Reviewerly/exHarmony huggingface dataset:
Serendipity Recommender System for Academic Disciplines

This paper proposes a system that encourages users to discover disciplines such as mathematics and biology, which they have not explored before. The recommendation of unexpected information is highly demanded in current recommender systems, which are used in various situations. To meet this need, the present study considers the serendipitous discovery of unexpected disciplines related to the information of interest to users. Serendipity means “the ability to see what others do not, and to discover useful things with your own wisdom.” Our system encourages users first to select a photograph of interest, and then to ponder the relevance of another photograph appearing on the display. The system is also equipped with a hint generation function that outputs an academic discipline related to the photos in order to foster users’ dictionary of unexpected relationship between the two photos. Whether users gain serendipity from the system was evaluated in an experiment. The results confirmed that the system induced serendipity by providing unexpected information through photographs linked to disciplines.
Hybrid Control for Power System Based on STATCOM and UPFC with Two 3-level 48-pulse under Different Conditions

This research presents the proposed model, to control the power flow in the transmission power system by applying Unified Power Flow Controller (UPFC), STATCOM and two 3-level 48-puls converter. This hybrid has been used to improve performance and reduce the maximum over shoot which is obtained from proposed model when the fault is occurring or suddenly system changes. The behavior of the system is analyzed under different three cases. The first case, the model is applied to plus load at bus 3. The second case, the model is operating at normal work and the third case, the three phase fault is occurred at bus4. In this research, the performance of system is studied under applied all cases of the current, voltage and power for the system. The numerical results of the proposed model are introduced to show the maximum over shoot and RMS values after applied proposed control at three different cases to prove the suggested model gave a good performance especially, during three phases fault and after fault clearance.
Optical Character Recognition for Printed Javanese Script Using Projection Profile Segmentation and Nearest Centroid Classifier

Preservation of cultural heritage manuscripts has been a constant challenge in countries where the societies lacked interest in doing a serious preservation. Therefore, digitisation become the most feasible option to help in preserving many forms of cultural legacy. In terms of manuscript preservation, optical character recognition (OCR) is the best option aside from keeping only the photograph form of a manuscript. OCR opens further possibilities to preserve not only the text but also the knowledge or wisdom contained within, as it is possible to provide a transliteration or translation using the stored digital text. This study proposes an OCR for Javanese script, one of the digitally under-resourced language which faced a usage decline in Indonesia. The recognition is performed using projection profile analysis for the segmentation and binary image features to be classified using nearest centroid classifier (NCC). Printed books of various Javanese font size and type are scanned and used for training and testing. Experiment results shown that projection profile (PP) provides an acceptable segmentation result, namely 93.88% for line segmentation and 73.59% for character segmentation. As for the classification, the combination of binary image features and NCC yields the result of 60.6% accuracy in recognition. This is mainly caused by wide array of character compound classes (over 600) produced by the usage of PP segmentation, with which the selected features are not strong enough to distinguish the classes.
Spectrum Sharing in CBRS Using Blockchain

We make a case for using the emerging blockchain technology for spectrum sharing in Citizen's Broadband Radio Service (CBRS). We show that blockchain can dynamically allocate channels in a transparent and fair manner by achieving distributed channel de-confliction. Furthermore, blockchain helps automate communication between the Spectrum Access Systems (SASs) belonging to competing, non-trusting organizations by providing consensus, while at the same time preserving confidentiality of sensitive information. We implement our CBRS blockchain architecture using the Hyperledger Fabric platform. In order to support high channel request rates, we also develop a novel feature that adaptively pre-processes channel allocation requests at the SAS before sending those to the blockchain network. We evaluate our blockchain solution under different settings and show that it meets the FCC latency requirements. We also analyze the scalability of our system and show that the latency and throughput guarantees can be maintained in scenarios involving multiple organizations.
KOI: An Architecture and Framework for Industrial and Academic Machine Learning Applications

A novel framework is presented, which simplifies the integration of machine learning into systems for industrial inspection and testing. In contrast to most approaches utilizing a centralized setup, the proposed work follows an edge-computing paradigm. The scope is not limited to inspection tasks but includes all requirements connected to such tasks. The support for continual and distributed learning, as well as distributed accumulation of training data, is a crucial feature of the proposed system. An integrated user rights management allows for the collaboration of multiple people with different background of expertise and tasks on the same machine learning models. Through platform-independent design and the use of a progressive web app as a user-interface, this framework supports the deployment in heterogeneous systems. Separation of concerns and clean object-oriented design makes the framework highly extensible and adaptable to other domains.
Perceptual Masking Model Using Lifting Wavelet Transform for Fingerprint Images

In this work, an efficient pixel-wise perceptual masking model that operates in the Lifting Wavelet Transform (LWT) is proposed for fingerprint images. The proposed model simulates the Human Visual System (HVS) behavior in a better way by exploiting four of its characteristics, namely: the frequency band sensitivity, the local brightness, the texture masking and the edge masking. It allows estimating the maximum unnoticeable noise for each coefficient. Experimental results show that the proposed model has high-noise tolerance when compared to a number of related state-of-the-art models.
Time-frequency Aliasing Separation Method of Radar Signal Based on Capsule Neural Network

To solve the time-frequency aliasing problem of various radar signals in a complex electromagnetic environment, a radar signal separation method based on capsule neural network is proposed. In this paper, the capsule neural network (Caps Net) is briefly introduced as preliminary. In the proposed method, the radar signal is firstly preprocessed to build a deep feature space, then pulses are separated based on a Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and used the capsule neural network to separate the aliasing points in the cluster. The performance of the proposed method is verified by numerical simulations. The results show that it can achieve an effective separation of 8 mixed radar signals. Compared with traditional method, the accuracy is significantly improved, and the recognition probability is over 95% with SNR=15dB.
Injecting Affective Computing into Online Virtual Training Through FPS Games

The online FPS (First Person Shooter) games are not only widely used in entertainment, but also in serious game scenarios such as military training. In FPS games, AI is usually utilized to build NPC(Non-Player Character) such as imaginary roles of teammates or enemies. In order to provide users with more credible and intelligent AI, thereby enhancing the realism of the simulation, this paper adds affective computing to the artificial intelligence system of the online networked FPS game "Glorious Mission Online". First, it improved a hierarchical OCC affective model [1] and integrated it into the original system to drive AI to control the decision-making and behavior of the role through affective factors. Finally, the design and implementation of the prototype system in Unreal Engine are given, and actual user test data are given. The results prove that the system has a good effect on enhancing user experience.
Orthogonal Mixture of Hidden Markov Models

Mixtures of Hidden Markov Models (MHMM) are widely used for clustering of sequential data, by letting each cluster correspond to a Hidden Markov Model (HMM). Expectation Maximization (EM) is the standard approach for learning the parameters of an MHMM. However, due to the non-convexity of the objective function, EM can converge to poor local optima. To tackle this problem, we propose a novel method, the Orthogonal Mixture of Hidden Markov Models (oMHMM), which aims to direct the search away from candidate solutions that include very similar HMMs, since those do not fully exploit the power of the mixture model. The directed search is achieved by including a penalty in the objective function that favors higher orthogonality between the transition matrices of the HMMs. Experimental results on both simulated and real-world datasets show that the oMHMM consistently finds equally good or better local optima than the standard EM for an MHMM; for some datasets, the clustering performance is significantly improved by our novel oMHMM (up to 55 percentage points w.r.t. the v-measure). Moreover, the oMHMM may also decrease the computational cost substantially, reducing the number of iterations down to a fifth of those required by MHMM using standard EM.
Local Privacy-Preserving Dynamic Worker Locations in Spatial Crowdsourcing

An outsourcing service named spatial crowdsourcing (SC) becomes popular, whereby the SC-server allocates nearby tasks to the workers based on the outsourced task and worker locations.Exposing real locations can cause serious privacy leakage.However, traditional differential privacy (DP) and encryption methods do not consider the dynamic worker location and correlation privacy.Here, a Local DP-based dynamic worker location protection (LDPDW) scheme is proposed to achieve high-quality task allocation and locally protect the correlation and location privacy of dynamic workers.Specifically, LDPDW generates noisy high correlated graph classes and obfuscates the worker locations in a static case by adopting a LDP-based correlation graph (LDPCG) algorithm and distance score-based LDP (DSLDP) algorithm, thereby achieving controlled noise addition and ensuring the correlation and location privacy.To support the privacy-preserving dynamic locations, a dynamic correlation graph-based location obfuscation (DCGLO) algorithm is proposed to allocate reasonable privacy budget , which ensures the data utility.Finally, a linear acceptance model-based task allocation (LAMTA) algorithm is used to allocate tasks to the workers with high acceptance rates.Privacy analysis and the extensive experimental results show that our LDPDW scheme follows -LDP while allocating tasks with high data utility.
Exploration of TransE in a Distributed Environment

Knowledge graph is popular in knowledge mining fields. TransE uses the structure information of triples ( <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">→</sup> e <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> + <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">→</sup> e <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> ≈ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">→</sup> e <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ) to embed knowledge graphs into a continuous vector space, which is a very important component in knowledge representations. However, current TransE models are only implemented on single-node machines. With the explosive growth of data volumes, single-node TransE cannot meet the demand for data processing of large knowledge graphs, so a distributed TransE is urgently needed. In this poster, we propose a distributed TransE written in MPI, which can run on HPC clusters. In our experiments, our distributed TransE exhibits high-performance speedup and accuracy.
Mind Genomics With Big Data for Digital Marketing on the Internet

This chapter sheds light on one very important application in the domain of digital economy – mind genomics. Mind genomics is an approach to targeted marketing which reaches each prospect with a different personalized message. This application requires acceleration coming from a data flow accelerator connected to a control flow host. It stresses equally the basic concept and its many applications. Innovation process is a step-by-step process. Once an important step up front is created and a new innovation finds its way into the commercial world, it is difficult to imagine that another dramatical step/leap forward is possible. However, such steps keep happening. Mind genomics is an example of one such step, unthinkable of until only a few years ago. Needs of the users could be served much more effectively not only in business domains or other lucrative domains, but also in the domains of public health, public happiness, public and individual quality of life, public and individual understanding of the environment around, etc.
Evaluating the effectiveness of risk containers to isolate change propagation

Previous studies indicate that error-proneness risks can be isolated into risk containers created from architectural designs, to help detect and mitigate such risks early on. Like error-proneness, change propagation may lead to higher implementation and maintenance costs. We used automated tools to analyse four software development projects using three risk container types, each type based on a different architectural perspective. A strong and significant correlation between design change propagation and implementation co-change was observed for all three container types. We found that Design Rule Containers (DRCs), based on class diagrams, are the most effective for isolating change propagation because they have the least amount of container overlap, highest levels of internal coupling, highest co-change probability between classes that share containers, and the most change sets isolated in containers. Developers from two projects were able to justify why design dependencies had resulted in the top five DRCs being predicted to isolate the most change propagation. This and the previous error-proneness research suggests DRCs are an effective technique to detect and contain code maintainability risks at the design stage. These results provide some evidence that class diagrams are more useful than use case sequence diagrams for analysing maintainability risks in designs.
14.3: A real‐time video defogging algorithm

This paper proposes a fast and efficient real‐time video defogging algorithm based on pyramid model, which solves the problems existing in the defogging algorithms, such as over‐estimation of transmittance, color distortion of sky area, and poor real‐time performance. Pyramid subsampling is used to obtain reduced images. The coarse transmittance of the reduced image is obtained by introducing pseudo‐defogging image and the confidence of dark primary color as correction factors. The image was returned to the original size, and the texture was refined with the combined bilateral filtering. Finally, the atmospheric light scattering model and the inter‐frame video defogging theory were combined to restore the degraded video. The experimental results show that this method has a complete defogging effect and can efficiently defog a variety of scenes. The improvement of the algorithm in PSNR and MSSIM is 15.7% and 11.6%, which effectively solves the problems of over‐estimation of transmittance, color distortion in the sky area, etc. Besides, it is fast and stable in operation, and suitable for real‐time video defogging.
Performance Improvement of Artificial Neural Networks by Addressing Class Overlapping Problem

The class overlapping problem is one of the most prominent challenges of Machine Learning in recent years. In spite of the rapid development of well performing machine learning models for classification tasks, majority of them suffers from overlapped classes. Artificial Neural Network (ANN), being an extremely successful machine learning model suffers from these issues. Studies have found the effect of imbalanced classes in ANNs. However, the effect of overlapped classes is still not well studied. Motivated by this, the present article proposes a soft membership based overlapping removal technique (SMORET) to improve the performance of ANNs in presence of overlapped classes. The proposed method is capable of detecting the data points in the overlapped region and removing them from the training phase dataset. The proposed method is tested using real and artificial datasets, specially designed to understand the effect of overlapped classes, in terms of various performance measuring metrics. The results have indicated that SMORET is capable of detecting the most significant and hardest-to-learn data points in the overlapping region. Further, it improves the performance of ANNs in classification tasks to a greater extent.
Automatic Text Summarization on Social Media

In the Natural Language Processing (NLP) community, automatic text summarization is considered to be a very difficult problem. The textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such massive amount of data, in order to extract the useful information, is a major undertaking and requires an automatic mechanism to aid with the extant repository of information. A good text summarization system should understand the whole text, reorganize information, and generate coherent, informative and remarkably short summaries to convey the important information of the original text. In this paper, an innovative text summarization model has been constructed, which combines BERT, reinforcement learning, sequence-to-sequence and other technologies. Our model is evaluated on the LCSTS[1] dataset, which is a high-quality corpus of Chinese short text summarization dataset constructed from "Sina Weibo", The experiment shows that our method has made a great breakthrough in Rouge Scores compared with other researches.
A Lightweight and Efficient Network for Logistics Truck Scene Semantic Segmentation

Intelligent video analytics (IVA) plays an increasingly important role in industrial applications such as autonomous driving, defect detection and logistic scene analytics. Semantic segmentation is a basic technique of IVA, as well as lightweight and efficient segmentation is especially significant in industrial applications. But most excellent methods focus on accuracy, rather than lightweight and fast. Therefore, we propose a unique lightweight and efficient semantic segmentation network aimed at logistics truck scene analysis. Firstly, the lightweight high-level semantic feature extractor, which is a deep branch, is proposed to get a larger receptive field with lower parameters. Secondly, the detail block as a shallow branch is designed to enrich the spatial detail information. Lastly, the feature guided aggregation module based on the novel two-branch methods proposed to guide feature fusion for improving the performance. The parameters of our model are only 1.05M, which are much less than other models, such as PSPNet, SegNet and BiseNet. Compared with some typical real-time segmentation methods, our approach achieves more competitive accuracy (83.94%) and higher inference speed (157 FPS) in the logistics truck scene dataset.
Smörgåsbords for Physical Computing

Physical computing concerns the design of systems that can sense and respond to the world around them, which is why it is often used in interaction design projects in educational settings. However, students who encounter physical computing for the first time are typically not aware of the form factors and the potential for interaction of the various sensing and actuating possibilities. To complement existing touchpoints that these students have with physical computing, we present electronic smörgåsbords: boards that display collections of physical computing components that are available inhouse in an organised and interactive way to support the initiation of interaction design projects. The development of the boards allowed us to articulate four principles for their design, which are intended to inspire the development of future educational material.
Arbitrary-Oriented Ship Detection Method Based on Improved Regression Model for Target Direction Detection Network

Arbitrary-oriented ship detection is one of the main applications of high-resolution remote sensing images. The current target direction detection methods, based on deep convolution neural network (DCNN), can estimate most of the ship directions (i.e. represented by angles). However, the performance of these networks is limited by the boundary discontinuity problem due to the angle-based regression model. In this paper, we propose an improved regression model based on coordinates in the complex plane to solve the boundary discontinuity problem. Experiments on real remote sensing dataset verify the effectiveness and robustness of our method.
Surface texture defect recognition method of machined parts based on machine vision

In order to improve the recognition rate of surface texture defects of machined parts, a machine vision-based method for identifying surface texture defects of machined parts is proposed. By analyzing the characteristics of the detection content of small parts, the image is filtered, the improved edge detection algorithm is used to accurately extract the edge position of the surface, the image is divided into different regions by the edge, the candidate defects are screened, and the BP network is finally used Algorithms classify and mark defects. The experimental results show that the method can stably recognize and detect the surface defects of small parts, the defect recognition rate is up to 97.05%, and the detection speed is also fast, reaching the expected detection target.
Overlapping Shadow Rendering for Outdoor Augmented Reality

Realism rendering methods of outdoor augmented reality (AR) is an interesting topic. Realism items in outdoor AR need advanced impacts like shadows, sunshine, and relations between unreal items. A few realistic rendering approaches were built to overcome this issue. Several of these approaches are not dealt with real-time rendering. However, the issue remains an active research topic, especially in outdoor rendering. This paper introduces a new approach to accomplish reality real-time outdoor rendering by considering the relation between items in AR regarding shadows in any place during daylight. The proposed method includes three principal stages that cover various outdoor AR rendering challenges. First, real shadow recognition was generated considering the sun’s location and the intensity of the shadow. The second step involves real shadow protection. Finally, we introduced a shadow production algorithm technique and shades through its impacts on unreal items in the AR. The selected approach’s target is providing a fast shadow recognition technique without affecting the system’s accuracy. It achieved an average accuracy of 95.1% and an area under the curve (AUC) of 92.5%. The outputs demonstrated that the proposed approach had enhanced the reality of outside AR rendering. The results of the proposed method outperformed other state-of-the-art rendering shadow techniques’ outcomes.