nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating operating system vulnerability to memory errors

Reliability is of great concern to the scalability of extreme-scale systems. Of particular concern are soft errors in main memory, which are a leading cause of failures on current systems and are predicted to be the leading cause on future systems. While great effort has gone into designing algorithms and applications that can continue to make progress in the presence of these errors without restarting, the most critical software running on a node, the operating system (OS), is currently left relatively unprotected. OS resiliency is of particular importance because, though this software typically represents a small footprint of a compute node's physical memory, recent studies show more memory errors in this region of memory than the remainder of the system. In this paper, we investigate the soft error vulnerability of two operating systems used in current and future high-performance computing systems: Kitten, the lightweight kernel developed at Sandia National Laboratories, and CLE, a high-performance Linux-based operating system developed by Cray. For each of these platforms, we outline major structures and subsystems that are vulnerable to soft errors and describe methods that could be used to reconstruct damaged state. Our results show the Kitten lightweight operating system may be an easier target to harden against memory errors due to its smaller memory footprint, largely deterministic state, and simpler system structure.
Participatory Visualization with Wordle

We discuss the design and usage of ldquoWordle,rdquo a Web-based tool for visualizing text. Wordle creates tag-cloud-like displays that give careful attention to typography, color, and composition. We describe the algorithms used to balance various aesthetic criteria and create the distinctive Wordle layouts. We then present the results of a study of Wordle usage, based both on spontaneous behaviour observed in the wild, and on a large-scale survey of Wordle users. The results suggest that Wordles have become a kind of medium of expression, and that a ldquoparticipatory culturerdquo has arisen around them.
Modelling and testing consumer trust dimensions in e-commerce

Prior research has found trust to play a significant role in shaping purchase intentions of a consumer. However there has been limited research where consumer trust dimensions have been empirically defined and tested. In this paper we empirically test a path model such that Internet vendors would have adequate solutions to increase trust. The path model presented in this paper measures the three main dimensions of trust, i.e. competence, integrity, and benevolence. And assesses the influence of overall trust of consumers. The paper also analyses how various sources of trust, i.e. consumer characteristics, firm characteristic, website infrastructure and interactions with consumers, influence dimensions of trust. The model is tested using 365 valid responses. Findings suggest that consumers with high overall trust demonstrate a higher intention to purchase online.
Neurocognitive models of aggression, the antisocial personality disorders, and psychopathy.

This paper considers neurocognitive models of aggression and relates them to explanations of the antisocial personality disorders. Two forms of aggression are distinguished: reactive aggression elicited in response to frustration/threat and goal directed, instrumental aggression. It is argued that different forms of neurocognitive model are necessary to explain the emergence of these different forms of aggression. Impairments in executive emotional systems (the somatic marker system or the social response reversal system) are related to reactive aggression shown by patients with "acquired sociopathy" due to orbitofrontal cortex lesions. Impairment in the capacity to form associations between emotional unconditioned stimuli, particularly distress cues, and conditioned stimuli (the violence inhibition mechanism model) is related to the instrumental aggression shown by persons with developmental psychopathy.
Efficient Progressive Sampling

Having access to massive amounts of data does not necessarily imply that induction algorithms must use them all. Samples often provide the same accuracy with far less computational cost. However, the correct sample size rarely is obvious. We analyze methods for progressive samplingusing progressively larger samples as long as model accuracy improves. We explore several notions of efficient progressive sampling. We analyze efficiency relative to induction with all instances; we show that a simple, geometric sampling schedule is asymptotically optimal, and we describe how best to take into account prior expectations of accuracy convergence. We then describe the issues involved in instantiating an efficient progressive sampler, including how to detect convergence. Finally, we provide empirical results comparing a variety of progressive sampling methods. We conclude that progressive sampling can be remarkably efficient .
On Covert Acoustical Mesh Networks in Air

Covert channels can be used to circumvent system and network policies by establishing communications that have not been considered in the design of the computing system. We construct a covert channel between different computing systems that utilizes audio modulation/demodulation to exchange data between the computing systems over the air medium. The underlying network stack is based on a communication system that was originally designed for robust underwater communication. We adapt the communication system to implement covert and stealthy communications by utilizing the ultrasonic frequency range. We further demonstrate how the scenario of covert acoustical communication over the air medium can be extended to multi-hop communications and even to wireless mesh networks. A covert acoustical mesh network can be conceived as a meshed botnet or malnet that is accessible via inaudible audio transmissions. Different applications of covert acoustical mesh networks are presented, including the use for remote keylogging over multiple hops. It is shown that the concept of a covert acoustical mesh network renders many conventional security concepts useless, as acoustical communications are usually not considered. Finally, countermeasures against covert acoustical mesh networks are discussed, including the use of lowpass filtering in computing systems and a host-based intrusion detection system for analyzing audio input and output in order to detect any irregularities.
Accounting Restatements and External Financing Choices

There is little research on how accounting information quality affects a firm’s external financing choices. In this paper, we use the occurrence of accounting restatements as a proxy for the reduced credibility of accounting information and investigate how restatements affect a firm’s external financing choices. We find that for firms that obtain external financing after restatements, they rely more on debt financing, especially private debt financing, and less on equity financing. The increase in debt financing is more pronounced for firms with more severe information problems and less pronounced for firms with prompt CEO/CFO turnover and auditor dismissal. Our evidence indicates that accounting information quality affects capital providers’ resource allocation and that debt holders help alleviate information problems after accounting restatements.
Development of ethnic, racial, and national prejudice in childhood and adolescence: a multinational meta-analysis of age differences.

This meta-analysis summarizes 113 research reports worldwide (121 cross-sectional and 7 longitudinal studies) on age differences in ethnic, racial, or national prejudice among children and adolescents. Overall, results indicated a peak in prejudice in middle childhood (5-7 years) followed by a slight decrease until late childhood (8-10 years). In addition to differences for the various operationalizations of prejudice, detailed findings revealed different age-related changes in prejudice toward higher versus lower status out-groups and positive effects of contact opportunities with the out-group on prejudice development. Results confirm that prejudice changes systematically with age during childhood but that no developmental trend is found in adolescence, indicating the stronger influence of the social context on prejudice with increasing age.
Reactance-domain ESPRIT algorithm for a hexagonally shaped seven-element ESPAR antenna

A direction-of-arrival (DoA) method that combines the reactance-domain (RD) technique and the ESPRIT algorithm is proposed for use with the 7-element electronically steerable parasitic array radiator (ESPAR) for the estimation of noncoherent sources. Simulations show that the method could resolve up to three incoming signals with an estimation performance that depends on the signal's angle of arrival. Moreover, the method is compared with the Cramer-Rao lower bound (CRB) and the MUSIC asymptotic error variance, both modified for the RD technique. Numerical comparison between this lower bound and the MUSIC algorithm confirmed that the proposed method can achieve the CRB and provide high-precision DoA estimation with a level of performance that is sufficient for many DoA finding applications. The proposed method could be demonstrated by means of experiments on DOA estimation conducted in an anechoic chamber.
Users Tagging Visual Moments: Timed Tags in Social Video

A timed tag is a tag that a user has assigned to a specific time point in a video. Although timed tags are supported by an increasing number of social video platforms on the Internet, multimedia research remains focused on conventional tags, here called "timeless tags", which users assign to the video as a whole, rather than to a specific moment. This paper presents a video data set consisting of social videos and user-contributed timed tags. A large crowdsourcing experiment was used to annotate this data set. The annotations allow us to better understand the phenomenon of timed tagging. We describe the design of the crowdsourcing experiment, and how it was executed. Then we present results of our analysis, which reveal the properties of timed tags, and their differences from timeless tags. The results suggest that the two differ with respect to what the user is attempting to express about the video. We close with an outlook that lays the groundwork for further study of timed tags in social video within the research community.
A Smart City Data Model based on Semantics Best Practice and Principles

Data management is crucial in modern smart cities. A good data model for smart cities has to be able to describe and integrate data from multiple domains, such as geographic information, public transportation, road maintenance, waste collection, and urban faults management. We describe our approach for creating a semantic platform for the Municipality of Catania, one of the main cities in Southern Italy. The ultimate goal is to boost the metropolis towards the route of a modern smart city and improve urban life. Our platform exhibits a consistent, minimal and comprehensive semantic data model for the city based on the Linked Open Data paradigm. Both the model and the data are publically accessible thorough dedicated user-friendly services, which allow citizens to observe and interact with the work of the public administration. Our platform also enables interested businesses and programmers to develop front-end services on the top of it. We describe the methodology used to extract data from sources, enrich them, building an ontology that describes them and publish them under the Linked Open Data paradigm. We include in our description employed tools and technologies. Our methodology is based on the standards of the W3C, on good practices of ontology design, on the guidelines issued by the Agency for Digital Italy and the Italian Index of Public Administration, as well as on the in-depth experience of the researchers in this field.
Efficiently Scaling up Crowdsourced Video Annotation

We present an extensive three year study on economically annotating video with crowdsourced marketplaces. Our public framework has annotated thousands of real world videos, including massive data sets unprecedented for their size, complexity, and cost. To accomplish this, we designed a state-of-the-art video annotation user interface and demonstrate that, despite common intuition, many contemporary interfaces are sub-optimal. We present several user studies that evaluate different aspects of our system and demonstrate that minimizing the cognitive load of the user is crucial when designing an annotation platform. We then deploy this interface on Amazon Mechanical Turk and discover expert and talented workers who are capable of annotating difficult videos with dense and closely cropped labels. We argue that video annotation requires specialized skill; most workers are poor annotators, mandating robust quality control protocols. We show that traditional crowdsourced micro-tasks are not suitable for video annotation and instead demonstrate that deploying time-consuming macro-tasks on MTurk is effective. Finally, we show that by extracting pixel-based features from manually labeled key frames, we are able to leverage more sophisticated interpolation strategies to maximize performance given a fixed budget. We validate the power of our framework on difficult, real-world data sets and we demonstrate an inherent trade-off between the mix of human and cloud computing used vs. the accuracy and cost of the labeling. We further introduce a novel, cost-based evaluation criteria that compares vision algorithms by the budget required to achieve an acceptable performance. We hope our findings will spur innovation in the creation of massive labeled video data sets and enable novel data-driven computer vision applications.
Animation of dynamic legged locomotion

This paper is about the use of control algorithms to animate dynamic legged locomotion. Control could free the animator from specifying the details of joint and limb motion while producing both physically realistic and natural looking results. We implemented computer animations of a biped robot, a quadruped robot, and a kangaroo. Each creature was modeled as a linked set of rigid bodies with compliant actuators at its joints. Control algorithms regulated the running speed, organized use of the legs, and maintained balance. All motions were generated by numerically integrating equations of motion derived from the physical models. The resulting behavior included running at various speeds, traveling with several gaits (run, trot, bound, gallop, and hop), jumping, and traversing simple paths. Whereas the use of control permitted a variety of physically realistic animated behavior to be generated with limited human intervention, the process of designing the control algorithms was not automated: the algorithms were "tweaked" and adjusted for each new creature.
Content delivery over TLS: a cryptographic analysis of keyless SSL

The Transport Layer Security (TLS) protocol is designed to allow two parties, a client and a server, to communicate securely over an insecure network. However, when TLS connections are proxied through an intermediate middlebox, like a Content Delivery Network (CDN), the standard endto- end security guarantees of the protocol no longer apply. In this paper, we investigate the security guarantees provided by Keyless SSL, a CDN architecture currently deployed by CloudFlare that composes two TLS 1.2 handshakes to obtain a proxied TLS connection. We demonstrate new attacks that show that Keyless SSL does not meet its intended security goals. These attacks have been reported to CloudFlare and we are in the process of discussing fixes. We argue that proxied TLS handshakes require a new, stronger, 3-party security definition. We present 3(S)ACCEsecurity, a generalization of the 2-party ACCE security definition that has been used in several previous proofs for TLS. We modify Keyless SSL and prove that our modifications guarantee 3(S)ACCE-security, assuming ACCE-security for the individual TLS 1.2 connections. We also propose a new design for Keyless TLS 1.3 and prove that it achieves 3(S)ACCEsecurity, assuming that the TLS 1.3 handshake implements an authenticated 2-party key exchange. Notably, we show that secure proxying in Keyless TLS 1.3 is computationally lighter and requires simpler assumptions on the certificate infrastructure than our proposed fix for Keyless SSL. Our results indicate that proxied TLS architectures, as currently used by a number of CDNs, may be vulnerable to subtle attacks and deserve close attention.
On-line Q-learning using connectionist systems

Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete nite-state Markovian problems, which is too restrictive for many real-world environments. Therefore, it is desirable to extend these methods to high dimensional continuous state-spaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of back-propagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number of di erent algorithms based around Q-Learning (Watkins 1989) combined with the Temporal Di erence algorithm (Sutton 1988), including a new algorithm (Modi ed Connectionist Q-Learning), and Q( ) (Peng and Williams 1994). In addition, we present algorithms for applying these updates on-line during trials, unlike backward replay used by Lin (1993) that requires waiting until the end of each trial before updating can occur. On-line updating is found to be more robust to the choice of training parameters than backward replay, and also enables the algorithms to be used in continuously operating systems where no end of trial conditions occur. We compare the performance of these algorithms on a realistic robot navigation problem, where a simulated mobile robot is trained to guide itself to a goal position in the presence of obstacles. The robot must rely on limited sensory feedback from its surroundings, and make decisions that can be generalised to arbitrary layouts of obstacles. These simulations show that on-line learning algorithms are less sensitive to the choice of training parameters than backward replay, and that the alternative update rules of MCQ-L and Q( ) are more robust than standard Q-learning updates.
Optimization of silver paste printed passive UHF RFID tags

Passive UHF (Ultra High Frequency) RFID (Radio Frequency Identification) is a promising technology for products tracking in logistics or routing packages in supply chain. Usually, the UHF RFID antenna is made by etched copper, or aluminum. These etching process introduce chemical wastes during the etching process. Recently, printing silver paste becomes popular due to its environmental friendly manufacturing process. However, there are two drawbacks on printed silver paste RFID antenna. One is low conductivity compared to metal and the other is the high material cost. In this paper, we exam the effects of printing thickness on the performance of UHF RFID tags and summarize the optimized thickness for different read range requirements. A commercial conductive silver paste is printed on PET film as RFID tag antennas. Both finite element simulations and experiments are conducted to evaluate the effects of printed thickness. The simulation results presents that the 10 um thick RFID antenna exhibits relatively good radiation efficiency. The results indicate that 10 um thick RFID tag antenna presents the minimum turn-on power of 18 dBm to 22 dBm which is enough for most applications. The simulation and measurement results present that thinner printed silver paste RFID tag antenna is a potential solution for low cost UHF RFID tags. The cost of these tags could be lower than current etched Al/Cu RFID tags because of material saving.
Smart Textiles and Wearable Technology – A

2 Foreword This report is the result of a study of the research field smart textiles, which has been carried out within the EU financed Baltic Fashion Project. The report gives an overview of projects combining smart textiles and clothing as a basis of discussions of how smart textile innovation could be introduced in fashion. The overview covers different projects, research as well as commercial projects, within smart textiles and clothing, with a certain focus on European activities. All information is based on literature studies, inquires from the Baltic Fashion partners and search for projects in different databases. The Swedish School of Textiles is the Swedish national contact for the Baltic Fashion project, which aims to promote fashion. Between 2011-2013, activities and research are carried out within various subjects, e.g. mapping production, innovation, smart textiles and wearable technology.  This report covers different projects, research as well as commercial projects, within smart textiles and clothing, with a certain focus on European activities. Each projects is categorized as either EU-project, University project not funded by EU and commercial projects. All information is based on literature studies, inquires from the Baltic Fashion Partners and search for projects in project databases. The collected materials shows that there are research initiative on both European and national levels. In these projects there has been a focus on medical applications and applications concentrated on enabling technologies. Despite an extensive research effort in several projects for over 10 years there are only few smart textile products on the market. In earlier investigations technical and production aspects are explained as barriers for the commercialisation of smart textiles. Fashion is rarely mentioned as a potential area for smart textiles. However, in this report it is most of the companies that are found are deals with fashion. The companies are certainly small and several authors dismiss the potential in their applications as more or less uninteresting. Despite these opinions the report shows that fashion players take private economical risks by producing and selling smart textile fashion products. The new companies also shows ability to take lead in using new developed technologies in their applications in order to avoid the technical barriers. The argument that the new companies are too small to consider also shows expectancy that smart textiles concern mass consumption and mass production, which is not necessarily the case. Several of the companies have focused on tailor-made solutions for …
Business Process Languages: An Ontology-Based Perspective

Business process modelling (BPM) notations describe processes using a graphical representation of process-relevant entities and their interplay. Despite the wide literature on the comparison between different modelling languages, the BPM community still lacks an ontological characterisation of process constructs. Purpose of this paper is to start filling this gap by providing a first ontological analysis of the main business process entities. The analysis and the resulting characterisation aim at illustrating the different perspectives that BPM languages implicitly take on business processes, as well as guiding the modellers in making an appropriate choice when selecting among different notations.