nils-herrmann/clean_scirepeval huggingface dataset:
Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss.

The psychomotor vigilance test (PVT) is among the most widely used measures of behavioral alertness, but there is large variation among published studies in PVT performance outcomes and test durations. To promote standardization of the PVT and increase its sensitivity and specificity to sleep loss, we determined PVT metrics and task durations that optimally discriminated sleep deprived subjects from alert subjects.



Repeated-measures experiments involving 10-min PVT assessments every 2 h across both acute total sleep deprivation (TSD) and 5 days of chronic partial sleep deprivation (PSD).



Controlled laboratory environment.



74 healthy subjects (34 female), aged 22-45 years.



TSD experiment involving 33 h awake (N = 31 subjects) and a PSD experiment involving 5 nights of 4 h time in bed (N = 43 subjects).



In a paired t-test paradigm and for both TSD and PSD, effect sizes of 10 different PVT performance outcomes were calculated. Effect sizes were high for both TSD (1.59-1.94) and PSD (0.88-1.21) for PVT metrics related to lapses and to measures of psychomotor speed, i.e., mean 1/RT (response time) and mean slowest 10% 1/RT. In contrast, PVT mean and median RT outcomes scored low to moderate effect sizes influenced by extreme values. Analyses facilitating only portions of the full 10-min PVT indicated that for some outcomes, high effect sizes could be achieved with PVT durations considerably shorter than 10 min, although metrics involving lapses seemed to profit from longer test durations in TSD.



Due to their superior conceptual and statistical properties and high sensitivity to sleep deprivation, metrics involving response speed and lapses should be considered primary outcomes for the 10-min PVT. In contrast, PVT mean and median metrics, which are among the most widely used outcomes, should be avoided as primary measures of alertness. Our analyses also suggest that some shorter-duration PVT versions may be sensitive to sleep loss, depending on the outcome variable selected, although this will need to be confirmed in comparative analyses of separate duration versions of the PVT. Using both sensitive PVT metrics and optimal test durations maximizes the sensitivity of the PVT to sleep loss and therefore potentially decreases the sample size needed to detect the same neurobehavioral deficit. We propose criteria to better standardize the 10-min PVT and facilitate between-study comparisons and meta-analyses.
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning

Research in automatic analysis of sign language has largely focused on recognizing the lexical (or citation) form of sign gestures as they appear in continuous signing, and developing algorithms that scale well to large vocabularies. However, successful recognition of lexical signs is not sufficient for a full understanding of sign language communication. Nonmanual signals and grammatical processes which result in systematic variations in sign appearance are integral aspects of this communication but have received comparatively little attention in the literature. In this survey, we examine data acquisition, feature extraction and classification methods employed for the analysis of sign language gestures. These are discussed with respect to issues such as modeling transitions between signs in continuous signing, modeling inflectional processes, signer independence, and adaptation. We further examine works that attempt to analyze nonmanual signals and discuss issues related to integrating these with (hand) sign gestures. We also discuss the overall progress toward a true test of sign recognition systems--dealing with natural signing by native signers. We suggest some future directions for this research and also point to contributions it can make to other fields of research. Web-based supplemental materials (appendicies) which contain several illustrative examples and videos of signing can be found at www.computer.org/publications/dlib.
Verification of On-Line Vehicle Collision Avoidance Warning System using DSRC

Many accidents were happened because of fast driving, habitual working overtime or tired spirit. This paper presents a solution of remote warning for vehicles collision avoidance using vehicular communication. The development system integrates dedicated short range communication (DSRC) and global position system (GPS) with embedded system into a powerful remote warning system. To transmit the vehicular information and broadcast vehicle position; DSRC communication technology is adopt as the bridge. The proposed system is divided into two parts of the positioning and vehicular units in a vehicle. The positioning unit is used to provide the position and heading information from GPS module, and furthermore the vehicular unit is used to receive the break, throttle, and other signals via controller area network (CAN) interface connected to each mechanism. The mobile hardware are built with an embedded system using X86 processor in Linux system. A vehicle is communicated with other vehicles via DSRC in non-addressed protocol with wireless access in vehicular environments (WAVE) short message protocol. From the position data and vehicular information, this paper provided a conflict detection algorithm to do time separation and remote warning with error bubble consideration. And the warning information is on-line displayed in the screen. This system is able to enhance driver assistance service and realize critical safety by using vehicular information from the neighbor vehicles. Dedicated short range communication, GPS, Control area network, Collision avoidance warning system.
Content-addressable memory (CAM) circuits and architectures: a tutorial and survey

We survey recent developments in the design of large-capacity content-addressable memory (CAM). A CAM is a memory that implements the lookup-table function in a single clock cycle using dedicated comparison circuitry. CAMs are especially popular in network routers for packet forwarding and packet classification, but they are also beneficial in a variety of other applications that require high-speed table lookup. The main CAM-design challenge is to reduce power consumption associated with the large amount of parallel active circuitry, without sacrificing speed or memory density. In this paper, we review CAM-design techniques at the circuit level and at the architectural level. At the circuit level, we review low-power matchline sensing techniques and searchline driving approaches. At the architectural level we review three methods for reducing power consumption.
Health Implications of High Dietary Omega-6 Polyunsaturated Fatty Acids

Omega-6 (n-6) polyunsaturated fatty acids (PUFA) (e.g., arachidonic acid (AA)) and omega-3 (n-3) PUFA (e.g., eicosapentaenoic acid (EPA)) are precursors to potent lipid mediator signalling molecules, termed "eicosanoids," which have important roles in the regulation of inflammation. In general, eicosanoids derived from n-6 PUFA are proinflammatory while eicosanoids derived from n-3 PUFA are anti-inflammatory. Dietary changes over the past few decades in the intake of n-6 and n-3 PUFA show striking increases in the (n-6) to (n-3) ratio (~15 : 1), which are associated with greater metabolism of the n-6 PUFA compared with n-3 PUFA. Coinciding with this increase in the ratio of (n-6) : (n-3) PUFA are increases in chronic inflammatory diseases such as nonalcoholic fatty liver disease (NAFLD), cardiovascular disease, obesity, inflammatory bowel disease (IBD), rheumatoid arthritis, and Alzheimer's disease (AD). By increasing the ratio of (n-3) : (n-6) PUFA in the Western diet, reductions may be achieved in the incidence of these chronic inflammatory diseases.
Open-end multi-level six-phase machine drive system with three three-phase DC-link converters

A novel multi-level AC six-phase motor drive is developed in this paper. The scheme is based on three conventional 2-level three-phase voltage source inverters (VSIs) supplying the open-end windings of a dual three-phase motor (six-phase induction machine). The proposed inverter is capable of supply the machine with multi-level voltage waveforms. The developed system is compared with the conventional solution and it is demonstrated that the drive system permits to reduce the harmonic distortion of the machine currents, to reduce the total semiconductor losses and to decrease the power processed by converter switches. The system model and the Pulse-Width Modulation (PWM) strategy are presented. The experimental verification was obtained by using IGBTs with dedicated drives and a digital signal processor (DSP) with plug-in boards and sensors.
Automated detection of centromere in G banded chromosomes

The centromere position is an important feature in analyzing chromosomes and to make karyogram. In the field of chromosome analysis the accurate determination centromere from the segmented chromosome image is a challenging task. Karyogram is an arrangement of 46 chromosomes, for finding out many genetic disorders, various abnormalities and cancers. There exist so many algorithms to detect centromere positions, but most of the algorithms cannot apply for all chromosomes because of their orientation in metaphase. Here we propose a novel algorithm that associates with some rules based on morphological features of chromosome, a GLM mask and rotation procedure. The algorithm is tested on publically available database (LK1) and images collected from RCC Trivandrum.
Analysis of amino acid composition in proteins of animal tissues and foods as pre-column o-phthaldialdehyde derivatives by HPLC with fluorescence detection.

Studies of protein nutrition and biochemistry require reliable methods for analysis of amino acid (AA) composition in polypeptides of animal tissues and foods. Proteins are hydrolyzed by 6M HCl (110°C for 24h), 4.2M NaOH (105°C for 20 h), or proteases. Analytical techniques that require high-performance liquid chromatography (HPLC) include pre-column derivatization with 4-chloro-7-nitrobenzofurazan, 9-fluorenyl methylchloroformate, phenylisothiocyanate, naphthalene-2,3-dicarboxaldehyde, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate, and o-phthaldialdehyde (OPA). OPA reacts with primary AA (except cysteine or cystine) in the presence of 2-mercaptoethanol or 3-mercaptopropionic acid to form a highly fluorescent adduct. OPA also reacts with 4-amino-1-butanol and 4-aminobutane-1,3-diol produced from oxidation of proline and 4-hydroxyproline, respectively, in the presence of chloramine-T plus sodium borohydride at 60°C, or with S-carboxymethyl-cysteine formed from cysteine and iodoacetic acid at 25°C. Fluorescence of OPA derivatives is monitored at excitation and emission wavelengths of 340 and 455 nm, respectively. Detection limits are 50 fmol for AA. This technique offers the following advantages: simple procedures for preparation of samples, reagents, and mobile-phase solutions; rapid pre-column formation of OPA-AA derivatives and their efficient separation at room temperature (e.g., 20-25°C); high sensitivity of detection; easy automation on the HPLC apparatus; few interfering side reactions; a stable chromatography baseline for accurate integration of peak areas; and rapid regeneration of guard and analytical columns. Thus, the OPA method provides a useful tool to determine AA composition in proteins of animal tissues (e.g., skeletal muscle, liver, intestine, placenta, brain, and body homogenates) and foods (e.g., milk, corn grain, meat, and soybean meal).
Triboelectrification of Granular Plastic Wastes in Vibrated Zigzag-Shaped Square Pipes in View of Electrostatic Separation

This paper presents an original device for the electrification of granular mixtures in vibrated zigzag-shaped pipes. Spatial movement of the granules introduced in the pipes is controlled by varying the oscillation amplitude and the frequency of a slider-crank mechanism. In the first set of experiments, two sorts of granular plastics (ABS and HIPS) were separately processed through the vibratory tribocharging device. Both ABS and HIPS charged negatively in contact with the aluminum pipes. The absolute value of charge/mass ratio increased with the amplitude and frequency of the vibratory movements to attain a maximum of 26 nC/g for the HIPS particles. In the second set of experiments, 100-g samples of 50% ABS + 50% HIPS were tribocharged, than introduced in a freefall electrostatic separator. A composite experimental design was performed for modeling the process. The output variable was the extraction of ABS while the speed and length of the crank were with the applied voltage the three control variables under investigation. ABS extractions higher than 85% were obtained for optimally chosen values of the control variables.
Multi-view 3D reconstruction by random-search and propagation with view-dependent patch maps

This paper proposes an efficient multi-view 3D reconstruction method based on randomization and propagation scheme. Our method progressively refines a 3D model of a given scene by randomly perturbing the initial guess of 3D points and propagating photo-consistent ones to their neighbors. While finding local optima is an ordinary method for better photo-consistency, our randomization and propagation takes lucky matchings to spread better points replacing old ones for reducing the computational complexity. Experiments show favorable efficiency of the proposed method accompanied by competitive accuracy with the state-of-the-art methods.
Text Segmentation as a Supervised Learning Task

Text segmentation, the task of dividing a document into contiguous segments based on its semantic structure, is a longstanding challenge in language understanding. Previous work on text segmentation focused on unsupervised methods such as clustering or graph search, due to the paucity in labeled data. In this work, we formulate text segmentation as a supervised learning problem, and present a large new dataset for text segmentation that is automatically extracted and labeled from Wikipedia. Moreover, we develop a segmentation model based on this dataset and show that it generalizes well to unseen natural text.
A SVM-based detection approach for GPS spoofing attacks to UAV

Today, the necessity of strong cyber security measures is self-evident. The proliferation of cyber attacks is causing increasing damage to companies, governments and individuals and it is not just the number of cyber security attacks that is increasing: the severity of these attacks is on the rise as well. In particular, aviation systems and Remotely Piloted Aircraft Systems are of highest importance in terms of safety and security. In this paper, we introduce a novel approach to the detection of Global Positioning System (GPS) spoofing attack to Unmanned Aerial Vehicle (UAV) based on the analysis of state estimation using the Support Vector Machine (SVM) as a tool for the anomaly detection.
Task-Based Authorization Controls (TBAC): A Family of Models for Active and Enterprise-Oriented Autorization Management

In this paper, we develop a new paradigm for access ontrol and authorization management, called task-based authorization control s (TBAC). TBAC models access controls from a task-oriented perspective th an e traditional subject-object one. Access mediation now involves authorizations at various points during the completion of tasks in accordance with some applica tion logic. By taking a taskoriented view of access control and authorizations, TBAC lays the foundation for research into a new breed of “active” security mode ls that are required for agentbased distributed computing and workflow management .
A systematic review of interventions to improve handwriting.

Handwriting difficulties are among the most common reasons for referral of children to occupational therapy.



To determine the effectiveness of handwriting interventions.



A systematic review was carried out. Included studies were randomized or nonrandomized controlled trials of interventions that could be used by an occupational therapist to improve written output (printing or writing) among school-aged children identified as having difficulties with handwriting. Electronic searches of relevant databases were conducted up to January 2010.



Eleven studies met the inclusion criteria. These studies tested (1) relaxation and practice with or without EMG, (2) sensory-based training without handwriting practice, and (3) handwriting-based practice (including sensory-focused or cognitive focused handwriting practice). Regardless of treatment type, interventions that did not include handwriting practice and those that included less than 20 practice sessions were ineffective.



Effective occupational therapy for improving handwriting must include adequate handwriting practice.
Brainstorming under constraints: why software developers brainstorm in groups

Group brainstorming is widely adopted as a design method in the domain of software development. However, existing brainstorming literature has consistently proven group brainstorming to be ineffective under the controlled laboratory settings. Yet, electronic brainstorming systems informed by the results of these prior laboratory studies have failed to gain adoption in the field because of the lack of support for group well-being and member support. Therefore, there is a need to better understand brainstorming in the field. In this work, we seek to understand why and how brainstorming is actually practiced, rather than how brainstorming practices deviate from formal brainstorming rules, by observing brainstorming meetings at Microsoft. The results of this work show that, contrary to the conventional brainstorming practices, software teams at Microsoft engage heavily in the constraint discovery process in their brainstorming meetings. We identified two types of constraints that occur in brainstorming meetings. Functional constraints are requirements and criteria that define the idea space, whereas practical constraints are limitations that prioritize the proposed solutions.
Massively-parallel single nucleus RNA-seq with DroNc-seq

Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.
A Sub-nW Multi-stage Temperature Compensated Timer for Ultra-Low-Power Sensor Nodes

Accurate measurement of synchronization cycle time is required for ultra-low power wireless sensor nodes with stringent power budgets. A multi-stage gate-leakage-based timer with boosted charging is proposed to address the high jitter of prior-art gate-leakage-based timers. The key approaches are faster load capacitor charging, wider voltage swing, and an improved gain sensing inverter. The proposed timer reduces RMS jitter by 8.1× and synchronization uncertainty by 4.1×, which allows hourly tracking with 200 ms uncertainty while consuming 660 pW. A novel closed-loop temperature compensation scheme with dynamic leakage adjustment is also proposed to achieve temperature sensitivity of 31 ppm/°C.
PositNN: Tapered Precision Deep Learning Inference for the Edge

The performance of neural networks, especially the currently popular form of deep 1 neural networks, is often limited by the underlying hardware. Computations in 2 deep neural networks are expensive, have large memory footprint, and are power 3 hungry. Conventional reduced-precision numerical formats, such as fixed-point 4 and floating point, cannot accurately represent deep neural network parameters 5 with a nonlinear distribution and small dynamic range. Recently proposed posit 6 numerical format with tapered precision represents small values more accurately 7 than the other formats. In this work, we propose an ultra-low precision deep neural 8 network, PositNN, that uses posits during inference. The efficacy of PositNN is 9 demonstrated on a deep neural network architecture with two datasets (MNIST 10 and Fashion MNIST), where an 8-bit PositNN outperforms other {5-8}-bit low11 precision neural networks and a 32-bit floating point baseline network.
Applying standards to systematize learning analytics in serious games

Learning Analytics is an emerging field focused on analyzing learners’ interactions with educational content. One of the key open issues in learning analytics is the standardization of the data collected. This is a particularly challenging issue in serious games, which generate a diverse range of data. This paper reviews the current state of learning analytics, data standards and serious games, studying how serious games are tracking the interactions from their players and the metrics that can be distilled from them. Based on this review, we propose an interaction model that establishes a basis for applying Learning Analytics into serious games. This paper then analyzes the current standards and specifications used in the field. Finally, it presents an implementation of the model with one of the most promising specifications: Experience API (xAPI). The Experience API relies on Communities of Practice developing profiles that cover different use cases in specific domains. This paper presents the Serious Games xAPI Profile: a profile developed to align with the most common use cases in the serious games domain. The profile is applied to a case study (a demo game), which explores the technical practicalities of standardizing data acquisition in serious games. In summary, the paper presents a new interaction model to track serious games and their implementation with the xAPI specification.
Mining Topically Coherent Patterns for Unsupervised Extractive Multi-document Summarization

Addressing the problem of information overload, automatic multi-document summarization (MDS) has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of MDS systems. In this paper, we proposed a novel unsupervised pattern-enhanced topic model (PETMSum) for the MDS task. PETMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative, non-redundant, and topically coherent sentences can be selected automatically to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2006 and 2007. The results prove the effectiveness and efficiency of our proposed approach.
1 N 3 Logic : A Logical Framework For the World Wide Web

The Semantic Web drives towards the use of the Web for interacting with logically interconnected data. Through knowledge models such as Resource Description Framework (RDF), the Semantic Web provides a unifying representation of richly structured data. Adding logic to the Web implies the use of rules to make inferences, choose courses of action, and answer questions. This logic must be powerful enough to describe complex properties of objects but not so powerful that agents can be tricked by being asked to consider a paradox. The Web has several characteristics that can lead to problems when existing logics are used, in particular, the inconsistencies that inevitably arise due to the openness of the Web, where anyone can assert anything. N3Logic is a logic that allows rules to be expressed in a Web environment. It extends RDF with syntax for nested graphs and quantified variables and with predicates for implication and accessing resources on the Web, and functions including cryptographic, string, math. The main goal of N3Logic is to be a minimal extension to the RDF data model such that the same language can be used for logic and data. In this paper, we describe N3Logic and illustrate through examples why it is an appropriate logic for the Web.