Reviewerly/exHarmony huggingface dataset:
Evaluating Query Strategies for Different Feedback Types in Interactive View Recommendation

Existing visualization recommendation approaches introduced an overwhelmingly large number of utility functions (UFs) to rank the visualizations (i.e., views). In order to discover the ideal UF and their tunable parameters that are most suited for a particular analysis context, recent works have proposed Interactive View Recommendation (IVR). IVR identifies the ideal UF by learning from the interactions with the user during the analysis process. We claim that the user is usually unable to provide an accurate real number between 0 and 1, indicating the view interestingness, and in this work, propose three alternative feedback types: binary, Likert scale, and pairwise comparison. We further developed ranking algorithms for the three feedback types. Finally, we propose different example selection strategies, and experimentally evaluate them on the three feedback types. We found that uncertainty-based and hybrid-based strategies usually outperforms the random strategy in terms of recommendation accuracy across all feedback types, while the interestingness-based strategy usually performs worse than the random strategy.
Smart Glove for Deaf

Abstract Smart glove is used to help deaf to interpret their communication. This tools is an interesting topic to be explored because its need improvement for the feature that user friendly and easy to used. In this paper, flex sensor and accelero-gyro is used. By using this tools, all of the sign language will be interpreted based on system database that registered before. Then, all of data will be displayed as text in the smartphone. The results prove that the proposed method can interpret the language system correctly.
Secure and Privacy-Preserving Data Computing Scheme Based on Blockchain for Double-Loop Governance of Smart Society

Society governance nowadays has reached a new level after cutting-edge technologies such as artificial intelligence applied to every field of society. This progress requires a novel data sharing and computation mechanism among different parties as a foundation of society governance. In this paper, we introduce a new typical society governance pattern that has been emerging in the most modern cities in the world. With the involvement of smart decision-making units that change the interaction among objects in the governing process, we name the new pattern as “double-loop governing”. And to meet the higher requirement of data security and privacy protection in the process of new society governing way, we proposed a multi-party data computation and sharing scheme based on blockchain which can guarantee that every participator in the process of society governance can efficiently conduct computation based on diverse resources of data while the ownership of data can be ensured. Moreover, we introduce the architecture and the computation mechanism of the proposed scheme.
Efficient Evolution of Variational Autoencoders

Despite the extensive successes of deep learning, designing effective neural networks remains an unsolved challenge in deep learning. The complexity of interactions between all the network hyperparameters such as activation type, layer size, number of layers, and connection patterns is far beyond current understanding. As a result, designing an effective neural network can be as much art as it is science. To that end, neural architecture search aims to automate the task of network design; however, many neural architecture search systems are not feasible for the typical research institution due to the high cost of operation. In this work we propose two simple techniques that have a dramatic impact on search cost, achieving as much as a 900% speed-up in per generation training time, with minimal impact on the efficacy of the search algorithm. Similarly, we achieve a 50% reduction in search time without sacrificing any performance.
Person Reidentification Model Based on Multiattention Modules and Multiscale Residuals

At present, person reidentification based on attention mechanism has attracted many scholars’ interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID.
Virtual Adversarial Active Learning

In traditional active learning, one of the most well-known strategies is to select the most uncertain data for annotation. By doing that, we acquire as most as we can obtain from the labeling oracle so that the training in the next run can be much more effective than the one from this run once the informative labeled data are added to training. The strategy however, may not be suitable when deep learning become one of the dominant modeling techniques. Deep learning is notorious for its failure to achieve a certain degree of effectiveness under the adversarial environment. Often we see the sparsity in deep learning training space which gives us a result with low confidence. Moreover, to have some adversarial inputs to fool the deep learners, we should have an active learning strategy that can deal with the aforementioned difficulties. We propose a novel Active Learning strategy based on Virtual Adversarial Training (VAT) and the computation of local distributional roughness (LDR). Instead of selecting the data that are closest to the decision boundaries, we select the data that are located in a place with rough enough surface if measured by the posterior probability. The proposed strategy called Virtual Adversarial Active Learning (VAAL) can help us to find the data with rough surface, reshape the model with smooth posterior distribution output thanks to the active learning framework. Moreover, we shall prefer the labeling data that own enough confidence once they are annotated from an oracle. In VAAL, we have the VAT that can not only be used as a regularization term but also helps us effectively and actively choose the valuable samples for active learning labeling. Experiment results show that the proposed VAAL strategy can guide the convolutional networks model converging efficiently on several well-known datasets.
Digital Technologies in Literary Work as a Modern Way of Visualizing Socio-economic Phenomena

Purpose: The article is devoted to the means of visualization in the network novel of the modern French writer Ph. De Jonckheere ‘‘La Cible’’. The purpose of this article is to analyze the impact of digital technologies on French literary text and to identify the functions of visual and graphic means in the novel. Design/methodology/approach: The general scientific methodology of the research was composed of works on the theory of text and the theory of visuality, as well as studies of domestic and foreign linguists in the field of visual semiotics and polycodicity, punctuation and graphic organization of visual. Findings: The modern era is characterized by the fragmentation of established traditions in all spheres of socio-economic activity and the search for new means of visualization. Thanks to the development of digital technologies at the beginning of the 21st century, electronic literature, including both text and multimedia components, came to the fore. At the same time, the network modified not only the visual space of the text, but also the reading process itself. Originality/value: It was found that the specifics of the visual and graphic design of the text of the network novel are manifested not only in the ways of presenting the text in the form of a blog, but also in the variety of planar and font variation, as well as due to the inclusion of various non-verbal elements in the text: crosswords, chess games, photographs, author's drawings. Digital technologies allowed the author of the novel ‘‘La Cible’’ to expand the set of text visualization tools. Hybrid texts by Ph. De Jonckheere, along with multicodicity, are characterized by hypertextuality. Multimedia and hypertext construction, creating a special visual appearance and idiosticism of the author, turn the text of a network novel into a literary game with the reader and an endless process of creating a text.
Neural Dependency Parser for Tibetan Sentences

The research of Tibetan dependency analysis is mainly limited to two challenges: lack of a dataset and reliance on expert knowledge. To resolve the preceding challenges, we first introduce a new Tibetan dependency analysis dataset, and then propose a neural-based framework that resolves the reliance on the expert knowledge issue by automatically extracting feature vectors of words and predicts their head words and type of dependency arcs. Specifically, we convert the words in the sentence into distributional vectors and employ a sequence to vector network to extract feature words. Furthermore, we introduce a head classifier and type classifier to predict the head word and type of dependency arc, respectively. Experiments demonstrate that our model achieves promising performance on the Tibetan dependency analysis task.
Extraction of multimodal features from depth and RGB images for recognising hand gestures

In this study, we propose a multimodal feature based framework for recognising hand gestures from RGB and depth images. In addition to the features from the RGB image, the depth image features are explored into constructing the discriminative feature labels of various gestures. Depth maps having powerful source of information, increases the performance level of various computer vision problems. A newly refined Gradient-Local Binary Pattern (G-LBP) is applied to extract the features from depth images and histogram of gradients (HOG) features are extracted from RGB images. The components from both RGB and depth channels, are concatenated to form a multimodal feature vector. In the final process, classification is performed using K-Nearest Neighbour and multi-class Support Vector Machines. The designed system is invariant to scale, rotation and illumination. The newly developed feature combination method is helpful to achieve superior recognition rates for future innovations.
ANALISIS PENGELOMPOKAN KUNJUNGAN WISATAWAN KE OBJEK WISATA UNGGULAN DI PROVINSI JAMBI BERBASIS DATA MINING

Tourist visits to Indonesia, especially the province of Jambi can improve the economy of the community in the tourist area Jambi Province is one of the provinces in Indonesia which is one of the tourist destinations for foreign and international tourists to come to Jambi Province. There are 174 leading tourist attractions in Jambi province according to the questionnaire data taken. The purpose of this study is to analyze the application of datamining in classifying the number of tourist visits to Prov. Jambi uses k-means. The source of research data comes from questionnaire data filled out by the people of Jambi Province. The research data used is the number of tourist visitors last year. The data is grouped into 3 clusters namely C2 = high tourist visits, C1 = moderate tourist visits and C0 = low tourist visits. The final centroid value used at C0 = 5, C1 = 35 and C2 = 17. In order to obtain the results of grouping C0 = 116 attractions, C1 = 18 attractions and C2 = 40 attractions. The results of this analysis can be used as a reference for the Jambi Tourism office to find out the leading tourism objects in Jambi Province according to the Jambi Province Community.
Foggy: A New Anonymous Communication Architecture Based on Microservices

This paper presents Foggy, an anonymous communication system focusing on providing users with anonymous web browsing. Foggy provides a microservice-based proxy for web browsing and other low-latency network activities without exposing users' metadata and browsed content to adversaries. It is designed with decentralized information management, web caching, and configurable service selection. Although Foggy seems to be more centralized compared with Tor, it gains an advantage in manageability while retaining anonymity. Foggy can be deployed by several agencies to become more decentralized. We prototype Foggy and test its performance. Our experiments show Foggy's low latency and deployability, demonstrating its potential to be a commercial solution for real-world deployment.
Grid Restoration Algorithm to Improve PO Calculation Accuracy in GRECO

When calculating RCS using the Graphical Electromagnetic Algorithm (GRECO), Firstly we must obtain the coordinate values and normal vectors of each point on the target illumination surface, which is limited to the computer hardware conditions. So when we calculating RCS at high frequencies often accompanied by insufficient pixel accuracy, therefore it can affect the calculation accuracy. The method of turning straight into a curve adopts a curved surface as close as possible to the real situation instead of the facet information represented by pixels, thereby improving the calculation accuracy of the model at high frequency.
Heuristic Learning in Domain-Independent Planning: Theoretical Analysis and Experimental Evaluation

Automated planning deals with the problem of finding a sequence of actions leading from a given state to a desired state. The state-of-the-art automated planning techniques exploit informed forward search guided by a heuristic which is used to estimate a distance from a state to a goal state. In this paper, we present a technique to automatically construct an efficient heuristic for a given domain. The proposed approach is based on training a deep neural network using a set of solved planning problems as training data. We use a novel way of extracting features for states developed specifically for planning applications. Our experiments show that the technique is competitive with state-of-the-art domain-independent heuristic. We also introduce a theoretical framework to formally analyze behaviour of learned heuristics. We state and prove several theorems that establish bounds on the worst-case performance of learned heuristics.
Designing a K-16 Cybersecurity Collaborative: CIPHER

Cyberattacks have become more common, sophisticated, and harmful, while, at the same time, there is a critical shortage of cybersecurity professionals. For example, from October 2019 to September 2020, there were more than 40,000 unfilled positions for information security analysts. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> While educational organizations have responded to this burgeoning demand, cybersecurity education and training institutions in the United States have found it difficult to keep pace with the growing call for cybertalent.
Drawing Network Visualizations on a Continuous, Spherical Surface

Despite the great literature regarding network visualizations, their graphic representation is hardly an object of investigation. Sometimes it is worth more attention, especially when individuals are represented. Visually translating communities in networks, for example, implies that some individuals are always situated at the borders of the representation. This assumption is clearly unfair, especially if each individual of the community is connected with everybody else. To solve this lack of design justice, the community is represented on a spherical network where the surface is continuous. In that space, individuals can be situated in a sparse area, but never on the edges. The spherical network is successively projected on the flat surface to improve the network readability making use of cartographic projections.
User Mobile App Encrypted Activity Detection

Mobile users install different types of applications on their mobile devices based on their interests and needs and perform various activities on them (known as in-app activities). In this paper, we demonstrate that a passive eavesdropper can identify fine grained in-app activities by analysing encrypted network traffic information obtained by sniffing a Wireless Local Area Network (WLAN). Even though encryption protocols are used to provide security over Internet communications, side channel data is still leaked from encrypted traffic. We utilise this data (frame length, inter arrival time and direction) to identify the in-app activities. Further as a first study of its kind, we show that it is possible to identify in-app activities accurately by observing a very small subset of traffic, rather than observing the entire transaction of an activity as presented in existing literature. To reach these observations, we evaluated 51 in-app activities from three popular social networking apps and identified more than 85% of them correctly using the Bayes Net machine learning algorithm.
Pixort: A Novel Approach for Effective Photo Album Clustering

Abstract Retrieving digital records on the internet and capturing, uploading images on social media or other platforms has become such a notable part of one’s daily existence. Digital content and videos constitute majority of this ever-growing deluge of information. Major problems are faced by people on a daily basis when they have to manually scan large databases to search for particular images thereby exhausting valuable time and resources. To overcome this, Content Based Image Retrieval in addition to Emotion Detection must be performed to efficiently store and retrieve very specific images. An effective image clustering application should be capable of accurately extracting images based on the given query image. It should also provide functionalities like Facial Detection and Recognition for customized usage and retrieval. In this study, texture-based feature extraction followed by the usage of a feed forward backpropagation neural network has been proposed for CBIR. For emotion detection, correlation has been proposed in addition to the Viola Jones algorithm which has been used for facial detection.
A joint STFT-HOC detection method for FH data link signals

The anti-interference, anti-interception, anti-spoofing ability and complex electromagnetic environment of the data link signal of the frequency hopping system pose formidable challenges to the reconnaissance of the frequency hopping signal. Therefore, this paper proposes a joint STFT-HOC detection method for FH data link signals. This method combines time-frequency analysis and high-order cumulant to realize effective detection of frequency hopping data link signals. Theoretical analysis and simulation experiments show that the proposed method can successfully detect the data link signal of the frequency hopping system when the signal-to-noise ratio is greater than −2 dB.
Computer File Signature Analysis Through Hexadecimal Editor Software

Computer file signature is a unique hexadecimal value written to a file header that acts as an identifying feature to identify a file type, as criminals are becoming more and more aware of digitalization these days; they tend to hide sensitive file information within the computer itself without destroying it using tricks to work for them. One of those tricks is to alter a file extension. Accordingly, a more adequate method of data analysis, known as file signature analysis, is needed to counter these measures, and it is done using hexadecimal editor software (HxD). A manipulated computer file can be opened in this software to get its file signature which can be further searched in online database (File Signatures Database) detecting the correct format (extension) of the file rendering criminals plotting to fail and by bringing the hidden information into light.
Foreground detection of human targets with abnormal behavior of mechanical operators

Abstract Complexity of mechanical production environment has brought a certain degree of interference to human target recognition, so it is necessary to preprocess the target video sequence before target detection. The advantages and disadvantages of various detection methods are compared. Research results show that the inter frame difference method has strong adaptability and the target contour is discontinuous. The optical flow method is suitable for the case of lens motion, which is complex in calculation and poor in noise resistance. ViBe algorithm has good effect in obtaining the target contour, clear edge and less holes, and the operation efficiency is not high. The background subtraction method basically retains the features needed by the moving foreground target behavior recognition and detection, and the processing speed is very fast, which can meet the requirements of real-time processing.
A variable patch approach with polling mechanism for intelligent human tracking

Human tracking is one of the challenging and significant components of intelligent tracking systems. Even though the correlation filter-based trackers accomplished the competitive outcomes in terms of accuracy and robustness, there is a need to improve tracking performance. In this paper, the vote mapping of patched confidence methodology is used to enhance a tracking system's performance by continuously increasing the number of patches. The system aims to provide robustness to occlusion and global scene changes by utilizing numerous patches from an image's bounding box. The proposed variable patch approach with a polling mechanism provides robustness to the occlusion in real-life tracking scenarios. The extensive quantitative and qualitative evaluation of a few challenging sequences is carried out using various patches. The tracking performance of proposed technique is better than existing techniques in terms of precision and success rate.
Deep Case Estimation and Japanese Anaphora Resolution with a Verb-Associative Concept Dictionary

In Japanese zero anaphora analysis, it is difficult to identify antecedents in cases where it is necessary to decipher the context and cases where common knowledge information such as common sense needs to be used. The purpose of this study is to improve the accuracy of zero anaphora analysis for these cases. To this end, we propose a method using a verb associative concept dictionary. Experimental results for the analysis of one sentence showed that, in the Top-5 candidate words of the antecedents, the precision of narrowing down the "agent" and the "object" was 91% and 69%, respectively. The results for multiple sentences showed that, in the Top-10 candidate words of the antecedents, the precision of narrowing down the "agent" and the "object" was 86% and 30%, respectively. In the proposed method, only "agent" showed a result close to human judgment. These results demonstrate that the verb associative concept dictionary is effective for anaphora resolution.