Reviewerly/exHarmony huggingface dataset:
An empirical study on pareto based multi-objective feature selection for software defect prediction

The performance of software defect prediction (SDP) models depend on the quality of considered software features. Redundant features and irrelevant features may reduce the performance of the constructed models, which require feature selection methods to identify and remove them. Previous studies mostly treat feature selection as a single objective optimization problem, and multi-objective feature selection for SDP has not been thoroughly investigated. In this paper, we propose a novel method MOFES (Multi-Objective FEature Selection), which takes two optimization objectives into account. One optimization objective is to minimize the number of selected features, this objective is related to the cost analysis of this problem. Another objective is to maximize the performance of the constructed SDP models, this objective is related to the benefit analysis of this problem. MOFES utilizes Pareto based multi-objective optimization algorithms (PMAs) to solve this problem. In our empirical study, we design and conduct experiments on RELINK and PROMISE datasets, which are gathered from real open source projects. Firstly, we analyze the influence of different PMAs on MOFES and find that NSGA-II can achieve the best performance on both datasets. Then, we compare MOFES method with 22 state-of-the-art filter based and wrapper based feature selection methods, and find that MOFES can effectively select fewer but closely related features to construct high-quality models. Moreover, we also analyze the frequently selected features by MOFES, and these findings can be used to provide guidelines on gathering high-quality SDP datasets. Finally, we analyze the computational cost of MOFES and find that MOFES only needs 107 seconds on average.
Do Independent Central Banks Exhibit Varied Behaviour in Election and Non-Election Years?: The Case of Fiscal Policy in Africa

The study primarily investigates if the behavior and effectiveness of CBI on fiscal policy varies between non-election and election years. It also examines whether the effectiveness of CBI in improving fiscal performance is enhanced by higher institutional quality. Using recent CBI data f on 48 African countries, 90 other developing countries and 40 developed countries over the period 1970–2012, we apply a two-stage system GMM with Windmeijer small sample robust correction estimator and find that due to the strong incentives of political authorities to influence economic outcomes in election years, CBI has stronger effects on fiscal performance in election years compared to non-election years in developed countries only. However, given higher levels of institutional quality, CBI has stronger effects on fiscal performance in election years compared to non-election years in Africa and other developing countries also.
On the Security of Two-Round Multi-Signatures

A multi-signature scheme allows a group of signers to collaboratively sign a message, creating a single signature that convinces a verifier that every individual signer approved the message.The increased interest in technologies to decentralize trust has triggered the proposal of highly efficient two-round Schnorr-based multi-signature schemes designed to scale up to thousands of signers, namely BCJ by Bagherzandi et al. (CCS 2008), MWLD by Ma et al. (DCC 2010), CoSi by Syta et al. (S&P 2016), and MuSig by Maxwell et al. (ePrint 2018).In this work, we point out serious security issues in all currently known two-round multi-signature schemes (without pairings).First, we prove that none of the schemes can be proved secure without radically departing from currently known techniques.Namely, we show that if the one-more discrete-logarithm problem is hard, then no algebraic reduction exists that proves any of these schemes secure under the discrete-logarithm or one-more discrete-logarithm problem.We point out subtle flaws in the published security proofs of the above schemes (except CoSi, which was not proved secure) to clarify the contradiction between our result and the existing proofs.Next, we describe practical subexponential attacks on all schemes, providing further evidence to their insecurity.Being left without two-round multi-signature schemes, we present mBCJ, a variant of the BCJ scheme that we prove secure under the discrete-logarithm assumption in the random-oracle model.Our experiments show that mBCJ barely affects scalability compared to CoSi, allowing 16384 signers to collaboratively sign a message in about 2 seconds, making it a highly practical and provably secure alternative for large-scale deployments.
Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems). To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS). To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various clustering datasets. The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications.
Conditional Speculation: An Effective Approach to Safeguard Out-of-Order Execution Against Spectre Attacks

Speculative execution side-channel vulnerabilities such as Spectre reveal that conventional architecture designs lack security consideration. This paper proposes a software transparent defense mechanism, named as Conditional Speculation, against Spectre vulnerabilities found on traditional out-of-order microprocessors. It introduces the concept of security dependence to mark speculative memory instructions which could leak information with potential security risk. More specifically, security-dependent instructions are detected and marked with suspect speculation flags in the Issue Queue. All the instructions can be speculatively issued for execution in accordance with the classic out-of-order pipeline. For those instructions with suspect speculation flags, they are considered as safe instructions if their speculative execution will not refill new cache lines with unauthorized privilege data. Otherwise, they are considered as unsafe instructions and thus not allowed to execute speculatively. To reduce the performance impact from not executing unsafe instructions speculatively, we investigate two filtering mechanisms, Cachehit based Hazard Filter and Trusted Page Buffer based Hazard Filter to filter out false security hazards. Our design philosophy is to speculatively execute safe instructions to maintain the performance benefits of out-of-order execution while blocking the speculative execution of unsafe instructions for security consideration. We evaluate Conditional Speculation in terms of performance, security and area. The experimental results show that the hardware overhead is marginal and the performance overhead is minimal.
Fast Adaptive K-Means Subspace Clustering for High-Dimensional Data

In many real-world applications, data are represented by high-dimensional features. Despite the simplicity, existing K-means subspace clustering algorithms often employ eigenvalue decomposition to generate an approximate solution, which makes the model less efficiency. Besides, their loss functions are either sensitive to outliers or small loss errors. In this paper, we propose a fast adaptive K-means (FAKM) type subspace clustering model, where an adaptive loss function is designed to provide a flexible cluster indicator calculation mechanism, thereby suitable for datasets under different distributions. To find the optimal feature subset, FAKM performs clustering and feature selection simultaneously without the eigenvalue decomposition, therefore efficient for real-world applications. We exploit an efficient alternative optimization algorithm to solve the proposed model, together with theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on several benchmark datasets demonstrate the advantages of FAKM compared to state-of-the-art clustering algorithms.
Vision-based detection and visualization of dynamic workspaces

Aligning workers and groups with workspaces in advance to enable peak performance and ensure safety is the essence of workspace planning. There are plans, while there have been few methods for examining these plans. We argue that capturing and visualizing actual dynamic workspaces is fundamental to plan examination. This paper describes an initial effort on integrating the latest computer vision methods to implement automatic detection and visualization of dynamic workspaces of workers on foot. To this end, object detection, multiple object tracking, and action recognition are adopted to collect two types of action data: action classes and action locations. A density-based spatial clustering algorithm is used to reason dynamic workspaces based on the action data. We evaluated each method of the integrated system and found that they have achieved the comparable performance in our envisaged settings with the original methods in their general settings. We also presented two demonstrations of the system. The research results represent an initial step towards developing a new management capability by capturing dynamic workspaces.
Analysis of the GPS Spoofing Vulnerability in the Drone 3DR Solo

At present, the boom in unmanned aerial vehicles (UAV) has been increasing in recent years, placing them in an important way in the commercial market. The use of UAV in the daily tasks of industry, commerce or as entertainment for children and adults becomes more recurrent. Each of the UAVs has a specific task, depending on the technologies that are provided, in addition to their basic functions with which they were manufactured. However, in most cases the security of these UAVs is not usually taken into account since some of them are inexpensive and do not have a robust security system that protects the data they send or receive for their operation, that can cause its communication system or the operating system that controls its basic functions of flight, landing, among others, to be compromised. These computer attacks could cause physical or moral harm to people around the same operator of the UAV because they could steal information related to the locations they have visited, or intercept images or videos taken by the UAV. This paper shows the exploitation of GPS vulnerability in the commercial drone of the company 3D Robotics, this vulnerability can cause a malicious user to have control of their autonomy, and carry out illicit activities, such as overflying in spaces not allowed as an airport and private areas. The exploitation of this vulnerability is important to make known that the UAVs should have a more robust security system and also give importance to the security of GPS since the only one that has security is the military GPS.
A Novel Adaptive EEMD Method for Switchgear Partial Discharge Signal Denoising

The elimination of a variety of noises such as the narrow-band interference in the detection of partial discharge (PD) signals in switchgear is an intractable issue. Furthermore, the self-adaptation in the denoising process is weak. A partial discharge-based novel adaptive ensemble empirical mode decomposition (Novel Adaptive EEMD, NAEEMD) method is proposed in this paper for noise reduction. First, the signal is decomposed using the EEMD, only the first-order natural mode is decomposed until the signal margin reaches the EEMD decomposed termination condition. After removing the first-order mode, noise is added to the residual signal, and the remaining signal components are decomposed in the next stage. At last, the intrinsic mode function (IMF) of the noise reduction reconstruction is adaptively selected. The latter is accomplished by combining the energy density and the average period of the IMF correlation coefficient method. Meanwhile, the proposed method provides a new strategy for pre-processing the PD signal of the switchgear. The outcomes of the proposed NAEEMD de-noising method have been compared with the conventional wavelet denoising algorithm (WDA) and EMD-based threshold denoising for validation. The simulation results showed a good denoising effect and effectiveness of the proposed method compared to the WDA and EMD-based threshold denoising. Furthermore, an experimental simulation utilizing actual switchgear PD signal has been performed to verify the noise reduction effectiveness of the proposed method.
Redundant feature pruning for accelerated inference in deep neural networks

This paper presents an efficient technique to reduce the inference cost of deep and/or wide convolutional neural network models by pruning redundant features (or filters). Previous studies have shown that over-sized deep neural network models tend to produce a lot of redundant features that are either shifted version of one another or are very similar and show little or no variations, thus resulting in filtering redundancy. We propose to prune these redundant features along with their related feature maps according to their relative cosine distances in the feature space, thus leading to smaller networks with reduced post-training inference computational costs and competitive performance. We empirically show on select models (VGG-16, ResNet-56, ResNet-110, and ResNet-34) and dataset (MNIST Handwritten digits, CIFAR-10, and ImageNet) that inference costs (in FLOPS) can be significantly reduced while overall performance is still competitive with the state-of-the-art.
Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders

Current deep supervised learning methods typically require large amounts of labeled data for training. Since there is a significant cost associated with clinical data acquisition and labeling, medical datasets used for training these models are relatively small in size. In this paper, we aim to alleviate this limitation by proposing a variational generative model along with an effective data augmentation approach that utilizes the generative model to synthesize data. In our approach, the model learns the probability distribution of image data conditioned on a latent variable and the corresponding labels. The trained model can then be used to synthesize new images for data augmentation. We demonstrate the effectiveness of the approach on two independent clinical datasets consisting of ultrasound images of the spine and magnetic resonance images of the brain. For the spine dataset, a baseline and a residual model achieve an accuracy of 85% and 92%, respectively, using our method compared to 78% and 83% using a conventional training approach for image classification task. For the brain dataset, a baseline and a U-net network achieve an accuracy of 84% and 88%, respectively, in Dice coefficient in tumor segmentation compared to 80% and 83% for the convention training approach.
Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

The Advanced Spaceborne Thermal Emission and Reflection Radiometer is one of five instruments operating on the National Aeronautics and Space Administration (NASA) Terra platform. Launched in 1999, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been acquiring optical data for 20 years. ASTER is a joint project between Japan’s Ministry of Economy, Trade and Industry; and U.S. National Aeronautics and Space Administration. Numerous reports of geologic mapping and mineral exploration applications of ASTER data attest to the unique capabilities of the instrument. Until 2000, Landsat was the instrument of choice to provide surface composition information. Its scanners had two broadband short wave infrared (SWIR) bands and a single thermal infrared band. A single SWIR band amalgamated all diagnostic absorption features in the 2–2.5 micron wavelength region into a single band, providing no information on mineral composition. Clays, carbonates, and sulfates could only be detected as a single group. The single thermal infrared (TIR) band provided no information on silicate composition (felsic vs. mafic igneous rocks; quartz content of sedimentary rocks). Since 2000, all of these mineralogical distinctions, and more, could be accomplished due to ASTER’s unique, high spatial resolution multispectral bands: six in the SWIR and five in the TIR. The data have sufficient information to provide good results using the simplest techniques, like band ratios, or more sophisticated analyses, like machine learning. A robust archive of images facilitated use of the data for global exploration and mapping.
A novel sparse representation model for pedestrian abnormal trajectory understanding

Pedestrian abnormal trajectory understanding based on video surveillance systems can improve public safety. However, manually identifying pedestrian abnormal trajectories is usually a prohibitive workload. The objective of this study is to propose an automatic method for understanding pedestrian abnormal trajectories. An improved sparse representation model, namely information entropy constrained trajectory representation method (IECTR), is developed for pedestrian trajectory classification. It aims to reduce the entropy for trajectory representation and to obtain superior analyzing results. In the proposed method, the orthogonal matching pursuit (OMP) is embedded in the expectation maximization (EM) method to iteratively obtain the selection probabilities and the sparse coefficients. In addition, the lower-bound sparser condition of Lp-minimization (0 < p < 1) is applied in the proposed method to guarantee salient solutions. In order to validate the performance and effectiveness of the proposed method, classification experiments are conducted using five pedestrian trajectory datasets. The results show that the identification accuracy of the proposed method is superior to the compared methods, including naïve Bayes classifier (NBC), support vector machine (SVM), k-nearest neighbor (kNN), and typical sparse representation-based methods.
Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran

Forecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria.It was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks.
Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm

Conventionally, the transition probabilities in the interacting multiple model (IMM) are often fixed based on the prior information. However, this conservative setting may result in inaccurate state estimations. To solve this problem, a Bayesian-based online correction function is proposed in this paper, which can adaptively adjust the transition probabilities. To deal with the response lag and the short-term peak estimation error problem during the respond to model jump, a model jumping threshold is defined, so that the current information of the models can be fully utilized by the IMM algorithm and the correction function of the transition probabilities can be further improved. Subsequently, an adaptive transition probability-based parallel IMM algorithm is proposed in this paper. Finally, three maneuvering target tracking simulations are conducted to verify the performance of the proposed algorithm, the results show that the proposed algorithm can improve the response speed of the system model jump and the state estimation accuracy. The effectiveness and feasibility of the algorithm are proven.
Comparing Heuristic and Machine Learning Approaches for Metric-Based Code Smell Detection

Code smells represent poor implementation choices performed by developers when enhancing source code. Their negative impact on source code maintainability and comprehensibility has been widely shown in the past and several techniques to automatically detect them have been devised. Most of these techniques are based on heuristics, namely they compute a set of code metrics and combine them by creating detection rules; while they have a reasonable accuracy, a recent trend is represented by the use of machine learning where code metrics are used as predictors of the smelliness of code artefacts. Despite the recent advances in the field, there is still a noticeable lack of knowledge of whether machine learning can actually be more accurate than traditional heuristic-based approaches. To fill this gap, in this paper we propose a large-scale study to empirically compare the performance of heuristic-based and machine-learning-based techniques for metric-based code smell detection. We consider five code smell types and compare machine learning models with DECOR, a state-of-the-art heuristic-based approach. Key findings emphasize the need of further research aimed at improving the effectiveness of both machine learning and heuristic approaches for code smell detection: while DECOR generally achieves better performance than a machine learning baseline, its precision is still too low to make it usable in practice.
Adaptive sliding window based activity recognition for assisted livings

The incessant diffusion of smart wearable devices and Body Area Networks (BANs) systems is pushing more and more researches on human activity recognition. Previous studies usually adopted fixed time window or chose an optimal window size depending on the characteristics of the activity. In this paper, we propose an adaptive time window-based algorithm of activity recognition, which addresses the problem of different types of activities having different time duration that usually causes poor recognition results. Multivariate Gaussian Distribution (MGD) method was used to detect the signal difference between each determined time window and the defined activity characteristics, and we more accurately define the time window size for each activity. To evaluate the proposed algorithm, we focused on the activities performed by wheelchair users in their daily life, so as to provide them with better healthcare services. We construct two different datasets, respectively considering static and dynamic wheelchair user activities on different ground surfaces. Then, we use time window expansion and contraction method to determine and dynamically adjust the window size for each activity. Different comparison criteria such as recognition precision and F-score were used to evaluate our algorithm. Experiment results revealed that, according to F-score, the proposed algorithm performs 15.3% better than traditional methods in static conditions. In dynamic scenarios we observed 6.4% and 24.5% improvements on flat and rough floors, respectively.