Reviewerly/exHarmony huggingface dataset:
Method for fast classification of MNIST digits on Arduino UNO board using LogNNet and linear congruential generator

Abstract The paper presents a method for forming a reservoir of a neural network LogNNet using a linear congruent pseudo-random number generator. This method made it possible to reduce the MNIST handwritten digit recognition time on the low-memory Arduino Uno board to 0.28 s for the LogNNet 784:20:10 configurations, with a classification accuracy of ~ 82%. It was found that the computations with integers gives an increase in the speed of the algorithm by more than 2 times in comparison with the algorithm using the real type when generating a chaotic time series. The developed method can be used to accelerate the calculations of edge devices in the field of “Internet of Things”, for example, for mobile medical devices, autonomous vehicle control systems and bionic suit control.
A Nonparametric Bayesian Framework for Multivariate Beta Mixture Models

In this paper, we propose a nonparametric Bayesian learning framework for clustering problem based on multivariate Beta mixture model. Mixture models have been widely used as an unsupervised learning method in many machine learning, data mining and pattern recognition applications. One critical challenge in applying mixture model is the selection of the proper number of mixture components which best describes the data. Our approach can be viewed as an extension of the finite mixture model to infinite to tackle the model selection problem. In particular, our learning approach is Bayesian and relies on estimation of posterior distribution using Markov Chain Monte Carlo technique. The performance of our proposed method is evaluated through multiple challenging applications and we show that clustering via infinite multivariate Beta mixture models provides a more powerful performance comparing with various other approaches.
Quantum annealing with twisted fields

Quantum annealing is a promising method for solving combinational optimization problems and performing quantum chemical calculations. The main sources of errors in quantum annealing are the effects of decoherence and non-adiabatic transition. We propose a method for suppressing both these effects using inhomogeneous twist operators corresponding to the twist angles of transverse fields applied to qubits. Furthermore, we adopt variational methods to determine the optimal inhomogeneous twist operator for minimizing the energy of the state after quantum annealing. Our approach is useful for increasing the energy gap and/or making the quantum states robust against decoherence during quantum annealing. In summary, our results can pave the way to a new approach for realizing practical quantum annealing.
Secure Deep Neural Network Models Publishing Against Membership Inference Attacks Via Training Task Parallelism

Vast data and computing resources are commonly needed to train deep neural networks, causing an unaffordable price for individual users. Motivated by the increasing demands of deep learning applications, sharing well-trained models becomes popular. The owner of a pre-trained model can share it by publishing the model directly or providing a prediction interface. Either way, individual users can benefit from deep learning without much cost, and computing resources can be saved. However, recent studies of machine learning security have identified severe threats to these model publishing approaches. This paper will focus on the privacy leakage issue of publishing well-trained deep neural network models. To tackle this problem, we propose a series of secure model publishing solutions based on training task parallelism. Specifically, we show how to estimate private model parameters through parallel model training and generate new model parameters in a privacy-preserving manner to replace the original ones for publishing. Based on data parallelism and parameter generating techniques, we design another two solutions concentrating on model quality and parameter privacy, respectively. Through privacy leakage analysis and experimental attack evaluation, we conclude that deep neural network models published with our solutions can provide on-demand model quality guarantees and resist membership inference attacks.
Human behavior recognition based on time correlation sampling two-stream heterogeneous grafting network

Aiming at the problem of confusion of recognition categories caused by insufficient feature information extraction and insufficient use of time correlation information between video frames in human behavior recognition, we propose a human behavior recognition algorithm based on time correlation sampling two-stream heterogeneous grafting network. In the video sampling, we adopt the time correlation sampling of video frames based on the TRN algorithm to remove redundant information in adjacent frames and make full use of the time correlation between different video stages. Based on their changes in the time dimension, many easily confused behaviors can be well distinguished. Due to the complexity and diversity of behavior feature information, we propose a two-stream heterogeneous network based on the basic structure of the traditional two-stream network. In order to improve the efficiency of network convolution kernel, filter weight grafting is carried out for invalid filters in DenseNet network and BNIception network based on filter grafting technology. It can effectively improve the network model's ability to express feature information. Then the improved DenseNet network and BNIception network are used to form a two-stream heterogeneous network to extract spatial and temporal information. The experimental results show that the accuracy of this method in UCF101 dataset and in KTH dataset are respectively 89.3% and 92.1%, which proves the effectiveness of the algorithm.
Retransmission steganography in real-world scenarios: a practical study

Retransmission steganography (RSTEG) is one of the state-of-the-art network steganography techniques which can be used for various network protocols that make use of a retransmission mechanism. Essentially, RSTEG works by intentionally not acknowledging a correctly received segment to invoke a retransmission and secret data is embedded in the payload of the retransmitted segment. In this paper, we explore the characteristic features of RSTEG in laboratory and real-world setups. To this aim, a client/server architecture has been built with a simple TCP implementation based on retransmission timeout (RTO) that includes the RSTEG functionality. The application is able to communicate using an HTTP implementation built on top of it. The performed experiments evaluate the steganographic bandwidth, robustness and detectability of the implemented method. An average bandwidth of 5.2 kB/s was achieved for a 5% of retransmission probability on a 122 kB/s TCP throughput in a LAN environment. Also, a 500 B/s bandwidth was obtained while connecting to a high latency host through the Internet and we measured the bandwidth for different payload sizes under network impairments such as delays and packet loss. Lastly, we have tried to detect the implemented method using a Network Intrusion Detection System such as Snort.
Voxstructor: Voice Reconstruction from Voiceprint

With the rapid development of machine learning technologies, voiceprint has become widely used as a personal identifier in daily life. Because of that, it is essential to determine to what extent a voiceprint derived from machine learning can be inverted to obtain the original speaker characteristic. However, the reconstruction of voiceprint templates is still a challenging issue. It has also not been proven whether the widespread use of voiceprint poses a privacy leakage risk. In this paper, we implement the first comprehensive, holistic, and systematic reconstruction study targeting voiceprint templates. We present Voxstructor, a voiceprint-based voice constructor that can be used for bulk template reconstruction attacks. An attacker can reconstruct a new voice based only on the victim’s voiceprint data instead of the voice itself. Specifically, we formalize the voice reconstruction work as an objective optimization problem and merge voice cloning with voiceprint template conversion work. We have conducted extensive experiments on multiple mapping models, loss functions, voiceprint template extraction models, scoring methods, and two types of speaker verification attacks. Thorough experiments show that our attacks are effective, achieving a fairly high success rate which is similar to the results generated by voice cloning methods. The time overhead of Voxstructor is far less than other attacks. Our study not only demonstrates the need for protection of voiceprint templates in speaker recognition systems, but also shows that Voxstructor can be used as a privacy measure tool for voiceprint privacy-preserving schemes.
Parallel Logic Programming: A Sequel

Abstract Multi-core and highly connected architectures have become ubiquitous, and this has brought renewed interest in language-based approaches to the exploitation of parallelism. Since its inception, logic programming has been recognized as a programming paradigm with great potential for automated exploitation of parallelism. The comprehensive survey of the first twenty years of research in parallel logic programming, published in 2001, has served since as a fundamental reference to researchers and developers. The contents are quite valid today, but at the same time the field has continued evolving at a fast pace in the years that have followed. Many of these achievements and ongoing research have been driven by the rapid pace of technological innovation, that has led to advances such as very large clusters, the wide diffusion of multi-core processors, the game-changing role of general-purpose graphic processing units, and the ubiquitous adoption of cloud computing. This has been paralleled by significant advances within logic programming, such as tabling, more powerful static analysis and verification, the rapid growth of Answer Set Programming, and in general, more mature implementations and systems. This survey provides a review of the research in parallel logic programming covering the period since 2001, thus providing a natural continuation of the previous survey. In order to keep the survey self-contained, it restricts its attention to parallelization of the major logic programming languages (Prolog, Datalog, Answer Set Programming) and with an emphasis on automated parallelization and preservation of the sequential observable semantics of such languages. The goal of the survey is to serve not only as a reference for researchers and developers of logic programming systems but also as engaging reading for anyone interested in logic and as a useful source for researchers in parallel systems outside logic programming.
Mobile Apps for COVID-19 Surveillance: Balancing Public Health Needs with the Privacy of Personal Data

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Detecting Snow Layer on Solar Panels using Deep Learning

Renewable energy now plays a significant role in meeting rising energy demand while protecting the environment. Solar energy, generated by enormous solar panel farms, is a rapidly developing environmentally friendly technology. However, its efficiency degrades due to some factors. The climate is one of the most impactful factors that affect the electricity generation of a photovoltaic cell—especially countries with snowy climates face those downside effects. Hence, detection and removal of the snow layer on the solar panels are crucial. Firstly, most of the snow detection approaches are based on time series or momentary sensor data. Secondly, the removal of snow is based on surface coatings, heating, and mechanical clearing. Nowadays, vision-based solutions for detecting and removal of snow are trending. Since eliminating the human factor is a priority in physical labor, drones are suitable for vision-based operations. This paper presents a new deep learning-based approach that can be deployed on drones for detecting snowy conditions on solar panels using deep learning-based algorithms. As they are state-of-the-art neural networks in computer vision applications, ResNet-50, VGG-19, and InceptionV3 have been selected. In order to increase generalization in the training phase, we augmented the dataset using different image manipulation techniques. Our results show that we obtain 100%, 99%, and 91% F1-Score from InceptionV3, VGG-19, and ResNet-50 respectively.
Two-dimensional Lee-Error-Correcting Codes on Hexagonal Signal Constellations

We construct linear codes over odd prime fields for correcting two-dimensional (2-D) Lee-errors on the hexagonal signal constellations. They are obtained by puncturing and enlarging either RS codes or BCH codes. We introduce 2-D Lee-weight on the hexagonal constellations in the same way as the method presented by the first author in ISIT’19, and propose an effective and efficient method for correcting Lee-errors of small weight. The concept of value-locator of an error, which was introduced implicitly by K. Nakamura in the late 1970s and early 1980s and inherited to the ISIT’19 paper, is a key for decoding Lee-error-correcting codes. Our method is based on the Buchberger algorithm for finding Gröbner bases of ideals in the multivariate polynomial ring. A result of simulations shows that our method works well for correcting Lee-errors of small weight.
Polynomial-Time Construction of Two-Channel Prefix-Free Codes with Given Codeword Lengths

Although n-channel prefix-free codes are natural extensions of their 1-channel counterpart, the extra dimensions greatly increase the complexity of the problem such that most classical results cannot be generalized directly. Recently, a greedy algorithm was developed for deciding if it is possible to construct a 2-channel prefix-free code from a given multiset of codeword lengths. By dropping the information about codeword assignments which are not necessary for the decision problem, the greedy algorithm runs in polynomial time. However, if we naively turn the decision algorithm into a search algorithm (for constructing a prefix-free code) by retaining the information about codeword assignments, the computational complexity becomes exponential. One puzzle left unsolved was that whether the search problem can also be solved in polynomial time. In this paper, we give an affirmative answer to this question by designing a tailor-made data structure and a new lazy evaluation technique.
Autonomous resource management in distributed stream processing systems

Resource management in Distributed Stream Processing Systems (DSPS) defines the way queries are deployed on in-network resources to deliver query results while fulfilling the Quality of Service (QoS) requirements of the end-users. Various resource management mechanisms have been proposed in DSPS; however, they become inefficient in challenging conditions imposed by the dynamic environment and heterogeneous resources. This is because they focus on pre-configuration of both single and static QoS requirements. In addition, they lack cooperation between heterogeneous resources which amplify the problem of coordination between resources. This could lead to severe performance degradation such as inconsistent and incorrect query results in comparison to homogeneous resources. To solve the above challenges, in this research work, we will propose mechanisms: (i) to forecast the performance of network and heterogeneous resources, (ii) to select an efficient resource management approach, and (iii) for cooperation between resources in a dynamic environment.
Egocentric Network Exploration for Immersive Analytics

Abstract To exploit the potential of immersive network analytics for engaging and effective exploration, we promote the metaphor of “Egocentrism”, where data depiction and interaction are adapted to the perspective of the user within a 3D network. Egocentrism has the potential to overcome some of the inherent downsides of virtual environments, e.g., visual clutter and cyber‐sickness. To investigate the effect of this metaphor on immersive network exploration, we designed and evaluated interfaces of varying degrees of Egocentrism. In a user study, we evaluated the effect of these interfaces on visual search tasks, efficiency of network traversal, spatial orientation, as well as cyber‐sickness. Results show that a simple Egocentric interface considerably improves visual search efficiency and navigation performance, yet does not decrease spatial orientation or increase cyber‐sickness. An occlusion‐free Ego‐Bubble view of the neighborhood only marginally improves the user's performance. We tie our findings together in an open online tool for Egocentric network exploration, providing actionable insights on the benefits of the Egocentric network exploration metaphor.
Processor Architecture Optimization for Spatially Dynamic Neural Networks

Spatially dynamic neural networks adjust network execution based on the input data, saving computations by skipping non-important image regions. Yet, GPU implementations fail to achieve speedups from these spatially dynamic execution patterns for most neural network architectures. This paper investigates hardware constraints preventing such speedup and proposes and compares novel processor architectures and dataflows enabling latency improvements due to the dynamic execution with minimal loss of utilization. The presented architectures flexibly support spatial execution of a broad range of networks. For the derived architectures, the spatial unrolling for each layer type is optimized and validated making use of the ZigZag design space exploration framework where appropriate. This allows to benchmark and compare the hardware architectures on NNs for classification and human pose estimation, increasing throughput up to $\times 1.9$ and $\times 2.3$ compared to their static executions, respectively. This is the same order of magnitude as other dynamic execution methods, while being complementary to those.
Is Homomorphic Encryption-Based Deep Learning Secure Enough?

As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology-which requires a large amount of analysis data-is activated in various service fields, the possibility of exposing sensitive information of users increases, and the user privacy problem is growing more than ever. As a solution to this user's data privacy problem, homomorphic encryption technology, which is an encryption technology that supports arithmetic operations using encrypted data, has been applied to various field including finance and health care in recent years. If so, is it possible to use the deep learning service while preserving the data privacy of users by using the data to which homomorphic encryption is applied? In this paper, we propose three attack methods to infringe user's data privacy by exploiting possible security vulnerabilities in the process of using homomorphic encryption-based deep learning services for the first time. To specify and verify the feasibility of exploiting possible security vulnerabilities, we propose three attacks: (1) an adversarial attack exploiting communication link between client and trusted party; (2) a reconstruction attack using the paired input and output data; and (3) a membership inference attack by malicious insider. In addition, we describe real-world exploit scenarios for financial and medical services. From the experimental evaluation results, we show that the adversarial example and reconstruction attacks are a practical threat to homomorphic encryption-based deep learning models. The adversarial attack decreased average classification accuracy from 0.927 to 0.043, and the reconstruction attack showed average reclassification accuracy of 0.888, respectively.
AutoProfile: Towards Automated Profile Generation for Memory Analysis

Despite a considerable number of approaches that have been proposed to protect computer systems, cyber-criminal activities are on the rise and forensic analysis of compromised machines and seized devices is becoming essential in computer security. This article focuses on memory forensics, a branch of digital forensics that extract artifacts from the volatile memory. In particular, this article looks at a key ingredient required by memory forensics frameworks: a precise model of the OS kernel under analysis, also known as profile . By using the information stored in the profile, memory forensics tools are able to bridge the semantic gap and interpret raw bytes to extract evidences from a memory dump. A big problem with profile-based solutions is that custom profiles must be created for each and every system under analysis. This is especially problematic for Linux systems, because profiles are not generic : they are strictly tied to a specific kernel version and to the configuration used to build the kernel. Failing to create a valid profile means that an analyst cannot unleash the true power of memory forensics and is limited to primitive carving strategies. For this reason, in this article we present a novel approach that combines source code and binary analysis techniques to automatically generate a profile from a memory dump, without relying on any non-public information. Our experiments show that this is a viable solution and that profiles reconstructed by our framework can be used to run many plugins, which are essential for a successful forensics investigation.
HiddenText: Cross-Trace Website Fingerprinting over Encrypted Traffic

Website fingerprinting can infer which website a user visits in Tor networks by eavesdropping and analyzing encrypted traffic patterns. Recent attacks built upon deep neural networks can achieve more than 98% accuracy. To mitigate the privacy leakage under website fingerprinting, effective defenses, such as Walkie-Talkie, have been proposed, where the attack accuracy can be mitigated to 50% at most. In this paper, we propose a cross-trace website fingerprinting, which leverages the semantic correlation of the content of webpages across traffic traces to improve attack accuracy when existing defenses are enabled. Our experimental results on real-world datasets demonstrate that our proposed cross-trace website fingerprinting can completely defeat Walkie-Talkie, in which an attacker can still achieve more than 70% accuracy over defended data.