Reviewerly/exHarmony huggingface dataset:
Efficient hinging hyperplanes neural network and its application in nonlinear system identification

In this paper, the efficient hinging hyperplanes (EHH) neural network is proposed, which is basically a single hidden layer neural network. Different from the dominant single hidden layer neural networks, the hidden layer in the EHH neural network can be seen as a directed acyclic graph (DAG) and all the nodes in the DAG contribute to the output. It is proved that for every EHH neural network, there is an equivalent adaptive hinging hyperplanes (AHH) tree, the model of which was proposed based on the hinging hyperplanes (HH) model and finds good applications in system identification. Analog to the proof for the AHH model, the universal approximation ability of the EHH neural network is provided. Different from other neural networks, the EHH neural network has interpretability, which can be easily obtained through its ANOVA decomposition (or interaction matrix). The interpretability can then be used as an indication for the importance of the input variables. The construction of the EHH neural network includes initial network generation and parameter optimization (including the structure and weights parameter optimization). A descent algorithm for searching the locally optimal EHH neural network is proposed and the worst-case complexity of the algorithm is also provided. The EHH neural network is applied in nonlinear system identification, the simulation results show that satisfactory accuracy can be achieved with relatively low computational cost, and at the same time, some insights into the importance of the regressors and the interactions among the regressors can be revealed.
Optimal Pricing for Serverless Computing

Serverless computing is an attractive cloud services paradigm, simultaneously promising reduced cost and greater flexibility for customers and increased revenues and higher resource utilization for cloud providers. In this paper, we present an analysis of the potential cost benefits of serverless computing for end customers and cloud providers. Using realistic cost models, queueing theoretic performance models, and a game theoretic formulation, we explore the tradeoffs between serverless computing (SC) and traditional cloud computing (virtual machine, VM). In the proposed framework, customers distribute their workload between SC and VM to minimize their cost while maintaining a particular performance constraint, while the cloud provider sets SC and VM prices to maximize its profit. We explore the impact of relative prices, customer workload, service capacity, and provider costs. Our main result is the identification and characterization of three optimal operational regimes for both customers and the provider that leverage either SC or VM only, or both, in a hybrid configuration. The various insights provided in this paper can help both cloud providers and customers better understand the tradeoffs and implications of a hybrid system that combines serverless and VM rental with corresponding pricing models.
On the Global Optima of Kernelized Adversarial Representation Learning

Adversarial representation learning is a promising paradigm for obtaining data representations that are invariant to certain sensitive attributes while retaining the information necessary for predicting target attributes. Existing approaches solve this problem through iterative adversarial minimax optimization and lack theoretical guarantees. In this paper, we first study the “linear” form of this problem i.e., the setting where all the players are linear functions. We show that the resulting optimization problem is both non-convex and non-differentiable. We obtain an exact closed-form expression for its global optima through spectral learning and provide performance guarantees in terms of analytical bounds on the achievable utility and invariance. We then extend this solution and analysis to non-linear functions through kernel representation. Numerical experiments on UCI, Extended Yale B and CIFAR100 datasets indicate that, (a) practically, our solution is ideal for “imparting” provable invariance to any biased pre-trained data representation, and (b) the global optima of the “kernel” form can provide a comparable trade-off between utility and invariance in comparison to iterative minimax optimization of existing deep neural network based approaches, but with provable guarantees.
Pose-Normalized and Appearance-Preserved Street-to-Shop Clothing Image Generation and Feature Learning

We tackle the task of street-to-shop clothing image synthesis. Given a daily person image with a particular clothing item captured in the street scenario, we aim to synthesize the frontal facing view of that item in the shop scenario. This problem has the following challenges: 1) the distinct visual discrepancy between the street and shop scenario; 2) the severe shape deformation of clothing in the presence of an arbitrary human pose; 3) the preservation of fine-grained details during the process of clothing image generation. In this paper, we jointly solve these difficulties by proposing a Pose-Normalized and Appearance-Preserved Generative Adversarial Network (PNAP-GAN). More specifically, conditioned on the clothing-agnostic representation (i.e., clothing landmarks and semantic parsing map), we disentangle the shape and appearance synthesis in a coarse-to-fine framework. Moreover, a semantic embedding loss is introduced to guide the domain transfer in the semantic level (i.e., keeping the clothing attributes). With the synthesized frontal shop image, a pose-normalized representation in complementary to the domain-invariant feature learnt from the original street image are integrated to facilitate the problem of street-to-shop clothing retrieval. Extensive experiments conducted demonstrate the effectiveness of the proposed PNAP-GAN on generating high quality frontal-view images and the excellence of the learnt pose-normalized features on the retrieval task than existing methods. In addition, we demonstrate that the pose-normalized retrieval feature benefits the cross-scenario (i.e., street-to-shop) clothing image generation in a semantic-preserved manner.
Privacy-Aware Edge Computing Based on Adaptive DNN Partitioning

Recent years have witnessed deep neural networks (DNNs) become the de facto tool in many applications such as image classification and speech recognition. But significant unmet needs remain in performing DNN inference tasks on mobile devices. Although edge computing enables complex DNN inference tasks to be performed in close proximity to the mobile device, performance optimization requires a carefully designed synergy between the edge and the mobile device. Moreover, the confidentiality of uploaded data to the possibly untrusted edge server is of great concern. In this paper, we investigate the impact of DNN partitioning on the inference latency performance and the privacy risks in edge computing. Based on the obtained insights, we design an offloading strategy that adaptively partitions the DNN in varying network environments to make the optimal tradeoff between performance and privacy for battery-powered mobile devices. This strategy is designed under the learning-aided Lyapunov optimization framework and has a provable performance guarantee. Finally, we build a small- scale testbed to demonstrate the efficacy of the proposed offloading scheme.
Accurate single image super-resolution using multi-path wide-activated residual network

In many recent image super-resolution (SR) methods based on convolutional neural networks (CNNs), the superior performance was achieved by training very large networks, which may not be suitable for real-world applications with limited computing resources. Therefore, it is necessary to develop more compact networks that achieve a better trade-off between the model size and the performance. In this paper, we propose an efficient and effective network called multi-path wide-activated residual network (MWRN). Firstly, as the basic building block of MWRN, the multi-path wide-activated residual block (MWRB) is presented to extract the multi-scale features. MWRB consists of three parallel wide-activated residual paths, where the dilated convolutions with different dilation factors are used to increase the receptive fields. Secondly, the fusional channel attention (FCA) module, which contains a bottleneck layer and a multi-path wide-activated residual channel attention (MWRCA) block, is designed to well exploit the multi-level features in MWRN. In each FCA, the MWRCA block refines the fused features by taking the interdependencies among feature channels into consideration. The experiments demonstrate that, compared with the state-of-the-art methods, the proposed MWRN model is able to provide very competitive performance with a relatively small number of parameters.
A Comprehensive Overview of Person Re-Identification Approaches

Person re-identification, identifying and tracking pedestrians in cross-domain monitoring systems, is an important technology in the computer vision field and of real significance for the construction of smart cities.With the development of deep learning techniques, especially convolutional neural networks, this technology has received more extensive attention and improvement in recent years and a large number of noteworthy achievements have emerged.This paper provides a comprehensive overview of person re-identification approaches to assist researchers in quickly understand this field with preference as well as to provide a more structured framework.By reviewing more than 300 re-identification related papers, the focus of these studies is summarized as information extraction, metric learning, post-processing, efficiency improvement, labeling cost reduction, and data type expansion.This classification is then organized based on different technologies, and on this basis, the pros and cons of each technology are analyzed.Moreover, this overview summarizes the difficulties and challenges of re-identification and discusses the possible research directions for reference.
DST-HRS: A topic driven hybrid recommender system based on deep semantics

Recommender Systems (RS) provide customized suggestion to users for specific item across the bulk of identical data such as media recommendations, electronic commerce web pages & social networks. RSs are being developed using the methods such as Collaborative Filtering (CF) and Contents Based Filtering (CB). However, CF suffers from sparseness problem wherein user-to-item data is sparse and CB filtering depends on feature extraction methods for item descriptions that require knowledge of content semantics and context of RS. In order to deal with the sparsity problem, various matrix factorization techniques embedded with pre-processed auxiliary information are used. On the other hand, currently employed techniques of feature extraction lack in deep semantics of items textual information as they individually cover either the semantic details or topic information. This paper proposes a hybrid RS model called Deep Semantic based Topic driven Hybrid RS (DST-HRS) that employs item description semantics influenced by its topics information. The proposed model extracts the embeddings by capturing the semantics of textual information and incorporates topic details into it. It further integrates these embeddings into Probabilistic Matrix Factorization (PMF), thus efficiently exploiting the semantics of items textual information such as reviews, synopsis, comments, plots etc to overcome the sparseness issue. The proposed DST-HRS can easily be deployed with lesser computation and time complexity. The model is validated on freely available datasets including Amazon Instant Video (AIV) and Movielens (1M & 10M). The validation exhibited a better performance with respect to sparse ratings of user-to-item as compared to the state-of-the-art.
Explaining recommendations by means of aspect-based transparent memories

Recommender Systems have seen substantial progress in terms of algorithmic sophistication recently. Yet, the systems mostly act as black boxes and are limited in their capacity to explain why an item is recommended. In many cases recommendations methods are employed in scenarios where users not only rate items, but also convey their opinion on various relevant aspects, for instance by the means of textual reviews. Such user-generated content can serve as a useful source for deriving explanatory information to increase system intelligibility and, thereby, the user's understanding.
Read-the-game: System for skill-based visual exploratory activity assessment with a full body virtual reality soccer simulation

We present a novel virtual reality (VR) system to measure soccer players' read-the-game ability. Read-the-game is a term that encompasses a conglomerate of visual exploratory behavioral patterns and cognitive elements required to make accurate in-game decisions. Our technological approach in the Sports Science domain focuses on the visuomotor component of targeted skill development in a VR simulation because VR is a powerful perception-action coupling training solution for visuomotor coordination due to its high sense of immersion and its psychological byproduct presence. Additionally, we analyze two critical aspects: psychological (i.e., sense of presence) and the human-computer interaction (HCI) domain (i.e., suitable input device for full-body immersion). To measure head movements related to visual explorations, the system tracks the user's head excursions. Specifically, the engaged visual exploratory activity (VEA) during a VR simulation is measured frame-by-frame at runtime to study the behavior of players making passing decisions while experiencing pressure from rivals during in-game situations recreated with computer graphics (CG). Additionally, the sense of presence elicited by our system is measured via the Igroup Presence Questionnaire applied to beginner and amateur soccer players (n = 24). Regarding the HCI aspect, a comparison of input options reveals that a high presence can be achieved when using full body interactions that integrate head and body motions via a combination of an HMD and kinetic body tracking. During our system verification, a difference in the VEA performance is observed between beginner and amateur players. Moreover, we demonstrate the capacity of the system to measure the VEA while evoking immersive soccer in-match experiences with a portable VR setup.
Usability Testing based on System Usability Scale and Net Promoter Score

In a variety of software development methods and their models of the activity process, no one does not have testing activities in their application. System testing is the last phase of testing that done by the developer in the internal work- space. However, phase of testing is not the end of the whole testing phase, since there is another stage to pass before the product deployment process, it is the acceptance testing. A popular method to perform the user acceptance testing is the usability testing, which in this method, the result provides the indication of the essential quality for the client to decide whether to approve or to deny the product. In this research of paper, we are going to do the usability testing by utilizing the System Usability Scale technique and the Net Promoter Score technique to measure appropriateness of the population information systems in the city of Tegal while we are also finding optimum testers required using the sampling technique. The result we earn trough this research is according to the Usability Testing result the proposed system developed by researchers is better than the current running system and the total testers participating on system testing is able to optimize using the sampling method.
Consistent Development of the Training Program “Innovation Management”

This paper gives an overview of how the Innovation Management undergraduate program at a Russian engineering university has been modernized for over twelve years. The program has developed from implementation of interdisciplinary training courses, to project training and practice of immersion into real businesses. The survey of fourth-year program graduates revealed barriers to opening startups. Based on the results of the survey, the curriculum was modified to reduce the identified barriers. Among the most effective methods of attracting young people to the field of entrepreneurship, the students indicated the following ones: organization of practical start-up training, organization of events for the exchange of experience, meetings with successful entrepreneurs, and organization of meetings with government representatives dedicated to entrepreneurship. In this paper, we share our experience in organization of a summer schools as one of the most effective methods of popularizing technological entrepreneurship turning the territory of the University campus into a platform for holding master classes and seminars during the event period. Every day young entrepreneurs come to share their experience of implementing their ideas and projects with the invited speakers who successfully work in real markets and increase motivation of school participants for start-ups.
A Self-Attention-Based Approach for Named Entity Recognition in Cybersecurity

With cybersecurity situation more and more complex, data-driven security has become indispensable. Numerous cybersecurity data exists in textual sources and data analysis is difficult for both security analyst and the machine. To convert the textual information into structured data for further automatic analysis, we extract cybersecurity-related entities and propose a self-attention-based neural network model for the named entity recognition in cybersecurity. Considering the single word feature not enough for identifying the entity, we introduce CNN to extract character feature which is then concatenated into the word feature. Then we add the self-attention mechanism based on the existing BiLSTM-CRF model. Finally, we evaluate the proposed model on the labelled dataset and obtain a better performance than the previous entity extraction model.
Market Basket Analysis Using Apriori and FP Growth Algorithm

Market basket analysis finds out customers’ purchasing patterns by discovering important associations among the products which they place in their shopping baskets. It not only assists in decision making process but also increases sales in many business organizations. Apriori and FP Growth are the most common algorithms for mining frequent itemsets. For both algorithms predefined minimum support is needed to satisfy for identifying the frequent itemsets. But when the minimum support is low, a huge number of candidate sets will be generated which requires large computation. In this paper, an approach has been proposed to avoid this large computation by reducing the items of dataset with top selling products. Various percentages of top selling products like 30%, 40%, 50%, 55% have been taken and for both algorithms frequent itemsets and association rules are generated. The results show that if top selling items are used, it is possible to get almost same frequent itemsets and association rules within a short time comparing with that outputs which are derived by computing all the items. From time comparison it is also found that FP Growth algorithm takes smaller time than Apriori algorithm.
Blockchain Technology: Characteristics, Security and Privacy; Issues and Solutions

The Blockchain technology is the most widely used and known of the cybersecurity techniques in recent years. This technology contained a lot of techniques, which are economic models, mathematics, cryptography, and algorithms. It is vital for several reasons, which are solving the synchronization problem and does not require the presence of more than two parties. In this paper, we survey Blockchain technology in terms of the consensus algorithms, the architecture, and the main features of this technology. This survey is constructed by explaining the importance of the privacy and security of the blockchain, issues, and some solutions if possible exists. This paper contributes to supporting the researchers who want to study the Blockchain Technology in terms of concept, applications, "security and privacy issues and solutions", and work mechanism of it.
GWO Based Task Allocation for Load Balancing in Containerized Cloud

On-demand provisioning of computing services such as analytics, intelligence, networking, storage, and servers, etc. over the internet is the main function of cloud computing. Several servers are connected in a distributed manner over the internet to execute tasks. Recently, container technology has gained enormous popularity as it can improve overall application performance by providing OS-level virtualization in cloud computing systems. Based on the resources available on server, a server can accommodate more than one container running on it. The process of distributing the incoming requests or user tasks among all available servers in such a way that all the servers will have almost equal workload is called load balancing. In this paper, we proposed a Grey Wolf Optimization(GWO) based technique for load distribution in the containerized cloud and also to reduce the makespan. We have compared our results with the Genetic algorithm and Particle Swarm Optimization(PSO) based algorithm. The experimental result indicate that the GWO based technique is performing better in terms of load balancing and also having reduced makespan.
A Multi-channel BiLSTM-CNN Model for Multilabel Emotion Classification of Informal Text

State-of-the-art research on emotion classification from text primarily focuses on binary or ternary classification. Yet, humans express a variety of emotions. Here we approach the classification of emotions from short, informal text as a multi-class problem, employing popular psychology models of basic and advanced human emotions. We account for imbalanced datasets differing in annotation schema, psychological models considered, and number of annotated emotions. We show that a multi-channel, multi-filter CNN-BiLSTM outperforms existing models, achieving 82.3% accuracy on the multi-label SemEval18-EC dataset.
Neural Machine Translation for the Bangla-English Language Pair

Due to the rapid advancement of different neural network architectures, the task of automated translation from one language to another is now in a new era of Machine Translation (MT) research. In the last few years, Neural Machine Translation (NMT) architectures have proven to be successful for resource-rich languages, trained on a large dataset of translated sentences, with variations of NMT algorithms used to train the model. In this study, we explore different NMT algorithms - Bidirectional Long Short Term Memory (LSTM) and Transformer based NMT, to translate the Bangla to English language pair. For the experiments, we used different datasets and our experimental results outperform the existing performance by a large margin on different datasets. We also investigated the factors affecting the data quality and how they influence the performance of the models. It shows a promising research avenue to enhance NMT for the Bangla-English language pair.