nils-herrmann/clean_scirepeval huggingface dataset:
Transcranial alternating current stimulation in the low kHz range increases motor cortex excitability.

External transcranial electric and magnetic stimulation techniques allow for the fast induction of sustained and measurable changes in cortical excitability. Here we aim to develop a paradigm using transcranial alternating current (tACS) in a frequency range higher than 1 kHz, which potentially interferes with membrane excitation, to shape neuroplastic processes in the human primary motor cortex (M1).



Transcranial alternating current stimulation was applied at 1, 2 and 5 kHz over the left primary motor cortex with a reference electrode over the contralateral orbit in 11 healthy volunteers for a duration of 10 min at an intensity of 1 mA. Monophasic single- pulse transcranial magnetic stimulation (TMS) was used to measure changes in corticospinal excitability, both during and after tACS in the low kHz range, in the right hand muscle. As a control inactive sham stimulation was performed.



All frequencies of tACS increased the amplitudes of motor- evoked potentials (MEPs) up to 30-60 min post stimulation, compared to the baseline. Two and 5 kHz stimulations were more efficacious in inducing sustained changes in cortical excitability than 1 kHz stimulation, compared to sham stimulation.



Since tACS in the low kHz range appears too fast to interfere with network oscillations, this technique opens a new possibility to directly interfere with cortical excitability, probably via neuronal membrane activation. It may also potentially replace more conventional repetitive transcranial magnetic stimulation (rTMS) techniques for some applications in a clinical setting.
Comparing the clinical usefulness of the Institute of Cognitive Neurology (INECO) Frontal Screening (IFS) and the Frontal Assessment Battery (FAB) in frontotemporal dementia.

We compared the utility of two executive-function brief screening tools, the Institute of Cognitive Neurology (INECO) Frontal Screening (IFS) and the Frontal Assessment Battery (FAB), in their ability to detect executive dysfunction in a group of behavioral variant frontotemporal dementia (bv-FTD, n = 25) and Alzheimer's disease (AD, n = 25) patients in the early stages of their disease and in comparison to a group of age-, gender-, and education-matched controls (n = 26). Relative to the FAB, the IFS showed (a) better capability to differentiate between types of dementia; (b) higher sensitivity and specificity for the detection of executive dysfunction; (c) stronger correlations with standard executive tasks. We conclude that while both tools are brief and specific for the detection of early executive dysfunction in dementia, the IFS is more sensitive and specific in differentiating bvFTD from AD, and its use in everyday clinical practice can contribute to the differential diagnosis between types of dementia.
Penile verrucous lesion: not necessarily a genital wart

Yaumatei Dermatology Clinic, 12/F Yaumatei Specialist Clinic (New Extension), 143 Battery Street, Yaumatei, Kowloon, Hong Kong A 46-year-old Chinese man presented with one year history of itchy verrucous lesions over penis and scrotum. Skin biopsy confirmed epidermolytic acanthoma. Epidermolytic acanthoma is a rare benign tumour. Before making such a diagnosis, exclusion of other diseases, especially genital warts and bowenoid papulosis is necessary. Treatment of multiple epidermolytic acanthoma remains unsatisfactory.
A Deep Learning Approach for Oriented Electrical Equipment Detection in Thermal Images

Due to the high precision and non-contact characteristics, infrared thermography has been widely used in equipment inspection to ensure the safety of electric power systems. A fundamental step toward automatic inspection and diagnosis is the detection of equipment in thermal images. Therefore, this paper presents a deep learning approach to detect equipment parts in real-time. Specifically, we propose a deep convolutional neural network that predicts the coordinates, orientation angle, and class type of each equipment part. A prior concerning orientation consistency between parts is also incorporated into our model to improve the prediction results. For evaluation, we construct a large image set containing various kinds of scenarios. Experiments on the data set show that our method is robust to noise, achieving 93.7% mean average precision when the intersection over union threshold is 0.5, and running at 20 fps on GPU. We believe that our high accurate detection results can benefit the subsequent diagnosis.
Vibration-driven mobile robots based on single actuated tensegrity structures

This paper describes a new concept for locomotion of mobile robots based on single actuated tensegrity structures. To discuss the working principle, two vibration-driven locomotion systems are considered. Due to the complex dynamics of the applied tensegrity structures with pronounced mechanical compliance, the movement performance of both systems is highly dependent on the driving frequency. By using single-actuation, the system design and also their control can be kept simple. The movement of the robots is depending on their configuration uniaxial bidirectional or planar. The working principle of both systems is discussed with the help of transient dynamic analyses and verified with experimental tests for a selected prototype.
Deep neural networks for energy load forecasting

Smartgrids of the future promise unprecedented flexibility in energy management. Therefore, accurate predictions/forecasts of energy demands (loads) at individual site and aggregate level of the grid is crucial. Despite extensive research, load forecasting remains to be a difficult problem. This paper presents a load forecasting methodology based on deep learning. Specifically, the work presented in this paper investigates the effectiveness of using Convolutional Neural Networks (CNN) for performing energy load forecasting at individual building level. The presented methodology uses convolutions on historical loads. The output from the convolutional operation is fed to fully connected layers together with other pertinent information. The presented methodology was implemented on a benchmark data set of electricity consumption for a single residential customer. Results obtained from the CNN were compared against results obtained by Long Short Term Memories LSTM sequence-to-sequence (LSTM S2S), Factored Restricted Boltzmann Machines (FCRBM), “shallow” Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the same dataset. Experimental results showed that the CNN outperformed SVR while producing comparable results to the ANN and deep learning methodologies. Further testing is required to compare the performances of different deep learning architectures in load forecasting.
Mobile Learning and Student Retention

Student retention in open and distance learning (ODL) is comparatively poor to traditional education and, in some contexts, embarrassingly low. Literature on the subject of student retention in ODL indicates that even when interventions are designed and undertaken to improve student retention, they tend to fall short. Moreover, this area has not been well researched. The main aim of our research, therefore, is to better understand and measure students’ attitudes and perceptions towards the effectiveness of mobile learning. Our hope is to determine how this technology can be optimally used to improve student retention at Bachelor of Science programmes at Indira Gandhi National Open University (IGNOU) in India. For our research, we used a survey. Results of this survey clearly indicate that offering mobile learning could be one method improving retention of BSc students, by enhancing their teaching/ learning and improving the efficacy of IGNOU’s existing student support system. The biggest advantage of this technology is that it can be used anywhere, anytime. Moreover, as mobile phone usage in India explodes, it offers IGNOU easy access to a larger number of learners. This study is intended to help inform those who are seeking to adopt mobile learning systems with the aim of improving communication and enriching students’ learning experiences in their ODL institutions.
Bulk optical properties of healthy female breast tissue.

We have measured the bulk optical properties of healthy female breast tissues in vivo in the parallel plate, transmission geometry. Fifty-two volunteers were measured. Blood volume and blood oxygen saturation were derived from the optical property data using a novel method that employed a priori spectral information to overcome limitations associated with simple homogeneous tissue models. The measurements provide an estimate of the variation of normal breast tissue optical properties in a fairly large population. The mean blood volume was 34 +/- 9 microM and the mean blood oxygen saturation was 68 +/- 8%. We also investigated the correlation of these optical properties with demographic factors such as body mass index (BMI) and age. We observed a weak correlation of blood volume and reduced scattering coefficient with BMI: correlation with age, however, was not evident within the statistical error of these experiments. The new information on healthy breast tissue provides insight about the potential contrasts available for diffuse optical tomography of breast tumours.
Predicting Instructor Performance Using Data Mining Techniques in Higher Education

Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this paper, four different classification techniques - decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis - are used to build classifier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics. Although all the classifier models show comparably high classification performances, C5.0 classifier is the best with respect to accuracy, precision, and specificity. In addition, an analysis of the variable importance for each classifier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The findings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve the measurement instruments.
Speech enhancement for non-stationary noise environments

In this paper, we present an optimally-modi#ed log-spectral amplitude (OM-LSA) speech estimator and a minima controlled recursive averaging (MCRA) noise estimation approach for robust speech enhancement. The spectral gain function, which minimizes the mean-square error of the log-spectra, is obtained as a weighted geometric mean of the hypothetical gains associated with the speech presence uncertainty. The noise estimate is given by averaging past spectral power values, using a smoothing parameter that is adjusted by the speech presence probability in subbands. We introduce two distinct speech presence probability functions, one for estimating the speech and one for controlling the adaptation of the noise spectrum. The former is based on the time–frequency distribution of the a priori signal-to-noise ratio. The latter is determined by the ratio between the local energy of the noisy signal and its minimum within a speci6ed time window. Objective and subjective evaluation under various environmental conditions con6rm the superiority of the OM-LSA and MCRA estimators. Excellent noise suppression is achieved, while retaining weak speech components and avoiding the musical residual noise phenomena. ? 2001 Elsevier Science B.V. All rights reserved.
Eye movements in reading and information processing: 20 years of research.

Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed.
LazyLSH: Approximate Nearest Neighbor Search for Multiple Distance Functions with a Single Index

Due to the "curse of dimensionality" problem, it is very expensive to process the nearest neighbor (NN) query in high-dimensional spaces; and hence, approximate approaches, such as Locality-Sensitive Hashing (LSH), are widely used for their theoretical guarantees and empirical performance. Current LSH-based approaches target at the L1 and L2 spaces, while as shown in previous work, the fractional distance metrics (Lp metrics with 0 < p < 1) can provide more insightful results than the usual L1 and L2 metrics for data mining and multimedia applications. However, none of the existing work can support multiple fractional distance metrics using one index. In this paper, we propose LazyLSH that answers approximate nearest neighbor queries for multiple Lp metrics with theoretical guarantees. Different from previous LSH approaches which need to build one dedicated index for every query space, LazyLSH uses a single base index to support the computations in multiple Lp spaces, significantly reducing the maintenance overhead. Extensive experiments show that LazyLSH provides more accurate results for approximate kNN search under fractional distance metrics.
A Non-Invasive Blood Insulin and Glucose Monitoring System Based on Near-Infrared Spectroscopy with Remote Data Logging

This paper presents the development of a portable, Non-Invasive device for monitoring patients’ blood Insulin and Glucose concentration. It is based on Near-Infrared(NIR) Spectroscopy. Perkin-Elmer Lambda 750 spectrometer was used to study the absorbance of Insulin and glucose at different wavelengths of NIR region. NIR LED (Light Emitting diode) of appropriate wavelengths were used to construct a finger clip. A separate Printed Circuit Board (PCB) was constructed; that connects to the finger clip and acquires the Photoplethysmography (PPG) signal at three separate NIR wavelengths. The PPG signal can be used to detect the concentartion of Glucose and Insulin in blood. The PCB has a WiFi module interfaced within it; that supports Internet connectivity and uploads the time series data of insulin and glucose to a server. The data can be viewed in a graphical format by authorized users. This can be used by a medical practitioner to understand whether the data shows an increasing or decreasing trend and hence, will enable to prognosticate the approaching critical condition of the patient much before the critical condition actually occurs.
Rhabdomyosarcoma of the clitoris.

Genitourinary rhabdomyosarcoma in females usually originates in the vagina or uterus, but rarely the vulva. The authors present a case of rhabdomyosarcoma originating in the clitoris. A 4-year-old with an alveolar rhabdomyosarcoma of the clitoris was treated with radical clitorectomy, radiation, and chemotherapy. Follow-up at 3 years showed no active disease.
Anti-ulcerogenic Evaluation of Gloriosa Superba Tuber Extracts In-vivo and Anthelmintic Activity In-vitro: A Comparison.

Gloriosa Superba (Liliaceae) is a semi-woody herbaceous climber native of tropical Asia, Africa and reported by tribal people of India for its several clinical uses. Traditionally, the tubers and other parts of the plant are widely being used in the treatment of ulcers, piles, inflammation, abdominal pain, infertility, parasitic skin infections and leprosy. The present study was carried out to investigate the antiulcerogenic activity of ethanolic extract of Gloriosa superba L. tuberous rhizomes (GST) and compare with Zingiber officinale (rhizomes) and Glycyrrhiza glabra (roots) ethanolic extracts on experimental ulcers induced by aspirin (chemically induced ulcers) couple with stress and on pylorus ligation-induced ulcers in Wistar rats. The anthelmintic activity of various extracts of GST was also evaluated in vitro against two types of intestinal worms; a) Earthworms and b) Ascardia Galli (a parasitic roundworm). Test extracts were administered orally 60 and 30 min before for aspirin and pylorus ligationinduced ulcer models respectively. GST extract showed superior protection when compared with other test extracts on both the ulcer models tested. The extract significantly increased the pH and decreased both the volume and total acidity of gastric secretion in pylorus ligationinduced ulcer model. The antiulcerogenic effect exhibited by GST extract was comparable with ranitidine (standard drug) in particular at higher dose. Aqueous, alcoholic and chloroform extracts of GST showed significant anthelmintic activity in vitro against both types of intestinal worms. Therefore, the results indicate that the herbal preparations containing GST would be beneficial to treat gastric ulcers more effectively for improved clinical outcome.
Mass circumcision with a novel plastic clamp technique.

To evaluate the safety and efficacy of mass circumcision performed using a plastic clamp.



A total of 2013 males, including infants, children, adolescents, and adults were circumcised during a 7-day period by using a plastic clamp technique. Complications were analyzed retrospectively in regard to 4 different age groups. Postcircumcision sexual function and satisfaction rates of the adult males were also surveyed.



The mean duration of circumcision was 3.6±1.2 minutes. Twenty-six males who were lost to follow-up were excluded from the study. The total complication rate was found to be 2.47% among the remaining 1987 males, with a mean age of 7.8±2.5 years. The highest complication rate (2.93%) was encountered among the children<2 years age, which was because of the high rate of buried penis (0.98%) and excessive foreskin (0.98%) observed in this group. The complication rates of older children, adolescents, and adults were slightly lower than the children<2 years age, at 2.39%, 2.51%, and 2.40%, respectively. Excessive foreskin (0.7%) was the most common complication observed after mass circumcision. Bleeding (0.6%), infection (0.55%), wound dehiscence (0.25%), buried penis (0.25%), and urine retention (0.1%) were other encountered complications. The erectile function and sexual libido in adolescents and adults was not affected by circumcision and a 96% satisfaction rate was obtained.



Mass circumcision performed by a plastic clamp technique was found to be a safe and time-saving method of circumcising a large number of males at any age.
Reply to comment Reply to criticism of the ‘ Orch OR qubit ’ – ‘ Orchestrated objective reduction ’ is scientifically justified

The critical commentary by Reimers et al.  regarding the Penrose–Hameroff theory of ‘orchestrated objective reduction’ (‘Orch OR’) is largely uninformed and basically incorrect, as they solely criticize non-existent features of Orch OR, and ignore (1) actual Orch OR features, (2) supportive evidence, and (3) previous answers to their objections (Section 5.6 in our review ). Here we respond point-by-point to the issues they raise.
Emotional abilities as predictors of risky driving behavior among a cohort of middle aged drivers.

The aim of this study is to analyze the relationship between emotional abilities and the influence of this relationship on self reported drivers' risky attitudes. The risky driving attitudes and emotional abilities of 177 future driving instructors were measured. The results demonstrate that risky attitudes correlate negatively with emotional abilities. Regression analysis showed that adaptability and interpersonal abilities explained the differences observed in the global risk attitude index. There were some differences in the specific risk factors. The variability observed in the speed and distraction and fatigue factors could also be explained by interpersonal and adaptability abilities. Nevertheless the tendency to take risks was explained by stress management and also interpersonal components. Emotional abilities have the weakest relation with alcohol and drugs factor, and in this case the variability observed was explained by the adaptability component. The results obtained highlight the importance take off including emotional abilities in prevention programs to reduce risky driving behaviors.
CORALINA: a universal method for the generation of gRNA libraries for CRISPR-based screening

The bacterial CRISPR system is fast becoming the most popular genetic and epigenetic engineering tool due to its universal applicability and adaptability. The desire to deploy CRISPR-based methods in a large variety of species and contexts has created an urgent need for the development of easy, time- and cost-effective methods enabling large-scale screening approaches. Here we describe CORALINA (comprehensive gRNA library generation through controlled nuclease activity), a method for the generation of comprehensive gRNA libraries for CRISPR-based screens. CORALINA gRNA libraries can be derived from any source of DNA without the need of complex oligonucleotide synthesis. We show the utility of CORALINA for human and mouse genomic DNA, its reproducibility in covering the most relevant genomic features including regulatory, coding and non-coding sequences and confirm the functionality of CORALINA generated gRNAs. The simplicity and cost-effectiveness make CORALINA suitable for any experimental system. The unprecedented sequence complexities obtainable with CORALINA libraries are a necessary pre-requisite for less biased large scale genomic and epigenomic screens.
OpenSHS: Open Smart Home Simulator

This paper develops a new hybrid, open-source, cross-platform 3D smart home simulator, OpenSHS, for dataset generation. OpenSHS offers an opportunity for researchers in the field of the Internet of Things (IoT) and machine learning to test and evaluate their models. Following a hybrid approach, OpenSHS combines advantages from both interactive and model-based approaches. This approach reduces the time and efforts required to generate simulated smart home datasets. We have designed a replication algorithm for extending and expanding a dataset. A small sample dataset produced, by OpenSHS, can be extended without affecting the logical order of the events. The replication provides a solution for generating large representative smart home datasets. We have built an extensible library of smart devices that facilitates the simulation of current and future smart home environments. Our tool divides the dataset generation process into three distinct phases: first design: the researcher designs the initial virtual environment by building the home, importing smart devices and creating contexts; second, simulation: the participant simulates his/her context-specific events; and third, aggregation: the researcher applies the replication algorithm to generate the final dataset. We conducted a study to assess the ease of use of our tool on the System Usability Scale (SUS).