nils-herrmann/clean_scirepeval huggingface dataset:
A 10000 frames/s CMOS digital pixel sensor

A 352 288 pixel CMOS image sensor chip with perpixel single-slope ADC and dynamic memory in a standard digital 0.18m CMOS process is described. The chip performs “snapshot” image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10 000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4 9.4 m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 V/e . At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.
Theory and Network Applications of Dynamic Bloom Filters

A bloom filter is a simple, space-efficient, randomized data structure for concisely representing a static data set, in order to support approximate membership queries. It has great potential for distributed applications where systems need to share information about what resources they have. The space efficiency is achieved at the cost of a small probability of false positive in membership queries. However, for many applications the space savings and short locating time consistently outweigh this drawback. In this paper, we introduce dynamic bloom filters (DBF) to support concise representation and approximate membership queries of dynamic sets, and study the false positive probability and union algebra operations. We prove that DBF can control the false positive probability at a low level by adjusting the number of standard bloom filters used according to the actual size of current dynamic set. The space complexity is also acceptable if the actual size of dynamic set does not deviate too much from the predefined threshold. Furthermore, we present multidimension dynamic bloom filters (MDDBF) to support concise representation and approximate membership queries of dynamic sets in multiple attribute dimensions, and study the false positive probability and union algebra operations through mathematic analysis and experimentation. We also explore the optimization approach and three network applications of bloom filters, namely bloom joins, informed search, and global index implementation. Our simulation shows that informed search based on bloom filters can obtain higher recall and success rate of query than the blind search protocol.
An extensive comparative study of cluster validity indices

The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cluster validity indices in many different environments with different characteristics. These results can serve as a guideline for selecting the most suitable index for each possible application and provide a deep insight into the performance differences between the currently available indices. & 2012 Elsevier Ltd. All rights reserved.
Data collection and analysis of GitHub repositories and users

In this paper, we present the collection and mining of GitHub data, aiming to understand GitHub user behavior and project success factors. We collected information about approximately 100K projects and 10K GitHub users//owners of these projects, via GitHub API. Subsequently, we statistically analyzed such data, discretized values of features via k-means algorithm, and finally we applied apriori algorithm via weka in order to find out association rules. Having assumed that project success could be measured by the cardinality of downloads we kept only the rules which had as right par a download cardinality higher than a threshold of 1000 downloads. The results provide intersting insight in the GitHub ecosystem and seven success rules for GitHub projects.
Assessing information security culture: A critical analysis of current approaches

Today's businesses operate in an interconnected and global environment allowing them to collaborate with one another and share information resources. At the same time this interconnectivity exposes the organization to many internal (employees) and external threats. Internal threat is among the top information security issues facing organizations as the human factor is regarded the weakest link in the security chain. To address this “human factor” researchers have suggested the fostering of an information security culture to address the human behavior so that information security becomes a second nature to employees. An important step in the fostering of an information security culture is the assessment of the current state of the culture. This paper focuses on the analysis and comparison of current information security culture assessment approaches, to evaluate their suitability specific for use in the culture change process.
What is LiFi?

This paper attempts to clarify the difference between visible light communication (VLC) and light-fidelity (LiFi). In particular, it will show how LiFi takes VLC further by using light emitting diodes (LEDs) to realise fully networked wireless systems. Synergies are harnessed as luminaries become LiFi attocells resulting in enhanced wireless capacity providing the necessary connectivity to realise the Internet-of-Things, and contributing to the key performance indicators for the fifth generation of cellular systems (5G) and beyond. It covers all of the key research areas from LiFi components to hybrid LiFi/wireless fidelity (WiFi) networks to illustrate that LiFi attocells are not a theoretical concept any more, but at the point of real-world deployment.
Radio frequency identification: technologies, applications, and research issues

A radio frequency identification (RFID) system is a special kind of sensor network to identify an object or a person using radio frequency transmission. A typical RFID system includes transponders (tags) and interrogators (readers): tags are attached to objects/persons, and readers communicate with the tags in their transmission ranges via radio signals. RFID systems have been gaining more and more popularity in areas such as supply chain management, automated identification systems, and any place requiring identifications of products or people. RFID technology is better than barcode in many ways, and may totally replace barcode in the future if certain technologies can be achieved such as low cost and protection of personal privacy. This paper provides a technology survey of RFID systems and various RFID applications. We also discuss five critical research issues: cost control, energy efficiency, privacy issue, multiple readers’ interference, and security issue.
Synchromesh Torque Estimation in an Electric Vehicle's Clutchless Automated Manual Transmission Using Unknown Input Observer

This paper studies the estimation of the frictional torque of the synchromesh during the gear shifting operation in an electric vehicle equipped with a clutchless automated manual transmission (AMT). The clutchless drivetrain of the electric vehicle is discussed and the dynamical model of the powertrain from the electric traction motor to the synchromesh system of a two-speed AMT is developed. In order to estimate the frictional torque of the synchromesh, which is indeed an unknown input to the dynamical system, it is assumed to be generated by a fictitious autonomous system. Thereafter, the augmented state- space representation of the actual and fictitious state variables, which forms the basis for the observer design, is provided and the observability of this augmented system is discussed. A deterministic Luenberger observer and a stochastic Kalman-Bucy filter are designed in order to estimate the frictional torque of the synchromesh. The estimation is based on the measuring angular velocities of the electric motor and synchro ring together with the known electromagnetic torque of the traction motor. The performance of the observers is assessed experimentally by means of a test rig. The results demonstrate the satisfactory performance of the stochastic observer when the system encounters process and measurement noise which are likely to happen in practice.
3D-Printed Origami Packaging With Inkjet-Printed Antennas for RF Harvesting Sensors

This paper demonstrates the combination of additive manufacturing techniques for realizing complex 3D origami structures for high frequency applications. A 3D-printed compact package for enclosing radio frequency (RF) electronics is built, that features on-package antennas for RF signal reception (for harvesting or communication) at orthogonal orientations. Conventional 3D printing technologies often require significant amounts of time and supporting material to realize certain structures, such as hollow packages. In this work, instead of fabricating the package in its final 3D form, it is 3D-printed as a planar structure with “smart” shape-memory hinges that allow origami folding to a 3D shape after heating. This significantly reduces fabrication time and effectively eliminates the need for supporting material, thus minimizing the overall manufacturing cost. Metallization on the package is performed by directly inkjet printing conductive inks on top of the 3D-printed surface with a modified inkjet-printed process without the need for surface treatment or processing. Inkjet-printed on-package conductive features are successfully fabricated, that are combined with RF energy harvesting electronics to showcase the proof-of-concept of utilizing origami techniques to build fully 3D RF systems. The methodologies presented in this paper will be enabling the manufacturing of numerous real-time shape-changing 3D complex structures for electromagnetic applications.
Slip-aware Model Predictive optimal control for Path following

Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skid-steered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.
Light warping for enhanced surface depiction

Recent research on the human visual system shows that our perception of object shape relies in part on compression and stretching of the reflected lighting environment onto its surface. We use this property to enhance the shape depiction of 3D objects by locally warping the environment lighting around main surface features. Contrary to previous work, which require specific illumination, material characteristics and/or stylization choices, our approach enhances surface shape without impairing the desired appearance.
 Thanks to our novel local shape descriptor, salient surface features are explicitly extracted in a view-dependent fashion at various scales without the need of any pre-process. We demonstrate our system on a variety of rendering settings, using object materials ranging from diffuse to glossy, to mirror or refractive, with direct or global illumination, and providing styles that range from photorealistic to non-photorealistic. The warping itself is very fast to compute on modern graphics hardware, enabling real-time performance in direct illumination scenarios.
 Note: Third-Party Material Attribution  Third-party material used in ACM Transactions on Graphics 28(3), Article 25 - "Light Warping for Enhanced Surface Depiction," by Vergne, Pacanowski, Barla, Granier, and Schlick - was used without proper attribution.
 The 3D model used in Figures 1, 3, and 5, as well as in the cover image of this volume of the journal, was downloaded from the Shape Repository of AIM@SHAPE Project (http://shapes.aimatshape.net) and is the property of CNR-IMATI.
 We regret this oversight.
Convergence Analysis of MAP Based Blur Kernel Estimation

One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown to prefer blurry images to sharp natural images. In this paper, we revisit this problem and provide an analysis on the convergence of MAP based approaches. We first introduce a slight modification to a conventional joint energy function for blind deconvolution. The reformulated energy function yields the same alternating estimation process, but more clearly reveals how blind deconvolution works. We then show the energy function can actually favor the right solution instead of the no-blur solution under certain conditions, which explains the success of previous MAP based approaches. The reformulated energy function and our conditions for the convergence also provide a way to compare the qualities of different blur kernels, and we demonstrate its applicability to automatic blur kernel size selection, blur kernel estimation using light streaks, and defocus estimation.
Big-Data Architecture for Cyber Attack Graphs Representing Security Relationships in NoSQL Graph Databases

The construction of cyber attack graphs is well studied in the literature. However, previous approaches generally apply only specific sets of algorithms against fixed data models. We introduce a much more flexible approach for attack graph modeling, analysis, and visualization based on general-purpose and extensible architecture. In this architecture, the model schema is implicit, and evolves with the available data sources, rather than being predetermined at design time. Graph patternmatching queries provide flexible analytic capabilities against arbitrary graph structures. We then process query results in a generic way for visualization. Our highly scalable implementation includes the MongoDB document-oriented NoSQL database, Apache Spark for cluster computing, and the Neo4j graph database. We illustrate our approach with a novel attack graph model that merges a complex blend of network data, including topology, firewall policies, vulnerabilities, attack patterns, and intrusion alerts, through standardized languages for cyber security data. attack graphs; topological vulnerability analysis; network attack modeling; cybersecurity standards; cluster computing; NoSQL databases
Crowdsourced Comprehension: Predicting Prerequisite Structure in Wikipedia

The growth of open-access technical publications and other open-domain textual information sources means that there is an increasing amount of online technical material that is in principle available to all, but in practice, incomprehensible to most. We propose to address the task of helping readers comprehend complex technical material, by using statistical methods to model the “prerequisite structure” of a corpus — i.e., the semantic impact of documents on an individual reader’s state of knowledge. Experimental results using Wikipedia as the corpus suggest that this task can be approached by crowdsourcing the production of ground-truth labels regarding prerequisite structure, and then generalizing these labels using a learned classifier which combines signals of various sorts. The features that we consider relate pairs of pages by analyzing not only textual features of the pages, but also how the containing corpora is connected and created.
Language Learning in Virtual Reality Environments: Past, Present, and Future

This study investigated the research trends in language learning in a virtual reality environment by conducting a content analysis of findings published in the literature from 2004 to 2013 in four top ranked computer-assisted language learning journals: Language Learning & Technology, CALICO Journal, Computer Assisted Language Learning, and ReCALL. Data from 29 articles were cross-analyzed in terms of research topics, technologies used, language learning settings, sample groups, and methodological approaches. It was found that the three most popular research topics for learners were interactive communication; behaviors, affections, and beliefs; and task-based instruction. However, the analysis results highlight the need for the inclusion of the impact of teacher. The data also revealed that more studies are utilizing triangulation of measurement processes to enable in-depth analysis. A trend of gathering data through informal learning procedures was also observed. This article concludes by highlighting particular fields related to VR in which further research is urgently needed.
Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management

Urban traffic gathers increasing interest as cities become bigger, crowded and “smart”. We present a system for heterogeneous stream processing and crowdsourcing supporting intelligent urban traffic management. Complex events related to traffic congestion (trends) are detected from heterogeneous sources involving fixed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, a crowdsourcing component handles and resolves sensor disagreement. Furthermore, to deal with data sparsity, a traffic modelling component offers information in areas with low sensor coverage. We demonstrate the system with a real-world use-case from Dublin city, Ireland.
An Analysis of Black-Box Web Application Security Scanners against Stored SQL Injection

Web application security scanners are a compilation of various automated tools put together and used to detect security vulnerabilities in web applications. Recent research has shown that detecting stored SQL injection, one of the most critical web application vulnerabilities, is a major challenge for black-box scanners. In this paper, we evaluate three state of art black-box scanners that support detecting stored SQL injection vulnerabilities. We developed our custom test bed that challenges the scanners capability regarding stored SQL injections. The results show that existing vulnerabilities are not detected even when these automated scanners are taught to exploit the vulnerability. The weaknesses of black-box scanners identified reside in many areas: crawling, input values and attack code selection, user login, analysis of server replies, miss-categorization of findings, and the automated process functionality. Because of the poor detection rate, we discuss the different phases of black-box scanners' scanning cycle and propose a set of recommendations that could enhance the detection rate of stored SQL injection vulnerabilities.
Wireless Magnetic Sensor Node for Vehicle Detection With Optical Wake-Up

Vehicle detectors provide essential information about parking occupancy and traffic flow. To cover large areas that lack a suitable electrical infrastructure, wired sensors networks are impractical because of their high deployment and maintenance costs. Wireless sensor networks (WSNs) with autonomous sensor nodes can be more economical. Vehicle detectors intended for a WSN should be small, sturdy, low power, cost-effective, and easy to install and maintain. Currently available vehicle detectors based on inductive loops, ultrasound, infrared, or magnetic sensors do not fulfill the requirements above, which has led to the search for alternative solutions. This paper presents a vehicle detector which includes a magnetic and an optical sensor and is intended as sensor node for use with a WSN. Magnetic sensors based on magnetoresistors are very sensitive and can detect the magnetic anomaly in the Earth's magnetic field that results from the presence of a car, but their continuous operation would drain more than 1.5 mA at 3 V, hence limiting the autonomy of a battery-supplied sensor node. Passive, low-power optical sensors can detect the shadow cast by car that covers them, but are prone to false detections. The use of optical triggering to wake-up a magnetic sensor, combined with power-efficient event-based software, yields a simple, compact, reliable, low-power sensor node for vehicle detection whose quiescent current drain is 5.5 μA. This approach of using a low-power sensor to trigger a second more specific sensor can be applied to other autonomous sensor nodes.
Information Flow in Social Groups

We present a study of information flow that takes into account the observation that an item relevant to one person is more likely to be of interest to individuals in the same social circle than those outside of it. This is due to the fact that the similarity of node attributes in social networks decreases as a function of the graph distance. An epidemic model on a scale-free network with this property has a finite threshold, implying that the spread of information is limited. We tested our predictions by measuring the spread of messages in an organization and also by numerical experiments that take into consideration the organizational distance among individuals.
Resin composite--state of the art.

The objective is to review the current state of the art of dental composite materials.



An outline of the most important aspects of dental composites was created, and a subsequent literature search for articles related to their formulation, properties and clinical considerations was conducted using PubMed followed by hand searching citations from relevant articles.



The current state of the art of dental composites includes a wide variety of materials with a broad range of mechanical properties, handling characteristics, and esthetic possibilities. This highly competitive market continues to evolve, with the major emphasis in the past being to produce materials with adequate strength, and high wear resistance and polishability retention. The more recent research and development efforts have addressed the issue of polymerization shrinkage and its accompanying stress, which may have a deleterious effect on the composite/tooth interfacial bond. Current efforts are focused on the delivery of materials with potentially therapeutic benefits and self-adhesive properties, the latter leading to truly simplified placement in the mouth.



There is no one ideal material available to the clinician, but the commercial materials that comprise the current armamentarium are of high quality and when used appropriately, have proven to deliver excellent clinical outcomes of adequate longevity.