nils-herrmann/clean_scirepeval huggingface dataset:
Beyond Web 2.0: mapping the technology landscapes of young learners

Boundaries between formal and informal learning settings are shaped by influences beyond learners’ control. This can lead to the proscription of some familiar technologies that learners may like to use from some learning settings. This contested demarcation is not well documented. In this paper, we introduce the term ‘digital dissonance’ to describe this tension with respect to learners’ appropriation of Web 2.0 technologies in formal contexts. We present the results of a study that explores learners’ inand out-of-school use of Web 2.0 and related technologies. The study comprises two data sources: a questionnaire and a mapping activity. The contexts within which learners felt their technologies were appropriate or able to be used are also explored. Results of the study show that a sense of ‘digital dissonance’ occurs around learners’ experience of Web 2.0 activity in and out of school. Many learners routinely cross institutionally demarcated boundaries, but the implications of this activity are not well understood by institutions or indeed by learners themselves. More needs to be understood about the transferability of Web 2.0 skill sets and ways in which these can be used to support formal learning.
Dialogue act segmentation for Vietnamese human-human conversational texts

Dialog act identification plays an important role in understanding conversations. It has been widely applied in many fields such as dialogue systems, automatic machine translation, automatic speech recognition, and especially useful in systems with human-computer natural language dialogue interfaces such as virtual assistants and chatbots. The first step of identifying dialog act is identifying the boundary of the dialog act in utterances. In this paper, we focus on segmenting the utterance according to the dialog act boundaries, i.e. functional segments identification, for Vietnamese utterances. We investigate carefully functional segment identification in two approaches: (1) machine learning approach using maximum entropy (ME) and conditional random fields (CRFs); (2) deep learning approach using bidirectional Long Short-Term Memory (LSTM) with a CRF layer (Bi-LSTM-CRF) on two different conversational datasets: (1) Facebook messages (Message data); (2) transcription from phone conversations (Phone data). To the best of our knowledge, this is the first work that applies deep learning based approach to dialog act segmentation. As the results show, deep learning approach performs appreciably better as to compare with traditional machine learning approaches. Moreover, it is also the first study that tackles dialog act and functional segment identification for Vietnamese.
Advanced learning algorithms for cross-language patent retrieval and classification

We study several machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingual sub-tasks, our learning algorithms exploit the bilingual training documents and learn a semantic representation from them. We study Japanese–English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. We also investigate learning algorithms for cross-language document classification. The learning algorithm are based on KCCA and Support Vector Machines (SVM). In particular, we study two ways of combining the KCCA and SVM and found that one particular combination called SVM_2k achieved better results than other learning algorithms for either bilingual or monolingual test documents.
Hyaluronic acid hydrogels for biomedical applications.

Hyaluronic acid (HA), an immunoneutral polysaccharide that is ubiquitous in the human body, is crucial for many cellular and tissue functions and has been in clinical use for over thirty years. When chemically modified, HA can be transformed into many physical forms-viscoelastic solutions, soft or stiff hydrogels, electrospun fibers, non-woven meshes, macroporous and fibrillar sponges, flexible sheets, and nanoparticulate fluids-for use in a range of preclinical and clinical settings. Many of these forms are derived from the chemical crosslinking of pendant reactive groups by addition/condensation chemistry or by radical polymerization. Clinical products for cell therapy and regenerative medicine require crosslinking chemistry that is compatible with the encapsulation of cells and injection into tissues. Moreover, an injectable clinical biomaterial must meet marketing, regulatory, and financial constraints to provide affordable products that can be approved, deployed to the clinic, and used by physicians. Many HA-derived hydrogels meet these criteria, and can deliver cells and therapeutic agents for tissue repair and regeneration. This progress report covers both basic concepts and recent advances in the development of HA-based hydrogels for biomedical applications.
Diagnosis and evaluation of renal cysts.

Renal cysts are commonly encountered in clinical practice. Although most cysts found on routine imaging studies are benign, there must be an index of suspicion to exclude a neoplastic process or the presence of a multicystic disorder. This article focuses on the more common adult cystic diseases, including simple and complex renal cysts, autosomal-dominant polycystic kidney disease, and acquired cystic kidney disease.
Open versus Percutaneous Stabilization of Thoracolumbar Spine Fractures: A Short-Term Functional and Radiological Follow-up.

A prospective cohort study evaluates the functional and radiological outcome of thoracolumbar spine fractures treated either with open or percutaneous dorsal instrumentation. In recent years, several studies advocate percutaneous stabilization of spinal fractures in patients without neurological deficits. However, it is still debated whether percutaneous stabilization is superior to open dorsal instrumentation in spinal trauma.



This study was performed between 2010 and 2012 at a Level 1 trauma center. Patients treated either with an open or a percutaneous dorsal instrumentation for traumatic fractures of the thoracolumbar spine (T11 to L2) were included. Fracture morphology, screw positioning and clinical parameters were analyzed. Standardized questionnaires (VAS-spine-score; Oswestry-disability-score; SF-36) and follow up radiographs were performed.



Overall 72 patients (29 percutaneous; 43 open) could be included. The surgical and the early postsurgical course were similar between both groups. Furthermore the operative approach had no influence on the functional and radiological outcome one year after surgery, but the questionnaires showed moderate impairments within both groups. Also both groups showed a significant loss of reduction after the first postoperative month (p < 0.01). Within the open group a significantly higher amount of fracture reduction (p < 0.01) and a significantly reduced intraoperative radiation exposure was seen (open 105.9 sec.; percutaneous 143.1 sec; p < 0.05); whereas the percutaneous approach was associated with significantly reduced intraoperative blood loss (open 2.2 g/dl; percutaneous 1.2 g/dl; p < 0.001).



The functional and the radiological outcome of both groups was comparable one year after trauma. Minor advantages of the percutaneous system was less blood loss, whereas the open approach was associated with a significantly higher amount of initial reduction and significantly less intraoperative radiation exposure. Independent from the type of posterior fixation loss of reduction was already significant in the early postoperative course.
Employing machine learning techniques for detection and classification of phishing emails

A phishing email is a legitimate-looking email which is designed to fool the recipient into believing that it is a genuine email, and either reveals sensitive information or downloads malicious software through clicking on malicious links contained in the body of the email. Given that phishing emails cost UK consumers £174m in 2015, this paper proposal is driven by a problem whose resolution will have a great impact on people's lives in the UK and in the world. In this paper, we proposed a Neural Network (NN)-based model for detections and classifications of phishing emails using publically available email datasets for both benign and phishing emails. The results of the experiments are presented in order to demonstrate the effectiveness of the model in terms of accuracy, true-positive rate, false-positive rate, network performance and error histogram.
Uni-CAVE: A Unity3D plugin for non-head mounted VR display systems

Unity3D has become a popular, freely available 3D game engine for design and construction of virtual environments. Unfortunately, the few options that currently exist for adapting Unity3D to distributed immersive tiled or projection-based VR display systems rely on closed commercial products. Uni-CAVE aims to solve this problem by creating a freely-available and easy to use Unity3D extension package for cluster-based VR display systems. This extension provides support for head and device tracking, stereo rendering and display synchronization. Furthermore, Uni-CAVE enables configuration within the Unity environment enabling researchers to get quickly up and running.
Enhanced human-machine interface in braking

Antilock brake system (ABS) technology with powerful electronic components has shown superior braking performance to conventional vehicles on test tracks. On real highways, however, the performance of the ABS-equipped car has been disappointing. The poor braking performance with ABS has resulted from the questionable design of the human-machine interface for the brake system. The goal of this study is to design brake systems that provide more intuitive brake control and proper braking-performance information for the driver. In this study automotive brake systems are modeled as a type of master-slave telemanipulator. Human force-displacement interaction at the brake pedal has a strong effect on braking performance. As a preliminary study in brake-system design, the characteristics of human leg motion and its underlying motor-control scheme are studied through experiments and simulations, and a model of braking motion by the driver's leg is developed. This paper proposes novel brake systems based on two new aspects. First, the mechanical impedance characteristics of the leg action of the driver are taken into consideration in designing the brake systems. Second, the brake systems provide the driver with kinesthetic feedback of braking conditions or performance. The effectiveness of the proposed designs in a combined driver-vehicle system is investigated using driving simulation.
A fast computation of pairwise sequence alignment scores between a protein and a set of single-locus variants of another protein

Recently we have developed a new algorithm, PROVEAN (<u>Pro</u>tein <u>V</u>ariation <u>E</u>ffect <u>An</u>alyzer), for predicting the functional effect of protein sequence variations, including single amino acid substitutions and small insertions and deletions . The prediction is based on the change, caused by a given variation, in the similarity of the query sequence to a set of its related protein sequences. For this prediction, the algorithm is required to compute a semi-global pairwise sequence alignment score between the query sequence and each of the related sequences. Using dynamic programming, it takes O(n · m) time to compute alignment score between the query sequence Q of length n and a related sequence S of length m. Thus given l different variations in Q, in a naive way it would take O(l · n · m) time to compute the alignment scores between each of the variant query sequences and S. In this paper, we present a new approach to efficiently compute the pairwise alignment scores for l variations, which takes O((n + l) · m) time when the length of variations is bounded by a constant. In this approach, we further utilize the solutions of overlapping subproblems, which are already used by dynamic programming approach. Our algorithm has been used to build a new database for precomputed prediction scores for all possible single amino acid substitutions, single amino acid insertions, and up to 10 amino acids deletions in about 91K human proteins (including isoforms), where l becomes very large, that is, l = O(n). The PROVEAN source code and web server are available at http://provean.jcvi.org.
Highly conductive PEDOT:PSS nanofibrils induced by solution-processed crystallization.

The fabrication of electronic devices based on organic materials, known as ’printed electronics’, is an emerging technology due to its unprecedented advantages involving fl exibility, light weight, and portability, which will ultimately lead to future ubiquitous applications.  The solution processability of semiconducting and metallic polymers enables the cost-effective fabrication of optoelectronic devices via high-throughput printing techniques.  These techniques require high-performance fl exible and transparent electrodes (FTEs) fabricated on plastic substrates, but currently, they depend on indium tin oxide (ITO) coated on plastic substrates. However, its intrinsic mechanical brittleness and inferior physical properties arising from lowtemperature ( T ) processing below the melting T of the plastic substrates (i.e., typically below 150 °C) have increased the demand for alternative FTE materials.
Slicing: A New Approach for Privacy Preserving Data Publishing

Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
Neonatal abdominal wall defects.

Gastroschisis and omphalocele are the two most common congenital abdominal wall defects. Both are frequently detected prenatally due to routine maternal serum screening and fetal ultrasound. Prenatal diagnosis may influence timing, mode and location of delivery. Prognosis for gastroschisis is primarily determined by the degree of bowel injury, whereas prognosis for omphalocele is related to the number and severity of associated anomalies. The surgical management of both conditions consists of closure of the abdominal wall defect, while minimizing the risk of injury to the abdominal viscera either through direct trauma or due to increased intra-abdominal pressure. Options include primary closure or a variety of staged approaches. Long-term outcome is favorable in most cases; however, significant associated anomalies (in the case of omphalocele) or intestinal dysfunction (in the case of gastroschisis) may result in morbidity and mortality.
A System for Detection of Distributed Denial of Service ( DDoS ) Attacks using KDD Cup Data Set

Distributed denial-of-service (DDoS) attacks are a major security threat, the prevention of which is very hard, like when it comes to highly distributed daemon-based attacks. The early discovery of these attacks, although difficult, is necessary to protect network resources as well as the end users. In this paper, we address the problem of DDoS attacks and present the foundation and algorithms of our IDS. The base of our system is composed of intrusion detection systems (IDSs) which use the KDD Cup dataset to detect intrusion. The IDS scans all the files being transmitted from the routers for malicious content and known virus signatures. The evaluation of our system, using the KDD testing dataset, shows a better ratio of detecting attacks and a low false positives ratio. It also supports easy modifiability, scalability and usability.
Dental impression compound as an effective splint for maintenance of ear elevation in microtia reconstruction

Ear projection is an important goal to be achieved after stage two (ear elevation) in cases of microtia. This is a retrospective study conducted on patients with microtia who underwent staged reconstruction for the same. This study has been carried out over a period of 10 years with 211 patients. Dental impression compound was used as a splint after ear elevation and split skin grafting to maintain the projection of the ear. Projection of the ear was measured both pre- and post-procedure and at every follow-up using goniometer and photographic documentation was simultaneously done. Statistical analysis was performed using t-test. Patients were reviewed every month and splint was continued until 6 months post-surgery. The splint was very effective in maintaining the ear projection of more than 20(°) even after prolonged follow-up of upto 2 years. There were no complications associated with the splint application or prolonged use.
Plastic accumulation in the North Atlantic subtropical gyre.

Plastic marine pollution is a major environmental concern, yet a quantitative description of the scope of this problem in the open ocean is lacking. Here, we present a time series of plastic content at the surface of the western North Atlantic Ocean and Caribbean Sea from 1986 to 2008. More than 60% of 6136 surface plankton net tows collected buoyant plastic pieces, typically millimeters in size. The highest concentration of plastic debris was observed in subtropical latitudes and associated with the observed large-scale convergence in surface currents predicted by Ekman dynamics. Despite a rapid increase in plastic production and disposal during this time period, no trend in plastic concentration was observed in the region of highest accumulation.
A cascading nonlinear magneto-optical effect in topological insulators

Topological insulators (TIs) are characterized by possessing metallic (gapless) surface states and a finite band-gap state in the bulk. As the thickness of a TI layer decreases down to a few nanometers, hybridization between the top and bottom surfaces takes place due to quantum tunneling, consequently at a critical thickness a crossover from a 3D-TI to a 2D insulator occurs. Although such a crossover is generally accessible by scanning tunneling microscopy, or by angle-resolved photoemission spectroscopy, such measurements require clean surfaces. Here, we demonstrate that a cascading nonlinear magneto-optical effect induced via strong spin-orbit coupling can examine such crossovers. The helicity dependence of the time-resolved Kerr rotation exhibits a robust change in periodicity at a critical thickness, from which it is possible to predict the formation of a Dirac cone in a film several quintuple layers thick. This method enables prediction of a Dirac cone using a fundamental nonlinear optical effect that can be applied to a wide range of TIs and related 2D materials.
Effects of intensive phonomotor treatment on reading in eight individuals with aphasia and phonological alexia.

The aim of this study was to investigate effects of a multimodal treatment of phonology, phonomotor treatment, on the reading abilities of persons with aphasia (PWA) with phonological alexia.



In a retrospective, single-group design, this study presents pre-, post-, and 3-months posttreatment data for 8 PWA with phonological alexia. Participants completed 60 hr of phonomotor treatment over 6 weeks. Wilcoxon signed-ranks tests and group effect sizes comparing pre-, immediately post-, and 3-months posttreatment performance on tests of phonological processing and reading were performed.



Group data showed phonological processing and oral reading of real words and nonwords improved significantly posttreatment; these gains were maintained 3 months later. No group improvement was found for reading comprehension; however, one individual did show improvement immediately post- and 3-months posttreatment.



This study provides support that phonomotor treatment is a viable approach to improve phonological processing and oral reading for PWA with phonological alexia. The lack of improvement with comprehension is inconsistent with prior work using similar treatments (Conway et al., 1998; Kendall et al., 2003). However, this difference can, in part, be accounted for by differences in variables, such as treatment intensity and frequency, outcome measures, and alexia severity.
Comparison of germinal center markers CD10, BCL6 and human germinal center-associated lymphoma (HGAL) in follicular lymphomas

Recently, human germinal center-associated lymphoma (HGAL) gene protein has been proposed as an adjunctive follicular marker to CD10 and BCL6.



Our aim was to evaluate immunoreactivity for HGAL in 82 cases of follicular lymphomas (FLs)--67 nodal, 5 cutaneous and 10 transformed--which were all analysed histologically, by immunohistochemistry and PCR.



Immunostaining for HGAL was more frequently positive (97.6%) than that for BCL6 (92.7%) and CD10 (90.2%) in FLs; the cases negative for bcl6 and/or for CD10 were all positive for HGAL, whereas the two cases negative for HGAL were positive with BCL6; no difference in HGAL immunostaining was found among different malignant subtypes or grades.



Therefore, HGAL can be used in the immunostaining of FLs as the most sensitive germinal center (GC)-marker; when applied alone, it would half the immunostaining costs, reserving the use of the other two markers only to HGAL-negative cases.
Deep Generative Networks For Sequence Prediction

This thesis investigates unsupervised time series representation learning for sequence prediction problems, i.e. generating nice-looking input samples given a previous history, for high dimensional input sequences by decoupling the static input representation from the recurrent sequence representation. We introduce three models based on Generative Stochastic Networks (GSN) for unsupervised sequence learning and prediction. GSNs are a probabilistic generalization of denoising auto-encoders that learn unsupervised hierarchical representations of complex input data, while being trainable by backpropagation. The first model, the Temporal GSN (TGSN), uses the latent state variables H learned by the GSN to reduce input complexity such that learning the representations H over time becomes linear. This means a simple linear regression step H → H can encode the next set of latent state variables describing the input data in the sequence, learning P (Ht+1|H t t−m) for an arbitrary history, or context, window of size m. The second model, the Recurrent GSN (RNN-GSN), uses a Recurrent Neural Network (RNN) to learn the sequences of GSN parameters H over time. By having the progression of H learned by an RNN instead of through regression like the TGSN, this model can learn sequences with arbitrary time dependencies. The third model, the Sequence Encoding Network (SEN), is a novel framework for learning deep sequence representations. It uses a hybrid approach of stacking alternating reconstruction generative network layers with recurrent layers, allowing the model to learn a deep representation of complex time dependencies.