Reviewerly/exHarmony huggingface dataset:
Variational Open Set Recognition (VOSR)

Open set recognition models address the real-world scenario where classes of data unobserved during training are encountered in testing after deployment. Closed set classifiers wrongly attempt to classify instances from an unknown class as belonging to one of the known classes from the training set, which reduces the model’s accuracy. Ideally, these unknown instances should be recognized as such, while known instances should continue to be accurately classified. Unfortunately, state-of-the-art open set methods solve this problem by making restrictive assumptions on the variance and/or boundedness of the distributions of known classes. In this paper, we propose a novel method, Variational Open-Set Recognition (VOSR) that eliminates these assumptions. VOSR incorporates a closed set classifier, an unknown detector, and a novel Structured Gaussian Mixture Variational Autoencoder (SGM-VAE) that guarantees separable class distributions with known variances in its la-tent space. Further, by encouraging a large distance between class-specific distributions, VOSR increases the likelihood that instances from unknown classes lie in low-probability regions and thus are more readily identifiable. In rigorous evaluation, we demonstrate that VOSR outperforms state-of-the-art open set classifiers with up to a 14% F1 score increase in identifying instances from unknown classes in multiple image classification and human activity recognition datasets.
Unsupervised Domain Adaptation via Attention Augmented Mutual Networks for Person Re-identification

Supervised learning has limited generalization ability across scenes due to its high cost of data annotation, and unsupervised learning and unsupervised domain adaptation have become the hot topics in recent years. With the applications of deep learning in the field of unsupervised domain adaptation (UDA) for person re-identification, pseudo label methods via clustering techniques have become the mainstream route. However, the clustering procedure inevitably leads to noisy pseudo-labels. To reduce the interference of clustering noise, mutual mean-teaching (MMT) is introduced to generate reliable soft pseudo labels, however, this method is easy to fall into the local optimum. In this paper, we propose a novel Attention Random Variation (ARV) module that can be integrated into the MMT framework to develop Attention Augmented Mutual Networks (AAMN). Our ARV module generates random differences between two collaborative networks under the MMT framework to avoid the networks converging to the same kind of noise. Specifically, we propose a parameter-free Random Variation module to produce differences by randomly enhancing units of feature maps, and then combine it with an attention mechanism to enlarge networks differences and complementarity. Experimental results show that our AAMN method improves mAP of baseline method by 1.9% and 6.3% on Market-to-Duke and Duke-to-Market UDA tasks respectively.
Detection of Stuck-at and Bridging Fault in Reversible Circuits using an Augmented Circuit

Low-power design is a major concern in the circuit design domain. The reversible circuit is an alternative for moving beyond the conventional ways of computing. For performing the high reliability and the correctness of the circuit, testing is necessary for evaluating the faults. This paper presents the fault detection method for classical fault models like stuck-at faults and bridging faults in reversible circuits using the negative-controlled augmented k-CNOT based circuit. We initially construct the n number of test vectors with n input lines for a given circuit. The constructed test vector sequences successfully attempt as the complete test set on the testable design augmented k-CNOT circuit for detecting faults. The proposed method applies to several benchmark circuits for detecting the stuck-at and bridging faults and also comparative analysis is prepared with some existing works.
Current Situation and Strategy of Information Technology in Art Class Teaching in Colleges Under the Background of “Internet+”

With the rise of artificial intelligence and the continuous development of information technology, the traditional teaching model no longer adapts to the all-round development of college students. Through the research, exploration and application of the resources of the flipped classroom and MOOC platform, aiming at the problems existing in the classroom teaching of fine arts in colleges, the article explores the current situation and strategies of information technology in fine arts classroom teaching in colleges under the background of “Internet+”.
WEWD: A Combined Approach for Measuring Cross-lingual Semantic Word Similarity Based on Word Embeddings and Word Definitions

Cross-lingual semantic word similarity (CLSW) ad- dresses the task of estimating the semantic distance between two words across languages. This task is an important component in many natural language processing applications. Recent studies have proposed several effective CLSW models for resource- rich language pairs such as English-German, English-French. However, This task has not been effectively addressed for language pairs consisting of Vietnamese and another one. In this paper, we propose a neural network model that exploits cross- lingual lexical resources to learn high-quality cross-lingual word embedding models. Since our neural network model is language- independent, it can learn a truly multilingual space. Furthermore, we introduce a novel cross-lingual semantic word similarity measurement method based on Word Embeddings and Word Definitions (WEWD). Last but not least, we introduce a standard Vietnamese-English dataset for the cross-lingual semantic word similarity measurement task (VESim-1000). The experimental results show that our proposed method is more robust and outperforms current state-of-the-art methods that are only based on word embeddings or lexical resources.
Complex Event Recognition via Spatial-Temporal Relation Graph Reasoning

Events in videos usually contain a variety of factors: objects, environments, actions, and their interaction relations, and these factors as the mid-level semantics can bridge the gap between the event categories and the video clips. In this paper, we present a novel video events recognition method that uses the graph convolution networks to represent and reason the logic relations among the inner factors. Considering that different kinds of events may focus on different factors, we especially use the transformer networks to extract the spatial-temporal features drawing upon the attention mechanism that can adaptively assign weights to concerned key factors. Although transformers generally rely more on large datasets, we show the effectiveness of applying a 2D convolution backbone before the transformers. We train and test our framework on the challenging video event recognition dataset UCF-Crime and conduct ablation studies. The experimental results show that our method achieves state-of-the-art performance, outperforming previous principal advanced models with a significant margin of recognition accuracy.
Research on Calculation Method of Software Service Risk Occurrence Probability Based on Markov Chain Theory

The new computing models represented by cloud computing, Internet of things, big data and artificial intelligence put forward higher requirements for the trustworthiness of software services. Software service risk occurrence probability is one of the key indicators of software service trustworthiness. The trustworthiness of software services with low risk occurrence probability is not necessarily high, but the trustworthiness of software services with high risk occurrence probability is certainly low. In order to solve the screening problem of software services with high risk probability in the process of software service selection, this paper proposes a software service risk probability calculation method based on Markov Chain theory, and verifies the effectiveness and feasibility of the proposed method through a case study.
Automatic Text Summarization Technology of Keyword Replacement Based on Seq2Seq

With the improvement of the data technology, the development of Internet technology has brought great convenience to people, and at the same time brought the problem of information overload. In order to effectively extract important information from documents to improve people's work efficiency, this paper proposes a method of using deep learning combined with keyword substitution to automatically generate Chinese short text summaries. First, the original input is encoded into a context vector by constructing a Seq2seq framework based on the Attention mechanism, and then the context vector is decoded and output by the improved Beamsearch (cluster search), and finally the generated abstract is replaced by the keywords of the original text. The final summary result. The comparison of the quality of the generated abstracts through three indicators of ROUGE-1, ROUGE-2 and ROUGE-L verifies the feasibility of the method and improves the accuracy of the abstract and the fluency of the language.
Research on the Application of Exchange Mode in Data Communication Technology

With the continuous development of economy and the continuous improvement of people's quality of life, data communication technology is also indispensable. Because of the sustainable development of data communication technology, the long-term development of various undertakings in China can be promoted. In the process of the development of data communication technology, the research on the exchange mode is an important way to expand the application of data communication mode. This paper mainly expounds the computer network technology and the technical principle and source of data communication, and explores the application way of exchange mode in data communication technology.
The Walsh Transform of a Class of Boolean Functions

The Walsh transform is an important tool to investigate cryptographic properties of Boolean functions. This paper is devoted to study the Walsh transform of a class of Boolean functions defined as [see formula in PDF], by making use of the known conclusions of Walsh transform and the properties of trace function, and the conclusion is obtained by generalizing an existing result.
A Compression and Recognition Joint Model for Structured Video Surveillance Storage

Structured data storage of surveillance video helps to reduce the time for information retrieval. However, modern surveillance systems have to perform the compressing and recognition in two separate devices, resulting in the waste of the processing time and hardware resource. This paper proposes a joint model to enable video recognition and compression simultaneously. The proposed model stores the video in a structured format, allowing high-level vision tasks such as structured storage, content analysis, and intelligent retrieval. The large and small features are extracted at intervals in the recognition network to reduce the computation cost significantly. The experimental results show that our compression module saves 68.4% bits per pixel (bpp) compared with H.264 while achieving the same Multi-scale Structural Similarity (MS-SSIM) for 0.965. Meanwhile, our recognition module reduces 47.2% of Giga Floating-point Operations (GFLOPs) compared with ARTNet, while achieving 64.1% top-1 and 84.2% top-5 accuracy. The proposed joint model saves nearly 28% of the number of computing resources.
Unlocking the Potential of MAPPO with Asynchronous Optimization

It almost reaches a consensus that off-policy algorithms dominated research benchmarks of multi-agent reinforcement learning, while recent work [] demonstrates that on-policy MARL algorithm, Multi-Agent Proximal Policy Optimization (MAPPO), can also attain comparable performance. In this paper, we propose a training framework based on MAPPO, named async-MAPPO, which supports scalable asynchronous training. We further re-examine async-MAPPO in StarCraftII micromanagement domain and obtain state-of-the-art performances on several hard and super-hard maps. Finally, we analyze three experimental phenomena and provide hypotheses behind the performance improvement of async-MAPPO.
DeltaVLAD: An efficient optimization algorithm to discriminate speaker embedding for text-independent speaker verification

&lt;abstract&gt; &lt;p&gt;Text-independent speaker verification aims to determine whether two given utterances in open-set task originate from the same speaker or not. In this paper, some ways are explored to enhance the discrimination of embeddings in speaker verification. Firstly, difference is used in the coding layer to process speaker features to form the DeltaVLAD layer. The frame-level speaker representation is extracted by the deep neural network with differential operations to calculate the dynamic changes between frames, which is more conducive to capturing insignificant changes in the voiceprint. Meanwhile, NeXtVLAD is adopted to split the frame-level features into multiple word spaces before aggregating, and subsequently perform VLAD operations in each subspace, which can significantly reduce the number of parameters and improve performance. Secondly, the margin-based softmax loss function and the few-shot learning-based loss function are proposed to be combined for more discriminative speaker embeddings. Finally, for a fair comparison, the experimental results are performed on Voxceleb-1 showing superior performance of speaker verification system and can obtain new state-of-the-art results.&lt;/p&gt; &lt;/abstract&gt;
Convergence &amp;amp; Conflict: Reflections on Global and Regional Human Rights Standards on Hate Speech

Hate speech is a hot topic globally, stubbornly persistent on online platforms just as those platforms, not to mention governments and international organizations, seek guidance from international human rights law in dealing with it. But what is hate speech? And how do key adjudicators – especially states, United Nations (UN) human rights mechanisms, and regional human rights bodies – interpret the law governing freedom of expression and hate speech? This Article seeks to illuminate two countervailing and under-reported trends: on the one hand, a growing consensus among UN experts and treaty bodies concerning interpretations of “hate speech” prohibitions in international law; and on the other, a failure of several regional human rights systems to develop approaches to hate speech that are consistent with the UN’s universal standards. The Article begins by analyzing the UN’s approach to freedom of expression and hate speech and examining how, in the last decade, various UN expert bodies have converged on an approach to hate speech and freedom of expression. The Article next compares this global standard with key developments in the Inter-American, European, African, Arab, Islamic, and Southeast Asian human rights systems. This comparative analysis reveals that, while certain systems converge with the UN’s approach, others diverge, sometimes marginally, sometimes significantly. For example, the European Court of Human Rights frequently lessens or removes the burden on governments to show hate speech restrictions are properly imposed, allows for the imposition of hate speech restrictions for reasons not accepted at the global level, and does not assess whether restrictions on speech are the least intrusive means to achieving legitimate public interest objectives. After analyzing this landscape of regional norms in convergence and conflict with UN standards, the Article provides several key observations on this complex state of affairs. The Article concludes by urging human rights defenders throughout the world to be cognizant of the areas in which regional human rights systems conflict with UN standards and to tackle this trend through proposed strategies to protect universal minimum standards for freedom of expression.
Automatic Malicious Worker Detection in Crowdsourced Paraphrases

Automatic paraphrasing has numerous applications such as query reformulation in query-answering systems, dialog systems, and machine translation. Recently, due to the limitations of automatic paraphrasing systems, crowdsourcing has been growing in popularity for obtaining corpora of paraphrases. Since crowdsourced paraphrases are generated by crowd workers with varied skills and motivations, such paraphrases may suffer from various quality issues (e.g., semantic and linguistic errors). Such quality issues stem from the lack of quality assessment methods for open-ended text in crowdsourcing platforms (e.g., Mechanical Turk). Thus unqualified workers and spammers may generate erroneous paraphrases. Crowdsourced paraphrases must be verified to remove erroneous paraphrases, imposing an extra cost. Detecting such erroneous paraphrases requires an understanding of how malicious workers generate paraphrases. In this paper, we provide a taxonomy of cheating behaviors in crowdsourced paraphrasing. We also propose an effective approach to detect cheating behaviors.
Detecting Motion and Orientation

Mobile computer devices like the iPhone and iPad essentially put a PC in your pocket, letting you use a computer wherever you happen to be. However, unlike a desktop or even a laptop PC, mobile computers can track movement and orientation. This can come in handy for tracking the user’s arm movements in a game or measure angles in objects around you.
A Real-time Multispectral Algorithm for Robust Pedestrian Detection

Low light conditions are known to create a notable challenge to the applicability of deep learning in a wide variety of computer vision applications. In this paper, we develop a detection method for real-time multispectral pedestrians that fuses color image (i.e., red-green-blue or RBG) with thermal image to provide a reliable object vision. Such combination is achieved using the confidence scores that are computed based on the illumination measure of a given input image. We evaluate the proposed algorithm on KAIST dataset. Such method is observed to give a 34.11% Log Average Miss Rate, operate in real-time, and thus, being ready to deploy in practice.
Synthesizing and Manipulating Natural Videos Using Image-to-Image Translation

Unpaired video-to-video translation is the mapping of elements of one video to another generated video using a neural network trained on unpaired data. While many methods have been developed in the attempt to generate naturalistic translated videos, many difficulties still exist. One major hurdle preventing the generation of naturalistic videos is the issue of texture consistency. This paper presents a modular video-to-video translation pipeline which leverages various fields of computer vision and image manipulation to produce high quality, texture-consistent translated videos. This paper also presents new paradigms for examining the performance of existing video-processing solutions, namely keyframe extraction methods, creating new metrics at which these methods are examined by. Experimental results show that the proposed method can increase the naturalistic appearance of these translated videos when compared to other existing unpaired methods.
Graphical System Design of Test Cases in Flight Control System

Based on the flight control system test and verification platform, explore the graphical design of test cases in a distributed test environment. Aiming at the problems of complicated test case form in distributed test environment and inconvenience for operators to record and call, analyze according to the actual needs of flight control system testers, adopt formal description method, and describe the execution process with state machine. A set of maintainable and easy to expand graphical system of test cases is designed. Experiments show that the system realizes the visual management of flight control system test cases. Through the conversion of test cases from text to graphics, it can be organically combined with complex test requirements and environments, and the flight control system can be improved in a complex application environment. Test efficiency and test quality.
Face Tracking via Content Aware Correlation Filter

Face tracking is an importance task in many computer vision based augment reality systems. Correlation filters (CFs) have been applied with great success to several computer vision problems including object detection, classification and tracking, but few CF-based methods are proposed for face tracking. As an essential research direction in computer vision, face tracking is very important in many human-computer applications. In this paper, we present a content aware CF for face tracking. In our work, face content refers to the locality sensitive histogram based foreground feature and the learning samples extracted from complex background. It means that both foreground and background information are considered in constructing the face tracker. The foreground feature is introduced into the objective function which could learn an efficient model to adapt to the face appearance variation. For evaluating the proposed face tracker, we build a dataset which contains 97 video sequences covering the 11 challenging attributes of face tracking. Extensive experiments are conducted on the dataset and the results demonstrate that the proposed face tracker shows superior performance to several state-of-the-art tracking algorithms.
Locally optimum image watermark detector based on statistical modeling of SWT-EFMs magnitudes

There are three indispensable, yet contrasting requirements for a watermarking scheme: perceptual transparency, watermark capacity, and robustness against attacks. Therefore, a watermarking scheme should provide a trade-off among these requirements from the information-theoretic perspective. Improving the ability of imperceptibility, watermark capacity, and robustness at the same time has been a challenge for all image watermarking algorithms. The statistical model-based transform domain multiplicative watermarking is an effective way to achieve the tradeoff among imperceptibility, robustness and data payload. In this paper, we propose a locally optimum statistical image watermark detector by incorporating stationary wavelet transform (SWT), exponent-Fourier moments (EFMs), and locally optimum (LO) decision rule. SWT has many characteristics, such as high redundancy, high reconstruction accuracy, low time complexity and multi-resolution representation, and EFMs can provide the excellent robustness. In order to obtain valuable samples, we first decompose the digital image by using SWT, and then perform EFMs calculation in the corresponding frequency domain. These robust frequency-domain EFMs magnitudes are treated as the basis for the following research work. In our scheme, the watermark is hidden into the target location of frequency-domain EFMs magnitudes by using multiplicative embedding method. These magnitudes at the target location are abbreviated as SWT-EFMs magnitudes for the convenience of subsequent description. We analyze the statistical characteristics of SWT-EFMs magnitudes in detail. In order to achieve accurate statistical modeling of SWT-EFMs magnitudes, the Beta Generalized Weibull mixtures-based hidden Markov tree (BGWM-HMT) model is constructed in SWT-EFMs magnitudes. The expectation-conditional maximization either (ECME) and the upward–downward algorithms are used to obtain model parameters. Our proposed statistical model can simultaneously capture the special distribution characteristics of SWT-EFMs magnitudes and the dependencies between scales. At the receiver, a statistical watermark detector based on the LO decision rule and BGWM-HMT model is developed in SWT-EFMs magnitudes. We derive closed-form expressions about the LO statistic and analyze the receiver operating characteristic (ROC) of the proposed detector. In addition, we measure the proposed scheme in terms of robustness, invisibility and time complexity. Experimental results on some test images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed statistical image watermark detector.