Miracle-dz/UniFAR-FTUs huggingface dataset:
DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN

In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralized control, scalability and reliability requirements. In such networking paradigm, controllers synchronize with each other to maintain a logically centralized network view. Despite various proposals of distributed SDN controller architectures, most existing works only assume that such logically centralized network view can be achieved with some synchronization designs, but the question of how exactly controllers should synchronize with each other to maximize the benefits of synchronization under the eventual consistency assumptions is largely overlooked. To this end, we formulate the controller synchronization problem as a Markov Decision Process (MDP) and apply reinforcement learning techniques combined with deep neural network to train a smart controller synchronization policy, which we call the Deep-Q (DQ) Scheduler. Evaluation results show that DQ Scheduler outperforms the anti-entropy algorithm implemented in the ONOS controller by up to 95.2% for inter-domain routing tasks.
Implementation of Differential Power Processing to Optimize the Solar Power Generation

The objective of this paper is to harvest the optimum power from solar panel using different MPPT techniques for low power application under non-uniform irradiance conditions. The overall power output of photovoltaic system is reduced due to partial shading. A new differential power converter along with INC control algorithm is introduced to reduce the partial shading effect and oscillations of output power.
Unraveling the Effects of Google Search on Volatility of Cryptocurrencies

Although cryptocurrencies have garnered enormous public attention in recent times, extensive fluctuations in their prices have deterred prospective investors. Due to the absence of a centralized valuation authority, the credibility of cryptocurrencies as a viable investment vehicle remains elusive. Building on attention theory, this study posits that prospective investors of cryptocurrencies are likely to search online for information before deciding whether to make a commitment. We hence investigate the effects of Google search on the return and risk of 268 cryptocurrencies over 181 trading days. Results indicate that the Google Search Volume Index (SVI) of a given cryptocurrency exerts significant and positive impact on its price and turnover.
Manifold-Structure Preserving Biometric Templates - A Preliminary Study On Fully Cancelable Smartphone Biometric Templates

The increasing use of smartphone biometrics proportionally increases the need to protect the biomemc data. Unlike the traditional passwords, biometric data once comprised cannot be replaced. The smartphones thus need to protect the biometric templates and store the protected templates instead of the raw biomemc images such as face image. The challenge in outaining optimal biometric templates without compromising the performance also relies on preserving the structural neighbourhood of the biometric characteristics. We therefore address these two problems by employing cancelaule biometric templates via hashing. The approach further is based on neighbourhood structure preserving manifold which we refer as Manifold-structure Preserving Biometric Template (MaP-BiT). The proposed approach also demonstrates the properties of an ideal biometric template protection scheme and obeys irreversibility, unlinkability and renewability. In order to validate the applicauility of the proposed approach, we employ a face and periocular database captured using the smart-phone that consists of 94 subjects in 15 different and independent sessions. The proposed approach results in a low Equal Error Rate $(\approx 0.80$%) with a Genuine Match Rate ($\approx 97.5$% at False Match Rate of 0.01%). The results indicate the promising nature of the proposed approach as compared to the traditional unprotected templates while outperforming the bloom-filter based template protection.
Study on Detection of Insulator Defects in Time Domain Terahertz Wave

The bonding performance of the composite inner interface is of great significance to the stable operation of the Insulation equipment. Terahertz wave has many applications in the fields of aviation, biomedicine and security because of its advantages of strong penetration. In order to explore its application in the detection of the interface performance of composite insulators, we conducted the experiments. The terahertz time domain system is used to test insulator models with different interface defects, and the characteristics of the time domain waveforms were analyzed. The test results proved the application value of terahertz wave in the detection of micro defects in the interface. The application scheme of the terahertz wave detection can be further explored. THz wave can be used in detecting the inner defects in the composite insulators. Combined with mathematical tools, the defects features in the defecting waves can be obtained.
A Framework for Descriptive Models Quality Assessment

Numerous systems engineering projects use descriptive models structured by standard metamodels such as SysML, UML, or NAF. Model-based systems engineering is now so well established in the world of systems engineering that there is no longer questions about the usefulness of such modeling activities. However, it is still difficult to determine what is the current level of quality and how far the models are from the expected project objectives. This paper proposes a framework which reduces the pain of elaborating a quality process aside of the main systems engineering tasks so that a project manager can quickly be aware of the modeling progress statuses. The proposed framework is based on a modeling method which is itself modeled so that to allow a predefined set of modeling artefacts self-learning the quality aspects, and generating the appropriate dashboards at different levels of details.
A Novel Approach for Youtube Video Spam Detection using Markov Decision Process

Social networking websites have become an integral part of the day to day lives of people. People turn to social media for interacting with other people, sharing ideas, gaining knowledge, for entertainment and staying informed about the events happening in the rest of the world. Among these sites, YouTube has emerged as the most popular website for sharing and viewing video content. This popularity of YouTube has also attracted spammers, who upload videos with the sole purpose of polluting the system content and causing dissatisfaction among other viewers. These spam videos may be unrelated to their title or may contain pornographic content. Therefore, it is very important to find a way to detect these videos and report them before they are viewed by innocent users. In this paper, we propose a Markov Decision Process approach to model the problem of YouTube video spam detection. We analyze the accuracy of the policy returned by the model and compare it with the accuracy of other data mining algorithms that have been proposed for video spam detection. We find that the proposed model gives a superior performance than the other models.
An Impact-Coupled MEMS Electrostatic Kinetic Energy Harvester and Its Predictive Model Taking Nonlinear Air Damping Effect Into Account

This paper describes a predictive and complete numeric model of a multi-mass and impact-coupled MEMS electrostatic kinetic energy harvester (e-KEH). A silicon resonator is coupled with elastic silicon beams (acting as a part of stoppers) and a miniature metallic ball. The model considers all these impacts as well as the electromechanical force and catches the frequency-up and spring-softening effects. More importantly, for the first time, it models accurately the evolving and non-linear squeeze-film air damping effect in a gap-closing transducer, which is induced by the large amplitude of displacement of the silicon mass. The predictions provided by the model are compared with experimental results, showing a good consistency. The key parameters of the KEH are analyzed with the proposed model, which can be of great help for further design optimization of similar devices. [2018-0174]
A Feature Set for the Similar Activity Recognition Using Smartphone

Human activity recognition(HAR) has been a hot topic in recent years. With the development of smartphone and sensor technique, using smartphone to recognize human activity seems possible. However, due to the diversity of activities, it is not easy to distinguish them, especially for the similar activities. In this paper, we focus on the HAR with the smartphone. We construct a 5-element vector according to the outputs from the 3-axis accelerometer to eliminate the effect of orientation. Then we study the features of activity data from three aspects time domain, frequency domain and time-frequency domain. After that a feature set is chosen for the similarity activity recognition. Experimental results show that this feature set works well under the Multi-Layer Perception classification, even for the general daily activities.
FTR-18: Collecting Rumours on Football Transfer News

This paper describes ongoing work on the creation of a multilingual rumour dataset on football transfer news, FTR-18. Transfer rumours are continuously published by sports media. They can both harm the image of player or a club or increase the player's market value. The proposed dataset includes transfer articles written in English, Spanish and Portuguese. It also comprises Twitter reactions related to the transfer rumours. FTR-18 is suited for rumour classification tasks and allows the research on the linguistic patterns used in sports journalism.
Energy Complexes Consisting of Hydro and Nuclear Power Plants with Low Power Nuclear Reactors

An algorithm for creating the electrical part of an energy complex consisting of power units of a low-power nuclear power plant and a hydroelectric power plant (HPP) has been developed. As an example, the results of various options for the development of an energy complex, suiting Kenya conditions, are presented: a low power nuclear plant having two power units with modular SVBR-100 reactors and a liquid-metal coolant-lead-bismuth alloy-of 100 MW, the creation of which is completed by the Russian State Nuclear Energy Corporation Rosatom, and a hydro power plant having two hydro-generators each with a capacity of 30 MW. As a result of the project implementation, the parameters and types of the main equipment of the energy complex (generators, transformers, hydro turbines) have been determined, the options for the power output scheme of the energy complex and schemes of the auxiliaries have also been proposed. The calculation of short-circuit currents has been performed and the types and parameters of the necessary modern electro-technical equipment for the energy blocks have been determined. The possibility of creating promising energy complexes with hydro and nuclear power plants with low-power nuclear reactors has been justified.
Enhanced Time-Frequency Masking by Using Neural Networks for Monaural Source Separation in Reverberant Room Environments

Deep neural networks (DNNs) have been used for dereverberation and denosing in the monaural source separation problem. However, the performance of current state-of-the-art methods is limited, particularly when applied in highly reverberant room environments. In this paper, we propose an enhanced time-frequency (T-F) mask to improve the separation performance. The ideal enhanced mask (IEM) consists of the dereverberation mask (DM) and the ideal ratio mask (IRM). The DM is specifically applied to eliminate the reverberations in the speech mixture and the IRM helps in denoising. The IEEE and the TIMIT corpora with real room impulse responses (RIRs) and noise from the NOISEX dataset are used to generate speech mixtures for evaluations. The proposed method outperforms the state-of-the-art methods specifically in highly reverberant and noisy room environments.
A Community Based Web Summarization in Near Linear Time

The rapid growth of web users searching for their topics of interest on web, pose challenges to the system, in particular to the search engines. Web content summarization is one crucial application which helps in leveraging the performance of search engines. However summarizing the totality of web content is a laborious task due to the massiveness of web data. Segmenting the web into communities and extracting only relevant pages from those communities for summarization could be a viable solution. This paper presents a novel technique for web summarization by extracting pages of the web according to their degree of authenticity. For this, a large collection of pages are crawled from the web and the communities are identified in linear time based on edge streaming in graph. Then, through link analysis, more authentic pages are identified for summarization. The proposed method is validated through experimentation using real and synthetic data. The results indicate that the proposed model is useful for building an optimized search engine.
Network Compression via Recursive Bayesian Pruning

Recently, compression and acceleration of deep neural networks are in critic need. Bayesian generalization of structured pruning represents an important research direction to solve the above problem. However, the existing Bayesian methods ignore the dependency among neurons and filters for computational simplicity. In this study, we explore, under Bayesian framework, a structured pruning method with layer-wise sequential dependency assumed, a more general learning setting. Based on the property of Dirac distribution, we further derive a new dropout noise, which makes it possible to approximate the posterior of dropout noise knowing that of the previous layer. With the Dirac-like dropout noise, we further propose a recursive strategy, named Recursive Bayesian Pruning (RBP), to train and prune networks in a layer-by-layer fashion. The unimportant neurons and filters are directly targeted and removed, taking the influence from the previous layer. Experiments on typical neural networks LeNet-300-100, LeNet5 and VGG-16 have demonstrated the proposed method are competitive with or even outperform the state-of-the-art methods in several compression and acceleration metrics.
Color Image Reconstruction with Perceptual Compressive Sensing

We propose a novel compressive sensing framework for color images. Recently, compressive sensing (CS) has gain its popularity with the development of deep learning. To our best knowledge, existing methods all deal with RGB images channel by channel. This brings redundancy of measurements. In this paper, we do a breakthrough work. Instead of recovering RGB images channel by channel uniformly, we adopt non-uniform sampling in different channels in YCbCr color space. The luminance component takes up more measurements while the other channels take up less in the proposed framework. It greatly enhances the performance on CS for color images. Moreover, perceptual loss gives a powerful ability to better capture the structure information. We give the measurement rate at 2% as an example in the experiments, and the results show the proposed method outperforms all the existing methods with better structure of images.
Patients’ preferences in periodontal disease treatment elicited alongside an IQWiG benefit assessment: a feasibility study

Background and purpose The German Institute for Quality and Efficiency in Health Care (IQWiG) previously tested two preference elicitation methods in pilot projects and regarded them as generally feasible for prioritizing outcome-specific results of benefit assessment. The present study aimed to investigate the feasibility of completing a discrete choice experiment (DCE) within 3 months and to determine the relative importance of attributes of periodontal disease and its treatment. Patients and methods This preference elicitation was conducted alongside the IQWiG benefit assessment of systematic treatments of periodontal diseases. Attributes were defined based on the benefit assessment, literature review, and patients’ and periodontologists’ interviews. The DCE survey was completed by patients with a history of periodontal disease. Preferences were elicited for the attributes “tooth loss within next 10 years”, “own costs for treatment, follow-up visits, re-treatment”, “complaints and symptoms”, and “frequency of follow-up visits”. Patients completed a self-administered questionnaire including 12 choice tasks. Data were analyzed using a random parameters logit model. The relative attribute importance was calculated based on level ranges. Results Within 3 months, survey development, data collection among 267 patients, data analysis, and provision of a study report could be completed. The analysis showed that tooth loss (score 0.73) was the most important attribute in patients’ decisions, followed by complaints and symptoms (0.22), frequency of follow-up visits (0.02), and costs (0.03) (relative importance scores summing up to 1). Conclusion A preference analysis performing a DCE can be generally feasible within 3 months; however, a good research infrastructure and access to patients is required. Outcomes used in benefit assessments might need to be adapted to be used in preference analyses.
Efficiency Optimization Strategy Based on Optimal Equivalent Load in Inductively Coupled Power Transfer System with Parallel Multi-Pickups

A novel efficiency optimization strategy based on optimal equivalent load in inductively coupled power transfer(ICPT)system is proposed to enhance system efficiency. The relationship between the load and the efficiency of ICPT systems with parallel multi-pickups is analyzed. An optimal load resistance is obtained with parameters of contactless transformer at which the efficiency is the highest. BOOST circuit is introduced to transfer the load into near optimal resistance region. A prototype has been constructed and the experiment has been carried out to verify the proposed strategy. The experimental results show the improved efficiency under light load.
Mindfulness – The Missing Link in the Relationship Between Leader–Follower Strategic Optimism (Mis)match and Work Engagement

Assuming a followership perspective and building on implicit leadership theory, this study examines the mediating role of followers’ mindfulness in the relationship between leader–follower strategic optimism (mis)match and work engagement. Specifically, we propose that a discrepancy between the respective levels of leaders’ and followers’ strategic optimism correlates with low levels of mindfulness and work engagement. A field study of 291 working professionals, using polynomial regression and response surface analysis, supports the (mis)match hypotheses. The results demonstrate that followers’ mindfulness mediates the relationship between leaders’ and followers’ matching levels of strategic optimism (whether at high-high and low-low leader-follower strategic optimism conditions) and work engagement. These findings have important implications for training and the extent to which interventions based on personal resources, such as strategic optimism and therefore mindfulness, foster higher work engagement.
I or You: Whom Should Online Reviewers Direct Their Attention To, and When?

Online reviews are pervasive and important in the digital age. However, people may write reviews with different focuses of attention: directed toward themselves or toward future readers. The present paper examines how a focus on future review readers (prospective consumers) influences these readers’ perception of review helpfulness. Despite the widely shared view that other-focus leads to positive outcomes, we propose that the effect of other-focus on review helpfulness perceptions is more nuanced. Drawing on the literature on attentional focus and persuasion, we develop a theoretical framework suggesting that reviewers’ other-focus may influence consumers’ perceived review helpfulness through both positive and negative processes, and that the overall effect of other-focus is dependent on the review’s two-sidedness. Results of an archival analysis using Apple’s App Store and two experiments provide consistent support for our hypotheses. Our findings challenge the predominant view of the positive effect of otherfocus, reveal a critical boundary condition and possible reasons, and provide important implications for product/service providers, review platforms and reviewers.
The Role of Cultural Differences when Using Different Classifications: An Experiment Design for Design Technique Selection

In order to deliver software which is enjoyable and easy to use, design experts are embedded in the global software development. Yet, the software industry witnesses a shortage in design experts. Design novices need to be trained to select appropriate design techniques for various task scenarios. Here, classifications of design techniques can support this selection. We suggest that the presence of classifications can lead to better task performance, whereas different types of classifications may lead to different task performances of novices. Furthermore, we suggest that cognitive styles can moderate the relationship between classification types and task performances. In order to test the hypotheses, we present the design of an online experiment. The experiment process is explained, and a prototype used in the online experiment and for collecting data is introduced. We expect to extend the theoretical understanding of the role of cultural differences in information systems research.
Indoor Positioning and Tracking by Multi-Point Observations of BLE Beacon Signal

This paper proposes an indoor positioning and tracking method with the aid of large-scale fingerprint observed by received signal strength indicator (RSSI) of Bluetooth low energy (BLE). The RSSI-based positioning is roughly classified into two types: user navigation and monitoring. Our target is the user monitoring scenario by using commercially available BLE dongles, which are compact, inexpensive, and low power consumptions. In this case, the user device sends beacon signals to a large number of receivers in the room. Due to the insufficient BLE transceiver for the accurate positioning, sophisticated support vector machine (SVM)-aided machine learning algorithm plays a vital role in dealing with impairments induced by BLE. Furthermore, according to contiguous movements of the object, prior information of the positioning can be yielded for improving the object tracking capability. Finally, Kalman filter is applied to smooth the tracking results. Experimental results demonstrate the validity of the proposed method.
Extending Denoising AutoEncoders for Feature Recognition

Image rendering techniques involve the addition of noise on the rendered samples. The characteristics of Monte Carlo rendered noisy images makes it difficult to denoise using conventional methods. The noise induced in this case is spatially sparse. Using traditional methods of denoising and feature recognition is time consuming for such images as they use the entire image space as their search space. This results in unnecessary calculations that can be avoided and therefore reduce the processing time significantly. A recurrent convolutional neural network that operates on varying spatial resolutions, also known as the auto encoder decoder structure perform very well on these rendered images. The partitioning into encoder and decoder phases lets the network operate on continuously decreasing and increasing spatial domains respectively. This can be coupled with classification techniques to incorporate feature recognition of noisy images. In this paper the pretrained autoencoder layers are supported with additional softmax and sigmoid layers to enable feature recognition capabilities.