Reviewerly/exHarmony huggingface dataset:
Text Classification Based on Hybrid Neural Network

Aiming at the problems of insufficient feature extraction and low classification accuracy in the classification of Chinese news texts by the neural network, this paper proposes a hybrid neural network text classification model which integrates time series convolutional network-TGNet. The new model utilizes Temporal Convolutional Network to capture the relationship between hidden features on different time scale, and uses Gated Tanh-ReLU Units (GTRU) as the activation layer to improve the expression ability of neural network to the model. Meanwhile, the Gated Recurrent Unit networks (GRU) is used to learn the semantic features of context. Finally, the extracted features are fused and input into Softmax for classification. Experimental results show that the text classification model proposed in this paper has achieved better classification results in the published Chinese news data sets SougoCS and FuDan.
Prior Knowledge Representation for Self-Attention Networks

Self-attention networks (SANs) have shown promising empirical results in various natural language processing tasks. Typically, it gradually learning language knowledge on the whole training dataset in parallel and stacked ways, thereby modeling language representation. In this paper, we propose a simple and general representation method to consider prior knowledge related to language representation from the beginning of training. Also, the proposed method allows SANs to leverage prior knowledge in a universal way compatible with neural networks. Furthermore, we apply it to one prior word frequency knowledge for the monolingual data and other prior translation lexicon knowledge for the bilingual data, respectively, thereby enhancing the language representation. Experimental results on WMT14 English-to-German and WMT17 Chinese-to-English translation tasks demonstrate the effectiveness and universality of the proposed method over a strong Transformer-based baseline.
Trusted Video Streams in Camera Sensor Networks

Proof of integrity in produced video data by surveillance cameras requires active forensic methods such as signatures, otherwise authenticity and integrity can be comprised and data becomes unusable e. g. for legal evidence. But a simple file- or stream-signature loses its validity when the stream is cut in parts or by separating data and signature. Using the principles of security in distributed systems similar to those of blockchain and distributed ledger technologies (BC/DLT), a chain which consists of the frames of a video which frame hash values will be distributed among a camera sensor network is presented. The backbone of this Framechain within the camera sensor network will be a camera identity concept to ensure accountability, integrity and authenticity according to the extended CIA triad security concept. Modularity by secure sequences, autarky in proof and robustness against natural modulation of data are the key parameters of this new approach. It allows the standalone data and even parts of it to be used as hard evidence.
Spectral Norm Normalized Network for Facial Expression Generation

Facial expression recognition (FER) is widely studied in the area of man-machine interaction. But in the current research on expression recognition technique, the degradation of recognition performance is caused by the problem of small sample and unbalance of sample size in expression database. In this paper, we study the generation of facial expression image in generative adversarial model, focusing on the solution to meet the Lipschitz constraint. We propose to generate the facial expression images based on spectral norm normalized networks (SNN-EG). The method satisfying K-Lipschitz regular constraints, can expand the number of expression samples, enrich sample diversity, and achieve the solution for the problems of small samples and unbalanced samples. The KDEF expression dataset is validated and evaluated. Experiments show that the proposed method has a better effect on synthesis and recognition of facial expressions.
An Empirical Study of Optimizers for Quantum Machine Learning

The hybrid quantum-classical framework has been attracted more researchers, no matter in classical machine learning or quantum physics and quantum chemistry. Although the existing models are implemented on small and intermediate scale system or datasets, they also provide the foresight of the large scale applications in the future. Because the hybrid framework consists of the parametrized quantum circuit and well-developed classical optimizers, the selection of appropriate optimizer for different task matters the final performance of the model. In this paper, we take nine widely employed optimizers, including gradient based and gradient-free based, into account, and present the empirical comparison between their performance on a typical scenario, i.e. supervised learning. In our experiments, we found the gradient based optimizer provides a relatively better solution in each case. However, the gradient-free based optimizers have less running time than that of gradient-based optimizers. For small scale problems, the gradient-free optimizers such as COBYLA are more practical. We believe what we found in this work might provide some help for researchers interested in the related problems.
Learning Japanese-English Bilingual Word Embeddings by Using Language Specificity

Cross-lingual word embeddings have been gaining attention because they can capture the semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform a word embedding space from one language to another. To improve bilingual word embeddings, we propose an advanced method that adds a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of the Japanese language. We evaluated our method by comparing it with single mapping-based-models on bilingual lexicon induction between Japanese and English. We determined that our method was more effective, with significant improvements on words of Japanese origin.
Exploring the Effects of User Control on Social Engagement in Virtual Reality

A key aspect of user experience in virtual reality (VR) is navigational control. However, when multiple users interact in a social VR environment, it is important that navigational control is designed to support social engagement. In this paper, we investigate the relationship between levels of navigational control, diminishing from fully independent to completely passive, and their potential impact on social engagement in a social VR environment. We conducted qualitative interviews with 12 participants who viewed video recordings demonstrating control methods and social interactions in a prototype social VR application. Our results demonstrate the diverse attitudes people hold about how social engagement is impacted by different navigational control methods, and how this might be impacted by individual preferences and cultural factors. Based on these results, we contribute design considerations for supporting group cohesion, interpersonal and cultural norms, as well as discoverability in social VR systems to better support social engagement.
An Architectural Approach to Provide Efficient Service with The Service Level Agreement

Cloud brokers have been as of late presented as an extra computational layer to encourage cloud choice and administration the board assignments for cloud shoppers. In any case, existing business plans on cloud administration choice ordinarily expect that brokers are totally trusted, and don’t give any assurance over the accuracy of the administration suggestions. It is then workable for an undermined or untrustworthy intermediary to effortlessly exploit the restricted abilities of the customers and give off base or deficient reactions. To address this issue, we propose a creative Cloud Service Selection Verification (CSSV) conspire and list structures to empower cloud customers to distinguish misconduct of cloud dealers during the administration determination measure. We show rightness and productivity of our methodologies both hypothetically and observationally.
Iterative Self-distillation for Precise Facial Landmark Localization

In this paper, we propose a novel training method to improve the precision of facial landmark localization. When a facial landmark localization method is applied to a facial video, the detected landmarks occasionally jitter, whereas the face apparently does not move. We hypothesize that there are two causes that induce the unstable detection: (1) small changes in input images and (2) inconsistent annotations. Corresponding to the causes, we propose (1) two loss terms to make a model robust to changes in the input images and (2) self-distillation training to reduce the effect of the annotation noise. We show that our method can improve the precision of facial landmark localization by reducing the variance using public facial landmark datasets, 300-W and 300-VW. We also show that our method can reduce jitter of predicted landmarks when applied to a video.
Toward Black-box Image Extraction Attacks on RBF SVM Classification Model

Image extraction attacks on machine learning models seek to recover semantically meaningful training imagery from a trained classifier model. Such attacks are concerning because training data include sensitive information. Research has shown that extracting training images is generally much harder than model inversion, which attempts to duplicate the functionality of the model. In this paper, we use the RBF SVM classifier to show that we can extract individual training images from models trained on thousands of images, which refutes the notion that these attacks can only extract an "average" of each class. Also, we correct common misperceptions about black-box image extraction attacks and developing a deep understanding of why some trained models are vulnerable to our attack while others are not. Our work is the first to show semantically meaningful images extracted from the RB F SVM classifier.
Visual Security Analytics

Despite the application of increasingly advanced methods and technologies to automate tasks within cyber security, human domain knowledge remains indispensable. Especially monitoring a system’s security posture as well as detecting and analyzing cyber threats requires involvement of security experts. However, the large amount of data relevant for these tasks poses a major impediment for any kind of manual analyses. It is therefore necessary, to enable security experts to efficiently deal with large amounts of data. Visual Security Analytics (VSA) aims to achieve this through generating interactive visual representations of log data or any other data relevant for monitoring, ensuring, and preserving cyber security and covering different ways of analyzing security data using visual approaches (Marty 2009). It is a combination of automated and visual analysis aiming for a “best-of-both” worlds approach. Thus, VSA is a highly interdisciplinary field covering information security, security analytics, information visualization, and human-computer interaction among several others.
Cultivation of Intercultural Communicative Competence in Japanese Teaching in Colleges and Universities Based on Computer Aid

Abstract Intercultural communication ability is an important factor in learning a foreign language, which can help us to have better daily communication. Therefore, to improve the intercultural communication ability of Japanese majors has become a hot topic of social concern, which is in urgent need of the reform of the current teaching mode in Universities. Through various ways, such as refined classroom teaching, extracurricular practice, micro class, MOOC class, etc., students can better understand Japanese culture, which will improve their intercultural communication ability. In computer-assisted Japanese teaching in colleges and universities, language teaching and cultural teaching are two independent systems, which have not been well integrated. Therefore, universities must improve the cross-cultural communication ability of Japanese students, which will meet the needs of the society for diversified and Applied Japanese talents [1-2] . First of all, this paper puts forward the focus of intercultural communication in Japanese teaching. Then, this paper puts forward some questions. Finally, some suggestions are put forward [3-4] .
Evaluation Method of Combat Effectiveness of Electronic Warfare Equipment Based on Improved Fruit Fly Algorithm

When using the original method to evaluate the operational effectiveness of electronic warfare equipment, due to the influence of too many states involved, it is impossible to build the combat simulation model of electronic warfare equipment, and the calculation results of unit damage probability in the range of 1000-3000 meters in the configuration area are inaccurate. Therefore, an operational effectiveness evaluation of electronic warfare equipment based on improved fruit fly algorithm is proposed method. The operational simulation model of EW equipment is established, which mainly includes battlefield environment model, entity model and combat action model. Using the method of decomposing and evaluating targets, the index set is initially drawn up, and the initial index set is compared, selected, and de-correlated. Through the screening index, the operational effectiveness evaluation model of electronic warfare equipment is constructed to realize the operational effectiveness evaluation of electronic warfare equipment. The original method is compared with this method, and the experimental results prove that the accuracy of the calculation result of the damage probability of the soldier group of the method is higher than that of the original method, and the performance is improved.
Aviation Surveillance Information Fusion Based on Multi Neural Network

This paper introduces an aviation surveillance information fusion method based on multi neural networks. With the rapid growth of the number of civil and military aircraft, the air traffic is more and more busy. In order to ensure the flight safety of the aircraft, the application of the aviation surveillance information processing system is indispensable. The aviation surveillance information fusion technology in the system is the key to obtain the accurate information of the flight target position. As the traditional method of aviation surveillance information fusion method, the Kalman filter has the characteristics that it does not need to retain the past measurement data, but only needs to be recursive according to the state equation. But the Kalman filter has the problem of high requirements for model error and calculation accuracy, and it requires a large number of professionals to conduct time-consuming parameter adjustment, which consumes a lot of human and material resources. Thus, it is necessary to find an efficient and accurate information fusion method of aviation surveillance. The multi neural network proposed in this paper can overcome the above shortcomings of the Kalman filter. Furthermore, the multi neural network can achieve better than single neural network, which can predict the position of the aircraft more accurately.
Wikipedia e le biblioteche: alleanze per costruire il futuro

The vision statement of the Wikimedia Foundation states: “Imagine a world in which every single human being can freely share in the sum of all knowledge”. If libraries had a shared mission statement, it would be something quite similar. However, librarians sometimes discount Wikipedia as being unreliable, low quality, and as a competitor to library services. In so doing, they may be missing an opportunity to leverage the omnipresence of this gentle giant in the online world. This paper highlights both the ways that OCLC has invested in partnerships with Wikimedia projects, as well as international success stories from academia, archives, museums, and libraries that all share a goal of connecting communities of knowledge.
A Survey on Linear Prediction Residual based Replay Attack Detection System

It is well known fact that modern automatic speaker verification (ASV) system is vulnerable to replay attacks, and this vulnerability has increased, in light of rapid progress in recording and playback device manufacturing technologies. Prior studies widely explored spectral features, modulation features and phase features in developing replay detection systems for ASV protection. In this order, focus on excitation source features has been comparatively very less. The linear prediction (LP) residual represents excitation source information implicitly. This paper provides a detailed review on prior LP residual derived excitation source features exploited for replay detection task. As outcome, we inferred that exploring LP residual as excitation source features using suitable signal processing algorithms may provide better solutions than existing. Further, these excitation source features are complementary to those of well performing spectral/modulation and phase features, and can be used in combination in order to obtained better solutions close to generalization. After this, the usefulness of LP residual information for replay detection task has been discussed from future perspective. It is concluded that capturing harmonic related excitation source information from LP residual signal may provide better results in replay speech detection context.
ShadowVM

With the development of the big data ecosystem, large-scale data analytics has become more prevalent in the past few years. Apache Spark, etc., provide a flexible approach for scalable processing upon massive data. However, they are not designed for handling computing-intensive workloads due to the restrictions of JVM runtime. In contrast, GPU has been the de facto accelerator for graphics rendering and deep learning in recent years. Nevertheless, the current architecture makes it difficult to take advantage of GPUs and other accelerators in the big data world.
Hybrid Anti-phishing Approach for Detecting Phishing Webpage Hosted on Hijacked Server and Zero-Day Phishing Webpage

Phishing is an information security issue of stealing confidential information (login credentials, credit card information, and identification information like SSN, etc.) using a fake website. In current scenario, search engine-based methods and machine learning-based methods are proposed for phishing detection. Search engine-based methods are highly efficient because of its fast responsive nature compare to machine learning approaches. We proposed a hybrid phishing detection technique that is a combination of a search engine-based approach and a hyperlink similarity-based methods for detecting phishing webpage hosted on the legitimate hijacked server and zero-day phishing webpage. We use title, domain, and copyright of webpage to create an efficient search query for a search engine-based approach. Based on the presence of a brand name in title or copyright and title language decide for the similarity phase. Our proposed method is practical and adaptable for detecting a phishing webpage hosted on the legitimate hijacked server and zero-day phishing webpage.
CrowdDepict: Know What and How to Generate Personalized and Logical Product Description using Crowd intelligence

A compelling product description on e-commerce platforms (e.g., Amazon) is vital in explaining the untouchable product and increasing consumers' purchase rate. However, hand-written product descriptions for each category of products is highly time-consuming and not professional due to the lack of related marketing knowledge. Prior works adopt either predefined templates or data-driven models to automatically generate the personalized product description, but the quality (e.g., readability, flexibility) of generated text is always constrained by the lack of personalized production description training samples. To further improve the product description quality, we propose a personalized product description generation model named CrowdDepict focusing on what proper permutation of attribute words should be taken to generate the description and how to describe the attribute words. Particularly, CrowdDepict integrates an Attribute Permutation-insensitive Encoder to enable the model to generate logical description with an appropriate attribute keywords organization without requiring a re-organized input attribute keywords and a Crowd Intelligence-aware Comment Encoder to capture crowd intelligence about how the attributes of products are described in real-world user comments. Experiment results demonstrate that CrowdDepict outperforms the baseline on various metrics, especially an improvement of 34% over state-of-the-art relative to BLEU, which shows that our model can generate personalized product description that consists of correct product attributes of consumer interests and the necessary product information.
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential Equations

Non-uniform sampling arises when an experimenter does not have full control over the sampling characteristics of the process under investigation. Moreover, it is introduced intentionally in algorithms such as Bayesian optimization and compressive sensing. We argue that Stochastic Differential Equations (SDEs) are especially well-suited for characterizing second order moments of such time series. We introduce new initial estimates for the numerical optimization of the likelihood, based on incremental estimation and initialization from autoregressive models. Furthermore, we introduce model truncation as a purely data-driven method to reduce the order of the estimated model based on the SDE likelihood. We show the increased accuracy achieved with the new estimator in simulation experiments. Finally, we apply the new estimator to experimental rainfall variability data.
Information and reference system for preparing for the main state exam in informatics and information and communication technologies

Nowadays, the task of preparing for the main state exam is becoming more and more popular because of the government’s orientation towards increasing the number of professionals trained in secondary vocational education programs. Enrollment in secondary vocational schools is based on the main state exam, which leads to an increase in demand for resources to prepare for this exam. The paper is devoted to the design and creation of a prototype information and reference system for preparing for the main state exam in computer science and information and communication technologies.
Bitcoin Cost Prediction using Deep Neural Network Technique

The accusative of this paper is to predict the bitcoin price accurately by taking various parameters into consideration which affects the bitcoin value. Here multi-layer perceptron algorithms under deep learning are used to predict the price of crypto-currency. Many researchers have analysed the crypto-currency features in many ways such as, market price prediction, the impact of cryptocurrency in real life. It has the ability to make long-term prediction of the exchange price in crypto-currencies particularly in US dollar, based on historical trends. The bitcoin cost prediction is done based on the data set which consists of 13 features relating to the crypto-currency price recorded daily over the period of particular range.
Formalization of Data Derivative Structures

The paper considers the topical issue of formal description of data structures. Data structures are widely used in modern object-oriented programming to solve various practical problems. The paper introduces functions for performing operations with such data structures as stack, queue, and priority queue. The conditions that these functions must satisfy are introduced as axioms. To describe the axioms, an approach based on an abstract data type, algebraic and programmatic approaches were used.