Reviewerly/exHarmony huggingface dataset:
DataMix: Efficient Privacy-Preserving Edge-Cloud Inference

Deep neural networks are widely deployed on edge devices (e.g., for computer vision and speech recognition). Users either perform the inference locally (i.e., edge-based) or send the data to the cloud and run inference remotely (i.e., cloud-based). However, both solutions have their limitations: edge devices are heavily constrained by insufficient hardware resources and cannot afford to run large models; cloud servers, if not trustworthy, will raise serious privacy issues. In this paper, we mediate between the resource-constrained edge devices and the privacy-invasive cloud servers by introducing a novel privacy-preserving edge-cloud inference framework, DataMix. We off-load the majority of the computations to the cloud and leverage a pair of mixing and de-mixing operation, inspired by mixup, to protect the privacy of the data transmitted to the cloud. Our framework has three advantages. First, it is privacy-preserving as the mixing cannot be inverted without the user’s private mixing coefficients. Second, our framework is accuracy-preserving because our framework takes advantage of the space spanned by images, and we train the model in a mixing-aware manner to maintain accuracy. Third, our solution is efficient on the edge since the majority of the workload is delegated to the cloud, and our mixing and de-mixing processes introduce very few extra computations. Also, our framework introduces small communication overhead and maintains high hardware utilization on the cloud. Extensive experiments on multiple computer vision and speech recognition datasets demonstrate that our framework can greatly reduce the local computations on the edge (to fewer than 20% of FLOPs) with negligible loss of accuracy and no leakages of private information.
Large quantum networks

Abstract Quantum networks that allow generating entangled states between distant qubits have enormous scientific and applied potential. They can be used for secure quantum cryptography and the teleportation of quantum states between cities and countries, in high-resolution astronomy, and in distributed quantum computing. The scattering of photons in an optical fiber and the difficulties in creating full-fledged quantum nodes impede the construction of large quantum networks. We review current approaches to the creation of such networks, with the emphasis on quantum repeaters intended for 'compensating' losses in optical fibers. We also discuss methods for increasing the range of quantum cryptography systems without using quantum repeaters.
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds

This paper investigates the formulation and implementation of Bayesian inverse problems to learn input parameters of partial differential equations (PDEs) defined on manifolds. Specifically, we study the inverse problem of determining the diffusion coefficient of a second-order elliptic PDE on a closed manifold from noisy measurements of the solution. Inspired by manifold learning techniques, we approximate the elliptic differential operator with a kernel-based integral operator that can be discretized via Monte Carlo without reference to the Riemannian metric. The resulting computational method is mesh-free and easy to implement, and can be applied without full knowledge of the underlying manifold, provided that a point cloud of manifold samples is available. We adopt a Bayesian perspective to the inverse problem, and establish an upper bound on the total variation distance between the true posterior and an approximate posterior defined with the kernel forward map. Supporting numerical results show the effectiveness of the proposed methodology.
Cognitive computing for big data systems over internet of things for enterprise information systems

(2020). Cognitive computing for big data systems over internet of things for enterprise information systems. Enterprise Information Systems: Vol. 14, Cognitive Computing for Big Data Systems over Internet of Things for Enterprise Information Systems, pp. 1233-1237.
Detecting locations in JavaScript programs affected by breaking library changes

JavaScript libraries are widely used and evolve rapidly. When adapting client code to non-backwards compatible changes in libraries, a major challenge is how to locate affected API uses in client code, which is currently a difficult manual task. In this paper we address this challenge by introducing a simple pattern language for expressing API access points and a pattern-matching tool based on lightweight static analysis. Experimental evaluation on 15 popular npm packages shows that typical breaking changes are easy to express as patterns. Running the static analysis on 265 clients of these packages shows that it is accurate and efficient: it reveals usages of breaking APIs with only 14% false positives and no false negatives, and takes less than a second per client on average. In addition, the analysis is able to report its confidence, which makes it easier to identify the false positives. These results suggest that the approach, despite its simplicity, can reduce the manual effort of the client developers.
Review of Current Machine Learning Approaches for Anomaly Detection in Network Traffic

Due to the advance in network technologies, the number of network users is growing rapidly, which leads to the generation of large network traffic data. This large network traffic data is prone to attacks and intrusions. Therefore, the network needs to be secured and protected by detecting anomalies as well as to prevent intrusions into networks. Network security has gained attention from researchers and network laboratories. In this paper, a comprehensive survey was completed to give a broad perspective of what recently has been done in the area of anomaly detection. Newly published studies in the last five years have been investigated to explore modern techniques with future opportunities. In this regard, the related literature on anomaly detection systems in network traffic has been discussed, with a variety of typical applications such as WSNs, IoT, high-performance computing, industrial control systems (ICS), and software-defined network (SDN) environments. Finally, we underlined diverse open issues to improve the detection of anomaly systems.
Cyber Security Challenges and its Emerging Trends on Latest Technologies

Abstract Today, due to the modern life style people have joined technology life and using more technology for shopping as well as financial transactions in their cyber space. At the same time, safeguarding of knowledge has become increasingly difficult. In addition, the heavy use and growth of social media, online crime or cybercrime has increased. In the world of information technology, data security plays a significant role. The information security has become one of today’s main challenges. Whenever we think of cyber security, we first of all think of ‘cybercrimes,’ which expand tremendously every day. Different government and businesses take various steps to avoid this form of cybercrime. In addition to numerous cyber protection initiatives, many people are also very worried about it. This paper focuses primarily on cyber security concerns related to the new technology. It also concentrates on the new technologies for cyber security, ethics and developments that impact cyber security.
Not Ready for Convergence in Data Infrastructures

Much research is dependent on Information and Communication Technologies (ICT). Researchers in different research domains have set up their own ICT systems (data labs) to support their research, from data collection (observation, experiment, simulation) through analysis (analytics, visualisation) to publication. However, too frequently the Digital Objects (DOs) upon which the research results are based are not curated and thus neither available for reproduction of the research nor utilization for other (e.g., multidisciplinary) research purposes. The key to curation is rich metadata recording not only a description of the DO and the conditions of its use but also the provenance – the trail of actions performed on the DO along the research workflow. There are increasing real-world requirements for multidisciplinary research. With DOs in domain-specific ICT systems (silos), commonly with inadequate metadata, such research is hindered. Despite wide agreement on principles for achieving FAIR (findable, accessible, interoperable, and reusable) utilization of research data, current practices fall short. FAIR DOs offer a way forward. The paradoxes, barriers and possible solutions are examined. The key is persuading the researcher to adopt best practices which implies decreasing the cost (easy to use autonomic tools) and increasing the benefit (incentives such as acknowledgement and citation) while maintaining researcher independence and flexibility.
Learning Efficient Binarized Object Detectors With Information Compression

In this paper, we propose a binarized neural network learning method (BiDet) for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly. On the contrary, our BiDet fully utilizes the representational capacity of the binary neural networks by redundancy removal, through which the detection precision is enhanced with alleviated false positives. Specifically, we generalize the information bottleneck (IB) principle to object detection, where the amount of information in the high-level feature maps is constrained and the mutual information between the feature maps and object detection is maximized. Meanwhile, we learn sparse object priors so that the posteriors are concentrated on informative detection prediction with false positive elimination. Since BiDet employs a fixed IB trade-off to balance the total and relative information contained in the high-level feature maps, the information compression leads to ineffective utilization of the network capacity or insufficient redundancy removal for input in different complexity. To address this, we further present binary neural networks with automatic information compression (AutoBiDet) to automatically adjust the IB trade-off for each input according to the complexity. Moreover, we further propose the class-aware sparse object priors by assigning different sparsity to objects in various classes, so that the false positives are alleviated more effectively without recall decrease. Extensive experiments on the PASCAL VOC and COCO datasets show that our BiDet and AutoBiDet outperform the state-of-the-art binarized object detectors by a sizable margin.
Temporal-Aware Self-Supervised Learning for 3D Hand Pose and Mesh Estimation in Videos

Estimating 3D hand pose directly from RGB images is challenging but has gained steady progress recently by training deep models with annotated 3D poses. However annotating 3D poses is difficult and as such only a few 3D hand pose datasets are available, all with limited sample sizes. In this study, we propose a new framework of training 3D pose estimation models from RGB images without using explicit 3D annotations, i.e., trained with only 2D information. Our framework is motivated by two observations: 1) Videos provide richer information for estimating 3D poses as opposed to static images; 2) Estimated 3D poses ought to be consistent whether the videos are viewed in the forward order or reverse order. We leverage these two observations to develop a self-supervised learning model called temporal-aware self-supervised network (TASSN). By enforcing temporal consistency constraints, TASSN learns 3D hand poses and meshes from videos with only 2D keypoint position annotations. Experiments show that our model achieves surprisingly good results, with 3D estimation accuracy on par with the state-of-the-art models trained with 3D annotations, highlighting the benefit of the temporal consistency in constraining 3D prediction models.
Relational proofs for quantum programs

Relational verification of quantum programs has many potential applications in quantum and post-quantum security and other domains. We propose a relational program logic for quantum programs. The interpretation of our logic is based on a quantum analogue of probabilistic couplings. We use our logic to verify non-trivial relational properties of quantum programs, including uniformity for samples generated by the quantum Bernoulli factory, reliability of quantum teleportation against noise (bit and phase flip), security of quantum one-time pad and equivalence of quantum walks.
Semantic Feature Learning via Dual Sequences for Defect Prediction

Software defect prediction (SDP) can help developers reasonably allocate limited resources for locating bugs and prioritizing their testing efforts.Existing methods often serialize an Abstract Syntax Tree (AST) obtained from the program source code into a token sequence, which is then inputted into the deep learning model to learn the semantic features.However, there are different ASTs with the same token sequence, and it is impossible to distinguish the tree structure of the ASTs only by a token sequence.To solve this problem, this paper proposes a framework called Semantic Feature Learning via Dual Sequences (SFLDS), which can capture the semantic and structural information in the AST for feature generation.Specifically, based on the AST, we select the representative nodes in the AST and convert the program source code into a simplified AST (S-AST).Our method introduces two sequences to represent the semantic and structural information of the S-AST, one is the result of traversing the S-AST node in pre-order, and another is composed of parent nodes.Then each token in the dual sequences is encoded as a numerical vector via mapping and word embedding.Finally, we use a bi-directional long short-term memory (BiLSTM) based neural network to automatically generate semantic features from the dual sequences for SDP.In addition, to leverage the statistical characteristics contained in the handcrafted metrics, we also propose a framework called Defect Prediction via SFLDS (DP-SFLDS) which combines the semantic features generated from SFLDS with handcrafted metrics to perform SDP.In our empirical studies, eight open-source Java projects from the PROMISE repository are chosen as our empirical subjects.Experimental results show that our proposed approach can perform better than several state-of-the-art baseline SDP methods.
An Evaluation Model for the Innovation and Entrepreneurship Thinking Ability of College Students Based on Neural Network

Mass innovation and entrepreneurship (I&amp;E) is a national campaign in China. In this context, it is important to encourage college students to engage in I&amp;E activities, and this calls for accurate and comprehensive evaluation of their I&amp;E thinking ability. Therefore, this paper proposes an evaluation model for the I&amp;E thinking ability of college students based on neural network (NN). Firstly, a reasonable evaluation index system was created for the I&amp;E thinking ability of college students, and the evaluation indices were preprocessed through fuzzy analytic hierarchy process (AHP). Then, a fuzzy neural network (FNN) was constructed based on GA rule optimization and the specific steps of the algorithm were given. Moreover, a few representative rules were selected by GA based on uncertain fuzzy knowledge rules, a 4-layer NN model with fuzzy inputs and outputs was established, and the evaluation flow of the I&amp;E thinking ability of college students was proposed. Finally, the effectiveness of the proposed model was verified through experiments. The research results of this paper provide a reference for the application of NN in the field of ability evaluation.
Informational flow on twitter-corona virus outbreak-topic modelling approach

The study focuses on the information flow on twitter during the corona virus outbreak Tweets related to #coronavirus are studied using sentiment analysis and topic modelling using Latent Dirichlet Allocation post preprocessing The study concluded that the information flow was accurate and reliable related to corona virus outbreak with minimum misinformation LDA analysis had identified the most relevant and accurate topics related to corona virus outbreak and sentiment analysis confirmed the prevalence of negative sentiments like fear along with positive sentiments like trust Governments and Healthcare authorities & institutions effectively utilized to spread accurate and reliable information on twitter © 2020 IAEME
DeepGIN: Deep Generative Inpainting Network for Extreme Image Inpainting

The degree of difficulty in image inpainting depends on the types and sizes of the missing parts. Existing image inpainting approaches usually encounter difficulties in completing the missing parts in the wild with pleasing visual and contextual results as they are trained for either dealing with one specific type of missing patterns (mask) or unilaterally assuming the shapes and/or sizes of the masked areas. We propose a deep generative inpainting network, named DeepGIN, to handle various types of masked images. We design a Spatial Pyramid Dilation (SPD) ResNet block to enable the use of distant features for reconstruction. We also employ Multi-Scale Self-Attention (MSSA) mechanism and Back Projection (BP) technique to enhance our inpainting results. Our DeepGIN outperforms the state-of-the-art approaches generally, including two publicly available datasets (FFHQ and Oxford Buildings), both quantitatively and qualitatively. We also demonstrate that our model is capable of completing masked images in the wild.
Representation transfer learning from deep end-to-end speech recognition networks for the classification of health states from speech

Representation transfer learning has been widely used across a range of machine learning tasks. One such notable approach seen in the speech literature is the use of Convolutional Neural Networks, pre-trained for image classification tasks, to extract features from spectrograms of speech signals. Interestingly, despite the strong performance of such approaches, there have been minimal research efforts exploring the suitability of using speech-specific networks to perform feature extraction. In this regard, a novel feature representation learning framework is presented herein. This approach is comprising the use of Automatic Speech Recognition (ASR) deep neural networks as feature extractors, the fusion of several extracted feature representations using Compact Bilinear Pooling (CBP), and finally inference via a specially optimised Recurrent Neural Network (RNN) classifier. To determine the usefulness of these feature representations, they are comprehensively tested on two representative speech-health classification tasks, namely the food-type being eaten and speaker intoxication. Key results indicate the promise of the extracted features, demonstrating comparable results to other state-of-the-art approaches in the literature.
Towards an optimized security approach to IoT devices with confidential healthcare data exchange

Reliable data exchange and efficient image transfer are currently significant research challenges in health care systems. To incentivize data exchange within the Internet of Things (IoT) framework, we need to ensure data sovereignty by facilitating secure data exchange between trusted parties. The security and reliability of data-sharing infrastructure require a community of trust. Therefore, this paper introduces an encryption frame based on data fragmentation. It also presents a novel, deterministic grey-scale optical encryption scheme based on fundamental mathematics. The objective is to use encryption as the underlying measure to make the data unintelligible while exploiting fragmentation to break down sensitive relationships between attributes. Thus, sensitive data distributed in separate data repositories for decryption and reconstruction using interpolation by knowing polynomial coefficients and personal values from the DBMS Database Management System. Aims also to ensure the secure acquisition of diagnostic images, micrography, and all types of medical imagery based on probabilistic approaches. Visual sharing of confidential medical imageries based on implementing a novel method, where transparencies ≤k - 1 out of n cannot reveal the original image.
Batch Optimization of Frequency-Modulated Pulses for Robust Two-Qubit Gates in Ion Chains

Two-qubit gates in trapped-ion quantum computers are generated by applying spin-dependent forces that temporarily entangle the internal state of the ion with its motion. Laser pulses are carefully designed to generate a maximally entangling gate between the ions while minimizing any residual entanglement between the motion and the ion. The quality of the gates suffers when the actual experimental parameters differ from the ideal case. Here, we improve the robustness of frequency-modulated M\o{}lmer-S\o{}rensen gates to motional mode-frequency offsets by optimizing the average performance over a range of systematic errors using batch optimization. We then compare this method with frequency-modulated gates optimized for ideal parameters that include an analytic robustness condition. Numerical simulations show good performance up to 12 ions, and the method is experimentally demonstrated on a two-ion chain.
The Unmet Data Visualization Needs of Decision Makers Within Organizations

When an organization chooses one course of action over alternatives, this task typically falls on a decision maker with relevant knowledge, experience, and understanding of context. Decision makers rely on data analysis, which is either delegated to analysts, or done on their own. Often the decision maker combines data, likely uncertain or incomplete, with non-formalized knowledge within a multi-objective problem space, weighing the recommendations of analysts within broader contexts and goals. As most past research in visual analytics has focused on understanding the needs and challenges of data analysts, less is known about the tasks and challenges of organizational decision makers, and how visualization support tools might help. Here we characterize the decision maker as a domain expert, review relevant literature in management theories, and report the results of an empirical survey and interviews with people who make organizational decisions. We identify challenges and opportunities for novel visualization tools, including trade-off overviews, scenario-based analysis, interrogation tools, flexible data input and collaboration support. Our findings stress the need to expand visualization design beyond data analysis into tools for information management.
Improved Linear Approximations to ARX Ciphers and Attacks Against ChaCha

In this paper, we present a new technique which can be used to find better linear approximations in ARX ciphers. Using this technique, we present the first explicitly derived linear approximations for 3 and 4 rounds of ChaCha and, as a consequence, it enables us to improve the recent attacks against ChaCha . Additionally, we present new differentials for 3 and 3.5 rounds of ChaCha that, when combined with the proposed technique, lead to further improvement in the complexity of the Differential-Linear attacks against ChaCha.