nils-herrmann/clean_scirepeval huggingface dataset:
Gesture Recognition Technology : A Review

Gesture Recognition Technology has evolved greatly over the years. The past has seen the contemporary Human – Computer Interface techniques and their drawbacks, which limit the speed and naturalness of the human brain and body. As a result gesture recognition technology has developed since the early 1900s with a view to achieving ease and lessening the dependence on devices like keyboards, mice and touchscreens. Attempts have been made to combine natural gestures to operate with the technology around us to enable us to make optimum use of our body gestures making our work faster and more human friendly. The present has seen huge development in this field ranging from devices like virtual keyboards, video game controllers to advanced security systems which work on face, hand and body recognition techniques. The goal is to make full use of the movements of the body and every angle made by the parts of the body in order to supplement technology to become human friendly and understand natural human behavior and gestures. The future of this technology is very bright with prototypes of amazing devices in research and development to make the world equipped with digital information at hand whenever and wherever required.
A Factorized Recurrent Neural Network Based Architecture for Medium to Large Vocabulary Language Modelling

Statistical language models are central to many applications that use semantics. Recurrent Neural Networks (RNN) are known to produce state of the art results for language modelling, outperforming their traditional n-gram counterparts in many cases. To generate a probability distribution across a vocabulary, these models require a softmax output layer that linearly increases in size with the size of the vocabulary. Large vocabularies need a commensurately large softmax layer and training them on typical laptops/PCs requires significant time and machine resources. In this paper we present a new technique for implementing RNN based large vocabulary language models that substantially speeds up computation while optimally using the limited memory resources. Our technique, while building on the notion of factorizing the output layer by having multiple output layers, improves on the earlier work by substantially optimizing on the individual output layer size and also eliminating the need for a multistep prediction process.
Reactive Power Compensation Technologies: State-of-the-Art Review

This paper presents an overview of the state of the art in reactive power compensation technologies. The principles of operation, design characteristics and application examples of Var compensators implemented with thyristors and self-commutated converters are presented. Static Var generators are used to improve voltage regulation, stability, and power factor in ac transmission and distribution systems. Examples obtained from relevant applications describing the use of reactive power compensators implemented with new static Var technologies are also described.
Radar Site Studies with Automated Propagation Effect Calculations Using QSiteAnalysis

Planning radar sites is very important for several civilian and military applications. Depending on the security or defence issue different requirements exist regarding the radar coverage and the radar sites. QSiteAnalysis offers several functions to automate, improve and speed up this highly complex task. Wave propagation effects such as diffraction, refraction, multipath and atmospheric attenuation are considered for the radar coverage calculation. Furthermore, an automatic optimisation of the overall coverage is implemented by optimising the radar sites. To display the calculation result, the calculated coverage is visualised in 2D and 3D. Therefore, QSiteAnalysis offers several functions to improve and automate radar site studies.
ALL-IN-1: Short Text Classification with One Model for All Languages

We present ALL-IN-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.
ATM: A distributed, collaborative, scalable system for automated machine learning

In this paper, we present Auto-Tuned Models, or ATM, a distributed, collaborative, scalable system for automated machine learning. Users of ATM can simply upload a dataset, choose a subset of modeling methods, and choose to use ATM's hybrid Bayesian and multi-armed bandit optimization system. The distributed system works in a load-balanced fashion to quickly deliver results in the form of ready-to-predict models, confusion matrices, cross-validation results, and training timings. By automating hyperparameter tuning and model selection, ATM returns the emphasis of the machine learning workflow to its most irreducible part: feature engineering. We demonstrate the usefulness of ATM on 420 datasets from OpenML and train over 3 million classifiers. Our initial results show ATM can beat human-generated solutions for 30% of the datasets, and can do so in 1/100th of the time.
Email Category Prediction

According to recent estimates, about 90% of consumer received emails are machine-generated. Such messages include shopping receipts, promotional campaigns, newsletters, booking confirmations, etc. Most such messages are created by populating a fixed template with a small amount of personalized information, such as name, salutation, reservation numbers, dates, etc. Web mail providers (Gmail, Hotmail, Yahoo) are leveraging the structured nature of such emails to extract salient information and use it to improve the user experience: e.g. by automatically entering reservation data into a user calendar, or by sending alerts about upcoming shipments. To facilitate these extraction tasks it is helpful to classify templates according to their category, e.g. restaurant reservations or bill reminders, since each category triggers a particular user experience. Recent research has focused on discovering the causal thread of templates, e.g. inferring that a shopping order is usually followed by a shipping confirmation, an airline booking is followed by a confirmation and then by a “ready to check in” message, etc. Gamzu et al. took this idea one step further by implementing a method to predict the template category of future emails for a given user based on previously received templates. The motivation is that predicting future emails has a wide range of potential applications, including better user experiences (e.g. warning users of items ordered but not shipped), targeted advertising (e.g. users that recently made a flight reservation may be interested in hotel reservations), and spam classification (a message that is part of a legitimate causal thread is unlikely to be spam). The gist of the Gamzu et al. approach is modeling the problem as a Markov chain, where the nodes are templates or temporal events (e.g. the first day of the month). This paper expands on their work by investigating the use of neural networks for predicting the category of emails that will arrive during a fixed-sized time window in the future. We consider two types of neural networks: multilayer perceptrons (MLP), a type of feedforward neural network; and long short-term memory (LSTM), a type of recurrent neural network. For each type of neural network, we explore the effects The work was completed at Google Research.  http://dx.doi.org/10.1145/3041021.3055166 of varying their configuration (e.g. number of layers or number of neurons) and hyper-parameters (e.g. drop-out ratio). We find that the prediction accuracy of neural networks vastly outperforms the Markov chain approach, and that LSTMs perform slightly better than MLPs. We offer some qualitative interpretation of our findings and identify some promising future directions.
Pre-Processing Steps for Segmentation of Retinal Blood Vessels

Segmentation of blood vessels in retinal images is an important part in retinal image analysis for diagnosis and treatment of eye diseases for large screening systems. In this paper, we addressed the problem of background and noise extraction from retinal images. Blood vessels usually have central light reflex and poor local contrast, hence the results yield by blood vessel segmentation algorithms are not satisfactory. We used different preprocessing steps which includes central light reflex removal, background homogenization and vessel enhancement to make retinal image noise-free for post-processing. We used mean and Gaussian filtering along with Top-Hat transformation for noise extraction. The preprocessing steps were applied on 40 retinal images of DRIVE database available publically. Results show the darker retinal structures like blood vessels, fovea, and possible presence of microaneurysms or hemorrhages, get enhanced as compared to original retinal image and the brighter structures like optic disc and possible presence of exudates were get removed . The presented technique will definitely improve automatic fundus images analysis also be very useful to eye specialists in their visual examination of retina.
Psychological Stress and Mitochondria: A Conceptual Framework.

The integration of biological, psychological, and social factors in medicine has benefited from increasingly precise stress response biomarkers. Mitochondria, a subcellular organelle with its own genome, produce the energy required for life and generate signals that enable stress adaptation. An emerging concept proposes that mitochondria sense, integrate, and transduce psychosocial and behavioral factors into cellular and molecular modifications. Mitochondrial signaling might in turn contribute to the biological embedding of psychological states.



A narrative literature review was conducted to evaluate evidence supporting this model implicating mitochondria in the stress response, and its implementation in behavioral and psychosomatic medicine.



Chronically, psychological stress induces metabolic and neuroendocrine mediators that cause structural and functional recalibrations of mitochondria, which constitutes mitochondrial allostatic load. Clinically, primary mitochondrial defects affect the brain, the endocrine system, and the immune systems that play a role in psychosomatic processes, suggesting a shared underlying mechanistic basis. Mitochondrial function and dysfunction also contribute to systemic physiological regulation through the release of mitokines and other metabolites. At the cellular level, mitochondrial signaling influences gene expression and epigenetic modifications, and modulates the rate of cellular aging.



This evidence suggests that mitochondrial allostatic load represents a potential subcellular mechanism for transducing psychosocial experiences and the resulting emotional responses-both adverse and positive-into clinically meaningful biological and physiological changes. The associated article in this issue of Psychosomatic Medicine presents a systematic review of the effects of psychological stress on mitochondria. Integrating mitochondria into biobehavioral and psychosomatic research opens new possibilities to investigate how psychosocial factors influence human health and well-being across the life-span.
Implicit Incremental Model Analyses and Transformations

In many engineering disciplines, abstract models are used to describe systems on a high level of abstraction. On this abstract level, it is often easier to gain insights about that system that is being described. When models of a system change – for example because the system itself has changed – any analyses based on these models have to be invalidated and thus have to be reevaluated again in order for the results to stay meaningful. In many cases, the time to get updated analysis results is critical. However, as most often only small parts of the model change, large parts of this reevaluation could be saved by using previous results but such an incremental execution is barely done in practice as it is non-trivial and error-prone. The approach of implicit incrementalization o ers a solution by deriving an incremental evaluation strategy implicitly from a batch speci cation of the analysis. This works by deducing a dynamic dependency graph that allows to only reevaluate those parts of an analysis that are a ected by a given model change. Thus advantages of an incremental execution can be gained without changes to the code that would potentially degrade its understandability. However, current approaches to implicit incremental computation only support narrow classes of analysis, are restricted to an incremental derivation at instruction level or require an explicit state management. In addition, changes are only propagated sequentially, meanwhile modern multi-core architectures would allow parallel change propagation. Even with such improvements, it is unclear whether incremental execution in fact brings advantages as changes may easily cause butter y e ects, making a reuse of previous analysis results pointless (i.e. ine cient). This thesis deals with the problems of implicit incremental model analyses by proposing multiple approaches that mostly can be combined. Further, the
Runtime Precoding: Enabling Multipoint Transmission in LTE-Advanced System-Level Simulations

System-level simulations have become an indispensable tool for predicting the behavior of wireless cellular systems. As exact link-level modeling is unfeasible due to its huge complexity, mathematical abstraction is required to obtain equivalent results by less complexity. A particular problem in such approaches is the modeling of multiple coherent transmissions. Those arise in multiple-input-multiple-output transmissions at every base station but nowadays so-called coordinated multipoint (CoMP) techniques have become very popular, allowing to allocate two or more spatially separated transmission points. Also, multimedia broadcast single frequency networks (MBSFNs) have been introduced recently in long-term evolution (LTE), which enables efficient broadcasting transmission suitable for spreading information that has a high user demand as well as simultaneously sending updates to a large number of devices. This paper introduces the concept of runtime-precoding, which allows to accurately abstract many coherent transmission schemes while keeping additional complexity at a minimum. We explain its implementation and advantages. For validation, we incorporate the runtime-precoding functionality into the Vienna LTE-A downlink system-level simulator, which is an open source tool, freely available under an academic noncommercial use license. We measure simulation run times and compare them against the legacy approach as well as link-level simulations. Furthermore, we present multiple application examples in the context of intrasite and intersite CoMP for train communications and MBSFN.
Dermoscopic features of extraocular sebaceous carcinoma.

Sebaceous carcinoma is a rare adenocarcinoma with variable degrees of sebaceous differentiation, most commonly found on periocular skin, but also occasionally occur extraocular. It can occur in isolation or as part of the MuirTorre syndrome. Sebaceous carcinomas are yellow or red nodules or plaques often with a friable surface, ulceration, or crusting. On histological examination, sebaceous carcinomas are typically poorly circumscribed, asymmetric, and infiltrative. Individual cells are pleomorphic with atypical nuclei, mitoses, and a coarsely vacuolated cytoplasm.
Measuring avoidance of pain: validation of the Acceptance and Action Questionnaire II-pain version.

Psychometric research on widely used questionnaires aimed at measuring experiential avoidance of chronic pain has led to inconclusive results. To test the structural validity, internal consistency, and construct validity of a recently developed short questionnaire: the Acceptance and Action Questionnaire II-pain version (AAQ-II-P). Cross-sectional validation study among 388 adult patients with chronic nonspecific musculoskeletal pain admitted for multidisciplinary pain rehabilitation in four tertiary rehabilitation centers in the Netherlands. Cronbach's α was calculated to analyze internal consistency. Principal component analysis was performed to analyze factor structure. Construct validity was analyzed by examining the association between acceptance of pain and measures of psychological flexibility (two scales and sum), pain catastrophizing (three scales and sum), and mental and physical functioning. Interpretation was based on a-priori defined hypotheses. The compound of the seven items of the AAQ-II-P shows a Cronbach's α of 0.87. The single component explained 56.2% of the total variance. Correlations ranged from r=-0.21 to 0.73. Two of the predefined hypotheses were rejected and seven were not rejected. The AAQ-II-P measures a single component and has good internal consistency, and construct validity is not rejected. Thus, the construct validity of the AAQ-II-P sum scores as indicator of experiential avoidance of pain was supported.
A Systematic Review of Topical Finasteride in the Treatment of Androgenetic Alopecia in Men and Women.

Currently, only topical minoxidil (MNX) and oral finasteride (FNS) are approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of androgenetic alopecia. Although FNS is efficacious for hair regrowth, its systemic use is associated with side effects limiting long-term utilization. Exploring topical FNS as an alternative treatment regimen may prove promising.



A search was conducted to identify studies regarding human in vivo topical FNS treatment efficacy including clinically relevant case reports, randomized controlled trials (RCTs), and prospective studies.



Seven articles were included in this systematic review. In all studies, there was significant decrease in the rate of hair loss, increase in total and terminal hair counts, and positive hair growth assessment with topical FNS. Both scalp and plasma DHT significantly decreased with application of topical FNS; no changes in serum testosterone were noted.



Preliminary results on the use of topical FNS are limited, but safe and promising. Continued research into drug-delivery, ideal topical concentration and application frequency, side effects, and use for other alopecias will help to elucidate the full extent of topical FNS' use.
SparkSNN: A density-based clustering algorithm on spark

Clustering is one of the most commonly used data mining techniques. Shared nearest neighbor clustering is an important density-based clustering technique that has been widely adopted in many application domains, such as environmental science and urban computing. As the size of data becomes extremely large nowadays, it is impossible for large-scale data to be processed on a single machine. Therefore, the scalability problem of traditional clustering algorithms running on a single machine must be addressed. In this paper, we improve the traditional density-based clustering algorithm by utilizing powerful programming platform (Spark) and distributed computing clusters. In particular, we design and implement Spark-based shared nearest neighbor clustering algorithm called SparkSNN, a scalable density-based clustering algorithm on Spark for big data analysis. We conduct our experiments using real data, i.e., Maryland crime data, to evaluate the performance of the proposed algorithm with respect to speed-up and scale-up. The experimental results well confirm the effectiveness and efficiency of the proposed SparkSNN clustering algorithm.
State of the Art of Sports Data Visualization

In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially-growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real-world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in-game actions and trajectories), and meta-data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high-level discussion of sports visualization research informed by our analysis—identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR’s website: https://sportsdataviz.github.io/.
Jet drive mechanisms in edge tones and organ pipes

Measurements of the phases of free jet waves relative to an acoustic excitation, and of the pattern and time phase of the sound pressure produced by the same jet impinging on an edge, provide a consistent model for Stage I frequencies of edge tones and of an organ pipe with identical geometry. Both systems are explained entirely in terms of volume displacement of air by the jet. During edge-tone oscillation, 180 ø of phase delay occur on the jet. Peak positive acoustic pressure on a given side of the edge occurs at the instant the jet profile crosses the edge and starts into that side. For the pipe, additional phase shifts occur that depend on the driving points for the jet current, the Q of the pipe, and the frequency of oscillation. Introduction of this additional phase shift yields an accurate prediction of the frequencies of a blown pipe and the blowing pressure at which mode jumps will occur.
VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images

The precise combination of image sensor and micro-lens array enables lenslet light field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from light field images. In turn, 3D models of the recorded objects can be recovered, which is a great advantage over other imaging system. However, reflective and texture-less areas in light field images have complicated conditions, making it hard to correctly calculate disparity with existing algorithms. To tackle this problem, we introduce a novel end-to-end network VommaNet to retrieve multi-scale features from reflective and texture-less regions for accurate disparity estimation. Meanwhile, our network has achieved similar or better performance in other regions for both synthetic light field images and real-world data compared to the state-ofthe-art algorithms. Currently, we achieve the best score for mean squared error (MSE) on HCI 4D Light Field Benchmark.