nils-herrmann/clean_scirepeval huggingface dataset:
Solving Steiner tree problems in graphs to optimality

In this paper, we present the implementation of a branch-and-cut algorithm for solving Steiner tree problems in graphs. Our algorithm is based on an integer programming formulation for directed graphs and comprises preprocessing, separation algorithms, and primal heuristics. We are able to solve nearly all problem instances discussed in the literature to optimality, including one problem that—to our knowledge—has not yet been solved. We also report on our computational experiences with some very large Steiner tree problems arising from the design of electronic circuits. All test problems are gathered in a newly introduced library called SteinLib that is accessible via the World Wide Web. q 1998 John Wiley & Sons, Inc. Networks 32: 207–232, 1998
Detection of parked vehicles from a radar based occupancy grid

For autonomous parking applications to become possible, knowledge about the parking environment is required. Therefore, a real-time algorithm for detecting parked vehicles from radar data is presented. These data are first accumulated in an occupancy grid from which objects are detected by applying techniques borrowed from the computer vision field. Two random forest classifiers are trained to recognize two categories of objects: parallel-parked vehicles and cross-parked vehicles. Performances of the classifiers are evaluated as well as the capacity of the complete system to detect parked vehicles in real world scenarios.
Extension of heuristic evaluation method: a review and reappraisal

As one of the major discount methods, the heuristic evaluation method (HE) is the most commonly used usability inspection method. We introduced the history and procedure of this method, as well as its strengths and weaknesses. We then reviewed the applications of this method to different Human-Computer Interaction systems with the adapted heuristic sets. We also reviewed many studies that extended the traditional HE method in different ways. Finally, the paper ends with a reappraisal of these extension methods and future research direction to improve the HE method.
Online anomaly detection with concept drift adaptation using recurrent neural networks

Anomaly detection in time series is an important task with several practical applications. The common approach of training one model in an offline manner using historical data is likely to fail under dynamically changing and non-stationary environments where the definition of normal behavior changes over time making the model irrelevant and ineffective. In this paper, we describe a temporal model based on Recurrent Neural Networks (RNNs) for time series anomaly detection to address challenges posed by sudden or regular changes in normal behavior. The model is trained incrementally as new data becomes available, and is capable of adapting to the changes in the data distribution. RNN is used to make multi-step predictions of the time series, and the prediction errors are used to update the RNN model as well as detect anomalies and change points. Large prediction error is used to indicate anomalous behavior or a change (drift) in normal behavior. Further, the prediction errors are also used to update the RNN model in such a way that short term anomalies or outliers do not lead to a drastic change in the model parameters whereas high prediction errors over a period of time lead to significant updates in the model parameters such that the model rapidly adapts to the new norm. We demonstrate the efficacy of the proposed approach on a diverse set of synthetic, publicly available and proprietary real-world datasets.
SQL Database Primitives for Decision Tree Classifiers

Scalable data mining in large databases is one of today's challenges to database technologies. Thus, substantial effort is dedicated to a tight coupling of database and data mining systems leading to database primitives supporting data mining tasks. In order to support a wide range of tasks and to be of general usage these primitives should be rather building blocks than implementations of specific algorithms. In this paper, we describe primitives for building and applying decision tree classifiers. Based on the analysis of available algorithms and previous work in this area we have identified operations which are useful for a number of classification algorithms. We discuss the implementation of these primitives on top of a commercial DBMS and present experimental results demonstrating the performance benefit.
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings

In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors. Shi et al. (2016) show that this kind of pseudo multi-senses can be eliminated by linear transformations. In this paper, we show that pseudo multi-senses may come from a uniform and meaningful phenomenon such as subjective and sentimental usage, though they are seemingly redundant. In this paper, we present an unsupervised algorithm to find a linear transformation which can minimize the transformed distance of a group of sense pairs. The major shrinking direction of this transformation is found to be related with subjective shift. Therefore, we can not only eliminate pseudo multi-senses in multisense embeddings, but also identify these subjective senses and tag the subjective and sentimental usage of words in the corpus automatically.
Efficient FIR Filter Design Using Modified Carry Select Adder & Wallace Tree Multiplier

An area-power-delay efficient design of FIR filter is described in this paper. In proposed multiplier unit high speed is achieved using XOR-XNOR column by column reduction compressors instead of compressors using full adder. The carry propagation delay and area of carry select adder is reduced by splitting carry select adder into equal bit groups. The proposed carry select adder unit consumes less power than conventional carry select adder unit. With proposed multiplier unit and carry select adder unit, the designed FIR Filter consumes 55% power less than the conventional filter without significant increase in area. The power & delay comparison is performed for both existing and proposed method of FIR filter. The design is implemented using 0.18μm technology. Index Terms FIR filter design, FDA Tool, Low Power VLSI, Wallace tree multiplication.
GentleRain: Cheap and Scalable Causal Consistency with Physical Clocks

GentleRain is a new causally consistent geo-replicated data store that provides throughput comparable to eventual consistency and superior to current implementations of causal consistency.
 GentleRain uses a periodic aggregation protocol to determine whether updates can be made visible in accordance with causal consistency. Unlike current implementations, it does not use explicit dependency check messages, resulting in a major throughput improvement at the expense of a modest increase in update visibility. Furthermore, GentleRain tracks causal consistency by attaching to updates scalar timestamps derived from loosely synchronized physical clocks. Clock skew does not cause violations of causal consistency, but may delay the visibility of updates. By encoding causality in a single scalar timestamp, GentleRain reduces storage and communication overhead for tracking causality.
 We evaluate GentleRain using Amazon EC2, and demonstrate that it achieves throughput equal to about 99% of eventual consistency, and 120% better than previous implementations of causal consistency.
Supraclavicular flap in head and neck reconstruction: experience in 50 consecutive patients

The supraclavicular flap (SCF) is a fasciocutaneous flap used to cover head, oral, and neck region defects after tumor resection. Its main vascular supply is the supraclavicular artery and accompanying veins and it can be harvested as a vascularised pedicled flap. The SCF serves as an excellent outer skin cover as well as a good inner mucosal lining after oral cavity and head-neck tumor resections. The flap has a wide arc of rotation and matches the skin colour and texture of the face and neck. Between March 2006 and March 2011, the pedicled supraclavicular flap was used for reconstruction in 50 consecutive patients after head and neck tumor resections and certain benign conditions in a tertiary university hospital setting. The flaps were tunnelized under the neck skin to cover the external cervicofacial defects or passed medial to the mandible to give an inner epithelial lining after the oral cavity and oropharyngeal tumor excision. Forty-four of the 50 patients had 100% flap survival with excellent wound healing. All the flaps were harvested in less than 1 h. There were four cases of distal tip desquamation and two patients had complete flap necrosis. Distal flap desquamation was observed in SCFs used for resurfacing the external skin defects after oral cavity tumor ablation and needed only conservative treatment measures. Total flap failure was encountered in two patients who had failed in previous chemoradiotherapy for squamous cell cancer of the floor of mouth and tonsil, respectively, and the SCF was used in mucosal defect closure after tumor ablation. The benefits of a pedicled fasciocutaneous supraclavicular flap are clear; it is thin, reliable, easy, and quick to harvest. In head, face and neck reconstructions, it is a good alternative to free fasciocutaneous flaps, regional pedicled myocutaneous flaps, and the deltopectoral flap.
Learning to Refine Source Representations for Neural Machine Translation

Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process. The encoder summarizes the representation of input sentence from scratch, which is potentially a problem if the sentence is ambiguous. When translating a text, humans often create an initial understanding of the source sentence and then incrementally refine it along the translation on the target side. Starting from this intuition, we propose a novel encoderrefiner-decoder framework, which dynamically refines the source representations based on the generated target-side information at each decoding step. Since the refining operations are time-consuming, we propose a strategy, leveraging the power of reinforcement learning models, to decide when to refine at specific decoding steps. Experimental results on both Chinese–English and English– German translation tasks show that the proposed approach significantly and consistently improves translation performance over the standard encoderdecoder framework. Furthermore, when refining strategy is applied, results still show reasonable improvement over the baseline without much decrease in decoding speed.
Threat-Driven Architectural Design of Secure Information Systems

To deal with software security issues in the early stages of system development, this paper presents a threatdriven approach to the architectural design and analysis of secure information systems. In this approach, we model security threats to systems with misuse cases and mitigation requirements with mitigation use cases at the requirements analysis phase. Then we drive system architecture design (including the identification of architectural components and their connections) by use cases, misuse cases, and mitigation use cases. According to the misuse case-based threat model, we analyze whether or not a candidate architecture is resistant to the identified security threats and what constraints must be imposed on the choices of system implementation. This provides a smooth transition from requirements specification to high-level design and greatly improves the traceability of security concerns in high assurance information systems. We demonstrate our approach through a case study on a security-intensive payroll information system.
Speech recognition with support vector machines in a hybrid system

While the temporal dynamics of speech can be represented very efficiently by Hidden Markov Models (HMMs), the classification of speech into single speech units (phonemes) is usually done with Gaussian mixture models which do not discriminate well. Here, we use Support Vector Machines (SVMs) for classification by integrating this method in a HMM-based speech recognition system. In this hybrid SVM/HMM system we translate the outputs of the SVM classifiers into conditional probabilities and use them as emission probabilities in a HMM-based decoder. SVMs are very appealing due to their association with statistical learning theory. They have already shown very good classification results in other fields of pattern recognition. We train and test our hybrid system on the DARPA Resource Management (RM1) corpus. Our results show better performance than HMM-based decoder using Gaussian mixtures.
Geo-parsing Messages from Microtext

Widespread use of social media during crises has become commonplace, as shown by the volume of messages during the Haiti earthquake of 2010 and Japan tsunami of 2011. Location mentions are particularly important in disaster messages as they can show emergency responders where problems have occurred. This article explores the sorts of locations that occur in disaster-related social messages, how well off-theshelf software identifies those locations, and what is needed to improve automated location identification, called geo-parsing. To do this, we have sampled Twitter messages from the February 2011 earthquake in Christchurch, Canterbury, New Zealand. We annotated locations in messages manually to make a gold standard by which to measure locations identified by a Named Entity Recognition software. The Stanford NER software found some locations that were proper nouns, but did not identify locations that were not capitalized, local streets and buildings, or nonstandard place abbreviations and mis-spellings that are plentiful in microtext. We review how these problems might be solved in software research, and model a readable crisis map that shows crisis location clusters via enlarged place labels.
Fast and robust fixed-point algorithms for independent component analysis

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.
Digital control of a half-bridge LLC resonant converter

Recently, LLC resonant converters are gaining more and more interest in DC/DC conversion due to their high efficiency and power density. Usually, they are used in low power applications, i.e. in the range of few hundred Watt and with constant output load, however they are very appealing in high power and renewable energy applications as well as in many other applications affected by wide variations of the load and/or the source voltage. This paper presents and discusses a novel half bridge DC/DC resonant converter with digital control and fuzzy logic. The proposed system is compared with a more traditional PID controller. A variable frequency control algorithms are implemented using a low cost, high performance Texas Instrument TMS320F28069 DSP for the regulation of the output voltage. Simulation results using MATLAB are presented to evaluate the quality of the obtained result. A laboratory prototype has been realized and experimental results obtained using both PID and Fuzzy controllers are shown and discussed.
with Emphasis to the Computer and Internet

This article discusses the Roles of ICT in education. Information communication technologies (ICT) at present are influencing every aspect of human life. They are playing salient roles in work places, business, education, and entertainment. Moreover, many people recognize ICTs as catalysts for change; change in working conditions, handling and exchanging information, teaching methods, learning approaches, scientific research, and in accessing information. Therefore, this review article discusses the roles of ICTs, the promises, limitations and key challenges of integration to education systems. The review attempts in answering the following questions: (1) What are the benefits of ICTs in education? (2) What are the existing promises of ICT use in education systems of some developing countries? (3) What are the limitations and key challenges of ICTs integration to education systems? The review concludes that regardless of all the limitations characterizing it, ICT benefits education systems to provide quality education in alignment with constructivism, which is a contemporary paradigm of learning.
A controlled clinical comparison of attention performance in children with ADHD in a virtual reality classroom compared to standard neuropsychological methods.

In this initial pilot study, a controlled clinical comparison was made of attention perforance in children with attention deficit-hyperactivity disorder (ADHD) in a virtual reality (VR) classroom. Ten boys diagnosed with ADHD and ten normal control boys participated in the study. Groups did not significantly differ in mean age, grade level, ethnicity, or handedness. No participants reported simulator sickness following VR exposure. Children with ADHD exhibited more omission errors, commission errors, and overall body movement than normal control children in the VR classroom. Children with ADHD were more impacted by distraction in the VR classroom. VR classroom measures were correlated with traditional ADHD assessment tools and the flatscreen CPT. Of note, the small sample size incorporated in each group and higher WISC-III scores of normal controls might have some bearing on the overall interpretation of results. These data suggested that the Virtual Classroom had good potential for controlled performance assessment within an ecologically valid environment and appeared to parse out significant effects due to the presence of distraction stimuli.
A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem

Over the last decade many metaheuristics have been applied t o the flowshop scheduling problem, ranging from Simulated Annealing or Tabu Search to complex hybrid techniques. Some of these methods provide excellent effectiveness and e fficiency at the expense of being utterly complicated. In fact, several published meth ods require substantial implementation efforts, exploit problem specific speed-up techniqu es that cannot be applied to slight variations of the original problem, and often re-implement ations of these methods by other researchers produce results that are quite different from t he original ones. In this work we present a new iterated greedy algorithm that applies two pha ses iteratively, namedestruction, were some jobs are eliminated from the incumbent solution, andconstruction, where the eliminated jobs are reinserted into the sequence using t he well known NEH construction Corresponding author
Indoor positioning of mobile devices with agile iBeacon deployment

Position of mobile devices and their users provides a great amount of added value and opportunities. The penetration of tracking devices with sensory such as GPS devices, accelerators and specifically smart phones has impacted human lives extensively. Nowadays, many applications on smart phones and mobile devices exploit different techniques and inputs for positioning. Wireless positioning is generally divided into two categories: outdoor positioning and indoor positioning, depending on not only where they are used but also how they work. Powerful as it is, indoor positing is still a challenging problem because satellite-based approaches do not work properly inside buildings. Therefore, for indoor positioning, we need to use other technologies creatively. iBeacon, the focus of this paper, is a new technology which provides a higher level of location awareness in indoor positioning. iBeacon is a built-in, cross-platform technology for Android and iOS devices, which utilizes Bluetooth Low Energy (BLE) for long-last services. This technology has significant advantages compared to other types of indoor positioning technologies, such as less expensive hardware, less energy consumption, needless to internet connection, and being capable of receiving notifications in background. This technology will provide huge benefits for future location awareness applications. It will change the way retailers, event organizers, and educational institutions communicate with people indoors. In this paper, we aim to provide a more accurate, cost efficient approach to indoor positioning of mobile devices using iBeacon.
Ontologies for Cultural Heritage

In the cultural heritage domain information systems are increasingly deployed, digital representations of physical objects are produced in immense numbers and there is a strong political pressure on memory institutions to make their holdings accessible to the public in digital form. The sector splits into a set of disciplines with highly specialized fields. Due to the resulting diversity, one can hardly speak about a “domain” in the sense of “domain ontologies” . On the other side, study and research of the past is highly interdisciplinary. Characteristically, archaeology employs a series of “auxiliary” disciplines, such as archaeometry, archaeomedicine, archaeobotany, archaeometallurgy, archaeoastronomy, etc., but also historical sources and social theories. Interoperability between various highly specialized systems, integrated information access and information integration increasingly becomes a demand to support research, professional heritage administration, preservation, public curiosity and education. Therefore the sector is characterized by a complex schema integration problem of associating complementary information from various dedicated systems, which can be efficiently addressed by formal ontologies [14,32,33]. There is a proliferation of specialized terminology, but terminology is less used as a means of agreement between experts than as an intellectual tool for hypothesis building based on discriminating phenomena. Automated classification is a long established discipline of archaeology, but few terminological systems are widely accepted. The sector is, however, more focused on establishing knowledge about facts and context in the past than about classes of things and the laws of their behavior. Respectively, the concatenation of related facts by co-reference [56] to particulars, such as things, people, places, periods is a major open issue. Knowledge Organisation Systems (KOS, [62]) describing people and places are employed to a certain degree, and pose similar technical problems as ontologies, but the required scale is very large. In this chapter, we describe how ontologies are and could be employed to improve information management in the cultural heritage sector.