Reviewerly/exHarmony huggingface dataset:
A Shopping Guide Text Generation System Based on Deep Neural Network

Many people shop on Taobao, Jingdong and other online platforms in China. More and more products are advertised through self-media. Shopping guide text is an effective means to improve the effectiveness of advertising. However, self-media companies need to hire a lot of professional writer to write shopping guide text, which leads to high labor cost. In this paper, we proposed a shopping guide text generator, which can automatically generate shopping guide text given an image of the product. In this paper, we focus on the shopping guide text generation of clothes. The proposed text generator consists of a convolutional neural network, a recurrent neural network with long-short-term-memory (LSTM) over shopping guide text, and a structured module which evaluates the related degree between the image and shopping guide text. The experimental results show that the proposed shopping guide text generation system can generate attractive text to advertise the given clothes.
Democratic Governance

In any democratic nation, electoral processes pave the way to democratic governance. In regards to this, a fully digitized online voting system should be introduced, and governments should invest efforts in studying the possibilities in adopting online voting system to curb the issues mentioned above. In that sense, any user should be allowed to cast his/her vote from any remote location, and the processes should be held in full confidentiality without any intervention or possible vulnerabilities. Therefore, this chapter investigates how governments can learn various initiatives and provide funding to further conduct research and implement such methodologies. The chapter begins by exploring some traditional and current practice of electoral system and further embarks into a review of secured online voting systems that has been proposed by the body of knowledge. The chapter then presents how some countries have adopted this secured online voting systems and giving an avenue to secured digital electoral governance.
Towards HDL-based Synthesis of Reversible Circuits with No Additional Lines

Reversible circuits are needed in different emerging technologies, but their design is still mainly conducted on low abstraction levels thus far. Hardware Description Languages (HDLs) provide suitable description means to lift the design process to higher levels of abstractions. However, synthesis of HDL descriptions thus far still relies on non-reversible building blocks even if the corresponding statements are purely reversible. This leads to reversible circuits with additional circuit lines (i.e., circuit signals)-rendering HDL-based synthesis infeasible for many applications such as quantum computing. In this work, we present a synthesis method which realizes many of the HDL statements with no additional lines at all. To this end, we consider the respective (reversible) HDL statements as an entirety rather than breaking it down into (possibly non-reversible) building blocks. For the first time, this allows to realize many HDL descriptions with no additional circuit lines.
Contextual bound states for qudit magic state distillation

Identifying necessary and sufficient conditions for universal quantum computing is a long-standing open problem for which contextuality is, perhaps, the only promising solution [Howard et al., Nature (London) 510, 351 (2014)]. To justify this conjecture, Howard et al. showed that contextuality is equivalent to Wigner negativity for qudits, and is therefore necessary and possibly sufficient for qudit magic state distillation. Here, we reformulate magic state distillation in the language of discrete phase space, to show that any finite qudit magic state distillation routine contains bound states outside the Wigner polytope; these states exhibit contextuality but are useless for magic state distillation.
Benchmarking Dynamic Searchable Symmetric Encryption Scheme for Cloud-Internet of Things Applications

Recently, the rapid development of Internet of things (IoT) has resulted in the generation of a considerable amount of data, which should be stored. Therefore, it is necessary to develop methods that can easily capture, save, and modify these data. The data generated using IoT contain private information; therefore sufficient security features should be incorporated to ensure that potential attackers cannot access the data. Researchers from various fields are attempting to achieve data security. One of the major challenges is that IoT is a paradigm of how each device in the Internet infrastructure is interconnected to a globally dynamic network. When searching in dynamic cloud-stored data, sensitive data can be easily leaked. IoT data storage and retrieval from untrusted cloud servers should be secure. Searchable symmetric encryption (SSE) is a vital technology in the field of cloud storage. SSE allows users to use keywords to search for data in an untrusted cloud server but the keywords and the data content are concealed from the server. However, an SSE database is seldom used by cloud operators because the data stored on the cloud server is often modified. The server cannot update the data without decryption because the data are encrypted by the user. Therefore, dynamic SSE (DSSE) has been developed in recent years to support the aforementioned requirements. Instead of decrypting the data stored by customers, DSSE adds or deletes encrypted data on the server. A number of DSSE systems based on linked list structures or blind storage (a new primitive) have been proposed. From the perspective of functionality, extensibility, and efficiency, these DSSE systems each have their own advantages and drawbacks. The most crucial aspect of a system that is used in the cloud industry is the trade-off between performance and security. Therefore, we compared the efficiency and security of multiple DSSE systems and identified their shortcomings to develop an improved system.
A Two-Stage Clustering Algorithm Based on Improved K-Means and Density Peak Clustering

The density peak clustering algorithm (DPC) has been widely concerned by researchers since it was proposed. Its advantage lies in its ability to achieve efficient clustering based on two simple assumptions. In DPC, a key step is to manually select the cluster centers according to the decision graph. The quality of the decision graph determines the quality of the selected cluster centers and the quality of the clustering result. The quality of the decision graph is determined by the parameter dc. Although the authors have proposed an empirical parameter selection method, this method does not work well in many real-world datasets. Therefore, in these data sets, the user needs to repeatedly adjust the parameter multiple times to get a good decision graph. Thus, manually selecting cluster centers is not an easy task. In this paper, combined with the clustering idea of K-means and DPC, we propose a two-stage clustering algorithm KDPC that can automatically acquire the cluster centers. In the first stage, KDPC uses an improved K-means algorithm to obtain high quality cluster centers. In the second stage, KDPC clusters the remaining data points according to the clustering idea of DPC. Experiments show that KDPC can achieve good clustering effect in both artificial data sets and real-world data sets. In addition, compared with DPC, KDPC can show better clustering effect in data sets with significant difference in density of clusters.
ENTYFI

Fiction and fantasy are archetypes of long-tail domains that lack comprehensive methods for automated language processing and knowledge extraction. We present ENTYFI, the first methodology for typing entities in fictional texts coming from books, fan communities or amateur writers. ENTYFI builds on 205 automatically induced high-quality type systems for popular fictional domains, and exploits the overlap and reuse of these fictional domains for fine-grained typing in previously unseen texts. ENTYFI comprises five steps: type system induction, domain relatedness ranking, mention detection, mention typing, and type consolidation. The recall-oriented typing module combines a supervised neural model, unsupervised Hearst-style and dependency patterns, and knowledge base lookups. The precision-oriented consolidation stage utilizes co-occurrence statistics in order to remove noise and to identify the most relevant types. Extensive experiments on newly seen fictional texts demonstrate the quality of ENTYFI.
Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation

Development of effective and efficient techniques for video analysis is an important research area in machine learning and computer vision. Matrix factorization (MF) is a powerful tool to perform such tasks. In this contribution, we present a hierarchical robust kernelized Bayesian matrix factorization (RKBMF) model to decompose a data set into low rank and sparse components. The RKBMF model automatically infers the parameters and latent variables including the reduced rank using variational Bayesian inference. Moreover, the model integrates the side information of similarity between frames to improve information extraction from the video. We employ RKBMF to extract background and foreground information from a traffic video. Experimental results demonstrate that RKBMF outperforms state-of-the-art approaches for background/foreground separation, particularly where the video is contaminated.
Analysis of Different Binarization Techniques within Whale Optimization Algorithm

In this study, Whale Optimization Algorithm (WOA), which is a recently proposed swarm intelligence-based algorithm, has been used to solve binary optimization problems. As the WOA algorithm has been developed for optimizing real-valued functions, binary versions of the WOA algorithm have been adopted for the binary optimization problems. The performance of modulation-, normalization-, s-shaped transfer function-, and angle modulation-based binarization approaches are compared. The proposed algorithms tested on the well-known benchmark problems, namely one-max, plateau, deceptive, and royal road. Our computational experiments show that angle modulation and normalization-based binarization approaches give the best results, and binary WOA is promising and has potential to handle difficult binary optimization problems.
Identification of Spontaneous Spoken Texts in Slovak

Abstract We propose a text classification method for the purpose of creating a language model for automatic recognition of spontaneous spoken speech. Transcripts from our departmental speech database served as spontaneous spoken texts. Using supervised machine learning methods, we have created multiple classification models (including neural networks), that were able to distinguish them from written texts with high accuracy. We subsequently verified the accuracy of our trained models on a database of texts containing direct speech extracted from newspaper articles.
An Overview of Recommendation System: Methods and Techniques

In recent years, different types of recommendation system have been developed based on the textual review, comparative opinion, user ratings, purchase patterns, user profiles, etc. These systems have changed the way online world of e-commerce and social media functions—from recommendation of friends on Facebook to purchasing products on Flipkart and choice of movie and music on Netflix. Recommendation system act as a family of information filtering systems that provide recommendation to the users based on his likes and dislikes. The relevance of recommendation becomes even higher in today’s world due to the abundance of information and options. As, the amount of information increased, it gave rise to a problem for users in selecting the items they actually want to buy or the service that they actually want to subscribe to. This is where recommendation system comes into play. This paper will briefly discuss the methods to implement recommendation system and also the techniques used by these methods.
Solar Power System Planning and Design

With growing concerns about greenhouse gas emissions, the security of conventional energy supplies, and the environmental safety of conventional energy production techniques, renewable energy systems are becoming increasingly important and are receiving much political attention [...]
Potentials of It-Supported Assistive Systems: Comparison of Two User Studies in The Manufacturing Industry

Since the demand for customized products increases steadily and the work tasks become more complex, the employees at the shop floor have to deal with a higher information density. Although it is obvious to use information technology to output the required information, work stations might require different supportive technologies. Besides finding adequate work stations to be supported by information technologies, there is also the need to have criteria on which technology is suitable for supporting a specific work task and how the needed information has to be displayed to the employees. In this paper, two user studies of implementing different supportive technologies in different manufacturing work tasks are presented, the potentials shown and compared to derive adequate recommendations for actions.
Robust Bayesian small area estimation based on quantile regression

Quantile and M-quantile regression have been applied successfully to small area estimation within the frequentist approach. Quantile regression is applied in the same context but from a Bayesian perspective. Joint modelling of the quantile function is considered, adopting a non parametric assumption on the data generating process that nonetheless explicitly includes the normal distribution as a special case. A specification of the random part of the model that is simple and consistent with the predictive aim of small area estimation is proposed. Although the main output of the method is the estimation of the whole quantile function, estimators of the small area means based on the integration of the quantile function are proposed and discussed. A simulation exercise is used to assess the frequentist properties of these proposed predictors, that result at least as efficient as frequentist small area estimators based on quantile regression in scenarios characterized by the presence of outliers. The proposed method is illustrated using data from the European survey on Income and Living Conditions (EU-SILC).
Blind Prediction of Original Image Quality for Sentinel Sar Data

Synthetic aperture radar (SAR) images are often subject to visual inspection and analysis. Many factors impact on visual quality of SAR images, such as properties of speckle, dynamic range of data, etc. Thus, the corresponding metrics have to be applied and it is worth predicting their values before one starts analyzing images. Using a set of input parameters (both statistical and spectral) and a trained neural network (NN), we show that full-reference visual quality metrics can be predicted for images acquired by modern SAR Sentinel-1. A prediction accuracy is studied and verified on real-life examples. The source codes and datasets will be made publicly available at https://github.com/asrubel/EUVIP2019.
Intelligent fuzzy algorithm for nonlinear discrete-time systems

This paper is concerned with the stability analysis and the synthesis of model-based fuzzy controllers for a nonlinear large-scale system. In evolved fuzzy NN (neural network) modeling, the NN model and LDI (linear differential inclusion) representation are established for the arbitrary nonlinear dynamics. The evolved bat algorithm (EBA) is first incorporated in the controlled algorithm of stability conditions, which could rapidly find the optimal solution and raise the control performance. This representation is constructed by taking advantage of sector nonlinearity that converts the nonlinear model to a multiple rule base linear model. A new sufficient condition guaranteeing asymptotic stability is implemented via the Lyapunov function in terms of linear matrix inequalities. Subsequently, based on this criterion and the decentralized control scheme, an evolved model-based fuzzy H infinity set is synthesized to stabilize the nonlinear large-scale system. Finally, a numerical example and simulation is given to illustrate the results.
A Cultural Scavenger Hunt Serious Game Based on Audio Augmented Reality

Augmented Reality is one of the most rapidly spreading technology used in several areas such as healthcare, education, entertainment, industry. For tourism and travel, visual augmented reality has been used for guidance, entertainment and exploration, etc. However, its main drawback is that it distracts the visitor from the main real attractions. In this paper, we present a scavenger hunts based on audio augmented reality and serious game concepts. The aim of this game is to create an interactive and immersive visit in order to guide visitors through different attraction places. We used audio augmented reality and some gamification elements to motivate the visitor to explore the main places in "Jnan Sbil" historical garden in Fez, Morocco.
A survey of fall detection model based on wearable sensor

Falls are the leading cause of injury deaths and injury-related hospitalization among elderly people. Due to the high impact of falls on health and healthcare costs, much research has been done on the topic of fall detection in the past decade. wearable sensors Inertial Measurement Units (IMU) (e.g. accelerometers and gyroscopes) are widely used in fall detection for the convenient. This paper is aimed to survey recent research based on the wearable fall detection model, introduced from the perspectives of data collection, feature extraction and classification method, finally a series of technical indicators for fall detection performance evaluation are summarized.
Accelerated Training Algorithms of General Fuzzy Min-Max Neural Network Using GPU for Very High Dimensional Data

One of the issues of training a general fuzzy min-max neural network (GFMM) on very high dimensional data is a long training time even if the number of samples is relatively low. This is a quite common problem shared by many prototype-based methods requiring frequently repeated distance or similarity calculations. This paper proposes the method of accelerating the learning algorithms of the GFMM by, first, reformulating and representing them in a format allowing for their parallel execution and subsequently leveraging the computational power of the graphics processing unit (GPU). The original implementation of GFMM is modified by matrix computations to be executed on the GPU for the very high-dimensional datasets. The empirical results on two very high-dimensional datasets indicated that the training and testing processes performed on Nvidia Quadro P5000 GPU were from 10 to 35 times faster compared to those running serially on the Xeon CPU while retaining the same classification accuracy.
Text Mining and Statistical Learning for the Analysis of the Voice of the Customer

This paper analyzes the content of texts through a Text Mining classification model for the particular case of the Tweets made about the Miniso brand in Mexico during the period from November 17 to 24, 2018. The analysis involves the extraction of the data, the cleaning of the text and supervised support models for high-dimensional data, obtaining as a result the classification of the tweets in the topics: Positive, Negative, Advertising or Requirements of new Branches. As well as the use of resampling techniques to measure the variability of the performance of the model and to improve the accuracy of the parameters. This practice allows to reduce time spent reading texts, especially in Social Networks, finding faster and more efficient trends that help decision-making and respond quickly to customer demand.
The effect of phase fluctuation and beam splitter fluctuation on two-photon quantum random walk*

In the optical quantum random walk system, phase fluctuation and beam splitter fluctuation are two unavoidable decoherence factors. These two factors degrade the performance of quantum random walk by destroying coherence, and even degrade it into a classical one. We propose a scheme for the simulation of quantum random walk using phase shifters, tunable beam splitters, and photodetectors. This proposed scheme enables us to analyze the effect of phase fluctuation and beam splitter fluctuation on two-photon quantum random walk. Furthermore, it is helpful to guide the control of phase fluctuation and beam splitter fluctuation in the experiment.
An exploratory analysis on challenges prevailing in small and medium IT firms

Abstract There is lot of challenges being faced by IT Firms. Be it the pressure on companies to deliver reliable software’s with lesser lead times, pressure to perform at par with market competitors, constant push to deliver quality software; there are several such challenges being faced by these firms. In this research paper, we want to focus our attention to small IT Firms and to understand their challenges by conducting a survey with the help of a structured questionnaire. The questionnaire is designed through the past problems highlighted in literature and a validated through responses from industry practitioners. The opinions are recorded and the relationship between the variables are analysed to give a better understanding of the present challenges in small IT Firms. The work presented in this paper is the first phase towards the development of a framework targeted towards SME IT firms for whom increased lead time is a primary bottleneck. In order to solve a problem, it is essential to understand it in depth and to analyse if there is any gap between the perceived bottles presented in the literature, and the actual problems faced in the industry. Hence, in order to bridge that gap, we have made an effort to structure out the questionnaire based on our findings and have tried to determine if the same can be validated through the responses acquired from the industry in practice.
Improving Supply Chain Resilience with a Hybrid System Architecture

The purpose of this research is to propose a new hybrid system architecture, which is a solution for reducing and even avoiding the complexity and vulnerability of the whole supply chain. In this paper, we propose a conceptional model which introduces some features of decentralized system architecture based on blockchain technologies, such as new consensus mechanism and smart contract into a typical supply chain system so that defects of traditional centralized system will be solved and supply chain resilience can be improved. Furthermore, some fatal flaws of current blockchain system can be shored up in an IoT infrastructure environment which is consolidating supply chain resilience. Last but not lease, we identify some unsettled issues and drawbacks derived from the hybrid system architecture and indicate the solutions regarding these challenges.
PARMA-CC

Clustering is a common component in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g. rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g. detecting critical situations, that are known to be accuracy-dependent), demand for novel approaches that respond faster while coping with large data sets. The latter is the challenge we address in this paper.