nils-herrmann/clean_scirepeval huggingface dataset:
Microsoft COCO: Common Objects in Context

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.
Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation.

Two experiments are reported which examined operators' trust in and use of the automation in a simulated supervisory process control task. Tests of the integrated model of human trust in machines proposed by Muir (1994) showed that models of interpersonal trust capture some important aspects of the nature and dynamics of human-machine trust. Results showed that operators' subjective ratings of trust in the automation were based mainly upon their perception of its competence. Trust was significantly reduced by any sign of incompetence in the automation, even one which had no effect on overall system performance. Operators' trust changed very little with experience, with a few notable exceptions. Distrust in one function of an automatic component spread to reduce trust in another function of the same component, but did not generalize to another independent automatic component in the same system, or to other systems. There was high positive correlation between operators' trust in and use of the automation; operators used automation they trusted and rejected automation they distrusted, preferring to do the control task manually. There was an inverse relationship between trust and monitoring of the automation. These results suggest that operators' subjective ratings of trust and the properties of the automation which determine their trust, can be used to predict and optimize the dynamic allocation of functions in automated systems.
Improved Collision Attack on Hash Function MD5

In this paper, we present a fast attack algorithm to find two-block collision of hash function MD5. The algorithm is based on the two-block collision differential path of MD5 that was presented by Wang et al. in the Conference EUROCRYPT 2005. We found that the derived conditions for the desired collision differential path were not sufficient to guarantee the path to hold and that some conditions could be modified to enlarge the collision set. By using technique of small range searching and omitting the computing steps to check the characteristics in the attack algorithm, we can speed up the attack of MD5 efficiently. Compared with the Advanced Message Modification technique presented by Wang et al., the small range searching technique can correct 4 more conditions for the first iteration differential and 3 more conditions for the second iteration differential, thus improving the probability and the complexity to find collisions. The whole attack on the MD5 can be accomplished within 5 hours using a PC with Pentium4 1.70GHz CPU.
Vehicle Routing with Drones

We introduce a package service model where trucks as well as drones can deliver packages. Drones can travel on trucks or fly; but while flying, drones can only carry one package at a time and have to return to a truck to charge after each delivery. We present a heuristic algorithm to solve the problem of finding a good schedule for all drones and trucks. The algorithm is based on two nested local searches, thus the definition of suitable neighbourhoods of solutions is crucial for the algorithm. Empirical tests show that our algorithm performs significantly better than a natural Greedy algorithm. Moreover, the savings compared to solutions without drones turn out to be substantial, suggesting that delivery systems might considerably benefit from using drones in addition to trucks.
Optimal pilot beam pattern design for massive MIMO systems

In this paper, channel estimation for massive multiple-input multiple-output (MIMO) systems with a large number of transmit antennas at the base station is considered, and a new algorithm for pilot beam pattern design for optimal channel estimation under the assumption of Gauss-Markov channel processes is proposed. The proposed algorithm designs the optimal pilot beam pattern sequentially by exploiting the statistics of the channel, antenna correlation, and temporal correlation. The algorithm provides a sequentially optimal sequence of pilot beam patterns for a given set of system parameters. Numerical results show the effectiveness of the proposed algorithm.
The Load Slice Core microarchitecture

Driven by the motivation to expose instruction-level parallelism (ILP), microprocessor cores have evolved from simple, in-order pipelines into complex, superscalar out-of-order designs. By extracting ILP, these processors also enable parallel cache and memory operations as a useful side-effect. Today, however, the growing off-chip memory wall and complex cache hierarchies of many-core processors make cache and memory accesses ever more costly. This increases the importance of extracting memory hierarchy parallelism (MHP), while reducing the net impact of more general, yet complex and power-hungry ILP-extraction techniques. In addition, for multi-core processors operating in power- and energy-constrained environments, energy-efficiency has largely replaced single-thread performance as the primary concern.
 Based on this observation, we propose a core microarchitecture that is aimed squarely at generating parallel accesses to the memory hierarchy while maximizing energy efficiency. The Load Slice Core extends the efficient in-order, stall-on-use core with a second in-order pipeline that enables memory accesses and address-generating instructions to bypass stalled instructions in the main pipeline. Backward program slices containing address-generating instructions leading up to loads and stores are extracted automatically by the hardware, using a novel iterative algorithm that requires no software support or recompilation. On average, the Load Slice Core improves performance over a baseline in-order processor by 53% with overheads of only 15% in area and 22% in power, leading to an increase in energy efficiency (MIPS/Watt) over in-order and out-of-order designs by 43% and over 4.7×, respectively. In addition, for a power- and area-constrained many-core design, the Load Slice Core outperforms both in-order and out-of-order designs, achieving a 53% and 95% higher performance, respectively, thus providing an alternative direction for future many-core processors.
The development of a patient-centered program based on the relaxation response: the Relaxation Response Resiliency Program (3RP).

Chronic daily stress has significant physical, emotional, and financial implications; levels of stress are increasing in the US. Dr. Benson highlighted how the mind and body function together in one's experience of the stress response and proposed the existence of the relaxation response (RR).



The current paper describes the foundation and development of an 8-session multimodal treatment program for coping with chronic stress: the Relaxation Response Resiliency Program (3RP).



We review the past decades of RR research, outline the development of the 3RP treatment, and provide an overview of the program's theory and content.



Extensive research and clinical work have examined how eliciting the RR may combat stress through down-regulation of the sympathetic nervous system. Related to this work are the multidimensional constructs of resiliency and allostatic load. The 3RP is based on principles from the fields of stress management, cognitive-behavioral therapy, and positive psychology, and has three core target areas: (1) elicitation of the RR; (2) stress appraisal and coping; and (3) growth enhancement. An 8-week patient-centered treatment program has been developed, with the purpose of assisting patients with a variety of psychological and medical issues to better cope with chronic stress.



Mastery of the RR is theorized to maximize one's ability to benefit from multimodal mind body strategies. The goal of the 3RP is to enhance individuals' adaptive responses to chronic stress through increasing awareness and decreasing the physiological, emotional, cognitive, and behavioral effects of the stress response, while simultaneously promoting the effects of being in the RR.
Efficient Hardware Architectures for Deep Convolutional Neural Network

Convolutional neural network (CNN) is the state-of-the-art deep learning approach employed in various applications. Real-time CNN implementations in resource limited embedded systems are becoming highly desired recently. To ensure the programmable flexibility and shorten the development period, field programmable gate array is appropriate to implement the CNN models. However, the limited bandwidth and on-chip memory storage are the bottlenecks of the CNN acceleration. In this paper, we propose efficient hardware architectures to accelerate deep CNN models. The theoretical derivation of parallel fast finite impulse response algorithm (FFA) is introduced. Based on FFAs, the corresponding fast convolution units (FCUs) are developed for the computation of convolutions in the CNN models. Novel data storage and reuse schemes are proposed, where all intermediate pixels are stored on-chip and the bandwidth requirement is reduced. We choose one of the largest and most accurate networks, VGG16, and implement it on Xilinx Zynq ZC706 and Virtex VC707 boards, respectively. We achieve the top-5 accuracy of 86.25% using an equal distance non-uniform quantization method. It is estimated that the average performances are 316.23 GOP/s under 172-MHz working frequency on Xilinx ZC706 and 1250.21 GOP/s under 170-MHz working frequency on VC707, respectively. In brief, the proposed design outperforms the existing works significantly, in particular, surpassing related designs by more than two times in terms of resource efficiency.
Measuring the impact of opening the London shared bicycle scheme to casual users

0968-090X/$ see front matter 2011  E-mail address: n.lathia@cs.ucl.ac.uk (N. Lathia). The increasing availability of sensor data in urban areas now offers the opportunity to perform continuous evaluations of transport systems and measure the effects of policy changes, in an empirical, large-scale, and non-invasive way. In this paper, we study one such example: the effect of changing the user-access policy in the London Barclays Cycle Hire scheme. When the scheme was launched in July 2010, users were required to apply for a key to access to the system. By December 2010, this policy was overridden in order to allow for ‘‘casual’’ usage, so that anyone in possession of a debit or credit card could gain access. While the transport authority measured the policy shift’s success by the increased number of trips, we set out to investigate how the change affected the system’s usage throughout the city. We present an extensive analysis of station data collected from the scheme’s web site both preand post-policy change, showing how differences in both global and local behaviour can be measured, and how the policy change correlates with a variety of effects observed around the city. We find that, as expected, quicker access to the system correlates with greater week end usage; it also reinforces the week-day commuting trend. In both the preand post-change periods, the geographic distribution of activity at individual stations forms concentric circles around central London. However, upon policy change, a number of stations undergo a complete usage change, now exhibiting an opposite trend with respect to that which they had prior to the policy change.
ICE: Item Concept Embedding via Textual Information

This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information. Specifically, in the proposed framework there are two stages: graph construction and embedding learning. In the first stage, we propose a generalized network construction method to build a network involving heterogeneous nodes and a mixture of both homogeneous and heterogeneous relations. The second stage leverages the concept of neighborhood proximity to learn the embeddings of both items and words. With the proposed carefully designed ICE networks, the resulting embedding facilitates both homogeneous and heterogeneous retrieval, including item-to-item and word-to-item retrieval. Moreover, as a distributed embedding approach, the proposed ICE approach not only generates related retrieval results but also delivers more diverse results than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, ICE encodes useful textual information and thus outperforms traditional methods in various item classification and retrieval tasks.
Evaluation of a Brute Forcing Tool that Extracts the RAT from a Malicious Document File

APT(Advanced Persistent Threat) attacks that cause unauthorized transfer of sensitive information from the targeted organization are serious threats. The attackers in APT attacks use RAT(Remote Access Trojan or Remote Administration Tool)s to take the control of the victim's computer. The attackers in APT attacks occasionally use malicious document files to camouflage themselves. If a RAT is embedded into a malicious document file, it is more difficult to detect and analyze the function. Analyzing malicious document files attached to spear phishing e-mails requires extracting the RAT. However, if we do not know the condition where the exploit code runs normally, it is difficult to extract the RAT by dynamic analysis. Therefore, we developed a brute forcing tool which decodes obfuscation and extracts the RAT from a malicious document file. This tool was developed based on the malicious document files which were used between 2009 and 2012 in APT attacks, and indicated how to embed a RAT in a malicious document file. However, whether our method can extract or detect recent RATs in APT attacks or not is uncertain. In this paper, we investigate recent malicious document files which were used between 2013 and 2015 in APT attacks, and reveal the recent trend of the encoding methods with this tool. Moreover, we compare the success rates with the other detection methods such as antivirus programs with the latest virus definitions. Even if a malicious document file which contains executable files does not perform malicious activities in dynamic analysis, this tool extracts the executable files automatically without executing the malicious document file. In addition, this tool hardly give false positives. We can use this tool to detect unknown malicious document files in dynamic analysis or at mail gateways.
Social sharing through interpersonal media: Patterns and effects on emotional well-being

Social sharing is the act of discussing significant emotional events with others. Using a daily diary methodology, this study investigated (1) patterns of media use for social sharing; and (2) effects of mediated social sharing on sharers’ emotions. Results show that easily accessible and non-intrusive media (i.e., texting, Twitter) were more likely to be used for sharing positive than negative events, and intrusive and rich media (i.e., phone calling) were more likely to be used for sharing negative than positive events. Highly intense positive events were more likely to be shared via Twitter than low-intensity positive events, and highly intense negative events were more likely to be shared face-to-face than low-intensity negative events. Regardless of the medium used, people experienced increased positive affect after sharing positive events, and increased negative affect after sharing negative events. The results extend the social sharing framework, and advance the media use and effects literature.
Comparison of supervised learning methods for spike time coding in spiking neural networks

In this review we focus our attention on the supervised learning methods for spike time coding in Spiking Neural Networks (SNN). This study is motivated by the recent experimental results on information coding in the biological neural systems which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We pose a fundamental question, what paradigms of neural temporal coding can be implemented with the recent learning methods. In order to answer this question we discuss various approaches to the considered learning task. We shortly describe the particular learning algorithms and report the results of experiments. Finally we discuss properties, assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature.
Two supervised learning approaches for name disambiguation in author citations

Due to name abbreviations, identical names, name misspellings, and pseudonyms inpublications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper investigates two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses Support Vector Machines(SVMs) and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: co-author names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the web, mainly publication lists from homepages, the other collected from the DBLPcitation databases.
Information Extraction: Techniques and Challenges

This volume takes a broad view of information extraction as any method for ltering information from large volumes of text. This includes the retrieval of documents from collections and the tagging of particular terms in text. In this paper we shall use a narrower de nition: the identi cation of instances of a particular class of events or relationships in a natural language text, and the extraction of the relevant arguments of the event or relationship. Information extraction therefore involves the creation of a structured representation (such as a data base) of selected information drawn from the text. The idea of reducing the information in a document to a tabular structure is not new. Its feasibility for sublanguage texts was suggested by Zellig Harris in the 1950's, and an early implementation for medical texts was done at New York University by Naomi Sager[20]. However, the speci c notion of information extraction described here has received wide currency over the last decade through the series of Message Understanding Conferences [1, 2, 3, 4, 14]. We shall discuss these Conferences in more detail a bit later, and shall use simpli ed versions of extraction tasks from these Conferences as examples throughout this paper.  1 shows a simpli ed example from one of the earlier MUC's, involving terrorist events (MUC-3) . For each terrorist event, the system had to determine the type of attack (bombing, arson, etc.), the date, location, perpetrator (if stated), targets, and e ects on targets. Other examples of extraction tasks are international joint ventures (where the arguments included the partners, the new venture, its product or service, etc.) and executive succession (indicating who was hired or red by which company for which position). Information extraction is a more limited task than \full text understanding". In full text understanding, we aspire to represent in a explicit fashion all the information in a text. In contrast, in information extraction we delimit in advance, as part of the speci cation of the task, the semantic range of the output: the relations we will represent, and the allowable llers in each slot of a relation.
Temporal RDF

The Resource Description Framework (RDF) is a metadata model and language recommended by the W3C. This paper presents a framework to incorporate temporal reasoning into RDF, yielding temporal RDF graphs. We present a semantics for temporal RDF graphs, a syntax to incorporate temporality into standard RDF graphs, an inference system for temporal RDF graphs, complexity bounds showing that entailment in temporal RDF graphs does not yield extra asymptotic complexity with respect to standard RDF graphs and sketch a temporal query language for RDF.
Business artifacts: An approach to operational specification

Any business, no matter what physical goods or services it produces, relies on business records. It needs to record details of what it produces in terms of concrete information. Business artifacts are a mechanism to record this information in units that are concrete, identifiable, self-describing, and indivisible. We developed the concept of artifacts, or semantic objects, in the context of a technique for constructing formal yet intuitive operational descriptions of a business. This technique, called OpS (Operational Specification), was developed over the course of many business-transformation and business-process-integration engagements for use in IBM’s internal processes as well as for use with customers. Business artifacts (or business records) are the basis for the factorization of knowledge that enables the OpS technique. In this paper we present a comprehensive discussion of business artifacts—what they are, how they are represented, and the role they play in operational business modeling. Unlike the more familiar and popular concept of business objects, business artifacts are pure instances rather than instances of a taxonomy of types. Consequently, the key operation on business artifacts is recognition rather than classification.
Comparing Blockchain and Cloud Services for Business Process Execution

Blockchain is of rising importance as a technology for engineering applications in cross-organizational settings, avoiding reliance on central trusted third-parties. The use of blockchain, instead of traditional databases or services, is an architectural choice in the development of a software system. The costs of execution and storage are important non-functional qualities, but as yet very little has been done to study them for blockchain-based systems. We investigate the cost of using blockchain using business process execution as a lens. Specifically, we compare the cost for computation and storage of business process execution on blockchain vs. a popular cloud service. First, we capture the cost models for both alternatives. Second, we implemented and measured the cost of business process execution on blockchain and cloud services for an example business process model from the literature. We observe two orders of magnitude difference in this cost.