nils-herrmann/clean_scirepeval huggingface dataset:
ACADEMIC PRESSURE AND IMPACT ON STUDENTS' DEVELOPMENT IN CHINA

o This paper examines the enormous pressure Chinese students must bear at home and in school in order to obtain high academic achievemento The authors look at students' lives from their own perspective and study the impact of home and school pressures on students' intellectral, psychological, and physical development. Cultural, political, and economic factors are analyzed to provide an explanation of the situation. The paper raises questions as to what is the purpose of education and argues for the importance of balancing educational goals with other aspects of students' lives. RtSUMto Cet article s'intéresse aux pressions considérables dont font l'objet les étudiants chinois à la maison et à l'école en vue de réussir sur le plan scolaire. Les auteurs étudient la vie des étudiants à la lumière de leurs propres points de vue de même que l'impact des pressions familliales et scolaires sur leur développement intellectuel, psychologique et physique. Les facteurs culturels, politiques et économiques sont analysés en vue d'expliquer la situation. L'article soulève des questions sur le but de l'éducation et insiste sur l'importance d'équilibrer les objetifs pédagogiques avec les autres aspects de la vie des
Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor

A paradigm shift is required to prevent the increasing automobile accident deaths that are mostly due to the inattentive behavior of drivers. Knowledge of gaze region can provide valuable information regarding a driver's point of attention. Accurate and inexpensive gaze classification systems in cars can improve safe driving. However, monitoring real-time driving behaviors and conditions presents some challenges: dizziness due to long drives, extreme lighting variations, glasses reflections, and occlusions. Past studies on gaze detection in cars have been chiefly based on head movements. The margin of error in gaze detection increases when drivers gaze at objects by moving their eyes without moving their heads. To solve this problem, a pupil center corneal reflection (PCCR)-based method has been considered. However, the error of accurately detecting the pupil center and corneal reflection center is increased in a car environment due to various environment light changes, reflections on glasses surface, and motion and optical blurring of captured eye image. In addition, existing PCCR-based methods require initial user calibration, which is difficult to perform in a car environment. To address this issue, we propose a deep learning-based gaze detection method using a near-infrared (NIR) camera sensor considering driver head and eye movement that does not require any initial user calibration. The proposed system is evaluated on our self-constructed database as well as on open Columbia gaze dataset (CAVE-DB). The proposed method demonstrated greater accuracy than the previous gaze classification methods.
Stereo Matching Using Optic Flow

A method for the determination of pixel correspondence in stereo image pairs is presented. The optic ̄ow vectors that result from the displacement of the point of projection are obtained and the correspondence between pixels of the various objects in the scene is derived from the optic ̄ow vectors. The proposed algorithm is implemented and the correspondence vectors are obtained. Various specialized improvements of the method are implemented and thoroughly tested on sequences of image pairs giving rise to interesting conclusions. The algorithm is highly-parallelizable and therefore suitable for real-time applications.
Deep Learning with unsupervised data labeling for weeds detection on UAV images

In recent years, weeds is responsible for most of the agricultural yield losses. To deal 1 with this threat, farmers resort to spraying pesticides throughout the field. Such method not only 2 requires huge quantities of herbicides but impact environment and humans health. One way to 3 reduce the cost and environmental impact is to allocate the right doses of herbicide at the right 4 place and at the right time (Precision Agriculture). Nowadays, Unmanned Aerial Vehicle (UAV) is 5 becoming an interesting acquisition system for weeds localization and management due to its ability 6 to obtain the images of the entire agricultural field with a very high spatial resolution and at low cost. 7 Despite the important advances in UAV acquisition systems, automatic weeds detection remains a 8 challenging problem because of its strong similarity with the crops. Recently Deep Learning approach 9 has shown impressive results in different complex classification problem. However, this approach 10 needs a certain amount of training data but, creating large agricultural datasets with pixel-level 11 annotations by expert is an extremely time consuming task. In this paper, we propose a novel fully 12 automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised 13 training dataset collection for weeds detection from UAV images. The proposed method consists 14 in three main phases. First we automatically detect the crop lines and using them to identify the 15 interline weeds. In the second phase, interline weeds are used to constitute the training dataset. 16 Finally, we performed CNNs on this dataset to build a model able to detect the crop and weeds in the 17 images. The results obtained are comparable to the traditional supervised training data labeling. The 18 accuracy gaps are 1.5% in the spinach field and 6% in the bean field.
DT-SLAM: Deferred Triangulation for Robust SLAM

Obtaining a good baseline between different video frames is one of the key elements in vision-based monocular SLAM systems. However, if the video frames contain only a few 2D feature correspondences with a good baseline, or the camera only rotates without sufficient translation in the beginning, tracking and mapping becomes unstable. We introduce a real-time visual SLAM system that incrementally tracks individual 2D features, and estimates camera pose by using matched 2D features, regardless of the length of the baseline. Triangulating 2D features into 3D points is deferred until key frames with sufficient baseline for the features are available. Our method can also deal with pure rotational motions, and fuse the two types of measurements in a bundle adjustment step. Adaptive criteria for key frame selection are also introduced for efficient optimization and dealing with multiple maps. We demonstrate that our SLAM system improves camera pose estimates and robustness, even with purely rotational motions.
Low complexity precoding for large millimeter wave MIMO systems

Millimeter wave (mmWave) systems must overcome heavy signal attenuation to support high-throughput wireless communication links. The small wavelength in mmWave systems enables beamforming using large antenna arrays to combat path loss with directional transmission. Beamforming with multiple data streams, known as precoding, can be used to achieve even higher performance. Both beamforming and precoding are done at baseband in traditional microwave systems. In mmWave systems, however, the high cost of mixed-signal and radio frequency chains (RF) makes operating in the passband and analog domains attractive. This hardware limitation places additional constraints on precoder design. In this paper, we consider single user beamforming and precoding in mmWave systems with large arrays. We exploit the structure of mmWave channels to formulate the precoder design problem as a sparsity constrained least squares problem. Using the principle of basis pursuit, we develop a precoding algorithm that approximates the optimal unconstrained precoder using a low dimensional basis representation that can be efficiently implemented in RF hardware. We present numerical results on the performance of the proposed algorithm and show that it allows mmWave systems to approach waterfilling capacity.
Using Heuristics to Evaluate the Overall User Experience of Video Games and Advanced Interaction Games

This book chapter describes an approach of evaluating user experience in video games and advanced interaction games (tabletop games) by using heuristics. We provide a short overview of computer games with a focus on advanced interaction games and explain the concept of user-centred design for games. Furthermore we describe the history of heuristics for video games and the role of user experience of games in general. We propose a framework consisting of three sets of heuristics (game play/game story, virtual interface and tabletop specific) to detect the most critical issues in video games as well as advanced interaction games. To assess its applicability we compare the results of expert evaluations of five current games with the user experience-based ratings of various game review sites. In the conclusion we provide an outlook on possible extensions of our approach.
Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping

Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing. For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net model parses the entire review before forming a decision. We argue that reading the entire input is not always necessary in practice, since a lot of reviews are often easy to classify, i.e., a decision can be formed after reading some crucial sentences or words in the provided text. In this paper, we present an approach of fast reading for text classification. Inspired by several well-known human reading techniques, our approach implements an intelligent recurrent agent which evaluates the importance of the current snippet in order to decide whether to make a prediction, or to skip some texts, or to re-read part of the sentence. Our agent uses an RNN module to encode information from the past and the current tokens, and applies a policy module to form decisions. With an end-to-end training algorithm based on policy gradient, we train and test our agent on several text classification datasets and achieve both higher efficiency and better accuracy compared to previous approaches.
AppPrint: Automatic Fingerprinting of Mobile Applications in Network Traffic

Increased adoption of mobile devices introduces a new spin to Internet: mobile apps are becoming a key source of user traffic. Surprisingly, service providers and enterprises are largely unprepared for this change as they increasingly lose understanding of their traffic and fail to persistently identify individual apps. App traffic simply appears no different than any other HTTP data exchange. This raises a number of concerns for security and network management. In this paper, we propose AppPrint, a system that learns fingerprints of mobile apps via comprehensive traffic observations. We show that these fingerprints identify apps even in small traffic samples where app identity cannot be explicitly revealed in any individual traffic flows. This unique AppPrint feature is crucial because explicit app identifiers are extremely scarce, leading to a very limited characterization coverage of the existing approaches. In fact, our experiments on a nationwide dataset from a major cellular provider show that AppPrint significantly outperforms any existing app identification. Moreover, the proposed system is robust to the lack of key app-identification sources, i.e., the traffic related to ads and analytic services commonly leveraged by the state-of-the-art identification methods.
Sentiment Analysis of Political Tweets: Towards an Accurate Classifier

We perform a series of 3-class sentiment classification experiments on a set of 2,624 tweets produced during the run-up to the Irish General Elections in February 2011. Even though tweets that have been labelled as sarcastic have been omitted from this set, it still represents a difficult test set and the highest accuracy we achieve is 61.6% using supervised learning and a feature set consisting of subjectivity-lexicon-based scores, Twitterspecific features and the top 1,000 most discriminative words. This is superior to various naive unsupervised approaches which use subjectivity lexicons to compute an overall sentiment score for a <tweet,political party> pair.
Neural Network Meets DCN: Traffic-driven Topology Adaptation with Deep Learning

The emerging optical/wireless topology reconfiguration technologies have shown great potential in improving the performance of data center networks. However, it also poses a big challenge on how to find the best topology configurations to support the dynamic traffic demands. In this work, we present xWeaver, a traffic-driven deep learning solution to infer the high-performance network topology online. xWeaver supports a powerful network model that enables the topology optimization over different performance metrics and network architectures. With the design of properly-structured neural networks, it can automatically derive the critical traffic patterns from data traces and learn the underlying mapping between the traffic patterns and topology configurations specific to the target data center. After offline training, xWeaver generates the optimized (or near-optimal) topology configuration online, and can also smoothly update its model parameters for new traffic patterns. We build an optical-circuit-switch-based testbed to demonstrate the function and transmission efficiency of our proposed solution. We further perform extensive simulations to show the significant performance gain of xWeaver, in supporting higher network throughput and smaller flow completion time.
Investigation of some medicinal plants traditionally used for treatment of malaria in Kenya as potential sources of antimalarial drugs.

Malaria is a major public health problem in many tropical and subtropical countries and the burden of this disease is getting worse, mainly due to the increasing resistance of Plasmodium falciparum against the widely available antimalarial drugs. There is an urgent need for discovery of new antimalarial agents. Herbal medicines for the treatment of various diseases including malaria are an important part of the cultural diversity and traditions of which Kenya's biodiversity has been an integral part. Two major antimalarial drugs widely used today came originally from indigenous medical systems, that is quinine and artemisinin, from Peruvian and Chinese ancestral treatments, respectively. Thus ethnopharmacology is a very important resource in which new therapies may be discovered. The present review is an analysis of ethnopharmacological publications on antimalarial therapies from some Kenyan medicinal plants.
A microcontroller-based multi-function solar tracking system

Renewable energy solutions are becoming increasingly popular. Photovoltaic (solar) systems are but one example. Maximizing power output from a solar system is desirable to increase efficiency. In order to maximize power output from solar panels, one needs to keep the panels aligned with the sun. As such, a means of tracking the sun is required. This is definitely a more cost effective solution than purchasing additional solar panels. It has been estimated that the yield from solar panels can be increased by 30 to 60 percent by utilizing a tracking system instead of a stationary array. In this paper, a prototype for a microcontroller-based multi-function solar tracking system is described, which will keep the solar panels aligned with the sun in order to maximize efficiency. The maximum power point tracking (MPPT) data can be transmitted in real time to other solar systems in need of this data.
Adult T-cell leukemia: clinical and hematologic features of 16 cases.

Clinical and hematologic studies of 16 adult patients whose leukemic cells had Tcell markers are reported from Japan, where the incidence of various lymphoproliferative diseases differs considerably from that in Western countries. Leukemic cells were studied by cytotoxicity tests with specific antisera against human T (ATS) and B cells (ABS) in addition to the usual Tand B-cell markers (E rosette, EAC rosette, and surface immunoglobulins). Characteristics of the clinical and hematologic findings were as follows: (1) onset in adulthood; (2) subacute or chronic leukemia with rapidly progressive terminal course; (3) leukemic cells killed by ATS and forming E rosettes; (4) Icykemic cells not morphologically monotonous and frequent cells with deeply indented or lobulated nuclei; (5) frequent skin involvement (9 patients); (6) common lymphadenopathy and hepatosplenomegaly; (7) no mediastinal mass; and, the most striking finding, (8) the clustering of the patients’ birthplaces, namely, 13 patients born in Kyushu. The relation. ship between our cases and other subacute or chronic adult T-ceIl malignancies such as chronic lymphocytic leukemia of T-cell origin, prolymphocytic leukemia with 1cell properties, S#{233}zarysyndrome, and mycosis fungoides is discussed.
Effect of Training Surface on Agility and Passing Skills of Prepubescent Female Volleyball Players

The purpose of the present study was to investigate the effect of different training surfaces (hard or sand surface) on agility and passing skills of prepubescent female volleyball players. 45 prepubescent girls (age: 11.1 ± 0.5 years) participated in this study and were separated in three groups. Groups S (N=15) and I (N=15) consisted of volleyball players, while group C (N=15) consisted of girls that had no volleyball training experience. All groups participated in a 10-week (3 days/week) volleyball training program that included technical and passing skills exercises. The program of groups S and I also included jumping and sprinting exercises. The training program of group S was conducted on sand surface, while groups I and C trained on hard surface. Measurements of agility (T-test and 505-test) and passing skills were conducted on both hard and sand surface before, in the middle (5th week) and after the end of the training program for groups S and I. Group C was tested only on hard surface before and after the training period. Data were analyzed using two-way ANOVA for independent samples. Agility T-test and 505-test were significantly (p<0.001) improved in all three groups after the 10-week training program. Agility improvement of group S was significantly (p<0.001) greater than the other two groups (I and C), regardless of the surface (hard or sand) that the test was executed. Group S achieved greater improvements than the other two groups in passing skills too. All three groups were significantly (p<0.001) improved in overhead and forearm passing accuracy after the 10-week training period, but it was group S that achieved the greatest improvement, regardless of the (hard or sand) that the test was executed. In conclusion training on sand surface could be a useful and effective tool for improving agility and passing skills in prepubescent female volleyball players.  Maria Maridaki, Associate Professor, Department of Physical Education & Sports Science, National & Kapodistrian University of Athens, Greece, Tel: +302107276043;  2013;  2013;  Gortsila E, Theos A, Nesic G, Maridaki M (2013) Effect of Training Surface on Agility and Passing Skills of Prepubescent Female Volleyball Players. J Sports Med Doping Stud 3: 128. doi:10.4172/2161-0673.1000128
Convolutional Neural Networks for Medical Clustering

A major challenge for Medical Image Retrieval (MIR) is the discovery of relationships between low-level image features (intensity, gradient, texture, etc.) and high-level semantics such as modality, anatomy or pathology. Convolutional Neural Networks (CNNs) have been shown to have an inherent ability to automatically extract hierarchical representations from raw data. Their successful application in a variety of generalised imaging tasks suggests great potential for MIR. However, a major hurdle to their deployment in the medical domain is the relative lack of robust training corpora when compared to general imaging benchmarks such as ImageNET and CIFAR. In this paper, we present the adaptation of CNNs to the medical clustering task at ImageCLEF 2015.
Effects of different photobiomodulation dosimetries on temporomandibular dysfunction: a randomized, double-blind, placebo-controlled clinical trial

Changes involving temporomandibular joint, masticatory musculature, and associated structures characterize temporomandibular dysfunction (TMD). The analgesic and anti-inflammatory effect produced by photobiomodulation has contributed to pain relief and functional improvement. However, the parameters to be used have not yet been well established. The aim of this study is to compare the efficacy of three different photobiomodulation dosimetries in the treatment of patients with TMD. A randomized, double-blind, placebo-controlled clinical trial with 44 subjects divided into the groups 8 J/cm2 (n = 11), 60 J/cm2 (n = 11), 105 J/cm2 (n = 11), and control (n = 11). Pain, symptom severity, and joint mobility were evaluated before and after a ten-session protocol of photobiomodulation with AlGaAs laser (830 nm), at a power density of 30 mW/cm2. The mouth opening increased in the 8-J/cm2 group from 10.49 ± 4.68 to 15.40 ± 6.43 degrees, and in the right protrusion from 9.80 ± 4.2 to 12.56 ± 5.40 degrees after the intervention protocol (p < 0.05). All groups significantly decreased pain (p < 0.05). 830-nm laser photobiomodulation was effective in reducing TMD pain and symptoms at all doses tested. Only the doses of 8 J/cm2 were effective regarding maximal opening and protrusion of the mandible.
GPS-GSM based inland vessel tracking system for automatic emergency detection and position notification

In this paper, an upgraded version of vehicle tracking system is developed for inland vessels. In addition to the features available in traditional VTS (Vehicle Tracking System) for automobiles, it has the capability of remote monitoring of the vessel's motion and orientation. Furthermore, this device can detect capsize events and other accidents by motion tracking and instantly notify the authority and/or the owner with current coordinates of the vessel, which is obtained using the Global Positioning System (GPS). This can certainly boost up the rescue process and minimize losses. We have used GSM network for the communication between the device installed in the ship and the ground control. So, this can be implemented only in the inland vessels. But using iridium satellite communication instead of GSM will enable the device to be used in any sea-going ships. At last, a model of an integrated inland waterway control system (IIWCS) based on this device is discussed.
A novel connected PIFA array with MIMO configuration for 5G mobile applications

A novel connected printed inverted-F antenna (PIFA) array with multiple-input multiple-output (MIMO) configuration is presented. The array is designed to operate at the 28 GHz band for 5G mobile applications. It consists of 4-element MIMO antenna system where each element includes 8 PIFA antennas forming a connected antenna array. A 1×8 power divider/combiner in the form of tapered T-junction is used to excite that array. The array operates at 28 GHz with 10 dB bandwidth of 1 GHz from 27.5 to 28.5 GHz. The proposed design is modeled on RO-4350B with the height of 0.76 mm and dielectric constant of 3.48. The dimensions of the substrate are 130×68×0.76 mm3, which matches the dimensions of modern smart phones. The proposed design is simple in structure, low profile and has a compact size that is suitable for mobile handsets.