nils-herrmann/clean_scirepeval huggingface dataset:
Challenges of Computational Processing of Code-Switching

This paper addresses challenges of Natural Language Processing (NLP) on non-canonical multilingual data in which two or more languages are mixed. It refers to code-switching which has become more popular in our daily life and therefore obtains an increasing amount of attention from the research community. We report our experience that covers not only core NLP tasks such as normalisation, language identification, language modelling, part-of-speech tagging and dependency parsing but also more downstream ones such as machine translation and automatic speech recognition. We highlight and discuss the key problems for each of the tasks with supporting examples from different language pairs and relevant previous work.
Deep Learning for Explicitly Modeling Optimization Landscapes

In all but the most trivial optimization problems, the structure of the solutions exhibit complex interdependencies between the input parameters. Decades of research with stochastic search techniques has shown the benefit of explicitly modeling the interactions between sets of parameters and the overall quality of the solutions discovered. We demonstrate a novel method, based on learning deep networks, to model the global landscapes of optimization problems. To represent the search space concisely and accurately, the deep networks must encode information about the underlying parameter interactions and their contributions to the quality of the solution. Once the networks are trained, the networks are probed to reveal parameter combinations with high expected performance with respect to the optimization task. These estimates are used to initialize fast, randomized, local search algorithms, which in turn expose more information about the search space that is subsequently used to refine the models. We demonstrate the technique on multiple optimization problems that have arisen in a variety of real-world domains, including: packing, graphics, job scheduling, layout and compression. The problems include combinatoric search spaces, discontinuous and highly non-linear spaces, and span binary, higher-cardinality discrete, as well as continuous parameters. Strengths, limitations, and extensions of the approach are extensively discussed and demonstrated.
Educational data mining applications and tasks: A survey of the last 10 years

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.
Bidirectional Transport of Amino Acids Regulates mTOR and Autophagy

Amino acids are required for activation of the mammalian target of rapamycin (mTOR) kinase which regulates protein translation, cell growth, and autophagy. Cell surface transporters that allow amino acids to enter the cell and signal to mTOR are unknown. We show that cellular uptake of L-glutamine and its subsequent rapid efflux in the presence of essential amino acids (EAA) is the rate-limiting step that activates mTOR. L-glutamine uptake is regulated by SLC1A5 and loss of SLC1A5 function inhibits cell growth and activates autophagy. The molecular basis for L-glutamine sensitivity is due to SLC7A5/SLC3A2, a bidirectional transporter that regulates the simultaneous efflux of L-glutamine out of cells and transport of L-leucine/EAA into cells. Certain tumor cell lines with high basal cellular levels of L-glutamine bypass the need for L-glutamine uptake and are primed for mTOR activation. Thus, L-glutamine flux regulates mTOR, translation and autophagy to coordinate cell growth and proliferation.
Selection of appropriate candidates for a type position using C4.5 decision tree

Recruitment and selection of new employees rank to the important processes of human potential management and development. Especially the process of employee selection prepares proper conditions for a successful work performance and decides on a future progress-ability of the organizations. In a unique sector of private security, the precise realization of employee selection can solve one of the most frequent problem of the private security organizations: high fluctuation/employee turnover. The paper focuses on an experimental possibility to assign systematically, and with the high measure of exactness, the required competences of candidates to the specification of clients' protected interests or objects. In presented experiment study, built on the basis of benchmarking, the paper presents the concept of well-known decision trees to choose best candidates. Demonstration of proposed system and method is done by public available data, where we achieved 97.27% accuracy of classification.
Comparative study of Hough Transform methods for circle finding

A variety of circle detection methods which are based on variations of the Hough Transform are investigated. The ,five methods considered are the standard Hough Trans,form, the Fast Hough Transform of Li et al.‘, a two stage Hough method, and two space saving approaches based on the method devised by Gerig and Klein2. The performance of each of the methods has been compared on synthetic imagery and real images from a metallurgical application. Figures and comments are presented concerning the accuracy, reliability, computational efficiency and storage requirements of each of the methods.
Geometry Guided Adversarial Facial Expression Synthesis

Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of large and non-linear face geometry variations. This paper proposes a Geometry-Guided Generative Adversarial Network (G2-GAN) for continuously-adjusting and identity-preserving facial expression synthesis. We employ facial geometry (fiducial points) as a controllable condition to guide facial texture synthesis with specific expression. A pair of generative adversarial subnetworks is jointly trained towards opposite tasks: expression removal and expression synthesis. The paired networks form a mapping cycle between neutral expression and arbitrary expressions, with which the proposed approach can be conducted among unpaired data. The proposed paired networks also facilitate other applications such as face transfer, expression interpolation and expression-invariant face recognition. Experimental results on several facial expression databases show that our method can generate compelling perceptual results on different expression editing tasks.
A retrospective study of SPECT/CT scans using SUV measurement of the normal pelvis with Tc-99m methylene diphosphonate.

To perform quantitative measurement based on the standardized uptake value (SUV) of Tc-99m methylene diphosphonate (MDP) in the normal pelvis using a single-photon emission tomography (SPECT)/computed tomography (CT) scanner.



This retrospective study was performed on 31 patients with cancer undergoing bone SPECT/CT scans with 99mTc-MDP. SUVmax and SUVmean of the normal pelvis were calculated based on the body weight. SUVmax and SUVmean of the bilateral anterior superior iliac spine, posterior superior iliac spine, facies auricularis ossis ilii, ischial tuberosity, and sacrum were also calculated. Furthermore, the correlation of SUVmax and SUVmean of all parts of pelvis with weight, height, and CT was assessed.



The data for 31 patients (20 women and 11 men; mean age 58.97±9.12 years; age range 37-87 years) were collected. SUVmax and SUVmean changed from 1.65±0.40 to 3.8±1.0 and from 1.15±0.25 to 2.07±0.58, respectively. The coefficient of variation of SUVmax and SUVmean ranged from 0.22 to 0.31. SUVmax and SUVmean had no statistically significant difference between men and women. SUVmax and SUVmean also showed no significant correlation with weight and height. However, part of SUVmax and SUVmean showed a significant correlation with CT. In addition, SUVmax and SUVmean of the bilateral ischial tuberosity showed a significant correlation with CT values.



Determination of the SUV value of the normal pelvis with 99m Tc-MDP SPECT/CT is feasible and highly reproducible. SUVs of the normal pelvis showed a relatively large variability. As a quantitative imaging biomarker, SUVs might require standardization with adequate reference data for the participant to minimize variability.
The Cubli: A reaction wheel based 3D inverted pendulum

The Cubli is a 15×15×15 cm cube with reaction wheels mounted on three of its faces. By applying controlled torques to the reaction wheels the Cubli is able to balance on its corner or edge. This paper presents the development of the Cubli. First, the mechatronic design of the Cubli is presented. Then the multi-body system dynamics are derived. The parameters of the nonlinear system are identified using a frequency domain based approach while the Cubli is balancing on its edge with a nominal controller. Finally, the corner balancing using a linear feedback controller is presented along with experimental results.
Broadband Millimeter-Wave Propagation Measurements and Models Using Adaptive-Beam Antennas for Outdoor Urban Cellular Communications

The spectrum crunch currently experienced by mobile cellular carriers makes the underutilized millimeter-wave frequency spectrum a sensible choice for next-generation cellular communications, particularly when considering the recent advances in low cost sub-terahertz/millimeter-wave complementary metal–oxide semiconductor circuitry. To date, however, little is known on how to design or deploy practical millimeter-wave cellular systems. In this paper, measurements for outdoor cellular channels at 38 GHz were made in an urban environment with a broadband (800-MHz RF passband bandwidth) sliding correlator channel sounder. Extensive angle of arrival, path loss, and multipath time delay spread measurements were conducted for steerable beam antennas of differing gains and beamwidths for a wide variety of transmitter and receiver locations. Coverage outages and the likelihood of outage with steerable antennas were also measured to determine how random receiver locations with differing antenna gains and link budgets could perform in future cellular systems. This paper provides measurements and models that may be used to design future fifth-generation millimeter-wave cellular networks and gives insight into antenna beam steering algorithms for these systems.
Turn-Taking Estimation Model Based on Joint Embedding of Lexical and Prosodic Contents

A natural conversation involves rapid exchanges of turns while talking. Taking turns at appropriate timing or intervals is a requisite feature for a dialog system as a conversation partner. This paper proposes a model that estimates the timing of turn-taking during verbal interactions. Unlike previous studies, our proposed model does not rely on a silence region between sentences since a dialog system must respond without large gaps or overlaps. We propose a Recurrent Neural Network (RNN) based model that takes the joint embedding of lexical and prosodic contents as its input to classify utterances into turn-taking related classes and estimates the turn-taking timing. To this end, we trained a neural network to embed the lexical contents, the fundamental frequencies, and the speech power into a joint embedding space. To learn meaningful embedding spaces, the prosodic features from each single utterance are pretrained using RNN and combined with utterance lexical embedding as the input of our proposed model. We tested this model on a spontaneous conversation dataset and confirmed that it outperformed the use of word embedding-based features.
Automating Intention Mining

Developers frequently discuss aspects of the systems they are developing online. The comments they post to discussions form a rich information source about the system. Intention mining, a process introduced by Di Sorbo et al., classifies sentences in developer discussions to enable further analysis. As one example of use, intention mining has been used to help build various recommenders for software developers. The technique introduced by Di Sorbo et al. to categorize sentences is based on linguistic patterns derived from two projects. The limited number of data sources used in this earlier work introduces questions about the comprehensiveness of intention categories and whether the linguistic patterns used to identify the categories are generalizable to developer discussion recorded in other kinds of software artifacts (e.g., issue reports). To assess the comprehensiveness of the previously identified intention categories and the generalizability of the linguistic patterns for category identification, we manually created a new dataset, categorizing 5,408 sentences from issue reports of four projects in GitHub. Based on this manual effort, we refined the previous categories. We assess Di Sorbo et al.’s patterns on this dataset, finding that the accuracy rate achieved is low (0.31). To address the deficiencies of Di Sorbo et al.’s patterns, we propose and investigate a convolution neural network (CNN)-based approach to automatically classify sentences into different categories of intentions. Our approach optimizes CNN by integrating batch normalization to accelerate the training speed, and an automatic hyperparameter tuning approach to tune appropriate hyperparameters of CNN. Our approach achieves an accuracy of 0.84 on the new dataset, improving Di Sorbo et al.’s approach by 171%. We also apply our approach to improve an automated software engineering task, in which we use our proposed approach to rectify misclassified issue reports, thus reducing the bias introduced by such data to other studies. A case study on four open source projects with 2,076 issue reports shows that our approach achieves an average AUC score of 0.687, which improves other baselines by at least 16%.
Thoracolumbar burst fractures without neurological deficit: the role for conservative treatment

Burst fractures are common in the thoracolumbar junction and account for 17% of all major spinal fractures. There is a considerable controversy on the efficacy of conservative treatment and the need for surgical intervention. Need for additional stability, prevention of neurological deterioration, attainment of canal clearance, prevention of kyphosis and early relief of pain are the commonly quoted reasons for surgical intervention. However, a careful review of literature does not validate any of the above arguments. The available randomised control trials prove that the results of conservative treatment for burst fractures are equal to that of surgery and also with lesser complications. Surgery for burst fractures may, however, have definite advantages in patients with polytrauma or in the rare event of deteriorating neurology. It is also important for the treating surgeon to clearly distinguish a burst fracture from other inherently unstable injuries like fracture dislocations, chance fractures and flexion rotation injuries which require surgical stabilisation.
IoT Planting: Watering system using mobile application for the elderly

Nowadays, Thailand has become an aged society. The elderly's mental health has decreased resulting from physiological changes. At present, technology has connect everything to the world of the Internet called the Internet of Things (IoT). IoT can be applied to many ways, which can improve the quality of life and reduce spaces between the elderly and technology. In this paper, we studied the situation of the elderly and proposed the IoT Planting which consists of gardening platform prototype with soil moisture, temperature, water sensor, grow light, and Android application for the elderly. An evaluation was performed by Technology Acceptance Model (TAM). The result represents that the elderly perceived the usefulness and found that it easy to use contrary to their expectations. Moreover, by reviewing their opinion, it was found out that they have a positive attitude toward our application and willingness to use it.
Microalgae as a raw material for biofuels production

Biofuels demand is unquestionable in order to reduce gaseous emissions (fossil CO2, nitrogen and sulfur oxides) and their purported greenhouse, climatic changes and global warming effects, to face the frequent oil supply crises, as a way to help non-fossil fuel producer countries to reduce energy dependence, contributing to security of supply, promoting environmental sustainability and meeting the EU target of at least of 10% biofuels in the transport sector by 2020. Biodiesel is usually produced from oleaginous crops, such as rapeseed, soybean, sunflower and palm. However, the use of microalgae can be a suitable alternative feedstock for next generation biofuels because certain species contain high amounts of oil, which could be extracted, processed and refined into transportation fuels, using currently available technology; they have fast growth rate, permit the use of non-arable land and non-potable water, use far less water and do not displace food crops cultures; their production is not seasonal and they can be harvested daily. The screening of microalgae (Chlorella vulgaris, Spirulina maxima, Nannochloropsis sp., Neochloris oleabundans, Scenedesmus obliquus and Dunaliella tertiolecta) was done in order to choose the best one(s), in terms of quantity and quality as oil source for biofuel production. Neochloris oleabundans (fresh water microalga) and Nannochloropsis sp. (marine microalga) proved to be suitable as raw materials for biofuel production, due to their high oil content (29.0 and 28.7%, respectively). Both microalgae, when grown under nitrogen shortage, show a great increase (~50%) in oil quantity. If the purpose is to produce biodiesel only from one species, Scenedesmus obliquus presents the most adequate fatty acid profile, namely in terms of linolenic and other polyunsaturated fatty acids. However, the microalgae Neochloris oleabundans, Nannochloropsis sp. and Dunaliella tertiolecta can also be used if associated with other microalgal oils and/or vegetable oils.
IC-CRIME : A Collaborative , Web-Based , 3 D System for the Investigation , Analysis , and Annotation of Crime Scenes

Modern day crime scene investigation methods are continually being enhanced by the application of new technologies to improve the analysis and presentation of crime scene information, helping to solve and prosecute crimes. This paper describes a new system called IC-CRIME that integrates several new technologies to meet both of these needs. IC-CRIME employs laser scanning to produce realistic 3D models of a crime scene which it then incorporates into a 3D virtual environment. ICCRIME creates an integrated platform that encourages investigators and forensic experts to view, explore, and annotate these models at any time from any web browser. A key goal of the system is to expand opportunities for collaboration between professionals whose geographic locations and time commitments would otherwise limit their ability to contribute to the analysis of the actual crime scene.
Ccdd: an Enhanced Standard ECG Database with its Management and Annotation Tools

1240020-1 Standard Electrocardiogram (ECG) database is created for validating and comparing different algorithms on feature detection and disease classification. At present, there are four frequently used standard databases: MIT-BIH arrhythmia database, QT database, CSE multi-lead database and AHA database. With the development in equipment and diagnosis approach, severe deficiencies are discovered and a new modern ECG database is needed for further research. So Chinese Cardiovascular Disease Database (CCDD or CCD database), which contains 12-Lead ECG data, detailed annotation features and beat diagnosis result is proposed. It is advanced for not only improving the raw ECG data’s technical parameters, but also introducing valuable morphology features which are utilized by experienced cardiologists effectively. CCDD is employed by our group as well as aiming for supporting other research groups that work in automated ECG analysis.
Proof Analysis in Modal Logic

A general method for generating contractionand cut-free sequent calculi for a large family of normal modal logics is presented. The method covers all modal logics characterized by Kripke frames determined by universal or geometric properties and it can be extended to treat also Gödel–Löb provability logic. The calculi provide direct decision methods through terminating proof search. Syntactic proofs of modal undefinability results are obtained in the form of conservativity theorems.
Single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp

A single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp is proposed. In the proposed converter, the operation principle is quite different from other boost bridgeless three-level converters. The charging stage is presented in the low side while the discharging stage in the high side and vice versa. As a result, nature voltage clamp is achieved. And the slow diodes are ‘shared’. They operate not only as rectifying diodes, but also as voltage clamping diodes. Besides, only single inductor is required and the device utilization factor is high. Besides, the common-mode(CM) noise is rather low. First, the operational principle are analyzed in detail. Then the performance evaluation and design consideration are presented. Finally, a 1kW experimental prototype is built. The result shows that the efficiency is above 94% at full load under 90V and the maximum efficiency is 98.4%.
Modeling of Political Discourse Framing on Twitter

Framing is a political strategy in which politicians carefully word their statements in order to control public perception and discussion of current issues. Previous works exploring political framing have focused on analysis of frames in longer texts, such as newspaper articles, or tweets relevant to specific events. We present the first in-depth analysis of issueindependent framing for political discourse in social media, specifically the microblogging platform Twitter. Building upon the fifteen frames designed by Boydstun, we propose three additional frames relevant to Twitter and provide insights into the dynamic usage of frames by party and over time. Finally we present a global probabilistic model for combining linguistic, issue, and party bias features of the tweets of politicians for the task of tweet frame prediction.