Reviewerly/exHarmony huggingface dataset:
A novel ensembling method to boost performance of neural networks

Classification is one of the important tasks in data mining. Over the last few decades, neural networks have proved to be very effective in solving this task. This paper presents a novel algorithm, called Neural Network Boosting (NNBOOST) to improve the classification performance of neural networks using ensembling technique. The ensembling technique proposed here is based on the boosting concept to ensemble classifiers. A boosting algorithm sequentially learns a new model by reassigning pattern weights. NNBOOST algorithm adapts a new technique to assign weights to patterns based on the Euclidean distances of patterns from the centroids of respective classes. It assigns more weight to patterns closest to the centroid. The pattern weights are updated subsequently after building or learning a neural network model, based on the updated centroids to build the next neural network model. Weighted Majority voting scheme is used to combine all the learned neural network models. Weight or confidence of a learned neural network model is calculated as the ratio of correctly classified to misclassified training patterns. The algorithm is validated with six real life data sets taken from UCI repository. Results show the effectiveness of the algorithm in ensembling neural networks.
Research on information retrieval model under scarcity theory and user cognition

In essence, information retrieval is a cognitive process, which is the matching process between the reader's or user's cognition and the system's pre-set cognition. At present, cognitive view has become a research hotspot in the field of information retrieval. The research of information retrieval cognitive model has laid a theoretical foundation for the development of information retrieval system. Based on scarcity theory, this paper analyses the dynamic change of user's cognitive ability and constructs a cognitive model of information retrieval based on scarcity theory. After analysis, this paper evaluates the effect of information retrieval cognitive model based on scarcity theory, and clarifies the specific problems that the model can solve.
Multi-source Multi-net Micro-video Recommendation with Hidden Item Category Discovery

As the sheer volume of available micro-videos often undermines the users' capability to choose the micro-videos, in this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users' best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users' interests, we interactively learn the user-item representations. The resulted categorical representations are interacted with user representations to model user preferences at different level of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines.
Complexity analysis and performance of double hashing sort algorithm

Sorting an array of n elements represents one of the leading problems in different fields of computer science such as databases, graphs, computational geometry, and bioinformatics. A large number of sorting algorithms have been proposed based on different strategies. Recently, a sequential algorithm, called double hashing sort (DHS) algorithm, has been shown to exceed the quick sort algorithm in performance by 10–25%. In this paper, we study this technique from the standpoints of complexity analysis and the algorithm's practical performance. We propose a new complexity analysis for the DHS algorithm based on the relation between the size of the input and the domain of the input elements. Our results reveal that the previous complexity analysis was not accurate. We also show experimentally that the counting sort algorithm performs significantly better than the DHS algorithm. Our experimental studies are based on six benchmarks; the percentage of improvement was roughly 46% on the average for all cases studied.
Feature Enhancement in Medical Ultrasound Videos Using Multifractal and Contrast Adaptive Histogram Equalization Techniques

Speckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this would require enhancement of features and fine details. In this paper, we consider the applications of multifractal features and contrast limit adaptive histogram equalization method for improving texture features, contrast, resolvable details, and image structures to which the human visual system is sensitive in ultrasound video frames. The experimental analysis considered various types of ultrasound video scans of the human anatomy e.g. breast cancer, uterine fibroids, transvaginal ovary, ovarian cyst, heart, and chest pleural effusion scan. Subjective assessments by four radiologists and experimental validation using three quality metrics clearly indicate that the proposed algorithm is able to reduce speckle effectively while preserving essential information and enhancing the overall visual quality.
Understanding the Reinforcement Learning

Artificial Intelligence has been a hot topic for a long time. It is a cross-discipline combined with many fields. Among them, machine learning plays the most important role. There are four core subjects in machine learning, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In this paper, we are going to talk about the reinforcement learning in the perspective of Markov Decision Process and Partially Observable Markov Decision Process, which are the core algorithms in reinforcement learning. Also, an example of Hearthstone is illustrated to show how to apply reinforcement learning in games for better understanding.
The Performative Mirror Space

Interactive mirrors, typically combining semi-transparent mirrors, digital screens and interaction mechanisms have been developed for a variety of application areas. Drawing on existing techniques to create interactive mirror spaces, we investigated their performative qualities through artistic discovery and collaborative prototyping. We document a linked set of design explorations and two public, site-specific experiences that brought together artists, communities, and HCI researchers. We illustrate the abstracted interactive mirror space that practitioners in the performance art, theatre and museum sectors can work with. In turn, we also discuss six performative design strategies concerning the use of physical context, movement and narrative that HCI researchers who wish to deploy interactive mirrors in more mainstream settings need to consider.
Assessment of aggregate-bitumen coverage using entropy-based image segmentation

Adhesion between bitumen and aggregates has a major impact on the performance of asphalt mixes. In order to avoid stripping and consequent premature failure of a pavement, methods to assess the adhesion have been developed and standardised. These methods, such as the frequently applied rolling bottle test defined in EN 1269-11, are based on visual inspection of specimens and subjective evaluation. This shortcoming can be eliminated by employing image analysis that accurately and consistently identifies the portion of aggregate covered by bitumen. However, the image segmentation procedure cannot be based purely on intensity thresholding because the light reflections on the glossy surface of bitumen merge together with light aggregates, and dark aggregates coincide with diffuse bitumen reflections. In this article, a method based on local entropy calculation is proposed to tackle this issue in the form of an open-source software tool. Its algorithms are thoroughly described here, and their impact is illustrated using a model example. Software outcomes are compared with an advanced, yet more complicated, approach. How the software might be used in other fields is also outlined and discussed.
Entropy and Compression Based Analysis of Web User Interfaces

In our paper we explore whether user visual perception of web interfaces (WUI) can be predicted by certain quantitative characteristics of WUI screenshots. The considered metrics are JPEG file size, PNG file size, and information entropy value calculated with frequency-based MATLAB's entropy(I) function. We ran survey with 70 subjects who provided subjective evaluations of complexity, aesthetics and orderliness for 497 website homepages. The results suggest that all the three metrics were significant, and the proposed regression models were considerably better than the respective baseline models that only used the popular JPEG-based metric. Remarkably, the entropy metric had significant positive correlations with aesthetic and orderliness evaluations, but not with the size of the image. We believe our findings might be used in development of automated WUI analysis tools to aid web engineers in their work.
Beat Sensors for Smart Environment Monitoring Systems

This paper introduces Beat Sensors which are suitable for smart environment monitoring as low power and small size IoT sensors. As a typical representative, the Ethanol Beat Sensor is realized to monitor the ethanol concentration from 0 to 200 ppm at almost 20 ppm resolution. Beat Sensor has large potential to address issues of smart environment monitoring such as air pollutions management. Moreover, the long distance (LoRa) communication protocol is applied to build a smart and energy efficient monitoring system.
Blockchain Solution Reference Architecture (BSRA)

Enterprise Blockchain network solutions facilitate the extension and optimization of processes across organizational boundaries. This increased level of sharing provides greater visibility, provenance of transactions to all parties involved, and reduces friction that arises due to organizational boundaries. Blockchain Solution Reference Architecture (BSRA) provides comprehensive guidance to architect and build end-to-end solutions based on Blockchain technologies. BSRA was primarily developed based on client engagements across industries such as retail, financial, supply chain, and telco, and addresses two major parts of a Blockchain solution: 1) building a Blockchain business network that receives, builds, and shares blocks in a secure manner; and 2) onboarding members on to the business network with the appropriate level of access and collaboration privileges. BSRA provides a foundational set of architectural artifacts that can help accelerate development of solutions based on Blockchain technologies. It also provides a starter list of architectural decisions that every project will have to set during development.
Modeling the failures of power‐aware data centers by leveraging heat recirculation

Summary With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data center. Additionally, data centers with a large number of nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, the traditional techniques do not take the situation of node failures into account. This paper proposes an airflow‐based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failures are considered. Furthermore, a genetic algorithm (GA) and a simulated annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the positions of node failures have a significant impact on the heat recirculation and the energy consumption of data centers, failures with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on the proposed failure model.
A Commit Messages-Based Bug Localization for Android Applications

Recently, there has been consistent growth in Android applications (apps). Under these circumstances, software maintenance for Android apps becomes an essential and important task. The core of software maintenance is to locate bugs in source files. Previous bug localization approaches mainly focus on open-source desktop software (e.g. Eclipse, Mozilla, GCC). Even though a few studies locate the bugs in the Android apps, they are dedicated to a special app named ZXing, without developing a general method to locate the bugs in Android apps by taking into account the unique characteristics of Android apps’ bug reports. Such characteristics include fewer number of historical bug reports, insufficient detailed description, etc. These characteristics hinder existing localization approaches from being directly delivered to Android apps, because lack of enough information degrades the performance of those localization approaches relying on historical bug reports. Commit messages include more informative data which can provide the details of reported bugs. Therefore, in this paper, we propose a novel information retrieval-based approach which utilizes commit messages to locate new bugs in Android apps. This approach not only considers the structured textual similarity between the given bug and the candidate source files, but also computes the unstructured textual similarities between the new bug and the commit messages linked to the corresponding source files. According to the experimental results on 10 popular open-source Android apps managed by GitHub, our approach outperforms the state-of-the-art bug localization methods that include BugLocator, BLUiR, and two-phase model.
Decomposition methods with deep corrections for reinforcement learning

Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently. An arbitrator is then responsible for combining the individual utilities and selecting an action in real time to solve the global problem. Although these techniques can perform well empirically, they rely on strong assumptions of independence between the local tasks and sacrifice the optimality of the global solution. This paper proposes an approach that improves upon such approximate solutions by learning a correction term represented by a neural network. We demonstrate this approach on a fisheries management problem where multiple boats must coordinate to maximize their catch over time as well as on a pedestrian avoidance problem for autonomous driving. In each problem, decomposition methods can scale to multiple boats or pedestrians by using strategies involving one entity. We verify empirically that the proposed correction method significantly improves the decomposition method and outperforms a policy trained on the full scale problem without utility decomposition.
Adversarial Learning for Content-Based Image Retrieval

In this paper, we propose a novel adversarial learning based framework, unsupervised adversarial image retrieval (UAIR) for content-based image retrieval. Different from most content-based image retrieval methods that use supervised learning in convolutional neural network to obtain semantic image features, we adopt adversarial training scheme to train the retrieval framework with unannotated information. A generative model and a discriminative model are designed for UAIR to learn together by pursuing competing goals. The generative model selects well-matched images and passes them to the discriminative model. The discriminative model judges the selected images as feedbacks to the generative model. Experimental results demonstrate the effectiveness of the proposed UAIR on two widely used databases. The performance of UAIR has been compared with other state-of-the-art image retrieval methods, including recently reported GAN-based methods. Experimental results show that the proposed UAIR achieves significant improvement in retrieval performance.
An Innovative Approach of Industrial Robot Programming Using Virtual Reality for the Design of Production Systems Layout

Current trends in the design of manufacturing systems are aimed at shortening the pre-production phase. Designing CAD (Computer Aided Design) data and creating simulation scenarios are currently strategic solutions for designing different variants of manufacturing systems. The research intention is to point out the possibilities of hardware connectivity and software tools of the present. The primary intent of this article is the capabilities of industrial robot programming by utilizing the interaction of several robot online method, OLP (Offline Robot Programming) and VR OLP computing methods with regard to the applicability of the proposed outputs to the real environment. As a supporting simulation tool ABB RobotStudio was used. As imaging hardware tools were used glasses designed to be displayed by the immersive VR environment HTC Vive. The article presents methods and experiments on a case study where the interaction between the immersive environment of VR and the area of classical OLP programming was studied. The immediate interaction of the classic OLP method of industrial robot programming in the PC desktop software environment and the immersive VR environment helps to shorten the time needed to create industrial robot programming. It is precisely such an interactive link between OLP programming and real-time imaging of an industrial robot in a VR environment is one of the Industry 4.0 strategy implementation area.
Listwaenitization and enrichment of precious metals in the hydrothermal mineralization zones of serpentinites in Sugeçer-Van (Eastern Anatolia, Turkey)

An ophiolitic complex containing hydrothermally altered serpentinized ultramafic rocks was studied in an area on the eastern side of Lake Van near the village of Sugecer, Eastern Anatolia. Field observations and petrographic and geochemical analyses clearly showed that the serpentinites were altered to listwaenites via silicification and carbonatization. The listwaenites were classified as type-I and type-II listwaenites based on their mineral composition. The type-I listwaenites contained carbonate minerals, rare serpentine minerals, chromite and mariposite, whereas the type-II listwaenites contained mainly silicate minerals. The listwaenites were enriched in Au, As, Cu, Ni and Co relative to the serpentinites. The listwaenites also showed chondrite-normalized enrichment in light rare earth elements and depletion in heavy rare earth elements. The effects of tectonism were widely observed in the study area. Mineral enrichment in the Sugecer region was shown to be associated with the transport of minerals by hydrothermal fluids under the influence of tectonism.
Threat Intelligence Sharing Community: A Countermeasure Against Advanced Persistent Threat

Advanced Persistent Threat (APT) having focused target along with advanced and persistent attacking skills under great concealment is a new trend followed for cyber-attacks. Threat intelligence helps in detecting and preventing APT by collecting a host of data and analyzing malicious behavior through efficient data sharing and guaranteeing the safety and quality of information exchange. For better protection, controlled access to intelligence information and a grading standard to revise the criteria in diagnosis for a security breach is needed. This paper analyses a threat intelligence sharing community model and proposes an improvement to increase the efficiency of sharing by rethinking the size and composition of a sharing community. Based on various external environment variables, it filters the low-quality shared intelligence by grading the trust level of a community member and the quality of a piece of intelligence. We hope that this research can fill in some security gaps to help organizations make a better decision in handling the ever-increasing and continually changing cyber-attacks.
A more robust multigrid algorithm for diffusion type registration models

Registration refers to the useful process of aligning two similar but different intensity image functions in order to either track changes or combine information. Variational models are capable of finding transform maps containing large and non-uniform deformations between such a pair of images. Since finding a transform map is an inverse problem, as with all models, suitable regularisation is necessary to overcome the non-uniqueness of the problem. In the case of diffusion type models regularisation terms impose smoothness on the transformation by minimising the gradient of the flow field. The diffusion model also coincides with the basic model for optical flow frameworks of Horn and Schunck (1981). The biggest drawback with variational models is the large computational cost required to solve the highly non-linear system of PDEs; Chumchob and Chen (2011) developed a non-linear multigrid (NMG) method to address this cost problem. However, a closer look at the analysis of the NMG scheme highlighted omissions which affected the convergence of the NMG scheme. Moreover, the NMG method proposed by Chumchob and Chen did not impose any control of non-physical folding which invalidates a map. This paper has proposed several key ideas. First we re-evaluate the analysis of the NMG method to show how the omissions in Chumchob and Chen (2011) have a noticeable impact on the convergence of the NMG method. In addition, we also provide a way of estimating the convergence rate of a solver on the coarsest grid in order to estimate the number of iterations that will be required to obtain a solution with appropriate accuracy. Second we propose an extension to the Chumchob–Chen NMG method which controls any folding within the deformation. Experimental results on the proposed multigrid framework demonstrate improvements in convergence and the accuracy of registrations compared with previous methods.