Reviewerly/exHarmony huggingface dataset:
A New Hybrid Algorithm for Solving Large Scale Global Optimization Problems

For large scale global optimization (LSGO) problems, many algorithms have been proposed in recent years. However, there are still some issues to be further handled since the search space grows exponentially and the problem solving becomes more and more difficult as the problem scale becomes larger and larger. In this paper, we propose a new hybrid algorithm for solving large-scale global optimization problems. First, we adopt an existing group algorithm to divide the large-scale problem into several small-scale problems. Second, a modified self-adaptive discrete scan method is designed to roughly scan the whole search space and then focus the search on the promising regions. Third, a hybrid search strategy is proposed, which adaptively chooses the one-dimensional search scheme or the covariance matrix adaptation evolutionary strategy to solve the subproblems of separable, partially (additively) separable problems or non-separable problems, respectively. To demonstrate the performance of the proposed algorithm, we conduct the experiments on 15 difficult LSGO problems in CEC'2013 benchmark suite and compare the performance of the proposed algorithm with that of the several state-of-the-art algorithms. The results show that the proposed algorithm is more effective than the compared algorithms in terms of solution accuracy.
Permissioned Blockchains and Virtual Nodes for Reinforcing Trust Between Aggregators and Prosumers in Energy Demand Response Scenarios

The advancement and penetration of distributed energy resources (DERs) and renewable energy sources (RES) are transforming legacy energy systems in an attempt to reduce carbon emissions and energy waste. Demand Response (DR) has been identified as a key enabler of integrating these, and other, Smart Grid technologies, while, simultaneously, ensuring grid stability and secure energy supply. The massive deployment of smart meters, IoT devices and DERs dictate the need to move to decentralized, or even localized, DR schemes in the face of the increased scale and complexity of monitoring and coordinating the actors and devices in modern smart grids. Furthermore, there is an inherent need to guarantee interoperability, due to the vast number of, e.g., hardware and software stakeholders, and, more importantly, promote trust and incentivize the participation of customers in DR schemes, if they are to be successfully deployed.In this work, we illustrate the design of an energy system that addresses all of the roadblocks that hinder the large scale deployment of DR services. Our DR framework incorporates modern Smart Grid technologies, such as fog-enabled and IoT devices, DERs and RES to, among others, automate asset handling and various time-consuming workflows. To guarantee interoperability, our system employs OpenADR, which standardizes the communication of DR signals among energy stakeholders. Our approach acknowledges the need for decentralization and employs blockchains and smart contracts to deliver a secure, privacy-preserving, tamper-resistant, auditable and reliable DR framework. Blockchains provide the infrastructure to design innovative DR schemes and incentivize active consumer participation as their aforementioned properties promote transparency and trust. In addition, we harness the power of smart contracts which allows us to design and implement fully automated contractual agreements both among involved stakeholders, as well as on a machine-to-machine basis. Smart contracts are digital agents that "live" in the blockchain and can encode, execute and enforce arbitrary agreements. To illustrate the potential and effectiveness of our smart contract-based DR framework, we present a case study that describes the exchange of DR signals and the autonomous instantiation of smart contracts among involved participants to mediate and monitor transactions, enforce contractual clauses, regulate energy supply and handle payments/penalties.
Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing

With changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented.
Kernel-Correlated Filtering Target Tracking Algorithm Based on Multi-Features Fusion

When the color of the target is similar to the color of the background or illumination of a scene, scale, and shape of the target change significantly, the traditional kernel-correlated filtering (KCF) algorithm often fails to track the target.Therefore, a multi-features fusion target tracking algorithm based on KCF is proposed.The algorithm makes the most of the illumination and rotation invariance of local binary patterns (LBP) feature, the effective capability of extracting color and contour information from the target by color Name (CN) feature and histogram of oriented gradient (HOG) feature, respectively.The multifeatures linear weighted fusion rule is established by calculating the HOG, LBP, and CN histograms of the current frame, and calculating the weighted fusion coefficients of the normalized histograms according to the variance of each histogram adaptively.On this basis, multi-scale sampling is used in the target areas and bilinear interpolation is used to adjust the sample size, which can improve the tracking robustness of the KCF algorithm to adapt to the target scale.The comparison experimental results of 50 video data sets in OTB-2015 show that the average success rate and precision of the proposed algorithm are 24.74% and 8% higher than that of the KCF tracking algorithm, respectively, and also significantly better than the other tracking algorithms ranked top ten in OTB-2015, UAV123, and LaSOT data sets, respectively.
6-DOF Robotic Obstacle Avoidance Path Planning Based on Artificial Potential Field Method

In this paper, a whole-arm path planning algorithm for 6-DOF industrial robot based on artificial potential field method is proposed. Multi-station conversion system used for welding robots. First, the environment is monitored by sensors and simplify obstacles and robotic arms. Then transform the Cartesian space obstacle into the C space of the manipulator through the geometric relationship, thus forming the free motion space of the robot arm, which provides conditions for the collision-free path planning of the manipulator. Finally, according to the obstacle repulsive vector summation and target point attraction vector, realize the intuitive rejection reaction, actively avoid the collision of the robot arm, and finally reach the target. We have successfully demonstrated our system on the MA1440 industrial robot.
A novel generalized logistic dependent model to predict the presence of breast cancer based on biomarkers

Summary Breast cancer is the second most common cancer in women in the United States. With the revolution of the machine learning era, many researchers currently aim to find pathways and develop tools that may help to detect breast cancer early on in its development. We propose a novel generalized logistic dependent model with considerations of the dependence among selected biomarkers for breast cancer detection based on a set of nine biomarker predictors such as age, glucose, BMI, resistin, HOMA, MCP‐1, leptin, insulin, and adiponectin. Our research findings demonstrate that the proposed model has the potential to predict breast cancer in women just based on five biomarkers, ie, glucose, age, BMI, resistin, and MCP‐1. We also compare our model results to several other machine‐learning modeling approaches including SVM, logistic regression, random forest, and multiple regression analyses using various training data sets (60%, 70%, 80% of all data) and all the dataset. It shows that the inclusion of the dependence among those five predictors in the proposed model is worth the extra model complexity and effort for achieving a significant accuracy prediction level of breast cancer detection in women. Further work in broader validation of the conclusion of our study and exploring the ability for artificial intelligence (AI) to be able to bolster these predictions based on biomarkers are also discussed.
Modeling and simulation of teaching quality in colleges based on BP neural network and training function

In order to realize the intelligent evaluation of effective teaching quality and make up for the lack of research in this aspect, in the research, BP neural network is used as the basis for model construction analysis. Political education in colleges and universities is an important course, and it s teaching quality evaluation is particularly important. Through comparative analysis, LMBP is selected as the learning algorithm, and the neural network evaluation model mechanism of college classroom teaching quality evaluation system is determined through theory and practical methods, and the simulation model is simulated by MATLAB as a simulation tool. At the same time, this paper uses the experimental method to carry out simulation training experiments in the MATLAB neural network toolbox, select the training algorithm for comparative analysis, and display the results in the form of statistical graphs. In addition, this paper sets the convergence speed and error curve as evaluation indicators, determines the appropriate training algorithm, and verifies the validity of the model. The research indicates that the BP teaching quality evaluation model based on BP neural network is a reasonable and feasible evaluation model and can provide theoretical reference for subsequent related research.
Deep Learning Face Hallucination via Attributes Transfer and Enhancement

Face hallucination technique aims to generate high-resolution (HR) face images from low-resolution (LR) inputs. Even though existing face hallucination methods have achieved great performance on the global region evaluation, most of them cannot reasonably restore local attributes, especially when ultra-resolving tiny LR face image (16 × 16 pixels) to its larger version (8× upscaling factor). In this paper, we propose a novel attribute-guided face transfer and enhancement network for face hallucination. Specifically, we first construct a face transfer network, which upsamples LR face images to HR feature maps, and then fuses facial attributes and the upsampled features to generate HR face images with rational attributes. Finally, a face enhancement network is developed based on generative adversarial network (GAN) to improve visual quality by exploiting a composite loss that combines image color, texture and content. Extensive experiments demonstrate that our method achieves superior face hallucination results and outperforms the state-of-the-art.
An Improved Artificial Potential Field Method Based on Chaos Theory for UAV Route Planning

This paper proposes a modified artificial potential field method based on chaos theory. In this algorithm, the search algorithm of chaos theory is introduced into the potential field function of artificial potential field method, which changes the repulsion coefficients of obstacles and the gravitational coefficients of target points. This method resolves the defects of the traditional artificial potential field method, such as the local optimum problem, the inability to find the path between the close obstacles, the oscillation in front of the obstacles, and the oscillation in the narrow channel. Simulation experiments show that this algorithm can not only effectively solve the problems of the unmanned aerial vehicle (UAV) in the route planning, such as easily falling into the minimum and wandering around the end point, but also realize the route planning in complex situations, reduce the flight cost, and improve the speed and accuracy of the UAV route planning.
BitVis: An Interactive Visualization System for Bitcoin Accounts Analysis

As an emerging payment method, bitcoin is receiving growing popularity for the different characteristics it shares with conventional fiat currencies. But the pseudonymous nature of bitcoin brings difficulties for regulators to effectively monitor bitcoin-related financial crimes. In this paper, we present an interactive system to visualize the relationship between bitcoin accounts, namely BitVis. With BitVis, users can easily filter transactions on demand, interact with the transaction networks to look for useful information, and analyze behavior of bitcoin accounts. Via BitVis, financial regulators can conveniently track suspicious accounts, while personal investors can easily investigate the activities of an interested account.
Governance and youth unemployment in Africa

The objective of this study is to identify governance indicators that can reduce youth unemployment in Africa. Thereby, the authors incorporated governance indicators into the growth model. The dynamic panel estimation of the model, based on data from the World Bank, the IMF and UNCTAD for the period 2002–2016 found that some governance indicators such as control of corruption and political stability have a negative influence on youth unemployment in Africa. Moreover, political stability does not reduce youth unemployment in highly corrupt countries which are rich in natural resources. Similarly, other governance indicators such as government effectiveness, regulatory quality, the rule of law, voice and accountability are not yet producing the expected effects in African countries. As a result, political authorities must improve governance indicators, including the control of corruption and political stability.
A Comparison of Bugs Across the iOS and Android Platforms of Two Open Source Cross Platform Browser Apps

Mobile app developers want to maximize their revenue and hence want to reach as large an audience as possible. In order to do this, they need to build apps for multiple platforms - like Google's Android and Apple's iOS, and maintain them in parallel. Past research has examined properties of the issues addressed in either Android or iOS, but not to compare the work between both. Our main motivation has been to determine if there were differences in how issues manifest themselves in iOS and Android, when we control for the projects, by considering the same apps across multiple platforms. In this paper, we compare issues across two mobile platforms - iOS and Android - for two open source browsers - Mozilla Firefox and Google Chromium. We consider three dimensions of study: frequency of issue report submission, fixing time of issues, and type of issues (using topic modeling on the issue description to generate the categories). We found that there were indeed differences; in particular, we found that there were more issues in the Android version of the apps and the gap with the iOS version is increasing. We observe that in both apps the fix time and type of issues are different for each platform. We also noted certain kinds of issues that may be more prevalent for different browser/platform combinations. This can advise project leads in identifying and allocating development resources to address key problem areas. Hence, issue reports seem more dependent on the platform than on the mobile app, making development and maintenance effort hard to estimate.
How to Assess the Quality of Compressed Surveillance Videos Using Face Recognition

Video surveillance plays an important role in public security. To store the growing volume of surveillance videos, video compression is beneficial for reducing video volume; however, it is simultaneously harmful to the video quality. Video quality assessment (VQA) methods help to achieve a tradeoff between the data volume and perceptual quality of compressed surveillance videos. Generally speaking, surveillance video quality assessment (SVQA) is different from conventional VQA, because surveillance videos are usually used for specific tasks, e.g., pedestrian recognition, rather than for entertainment purposes. Therefore, in this paper, we propose two full-reference SVQA methods based on the concept of quality of recognition. We first design two new tasks, distorted face verification (DFV) and distorted face identification (DFI), based on which we further propose two SVQA methods, DFV-SVQA and DFI-SVQA, and corresponding quality metrics. The core components of the DFV-SVQA and DFI-SVQA methods are feature extractors (a DFV model and a DFI model), which we construct using convolutional-neural-network-based face recognition models. In addition, we construct a real-world surveillance video data set, based on which we analyze how various factors, including the video codec, compression level, face resolution, and light intensity, affect the quality of compressed surveillance videos. We find that, compared with conventional VQA methods, our methods are more effective in measuring the quality of surveillance videos while maintaining an acceptable time efficiency.
Optimal Policies in Complex Large-scale UAS Traffic Management

There is currently a worldwide effort to develop UAS Traffic Management (UTM) systems that help ensure safe and reliable operation of Unmanned Autonomous Systems (UAS) in urban environments. A large number of factors must be considered in planning such flights, including GIS (roads, topography, etc.), weather (temperature, wind, precipitation), localization and navigation (GPS, V2X communication), infrastructure obstacles (buildings, towers, etc.), excluded zones of operation, etc. We have developed a cloud-based geospatial intelligence system, BRECCIA, which brokers such information among a set of intelligent agents. In this work, an extended version of BRECCIA is proposed as a universal-UTM (U-UTM) which allows the specification of urban airways constrained to be above roadways. In addition, we develop a reinforcement learning approach for the determination of optimal flight policies through such airways, where these policies can take into account a variety of factors (wind, precipitation, communication, etc.) which impact UAV path following capabilities. A novel context-based probabilistic state transition function is introduced. Simulation experiments are performed to demonstrate the performance of the approach.
Blind Signal Classification for Non-Orthogonal Multiple Access in Vehicular Networks

In this paper, blind signal classification and detection in a non-orthogonal multiple access (NOMA) system are explored. Since a NOMA scheme superposes the multiple-user signals within nonorthogonal resources, classical modulation classification methods used in orthogonal multiple access (OMA) systems are not sufficient to process the superposed NOMA signal. NOMA receivers require information about the multiple access schemes such as modulation order and need interference cancellation of the co-scheduled user's signal; therefore, a NOMA system causes more high-layer signaling overheads than OMA during packet scheduling. Blind detection algorithms used for multiplexing information are considered to be possible solutions; however, they pose various challenges and could cause performance loss while performing blind modulation classification in the order of OMA/NOMA classification, co-scheduled user's modulation classification, and classification of the signal, due to the necessity for successive interference cancellation. To improve the performance of blind detection, we propose a NOMA transmission scheme that applies phase rotation to data or pilot symbols depending on the NOMA multiplexing format, as an aid to the blind detection. The proposed classification algorithm can implicitly provide essential information on NOMA multiplexing without the need for any extra high layer signaling or resources. The performance improvement is verified through simulation studies, and it is found that the proposed algorithm provides a gain of more than 1 dB compared to the existing blind signal classification methods and shows almost equivalent performance as the genie information scheme.
Costing Secure Software Development

Building more secure software is a recent concern for software engineers due to increasing incidences of data breaches and other types of cyber attacks. However, software security, through the introduction of specialized practices in the software development life cycle, leads to an increase in the development cost. Although there are many studies on software cost models, few address the additional costs required to build secure software. We conducted a systematic review in the form of a mapping study to classify and analyze the literature related to the impact of security in software development costs. Our search strategy strove to achieve high completeness by the identification of a quasi-gold-standard set of papers, which we then used to establish a search string and retrieve papers from research databases automatically. The application of inclusion/exclusion criteria resulted in a final set of 54 papers, which were categorized according to the approach to software security cost analysis. Perform Security Review, Apply Threat Modeling, and Perform Security Testing were the three most frequent activities related to cost, and Common Criteria was the most applied standard. We also identified ten approaches to estimating software security costs for development projects; however, their validation remains a challenge, which could be addressed in future studies.
Receding horizon estimation and control with structured noise blocking for mobile robot slip compensation

The control of field robots in varying and uncertain terrain conditions presents a challenge for autonomous navigation. Online estimation of the wheel-terrain slip characteristics is essential for generating the accurate control predictions necessary for tracking trajectories in off-road environments. Receding horizon estimation (RHE) provides a powerful framework for constrained estimation, and when combined with receding horizon control (RHC), yields an adaptive optimisation-based control method. Presently, such methods assume slip to be constant over the estimation horizon, while our proposed structured blocking approach relaxes this assumption, resulting in improved state and parameter estimation. We demonstrate and compare the performance of this method in simulation, and propose an overlapping-block strategy to ameliorate some of the limitations encountered in applying noise-blocking in a receding horizon estimation and control (RHEC) context.