Reviewerly/exHarmony huggingface dataset:
Efficient personalized search over encrypted data for mobile edge-assisted cloud storage

Cloud storage services allow a data owner to share her/his outsourced data with other users, and enable the users to search target data by keywords. To ensure the data confidentiality, data owner always encrypt data using traditional encryption schemes before outsourcing. Whereas, it makes efficiently searching impossible. Symmetric searchable encryption (SSE) is a cryptographic primitive that resolves this tension. However, most existing SSE schemes do not consider the individual characteristics of users during the search, such that they cannot support personalized search services over encrypted data. Meanwhile, security and efficiency issues in the cloud service model have also severely affected the user's search experience, and the introduction of mobile edge servers can solve these problems to some extent. In this paper, we propose a personalized searchable encryption scheme (PSED) for mobile edge-assisted cloud storage. Our contribution consists of three aspects. First, we incorporate the user's preference factors into the user's query which enable users to get accurate personalized search results. Second, the computational overhead of the cloud server is reduced by calculating the relevance scores of the subqueries and subindexes on mobile edge servers. Third, by cutting the index and the query matrix, the encryption efficiency of the index and the query matrix is improved. Security analysis shows that PSED can guarantee the privacy of the data and the user. Experimental results demonstrate that the proposed schemes are highly efficient and accurate.
Recent Study on Image Denoising using Deep CNN Techniques

Image denoising is one of the important subjects in the examination field. On the presentation of profound CNN in picture denoising, the picture is by all accounts more exact from commotion when contrasted with the non-cnn picture denoising strategies. Thusly, this paper is an audit on some CNN models for denoising boisterous picture including Deep CNN utilizing leftover learning with and without group standardization, Deep shrinkage CNN, Mixed commotion decrease CNN, Residual learning of profound CNN with Symmetry-Rectifier Linear Unit (SyReLU), Dilated Residual Networks with Symmetric Skip Connection (DSNet), Fast and adaptable denoising CNN (FFDNet), Batch-renormalization denoising network (BRDNet) and Attention-guided denoising convolutional neural organization (ADNet).The denoising achievements of the above models are analyzed on different gray and colour.
Cross-Language Transfer Learning and Domain Adaptation for End-to-End Automatic Speech Recognition

In this paper, we demonstrate the efficacy of transfer learning and continuous learning for various automatic speech recognition (ASR) tasks using end-to-end models trained with CTC loss. We start with a large pre-trained English ASR model and show that transfer learning can be effectively and easily performed on: (1) different English accents, (2) different languages (from English to German, Spanish, Russian, or from Mandarin to Cantonese) and (3) application-specific domains. Our extensive set of experiments demonstrate that in all three cases, transfer learning from a good base model has higher accuracy than a model trained from scratch. Our results indicate that, for fine-tuning, larger pre-trained models are better than small pre-trained models, even if the dataset for fine-tuning is small. We also show that transfer learning significantly speeds up convergence, which could result in significant cost savings when training with large datasets.
HAN-BSVD: A hierarchical attention network for binary software vulnerability detection

Deep learning has shown effectiveness in binary software vulnerability detection due to its outstanding feature extraction capability independent of human expert experience. However, detection approaches such as Instruction2vec still have the following defects: (1) the context between an instruction’s elements (opcode, registers, etc.) is not fully incorporated when embedding a single instruction into its vector representation; (2) the crucial regions that related to vulnerability are not highlighted when extracting features of the vulnerable code. In this paper, we propose a hierarchical attention network for binary software vulnerability detection (HAN-BSVD). Through HAN-BSVD, the contextual information is first enriched by the preprocessor with unifying jump address and normalizing instruction, and then preserved by the instruction embedding network that composed of Bi-GRU and word-attention module; the local features are captured and the crucial regions are highlighted by the feature extraction network that composed of Text-CNN and spatial-attention module. The proposed approach is evaluated on the Juliet Test Suite dataset and the ICLR19 dataset, detection result performs better than the other compared approaches. Extensive ablation studies are also conducted to further prove the effectiveness of each design choice.
Multi-Frame Super-Resolution Algorithm Based on a WGAN

Image super-resolution reconstruction has been widely used in remote sensing, medicine and other fields. In recent years, due to the rise of deep learning research and the successful application of convolutional neural networks in the image field, the super-resolution reconstruction technology based on deep learning has also achieved great development. However, there are still some problems that need to be solved. For example, the current mainstream image super-resolution algorithms based on single or multiple frames pursue high performance indicators such as PSNR and SSIM, while the reconstructed image is relatively smooth and lacks many high-frequency details. It is not conducive to application in a real environment. To address such problem, this paper proposes a super-resolution reconstruction model of sequential images based on Generative Adversarial Networks (GAN). The proposed approach combines the registration module to fuse adjacent frames, effectively use the detailed information in multiple consecutive frames, and enhances the spatio-temporality of low-resolution images in sequential images. While the GAN was used to improve the effect of image high-frequency texture detail reconstruction, WGAN was introduced to optimize model training. The reconstruction results not only improved the PSNR and SSIM indexes but also reconstructed more high-frequency detail textures. Finally, in order to further improve the perception effect, an additional registration loss item RLT is introduced in the GAN network perception loss. Through extensive experiments, it shows that the model proposed in this paper effectively obtains the information between the sequence images. When the PSNR and SSIM indicators are improve, it can reconstruct better high-frequency texture details than the current advanced multi-frame algorithms.
Unsupervised Remoting Sensing Super-Resolution via Migration Image Prior

Recently, satellites with high temporal resolution have fostered wide attention in various practical applications. Due to limitations of bandwidth and hardware cost, however, the spatial resolution of such satellites is considerably low, largely limiting their potentials in scenarios that require spatially explicit information. To improve image resolution, numerous approaches based on training low-high resolution pairs have been proposed to address the super-resolution (SR) task. De-spite their success, however, low/high spatial resolution pairs are usually difficult to obtain in satellites with a high temporal resolution, making such approaches in SR impractical to use. In this paper, we proposed a new unsupervised learning framework, called "MIP", which achieves SR tasks without low/high resolution image pairs. First, random noise maps are fed into a designed generative adversarial network (GAN) for reconstruction. Then, the proposed method converts the reference image to latent space as the migration image prior. Finally, we update the input noise via an implicit method, and further transfer the texture and structured information from the reference image. Extensive experimental results on the Draper dataset show that MIP achieves significant improvements over state-of-the-art methods both quantitatively and qualitatively. The proposed MIP is open-sourced at https://github.com/jiaming-wang/MIP.
Study of the Influence of Non-Deposit Locations in Data-Driven Mineral Prospectivity Mapping: A Case Study on the Iskut Project in Northwestern British Columbia, Canada

The accuracy of data-driven predictive mineral prospectivity models relies heavily on the training datasets used. These models are usually trained using data for “known” deposit locations as well as “non-deposit” locations that are based on randomly generated point patterns. In this study, data related to the Seabridge Gold Inc Iskut project, an epithermal Au deposit in northwestern British Columbia (BC), Canada, are used to test the utility of data-driven mineral prospectivity modeling. The input spatial dataset is comprised mostly of publicly available data. Data for 18 vein and epithermal Au known mineral occurrences (KMO) are obtained from the BC Geological Survey’s MINFILE repository and selected as training deposit locations. A total of eleven sets of non-deposit locations (NDL) were also created, including one set of selected non-prospective KMO for Au deposits from the MINFILE and ten sets of random point patterns. Given the scale of this study, most of the KMO recorded on the property are of the epithermal deposit type. Hence, they could not be used as a selection criterion. Data-driven mineral potential models are generated using the random forest (RF) algorithm and trained on multiple data sets. The comparison of RF models demonstrated that using non-prospective KMO generates more accurate predictions than the random point pattern. The produced mineral prospectivity maps delineated multiple areas with higher discovery potential, which matched viable targets for the Au-Cu epithermal-porphyry system identified through previous Seabridge Gold Inc. (Toronto, ON, Canada) field reconnaissance and drilling programs.
A Real-Time Semi-Supervised Deep Tone Mapping Network

Tone mapping operators (TMOs) can compress the range of high dynamic range (HDR) images so that they can be displayed normally on the low dynamic range (LDR) devices. Recent TMOs based on deep neural networks can produce impressive results, but there are still some shortcomings. On the one hand, their supervised learning procedure requires a high-quality paired dataset which is hard to be accessed. On the other hand, they are too slow and heavy to meet the needs of practical applications. This paper proposes a real-time deep semi-supervised learning TMO to solve the above problems. The proposed method learns in a semi-supervised manner by combining the adversarial loss, cycle consistency loss, and the pixel-wise loss. The first two can simulate the image distributions in the real world from the unpaired LDR data and the latter can learn the guidance of paired LDR labels. In this way, the proposed method only requires HDR sources, unpaired high-quality LDR images, and a few well tone-mapped HDR-LDR pairs as training data. Furthermore, the proposed method divides tone mapping into luminance mapping and saturation adjustment and then processes them simultaneously. By this strategy, we can reconstruct each component more precisely. Based on the aforementioned improvements, we propose a lightweight tone mapping network that is efficient in tone mapping task (up to 5000x parameters-saving and 27x time-saving compared to the learning-based TMOs). Both quantitative and qualitative results demonstrate that the proposed method performs favorable against state-of-the-art TMOs.
An Optimized k-means Algorithm Based on Information Entropy

Abstract Clustering is a widely used technique in data mining applications and various pattern recognition applications, in which data objects are divided into groups. K-means algorithm is one of the most classical clustering algorithms. In this algorithm, the initial clustering centers are randomly selected, this results in unstable clustering results. To solve this problem, an optimized algorithm to select the initial centers is proposed. In the proposed algorithm, dispersion degree is defined, which is based on entropy. In the algorithm, all the objects are firstly grouped into a big cluster, and the object that has the maximum dispersion degree and the object that has the minimum dispersion degree are selected as the initial clustering centers from the initial big cluster. And then other objects in the biggest cluster are partitioned to the initial clusters to which the objects are nearest. The partition process will be repeated until the cluster number is equal to the specified value k. Finally, the partitioned k clusters and their cluster centers are applied to k-means algorithm as initial clusters and centers. Several experiments are conducted on real data sets to evaluate the proposed algorithm. The proposed algorithm is compared with traditional k-means algorithm and max-min distance clustering algorithm, and experimental results show that the improved k-means algorithm is stable in selecting initial clustering, because it can select unique initial clustering centers. The optimized algorithm’s effectiveness and feasibility are also verified by experiments, and the algorithm can reduce the times of iterations and has more stable clustering results and higher accuracy.
Understanding the perceived relevance of capability measures: A survey of Agile Software Development practitioners

In the light of the swift and iterative nature of Agile Software Development (ASD) practices, establishing deeper insights into capability measurement within the context of team formation is crucial, as the capability of individuals and teams can affect team performance and productivity Although a former Systematic Literature Review (SLR) synthesized the state of the art in relation to capability measurement in ASD – with a focus on selecting individuals to agile teams, and capabilities related to team performance, productivity and success determining to what degree the SLR's results apply to practice can provide progressive insights to both research and practice. Our study investigates how agile practitioners perceive the relevance of individual and team level measures for characterizing the capability of an agile team and its members. Here, the emphasis was also on selecting individuals to agile teams, and capabilities associated with effective teams in terms of their performance, productivity and success. Furthermore, to scrutinize variations in practitioners' perceptions, our study further analyzes perceptions across stratified demographic groups. We undertook a Web-based survey using a questionnaire built based on the capability measures identified from a previously conducted SLR. Our survey responses (60) indicate that 127 individual and 28 team capability measures were considered as relevant by the majority of practitioners. We also identified seven individual and one team capability measure that have not been previously characterized by our SLR. The surveyed practitioners suggested that an agile team member's responsibility and questioning skills significantly represent the member's capability. Results from our survey align with our SLR's findings. Measures associated with social aspects were observed to be dominant compared to technical and innovative aspects. Our results can support agile practitioners in their team composition decisions.
GSVNET: Guided Spatially-Varying Convolution for Fast Semantic Segmentation on Video

This paper addresses fast semantic segmentation on video. Video segmentation often calls for real-time, or even faster than real-time, processing. One common recipe for conserving computation arising from feature extraction is to propagate features of few selected keyframes. However, recent advances in fast image segmentation make these solutions less attractive. To leverage fast image segmentation for furthering video segmentation, we propose a simple yet efficient propagation framework. Specifically, we perform lightweight flow estimation in 1/8-downscaled image space for temporal warping in segmentation outpace space. Moreover, we introduce a guided spatially-varying convolution for fusing segmentations derived from the previous and current frames, to mitigate propagation error and enable lightweight feature extraction on non-keyframes. Experimental results on Cityscapes and CamVid show that our scheme achieves the state-of-the-art accuracy-throughput trade-off on video segmentation.
Change point detection for compositional multivariate data

Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault detection in industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem of change point detection on compositional multivariate data (each sample is a probability mass function), which is a practically important sub-class of general multivariate data. While the problem of change-point detection is well studied in univariate setting, and there are few viable implementations for a general multivariate data, the existing methods do not perform well on compositional data. In this paper, we propose a parametric approach for change point detection in compositional data. Moreover, using simple transformations on data, we extend our approach to handle any general multivariate data. Experimentally, we show that our method performs significantly better on compositional data and is competitive on general data compared to the available state of the art implementations.
Audio-Visual Autoencoding for Privacy-Preserving Video Streaming

The demand of sharing video streaming extremely increases due to the proliferation of Internet of Things (IoT) devices in recent years, and the explosive development of artificial intelligent (AI) detection techniques has made visual privacy protection more urgent and difficult than ever before. Although a number of approaches have been proposed, their essential drawbacks limit the effect of visual privacy protection in real applications. In this article, we propose a cycle vector-quantized variational autoencoder (cycle-VQ-VAE) framework to encode and decode the video with its extracted audio, which takes the advantage of multiple heterogeneous data sources in the video itself to protect individuals’ privacy. In our cycle-VQ-VAE framework, a fusion mechanism is designed to integrate the video and its extracted audio. Particularly, the extracted audio works as the random noise with a nonpatterned distribution, which outperforms the noise that follows a patterned distribution for hiding visual information in the video. Under this framework, we design two models, including the frame-to-frame (F2F) model and video-to-video (V2V) model, to obtain privacy-preserving video streaming. In F2F, the video is processed as a sequence of frames; while, in V2V, the relations between frames are utilized to deal with the video, greatly improving the performance of privacy protection, video compression, and video reconstruction. Moreover, the video streaming is compressed in our encoding process, which can resist side-channel inference attack during video transmission and reduce video transmission time. Through the real-data experiments, we validate the superiority of our models (F2F and V2V) over the existing methods in visual privacy protection, visual quality preservation, and video transmission efficiency. The codes of our model implementation and more experimental results are now available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ahahnut/cycle-VQ-VAE</uri> .
Cyberbullying Classification Methods for Arabic: A Systematic Review

Across the globe, bullying and cyberbullying are present and have significant implications for people and communities. Over the last years, the number of research studies for cyberbullying classification in texts has grown exponentially, but most studies have been examined on English datasets. Most of the new methods in text classification are based on linguistic features of the text since the Arabic language has different rules and structures. This research intended to review articles about cyberbullying classification methods for Arabic texts and analyzed them The review includes the used methods, quality measuring performance, and details of the dataset (dialect, annotation method, source). This will guide future research by giving researchers a more consistent and compatible view on the topic. Three major databases were systematically searched for articles on related topics (Science Direct, IEEE, Google Scholar). Through a systematic study, nine research papers related to this topic are obtained. Results show that this topic's work is still recent, and many methods have not yet been tested to figure out the best approach for classifying Arabic texts as cyberbullying. Also, the number of available datasets for cyberbullying classification in Arabic text is still limited compared to the English language. Therefore, generalizing the best method and dataset for classifying cyberbullying is so difficult. Furthermore, the majority of studies used Twitter for collecting the datasets. SVM is the most used classifier, Whereas CNN, is the most commonly used neural network for the cyberbullying classification.
SCARI: Separate and conquer algorithm for action rules and recommendations induction

This article describes an action rule induction Algorithm based on a sequential covering. Two variants of the Algorithm are presented. The Algorithm allows the action rule induction from a source and a target decision class point of view. The application of rule quality measures enables the induction of action rules that meet various quality criteria. The article also presents a method for recommendation induction. A recommendation indicates the actions required to move an example representing a given source class to the appropriate target class. The recommendation method is based on a set of induced action rules. The experimental part of the article presents the results of the Algorithm operation on sixteen data sets. As a result of the conducted research, the Ac-Rules package was made available.