nils-herrmann/clean_scirepeval huggingface dataset:
Tag-aware recommender systems based on deep neural networks

Many researchers have introduced tag information to recommender systems to improve the performance of traditional recommendation techniques. However, user-de ned tags will usually su er from many problems, such as sparsity, redundancy, and ambiguity. To address these problems, we propose a new recommendation algorithm based on deep neural networks. In the proposed algorithm, users' pro les are initially represented by tags and then a deep neural network model is used to extract the in-depth features from tag space layer by layer. In this way, representations of the raw data will become more abstract and advanced, and therefore the unique structure of tag space will be revealed automatically. Based on those extracted abstract features, users' pro les are updated and used for making recommendations. The experimental results demonstrate the usefulness of the proposed algorithm and show its superior performance over the clustering based recommendation algorithms. In addition, the impact of network depth on the algorithm performance is also investigated.
An Approach for Learning and Construction of Expressive Ontology from Text in Natural Language

In this paper, we present an approach based on Ontology Learning and Natural Language Processing for automatic construction of expressive Ontologies, specifically in OWL DL with ALC expressivity, from a natural language text. The viability of our approach is demonstrated through the generation of descriptions of complex axioms from concepts defined by users and glossaries found at Wikipedia. We evaluated our approach in an experiment with entry sentences enriched with hierarchy axioms, disjunction, conjunction, negation, as well as existential and universal quantification to impose restriction of properties. The obtained results prove that our model is an effective solution for knowledge representation and automatic construction of expressive Ontologies. Thereby, it assists professionals involved in processes for obtain, construct and model knowledge domain.
Addressing Legal Requirements in Requirements Engineering

Legal texts, such as regulations and legislation, are playing an increasingly important role in requirements engineering and system development. Monitoring systems for requirements and policy compliance has been recognized in the requirements engineering community as a key area for research. Similarly, regulatory compliance is critical in systems that are governed by regulations and law, especially given that non-compliance can result in both financial and criminal penalties. Working with legal texts can be very challenging, however, because they contain numerous ambiguities, cross-references, domain-specific definitions, and acronyms, and are frequently amended via new regulations and case law. Requirements engineers and compliance auditors must be able to identify relevant regulations, extract requirements and other key concepts, and monitor compliance throughout the software lifecycle. This paper surveys research efforts over the past 50 years in handling legal texts for systems development. These efforts include the use of symbolic logic, logic programming, first-order temporal logic, deontic logic, defeasible logic, goal modeling, and semi-structured representations. This survey can aid requirements engineers and auditors to better specify, monitor, and test software systems for compliance.
Arc-Length Parameterized Spline Curves for Real-Time Simulation

Parametric curves are frequently used in computer animation and virtual environments to control the movements of synthetic objects. In many of these applications it is essential to efficiently relate parameter values to the arc length of the curve. Current approaches to compute arc length or to construct an arc-length parameterized curve are impractical to use in real-time applications. This paper presents a simple and efficient technique to generate approximately arc-length parameterized spline curves that closely match spline curves typically used to model roads in high-fidelity driving simulators.
A topology for three-stage Solid State Transformer

Solid State Transformer (SST) is a new type of power transformer based on power electronic converters and high frequency transformers. The SST realizes voltage transformation, galvanic isolation, and power quality improvements in a single device. In the literature, a number of topologies have been introduced for the SST. In this work, employing a modular multilevel converter, a new SST topology is introduced which provides not only high-voltage AC (HVAC), low-voltage AC (LVAC) and low-voltage DC (LVDC) ports, but also high-voltage DC (HVDC) port. Besides, the proposed topology is easily scalable to higher voltage levels.
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input

We demonstrate that current state-of-theart approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly training the local coherence model with a state-of-the-art AES model. We evaluate our approach against a number of baselines and experimentally demonstrate its effectiveness on both the AES task and the task of flagging adversarial input, further contributing to the development of an approach that strengthens the validity of neural essay scoring models.
Applied Internet of Things (IoT): Car monitoring system using IBM BlueMix

Wide application of Internet technology brings the technology Internet of Things (IoT), a technology where any object can be on the internet and communicate with each other. This research will bring IoT technology to build the smart car system, which can be monitored and can actively report the condition to predict routine maintenance and provide information in order to achieve an efficient way of driving. Smart Car is realized by utilizing a standard On-Board Diagnostic (OBD) which is applied to the vehicles available at this time. OBD allows any vehicle parameters can be monitored electronically using the OBD Scanner tool. Monitored parameters will be uploaded to a cloud server utilizing IBM BlueMix applications and make the car as the IoT that can be analyzed in order to obtain information for predictive maintenance and monitoring of the Smart Car. The purpose of this research is to apply IoT in the smart car system so that car's predictive maintenance and monitoring functions can be realized. Big data analysist from the cloud would be informed to either driver, owners, or technician. This system is expected to be able to increase the efficiency of the car because of observed and supervised driving behavior which is able to reduce fuel consumption and exhaust emissions.
Bijective parameterization with free boundaries

We present a fully automatic method for generating guaranteed bijective surface parameterizations from triangulated 3D surfaces partitioned into charts. We do so by using a distortion metric that prevents local folds of triangles in the parameterization and a barrier function that prevents intersection of the chart boundaries. In addition, we show how to modify the line search of an interior point method to directly compute the singularities of the distortion metric and barrier functions to maintain a bijective map. By using an isometric metric that is efficient to compute and a spatial hash to accelerate the evaluation and gradient of the barrier function for the boundary, we achieve fast optimization times. Unlike previous methods, we do not require the boundary be constrained by the user to a non-intersecting shape to guarantee a bijection, and the boundary of the parameterization is free to change shape during the optimization to minimize distortion.
Multiresolution Bilateral Filtering for Image Denoising

The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided.
Holographic Photolysis for Multiple Cell Stimulation in Mouse Hippocampal Slices

Advanced light microscopy offers sensitive and non-invasive means to image neural activity and to control signaling with photolysable molecules and, recently, light-gated channels. These approaches require precise and yet flexible light excitation patterns. For synchronous stimulation of subsets of cells, they also require large excitation areas with millisecond and micrometric resolution. We have recently developed a new method for such optical control using a phase holographic modulation of optical wave-fronts, which minimizes power loss, enables rapid switching between excitation patterns, and allows a true 3D sculpting of the excitation volumes. In previous studies we have used holographic photololysis to control glutamate uncaging on single neuronal cells. Here, we extend the use of holographic photolysis for the excitation of multiple neurons and of glial cells.



The system combines a liquid crystal device for holographic patterned photostimulation, high-resolution optical imaging, the HiLo microscopy, to define the stimulated regions and a conventional Ca(2+) imaging system to detect neural activity. By means of electrophysiological recordings and calcium imaging in acute hippocampal slices, we show that the use of excitation patterns precisely tailored to the shape of multiple neuronal somata represents a very efficient way for the simultaneous excitation of a group of neurons. In addition, we demonstrate that fast shaped illumination patterns also induce reliable responses in single glial cells.



We show that the main advantage of holographic illumination is that it allows for an efficient excitation of multiple cells with a spatiotemporal resolution unachievable with other existing approaches. Although this paper focuses on the photoactivation of caged molecules, our approach will surely prove very efficient for other probes, such as light-gated channels, genetically encoded photoactivatable proteins, photoactivatable fluorescent proteins, and voltage-sensitive dyes.
Face recognition using LDA-based algorithms

Low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR tasks. In this paper, we propose a new algorithm that deals with both of the shortcomings in an efficient and cost effective manner. The proposed method is compared, in terms of classification accuracy, to other commonly used FR methods on two face databases. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the eigenfaces, fisherfaces, and D-LDA methods.
Differentiating anxiety forms and their role in academic performance from primary to secondary school

Individuals with high levels of mathematics anxiety are more likely to have other forms of anxiety, such as general anxiety and test anxiety, and tend to have some math performance decrement compared to those with low math anxiety. However, it is unclear how the anxiety forms cluster in individuals, or how the presence of other anxiety forms influences the relationship between math anxiety and math performance.



We measured math anxiety, test anxiety, general anxiety and mathematics and reading performance in 1720 UK students (year 4, aged 8-9, and years 7 and 8, aged 11-13). We conducted latent profile analysis of students' anxiety scores in order to examine the developmental change in anxiety profiles, the demographics of each anxiety profile and the relationship between profiles and academic performance.



Anxiety profiles appeared to change in specificity between the two age groups studied. Only in the older students did clusters emerge with specifically elevated general anxiety or academic anxiety (test and math anxiety). Our findings suggest that boys are slightly more likely than girls to have elevated academic anxieties relative to their general anxiety. Year 7/8 students with specifically academic anxiety show lower academic performance than those who also have elevated general anxiety.



There may be a developmental change in the specificity of anxiety and gender seems to play a strong role in determining one's anxiety profile. The anxiety profiles present in our year 7/8 sample, and their relationships with math performance, suggest a bidirectional relationship between math anxiety and math performance.
From natural language requirements to UML class diagrams

Unified Modeling Language (UML) is the most popular modeling language for analysis, design and development of the software system. There has been a lot of research interest in generating these UML models, especially class diagrams, automatically from Natural Language requirements. The interest in class diagrams can be attributed to the fact that classes represent the abstractions present in the system to be developed. However, automated generation of UML class diagrams is a challenging task as it involves lot of pre-processing or manual intervention at times. In this paper, we present dependency analysis based approach to derive UML class diagrams automatically from Natural Language requirements. We transform the requirements statements to an intermediary frame-based structured representation using dependency analysis of requirements statements and the Grammatical Knowledge Patterns. The knowledge stored in the frame-based structured representation is used to derive class diagrams using rule-based algorithm. Our approach has generated similar class diagrams as reported in earlier works based on linguistic analysis with either annotation or manual intervention. We present the effectiveness of our approach in terms of recall and precision for the case-studies presented in earlier works.
Information Extraction Supported Question Answering

This paper discusses the use of our information extraction (IE) system, Textract, in the questionanswering (QA) track of the recently held TREC-8 tests. One of our major objectives is to examine how IE can help IR (Information Retrieval) in applications like QA. Our study shows: (i) IE can provide solid support for QA; (ii ) low-level IE like Named Entity tagging is often a necessary component in handling most types of questions; (iii ) a robust natural language shallow parser provides a structural basis for handling questions; (iv) high-level domain independent IE, i.e. extraction of multiple relationships and general events, is expected to bring about a breakthrough in QA.
A Multicriteria Approach for Distributed Planning and Conflict Resolution for Multi-Agent Systems

A multicriteria approach for distributed planning and conflict resolution for multiagent systems is presented in the paper. A graph representation is used to model the plans of each agent and a multicriteria "best" path procedure can be used in order to obtain the "best" path for each agent considering its private and local goals. In our approach private and local goals do not necessarily coincide. A conflict and/or positive cooperation may occur and a detection procedure is triggered as soon as the agents broadcast their "best plans". A negotiation process is then established if necessary. Such a process iterates the use of the graph representation and of the "best" path algorithm to a level including the agents that have to negotiate. The parameters of the multicriteria model used to evaluate the paths are then negotiated or even the model itself. Some open problems conclude the paper.
Accurate odometry and error modelling for a mobile robot

This paper presents a low cost novel odometry design capable of achieving high accuracy deadreckoning. It also develops a statistical error model for estimating position and orientation errors of a mobile robot using odometry. Previous work on propagating odometry error covariance relies on incrementally updating the covariance matrix in small time steps. The approach taken here sums the noise theoretically over the entire path length to produce simple closed form expressions, allowing efficient covariance matrix updating after the completion of path segments. Closed form error covariance matrix is developed for a general circular arc and two special cases : (I) straight line and (II) turning about the centre of axle of the robot. Other paths can be composed of short segments of constant curvature arcs without great loss of accuracy. The model assumes that wheel distance measurement errors are exclusively random zero mean white noise. Systematic errors due to wheel radius and wheel base measurement were first calibrated with UMBmark [BorFen94]. Experimental results show that, despite its low cost, our system’s performance, with regard to dead-reckoning accuracy, is comparable to some of the best, award-winning vehicles around. The statistical error model, on the other hand, needs to be improved in light of new insights.
Enhanced Search with Wildcards and Morphological Inflections in the Google Books Ngram Viewer

We present a new version of the Google Books Ngram Viewer, which plots the frequency of words and phrases over the last five centuries; its data encompasses 6% of the world’s published books. The new Viewer adds three features for more powerful search: wildcards, morphological inflections, and capitalization. These additions allow the discovery of patterns that were previously difficult to find and further facilitate the study of linguistic trends in printed text.
Sliding Window Trend Analysis: A Method for Short and Open Circuit Detection in Copper Electrorefining

This work presents a system to detect short and open circuits suitable for Optibar® inter-cell bars in copper electrorefining processes. The system is based in Optibar's voltage connecting segments. Voltages are sampled and transmitted via WI-FI to a compatible SQL database every five minutes. The technique proposed performs sliding window analysis on anode-cathode voltages slopes to detect short and open circuits. The system was implemented in a refinery section of 20 cells with 1,100 cathodes.
A decision model for evaluating third-party logistics providers using fuzzy analytic hierarchy process

As a consequence of an increasing trend toward the outsourcing of logistics activities, shippers have been faced with the inevitability of selecting an appropriate third-party logistics (3PL) provider. The selection process for the identification of a 3PL provider that best fits user requirements involves multiple criteria and alternatives and may be one of the most complex decisions facing logistics users. In this regard, this study proposes an evaluation framework and methodology for selecting a suitable 3PL provider and illustrates the process of evaluation and selection through a case study. It is expected that the results of this study will provide a practical reference for logistics managers who want to engage the best 3PL provider. Future research using different datasets is warranted to verify the generalizability of the findings.
Evidence-based software engineering

Our objective is to describe how softwareengineering might benefit from an evidence-basedapproach and to identify the potential difficultiesassociated with the approach. We compared the organisation and technicalinfrastructure supporting evidence-based medicine (EBM)with the situation in software engineering. We consideredthe impact that factors peculiar to software engineering(i.e. the skill factor and the lifecycle factor) would haveon our ability to practice evidence-based softwareengineering ( EBSE promises a number of benefits byencouraging integration of research results with a view tosupporting the needs of many different stakeholdergroups. However, we do not currently have theinfrastructure needed for widespread adoption of EBSE.The skill factor means software engineering experimentsare vulnerable to subject and experimenter bias. Thelifecycle factor means it is difficult to determine howtechnologies will behave once deployed. Software engineering would benefit fromadopting what it can of the evidence approach providedthat it deals with the specific problems that arise from thenature of software engineering.
Analysis of DCTCP: stability, convergence, and fairness

Cloud computing, social networking and information networks (for search, news feeds, etc) are driving interest in the deployment of large data centers. TCP is the dominant Layer 3 transport protocol in these networks. However, the operating conditions---very high bandwidth links, low round-trip times, small-buffered switches---and traffic patterns cause TCP to perform very poorly. The Data Center TCP (DCTCP) algorithm has recently been proposed as a TCP variant for data centers and addresses these shortcomings.
 In this paper, we provide a mathematical analysis of DCTCP. We develop a fluid model of DCTCP and use it to analyze the throughput and delay performance of the algorithm, as a function of the design parameters and of network conditions like link speeds, round-trip times and the number of active flows. Unlike fluid model representations of standard congestion control loops, the DCTCP fluid model exhibits limit cycle behavior. Therefore, it is not amenable to analysis by linearization around a fixed point and we undertake a direct analysis of the limit cycles, proving their stability. Using a hybrid (continuous- and discrete-time) model, we analyze the convergence of DCTCP sources to their fair share, obtaining an explicit characterization of the convergence rate. Finally, we investigate the "RTT-fairness" of DCTCP; i.e., the rate obtained by DCTCP sources as a function of their RTTs. We find a very simple change to DCTCP which is suggested by the fluid model and which significantly improves DCTCP's RTT-fairness. We corroborate our results with ns2 simulations.
A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease

This paper presents a particle swarm optimization (PSO)-based fuzzy expert system for the diagnosis of coronary artery disease (CAD). The designed system is based on the Cleveland and Hungarian Heart Disease datasets. Since the datasets consist of many input attributes, decision tree (DT) was used to unravel the attributes that contribute towards the diagnosis. The output of the DT was converted into crisp if– then rules and then transformed into fuzzy rule base. PSO was employed to tune the fuzzy membership functions (MFs). Having applied the optimized MFs, the generated fuzzy expert system has yielded 93.27% classification accuracy. The major advantage of this approach is the ability to interpret the decisions made from the created fuzzy expert system, when compared with other approaches.