Reviewerly/exHarmony huggingface dataset:
Private Linear Transformation: The Individual Privacy Case

This paper considers the single-server Private Linear Transformation (PLT) problem when individual privacy is required. In this problem, there is a user that wishes to obtain <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> linear combinations of a D-subset of messages belonging to a dataset of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> messages stored on a single server. The goal is to minimize the download cost while keeping the identity of every message required for the computation individually private. We focus on the setting in which the matrix of coefficients pertaining to the required linear combinations is the generator matrix of a maximum distance separable code. We establish lower and upper bounds on the capacity of PLT with individual privacy, where the capacity is defined as the supremum of all achievable download rates. We show that our bounds are tight under certain divisibility conditions. In addition, we present lower bounds on the capacity of the settings in which the user has a prior side information about a subset of messages.
Quantum Machine Learning for Health State Diagnosis and Prognostics

Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum computing. The availability of quantum processing units (QPUs) to the general scientific community through open API’s (e.g., IBM’s Qiskit) have kindled the interest in developing and testing new approaches to old problems. In this paper, we present a hybrid quantum machine learning framework for health state diagnostics and prognostics. The framework is exemplified using a problem involving ball bearings dataset. To the best of our knowledge, this is the first attempt to harvest and leverage quantum computing to develop and apply a hybrid quantum-classical machine learning approach to a prognostics and health management (PHM) problem. We hope that this paper initiates the exploration and application of quantum machine learning algorithms in areas of risk and reliability.
Video object segmentation via random walks on two-frame graphs comprising superpixels

We propose a novel video object segmentation method employing random walkers to travel on graphs constructed on two consecutive frames. First, we estimate the initial foreground and background distributions by minimising an energy function that incorporates the stationary distributions of the random walks. The random walkers frequently travel between similar nodes of the graph constructed on two adjacent frames, which enables the incorporation of the inter-frame information into the energy function effectively and elegantly. Then, we refine the initial results by simulating the movements of multiple random walkers. We process the sequence in a recursive manner, which naturally propagates the previous segmentation labels to the subsequent frames. Additionally, we develop a strategy for adjusting the superpixel number using region similarity and the average Frobenius norm of optical flow gradient. This strategy can improve performance significantly. Furthermore, we discuss the feature selection problem in the method to select a more effective feature representation. Extensive and comparable experiments on Segtrack and Segtrack v2 demonstrate that the proposed algorithm yields higher performance than several recent state-of-the-art approaches.
Recent Advances in Intelligent Source Code Generation: A Survey on Natural Language Based Studies

Source Code Generation (SCG) is a prevalent research field in the automation software engineering sector that maps specific descriptions to various sorts of executable code. Along with the numerous intensive studies, diverse SCG types that integrate different scenarios and contexts continue to emerge. As the ultimate purpose of SCG, Natural Language-based Source Code Generation (NLSCG) is growing into an attractive and challenging field, as the expressibility and extremely high abstraction of the input end. The booming large-scale dataset generated by open-source code repositories and Q&amp;A resources, the innovation of machine learning algorithms, and the development of computing capacity make the NLSCG field promising and give more opportunities to the model implementation and perfection. Besides, we observed an increasing interest stream of NLSCG relevant studies recently, presenting quite various technical schools. However, many studies are bound to specific datasets with customization issues, producing occasional successful solutions with tentative technical methods. There is no systematic study to explore and promote the further development of this field. We carried out a systematic literature survey and tool research to find potential improvement directions. First, we position the role of NLSCG among various SCG genres, and specify the generation context empirically via software development domain knowledge and programming experiences; second, we explore the selected studies collected by a thoughtfully designed snowballing process, clarify the NLSCG field and understand the NLSCG problem, which lays a foundation for our subsequent investigation. Third, we model the research problems from technical focus and adaptive challenges, and elaborate insights gained from the NLSCG research backlog. Finally, we summarize the latest technology landscape over the transformation model and depict the critical tactics used in the essential components and their correlations. This research addresses the challenges of bridging the gap between natural language processing and source code analytics, outlines different dimensions of NLSCG research concerns and technical utilities, and shows a bounded technical context of NLSCG to facilitate more future studies in this promising area.
Impact of Interaction Strategies on User Relevance Feedback

User Relevance Feedback (URF) is a class of interactive learning methods that rely on the interaction between a human user and a system to analyze a media collection. To improve URF system evaluation and design better systems, it is important to understand the impact that different interaction strategies can have. Based on the literature and observations from real user sessions from the Lifelog Search Challenge and Video Browser Showdown, we analyze interaction strategies related to (a) labeling positive and negative examples, and (b) applying filters based on users' domain knowledge. Experiments show that there is no single optimal labeling strategy, as the best strategy depends on both the collection and the task. In particular, our results refute the common assumption that providing more training examples is always beneficial: strategies with a smaller number of prototypical examples lead to better results in some cases. We further observe that while expert filtering is unsurprisingly beneficial, aggressive filtering, especially by novice users, can hinder the completion of tasks. Finally, we observe that combining URF with filters leads to better results than using filters alone.
IoT in the Wild: An expedition of discovery for remote monitoring.

Free-living assessment and remote monitoring is important for healthcare researchers. Moving research beyond the laboratory provides habitual environments for remote assessment that allows research to remain agile even when facing uncontrollable external factors e.g., the SARS-COV-2 pandemic. Emergent technologies have the potential to make this form of assessment feasible by providing accessible and affordable mechanisms for conducting free-living research. This paper presents findings from a study that was halted due to the pandemic, but this work highlighted a series of challenges that may present themselves to researchers conducting similar work. By transparently reporting the challenges and solutions rather than just methods, it is hoped that the lessons learned from this study could provide researchers with greater awareness in future studies.
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization

Differentiable ARchiTecture Search (DARTS) uses a continuous relaxation of network representation and dramatically accelerates Neural Architecture Search (NAS) by almost thousands of times in GPU-day. However, the searching process of DARTS is unstable, which suffers severe degradation when training epochs become large, thus limiting its application. In this article, we claim that this degradation issue is caused by the imbalanced norms between different nodes and the highly correlated outputs from various operations. We then propose an improved version of DARTS, namely iDARTS, to deal with the two problems. In the training phase, it introduces node normalization to maintain the norm balance. In the discretization phase, the continuous architecture is approximated based on the similarity between the outputs of the node and the decorrelated operations rather than the values of the architecture parameters. Extensive evaluation is conducted on CIFAR-10 and ImageNet, and the error rates of 2.25% and 24.7% are reported within 0.2 and 1.9 GPU-day for architecture search, respectively, which shows its effectiveness. Additional analysis also reveals that iDARTS has the advantage in robustness and generalization over other DARTS-based counterparts.
An Efficient Combined Approach For Denoising Fibrous Dysplasia Images

Image denoising and Reconstruction are necessary for medical image processing. It helps to crate a lot of research scope in the medical image analytics. The proposed model of this paper is a combined approach for reducing the noisy details from the bone images. It also predicts the Peak Signal to Noise Ratio (PSNR) ratio between the quality and noise. This approach is greater idea to track the fibrous tissue growth in the bones. This framework is the combination of Auto-Encoder (AE) and Convolutional Neural Network (CNN). AutoEncoder is used for denoising the image with higher quality. Enhanced Convolutional Neural Network predicts the minor noisy details in the image with deep hidden layers. The convolution algorithm creates the major optimism for reconstruction the highly corrupted images. Similarly this framework helps to process denoising and extracting the important features from the image for grouping the regions. Our proposed model achieves the PSNR range for denoised image is 65.369 db.
A primer on insider threats in cybersecurity

Though human factors are increasingly being acknowledged as a contributor to cybersecurity incidents, this domain is not widely understood by those in technical and applied disciplines. Humans can be influenced, are not always rational or predictable, and must be studied through psychology rather than technology. Consequently, this domain may represent uncharted territory for the technical practitioner leaving many promising areas of research and practice unexplored. This paper provides a broad primer on human factors in cybersecurity, specifically focusing on the threat posed by organizational insiders. We emphasize the pivotal role that users play in determining overall system security and aim to introduce non-experts to this field, stimulating new interest in this intersection of humans and computers.
Closed-set thermal face recognition via the synthesis approach

Thermal face recognition has various applications in security, video-surveillance, military and government industries, etc. In contrast to the visible-light domain case, there is a lack of sufficiently large databases of thermal-face images. Therefore, it is difficult to construct a good face recognition system directly through learning procedures. We address this problem by using the synthesis approach, in which artificial visible-domain images of faces are generated from thermal face images and then identified in the visible-light domain. The generation of the artificial face images is done using a conditional Generative Adversarial Network, and the face identification is done by another, separate, deep neural network. Generating visible face images from thermal ones using DNNs requires large datasets of pairs of thermal and visible face images of the same persons, taken from the same position, time and imaging conditions, with two different cameras. The available datasets of such pairs are of relatively very low amount of subjects. Therefore, as in other similar studies, we approach this problem as a closed-set face recognition task, in which all testing identities are predefined in the training set. We train the face identification network itself using the synthesized images together with the original ground-truth visible-domain images to create a recognition system that is suitable to the characteristics of the artificially generated images. Thus, the recognition network will be optimized specifically for the thermal face recognition.
Autonomous mobile robot navigation in uncertain dynamic environments based on deep reinforcement learning

In this paper, we study autonomous end-to-end navigation for wheeled robots based on deep reinforcement learning (DRL) in an unknown environment without a priori map. The DRL network is mainly based on deep deterministic policy gradient algorithm together with long short-term memory. The input for the network is the data from a 2D lidar as well as the relative position to the target point, while the outputs are the linear velocity and angular velocity that actuate the robot. A novel reward function is proposed to avoid the collision with dynamic obstacles and to generate a smooth trajectory for the robot. The network is trained without supervision in an unknown dynamic environment, the random Gaussian noise is added to the input data of long short-term memory to avoid local optimum. Besides, different unstructured environments are also considered in the training to increase the robustness of the developed network. Experiments performed on public dataset have showed that the developed network makes the robot navigate in unstructured environments safely and outperform several DRL methods.
Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

With the remarkable growth of textual data sources in recent years, easy, fast, and accurate text processing has become a challenge with significant payoffs. Automatic text summarization is the process of compressing text doc... | Find, read and cite all the research you need on Tech Science Press
Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

With the popularity of e-learning, personalization and ubiquity have become important aspects of online learning. To make learning more personalized and ubiquitous, we propose a learner model for a query-based personalized le... | Find, read and cite all the research you need on Tech Science Press
Image Restoration with Structured Deep Image Prior

In this study, a novel image restoration method is proposed by introducing a structured convolutional neural network (CNN) in the deep image prior (DIP) framework. CNN has shown significance for image restoration as well as classification. DIP uses CNN structures as an image prior and shows a significant performance without explicit training of the generative model. Although CNN improves their ability by training with a large number of example images, DIP requests no training set of images for constructing the prior. The conventional DIP, however, uses a U-Net for the prior, which contains a huge number of design parameters. In order to reduce the number of parameters, this study proposes to use a structured CNN based on the non-separable oversampled lapped transform (NSOLT). By showing some simulation results, the significance of the proposed method is verified.
Video-based fusion of multiple detectors to counter terrorism

Terrorism is an international security challenge. The early detection of threats (e.g., explosives or firearms) could provide a valuable contribution to the ability to prevent, protect and respond to terrorism. This paper presents a system for the management of a plurality of sensors to improve the threat-detection capabilities without disrupting the flow of passengers. The system improves the prevention capabilities of soft targets (such as airports, undergrounds and railway stations) with a high number of daily commuters. The system architecture consists of three main components. The first component is 2D video tracking and re-identification (Re-ID), which allows the labelling and tracking of commuters in a small area. Thereby, it supports the fusion of sensors at different locations. The Re-ID has a smart training strategy with anonymized snippets to increase flexibility for new environments. The second component is 3D video tracking with a stereo camera, which gives a more accurate location measurement than 2D video. Location prediction is used to compensate for latency in the control of active elements in the threat detection sensor. Recurrent neural networks for location prediction were trained by using real 3D tracking data from a railway station. The performance is evaluated with a ground-truth based on Ultra-Wide Band (UWB) radio positioning and a coordinate conversion method was created to compensate for identified inaccuracies. The third component is Command & Control (C&C), which consists of three submodules: message broker, data-fusion and security client. The message broker is a publish-subscribe middleware layer to enable flexible integration of the various sensors and components. The data-fusion combines outputs of multiple sensors. In case of a suspect person, the security client triggers an alarm and a comprehensive report is sent to the security guards.
CTI Blockchain-Based Sharing using Proof-of-Quality Consensus Algorithm

The importance of Cyber Threat Intelligence (CTI) lies in its ability to be shared, consumed and actioned. For threat intelligence to be actionable, it must be shared with the right audience, at the right time and must fulfil quality standards. Without actionable and contextualised CTI, security teams will be making best guesses and assumptions instead of intelligence-based informed decisions. This paper proposes a decentralised, tamper-proof and reputation based solution for delivering vetted CTI information, using quality based CTI parameters in order to further evaluate the quality of CTI Feeds and the reputation of CTI Feed Sources.
Doing more with less

Automated machine learning (AutoML) promises to democratize machine learning by automatically generating machine learning pipelines with little to no user intervention. Typically, a search procedure is used to repeatedly generate and validate candidate pipelines, maximizing a predictive performance metric, subject to a limited execution time budget. While this approach to generating candidates works well for small tabular datasets, the same procedure does not directly scale to larger tabular datasets with 100,000s of observations, often producing fewer candidate pipelines and yielding lower performance, given the same execution time budget. We carry out an extensive empirical evaluation of the impact that downsampling - reducing the number of rows in the input tabular dataset - has on the pipelines produced by a genetic-programming-based AutoML search for classification tasks.
LASH: Large-scale Academic Deep Semantic Hashing

With the explosively increasing of academic papers, efficient academic document retrieval is becoming an essential requirement for large-scale information retrieval systems. Inspired by the success of deep semantic hashing in normal document retrieval, deep semantic hashing is a promising approach for academic document retrieval by mapping academic documents into efficient hash codes. However, for academic document retrieval, the existing deep semantic hashing methods suffer from following two problems: (1) they cannot differentiate the importance of different field labels; (2) they cannot plenty utilize the structure information in paper citations. To address these problems, we propose a novel Large-scale Academic deep Semantic Hashing, called LASH. Specifically, LASH first treats paper citations as a citation network, and then employs a multi-input variational deep autoencoder to directly encode both structure information of the citation network and semantic information of academic documents into unified hash codes. Moreover, a weighted percentage similarity is designed to measure the importance of different field labels, which is a linear combination of Jaccard and Cosine similarity. Supervised by the similarity, the learned unified hash codes can further preserve the importance of different field labels. Extensive experiments show LASH significantly outperforms state-of-the-art baselines over proposed three real-world large-scale academic datasets.
PR-Net: Preference Reasoning for Personalized Video Highlight Detection

Personalized video highlight detection aims to shorten a long video to interesting moments according to a user’s preference, which has recently raised the community’s attention. Current methods regard the user’s history as holistic information to predict the user’s preference but negating the inherent diversity of the user’s interests, resulting in vague preference representation. In this paper, we propose a simple yet efficient preference reasoning framework (PR-Net) to explicitly take the diverse interests into account for frame-level highlight prediction. Specifically, distinct user-specific preferences for each input query frame are produced, presented as the similarity weighted sum of history highlights to the corresponding query frame. Next, distinct comprehensive preferences are formed by the user-specific preferences and a learnable generic preference for more overall highlight measurement. Lastly, the degree of highlight and non-highlight for each query frame is calculated as semantic similarity to its comprehensive and non-highlight preferences, respectively. Besides, to alleviate the ambiguity due to the incomplete annotation, a new bidirectional contrastive loss is proposed to ensure a compact and differentiable metric space. In this way, our method significantly outperforms state-of-the-art methods with a relative improvement of 12% in mean accuracy precision.
Automatic de-identification of data download packages

The General Data Protection Regulation (GDPR) grants all natural persons the right to access their personal data if this is being processed by data controllers. The data controllers are obliged to share the data in an electronic format and often provide the data in a so called Data Download Package (DDP). These DDPs contain all data collected by public and private entities during the course of a citizens’ digital life and form a treasure trove for social scientists. However, the data can be deeply private. To protect the privacy of research participants while using their DDPs for scientific research, we developed a de-identification algorithm that is able to handle typical characteristics of DDPs. These include regularly changing file structures, visual and textual content, differing file formats, differing file structures and private information like usernames. We investigate the performance of the algorithm and illustrate how the algorithm can be tailored towards specific DDP structures.