Miracle-dz/UniFAR-FTUs huggingface dataset:
PerceptNet: Learning Perceptual Similarity of Haptic Textures in Presence of Unorderable Triplets

In order to design haptic icons or build a haptic vocabulary, we require a set of easily distinguishable haptic signals to avoid perceptual ambiguity, which in turn requires a way to accurately estimate the perceptual (dis)similarity of such signals. In this work, we present a novel method to learn such a perceptual metric based on data from human studies. Our method is based on a deep neural network that projects signals to an embedding space where the natural Euclidean distance accurately models the degree of dissimilarity between two signals. The network is trained only on non-numerical comparisons of triplets of signals, using a novel triplet loss that considers both types of triplets that are easy to order (inequality constraints), as well as those that are unorderable/ambiguous (equality constraints). Unlike prior MDS-based non-parametric approaches, our method can be trained on a partial set of comparisons and can embed new haptic signals without retraining the model from scratch. Extensive experimental evaluations show that our method is significantly more effective at modeling perceptual dissimilarity than alternatives.
The evaluation of the diet/disease relation in the EPIC study: considerations for the calibration and the disease models.

BACKGROUND International multicentre studies on diet and cancer are relatively new in epidemiological research. They offer a series of challenging methodological issues for the evaluation of the association between dietary exposure and disease outcomes, which can both be quite heterogeneous across different geographical regions. This requires considerable work to standardize dietary measurements at the food and the nutrient levels. METHODS Within the European Prospective Investigation into Cancer and Nutrition (EPIC), a calibration study was set up to express individual dietary intakes according to the same reference scale. A linear regression calibration model was used to correct the association between diet and disease for measurement errors in dietary exposures. In the present work, we describe an approach for analysing the EPIC data, using as an example the evaluation of the association between fish intake and colorectal cancer incidence. RESULTS Sex- and country-specific attenuation factors ranged from 0.083 to 0.784, with values overall higher for men compared with women. Hazard ratio estimates of colorectal cancer for a 10 g/day increase in fish intake were 0.97 [95% confidence interval (CI): 0.95-0.99] and 0.93 (0.88-0.98), before and after calibration, respectively. CONCLUSIONS In a multicentre study, the diet/disease association can be evaluated by exploiting the whole variability of intake over the entire study. Calibration may reduce between-centre heterogeneity in the diet-disease relationship caused by differential impact of measurement errors across cohorts.
Comprehensive Dynamic Spectrum Allocation in multi-PU multi-SU CRN using Coalition Game Theory

For optimal Dynamic Spectrum Allocation (DSA) in multi-PU (Primary User) multi-SU (Secondary User) Cognitive Radio Network (CRN), it is necessary to study the comprehensive behavior of each SU. In this paper, we have designed a database assisted CRN using Coalition Game Theory (CGT) to increase the involvement of the maximum number of SUs in the spectrum sensing and allocation. In this model, we have formed Pareto optimal coalitions considering hidden/mobile/remotely located SUs. To enlarge the spectrum allocation rate of this CRN, we have proposed a DSA technique using an even and odd channel allocation scheme. In this scheme, we have used a comprehensive function to determine the payoff of each SU. The simulation result shows that the proposed channel allocation scheme enriching in the payoff of each SU and spectrum allocation rate of the CRN compared with the former coalition game scheme.
Development of a Location-based Approach for Countering Primary User Emulation Attack in Cognitive Radio Network

With the ever-increasing demand for wireless spectrum for various applications, the need for intelligent and efficient utilization of the limited resource has been felt tremendously in the last few years. Cognitive radio network (CRN) with its dynamic spectrum sharing techniques coupled with flexibility, reliability, awareness and adaptability offers a fitting solution. Unfortunately, this brings along myriads of threats and attacks specific to CRN. In this paper, a specific kind of attack called Primary User Emulation Attack (PUEA) by Selfish Secondary Users has been considered and various algorithms to counter this have been explored. In PUEA an adversary’s CR transmits signals whose characteristics emulate those of the Primary User (PU). This paper focuses on location-based security algorithms to counter PUEA which include Distance Ratio Test (DRT) and Distance Difference Test (DDT) for transmitter verification. Certain modifications to DRT and DDT have been proposed to alleviate the inherent drawbacks of the techniques. This includes using a random configuration of the location verifiers (LVs) to counter an adversary trying positions equivalent to that of a PU. In light of certain shortcomings of this initial proposed approach, a superior DRT over DDT assortment algorithm is proposed. Also, an LV location optimization algorithm using genetic algorithm based on similarity index is designed to optimize the locations of the LVs.
A Practical Approach of Active Cell Balancing in a Battery Management System

Battery is the heart of electric vehicle and a way of improving the battery life is to equalize the energy of its cells. This can be done by either dissipating excess energy in the form of heat (passive cell balancing) or charging the low voltage cells through high voltage cells (active cell balancing). This paper presents a practical approach of active cell balancing along with a brief comparative study of passive and active cell balancing techniques. To improve the inconsistency present in the series-parallel connected lithium ion (Li-Ion) cells, a cell balancing scheme based on switch-matrix and forward converter with active clamp driver topology is presented. This cell balancing scheme is based on transferring the energy from the over-charged cell to auxiliary battery and from auxiliary battery to less charged cell. The balancing takes place for one cell at a time and the balancing current can be continuously monitored. The energy transfer efficiency of 90% for charging and 72% for discharging is achieved. The proposed battery management system can be used for communicating the faults generated in the battery pack to the master controller and corrective actions can be taken to avoid the damage to the battery.
Temperature Distribution of 250 MW Hydro Turbine Synchronous Generator at Continuous Overloading Conditions

Hydrogenerators are usually operated at over-load condition during the period when excess water, within permissible silt content, would be available in the river. The period may be extended to many weeks under extensive conditions e.g. six weeks in case of Karcham Wangtoo Hydropower Station (India) at 20% overloads. High-power, low-speed, direct-driven Hydro Turbine Synchronous Generators (HTSGs) apply their own cooling techniques to withstand temperature rise at various load points. This paper presents the temperature distribution of a 250 MW CB 870/300-28 HTSG running under distinct stages of long time interval overloads. The machine modelling and simulations are carried out by means of three-dimensional commercial finite-element analysis (FEA) software package MagNet 7.5. The analysis is initiated with a basic electromagnetic design of CB 870/300-28 using FEA solver. Then, extended to the investigation of thermal distribution via a coupled thermal and fluid-dynamical model based on ThermNet 7.5. The simulated thermal metaphors at continuous overloads are presented and validated with a case study of 250 MW HTSG. The utility of these generalized results of thermal distribution guarantees the desirable operation of this kind of machine. Moreover, this paper shows the feasibility to design HTSG which intends to operate at continuous overloads.
Accurate continuous geographic assignment from low- to high-density SNP data

MOTIVATION Large-scale genotype datasets can help track the dispersal patterns of epidemiological outbreaks and predict the geographic origins of individuals. Such genetically-based geographic assignments also show a range of possible applications in forensics for profiling both victims and criminals, and in wildlife management, where poaching hotspot areas can be located. They, however, require fast and accurate statistical methods to handle the growing amount of genetic information made available from genotype arrays and next-generation sequencing technologies. RESULTS We introduce a novel statistical method for geopositioning individuals of unknown origin from genotypes. Our method is based on a geostatistical model trained with a dataset of georeferenced genotypes. Statistical inference under this model can be implemented within the theoretical framework of Integrated Nested Laplace Approximation, which represents one of the major recent breakthroughs in statistics, as it does not require Monte Carlo simulations. We compare the performance of our method and an alternative method for geospatial inference, SPA in a simulation framework. We highlight the accuracy and limits of continuous spatial assignment methods at various scales by analyzing genotype datasets from a diversity of species, including Florida Scrub-jay birds Aphelocoma coerulescens, Arabidopsis thaliana and humans, representing 41-197,146 SNPs. Our method appears to be best suited for the analysis of medium-sized datasets (a few tens of thousands of loci), such as reduced-representation sequencing data that become increasingly available in ecology. AVAILABILITY AND IMPLEMENTATION http://www2.imm. dtu.dk/∼gigu/Spasiba/ CONTACT gilles.b. guillot@gmail.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Comparison of Si and GaN Power Devices Based SMPS for Satellite Application

Gallium Nitride(GaN) power devices offer benefits like faster switching, lower on-resistance, lower device capacitances as compared to the current silicon(Si) devices. These benefits could be leveraged to reduce the size and weight of GaN based power electronic supplies. This paper aims to evaluate the benefits and challenges of using GaN devices by experimentally comparing Si and GaN based Switch Mode Power Supply(SMPS) for satellite application. Two multi output flyback converter prototypes with Si and GaN switches are developed for the same specifications to observe the direct benefits and challenges of using GaN switches. It is observed that for same efficiency, GaN converter can have upto 10 times the switching frequency as compared to a Si converter. This results in significant reduction in capacitor and inductor size, resulting in a two fold improvement in overall power density. Further, this paper also discusses the issues arising from high frequency switching in presence of circuit parasitics.
Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts

In this paper, a forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented. We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the demosaicking algorithm. The proposed method is based on a new feature measuring the presence of demosaicking artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2 image block without requiring to know a priori the position of the forged region. Experimental results on different cameras equipped with different demosaicking algorithms demonstrate both the validity of the theoretical model and the effectiveness of our scheme.
Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

Reinforcement learning (RL), which is a class of machine learning, provides a framework by which a system can learn from its previous interactions with its environment to efficiently select its actions in the future. RL has been used in a number of application fields, including game playing, robotics and control, networks, and telecommunications, for building autonomous systems that improve themselves with experience. It is commonly accepted that RL is suitable for solving optimization problems related to distributed systems in general and to routing in networks in particular. RL also has reasonable overhead—in terms of control packets, memory and computation—compared to other optimization techniques used to solve the same problems. Since the mid-1990s, over 60 protocols have been proposed, with major or minor contributions in the field of optimal route selection to convey packets in different types of communication networks under various user QoS requirements. This paper provides a comprehensive review of the literature on the topic. The review is structured in a way that shows how network characteristics and requirements were gradually considered over time. Classification criteria are proposed to present and qualitatively compare existing RL-based routing protocols.
Proposing a new feature set for recognition of moving humans in video frames

Human detection and recognition at a distance is recently a matter of great concern among computer vision researchers. This paper introduces a new set of human body features for the recognition of detected human as an object. The feature extraction is performed by an established human model consisting of five parts. These features consist of geometric calculations of detected object and their different ratios. Detection of moving object is achieved using average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. Recognition of detected object has been carried out using Support vector machine (SVM). Experimental results show better recognition rates as were achieved previously using a relatively smaller feature set.
V-Matrix-Based Scalable Data Aggregation Scheme in WSN

Data aggregation is one of the most important functions provided by wireless sensor networks (WSNs). Among a variety of data aggregation schemes, the coding-based approaches (such as Compressive sensing (CS) and other similar programs) can significantly reduce traffic quantity by encoding the raw sensed data using weight vectors. The critical feature to design a coding-based data aggregation protocol is to construct a weight/measurement matrix for the application scenario. After that, the sink node assigns the column of the matrix, which is treated as the weight vector during the encoding process, to each sensor node respectively. However, for a dynamic scenario where the number of sensor nodes changes frequently, the existing approaches have to reconfigure the network by regenerating the measurement matrix and allocating the new weight vectors for all the existing nodes, which causes a considerable energy consumption and affects the regular monitoring tasks. To solve this problem, we propose a Vandermonde matrix-based scalable data aggregation protocol (VSDA), which preserves the advantages of coding-based schemes and addresses the issues mentioned above. In VSDA, as new nodes join into the scaled-up network, the original weight vectors owned by the original nodes do not need to regenerate the weight vectors entirely but add some new entries by itself at all. It outperforms the existing schemes by saving the energy in network scaling-up. Besides, we propose a concise hardware framework to quantify the data encoding process of VSDA, which provides a performance analysis process that is closer to practical application. The numeric tests validate the performance of VSDA compared with the existing schemes in several aspects, such as, the number of transmissions, energy consumption, and storage space showing the outperformance of VSDA scheme.
A Fully Automatic based Deep Learning Approach for Aneurysm Detection in DSA Images

Intracranial Aneurysm (IA) is a weaknesses of the cerebral arteries. Their rupture causes heavy consequences. Therefore, accurately assessing the risk of IA rupture and intervening timely is critical for improving the survival rate and prognosis of patients. We present in this paper a new IA detection method based on Convolutional Neural Networks (CNN). All the IA are detected in 2D-DSA dataset by the proposed method with zero false negative.
Word embeddings combination and neural networks for robustness in ASR error detection

This study focuses on error detection in Automatic Speech Recognition (ASR) output. We propose to build a confidence classifier based on a neural network architecture, which is in charge to attribute a label (error or correct) for each word within an ASR hypothesis. This classifier uses word embed-dings as inputs, in addition to ASR confidence-based, lexical and syntactic features. We propose to evaluate the impact of three different kinds of word embeddings on this error detection approach, and we present a solution to combine these three different types of word embeddings in order to take advantage of their complementarity. In our experiments, different approaches are evaluated on the automatic transcriptions generated by two different ASR systems applied on the ETAPE corpus (French broadcast news). Experimental results show that the proposed neural architectures achieve a CER reduction comprised between 4% and 5.8% in error detection, depending on test dataset, in comparison with a state-of-the-art CRF approach.
Simulation and Harmonic Analysis of Six Phase Three Level Neutral Point Clamped Inverter

In a modern nuclear industry special applications like primary coolant pump, component cooling water pumps etc. In a nuclear reactor requires smooth operation with high reliability. Besides, several research applications require adjustable speed drives with precise speed control feature. This paper presents six phase multilevel inverter using three level Neutral Point Clamped topology. Multilevels in inverter serve smooth operation and multiphase ensures high reliability of system. Harmonic analysis of multilevel inverter output voltage is also presented using unit cell analysis and double Fourier series for Pulse Width Modulation method. Simulations are performed for proving design concept and performance.
A Minimum Cost Real-Time Ubiquitous Computing System Using Edge-Fog-Cloud

With the development of diverse ubiquitous computing applications, tremendous amount of sensor data is generated. This data requires efficient localized processing and storage. A real-time ubiquitous system requires latency-aware processing to satisfy the deadline of the ubiquitous applications. Performing processing near the network, using Edge and Fog devices, meets this need. The cost of the ubiquitous system depends on the pricing of the processing and storage. In this paper, we present an Edge, Fog, and Cloud layers based ubiquitous computing system, which not only deals with the deadline of a given application but also minimizes the cost of the system. We derive expressions to estimate the cost of computing and storage of the ubiquitous computing system and delay of the network. We demonstrate an application of the analysis in the design of a minimum cost ubiquitous computing system. We propose an algorithm to determine the layers for executing the machine learning techniques required to satisfy the deadline of the system and simultaneously minimize the cost of the network.
Validation of the Catherine Bergego Scale in patients with unilateral spatial neglect after stroke

ABSTRACT. The Catherine Bergego Scale (CBS) is a scale to evaluates the impact of unilateral spatial neglect (USN) on everyday life of patients after stroke. Objective: The aim of this study was to evaluate the reliability and comprehension of the Portuguese version of the CBS for patients with USN after stroke. Methods: This was a cross-sectional study in patients with stroke and USN. The CBS was translated, culturally adapted and applied by two independent investigators. The patients were also evaluated by the Behavioural Inattention Test (BIT), NIHSS, mRS and Barthel scale to assess USN severity, neurological function, disability and autonomy consecutively. Consistency and coherence were analysed using Cronbach’s α, inter-observer reliability by Kappa, and the correlation between the CBS, BIT, NIHSS, mRS, and Barthel was determined using Pearson correlation. Results: Twenty-two patients were evaluated and the observed Cronbach’s α=0.913. For intra-observer reproducibility, the 10 items showed a reasonable and high reliability between evaluators. The CBS showed a negative correlation with the BIT. There was a low correlation between the BIT and NIHSS, mRS and Barthel index. Conclusion: The CBS is an adequate and validated scale for assessing patients with USN after stroke in a Brazilian population.
Algorithm development for assessing the risk of damage to the health of workers in the energy industry

The article is devoted to the problem of improving the safety of employees of energy enterprises, which for several decades has been one of the most important organizational tasks in the enterprise in the process of labor activity. The paper analyzes the existing indicators of assessment of working conditions, scientifically substantiated a new algorithm for assessing the risk of damage to the health of employees of the energy industry, allowing to further develop a list of measures that reduce the impact of harmful factors on employees.
Quality of Life, Depression, and Healthcare Resource Utilization among Adults with Type 2 Diabetes Mellitus and Concomitant Hypertension and Obesity: A Prospective Survey

Background. This study compared quality of life, depression, and healthcare resource utilization among adults with type 2 diabetes mellitus (T2DM) and comorbid hypertension (HTN) and obesity with those of adults reporting T2DM alone. Methods. Respondents to the US SHIELD survey self-reported their height, weight, comorbid conditions, hospitalizations, and outpatient visits and completed the Short Form-12 (SF-12) and Patient Health Questionnaire (PHQ-9). Respondents reporting T2DM and HTN and obesity (body mass index, BMI, ≥30 kg/m2) were compared with a T2DM-alone group. Results. Respondents with T2DM, HTN, and obesity (n = 1292) had significantly lower SF-12 Physical and Mental Component Summary scores (37.3 and 50.9, resp.) than T2DM-alone respondents (n = 349) (45.8 and 53.5, resp., P < 0.0001). Mean PHQ-9 scores were significantly higher among T2DM respondents with comorbid HTN and obesity (5.0 versus 2.5, P < 0.0001), indicating greater depression burden. Respondents with T2DM, HTN, and obesity had significantly more resource utilization with respect to physician visits and emergency room visits but not hospitalizations than respondents with T2DM alone (P = 0.03). Conclusions. SHIELD respondents with comorbid conditions of T2DM, HTN, and obesity reported greater healthcare resource utilization, more depression symptoms, and lower quality of life than the T2DM-alone group.
A System for Compound Noun Multiword Expression Extraction for Hindi

Identifying compound noun multiword expressions is important for applications like machine translation and information retrieval. We describe a system for extracting Hindi compound noun multiword expressions (MWE) from a given corpus. We identify major categories of compound noun MWEs, based on linguistic and psycholinguistic principles. Our extraction methods use various statistical co-occurrence measures to exploit the statistical idiosyncrasy of MWEs. We make use of various lexical cues from the corpus to enhance our methods. We also address the extraction of reduplicative expressions using lexical, semantic and phonetic knowledge. We have also built an evaluation resource of compound noun MWEs for Hindi. Our methods give a recall of 80% and precision of 23% at rank 1000.