nils-herrmann/clean_scirepeval huggingface dataset:
Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks

The storage and computation requirements of Convolutional Neural Networks (CNNs) can be prohibitive for exploiting these models over low-power or embedded devices. This paper reduces the computational complexity of the CNNs by minimizing an objective function, including the recognition loss that is augmented with a sparsity-promoting penalty term. The sparsity structure of the network is identified using the Alternating Direction Method of Multipliers (ADMM), which is widely used in large optimization problems. This method alternates between promoting the sparsity of the network and optimizing the recognition performance, which allows us to exploit the two-part structure of the corresponding objective functions. In particular, we take advantage of the separability of the sparsity-inducing penalty functions to decompose the minimization problem into sub-problems that can be solved sequentially. Applying our method to a variety of state-of-the-art CNN models, our proposed method is able to simplify the original model, generating models with less computation and fewer parameters, while maintaining and often improving generalization performance. Accomplishments on a variety of models strongly verify that our proposed ADMM-based method can be a very useful tool for simplifying and improving deep CNNs.
Efficient time compression earthquake database using hadoop Hive ORC format

Today is an age of Big data. Big data is the normally unstructured data. Apache Hive is largely used for analysis in process of huge data. Because it is like SQL so easy to get analytical report. The main problem is that unstructured data loading and storage as well as Fast and timely analysis of large amount of data. There are data Compression columnar format like ORC(Optimized Row And Columnar) and Parquet columnar format. In this paper we used USGS (United States Geological Survey) Earthquake dataset. USGS provides the multi-Dimension dataset of earthquake of every day, week and month. We applied hadoop Hive's ORC format On monthly USGS earthquake dataset. ORC format Stored dataset efficiently without lose so that the most important data without losing stored on HDFS. We compare result of ORC Sorted and Unsorted dataset on the basses of time required to load the dataset on HDFS.
Review of Complexity Metrics for Object Oriented Software Products

It is widely accepted that sizing or predicting the volumes of various kinds of software deliverable items is one of the first and most dominant aspects of software cost estimating. Most of the cost estimation model or techniques usually assume that software size or structural complexity is the integral factor that influences software development effort. Although sizing and complexity measure is a very critical due to the need of reliable size estimates in the utilization of existing software project cost estimation models and complex problem for software cost estimating, advances in sizing technology over the past 30 years have been impressive. This paper attempts to review the 12 object-oriented software metrics proposed in 90s’ by Chidamber, Kemerer and Li.
Using Model Checking to Analyze the System Behavior of the LHC Production Grid

DIRAC (Distributed Infrastructure with Remote Agent Control) is the grid solution designed to support production activities as well as user data analysis for the Large Hadron Collider "beauty" experiment. It consists of cooperating distributed services and a plethora of light-weight agents delivering the workload to the grid resources. Services accept requests from agents and running jobs, while agents actively fulfill specific goals. Services maintain database back-ends to store dynamic state information of entities such as jobs, queues, or requests for data transfer. Agents continuously check for changes in the service states, and react to these accordingly. The logic of each agent is rather simple, the main source of complexity lies in their cooperation. These agents run concurrently, and communicate using the services' databases as a shared memory for synchronizing the state transitions. Despite the effort invested in making DIRAC reliable, entities occasionally get into inconsistent states. Tracing and fixing such behaviors is difficult, given the inherent parallelism among the distributed components and the size of the implementation. In this paper we present an analysis of DIRAC with mCRL2, process algebra with data. We have reverse engineered two critical and related DIRAC subsystems, and subsequently modeled their behavior with the mCRL2 toolset. This enabled us to easily locate race conditions and live locks which were confirmed to occur in the real system. We further formalized and verified several behavioral properties of the two modeled subsystems.
A Video Database Management System for Advancing Video Database Research

The most useful environments for advancing research and development in video databases are those that provide complete video database management, including (1) video preprocessing for content representation and indexing, (2) storage management for video, metadata and indices, (3) image and semantic -based query processing, (4) realtime buffer management, and (5) continuous media streaming. Such environments support the entire process of investigating, implementing, analyzing and evaluating new techniques, thus identifying in a concrete way which techniques are truly practical and robust. In this paper we present a video database research initiative that culminated in the successful development of VDBMS, a video database research platform that supports comprehensive and efficient database management for digital video. We describe key video processing components of the system and illustrate the value of VDBMS as a research platform by describing several research projects carried out within the VDBMS environment. These include MPEG7 document support for video feature import and export, a new query operator for optimal multi-feature image similarity matching, secure access control for streaming video, and the mining of medical video data using hierarchical content organization.
On discussion of AC and DC side EMI filters design for conducted noise suppression in DC-fed three phase motor drive system

This paper discusses EMI filter design methodology for DC-fed three-phase motor drive system. An analysis of noise source and propagation path impedances of motor drive systems is conducted, a new filter design model for both AC side and DC side filter is proposed based on the impedance mismatching between the EMI filter impedances and noise source/load impedances. Using this model, the in circuit attenuation of EMI filter can be accurately predicted. The interaction between AC and DC filters is also studied in this analysis. By applying some constraint, EMI filters can be designed. This new design procedure is suitable for both DC and AC side EMI filters. Simulation and experimental results give a firm support to the proposed method.
Antibacterial activity of turmeric oil: a byproduct from curcumin manufacture.

Curcumin, the yellow color pigment of turmeric, is produced industrially from turmeric oleoresin. The mother liquor after isolation of curcumin from oleoresin contains approximately 40% oil. The oil was extracted from the mother liquor using hexane at 60 degrees C, and the hexane extract was separated into three fractions using silica gel column chromatography. These fractions were tested for antibacterial activity by pour plate method against Bacillus cereus, Bacillus coagulans, Bacillus subtilis, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Fraction II eluted with 5% ethyl acetate in hexane was found to be most active fraction. The turmeric oil, fraction I, and fraction II were analyzed by GC and GC-MS. ar-Turmerone, turmerone, and curlone were found to be the major compounds present in these fractions along with other oxygenated compounds.
The Manipulation Potential of Libor and Euribor

The London Interbank Offered Rate (Libor) and the Euro Interbank Offered Rate (Euribor) are two key market benchmark interest rates used in a plethora of financial contracts with notional amounts running into the hundreds of trillions of dollars. The integrity of the rate-setting process for these benchmarks has been under intense scrutiny ever since the first reports of attempts to manipulate these rates surfaced in 2007. In this paper, we analyze Libor and Euribor rate submissions by the individual panel banks and shed light on the underlying manipulation potential, by quantifying their effects on the final rate set (the “fixing”). We explicitly take into account the possibility of collusion between several market participants. Our setup allows us to quantify such effects for the actual rate-setting process that is in place at present, and compare it to several alternative rate-setting procedures. We find that such alternative rate fixings, particularly methodologies that eliminate outliers based on the median of submitted rates and the time-series of past submissions, could significantly reduce the effect of manipulation. Furthermore, we discuss the role of the sample size and the particular questions asked of the panel banks, which are different for Libor and Euribor, and examine the need for a transactions database to validate individual submissions.
A 32 Gb/s Data-Interpolator Receiver With Two-Tap DFE Fabricated With 28-nm CMOS Process

A 32-Gb/s data-interpolator receiver for electrical chip-to-chip communications is introduced. The receiver front-end samples incoming data by using a blind clock signal, which has a plesiochronous frequency-phase relation with the data. Phase alignment between the data and decision timing is achieved by interpolating the input-signal samples in the analog domain. The receiver has a continuous-time linear equalizer and a two-tap loop unrolled DFE using adjustable-threshold comparators. The receiver occupies 0.24 mm2 and consumes 308.4 mW from a 0.9-V supply when it is implemented with a 28-nm CMOS process.
A systematic review of suicide prevention interventions targeting indigenous peoples in Australia, United States, Canada and New Zealand

Indigenous peoples of Australia, Canada, United States and New Zealand experience disproportionately high rates of suicide. As such, the methodological quality of evaluations of suicide prevention interventions targeting these Indigenous populations should be rigorously examined, in order to determine the extent to which they are effective for reducing rates of Indigenous suicide and suicidal behaviours. This systematic review aims to: 1) identify published evaluations of suicide prevention interventions targeting Indigenous peoples in Australia, Canada, United States and New Zealand; 2) critique their methodological quality; and 3) describe their main characteristics.



A systematic search of 17 electronic databases and 13 websites for the period 1981-2012 (inclusive) was undertaken. The reference lists of reviews of suicide prevention interventions were hand-searched for additional relevant studies not identified by the electronic and web search. The methodological quality of evaluations of suicide prevention interventions was assessed using a standardised assessment tool.



Nine evaluations of suicide prevention interventions were identified: five targeting Native Americans; three targeting Aboriginal Australians; and one First Nation Canadians. The main intervention strategies employed included: Community Prevention, Gatekeeper Training, and Education. Only three of the nine evaluations measured changes in rates of suicide or suicidal behaviour, all of which reported significant improvements. The methodological quality of evaluations was variable. Particular problems included weak study designs, reliance on self-report measures, highly variable consent and follow-up rates, and the absence of economic or cost analyses.



There is an urgent need for an increase in the number of evaluations of preventive interventions targeting reductions in Indigenous suicide using methodologically rigorous study designs across geographically and culturally diverse Indigenous populations. Combining and tailoring best evidence and culturally-specific individual strategies into one coherent suicide prevention program for delivery to whole Indigenous communities and/or population groups at high risk of suicide offers considerable promise.
Big Data Analytics: Applications, Prospects and Challenges

In the era of the fourth industrial revolution (Industry 4.0), big data has major impact on businesses, since the revolution of networks, platforms, people and digital technology have changed the determinants of firms’ innovation and competitiveness. An ongoing huge hype for big data has been gained from academics and professionals, since big data analytics leads to valuable knowledge and promotion of innovative activity of enterprises and organizations, transforming economies in local, national and international level. In that context, data science is defined as the collection of fundamental principles that promote information and knowledge gaining from data. The techniques and applications that are used help to analyze critical data to support organizations in understanding their environment and in taking better decisions on time. Nowadays, the tremendous increase of data through the Internet of Things (continuous increase of connected devices, sensors and smartphones) has contributed to the rise of a “data-driven” era, where big data analytics are used in every sector (agriculture, health, energy and infrastructure, economics and insurance, sports, food and transportation) and every world economy. The growing expansion of available data is a recognized trend worldwide, while valuable knowledge arising from the information come from data analysis processes. In that context, the bulk of organizations are collecting, storing and analyzing data for strategic business decisions leading to valuable knowledge. The ability to manage, analyze and act on data (“data-driven decision systems”) is very important to organizations and is characterized as a significant asset. The prospects of big data analytics are important and the benefits for data-driven organizations are K. Vassakis (✉) ⋅ E. Petrakis Department of Economics, University of Crete, Gallos Campus, Rethymno, Crete 74100, Greece e-mail: k.vassakis@e-bilab.gr E. Petrakis e-mail: petrakis@uoc.gr I. Kopanakis Department of Business Administration, Technological Educational Institute of Crete, Agios Nikolaos, Crete 72100, Greece e-mail: i.kopanakis@teicrete.gr
Refractive indices of liquid crystals for display applications

This paper reviews the extended Cauchy model and the four-parameter model for describing the wavelength and temperature effects of liquid crystal (LC) refractive indices. The refractive indices of nine commercial LCs, MLC-9200-000, MLC-9200-100, MLC-6608, MLC-6241-000, 5PCH, 5CB, TL-216, E7, and E44 are measured by the Multi-wavelength Abbe Refractometer. These experimental data are used to validate the theoretical models. Excellent agreement between experiment and theory is obtained.
Cellulose chemical markers in transformer oil insulation Part 1: Temperature correction factors

The concentrations of cellulose chemical markers, in oil, are influenced by various parameters due to the partition between the oil and the cellulose insulation. One major parameter is the oil temperature which is a function of the transformer load, ambient temperature and the type of cooling. To accurately follow the chemical markers concentration trends during all the transformer life, it is crucial to normalize the concentrations at a specific temperature. In this paper, we propose equations for the normalization of methanol, ethanol and 2-furfural at 20 °C. The proposed equations have been validated on some real power transformers.
Learning with Memory Embeddings

Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Latent variable models are well suited to deal with the high dimensionality and sparsity of typical knowledge graphs. In recent publications the embedding models were extended to also consider temporal evolutions, temporal patterns and subsymbolic representations. In this paper we map embedding models, which were developed purely as solutions to technical problems for modelling temporal knowledge graphs, to various cognitive memory functions, in particular to semantic and concept memory, episodic memory, sensory memory, short-term memory, and working memory. We discuss learning, query answering, the path from sensory input to semantic decoding, and relationships between episodic memory and semantic memory. We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models. There are three main hypotheses. The first one is that semantic memory is described as triples and that episodic memory is described as triples in time. A second main hypothesis is that generalized entities have unique latent representations which are shared across memory functions and that are the basis for prediction, decision support and other functionalities executed by working memory. A third main hypothesis is that the latent representation for a time t, which summarizes all sensory information available at time t, is the basis for episodic memory. The proposed model includes both a recall of previous memories and the mental imagery of future events and sensory impressions.
ROSA: R Optimizations with Static Analysis

R is a popular language and programming environment for data scientists. It is increasingly co-packaged with both relational and Hadoop-based data platforms and can often be the most dominant computational component in data analytics pipelines. Recent work has highlighted inefficiencies in executing R programs, both in terms of execution time and memory requirements, which in practice limit the size of data that can be analyzed by R. This paper presents ROSA, a static analysis framework to improve the performance and space efficiency of R programs. ROSA analyzes input programs to determine program properties such as reaching definitions, live variables, aliased variables, and types of variables. These inferred properties enable program transformations such as C++ code translation, strength reduction, vectorization, code motion, in addition to interpretive optimizations such as avoiding redundant object copies and performing in-place evaluations. An empirical evaluation shows substantial reductions by ROSA in execution time and memory consumption over both CRAN R and Microsoft R Open.
A sparse modular GCD algorithm for polynomials over algebraic function fields

We present a first sparse modular algorithm for computing a greatest common divisor of two polynomials <i>f</i><sub>1</sub>, <i>f</i><sub>2</sub> ε <i>L</i>[<i>x</i>] where <i>L</i> is an algebraic function field in <i>k</i> ≥ <i>0</i> parameters with <i>r</i> ≥ <i>0</i> field extensions. Our algorithm extends the dense algorithm of Monagan and van Hoeij from 2004 to support multiple field extensions and to be efficient when the gcd is sparse. Our algorithm is an output sensitive Las Vegas algorithm.
 We have implemented our algorithm in Maple. We provide timings demonstrating the efficiency of our algorithm compared to that of Monagan and van Hoeij and with a primitive fraction-free Euclidean algorithm for both dense and sparse gcd problems.
Growth at puberty.

Somatic growth and maturation are influenced by a number of factors that act independently or in concert to modify an individual's genetic potential. The secular trend in height and adolescent development is further evidence for the significant influence of environmental factors on an individual's genetic potential for linear growth. Nutrition, including energy and specific nutrient intake, is a major determinant of growth. Paramount to normal growth is the general health and well-being of an individual; in fact, normal growth is a strong testament to the overall good health of a child. More recently the effect of physical activity and fitness on linear growth, especially among teenage athletes, has become a topic of interest. Puberty is a dynamic period of development marked by rapid changes in body size, shape, and composition, all of which are sexually dimorphic. One of the hallmarks of puberty is the adolescent growth spurt. Body compositional changes, including the regional distribution of body fat, are especially large during the pubertal transition and markedly sexually dimorphic. The hormonal regulation of the growth spurt and the alterations in body composition depend on the release of the gonadotropins, leptin, the sex-steroids, and growth hormone. It is very likely that interactions among these hormonal axes are more important than their main effects, and that alterations in body composition and the regional distribution of body fat actually are signals to alter the neuroendocrine and peripheral hormone axes. These processes are merely magnified during pubertal development but likely are pivotal all along the way from fetal growth to the aging process.
On 64-APSK constellation design optimization

This paper investigates the design of power and spectrally efficient coded modulations based on amplitude phase shift keying (APSK) with application to broadband satellite communications. Emphasis is put on 64APSK constellations. The APSK modulation has merits for digital transmission over nonlinear satellite channels due to its power and spectral efficiency combined with its inherent robustness against nonlinear distortion. This scheme has been adopted in the DVB-S2 Standard for satellite digital video broadcasting. Assuming an ideal rectangular transmission pulse, for which no nonlinear inter-symbol interference is present and perfect pre-compensation of the nonlinearity takes place, we optimize the 64APSK constellation design by employing an optimization criterion based on the mutual information. This method generates an optimum constellation for each operating SNR point, that is, for each spectral efficiency. Two separate cases of interest are particularly examined: (i) the equiprobable case, where all constellation points are equiprobable and (ii) the non-equiprobable case, where the constellation points on each ring are assumed to be equiprobable but the a priory symbol probability associated per ring is assumed different for each ring. Following the mutual information-based optimization approach in each case, detailed simulation results are obtained for the optimal 64APSK constellation settings as well as the achievable shaping gain.
Railway Track Finding System with RFID Application

The Indian Railways is world&apos;s fourth largest railway network in the world after USA, Russia and China. There is a severe problem of collisions of trains. So Indian railway is working in this aspect to promote the motto of &quot;SAFE JOURNEY&quot;. A RFID based railway track finding system for railway has been proposed in this paper. In this system the RFID tags and reader are used which are attached in the tracks and engine consecutively. So Train engine automatically get the data of path by receiving it from RFID tag and detect it. If path is correct then train continue to run on track and if it is wrong then a signal is generated and sent to the control station and after this engine automatically stop in a minimum time and the display of LCD show the &quot;WRONG PATH&quot;. So the collision and accident of train can be avoided. With the help of this system the train engine would be programmed to move according to the requirement. The another feature of this system is automatic track changer by which the track jointer would move automatically according to availability of trains.