nils-herrmann/clean_scirepeval huggingface dataset:
Air-swipe gesture recognition using OpenCV in Android devices

There is a need to enhance communication between human and computers which is being greatly defined in this changing era of technology with Human Computer Interaction (HCI) which is helping in determining new communication models and accordingly new ways of interacting with machines. Current smartphone inputs are limited to physical buttons, touchscreen input, cameras or built-in sensors. The rapid development of Smartphone's in the last decade was mainly due to interaction and visual innovations. For Example the given approaches of input either require a dedicated surface or Line-of-Sight for interaction. But in today's scenario of increasing computability of smartphone or other gadgets and their decreasing sizes have raised a need for such touch free operations over these gadgets. In such an intendment we introduce Air-Swipe Gesture Recognition System which can be useful to enable user to make In-Air gestures in front of the camera and to do different operations. This System can give a user-friendly and a live-experience of interaction and visualization, enhancing the usability and making the android device more interactive. It does not require any hardware changes instead only uses the native camera of the device and a machine learning software such as Open Source Computer Vision (OpenCV) algorithms to detect the changes in environment and respond accordingly in varying conditions. We tested this classification and found out the result that considering the frames to be divided into quadrants along x-axis and y-axis and found that the value of the frame matrix changes. Our approach has the capability of recognizing gestures with precision of almost 96%.
Sequential Encoding of Tamil Kolam Patterns

The kolam is a traditional hand-drawn art form in Tamil Nadu and South India comprised of many subfamilies. Kolam patterns are most commonly drawn with chalk or rice powder by women on the thresholds of homes and temples and are of significant cultural importance in Tamil society. Academic investigations of Kolam patterns have used many different terms for different kolam pattern types. I introduce a global typology of kolam types, and present an extension to the square loop kolam (SLK) patterns studied in the past. Square loop kolam patterns are composed of an initial orthogonal matrix of dots, defining a space around which curving lines are drawn to complete one or many loops. SLK patterns can be decomposed into the gestures made by the artist’s hand, and previous studies have created sequential languages to represent SLK patterns. Prior kolam languages used a limited gestural lexicon and could not account for the diversity of SLK patterns produced by artists. The current paper introduces an expanded sequential gestural lexicon for square loop kolams, and describes a system for the digitization of SLK patterns using this expanded and expandable language. Original Paper___________________________________________________Waring, 2011
Modeling changing dependency structure in multivariate time series

We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmentation, as well as how to draw perfect samples from the posterior over segmentations, simultaneously accounting for uncertainty about the number and location of changepoints, as well as uncertainty about the covariance structure. We illustrate the technique by applying it to financial data and to bee tracking data.
Frequency compensation of high-speed, low-voltage CMOS multistage amplifiers

This paper presents the frequency compensation of high-speed, low-voltage multistage amplifiers. Two frequency compensation techniques, the Nested Miller Compensation with Nulling Resistors (NMCNR) and Reversed Nested Indirect Compensation (RNIC), are discussed and employed on two multistage amplifier architectures. A four-stage pseudo-differential amplifier with CMFF and CMFB is designed in a 1.2 V, 65-nm CMOS process. With NMCNR, it achieves a phase margin (PM) of 59° with a DC gain of 75 dB and unity-gain frequency (fug) of 712 MHz. With RNIC, the same four-stage amplifier achieves a phase margin of 84°, DC gain of 76 dB and fug of 2 GHz. Further, a three-stage single-ended amplifier is designed in a 1.1-V, 40-nm CMOS process. The three-stage OTA with RNIC achieves PM of 81°, DC gain of 80 dB and fug of 770 MHz. The same OTA achieves PM of 59° with NMCNR, while maintaining a DC gain of 75 dB and fug of 262 MHz. Pole-splitting, to achieve increased stability, is illustrated for both compensation schemes. Simulations illustrate that the RNIC scheme achieves much higher PM and fug for lower values of compensation capacitance compared to NMCNR, despite the growing number of low voltage amplifier stages.
Calcium currents in hair cells isolated from semicircular canals of the frog.

L-type and R-type Ca(2+) currents were detected in frog semicircular canal hair cells. The former was noninactivating and nifedipine-sensitive (5 microM); the latter, partially inactivated, was resistant to omega-conotoxin GVIA (5 microM), omega-conotoxin MVIIC (5 microM), and omega-agatoxin IVA (0.4 microM), but was sensitive to mibefradil (10 microM). Both currents were sensitive to Ni(2+) and Cd(2+) (>10 microM). In some cells the L-type current amplitude increased almost twofold upon repetitive stimulation, whereas the R-type current remained unaffected. Eventually, run-down occurred for both currents, but was prevented by the protease inhibitor calpastatin. The R-type current peak component ran down first, without changing its plateau, suggesting that two channel types generate the R-type current. This peak component appeared at -40 mV, reached a maximal value at -30 mV, and became undetectable for voltages > or =0 mV, suggestive of a novel transient current: its inactivation was indeed reversibly removed when Ba(2+) was the charge carrier. The L-type current and the R-type current plateau were appreciable at -60 mV and peaked at -20 mV: the former current did not reverse for voltages up to +60 mV, the latter reversed between +30 and +60 mV due to an outward Cs(+) current flowing through the same Ca(2+) channel. The physiological role of these currents on hair cell function is discussed.
Bursty event detection from microblog: a distributed and incremental approach

As a new form of social media, microblogs (e.g., Twitter and Weibo) are playing an important role in people’s daily life. With the rise in popularity and size of microblogs, there is a need for distributed approaches that can detect bursty event with low latency from the short-text data stream. In this paper, we propose a distributed and incremental temporal topic model for microblogs called Bursty Event dEtection (BEE+). BEE+ is able to detect bursty events from short-text dataset and model the temporal information. And BEE+ processes the post-stream incrementally to track the topic drifting of events over time. Therefore, the latent semantic indices are preserved from one time period to the next. In order to achieve real-time processing, we design a distributed execution framework based on Spark engine. To verify its ability to detect bursty event, we conduct experiments on a Weibo dataset of 6,360,125 posts. The results show that BEE+ can outperform the baselines for detecting the meaningful bursty events and track the topic drifting.
Modeling disease progression via fused sparse group lasso

Alzheimer's Disease (AD) is the most common neurodegenerative disorder associated with aging. Understanding how the disease progresses and identifying related pathological biomarkers for the progression is of primary importance in Alzheimer's disease research. In this paper, we develop novel multi-task learning techniques to predict the disease progression measured by cognitive scores and select biomarkers predictive of the progression. In multi-task learning, the prediction of cognitive scores at each time point is considered as a task, and multiple prediction tasks at different time points are performed simultaneously to capture the temporal smoothness of the prediction models across different time points. Specifically, we propose a novel convex fused sparse group Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty. The proposed formulation is challenging to solve due to the use of several non-smooth penalties. We show that the proximal operator associated with the proposed formulation exhibits a certain decomposition property and can be computed efficiently; thus cFSGL can be solved efficiently using the accelerated gradient method. To further improve the model, we propose two non-convex formulations to reduce the shrinkage bias inherent in the convex formulation. We employ the difference of convex programming technique to solve the non-convex formulations. Our extensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative demonstrate the effectiveness of the proposed progression models in comparison with existing methods for disease progression. We also perform longitudinal stability selection to identify and analyze the temporal patterns of biomarkers in disease progression.
The state of the art in underwater acoustic telemetry

Progress in underwater acoustic telemetry since 1982 is reviewed within a framework of six current research areas: (1) underwater channel physics, channel simulations, and measurements; (2) receiver structures; (3) diversity exploitation; (4) error control coding; (5) networked systems; and (6) alternative modulation strategies. Advances in each of these areas as well as perspectives on the future challenges facing them are presented. A primary thesis of this paper is that increased integration of high-fidelity channel models into ongoing underwater telemetry research is needed if the performance envelope (defined in terms of range, rate, and channel complexity) of underwater modems is to expand.
When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages

We present the results of our participation in the VarDial 4 shared task on discriminating closely related languages. Our submission includes simple traditional models using linear support vector machines (SVMs) and a neural network (NN). The main idea was to leverage language group information. We did so with a two-layer approach in the traditional model and a multi-task objective in the neural network. Our results confirm earlier findings: simple traditional models outperform neural networks consistently for this task, at least given the amount of systems we could examine in the available time. Our two-layer linear SVM ranked 2nd in the shared task.
Using edit distance and junction feature to detect and recognize arrow road marking

Arrow road markings usually appear on freeway surface and they convey important navigation information to autonomous driving. But detecting and recognizing them is a tough task because they suffer from numerous deviations like objects' interference and themselves' abrasion, etc. We therefore propose a novel local junction feature (L-junction) to describe each road marking as a junction string, different deviation is dispersed into different junction. We encode those junctions within a range as the same code. To measure the similarity between detected junction string and ground truth junction string, we design a weighted edit distance strategy and assign different deviation with different weight so that our framework is robust enough to deviations in arrow road marking but sensitive to non- arrow road markings' deviations. To test our framework, we collect three freeway datasets with our self-driving car: clean/dirty arrow road marking images (300 images respectively), a video dataset (arrow road marking and non-road marking images (670 images)). Another deep learning framework (Boosting+Convolutional Deep Neural Network (CDNN)) is also implemented for comparison. Extensive experimental results well demonstrate the superior performance of our framework.
Electronic Shopping

he global electronic market will have a profound impact on commerce in the 21st Century. While current U.S. sales in cyberspace ($1 billion in 1995) are small in comparison to total U.S. retail sales ($1.7 trillion in 1995) , U.S. cybersales projections for the year 2000 range from $7 to $117 billion . More importantly, most experts predict a radical shift in how business will be conducted in the next century. This shift not only has businesses scrambling to meet this new marketing reality, but also raises many important research questions about business strategy, technical infrastructure, government policies, the electronic market demographics as well as how people will use the technology. Issues of technology usage become critical as businesses and retailers attempt to exploit the boom in electronic marketing. There are large differences between a physical store and its electronic counterpart. A help button on the home page of the Web shopping site replaces the sales clerk’s friendly advice and service. The familiar layout of the physical store becomes a maze of pull-down menus, product indices, and search features. Now more than ever, the promise of electronic commerce and online shopping will depend to a great extent upon the interface and how people interact with the computer. Account managers, production staff, and merchant partners should not assume customers do not want an item in an online retail store if it is not selling. Nor should they conclude that a poor response to a given store design is due to the merchandising mix. It is important to look at the relationship between sales and user interface design. Limited menus, poorly designed navigation, and the difficulty in comparing multiple products on the same screen all have adverse effects on electronic shopping . Can customers find what they want in the stores? Are customers aware of what products are available? After all, diligence in browsing a store is not a virtue retailers should expect from its online customers. We review online retail store attributes such as the number of links into the store, image sizes, number of products, and store navigation features. By reviewing the user interface features in the context of electronic shopping, we hope to facilitate the process of designing and evaluating alternative storefronts by identifying key features that impact traffic and sales. While this article specifically addresses user interface design issues for electronic shopping, user interface design is an important component of all software development [10] with important implications for productivity . Unfortunately, Web information systems (WIS) designers sometimes do not heed the importance of the message advocated by user interface design pundits such as Jakob Nielsen [11] and Vincent Flanders (www.webpagesthatsuck.com). Many of the lessons learned from online stores also apply to WIS applications.
On the use of words and n-grams for Chinese information retrieval

In the processing of Chinese documents and queries in information retrieval (IR), one has to identify the units that are used as indexes. Words and n-grams have been used as indexes in several previous studies, which showed that both kinds of indexes lead to comparable IR performances. In this study, we carry out more experiments on different ways to segment documents and queries, and to combine words with n-grams. Our experiments show that a combination of the longest-matching algorithm with single characters is the best choice.
Massaging human feet by a redundant manipulator equipped with a tactile sensor

Reflexology is a kind of assisting physical care through massaging or applying pressure to parts of the feet where will reflect an image of the human body in order to improve general health of the human. Based on this principle, we propose a new application of 7-DOF(degree of freedom) redundant manipulator to do the massaging work for human feet with the tactile sensor equipped to the end-effector. To facilitate the flexible and dexterous manipulation of redundant manipulator, the hybrid impedance control is adopted and extended to include the null space motion, not only to generate a desired motion of the end-effector, but also manage the contact force between the end-effector and the human feet. In order to improve the safety and comfortability, the reactive forces of the tactile sensor cells are monitored and recorded. A real massaging experiment is carried out on the human feet and the results demonstrate the effectiveness of the proposed control system.
Image annotation via deep neural network

Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two-stage learning scheme that consists of (i) learning to fune-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on the NUS-WIDE dataset evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms.
Calibration for Channel Reciprocity in Industrial Massive MIMO Antenna Systems

Massive multiple input multiple output (MIMO) antenna systems have received a great deal of interest due to their applicability to industrial network systems. One of the major obstacles that reducing the performance of massive MIMO system is reference signal (RS) overhead which can be increased as the number of transmitter antenna increases in frequency division duplexing (FDD) system. Using channel reciprocity in time division duplexing (TDD) system can significantly increase the RS overhead performance using channel reciprocity. However, to use the channel reciprocity, channel calibration is a significant challenge to overcome in real system design. There are various base station (BS) calibration methods that have already successfully applied to the current BS, while there are few methods for the calibration of distributed user entities (UEs) and/or industrial Internet of things (IIoT) devices. In this paper, we propose a distributed UE RF calibration method of massive MIMO systems that uses the power headroom report. The power headroom is typically reported from the UEs and/or IIoT devices to the BS periodically or aperiodically. By including additional amplitude RF impairment information in the power headroom report, we can successfully transfer the necessary information from UE to BS. The proposed scheme does not require additional feedback, and is in compliance with current standards. There are five schemes based on the kind of information transferred to BS and the way of making a calibration factor that can be multiplied to the estimated channel. Numerical analysis shows that the proposed schemes can significantly increase the spectral efficiency with little system burden, and thus can be a core technology for the realization of massive MIMO for industrial network systems.
Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion

Present object detection methods working on 3D range data are so far either optimized for unstructured offroad environments or flat urban environments. We present a fast algorithm able to deal with tremendous amounts of 3D Lidar measurements. It uses a graph-based approach to segment ground and objects from 3D lidar scans using a novel unified, generic criterion based on local convexity measures. Experiments show good results in urban environments including smoothly bended road surfaces.
Mass Cytometry: Single Cells, Many Features

Technology development in biological research often aims to either increase the number of cellular features that can be surveyed simultaneously or enhance the resolution at which such observations are possible. For decades, flow cytometry has balanced these goals to fill a critical need by enabling the measurement of multiple features in single cells, commonly to examine complex or hierarchical cellular systems. Recently, a format for flow cytometry has been developed that leverages the precision of mass spectrometry. This fusion of the two technologies, termed mass cytometry, provides measurement of over 40 simultaneous cellular parameters at single-cell resolution, significantly augmenting the ability of cytometry to evaluate complex cellular systems and processes. In this Primer, we review the current state of mass cytometry, providing an overview of the instrumentation, its present capabilities, and methods of data analysis, as well as thoughts on future developments and applications.
Comparison of perioperative outcomes between robotic and laparoscopic partial nephrectomy: a systematic review and meta-analysis.

Robotic partial nephrectomy (RPN) is rapidly increasing; however, the benefit of RPN over laparoscopic partial nephrectomy (LPN) is controversial.



To compare perioperative outcomes of RPN and LPN.



We searched Ovid-Medline, Ovid-Embase, the Cochrane Library, KoreaMed, KMbase, KISS, RISS, and KisTi from their inception through August 2013. Two independent reviewers extracted data using a standardized form. Quality of the selected studies was assessed using the methodological index for nonrandomized studies.



A total of 23 studies and 2240 patients were included. All studies were cohort studies with no randomization, and the methodological quality varied. There was no significant difference between the two groups regarding complications of Clavien-Dindo classification grades 1-2 (p=0.62), Clavien-Dindo classification grades 3-5 (p=0.78), change of serum creatinine (p=0.65), operative time (p=0.35), estimated blood loss (p=0.76), and positive margins (p=0.75). The RPN group had a significantly lower rate of conversion to open surgery (p=0.02) and conversion to radical surgery (p=0.0006), shorter warm ischemia time (WIT; p=0.005), smaller change of estimated glomerular filtration rate (eGFR; p=0.03), and shorter length of stay (LOS; p=0.004).



This meta-analysis shows that RPN is associated with more favorable results than LPN in conversion rate to open or radical surgery, WIT, change of eGFR, and shorter LOS. To establish the safety and effectiveness outcomes of robotic surgery, well-designed randomized clinical studies with long-term follow-up are needed.



Robotic partial nephrectomy (PN) is more favorable than laparoscopic PN in terms of lower conversion rate to radical nephrectomy, a favorable renal function indexed estimated glomerular filtration rate, shorter length of hospital stay, and shorter warm ischemia time.