nils-herrmann/clean_scirepeval huggingface dataset:
I want to answer; who has a question?: Yahoo! answers recommender system

Yahoo! Answers is currently one of the most popular question answering systems. We claim however that its user experience could be significantly improved if it could route the "right question" to the "right user." Indeed, while some users would rush answering a question such as "what should I wear at the prom?," others would be upset simply being exposed to it. We argue here that Community Question Answering sites in general and Yahoo! Answers in particular, need a mechanism that would expose users to questions they can relate to and possibly answer.
 We propose here to address this need via a multi-channel recommender system technology for associating questions with potential answerers on Yahoo! Answers. One novel aspect of our approach is exploiting a wide variety of content and social signals users regularly provide to the system and organizing them into channels. Content signals relate mostly to the text and categories of questions and associated answers, while social signals capture the various user interactions with questions, such as asking, answering, voting, etc. We fuse and generalize known recommendation approaches within a single symmetric framework, which incorporates and properly balances multiple types of signals according to channels. Tested on a large scale dataset, our model exhibits good performance, clearly outperforming standard baselines.
Multimedia Cloud Computing

This article introduces the principal concepts of multimedia cloud computing and presents a novel framework. We address multimedia cloud computing from multimedia-aware cloud (media cloud) and cloud-aware multimedia (cloud media) perspectives. First, we present a multimedia-aware cloud, which addresses how a cloud can perform distributed multimedia processing and storage and provide quality of service (QoS) provisioning for multimedia services. To achieve a high QoS for multimedia services, we propose a media-edge cloud (MEC) architecture, in which storage, central processing unit (CPU), and graphics processing unit (GPU) clusters are presented at the edge to provide distributed parallel processing and QoS adaptation for various types of devices.
Performance Evaluation of Symmetric Encryption Algorithms on Power Consumption for Wireless Devices

—s the popularity of wireless networks increases, so does the need to protect them. Encryption algorithms play a main role in information security systems. On the other side, those algorithms consume a significant amount of computing resources such as CPU time, memory, and battery power. This paper illustrates the key concepts of security, wireless networks, and security over wireless networks. Wireless security is demonstrated by applying the common security standards like (802.11 WEP and 802.11i WPA,WPA2) and provides evaluation of six of the most common encryption algorithms on power consumption for wireless devices namely: AES comparison has been conducted for those encryption algorithms at different settings for each algorithm such as different sizes of data blocks, different data types, battery power consumption, date transmission through wireless network and finally encryption/decryption speed. Experimental results are given to demonstrate the effectiveness of each algorithm.
An over 20,000 quality factor on-chip relaxation oscillator using Power Averaging Feedback with a Chopped Amplifier

This paper describes the first achievement of over 20,000 quality factors among on-chip relaxation oscillators. The proposed Power Averaging Feedback with a Chopped Amplifier enables such a high Q which is close to MEMS oscillators. 1/f noise free design and rail-to-rail oscillation result in low phase noise with small area and low power consumption. The proposed oscillator can be applied to low noise applications (e.g. digital audio players) implemented onto a System on a Chip.
Deployment orchestration of microservices with geographical constraints for Edge computing

Nowadays, Edge computing allows to push the application intelligence at the boundaries of a network in order to get high-performance processing closer to both data sources and end-users. In this scenario, the Horizon 2020 BEACON project - enabling federated Cloud-networking - can be used to setup Fog computing environments were applications can be deployed in order to instantiate Edge computing applications. In this paper, we focus on the deployment orchestration of Edge computing distributed services on such fog computing environments. We assume that a distributed service is composed of many microservices. Users, by means of geolocation deployment constrains can select regions in which microservices will be deployed. Specifically, we present an Orchestration Broker that starting from an ad-hoc OpenStack-based Heat Orchestraton Template (HOT) service manifest of an Edge computing distributed service produces several HOT microservice manifests including the the deployment instruction for each involved Fog computing node. Experiments prove the goodness of our approach.
Detecting urban black holes based on human mobility data

Many types of human mobility data, such as flows of taxicabs, card swiping data of subways, bike trip data and Call Details Records (CDR), can be modeled by a Spatio-Temporal Graph (STG). STG is a directed graph in which vertices and edges are associated with spatio-temporal properties (e.g. the traffic flow on a road and the geospatial location of an intersection). In this paper, we instantly detect interesting phenomena, entitled black holes and volcanos, from an STG. Specifically, a black hole is a subgraph (of an STG) that has the overall inflow greater than the overall outflow by a threshold, while a volcano is a subgraph with the overall outflow greater than the overall inflow by a threshold (detecting volcanos from an STG is proved to be equivalent to the detection of black holes). The online detection of black holes/volcanos can timely reflect anomalous events, such as disasters, catastrophic accidents, and therefore help keep public safety. The patterns of black holes/volcanos and the relations between them reveal human mobility patterns in a city, thus help formulate a better city planning or improve a system's operation efficiency. Based on a well-designed STG index, we propose a two-step black hole detection algorithm: The first step identifies a set of candidate grid cells to start from; the second step expands an initial edge in a candidate cell to a black hole and prunes other candidate cells after a black hole is detected. Then, we adapt this detection algorithm to a continuous black hole detection scenario. We evaluate our method based on Beijing taxicab data and the bike trip data in New York, finding urban anomalies and human mobility patterns.
Kangaroo: A Tenant-Centric Software-Defined Cloud Infrastructure

Applications on cloud infrastructures acquire virtual machines (VMs) from providers when necessary. The current interface for acquiring VMs from most providers, however, is too limiting for the tenants, in terms of granularity in which VMs can be acquired (e.g., small, medium, large, etc.), while giving very limited control over their placement. The former leads to VM underutilization, and the latter has performance implications, both translating into higher costs for the tenants. In this work, we leverage nested virtualization and a networking overlay to tackle these problems. We present Kangaroo, an Open Stack-based virtual infrastructure provider, and IPOPsm, a virtual networking switch for communication between nested VMs over different infrastructure VMs. In addition, we design and implement Skippy, the realization of our proposed virtual infrastructure API for programming Kangaroo. Our benchmarks show that through careful mapping of nested VMs to infrastructure VMs, Kangaroo achieves up to an order of magnitude better performance, with only half the cost on Amazon EC2. Further, Kangaroo's unified Open Stack API allows us to migrate an entire application between Amazon EC2 and our local Open Nebula deployment within a few minutes, without any downtime or modification to the application code.
A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers

Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we proposed a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under diverse and even unforeseen scenarios. Particularly, we treated both state space and action space as continuous, and designed a Q-function approximator that has a closed-form greedy policy, which contributes to the computation efficiency of our deep Q-learning algorithm. Extensive simulations are conducted for training the algorithm, and the results illustrate that the Reinforcement Learning based vehicle agent is capable of learning a smooth and efficient driving policy for lane change maneuvers.
Simulation and control of a two-wheeled self-balancing robot

A two-wheeled self-balancing robot is a special type of wheeled mobile robot, its balance problem is a hot research topic due to its unstable state for controlling. In this paper, human transporter model has been established. Kinematic and dynamic models are constructed and two control methods: Proportional-integral-derivative (PID) and Linear-quadratic regulator (LQR) are implemented to test the system model in which controls of two subsystems: self-balance (preventing system from falling down when it moves forward or backward) and yaw rotation (steering angle regulation when it turns left or right) are considered. PID is used to control both two subsystems, LQR is used to control self-balancing subsystem only. By using simulation in Matlab, two methods are compared and discussed. The theoretical investigations for controlling the dynamic behavior are meaningful for design and fabrication. Finally, the result shows that LQR has a better performance than PID for self-balancing subsystem control.
Im2Sketch: Sketch generation by unconflicted perceptual grouping

Effectively solving the problem of sketch generation, which aims to produce human-drawing-like sketches from real photographs, opens the door for many vision applications such as sketch-based image retrieval and nonphotorealistic rendering. In this paper, we approach automatic sketch generation from a human visual perception perspective. Instead of gathering insights from photographs, for the first time, we extract information from a large pool of human sketches. In particular, we study how multiple Gestalt rules can be encapsulated into a unified perceptual grouping framework for sketch generation. We further show that by solving the problem of Gestalt confliction, i.e., encoding the relative importance of each rule, more similar to human-made sketches can be generated. For that, we release a manually labeled sketch dataset of 96 object categories and 7,680 sketches. A novel evaluation framework is proposed to quantify human likeness of machinegenerated sketches by examining how well they can be classified using models trained from human data. Finally, we demonstrate the superiority of our sketches under the practical application of sketch-based image retrieval.
JSNOSE: Detecting JavaScript Code Smells

JavaScript is a powerful and flexible prototype-based scripting language that is increasingly used by developers to create interactive web applications. The language is interpreted, dynamic, weakly-typed, and has first-class functions. In addition, it interacts with other web languages such as CSS and HTML at runtime. All these characteristics make JavaScript code particularly error-prone and challenging to write and maintain. Code smells are patterns in the source code that can adversely influence program comprehension and maintainability of the program in the long term. We propose a set of 13 JavaScript code smells, collected from various developer resources. We present a JavaScript code smell detection technique called JSNOSE. Our metric-based approach combines static and dynamic analysis to detect smells in client-side code. This automated technique can help developers to spot code that could benefit from refactoring. We evaluate the smell finding capabilities of our technique through an empirical study. By analyzing 11 web applications, we investigate which smells detected by JSNOSE are more prevalent.
Solution of differential-difference equations by using differential transform method

In this work, we successfully extended differential transform method (DTM), by presenting and proving new theorems, to the solution of differential–difference equations (DDEs). Theorems are presented in the most general form to cover a wide range of DDEs, being linear or nonlinear and constant or variable coefficient. In order to show the power and the robustness of the method and to illustrate the pertinent features of related theorems, examples are presented.
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers

Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel DenseNet based FCN architecture for cardiac segmentation which is parameter and memory efficient. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in conventional FCN based architectures. In order to process the input images at multiple scales and view points simultaneously, we propose to incorporate Inception module's parallel structures. We propose a novel dual loss function whose weighting scheme allows to combine advantages of cross-entropy and Dice loss leading to qualitative improvements in segmentation. We demonstrate computational efficacy of incorporating conventional computer vision techniques for region of interest detection in an end-to-end deep learning based segmentation framework. From the segmentation maps we extract clinically relevant cardiac parameters and hand-craft features which reflect the clinical diagnostic analysis and train an ensemble system for cardiac disease classification. We validate our proposed network architecture on three publicly available datasets, namely: (i) Automated Cardiac Diagnosis Challenge (ACDC-2017), (ii) Left Ventricular segmentation challenge (LV-2011), (iii) 2015 Kaggle Data Science Bowl cardiac challenge data. Our approach in ACDC-2017 challenge stood second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100% on a limited testing set (n=50). In the LV-2011 challenge our approach attained 0.74 Jaccard index, which is so far the highest published result in fully automated algorithms. In the Kaggle challenge our approach for LV volume gave a Continuous Ranked Probability Score (CRPS) of 0.0127, which would have placed us tenth in the original challenge. Our approach combined both cardiac segmentation and disease diagnosis into a fully automated framework which is computationally efficient and hence has the potential to be incorporated in computer-aided diagnosis (CAD) tools for clinical application.
Long short term memory neural network for keyboard gesture decoding

Gesture typing is an efficient input method for phones and tablets using continuous traces created by a pointed object (e.g., finger or stylus). Translating such continuous gestures into textual input is a challenging task as gesture inputs exhibit many features found in speech and handwriting such as high variability, co-articulation and elision. In this work, we address these challenges with a hybrid approach, combining a variant of recurrent networks, namely Long Short Term Memories  with conventional Finite State Transducer decoding . Results using our approach show considerable improvement relative to a baseline shape-matching-based system, amounting to 4% and 22% absolute improvement respectively for small and large lexicon decoding on real datasets and 2% on a synthetic large scale dataset.
A CMOS buffer without short-circuit power consumption

A new CMOS buffer without short-circuit power consumption is proposed. The gatedriving signal of the output pull-up (pull-down) transistor is fed back to the output pull-down (pull-up) transistor to get tri-state output momentarily, eliminating the short-circuit power consumption. The HSPICE simulation results verified the operation of the proposed buffer and showed the power-delay product is about 15% smaller than conventional tapered CMOS buffer.
Network Sketching: Exploiting Binary Structure in Deep CNNs

Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks. However, deep networks are typically resource-intensive and thus difficult to be deployed on mobile devices. Recently, CNNs with binary weights have shown compelling efficiency to the community, whereas the accuracy of such models is usually unsatisfactory in practice. In this paper, we introduce network sketching as a novel technique of pursuing binary-weight CNNs, targeting at more faithful inference and better trade-off for practical applications. Our basic idea is to exploit binary structure directly in pre-trained filter banks and to produce binary-weight models via tensor expansion. The whole process can be treated as a coarse-to-fine model approximation, akin to the pencil drawing steps of outlining and shading. To further speedup the generated models, namely the sketches, we also propose an associative implementation of binary tensor convolutions. Experimental results demonstrate that a proper sketch of AlexNet (or ResNet) outperforms the existing binary-weight models by large margins on the ImageNet large scale classification task, while the committed memory for network parameters only exceeds a little.
Modelling Provenance for GDPR Compliance using Linked Open Data Vocabularies

The upcoming General Data Protection Regulation (GDPR) requires justification of data activities to acquire, use, share, and store data using consent obtained from the user. Failure to comply may result in significant heavy fines which incentivises creation and maintenance of records for all activities involving consent and data. Compliance documentation therefore requires provenance information outlining consent and data lifecycles to demonstrate correct usage of data in accordance with the related consent provided and updated by the user. In this paper, we present GDPRov, a linked data ontology for expressing provenance of consent and data lifecycles with a view towards documenting compliance. GDPRov is an OWL2 ontology that extends PROV-O and P-Plan to model the provenance, and uses SPARQL to express compliance related
Robotic Grasping and Fine Manipulation Using Soft Fingertip

The ability to create stable, encompassing grasps with subsets of fingers is greatly increased by using soft fingertips that deform during contact and apply a larger space of frictional forces and moments than their rigid counterparts. This is true not only for human grasping, but also for robotic hands using fingertips made of soft materials. The superiority of deformable human fingertips as compared to hard robot gripper fingers for grasping and manipulation has led to a number of investigations with robot hands employing elastomers or materials such as fluids or powders beneath a membrane at the fingertips. When the fingers are soft, during holding and for manipulation of the object through precise dimensions, their property of softness maintains the area contact between, the fingertips and the manipulating object, which restraints the object and provides stability. In human finger there is a natural softness which is a combination of elasticity and damping. This combination of elasticity and damping is produced by nature due to flesh and blood beneath the skin. This keeps the contact firm and helps in holding the object firmly and stably.
Survival horror games-an uncanny modality .

This study investigates the relationship between the perceived eeriness of a virtual character with the perception of human-likeness for some attributes of motion and sound. 100 participants were asked to rate 13 video clips of 12 different virtual characters and one human. The results indicate that attributes of motion and sound do exaggerate the uncanny phenomenon and how frightening that character is perceived to be. Strong correlations were identified for the perceived eeriness for a character with how human-like a character‘s voice sounded, how human-like facial expression appeared and the synchronization of the character‘s sound with lip movement; characters rated as the least synchronized were perceived to be the most frightening. Based on the results of this study, this paper seeks to define an initial set of hypotheses for the fearevoking aspects of character facial rendering and vocalization in survival horror games that can be used by game designers seeking to increase the fear factor in the genre and that will form the basis of further experiments which, it is hoped, will lead to a conceptual framework for the uncanny. Survival Horror Games 3
Small antenna embedded in a wrist-watch for application in telemedicine

In this paper, the design and radiation performance of a miniature antenna embedded in small devices with a wrist-watch form factor, operating in both MICS (Medical Implant Communication Service) (402.0-405.0 MHz) and Bluetooth (2.4-2.483 GHz) bands, are presented. The radiating element, based on a spiral shape Planar Inverted-F Antenna (PIFA), was fabricated and tested. Simulated and experimental results of the structure are compared and discussed. As this antenna is dedicated to be embedded in a watch, the influence of its interaction with the other parts of the device such as batteries, transceiver, and the LCD is investigated. The influence of the wrist of the user on the antenna performance is also considered.
Automated Evaluation of Scientific Writing: AESW Shared Task Proposal

The goal of the Automated Evaluation of Scientific Writing (AESW) Shared Task is to analyze the linguistic characteristics of scientific writing to promote the development of automated writing evaluation tools that can assist authors in writing scientific papers. The proposed task is to predict whether a given sentence requires editing to ensure its “fit” with the scientific writing genre. We describe the proposed task, training, development, and test data sets, and evaluation metrics. Quality means doing it right when no one is looking. – Henry Ford
Simulink model for a PEM electrolyzer based on an equivalent electrical circuit

In this paper, an electrical equivalent circuit for a proton exchange membrane (PEM) electrolyzer is used to develop a Simulink block diagram. The I-V characteristic for a single PEM electrolyzer cell is modeled under a steady-state and elaborated using an electrical equivalent circuit. Hydrogen production rates characteristics are developed with respect to the input current and power. It is clearly seen that the electrolytic hydrogen production rate increases with the input current in a linear fashion and the variation of hydrogen production rate with the input electrical power is non-linear (i.e. logarithmic). These characteristics are simulated using the developed Simulink/Matlab model of PEM electrolyzer cell. The parameters of the developed model can also be defined by taking into account temperature and pressure effects.