nils-herrmann/clean_scirepeval huggingface dataset:
Sparse code multiple access

Multicarrier CDMA is a multiplexing approach in which modulated QAM symbols are spread over multiple OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequence allowing us to take advantage of a near optimal ML receiver with practically feasible complexity. In this paper, we propose a new multiple access scheme so called sparse code multiple access (SCMA) which still enjoys the low complexity reception technique but with better performance compared to LDS. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to a multidimensional codeword of an SCMA codebook set. Each layer or user has its dedicated codebook. Shaping gain of a multidimensional constellation is the main source of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. In general, SCMA codebook design is an optimization problem. A systematic sub-optimal approach is proposed here for SCMA codebook design.
INTEGRATION OF BIM AND GIS : THE DEVELOPMENT OF THE CITYGML GEOBIM EXTENSION

There is a growing interest in the integration of BIM and GIS. However, most of the research is focused on importing BIM data in GIS applications and vice versa. Real integration of BIM and GIS is using the strong parts of the GIS technology in BIM, and of course the strong parts from BIM technology in GIS. In this paper a mix of strong parts from both worlds is integrated in a single project. The paper describes the development of a CityGML extension called GeoBIM to get semantic IFC data into a GIS context. The conversion of IFC to CityGML (including the GeoBIM extension) is implemented in the open source Building Information Modelserver. This contribution was selected in a double blind review process to be published within the Lecture Notes in Geoinformation and Cartography series (Springer-Verlag, Heidelberg). Advances in 3D Geo-Information Sciences Kolbe, Thomas H.; König, Gerhard; Nagel, Claus (Eds.) 2011, X ISBN 978-3-642-12669-7, Hardcover Date of Publication: January 5, 2011 Series Editors: Cartwright, W., Gartner, G., Meng, L., Peterson,  2010, Berlin, Germany 193
Understanding holistic human pose using class-specific convolutional neural network

This paper presents a method to capture human pose from individual real-world RGB images using a deep learning technique. The current works on estimating human pose by deep learning are designed in a detection or a regression framework, and in a part-based manner. As a new perspective, we introduce a classification scheme for this problem, which reasons the pose holistically. To the best of our knowledge, this is the first work for holistic human pose classification task that owes its feasibility to the great power of convolutional neural networks in feature learning. After training a convolutional neural network to classify the input image to one of the KeyPoses, the final pose is computed as a linear combination of several KeyPoses. In this new holistic classification attitude, the vast and high degree of freedom human pose space is divided into a finite number of subspaces and the convolutional neural network shows promising results in learning the features of each subspace. Empirical results (PCP and PCK rates) demonstrate that the proposed scheme is successfully able to understand human pose (i.e., predict a valid, true and coarse pose) in real-world unconstrained images with challenges like severe occlusion, high articulation, low quality and cluttered background. Furthermore, using the proposed method, the need for defining a complex model (such as appearance model or joints pairwise relations) is relieved. We have also verified a potential application of our proposed method in semantic image retrieval based on human pose.
Web-scale knowledge extraction from semi-structured tables

A wealth of knowledge is encoded in the form of tables on the World Wide Web. We propose a classification algorithm and a rich feature set for automatically recognizing layout tables and attribute/value tables. We report the frequencies of these table types over a large analysis of the Web and propose open challenges for extracting from attribute/value tables semantic triples (knowledge). We then describe a solution to a key problem in extracting semantic triples: protagonist detection, i.e., finding the subject of the table that often is not present in the table itself. In 79% of our Web tables, our method finds the correct protagonist in its top three returned candidates.
Generative Deep Deconvolutional Learning

A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. After learning the deep convolutional dictionary, testing is implemented via deconvolutional inference. To speed up this inference, a new statistical approach is proposed to project the top-layer dictionary elements to the data level. Following this, only one layer of deconvolution is required during testing. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images, and excellent classification results are obtained on the MNIST and Caltech 101 datasets.
Music Feature Maps with Convolutional Neural Networks for Music Genre Classification

Nowadays, deep learning is more and more used for Music Genre Classification: particularly Convolutional Neural Networks (CNN) taking as entry a spectrogram considered as an image on which are sought different types of structure.
 But, facing the criticism relating to the difficulty in understanding the underlying relationships that neural networks learn in presence of a spectrogram, we propose to use, as entries of a CNN, a small set of eight music features chosen along three main music dimensions: dynamics, timbre and tonality. With CNNs trained in such a way that filter dimensions are interpretable in time and frequency, results show that only eight music features are more efficient than 513 frequency bins of a spectrogram and that late score fusion between systems based on both feature types reaches 91% accuracy on the GTZAN database.
Motion Detection Techniques Using Optical Flow

Motion detection is very important in image processing. One way of detecting motion is using optical flow. Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. The method used for finding the optical flow in this project is assuming that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. This technique is later used in developing software for motion detection which has the capability to carry out four types of motion detection. The motion detection software presented in this project also can highlight motion region, count motion level as well as counting object numbers. Many objects such as vehicles and human from video streams can be recognized by applying optical flow technique. Background modeling, Motion detection, Optical flow, Velocity smoothness constant, motion trajectories.
An approch for monitoring and smart planning of urban solid waste management using smart-M3 platform

Solid waste management is one of the most important challenges in urban areas throughout the world and it is becoming a critical issue in developing countries where a rapid increase in population has been observed. Waste collection is a complex process that requires the use of large amount of money and an elaborate management of logistics. In this paper an approch to smart waste collection is proposed able to improve and optimize the handling of solid urban waste. Context of smart waste management requires interconnection among heterogeneous devices and data sharing involving a large amount of people. Smart-M3 platform solves these problems offering a high degree of decoupling and scalability.Waste collection is made by real-time monitoring the level of bin's fullness through sensors placed inside the containers. This method enables to exempt from collecting semi-empty bins. Furthermore, incoming data can be provided to decisional algorithms in order to determine the optimal number of waste vehicles or bins to distribute in the territory. The presented solution gives important advantages for both service providers and consumers. The formers could obtain a sensible cost reduction. On the other hand, users may benefit from a higher level of service quality. In order to make users feel closer to their community, they can interact with the system to be aware about the fulness state of the nearest bins. Finally, a mechanism for collecting “green points” was introduced for encouraging citizens to recycle.
Cancer statistics, 2013.

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. A total of 1,660,290 new cancer cases and 580,350 cancer deaths are projected to occur in the United States in 2013. During the most recent 5 years for which there are data (2005-2009), delay-adjusted cancer incidence rates declined slightly in men (by 0.6% per year) and were stable in women, while cancer death rates decreased by 1.8% per year in men and by 1.5% per year in women. Overall, cancer death rates have declined 20% from their peak in 1991 (215.1 per 100,000 population) to 2009 (173.1 per 100,000 population). Death rates continue to decline for all 4 major cancer sites (lung, colorectum, breast, and prostate). Over the past 10 years of data (2000-2009), the largest annual declines in death rates were for chronic myeloid leukemia (8.4%), cancers of the stomach (3.1%) and colorectum (3.0%), and non-Hodgkin lymphoma (3.0%). The reduction in overall cancer death rates since 1990 in men and 1991 in women translates to the avoidance of approximately 1.18 million deaths from cancer, with 152,900 of these deaths averted in 2009 alone. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population, with an emphasis on those groups in the lowest socioeconomic bracket and other underserved populations.
Concrete Crack Detection by Multiple Sequential Image Filtering

This article presents a new robust automated image processing method for detecting cracks in surface images of concrete structures. This method involves two steps: (1) development of an image filter for detecting major cracks using genetic programming (GP), and (2) elimination of residual noise after filtering and detection of indistinct cracks by iterative applications of the image filter to the local regions surrounding the cracks. The proposed method can be used for the accurate detection of cracks in surface images recorded under various conditions. Moreover, the widths of the detected cracks can be quantified on the basis of the spatial derivatives of the brightness patterns. The estimated crack widths are in good agreement with those measured manually.
A unified view of gradient-based attribution methods for Deep Neural Networks

Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, only few attempts to analyze them from a theoretical perspective have been made in the past. In this work we analyze various state-of-the-art attribution methods and prove unexplored connections between them. We also show how some methods can be reformulated and more conveniently implemented. Finally, we perform an empirical evaluation with six attribution methods on a variety of tasks and architectures and discuss their strengths and limitations.
Ternary CAM Power and Delay Model: Extensions and Uses

Applications in computer networks often require high throughput access to large data structures for lookup and classification. While advanced algorithms exist to speed these search primitives on network processors and even custom application-specific integrated circuits (ASICs), achieving tight bounds on worst case performance with standard memories often requires a very careful analysis of all possible access patterns. An alternative, and often times more simple solution, is possible if a ternary CAM (TCAM) is used to perform a fully parallel search across the entire data set. Unfortunately, this parallelism means that large portions of the chip are switching during each cycle, causing large amounts of power to be consumed. While researchers at all levels of design (from algorithms to circuits) have begun to explore new ways of managing the power consumption, quantifying design alternatives is difficult due to a lack of available models. In this paper, we examine the structure of a modern TCAM and present a simple, yet accurate, power and delay model. We present techniques to estimate the dynamic power consumption and leakage power of a TCAM structure and validate the model using a combination of industrial TCAM datasheets and prior published works. Such a model is a critical first step in bridging the intellectual divide between circuit-level and algorithm-level optimizations. To demonstrate the utility of our model, we present an extensive analysis of the model by varying various architectural parameters and describe how our model can be easily extended to handle several circuit optimizations in the TCAM structure. In addition, we present a comparative study of SRAM and TCAM energy consumption to directly quantify the many design options which will be very useful for network designers to explore various power management schemes.
Does the technology acceptance model predict actual use? A systematic literature review

0950-5849/$ see front matter 2009  A doi:10.1016/j.infsof.2009.11.005 * Corresponding author.  E-mail address: m.turner@cs.keele.ac.uk (M. Turne Context: The technology acceptance model (TAM) was proposed in 1989 as a means of predicting technology usage. However, it is usually validated by using a measure of behavioural intention to use (BI) rather than actual usage.  This review examines the evidence that the TAM predicts actual usage using both subjective and objective measures of actual usage.  We performed a systematic literature review based on a search of six digital libraries, along with vote-counting meta-analysis to analyse the overall results.  The search identified 79 relevant empirical studies in 73 articles. The results show that BI is likely to be correlated with actual usage. However, the TAM variables perceived ease of use (PEU) and perceived usefulness (PU) are less likely to be correlated with actual usage.  Care should be taken using the TAM outside the context in which it has been validated.
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new tool for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at https://allenai.github.io/ scispacy/.
Automatic generation of high-performance modular multipliers for arbitrary mersenne primes on FPGAs

Modular multiplication is a fundamental and performance determining operation in various public-key cryptosystems. High-performance modular multipliers on FPGAs are commonly realized by several small-sized multipliers, an adder tree for summing up the digit-products, and a reduction circuit. While small-sized multipliers are available in pre-fabricated high-speed DSP slices, the adder tree and the reduction circuit are implemented in standard logic. The latter operations represent the performance bottleneck to high-performance implementations. Previous works attempted to minimize the critical path of the adder tree by rearranging digit-products on digit-level. We report improved performance by regrouping digit-products on bit-level, while incorporating the reduction for Mersenne primes. Our approach leads to very fast modular multipliers, whose latency and throughput characteristics outperform all previous results. We formalize our approach and provide algorithms to automatically generate high-performance modular multipliers for arbitrary Mersenne primes from any small-sized multipliers.
Interactions with big data analytics

Increasingly in the 21st century, our daily lives leave behind a detailed digital record: our shifting thoughts and opinions shared on Twitter, our social relationships, our purchasing habits, our information seeking, our photos and videos—even the movements of our bodies and cars. Naturally, for those interested in human behavior, this bounty of personal data is irresistible. Decision makers of all kinds, from company executives to government agencies to researchers and scientists, would like to base their decisions and actions on this data. In response, a new discipline of big data analytics is forming. Fundamentally, big data analytics is a workflow that distills terabytes of low-value data (e.g., every tweet) down to, in some cases, a single bit of high-value data (Should Company X acquire Company Y? Can we reject the null hypothesis?). The goal is to see the big picture from the minutia of our digital lives. It is no surprise today that big data is useful for HCI researchers and user interface design. As one example, A/B testing is a standard practice in the usability community to help determine relative differences in user performance using different interfaces. For many years, we have used strict laboratory conditions to evaluate interfaces, but more recently we have seen the ability to implement those tests quickly and on a large population by running controlled Interactions with Big Data Analytics
A preliminary study on ASR-based detection of Chinese mispronunciation by Japanese learners

Detecting mispronunciations produced by non-native speakers and providing detailed instructive feedbacks are desired in computer assisted pronunciation training system (CAPT), as it is helpful to L2 learners to improve their pronunciation more effectively. In this paper, we present our preliminary study on detecting phonetic segmental mispronunciations on account of the erroneous articulation tendencies, including the place of articulation and the manner of articulation. Through modeling and detecting these error patterns, feedbacks based on articulation-placement and articulation-manner could be given. Moreover, Japanese learners of Chinese are focused on in this study. The experimental results show that the approach can detect the mostly representative pronunciation errors moderately well, achieving a false rejection rate of 8.0% and a false acceptance rate 32.6%. The diagnostic accuracy is 86.0%.
Neuronal population coding of movement direction.

Although individual neurons in the arm area of the primate motor cortex are only broadly tuned to a particular direction in three-dimensional space, the animal can very precisely control the movement of its arm. The direction of movement was found to be uniquely predicted by the action of a population of motor cortical neurons. When individual cells were represented as vectors that make weighted contributions along the axis of their preferred direction (according to changes in their activity during the movement under consideration) the resulting vector sum of all cell vectors (population vector) was in a direction congruent with the direction of movement. This population vector can be monitored during various tasks, and similar measures in other neuronal populations could be of heuristic value where there is a neural representation of variables with vectorial attributes.
Paternal filicide in Québec.

In this retrospective study, relevant demographic, social, and clinical variables were examined in 77 cases of paternal filicide. Between 1991 and 2001, all consecutive coroners' files on domestic homicide in Québec, Canada, were reviewed, and 77 child victims of 60 male parent perpetrators were identified. The results support data indicating that more fathers commit filicide than do mothers. A history of family abuse was characteristic of a substantial number of cases, and most of the cases involved violent means of homicide. Filicide was frequently (60%) followed by the suicide of the perpetrator and more so (86%) in cases involving multiple sibling victims. The abuse of drugs and alcohol was rare. At the time of the offense, most of the perpetrators were suffering from a psychiatric illness, usually depressive disorder. Nearly one-third were in a psychotic state. The proportion of fatal abuse cases was comparatively low. Many of the perpetrators had had contact with health professionals prior to the offense, although none had received treatment for a psychiatric illness.
An Optimization Approach for Utilizing Cloud Services for Mobile Devices in Cloud Environment

Mobile cloud computing has emerged aiming at assisting mobile devices in processing computationally or data intensive tasks using cloud resources. This paper presents an optimization approach for utilizing cloud services for mobile client in mobile cloud, which considers the benefit of both mobile device users and cloud datacenters. The mobile cloud service provisioning optimization is conducted in parallel under the deadline, budget and energy expenditure constraint. Mobile cloud provider runs multiple VMs to execute the jobs for mobile device users, the cloud providers want to maximize the revenue and minimize the electrical cost. The mobile device user gives the suitable payment to the cloud datacenter provider for available cloud resources for optimize the benefit. The paper proposes a distributed optimization algorithm for utilizing cloud services for mobile devices. The experiment is to test convergence of the proposed algorithm and also compare it with other related work. The experiments study the impacts of job arrival rate, deadline and mobility speeds on energy consumption ratio, execution success ratio, resource allocation efficiency and cost. The experiment shows that the proposed algorithm outperforms other related work in terms of some performance metrics such as allocation efficiency.
Polymer-supported membranes as models of the cell surface

Lipid-bilayer membranes supported on solid substrates are widely used as cell-surface models that connect biological and artificial materials. They can be placed either directly on solids or on ultrathin polymer supports that mimic the generic role of the extracellular matrix. The tools of modern genetic engineering and bioorganic chemistry make it possible to couple many types of biomolecule to supported membranes. This results in sophisticated interfaces that can be used to control, organize and study the properties and function of membranes and membrane-associated proteins. Particularly exciting opportunities arise when these systems are coupled with advanced semiconductor technology.