Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimum Grid Size Estimation using Fuzzy-AHP for Radio Fingerprinting based Indoor Localization

In the last few decades, Radio Fingerprinting based Indoor Localization Systems (RFILSs) are widely adopted for various location based services. These systems determine the position of mobile device using a radio map. This radio map is constructed by calibrating the site into number of equal sized grids and measuring Wi-Fi signals from various access points on each grid. However, the use of optimum grid size to construct the radio map not only affects the calibration effort, it also influences other performance criteria i.e., localization error, precision rate, and response time. Therefore, taking this into consideration, it becomes essential to choose the optimum grid size based on the multiple criteria. In this paper, firstly, the influence of various grid sizes is experimentally evaluated on each performance criterion. Then, Fuzzy Analytical Hierarchy Process (FAHP) is applied on them to determine the optimum grid size. The experiments are performed by logically dividing the testbed into number of grids in the range of 1 × 1\ m2 to 3× 3 m2 on the variation of 0.5 × 0.5 m2.
Integrated Guidance, Navigation, and Control System for a UAV in a GPS Denied Environment

This paper proposes an integrated guidance, navigation, and control system for operations of a UAV in GPS denied environments. The proposed system uses a sensor combination, which consists of an image sensor and a range sensor. The main idea of the system developed is that it replaces the conventional navigation information with the measurement from the image processing. For example, it is possible to substitute the look angle and look angle rate from the image sensor for the conventional navigation information like the relative target position and the body angular rate. As the preliminary study, the integrated guidance and control system is designed with a nonlinear back-stepping approach to investigate the possibility of the proposed system. And the proposed integrated guidance and control system is verified by the numerical simulation.
A pHRI Framework for Modifying a Robot's Kinematic Behaviour via Varying Stiffness and Dynamical System Synchronization

In this work, we propose a robot control framework for modifying a desired robot kinematic behavior encoded in dynamical movement primitives (DMP) by physically interacting with the robot during its autonomous operation. The proposed method is based on variable stiffness and DMP time synchronization with the user during the interaction. The overall controlled system is proved to be stable. After the user stops interacting with the robot, the robot motion continues according to the learned kinematic behavior until it reaches the final task goal. At the next execution cycle a new DMP can been learned to generate the modified trajectory. In this way, explicit robot programming and separation of learning and execution stages is eliminated. The proposed approach is implemented and evaluated on a 7-degree-of-freedom KUKA LWR4.
Moving Away from a Graded System

This book presents an analysis of a non-retention policy in Primary Education in Brazil called as Cycles of Learning and its implementation in a sample of Brazilian primary schools. The policy investigated was designed to reduce retention and dropout rates and age/grade mismatch, provide students with more time to learn and ultimately create a more inclusive educational system. The theoretical framework used in the thesis is based on the ‘policy cycle'' approach developed by Stephen Ball and his colleagues and Basil Bernstein''s theories of recontextualisation and pedagogies. The data, which are largely qualitative, derive from interviews with policy makers and analysis of policy documents. The research also involved qualitative investigation of four primary schools, consisting of interviews with 20 teachers and eight heads and observations of classrooms and schools'' activities. Overall the research has found that the policy''s implementation has been contested and patchy. This book demonstrates the need to analyse the mode of implementation and the nature of the policy itself so that the process of reform can be better understood.
Estimating Rock Moisture Based on Ground Penetration Radar Survey in Frozen and Thawed States

Possibilities of estimating rock moisture based on ground penetrating radar data were considered. A methodology for estimating moisture of dispersed rocks has been proposed which uses an empirical formula based on the relative change of delay time $(\mathbf{N}_{\mathrm{t}})$ of GPR signals reflected from the medium interface in the frozen ($(\mathbf{t}_{\mathbf{M}}$., ns) and thawed ($(\mathbf{t}_{\mathbf{T}}$, ns) states. It was approbated in the natural environment. , on two sites in Central Yakutia. GPR data within the active layer during the period of complete freezing and thawing of rocks was examined. On the first testing site. , data values were selected from a section segment in the vicinity of the check hole. The average value of the signal delay time from the reference boundary at a depth of 1.7 m was calculated based on three adjoining sounding locations. The relative change of the signal delay time $\mathbf{N}_{\mathbf{t}}$ was evaluated. The average gravimetric water content was calculated according to the proposed formula. Moisture distribution along the GPR section on the second approbation site was also determined in accordance with the methodology. The proposed methodology enables remote evaluation of dispersed rock moisture and its changes under the influence of various natural and anthropogenic factors within the active layer of the cryolithozone rock massif.
Incremental parallel computing for continuous queries in dynamic graphs using a transactional model

In dynamically evolving graphs, one may be interested in continuously observing certain properties of the graph. One approach for continuous monitoring is to re‐execute the graph analytics program on the entire graph after it is updated. However, this approach can lead to high computation cost and latency in case of large graphs. An alternate approach, which we present in this paper, is to execute the analytics program only initially and then perform incremental computations for supporting continuous queries as the graph is modified. The goal of our work is to develop incremental parallel computing techniques to continuously monitor a graph as it is updated to check for the properties of interest. We present the results of our investigation of utilizing a transactional model of parallel programming for supporting continuous queries on dynamic and evolving graphs. In our model, the graph updates are performed as transactions, which trigger the execution of a set of transactional tasks to perform incremental computations. In our testbed system, the graph data is stored in the RAM of cluster nodes, and continuous queries involve the parallel execution of transactional tasks on the cluster nodes. Using a set of graph problems, we illustrate this approach and evaluate its performance.
Accelerated proximal boosting

Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure of gradient boosting mimics a gradient descent on a functional variable. This paper proposes to build upon the proximal point algorithm when the empirical risk to minimize is not differentiable. In addition , the novel boosting approach, called accelerated proximal boosting, benefits from Nesterov's acceleration in the same way as gradient boosting [Biau et al., 2018]. Advantages of leveraging proximal methods for boosting are illustrated by numerical experiments on simulated and real-world data. In particular, we exhibit a favorable comparison over gradient boosting regarding convergence rate and prediction accuracy.
Designing of Road Loads Simulator for EV

The task of proposed simulator is to load the traction drive by taking into account the structure and parameters of an electric vehicle (EV). Therefore, this design leads to determination of vehicle performance in the laboratory instead of road tests. In this paper presents an equivalent loading of the electric motor driving the vehicle has been described. Most of the significant forces acting on EV are considered and recalculated to the motor shaft. Loading torque caused by EV is realized by the DC electric generator with the field winding current control. The control system and its operation was described in the paper.
Deep Learning with a Spatiotemporal Descriptor of Appearance and Motion Estimation for Video Anomaly Detection

The automatic detection and recognition of anomalous events in crowded and complex scenes on video are the research objectives of this paper. The main challenge in this system is to create models for detecting such events due to their changeability and the territory of the context of the scenes. Due to these challenges, this paper proposed a novel HOME FAST (Histogram of Orientation, Magnitude, and Entropy with Fast Accelerated Segment Test) spatiotemporal feature extraction approach based on optical flow information to capture anomalies. This descriptor performs the video analysis within the smart surveillance domain and detects anomalies. In deep learning, the training step learns all the normal patterns from the high-level and low-level information. The events are described in testing and, if they differ from the normal pattern, are considered as anomalous. The overall proposed system robustly identifies both local and global abnormal events from complex scenes and solves the problem of detection under various transformations with respect to the state-of-the-art approaches. The performance assessment of the simulation outcome validated that the projected model could handle different anomalous events in a crowded scene and automatically recognize anomalous events with success.
The University of Cambridge’s Machine Translation Systems for WMT18

The University of Cambridge submission to the WMT18 news translation task focuses on the combination of diverse models of translation. We compare recurrent, convolutional, and self-attention-based neural models on German-English, English-German, and Chinese-English. Our final system combines all neural models together with a phrase-based SMT system in an MBR-based scheme. We report small but consistent gains on top of strong Transformer ensembles.
The Role of Midkine in Arteriogenesis, Involving Mechanosensing, Endothelial Cell Proliferation, and Vasodilation

Mechanical forces in blood circulation such as shear stress play a predominant role in many physiological and pathophysiological processes related to vascular responses or vessel remodeling. Arteriogenesis, defined as the growth of pre-existing arterioles into functional collateral arteries compensating for stenosed or occluded arteries, is such a process. Midkine, a pleiotropic protein and growth factor, has originally been identified to orchestrate embryonic development. In the adult organism its expression is restricted to distinct tissues (including tumors), whereby midkine is strongly expressed in inflamed tissue and has been shown to promote inflammation. Recent investigations conferred midkine an important function in vascular remodeling and growth. In this review, we introduce the midkine gene and protein along with its cognate receptors, and highlight its role in inflammation and the vascular system with special emphasis on arteriogenesis, particularly focusing on shear stress-mediated vascular cell proliferation and vasodilatation.
Study of User Behaviors in OSNs

Almost every individual around the world are in touch with each other using OSN. Using OSN user can cooperate with each other and share their knowledge and wisdom. Approximately OSNs are created every day however only few of them grow to be popular worldwide and other become extinct. Success and failure of OSN both depends upon behavior of user. Understanding user's behavior is key to the success of OSNs. The status of OSN is demonstrated by the number of people connecting, interacting and sharing data on the online social network and consider as collective social action. With maximum utilization of OSN, regular customs of human nature has taken drastic change. Traditional routines in progress bought revolution to online shopping, the way of communication through internet, online education or e-learning, online entertainment, online advertising and online marketing etc. Almost everything substantial or non-substantial is now just a click away by using OSN. OSN is also boosting in the field of sociology as OSN stands on online human interaction, online gaming platforms, blogs, instant messages services, sharing information. Such drastic revolution in society exposed too many discoveries in human behavior and equilibrium. This enhanced new area of research and scientific community. In This paper we focus on analysis of different user behavior in online social networks
Improving Short Text Classification Using Fast Semantic Expansion on Multichannel Convolutional Neural Network

Nowadays, text classification is recognized as one of crucial tools for business users to gain more insights from customers. However, textual data from customers such as comments are usually short. Thus, there are two main issues in short text categorization: (1) insufficient contextual information and (2) noisy data due to misspellings. Recently, there is a prior attempt to propose a deep learning approach for short text categorization by expanding semantic using word embeddings clustering via an algorithm based on density peaks searching. Since the number of words in word embeddings is usually large, the clustering algorithm does not scale with the size of data set, thus demanding unacceptable computational cost. In this paper, we aim to propose a fast short-text categorization framework. Rather than using a CNN with k-max pooling layer, we propose to use a faster version of Convolutional Neural Network (CNN) called “multichannel CNN. ” To speed up the semantic expansion process, we propose to employ mini batch K-Means++, which is considerably faster and scales well with the size of data set. Furthermore, we also introduce an additional preprocessing step to increase vocabulary coverage rate on word embeddings. We conducted experiments on four public data sets: Google Snippets, TREC, MR, and Subj. The results showed that the proposed framework does not only improve an accuracy on all data sets, but also reduces computational costs from several days to a few hours.
An MPC with Disturbance Forecasting for the Control of the Level of a Tank with Limited Buffer Capacity

The paper deals with the behavior of an MPC for the control of a level of a tank, whose inflow is subject to persistent plantwide disturbances. It is shown that the response of an industrial MPC can be aggressive and oscillatory in such situations. The result is that the disturbance propagates further. The reason is the assumption made by the industrial MPCs regarding the future evolution of the disturbance. To improve the response in presence of plantwide disturbances, an MPC with disturbance forecasting is proposed. Such MPC is able to handle tight constraints and still reduce the movement of the outflow of the tank, therefore reducing the disturbance propagation. To compare the MPC with prediction forecasting with other two strategies used in industrial practice to handle measured disturbances, the paper uses sinusoidal disturbances and real disturbances coming from a refinery.
The opportunities and challenges of serious games for people with an intellectual disability

Abstract: Serious Games (SG) are increasingly used to deliver education and training to support well‐being, social skills, independent living and online safety. This presents opportunities and challenges for users, developers and educational providers to ensure their benefits are available to all. People with intellectual disability (ID) experience high levels of digital exclusion. This article reviews and synthesises the literature on the use of SG with people with ID. Recent work indicates the importance of the psycho‐social profile of the gameplayer, mechanics and game context. We consider how these factors influence the inclusivity of SG and how they could be addressed. We demonstrate how a psychological perspective illuminates the impact of the user's psychological profile on game use and effectiveness, and explore how culture, personalisation and learning analytics can increase inclusivity of SG. We conclude by identifying wider challenges such as the need for multi‐disciplinary working and a more integrated, conceptual framework for games design which addresses diversity in learning needs. [ABSTRACT FROM AUTHOR]
3D Dense-U-Net for MRI Brain Tissue Segmentation

This paper presents a fully automatic method for 3D segmentation of brain tissue on MRI scans using modern deep learning approach and proposes 3D Dense-U-Net neural network architecture using densely connected layers. In contrast with many previous methods, our approach is capable of precise segmentation without any preprocessing of the input image and achieved accuracy 99.70 percent on testing data which outperformed human expert results. The architecture proposed in this paper can also be easily applied to any project already using U-net network as a segmentation algorithm to enhance its results. Implementation was done in Keras on Tensorflow backend and complete source-code was released online.
A Survey of Opportunistic Offloading

This paper surveys the literature of opportunistic offloading. Opportunistic offloading refers to offloading traffic originally transmitted through the cellular network to opportunistic network, or offloading computing tasks originally executed locally to nearby devices with idle computing resources through opportunistic network. This research direction is recently emerged, and the relevant research covers the period from 2009 to date, with an explosive trend over the last four years. We provide a comprehensive review of the research field from a multi-dimensional view based on application goal, realizing approach, offloading direction, etc. In addition, we pinpoint the major classifications of opportunistic offloading, so as to form a hierarchical or graded classification of the existing works. Specifically, we divide opportunistic offloading into two main categories based on application goal: traffic offloading or computation offloading. Each category is further divided into two smaller categories: with and without offloading node selection, which bridges between subscriber node and the cellular network, or plays the role of computing task executor for other nodes. We elaborate, compare, and analyze the literatures in each classification from the perspectives of required information, objective, etc. We present a complete introductory guide to the researches relevant to opportunistic offloading. After summarizing the development of the research direction and offloading strategies of the current state-of-the-art, we further point out the important future research problems and directions.
O2 Pulse Patterns in Male Master Athletes with Normal and Abnormal Exercise Tests

Purpose The clinical relevance of abnormal exercise testing (ET) results (at least 0.1 mV ST segment depression measured during exercise or recovery in three consecutive beats) in athletes without obstructive coronary artery disease (CAD) is not well understood. It is unknown whether this phenomenon reflects a physiological adaptation to sport or a truly ischemic response and a concomitant attenuated stroke volume (SV) response. The aim of this study was to investigate if athletes with abnormal ET results without obstructive CAD showed signs of an attenuated SV response using cardiopulmonary ET parameters. Methods A total of 78 male master athletes with abnormal ET results without obstructive CAD underwent cardiopulmonary ET. &Dgr;O2 pulse/&Dgr;work rate (WR), &Dgr;V˙O2/&Dgr;WR, and &Dgr;heart rate (HR)/&Dgr;WR were assessed and compared with data from 78 male master athletes with normal ET results, matched for age, sports characteristics, and exercise capacity. Results The &Dgr;O2 pulse/&Dgr;WR ratio beyond anaerobic threshold in athletes with abnormal ET results was lower than that in athletes with normal ET results (0.73 ± 0.41 vs 1.12 ± 0.54, respectively, P < 0.001). The &Dgr;V˙O2/&Dgr;WR ratio was also lower in athletes with abnormal ET results (0.9 ± 0.2 vs 1.0 ± 0.3, respectively, P = 0.041). Furthermore, these athletes showed a greater increase in HR in the last 2 min of exercise (&Dgr;HR/&Dgr;WR ratio: 1.19 ± 0.5 vs 0.80 ± 0.6, P < 0.001). Conclusion Athletes with abnormal ET results without obstructive CAD showed an attenuated O2 pulse slope, decreased &Dgr;V˙O2/&Dgr;WR ratio, and increased &Dgr;HR/&Dgr;WR ratio beyond anaerobic threshold when compared with athletes with a normal ET result. These results support the hypothesis that at least a part of the athletes with an abnormal ET in absence of obstructive CAD have an attenuated SV response at high-intensity exercise.
The toxicity of ZnO nanomaterials to HepG2 cells: the influence of size and shape of particles

Understanding the possible role of physicochemical properties in determining the toxicity of ZnO nanomaterials (NMs) is crucial for the safe use of ZnO‐based materials. In this study, we synthesized four types of ZnO NMs, and characterized them as ZnO nanorods (NRs; length 400‐500 nm, diameter 150‐200 nm), ZnO Mini‐NRs (length 50‐100 nm, diameter 15‐20 nm), amorphous ZnO microspheres (a‐ZnO MS) and crystalline ZnO MS (c‐ZnO MS; the a/c‐ZnO MS are nanoflowers with an extensive growth of sheet‐like structures). ZnO NMs and ZnO Mini‐NRs were significantly more cytotoxic than a/c‐ZnO MS, and this trend was similar in both HepG2 cells and human umbilical vein endothelial cells. Intracellular reactive oxygen species was only modestly induced by c‐ZnO MS, whereas intracellular Zn ions were dose‐dependently increased in HepG2 cells by the exposure of all types of ZnO NMs. The expression of endoplasmic reticulum stress marker DDIT3 was induced following an order of ZnO NRs > a‐ZnO MS > c‐ZnO MS > ZnO Mini‐NRs, and the apoptosis gene CASP12 was induced following an order of a‐ZnO MS > ZnO NRs > c‐ZnO MS > ZnO Mini‐NRs. Combined, these results suggested that ZnO NM‐induced cytotoxicity and expression of endoplasmic reticulum stress‐apoptosis genes could be influenced by the size and shape of ZnO NMs.
Security Testing for Monitoring Web Service using Cloud

Security testing of a web service is the most integral part of its existence in Web service life cycle monitoring. Through testing, this system can monitor the possibility of attacks and redesign the services using the considerations provided by monitoring system. There is a never ending need to produce cyber safe web services which can be trusted. Penetration Testing is a professional testing where the test engineer is responsible for auditing the web service for its ability to withstand security attacks. The present work provides a monitoring system which includes penetration testing for vulnerability/malware detection. We introduced the survey done for vulnerabilities found in WSLC phases, include sample testing methodologies and propose the monitoring framework to identify vulnerabilities in requests for accessing phases of web services launched by worlds wide users. The system architecture is a cost-effective solution to provide security from malware. This system introduces a perimeter defence strategy.