nils-herrmann/clean_scirepeval huggingface dataset:
Detection of malingering in assessment of adult ADHD.

Comparisons of two assessment measures for ADHD: the ADHD Behavior Checklist and the Integrated Visual and Auditory Continuous Performance Test (IVA CPT) were examined using undergraduates (n=44) randomly assigned to a control or a simulated malingerer condition and undergraduates with a valid diagnosis of ADHD (n=16). It was predicted that malingerers would successfully fake ADHD on the rating scale but not on the CPT for which they would overcompensate, scoring lower than all other groups. Analyses indicated that the ADHD Behavior Rating Scale was successfully faked for childhood and current symptoms. IVA CPT could not be faked on 81% of its scales. The CPT's impairment index results revealed: sensitivity 94%, specificity 91%, PPP 88%, NPP 95%. Results provide support for the inclusion of a CPT in assessment of adult ADHD.
Mobile-Based Eye-Blink Detection Performance Analysis on Android Platform

In this article, we develop a real-time mobile phone-based gaze tracking and eye-blink detection system on Android platform. Our eye-blink detection scheme is developed based on the time difference between two open eye states. We develop our system by finding the greatest circle—pupil of an eye. So we combine the both Haar classifier and Normalized Summation of Square of Difference template-matching method. We define the eyeball area that is extracted from the eye region as the region of interest (ROI). The ROI helps to differentiate between the open state and closed state of the eyes. The output waveform of the scheme is analogous to binary trend, which alludes the blink detection distinctly. We categorize short, medium, and long blink, depending on the degree of closure and blink duration. Our analysis is operated on medium blink under 15 frames/s. This combined solution for gaze tracking and eye-blink detection system has high detection accuracy and low time consumption. We obtain 98% accuracy at 0° angles for blink detection from both eyes. The system is also extensively experimented with various environments and setups, including variations in illuminations, subjects, gender, angles, processing speed, RAM capacity, and distance. We found that the system performs satisfactorily under varied conditions in real time for both single eye and two eyes detection. These concepts can be exploited in different applications, e.g., to detect drowsiness of a driver, or to operate the computer cursor to develop an eye-operated mouse for disabled people.
Regression analysis of spatial data.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.
External visual interface for a Nikon 6D autocollimator

The goal of this paper is to describe the potential of a visual interface applied to a Nikon 6B/6D autocollimator in order to replace human operator within a “laboratory grade” measurement. The optical interface implemented consists of a Basler HD camera attached to the autocollimator's eyepiece, camera positioning devices, and a proprietary digital image processing package, built around Matlab environment. The whole system is used to analyze the reticle image to detect both the scale and the measuring crosshair lines at sub-pixel level. The system performance was compared in a controlled experiment against an electronic level with internationally traceable certification. The resolution of the pitch angles obtained was 10 times better than that originally obtained with the instrument. This example shows that the value of 0.02 pixels for the uncertainty associated with sub-pixel position of reticle lines is realistic, allowing for continue working on a robust interface for all angle parameters that can be measured using autocollimators.
Growth Hormone Insensitivity (Laron Syndrome)

In 1966 our group reported three siblings with clinical and biochemical features of GH deficiency but who had high serum GH levels [1] in the range of active acromegaly. Within 2 years we were able to collect 22 patients  all Oriental Jews. The first hypothesis that the defect is due to an abnormal GH molecule was disproved by finding that the circulating GH of these patients is normal by immunologic [5,6] and radioreceptor tests using GH receptors (GH-R) prepared from human liver membranes [7,8]. Growth hormone insensitivity was diagnosed by the low serum levels of insulin-like growth factor I (IGF-I) (at that time called somatomedin-C) in the presence of high endogenous GH levels and by the inability to generate IGF-I by the administration of exogenous hGH [9,10]. The cause of the GH resistance was also demonstrated by our group in 1984 by showing that GH-R prepared from liver membranes obtained by biopsies from two patients with LS did not bind radio-labelled human GH [11]. The subsequent cloning by Leung et al., of the GH-R , its characterization by Godowski et al. of the GH-R [13] and the introduction of new techniques in molecular biology enabled the localization of the molecular defects of the GH receptor gene. Geographical distribution and genetic aspects Since the first description of patients with LS in Israel of Oriental Jewish origin (Yemen, Iran, Afghanistan, Middle East and North Africa [1,4,14], more and more patients have been diagnosed both in our and other countries, the majority originating in the Mediterranean area, of North African, Spanish or Italian origin and Middle East of Jewish, Arab, Turkish, Iranian or Pakistani origin or in descendants of subjects originating from these regions. A large genetic isolate has been reported from Ecuador [15] suspected to have originated from Jewish conversos fleeing the Spanish Inquisition, and a smaller one in the Bahamas [16]. There are also numerous patients in Turkey [17], Pakistan and India . In most of the above populations consanguinity was or is still in practice. Isolated patients not of Mediterranean or Oriental descent have been reported from Denmark, The Netherlands, Slovakia, Russia, Slovenia, Japan and the USA. A more detailed listing appears in recent reviews of this syndrome [20–22]. At present many hundreds of patients have been diagnosed and the number increases constantly. Analysis of the Israeli cohort led to the conclusion that Laron syndrome is caused by an autosomal fully penetrant recessive mechanism [23].
Clinical course of primary progressive aphasia: clinical and FDG-PET patterns

Primary progressive aphasia (PPA) may be the onset of several neurodegenerative diseases. This study evaluates a cohort of patients with PPA to assess their progression to different clinical syndromes, associated factors that modulate this progression, and patterns of cerebral metabolism linked to different clinical evolutionary forms. Thirty-five patients meeting PPA criteria underwent a clinical and neuroimaging 18F-Fluorodeoxyglucose PET evaluation. Survival analysis was performed using time from clinical onset to the development of a non-language symptom or deficit (PPA-plus). Cerebral metabolism was analyzed using Statistical Parametric Mapping. Patients classified into three PPA variants evolved to atypical parkinsonism, behavioral disorder and motor neuron disease in the agrammatic variant; to behavioral disorder in the semantic; and to memory impairment in the logopenic. Median time from the onset of symptoms to PPA-plus was 36 months (31–40, 95 % confidence interval). Right laterality, and years of education were associated to a lower risk of progression, while logopenic variant to a higher risk. Different regions of hypometabolism were identified in agrammatic PPA with parkinsonism, motor neuron disease and logopenic PPA-plus. Clinical course of PPA differs according to each variant. Left anterior temporal and frontal medial hypometabolism in agrammatic variant is linked to motor neuron disease and atypical parkinsonism, respectively. PPA variant, laterality and education may be associated to the risk of progression. These results suggest the possibility that clinical and imaging data could help to predict the clinical course of PPA.
Konnect: An Internet of Things(IoT) based smart helmet for accident detection and notification

The objective of the smart helmet is to provide a means and apparatus for detecting and reporting accidents. Sensors, Wi-Fi enabled processor, and cloud computing infrastructures are utilised for building the system. The accident detection system communicates the accelerometer values to the processor which continuously monitors for erratic variations. When an accident occurs, the related details are sent to the emergency contacts by utilizing a cloud based service. The vehicle location is obtained by making use of the global positioning system. The system promises a reliable and quick delivery of information relating to the accident in real time and is appropriately named Konnect. Thus, by making use of the ubiquitous connectivity which is a salient feature for the smart cities, a smart helmet for accident detection is built.
Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial.

Deep brain stimulation (DBS) of the subcallosal cingulate white matter has shown promise as an intervention for patients with chronic, unremitting depression. To test the safety and efficacy of DBS for treatment-resistant depression, a prospective, randomised, sham-controlled trial was conducted.



Participants with treatment-resistant depression were implanted with a DBS system targeting bilateral subcallosal cingulate white matter and randomised to 6 months of active or sham DBS, followed by 6 months of open-label subcallosal cingulate DBS. Randomisation was computer generated with a block size of three at each site before the site started the study. The primary outcome was frequency of response (defined as a 40% or greater reduction in depression severity from baseline) averaged over months 4-6 of the double-blind phase. A futility analysis was performed when approximately half of the proposed sample received DBS implantation and completed the double-blind phase. At the conclusion of the 12-month study, a subset of patients were followed up for up to 24 months. The study is registered at ClinicalTrials.gov, number NCT00617162.



Before the futility analysis, 90 participants were randomly assigned to active (n=60) or sham (n=30) stimulation between April 10, 2008, and Nov 21, 2012. Both groups showed improvement, but there was no statistically significant difference in response during the double-blind, sham-controlled phase (12 [20%] patients in the stimulation group vs five [17%] patients in the control group). 28 patients experienced 40 serious adverse events; eight of these (in seven patients) were deemed to be related to the study device or surgery.



This study confirmed the safety and feasibility of subcallosal cingulate DBS as a treatment for treatment-resistant depression but did not show statistically significant antidepressant efficacy in a 6-month double-blind, sham-controlled trial. Future studies are needed to investigate factors such as clinical features or electrode placement that might improve efficacy.



Abbott (previously St Jude Medical).
Dermoscopy of pigmented mucosal lesions and considerations for a case of melanoma of the lip

Financial support: None Conflict of interest: None ABSTRACT The observation of mucous membranes should be part of a dermatological examination. It is known that early diagnosis is critical for the prognosis of patients with malignant melanocytic lesions. Nevertheless, integrating this step into the examination routine and performing a differential diagnosis between benign and malignant mucosal lesions with only clinical signs, are great challenges. Dermoscopy is still seldom-used for pigmented lesions of mucous membranes, however recent studies have shown its potential. In light of a case of melanoma of the lip, the authors provide tips and data from the literature that highlight the usefulness of the technique, and support the use of dermoscopic examination in the dermatologist’s routine.
Toward Marker-Free 3D Pose Estimation in Lifting: A Deep Multi-View Solution

Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks, which requires very accurate 3D pose. Existing approaches mainly utilize marker-based sensors to collect 3D information. However, these methods are usually expensive to setup, timeconsuming in process, and sensitive to the surrounding environment. In this study, we propose a multi-view based deep perceptron approach to address aforementioned limitations. Our approach consists of two modules: a "view-specific perceptron" network extracts rich information independently from the image of view, which includes both 2D shape and hierarchical texture information; while a "multi-view integration" network synthesizes information from all available views to predict accurate 3D pose. To fully evaluate our approach, we carried out comprehensive experiments to compare different variants of our design. The results prove that our approach achieves comparable performance with former marker-based methods, i.e. an average error of 14:72 ± 2:96 mm on the lifting dataset. The results are also compared with state-of-the-art methods on HumanEva- I dataset , which demonstrates the superior performance of our approach.
Performance of AdaBoost classifier in recognition of superposed modulations for MIMO TWRC with physical-layer network coding

Modulation recognition algorithms have recently received a great deal of attention in academia and industry. In addition to their application in the military field, these algorithms found civilian use in reconfigurable systems, such as cognitive radios. Most previously existing algorithms are focused on recognition of a single modulation. However, a multiple-input multiple-output two-way relaying channel (MIMO TWRC) with physical-layer network coding (PLNC) requires the recognition of the pair of sources modulations from the superposed constellation at the relay. In this paper, we propose an algorithm for recognition of sources modulations for MIMO TWRC with PLNC. The proposed algorithm is divided in two steps. The first step uses the higher order statistics based features in conjunction with genetic algorithm as a features selection method, while the second step employs AdaBoost as a classifier. Simulation results show the ability of the proposed algorithm to provide a good recognition performance at acceptable signal-to-noise values.
Toward a merged approach for cross-platform applications (web, mobile and desktop)

Several approaches are available to create cross-platform applications. The majority of these approaches focus on purely mobile platforms. Their principle is to develop the application once and be able to deploy it to multiple mobile platforms with different operating systems (Android (Java), IOS (Objective C), Windows Phone 7 (C#), etc.). In this article, we propose a merged approach and cross-platform called ZCA "ZeroCouplage Approach". Merged to regroup the strong points of approaches: "Runtime", "Component-Based" and "Cloud-Based" thank to a design pattern which we created and named M2VC (Model-Virtual-View-Controller). Cross-platform allows creating a unique application that is deployable directly on many platforms: Web, Mobile and Desktop. In this article, we also compare our ZCA approach with others to approve its added value. Our idea, contrary to mobile approaches, consists of a given technology to implement cross-platform applications. To validate our approach, we have developed an open source framework named ZCF "ZeroCouplage Framework" for Java technology.
An Economic Evaluation of the Moneyball Hypothesis

I n his 2003 book Moneyball, financial reporter Michael Lewis made a striking claim: the valuation of skills in the market for baseball players was grossly inefficient. The discrepancy was so large that when the Oakland Athletics hired an unlikely management group consisting of Billy Beane, a former player with mediocre talent, and two quantitative analysts, the team was able to exploit this inefficiency and outproduce most of the competition, while operating on a shoestring budget. The publication of Moneyball triggered a firestorm of criticism from baseball insiders (Lewis, 2004), and it raised the eyebrows of many economists as well. Basic price theory implies a tight correspondence between pay and productivity when markets are competitive and rich in information, as would seem to be the case in baseball. The market for baseball players receives daily attention from the print and broadcast media, along with periodic in-depth analysis from lifelong baseball experts and academic economists. Indeed, a case can be made that more is known about pay and quantified performance in this market than in any other labor market in the American economy. In this paper, we test the central portion of Lewis’s (2003) argument with elementary econometric tools and confirm his claims. In particular, we find that hitters’ salaries during this period did not accurately reflect the contribution of various batting skills to winning games. This inefficiency was sufficiently large that knowledge of its existence, and the ability to exploit it, enabled the Oakland Athletics to gain a substantial advantage over their competition. Further, we find
Why and how Java developers break APIs

Modern software development depends on APIs to reuse code and increase productivity. As most software systems, these libraries and frameworks also evolve, which may break existing clients. However, the main reasons to introduce breaking changes in APIs are unclear. Therefore, in this paper, we report the results of an almost 4-month long field study with the developers of 400 popular Java libraries and frameworks. We configured an infrastructure to observe all changes in these libraries and to detect breaking changes shortly after their introduction in the code. After identifying breaking changes, we asked the developers to explain the reasons behind their decision to change the APIs. During the study, we identified 59 breaking changes, confirmed by the developers of 19 projects. By analyzing the developers' answers, we report that breaking changes are mostly motivated by the need to implement new features, by the desire to make the APIs simpler and with fewer elements, and to improve maintainability. We conclude by providing suggestions to language designers, tool builders, software engineering researchers and API developers.
Active front-end for shaft power generation and voltage control in FREMM frigates integrated power system: Modeling and validation

Italian Navy is being equipping its fleet with the new FREMM class of frigates, developed under a joint program by Italian and French Navies. They are conceptually new war ships endowed with a hybrid propulsion system, composed by a gas turbine and two electric drives, which can be operated also as shaft-generators. University of Trieste has been charged with developing a software simulator of the FREMM integrated electrical power system and its propulsion drives, which has been recently delivered. The simulator will be employed by Italian Navy for virtual tests and personnel training activities. The paper is focused on the shaft-generator operation of the electric propulsion drives and their simulation. Numeric results are displayed in the paper and compared with factory tests in order to validate the simulator.
Review on mechanisms for detecting sinkhole attacks on RPLs

(IPv6) over Low power Wireless Personal Area Networks (6LoWPAN) is extensively used in wireless sensor networks (WSNs) due to its ability to transmit IPv6 packet with low bandwidth and limited resources. 6LoWPAN has several operations in each layer. Most existing security challenges are focused on the network layer, which is represented by its routing protocol for low-power and lossy network (RPL). RPL components include WSN nodes that have constrained resources. Therefore, the exposure of RPL to various attacks may lead to network damage. A sinkhole attack is a routing attack that could affect the network topology. This paper aims to investigate the existing detection mechanisms used in detecting sinkhole attack on RPL-based networks. This work categorizes and presents each mechanism according to certain aspects. Then, their advantages and drawbacks with regard to resource consumption and false positive rate are discussed and compared.
Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks.

One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BNs) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analysed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.