nils-herrmann/clean_scirepeval huggingface dataset:
Update TFCC: histology and pathology, classification, examination and diagnostics

The TFCC is a crucial stabilizer of the DRUJ. Based on its superficial and deep fibers, the TFCC guarantees unrestricted pronation and supination which is essential for performing sophisticated tasks. The ability to perform complex movements is of uppermost importance for hand function. Therefore, a functional intact TFCC is a prerequisite in this context. The articular disc of the TFCC is a fibrocartilaginous extension of the superficial zone of hyaline articular cartilage which arises from the radius. The peripheral 10–40 % of the TFC is vascularized. Degeneration of the articular disc is common with increasing age. Even though the central part of the articular disc is avascular, potential regeneration of lesions could be detected. The Palmer and Atzei classifications of TFCC lesions are complementary. TFCC innervation is based on different nerves. There is a high variability. A diligent clinical examination facilitates specific tests which help to allocate symptoms to the pathology. Therefore, a thorough clinical examination is not dispensable. Wrist arthroscopy remains the “gold standard” for diagnosing TFCC pathologies despite technical progress in imaging modalities. MR arthrography may have the potential to become a real alternative to wrist arthroscopy for diagnosing TFCC pathologies with technical progress in the future.
Automatic dynamic range adjustment of a controlled-temperature thermoresistive-based anemometer

The thermoresistive-based hot-wire anemometer operating at a controlled temperature is a classical architecture that is vastly found in the literature. Nevertheless, this architecture presents a problem due to dynamic range variation with respect to the fluid temperature. In this work an alternative architecture of a controlled temperature anemometer with automatic adjustment of the dynamic range is proposed. Simulations of the proposed system using the parameters from a commercial sensor are presented and compared with the results from a classical configuration.
Effectiveness of Japanese SHARE model in improving Taiwanese healthcare personnel's preference for cancer truth telling.

Communication skills training (CST) based on the Japanese SHARE model of family-centered truth telling in Asian countries has been adopted in Taiwan. However, its effectiveness in Taiwan has only been preliminarily verified. This study aimed to test the effect of SHARE model-centered CST on Taiwanese healthcare providers' truth-telling preference, to determine the effect size, and to compare the effect of 1-day and 2-day CST programs on participants' truth-telling preference.



For this one-group, pretest-posttest study, 10 CST programs were conducted from August 2010 to November 2011 under certified facilitators and with standard patients. Participants (257 healthcare personnel from northern, central, southern, and eastern Taiwan) chose the 1-day (n = 94) or 2-day (n = 163) CST program as convenient. Participants' self-reported truth-telling preference was measured before and immediately after CST programs, with CST program assessment afterward.



The CST programs significantly improved healthcare personnel's truth-telling preference (mean pretest and posttest scores ± standard deviation (SD): 263.8 ± 27.0 vs. 281.8 ± 22.9, p < 0.001). The CST programs effected a significant, large (d = 0.91) improvement in overall truth-telling preference and significantly improved method of disclosure, emotional support, and additional information (p < 0.001). Participation in 1-day or 2-day CST programs did not significantly affect participants' truth-telling preference (p > 0.05) except for the setting subscale. Most participants were satisfied with the CST programs (93.8%) and were willing to recommend them to colleagues (98.5%).



The SHARE model-centered CST programs significantly improved Taiwanese healthcare personnel's truth-telling preference. Future studies should objectively assess participants' truth-telling preference, for example, by cancer patients, their families, and other medical team personnel and at longer times after CST programs.
Incidence of trichostasis spinulosa at a single institution in Yemen.

Trichostasis spinulosa (TS) is a common, underdiagnosed cosmetic skin condition.



The main objectives of this study were to determine the occurrence of TS relative to age and gender, to analyze its cutaneous distribution, and to investigate any possible familial basis for this condition, its impact on patients, and the types and efficacy of previous treatments.



All patients presenting to the outpatient dermatology clinic at the study institution and their relatives were examined for the presence of TS and were questioned about family history and previous treatment. Photographs and biopsies of suspected cases of TS were obtained.



Of 2400 patients seen between August and December 2013, 286 patients were diagnosed with TS (135 males, 151 females; prevalence: 11.9%). Women presented more frequently than men with complaints of TS (6.3 vs. 4.2%), and more women had received prior treatment for TS (10.5 vs. 2.8%). The most commonly affected sites were the face (100%), interscapular area (10.5%), and arms (3.1%). Lesions involved the nasal alae in 96.2%, the nasal tip in 90.9%, the chin in 55.9%, and the cheeks in 52.4% of patients. Only 15.7% of patients had forehead lesions, and only 4.5% had perioral lesions. Among the 38 previously treated patients, 65.8% reported temporary improvement.



Trichostasis spinulosa is a common condition that predominantly affects the face in patients of all ages. Additional studies employing larger cohorts from multiple centers will be required to determine the prevalence of TS in the general population.
When SIGNAL hits the Fan: On the Usability and Security of State-of-the-Art Secure Mobile Messaging

In this paper we analyze the security and usability of the state-of-the-art secure mobile messenger SIGNAL. In the first part of this paper we discuss the threat model current secure mobile messengers face. In the following, we conduct a user study to examine the usability of SIGNAL’s security features. Specifically, our study assesses if users are able to detect and deter man-in-the-middle attacks on the SIGNAL protocol. Our results show that the majority of users failed to correctly compare keys with their conversation partner for verification purposes due to usability problems and incomplete mental models. Hence users are very likely to fall for attacks on the essential infrastructure of today’s secure messaging apps: the central services to exchange cryptographic keys. We expect that our findings foster research into the unique usability and security challenges of state-of-theart secure mobile messengers and thus ultimately result in strong protection measures for the average user.
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning

Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required. After that, we designed a model which still keeps an RNN as the encoder, while using a non-autoregressive convolutional decoder. We further combine a suite of effective designs to significantly improve model efficiency while also achieving better performance. Our model is trained on two different large unlabelled corpora, and in both cases the transferability is evaluated on a set of downstream NLP tasks. We empirically show that our model is simple and fast while producing rich sentence representations that excel in downstream tasks.
Pre-analytical errors management in the clinical laboratory: a five-year study

This study describes quality indicators for the pre-analytical process, grouping errors according to patient risk as critical or major, and assesses their evaluation over a five-year period.



A descriptive study was made of the temporal evolution of quality indicators, with a study population of 751,441 analytical requests made during the period 2007-2011. The Runs Test for randomness was calculated to assess changes in the trend of the series, and the degree of control over the process was estimated by the Six Sigma scale.



The overall rate of critical pre-analytical errors was 0.047%, with a Six Sigma value of 4.9. The total rate of sampling errors in the study period was 13.54% (P = 0.003). The highest rates were found for the indicators "haemolysed sample" (8.76%), "urine sample not submitted" (1.66%) and "clotted sample" (1.41%), with Six Sigma values of 3.7, 3.7 and 2.9, respectively.



The magnitude of pre-analytical errors was accurately valued. While processes that triggered critical errors are well controlled, the results obtained for those regarding specimen collection are borderline unacceptable; this is particularly so for the indicator "haemolysed sample".
CAEMSI : A Cross-Domain Analytic Evaluation Methodology for Style Imitation

We propose CAEMSI, a cross-domain analytic evaluation methodology for Style Imitation (SI) systems, based on a set of statistical significance tests that allow hypotheses comparing two corpora to be tested. Typically, SI systems are evaluated using human participants, however, this type of approach has several weaknesses. For humans to provide reliable assessments of an SI system, they must possess a sufficient degree of domain knowledge, which can place significant limitations on the pool of participants. Furthermore, both human bias against computer-generated artifacts, and the variability of participants’ assessments call the reliability of the results into question. Most importantly, the use of human participants places limitations on the number of generated artifacts and SI systems which can be feasibly evaluated. Directly motivated by these shortcomings, CAEMSI provides a robust and scalable approach to the evaluation problem.  Distance, a domain-independent distance metric, is used to measure the distance between individual artifacts within a corpus. The difference between corpora is measured using test statistics derived from these inter-artifact distances, and permutation testing is used to determine the significance of the difference. We provide empirical evidence validating the statistical significance tests, using datasets from two distinct domains.
Individual teeth segmentation in CBCT and MSCT dental images using watershed

Teeth segmentation is an important step in human identification and Content Based Image Retrieval (CBIR) systems. This paper proposes a new approach for teeth segmentation using morphological operations and watershed algorithm. In Cone Beam Computer Tomography (CBCT) and Multi Slice Computer Tomography (MSCT) each tooth is an elliptic shape region that cannot be separated only by considering their pixels' intensity values. For segmenting a tooth from the image, some enhancement is necessary. We use morphological operators such as image filling and image opening to enhance the image. In the proposed algorithm, a Maximum Intensity Projection (MIP) mask is used to separate teeth regions from black and bony areas. Then each tooth is separated using the watershed algorithm. Anatomical constraints are used to overcome the over segmentation problem in watershed method. The results show a high accuracy for the proposed algorithm in segmenting teeth. Proposed method decreases time consuming by considering only one image of CBCT and MSCT for segmenting teeth instead of using all slices.
The use of probiotics in aquaculture

The research of probiotics for aquatic animals is increasing with the demand for environmentfriendly aquaculture. The probiotics were defined as live microbial feed supplements that improve health of man and terrestrial livestock. The gastrointestinal microbiota of fish and shellfish are peculiarly dependent on the external environment, due to the water flow passing through the digestive tract. Most bacterial cells are transient in the gut, with continuous intrusion of microbes coming from water and food. Some commercial products are referred to as probiotics, though they were designed to treat the rearing medium, not to supplement the diet. This extension of the probiotic concept is pertinent when the administered microbes survive in the gastrointestinal tract. Otherwise, more general terms are suggested, like biocontrol when the treatment is antagonistic to pathogens, or bioremediation when water quality is improved. However, the first probiotics tested in fish were commercial preparations devised for land animals. Though some effects were observed with such preparations, the survival of these bacteria was uncertain in aquatic environment. Most attempts to propose probiotics have been undertaken by isolating and selecting strains from aquatic environment. These microbes were Vibrionaceae, pseudomonads, lactic acid bacteria, Bacillus spp. and yeasts. Three main characteristics have been searched in microbes as candidates Ž . to improve the health of their host. 1 The antagonism to pathogens was shown in vitro in most Ž . Ž . cases. 2 The colonization potential of some candidate probionts was also studied. 3 Challenge tests confirmed that some strains could increase the resistance to disease of their host. Many other beneficial effects may be expected from probiotics, e.g., competition with pathogens for nutrients or for adhesion sites, and stimulation of the immune system. The most promising prospects are sketched out, but considerable efforts of research will be necessary to develop the applications to aquaculture. q 1999 Elsevier Science B.V. All rights reserved.
A contactless heart rate sensor system for driver health monitoring

This paper describes a non-contact heart rate monitoring system using a 24-GHz microwave Doppler sensor for driver health monitoring. The objective of this work is to detect an instantaneously heart rate using this non-contact system in a car. The instantaneously heart rate can contribute to prevent heart disasters and to detect mental stress state. However, the Doppler sensor system is very sensitive and it can be easily contaminated by a motion artifact especially while driving. To address this problem, time frequency analysis and autocorrelation method are employed. Measurement results show that the Doppler sensor, which is pasted on the clothing surface, can successfully extract the heart rate through clothes. The proposed method achieves 13.1-ms RMS error in heart rate measurement with 11 subjects and 40km/h driving speed on average.
VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time–frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.
Coding theory: tutorial & survey

Co dingtheory has playe da central role in the theoretical computer science. Computer scientists have long exploite d notions, constructions, theorems and techniques of coding theory. More recently, theoretical computer science has also been c ontributing to the theory of error-correcting c odesin particular in making progress on some fundamental algorithmic connections. Here we survey some of the centr al go als of c oding theory and the progress made via algebraic methods. We stress that this is a very partial view of coding theory and a lot of promising combinatorial and probabilistic approaches are not cover ed by this survey.
Multimodal page classification in administrative document image streams

In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an $$n$$ n -gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.
Global Laparoscopy Positioning System with a Smart Trocar

in the context of minimally invasive surgery, navigation in the patients abdomen is crucial, but is also more complicated than during open procedures. While the advantages for the patient are numerous, surgeons may not always know where they are in this complex environment, and getting used to this technique takes a lot of practice.  we present a novel smart trocar device that provides a global positioning system for the laparoscopic instruments that are inserted through the trocars. We augment current trocars with a camera pointing towards the ceiling. At any point in time, and by using computer vision as well as projective geometry, our smart trocar can provide the position of rigid instruments.  with our system, we reach one-millimeter accuracy on translation and half a degree on rotation with the only limitation being the velocity of surgeons movements. But this limitation has not been an obstacle in real practice.  the trocar can track specific landmarks found on the ceiling of the OR, and, from these, accurately determine its global position in the OR in real time. Applications of this invention are first, assessment and/or training of surgical skills second, once our system is coupled to a real-time three-dimensional simulator of patient anatomy, we envision that our system can automatically provide surgical guidance during the procedure.
The American Association of Oral and Maxillofacial Surgeons Age-Related Third Molar Study.

The purpose of this investigation was to assess the frequency of complications of third molar surgery, both intraoperatively and postoperatively, specifically for patients 25 years of age or older.



This prospective study evaluated 3,760 patients, 25 years of age or older, who were to undergo third molar surgery by oral and maxillofacial surgeons practicing in the United States. The predictor variables were categorized as demographic (age, gender), American Society of Anesthesiologists classification, chronic conditions and medical risk factors, and preoperative description of third molars (present or absent, type of impaction, abnormalities or association with pathology). Outcome variables were intraoperative and postoperative complications, as well as quality of life issues (days of work missed or normal activity curtailed). Frequencies for data collected were tabulated.



The sample was provided by 63 surgeons, and was composed of 3,760 patients with 9,845 third molars who were 25 years of age or older, of which 8,333 third molars were removed. Alveolar osteitis was the most frequently encountered postoperative problem (0.2% to 12.7%). Postoperative inferior alveolar nerve anesthesia/paresthesia occurred with a frequency of 1.1% to 1.7%, while lingual nerve anesthesia/paresthesia was calculated as 0.3%. All other complications also occurred with a frequency of less than 1%.



The findings of this study indicate that third molar surgery in patients 25 years of age or older is associated with minimal morbidity, a low incidence of postoperative complications, and minimal impact on the patients quality of life.
Hysteroscopic resection of uterine submucous leiomyoma protruding through hymen in a 16-year-old adolescent.

Uterine leiomyomas are rarely seen in adolescent and to date nine leiomyoma cases have been reported under age 17. Eight of these have been treated surgically via laparotomic myomectomy.



A 16-year-old girl presented with a painless, lobulated necrotic mass protruding through the introitus. The mass originated from posterior uterine wall resected using hysteroscopy. Final pathology report revealed a submucous uterine leiomyoma.



Submucous uterine leiomyomas may present as a vaginal mass in adolescents and can be safely treated using hysteroscopy.
Detecting Both Machine and Human Created Fake Face Images In the Wild

Due to the significant advancements in image processing and machine learning algorithms, it is much easier to create, edit, and produce high quality images. However, attackers can maliciously use these tools to create legitimate looking but fake images to harm others, bypass image detection algorithms, or fool image recognition classifiers. In this work, we propose neural network based classifiers to detect fake human faces created by both 1) machines and 2) humans. We use ensemble methods to detect GANs-created fake images and employ pre-processing techniques to improve fake face image detection created by humans. Our approaches focus on image contents for classification and do not use meta-data of images. Our preliminary results show that we can effectively detect both GANs-created images, and human-created fake images with 94% and 74.9% AUROC score.
A Survey on Bayesian Nonparametric Learning

Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning’s great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field due to its greater modelling flexibility and representation power. Instead of playing with the fixed-dimensional probabilistic distributions of Bayesian learning, BNL creates a new “game” with infinite-dimensional stochastic processes. BNL has long been recognised as a research subject in statistics, and, to date, several state-of-the-art pilot studies have demonstrated that BNL has a great deal of potential to solve real-world machine-learning tasks. However, despite these promising results, BNL has not created a huge wave in the machine-learning community. Esotericism may account for this. The books and surveys on BNL written by statisticians are overcomplicated and filled with tedious theories and proofs. Each is certainly meaningful but may scare away new researchers, especially those with computer science backgrounds. Hence, the aim of this article is to provide a plain-spoken, yet comprehensive, theoretical survey of BNL in terms that researchers in the machine-learning community can understand. It is hoped this survey will serve as a starting point for understanding and exploiting the benefits of BNL in our current scholarly endeavours. To achieve this goal, we have collated the extant studies in this field and aligned them with the steps of a standard BNL procedure—from selecting the appropriate stochastic processes through manipulation to executing the model inference algorithms. At each step, past efforts have been thoroughly summarised and discussed. In addition, we have reviewed the common methods for implementing BNL in various machine-learning tasks along with its diverse applications in the real world as examples to motivate future studies.