nils-herrmann/clean_scirepeval huggingface dataset:
Clinico-epidemiological study of Schistosomiasis mansoni in Waja-Timuga, District of Alamata, northern Ethiopia

Intestinal schistosomiasis, caused by digenetic trematodes of the genus Schistosoma, is the most prevalent water related disease that causes considerable morbidity and mortality. Although prevalence of Schistosoma mansoni infection has been reported for the present study area, earlier studies have not estimated intensity of infections in relation to periportal fibrosis, which would have been crucial for epidemiological and clinical evaluations. Hence, a community based cross sectional study was conducted from December 2011 to March 2012 to assess prevalence of infection and schistosomal periportal fibrosis in Waja-Timuga, northern Ethiopia. In a cross sectional study involving 371 randomly selected individuals, fresh stool samples were collected and processed by the Kato-Katz method and examined microscopically. Ultrasonography was used to determine status of schistosomal periportal fibrosis and to detect hepatomegaly and/or splenomegaly. Serum was collected for assay of hepatic activity. Statistical analysis was performed using STATA 11 statistical soft ware. P-value <0.05 was reported as statistically significant. The prevalence of S.mansoni infection was 73.9%, while the prevalence of schistosomal periportal fibrosis was 12.3% and mean intensity of infection was 234 eggs per gram of stool. Peak prevalence and intensity of S.mansoni infection was documented in the age range of 10–20 years. Among the study individuals, hepatomegaly was recorded in 3.7% and splenomegaly was recorded in 7.4% of the study individuals. Similarly, among the study individuals who had definite periportal fibrosis, 5.9% had elevated liver enzyme levels. The high prevalence of Schistosoma mansoni infection and schistosomal periportal fibrosis observed in the study area calls for a periodic deworming program to reduce disease, morbidity and transmission. Preventive chemotherapy complemented with other control measures is highly required for sustainable control of schistosomiasis in the study area.
RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms

Reinforcement learning (RL) has recently achieved tremendous success in solving complex tasks. Careful considerations are made towards reproducible research in machine learning. Reproducibility in RL often becomes more difficult, due to the lack of standard evaluation method and detailed methodology for algorithms and comparisons with existing work. In this work, we highlight key differences in evaluation in RL compared to supervised learning, and discuss specific issues that are often non-intuitive for newcomers. We study the importance of reproducibility in evaluation in RL, and propose an evaluation pipeline that can be decoupled from the algorithm code. We hope such an evaluation pipeline can be standardized, as a step towards robust and reproducible research in RL.
Analysis of circadian properties and healthy levels of blue light from smartphones at night

This study proposes representative figures of merit for circadian and vision performance for healthy and efficient use of smartphone displays. The recently developed figures of merit for circadian luminous efficacy of radiation (CER) and circadian illuminance (CIL) related to human health and circadian rhythm were measured to compare three kinds of commercial smartphone displays. The CIL values for social network service (SNS) messenger screens from all three displays were higher than 41.3 biolux (blx) in a dark room at night, and the highest CIL value reached 50.9 blx. These CIL values corresponded to melatonin suppression values (MSVs) of 7.3% and 11.4%, respectively. Moreover, smartphone use in a bright room at night had much higher CIL and MSV values (58.7 ~ 105.2 blx and 15.4 ~ 36.1%, respectively). This study also analyzed the nonvisual and visual optical properties of the three smartphone displays while varying the distance between the screen and eye and controlling the brightness setting. Finally, a method to possibly attenuate the unhealthy effects of smartphone displays was proposed and investigated by decreasing the emitting wavelength of blue LEDs in a smartphone LCD backlight and subsequently reducing the circadian effect of the display.
Conversion of Jatropha curcas Oil to Ester Biolubricant Using Solid Catalyst Derived from Saltwater Clam Shell Waste ( SCSW ) T

The discarded clam shell waste, fossil and edible oil as biolubricant feedstocks create environmental impacts and food chain dilemma, thus this work aims to circumvent these issues by using activated saltwater clam shell waste (SCSW) as solid catalyst for conversion of Jatropha curcas oil as non-edible sources to ester biolubricant. The characterization of solid catalyst was done by Differential Thermal Analysis-Thermo Gravimetric Analysis (DTATGA), X-Ray Fluorescence (XRF), X-Ray Diffraction (XRD), Brunauer-Emmett-Teller (BET), Field Emission Scanning Electron Microscopy (FESEM) and Fourier Transformed Infrared Spectroscopy (FTIR) analysis. The calcined catalyst was used in the transesterification of Jatropha oil to methyl ester as the first step, and the second stage was involved the reaction of Jatropha methyl ester (JME) with trimethylolpropane (TMP) based on the various process parameters. The formated biolubricant was analyzed using the capillary column (DB-5HT) equipped Gas Chromatography (GC). The conversion results of Jatropha oil to ester biolubricant can be found nearly 96.66%, and the maximum distribution composition mainly contains 72.3% of triester (TE). Conversion, ester biolubricant, Jatropha curcas oil, solid catalyst.
Survey of dietary habits of in-school adolescents in Damascus, Syrian Arab Republic.

This study aimed to describe dietary habits of Syrian adolescents attending secondary schools in Damascus and the surrounding areas. A descriptive, cross-sectional study was carried out on 3507 students in 2001. A stratified, 2-stage random cluster sample was used to sample the students. The consumption pattern of food items during the previous week was described. More than 50% of the students said that they had not consumed green vegetables and more than 35% had not consumed meat. More than 35% said that they consumed cheese and milk at least once a day. Only 11.8% consumed fruit 3 times or more daily. Potential determinants of the pattern of food consumption were arialysed. Weight control practices and other eating habits were also described.
Comparisons of BKT , RNN and LSTM for Predicting Student Learning Gains

The objective of this study is to develop effective computational models that can predict student learning gains, preferably as early as possible. We compared a series of Bayesian Knowledge Tracing (BKT) models against vanilla RNNs and Long Short Term Memory (LSTM) based models. Our results showed that the LSTM-based model achieved the highest accuracy and the RNN based model have the highest F1-measure. Interestingly, we found that RNN can achieve a reasonably accurate prediction of student final learning gains using only the first 40% of the entire training sequence; using the first 70% of the sequence would produce a result comparable to using the entire sequence.
Fraud detection in reputation systems in e-markets using logistic regression and stepwise optimization

Reputation is the opinion of the public toward a person, a group of people, or an organization. Reputation systems are particularly important in e-markets, where they help buyers to decide whether to purchase a product or not. Since a higher reputation means more profit, some users try to deceive such systems to increase their reputation. E-markets should protect their reputation systems from attacks in order to maintain a sound environment. This work addresses the task of finding attempts to deceive reputation systems in e-markets. Our goal is to generate a list of users (sellers) ranked by the probability of fraud. Firstly we describe characteristics related to transactions that may indicate frauds evidence and they are expanded to the sellers. We describe results of a simple approach that ranks sellers by counting characteristics of fraud. Then we incorporate characteristics that cannot be used by the counting approach, and we apply logistic regression to both, improved and not improved. We use real data from a large Brazilian e-market to train and evaluate our methods and the improved set with logistic regression performs better, specially when we apply stepwise optimization. We validate our results with specialists of fraud detection in this market place. In the end, we increase by 112% the number of identified fraudsters against the reputation system. In terms of ranking, we reach 93% of average precision after specialists' review in the list that uses Logistic Regression and Stepwise optimization. We also detect 55% of fraudsters with a precision of 100%.
Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition

Spatio-temporal feature encoding is essential for encoding the dynamics in video sequences. Recurrent neural networks, particularly long short-term memory (LSTM) units, have been popular as an efficient tool for encoding spatio-temporal features in sequences. In this work, we investigate the effect of mode variations on the encoded spatio-temporal features using LSTMs. We show that the LSTM retains information related to the mode variation in the sequence, which is irrelevant to the task at hand (e.g. classification facial expressions). Actually, the LSTM forget mechanism is not robust enough to mode variations and preserves information that could negatively affect the encoded spatio-temporal features. We propose the mode variational LSTM to encode spatio-temporal features robust to unseen modes of variation. The mode variational LSTM modifies the original LSTM structure by adding an additional cell state that focuses on encoding the mode variation in the input sequence. To efficiently regulate what features should be stored in the additional cell state, additional gating functionality is also introduced. The effectiveness of the proposed mode variational LSTM is verified using the facial expression recognition task. Comparative experiments on publicly available datasets verified that the proposed mode variational LSTM outperforms existing methods. Moreover, a new dynamic facial expression dataset with different modes of variation, including various modes like pose and illumination variations, was collected to comprehensively evaluate the proposed mode variational LSTM. Experimental results verified that the proposed mode variational LSTM encodes spatio-temporal features robust to unseen modes of variation.
Human contour extraction from RGBD camera for action recognition

Spatio-temporal cuboid pyramid (STCP) for action recognition using depth motion sequences  is influenced by depth camera error which leads the depth motion sequence (DMS) existing many kinds of noise, especially on the surface. It means that the dimension of DMS is awfully high and the feature for action recognition becomes less apparent. In this paper, we present an effective method to reduce noise, which is to segment foreground. We firstly segment and extract human contour in the color image using convolutional network model. Then, human contour is re-segmented utilizing depth information. Thirdly we project each frame of the segmented depth sequence onto three views. We finally extract features from cuboids and recognize human actions. The proposed approach is evaluated on three public benchmark datasets, i.e., UTKinect-Action Dataset, MSRActionPairs Dataset and 3D Online Action Dataset. Experimental results show that our method achieves state-of-the-art performance.
Follicular porokeratosis: four new cases.

Porokeratosis, a disorder of keratinisation, is clinically characterized by the presence of annular plaques with a surrounding keratotic ridge. Clinical variants include linear, disseminated superficial actinic, verrucous/hypertrophic, disseminated eruptive, palmoplantar and porokeratosis of Mibelli (one or two typical plaques with atrophic centre and guttered keratotic rim). All of these subtypes share the histological feature of a cornoid lamella, characterized by a column of 'stacked' parakeratosis with focal absence of the granular layer, and dysmaturation (prematurely keratinised cells in the upper spinous layer). In recent years, a proposed new subtype, follicular porokeratosis (FP_, has been described, in which the cornoid lamella are exclusively located in the follicular ostia. We present four new cases that showed typical histological features of FP.
Development of a claw pole permanent magnet motor with a molded low-density soft magnetic composite stator core

SMC (soft magnetic composite) materials and SMC electrical machines have undergone significant development in the past decade. For the molding of the SMC core, if a high productivity low pressure press is used, the manufacturing cost can be dramatically reduced. However, the magnetic properties of the SMC core and the machine performance are highly dependant on the mass density of the core. This paper presents the development of a claw pole permanent magnet motor with a low mass density SMC stator core molded at low pressure which can replace the existing single phase induction motor in a dish washer pump. The developed SMC motor has been prototyped and tested. Both the parameter computation and performance prediction are validated by the experimental results. These show that the low mass density SMC core is suitable for low cost mass production of SMC electrical machines.
Detecting splicing in digital audios using local noise level estimation

One common form of tampering in digital audio signals is known as splicing, where sections from one audio is inserted to another audio. In this paper, we propose an effective splicing detection method for audios. Our method achieves this by detecting abnormal differences in the local noise levels in an audio signal. This estimation of local noise levels is based on an observed property of audio signals that they tend to have kurtosis close to a constant in the band-pass filtered domain. We demonstrate the efficacy and robustness of the proposed method using both synthetic and realistic audio splicing forgeries.
Intellectual Property Strategy in the Global Cosmetics Industry

In this paper we analyze aspects of the intellectual property strategies of firms in the global cosmetics and toilet preparations industry. Using detailed data on all 4,205 EPO patent grants in the relevant IPC class between 1980 and 2001, we find that about 15 percent of all patents are challenged in EPO opposition proceedings, a rate about twice as high as in the overall population of EPO patents. Moreover, opposition in this sector is more frequent than in chemicals-based high technology industries such as biotechnology and pharmaceuticals. About one third of the opposition cases involve multiple opponents. We search for rationales that could explain this surprisingly strong “IP litigation” activity. In a first step, we use simple probability models to analyze the likelihood of opposition as a function of characteristics of the attacked patent. We then introduce owner firm variables and find that major differences across firms in the likelihood of having their patents opposed prevail even after accounting for other influences. Aggressive opposition in the past appears to be associated with a reduction of attacks on own patents. In future work we will look at the determinants of outcomes and duration of these oppositions, in an attempt to understand the firms’ strategies more fully. Acknowledgements This version of the paper was prepared for presentation at the Productivity Program meetingsof the NBER Summer Institute.  We would like to thank the participants and in particular Franz Palm and John Hagedoorn for their helpful comments.
MECHANICAL DESIGN OF A LEGGED-WHEEL HYBRID QUADRUPED ROBOT FOR MULTI-TERRAIN NAVIGATION

The two most common modes of locomotion used by humans are legged mode and wheeled mode, the former an inherent gift while the latter being an ingenious invention on their part. While both have their advantages, they may fall short in some aspects for instance, legs may fail in terms of high speeds and wheels might prove not so handy in the more demanding and uneven terrains Thus, a leg-wheel hybrid platform promises to ensure both high speeds and good stability on a variety of terrains .
Managing injuries of the great toe.

Most of the common great-toe injuries that affect active people are self-limiting and easily treated if detected early. Reviewed here are the causes, symptoms, diagnosis, and treatment of hallux valgus, turf toe, hallux rigidus, sesamoid dysfunction, nail abnormalities, dislocations and fractures, calluses, and blisters. Conservative treatment will usually enable patients to return to activity relatively quickly. Continued disability may require referral to an orthopedist.
Bio-inspired fluidic thermal angular accelerometer

This paper reports on a bio-inspired angular accelerometer based on a two-mask microfluidic process using a PDMS mold. The sensor is inspired by the semicircular canals in mammalian vestibular systems and pairs a fluid-filled microtorus with a thermal detection principle based on thermal convection. With inherent linear acceleration insensitivity, the sensor features a sensitivity of 29.8μV/deg/s2=1.7mV/rad/s2, a dynamic range of 14,000deg/s2 and a detection limit of ~20deg/s2.
UAV LiDAR for below-canopy forest surveys

Remote sensing tools are increasingly being used to survey forest structure. Most current methods rely on GPS signals, which are available in above-canopy surveys or in below-canopy surveys of open forests, but may be absent in below-canopy environments of dense forests. We trialled a technology that facilitates mobile surveys in GPS-denied below-canopy forest environments. The platform consists of a battery-powered UAV mounted with a LiDAR. It lacks a GPS or any other localisation device. The vehicle is capable of an 8 min flight duration and autonomous operation but was remotely piloted in the present study. We flew the UAV around a 20 m × 20 m patch of roadside trees and developed postprocessing software to estimate the diameter-at-breast-height (DBH) of 12 trees that were detected by the LiDAR. The method detected 73% of trees greater than 200 mm DBH within 3 m of the flight path. Smaller and more distant trees could not be detected reliably. The UAV-based DBH estimates of detected trees were positively correlated with the humanbased estimates (R = 0.45, p = 0.017) with a median absolute error of 18.1%, a root-meansquare error of 25.1% and a bias of −1.2%. We summarise the main current limitations of this technology and outline potential solutions. The greatest gains in precision could be achieved through use of a localisation device. The long-term factor limiting the deployment of below-canopy UAV surveys is likely to be battery technology.
Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications

The authors present an approach for designing self-monitoring technology called "semi-automated tracking," which combines both manual and automated data collection methods. Through this approach, they aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve their self-monitoring goals. They first specify three design considerations for semi-automated tracking: data capture feasibility, the purpose of self-monitoring, and the motivation level. They then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies they developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations.
A Comparative Study on Vulnerabilities in Categories of Clones and Non-cloned Code

Code clones are serious code smells. To investigate bug-proneness of clones as opposed to clone-free source code, earlier attempts have studied the stability of code clones and their contributions to program faults. This paper presents a comparative study on different types of clones and non-cloned code on the basis of their vulnerabilities, which may lead to software defects and issues in future. The empirical study along this new dimension examines source code of 97 software systems and derives results based on quantitative analysis with statistical significance. The findings from this work add to our under-standing of the characteristics and impacts of clones, which can be useful in clone-aware software development and in devising techniques for minimizing the negative impacts of code clones.
Partial discharge and dissipation factor behavior of model insulating systems for high voltage rotating machines under different stresses

This article describes the change in partial discharge (PD) pattern of high voltage rotating machines and the change in the tan /spl delta/ as a function of the applied test voltage during the aging processes as caused by the application of different stresses on stator bars. It also compares the PD patterns associated with internal, slot, and end-winding discharges, which were produced in well-controlled laboratory conditions. In addition, the influence of different temperature conditions on the partial discharge activities are shown. The investigations in this work were performed on model stator bars under laboratory conditions, and the results might be different from those obtained for complete machines, as rotating machines are complex PD test objects, and for example, the detected PD signals in a complete machine significantly depend on the transmission path from the PD source to the measurement device.
Quantum radar technology and its developments

Quantum metrology exploits quantum phenomena to improve the measurement sensitivity. Theoretical analysis shows that quantum measurement can break through the standard quantum limits and reach super sensitivity level. Quantum radar systems based on quantum measurement can fufill not only conventional target detection and recognition tasks but also capable of detecting and identifying the RF stealth platform and weapons systems. The theoretical basis, classification, physical realization of quantum radar is discussed comprehensively in this paper. And the technology state and open questions of quantum radars is reviewed at the end.
Technical Report for Collaborative Research Center Sfb 876 Providing Information by Resource- Constrained Data Analysis Subproject A1 Data Mining for Ubiquitous System Software Machine Learning on Fpgas

With increasing volumes in data and more sophisticated Machine Learning algorithms, the demand for fast and energy efficient computation systems is also growing. The combination of classical CPU systems with more specialized hardware such as FPGAs offer one way to meet this demand. FPGAs are fast and energy efficient reconfigurable hardware devices allowing new design explorations for algorithms and their implementations. This report briefly discusses FPGAs as computational hardware and their application in the domain of Machine Learning, specifically in combination with Gaussian Processes.