nils-herrmann/clean_scirepeval huggingface dataset:
Cyber/Physical Co-verification for Developing Reliable Cyber-physical Systems

Cyber-Physical Systems (CPS) tightly integrate cyber and physical components and transcend discrete and continuous domains. It is greatly desired that the physical components being controlled and the software implementation of control algorithms can be verified together. We present an efficient approach to reachability analysis of Hybrid Automata Pushdown System (HAPS) models for cyber/physical co-verification of CPS. We have realized this approach and applied it to real-world control systems. The evaluation has shown that HAPS is an effective model for co-verification of CPS and our approach has major potential in verifying system-level properties of CPS, therefore improving the reliability of CPS.
The Productivity Paradox of Information Technology

service sector, seems to have stagnated . Given the enormous promise of IT to usher in "the biggest technological revolution men have known" [29], disi l lusionment and even frustrat ion with the technology is increasingly evident in statements like "No, computers do not boost productivity, at least not most of the time" . The increased interest in the "productivity paradox," as it has become known, has engendered a significant amount of research, but thus far, this has only deepened the mystery. The Nobel Laureate economist Robert Solow has cleverly characterized the results: "we see computers everywhere except in the productivity statistics." Al though similar conclusions are repeated by an alarming number of researchers in this area, we must be careful not to over in terpre t these findings; a shortfall of evidence is not necessarily evidence of a shortfall. Fur thermore , recent work  suggests that the re turn to I T spending may in fact be much higher than previously estimated. This article summarizes what we know and do not know, distinguishes the central issues from diversions, and clarifies the questions that can be profitably explored in future research. After reviewing and assessing the research to date, it appears that the shortfall of IT productivity is as much due to deficiencies in our measurement and methodological tool kit as to mismanagement by developers and users of IT. The research considered in this article reflects the results of a computer ized l i terature search of 30 of the leading journals in both information systems (IS) and economics (see sidebar for a comprehensive list of l i terature searched), as well as discussions with leading researchers in the field. In what follows, the key findings and essential research references are highlighted and discussed.
Serotonin transporter occupancy with TCAs and SSRIs: a PET study in patients with major depressive disorder

The aim of the present clinical positron emission tomography study was to examine if the 5-HTT is a common target, both for tricyclic antidepressants (TCAs) and selective serotonin reuptake inhibitors (SSRIs). Serotonin transporter (5-HTT) occupancy was estimated during treatment with TCA, SSRI and mirtazapine in 20 patients in remission from depression. The patients were recruited from out-patient units and deemed as responders to antidepressive treatment. The radioligand [¹¹C]MADAM was used to determine the 5-HTT binding potential. The mean 5-HTT occupancy was 67% (range 28-86%). There was no significant difference in 5-HTT occupancy between TCA (n=5) and SSRI (n=14). 5-HTT affinity correlated with the recommended clinical dose. Mirtazapine did not occupy the serotonin transporter. The results support that TCAs and SSRIs have a shared mechanism of action by inhibition of 5-HTT.
An Evaluation of Unified Memory Technology on NVIDIA GPUs

Unified Memory is an emerging technology which is supported by CUDA 6.X. Before CUDA 6.X, the existing CUDA programming model relies on programmers to explicitly manage data between CPU and GPU and hence increases programming complexity. CUDA 6.X provides a new technology which is called as Unified Memory to provide a new programming model that defines CPU and GPU memory space as a single coherent memory (imaging as a same common address space). The system manages data access between CPU and GPU without explicit memory copy functions. This paper is to evaluate the Unified Memory technology through different applications on different GPUs to show the users how to use the Unified Memory technology of CUDA 6.X efficiently. The applications include Diffusion3D Benchmark, Parboil Benchmark Suite, and Matrix Multiplication from the CUDA SDK Samples. We changed those applications to corresponding Unified Memory versions and compare those with the original ones. We selected the NVIDIA Keller K40 and the Jetson TK1, which can represent the latest GPUs with Keller architecture and the first mobile platform of NVIDIA series with Keller GPU. This paper shows that Unified Memory versions cause 10% performance loss on average. Furthermore, we used the NVIDIA Visual Profiler to dig the reason of the performance loss by the Unified Memory technology.
Preference based evaluation measures for novelty and diversity

Novel and diverse document ranking is an effective strategy that involves reducing redundancy in a ranked list to maximize the amount of novel and relevant information available to users. Evaluation for novelty and diversity typically involves an assessor judging each document for relevance against a set of pre-identified subtopics, which may be disambiguations of the query, facets of an information need, or nuggets of information. Alternately, when expressing a \emph{preference} for document A or document B, users may implicitly take subtopics into account, but may also take into account other factors such as recency, readability, length, and so on, each of which may have more or less importance depending on user. A \emph{user profile} contains information about the extent to which each factor, including subtopic relevance, plays a role in the user's preference for one document over another. A preference-based evaluation can then take this user profile information into account to better model utility to the space of users.
 In this work, we propose an evaluation framework that not only can consider implicit factors but also handles differences in user preference due to varying underlying information need. Our proposed framework is based on the idea that a user scanning a ranked list from top to bottom and stopping at rank $k$ gains some utility from every document that is relevant their information need. Thus, we model the expected utility of a ranked list by estimating the utility of a document at a given rank using preference judgments and define evaluation measures based on the same. We validate our framework by comparing it to existing measures such as $\alpha$-nDCG, ERR-IA, and subtopic recall that require explicit subtopic judgments We show that our proposed measures correlate well with existing measures while having the potential to capture various other factors when real data is used. We also show that the proposed measures can easily handle relevance assessments against multiple user profiles, and that they are robust to noisy and incomplete judgments.
Understanding Neural Networks via Rule Extraction

Although backpropagation neural networks generally predict better than decision trees do for pattern classi cation problems they are of ten regarded as black boxes i e their predic tions are not as interpretable as those of deci sion trees This paper argues that this is be cause there has been no proper technique that enables us to do so With an algorithm that can extract rules by drawing parallels with those of decision trees we show that the predic tions of a network can be explained via rules ex tracted from it thereby the network can be un derstood Experiments demonstrate that rules extracted from neural networks are compara ble with those of decision trees in terms of pre dictive accuracy number of rules and average number of conditions for a rule they preserve high predictive accuracy of original networks
C-Phrase: A system for building robust natural language interfaces to databases

This article presents C-Phrase, a natural language interface system that can be configured by normal, non-specialized, web-based technical teams. C-Phrase models queries in an extended version of Codd’s tuple calculus and uses synchronous context free grammars with lambda-expressions to represent semantic grammars. Given an arbitrary relational database, authors rapidly build an NLI using what we term the name-tailor-define protocol. We present a small study demonstrating the effectiveness of this approach for the GEO corpus and we introduce the evaluation metric of willingness that complements the standard metrics of precision and recall. However our true evaluation comes as we open-source C-Phrase.
A Distributed Reinforcement Learning Scheme for Network Routing

In this paper we describe a self-adjusting algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used to keep accurate statistics at each node on which routing policies lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, this learning approach proves superior to a nonadaptive algorithm based on precomputed shortest paths. The authors would like to thank for their support the Bellcore Cognitive Science Research Group, the National Defense Science and Engineering Graduate fellowship program, and National Science Foundation Grant IRI-9214873. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the o cial policies, either expressed or implied, of Bellcore, the National Science Foundation or the U.S. Government.
Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions

The main objective of this research is to identify, categorize, and analyze barriers perceived by physicians to the adoption of Electronic Medical Records (EMRs) in order to provide implementers with beneficial intervention options.



A systematic literature review, based on research papers from 1998 to 2009, concerning barriers to the acceptance of EMRs by physicians was conducted. Four databases, "Science", "EBSCO", "PubMed" and "The Cochrane Library", were used in the literature search. Studies were included in the analysis if they reported on physicians' perceived barriers to implementing and using electronic medical records. Electronic medical records are defined as computerized medical information systems that collect, store and display patient information.



The study includes twenty-two articles that have considered barriers to EMR as perceived by physicians. Eight main categories of barriers, including a total of 31 sub-categories, were identified. These eight categories are: A) Financial, B) Technical, C) Time, D) Psychological, E) Social, F) Legal, G) Organizational, and H) Change Process. All these categories are interrelated with each other. In particular, Categories G (Organizational) and H (Change Process) seem to be mediating factors on other barriers. By adopting a change management perspective, we develop some barrier-related interventions that could overcome the identified barriers.



Despite the positive effects of EMR usage in medical practices, the adoption rate of such systems is still low and meets resistance from physicians. This systematic review reveals that physicians may face a range of barriers when they approach EMR implementation. We conclude that the process of EMR implementation should be treated as a change project, and led by implementers or change managers, in medical practices. The quality of change management plays an important role in the success of EMR implementation. The barriers and suggested interventions highlighted in this study are intended to act as a reference for implementers of Electronic Medical Records. A careful diagnosis of the specific situation is required before relevant interventions can be determined.
Dynamic Facial Expression Recognition Based on Convolutional Neural Networks with Dense Connections

Facial expression recognition (FER) is a challenging problem with important applications. Applying deep learning techniques for dynamic FER is advantageous in terms of automatically generating discriminative expression features. However, inadequate training data in small expression databases aggravates overfitting and hinders the performance of deep networks. Recent studies have shown that dense connectivity in convolutional neural networks can encourage feature sharing and alleviate overfitting when training with small datasets. Still, traditional dense structures are too deep for FER with insufficient training samples. In this paper, we propose a relatively shallow CNN structure with densely connected short paths for FER. Instead of using transition layers to down-sample feature maps between dense blocks, we introduce dense connectivity across pooling to enforce feature sharing in the shallow CNN structure. Extensive experiments show that our method achieves competitive performance on benchmark datasets CK+ and Oulu-CASIA.
Faster and Smaller N-Gram Language Models

N -gram language models are a major resource bottleneck in machine translation. In this paper, we present several language model implementations that are both highly compact and fast to query. Our fastest implementation is as fast as the widely used SRILM while requiring only 25% of the storage. Our most compact representation can store all 4 billion n-grams and associated counts for the Google n-gram corpus in 23 bits per n-gram, the most compact lossless representation to date, and even more compact than recent lossy compression techniques. We also discuss techniques for improving query speed during decoding, including a simple but novel language model caching technique that improves the query speed of our language models (and SRILM) by up to 300%.
Turret: A Platform for Automated Attack Finding in Unmodified Distributed System Implementations

Security and performance are critical goals for distributed systems. The increased design complexity, incomplete expertise of developers, and limited functionality of existing testing tools often result in bugs and vulnerabilities that prevent implementations from achieving their design goals in practice. Many of these bugs, vulnerabilities, and misconfigurations manifest after the code has already been deployed making the debugging process difficult and costly. In this paper, we present Turret, a platform for automatically finding performance attacks in unmodified implementations of distributed systems. Turret does not require the user to provide any information about vulnerabilities and runs the implementation in the same operating system setup as the deployment, with an emulated network. Turret uses a new attack finding algorithm and several optimizations that allow it to find attacks in a matter of minutes. We ran Turret on 5 different distributed system implementations specifically designed to tolerate insider attacks, and found 30 performance attacks, 24 of which were not previously reported to the best of our knowledge.
A Three-Dimensional Edge Operator

Modern scanning techniques, such as computed tomography, have begun to produce true three-dimensional imagery of internal structures. The first stage in finding structure in these images, like that for standard two-dimensional images, is to evaluate a local edge operator over the image. If an edge segment in two dimensions is modeled as an oriented unit line segment that separates unit squares (i.e., pixels) of different intensities, then a three-dimensional edge segment is an oriented unit plane that separates unit volumes (i.e., voxels) of different intensities. In this correspondence we derive an operator that finds the best oriented plane at each point in the image. This operator, which is based directly on the 3-D problem, complements other approaches that are either interactive or heuristic extensions of 2-D techniques.
Apoidea: A Decentralized Peer-to-Peer Architecture for Crawling the World Wide Web

1 This paper describes a decentralized peer-to-peer model for building a Web crawler. Most of the current systems use a centralized client-server model, in which the crawl is done by one or more tightly coupled machines, but the distribution of the crawling jobs and the collection of crawled results are managed in a centralized system using a centralized URL repository. Centralized solutions are known to have problems like link congestion, being a single point of failure, and expensive administration. It requires both horizontal and vertical scalability solutions to manage Network File Systems (NFS) and load balancing DNS and HTTP requests. In this paper, we present an architecture of a completely distributed and decentralized Peer-to-Peer (P2P) crawler called Apoidea, which is self-managing and uses geographical proximity of the web resources to the peers for a better and faster crawl. We use Distributed Hash Table (DHT) based protocols to perform the critical URL-duplicate and content-duplicate tests.
QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees

Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effective for ranking query results returned by Web search engines, a scenario where quality and efficiency requirements are very demanding. Unfortunately, the computational cost of these ranking models is high. Thus, several works already proposed solutions aiming at improving the efficiency of the scoring process by dealing with features and peculiarities of modern CPUs and memory hierarchies. In this paper, we present QuickScorer, a new algorithm that adopts a novel bitvector representation of the tree-based ranking model, and performs an interleaved traversal of the ensemble by means of simple logical bitwise operations. The performance of the proposed algorithm are unprecedented, due to its cache-aware approach, both in terms of data layout and access patterns, and to a control flow that entails very low branch mis-prediction rates. The experiments on real Learning-to-Rank datasets show that QuickScorer is able to achieve speedups over the best state-of-the-art baseline ranging from 2x to 6.5x.
Transductive Face Sketch-Photo Synthesis

Face sketch-photo synthesis plays a critical role in many applications, such as law enforcement and digital entertainment. Recently, many face sketch-photo synthesis methods have been proposed under the framework of inductive learning, and these have obtained promising performance. However, these inductive learning-based face sketch-photo synthesis methods may result in high losses for test samples, because inductive learning minimizes the empirical loss for training samples. This paper presents a novel transductive face sketch-photo synthesis method that incorporates the given test samples into the learning process and optimizes the performance on these test samples. In particular, it defines a probabilistic model to optimize both the reconstruction fidelity of the input photo (sketch) and the synthesis fidelity of the target output sketch (photo), and efficiently optimizes this probabilistic model by alternating optimization. The proposed transductive method significantly reduces the expected high loss and improves the synthesis performance for test samples. Experimental results on the Chinese University of Hong Kong face sketch data set demonstrate the effectiveness of the proposed method by comparing it with representative inductive learning-based face sketch-photo synthesis methods.
Information Security and Privacy: Rethinking Governance Models

Concerns about information security and privacy continue to make headlines in the media and rate as serious issues in business and public surveys. Conventional thinking suggests that enhancements to governance models and business practices improve the performance of individual businesses. However, is this enough? Good governance is based on clear rights and responsibilities, and this panel will address the contention that such clarity is lacking in the treatment of personal information. As a result, certain types of business innovation may be constrained and good practices may remain tick box exercises, disconnected from wider business objectives. More radical thinking would go beyond incremental improvements to the status quo and recognize the profound challenges to privacy and information security created by digital technology. This panel will bring together a range of research disciplines and senior business representatives to critique current practice and develop a future research agenda.
Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning

Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.
Does working memory training work? The promise and challenges of enhancing cognition by training working memory.

A growing body of literature shows that one's working memory (WM) capacity can be expanded through targeted training. Given the established relationship between WM and higher cognition, these successful training studies have led to speculation that WM training may yield broad cognitive benefits. This review considers the current state of the emerging WM training literature, and details both its successes and limitations. We identify two distinct approaches to WM training, strategy training and core training, and highlight both the theoretical and practical motivations that guide each approach. Training-related increases in WM capacity have been successfully demonstrated across a wide range of subject populations, but different training techniques seem to produce differential impacts upon the broader landscape of cognitive abilities. In particular, core WM training studies seem to produce more far-reaching transfer effects, likely because they target domain-general mechanisms of WM. The results of individual studies encourage optimism regarding the value of WM training as a tool for general cognitive enhancement. However, we discuss several limitations that should be addressed before the field endorses the value of this approach.