nils-herrmann/clean_scirepeval huggingface dataset:
IMPLEMNTATION OF SIMULINK BASED MODEL USING SOBEL EDGE DETECTOR FOR DENTAL PROBLEMS Deepika Nagpal

Image Segmentation is the process of partitioning a digital image into multiple regions or sets of pixels.Edge Detection is one of the main Technique used in Segmentation.In this paper we used Sobel edge detector for segmenting the dental X-ray image.Using MATLAB,Image is segmented.Sysytem Test tool is used for the verification of the Simulink Model. The Simulink Model based Image Segmentation is a new function in image processing and offers a model based design for processing. Dental Caries is the main problem occurred in the teeths.Segmentation help to identify the places where the problems of dental caries are present.
A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets

In this study, we provide a direct comparison of the Stochastic Maximum Likelihood algorithm and Contrastive Divergence for training Restricted Boltzmann Machines using the MNIST data set. We demonstrate that Stochastic Maximum Likelihood is superior when using the Restricted Boltzmann Machine as a classifier, and that the algorithm can be greatly improved using the technique of iterate averaging from the field of stochastic approximation. We further show that training with optimal parameters for classification does not necessarily lead to optimal results when Restricted Boltzmann Machines are stacked to form a Deep Belief Network. In our experiments we observe that fine tuning a Deep Belief Network significantly changes the distribution of the latent data, even though the parameter changes are negligible.
A flexible 100-antenna testbed for Massive MIMO

Massive multiple-input multiple-output (MIMO) is one of the main candidates to be included in the fifth generation (5G) cellular systems. For further system development it is desirable to have real-time testbeds showing possibilities and limitations of the technology. In this paper we describe the Lund University Massive MIMO testbed - LuMaMi. It is a flexible testbed where the base station operates with up to 100 coherent radio-frequency transceiver chains based on software radio technology. Orthogonal Frequency Division Multiplex (OFDM) based signaling is used for each of the 10 simultaneous users served in the 20 MHz bandwidth. Real time MIMO precoding and decoding is distributed across 50 Xilinx Kintex-7 FPGAs with PCI-Express interconnects. The unique features of this system are: (i) high throughput processing of 384 Gbps of real time baseband data in both the transmit and receive directions, (ii) low-latency architecture with channel estimate to precoder turnaround of less than 500 micro seconds, and (iii) a flexible extension up to 128 antennas. We detail the design goals of the testbed, discuss the signaling and system architecture, and show initial measured results for a uplink Massive MIMO over-the-air transmission from four single-antenna UEs to 100 BS antennas.
Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback

Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population.
Characterisation of acoustic scenes using a temporally-constrained shift-invariant model

In this paper, we propose a method for modeling and classifying acoustic scenes using temporally-constrained shift-invariant probabilistic latent component analysis (SIPLCA). SIPLCA can be used for extracting time-frequency patches from spectrograms in an unsupervised manner. Component-wise hidden Markov models are incorporated to the SIPLCA formulation for enforcing temporal constraints on the activation of each acoustic component. The time-frequency patches are converted to cepstral coefficients in order to provide a compact representation of acoustic events within a scene. Experiments are made using a corpus of train station recordings, classified into 6 scene classes. Results show that the proposed model is able to model salient events within a scene and outperforms the non-negative matrix factorization algorithm for the same task. In addition, it is demonstrated that the use of temporal constraints can lead to improved performance.
“I am active”: effects of a program to promote active aging

Active aging involves a general lifestyle strategy that allows preservation of both physical and mental health during the aging process. "I am Active" is a program designed to promote active aging by increased physical activity, healthy nutritional habits, and cognitive functioning. The purpose of this study was to assess the effectiveness of this program.



Sixty-four healthy adults aged 60 years or older were recruited from senior centers and randomly allocated to an experimental group (n=31) or a control group (n=33). Baseline, post-test, and 6-month follow-up assessments were performed after the theoretical-practical intervention. Effect sizes were calculated.



At the conclusion of the program, the experimental group showed significant improvement compared with the control group in the following domains: physical activity (falls risk, balance, flexibility, self-efficacy), nutrition (self-efficacy and nutritional status), cognitive performance (processing speed and self-efficacy), and quality of life (general, health and functionality, social and economic status). Although some declines were reported, improvements at follow-up remained in self-efficacy for physical activity, self-efficacy for nutrition, and processing speed, and participants had better nutritional status and quality of life overall.



Our findings show that this program promotes improvements in domains of active aging, mainly in self-efficacy beliefs as well as in quality of life in healthy elders.
GUIDANCE LAW EVALUATION FOR MISSILE GUIDANCE SYSTEMS

In missile guidance system, to reduce the interception “miss distance,” it is important to choose a suitable guidance law and navigation constant. This paper investigates and compares the system behavior of guidance laws under different navigation constants. Based on use of the adjoint technique, miss distance sensitivity analyses which consider the system noise, target step maneuver, initial heading error and system parameters for different guidance laws and navigation constants are presented. Based on these analyses, some suggestions for choosing a suitable guidance law and navigation constant are given for the design of missile guidance systems. Also, a suggestion for the optimal escape time for pilots of fighter planes is given.
Could on-line voting boost desire to vote? - Technology acceptance perceptions of young Hungarian citizens

Article history: Received 24 May 2015 Received in revised form 2 October 2016 Accepted 18 November 2016 In our paper we develop and test the argument that intent to i-vote (to use on-line voting systems) drives intent to vote, while intent to i-vote is influenced by four key attitudes: performance expectation, perception on ease of use, trust in the internet and trust in the government.We show that these findings contradict thosewhich exclusively identified economical, legal, and cultural drivers to enhance democratic participation in the Central and Eastern European region. Rooted cardinally in the Technology Acceptance Model (TAM) six hypotheses were set, and then tested with partial least square (PLS) structural equation modelling. In the context of young, educated and internet-ready Hungarian voters the testing of the hypotheses has shown high level of on-line voting intent and that perception of on-line voting would enhance voting desire amongst young Hungarian internet users. Also, our findings show that performance expectation, perception on ease of use and trust in the internet are positively associated with i-voting intent.
Insulating electrodes: a review on biopotential front ends for dielectric skin-electrode interfaces.

Insulating electrodes, also known as capacitive electrodes, allow acquiring biopotentials without galvanic contact with the body. They operate with displacement currents instead of real charge currents, and the electrolytic electrode-skin interface is replaced by a dielectric film. The use of insulating electrodes is not the end of electrode interface problems but the beginning of new ones: coupling capacitances are of the order of pF calling for ultra-high input impedance amplifiers and careful biasing, guarding and shielding techniques. In this work, the general requirements of front ends for capacitive electrodes are presented and the different contributions to the overall noise are discussed and estimated. This analysis yields that noise bounds depend on features of the available devices as current and voltage noise, but the final noise level also depends on parasitic capacitances, requiring a careful shield and printed circuit design. When the dielectric layer is placed on the skin, the present-day amplifiers allow achieving noise levels similar to those provided by wet electrodes. Furthermore, capacitive electrode technology allows acquiring high quality ECG signals through thin clothes. A prototype front end for capacitive electrodes was built and tested. ECG signals were acquired with these electrodes in direct contact with the skin and also through cotton clothes 350 µm thick. They were compared with simultaneously acquired signals by means of wet electrodes and no significant differences were observed between both output signals.
Improved Algorithm for Intrusion Detection Using Genetic Algorithm and SNORT

Intrusion Detection Systems (IDSs) detects the network attacks by scanning each data packets. There are various approaches were done for making better IDS, though traditional IDS approaches are insufficient in terms of automating detection of attacks as attackers evolved. This research is focused on Genetic Algorithm approach used in Intrusion Detection. This research shows that how the traditional SNORT and Genetic Algorithm combined together so that the number of rules of SNORT is decreased. Experimental results Shows That IDS using Genetic Algorithm and SNORT can decrease the amount of detection time, CPU Utilization and Memory Utilization. Genetic Algorithm, SNORT, Fitness Function,
Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing

The efficient processing of video streams is a key component in many emerging Internet of Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self-driving cars. These applications require real-time high-throughput video processing. This can be attained via a collaborative processing model between the edge and the cloud—called an Edge-Cloud model. To this end, many approaches were proposed to optimize the latency and bandwidth consumption of Edge-Cloud video processing, especially for Neural Networks (NN)-based methods. In this demonstration. We investigate the efficiency of these NN techniques, how they can be combined, and whether combining them leads to better performance. Our demonstration invites participants to experiment with the various NN techniques, combine them, and observe how the underlying NN changes with different techniques and how these changes affect accuracy, latency and bandwidth consumption.  Philipp M. Grulich and Faisal Nawab. Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing. PVLDB, 11 (12): 2046-2049, 2018.
OpenSense: open community driven sensing of environment

This paper outlines a vision for community-driven sensing of our environment. At its core, community sensing is a dynamic new form of mobile geosensor network. We believe that community sensing networks, in order to be widely deployable and sustainable, need to follow utilitarian approaches towards sensing and data management. Current projects exploring community sensing have paid less attention to these underlying fundamental principles. We illustrate this vision through OpenSense -- a large project that aims to explore community sensing driven by air pollution monitoring.
ADAM: Detecting Intrusions by Data Mining

Intrusion detection systems have traditionally been based on the characterization of an attack and the tracking of the activity on the system to see if it matches that characterization. Recently, new intrusion detection systems based on data mining are making their appearance in the field. This paper describes the design and experiences with the ADAM ( Audit Data Analysis and Mining) system, which we use as a testbed to study how useful data mining techniques can be in intrusion detection. Intrusion Detection, Data Mining, Association Rules, Classifiers.
VADI: GPU Virtualization for an Automotive Platform

Modern vehicles are evolving with more electronic components than ever before (In this paper, “vehicle” means “automotive vehicle.” It is also equal to “car.”) One notable example is graphical processing unit (GPU), which is a key component to implement a digital cluster. To implement the digital cluster that displays all the meters (such as speed and fuel gauge) together with infotainment services (such as navigator and browser), the GPU needs to be virtualized; however, GPU virtualization for the digital cluster has not been addressed yet. This paper presents a Virtualized Automotive DIsplay (VADI) system to virtualize a GPU and its attached display device. VADI manages two execution domains: one for the automotive control software and the other for the in-vehicle infotainment (IVI) software. Through GPU virtualization, VADI provides GPU rendering to both execution domains, and it simultaneously displays their images on a digital cluster. In addition, VADI isolates GPU from the IVI software in order to protect it from potential failures of the IVI software. We implement VADI with Vivante GC2000 GPU and perform experiments to ensure requirements of International Standard Organization (ISO) safety standards. The results show that VADI guarantees 30 frames per second (fps), which is the minimum frame rate for digital cluster mandated by ISO safety standards even with the failure of the IVI software. It also achieves 60 fps in a synthetic workload.
Aware and smart member card: RFID and license plate recognition systems integrated applications at parking guidance in shopping mall

Member card can provide personal identification, authentication, data storage, and application processing. It may provide strong marketing media for customer relationship within business organizations. In this paper, the integrated applications of passive radio frequency identification (RFID) and license plate recognition (LPR) are presented. We applied RFID and LPR techniques integrated, meanwhile, we collected the vehicles self-adhered e-Tag ID data. All of these development to compile on the member card as a linking media, it provided the more premium services for card holder. By the cumulative utilization and analysis data of member card, it has become an essential connection between the customers and the companies. It is not only added card self-valued, but also do grasp customers' preference. This proposed system is composed of three main modules at RFID (3M e-Tag), LPR (Image processing), and RFID (NXP MIFARE), respectively. It is designed to meet the requirements of performance and can be generally applied to the commercial markets (e.g., other malls, marts, department stores) that also operate parking lots which face similar problems.
Unsupervised speech representation learning using WaveNet autoencoders

We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. The behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE (VQVAE). We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQVAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.
Bach 10 Dataset---- A Versatile Polyphonic Music Dataset

This is a polyphonic music dataset which can be used for versatile research problems, such as Multi-pitch Estimation and Tracking, Audio-score Alignment, Source Separation, etc. This dataset consists of the audio recordings of each part and the ensemble of ten pieces of four-part J.S. Bach chorales, as well as their MIDI scores, the ground-truth alignment between the audio and the score, the ground-truth pitch values of each part and the ground-truth notes of each piece. The audio recordings of the four parts (Soprano, Alto, Tenor and Bass) of each piece are performed by violin, clarinet, saxophone and bassoon, respectively.
A three-level, T-type, power electronics building block using Si-SiC hybrid switch for high-speed drives

This paper presents the design and implementation of a 100 kW three-level T-type (3L-T2), single-phase power electronics building block (PEBB). The PEBB features switching power devices with associated gate drivers, DC-link capacitors and interconnection busbar. A hybrid switch (HyS), which includes a Si IGBT and a SiC MOSFET in parallel, forms the switching power device in the PEBB. An low-inductive multi-layer laminated busbar was designed to have symmetrical current commutation loops. A major contribution is that this is achieved while no three-level T-type power module was used. A loop inductance of 29 nH was achieved which is lower than the existing state-of-the-art three-level designs in literature. The fabricated prototype PEBB achieves a specific power density of 27.7 kW/kg and a volumetric power density of 308.61 W/in3. Single-phase operation of the PEBB has been demonstrated at the switching frequency of 28 kHz.
A framework for automatic question generation from text using deep reinforcement learning

Automatic question generation (QG) is a useful yet challenging task in NLP. Recent neural network-based approaches represent the state-of-the-art in this task, but they are not without shortcomings. Firstly, these models lack the ability to handle rare words and the word repetition problem. Moreover, all previous works optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation measure). In this paper, we present a novel deep reinforcement learning based framework for automatic question generation. The generator of the framework is a sequence-to-sequence model, enhanced with the copy mechanism to handle the rare-words problem and the coverage mechanism to solve the word repetition problem. The evaluator model of the framework evaluates and assigns a reward to each predicted question. The overall model is trained by learning the parameters of the generator network which maximizes the reward. Our framework allows us to directly optimize any task-specific score including evaluation measures such as BLEU, GLEU, ROUGE-L, etc., suitable for sequence to sequence tasks such as QG. Our comprehensive evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark in both automatic and human evaluation.
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning

Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each individual problem by a qualified engineer. Within the learning community there is a long history of insertion research, but existing approaches are typically either too sample-inefficient to run on real robots, or assume access to high-level object features, e.g. socket pose. In this paper we show that relatively minor modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly. Our approach requires no modeling or simulation, no parameterized search or alignment behaviors, no vision system aside from raw images, and no reward shaping. We evaluate our approach on a narrowclearance peg-insertion task and a deformable clip-insertion task, both of which include variability in the socket position. Our results show that these tasks can be solved reliably on the real robot in less than 10 minutes of interaction time, and that the resulting policies are robust to variance in the socket position and orientation.
Ontology Engineering: a Survey and a Return on Experience

Ontology is a new object of IA that recently came to maturity and a powerful conceptual tool of Knowledge Modeling. It provides a coherent base to build on, and a shared reference to align with, in the form of a consensual conceptual vocabulary, on which one can build descriptions and communication acts. This report presents the object that is called "an ontology" and a state of the art of engineering techniques for ontologies. Then it describes a project for which we developed an ontology and used it to improve knowledge management. Finally it describes the design process and discuss the resulting ontology.
An edge computing architecture integrating virtual IoT devices

The current IoT ecosystem is Cloud centric which can not handle a diverse set of IoT applications and services, especially those demanding real time response. This paper proposes an Edge Computing (EC) architecture that provides an intermediate computing layer for IoT data. The proposed architecture utilizes Virtual IoT Devices (VID) for local data processing, management of physical IoT devices and quick reaction using actuators. A prototype of the EC system is developed and early performance results are reported. Applications of the EC system for roadside assistance services and emerging autonomous vehicles are outlined.