Reviewerly/exHarmony huggingface dataset:
GrAALF: Supporting graphical analysis of audit logs for forensics

<h2>Abstract</h2> System-level logs play a critical role in computer forensics. They capture interactions between programs and users in detail. However, a typical computer generates more than 2.5 million system events hourly, making finding malicious activities in such logs compute and time-intensive. We introduce GrAALF a graphical system for efficiently loading, storing, processing, querying, and displaying system events for computer forensics. In comparison to similar systems, GrAALF offers the flexibility of storage, intuitive querying, and the tracing power for longer sequences of events in real-time to help identify attacks. GrAALF is a robust solution for analysis to support computer forensics.
Fuzzy Rank Based Parallel Online Feature Selection Method using Multiple Sliding Windows

Abstract Nowadays, in real-world applications, the dimensions of data are generated dynamically, and the traditional batch feature selection methods are not suitable for streaming data. So, online streaming feature selection methods gained more attention but the existing methods had demerits like low classification accuracy, fails to avoid redundant and irrelevant features, and a higher number of features selected. In this paper, we propose a parallel online feature selection method using multiple sliding-windows and fuzzy fast-mRMR feature selection analysis, which is used for selecting minimum redundant and maximum relevant features, and also overcomes the drawbacks of existing online streaming feature selection methods. To increase the performance speed of the proposed method parallel processing is used. To evaluate the performance of the proposed online feature selection method k-NN, SVM, and Decision Tree Classifiers are used and compared against the state-of-the-art online feature selection methods. Evaluation metrics like Accuracy, Precision, Recall, F1-Score are used on benchmark datasets for performance analysis. From the experimental analysis, it is proved that the proposed method has achieved more than 95% accuracy for most of the datasets and performs well over other existing online streaming feature selection methods and also, overcomes the drawbacks of the existing methods.
Few-weight quaternary codes via simplicial complexes

<abstract> In this paper, we construct quaternary linear codes via simplicial complexes and we also determine the weight distributions of these codes. Moreover, we present an infinite family of minimal quaternary linear codes, which also meet the Griesmer bound. </abstract>
Alarm Collector in Smart Train Based on Ethereum Blockchain Events-Log

The European Union is moving toward the “smart” era having as one of the key topics the smart mobility. What is more, the European union (EU) is moving toward Mobility as a Service (MaaS). The key concept behind MaaS is the capability to offer both the traveler's mobility and goods' transport solutions based on travel needs. For example, unique payment methods, intermodal tickets, passenger services, freight transport services, etc. The introduction of new services implies the integration of many Internet-of-Things (IoT) sensors. At this point, security gains a key role in the railway sector. Considering an environment where sensor data are monitored from sensor events, and alarms are detected and emitted when events contain an anomaly, this document proposes the development of an alarms collection system, which ensures both traceability and privacy of these alarms. This system is based on Ethereum blockchain events-log, as an efficient storage mechanism, which guarantees that any railway entity can participate in the network, ensuring both entity security and information privacy.
Stochastic Adaptive Line Search for Differentially Private Optimization

The performance of private gradient-based optimization algorithms is highly dependent on the choice of step size (or learning rate) which often requires non-trivial amount of tuning. In this paper, we introduce a stochastic variant of classic backtracking line search algorithm that satisfies Rényi differential privacy. Specifically, the proposed algorithm adaptively chooses the step size satisfying the the Armijo condition (with high probability) using noisy gradients and function estimates. Furthermore, to improve the probability with which the chosen step size satisfies the condition, it adjusts per-iteration privacy budget during runtime according to the reliability of noisy gradient. A naive implementation of the backtracking search algorithm may end up using unacceptably large privacy budget as the ability of adaptive step size selection comes at the cost of extra function evaluations. The proposed algorithm avoids this problem by using the sparse vector technique combined with the recent privacy amplification lemma. We also introduce a privacy budget adaptation strategy in which the algorithm adaptively increases the budget when it detects that directions pointed by consecutive gradients are drastically different. Extensive experiments on both convex and non-convex problems show that the adaptively chosen step sizes allow the proposed algorithm to efficiently use the privacy budget and show competitive performance against existing private optimizers.
All You Need Is a Second Look: Towards Arbitrary-Shaped Text Detection

Arbitrary-shaped text detection is a challenging task since curved texts in the wild are of the complex geometric layouts. Existing mainstream methods follow the instance segmentation pipeline to obtain the text regions. However, arbitrary-shaped texts are difficult to be depicted through one single segmentation network because of the varying scales. In this paper, we propose a two-stage segmentation-based detector, termed as NASK (Need A Second looK), for arbitrary-shaped text detection. Compared to the traditional single-stage segmentation network, our NASK conducts the detection in a coarse-to-fine manner with the first stage segmentation spotting the rectangle text proposals and the second one retrieving compact representations. Specifically, NASK is composed of a Text Instance Segmentation (TIS) network ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^{st}$ </tex-math></inline-formula> stage), a Geometry-aware Text RoI Alignment (GeoAlign) module, and a Fiducial pOint eXpression (FOX) module ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{nd}$ </tex-math></inline-formula> stage). Firstly, TIS extracts the augmented features with a novel Group Spatial and Channel Attention (GSCA) module and conducts instance segmentation to obtain rectangle proposals. Then, GeoAlign converts these rectangles into the fixed size and encodes RoI-wise feature representations. Finally, FOX disintegrates the text instance into serval pivotal geometrical attributes to refine the detection results. Extensive experimental results on four public benchmarks including Total-Text, SCUT-CTW1500, ICDAR 2015 and ICDAR 2017 MLT verify that our NASK outperforms recent state-of-the-art methods.
Bayesian optimization with approximate set kernels

We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input. Because set inputs are permutation-invariant, traditional Gaussian process-based Bayesian optimization strategies which assume vector inputs can fall short. To address this, we develop a Bayesian optimization method with set kernel that is used to build surrogate functions. This kernel accumulates similarity over set elements to enforce permutation-invariance, but this comes at a greater computational cost. To reduce this burden, we propose two key components: (i) a more efficient approximate set kernel which is still positive-definite and is an unbiased estimator of the true set kernel with upper-bounded variance in terms of the number of subsamples, (ii) a constrained acquisition function optimization over sets, which uses symmetry of the feasible region that defines a set input. Finally, we present several numerical experiments which demonstrate that our method outperforms other methods.
Quantum Speed Limit and Divisibility of the Dynamical Map

The quantum speed limit (QSL) is the theoretical lower limit of the time for a quantum system to evolve from a given state to another one. Interestingly, it has been shown that non-Markovianity can be used to speed-up the dynamics and to lower the QSL time, although this behavior is not universal. In this paper we further carry on the investigation on the connection between QSL and non-Markovianity by looking at the effects of P- and CP-divisibility of the dynamical map to the quantum speed limit. We show that the speed-up can also be observed under P- and CP-divisible dynamics, and that the speed-up is not necessarily tied to the transition from P-divisible to non-P-divisible dynamics.
FG-SMOTE: Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution

Purpose Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance. The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset. This issue is known as the imbalance problem, which is one of the most common issues occurring in real-time applications. Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining. Imbalanced data degrades the performance of the classifier by producing inaccurate results. Design/methodology/approach In the proposed work, a novel fuzzy-based Gaussian synthetic minority oversampling (FG-SMOTE) algorithm is proposed to process the imbalanced data. The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets. The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique. Findings The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC: 93.7%. F1 Score Prediction: 94.2%, Geometric Mean Score: 93.6% predicted from confusion matrix. Research limitations/implications The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment. Originality/value The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data. FG-SMOTE has aided in balancing minority and majority class datasets.
DCGAN-generated Synthetic Images Effect on White Blood Cell Classification

Abstract White blood cell can give information about someone’s health. Imbalanced amount of white blood cells indicates someone’s disease. The disease detection using white blood cell can be done by using flow cytometry, however the use of the device has many drawbacks. The drawbacks can be solved by using computer aided classification. However, another problem arises, that the available white blood cell dataset has small dataset and the class distribution is imbalanced. This problem can be solved by using classic data augmentation and DCGAN to create synthetic image in order to balance the amount of white blood cell dataset. The balanced dataset then trained using ResNet50 model as the classification model. Accuracy, precision, recall and Fl-score are used as the performance measures of the classification. The result shows that the model obtain accuracy of 82.5%. ResNet50 was chosen because it can produce good accuracy when used in medical image classification.
Gaussian Process-based Feature-Enriched Blind Image Quality Assessment

The objective of blind-image quality assessment (BIQA) research is the prediction of perceptual quality of images, without reference information. The human’s perceptual assessment of quality of an image is the backbone of BIQA research. Therefore, human-provided, mean opinion score (perceptual quality) has been analyzed in detail, and it has been observed to follow the Gaussian distribution and thus can be ideally modeled by the same. In this paper, we have proposed an integrated two-stage Gaussian process-based hybrid-feature selection algorithm for the BIQA problem. Moreover, a new consolidated feature set (obtained from the proposed algorithm), consisting of momentous Natural Scene Statistics (NSS)-based features is used in combination with the Gaussian process regression algorithm for the design of a new blind-image quality evaluator, referred to as GPR-BIQA. The proposed evaluator is tested on eight IQA legacy databases, and it is found that the proposed evaluator proficiently correlate with the human opinion, and outperformed a substantial number of existing approaches.
Addressing Hate Speech with Data Science: An Overview from Computer Science Perspective

From a computer science perspective, addressing on-line hate speech is a challenging task that is attracting the attention of both industry (mainly social media platform owners) and academia. In this chapter, we provide an overview of state-of-the-art data-science approaches - how they define hate speech, which tasks they solve to mitigate the phenomenon, and how they address these tasks. We limit our investigation mostly to (semi-)automatic detection of hate speech, which is the task that the majority of existing computer science works focus on. Finally, we summarize the challenges and the open problems in the current data-science research and the future directions in this field. Our aim is to prepare an easily understandable report, capable to promote the multidisciplinary character of hate speech research. Researchers from other domains (e.g., psychology and sociology) can thus take advantage of the knowledge achieved in the computer science domain but also contribute back and help improve how computer science is addressing that urgent and socially relevant issue which is the prevalence of hate speech in social media.
From the euclidean distance to compositional dissimilarity: What is gained and what is lost

A well-known problem of the Euclidean distance is that it is not always appropriate to explore the multivariate structure of community composition. This is because all distances of the ‘Euclidean family’ may lead to the paradoxical condition for which two samples with no species in common may be more similar than two samples with the same species list. This effect is generally known as the Orlóci paradox. To avoid this problem, some kind of data normalization has to be performed. Therefore, the main question of this short paper is: what is gained and what is lost by moving from the Euclidean distance to compositional dissimilarity? To answer this question, I first explore the causes of the Orlóci paradox. Next, I discuss how the normalization methods that are included in virtually all compositional dissimilarity measures affect the way in which species abundances are modeled.
Implementing a quantum search algorithm with nonorthogonal states

The search algorithm is one of the most widely studied quantum algorithms, while its complete implementation in experiments is difficult and only a few experimental works have been reported. Based on Grover's algorithm, we propose a scheme of quantum search using nonorthogonal states and experimentally implement the entire scheme in a linear optical system. With this scheme, a large-sized search space can be encoded using only one qubit and the search probability of the target state is $100%$ in theory. The experimental setup and the operations for implementing this scheme are extremely simple. In our linear optical experiment, different target states are successfully found from a database of nine elements with all fidelities $>0.99$, the state evolution from the initial state to a target state is studied, and the medium states and the final state are shown in a Bloch sphere. The scheme is general and can be easily realized in a wide range of quantum systems. This work may pave an avenue to implement quantum algorithms with nonorthogonal states.
A Quantum Approximate Optimization Algorithm with Metalearning for MaxCut Problem and Its Simulation via TensorFlow Quantum

A quantum approximate optimization algorithm (QAOA) is a polynomial-time approximate optimization algorithm used to solve combinatorial optimization problems. However, the existing QAOA algorithms have poor generalization performance in finding an optimal solution from a feasible solution set of combinatorial problems. In order to solve this problem, a quantum approximate optimization algorithm with metalearning for the MaxCut problem (MetaQAOA) is proposed. Specifically, a quantum neural network (QNN) is constructed in the form of the parameterized quantum circuit to detect different topological phases of matter, and a classical long short-term memory (LSTM) neural network is used as a black-box optimizer, which can quickly assist QNN to find the approximate optimal QAOA parameters. The experiment simulation via TensorFlow Quantum (TFQ) shows that MetaQAOA requires fewer iterations to reach the threshold of the loss function, and the threshold of the loss value after training is smaller than comparison methods. In addition, our algorithm can learn parameter update heuristics which can generalize to larger system sizes and still outperform other initialization strategies of this scale.
ASKs: Convolution with any-shape kernels for efficient neural networks

Despite the outstanding performance, deep convolutional neural networks (CNNs) are computationally expensive and contain a large number of redundant parameters, hindering their deployment on resource constrained platforms. To address this issue, many model compression methods have been proposed. However, these methods mainly focus on pruning redundant parameters or designing efficient architectures, the redundancy in convolution kernels has rarely been investigated. In this paper, we find that the contributions of parameters at different locations in the traditional 3×3 kernels are not the same, and this distribution varies considerably in different layers. Motivated by this, we propose to use irregular kernels and present a novel approach to implementing convolution with any-shape kernels (ASKs) efficiently. The proposed ASKs are plug-and-play and can be readily embedded into existing CNNs, providing efficient modules for building compact CNNs. Experiments on benchmarks demonstrate the effectiveness of the proposed method. We improve the accuracy of VGG-16 on CIFAR-10 dataset from 93.45% to 94.04% simply by replacing the regular 3×3 kernel with cross-shaped kernel, which takes up only about 5/9 of the original storage and computing resources. Compared to state-of-the-art model compression methods, our ASKs achieve a better trade-off between accuracy and compression ratio.
Certified Abstract Cost Analysis

Abstract A program containing placeholders for unspecified statements or expressions is called an abstract (or schematic) program. Placeholder symbols occur naturally in program transformation rules, as used in refactoring, compilation, optimization, or parallelization. We present a generalization of automated cost analysis that can handle abstract programs and, hence, can analyze the impact on the cost of program transformations. This kind of relational property requires provably precise cost bounds which are not always produced by cost analysis. Therefore, we certify by deductive verification that the inferred abstract cost bounds are correct and sufficiently precise. It is the first approach solving this problem. Both, abstract cost analysis and certification, are based on quantitative abstract execution (QAE) which in turn is a variation of abstract execution, a recently developed symbolic execution technique for abstract programs. To realize QAE the new concept of a cost invariant is introduced. QAE is implemented and runs fully automatically on a benchmark set consisting of representative optimization rules.
Perceived Formal and Informal Sanctions in Deterring Cybercrime in a College Sample

The threat of formal sanctions is the criminal justice system’s primary tool to discourage online and offline deviant behavior. Yet, scholars have expressed strong concerns about the effectiveness of formal sanctions to deter cybercrime. Even more surprising is the sparsity of deterrence research in the cybercrime literature. This study examined the effects of perceived formal and informal sanctions on digital piracy, computer hacking, and online harassment in a large American college sample. Perceived formal sanctions was negatively correlated with software piracy, media piracy, password cracking, accessing accounts, sending mean messages privately online, and posting mean messages. Higher levels of perceived formal sanctions did not significantly predict any form of cybercrime, however, when controlling for informal sanctions and deviant peer associations. The implications of the findings for our ability to deter deviant behavior in cyberspace are explored.
An Improved SPSIM Index for Image Quality Assessment

Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.
Nonclassical effects in a nonlinear two trapped-particles system under intrinsic decoherence

In this paper, we analytically explore the dynamics of a nonlinear two-qubit system derived from a physical model that describes laser-irradiated two trapped particles in the Lamb-Dicke regime under appropriate resonance conditions and the intrinsic decoherence. The dynamics of the particle population inversion, the entanglement between the two trapped-particles and their center-of-mass modes, and the entanglement between the two trapped-particles are investigated under the Lamb-Dicke parameter and the intrinsic decoherence. In is found that, in the absence of decoherence, the generated particle-field and two trapped-particles entanglement can be enhanced by increasing the Lamb-Dicke parameter. The Lamb-Dicke nonlinearity effect on the generated stationary entanglement, the sudden death and sudden birth of the entanglement, and the intrinsic decoherence effect become more pronounced with its large values. For the high Lamb-Dicke non-linearity, the decoherence effect on the generated nonclassical effects can be weakened.