nils-herrmann/clean_scirepeval huggingface dataset:
FLIR Image Segmentation and Natural Object Classification

In this paper we compare four classification techniques for classifying texture data of various natural objects found in FLIR images. The techniques compared include Linear Discriminant Analysis, Mean Classifier and two different models of K-Nearest Neighbour methods. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. A total of 2680 samples for a total of twelve different classes are used for object recognition. The results on correctly identifying twelve natural objects in scenes are compared across the four classifiers on both unnormalised and normalised data. On unnormalised data, the average best recognition rate obtained using a ten fold cross-validation is 96.5%, and on unnormalised data it is 86.1% with a single nearest neighbour technique.
The impact of intent selection on diversified search evaluation

To construct a diversified search test collection, a set of possible subtopics (or intents) needs to be determined for each topic, in one way or another, and perintent relevance assessments need to be obtained. In the TREC Web Track Diversity Task, subtopics are manually developed at NIST, based on results of automatic click log analysis; in the NTCIR INTENT Task, intents are determined by manually clustering 'subtopics strings' returned by participating systems. In this study, we address the following research question: Does the choice of intents for a test collection affect relative performances of diversified search systems? To this end, we use the TREC 2012 Web Track Diversity Task data and the NTCIR-10 INTENT-2 Task data, which share a set of 50 topics but have different intent sets. Our initial results suggest that the choice of intents may affect relative performances, and that this choice may be far more important than how many intents are selected for each topic
Challenges with Current Wearable Technology in Monitoring Health Data and Providing Positive Behavioural Support

There is a current trend of wearable sensing with regards to health. Wearable sensors and devices allow us to monitor various aspects of our lives. Through this monitoring, wearable systems can utilise data to positively influence an individual’s overall health and wellbeing. We envisage a future where technology can effectively help us to become fitter and healthier, but the current state of wearables and future directions are unclear. In this paper, we present an overview of current methods used within wearable applications to monitor and support positive health and wellbeing within an individual. We then highlight issues and challenges outlined by previous studies and describe the future focuses of work.
Associations Between Different Parenting Styles and Child Behavior

Attention-Deficit/Hyperactivity Disorder (ADHD) is a chronic, behavioral control disorder, which is most frequently diagnosed in children. ADHD is traditionally conceptualized as a neurological disorder; however, there are important environmental factors that affect symptom presentation. Parental involvement is a critical factor in virtually every form of treatment for ADHD, yet the specific parenting styles employed by parents of children with ADHD has received little attention. This study sought to address this issue through identifying associations between parenting styles, ADHD symptoms, and homework problems. Participants were recruited from a community sample, using a snowball sampling method. Participants were required to complete three rating scales, which assessed for parenting style, child’s ADHD symptoms, and child’s homework problems. Results indicated that parents who had high scores on the authoritative scale had children with more ADHD Inattentive and ADHD Combined symptoms than did parents who had lower scores on the authoritative scale. Results also indicated that higher scores on the authoritative scale were associated with a greater number of homework problems. These findings indicate that authoritative parenting is not a unitary construct, but that it has various expressions on a continuum of demandingness and responsiveness. Differences in authoritative parenting may contribute to ADHD symptom presence and to homework problems in male children. DIFFERENCES BETWEEN PARENTING STYLES AND CHILD BEHAVIOR v TABLE OF CONTENTS Acknowledgements.................................................................................... iii Abstract.................................................................................................. iv Table of
Spasmodic torticollis associated with sertraline in a child and an adolescent.

Movement disorders or extrapyramidal symptoms (EPS) associated with selective serotonin reuptake inhibitors (SSRIs) have been reported. Although akathisia was found to be the most common EPS, and fluoxetine was implicated in the majority of the adverse reactions, there were also cases with EPS due to sertraline treatment. We present a child and an adolescent who developed torticollis (cervical dystonia) after using sertraline. To our knowledge, the child case is the first such report of sertraline-induced torticollis, and the adolescent case is the third in the literature.
Improving beamforming performance by phased synthetic aperture imaging in medical ultrasound

Ultrasound image quality is related to the receive beamformer’s ability. Delay and sum (DAS), a conventional beamformer, is combined with the coherence factor (CF) technique to suppress side lobe levels. The purpose of this study is to improve these beamformer’s abilities. It has been shown that extension of the receive aperture can improve the receive beamformer’s ability in radar studies. This paper shows that the focusing quality of CF and CF+DAS in medical ultrasound can be increased by extension of the receive aperture’s length in phased synthetic aperture (PSA) imaging. The 3-dB width of the main lobe in the receive beam related to CF focusing decreased to 0.55 mm using the proposed PSA compared to the conventional phased array (PHA) imaging, whose FWHM is about 0.9 mm. The clutter-to-total-energy ratio (CTR) represented by R20 dB showed an improvement of 50 and 33% for CF and CF+DAS beamformers, respectively, with PSA as compared to PHA. In addition, simulation results validated the effectiveness of PSA versus PHA. In applications where there are no important limitations on the SNR, PSA imaging is recommended as it increases the ability of the receive beamformer for better focusing.
MRI of acute myocarditis: a comprehensive approach based on various imaging sequences.

To explore the diagnostic performance of MRI for the diagnosis of acute myocarditis, using a comprehensive imaging approach.



Twenty patients with myocarditis and 7 age-matched and gender-matched control subjects underwent comprehensive MRI. Magnetic resonance (MR) examinations included axial T2-weighted sequences, precontrast and postcontrast ECG-gated T1-weighted sequences in axial and short heart axis, cine-MRI, and serial dynamic turbo fast low-angle shot (turboFLASH) acquisitions in the short axis following Gd injection for a period of 2 min. Precontrast and postcontrast images were postprocessed using subtraction. Two observers read all images qualitatively and quantitatively. Myocardial enhancement was compared between patients and control subjects.



Myocardial involvement was focal in 6 patients examined within 1 week from clinical onset, and diffuse in the remaining 14 patients examined later.



Qualitatively, contrast-enhanced T1-weighted subtracted images had 100% sensitivity and specificity for myocardial involvement. Postcontrast T1-weighted images were able to discriminate the early phase (nodular enhancement) from the later phase of myocarditis (diffuse enhancement). Quantitatively, myocardial enhancement was 56% +/- 3.2% in patients, vs 29% +/- 3.1% in control subjects using T1-weighted MRI (p < 0.0001). Serial turboFLASH images displayed greater myocardial enhancement between 25 s and 120 s in patients than in control subjects (p < 0.0001); however, there was marked enhancement of skeletal muscles in both early and late stages of myocarditis compared to control subjects (p < 0.0001).



On the basis of subtracted cardiac-gated T1-weighted images and serial postinjection turboFLASH images, our study shows that myocarditis is largely, at least in the early stages, a focal process in the myocardium. It also provides evidence of transient skeletal muscle involvement, which may actually be useful for diagnosis.
Wireless sensors powered by microbial fuel cells.

Monitoring parameters characterizing water quality, such as temperature, pH, and concentrations of heavy metals in natural waters, is often followed by transmitting the data to remote receivers using telemetry systems. Such systems are commonly powered by batteries, which can be inconvenient at times because batteries have a limited lifetime and must be recharged or replaced periodically to ensure that sufficient energy is available to power the electronics. To avoid these inconveniences, a microbial fuel cell was designed to power electrochemical sensors and small telemetry systems to transmit the data acquired by the sensors to remote receivers. The microbial fuel cell was combined with low-power, high-efficiency electronic circuitry providing a stable power source for wireless data transmission. To generate enough power for the telemetry system, energy produced by the microbial fuel cell was stored in a capacitor and used in short bursts when needed. Since commercial electronic circuits require a minimum 3.3 V input and our cell was able to deliver a maximum of 2.1 V, a DC-DC converter was used to boost the potential. The DC-DC converter powered a transmitter, which gathered the data from the sensor and transmitted it wirelessly to a remote receiver. To demonstrate the utility of the system, temporal variations in temperature were measured, and the data were wirelessly transmitted to a remote receiver.
A Batch Normalization Free Binarized Convolutional Deep Neural Network on an FPGA (Abstract Only)

A pre-trained convolutional deep neural network (CNN) is a feed-forward computation perspective, which is widely used for the embedded systems, requires high power-and-area efficiency. This paper realizes a binarized CNN which treats only binary 2-values (+1/-1) for the inputs and the weights. In this case, the multiplier is replaced into an XNOR circuit instead of a dedicated DSP block. For hardware implementation, using binarized inputs and weights is more suitable. However, the binarized CNN requires the batch normalization techniques to retain the classification accuracy. In that case, the additional multiplication and addition require extra hardware, also, the memory access for its parameters reduces system performance. In this paper, we propose the batch normalization free CNN which is mathematically equivalent to the CNN using batch normalization. The proposed CNN treats the binarized inputs and weights with the integer bias. We implemented the VGG-16 benchmark CNN on the NetFPGA-SUME FPGA board, which has the Xilinx Inc. Virtex7 FPGA and three off-chip QDR II+ Synchronous SRAMs. Compared with the conventional FPGA realizations, although the classification error rate is 6.5% decayed, the performance is 2.82 times faster, the power efficiency is 1.76 times lower, and the area efficiency is 11.03 times smaller. Thus, our method is suitable for the embedded computer system.
A Probabilistic Traffic-Theoretic Network Loading Model Suitable for Large-Scale Network Analysis

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Design of a Web-Based LBS Framework Addressing Usability, Cost, and Implementation Constraints

This research investigates barriers that prevent Location Based Services (LBS) from reaching its full potential. The different constraints, including poor usability, lack of positioning support, costs, and integration difficulties are highlighted. A framework was designed incorporating components based on existing and new technologies that could help address the constraints of LBS and increase end-user acceptance. This research proposes that usability constraints can be addressed by adapting a system to user characteristics which are inferred on the basis of captured user context and interaction data. A prototype LBS system was developed to prove the feasibility and benefit of the framework design, demonstrating that constraints of positioning, cost, and integration can be overcome. Volunteers were asked to use the system, and to answer questions in relation to their proficiency and experience. User-feedback showed that the proposed combination of functionality was well-received, and the prototype was appealing to many users. Ground-truths from the survey were related back to data captured with a user monitoring component in order to investigate whether users can be classified according to their context and how they interact. The results have shown that statistically significant relationships exist, and that by using the C4.5 decision-tree, computer proficiency can be estimated within one class-width in 76.7% of the cases. These results suggest that it may be possible to build a user-model to estimate computer proficiency on the basis of user-interaction data. The user model could then used to improve usability through adaptive user-specific customisations.
The Intended and Actual Adoption of Online Purchasing : A Brief Review of Recent Literature

The rapid growth of e-commerce is imposing profound impacts on modern society. On the supply side, the emergence of e-commerce is greatly changing the operation behavior of some retailers and is increasing product internationalization due to its geographically unlimited nature. On the demand side, the pervasiveness of e-commerce affects how, where, and when consumers shop, and indirectly influences the way in which we live our lives. However, the development of e-commerce is still in an early stage, and why consumers choose (or do not choose) online purchasing is far from being completely understood. To better evaluate and anticipate those profound impacts of e-commerce, therefore, it is important to further refine our understanding of consumers' e-shopping behavior. A number of studies have investigated e-shopping behavior, and reviewing them is valuable for further improving our understanding. This report aims to summarize previous e-shopping research in a systematic way. In this review, we are interested primarily in the potential benefits and costs that the internet offers for the business-to-consumer segment of e-commerce in the transaction (purchase) channel. An overview of the 65 empirical studies analyzed in this report is provided in the Appendix. Most previous studies fall into one or more of several theoretical frameworks, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory, and others. Among them, social psychological theories (the theory of reasoned action, the theory of planned behavior, the technology acceptance model) were widely applied. As shown in the applications of different theories, e-shopping behavior is not a simple decision process, and thus an integration of various theories is necessary to deal with its complexities. We suggest synthesizing these theories through the development of a comprehensive list of benefits and costs, using each of the key constructs of the pertinent theories as a guide to identifying the nature of those benefits and costs. The dependent variables mainly include e-shopping intention and actual e-shopping behavior (a few studies used attitudes toward e-shopping). E-shopping intention was measured by various dimensions. Among them, the directly-stated intention to purchase online is the most frequently used measure. Although some studies used a unidimensional measure, most adopted a latent construct to assess consumers' e-shopping intentions. Actual e-shopping behavior mainly includes three dimensions: adoption, spending, and frequency. Most studies examined one or more of these three dimensions directly, while a few studies constructed a latent …
Tailored displays to compensate for visual aberrations

We introduce tailored displays that enhance visual acuity by decomposing virtual objects and placing the resulting anisotropic pieces into the subject's focal range. The goal is to free the viewer from needing wearable optical corrections when looking at displays. Our tailoring process uses aberration and scattering maps to account for refractive errors and cataracts. It splits an object's light field into multiple instances that are each in-focus for a given eye sub-aperture. Their integration onto the retina leads to a quality improvement of perceived images when observing the display with naked eyes. The use of multiple depths to render each point of focus on the retina creates multi-focus, multi-depth displays. User evaluations and validation with modified camera optics are performed. We propose tailored displays for daily tasks where using eyeglasses are unfeasible or inconvenient (e.g., on head-mounted displays, e-readers, as well as for games); when a multi-focus function is required but undoable (e.g., driving for farsighted individuals, checking a portable device while doing physical activities); or for correcting the visual distortions produced by high-order aberrations that eyeglasses are not able to.
Do Process Modelling Techniques Get Better? A Comparative Ontological Analysis of BPMN

Current initiatives in the field of Business Process Management (BPM) strive for the development of a BPM standard notation by pushing the Business Process Modeling Notation (BPMN). However, such a proposed standard notation needs to be carefully examined. Ontological analysis is an established theoretical approach to evaluating modelling techniques. This paper reports on the outcomes of an ontological analysis of BPMN and explores identified issues by reporting on interviews conducted with BPMN users in Australia. Complementing this analysis we consolidate our findings with previous ontological analyses of process modelling notations to deliver a comprehensive assessment of BPMN.
Why the Diagnosis of Attention Deficit Hyperactivity Disorder Matters

Attention Deficit Hyperactivity disorder (ADHD) is one of the most common and challenging childhood neurobehavioral disorders. ADHD is known to negatively impact children, their families, and their community. About one-third to one-half of patients with ADHD will have persistent symptoms into adulthood. The prevalence in the United States is estimated at 5-11%, representing 6.4 million children nationwide. The variability in the prevalence of ADHD worldwide and within the US may be due to the wide range of factors that affect accurate assessment of children and youth. Because of these obstacles to assessment, ADHD is under-diagnosed, misdiagnosed, and undertreated.



We examined factors associated with making and receiving the diagnosis of ADHD. We sought to review the consequences of a lack of diagnosis and treatment for ADHD on children's and adolescent's lives and how their families and the community may be involved in these consequences.



We reviewed scientific articles looking for factors that impact the identification and diagnosis of ADHD and articles that demonstrate naturalistic outcomes of diagnosis and treatment. The data bases PubMed and Google scholar were searched from the year 1995 to 2015 using the search terms "ADHD, diagnosis, outcomes." We then reviewed abstracts and reference lists within those articles to rule out or rule in these or other articles.



Multiple factors have significant impact in the identification and diagnosis of ADHD including parents, healthcare providers, teachers, and aspects of the environment. Only a few studies detailed the impact of not diagnosing ADHD, with unclear consequences independent of treatment. A more significant number of studies have examined the impact of untreated ADHD. The experience around receiving a diagnosis described by individuals with ADHD provides some additional insights.



ADHD diagnosis is influenced by perceptions of many different members of a child's community. A lack of clear understanding of ADHD and the importance of its diagnosis and treatment still exists among many members of the community including parents, teachers, and healthcare providers. More basic and clinical research will improve methods of diagnosis and information dissemination. Even before further advancements in science, strong partnerships between clinicians and patients with ADHD may be the best way to reduce the negative impacts of this disorder.
Femoral cortical index: an indicator of poor bone quality in patient with hip fracture.

Osteoporosis is a common disease in elderly, characterized by poor bone quality as a result of alterations affecting trabecular bone. However, recent studies have described also an important role of alterations of cortical bone in the physiopathology of osteoporosis. Although dual-energy X-ray absorptiometry (DXA) is a valid method to assess bone mineral density, in the presence of comorbidities real bone fragility is unable to be evaluated. The number of hip fractures is rising, especially in people over 85 years old.



The aim is to evaluate an alternative method so that it can indicate fracture risk, independent of bone mineral density (BMD). Femoral cortical index (FCI) assesses cortical bone stock using femur X-ray.



A retrospective study has been conducted on 152 patients with hip fragility fractures. FCI has been calculated on fractured femur and on the opposite side. The presence of comorbidities, osteoporosis risk factors, vitamin D levels, and BMD have been analyzed for each patient.



Average values of FCI have been 0.42 for fractured femurs and 0.48 at the opposite side with a statistically significant difference (p = 0.002). Patients with severe hypovitaminosis D had a minor FCI compared to those with moderate deficiency (0.41 vs. 0.46, p < 0.011). 42 patients (27.6%) with osteopenic or normal BMD have presented low values of FCI.



A significant correlation among low values of FCI, comorbidities, severe hypovitaminosis D. and BMD in patients with hip fractures has been found. FCI could be a useful tool to evaluate bone fragility and to predict fracture risk even in the normal and osteopenic BMD patients.
Thermal analysis of both ventilated and full disc brake rotors with frictional heat generation

In automotive engineering, the safety aspect has been considered as a number one priority in development of a new vehicle. Each single system has been studied and developed in order to meet safety requirements. Instead of having air bags, good suspension systems, good handling and safe cornering, one of the most critical systems in a vehicle is the brake system. The objective of this work is to investigate and analyze the temperature distribution of rotor disc during braking operation using ANSYS Multiphysics. The work uses the finite element analysis techniques to predict the temperature distribution on the full and ventilated brake discs and to identify the critical temperature of the rotor. The analysis also gives us the heat flux distribution for the two discs.