nils-herrmann/clean_scirepeval huggingface dataset:
A robust eye gaze estimation using geometric eye features

Gaze estimation is the process of determining the point of gaze in the space, or the visual axis of an eye. It plays an important role in representing human attention; therefore, it can be most appropriately used in Human Computer Interaction as a means of an advance computer input. Here, the focus is to develop a gaze estimation method for Human Computer Interaction using an ordinary webcam mounted on the top of the computer screen without any additional or specialized hardware. The eye center coordinates are obtained with the geometrical eye model and edge gradients. To improve the reliability, the estimates from two eye centers are combined to reduce the noise and improve the accuracy. Facial land marking is done to identify a precise reference point on the face between the nose. The ellipse fitting and RANSAC method is used to estimate the gaze coordinates and to reject the outliers. This approach can estimate the gaze coordinates with high degree of accuracy even when significant numbers of outliers are present in the data set. Several refinements such as feedback and masking, queuing and averaging are proposed to make the system more stable and useful practically. The results show that the proposed method can be successfully applied to commercial gaze tracking systems using ordinary webcams.
Chip design for turbo encoder module for in-vehicle system

This paper studies design and implementation of the Turbo encoder to be an embedded module in the in-vehicle system (IVS) chip. Field programmable gate array (FPGA) is employed to develop the Turbo encoder module. Both serial and parallel computations for the encoding technique are studied. The two design methods are presented and analyzed. Developing the parallel computation method, it is shown that both chip size and processing time are improved. The logic utilization is enhanced by 73% and the processing time is reduced by 58%. The Turbo encoder module is designed, simulated, and synthesized using Xilinx tools. Xilinx Zynq-7000 is employed as an FPGA device to implement the developed module. The Turbo encoder module is designed to be a part of the IVS chip on a single programmable device.
Neuropathology of prodromal Lewy body disease.

Current evidence suggests that there is a prodromal stage in Parkinson disease characterized by a variety of nonmotor symptoms.



A 69-year-old man presented to our sleep center with isolated rapid eye movement sleep behavior disorder. During a 10-year follow-up period, longitudinal clinical and laboratory assessments indicated the development of hyposmia, depression, mild cognitive impairment, and constipation. Parkinsonism was absent, but dopamine transporter imaging showed subclinical substantia nigra damage. Postmortem examination demonstrated neuronal loss and Lewy body pathology in the peripheral autonomic nervous system (eg, cardiac and myenteric plexus), olfactory bulb, medulla, pons, substantia nigra pars compacta (estimated cell loss, 20%-30%), nucleus basalis of Meynert, and amygdala, sparing the neocortex.



Our observations indicate that nonmotor symptoms plus widespread peripheral and central nervous system pathological changes occur before parkinsonism and dementia onset in diseases associated with Lewy pathology. The current diagnostic criteria for Parkinson's disease miss these patients, who present only with nonmotor symptoms.
Wireless Digital Water Meter with Low Power Consumption for Automatic Meter Reading

Recently, several papers for reading meters remotely using RFID/USN technologies have been presented. In the case of wireless water meters, there has been neither commercial product nor paper. In this paper, we describe the design and implementation of wireless digital water meter with low power consumption. We use magnetic hole sensors to compute the amount of water consumption. The meter of water consumption is transferred via ZigBee wireless protocol to a gateway. Low power consumption design is essential since a battery should last till the life time of water meter. We suggest that dual batteries having 3 V, 3000 mAh, would last 8 years by analyzing the real power consumption of our water meter.
NUMERICAL ANALYSIS OF A SMALL ULTRA WIDEBAND MICROSTRIP-FED TAP MONOPOLE ANTENNA

This paper presents a planar microstrip-fed tab monopole antenna for ultra wideband wireless communications applications. The impedance bandwidth of the antenna is improved by adding slit in one side of the monopole, introducing a tapered transition between the monopole and the feed line, and adding two-step staircase notch in the ground plane. Numerical analysis for the antenna dimensional parameters using Ansoft HFSS is performed and presented. The proposed antenna has a small size of 16 × 19 mm, and provides an ultra wide bandwidth from 2.8 to 28 GHz with low VSWR level and good radiation characteristics to satisfy the requirements of the current and future wireless communications systems.
Efficient fingerprint search based on database clustering

Fingerprint identification has been a great challenge due to its complex search of database. This paper proposes an efficient fingerprint search algorithm based on database clustering, which narrows down the search space of fine matching. Fingerprint is non-uniformly partitioned by a circular tessellation to compute a multi-scale orientation field as the main search feature. The average ridge distance is employed as an auxiliary feature. A modified K-means clustering technique is proposed to partition the orientation feature space into clusters. Based on the database clustering, a hierarchical query processing is proposed to facilitate an efficient fingerprint search, which not only greatly speeds up the search process but also improves the retrieval accuracy. The experimental results show the effectiveness and superiority of the proposed fingerprint search algorithm. 2006 Pattern Recognition Society.
GDNF: a glial cell line-derived neurotrophic factor for midbrain dopaminergic neurons.

A potent neurotrophic factor that enhances survival of midbrain dopaminergic neurons was purified and cloned. Glial cell line-derived neurotrophic factor (GDNF) is a glycosylated, disulfide-bonded homodimer that is a distantly related member of the transforming growth factor-beta superfamily. In embryonic midbrain cultures, recombinant human GDNF promoted the survival and morphological differentiation of dopaminergic neurons and increased their high-affinity dopamine uptake. These effects were relatively specific; GDNF did not increase total neuron or astrocyte numbers nor did it increase transmitter uptake by gamma-aminobutyric-containing and serotonergic neurons. GDNF may have utility in the treatment of Parkinson's disease, which is marked by progressive degeneration of midbrain dopaminergic neurons.
A portable near infrared spectroscopy system for bedside monitoring of newborn brain

Newborns with critical health conditions are monitored in neonatal intensive care units (NICU). In NICU, one of the most important problems that they face is the risk of brain injury. There is a need for continuous monitoring of newborn's brain function to prevent any potential brain injury. This type of monitoring should not interfere with intensive care of the newborn. Therefore, it should be non-invasive and portable.



In this paper, a low-cost, battery operated, dual wavelength, continuous wave near infrared spectroscopy system for continuous bedside hemodynamic monitoring of neonatal brain is presented. The system has been designed to optimize SNR by optimizing the wavelength-multiplexing parameters with special emphasis on safety issues concerning burn injuries. SNR improvement by utilizing the entire dynamic range has been satisfied with modifications in analog circuitry.



As a result, a shot-limited SNR of 67 dB has been achieved for 10 Hz temporal resolution. The system can operate more than 30 hours without recharging when an off-the-shelf 1850 mAh-7.2 V battery is used. Laboratory tests with optical phantoms and preliminary data recorded in NICU demonstrate the potential of the system as a reliable clinical tool to be employed in the bedside regional monitoring of newborn brain metabolism under intensive care.
A Review on Modern Distributed Computing Paradigms: Cloud Computing, Jungle Computing and Fog Computing

Cloud computing brings a number of advantages to consumers in terms of accessibility and elasticity. It is based on centralization of resources that possess huge processing power and storage capacities. Fog computing, in contrast, is pushing the frontier of computing away from centralized nodes to the edge of a network, to enable computing at the source of the data. On the other hand, Jungle computing includes a simultaneous combination of clusters, grids, clouds, and so on, in order to gain maximum potential computing power.
Design and demonstration of ultra-thin 3D glass-based 5G modules with low-loss interconnects

This paper demonstrates six-metal-layer antenna-to-receiver signal transitions on panel-scale processed ultra-thin glass-based 5G module substrates with 50-Ω transmission lines and micro-via transitions in re-distribution layers. The glass modules consist of low-loss dielectric thin-films laminated on 100-μm glass cores. Modeling, design, fabrication, and characterization of the multilayered signal interconnects were performed at 28-GHz band. The surface planarity and dimensional stability of glass substrates enabled the fabrication of highly-controlled signal traces with tolerances of 2% inside the re-distribution layers on low-loss dielectric build-up thin-films. The fabricated transmission lines showed 0.435 dB loss with 4.19 mm length, while microvias in low-loss dielectric thin-films showed 0.034 dB/microvia. The superiority of glass substrates enable low-loss link budget with high precision from chip to antenna for 5G communications.
TRUST IN TYPICAL AND HIGH-RELIABILITY CONTEXTS : BUILDING AND REACTING TO TRUST AMONG FIREFIGHTERS

We develop theory that distinguishes trust among employees in typical task contexts (marked by low levels of situational unpredictability and danger) from trust in “highreliability” task contexts (those marked by high levels of situational unpredictability and danger). A study of firefighters showed that trust in high-reliability task contexts was based on coworkers’ integrity, whereas trust in typical task contexts was also based on benevolence and identification. Trust in high-reliability contexts predicted physical symptoms, whereas trust in typical contexts predicted withdrawal. Job demands moderated linkages with performance: trust in high-reliability task contexts was a more positive predictor of performance when unpredictable and dangerous calls were more frequent.
Moment-to-moment brain signal variability: A next frontier in human brain mapping?

Neuroscientists have long observed that brain activity is naturally variable from moment-to-moment, but neuroimaging research has largely ignored the potential importance of this phenomenon. An emerging research focus on within-person brain signal variability is providing novel insights, and offering highly predictive, complementary, and even orthogonal views of brain function in relation to human lifespan development, cognitive performance, and various clinical conditions. As a result, brain signal variability is evolving as a bona fide signal of interest, and should no longer be dismissed as meaningless noise when mapping the human brain.
A modular neural model of motor synergies

Animals such as reptiles, amphibians and mammals (including humans) are mechanically extremely complex. It has been estimated that the human body has between 500 and 1400 degrees of freedom! And yet, these animals can generate an infinite variety of very precise, complicated and goal-directed movements in continuously changing and uncertain environments. Understanding how this is achieved is of great interest to both biologists and engineers. There are essentially two questions that must be addressed: (1) What type of control strategy is used to handle the large number of degrees of freedom involved? and (2) How is this strategy instantiated in the substrate of neural and musculoskeletal elements comprising the animal bodies? The first question has been studied intensively for several decades, providing strong indications that, rather than using standard feedback control based on continuous tracking of desired trajectories, animals' movements emerge from the controlled combination of pre-configured movement primitives or synergies. These synergies represent coordinated activity patterns over groups of muscles, and can be triggered as a whole with controlled amplitude and temporal offset. Complex movements can thus be constructed from the appropriate combination of a relatively small number of synergies, greatly simplifying the control problem. Although experimental studies on animal movements have confirmed the existence of motor synergies, and their utility has been demonstrated in the control of fairly complex robots, their neural basis remains poorly understood. In this paper, we introduce a simple but plausible and general neural model for motor synergies based on the principle that these functional modules reflect the structural modularity of the underlying physical system. Using this model, we show that a small set of synergies selected through a redundancy-reduction principle can generate a rich motor repertoire in a model two-jointed arm system. We investigate the synergies generated by this model systematically with respect to various parameters, and compare them to those observed in experiments.
Mobile Encrypted Traffic Classification Using Deep Learning

The massive adoption of hand-held devices has led to the explosion of mobile traffic volumes traversing home and enterprise networks, as well as the Internet. Procedures for inferring (mobile) applications generating such traffic, known as Traffic Classification (TC), are the enabler for highly-valuable profiling information while certainly raise important privacy issues. The design of accurate classifiers is however exacerbated by the increasing adoption of encrypted protocols (such as TLS), hindering the applicability of highly-accurate approaches, such as deep packet inspection. Additionally, the (daily) expanding set of apps and the moving-target nature of mobile traffic makes design solutions with usual machine learning, based on manually-and expert-originated features, outdated. For these reasons, we suggest Deep Learning (DL) as a viable strategy to design traffic classifiers based on automatically-extracted features, reflecting the complex mobile-traffic patterns. To this end, different state-of-the-art DL techniques from TC are here reproduced, dissected, and set into a systematic framework for comparison, including also a performance evaluation workbench. Based on three datasets of real human users' activity, performance of these DL classifiers is critically investigated, highlighting pitfalls, design guidelines, and open issues of DL in mobile encrypted TC.
Context Aware Citation Recommendation System

This paper present a machine learning method for generating citation recommendations within research papers, articles, and other technical documents. This is potentially useful for authors who include some statement of fact within their work and go back only later to provide appropriate citation for such content. To facilitate this, we phrase this problem as an instance of multi-class learning in which we map so-called ’local contexts’ in a paper to possible documents that would best serve as a cited source. Within this paper, we look at the effectiveness of a support-vector machine classifier alongside a feature set consisting of a mix of bag-of-words document descriptions and similarity measures between documents, and the difficulty of scaling such techniques given the massive amounts of data available and necessary for proper classification. We closely analyze specific results over a subset of this data taken from CiteseerX .
Student2student: arduino project-based learning

In this paper we report the work that jeKnowledge (Júnior Empresa da Faculdade de Ciências e Tecnologias da Universidade de Coimbra), a student-led initiative, has done in the 'jeKnowledge academy' Summer School to actively engage Portuguese high-school students in STEM education through hands-on projects based on the low-cost Arduino platform. F2F activities are complemented with tutorials and more advanced project suggestions in a blog. The course resulted in high degrees of satisfaction for both the students in the teaching role and the younger students and even influenced the Physics Department where it took place, which included Arduino workshops in the first-year syllabus of the Physics Engineering course.
Architecture, design and source code comparison of ns-2 and ns-3 network simulators

Ns-2 and its successor ns-3 are discrete-event simulators. Ns-3 is still under development, but offers some interesting characteristics for developers while ns-2 still has a big user base. This paper remarks current differences between both tools from developers point of view. Leaving performance and resources consumption aside, technical issues described in the present paper might help to choose one or another alternative depending of simulation and project management requirements.
60GHz wireless technologies for WiGig/IEEE 802.11ad multi-gigabit systems

In recent times, 60 GHz millimeter wave systems have become increasingly attractive due to the escalating demand for multi-Gb/s wireless communication. Recent works have demonstrated the ability to realize a 60 GHz transceiver by means of a cost-effective CMOS process. This paper aims to give the most up-to-date status of the 60 GHz wireless transceiver development, with an emphasis on realizing low power consumption and small form factor that is applicable for mobile terminals. To make 60 GHz wireless more robust and ease of use in various applications, broadband propagation and interference characteristics are measured at the 60 GHz band in an application-oriented office environment, considering the concurrent use of multiple frequency channels and multiple terminals. Moreover, this paper gives an overview of future millimeter wave systems.
A Case of Cutis Pleonasmus.

In 2005, Kreidstein first proposed the term "Cutis pleonasmus," a Greek term meaning "redundancy," which refers to the excessive skin that remains after massive weight loss. Cutis pleonasmus is clearly distinguishable from other diseases showing increased laxity of the skin, such as pseudoxanthoma elasticum, congenital and acquired generalized cutis laxa. Although individuals who are severely overweight are few and bariatric surgeries are less common in Korea than in the West, the number of these patients is increasing due to changes to Western life styles. We report a case for a 24-year-old man who presented with generalized lax and loose skin after massive weight loss. He was diagnosed with cutis pleonasmus based on the history of great weight loss, characteristic clinical features and normal histological findings. To the best of our knowledge, this is the first report of cutis pleonasmus in Korea.
Detecting Hand Bone Fractures in X-Ray Images

Computer aided diagnosis is a hot research field. Systems with the ability to provide a highly accurate diagnosis using little resources are highly desirable. One type of such systems depend on medical images to provide instantaneous diagnosis based on some discriminative features extracted from the images after processing them for noise removal and enhancement. In this paper, we propose a system to automatically detect fractures in hand bones using x-ray images. To the best of our knowledge, this problemhave never been addressed before. For a first attempt to tackle such a difficult problem, our system performed incredibly good with a 91.8% accuracy.
Bovine herpesvirus-1 (BHV-1) - a re-emerging concern in livestock: a revisit to its biology, epidemiology, diagnosis, and prophylaxis.

Bovine herpesvirus-1 (BHV-1) is known to cause several diseases worldwide. It is a double-stranded DNA virus consisting of 33 structural proteins out of which 13 are associated with the envelope. Based on genomic analysis and viral peptide patterns, BHV-1 virus can be divided into several subtypes like BHV-1.1, BHV-1.2, and BHV-1.3. However, all subtypes are antigenically similar. The symptoms of the related diseases are mainly non-life-threatening but have a rather wide host range that limits animal trade. The different modes of transmission as unique feature of this virus and the tendency to cause infection in the early age with latency development in trigeminal and sacral ganglion cause huge economic losses around the world. The virus also affects endangered bovine species like mithun (Bos frontalis) and yak (Poephagus grunniens). The disease can be diagnosed by using conventional procedures (like cell culture, immune-histopathology, and enzyme-linked immunosorbent assay (ELISA)) as well as highly sensitive modern techniques (like nested PCR and southern hybridization) with the virus neutralization test regarded as gold standard. With the currently available diagnostic tests it is not possible to identify animals which have a latent BHV-1 infection. Different types of modern and conventional vaccines are available for immunoprophylaxis. Inactivated vaccines are not as efficacious as modified live virus (MLV) vaccines. Marker vaccines allow the distinction between vaccinated and naturally infected animals. In this review the present status of BHV-1 around the world will be addressed besides the current knowledge with regard to its biology, epidemiology, diagnosis, and prophylaxis.
Training a convolutional neural network for multi-class object detection using solely virtual world data

Convolutional neural networks are a popular choice for current object detection and classification systems. Their performance improves constantly but for effective training, large, hand-labeled datasets are required. We address the problem of obtaining customized, yet large enough datasets for CNN training by synthesizing them in a virtual world, thus eliminating the need for tedious human interaction for ground truth creation. We developed a CNN-based multi-class detection system that was trained solely on virtual world data and achieves competitive results compared to state-of-the-art detection systems.