nils-herrmann/clean_scirepeval huggingface dataset:
A rule-based language model for reading recognition

Systems for assessing and tutoring reading skills place unique requirements on underlying ASR technologies. Most responses to a “read out loud” task can be handled with a low perplexity language model, but the educational setting of the task calls for diagnostic measures beyond plain accuracy. Pearson developed an automatic assessment of oral reading fluency that was administered in the field to a large, diverse sample of American adults. Traditional N-gram methods for language modeling are not optimal for the special domain of reading tests because N-grams need too much data and do not produce as accurate recognition. An efficient rule-based language model implemented a set of linguistic rules learned from an archival body of transcriptions, using only the text of the new passage and no passage-specific training data. Results from operational data indicate that this rule-based language model can improve the accuracy of test results and produce useful diagnostic information.
Modeling multiway data with linearly dependent loadings y

A generalization/specialization of the PARAFAC model is developed that improves its properties when applied to multi-way problems involving linearly dependent factors. Thismodel is called PARALIND (PARAllel profiles with LINear Dependences). Linear dependences can arise when the empirical sources of variation being modeled by factors are causally or logically linked during data generation, or circumstantially linked during data collection. For example, this can occur in a chemical context when end products are related to the precursor or in a psychological context when a single stimulus generates two incompatible feelings at once. For such cases, the most theoretically appropriate PARAFAC model has loading vectors that are linearly dependent in at least one mode, and when collinear, are nonunique in the others. However, standard PARAFAC analysis of fallible data will have neither of these features. Instead, latent linear dependences become high surface correlations and any latent nonuniqueness is replaced by a meaningless surface-level ‘unique orientation’ that optimally fits the particular random noise in that sample. To avoid these problems, any set of components that in theory should be rank deficient are re-expressed in PARALIND as a product of two matrices, one that explicitly represents their dependency relationships and another, with fewer columns, that captures their patterns of variation. To demonstrate the approach, we apply it first to fluorescence spectroscopy (excitation-emission matrices, EEM) data in which concentration values for two analytes covary exactly, and then to flow injection analysis (FIA) data in which subsets of columns are logically constrained to sum to a constant, but differently in each of twomodes. In the PARAFAC solutions of the EEMdata, all factors are ‘unique’ but this is onlymeaningful for two of the factors that are also unique at the latent level. In contrast, the PARALIND solutions directly display the extent and nature of partial nonuniqueness present at the latent level by exhibiting a corresponding partial uniqueness in their recovered loadings. For the FIA data, PARALIND constraints restore latent uniqueness to the concentration estimates. Comparison of the solutions shows that PARALINDmore accurately recovers latent structure, presumably because it uses fewer parameters and hence fits less error.
GMM for offline signature forgery detection

As signature continues to play a crucial part in personal identification for number of applications including financial transaction, an efficient signature authentication system becomes more and more important. Various researches in the field of signature authentication has been dynamically pursued for many years and its extent is still being explored. Signature verification is the process which is carried out to determine whether a given signature is genuine or forged. It can be distinguished into two types such as the Online and the Offline. In this paper we presented the Offline signature verification system and extracted some new local and geometric features like QuadSurface feature, Area ratio, Distance ratio etc. For this we have taken some genuine signatures from 5 different persons and extracted the features from all of the samples after proper preprocessing steps. The training phase uses Gaussian Mixture Model (GMM) technique to obtain a reference model for each signature sample of a particular user. By computing Euclidian distance between reference signature and all the training sets of signatures, acceptance range is defined. If the Euclidian distance of a query signature is within the acceptance range then it is detected as an authenticated signature else, a forged signature.
Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks

Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access under-utilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect signals from licensed primary radios to avoid harmful interference. However, due to the effects of channel fading/shadowing, individual cognitive radios may not be able to reliably detect the existence of a primary radio. In this paper, we propose an optimal linear cooperation framework for spectrum sensing in order to accurately detect the weak primary signal. Within this framework, spectrum sensing is based on the linear combination of local statistics from individual cognitive radios. Our objective is to minimize the interference to the primary radio while meeting the requirement of opportunistic spectrum utilization. We formulate the sensing problem as a nonlinear optimization problem. By exploiting the inherent structures in the problem formulation, we develop efficient algorithms to solve for the optimal solutions. To further reduce the computational complexity and obtain solutions for more general cases, we finally propose a heuristic approach, where we instead optimize a modified deflection coefficient that characterizes the probability distribution function of the global test statistics at the fusion center. Simulation results illustrate significant cooperative gain achieved by the proposed strategies. The insights obtained in this paper are useful for the design of optimal spectrum sensing in cognitive radio networks.
Automatic differentiation of melanoma from dysplastic nevi

Malignant melanoma causes the majority of deaths related to skin cancer. Nevertheless, it is the most treatable one, depending on its early diagnosis. The early prognosis is a challenging task for both clinicians and dermatologist, due to the characteristic similarities of melanoma with other skin lesions such as dysplastic nevi. In the past decades, several computerized lesion analysis algorithms have been proposed by the research community for detection of melanoma. These algorithms mostly focus on differentiating melanoma from benign lesions and few have considered the case of melanoma against dysplastic nevi. In this paper, we consider the most challenging task and propose an automatic framework for differentiation of melanoma from dysplastic nevi. The proposed framework also considers combination and comparison of several texture features beside the well used colour and shape features based on "ABCD" clinical rule in the literature. Focusing on dermoscopy images, we evaluate the performance of the framework using two feature extraction approaches, global and local (bag of words) and three classifiers such as support vector machine, gradient boosting and random forest. Our evaluation revealed the potential of texture features and random forest as an almost independent classifier. Using texture features and random forest for differentiation of melanoma and dysplastic nevi, the framework achieved the highest sensitivity of 98% and specificity of 70%.
Half-Mode Substrate Integrated Waveguide Transverse Slot Array Antennas

Transverse slot array antennas fed by a half-mode substrate integrated waveguide (HMSIW) are proposed and developed in this paper. The design concept of these new radiating structures is based on the study of the field distribution and phase constant along the HMSIW as well as on the resonant characteristics of a single slot etched on its top conducting wall. Two types of HMSIW-fed slot array antennas, operating, respectively, in X-band and Ka-band, are designed following a procedure similar to the design of slot array antennas fed by a dielectric-filled rectangular waveguide. Compared with slot array antennas fed by a conventional rectangular waveguide, such proposed HMSIW-fed slot array antennas possess the advantages of low profile, compact size, low cost, and easy integration with other microwave and millimeter wave planar circuits. It is worth noting that the width of HMSIW slot array antennas is reduced by nearly half compared to that of slot array antennas fed by a substrate integrated waveguide.
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by 48% to 78% and energy consumption by 37% to 69% compared with the state-of-the-art compression algorithms.
Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading

Physical library collections are valuable and long standing resources for knowledge and learning. However, managing books in a large bookshelf and finding books on it often leads to tedious manual work, especially for large book collections where books might be missing or misplaced. Recently, deep neural models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have achieved great success for scene text detection and recognition. Motivated by these recent successes, we aim to investigate their viability in facilitating book management, a task that introduces further challenges including large amounts of cluttered scene text, distortion, and varied lighting conditions. In this paper, we present a library inventory building and retrieval system based on scene text reading methods. We specifically design our scene text recognition model using rich supervision to accelerate training and achieve state-of-the-art performance on several benchmark datasets. Our proposed system has the potential to greatly reduce the amount of human labor required in managing book inventories as well as the space needed to store book information.
The Measurement of Quality of Life : Conceptualization Comes First A Four-Qualities-of-Life Conceptual Framework and an Illustration to Latin America

This investigation was concerned with providing a rationale for the understanding and measurement of quality of life. The investigation proposes a modified version of Veenhoven’s Four-Qualities-of-Life Framework. Its main purpose is to bring order to the vast literature on measuring quality of life; another purpose is to provide a richer framework to guide public policy in the procurement of a better society. The framework is used to assess quality of life in Latin America; the purpose of this exercise is to illustrate the utility of the framework and to show that importance of conceptualizing what quality of life is before any attempt to measure it is undertaken.
Medical diagnosis system using fuzzy logic toolbox

Medical diagnosis is basically a pattern classification phenomena: based on some input provided by a patient, an expert gives a conclusion on the basis of its knowledge, which is normally stored in a binary form, and finally the result is calculated i.e. either the patient suffering from a certain disease or not. There are a number of properties in fuzzy set theory has a number of facilities that make it suitable for medical diagnosis. The results and findings from the study had shown that the technique of fuzzy logic can contribute a reliable result in order to notify the disease. Human subject has been used to test the system. In this paper we have proposed a system for medical diagnosis which is a generated using Fuzzy Logic Toolbox in MATLAB. Specifically, it focuses on medical diagnosis.
Object-centric Sampling for Fine-grained Image Classification

This paper proposes to go beyond the state-of-the-art deep convolutional neural network (CNN) by incorporating the information from object detection, focusing on dealing with fine-grained image classification. Unfortunately, CNN suffers from over-fiting when it is trained on existing finegrained image classification benchmarks, which typically only consist of less than a few tens of thousands training images. Therefore, we first construct a large-scale finegrained car recognition dataset that consists of 333 car classes with more than 150 thousand training images. With this large-scale dataset, we are able to build a strong baseline for CNN with top-1 classification accuracy of 81.6%. One major challenge in fine-grained image classification is that many classes are very similar to each other while having large within-class variation. One contributing factor to the within-class variation is cluttered image backgroun d. However, the existing CNN training takes uniform window sampling over the image, acting as blind on the location of the object of interest. In contrast, this paper proposes an object-centric sampling(OCS) scheme that samples image windows based on the object location information. The challenge in using the location information lies in how to design powerful object detector and how to handle the imperfectness of detection results. To that end, we design a saliency-aware object detection approach specific for the setting of fine-grained image classification, and the uncertainty of detection results are naturally handled in our OCS scheme. Our framework is demonstrated to be very effective, improving top-1 accuracy to 89.3% (from 81.6%) on the large-scale fine-grained car classification dataset.
Critical Illness in Pregnancy: Part II: Common Medical Conditions Complicating Pregnancy and Puerperium.

The first of this two-part series on critical illness in pregnancy dealt with obstetric disorders. In Part II, medical conditions that commonly affect pregnant women or worsen during pregnancy are discussed. ARDS occurs more frequently in pregnancy. Strategies commonly used in nonpregnant patients, including permissive hypercapnia, limits for plateau pressure, and prone positioning, may not be acceptable, especially in late pregnancy. Genital tract infections unique to pregnancy include chorioamnionitis, group A streptococcal infection causing toxic shock syndrome, and polymicrobial infection with streptococci, staphylococci, and Clostridium perfringens causing necrotizing vulvitis or fasciitis. Pregnancy predisposes to VTE; D-dimer levels have low specificity in pregnancy. A ventilation-perfusion scan is preferred over CT pulmonary angiography in some situations to reduce radiation to the mother's breasts. Low-molecular-weight or unfractionated heparins form the mainstay of treatment; vitamin K antagonists, oral factor Xa inhibitors, and direct thrombin inhibitors are not recommended in pregnancy. The physiologic hyperdynamic circulation in pregnancy worsens many cardiovascular disorders. It increases risk of pulmonary edema or arrhythmias in mitral stenosis, heart failure in pulmonary hypertension or aortic stenosis, aortic dissection in Marfan syndrome, or valve thrombosis in mechanical heart valves. Common neurologic problems in pregnancy include seizures, altered mental status, visual symptoms, and strokes. Other common conditions discussed are aspiration of gastric contents, OSA, thyroid disorders, diabetic ketoacidosis, and cardiopulmonary arrest in pregnancy. Studies confined to pregnant women are available for only a few of these conditions. We have, therefore, reviewed pregnancy-specific adjustments in the management of these disorders.
Efficient Exploitation of Mobile Edge Computing for Virtualized 5G in EPC Architectures

To pave the way towards disclosing the full potential of 5G networking, emerging Mobile Edge Computing techniques are gaining momentum in both academic and industrial research as a means to enhance infrastructure scalability and reliability by moving control functions close to the edge of the network. After the promising results under achievement within the EU Mobile Cloud Networking project, we claim the suitability of deploying Evolved Packet Core (EPC) support solutions as a Service (EPCaaS) over a uniform edge cloud infrastructure of Edge Nodes, by following the concepts of Network Function Virtualization (NFV). This paper originally focuses on the support needed for efficient elasticity provisioning of EPCaaS stateful components, by proposing novel solutions for effective subscribers' state management in quality-constrained 5G scenarios. In particular, to favor flexibility and high-availability against network function failures, we have developed a state sharing mechanism across different data centers even in presence of firewall/network encapsulation. In addition, our solution can dynamically select which state portions should be shared and to which Edge Nodes. The reported experimental results, measured over the widely recognized Open5GCore testbed, demonstrate the feasibility and effectiveness of the approach, as well as its capability to satisfy "carrier-grade" quality requirements while ensuring good elasticity and scalability.
A fingerprint-based technique for indoor localization using fuzzy Least Squares Support Vector Machine

Considering the growing demand of location-based services in indoor environments and development of Wi-Fi in recent years, indoor localization based on fingerprinting has attracted many researchers interest. In this paper, we introduce a novel fuzzy Least Squares Support Vector Machine (LS-SVM) based indoor fingerprinting system by using the received signal strength (RSS). In the offline phase, RSS values of all Wi-Fi signals detected from the available access points are collected at different reference points with known locations and are stored in a database. In the online phase, the target position is estimated by calculating fuzzy membership functions of samples and using formulation of fuzzy LS-SVM method. Simulation results show that average estimation error of the proposed method is 2.56m, while average positioning error of traditional LS-SVM methods was 4.61m.
The Role of Gravity in Periorbital and Midfacial Aging.

With respect to the pathogenesis of periorbital and midfacial aging, gravity may play a greater role than volume loss.



The authors determined the effect of shifting from the upright to the supine position on specific attributes of facial appearance and ascertained whether facial appearance in the supine position bore any resemblance to its appearance in youth.



Participants who showed signs of midface aging were positioned in the upright and supine positions, and photographs were obtained during smiling and repose. For each photograph, examiners graded the following anatomic parameters, using a standardized scale: brow position, tear trough length and depth, steatoblepharon, cheek volume, malar bags/festoons, and nasolabial folds. Some participants provided photographs of themselves taken 10 to 15 years earlier; these were compared with the study images.



Interobserver correlation was strong. When participants were transferred from upright to supine, all anatomic parameters examined became more youthful in appearance; findings were statistically significant. The grading of anatomic parameters of the earlier photographs most closely matched that of current supine photographs of the subjects smiling.



In the supine position, as opposed to the upright position, participants with signs of midface aging appear to have much more volume in the periorbita and midface. For the subset of participants who provided photographs obtained 10 to 15 years earlier, the appearance of facial volume was similar between those images and the current supine photographs. This suggests that volume displacement due to gravitational forces plays an integral role in the morphogenesis of midface aging.
An investigation on the cytotoxicity and caspase-mediated apoptotic effect of biologically synthesized silver nanoparticles using Podophyllum hexandrum on human cervical carcinoma cells.

Now-a-days synthesis and characterization of silver nanoparticles (AgNPs) through biological entity is quite interesting to employ AgNPs for various biomedical applications in general and treatment of cancer in particular. This paper presents the green synthesis of AgNPs using leaf extract of Podophyllum hexandrum Royle and optimized with various parameters such as pH, temperature, reaction time, volume of extract and metal ion concentration for synthesis of AgNPs. TEM, XRD and FTIR were adopted for characterization. The synthesized nanoparticles were found to be spherical shaped with average size of 14 nm. Effects of AgNPs were analyzed against human cervical carcinoma cells by MTT Assay, quantification of ROS, RT-PCR and western blotting techniques. The overall result indicates that AgNPs can selectively inhibit the cellular mechanism of HeLa by DNA damage and caspase mediated cell death. This biological procedure for synthesis of AgNPs and selective inhibition of cancerous cells gives an alternative avenue to treat human cancer effectively.
Control of motorcycle steering instabilities

The establishment of damper settings that provide an optimal compromise between wobble- and weave-mode damping is discussed. The conventional steering damper is replaced with a network of interconnected mechanical components comprised of springs, dampers and inerters - that retain the virtue of the damper, while improving the weave-mode performance. The improved performance is due to the fact that the network introduces phase compensation between the relative angular velocity of the steering system and the resulting steering technique