nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning for Answer Sentence Selection

Answer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger context of open domain question answering. We propose a novel approach to solving this task via means of distributed representations, and learn to match questions with answers by considering their semantic encoding. This contrasts prior work on this task, which typically relies on classifiers with large numbers of hand-crafted syntactic and semantic features and various external resources. Our approach does not require any feature engineering nor does it involve specialist linguistic data, making this model easily applicable to a wide range of domains and languages. Experimental results on a standard benchmark dataset from TREC demonstrate that—despite its simplicity—our model matches state of the art performance on the answer sentence selection task.
Local Neighborhood Intensity Pattern-A new texture feature descriptor for image retrieval

In this paper, a new texture descriptor based on the local neighborhood intensity difference is proposed for content based image retrieval (CBIR). For computation of texture features like Local Binary Pattern (LBP), the center pixel in a 3×3 window of an image is compared with all the remaining neighbors, one pixel at a time to generate a binary bit pattern. It ignores the effect of the adjacent neighbors of a particular pixel for its binary encoding and also for texture description. The proposed method is based on the concept that neighbors of a particular pixel hold significant amount of texture information that can be considered for efficient texture representation for CBIR. The main impact of utilizing the mutual relationship among adjacent neighbors is that we do not rely on the sign of the intensity difference between central pixel and one of its neighbors (Ii) only, rather we take into account the sign of difference values between
Domain Transfer Through Deep Activation Matching

We introduce a layer-wise unsupervised domain adaptation approach for semantic segmentation. Instead of merely matching the output distributions of the source and target domains, our approach aligns the distributions of activations of intermediate layers. This scheme exhibits two key advantages. First, matching across intermediate layers introduces more constraints for training the network in the target domain, making the optimization problem better conditioned. Second, the matched activations at each layer provide similar inputs to the next layer for both training and adaptation, and thus alleviate covariate shift. We use a Generative Adversarial Network (or GAN) to align activation distributions. Experimental results show that our approach achieves stateof-the-art results on a variety of popular domain adaptation tasks, including (1) from GTA to Cityscapes for semantic segmentation, (2) from SYNTHIA to Cityscapes for semantic segmentation, and (3) adaptations on USPS and MNIST for image classification.
Generic Tubelet Proposals for Action Localization

We develop a novel framework for action localization in videos. We propose the Tube Proposal Network (TPN), which can generate generic, class-independent, video-level tubelet proposals in videos. The generated tubelet proposals can be utilized in various video analysis tasks, including recognizing and localizing actions in videos. In particular, we integrate these generic tubelet proposals into a unified temporal deep network for action classification. Compared with other methods, our generic tubelet proposal method is accurate, general, and is fully differentiable under a smoothL1 loss function. We demonstrate the performance of our algorithm on the standard UCF-Sports, JHMDB21, and UCF-101 datasets. Our class-independent TPN outperforms other tubelet generation methods, and our unified temporal deep network achieves state-of-the-art localization results on all three datasets.
Multimode and Wideband Printed Loop Antenna Based on Degraded Split-Ring Resonators

In this paper, a compound multimode printed loop antenna based on degraded spilt-ring resonators (SRRs) for broadband wireless applications is proposed. The proposed antenna consists of an outer split ring and one inner closed ring with a big difference in size, which can be considered as degraded SRRs. By virtue of the orthogonal radiating mode of two rings, the compound multimode loop antenna can achieve multi-band resonances. Collaborating with the two coupled rings, a parasitic strip is placed close to the feed gap of the inner ring, which not only induces a new resonant frequency at upper band but also greatly improves the impedance matching in all high-frequency bands. The proposed antenna not only covers the first split ring mode at 816 MHz but also exhibits a broadband property covering from 2.3 to 4.6 GHz (reflection coefficient <inline-formula> <tex-math notation="LaTeX">$|S_{11}| < -10~\textrm {dB}, 67\% $ </tex-math></inline-formula> fractional bandwidth). The antenna operational principle and physical mechanism are analyzed by carrying out the studies of mode analysis and surface current distribution. To demonstrate the effectiveness of the proposed antenna, an antenna prototype is fabricated and tested. It is numerically and experimentally proved that broadband and multimode characteristics, stable radiation patterns with a peak gain of 1.2 and 5.1 dBi in the low- and high-frequency bands respectively, and more than 80% efficiency can be achieved. With merits of a compact size of <inline-formula> <tex-math notation="LaTeX">$0.12\lambda _{0}\times 0.11\lambda _{0}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the wavelength at the lowest operating frequency), uniplanar and simple printed structure and wide bandwidth characteristics make it suitable for a wide range of wireless applications.
Syntactic Representation Learning for Open Information Extraction on Web

This paper proposes a representation learning based method to discover new relations between entities from web, which is more general than existing Open Information Extraction(OIE) methods. Given dependency sequences on the expandPath as input, a convolutional neural network(CNN) is adopted to learn the representation layer features of the syntactic dependency patterns which indicate the relations. Experimental results show that compared with the state-of-art OIE methods, the proposed method obviously improves recall without much expense of precision.
Machine Learning Algorithms for Characterization of EMG Signals

—In the last decades, the researchers of the human arm prosthesis are using different types of machine learning algorithms. This review article firstly gives a brief explanation about type of machine learning methods. Secondly, some recent applications of myoelectric control of human arm prosthesis by using machine learning algorithms are compared. This study presents two different comparisons based on feature extraction methods which are time series modeling and wavelet transform of EMG signal. Finally, of characterization of EMG for of human arm prosthesis have been and discussed.
Towards a Rigorous Definition of Information System Survivability

The computer systems that provide the information underpinnings for critical infrastructure applications, both military and civilian, are essential to the operation of those applications. Failure of the information systems can cause a major loss of service, and so their dependability is a major concern. Current facets of dependability, such as reliability and availability, do not address the needs of critical information systems adequately because they do not include the notion of degraded service as an explicit requirement. What is needed is a precise notion of what forms of degraded service are acceptable to users, under what circumstances each form is most useful, and the fraction of time such degraded service levels are acceptable. This concept is termed survivability. In this paper, we present the basis for a rigorous definition of survivability and an example of its use.
DESH: Database evaluation system with hibernate ORM framework

Relational databases have been the predominant choice for back-ends in enterprise applications for several decades. JDBC - a Java API - that is used for developing such applications and persisting data on the back-end requires enormous time and effort. JDBC makes the application logic to become tightly coupled with the database and consequently is inadequate for building enterprise applications that need to adopt to dynamic requirements. Hence, ORM frameworks such as Hibernate, became prominent. However, even with ORM, the relational back-end often faces a drawback of lack of scalability and flexibility. In this context, NoSQL databases are increasingly gaining popularity. Existing research works have either benchmarked Hibernate with an RDBMS or with one of the NoSQL databases. However, it has not been attempted in the literature to test both an RDBMS and a NoSQL database for their performance within the context of a single application developed using the features of Hibernate. This kind of a study will provide an insight that using Hibernate ORM solution would help us to develop database-independent applications and how much performance gain can be achieved when an application is ported from an RDBMS back-end to a NoSQL database backend. The objective of this work is to develop a business application using Hibernate framework that can individually communicate with an RDBMS as well as a specific NoSQL database and to evaluate the performance of both these databases.
An ear-worn continuous ballistocardiogram (BCG) sensor for cardiovascular monitoring

Traditionally, ballistocardiogram (BCG) has been measured using large and stationary devices. In this work, we demonstrate a portable and continuous BCG monitor that is wearable at the ear. The device has the form factor of a hearing aid and is wirelessly connected to a PC for data recording and analysis. With the ear as an anchoring point, the device uses a MEMS tri-axial accelerometer to measure BCG at the head. Morphological differences exist between head BCG and traditional BCG, but the principal peaks (J waves) and their vectors are preserved. The frequency of J waves corresponds to heart rate, and when used in conjunction with an electrocardiogram's (ECG) R wave, the timing of J waves yields the RJ interval. Results from our clinical study show linear correlation between the RJ interval and the heart's pre-ejection period during hemodynamic maneuvers, thus revealing important information about cardiac contractility and its regulation.
Multi-terminal transport measurements of MoS2 using a van der Waals heterostructure device platform.

Atomically thin two-dimensional semiconductors such as MoS2 hold great promise for electrical, optical and mechanical devices and display novel physical phenomena. However, the electron mobility of mono- and few-layer MoS2 has so far been substantially below theoretically predicted limits, which has hampered efforts to observe its intrinsic quantum transport behaviours. Potential sources of disorder and scattering include defects such as sulphur vacancies in the MoS2 itself as well as extrinsic sources such as charged impurities and remote optical phonons from oxide dielectrics. To reduce extrinsic scattering, we have developed here a van der Waals heterostructure device platform where MoS2 layers are fully encapsulated within hexagonal boron nitride and electrically contacted in a multi-terminal geometry using gate-tunable graphene electrodes. Magneto-transport measurements show dramatic improvements in performance, including a record-high Hall mobility reaching 34,000 cm(2) V(-1) s(-1) for six-layer MoS2 at low temperature, confirming that low-temperature performance in previous studies was limited by extrinsic interfacial impurities rather than bulk defects in the MoS2. We also observed Shubnikov-de Haas oscillations in high-mobility monolayer and few-layer MoS2. Modelling of potential scattering sources and quantum lifetime analysis indicate that a combination of short-range and long-range interfacial scattering limits the low-temperature mobility of MoS2.
A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder.

To determine the empirical evidence for deficits in working memory (WM) processes in children and adolescents with attention-deficit/hyperactivity disorder (ADHD).



Exploratory meta-analytic procedures were used to investigate whether children with ADHD exhibit WM impairments. Twenty-six empirical research studies published from 1997 to December, 2003 (subsequent to a previous review) met our inclusion criteria. WM measures were categorized according to both modality (verbal, spatial) and type of processing required (storage versus storage/manipulation).



Children with ADHD exhibited deficits in multiple components of WM that were independent of comorbidity with language learning disorders and weaknesses in general intellectual ability. Overall effect sizes for spatial storage (effect size = 0.85, CI = 0.62 - 1.08) and spatial central executive WM (effect size = 1.06, confidence interval = 0.72-1.39) were greater than those obtained for verbal storage (effect size = 0.47, confidence interval = 0.36-0.59) and verbal central executive WM (effect size = 0.43, confidence interval = 0.24-0.62).



Evidence of WM impairments in children with ADHD supports recent theoretical models implicating WM processes in ADHD. Future research is needed to more clearly delineate the nature, severity, and specificity of the impairments to ADHD.
Research Advances in Cloud Computing

Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programmingmodels, abstractions, and platforms, and is a testament to the maturity and wide adoption of cloud technologies. In this chapter, we survey existing serverless platforms from industry, academia, and open-source projects, identify key characteristics and use cases, and describe technical challenges and open problems. I. Baldini · P. Castro (B) · K. Chang · P. Cheng · S. Fink · N. Mitchell · V. Muthusamy · R. Rabbah · A. Slominski IBM Research, New York, USA e-mail: ioana@us.ibm.com P. Castro  castrop@us.ibm.com K. Chang e-mail: Kerry.Chang@ibm.com P. Cheng e-mail: perry@us.ibm.com S. Fink e-mail: sjfink@us.ibm.com N. Mitchell e-mail: nickm@us.ibm.com V. Muthusamy (B) e-mail: vmuthus@us.ibm.com R. Rabbah e-mail: rabbah@us.ibm.com A. Slominski (B) e-mail: aslom@us.ibm.com V. Ishakian (B) Bentley University, Waltham, USA e-mail: vishakian@bentley.edu P. Suter Two Sigma, New York, USA © Springer Nature Singapore Pte Ltd. 2017 S. Chaudhary et al. (eds.),
Balancing and Velocity Control of a Unicycle Robot Based on the Dynamic Model

This paper presents a dynamic-model-based control scheme for the balancing and velocity control of a unicycle robot. Unicycle robot motion consists of a pitch that is controlled by an in-wheel motor and a roll that is controlled by a reaction wheel pendulum. The unicycle robot lacks an actuator for yaw-axis control, which makes the derivation of the dynamics relatively simple although it may limit the motion control. The Euler-Lagrange equation is applied to derive the dynamic equations of the unicycle robot to implement dynamic speed control. To achieve real-time speed control, a sliding-mode control and a nonzero set-point linear quadratic regulator (LQR) are utilized to guarantee stability while maintaining the desired speed-tracking performance. In the roll controller, a sigmoid-function-based sliding-mode controller has been adopted to minimize switching-function chattering. An LQR controller has been implemented for pitch control to drive the unicycle robot to follow the desired velocity trajectory in real time using the state variables of pitch angle, angular velocity, wheel angle, and angular velocity. The control performance of the two control systems using a single dynamic model has been experimentally demonstrated.
Integrated Phased Array Systems in Silicon

Silicon offers a new set of possibilities and challenges for RF, microwave, and millimeter-wave applications. While the high cutoff frequencies of the SiGe heterojunction bipolar transistors and the ever-shrinking feature sizes of MOSFETs hold a lot of promise, new design techniques need to be devised to deal with the realities of these technologies, such as low breakdown voltages, lossy substrates, low-Q passives, long interconnect parasitics, and high-frequency coupling issues. As an example of complete system integration in silicon, this paper presents the first fully integrated 24-GHz eight-element phased array receiver in 0.18-/spl mu/m silicon-germanium and the first fully integrated 24-GHz four-element phased array transmitter with integrated power amplifiers in 0.18-/spl mu/m CMOS. The transmitter and receiver are capable of beam forming and can be used for communication, ranging, positioning, and sensing applications.
RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots

Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.
House Price Prediction : Hedonic Price Model vs . Artificial Neural Network

The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.
Quantum-Inspired Immune Clonal Algorithm for Global Optimization

Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum NOT gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.
Microbial Fuel Cells for Wastewater Treatment

1.1 Screening The influent is strained to remove all large objects carried in the sewage stream. This is most commonly performed with an automated mechanically-raked bar screen in modern plants serving large populations, whilst in smaller or less modern plants a manually-cleaned screen may be used. The raking action of a mechanical bar screen is typically paced according to the accumulation on the bar screens and/or flow rate. The solids are collected and later disposed of in landfill or incinerated. Bar screens or mesh screens of varying sizes may be used to optimise solids removal, so as to trap and remove the floating matter, such as pieces of cloth, paper, wood, kitchen refuse, etc. These floating materials will choke pipes or adversely affect the working of the pumps if not removed. They should be placed before the grit chambers. However, if the quality of grit is not of much importance, as in the case of landfilling etc., screens may even be placed after the grit chambers. They may sometimes be accommodated in the body of the grit chambers themselves.
Enterprise Architecture: Enabling Integration, Agility And Change

Three core imperatives are essential for modern businesses and organizations: seamless integration of customer and operational processes, agility, and the ability to change. These imperatives are thus relevant in view of successfully executing strategic choices, but all too often not satisfied. Businesses and organizations can be viewed as complex adaptive socio-technical systems. Two fundamentally different perspectives on systems play a role: the functional (black-box) perspective and the constructional (white-box) perspective. Management and governance of businesses and organizations regard the functional, black-box perspective, which is inherently ill-suited for addressing the imperatives mentioned. Rather, establishing system integration, agility and change requires a focus on the system’s design, hence entails the constructional perspective. Both perspectives are relevant. The concept of architecture plays a fundamental role for operationalizing the constructional perspective. Next to the more familiar notion of technology architecture, the concepts of business and organizational architecture are formally introduced and elucidated. In view of the important role of information in customer and operational processes, the notion of information architecture will be additionally discussed, specifically the relationships with other architectures. Various domains within these architectures will be highlighted, whereby the importance of coherence and consistency is stressed, especially in view of the ability to change. Collectively, the four architectures are labeled Enterprise Architecture. Finally, enterprise architecture will be positioned as a crucial means for linking strategy development and execution. The functional and constructional perspective Function and construction A system may be defined as an identifiable bounded set of functionally and/or methodically related elements or principles with a certain operational purpose. Within this definition, a plethora of systems can be mentioned: a payment system, an accounting system, a transport