nils-herrmann/clean_scirepeval huggingface dataset:
Bilingual Language Switching in Naming : Asymmetrical Costs of Language Selection

In an experimental study of language switching and selection, bilinguals named numerals in either their first or second language unpredictably. Response latencies (RTs) on switch trials (where the response language changed from the previous trial) were slower than on nonswitch trials. As predicted, the language-switching cost was consistently larger when switching to the dominant L 1 from the weaker L2 than vice versa such that, on switch trials, L 1 responses were slower than in L 2. This “paradoxical” asymmetry in the cost of switching languages is explained in terms of differences in relative strength of the bilingual’s two languages and the involuntary persistence of the previous language set across an intended switch of language. Naming in the weaker language, L 2, requires active inhibition or suppression of the stronger competitor language, L 1; the inhibition persists into the following (switch) trial in the form of “negative priming” of the L 1 lexicon as a whole.
A Survey on License Plate Recognition Systems

License Plate Recognition (LPR) is a well known image processing technology. LPR system consists of four steps: capture the image from digital camera, pre-processing, character segmentation and character recognition. License plates are available in various styles and colors in various countries. Every country has their own license plate format. So each country develops the LPR system appropriate for the vehicle license plate format. Difficulties that the LPR systems face are the environmental and non-uniform outdoor illumination conditions. Therefore, most of the systems work under restricted environmental conditions like fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Each LPR system use different combination of algorithms. From the papers being surveyed, it is realized that a good success rate of 93. 7% is obtained by the combination of fuzzy logic for license plate detection and Self Organizing (SO) neural network for character recognition. Comparisons of different LPR systems are discussed in this paper.
The usage and adoption of cloud computing by small and medium businesses

Cloud computing has become the buzzword in the industry today. Though, it is not an entirely new concept but in today’s digital age, it has become ubiquitous due to the proliferation of Internet, broadband, mobile devices, better bandwidth and mobility requirements for end-users (be it consumers, SMEs or enterprises). In this paper, the focus is on the perceived inclination of micro and small businesses (SMEs or SMBs) toward cloud computing and the benefits reaped by them. This paper presents five factors nfrastructure-as-a-Service (IaaS) mall and medium enterprises (SMEs’) mall and medium businesses (SMBs’) influencing the cloud usage by this business community, whose needs and business requirements are very different from large enterprises. Firstly, ease of use and convenience is the biggest favorable factor followed by security and privacy and then comes the cost reduction. The fourth factor reliability is ignored as SMEs do not consider cloud as reliable. Lastly but not the least, SMEs do not want to use cloud for sharing and collaboration and prefer their old conventional methods for sharing and collaborating with their stakeholders.
Benchmarking Graph Databases on the Problem of Community Detection

Thanks to the proliferation of Online Social Networks (OSNs) and Linked Data, graph data have been constantly increasing, reaching massive scales and complexity. Thus, tools to store and manage such data efficiently are absolutely essential. To address this problem, various technologies have been employed, such as relational, object and graph databases. In this paper we present a benchmark that evaluates graph databases with a set of workloads, inspired from OSN mining use case scenarios. In addition to standard network operations, the paper focuses on the problem of community detection and we propose the adaptation of the Louvain method on top of graph databases. The paper reports a comprehensive comparative evaluation between three popular graph databases, Titan, OrientDB and Neo4j. Our experimental results show that, in the current development status, OrientDB is the fastest solution with respect to the Louvain method, while Neo4j performs the query workloads fastest. Moreover, Neo4j and Titan handle better massive and single insertion operations respectively.
Current Studies On Intrusion Detection System, Genetic Algorithm And Fuzzy Logic

Nowadays Intrusion Detection System (IDS) which is increasingly a key element of system security is used to identify the malicious activities in a computer system or network. There are different approaches being employed in intrusion detection systems, but unluckily each of the technique so far is not entirely ideal. The prediction process may produce false alarms in many anomaly based intrusion detection systems. With the concept of fuzzy logic, the false alarm rate in establishing intrusive activities can be reduced. A set of efficient fuzzy rules can be used to define the normal and abnormal behaviors in a computer network. Therefore some strategy is needed for best promising security to monitor the anomalous behavior in computer network. In this paper I present a few research papers regarding the foundations of intrusion detection systems, the methodologies and good fuzzy classifiers using genetic algorithm which are the focus of current development efforts and the solution of the problem of Intrusion Detection System to offer a realworld view of intrusion detection. Ultimately, a discussion of the upcoming technologies and various methodologies which promise to improve the capability of computer systems to detect intrusions is offered.
TasteWeights: a visual interactive hybrid recommender system

This paper presents an interactive hybrid recommendation system that generates item predictions from multiple social and semantic web resources, such as Wikipedia, Facebook, and Twitter. The system employs hybrid techniques from traditional recommender system literature, in addition to a novel interactive interface which serves to explain the recommendation process and elicit preferences from the end user. We present an evaluation that compares different interactive and non-interactive hybrid strategies for computing recommendations across diverse social and semantic web APIs. Results of the study indicate that explanation and interaction with a visual representation of the hybrid system increase user satisfaction and relevance of predicted content.
Security Access Protocols in IoT Capillary Networks

Smart city services are enabled by a massive use of Internet of Things (IoT) technologies. The huge amount of sensors, and terminals with a great variety of typologies and applications, requires a secure way to manage them. Capillary networks can be seen as a short range extension of conventional access network in order to efficiently capture the IoT traffic, and are enablers for smart city services. They can include both IP and non-IP devices, and security can become an issue, especially when simple unidirectional communication devices are considered. The main goal of this paper is to analyze security aspects in IoT capillary networks including unidirectional and bidirectional IP or non-IP devices. We propose an algorithm for secure access for uni- and bi-directional devices. The security procedure is based on a secure key renewal (without any exchange in air), considering a local clock time and a time interval of key validity. Following previous work in 2014 by Giuliano et al., in this paper we assess the duration of the validity of the time window, and present extended simulation results in terms of (average) transmission time in a realistic scenario, i.e., including the presence of disturber(s), then providing indications for the setting of the duration of the key validity time window. Finally, we present the benchmark analysis in order to assess the effectiveness of our approach with respect to other existing standards, as well as the security analysis in terms of typical attacks.
Microplastics in bivalves cultured for human consumption.

Microplastics are present throughout the marine environment and ingestion of these plastic particles (<1 mm) has been demonstrated in a laboratory setting for a wide array of marine organisms. Here, we investigate the presence of microplastics in two species of commercially grown bivalves: Mytilus edulis and Crassostrea gigas. Microplastics were recovered from the soft tissues of both species. At time of human consumption, M. edulis contains on average 0.36 ± 0.07 particles g(-1) (wet weight), while a plastic load of 0.47 ± 0.16 particles g(-1) ww was detected in C. gigas. As a result, the annual dietary exposure for European shellfish consumers can amount to 11,000 microplastics per year. The presence of marine microplastics in seafood could pose a threat to food safety, however, due to the complexity of estimating microplastic toxicity, estimations of the potential risks for human health posed by microplastics in food stuffs is not (yet) possible.
FPGA: what's in it for a database?

While there seems to be a general agreement that next years' systems will include many processing cores, it is often overlooked that these systems will also include an increasing number of different cores (we already see dedicated units for graphics or network processing). Orchestrating the diversity of processing functionality is going to be a major challenge in the upcoming years, be it to optimize for performance or for minimal energy consumption.
 We expect field-programmable gate arrays (FPGAs or "programmable hardware") to soon play the role of yet another processing unit, found in commodity computers. It is clear that the new resource is going to be too precious to be ignored by database systems, but it is unclear how FPGAs could be integrated into a DBMS. With a focus on database use, this tutorial introduces into the emerging technology, demonstrates its potential, but also pinpoints some challenges that need to be addressed before FPGA-accelerated database systems can go mainstream. Attendees will gain an intuition of an FPGA development cycle, receive guidelines for a "good" FPGA design, but also learn the limitations that hardware-implemented database processing faces. Our more high-level ambition is to spur a broader interest in database processing on novel hardware technology.
Network pharmacology: the next paradigm in drug discovery.

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.
Estimation of Tire Slip Angle and Friction Limits Using Steering Torque

Knowledge of the vehicle's lateral handling limits is important for vehicle control systems which aim to enhance vehicle handling and passenger safety. This paper presents a model-based estimation method that utilizes pneumatic trail information in steering torque to identify a vehicle's lateral handling limits, which are defined by the tire slip angle and peak lateral force limits. This method uses measurements available on many current production vehicles. Most importantly, it takes advantage of the early friction information encoded in the tire pneumatic trail. Pneumatic trail decreases as a function of the tire parameters even in the linear handling region, enabling early detection of the limits before they are reached. Experimental results on an independent front steering steer-by-wire research vehicle demonstrate the observer's ability to provide accurate real-time estimates of tire slip angle up to the limits of handling. Testing conditions include maneuvers performed on dry, flat paved road, as well as on lower-friction, dry gravel.
Ripple current cancellation circuit

A ripple current cancellation technique injects AC current into the output voltage bus of a converter that is equal and opposite to the normal converter ripple current. The output current ripple is ideally zero, leading to ultra-low noise converter output voltages. The circuit requires few additional components, no active circuits are required. Only an additional filter inductor winding, an auxiliary inductor, and small capacitor are required. The circuit utilizes leakage inductance of the modified filter inductor as all or part of the required auxiliary inductance. Ripple cancellation is independent of switching frequency, duty cycle, and other converter parameters. The circuit eliminates ripple current in both continuous conduction mode and discontinuous conduction mode. Experimental results provide better than an 80/spl times/ ripple current reduction.
Deep-Deep Neural Network Language Models for Predicting Mild Cognitive Impairment

Early diagnosis of Mild Cognitive Impairment (MCI) is currently a challenge. Currently, MCI is diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. As such we propose an automated diagnostic technique using a variant of deep neural networks language models (DNNLM) on the verbal utterances of MCI patients. Motivated by the success of DNNLM on natural language tasks, we propose a combination of deep neural network and deep language models (D2NNLM) to predict MCI. Results on the DementiaBank language transcript clinical dataset show that D2NNLM sufficiently learned several linguistic biomarkers in the form of higher order n-grams and skip-grams to distinguish the MCI group from the healthy group with reasonable accuracy, which could help clinical diagnosis even in the absence of sufficient training data.
Evaluating the Cost of Software Quality

There is some confusion about the business value of quality even outside the software development context. On the one hand, there are those who believe that it is economical to maximize quality. This is the “quality is free” perspective espoused by Crosby [7], Juran and Gryna , and others. Their key argument is that as the voluntary costs of defect prevention are increased, the involuntary costs of rework decrease by much more than the increase in prevention costs. The net result is lower total costs, and thus quality is free. On the other hand, there are those who believe it is uneconomical to have high levels of quality and assume they must sacrifice quality to achieve other objectives such as reduced development cycles. For example, a study of adoption of the Software Engineering Institute’s Capability Maturity Model (CMM) reports the following quote from a software manager: “I’d rather have it wrong than have it late. We can always fix it later” [11]. Experiences in manufacturing relating to the cost
Wideband Circularly Polarized Cavity-Backed Asymmetric Crossed Bowtie Dipole Antenna

A single-feed, cavity-backed, circularly polarized (CP) antenna is designed for wideband wireless applications. The antenna is made up of asymmetric crossed bowtie dipoles and achieves broadband characteristics in terms of impedance and 3-dB axial-ratio (AR) bandwidths. A double vacant-quarter printed ring with orthogonal asymmetric bowties is employed for CP radiation and then combined with slot-mode radiation to significantly enhance the CP operation bandwidth. Measurements of an optimized antenna prototype with an overall size of 0.88λo × 0.88λo × 0.23λo at 2.4 GHz show that it can yield an impedance bandwidth of approximately 57% and a 3-dB AR bandwidth of approximately 51%. In addition, a unidirectional CP radiation pattern with a high broadside average gain of 9.6 dBic was achieved across the operating bandwidth.
Automatic virtual machine configuration for database workloads

Virtual machine monitors are becoming popular tools for the deployment of database management systems and other enterprise software. In this article, we consider a common resource consolidation scenario in which several database management system instances, each running in a separate virtual machine, are sharing a common pool of physical computing resources. We address the problem of optimizing the performance of these database management systems by controlling the configurations of the virtual machines in which they run. These virtual machine configurations determine how the shared physical resources will be allocated to the different database system instances. We introduce a virtualization design advisor that uses information about the anticipated workloads of each of the database systems to recommend workload-specific configurations offline. Furthermore, runtime information collected after the deployment of the recommended configurations can be used to refine the recommendation and to handle changes in the workload. To estimate the effect of a particular resource allocation on workload performance, we use the query optimizer in a new what-if mode. We have implemented our approach using both PostgreSQL and DB2, and we have experimentally evaluated its effectiveness using DSS and OLTP workloads.
Efficient antialiased rendering of 3-D linear fractals

Object instancing is the efficient method of representing an hierarchical object with a directed graph instead of a tree. If this graph contains a cycle then the object it represents is a linear fractal. Linear fractals are difficult to render for three specific reasons: (1) ray-fractal intersection is not trivial, (2) surface normals are undefined and (3) the object aliases at all sampling resolutions.Ray-fractal intersections are efficiently approximated to sub-pixel accuracy using procedural bounding volumes and a careful determination of the size of a pixel, giving the perception that the surface is infinitely detailed. Furthermore, a surface normal for these non-differentiable surfaces is defined and analyzed. Finally, the concept of antialiasing "covers" is adapted and used to solve the problem of sampling fractal surfaces.An initial bounding volume estimation method is also described, allowing a linear fractal to be rendered given only its iterated, function system. A parallel implementation of these methods is described and applications of these results to the rendering of other fractal models are given.
Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts

The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing.
Towards Better Availability and Accountability for IoT Updates by Means of a Blockchain

Building the Internet of Things requires deploying a huge number of objects with full or limited connectivity to the Internet. Given that these objects are exposed to attackers and generally not secured-by-design, it is essential to be able to update them, to patch their vulnerabilities and to prevent hackers from enrolling them into botnets. Ideally, the update infrastructure should implement the CIA triad properties, i.e., confidentiality, integrity and availability. In this work, we investigate how the use of a blockchain infrastructure can meet these requirements, with a focus on availability. In addition, we propose a peer-to-peer mechanism, to spread updates between objects that have limited access to the Internet. Finally, we give an overview of our ongoing prototype implementation.
Enabling JSON Document Stores in Relational Systems

In recent years, “document store” NoSQL systems have exploded in popularity. A large part of this popularity has been driven by the adoption of the JSON data model in these NoSQL systems. JSON is a simple but expressive data model that is used in many Web 2.0 applications, and maps naturally to the native data types of many modern programming languages (e.g. Javascript). The advantages of these NoSQL document store systems (like MongoDB and CouchDB) are tempered by a lack of traditional RDBMS features, notably a sophisticated declarative query language, rich native query processing constructs (e.g. joins), and transaction management providing ACID safety guarantees. In this paper, we investigate whether the advantages of the JSON data model can be added to RDBMSs, gaining some of the traditional benefits of relational systems in the bargain. We present Argo, an automated mapping layer for storing and querying JSON data in a relational system, and NoBench, a benchmark suite that evaluates the performance of several classes of queries over JSON data in NoSQL and SQL databases. Our results point to directions of how one can marry the best of both worlds, namely combining the flexibility of JSON to support the popular document store model with the rich query processing and transactional properties that are offered by traditional relational DBMSs.