on videos with other viewers. Danmu form community discussions of video content and frequently provoke extensive further contributions. The motivation to add danmu comments at specific points in videos are not obvious. In this paper, we explore the potential for predicting user online-video comment distributions using multi-modal signals from the video content stream. To address this task we integrate multiple sources of information, including video frame content, the audio signal, as well as video subtitles, in an end-to-end neural framework. Specifically, text, visual and audio input are encoded respectively and then a transformer framework is used to learn and combine attention aware representation of three modalities. We evaluate the system using retrieval-based evaluation metrics, including mean average precision (mAP) and normalized discounted cumulative gain (NDCG). We conduct experiments on an expanded publicly available danmu commenting dataset. Our model significantly outperformed an LSTM multi-modal baseline method.
Detection of Emphasis Words in Short Texts – A Context Aware Label Distribution Learning Approach

In multi-label classification problems, the predominant approach is to transform the problem into a single-label classification problem that can result in the affirmative classification of multiple labels of a single sample. However, for data such as image, video, or text segment - a realistic scenario is that of a distribution across multiple labels for any particular sample. This work adopts a label distribution approach to determine the segments of text that need emphasis. Additionally, it focuses on short text samples that make it challenging to understand the context and the author’s intent. A context-aware approach is proposed by employing a composite embedding, thereby obtaining contextual information while also focusing on fine-grained details about the text samples. The proposed model outperforms the selected baseline by a significant amount, rendering the amalgamation of the context-aware embeddings and the non-binary nature of the Label Distribution Learning fittest for estimating emphasis.
Swift: Reliable and Low-Latency Data Processing at Cloud Scale

Nowadays, it is a rapidly rising demand yet challenging issue to run large-scale applications on shared infrastructures such as data centers and clouds with low execution latency and high resource utilization. This paper reports our experience with Swift, a system capable of efficiently running real-time and interactive data processing jobs at cloud scale. Taking directed acyclic graph DAG as the job model, Swift achieves the design goal by three new mechanisms: 1 fine-grained scheduling that can efficiently partition a job into graphlets i.e., sub-graphs based on new shuffle heuristics and that does scheduling in the unit of graphlet, thus avoiding resource fragmentation and waste, 2 adaptive memory-based in-network shuffling that reduces IO overhead and data transfer time by doing shuffle in memory and allowing jobs to select the most efficient way to fulfill shuffling, and 3 lightweight fault tolerance and recovery that only prolong the whole job execution time slightly with the help of timely failure detection and fine-grained failure recovery. Experimental results show that Swift can achieve an average speedup of 2.11× on TPC-H, and 14.18× on Terasort when compared with Spark. Swift has been deployed in production, supporting as many as 140,000 executors and processing millions of jobs per day. Experiments with production traces show that Swift outperforms JetScope and Bubble Execution by 2.44× and 1.23× respectively.
Anomaly Link Detection Based on Weighted Dynamic Network Representation Learning

Network representation learning has been proved to have distinct advantages in capturing the network structure facing complex network data. However, the existing methods cannot learn the corresponding relationship between the edge and the edge weight well when facing weighted dynamic networks. Moreover, when performing anomaly link detection tasks, these methods cannot effectively identify edges with anomalous edge weights. A network representation learning method for weighted dynamic networks is proposed, named WeightWalk. This method regards weights as particular nodes and uses deep Auto-encoding neural network to obtain the vector representations of nodes. This method can learn network structure information and edge weight information at the same time. As the basis of WeightWalk, the task of unnatural links and abnormal weights detection can be regarded as the task of evaluating the difference between the predicted result and the real edge. Our method has been tested on various datasets. Compared with the traditional network representation learning method, the accuracy of anomaly-link detection is improved by about 10%.
API Topics Issues in Stack Overflow Q&amp;As Posts: An Empirical Study

Application Programming Interfaces (APIs) have become one of the key assets within modern businesses, facilitating the linking and integration of intra- and inter-organizational data and systems in the context of complex and heterogeneous technology ecosystems. APIs allow organizations to monetize data, build profitable partnerships and foster innovation and growth. Understanding APIs and their usage are therefore key to building solutions for enabling successful business operations. This paper aims at understanding API topic issues posted on Stack Overflow (SO), a Community Question Answering (CQA) site for programmers. We conduct an empirical analysis on a sample of 400 randomly-selected Q&As threads to help identify API-related issues and their main topics. A thematic analysis performed on this sample reveals eight main topics related to APIs, among which API usage, debugging, API constraints and API security emerged as the major ones. We also exemplify the types of support provided by SO community in addressing each of the identified topics and discuss possible venues on how to further leverage this knowledge.
Online Interactive Ideological Education with Multimedia and Face Verifications

The rapid development of computer vision promotes the development of face recognition technology from simple application scenarios to complex application scenarios. Nevertheless, the existing face recognition system has a high recognition success rate under certain constraints, but the actual results obtained will be influenced by the external environment. Therefore, the image captured by the camera is fuzzy and low resolution, which leads to the decline of recognition accuracy. Meanwhile, the emergence of digital multimedia technology has brought a lot of convenience to people's lives, and increased the way of cultural exchange. Multimedia technology has been applied in every field of society, which makes the dissemination of information culture has been greatly improved. Under this background, the thinking mode of contemporary college students presents new characteristics, and the value demand presents new orientation. Therefore, the education mode in colleges and universities needs the correct spirit guiding mode and more intuitive behavior demonstration. In this paper, face verification and multimedia technology are applied in the ideological and political education system to provide better suggestions.
Exploiting API Description Information to Improve Code Comment Generation

Code comments can improve the readability of program and help the programmer to efficiently promote the maintenance of the software. Therefore, the code comment generation task has important research significance. However, generating high-quality code comments is very challenging, because the function methods called in the code hide a lot of functional information, which is often related to the functionality of the source code. Therefore, we propose to use the function description information to improve the code comment generation model. In the paper, we have designed two models that combine code sequences and API description information to implement the code comment generation task, and conducted experiments on two open source data sets. The experimental results demonstrate the effectiveness of API function description information for code comment generation task.
The International Conference on History, Theory and Methodology of Learning (ICHTML 2020)

Proceedings of the International Conference on History, Theory and Methodology of Learning (ICHTML 2020). Kryvyi Rih, Ukraine, May 13-15, 2020