Reviewerly/exHarmony huggingface dataset:
Additional kernel observer: privilege escalation attack prevention mechanism focusing on system call privilege changes

Abstract Cyberattacks, especially attacks that exploit operating system vulnerabilities, have been increasing in recent years. In particular, if administrator privileges are acquired by an attacker through a privilege escalation attack, the attacker can operate the entire system and cause serious damage. In this paper, we propose an additional kernel observer (AKO) that prevents privilege escalation attacks that exploit operating system vulnerabilities. We focus on the fact that a process privilege can be changed only by specific system calls. AKO monitors privilege information changes during system call processing. If AKO detects a privilege change after system call processing, whereby the invoked system call does not originally change the process privilege, AKO regards the change as a privilege escalation attack and applies countermeasures against it. AKO can therefore prevent privilege escalation attacks. Introducing the proposed method in advance can prevent this type of attack by changing any process privilege that was not originally changed in a system call, regardless of the vulnerability type. In this paper, we describe the design and implementation of AKO for Linux x86 64-bit. Moreover, we show that AKO can be expanded to prevent the falsification of various data in the kernel space. Then, we present an expansion example that prevents the invalidation of Security-Enhanced Linux. Finally, our evaluation results show that AKO is effective against privilege escalation attacks, while maintaining low overhead.
Time-Release Cryptography from Minimal Circuit Assumptions

Time-release cryptography requires problems that take a long time to solve and take just as long even with significant computational resources. While time-release cryptography originated with the seminal paper of Rivest, Shamir and Wagner ('96), it has gained special visibility recently due to new time-release primitives, like verifiable delay functions (VDFs) and sequential proofs of work, and their novel blockchain applications. In spite of this recent progress, security definitions remain inconsistent and fragile, and foundational treatment of these primitives is scarce. Relationships between the various time-release primitives are elusive, with few connections to standard cryptographic assumptions.
Developing and applying a GIS-Fuzzy AHP assisted approach to locating a hybrid renewable energy system with high potential: Case of Dakhla region–Morocco-

Recently, the renewable energy sector, particularly wind and solar photovoltaic, has seen a great evolution in the world thanks to various technological advances and to incentive programs. But the main disadvantage of these systems is their dependence on weather and climate conditions. However, this problem can be solved by integrating two or more energy sources called hybrid systems. Thus, the weakness of one system could be compensated by the strengths of other sources and thus an efficient system can be achieved that ensures the continuity of electricity supply. The main objective of this paper is to present a model based on geographical information systems (GIS) coupled with a multi-criteria decision-making analysis (MCDA) using the fuzzy analytical hierarchy process (Fuzzy AHP) method that will make it possible to locate a hybrid solarwind energy system with high potential in the Dakhla region of the southern Kingdom of Morocco, taking into account three essential factors: Climatic, economic and social. The results obtained indicate that 17.62% (198 km2), 25.19% (283 km2), 30.62% (344 km2) and 26.57% (298 km2) of the region of Dakhla are positioned as areas strongly, moderately, marginally adapted and excluded, respectively.
Light Field Image Coding Based on Hybrid Data Representation

This paper proposes a novel efficient light field coding approach based on a hybrid data representation. Current state-of-the-art light field coding solutions either operate on micro-images or sub-aperture images. Consequently, the intrinsic redundancy that exists in light field images is not fully exploited, as is demonstrated. This novel hybrid data representation approach allows to simultaneously exploit four types of redundancies: i) sub-aperture image intra spatial redundancy, ii) sub-aperture image inter-view redundancy, iii) intra-micro-image redundancy, and iv) inter-micro-image redundancy between neighboring micro-images. The proposed light field coding solution allows flexibility for several types of baselines, by adaptively exploiting the most predominant type of redundancy on a coding block basis. To demonstrate the efficiency of using a hybrid representation, this paper proposes a set of efficient pixel prediction methods combined with a pseudo-video sequence coding approach, based on the HEVC standard. Experimental results show consistent average bitrate savings when the proposed codec is compared to relevant state-of-the-art benchmarks. For lenslet light field content, the proposed coding algorithm outperforms the HEVC-based pseudo-video sequence coding benchmark by an average bitrate savings of 23%. It is shown for the same light field content that the proposed solution outperforms JPEG Pleno verification models MuLE and WaSP, as these codecs are only able to achieve 11% and -14% bitrate savings over the same HEVC-based benchmark, respectively. The performance of the proposed coding approach is also validated for light fields with wider baselines, captured with high-density camera arrays, being able to outperform both the HEVC-based benchmark, as well as MuLE and WaSP.
A Combination of Machine Learning and Lexicon Based Techniques for Sentiment Analysis

Today millions of web users put their opinions on the internet about various topics. Development of methods that automatically categorize these opinions to positive, negative or neutral is important. Opinion mining or sentiment analysis is known as mining of behavior, opinions and sentiments of the text, chat, etc. using natural language processing and information retrieval methods. The paper is aimed to study the effect of combining machine learning methods in a meta-classifier for sentiment analysis. The machine learning methods use the output of lexicon-based techniques. In this way, the score of SentiWordNet dictionary, Liu's sentiment list, SentiStrength and sentimental words ratios are computed and used as the input of machine learning techniques. Adjectives, adverbs and verbs of an opinion are used for opinion modeling and score of these words are extracted from lexicons. Experimental results show that the meta-classifier improve the accuracy of classification 0.9% and 1.09% for Amazon and IMDB reviews in comparison with the four machine learning techniques evaluated here.
Spreadsheets for Legal Reasoning: The Continued Promise of Declarative Logic Programming in Law

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Nonlinear filtering of light-curve data

A particle filter with an expectation-maximization (EM) clustering algorithm to approximate the state’s distribution with Gaussian mixture models (GMMs) is proposed to simultaneously estimate the position, velocity, attitude, angular rates, and surface parameters of a space object (SO) in the near-Geostationary Earth Orbit (GEO). Recent work shows that the unscented Kalman filter applied to this problem is susceptible to divergence due to the presence of the many uncertain states estimated. The underlying reasons of the filter’s divergence have not yet been completely revealed and preliminary attributed to information dilution. Under the scenario considered in this paper, it is demonstrated through numerical simulation that the underlying reason for the filter’s divergence in SO tracking is due to the severe nonlinearities of light-curve measurement data coupled with weak observability; rather than information dilution. In addition to the novel particle filter, two alternative estimation techniques based on modifications of the extended Kalman filter (EKF) and unscented Kalman filter (UKF) are introduced to reduce the computational burden while still mitigating filter divergence.
A Comparative Study of Computational Intelligence for Identification of Breast Cancer

Breast cancer is a type of invasive cancer that occurs in women. Breast cancer accounts for 18% of all cancer related deaths among women according to World Health Organization. After Lung Cancer, breast cancer is the leading cause of death of women in India. Due to inaccessibility, especially in rural areas, it is impossible for everyone to get diagnosed in time. If breast cancer is detected at an early stage, the doctor will be aided in suggesting an efficient way to proceed with the treatment of the patient, thus reducing the mortality rate and medical expenses. So, in this paper a comparative study on machine learning and computational intelligence techniques has been performed to optimize the process and achieve better accuracy and precision. The focus of this review article is to survey several articles existing on breast cancer majorly on Wisconsin dataset which is obtained from UCI repository. This review article has been concluded with suggestions for future directions.
Outlier-Aware, density-Based gaze fixation identification

Eye tracking is an advancing technology holding significant promise to improve our understanding of human behavior and decision making. Gaze data gathered by eye trackers contain events known as fixations. Fixations indicate visual attention and awareness, and are identified by algorithms that parse eye-tracking data into a sequence of gaze point clusters. While great potential exists, eye-tracker imprecision often results in noisy gaze data, such as what arises from calibration errors, erratic eye movements, or other system noise. Noise can cause inaccurate identification of fixations in eye-tracking applications, resulting in misleading behavioral interpretations and conclusions. Therefore, fixation identification algorithms should be robust against data noise. To resolve such inaccuracies, we propose FID+: outlier-aware fixation identification via fixation inner-density. We represent the problem of detecting outliers in fixation gaze data through a novel mixed-integer optimization formulation, and subsequently strengthen the formulation using two geometric arguments to provide enhanced bounds. We show that neither bound dominates the other, and that both are effective in reducing the overall solution runtime. Our experiments on real gaze recordings demonstrate that accommodating for the reality of fixation outliers enhances the ability to identify fixations with greater density in reasonable runtime.
Design of Modified Dual-CLCG Algorithm for Pseudo-Random Bit Generator

In this paper, modified algorithm of dual-coupled linear congruential generator (dual-CLCG) for pseudo-random bit generation is proposed to achieve the high randomness properties of generated bit sequence with minimum VLSI complexity. The proposed method relies based on randomly chosen inequality comparisons bits by the multiplexer circuit and its select line is control by current seeds value to generates final random bits. Therefore, a new method for random bit generator i.e. modified dual-CLCG and its VLSI architecture are proposed in this work to mitigate the more secure random bit generator. The effect of modification on the design of a dual-CLCG is observed to evaluate the some of the essential timing performance parameters such as initial clock latency, maximum possible frequency of bit generation and output -to-output latency. Also find the power dissipation and utilize chip area. The proposed architecture with 8, 16 and 32-bit length is design using Verilog-HDL and its synthesis is done based on 90-nm CMOS technology (GPDK) using Cadence Tool. The physical layout of proposed architecture using 90-nm CMOS process is also obtained in this work.
Research on Intelligent Target Detection and Coder-decoder Technology Based on Embedded Platform

In order to meet the embedded application requirements of machine learning algorithm, the intelligent target detection and recognition algorithm based on convolutional neural network and corresponding optimal process are studied. Detailed network structure analysis and network performance analysis are carried out. Based on GPU embedded platform, TensorRT technology is used to accelerate the embedded application of intelligent target detection and recognition algorithm, including fp16 and int8 inference modes. Satisfactory verification results are achieved on embedded platform. In addition, an integrated system of real-time machine learning and H.265 encoding and decoding technology is realized. Firstly, the compressed image data sent by the camera is received by embedded platform and decoded in real time in H.265 format. Then the real-time intelligent target detection and recognition algorithm basing on TensorRT technology is done for RGB data obtained by hardware decoding process. Finally, the data is compressed in H.265 format, and subsequently storage and data transmission are carried out. The experimental results show that TensorRT technology can improve the inference speed of neural network in embedded platform. The network structure optimized by TensorRT technology can achieve three times the speed increase, with limited accuracy loss. Hardware coding and decoding of H.265 can also cause corresponding delay to program inevitably.
A New Black Box Attack Generating Adversarial Examples Based on Reinforcement Learning

Machine learning can be misled by adversarial examples, which is formed by making small changes to the original data. Nowadays, there are kinds of methods to produce adversarial examples. However, they can not apply non-differentiable models, reduce the amount of calculations, and shorten the sample generation time at the same time. In this paper, we propose a new black box attack generating adversarial examples based on reinforcement learning. By using deep Q-learning network, we can train the substitute model and generate adversarial examples at the same time. Experimental results show that this method only needs 7.7ms to produce an adversarial example, which solves the problems of low efficiency, large amount of calculation and inapplicable to non-differentiable model.
Variation in QoE of Passive Gaming Video Streaming for Different Packet Loss Ratios

The rise in popularity of the streaming platforms for gaming videos like Twitch.tv results in more users watching and streaming gaming videos passively. These streaming videos show great dependence on Quality of Service (QoS) parameters like latency and packet loss. This motivates us to evaluate the Quality of Experience (QoE) of three popular streaming game videos for different Packet Loss Ratios (PLR). In this paper, using streaming gaming videos, we demonstrated the limitations of using Mean Opinion Score (MOS) to evaluate QoE. Using a novel method of data representation, we presented our results as distributions of user-rating at different PLR configuration and compared them with other subjective QoE evaluation methods like MOS and standard deviation of Opinion score (SOS). Our results show that evaluating QoE using MOS loses information about user diversity. Our findings also include SOS retaining user diversity but posing challenges to quantifying the QoE at varying PLR values. For streaming video games we show that QoE distributions evaluate user perception accurately. Our work in this paper also studies how different games with different content complexity, genre and pace are affected by PLR. Our results show that slow-paced games like FIFA 20 get affected more significantly by packet loss.
Push for More: On Comparison of Data Augmentation and SMOTE with Optimised Deep Learning Architecture for Side-Channel

Side-channel analysis has seen rapid adoption of deep learning techniques over the past years. While many paper focus on designing efficient architectures, some works have proposed techniques to boost the efficiency of existing architectures. These include methods like data augmentation, oversampling, regularization etc. In this paper, we compare data augmentation and oversampling (particularly SMOTE and its variants) on public traces of two side-channel protected AES. The techniques are compared in both balanced and imbalanced classes setting, and we show that adopting SMOTE variants can boost the attack efficiency in general. Further, we report a successful key recovery on ASCAD(desync=100) with 180 traces, a 50% improvement over current state of the art.
Neural Networks for Relational Data

While deep networks have been enormously successful, they rely on flat-feature vector representations. Using them in structured domains requires significant feature engineering. Such domains rely on relational representations to capture complex relationships between entities and their attributes. Thus, we consider the problem of learning neural networks for relational data. We distinguish ourselves from current approaches that rely on expert hand-coded rules by learning higher-order random-walk features to capture local structural interactions and the resulting network architecture. We further exploit parameter tying, where instances of the same rule share parameters. Our experimental results demonstrate the effectiveness of the proposed approach over multiple neural net baselines as well as state-of-the-art relational models.
Imbalanced Learning in Massive Phishing Datasets

Phishing is one of the major threats facing internet users in today’s work. Such attacks continue costing billions of dollars to companies around the words thus requiring more efficient detection techniques to curb the danger. This paper proposes a big data friendly implementation of Multiclass Imbalance Learning in Ensembles through Selective Sampling (MILES) that detects phishing attacks with high accuracy. The proposed method is compatible with SPARK, can be trained on a cluster of nodes in parallel, thus reduce the training time by increasing the size of the cluster. In addition, a comparative study of classic machine learning techniques like Random Forest, Naive Bayes, and Decision Trees show that the proposed MILES method provides significantly higher precision and recall.
Quality Assessment of Gaming Videos Compressed via AV1

With the increasing demand in video traffic, delivering appropriate Quality of Experience may appear as a challenge for highly popular services such as Twitch.tv and YouTube-Gaming. This paper presents the evaluation of three widely used encoders, namely H.264, H.265 and AV1, for the case of gaming videos, assuming streaming scenario. The performance of the codecs has been assessed in terms of objective video quality metrics (PSNR, SSIM and VMAF) and subjective video quality assessment. The encoding settings were kept similar for all the three aforementioned encoders. In the considered setting, AV1 achieves a better level of video quality both in terms of objective and subjective VQA, for almost all bitrates and content considered. The improvement is remarkable in particular for the lower range of bitrates considered.
Low-Light Image Enhancement Using Volume-Based Subspace Analysis

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under challenging illumination conditions. Even though the significant progress has been made for enhancing the poor visibility, the intrinsic noise amplified in low-light areas still remains as an obstacle for further improvement in visual quality. In this paper, a novel and simple method for low-light image enhancement is proposed. Specifically, the subspace, which has an ability to separately reveal illumination and noise, is constructed from a group of similar image patches, so-called volume, at each pixel position. Based on the principal energy analysis onto this volume-based subspace, the illumination component is accurately inferred from a given image while the unnecessary noise is simultaneously suppressed. This leads to clearly unveiling the underlying structure in low-light areas without loss of details. Experimental results show the efficiency and robustness of the proposed method for low-light image enhancement compared to state-of-the-art methods.
A comparison of GEOBIA Vs PBIA machine learning methods for lithological mapping using Sentinel 2 imagery: Case study of Skhour Rehamna, Morocco

An extensive range of remote sensing-based approaches has been used to lithological mapping in conjunction with multispectral satellite imagery. Thus, this study focuses on mapping and comparing thematic classification methods of lithologies using Sentinel 2 imagery in the southern of Skhour Rehamna. First, Pixel-Based Image analysis (PBIA) techniques were evaluated, particularly, RF and K-NN machine learning algorithms (MLAs), both methods generalized lithological maps that is characterized with “salt and pepper” noise of misclassified pixels and poorly defined boundaries. The second approach is Geographic ObjectBased Image Analysis (GEOBIA), which involved multiresolution segmentation part based on several predefined scale, shape and compactness parameters that must be subjected to relative weighting in order segment the image to homogeneous areas, whilst, standard Nearest Neighbor classification was applied to the given samples. Then, classifications accuracies were assessed by comparing both methods using confusion matrix and Kappa coefficient. The results show that the GEOBIA approach provides the best mapping results with an overall accuracy of approximately 83.46% and Kappa coefficient of 0.76 compared with the results obtained by PBIA approach, with 81.92% and 80.79%, and Kappa coefficient of 0.72 and 0.72, for RF and K-NN respectively. As a conclusion, the results clearly indicate the potential of GEOBIA technique for lithological mapping.
Extended Target Tracking With Multipath Detections, Terrain-Constrained Motion Model and Clutter

To address the problem of extended target tracking (ETT) with measurement-origin uncertainty in a multipath environment with terrain-constrained motion model, a new generalized version of the standard probabilistic data association (PDA) filter, called MP-ET-PDA, based on random matrices (RM) is proposed in this paper. In the MP-ET-PDA filter, we assume that multipath detections and clutter are possible in the extended target tracking problem, which are prevalent in practical systems but barely addressed in the literature. Further, a clustering-aided MP-ET-PDA algorithm with a reduced computational complexity that makes use of the Variational Bayesian (VB) technique, called MP-ET-PDA-VB, is presented to provide near real-time processing capability in ETT problems with an uncertain multipath environment. In addition to using a constant velocity motion model, a new terrain-constrained motion model is presented for scenarios where terrain-following is required by motion-constrained autonomous vehicles. The posterior Cramér-Rao lower bound (PCRLB), which quantifies the best possible accuracy in realistic ETT problems with multipath detections and measurement-origin uncertainty, is derived as the benchmark for performance evaluation. Simulations results demonstrate the improved performance of the proposed algorithms.