nils-herrmann/clean_scirepeval huggingface dataset:
2 Stylized Examples of Shading ’ s Effect on CDA Equilibrium Efficiency

Strategic shading of bids is typically viewed as a factor degrading the efficiency of auction outcomes. In continuous double auctions (CDAs), however, strategic bid-shading can actually improve allocative efficiency, as it counteracts the inherent inefficiency of CDAs induced by vagaries in order arrival sequence. This has been demonstrated most clearly in a standard CDA model by Zhan and Friedman (2007). Through a systematic simulation-based game-theoretic study, we show that the phenomenon also exhibits in symmetric mixed-strategy equilibria of Zhan and Friedman’s original game, and in equilibria of a richer dynamic model, more representative of financial markets. We find that the presence and degree of the effect can vary significantly, with the greatest benefit of strategic shading in markets with many traders or in the presence of factors such as adverse selection and limited trading opportunity.
Evaluating the influence of YouTube advertising for attraction of young customers

Nowadays, we have been faced with an increasing number of people who are spending tremendous amounts of time all around the world on YouTube. To date, the factors that persuade customers to accept YouTube advertising as an advertising medium are not yet fully understood. The present paper identified four dimensions towards YouTube advertising (i.e., entertainment, Informativeness, Customization and irritation) which may be affected on advertising value as well as brand awareness, and accordingly on purchase intention of consumers. The conceptual model hypothesizes that ad value strategies are positively associated with brand awareness, which in turn influence perceived usefulness of You Tube and continued purchase behavior. For this study, data were collected from students studying at the Sapienza University of Rome. In total, 315 usable questionnaires were chosen in order to analysis of data for the variables. The results show that entertainment, informativeness and customization are the strongest positive drivers, while irritation is negatively related to YouTube advertising. On the other hand, advertising value through YouTube affects both brand awareness and purchase intention of con-
Design Methodology for Highly Reliable, High Performance ReRAM and 3-Bit/Cell MLC NAND Flash Solid-State Storage

This paper proposes design methodology for highly reliable, high performance ReRAM and 3-bit/cell multi-level cell (MLC) NAND flash solid-state storage. Six techniques, calibrated RRef (CR), flexible RRef (FR), adaptive asymmetric coding (AAC), verify trials reduction (VTR), bits/cell optimization (BCO), and balanced RAID-5/6 are proposed. CR, FR, AAC, and VTR are for ReRAM. CR and FR change the read-reference resistance (RRef) to reduce the BER. AAC first increases the population of Set and then Reset. The BER reduction with FR and AAC is 69 and 78% with 60 and 75% asymmetry, respectively. In VTR, by changing the number of acceptable bit-errors, the total Reset time is reduced by 97% at maximum with small ECC calculation overhead. The reliability of 3-bit/cell MLC NAND flash memory is improved by BCO and balanced RAID-5/6. BCO reallocates 3-bit/cell MLC to 2-bit/cell MLC and single-level cell (SLC) and the write/erase cycle increases by over 22-times. Balanced RAID-5/6 evenly allocates upper/middle/lower pages to a stripe to reduce the RAID failure rate by 98%.
Multi camera image tracking

This paper presents a method for multi camera image tracking in the context of image surveillance. The approach differs from most methods in that we exploit multiple camera views to resolve object occlusion. Moving objects are detected by using background subtraction. Viewpoint correspondence between the detected objects is then established by using the ground plane homography constraint. The Kalman Filter is then used to facilitate the tracking of objects in 3D. Tracking in 3D offers benefits in terms of allowing multiple views to be combined to generate a network field of view (FOV), i.e. the FOV of all the cameras combined. In addition, tracking in 3D allows us to use the Linear Kalman Filter, which is less cumbersome to implement than the Extended Kalman Filter (EKF).
Requirements of Quality of Service in Wireless Sensor Network

Sensor networks are distributed networks made up of small sensing devices equipped with processors, memory, and short-range wireless communication. They differ from traditional computer networks in that they have resource constraints, unbalanced mixture traffic, data redundancy, network dynamics, and energy balance. Work within wireless sensor networks (WSNs) Quality of service (QoS) has been isolated and specific either on certain functional layers or application scenarios. However the area of sensor network quality of service (QoS) remains largely open. In this paper we define WSNs QoS requirements within a WSNs application, and then analyzing Issues for QoS Monitoring.
Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek

Online transportation, such as GoJek, is preferred by many users especially in areas where public transport is difficult to access or when there is a traffic jam. Twitter is a popular social networking site in Indonesia that can generate information from users' tweets. In this study, we proposed a system that detect public sentiments based on Twitter post about online transportation services especially GoJek. The system will collect tweets, analyze the tweets sentiments using SVM, and group them into positive and negative sentiment.
Clustering versus faceted categories for information exploration

Information seekers often express a desire for a user interface that organizes search results into meaningful groups, in order to help make sense of the results, and to help decide what to do next. A longitudinal study in which participants were provided with the ability to group search results found they changed their search habits in response to having the grouping mechanism available . There are many open research questions about how to generate useful groupings and how to design interfaces to support exploration using grouping. Currently two methods are quite popular: clustering and faceted categorization. Here, I describe both approaches and summarize their advantages and disadvantages based on the results of usability studies. Clustering refers to the grouping of items according to some measure of similarity. In document clustering, similarity is typically computed using associations and commonalities among features, where features are typically words and phrases . One of the better implementations of clustering of Web results can be found at Clusty.com. The greatest advantage of clustering is that it is fully automatable and can be easily applied to any text collection. Clustering can also reveal interesting and potentially unexpected or new trends in a group of documents. A query on “New Orleans”’ run on Clusty.com on Sept. 16, 2005 (shortly after the devastation wreaked by Hurricane Katrina), revealed a top-ranked cluster titled Hurricane, followed by the more standard groupings of Hotels, Louisiana, University, and Mardi Gras. Clustering can be useful for clarifying and sharpening a vague query, by showing users the dominant themes of the returned results . Clustering also works well for disambiguating ambiguous queries; particularly acronyms. For example, ACL can stand for Anterior Cruciate Ligament, Association for Computational Linguistics, Atlantic Coast Line Railroad, among others. Unfortunately, because clustering algorithms are imperfect, they do not neatly group all occurrences of each acronym into one cluster, nor do they allow users to issue follow-up queries that only return documents from the intended sense (for example, “ACL meeting” will return meetings for multiple senses of the term). By Marti A. Hearst
Artificial Life and Real Robots

The first part of this paper explores the general issues in using Artificial Life techniques to program actual mobile robots. In particular it explores the difficulties inherent in transferring programs evolved in a simulated environment to run on an actual robot. It examines the dual evolution of organism morphology and nervous systems in biology. It proposes techniques to capture some of the search space pruning that dual evolution offers in the domain of robot programming. It explores the relationship between robot morphology and program structure, and techniques for capturing regularities across this mapping. The second part of the paper is much more specific. It proposes techniques which could allow realistic explorations concerning the evolution of programs to control physically embodied mobile robots. In particular we introduce a new abstraction for behavior-based robot programming which is specially tailored to be used with genetic programming techniques. To compete with hand coding techniques it will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain. Considerable extensions to previously reported approaches to genetic programming are necessary in order to achieve this goal.
High performance CAN/FlexRay gateway design for in-vehicle network

A gateway in in-vehicle network (IVN) is an indispensable component as a conversion device between different communication protocols. Due to meet more complex network for IVN, how to improving the performance of gateway is more important. In this paper, a high efficient of CAN/FlexRay gateway using hardware/software (HW/SW) co-design method is proposed which is implemented with Xilinx FPGA system. The CAN controller and FlexRay controller are designed by SW and HW, respectively. The interface of HW and SW are used AXI-bus. The convert module is evaluated by a cost function. In addition, a transceiver PCB for connecting between the proposed gateway and external CAN/FlexRay-based ECUs is implemented. Experimental results show that the proposed gateway system can reduce at most 94.7% on execution time with related works which is more suited for IVN.
Simplified markov random fields for efficient semantic labeling of 3D point clouds

In this paper, we focus on 3D point cloud classification by assigning semantic labels to each point in the scene. We propose to use simplified Markov networks to model the contextual relations between points, where the node potentials are calculated from point-wise classification results using off-the-shelf classifiers, such as Random Forest and Support Vector Machines, and the edge potentials are set by physical distance between points. Our experimental results show that this approach yields comparable if not better results with improved speed compared with state-of-the-art methods. We also propose a novel robust neighborhood filtering method to exclude outliers in the neighborhood of points, in order to reduce noise in local geometric statistics when extracting features and also to reduce number of false edges when constructing Markov networks. We show that applying robust neighborhood filtering improves the results when classifying point clouds with more object categories.
Cerebral bases of subliminal speech priming

While the neural correlates of unconscious perception and subliminal priming have been largely studied for visual stimuli, little is known about their counterparts in the auditory modality. Here we used a subliminal speech priming method in combination with fMRI to investigate which regions of the cerebral network for language can respond in the absence of awareness. Participants performed a lexical decision task on target items preceded by subliminal primes, which were either phonetically identical or different from the target. Moreover, the prime and target could be spoken by the same speaker or by two different speakers. Word repetition reduced the activity in the insula and in the left superior temporal gyrus. Although the priming effect on reaction times was independent of voice manipulation, neural repetition suppression was modulated by speaker change in the superior temporal gyrus while the insula showed voice-independent priming. These results provide neuroimaging evidence of subliminal priming for spoken words and inform us on the first, unconscious stages of speech perception.
MOA: Massive Online Analysis

MassiveOnline Analysis (MOA) is a software environment for implementing al orithms and running experiments for online learning from evolving data str eams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Na ı̈ve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.
Interpretation of Overtracing Freehand Sketching for Geometric Shapes

This paper presents a novel method for interpreting overtracing freehand sketch. The overtracing strokes are interpreted as sketch content and are used to generate 2D geometric primitives. The approach consists of four stages: stroke classification, strokes grouping and fitting, 2D tidy-up with endpoint clustering and parallelism correction, and in-context interpretation. Strokes are first classified into lines and curves by a linearity test. It is followed by an innovative strokes grouping process that handles lines and curves separately. The grouped strokes are fitted with 2D geometry and further tidied-up with endpoint clustering and parallelism correction. Finally, the in-context interpretation is applied to detect incorrect stroke interpretation based on geometry constraints and to suggest a most plausible correction based on the overall sketch context. The interpretation ensures sketched strokes to be interpreted into meaningful output. The interface overcomes the limitation where only a single line drawing can be sketched out as in most existing sketching programs, meanwhile is more intuitive to the user.
Tracking in cell and developmental biology.

The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.
Abacus: A Processor Family for Education

We present the Abacus processor family and its compiler framework for the MiniC language that we have developed for teaching processor architectures. Besides typical RISC instructions, Abacus also offers instructions for vector processing and thread synchronization, but it is still small enough to be discussed completely in a class. With reasonable effort, students can therefore modify given implementations of micro-architectures and code generators to deepen their understanding of the theoretical concepts. Moreover, using benchmark examples, they can explore the quantitative aspects of their optimizations. In contrast to commercial and other educational processors, we provide many micro-architectures that are based on a pure concept only rather than on a combination of concepts, and we provide code generators which contain the core ideas of some architectures.
R 3 -Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos.

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudN0.
ONLINE MARKETING AND CONSUMER PURCHASE BEHAVIOUR : A STUDY OF NIGERIAN FIRMS

Businesses are spending more on and partaking in online marketing than ever before, the world over. Understanding the consumer behavioural factors that influence emarketing effectiveness is crucial. While some researchers have addressed this issue, few studies draw their conclusions focusing on the customers’ angle. More also is the fact that the study of the developing countries in this regards have been lesser than expected. The work seeks to validate empirically, while analyzing Nigeria firm engaging in internet marketing, the impact of the same on consumers’ purchase behaviour. We seek to understand to what extent the functionality of the infrastructure of the internet and the internet security issues impact consumers’ decision to eventually purchase. The survey research used a structured questionnaire to elicit data from selected firms in Lagos State, Nigeria. A reliable Cronbach’s Alpha was used to determine the reliability of the questionnaire. The data was analyzed using simple regression while the hypotheses drawn were tested. The findings show that online marketing has impacted consumer purchase decisions in Nigeria firms. There is a significant relationship between consumer purchase decisions and infrastructure of the internet in Nigeria. There also exists relationship between internet security and consumer purchase behaviour. These simply imply that one variable influences the other.
Security in Container-Based Virtualization through vTPM

Cloud computing is a wide-spread technology that enables the enterprises to provide services to their customers with a lower cost, higher performance, better availability and scalability. However, privacy and security in cloud computing has always been a major challenge to service providers and a concern to its users. Trusted computing has led its way in securing the cloud computing and virtualized environment, during the past decades.
 In this paper, first we study virtualized trusted platform modules and integration of vTPM in hypervisor-based virtualization. Then we propose two architectural solutions for integrating the vTPM in container-based virtualization model.
Tendon involvement in the feet of patients with gout: a dual-energy CT study.

To examine the frequency and patterns of monosodium urate (MSU) crystal deposition in tendons and ligaments in patients with gout using dual-energy CT (DECT).



Ninety-two patients with tophaceous gout had DECT scanning of both feet. Two readers scored the DECT scans for MSU crystal deposition at 20 tendon/ligament sites and 42 bone sites (total 1840 tendon/ligament sites and 3864 bone sites).



MSU crystal deposition was observed by both readers in 199/1840 (10.8%) tendon/ligament sites and in 399/3864 (10.3%) bone sites (p=0.60). The Achilles tendon was the most commonly involved tendon/ligament site (39.1% of all Achilles tendons), followed by the peroneal tendons (18.1%). Tibialis anterior and the extensor tendons were involved less commonly (7.6-10.3%), and the other flexor tendons, plantar fascia and deltoid ligaments were rarely involved (<5%) (p<0.0001 between sites). Involvement of the enthesis alone was more common in the Achilles tendon (OR (95% CI) 74.5 (4.4 to 1264), p<0.0001), as was any involvement of the enthesis (OR (95% CI) 6.8 (3.6 to 13.0), p<0.0001).



Tendons are commonly affected by MSU crystal deposition in patients with tophaceous gout. The patterns of MSU crystal deposition suggest that biomechanical strain or other local factors may contribute to deposition of MSU crystals.
Fragmentation Design for Efficient Query Execution over Sensitive Distributed Databases

The balance between privacy and utility is a classical problem with an increasing impact on the design of modern information systems. On the one side it is crucial to ensure that sensitive information is properly protected; on the other side, the impact of protection on the workload must be limited as query efficiency and system performance remain a primary requirement. We address this privacy/efficiency balance proposing an approach that, starting from a flexible definition of confidentiality constraints on a relational schema, applies encryption on information in a parsimonious way and mostly relies on fragmentation to protect sensitive associations among attributes. Fragmentation is guided by workload considerations so to minimize the cost of executing queries over fragments. We discuss the minimization problem when fragmenting data and provide a heuristic approach to its solution.