Reviewerly/exHarmony huggingface dataset:
Cooperative Multi-task Assignment of Multiple UAVs with Improved Genetic Algorithm Based on Beetle Antennae Search

With the number of UAVs and targets increasing, the quantity of task combinations increases exponentially. This article presents an improved genetic algorithm based on the beetle antennae search algorithm to Multi-UAV task allocation. Firstly, a target sequence is searched through beetle antennae search algorithm, and then the method of twice crossing operators is used to increase the diversity of the target sequence arrangements and retain the global optimality of the population. Finally, the method of dynamically adjusting the mutation probability is used to increase the local search ability of the algorithm to avoid locally optimal. In the simulation part, the effect of the proposed algorithm, both on searching capability and convergence speed, is demonstrated by comparison with other algorithms. The results show that the proposed algorithm outperforms other algorithms in solving the problem of Multi-UAV task allocation.
Application of a Novel Political Optimization in Optimal Parameter Design of PI Controller for the BLDC motor Speed Control

BLDC motors are getting popularity in recent times in various applications which necessitates an efficient speed control techniques. This paper proposes a speed control techniques for the BLDC motor by designing optimal parameters of PI controller using a novel political optimization (PO). The traditional methods of parameter design of the PI controller are complicated, and also these may result in large overshoot. Even though PSO based methods are widely used in literature to optimize the PI control parameters of the speed control of BLDC motor, these methods require the proper setting of more number of algorithm control parameters to get better performance. Hence, in this paper, a novel political optimization based optimal parameter design of the PI controller for the speed control of BLDC motor is proposed. The optimum values of PI control parameters are required for achieving the desired speed of the motor for any dynamic condition. A MATLAB/Simulink model of BLDC motor is used for testing the performance of the proposed algorithm.
Automated Analysis of the Origin of Movement: An Approach Based on Cooperative Games on Graphs

In this work, a computational method is proposed to automatically investigate the perception of the origin of full-body human movement and its propagation. The method is based on a mathematical game built over a suitably defined graph structure representing the human body. The players of this game are the graph vertices, which form a subset of body joints. Since each vertex contributes to a shared goal (i.e., to the way in which a specific movement-related feature is transferred among the joints), a cooperative game-theoretical model (specifically a transferable-utility game) is adopted, which is able (via the Shapley value) to measure the relevance of the various joints in human movement when performing full-body movement analysis. The method is theoretically investigated and applied to a motion capture dataset obtained from subjects who performed expressive movements. Finally, the method is validated through an online survey, in which several dancers/nondancers participated. The results show the capability of the proposed approach to represent the evolution of the most important joint responsible for originating each dancer's movement.
Clustering Characteristics of UCI Dataset

The UCI (University of California Irvine) machine learning repository currently maintain 488 datasets of various characteristics as a service to the machine learning community. Since the past decades, owing to available cluster labels and data attributes, the UCI datasets have been playing an important role in clustering analysis field. Nevertheless, as the common benchmark to evaluate clustering results, high dimensionality and complexity in UCI datasets lead to that main clustering characteristics fail to be recovered, such as shape, size, density, overlap or separation, subspace or component, noise, key point distributions, and so on. Consequently, the evaluation effect of clustering results from UCI is incomplete, uncertain and inconsistent, and the natural characteristics of the tested datasets cannot be effectively found. In this paper, we apply three most used clustering algorithms to recover the clustering characteristics of UCI datasets, including CM(C-Means), DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and DPC (Density Peak clustering). To deal with high-dimensionality and complexity problem in UCI, MDS (Multidimensional Scaling) is used to map high dimensional data to two dimensional Cartesian coordinates under nearly keeping the distance of any pair of points unchangeable. Experimental results show the clustering characteristics of UCI datasets, and this is very helpful to assess clustering results in real applications.
Scalable Computation of Predictive Probabilities in Probit Models with Gaussian Process Priors

Predictive models for binary data are fundamental in various fields, and the growing complexity of modern applications has motivated several flexible specifications for modeling the relationship between the observed predictors and the binary responses.A widely-implemented solution is to express the probability parameter via a probit mapping of a Gaussian process indexed by predictors.However, unlike for continuous settings, there is a lack of closed-form results for predictive distributions in binary models with Gaussian process priors.Markov chain Monte Carlo methods and approximation strategies provide common solutions to this problem, but state-of-the-art algorithms are either computationally intractable or inaccurate in moderate-to-high dimensions.In this article, we aim to cover this gap by deriving closed-form expressions for the predictive probabilities in probit Gaussian processes that rely either on cumulative distribution functions of multivariate Gaussians or on functionals of multivariate truncated normals.To evaluate these quantities we develop novel scalable solutions based on tile-low-rank Monte Carlo methods for computing multivariate Gaussian probabilities, and on mean-field variational approximations of multivariate truncated normals.Closed-form expressions for the marginal likelihood and for the posterior distribution of the Gaussian process are also discussed.As shown in simulated and real-world empirical studies, the proposed methods scale to dimensions where state-of-the-art solutions are impractical.
Self-organized hybrid fuzzy neural networks driven with the aid of probability-based node selection and enhanced input strategy

Polynomial neural network (PNN) is a flexible self-organizing network model, and there have been lots of improvements to it. However, various and improved models of PNN do not properly balance their performance and efficiency. In this study, we present novel self-organized hybrid fuzzy neural networks (SHFNN). The purpose of this study is to develop a new design methodology of constructing a hybrid fuzzy model through enhanced input strategy (EIS) and probability-based node selection (PNS) to reinforce the performance of the model without sacrificing efficiency. l2-norm regularization (l2) is utilized to mitigate overfitting as well as enhance generalization capability. The key points of SHFNN can be summarized as follows. First, a hybrid network structure is constructed by combining fuzzy radial basis function neural networks (FRBFNN) and PNN. Second, we propose a probability-based node selection strategy for node (neuron) selection. Third, an enhanced input strategy (EIS) is proposed to design the enhanced input set by merging the general candidate input set (GCIS) and the original candidate input set (OCIS). The regularization coefficient estimation method based on l2 is used in conjunction with EIS and PNS to improve the performance and enhance the stability of the model. The effectiveness of SHFNN is validated using synthetic and machine learning dataset. The experimental results show that the prediction accuracy of the proposed model is improved by 4–22% when compared with some methods reported in the literature. Statistical analysis is also considered to demonstrate the superiority of the proposed model.
Secure Data De-Duplication Based on Threshold Blind Signature and Bloom Filter in Internet of Things

Within the cloud environment, the availability of storage, as well as bandwidth, can be effectively preserved in virtue of data de-duplication.However, refraining redundancy from additional storage or communication is not trivial due to security concerns.Though intensive researches have been addressed on a convergent cryptosystem for secure data de-duplication, the conflicts amongst functionality, confidentiality, and authority remain unbalanced.More concretely, although data are obfuscated under convergent encryption, a violent dictionary attack is still efficacious since the whole pseudorandom process relies heavily on plaintexts.As for data ownership, the download privilege, which depends on hash value, may also be infringed due to the same reason.To dispose of these problems, we presented a conspiracy-free data de-duplication protocol based on a threshold blind signature in this article.With the help of multiple key servers, the outsourced file and de-duplication label will be computationally indistinguishable from random strings.We used the Boom filter as a tool to implement a proof of ownership, ensuring that the ownership claims made by users are real.It effectively prevents the attacker from using the stolen tag to get the whole file to gain file access without authorization.The most significant innovation of this article is to use homomorphism computation to aggregate and generate partial signature tags, and to introduce a secret sharing mechanism based on The Chinese Remainder Theorem to hide signature keys, thus balancing the security concerns of cloud and client.Compared with existing schemes, both communication and computation performances are preferable in our protocol.As far as we know, our scheme is the only data de-duplication scheme that satisfies the semantic security of ciphertext and label.
ProxiTrak

In the wake of recent COVID-19 pandemic, contact tracing has turned out to be an indispensable technique to help administrative authorities contain localized infections efficiently. In the absence of a definitive and an official vaccine for the infection, practicing social distancing has proved to be an effective norm to prevent the risk of infection. In this paper, we present 'ProxiTrak', a smartphone based solution for an enterprise scenario capable of not only tracing the chain of possible infection transmission among a set of population, but also guiding the users towards following social distancing norms by alerting them in real-time about any possible violation of proximity norms on their smartphones. We devise an effective classification model to make proximity decisions on the smartphone itself using Received Signal Strength Indicator (RSSI) data of on-board Bluetooth Low Energy (BLE) module using multiple mobile devices in different environments, with novel addition of using temporal features from BLE data to boost the model's accuracy. We briefly discuss ProxiTrak's corresponding server-side framework for tracing a possible chain of infection and analysing social connectivities graphically. We also propose on-device decision aggregation and server-side pruning of proximity events to lower the false positive events. Our model is capable of making strong proximity decisions to an accuracy of up to 94% on the devices trained with the model.
Color corrected single scale retinex based haze removal and color correction for underwater images

Abstract Underwater images are affected by scattering of dust particles leading to hazy images. The difference in absorption of light at different wavelength leads to colorcast. Restoring underwater images from haze and colorcast degradation is a challenging task. This article proposes color corrected single scale retinex algorithm for the restoration of hazy and colorcast underwater images. Further, the illumination of the underwater image is enhanced through illumination enhancer. In order to correct colorcast, the underwater images intensity shifters are proposed. Blur, natural image quality evaluator, blind/referenceless image spatial quality evaluator, entropy, peak signal to noise ratio are the metrics used to evaluate the performance of the proposed restoration technique and compared the output with the existing techniques. Compared to the existing techniques the proposed algorithm prominently removes haze and colorcast present in underwater images. The study excludes retinex‐with‐reset models from consideration.
Secure Energy Trading for Electric Vehicles using Consortium Blockchain and k-Nearest Neighbor

In this paper, we deal with some major energy issues related to the charging of vehicles in vehicular network. The exponential increase of Electric Vehicles (EVs) has led to the more complex problems. In general, there are two major issues related to th EVs. First, its difficult to find a nearest charging station with required energy. Second, how much energy is needed to reach charging station from current location. In traditional systems, the energy trading between charging station and EVs is not secured due to centralized girds. To deal with this problem, a consortium blockchain based secure energy trading system is proposed. Blockchain is used for secure energy trading with moderate cost. The main purpose of the proposed system is resource reduction and find out the present state of charging stations. Simulations and results show that the proposed schemes outperform the conventional schemes in terms of minimizing the charging cost of battery and expenses of EVs.
Exploring alternatives with Unreal Engine’s Blueprints Visual Scripting System

We present BPAlt – a system which allows game developers to create and manage alternatives for Unreal Engine's Blueprints Visual Scripting System. BPAlt allows the user to create, save, organize and swap Blueprint alternatives for rapid testing and experimentation. BPAlt also supports level alternatives, alternatives of alternatives through alternative scenarios, and allows merging of alternatives. We conducted a preliminary user study with 10 moderately skilled participants where we compared the usability, usefulness and creativity support of BPAlt to Unreal Engine alone for prototyping alternatives of game objects and mechanics in four different games. The findings suggest that the support for alternatives with the main features of BPAlt is beneficial in the game developers' workflow as it supports exploration particularly well.
VTD-Net: Depth Face Forgery Oriented Video Tampering Detection based on Convolutional Neural Network

Face is the basis of identity authentication in many software, and the rise of generative adversary network makes the forgery of face easier than ever, which brings great challenges to information security. We propose a novel deep convolution neural network called VTD-Net to recognize faces generated by adversarial learning. The network is full-pipeline which com-posed of face location, interception, scaling and detection. In the experiment, we use the latest challenging face forgery dataset Celeb-DF, evaluated the forgery detection performance at frame level and video level, and achieved state-of-the-art results.
Identification of Cybersecurity Specific Content Using Different Language Models

Given the sheer amount of digital texts publicly available on the Internet, it becomes more challenging for security analysts to identify cyber threat related content. In this research, we proposed to build an autonomous system to identify cyber threat information from publicly available information sources. We examined different language models to utilize as a cybersecurity-specific filter for the proposed system. Using the domain-specific training data, we trained Doc2Vec and BERT models and compared their performance. According to our evaluation, the BERT-based Natural Language Filter is able to identify and classify cybersecurity-specific natural language text with 90% accuracy.
Gray Literature Versus Academic Literature in Software Engineering: A Call for Epistemological Analysis

Many software engineering (SE) practitioners share technical materials online, without formal peer review. Contrarily, SE researchers write technical articles that are peer reviewed and published as academic literature. We discuss how combining both can lead to integrated knowledge exchange and dissemination in SE.
Mining Attribute Evolution Rules in Dynamic Attributed Graphs

A dynamic attributed graph is a graph that changes over time and where each vertex is described using multiple continuous attributes. Such graphs are found in numerous domains, e.g., social network analysis. Several studies have been done on discovering patterns in dynamic attributed graphs to reveal how attribute(s) change over time. However, many algorithms restrict all attribute values in a pattern to follow the same trend (e.g. increase) and the set of vertices in a pattern to be fixed, while others consider that a single vertex may influence its neighbors. As a result, these algorithms are unable to find complex patterns that show the influence of multiple vertices on many other vertices in terms of several attributes and different trends. This paper addresses this issue by proposing to discover a novel type of patterns called attribute evolution rules (AER). These rules indicate how changes of attribute values of multiple vertices may influence those of others with a high confidence. An efficient algorithm named AER-Miner is proposed to find these rules. Experiments on real data show AER-Miner is efficient and that AERs can provide interesting insights about dynamic attributed graphs.
Side-Channel Attacks With Multi-Thread Mixed Leakage

Side-channel attacks are one of the greatest practical threats to security-related applications, because they are capable of breaking ciphers that are assumed to be mathematically secure. Lots of studies have been devoted to power or electro-magnetic (EM) analysis against desktop CPUs, mobile CPUs (including ARM, MSP, AVR, etc) and FPGAs, but rarely targeted modern GPUs. Modern GPUs feature their special and specific single instruction multiple threads (SIMT) execution fashion, which makes their power/EM leakage more sophisticated in practical scenarios. In this article, we study side-channel attacks with leakage from SIMT systems, and propose leakage models suited to any SIMT systems and specifically to CUDA-enabled GPUs. Afterwards, we instantiate the models with a GPU AES implementation, which is also used for performance evaluations. In addition to the models, we provide optimizations on the attacks that are based on the models. To evaluate the models and optimizations, we run the GPU AES implementation on a CUDA-enabled GPU and, at the same time, collect its EM leakage. The experimental results show that the proposed models are more efficient and the optimizations are effective as well. Our study suggests that GPU-based cryptographic implementations may be much vulnerable to microarchitecture-based side-channel attacks. Therefore, GPU-specific countermeasures should be considered for GPU-based cryptographic implementations in practical applications.
DK-CNNs: Dynamic kernel convolutional neural networks

This paper introduces dynamic kernel convolutional neural networks (DK-CNNs), an enhanced type of CNN, by performing line-by-line scanning regular convolution to generate a latent dimension of kernel weights. The proposed DK-CNN applies regular convolution to the DK weights, which rely on a latent variable, and discretizes the space of the latent variable to extend a new dimension; this process is named "DK convolution". DK convolution increases the expressive capacity of the convolution operation without increasing the number of parameters by searching for useful patterns within the new extended dimension. In contrast to conventional convolution, which applies a fixed kernel to analyse the changed features, DK convolution employs a DK to analyse fixed features. In addition, DK convolution can replace a standard convolution layer in any CNN network structure. The proposed DK-CNNs were compared with different network structures with and without a latent dimension on the CIFAR and FashionMNIST datasets. The experimental results show that DK-CNNs can achieve better performance than regular CNNs.
Approximation-Aware Design of an Image-Based Control System

Image-based control (IBC) systems are common in many modern applications.In such systems, image-based sensing imposes massive compute workload, making them challenging to implement on embedded platforms.Approximate image processing is a way to handle this challenge.In essence, approximation reduces the workload at the cost of additional sensor noise.In this work, we propose an approximation-aware design approach for optimizing the energy, memory and performance of an IBC system, making it suitable for embedded implementation.First, we perform compute-and data-centric approximations and evaluate its impact on the energy efficiency, memory utilization and closed-loop qualityof-control (QoC) of the IBC system.We observe that the workload reductions due to approximations allow mapping these lighter approximated IBC tasks to embedded platforms with lower power consumption while still ensuring proper system functionality.Therefore, we explore the interplay between approximations and platform mappings to improve the energy-efficiency of IBC systems.Further, an IBC system operates under several environmental scenarios e.g., weather conditions.We evaluate the sensitivity of the IBC system to our approximation-aware design approach when operated under different scenarios and perform a failure probability (FP) analysis using Monte-Carlo simulations to analyze the robustness of the approximate system.Finally, we design an optimal approximation-aware controller that models the approximation error as sensor noise and show QoC improvements.We demonstrate the effectiveness of our approach using a concrete case-study of a lane keeping assist system (LKAS) using a heterogeneous NVIDIA AGX Xavier embedded platform in a hardware-in-the-loop (HiL) framework.We show energy and memory reduction of up to 92% and 88% respectively, for 44% QoC improvements with respect to the accurate implementation.We show that our approximation-aware design approach has an FP (per km) ≤ 9.6 × 10 -6 %.
SecDedup: Secure Encrypted Data Deduplication With Dynamic Ownership Updating

Deduplication eliminates duplicated data copies and reduces storage costs of cloud service providers. However, deduplication of encrypted data is difficult. Current solutions rely heavily on trusted third parties, and do not address the popularity of data, resulting in unsatisfying security and efficiency. A secure encrypted data deduplication scheme based on data popularity is proposed. Check tags are calculated via bilinear mapping to determine whether different encrypted data originate from the same plaintext. Ciphertext policy attribute-based encryption is applied to protect the tags. A secure key delivery scheme is designed to pass the data encryption key from an initial data uploader to subsequent uploaders via the cloud server in an offline manner. The cloud server can perform deduplication without the assistance of any online third party. Security analysis and simulation experiments are provided, proving the practicability and efficiency of the proposed scheme.