nils-herrmann/clean_scirepeval huggingface dataset:
A Neuro-fuzzy System for Fraud Detection in Electricity Distribution

The volume of energy loss that Brazilian electrical utilities have to deal with has been ever increasing. Electricity distribution companies have suffered significant and increasing losses in the last years, due to theft, measurement errors and other irregularities. Therefore there is a great concern to identify the profile of irregular customers, in order to reduce the volume of such losses. This paper presents a combined approach of a neural networks committee and a neuro-fuzzy hierarchical system intended to increase the level of accuracy in the identification of irregularities among low voltage consumers. The data used to test the proposed system are from Light S.A., the distribution company of Rio de Janeiro. The results obtained presented a significant increase in the identification of irregular customers when compared to the current methodology employed by the company.  neural nets, hierarchical neuro-fuzzy systems, binary space partition, electricity distribution, fraud detection.
Rotation Equivariant Vector Field Networks

In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image. If this relationship is explicitly encoded, instead of treated as any other variation, the complexity of the problem is decreased, leading to a reduction in the size of the required model. In this paper, we propose the Rotation Equivariant Vector Field Networks (RotEqNet), a Convolutional Neural Network (CNN) architecture encoding rotation equivariance, invariance and covariance. Each convolutional filter is applied at multiple orientations and returns a vector field representing magnitude and angle of the highest scoring orientation at every spatial location. We develop a modified convolution operator relying on this representation to obtain deep architectures. We test RotEqNet on several problems requiring different responses with respect to the inputs’ rotation: image classification, biomedical image segmentation, orientation estimation and patch matching. In all cases, we show that RotEqNet offers extremely compact models in terms of number of parameters and provides results in line to those of networks orders of magnitude larger.
Friending, IMing, and hanging out face-to-face: overlap in adolescents' online and offline social networks.

Many new and important developmental issues are encountered during adolescence, which is also a time when Internet use becomes increasingly popular. Studies have shown that adolescents are using these online spaces to address developmental issues, especially needs for intimacy and connection to others. Online communication with its potential for interacting with unknown others, may put teens at increased risk. Two hundred and fifty-one high school students completed an in-person survey, and 126 of these completed an additional online questionnaire about how and why they use the Internet, their activities on social networking sites (e.g., Facebook, MySpace) and their reasons for participation, and how they perceive these online spaces to impact their friendships. To examine the extent of overlap between online and offline friends, participants were asked to list the names of their top interaction partners offline and online (Facebook and instant messaging). Results reveal that adolescents mainly use social networking sites to connect with others, in particular with people known from offline contexts. While adolescents report little monitoring by their parents, there was no evidence that teens are putting themselves at risk by interacting with unknown others. Instead, adolescents seem to use the Internet, especially social networking sites, to connect with known others. While the study found moderate overlap between teens' closest online and offline friends, the patterns suggest that adolescents use online contexts to strengthen offline relationships.
Extracting Chatbot Knowledge from Online Discussion Forums

This paper presents a novel approach for extracting high-quality <thread-title, reply> pairs as chat knowledge from online discussion forums so as to efficiently support the construction of a chatbot for a certain domain. Given a forum, the high-quality <thread-title, reply> pairs are extracted using a cascaded framework. First, the replies logically relevant to the thread title of the root message are extracted with an SVM classifier from all the replies, based on correlations such as structure and content. Then, the extracted <thread-title, reply> pairs are ranked with a ranking SVM based on their content qualities. Finally, the Top-N <thread-title, reply> pairs are selected as chatbot knowledge. Results from experiments conducted within a movie forum show the proposed approach is effective.
Folksonomies, the Semantic Web, and Movie Recommendation

While the Semantic Web has evolved to support the meaningful exchange of heterogeneous data through shared and controlled conceptualisations, Web 2.0 has demonstrated that large-scale community tagging sites can enrich the semantic web with readily accessible and valuable knowledge. In this paper, we investigate the integration of a movies folksonomy with a semantic knowledge base about usermovie rentals. The folksonomy is used to enrich the knowledge base with descriptions and categorisations of movie titles, and user interests and opinions. Using tags harvested from the Internet Movie Database, and movie rating data gathered by Netflix, we perform experiments to investigate the question that folksonomy-generated movie tag-clouds can be used to construct better user profiles that reflect a user’s level of interest in different kinds of movies, and therefore, provide a basis for prediction of their rating for a previously unseen movie.
The Use of Phrases and Structured Queries in Information Retrieval

Both phrases and Boolean queries have a long history in information retrieval, particularly in commercial systems. In previous work, Boolean queries have been used as a source of phrases for a statistical retrieval model, This work, like the majority of research on phrases, resulted in little improvement in retrieval effectiveness, In this paper, we describe an approach where phrases identified in natural language queries are used to build structured queries for a probabilistic retrieval model. Our results show that using phrases in this way can improve performance, and that phrases that are automatically extracted from a natural language query perform nearly as well as manually selected phrases. 1  The use of phrases as part of a text representation or indexing language has been investigated since the early days of information retrieval research. Cleverdon, for example, included phrase-based indexing in the Cran-field studies (1966). Salton (1968) also described a variety of experiments using phrases in the SMART system. Certainly, there has always been the feeling that phrases, if used correctly, should improve the specificity of the indexing language and, consequently, the quality of the text representation. The experimental results obtained with phrases do not, however, support this intuition. These results have been very mixed, ranging from small improvements in some collections to decreases in * Current address: West F'ubtishing Company, Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Aeeociation for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. @ 1991 ACM O-8979 J-448-J/9 j[O009/0032... $J .50 effectiveness in othersl, Fagan's recent theeis (1987) is one of the most comprehensive studies of automatic indexing ueing phrases, in that he used both " statistical " and " syntactic " phrasee and varied a number of factors in the phrase formation process. A statistical phrase is defined by constraints on the number of occurrences and co-occurrences of its component words and/or the proximity between occurrences of components in a document. A syntactic phrase may be characterized by some of the came criteria as a statistical phrase, but in addition must obey some constraint on the syntactic relationships among its component words. Fagan's results showed …
A multinomial logistic regression modeling approach for anomaly intrusion detection

Although researchers have long studied using statistical modeling techniques to detect anomaly intrusion and profile user behavior, the feasibility of applying multinomial logistic regression modeling to predict multi-attack types has not been addressed, and the risk factors associated with individual major attacks remain unclear. To address the gaps, this study used the KDD-cup 1999 data and bootstrap simulation method to fit 3000 multinomial logistic regression models with the most frequent attack types (probe, DoS, U2R, and R2L) as an unordered independent variable, and identified 13 risk factors that are statistically significantly associated with these attacks. These risk factors were then used to construct a final multinomial model that had an ROC area of 0.99 for detecting abnormal events. Compared with the top KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm, the multinomial logistic model-based classification results had similar sensitivity values in detecting normal and a significantly lower overall misclassification rate (18.9% vs. 35.7%). The study emphasizes that the multinomial logistic regression modeling technique with the 13 risk factors provides a robust approach to detect anomaly intrusion.
A 500 W push-pull dc-dc power converter with a 30 MHz switching frequency

DESIGNERS of power conversion circuits are under relentless pressure to increase power density while maintaining high efficiency. Increased switching frequency is a primary path to higher power density. Prior work has shown that the use of switching frequencies in the VHF band (30 MHz-300 MHz) are a viable path to the achievement of gains in power density. A promising topology for VHF operation is the voltage-fed Class EF2 (Class Φ2) inverter based topology, where the use of controlled impedance at the switching frequency and its 2nd and 3rd harmonics provides both full soft switching and substantially reduced voltage stress compared to topologies such as Class E. However, such converters contain multiple resonant elements, and the tuning of the converter can be complicated due in part to the interaction of said elements. It is proposed that a push-pull version of the Class EF2 inverter can alleviate some of these difficulties. In particular, it is shown that odd and even frequency components can be independently tuned without interaction, and furthermore that center-tapped inductors may be used to reduce the total volume occupied by said inductors. The benefits include simplified design and increased power density. Evidence is presented in the form of a push-pull Class EF2 (Class Φ2) unregulated 500 W prototype dc-dc converter with a 30 MHz switching frequency, an input voltage 150 VDC, and an output voltage of 65 VDC. This converter has an efficiency of > 81% under nominal conditions, including gate drive power.
The anatomy of phonological and semantic processing in normal subjects.

We assessed brain activation of nine normal right-handed volunteers in a positron emission tomography study designed to differentiate the functional anatomy of the two major components of auditory comprehension of language, namely phonological versus lexico-semantic processing. The activation paradigm included three tasks. In the reference task, subjects were asked to detect rising pitch within a series of pure tones. In the phonological task, they had to monitor the sequential phonemic organization of non-words. In the lexico-semantic task, they monitored concrete nouns according to semantic criteria. We found highly significant and different patterns of activation. Phonological processing was associated with activation in the left superior temporal gyrus (mainly Wernicke's area) and, to a lesser extent, in Broca's area and in the right superior temporal regions. Lexico-semantic processing was associated with activity in the left middle and inferior temporal gyri, the left inferior parietal region and the left superior prefrontal region, in addition to the superior temporal regions. A comparison of the pattern of activation obtained with the lexico-semantic task to that obtained with the phonological task was made in order to account for the contribution of lower stage components to semantic processing. No difference in activation was found in Broca's area and superior temporal areas which suggests that these areas are activated by the phonological component of both tasks, but activation was noted in the temporal, parietal and frontal multi-modal association areas. These constitute parts of a large network that represent the specific anatomic substrate of the lexico-semantic processing of language.
Unikernels Everywhere: The Case for Elastic CDNs

Video streaming dominates the Internet's overall traffic mix, with reports stating that it will constitute 90% of all consumer traffic by 2019. Most of this video is delivered by Content Delivery Networks (CDNs), and, while they optimize QoE metrics such as buffering ratio and start-up time, no single CDN provides optimal performance. In this paper we make the case for elastic CDNs, the ability to build virtual CDNs on-the-fly on top of shared, third-party infrastructure at a scale. To bring this idea closer to reality we begin by large-scale simulations to quantify the effects that elastic CDNs would have if deployed, and build and evaluate MiniCache, a specialized, minimalistic virtualized content cache that runs on the Xen hypervisor. MiniCache is able to serve content at rates of up to 32 Gb/s and handle up to 600K reqs/sec on a single CPU core, as well as boot in about 90 milliseconds on x86 and around 370 milliseconds on ARM32.
Enhancing Performance Prediction Robustness by Combining Analytical Modeling and Machine Learning

Classical approaches to performance prediction rely on two, typically antithetic, techniques: Machine Learning (ML) and Analytical Modeling (AM). ML takes a black box approach, whose accuracy strongly depends on the representativeness of the dataset used during the initial training phase. Specifically, it can achieve very good accuracy in areas of the features' space that have been sufficiently explored during the training process. Conversely, AM techniques require no or minimal training, hence exhibiting the potential for supporting prompt instantiation of the performance model of the target system. However, in order to ensure their tractability, they typically rely on a set of simplifying assumptions. Consequently, AM's accuracy can be seriously challenged in scenarios (e.g., workload conditions) in which such assumptions are not matched.
 In this paper we explore several hybrid/gray box techniques that exploit AM and ML in synergy in order to get the best of the two worlds. We evaluate the proposed techniques in case studies targeting two complex and widely adopted middleware systems: a NoSQL distributed key-value store and a Total Order Broadcast (TOB) service.
Analysis of assets for virtual reality applications in neuropsychology

Virtual reality (VR) technology offers new opportunities for the development of innovative neuropsychological assessment and rehabilitation tools. VR-based testing and training scenarios that would be difficult, if not impossible, to deliver using conventional neuropsychological methods are now being developed that take advantage of the assets available with VR technology. If empirical studies continue to demonstrate effectiveness, virtual environment applications could provide new options for targeting cognitive and functional impairments due to traumatic brain injury, neurological disorders, and learning disabilities. This article focuses on specifying the assets that are available with VR for neuropsychological applications along with discussion of current VR-based research that serves to illustrate each asset. VR allows for the precise presentation and control of dynamic multi-sensory 3D stimulus environments, as well as providing advanced methods for recording behavioural responses. This serves as the basis for a diverse set of VR assets for neuropsychological approaches that are detailed in this article. We take the position that when combining these assets within the context of functionally relevant, ecologically valid virtual environments, fundamental advancements can emerge in how human cognition and functional behaviour is assessed and rehabilitated.
CUDAlign: using GPU to accelerate the comparison of megabase genomic sequences

Biological sequence comparison is a very important operation in Bioinformatics. Even though there do exist exact methods to compare biological sequences, these methods are often neglected due to their quadratic time and space complexity. In order to accelerate these methods, many GPU algorithms were proposed in the literature. Nevertheless, all of them restrict the size of the smallest sequence in such a way that Megabase genome comparison is prevented. In this paper, we propose and evaluate CUDAlign, a GPU algorithm that is able to compare Megabase biological sequences with an exact Smith-Waterman affine gap variant. CUDAlign was implemented in CUDA and tested in two GPU boards, separately. For real sequences whose size range from 1MBP (Megabase Pairs) to 47MBP, a close to uniform GCUPS (Giga Cells Updates per Second) was obtained, showing the potential scalability of our approach. Also, CUDAlign was able to compare the human chromosome 21 and the chimpanzee chromosome 22. This operation took 21 hours on GeForce GTX 280, resulting in a peak performance of 20.375 GCUPS. As far as we know, this is the first time such huge chromosomes are compared with an exact method.
Coherent Citation-Based Summarization of Scientific Papers

In citation-based summarization, text written by several researchers is leveraged to identify the important aspects of a target paper. Previous work on this problem focused almost exclusively on its extraction aspect (i.e. selecting a representative set of citation sentences that highlight the contribution of the target paper). Meanwhile, the fluency of the produced summaries has been mostly ignored. For example, diversity, readability, cohesion, and ordering of the sentences included in the summary have not been thoroughly considered. This resulted in noisy and confusing summaries. In this work, we present an approach for producing readable and cohesive citation-based summaries. Our experiments show that the proposed approach outperforms several baselines in terms of both extraction quality and fluency.
Review of locking devices used in robotics

Locking devices are widely used in robotics, for instance to lock springs, joints or to reconfigure robots. This review paper classifies the locking devices currently described in literature and preforms a comparative study. Designers can therefore better determine which locking device best matches the needs of their application. The locking devices are divided into three main categories based on different locking principles: mechanical locking, friction-based locking and singularity locking. Different locking devices in each category can be passive or active. Based on an extensive literature survey, the paper summarizes the findings by comparing different locking devices on a set of properties of an ideal locking device.
MRCP-RM: A Technique for Resource Allocation and Scheduling of MapReduce Jobs with Deadlines

Resource allocation and scheduling on clouds are required to harness the power of the underlying resource pool such that the service provider can meet the quality of service requirements of users, which are often captured in service level agreements (SLAs). This paper focuses on resource allocation and scheduling on clouds and clusters that process MapReduce jobs with SLAs. The resource allocation and scheduling problem is modelled as an optimization problem using constraint programming, and a novel MapReduce Constraint Programming based Resource Management algorithm (MRCP-RM) is devised that can effectively process an open stream of MapReduce jobs where each job is characterized by an SLA comprising an earliest start time, a required execution time, and an end-to-end deadline. A detailed performance evaluation of MRCP-RM is conducted for an open system subjected to a stream of job arrivals using both simulation and experimentation on a real system. The experiments on a real system are performed on a Hadoop cluster (deployed on Amazon EC2) that runs our new Hadoop Constraint Programming based Resource Management algorithm (HCP-RM) that incorporates a technique for handling data locality. The results of the performance evaluation demonstrate the effectiveness of MRCP-RM/HCP-RM in generating a schedule that leads to a low proportion of jobs missing their deadlines (P) and also provide insights into system behaviour and performance. In the simulation experiments, it is observed that MRCP-RM achieves on average an 82 percent lower P compared to a technique from the existing literature when processing a synthetic workload from Facebook. Furthermore, in the experiments performed on a Hadoop cluster deployed on Amazon EC2, it is observed that HCP-RM achieved on average a 63 percent lower P compared to an EDF-Scheduler for a wide variety of workload and system parameters experimented with.
Effects of microplastic on fitness and PCB bioaccumulation by the lugworm Arenicola marina (L.).

It has been speculated that marine microplastics may cause negative effects on benthic marine organisms and increase bioaccumulation of persistent organic pollutants (POPs). Here, we provide the first controlled study of plastic effects on benthic organisms including transfer of POPs. The effects of polystyrene (PS) microplastic on survival, activity, and bodyweight, as well as the transfer of 19 polychlorinated biphenyls (PCBs), were assessed in bioassays with Arenicola marina (L.). PS was pre-equilibrated in natively contaminated sediment. A positive relation was observed between microplastic concentration in the sediment and both uptake of plastic particles and weight loss by A. marina. Furthermore, a reduction in feeding activity was observed at a PS dose of 7.4% dry weight. A low PS dose of 0.074% increased bioaccumulation of PCBs by a factor of 1.1-3.6, an effect that was significant for ΣPCBs and several individual congeners. At higher doses, bioaccumulation decreased compared to the low dose, which however, was only significant for PCB105. PS had statistically significant effects on the organisms' fitness and bioaccumulation, but the magnitude of the effects was not high. This may be different for sites with different plastic concentrations, or plastics with a higher affinity for POPs.
Real-Time Analytical Processing with SQL Server

Over the last two releases SQL Server has integrated two specialized engines into the core system: the Apollo column store engine for analytical workloads and the Hekaton in-memory engine for high-performance OLTP workloads. There is an increasing demand for real-time analytics, that is, for running analytical queries and reporting on the same system as transaction processing so as to have access to the freshest data. SQL Server 2016 will include enhancements to column store indexes and in-memory tables that significantly improve performance on such hybrid workloads. This paper describes four such enhancements: column store indexes on inmemory tables, making secondary column store indexes on diskbased tables updatable, allowing B-tree indexes on primary column store indexes, and further speeding up the column store scan operator.