Miracle-dz/UniFAR-FTUs huggingface dataset:
Approaches for Disaster Communication Backbone Network in Operator Independent Scenario

Communication system performance is very crucial during disaster relief operations. The wireless communication systems are expected to perform at their best in hostile conditions with limited resources since thousands of lives are at stake. Need of communication in disaster, deals with many challenges like Energy/power efficiency, reliable connectivity, availability, throughput, QoS and interoperability, so considering all these aspects, we propose an approach for the disaster communication network through existing Base Station (BS) by optimal selection of BS under the constraint of cell coverage and capacity. Those selected BS can be from any operator and where we install emergency system (ES). Which design to work in operator independent mode on some pre-defined spectrum shared by all the operator and has the flexibility to increase the height according to need through helium balloon as considered by the BLUE-COM+ project. Those ES provides the necessary communication network to the victim and helps the rescue team with effective service at the time of disaster. Simulation result shows that the increment in height of BS through ES can be beneficial in respect to cover the user of blackout area and demonstrate how coverage is related with the number of user and user data rate in a considered band of spectrum.
Split-Type Input-Reflectionless Multiband Filters

Split-type multiband bandpass filters (BPFs) and bandstop filters (BSFs) with input-reflectionless capabilities are reported in this letter. In the multiband BPF case, they consist of a duplexing network with a split-type multiband BPF channel—main channel—and its complementary resistively terminated multiband BSF channel—auxiliary channel—that absorbs the input-signal energy that is not transmitted by the overall filter. The multiband BSF counterpart is obtained from the latter by interchanging the roles of the main and auxiliary channels. The operational foundations, theoretical design rules, and synthesis examples of the devised split-type input-reflectionless multiband filters under a coupling-matrix-based formulation are described. Furthermore, for practical validation purposes, two microstrip prototypes corresponding to 3.85-/4.55-GHz second-order dual-band BPF and BSF circuits are constructed and measured.
Spectral-Spatial Sparse Subspace Clustering Based On Three-Dimensional Edge-Preserving Filtering For Hyperspectral Image

Due to the 3-D property of raw HSI cubes, 3-D spectral-spatial filter becomes an effective way for extracting spectral and spatial signatures from HSI. In this paper, a new spectral-spatial sparse subspace clustering framework based on 3-D edge-preserving filtering is proposed to improve the clustering accuracy of HSI. First, the initial sparse coefficient matrix is obtained in the s-parse representation process of the classical SSC model. Then, a 3-D edge-preserving filtering is conducted on the initial sparse coefficient matrix to get a more accurate one, which is used to build the similarity graph. Finally, the clustering result of H-SI data is achieved by employing the spectral clustering algorithm to the similarity graph. Specifically, the filtered matrix can not only capture the spectral-spatial features but the intensity differences. Experimental results demonstrate the potential of including the proposed 3-D edge-preserving filtering into the SSC framework can improve the clustering accuracy.
Pressure Variation Study in Human-Human and Human-Robot Handshakes: Impact of the Mood

With the development of compliant mechanisms and tactile skins, social robots are more inclined to physically interact with humans. Touch has been proved highly beneficial in interpersonal social interaction. This motivates social robots to exhibit affective touch abilities. However, most of the studies tackle the consequences and the perception of touch instead of how touching is performed. Understanding the touch manner enables to control robots in a more rich and natural way, but also to detect the intention of the user while touching the robot. This paper focuses on one specific social interaction based on touch, the handshake, and it analyses the pressure exerted by the participants depending on their mood. A corpus of daily repeated handshakes of 11 participants, during 16 days in Human-Human and Human-Robot interactions was produced. A handshake descriptor of 6 variables was computed and allowed to discriminate the participants' behavior. One of the variables (i.e., the maximum pressure on the fingers) enabled to discriminate the mood “Bored” from the others.
Compliant Polymer Interface Demonstration with Standard Plug-In Connection to Fiber Cables

We demonstrate a mechanically-compliant polymer interface, between standard fiber cables and nanophotonic waveguides, with passive self-alignment at both the fiber and the chip connections. We show a peak transmission of −1.8dB with less than 0.7dB penalty over a 100nm bandwidth and all polarizations.
Preliminary data on clinical performance of bulk-fill restorations in primary molars

Context: In pediatric clinic practice, bulk fill composite is gaining importance for shortened clinical time with a limited shrinkage. Aims: The present study evaluated the 1 year clinical performance of bulk fill composite and conventional composite material in occlusal caries of primary molars. Settings and Design: The study was designed as randomized single blind clinical trial and a total of 160 restorations were placed in the cavities of the 80 patients. Materials and Methods: Each patient received two restorations: one with Filtek Z250 (3M ESPE, St Paul, MN 55144, USA); the other restored with Filtek Bulk-Fill Restorative (FBF) (3M ESPE, St Paul, MN, USA). All restorations were clinically evaluated after baseline, 6 months, and 1 year in terms of retention, color matching, marginal discoloration, marginal adaptation, secondary caries, surface texture, anatomic form, and postoperative sensitivity. Statistical Analysis Used: Besides the descriptive statistical methods, the Friedman test and the Wilcoxon Signed Ranks were used. Results: Bulk fill was found to be worse compared to control with regard to postoperative sensitivity at baseline without statistical significance (P > 0.05). All of the evaluated restorations were retained and were still in function after 1 year (P > 0.05). With respect to marginal discoloration and marginal integrity, there were no significant differences between bulk fill and composite restorations at all intervals (P > 0.05). Conclusions: Based on this short term data, restoration of Class I cavities with both bulk fill and conventional composite restorations can be performed successfully. Postoperative sensitivity can be an issue with the restorations completed with Bulk fill restorative.
Plane Depth Estimation Based on Single Image and Projection of Four Laser Points

Single image depth estimation is a challenging problem and provides many application scenarios like object detection, pose calculation and path planning. To avoid the large data restriction in specific applications with deep learning based methods, this paper proposes a method of depth estimation for an arbitrary spatial plane using four laser points projected in the plane. Single plane is addressed at this work and the experimental results based on construction robot show that the proposed depth estimation method is feasible and valid.
INARTE: An Indonesian Dataset for Recognition Textual Entailment

Recognition Textual Entailment (RTE) try to solve variability problem that commonly encountered in natural language-based systems. The basic idea is to detect whether the meaning of a text can be inferred by another text. The need dataset in language other than English is necessary to accelerate research development in RTE. We created RTE dataset for Indonesian by retrieval text from Web and generate text-hypothesis pairs as many as possible. The subset technique is used to decide whether Text (T) entails Hypothesis (H). The initial data used 400 question-answer pairs obtained 1,577 entailment pairs, where 481 entailment pairs obtained from the accuracy above 50%.
Evaluation Method of Distribution Network Resilience Focusing on Critical Loads

With the frequent occurrence of extreme weather events in the world, the ability of power system to resist disasters has attracted more attention. In this paper, a method to evaluate the resilience of distribution networks by focusing on the impact of critical loads under extreme weather events is proposed, and typhoon is taken as the representative to formulate a vulnerability curve of components. Furthermore, the Monte Carlo method is used to simulate the whole process of extreme weather disaster and to generate the fault scenario. Different weights are assigned to different levels of load according to the importance of load, and the weighted loss of load is selected as the evaluation index of resilience. Finally, the method is verified by the IEEE-33 bus system. The results demonstrate that this method is effective to quantify the resilience of distribution networks under extreme weather events.
Towards an Open Source Stack to Create a Unified Data Source for Software Analysis and Visualization

The beginning of every software analysis and visualization process is data acquisition. However, there are various sources of data about a software system. The methods used to extract the relevant data are as diverse as the sources are. Furthermore, integration and storage of heterogeneous data from different software artifacts to form a unified data source are very challenging. In this paper, we introduce an extensible open source stack to take the first step to solve these challenges. We show its feasibility by analyzing and visualizing JUnit and provide answers regarding the schema, selection, and implementation of software artifacts' data.
Automatic Stand for Testing the Influence of Humidity and Temperature on Dielectric Charges

Used for their excellent electrical behavior, as well as for their other good general properties, polymers are today applied mostly for the electrical insulation of underground BV/MV power line cables. Observed from the microscopic level, these dielectrics are not homogeneous. They present a mix of amorphous and crystalline volumes and the interface between these regions offers boudary junctions. This microscopic inhomogeneity detemines an increased space charge at the microscopic place. The existence of space charge causes an electric stress enhancement which may reduce the lifespan of the dielectric material. In this paper, partial discharges occurring in an air-filled gap between two closed high density polyethylene cavities are experimentally investigated. Measurements are performed at different temperatures, from 10°C up to 90°C and for different relative humidity ratios, from 20 up to 70%. The influence of these environmental parameters on PDIV, PD phase distribution and occurance numbers is clealy described.
Modeling for Optimality in Design of Planetary Ring Gear to Reduce Stress Development a Response Surface Methodology Based Parametric Modelling Approach

Parametric modeling and optimization is emphasized in this work utilizing response surface methodology for a ring gear, the largest component in a planetary gear mechanism. The purpose of this paper is to disseminate design optimization based information for developing robust transmission system using finite element analysis. The methodology employed for assessing failure in gear components is modeled and optimized to reduce stress development. Analysis based on combination of statistical and mathematical modeling tools is employed for achieving desirable result. The mathematical modeling concepts exemplified for the ring gear can be used for other gear members. This paper serves the purpose of deducing strategic design related decision from design optimization concepts for achieving the best performance characteristics.
An Offline Roadside Unit ON-OFF Scheduling Algorithm for Energy Efficiency of Ad Hoc Networks

This paper considers the off-line roadside unit (RSU) ON/ OFF scheduling problem in a sparse vehicular ad hoc network (VANET). We formulate the problem as a mixed integer non-linear programming problem and propose a heuristic scheduling algorithm called Multi-Level Greedy (MLG) algorithm to find a suboptimal approximation. In the proposed MLG algorithm, we introduce the concepts of optimal service location and suboptimal service location. Then, we obtain the ON-OFF state matrix of RSUs. Simulation results show that the MLG algorithm can significantly reduce the energy consumption of RSUs deployed and can provide a reference basis for energy efficient scheduling in VANETs.
The Impact of e-CRM Implementation to Bank Performance Through e-Service Quality

The purpose of this research was to analyze the impact of e-CRM implementation on bank performance through e-Service Quality and what are the features to be concerned further. The study was conducted in the business process of public bank in Jakarta. The questionnaires were distributed to 58 corporate customers of this public bank using cluster sampling method. Analysis obtained using quantitative research with partial least square (PLS) model. The result of this study found that e-CRM system helps bank performance to accelerate the registration process, product implementation, and help customer dealing with various feature offered.
Human Motion Analysis and Its Application to Walking Stabilization With COG and ZMP

A stability index has been generally used in a gait system of formulaic structure. In this standardized structure, a human model has a center of gravity (COG), while a robot device has a zero moment point (ZMP) with COG as the stability standard in the walking motion. In the gait system of wearable type, the stability of human model is closely related with the robot device, in other words, the COG and ZMP are located in the limited domain of human motion and robot task. In this paper, the human and device are designed individually and the knee joint of human is unconstrained from the device work to allow the human reliance. The stability index that includes the COG and ZMP is defined. The validation of this index is guaranteed through a few tests to describe a correlation between the stability index and the human model condition. Based on the validity of the stability index, the experiment for the walking stabilization is implemented with the ZMP estimation by reaction torque observer.
Clustering Data Gathering Method Based on Compressed Sensing in Wireless Sensor Networks

In practical applications of compressive data gathering (CDG) in large-scale wireless sensor networks (WSNs), methods based on compressed sensing (CS) which use traditional measurement matrix can only save a small amount of energy but lead to high computational complexity. To prolong the lifetime of network and reduce computational complexity of the measurement process, a clustering data gathering method based on sparse circulant matrix (SCM) is proposed in this paper. The proposed method is composed of two steps. First, a structural sparse circulant matrix is designed. And then the matrix is applied to clustering data gathering process. The theoretical analysis shows the proposed method obtains satisfactory computational complexity. Extensive simulation results verify that the proposed method outperforms the traditional data gathering method in respect to energy saving with the same recovery accuracy.
Object-specific attenuation correction of SPECT with correlated dual-energy X-ray CT

We have developed a prototype emission-transmission computed tomographic (ETCT) system, which can acquire both radionuclide-emission and dual-energy X-ray transmission data. The dual-energy X-ray data are used to synthesize an object- and energy-specific attenuation map which is incorporated into an ML-EM algorithm to reconstruct an attenuationcorrected SPECT image. The technique was applied to a thoraxlike phantom with regions simulating lung, background and cardiac regions, and bone. SPECT images reconstructed with no attenuation correction, or with a uniform water-equivalent map, contained increased count levels in the lung region and reduced count levels in the cardiac region as compared with the radioactivity concentrations contained in the phantom. Use of an object- specific attenuation map improved the accuracy of the reconstruction, but overestimated the activity in the cardiac chamber by about 10%. This error was reduced to about 5% when the attenuation coefficients were modified to include the effects of scattered radiation.
Parameter Estimation Of Gaussian Mixture Model Based On Variational Bayesian Learning

Parameter estimation of gaussian mixture model has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for researching on global optimal convergence and interesting practical application- s. In this paper, we present novel evaluation algorithm for estimating the parameter of gaussian mixture model based on Variational Bayesian Learning (VBL) principles. Starting from a traditional VBEM algorithm and analysing the convergence of the algorithm as an initial-value constraint, we develop an approach that is very effective in estimating the parameter of gaussian mixture model while providing high astringency performance. We provide empirical results and comparison with current VBEM algorithm that illustrate the effectiveness of this approach.
Ex vivo dielectric properties of fat: influence of the experimental conditions on the measured data

The recent developments of medical applications of electromagnetic fields in both diagnostic and therapeutic scenarios has posed the attention on the accurate knowledge of the dielectric properties of human tissues. Accordingly, several studies were recently devoted to the measurements of such properties under various physiological conditions. Since biological tissues show a high variability, to compare the different data it is necessary to thoroughly control the experimental conditions. In this work, an ex vivo study is performed on rat fat tissue to look for possible confounders linked to the time between excision and measurement.
Customer Segmentation in XYZ Bank Using K-Means and K-Medoids Clustering

The Internet banking customers has growth very fast. Customer segmentation can be applied based on Internet banking data. Clustering is unsupervised data mining technique that can be used for customer segmentation. This research build clustering models on customer profile data based on their usage of internet Banking in XYZ bank. The clustering methods employed K-Means method and K-Medoids method based on RFM score of customer's Internet Banking transactions. This research used Knowledge Discovery methodology [18]. The performances of both methods were measured and compared. The result shows that K-Means method outperformed K-Medoids method based on intra cluster (A WC) distance. While based on Davies-Bouldin index, K-Means performs slightly better than K-Medoids.
Resveratrol glucuronidation in vitro: potential implications of inhibition by probenecid

Resveratrol is a naturally occurring antioxidant with therapeutic potential in prevention and treatment of neoplastic disease and other human disorders. However, net clearance of resveratrol in humans is very high, mainly due to glucuronide conjugation. This leads to extensive presystemic extraction and low plasma concentrations after oral dosage. The present study evaluated the effect of probenecid, an inhibitor of glucuronide conjugation, on resveratrol metabolism in vitro.
A Linear Approach to Optimize an EMG-Driven Neuromusculoskeletal Model for Movement Intention Detection in Myo-Control: A Case Study on Shoulder and Elbow Joints

The growing interest of the industry production in wearable robots for assistance and rehabilitation purposes opens the challenge for developing intuitive and natural control strategies. Myoelectric control, or myo-control, which consists in decoding the human motor intent from muscular activity and its mapping into control outputs, represents a natural way to establish an intimate human-machine connection. In this field, model based myo-control schemes (e.g., EMG-driven neuromusculoskeletal models, NMS) represent a valid solution for estimating the moments of the human joints. However, a model optimization is needed to adjust the model's parameters to a specific subject and most of the optimization approaches presented in literature consider complex NMS models that are unsuitable for being used in a control paradigm since they suffer from long-lasting setup and optimization phases. In this work we present a minimal NMS model for predicting the elbow and shoulder torques and we compare two optimization approaches: a linear optimization method (LO) and a non-linear method based on a genetic algorithm (GA). The LO optimizes only one parameter per muscle, whereas the GA-based approach performs a deep customization of the muscle model, adjusting 12 parameters per muscle. EMG and force data have been collected from 7 healthy subjects performing a set of exercises with an arm exoskeleton. Although both optimization methods substantially improved the performance of the raw model, the findings of the study suggest that the LO might be beneficial with respect to GA as the latter is much more computationally heavy and leads to minimal improvements with respect to the former. From the comparison between the two considered joints, it emerged also that the more accurate the NMS model is, the more effective a complex optimization procedure could be. Overall, the two optimized NMS models were able to predict the shoulder and elbow moments with a low error, thus demonstrating the potentiality for being used in an admittance-based myo-control scheme. Thanks to the low computational cost and to the short setup phase required for wearing and calibrating the system, obtained results are promising for being introduced in industrial or rehabilitation real time scenarios.
Priority Based Sentiment Analysis for Quick Response to Citizen Complaints

Today metropolitan citizens need a common platform to register their complaints. In the traditional telephonic system task of complaint registration is very time-consuming process so, citizen have to wait until call is received by the service executive. The proposed framework is developed for Pune Municipal corporation (PMC) will be very helpful for registering queries in natural language and get immediate response. Understanding the short text is the main challenge of the system as short text do not follow syntax of the written language, short text does not have sufficient statistics to support approach of text mining, short text is noisy and ambiguous. So, traditional Part-of-Speech (POS) tagging tools cannot be easily applied. In proposed framework for understanding natural language semantic knowledge provided by well-known knowledgebase WordNet is used. In prequery citizens inserts complaint to system and get immediate response to query with the help of knowledgebase and machine learning algorithm. In postquery system analyses the citizen sentiment to handle grievance level and accordingly prioritize the citizens by sentiment analysis. The proposed framework will help many organizations to ensure quality service provision and customer satisfaction with less human efforts.