nils-herrmann/clean_scirepeval huggingface dataset:
Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments

Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages while abstracting the specific details of a sensor. Different tracking models are used depending on the current set of sensors detecting the object. A sensor independent algorithm for classifying objects regarding their current and past movement state is presented. The described architecture and algorithms have been successfully implemented in Tartan racingpsilas autonomous vehicle for the urban grand challenge. Results are presented and discussed.
Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums

Recent initiatives in IR community have shown the importance of going beyond factoid Question Answering (QA) in order to design useful real-world applications. Questions asking for descriptions or explanations are much more difficult to be solved, e.g., the machine learning models cannot focus on specific answer words or their lexical type. Thus, researchers have started to explore powerful methods for feature engineering. Two of the most promising methods are convolution tree kernels (CTKs) and convolutional neural networks (CNNs) as they have been shown to obtain high performance in the task of answer sentence selection in factoid QA. In this paper, we design state-of-the-art models for non-factoid QA also carried out on noisy data. In particular, we study and compare models for comment selection in a community QA (cQA) scenario, where the majority of questions regard descriptions or explanations. To deal with such complex task, we incorporate relational information holding between questions and comments as well as domain-specific features into both convolutional models above.
 Our experiments on a cQA corpus show that both CTK and CNN achieve the state of the art, also according to a direct comparison with the results obtained by the best systems of the SemEval cQA challenge.
SoftBound: highly compatible and complete spatial memory safety for c

The serious bugs and security vulnerabilities facilitated by C/C++'s lack of bounds checking are well known, yet C and C++ remain in widespread use. Unfortunately, C's arbitrary pointer arithmetic, conflation of pointers and arrays, and programmer-visible memory layout make retrofitting C/C++ with spatial safety guarantees extremely challenging. Existing approaches suffer from incompleteness, have high runtime overhead, or require non-trivial changes to the C source code. Thus far, these deficiencies have prevented widespread adoption of such techniques.
 This paper proposes SoftBound, a compile-time transformation for enforcing spatial safety of C. Inspired by HardBound, a previously proposed hardware-assisted approach, SoftBound similarly records base and bound information for every pointer as disjoint metadata. This decoupling enables SoftBound to provide spatial safety without requiring changes to C source code. Unlike HardBound, SoftBound is a software-only approach and performs metadata manipulation only when loading or storing pointer values. A formal proof shows that this is sufficient to provide spatial safety even in the presence of arbitrary casts. SoftBound's full checking mode provides complete spatial violation detection with 67% runtime overhead on average. To further reduce overheads, SoftBound has a store-only checking mode that successfully detects all the security vulnerabilities in a test suite at the cost of only 22% runtime overhead on average.
Accelerating Differential Evolution Using an Adaptive Local Search

We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.
Image-to-Markup Generation with Coarse-to-Fine Attention

We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism. Our method is evaluated in the context of imageto-LaTeX generation, and we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup. We show that unlike neural OCR techniques using CTCbased models, attention-based approaches can tackle this non-standard OCR task. Our approach outperforms classical mathematical OCR systems by a large margin on in-domain rendered data, and, with pretraining, also performs well on out-of-domain handwritten data. To reduce the inference complexity associated with the attention-based approaches, we introduce a new coarse-to-fine attention layer that selects a support region before applying attention.
DYNAMIC ASSET ALLOCATION WITH EVENT RISK

An inherent risk facing investors in financial markets is that a major event may trigger a large abrupt change in stock prices and market volatility. This paper studies the implications of jumps in prices and volatility on investment strategies. Using the event-risk framework of Duffie, Pan, and Singleton, we provide an analytical solution to the optimal portfolio problem. We find that event risk dramatically affects the optimal strategy. An investor facing event risk is less willing to take leveraged or short positions. In addition, the investor acts as if some portion of his wealth may become illiquid and the optimal strategy blends elements of both dynamic and buy-and-hold portfolio strategies. Jumps in prices and volatility both have an important influence on the optimal strategy.
Ad Hoc Retrieval Experiments Using WordNet and Automatically Constructed Thesauri

This paper describe our method in automatic-adhoc task of TREC-7. We propose a method to improve the performance of information retrieval system by expanded the query using 3 diffferent types of thesaurus. The expansion terms are taken from handcrafted thesaurus (WordNet), co-occurrence-based automatically constructed thesaurus, and syntactically predicate-argument based automatically constructed thesaurus.
A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration After Incomplete Cervical Spinal Cord Injury

This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of nine subjects with incomplete cervical SCI while they tried to perform six different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (>; 97%) were achieved in classification of seven different classes (six intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury.
An overview of online trust: Concepts, elements, and implications

Lack of trust has been repeatedly identified as one of the most formidable barriers to people for engaging in e-commerce, involving transactions in which financial and personal information is submitted to merchants via the Internet. The future of e-commerce is tenuous without a general climate of online trust. Building consumer trust on the Internet presents a challenge for online merchants and is a research topic of increasing interest and importance. This paper provides an overview of the nature and concepts of trust from multi-disciplinary perspectives, and it reviews relevant studies that investigate the elements of online trust. Also, a framework of trust-inducing interface design features articulated from the existing literature is presented. The design features were classified into four dimensions, namely (1) graphic design, (2) structure design, (3) content design, and (4) social-cue design. By applying the design features identified within this framework to e-commerce web site interfaces, online merchants might then anticipate fostering optimal levels of trust in their customers.
An autonomous polymerization motor powered by DNA hybridization.

We present a synthetic molecular motor capable of autonomous nanoscale transport in solution. Inspired by bacterial pathogens such as Rickettsia rickettsii, which locomote by inducing the polymerization of the protein actin at their surfaces to form 'comet tails', the motor operates by polymerizing a double-helical DNA tail2. DNA strands are propelled processively at the living end of the growing polymers, demonstrating autonomous locomotion powered by the free energy of DNA hybridization.
Healthy Menus Recommendation: Optimizing the Use of the Pantry

We are often unable to plan menus ahead, thus making poor and unhealthy choices of meals. Besides healthy, one may want menus in which ingredients harmonize and cover well the available ingredients in the pantry. In this paper, we propose a novel multi-objective-based recommender of menus that features an optimal balance between nutritional aspects, harmony and coverage of available ingredients. We conduct experiments on real-world and synthetic datasets and show that our approach achieves the desired levels of nutrients, harmonization and coverage of ingredients.
Generation from Abstract Meaning Representation using Tree Transducers

Language generation from purely semantic representations is a challenging task. This paper addresses generating English from the Abstract Meaning Representation (AMR), consisting of re-entrant graphs whose nodes are concepts and edges are relations. The new method is trained statistically from AMRannotated English and consists of two major steps: (i) generating an appropriate spanning tree for the AMR, and (ii) applying tree-tostring transducers to generate English. The method relies on discriminative learning and an argument realization model to overcome data sparsity. Initial tests on held-out data show good promise despite the complexity of the task. The system is available open-source as part of JAMR at: http://github.com/jflanigan/jamr
The impact of the MIT-BIH Arrhythmia Database

The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.
Korat: automated testing based on Java predicates

This paper presents Korat, a novel framework for automated testing of Java programs. Given a formal specification for a method, Korat uses the method precondition to automatically generate all (nonisomorphic) test cases up to a given small size. Korat then executes the method on each test case, and uses the method postcondition as a test oracle to check the correctness of each output.To generate test cases for a method, Korat constructs a Java predicate (i.e., a method that returns a boolean) from the method's pre-condition. The heart of Korat is a technique for automatic test case generation: given a predicate and a bound on the size of its inputs, Korat generates all (nonisomorphic) inputs for which the predicate returns true. Korat exhaustively explores the bounded input space of the predicate but does so efficiently by monitoring the predicate's executions and pruning large portions of the search space.This paper illustrates the use of Korat for testing several data structures, including some from the Java Collections Framework. The experimental results show that it is feasible to generate test cases from Java predicates, even when the search space for inputs is very large. This paper also compares Korat with a testing framework based on declarative specifications. Contrary to our initial expectation, the experiments show that Korat generates test cases much faster than the declarative framework.
Conditioning Prices on Purchase History

T rapid advance in information technology now makes it feasible for sellers to condition their price offers on consumers’ prior purchase behavior. In this paper we examine when it is profitable to engage in this form of price discrimination when consumers can adopt strategies to protect their privacy. Our baseline model involves rational consumers with constant valuations for the goods being sold and a monopoly merchant who can commit to a pricing policy. Applying results from the prior literature, we show that although it is feasible to price so as to distinguish high-value and low-value consumers, the merchant will never find it optimal to do so. We then consider various generalizations of this model, such as allowing the seller to offer enhanced services to previous customers, making the merchant unable to commit to a pricing policy, and allowing competition in the marketplace. In these cases we show that sellers will, in general, find it profitable to condition prices on purchase history.
BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology

Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis. We describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology. BiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.
Distributed Denial of Service (DDoS) Attack in Cloud- Assisted Wireless Body Area Networks: A Systematic Literature Review

Wireless Body Area Networks (WBANs) have emerged as a promising technology that has shown enormous potential in improving the quality of healthcare, and has thus found a broad range of medical applications from ubiquitous health monitoring to emergency medical response systems. The huge amount of highly sensitive data collected and generated by WBAN nodes requires an ascendable and secure storage and processing infrastructure. Given the limited resources of WBAN nodes for storage and processing, the integration of WBANs and cloud computing may provide a powerful solution. However, despite the benefits of cloud-assisted WBAN, several security issues and challenges remain. Among these, data availability is the most nagging security issue. The most serious threat to data availability is a distributed denial of service (DDoS) attack that directly affects the all-time availability of a patient’s data. The existing solutions for standalone WBANs and sensor networks are not applicable in the cloud. The purpose of this review paper is to identify the most threatening types of DDoS attacks affecting the availability of a cloud-assisted WBAN and review the state-of-the-art detection mechanisms for the identified DDoS attacks.
Optimal Lower Bounds on Regular Expression Size Using Communication Complexity

The problem of converting deterministic finite automata into (short) regular expressions is considered. It is known that the required expression size is 2 in the worst case for infinite languages, and for finite languages it is n log n) and n , if the alphabet size grows with the number of states n of the given automaton. A new lower bound method based on communication complexity for regular expression size is developed to show that the required size is indeed n . For constant alphabet size the best lower bound known to date is Ω(n), even when allowing infinite languages and nondeterministic finite automata. As the technique developed here works equally well for deterministic finite automata over binary alphabets, the lower bound is improved to n .
FPGA architecture for real-time barrel distortion correction of colour images

This paper presents a hardware architecture for real time barrel distortion correction of YUV 4∶2∶2 encoded color images. In our solution we are presenting an alternative implementation of the correction engine which is based on multiplications rather than trigonometric transforms. The system makes minimal use of resources thus being a good candidate for embedding into a single chip. Proposed solution is implemented in a cost-effective Spartan 3 Field Programmable Gate Array (FPGA). Our architecture is capable of processing QQVGA images at the rate of 30 frames per second (fps). Qualitative and quantitative examination confirm correct preservation of color information in processed images.
VR Learning : Potential and Challenges for the Use of 3 D Environments in Education and Training

There is some evidence that Virtual Reality (VR) can contribute to raise interest and motivation in students and to effectively support knowledge transfer, since the learning process can be settled within an experiential framework. However, the practical potential of VR is still being explored: understanding how to use Virtual Reality to support training and learning activities presents a substantial challenge for the designers and evaluators of this learning technology. This chapter has the main aim of discussing the rationale and main benefits for the use of virtual environments in education and training. A number of key attributes of VR environments will be described and discussed in relationship to educational theory and pedagogical practice, in order to establish a possible theoretical basis for VR learning. Significant research and projects carried out in this field will be also presented, together with suggestions and guidelines for future development of VR learning systems. However, further research is required, both on technological side and on key issues such as transfer of learning, appropriate curriculum implementation, elements of effective VR design, and the psychological and social impact of the technology use.
Nondissipative turn-off snubber alleviates switching power dissipation, second-breakdown stress and VCE overshoot: Analysis, design procedure and experimental verification

A nondissipative, resonant turn-off snubber eliminates the power dissipation of the traditional R-C-diode snubber, while providing protection against transistor breakdown from energy stored in the power-transformer leakage inductance. Four operating modes are analyzed. Complete design procedures are given, with experimental verification.
Computer security technology planning study

Approved for pubJic reJease; distribution unri mited. When U.S. Government drawings, specifications or other data are used for any purpose other than a definitely related government procurement operation, the government thereby incurs no responsibility nor any obligation whatsoever; and the fact that the government may have formulated, fui·nished, or in any way sup­ plied the said drawings, specifications, or other data is not to be regarded by implication or otherwise as in any manner licensing the holder or any other person or conveying any rights or permission to manufacture, use, or sell any patented invention that may in any way be related thereto.
Adversarial Deep Structural Networks for Mammographic Mass Segmentation

Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INBreast and DDSM-BCRP, demonstrate that our end-to-end network achieves the state-of-the-art results.