nils-herrmann/clean_scirepeval huggingface dataset:
A Literature Survey on Digital Image Processing Techniques in Character Recognition of Indian Languages

Handwritten character recognition is always a frontier area of research in the field of pattern recognition. There is a large demand for OCR on hand written documents in Image processing. Even though, sufficient studies have performed in foreign scripts like Arabic, Chinese and Japanese, only a very few work can be traced for handwritten character recognition mainly for the south Indian scripts. OCR system development for Indian script has many application areas like preserving manuscripts and ancient literatures written in different Indian scripts and making digital libraries for the documents. Feature extraction and classification are essential steps of character recognition process affecting the overall accuracy of the recognition system. This paper presents a brief overview of digital image processing techniques such as Feature Extraction, Image Restoration and Image Enhancement. A brief history of OCR and various approaches to character recognition is also discussed in this paper.
Realistic 3D Face Modeling by Fusing Multiple 2D Images

In this paper, we propose a fully automatic and efficient algorithm for realistic 3D face reconstruction by fusing multiple 2D face images. Firstly, an efficient multi-view 2D face alignment algorithm is utilized to localize the facial points of the face images; and then the intrinsic shape and texture models are inferred by the proposed Syncretized Shape Model (SSM) and Syncretized Texture Model (STM), respectively. Compared with other related works, our proposed algorithm has the following characteristics: 1) the inferred shape and texture are more realistic owing to the constraints and co-enhancement among the multiple images; 2) it is fully automatic, without any user interaction; and 3) the shape and pose parameter estimation is efficient via EM approach and unit quaternion based pose representation, and is also robust as a result of the dynamic correspondence approach. The experimental results show the effectiveness of our proposed algorithm for 3D face reconstruction.
Energy-Efficient Broadcast and Multicast Trees in Wireless Networks

The wireless networking environment presents formidable challenges to the study of broadcasting and multicasting problems. After addressing the characteristics of wireless networks that distinguish them from wired networks, we introduce and evaluate algorithms for tree construction in infrastructureless, all-wireless applications. The performance metric used to evaluate broadcast and multicast trees is energyefficiency. We develop the Broadcast Incremental Power Algorithm, and adapt it to multicast operation as well. This algorithm exploits the broadcast nature of the wireless communication environment, and addresses the need for energy-efficient operation. We demonstrate that our algorithm provides better performance than algorithms that have been developed for the link-based, wired environment.
Usage of Complementary and Alternative Medicine (CAM) among osteoarthritis patients attending an urban multi-specialist hospital in Lagos, Nigeria.

Osteoarthritis (OA), a chronic degenerative disease of synovial joints is characterised by pain and stiffness. Aim of treatment is pain relief. Complementary and alternative medicine (CAM) refers to practices which are not an integral part of orthodox medicine.



To determine the pattern of usage of CAM among OA patients in Nigeria.



Consecutive patients with OA attending orthopaedic clinic of Havana Specialist Hospital, Lagos, Nigeria were interviewed over a 6- month period st st of 1 May to 31 October 2007 on usage of CAM. Structured and open-ended questions were used. Demographic data, duration of OA and treatment as well as compliance to orthodox medications were documented.



One hundred and sixty four patients were studied.120 (73.25%) were females and 44(26.89%) were males. Respondents age range between 35-74 years. 66(40.2%) patients used CAM. 35(53.0%) had done so before presenting to the hospital. The most commonly used CAM were herbal products used by 50(75.8%) of CAM users. Among herbal product users, 74.0% used non- specific local products, 30.0% used ginger, 36.0% used garlic and 28.0% used Aloe Vera. Among CAM users, 35(53.0%) used local embrocation and massage, 10(15.2%) used spiritual methods. There was no significant difference in demographics, clinical characteristics and pain control among CAM users and non-users.



Many OA patients receiving orthodox therapy also use CAM. Medical doctors need to keep a wary eye on CAM usage among patients and enquire about this health-seeking behaviour in order to educate them on possible drug interactions, adverse effects and long term complications.
Disparities in genetic testing: thinking outside the BRCA box.

The impact of predictive genetic testing on cancer care can be measured by the increased demand for and utilization of genetic services as well as in the progress made in reducing cancer risks in known mutation carriers. Nonetheless, differential access to and utilization of genetic counseling and cancer predisposition testing among underserved racial and ethnic minorities compared with the white population has led to growing health care disparities in clinical cancer genetics that are only beginning to be addressed. Furthermore, deficiencies in the utility of genetic testing in underserved populations as a result of limited testing experience and in the effectiveness of risk-reducing interventions compound access and knowledge-base disparities. The recent literature on racial/ethnic health care disparities is briefly reviewed, and is followed by a discussion of the current limitations of risk assessment and genetic testing outside of white populations. The importance of expanded testing in underserved populations is emphasized.
Computing the Matrix Cosine

An algorithm is developed for computing the matrix cosine, building on a proposal of Serbin and Blalock. The algorithm scales the matrix by a power of 2 to make the ∞-norm less than or equal to 1, evaluates a Padé approximant, and then uses the double angle formula cos (2A)=2cos (A)2−I to recover the cosine of the original matrix. In addition, argument reduction and balancing is used initially to decrease the norm. We give truncation and rounding error analyses to show that an  Padé approximant produces the cosine of the scaled matrix correct to machine accuracy in IEEE double precision arithmetic, and we show that this Padé approximant can be more efficiently evaluated than a corresponding Taylor series approximation. We also provide error analysis to bound the propagation of errors in the double angle recurrence. Numerical experiments show that our algorithm is competitive in accuracy with the Schur–Parlett method of Davies and Higham, which is designed for general matrix functions, and it is substantially less expensive than that method for matrices of ∞-norm of order 1. The dominant computational kernels in the algorithm are matrix multiplication and solution of a linear system with multiple right-hand sides, so the algorithm is well suited to modern computer architectures.
The role of FDG-PET in Hodgkin lymphoma

18-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET)/computed tomography (CT) is currently the most valuable imaging technique in Hodgkin lymphoma. Since its first use in lymphomas in the 1990s, it has become the gold standard in the staging and end-of-treatment remission assessment in patients with Hodgkin lymphoma. The possibility of using early (interim) PET during first-line therapy to evaluate chemosensitivity and thus personalize treatment at this stage holds great promise, and much attention is now being directed toward this goal. With high probability, it is believed that in the near future, the result of interim PET-CT would serve as a compass to optimize treatment. Also the role of PET in pre-transplant assessment is currently evolving. Much controversy surrounds the possibility of detecting relapse after completed treatment with the use of PET in surveillance in the absence of symptoms suggestive of recurrence and the results of published studies are rather discouraging because of low positive predictive value. This review presents current knowledge about the role of 18-FDG-PET/CT imaging at each point of management of patients with Hodgkin lymphoma.
Selecting Training Data for Cross-Corpus Speech Emotion Recognition: Prototypicality vs. Generalization

We investigate strategies for selection of databases and instances for training cross-corpus emotion recognition systems, that is, systems that generalize across different labelling concepts, languages and interaction scenarios. We propose objective measures for prototypicality based on distances in a large space of brute-forced acoustic features and show their relation to the expected performance in cross-corpus testing. We perform extensive evaluation on eight commonly used corpora of emotional speech reaching from acted to fully natural emotion and limited phonetic content to conversational speech. In the result, selecting prototypical training instances by the proposed criterion can deliver a gain of up to 7.5 % unweighted accuracy in cross-corpus arousal recognition, and there is a correlation of .571 between the proposed prototypicality measure of databases and the expected unweighted accuracy in cross-corpus testing by Support Vector Machines.
DROP (DRone Open source Parser) Your Drone: Forensic Analysis of the DJI Phantom III

The DJI Phantom III drone has already been used for malicious activities (to drop bombs, remote surveillance and plane watching) in 2016 and 2017. At the time of writing, DJI was the drone manufacturer with the largest market share. Our work presents the primary thorough forensic analysis of the DJI Phantom III drone, and the primary account for proprietary file structures stored by the examined drone. It also presents the forensically sound open source tool DRone Open source Parser (DROP) that parses proprietary DAT files extracted from the drone's nonvolatile internal storage. These DAT files are encrypted and encoded. The work also shares preliminary findings on TXT files, which are also proprietary, encrypted, encoded, files found on the mobile device controlling the drone. These files provided a slew of data such as GPS locations, battery, flight time, etc. By extracting data from the controlling mobile device, and the drone, we were able to correlate data and link the user to a specific device based on extracted metadata. Furthermore, results showed that the best mechanism to forensically acquire data from the tested drone is to manually extract the SD card by disassembling the drone. Our findings illustrated that the drone should not be turned on as turning it on changes data on the drone by creating a new DAT file, but may also delete stored data if the drone's internal storage is full.
GABA: homeostatic and pharmacological aspects.

The central nervous system (CNS) operates by a fine-tuned balance between excitatory and inhibitory signalling. In this context, the inhibitory neurotransmission may be of particular interest as it has been suggested that such neuronal pathways may constitute 'command pathways' and the principle of 'dis-inhibition' leading ultimately to excitation may play a fundamental role (Roberts, E. (1974).  127-143). The neurotransmitter responsible for this signalling is gamma-aminobutyrate (GABA) which was first discovered in the CNS as a curious amino acid (Roberts, E., Frankel, S. (1950).  55-63) and later proposed as an inhibitory neurotransmitter (Curtis, D.R., Watkins, J.C. (1960).  117-141; Krnjevic, K., Schwartz, S. (1967). ). The present review will describe aspects of GABAergic neurotransmission related to homeostatic mechanisms such as biosynthesis, metabolism, release and inactivation. Additionally, pharmacological and therapeutic aspects of this will be discussed.
RCS Diversity of Electromagnetic Wave Carrying Orbital Angular Momentum

An electromagnetic (EM) wave with orbital angular momentum (OAM) has a helical wave front, which is different from that of the plane wave. The phase gradient can be found perpendicular to the direction of propagation and proportional to the number of OAM modes. Herein, we study the backscattering property of the EM wave with different OAM modes, i.e., the radar cross section (RCS) of the target is measured and evaluated with different OAM waves. As indicated by the experimental results, different OAM waves have the same RCS fluctuation for the simple target, e.g., a small metal ball as the target. However, for complicated targets, e.g., two transverse-deployed small metal balls, different RCSs can be identified from the same incident angle. This valuable fact helps to obtain RCS diversity, e.g., equal gain or selective combining of different OAM wave scattering. The majority of the targets are complicated targets or expanded targets; the RCS diversity can be utilized to detect a weak target traditionally measured by the plane wave, which is very helpful for anti-stealth radar to detect the traditional stealth target by increasing the RCS with OAM waves.
Active-Braid, a Bioinspired Continuum Manipulator

This letter reports the concept and design of the active-braid, a novel bioinspired continuum manipulator with the ability to contract, extend, and bend in three-dimensional space with varying stiffness. The manipulator utilizes a flexible crossed-link helical array structure as its main supporting body, which is deformed by using two radial actuators and a total of six longitudinal tendons, analogously to the three major types of muscle layers found in muscular hydrostats. The helical array structure ensures that the manipulator behaves similarly to a constant volume structure (expanding while shortening and contracting while elongating). Numerical simulations and experimental prototypes are used in order to evaluate the feasibility of the concept.
Class-wise deep dictionaries for EEG classification

In this work we propose a classification framework called class-wise deep dictionary learning (CWDDL). For each class, multiple levels of dictionaries are learnt using features from the previous level as inputs (for first level the input is the raw training sample). It is assumed that the cascaded dictionaries form a basis for expressing test samples for that class. Based on this assumption sparse representation based classification is employed. Benchmarking experiments have been carried out on some deep learning datasets (MNIST and its variations, CIFAR and SVHN); our proposed method has been compared with Deep Belief Network (DBN), Stacked Autoencoder, Convolutional Neural Net (CNN) and Label Consistent KSVD (dictionary learning). We find that our proposed method yields better results than these techniques and requires much smaller run-times. The technique is applied for Brain Computer Interface (BCI) classification problems using EEG signals. For this problem our method performs significantly better than Convolutional Deep Belief Network(CDBN).
The long-term effects of alar base reduction.

To statistically analyze the long-term results of alar base reduction after rhinoplasty.



Among a consecutive series of 100 rhinoplasty cases, 19 patients required alar base reduction. The mean (SD) follow-up time was 11 (9) months (range, 2 months to 3 years). Using preoperative and postoperative photographs, comparisons were made of the change in the base width (width of base between left and right alar-facial junctions), flare width (width on base view between points of widest alar flare), base height (distance from base to nasal tip on base view), nostril height (distance from base to anterior edge of nostril), and vertical flare (vertical distance from base to the widest alar flare). Notching at the nasal sill was recorded as none, minimal, mild, moderate, and severe.



Changes in vertical flare (P<.05) and nostril height (P<.05) were the only significant differences seen in the patients who required alar reduction. No significant change was seen in base width (P=.92), flare width (P=.41), or base height (P=.22). No notching was noted.



It would have been preferable to study patients undergoing alar reduction without concomitant rhinoplasty procedures, but this approach is not practical. To our knowledge, the present study represents the most extensive attempt in the literature to characterize and quantify the postoperative effects of alar base reduction.
Lipid-associated sialic acid test for the detection of human cancer.

A rapid method for the measurement of serum and/or plasma, lipid-associated sialic acid levels has been developed. This test has been applied to 850 human sera of which 670 came from patients with nine categories of malignant disease, 80 from persons with benign disorders, and 100 from normal individuals. Lipid-associated sialic acid concentrations were found to be significantly increased (p less than 0.001) in all groups of cancer patients as compared to both those with benign diseases and normal controls. Test sensitivity in the detection of cancer ranged from 77 to 97%. Specificity was, respectively, 81 and 93% for the benign and normal groups. In small samples of patients, no association between test values and tumor burden was found. This test compares favorably with the most widely used tumor marker test, that for carcinoembryonic antigen.
MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds

We consider the stochastic optimization of finite sums over a Riemannian manifold where the functions are smooth and convex. We present MASAGA, an extension of the stochastic average gradient variant SAGA on Riemannian manifolds. SAGA is a variance-reduction technique that typically outperforms methods that rely on expensive full-gradient calculations, such as the stochastic variance-reduced gradient method. We show that MASAGA achieves a linear convergence rate with uniform sampling, and we further show that MASAGA achieves a faster convergence rate with non-uniform sampling. Our experiments show that MASAGA is faster than the recent Riemannian stochastic gradient descent algorithm for the classic problem of finding the leading eigenvector corresponding to the maximum eigenvalue.
Decanting the Contribution of Instruction Types and Loop Structures in the Reuse of Traces

Reuse has been proposed as a microarchitecture-level mechanism to reduce the amount of executed instructions, collapsing dependencies and freeing resources for other instructions. Previous works have used reuse domains such as memory accesses, integer or not floating point, based on the reusability rate. However, these works have not studied the specific contribution of reusing different subsets of instructions for performance. In this work, we analysed the sensitivity of trace reuse to instruction subsets, comparing their efficiency to their complementary subsets. We also studied the amount of reuse that can be extracted from loops. Our experiments show that disabling trace reuse outside loops does not harm performance but reduces in 12% the number of accesses to the reuse table. Our experiments with reuse subsets show that most of the speedup can be retained even when not reusing all types of instructions previously found in the reuse domain. 1 ar X iv :1 71 1. 06 67 2v 1 [ cs .A R ] 1 7 N ov 2 01 7
Sparse Factorization Machines for Click-through Rate Prediction

With the rapid development of E-commerce, recent years have witnessed the booming of online advertising industry, which raises extensive concerns of both academic and business circles. Among all the issues, the task of Click-through rates (CTR) prediction plays a central role, as it may influence the ranking and pricing of online ads. To deal with this task, the Factorization Machines (FM) model is designed for better revealing proper combinations of basic features. However, the sparsity of ads transaction data, i.e., a large proportion of zero elements, may severely disturb the performance of FM models. To address this problem, in this paper, we propose a novel Sparse Factorization Machines (SFM) model, in which the Laplace distribution is introduced instead of traditional Gaussian distribution to model the parameters, as Laplace distribution could better fit the sparse data with higher ratio of zero elements. Along this line, it will be beneficial to select the most important features or conjunctions with the proposed SFM model. Furthermore, we develop a distributed implementation of our SFM model on Spark platform to support the prediction task on mass dataset in practice. Comprehensive experiments on two large-scale real-world datasets clearly validate both the effectiveness and efficiency of our SFM model compared with several state-of-the-art baselines, which also proves our assumption that Laplace distribution could be more suitable to describe the online ads transaction data.
A Visual Approach for Interactive Keyterm-Based Clustering

The keyterm-based approach is arguably intuitive for users to direct text-clustering processes and adapt results to various applications in text analysis. Its way of markedly influencing the results, for instance, by expressing important terms in relevance order, requires little knowledge of the algorithm and has predictable effect, speeding up the task. This article first presents a text-clustering algorithm that can easily be extended into an interactive algorithm. We evaluate its performance against state-of-the-art clustering algorithms in unsupervised mode. Next, we propose three interactive versions of the algorithm based on keyterm labeling, document labeling, and hybrid labeling. We then demonstrate that keyterm labeling is more effective than document labeling in text clustering. Finally, we propose a visual approach to support the keyterm-based version of the algorithm. Visualizations are provided for the whole collection as well as for detailed views of document and cluster relationships. We show the effectiveness and flexibility of our framework, Vis-Kt, by presenting typical clustering cases on real text document collections. A user study is also reported that reveals overwhelmingly positive acceptance toward keyterm-based clustering.
Automatic Mashup Creation by Considering both Vertical and Horizontal Mashabilities

In this paper, we proposed a system to effectively create music mashups – a kind of re-created music that is made by mixing parts of multiple existing music pieces. Unlike previous studies which merely generate mashups by overlaying music segments on one single base track, the proposed system creates mashups with multiple background (e.g. instrumental) and lead (e.g. vocal) track segments. So, besides the suitability between the vertically overlaid tracks (i.e. vertical mashability) used in previous studies, we proposed to further consider the suitability between the horizontally connected consecutive music segments (i.e. horizontal mashability) when searching for proper music segments to be combined. On the vertical side, two new factors: “harmonic change balance” and “volume weight” have been considered. On the horizontal side, the methods used in the studies of medley creation are incorporated. Combining vertical and horizontal mashabilities together, we defined four levels of mashability that may be encountered and found the proper solution to each of them. Subjective evaluations showed that the proposed four levels of mashability can appropriately reflect the degrees of listening enjoyment. Besides, by taking the newly proposed vertical mashability measurement into account, the improvement in user satisfaction is statistically significant.