nils-herrmann/clean_scirepeval huggingface dataset:
Human-competitive tagging using automatic keyphrase extraction

This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, “Maui”, that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.
Arabic Optical Character Recognition Software : A Review 1

This paper provides a thorough evaluation of a set of six important Arabic OCR systems available in the market; namely: Abbyy FineReader, Leadtools, Readiris, Sakhr, Tesseract and NovoVerus. We test the OCR systems using a randomly selected images from the well known Arabic Printed Text Image database (250 images from the APTI database) and using a set of 8 images from an Arabic book. The APTI database contains 45.313.600 of both decomposable and non-decomposable word images. In the evaluation, we conduct two tests. The first test is based on usual metrics used in the literature. In the second test, we provide a novel measure for Arabic language, which can be used for other non-Latin languages.
Robot_gym: accelerated robot training through simulation in the cloud with ROS and Gazebo

Rather than programming, training allows robots to achieve behaviors that generalize better and are capable to respond to real-world needs. However, such training requires a big amount of experimentation which is not always feasible for a physical robot. In this work, we present robot gym, a framework to accelerate robot training through simulation in the cloud that makes use of roboticists’ tools, simplifying the development and deployment processes on real robots. We unveil that, for simple tasks, simple 3DoF robots require more than 140 attempts to learn. For more complex, 6DoF robots, the number of attempts increases to more than 900 for the same task. We demonstrate that our framework, for simple tasks, accelerates the robot training time by more than 33% while maintaining similar levels of accuracy and repeatability.
Inflammation and atherosclerosis.

Atherosclerosis, formerly considered a bland lipid storage disease, actually involves an ongoing inflammatory response. Recent advances in basic science have established a fundamental role for inflammation in mediating all stages of this disease from initiation through progression and, ultimately, the thrombotic complications of atherosclerosis. These new findings provide important links between risk factors and the mechanisms of atherogenesis. Clinical studies have shown that this emerging biology of inflammation in atherosclerosis applies directly to human patients. Elevation in markers of inflammation predicts outcomes of patients with acute coronary syndromes, independently of myocardial damage. In addition, low-grade chronic inflammation, as indicated by levels of the inflammatory marker C-reactive protein, prospectively defines risk of atherosclerotic complications, thus adding to prognostic information provided by traditional risk factors. Moreover, certain treatments that reduce coronary risk also limit inflammation. In the case of lipid lowering with statins, this anti-inflammatory effect does not appear to correlate with reduction in low-density lipoprotein levels. These new insights into inflammation in atherosclerosis not only increase our understanding of this disease, but also have practical clinical applications in risk stratification and targeting of therapy for this scourge of growing worldwide importance.
Mining Invariants from Console Logs for System Problem Detection

Detecting execution anomalies is very important to the maintenance and monitoring of large-scale distributed systems. People often use console logs that are produced by distributed systems for troubleshooting and problem diagnosis. However, manually inspecting console logs for the detection of anomalies is unfeasible due to the increasing scale and complexity of distributed systems. Therefore, there is great demand for automatic anomaly detection techniques based on log analysis. In this paper, we propose an unstructured log analysis technique for anomaly detection, with a novel algorithm to automatically discover program invariants in logs. At first, a log parser is used to convert the unstructured logs to structured logs. Then, the structured log messages are further grouped to log message groups according to the relationship among log parameters. After that, the program invariants are automatically mined from the log message groups. The mined invariants can reveal the inherent linear characteristics of program work flows. With these learned invariants, our technique can automatically detect anomalies in logs. Experiments on Hadoop show that the technique can effectively detect execution anomalies. Compared with the state of art, our approach can not only detect numerous real problems with high accuracy but also provide intuitive insight into the problems.
A Blockchain-based Security-Oriented Framework for Cloud Federation

Cloud federations have been formed to share the services, prompt and support cooperation, as well as interoperability among their already deployed cloud systems. However, the creation and management of the cloud federations lead to various security issues such as confidentially, integrity and availability of the data. Despite the access control policies in place, an attacker may compromise the communication channel processing the access requests and the decisions between the access control systems and the members(users) and vice-versa. In cloud federation, the rating of the services offered by different cloud members becomes integral to providing the users with the best quality services. Hence, we propose an innovative blockchainbased framework that on the one hand permits secure communication between the members of the federation and the access control systems, while on the other hand provides the quality services to the members by considering the service constraints imposed by them.
Does Microfinance Really Help the Poor ? New Evidence from Flagship Programs in Bangladesh

The microfinance movement has built on innovations in financial intermediation that reduce the costs and risks of lending to poor households. Replications of the movement’s flagship, the Grameen Bank of Bangladesh, have now spread around the world. While programs aim to bring social and economic benefits to clients, few attempts have been made to quantify benefits rigorously. This paper draws on a new cross-sectional survey of nearly 1800 households, some of which are served by the Grameen Bank and two similar programs, and some of which have no access to programs. Households that are eligible to borrow and have access to the programs do not have notably higher consumption levels than control households, and, for the most part, their children are no more likely to be in school. Men also tend to work harder, and women less. More favorably, relative to controls, households eligible for programs have substantially (and significantly) lower variation in consumption and labor supply across seasons. The most important potential impacts are thus associated with the reduction of vulnerability, not of poverty per se. The consumption-smoothing appears to be driven largely by income-smoothing, not by borrowing and lending. The evaluation holds lessons for studies of other programs in low-income countries. While it is common to use fixed effects estimators to control for unobservable variables correlated with the placement of programs, using fixed effects estimators can exacerbate biases when, as here, programs target their programs to specific populations within larger communities.
Towards IP over LPWANs technologies: LoRaWAN, DASH7, NB-IoT

In this paper, we discuss a set of solutions that are proposed in order to run IPv6 over IoT and we investigate its applicability over the three Low Power Wide Area Networks (LPWANs) technologies: LoRaWAN, DASH7, NB-IoT. LPWANs are wireless technologies that are used to connect things to the Internet. These technologies are characterized by their large coverage area, long battery operation, low bandwidth, small frame payload size, and the use of asymmetric and non-synchronized communication. Based on this investigation, we highlight the schemes that can be adopted for IP-based LPWANs technologies.
The entanglement of leader character and leader competence and its impact on performance

Article history: Received 22 May 2015 Received in revised form 28 November 2016  in relation to competencies and performance has been to a large extent neglected. Our work seeks to shift the scholarly dialogue by introducing the concept of character-competence entanglement, which reflects the binding between character and competence over time. The highest degree of entanglement represents the deep and more persistent interconnection and mutually-reinforcing effect between highly-developed leader character and highly-developed leader competence, whereas in cases of low entanglement, character can be activated temporarily in a particular context to help strengthen the relationship between competence and performance. Our core proposition is that high character-competence entanglement will lead to extraordinary performance over time. In addition, we emphasize that relying on naturally-occurring learning opportunities and the processes of “learning-by-living” both outside and inside the organization will positively impact the development of character-competence entanglement.
Analysis of Software Defined Network firewall (SDF)

Software Defined Networks (SDN) technology can alternate the network, unlock critical intelligence, and help deliver the new services and analytics needed to run on-demand applications for today's businesses and consumers. So, SDN has been much request for adoption in mostly data centre. SDN provides network administrators to clear overview of the entire network architecture. It decouples a network's control and forwarding functions such that the physical and logical networks can be treated independently. This method allows network traffic flows to be programmatically and effectively reallocated to meet changing demands. That's what SDN does. Software defined network have too many scarcity like throughput, packet loss and network attack defense. Software defined firewalls helps to overcome lack of SDN and also reduce ratios of attack traffic. Here we analyze some SDF techniques and challenges related to software defined network.
Estimation of construction and demolition waste volume generation in new residential buildings in Spain.

The management planning of construction and demolition (C&D) waste uses a single indicator which does not provide enough detailed information. Therefore the determination and implementation of other innovative and precise indicators should be determined. The aim of this research work is to improve existing C&D waste quantification tools in the construction of new residential buildings in Spain. For this purpose, several housing projects were studied to determine an estimation of C&D waste generated during their construction process. This paper determines the values of three indicators to estimate the generation of C&D waste in new residential buildings in Spain, itemizing types of waste and construction stages. The inclusion of two more accurate indicators, in addition to the global one commonly in use, provides a significant improvement in C&D waste quantification tools and management planning.
Radiation Efficiency Measurement Method for Passive UHF RFID Dipole Tag Antennas

Concurrently with the continuously developing radio frequency identification (RFID) technology, new types of tag antenna materials and structures are emerging to fulfill the requirements encountered within the new application areas. In this work, a radiation efficiency measurement method is developed and verified for passive ultra-high frequency (UHF) RFID dipole tag antennas. In addition, the measurement method is applied to measure the radiation efficiency of sewed dipole tag antennas for wearable body-centric wireless communication applications. The acquired information from measurements can be used to characterize tag antenna material structures losses and to further both improve and optimize tag antenna performance and reliability.
A reconfigurable microstrip antenna for switchable polarization

A novel reconfigurable microstrip antenna with switchable polarization sense is proposed. The proposed antenna has a simple structure, consisting of a corner-truncated square radiating patch, four small triangular conductors, and a microstrip line feed. Using independently biased PIN diodes on the patch, it can produce linear polarization, or left- or right-hand circular polarization according to bias voltages. From the measured results, low cross-polarization levels when operated in the linear state and good axial ratios in the circular state are observed.
Information Fusion for Diabetic Retinopathy CAD in Digital Color Fundus Photographs

The purpose of computer-aided detection or diagnosis (CAD) technology has so far been to serve as a second reader. If, however, all relevant lesions in an image can be detected by CAD algorithms, use of CAD for automatic reading or prescreening may become feasible. This work addresses the question how to fuse information from multiple CAD algorithms, operating on multiple images that comprise an exam, to determine a likelihood that the exam is normal and would not require further inspection by human operators. We focus on retinal image screening for diabetic retinopathy, a common complication of diabetes. Current CAD systems are not designed to automatically evaluate complete exams consisting of multiple images for which several detection algorithm output sets are available. Information fusion will potentially play a crucial role in enabling the application of CAD technology to the automatic screening problem. Several different fusion methods are proposed and their effect on the performance of a complete comprehensive automatic diabetic retinopathy screening system is evaluated. Experiments show that the choice of fusion method can have a large impact on system performance. The complete system was evaluated on a set of 15 000 exams (60 000 images). The best performing fusion method obtained an area under the receiver operator characteristic curve of 0.881. This indicates that automated prescreening could be applied in diabetic retinopathy screening programs.
Cognitive perspectives on SLA The Associative-Cognitive CREED *

This paper outlines current cognitive perspectives on second language acquisition (SLA). The Associative-Cognitive CREED holds that SLA is governed by the same principles of associative and cognitive learning that underpin the rest of human knowledge. The major principles of the framework are that SLA is Construction-based, Rational, Exemplar-driven, Emergent, and Dialectic. Language learning involves the acquisition of constructions that map linguistic form and function. Competence and performance both emerge from the dynamic system that is the frequency-tuned conspiracy of memorized exemplars of use of these constructions, with competence being the integrated sum of prior usage and performance being its dynamic contextualized activation. The system is rational in that it optimally reflects prior first language (L1) usage. The L1 tunes the ways in which learners attend to language. Learned-attention transfers to L2 and it is this L1 entrenchment that limits the endstate of usage-based SLA. But these limitations can be overcome by recruiting learner consciousness, putting them into a dialectic tension between the conflicting forces of their current stable states of interlanguage and the evidence of explicit form-focused feedback, either linguistic, pragmatic, or metalinguistic, that allows socially scaffolded development. The paper directs the reader to recent review articles in these key areas and weighs the implications of this framework.
A Neural Conversational Model

Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require handcrafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversational training dataset. Our preliminary results suggest that, despite optimizing the wrong objective function, the model is able to converse well. It is able extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. On a domainspecific IT helpdesk dataset, the model can find a solution to a technical problem via conversations. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning. As expected, we also find that the lack of consistency is a common failure mode of our model.
Study on Virtual Control of a Robotic Arm via a Myo Armband for the Self-Manipulation of a Hand Amputee

This paper proposes a Myo device that has electromyography (EMG) sensors for detecting electrical activities from different parts of the forearm muscles; it also has a gyroscope and an accelerometer. EMG sensors detect and provide very clear and important data from muscles compared with other types of sensors. The Myo armband sends data from EMG, gyroscope, and accelerometer sensors to a computer via Bluetooth and uses these data to control a virtual robotic arm which was built in Unity 3D. Virtual robotic arms based on EMG, gyroscope, and accelerometer sensors have different features. A robotic arm based on EMG is controlled by using the tension and relaxation of muscles. Consequently, a virtual robotic arm based on EMG is preferred for a hand amputee to a virtual robotic arm based on a gyroscope and an accelerometer
A stepped-carrier 77-GHz OFDM MIMO radar system with 4 GHz bandwidth

This paper presents a 77-GHz orthogonal frequency-division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar system with 4 GHz bandwidth. Due to the limited bandwidth of the analog-to-digital and digital-to-analog converters a stepped-carrier approach is used. OFDM radar measurements with a bandwidth of 200MHz at 20 different carrier frequencies are combined in signal processing to achieve a high range resolution OFDM MIMO radar. Spectrally-interleaved OFDM signals are used to allow for simultaneous transmission and thus MIMO operation. The proposed approach is verified in practical measurements of a static target scenario and a slowly moving target scenario. Therefore a self-developed software defined radar evaluation platform with four transmitters and four receivers is used. The expected high range resolution could be achieved and range/angle/velocity measurements show the suitability for detecting slowly moving targets like pedestrians.
A new Molyneux's problem: Sounds, shapes and arbitrary crossmodal correspondences

Several studies in cognitive sciences have highlighted the existence of privileged and universal psychological associations between shape attributes, such as angularity, and auditory dimensions, such as pitch. These results add a new puzzle to the list of arbitrary-looking crossmodal matching tendencies whose origin is hard to explain. The puzzle is all the more general in the case of shape that the shapes-sounds correspondences have a wide set of documented effects on perception and behaviour: Sounds can for instance influence the way a certain shape is perceived (Sweeny et al., 2012). In this talk, we suggest that the study of these crossmodal correspondences can be related to the more classical cases of crossmodal transfer of shape between vision and touch documented as part of Molyneux’s question, and reveal the role that movement plays as an amodal invariant in explaining the variety of multimodal associations around shape.
Automatic Shortlisting of Candidates in Recruitment

Talent acquisition is an important, complex and effort-intensive function within Human Resources (HR) management. We describe a system called TEAC that processes a set of given free-form textual resumes (in English for IT domain), creates a standardized profile for each candidate, and for a given job description, identifies a ranked shortlist of k candidates, along with a matching score for each. The resume scoring function is hand-crafted, hierarchical, and uses domain-knowledge from recruitment experts. We describe a simple neural-network system that automatically learns some weights used in the scoring function, based on feedback about whether the candidate was SELECTED or REJECTED. The TEAC system is deployed in a large multinational IT services organization.
Semi-Supervised Kernel Matching for Domain Adaptation

In this paper, we propose a semi-supervised kernel matching method to address domain adaptation problems where the source distribution substantially differs from the target distribution. Specifically, we learn a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. Our empirical results show the proposed kernel matching method significantly outperforms alternative methods on the task of cross domain sentiment classification.
Laplacian Auto-Encoders: An explicit learning of nonlinear data manifold

A key factor contributing to the success of many auto-encoders based deep learning techniques is the implicit consideration of the underlying data manifold in their training criteria. In this paper, we aim to make this consideration more explicit by training auto-encoders completely from the manifold learning perspective. We propose a novel unsupervised manifold learning method termed Laplacian Auto-Encoders (LAEs). Starting from a general regularized function learning framework, LAE regularizes training of autoencoders so that the learned encoding function has the locality-preserving property for data points on the manifold. By exploiting the analog relation between the graph Laplacian and the Laplace–Beltrami operator on the continuous manifold, we derive discrete approximations of the firstand higher-order auto-encoder regularizers that can be applied in practical scenarios, where only data points sampled from the distribution on the manifold are available. Our proposed LAE has potentially better generalization capability, due to its explicit respect of the underlying data manifold. Extensive experiments on benchmark visual classification datasets show that LAE consistently outperforms alternative auto-encoders recently proposed in deep learning literature, especially when training samples are relatively scarce. & 2015 Elsevier B.V. All rights reserved.