Reviewerly/exHarmony huggingface dataset:
Adaptive comb-type filtering method for stripe noise removal in infrared images

Occasionally, stripe noise infiltrates infrared images during acquisition, causing the image quality to severely deteriorate and affecting subsequent image analysis. To remove stripe noise, an adaptive comb-type notch filtering method is proposed, and a spatial mathematical stripe noise model and the comb-like impulse spectrum characteristics of stripe noise are theoretically and experimentally analyzed. The analysis results demonstrate that when periodic stripe noise consists of only intact stripes, the amplitude spectrum of the periodic stripe noise is an ideal impulse comb spectrum, and the noise frequency energy is concentrated; by contrast, when the periodic stripe noise contains incomplete stripes, spectral leakage occurs, and the noise frequency energy spreads to all frequencies. On this basis, optimal methods of estimating the stripe width and the size of the subimages to be processed are also presented. First, we crop the original image into two subimages of the optimal size to focus the comb-like impulse spectrum characteristics of the stripe noise, thereby enabling easier removal of the stripe noise patterns in the subimages. Then we detect the peaks in the amplitude spectra of the subimages based on the characteristics of the comb-like impulse spectrum and eliminate those peaks using an adaptive comb-type notch filter. Finally, we fuse the two filtered subimages into a final image. Experimental results show that the proposed method achieves a good effect in removing stripe noise from infrared images.
Panoramic Light Field From Hand-Held Video and Its Sampling for Real-Time Rendering

By providing angular and spatial information of light rays, light field images are widely used in many applications. To capture large field of view light field, existing approaches either stitch small field of view light fields, whose apertures are also small, or leverage specialized equipments, which are not accessible to ordinary users. In this paper, we present a method to extract fully 360° field of view panoramic light fields from densely sampled hand-held videos. Unlike previous works, our method handles the large exposure variation and motion blur problems, which are common in the panoramic capture and hand-held videos. To provide real-time rendering performance, our method applies light field sampling to the extracted panoramic light field. Based on the unstructured representation, our method can sample the light field without explicit parameterization. We formulate the sampling problem into a set multicover problem and solve it globally using integer linear programming. Compared with the greedy based method, our approach can use much less video frames to represent the panoramic light field, but still can get better rendering results.
Intelligent Recommender System for Big Data Applications Based on the Random Neural Network

Online market places make their profit based on their advertisements or sales commission while businesses have the commercial interest to rank higher on recommendations to attract more customers. Web users cannot be guaranteed that the products provided by recommender systems within Big Data are either exhaustive or relevant to their needs. This article analyses the product rank relevance provided by different commercial Big Data recommender systems (Grouplens film, Trip Advisor and Amazon); it also proposes an Intelligent Recommender System (IRS) based on the Random Neural Network; IRS acts as an interface between the customer and the different Recommender Systems that iteratively adapts to the perceived user relevance. In addition, a relevance metric that combines both relevance and rank is presented; this metric is used to validate and compare the performance of the proposed algorithm. On average, IRS outperforms the Big Data recommender systems after learning iteratively from its customer.
SpamTracer: Manual Fake Review Detection for O2O Commercial Platforms by Using Geolocation Features

Nowadays, O2O commercial platforms are playing a crucial role in our daily purchases. However, some people are trying to manipulate the online market maliciously by opinion spamming, a kind of web fraud behavior like writing fake reviews, due to fame and profits, which will harm online purchasing environment and should be detected and eliminated. Moreover, manual fake reviewers are more deceptive compared with old web spambots. Although several efficient methods were proposed in the fake review detection field, the manual fake reviewers are also evolving rapidly. They imitate to be benign users to control the velocity of review fraud actions, and deceive the detection system. Our investigation presented that geolocation factor is potential and can well reflect the distinctions between fake reviewers and benign users. In this research, we analyzed the geolocations of shops in reviews, found the distinct distribution features of those in fake reviewers and benign users, and proposed a SpamTracer model that can identify fake reviewers and benign users by exploiting an improved HMM (Hidden Markov Model). Our experiment demonstrated that SpamTracer could achieve 71% accuracy and 76% recall in the unbalanced dataset, outperforming some excellent classical approaches in the aspect of stability. Furthermore, SpamTracer can help to analyze the regularities of review fraud actions. Those regularities reflect the time and location in which online shops are likely to hire fake reviewers to increase their turnover. We also found that a small group of fake reviewers tend to work with plural shops located in a small business zone.
GLOBDEF: A Framework for Dynamic Pipelines of Semantic Data Enrichment Tools

Semantic data enrichment adds information to raw data to allow computational reasoning based on the meaning of data. With the introduction of Linked Data a lot of work is spent on combining existing tools for specific enhancement needs into multi-domain reusable enhancement pipeline. As part of the GloBIG project, we are working on the development of a framework for data enhancement, which attempts to be domain-agnostic and dynamically configurable. It works with pluggable enhancement modules, which are dynamically activated to create on-the-fly pipelines for data enhancement. Our research goal is to find a way for processing large amounts of data and automatically enhancing it while leveraging variety of domain knowledge sources and tools by selecting and using the most suitable ones according to the data. In this paper we present our proof-of-concept implementation of the so called GLOBDEF framework and discuss the challenges and next steps on its development.
Saliency prediction by Mahalanobis distance of topological feature on deep color components

A new saliency prediction method via extracting topological feature and calculating Mahalanobis distance on deep color components is presented in this paper. Specifically, four selectable schemes of color components are considered and a deep convolutional network is used to learn the best scheme. Then the topological feature maps of an input image are extracted on the learned color components by the analysis of connectivity and adjacency. To achieve the final saliency map, a new fusion method is proposed by calculating the Mahalanobis distance between the feature maps and their means with their covariance matrices rather than summating the feature maps linearly. The numerical and visual evaluation shows that a competitive performance compared with fourteen state-of-the-art models is achieved by the proposed method.
Modeling the Automatic Voltage Regulator (AVR) Using Artificial Neural Network

The artificial neural network (ANN) has been successfully applied to many systems' models with the objective of improving their performance. This study presents the ANN application to the automated voltage regulator (AVR) model of the synchronous machine. The studied system consists of the single-machine infinite bus (SMIB) power system. Data was extracted over a diverse range of conditions. Different fault types were also considered for collecting a large number of data to avoid overfitting. The multilayer feedforward neural network (MLFFNN) approach was applied to design the paradigm and system equations were solved using the MATLAB Simulink software.
The Subject Specialist is Dead. Long Live the Subject Specialist!

The role of the subject specialist as selector must transform! Libraries need to recognize that the scholarly record is evolving, and they need to develop roles that reflect this reality. Scholarly outputs are shifting away from static and text-based documentation of final research outcomes to dynamic and fluid digital outputs produced throughout the research process. It is imperative that libraries develop workflows and roles to identify and capture these outputs. This article will discuss and describe how the subject specialist’s traditional skills and responsibilities position them to be highly relevant and effective as libraries transition to new collecting approaches.
Cybersecurity and the Evolutions of Healthcare: Challenges and Threats Behind Its Evolution

HealthcareHealthcare is among the fields that adopted ICT very early to improve physicians’ work. The digital transformation in healthcare started already some years ago, with the computerization of hospitals. Todays’ healthcare is at the forefront again, as one of the most attacked and profitable areas of exploitationExploitation for cybercriminals and cyberterrorists. The overabundance of valuable information, its nature of critical infrastructure and its mobile services, are at the centre of cybercriminals attentions. Besides, patients and physicians, both went through a massive digital transformation; nowadays, healthcare operators and users are highly digitalized and mobile. This evolution influences how, respectively, healthcare operators and patients offer and consume services. The present chapter starts from a presentation of how the modern workforces changed their working paradigms and then introduces the concepts of Hospital 2.0 and patient ecosystem. The chapter also explores the cyberterrorism and cybercrime, present and future threats landscapes, including the mobile healthMobile health example.
Software Module Clustering Based on the Fuzzy Adaptive Teaching Learning Based Optimization Algorithm

Although showing competitive performances in many real-world optimization problems, Teaching Learning based Optimization Algorithm (TLBO) has been criticized for having poor control on exploration and exploitation. Addressing these issues, a new variant of TLBO called Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) has been developed in the literature. This paper describes the adoption of Fuzzy Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) for software module clustering problem. Comparative studies with the original Teaching Learning based Optimization (TLBO) and other Fuzzy TLBO variant demonstrate that ATLBO gives superior performance owing to its adaptive selection of search operators based on the need of the current search.
Image Enhancement Based on Pulse Coupled Neural Network in the Nonsubsample Shearlet Transform Domain

In this study, pulse coupled neural network (PCNN) was modified and applied to the enhancement of blur images. In the transform domain of nonsubsample shearlet transform (NSST), PCNN was used to enhance the details of images in the low- and high-frequency subbands, and then the enhanced low- and high-frequency coefficients were used for NSST inverse transformation to obtain the enhanced images. The results showed that the proposed method can produce higher-quality images and suppress noise better than traditional image enhancement strategies.
Control of entropic uncertainty in circuit QED

We investigate the dynamics of quantum-memory-assisted entropic uncertainty relations under two typical categories of noise: phase damping channel and amplitude damping channel. It is shown that, the entropic uncertainty monotonically increases and tends to a steady-state value with the increase of decoherence in phase damping channel, and can always keep its lower bound during the evolution when the initial state is the maximum entangled state. In contrast, under the amplitude damping channel with memory, the entropic uncertainty always fails to reach its lower bound, even in the decoherence-free subspace formed by it. Besides, the entropic uncertainty and its lower bound firstly increase with time, then turn down and tend to steady-state values. Our investigations might offer an insight into the dynamics of the measurement uncertainty under decoherence, and are important to quantum precision measurement in open systems.
See Extensively While Focusing on the Core Area for Pedestrian Detection

Pedestrian detection attracts much attention from the academic community since it is an essential and significant component of autonomous driving. Despite many similarities with general object detection, pedestrian detection still has unique challenges such as background false positives and missed detections in a crowd. In this paper, we explore the value of contextual information for pedestrian detection, arguing that the contextual information is not a panacea in all cases. Despite the importance of enriching context, a method should focus on the core area where there is the target instance. To this end, we propose a pedestrian detection framework that extends proposals to enrich the context and relieves the confusions caused by contextual information through a supervised attention module. The ablation study demonstrates that the proposed extension is effective for both crowded and non-crowded instances; moreover, the attention module narrows the precision gap between these two cases. The extensive experiments on commonly used pedestrian benchmarks are conducted, which show the superior performance of our method. We achieve the average precision of 74.78% on KITTI pedestrian benchmark with the hard metric level, ranking the first places among all methods.
Effects of Self-Enhancement on Eye Movements During Reading

Previous studies show that readers’ eye movements are influenced by text properties and readers’ personal cognitive characteristics. In the current study, we further show that readers’ eye movements are influenced by a social motivation of self-enhancement. We asked participants to silently read sentences that describe self or others with positive or negative traits while their eyes were monitored. First-fixation duration and gaze duration were longer when positive words were used to describe self than to describe others, but with no corresponding effect for negative words. These results therefore suggest that eye movements can be influenced by the motivation of self-enhancement in addition to various stimuli features and cognitive factors. This finding indicates that eye movements are sensitive to high level cognitive processes such as self enhancement, and therefore the eye movement methodology can potentially be used to study implicit social cognition.
Enhanced Quantum Teleportation between Noninertial Moved Parties by Weak Measurements

Abstract Quantum decoherence caused by the interaction between the quantum system and the environment is the most limiting factor for the applications of quantum teleportation. In this paper, investigated are the dynamic behavior of the coherence of the entangled source and the fidelity of the teleportation for two non‐inertial moved parties suffering the quantum noise induced by the background quantum fields, and a general protection scheme using weak measurements is provided. Explicitly, comparing the cases of using the initial state as well as the initial state , the coherence of the entangled state and teleportation fidelity with and without probabilistic weak measurements are studied. The results show that the decoherence caused by the thermalization of the two parties induced by their non‐inertial movements can be eliminated. The coherence and fidelity can be totally recovered with proper weak measurements and the optimal weak values are shown in relation with the spontaneous emission and excitation rates of the systems. However, the successful probability in the case of using the initial state is found to be much larger than that in the case of using the state and the preweak measurement is not needed with the initial state .
The chemical properties of seaweed for modify concrete

Seaweed is one of the natural sources usually use in industrial food, cosmetic, pharmaceutical and fertiliser. A natural polymer material such as seaweed is found to have excellent bonding mechanism and also the critical factor to achieve sustainability. Malaysia is also rich with seaweed species like Eucheuma Cottonii. The future study was carried out seaweed species from Malaysia especially seaweed from Sabah. This research aimed to investigate the relationship of a few chemical properties namely physical properties, metal element content, microstructure image and chemical component of seaweed type of Eucheuma Cottonii for modifying concrete. This research is mainly base on experimental works. That powdered seaweed from Eucheuma Cottonii was analysis using Energy Dispersive X-Ray (EDX), Scanning Electron Analysis (SEM) and Fourier Transform Infra-Red (FT-IR). All the procedure of the laboratory work complies with the specified and relevant standard. The research had shown that Euchema Cottonii powder content suitable for metal and component to bind together with concrete. Concrete with seaweed powder is alternative to become green construction material for sustainable concrete
Multidirectional wavelet-based fringe analysis for surface diagnosis by optical profilometry

Scientists, engineers, and manufacturers have a critical need for better techniques of diagnosis and quality control. The optical metrology uses the practical light and computer sciences, in order to perform simulation, design, computing and inspection for many scientific and industrial applications, such as optics, mechanics, aeronautics, electronics and many others. The fringe pattern analysis is a method to perform some operations on the optical images, in order to get an interferometric phase map and then to extract some useful information from it. In this paper, an exciting improvements of local fringe demodulation algorithm are presented, which are based on a new multidirectional wavelet. The numerical and experimental works show interesting gains compared to the other standard algorithms. Our approach executes as fast as the popular phase retrieval methods, but with substantially improved accuracy in demodulating noisy fringes. All of this comes without any pre-processing by filtering models.
Device for mHealth

Nowadays, wearable technology is the most promising and market growing technology. Wearable can be considered the winning card up to the mHealth sleeve. Despite mHealth born around the 2000s, only in the last lustrum, it has seen a massive diffusion both for monitoring and diagnosis. Moreover, many existing devices and products have been equipped with data transmission technologies in order to improve the capability of communicating data over the Internet by means of mobile devices (smartphone or tablet) or direct connection. Data transmission allow for communicating health data directly to physicians. This permits to monitor the patient from a distance directly from home, increasing their life quality and, in the meantime, decreasing the welfare costs. This chapter wants to be a compendium of the existing solution in term of wearable, but also non-wearable devices for mobile health. The last paragraph of the chapter reports current and future development of wearable devices, with invisible technology, smart garments, and Wearable 2.0.
The Review of Recommendation System

With the development of the Internet, the amount of information continues to increase, and the problem of "information overloading" is becoming more and more obvious. Simple information retrieval can no longer satisfies the needs of users to search for accurate information, and the recommendation system emerges. Although the recommendation system is widely used in e-commerce, the recommended algorithm faces more difficulties. The paper firstly introduces the related concepts, the directions of application and the principles of the recommendation system, then the paper analyzes the advantages and disadvantages of these algorithms. Finally, it summarizes some main problems and the directions of the research the recommendation system needs to solve.
Empathic Autonomous Agents

Identifying and resolving conflicts of interests is a key challenge when designing autonomous agents. For example, such conflicts often occur when complex information systems interact persuasively with humans and are in the future likely to arise in non-human agent-to-agent interaction. We introduce a theoretical framework for an empathic autonomous agent that proactively identifies potential conflicts of interests in interactions with other agents (and humans) by considering their utility functions and comparing them with its own preferences using a system of shared values to find a solution all agents consider acceptable. To illustrate how empathic autonomous agents work, we provide running examples and a simple prototype implementation in a general-purpose programing language. To give a high-level overview of our work, we propose a reasoning-loop architecture for our empathic agent.
Leveraging contextual information for cold‐start Web service recommendation

Summary Web service recommendation becomes an increasingly important issue when more and more services are published on the Internet. Many Web service recommendation methods have been proposed in recent years, most of which adopted collaborative filtering (CF) techniques. In general, these approaches have two limitations. Firstly, they rarely leverage user ratings since this kind of explicit feedback is difficult to collect for Web services. Secondly, the new user cold‐start problem is an inherent limitation of CF because the new users have not yet cast sufficient numbers of votes. In this paper, pseudo ratings of services constructed based on plenty of user‐service interactions, also known as a kind of implicit feedback, are provided to represent users' preferences on services. Based on these pseudo ratings, we present a novel Web service recommendation approach, which can alleviate the cold‐start problem by integrating contextual information and an online learning model. Experiments conducted on a real world data set show that, compared with the method without contextual information, our proposed approach that handles the cold start problem by integrating contextual information can achieve better F‐Measure performance (5.08 times increase on average). Moreover, the proposed online recommendation approach can dramatically decrease the time overhead while keeping the similar recommendation performance.