nils-herrmann/clean_scirepeval huggingface dataset:
An architecture of agent-based multi-layer interactive e-learning and e-testing platform

E-learning is the synthesis of multimedia and social media platforms powered by Internet and mobile technologies. Great popularity of e-learning is encouraging governments and educational institutions to adopt and develop e-learning cultures in societies in general and universities in particular. In traditional e-learning systems, all components (agents and services) are tightly coupled into a single system. In this study, we propose a new architecture for e-learning with two subsystems, namely, e-learning and e-testing. The motivation of the research is to improve the effectiveness of the learning process by extracting relevant features for elastic learning and testing process. We employ a multi-agent system because it contains five-layer architecture, including agents at various levels. We also propose a novel method for updating content through question and answer between e-learners and intelligent agents. To achieve optimization, we design a system that applies various technologies, which M. Arif (B)· A. Karim · K. A. Alam · S. Farid · S. Iqbal Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: arifmuhammad36@siswa.um.edu.my M. Arif · M. Illahi Departmnet of Computer Science, COMSATS Institute of Information Technology Islamabad, Islamabad, Pakistan S. Shamshirband Faculty of Computer Science and Information Technology, Department of Computer System and Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: shamshirband1396@gmail.com S. Shamshirband Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), Chalous, 46615-397 Mazandaran, Iran Z. Buang Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia V. E. Balas Department of Automation and Applied Informatics, University of Arad, 310130 Arad, Romania
Mass-produced parts traceability system based on automated scanning of “Fingerprint of Things”

This paper presents a prototype of parts traceability system which employs the “Fingerprint of Things”-based individual identification technique. Traceability of mass-produced tiny parts such as bolts and nuts are required to ensure quality and safety of big machines. However conventional systems using ID tags or serial marking are not applicable because of quantity and tiny size. To overcome this problem, we propose a tag-less traceability system which uses their appearance images as “fingerprints” to identify each of them. Our traceability system consists of three components; (i) automated fingerprints scanning machine for enrollment, (ii) mobile device for query and (iii) cloud server for identification from database. The key to success of our traceability system is enabling us to capture repeatable image features from the same parts in both of (i) enrollment and (ii) query. To this end, we designed the two lighting mechanisms; one for fast scanning of numerous bolts by automatic feeding, and another for a mobile device to capture one parts by hand. In our experiments, we achieve that 1,000 metal bolts produced with the same mold are perfectly identified by matching their surface images captured with our automatic scanning machine and a smart phone.
A universal SNP and small-indel variant caller using deep neural networks

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.
Brain Controlled Robot for Indian Armed Force

Currently border security that incorporates social, cultural, activity and structure aspects of interactions among border security forces, smugglers and therefore the population and represent integrated technology architectures created of mounted and mobile detector and police work networks. These tools give important capabilities that influence border security operations, planning, analysis and coaching. Sensors are being deployed to enhance border security generating monumental collections of knowledge and databases. Sadly, these sensors will reply to a range of stimuli, typically reacting to important events and typically triggered by random events that are thought of false alarms. The intent of this project is to supplement human intelligence in a very detector network framework which will assist in filtering and period of time higher cognitive process from the massive volume of knowledge generated. In our project, the projected system that has secured to the motherland by victimization ideas of Wireless Integrated Network Sensors, GPS pursuit and object and metal detection and tracking of vehicles with within the country. By Object identification system we will be ready to get the images of that exact space wherever the strangers has returned in addition, because the details of objects or folks that are gift there. And later the metal police work sensors and bomb noticed signals can detect the existence of explosives and weapons(metals) with them. Presently the Indian government is coming up with to-implement a similar technology for pursuit the vehicles with within the country that carry illegitimate commodities ( like government issued sugar , rice to be distributed among lots however send to alternative states without legal permission). The vehicles that carry explosive materials for industrial functions are often half-tracked.
Electrical modeling and characterization of through-silicon vias (TSVs) for 3-D integrated circuits

The integration of chips in the third dimension has been explored to address various physical and system level limitations currently undermining chip performance. In this paper, we present a comprehensive analysis of the electrical properties of through silicon vias and microconnects with an emphasis on single via characteristics as well as inter-TSV capacitive and inductive coupling in the presence of either a neighboring ground tap or a grounded substrate back plane. We also analyze the impact of technology scaling on TSV electrical parasitics, and investigate the power and delay trend in 3-D interstratum IO drivers with those of global wire in 2-D circuits over various technology nodes. We estimate the global wire length necessary to produce an equivalent 3-D IO delay, a metric useful in early stage design tools for 3D floorplanning that considers the electrical characteristics of 3D connections with TSVs and microconnects. & 2009 Elsevier Ltd. All rights reserved.
Stippling by Example

In this work, we focus on stippling as an artistic style and discuss our technique for capturing and reproducing stipple features unique to an individual artist. We employ a texture synthesis algorithm based on the gray-level co-occurrence matrix (GLCM) of a texture field. This algorithm uses a texture similarity metric to generate stipple textures that are perceptually similar to input samples, allowing us to better capture and reproduce stipple distributions. First, we extract example stipple textures representing various tones in order to create an approximate tone map used by the artist. Second, we extract the stipple marks and distributions from the extracted example textures, generating both a lookup table of stipple marks and a texture representing the stipple distribution. Third, we use the distribution of stipples to synthesize similar distributions with slight variations using a numerical measure of the error between the synthesized texture and the example texture as the basis for replication. Finally, we apply the synthesized stipple distribution to a 2D grayscale image and place stipple marks onto the distribution, thereby creating a stippled image that is statistically similar to images created by the example artist.
SnortView: visualization system of snort logs

False detection is a major issue in deploying and maintaining Network-based Intrusion Detection Systems (NIDS). Traditionally, it is recommended to customize its signature database (DB) to reduce false detections. However, it requires quite deep knowledge and skills to appropriately customize the signature DB. Inappropriate customization causes the increase of false negatives as well as false positives. In this paper, we propose a visualization system of a NIDS log, named SnortView, which supports administrators in analyzing NIDS alerts much faster and much more easily. Instead of customizing the signature DB, we propose to utilize visualization to recognize not only each alert but also false detections. The system is based on a 2-D time diagram and alerts are shown as icons with different styles and colors. In addition, the system introduces some visualization techniques such as overlayed statistical information, source-destination matrix, and so on. The system was used to detect real attacks while recognizing some false detections.
Reservoir Computing by Felix Grezes

Even before Artificial Intelligence was its own field of computational science, men have tried to mimic the activity of the human brain. In the early 1940s the first artificial neuron models were created as purely mathematical concepts. Over the years, ideas from neuroscience and computer science were used to develop the modern Neural Network. The interest in these models rose quickly but fell when they failed to be successfully applied to practical applications, and rose again in the late 2000s with the drastic increase in computing power, notably in the field of natural language processing, for example with the state-of-the-art speech recognizer making heavy use of deep neural networks. Recurrent Neural Networks (RNNs), a class of neural networks with cycles in the network, exacerbates the difficulties of traditional neural nets. Slow convergence limiting the use to small networks, and difficulty to train through gradient-descent methods because of the recurrent dynamics have hindered research on RNNs, yet their biological plausibility and their capability to model dynamical systems over simple functions makes then interesting for computational researchers. Reservoir Computing emerges as a solution to these problems that RNNs traditionally face. Promising to be both theoretically sound and computationally fast, Reservoir Computing has already been applied successfully to numerous fields: natural language processing, computational biology and neuroscience, robotics, even physics. This survey will explore the history and appeal of both traditional feed-forward and recurrent neural networks, before describing the theory and models of this new reservoir computing paradigm. Finally recent papers using reservoir computing in a variety of scientific fields will be reviewed.
Emerging
Applications of Liquid Metals Featuring Surface Oxides

Gallium and several of its alloys are liquid metals at or near room temperature. Gallium has low toxicity, essentially no vapor pressure, and a low viscosity. Despite these desirable properties, applications calling for liquid metal often use toxic mercury because gallium forms a thin oxide layer on its surface. The oxide interferes with electrochemical measurements, alters the physicochemical properties of the surface, and changes the fluid dynamic behavior of the metal in a way that has, until recently, been considered a nuisance. Here, we show that this solid oxide "skin" enables many new applications for liquid metals including soft electrodes and sensors, functional microcomponents for microfluidic devices, self-healing circuits, shape-reconfigurable conductors, and stretchable antennas, wires, and interconnects.
Development of Fire alarm system using Raspberry Pi and Arduino Uno

The proposed Fire alarm system is a real-time monitoring system that detects the presence of smoke in the air due to fire and captures images via a camera installed inside a room when a fire occurs. The embedded systems used to develop this fire alarm system are Raspberry Pi and Arduino Uno. The key feature of the system is the ability to remotely send an alert when a fire is detected. When the presence of smoke is detected, the system will display an image of the room state in a webpage. The system will need the user confirmation to report the event to the Firefighter using Short Message Service (SMS). The advantage of using this system is it will reduce the possibility of false alert reported to the Firefighter. The camera will only capture an image, so this system will consume a little storage and power.
A Novel Metal-Plate Monopole Antenna for DTV Application

In this paper, a novel metal-plate monopole antenna is presented for indoor digital television (DTV) signal coverage in the 468-880MHz band. The proposed antenna consists of two orthorhombic metal-plates with symmetrical bevels, a top-uploaded metal-plate and a ground plane. Simulation results show that the antenna achieves a bandwidth ranging from 468MHz to 880MHz which covers the DTV band (470-860MHZ) and shows stable radiation patterns in three coordinate planes.
A 100 W 5.1-Channel Digital Class-D Audio Amplifier With Single-Chip Design

A 100 W, 5.1-channel, single-chip, digital-input class-D audio amplifier with a low-voltage (LV) digital circuit and high-voltage (HV) switching power stage is designed for moderate-performance and cost-effective speaker systems. The LV portion, including multi-channel audio processors, delta-sigma modulators (DSMs), and pulse-width modulation (PWM) generators, is implemented with a standard CMOS digital cell-library. A dual-loop resonator is proposed to increase the stable input range of the DSM so that the low-distortion output power of the class-D amplifier can be increased. For the HV portion, distortion caused by parasitic resistances of the power stage is analyzed to obtain a better design. A multi-phase PWM switching technique is proposed to prevent the multi-channel output stages from simultaneously switching, and thus the supply bouncing can be reduced. An over-current protection circuit with high supply noise immunity is also presented. Fabricated with 0.35/3-μm 3.3/18-V 1P3M CMOS technology, the 5.1-channel amplifier achieves a total root-mean-square (RMS) output power of 100 W, a distortion of less than 0.7%, and a power efficiency of 88% with a total chip area of 48.9 mm2.
A High-Speed Sliding-Mode Observer for the Sensorless Speed Control of a PMSM

This paper proposes a sensorless speed control strategy for a permanent-magnet synchronous motor (PMSM) based on a new sliding-mode observer (SMO), which substitutes a sigmoid function for the signum function with a variable boundary layer. In order to apply a sensorless PMSM control which is robust against parameter fluctuations and disturbances, a high-speed SMO is proposed, which estimates the rotor position and the angular velocity from the back EMF. In the conventional SMO, a low-pass filter and an additional position compensation of the rotor are used to reduce the chattering problem that is commonly found in the SMO using the signum function. In order to overcome the time delay caused by the low-pass filter, in this research, a sigmoid function is used for the switching function instead of the signum function. Also, the variation in the stator resistance is estimated to improve the steady-state performance of the SMO. The stability of the proposed SMO was verified using the Lyapunov second method to determine the observer gain. The validity of the proposed high-speed PMSM sensorless velocity control has been demonstrated with simulations and real experiments.
Topology Derivation of Nonisolated Three-Port DC–DC Converters From DIC and DOC

A systematic approach is proposed for the derivation of nonisolated three-port converter (TPC) topologies based on dual-input converters (DIC) and dual-output converters (DOC), which serves as an interface for a renewable source, a storage battery, and a load simultaneously. The power flow in a TPC is analyzed and compared with that in a DIC or a DOC, respectively. Beginning with building the power flow paths of a TPC from a DIC or a DOC, the general principles and detailed procedures for the generation and optimization of TPC topologies are presented. Based on these works, a family of nonisolated TPC topologies is developed. The derived TPCs feature single-stage power conversion between any two of the three ports, and result in high integration and high efficiency. One of the proposed TPCs, named Boost-TPC, is taken as an example for verifying the performance of its circuit topology with related operational methods. Pulsewidth modulation and power management methods, used in this circuit, are analyzed in detail. Experiments have been carried out on a 1-kW prototype of the Boost-TPC, which demonstrate the feasibility and effectiveness of the proposed topology derivation method.
A Lie Never Lives to be Old: The Effects of Fake Social Information on Consumer Decision-Making in Crowdfunding

The growing success of social media led to a strong presence of social information such as customer product reviews and product ratings in electronic markets. While this information helps consumers to better assess the quality of goods before purchase, its impact on consumer decision-making also incentivizes sellers to game the system by creating fake data in favor of specific goods in order to deliberately mislead consumers. As a consequence, consumers could make suboptimal choices or could choose to disregard social information altogether. In this exploratory study, we assess the effects nongenuine social information has on the consumer’s decision-making in the context of reward-based crowdfunding. Specifically, we capture unnatural peaks in the number of Facebook Likes that a specific crowdfunding campaign receives on the platforms Kickstarter and Indiegogo and observe subsequent campaign performance. Our results show that fake Facebook Likes have a very short-term positive effect on the number of backers participating in the respective crowdfunding campaign. However, this peak in participation is then followed by a period in which participation is lower than prior to the existence of the non-genuine social information. We further discuss circumstances that foster this artificial manipulation of quality signals.
Big data and machine learning driven handover management and forecasting

Handover (HO), as a key aspect of mobility management, plays an important role in improving network quality and mobility performance in mobile networks. Especially, in 5G networks, heterogeneous networks (HetNets) deployment of macro cells and small cells, and the deployment of ultra-dense networks (UDNs) make HO management become more challenging. Besides, the understanding of HO behavior in a cell is quite limited in existing studies, thus the forecasting HO for an individual cell is complicated, even impossible. This challenge led the authors to propose a practical process for managing and forecasting HO for a huge number of cells, based on machinelearning (ML) algorithms and big data. Moreover, based on HO forecasting, the authors also propose an approach to detect any abnormal HO in cells. The performance of the proposed approaches was evaluated by applying it to a real dataset that collected HO KPI of more than 6000 cells of a real network during the years, 2016 and 2017. The results show that the study was successful in identifying, separating HO behavior, forecasting the future number of HO attempts, and detecting abnormal HO behaviors of cells.
To See or not to See – Innovative Display Technologies as Enablers for Ergonomic Cockpit Concepts Ergonomic requirements future mobility future functionality

Given the fact that the driving task will remarkably change by the increase of assistance and information systems in the future innovative display technologies play a central role for the ergonomic realization of the driver vehicle interaction. Increasing assistance and even automation of the driving task do not lead to a decrease or disappearance of visual information but instead request new and in some cases revolutionary concepts to close the loop between driver, vehicle and traffic environment. Augmenting information in contact analog head-up-displays for navigation and driver assistance is a very promising approach. Replacement of mirrors via camera monitor systems is a further example. Free programmable cluster instruments in combination with HUD promise to resolve the problem of information density produced by an increasing amount of ADAS and IVIS functionality.
Design and implementation of intrusion detection system using convolutional neural network for DoS detection

Nowadays, network is one of the essential parts of life, and lots of primary activities are performed by using the network. Also, network security plays an important role in the administrator and monitors the operation of the system. The intrusion detection system (IDS) is a crucial module to detect and defend against the malicious traffics before the system is affected. This system can extract the information from the network system and quickly indicate the reaction which provides real-time protection for the protected system. However, detecting malicious traffics is very complicating because of their large quantity and variants. Also, the accuracy of detection and execution time are the challenges of some detection methods. In this paper, we propose an IDS platform based on convolutional neural network (CNN) called IDS-CNN to detect DoS attack. Experimental results show that our CNN based DoS detection obtains high accuracy at most 99.87%. Moreover, comparisons with other machine learning techniques including KNN, SVM, and Naïve Bayes demonstrate that our proposed method outperforms traditional ones.