Reviewerly/exHarmony huggingface dataset:
Improvement and Application of Chicken Swarm Optimization for Constrained Optimization

Aiming at the problem of slow convergence speed and ease of falling into local optimum when solving high dimensional problems, this paper proposes an improved chicken swarm optimization algorithm.The improved chicken swarm optimization includes four aspects, namely, cock position update mode, hen position update mode, chick position update mode, and population update strategy, so it is abbreviated as ICSO-RHC.On the basis of algorithm improvement, the influence of the number of retained elite individuals and control parameters on the convergence speed of the algorithm is discussed.The calculation results of the test function show that when the number of elite individuals in the population is 1, and the control parameters is a random number uniformly distributed between [0, 1], the algorithm has a faster convergence speed.In addition, in order to verify the performance of ICSO-RHC, 30 test functions and CEC 2005 benchmark functions were selected.The calculation results of these test functions show that the success rate of ICSO-RHC is significantly higher than other algorithms, both for low-dimensional and highdimensional optimization problems.The average iteration number and average running time are significantly lower than other algorithms.Finally, ICSO-RHC and other improved algorithms in the literature are used to optimize the parameters of four practical engineering problems.The optimization results show that the statistical results obtained by ICSO-RHC are significantly better than other algorithms.The calculation results of the test functions and the actual engineering problems show that the performance of ICSO-RHC proposed in this paper is significantly better than other algorithms.
Optimal rough fuzzy clustering for user profile ontology based web page recommendation analysis

Personalized information recommendation in view of social labeling is a hot issue in the scholarly community and this web page data collected from the Internet of Things (IoT). To accomplish personalized web pages, the current investigation proposes a recommendation framework with two methodologie s on user access behavior using Rough-Fuzzy Clustering (RFC) technique. In this paper, Fuzzy-based Web Page Recommendation (WPR) framework is provided with the user profile and ontology design. At first, the weblog documents were gathered from IoT to clean the data and undergo learning process. In the profile ontology module, the learner profile was spared as the ontology with an obvious structure and data. For identification of the similar data, innovative similarity measure was considered and for effective WPR process, the generated rules in RFC were optimized with the help of Chicken Swarm Optimization (CSO) technique. Finally, these optimal rules-based output recommends e-commence shopping websites with better performances. A group of randomly-selected users was isolated and on the basis of the obtained data, their clustering was performed by cluster analysis. Based on the current proposed model, the results were analyzed with performance measures and a number of top recommended pages were provided to users compared to existing clustering tech-niques.
Metacognitive Octonion-Valued Neural Networks as They Relate to Time Series Analysis

In this paper, a metacognitive octonion-valued neural network (Mc-OVNN) learning algorithm and its application to diverse time series prediction are presented. The Mc-OVNN is comprised of two components: the octonion-valued neural network that represents the cognitive component and the metacognitive component that serves to self-regulate the learning algorithm. At each epoch, the metacognitive component decides if, how, and when learning occurs. The algorithm deletes unneeded samples and only stores those that will be used. This decision is determined by the octonion magnitude and the seven phases. To evaluate the Mc-OVNN algorithm's performance, it is applied to five real-world forecasting problems: the power consumption of a home in Honolulu, HI, USA, Box and Jenkins J series, Euro to Algerian Dinar (DZ) real-time conversion rates, the Mackey-Glass equation, and Europe Brent oil price prediction in a time series. When comparing the Mc-OVNN to other relevant techniques, Mc-OVNN displays its capability for efficient time series prediction. The real-time evaluation of the proposed algorithm is presented using the power consumption of a home in Boumerdès, Algeria, as a case study.
A Walk on the Child Side

Technology increasingly offers parents more and more opportunities to monitor children, reshaping the way control and autonomy are negotiated within families. This paper investigates the views of parents and primary school children on mobile technology designed to support child independent mobility in the context of the local walking school buses. Based on a school-year long field study, we report findings on children's and parents' experience with proximity detection devices. The results provide insights into how the parents and children accepted and socially appropriated the technology into the walking school bus activity, shedding light on the way they understand and conceptualize a technology that collects data on children's proximity to the volunteers' smartphone. We discuss parents' needs and concerns toward monitoring technologies and the related challenges in terms of trust-control balance. These insights are elaborated to inform the future design of technology for child independent mobility.
Compressed sensing for OMA using full-field vibration images

Optically-acquired data, typically from digital image correlation, is increasingly being used in the area of structural dynamics, particularly modal testing and damage identification. One of the problems with such data is its extremely large size. Single images regularly extend to tens or even hundreds of thousands of data points and many thousands of images may be required for a vibration test. Such data must be stored and transmitted efficiently for later remote reconstruction and analysis, typically operational modal analysis. It is this requirement that is addressed in the research presented in this paper. This research builds upon previous work whereby digitised optical data was projected onto an orthogonal basis with coefficients (shape descriptors) of either greater or lesser significance; those deemed to be insignificant, according to a chosen threshold being removed. Data reduction by a combination of shape-descriptor decomposition and compressed-sensing is applied to an industrial printed circuit board and reconstructed for operational modal analysis by ℓ1 optimisation.
A Survey on Various Attacks in Bitcoin and Cryptocurrency

Bitcoin is a popular cryptocurrency scheme which has decentralized electronic payment system. The transaction records of bitcoin are stored in public ledger which is connected to the blockchain. The reliability of bitcoin is based on the incentive with compatible proof-of-work that depends on the distributed concord protocol. For a specific state of problem, the attacker can maximize his\her revenue by understanding cryptographic scheme in order to utilize his\her computational power. It shows that a selfish miner earn huge amount of incentives for initiating a block withholding attack within a mining pool. In this work, we implemented various attacks to prevent the pools from rogue miners and rogue pool administrator.
Vehicle re-identification in still images: Application of semi-supervised learning and re-ranking

Vehicle re-identification (re-ID), namely, finding exactly the same vehicle from a large number of vehicle images, remains a great challenge in computer vision. Most existing vehicle re-ID approaches follow a fully-supervised learning methodology, in which sufficient labeled training data is required. However, this limits their scalability to realistic applications, due to the high cost of data labeling. In this paper, we adopted a Generative Adversarial Network (GAN) to generate unlabeled samples and enlarge the training set. A semi-supervised learning scheme with the Convolutional Neural Networks (CNN) was proposed accordingly, which assigns a uniform label distribution to the unlabeled images to regularize the supervised model and improve the performance of the vehicle re-ID system. Besides, an improved re-ranking method based on the Jaccard distance and k-reciprocal nearest neighbors is proposed to optimize the initial rank list. Extensive experiments over the benchmark datasets VeRi-776, VehicleID and VehicleReID have demonstrated that the proposed method outperforms the state-of-the-art approaches for vehicle re-ID.
How Developers Discuss Architecture Smells? An Exploratory Study on Stack Overflow

Architecture Smells (ASs) are design decisions that can have significant negative effects on a system's quality attributes such as reusability and testability. ASs are focused on higher level of software systems than code smells, which are implementation-level constructs. ASs can have much wider impact on a system than code smells. However, ASs usually receive less attention than code smells in both research and practice. We have conducted an exploratory study of developers' conception of ASs by analyzing related discussions in Stack Overflow. We used 14 ASs related terms to search the relevant posts in Stack Overflow and extracted 207 posts. We used Grounded Theory method for analyzing the extracted posts about developers' description of ASs, causes of ASs, approaches and tools for detecting and refactoring ASs, quality attributes affected by ASs, and difficulties in detecting and refactoring ASs. Our findings show that: (1) developers often describe ASs with some general terms; (2) ASs are mainly caused by violating architecture patterns, design principles, or misusing architecture antipatterns; (3) there is a lack of dedicated tools for detecting and refactoring ASs; (4) developers mainly concern about the maintainability and performance of systems affected by ASs; and (5) the inability to quantify the cost and benefit as well as the lack of approaches and tools makes detecting and refactoring ASs difficult.
Dragonfly: a Tool for Simulating Self-Adaptive Drone Behaviours

Drone simulators can provide an abstraction of different applications of drones and facilitate reasoning about distinct situations, in order to evaluate the effectiveness of these applications. In this paper we describe Dragonfly, a simulator of the behaviours of individual and collection of drones in various environments, involving random contextual variables and different environmental settings. Dragonfly supports the use of several drones in applications and evaluates the satisfaction of requirements under normal and exceptional situations. It simulates adaptive behaviours of drones due to exceptional situations. The adaption of drones is based on the use of wrappers implemented using aspect-oriented programming.
Learn to synthesize and synthesize to learn

Attribute guided face image synthesis aims to manipulate attributes on a face image. Most existing methods for image-to-image translation can either perform a fixed translation between any two image domains using a single attribute or require training data with the attributes of interest for each subject. Therefore, these methods could only train one specific model for each pair of image domains, which limits their ability in dealing with more than two domains. Another disadvantage of these methods is that they often suffer from the common problem of mode collapse that degrades the quality of the generated images. To overcome these shortcomings, we propose attribute guided face image generation method using a single model, which is capable to synthesize multiple photo-realistic face images conditioned on the attributes of interest. In addition, we adopt the proposed model to increase the realism of the simulated face images while preserving the face characteristics. Compared to existing models, synthetic face images generated by our method present a good photorealistic quality on several face datasets. Finally, we demonstrate that generated facial images can be used for synthetic data augmentation, and improve the performance of the classifier used for facial expression recognition.
Ways of Qualitative Coding

The process of qualitative coding often involves multiple coders coding the same data to ensure reliable codes and a consistent understanding of the codebook. One aspect of qualitative coding includes resolving disagreements, where coders discuss differences in coding to reach a consensus. We conduct a case study to evaluate four strategies of disagreement resolution and understand their impact on the coding process. We find that an open discussion and the n-ary tree metric lead coders to focus more on the disagreement of a particular data instance, whereas kappa values and Code Wizard direct coders to compare code definitions. We discuss opportunities for using different strategies at different stages of the coding process for more effective disagreement resolution.
A Novel Aspect-based Sentiment Analysis Network Model Based on Multilingual Hierarchy in Online Social Network

Abstract In recent years, sentiment analysis based on aspects has become one of the research hotspots in the field of natural language processing. Aiming at the fact that the existing network model cannot fully obtain the interrelationship between sentences in the same comment and the long-distance dependence of specific aspects in the whole comment, a multilingual deep hierarchical model combining regional convolutional neural network and bidirectional LSTM network is proposed. The model obtains the time series relationship of different sentences in the comments through the regional CNN, and obtains the local features of the specific aspects in the sentence and the long-distance dependence in the whole comment through the hierarchical attention network. In addition, the model improves the word vector representation based on the gate mechanism to make the model completely independent of the language. Experimental results for different domain datasets in multi-language show that the proposed model achieves better classification results than the traditional deep network model, the network model combining with the attention mechanism and considering the relationship between sentences.
Weighted feature selection via discriminative sparse multi-view learning

The matrix-based structured sparsity-inducing multi-view feature selection has received much attention because it can select the relevant features through the information-rich multi-view data instead of the single-view data. In this paper, a novel supervised sparse multi-view feature selection model is proposed based on the separable weighted loss term and the discriminative regularization terms. The proposed model adopts the separable strategy to enforce the weighted penalty for each view instead of using the concatenated feature vectors to calculate the penalty. Therefore, the proposed model is established by considering both the complementarity of multiple views and the specificity of each view. The derived model can be split into several small-scale problems in the process of optimization, and be solved efficiently via an iterative algorithm with low complexity. Furthermore, the convergence of the proposed iterative algorithm is investigated from both theoretical and experimental aspects. The extensive experiments compared with several state-of-the-art matrix-based feature selection methods on the widely used multi-view datasets show the effectiveness of the proposed method.
Mitch: A Machine Learning Approach to the Black-Box Detection of CSRF Vulnerabilities

Cross-Site Request Forgery (CSRF) is one of the oldest and simplest attacks on the Web, yet it is still effective on many websites and it can lead to severe consequences, such as economic losses and account takeovers.Unfortunately, tools and techniques proposed so far to identify CSRF vulnerabilities either need manual reviewing by human experts or assume the availability of the source code of the web application.In this paper we present Mitch, the first machine learning solution for the black-box detection of CSRF vulnerabilities.At the core of Mitch there is an automated detector of sensitive HTTP requests, i.e., requests which require protection against CSRF for security reasons.We trained the detector using supervised learning techniques on a dataset of 5,828 HTTP requests collected on popular websites, which we make available to other security researchers.Our solution outperforms existing detection heuristics proposed in the literature, allowing us to identify 35 new CSRF vulnerabilities on 20 major websites and 3 previously undetected CSRF vulnerabilities on production software already analyzed using a state-of-the-art tool.
A Comparison of Skin Detection Algorithms for Hand Gesture Recognition

Hand gesture recognition software is becoming more accessible with the advances in depth cameras and sensors, but these sensors are still expensive and not freely available. A real time Hand Gesture Recognition software is designed to work with a low cost monocular web camera. Skin detection and skin extraction is a common form of image processing used for gesture recognition. A comparison of three different skin detection algorithms is performed. The three algorithms are: YCbCr thresholding, RGB-H-CrCb thresholding and KNN Classification. The results obtained for each algorithm show that the algorithms are unreliable with a low mean and a large standard deviation. It was concluded that the uncertainty of the accuracy of each algorithm reduces the effectiveness of the hand gesture recognition software and it is not implemented in the final design. Alternative skin detection algorithms are suggested to improve on the accuracies and latencies obtained.
Toward Empirically Investigating Non-Functional Requirements of iOS Developers on Stack Overflow

Context: Mobile application developers are getting more concerned due to the importance of quality requirements or non-functional requirements (NFR) in software quality. Developers around the globe are actively asking a question(s) and sharing solutions to the problems related to software development on Stack Overflow (SO). The knowledge shared by developers on SO contains useful information related to software development such as feature requests (functional/non-functional), code snippets, reporting bugs or sentiments. Extracting the NFRs shared by iOS developers on programming Q&A website SO has become a challenge and a less researched area. Objective: To identify and understand the real problems, needs, trends, and the critical NFRs or quality requirements discussed on Stack Overflow related to iOS mobile application development. Method: We extracted and used only the iOS posts data of SO. We applied the well-known statistical topical model Latent Dirichlet Allocation (LDA) to identify the main topics in iOS posts on SO. Then, we labeled the extracted topics with quality requirements or NFRs by using the wordlists to assess the trend, evolution, hot and unresolved NFRs in all iOS discussions. Results: Our findings revealed that the highly frequent topics the iOS developers discussed are related to usability, reliability, and functionality followed by efficiency. Interestingly, the most problematic areas unresolved are also usability, reliability, and functionality though followed by portability. Besides, the evolution trend of each of the six different quality requirements or NFRs over time is depicted through comprehensive visualization. Conclusion: Our first empirical investigation on approximately 1.5 million iOS posts and comments of SO gives insight on comprehending the NFRs in iOS application development through the lens of real-world practitioners.
Algorithm and Architecture Design of a Hardware-Efficient Image Dehazing Engine

The function of an image dehazing algorithm restores a hazy image back to a clear one. This technique can be equipped in Advanced Driver Assistance Systems (ADAS) to further improve driving safety. Advanced display resolutions and high frame rates are critical to improve the reliability of ADAS. However, they cause a serious limitation on the hardware realization of an image dehazing algorithm. In this paper, an algorithm and architecture design of a hardware-efficient image dehazing engine are proposed. This dehazing algorithm comprises two core techniques: a low-complexity airlight estimation (LAE) and an independent transmission rate estimation (ITRE). LAE can efficiently predict the airlight without massive sorting processes and a computationally intensive filtering. Moreover, in its hardware architecture, a preluded airlight estimation is proposed to yield an efficient data scheduling. Being independent of the airlight, ITRE is able to solely predict transmission rates. This enables LAE and ITRE to be concurrently processed, improving the throughput of their hardware architectures. Compared with other sophisticated studies, this study demonstrates a competitive performance in peak signal-to-noise ratio, structural similarity, contrast distortion, and computational efficiency. The hardware architecture is realized using Taiwan Semiconductor Manufacturing Company CMOS 0.18-μm technology, and logic gate count is 32.6 K. Its working frequency is 200 MHz with a power consumption of 16.5 mW. The maximum throughput is as high as 600 megapixels/s, which can support 4096×2160 at 60 frames/s. The experimental results indicate that this paper demonstrates superior energy and area efficiencies.
Should we smoke it for you as well? An ethnographic analysis of a drug cryptomarket environment

Why aren't cryptomarkets more widely used? Researchers from a wide array of fields have attempted to answer this question by studying the size of drug cryptomarkets, the substances trafficked, and their structure. In this paper we address the potential acceptance of drug cryptomarkets by studying their perceived ease of use.This paper draws on observational data collected over 350 h of unstructured observation during an ethnographic study conducted in April-September 2016 of the two most prominent drug cryptomarkets at the time, in addition to seven face-to-face semi-structured qualitative interviews.Use of cryptomarkets relies on specialised knowledge. The administrators of the cryptomarkets do not play a publicly visible role in facilitating or easing cryptomarket use while simultaneously expecting cryptomarket users to exhibit self-reliance. We argue that the current levels of complexity and obfuscation constructed in the cryptomarket environment act as a barrier to the widespread acceptance of this technology.Through studying cryptomarkets' potential for widespread use we have shed light on current barriers to their growth requirement of specialised knowledge in order to use cryptomarkets and no public efforts to bridge the knowledge gap on behalf of cryptomarket administrators. As cryptomarkets continue to develop the ease of use of these platforms are bound to change, and with them the likelihood that cryptomarket usage may increase.