nils-herrmann/clean_scirepeval huggingface dataset:
Performance improvement of multi dimensional indexing system for big data analysis
Venue: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS)

This paper proposes the methods for improving performance of multi dimensional index system. In the previous system, data are inserted into a reception node, and are stored in a reception index. When a reception index stores a certain number of data, the reception index is moved to a normal node, and becomes a partial index. At this time, insertion is paused. In addition, if the numbers of data in a normal node are skewed, the retrieval performance degrades. This paper proposes an architecture enabling data to be inserted without a pause. Moreover, when a reception index is moved to a normal node, all normal nodes are tried to store partial indexes equally. It is experimentally clarified that the insertion time decreases by the proposed architecture. It is also clarified that partial indexes can be distributed equally based on the proposed method, and that the retrieval performance can be improved.
Information security maturity model: A best practice driven approach to PCI DSS compliance
Venue: 2016 IEEE Region 10 Symposium (TENSYMP)

A successful of PCI DSS implementation depends on the capability of the organization's information security in providing the effective safeguard of their information asset, while cardholder data security is the main concern. Many organizations failed to comply with the standard, and this eventually results in fines or even termination of the ability to process credit cards. Clearly, an evaluation mechanism or tool used to measure the current state of the organization's information security is needed. In this paper, an Information Security Maturity Model for PCI DSS (ISMM PCI) with four maturity level None, Initial, Basic and Capable was proposed. The ISMM PCI utilizes the use of quantitative and qualitative analysis, enhancing the PCI DSS to ISO/IEC 27001 mapping, and focuses on improving the quality of people, process and technology. The model assists the organizations to easily identify the key success factors and gaps (point of weaknesses) provides the guideline to better manage information security and formulate the best strategy for the enhancement, improving the overall information security state by selecting the best security countermeasures (controls) to protect their information assets from the emerging cyber attacks, while achieving PCI DSS full compliant. The main advantage of ISMM PCI over other ISMMs is its ease of use. The comparative analysis of the case results affirms the statement. ISMM PCI may be used by a wide range of organizations regardless of the size.
A Meta Analysis on the Efficacy of Technology Mediated CBT for Anxious Children and Adolescents

Several meta analyses indicate that cognitive behavioral therapy (CBT) via electronic/technological devices or applications (i.e. eCBT) is an effective alternative to standard therapist delivered CBT for anxious adults. However, we know little about the efficacy of eCBT interventions for anxious children and adolescents. The present meta analysis set out to investigate the efficacy of eCBT in comparison to standard CBT or waitlist control for anxious children and adolescents. Eight randomized controlled studies (N 404 participants) that targeted anxiety at post intervention and follow up were included in the analysis. The results indicated that eCBT was as effective as standard CBT (g .295) and more effective than waitlist (g 1.410) in reducing anxiety symptoms. Moderation analyses revealed that anxious children and adolescents benefited the most from eCBT in the minimal therapist involvement condition (g 2.682) in contrast to the significant therapist involvement group (g .326) Furthermore, older participants seemed to extract greater clinical benefits from eCBT in contrast to younger participants (slope .514) Current eCBT interventions for anxious children and adolescents appear to be promising, but require further investigation.
Object Oriented Operational Semantics
Venue: SAM

Operational semantics is one way of providing meaning to an executable language. On a high level of abstraction, operational semantics means to define an interpreter or an abstract machine for the language. In this article, we review the concept of operational semantics in the scope of meta model based language definitions and identify challenges and issues. We provide a clean conceptual approach using an object oriented runtime environment and state change operations, which relies on an underlying abstract virtual machine. We present the approach using a sample language.
Optimizing Sideways Composition: Fast Context oriented Programming in ContextPyPy
Venue: COP@ECOOP

The prevalent way of code sharing in many current object systems is static and/or single inheritance; both are limiting in situations that call for multi dimensional decomposition. Sideways composition provides a technique to reduce their limitations. Context oriented programming (COP) notably applies sideways composition to achieve better modularity. However, most COP implementations have a substantial performance overhead. This is partly because weaving and execution of layered methods violate assumptions that common language implementations hold about lookup. Meta tracing just in time (JIT) compilers have unique characteristics that can alleviate the performance overhead, as they can treat lookup differently. We show that meta tracing JIT compilers are good at optimizing sideways composition and give initial, supporting results. Furthermore, we suggest that explicit communication with the JIT compiler in a COP implementation can improve performance further.
CsPbX3 Quantum Dots for Lighting and Displays: Room Temperature Synthesis, Photoluminescence Superiorities, Underlying Origins and White Light Emitting Diodes

Recently, Kovalenko and co workers and Li and co workers developed CsPbX3 (X Cl, Br, I) inorganic perovskite quantum dots (IPQDs) which exhibited ultrahigh photoluminescence (PL) quantum yields (QYs) low threshold lasing, and multicolor electroluminescence. However, the usual synthesis needs high temperature, inert gas protection, and localized injection operation, which are severely against applications. Moreover, the so unexpectedly high QYs are very confusing. Here, for the first time, the IPQDs' room temperature (RT) synthesis, superior PL, underlying origins and potentials in lighting and displays are reported. The synthesis is designed according to supersaturated recrystallization (SR) which is operated at RT, within few seconds, free from inert gas and injection operation. Although formed at RT, IPQDs' PLs have QYs of 80% 95% 70% and FWHMs of 35, 20, and 18 nm for red, green, and blue emissions. As to the origins, the observed 40 meV exciton binding energy, halogen self passivation effect, and CsPbX3@X quantum well band alignment are proposed to guarantee the excitons generation and high rate radiative recombination at RT. Moreover, such superior optical merits endow them with promising potentials in lighting and displays, which are primarily demonstrated by the white light emitting diodes with tunable color temperature and wide color gamut.
Reference Section for the Horn River Group and Definition of the Bell Creek Member, Hare Indian Formation in central Northwest Territories

A stratigraphically complete section, exposed along a tributary to the Mountain River in the Northwest Territories, provides a reference section for the Devonian Horn River Group (Hare Indian, Ramparts, and Canol formations) Detailed whole rock geochemical data and total organic carbon content variations are related to the lithostratigraphy, thus providing a standard for regional correlation. The name 'Bell Creek Member' is proposed for the upper part of the Hare Indian Formation known as the "grey shale member" and is defined at the Mountain River section. This member is heterogeneous and consists of two lithofacies: 1) a green grey, silty shale and limestone facies (Bell Creek Member, grey facies) and 2) a dark grey, silty shale and limestone facies (Bell Creek Member, dark facies) The dark grey facies is described from a reference section along the Carcajou River, and is distributed south of 65degN latitude. The Bell Creek Member is Givetian in age.
Scaffolding the Mathematical "Connections" A New Approach to Preparing Teachers for the Teaching of Lower Secondary Algebra.

This paper discusses the results of a three year mixed methods study into the effectiveness of a mathematics education unit. This was written for both pre service primary education students and re training in service teachers, to prepare them for the teaching of prealgebra and early algebra. The unit was taught rom 2013 to 2015 inclusively in a School of Education setting of a university in an Australian capital city. Focusing on the Number and Algebra strand in the Australian Curriculum, its purpose was to better prepare some novice teachers through modelling a more coherent approach to mathematics teaching. The unit's genesis lies in the author's belief that many mathematics teachers conduct their classes in isolated "pockets" of instruction that are not sufficiently informed by a broader, connected understanding of the mathematics. The unit was also prepared as a contribution to the recent call by the Australian Association of Mathematics Teachers for more targeted initiatives to combat the decrease of STEM skills in our schools (AAMT, 2014) Results from the analysis of this study suggest that there might be much to be gained from this new approach.
Leadership Structure, Gender Diversity and Audit Quality Influence on Earnings Management in Malaysian Listed Companies

The aim of this paper is to examine the effect of CEO duality, women directors and BIG 4 audit firm on real earnings manipulation (REM) proxied by cash flow from operation and discretionary expenses (DE) The sample of the study was Malaysian Public Listed Companies in year 2009 2012 with a sample of 1597 firm year observations. Using regression analysis, this study evidences that CEO duality promote REM. Interestingly, by having BIG 4 as auditors, firms failed to prevent managers from managing the earnings figure. Further, having women as directors in companies also do not help firms to mitigate the earnings manipulation problem.This paper contributes to the literature on earnings management by presenting evidence on the management of cash flows items and DE, which has received little attention to date.
THIAMINE HYDROCHLORIDE (VIT B1) AN OPTIMIZED GREEN ALTERNATIVE FOR THE SYNTHESIS OF POLYHYDROQUINOLINE DERIVATIVES

Thiamine hydrochloride (Vitamin B1) has been utilized as an effective biodegradable catalyst for the one pot synthesis of polyhydroquinoline derivatives via four component reaction of dimedone, aldehyde, ethylacetoacetate and ammonium acetate in ethanol under mild conditions. The method presented here offers several advantages over reported procedures in terms of environmentally benign catalytic system and excellent product yields (75 98% of polyhydroqninoline compounds. Aromatic and heteroaryl aldehyde with electron donating as well as electron withdrawing group undergo smooth reaction to afford the corresponding polyhydroquinoline derivatives. The synthesized polyhydroquinoline compounds were characterized by IR, NMR and Mass spectral data. The present article describes the new application of Vitamin B1.
Adjusting the Warm Glow Thermostat: How Incentivizing Participation in Voluntary Green Programs Moderates Their Impact on Service Satisfaction

In Study 1, the authors find that people are more satisfied with a service experience when they choose to participate in the provider's voluntary green program (e.g. recycling) an effect mediated by the "warm glow" of participation. The downside, however, is that this same mechanism decreases satisfaction among people who choose not to participate. In Study 2, analysis of data from the J.D. Power Guest Satisfaction Index suggests that incentivizing the program (i.e. compensating the program participants) paradoxically increases satisfaction for those who do not participate but decreases satisfaction among those who do. Studies 3 and 4 explore how manipulating incentive characteristics might enable managers to maximize satisfaction for both groups. Study 3 indicates that, compared with no incentive, an "other benefiting" incentive increases warm glow and satisfaction for green program participants but decreases them among nonparticipants. Study 4, however, suggests that mixed incentive bundles (i.e. providing both self benefiting and other benefiting options) maximize warm glow and satisfaction for both groups the ideal outcome for managers.
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.
Venue: Molecular pharmaceutics

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF 7, and PC 3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.
In silico methods for drug repurposing and pharmacology
Venue: Wiley interdisciplinary reviews. Systems biology and medicine

Data in the biological, chemical, and clinical domains are accumulating at ever increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action.  2016, 8:186 210. doi: 10.1002/wsbm.1337
Modeling and Optimization of Memristor and STT RAM Based Memory for Low Power Applications
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Conventional charge based memory usage in low power applications is facing major challenges. Some of these challenges are leakage current for static random access memory (SRAM) and dynamic random access memory (DRAM) additional refresh operation for DRAM, and high programming voltage for Flash. In this paper, two emerging resistive random access memory (ReRAM) technologies are investigated, memristor and spin transfer torque (STT) RAM, as potential universal memory candidates to replace traditional ones. Both of these nonvolatile memories support zero leakage and low voltage operation during read access, which makes them ideal for devices with long sleep time. To date, high write energy for both memristor and STT RAM is one of the major inhibitors for adopting the technologies. The primary contribution of this paper is centered on addressing the high write energy issue by trading off retention time with noise margin. In doing so, the memristor and STT RAM power has been compared with the traditional six transistor SRAM based memory power and potential application in wireless sensor nodes is explored. This paper uses 45 nm foundry process technology data for SRAM and physics based mathematical models derived from real devices for memristor and STT RAM. The simulations are conducted using MATLAB and the results show a potential power savings of 87% and 77% when using memristor and STT RAM, respectively, at 1% duty cycle.
Energy Optimization of Racetrack Memory Based SIMON Block Cipher
Venue: 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

Spin based memory devices are gaining importance due to multiple advantages like, zero standby power, high write endurance and fast read, write operations. Besides storage, Spin Torque Transfer (STT) based Magnetic Tunnel Junctions (MTJs) and Racetrack Memories (RMs) are also being investigated for logic applications, especially in the context of in memory computing and neuromorphic architectures. Despite multiple innovations at technology device and circuit level, spin based circuits suffer from poor energy efficiency, due to the high energy consumption of write operations. In this paper, we propose design optimizations to reduce the number of write operations in RM based logic circuits, and therefore, achieve overall gain in energy performance. We performed in depth study of the cutting edge cryptographic primitive, block cipher SIMON, using experimentally validated Verilog A models of MTJ and RM. For this benchmark, simulations demonstrate 4.65x reduction in computation energy, 2.66x improvement in computation delay and 1.71x reduction in transistor count compared to its base implementation using RM.
Solidarity economy as a counterpoint to classical economics Possibilities of changes

In the current context of social arrhythmia promoted by the culture of excess, consumerism and individualism the purpose of this article is to discuss the need to promote changes in tourism and society towards a more human development, based on the pillars of the solidarity economy. The article extends discussions on the solidarity economy into the context of classical economics, with new offers and demands that are focused not on capital accumulation only, but on the rights and development of human beings. To achieve this goal, we analyse four key foundations of the solidarity economy solidarity, social equality, cooperation and sharing. The original contribution of the paper is to present cutting edge ideas that show a counterpoint to capitalism and consumerism; one that is excluded from global tourism's contradictory path by a form of production that values social relations, and quality of life in community experiences and community tourism.
A Comment on Sraffa's 'Classical Economics'

This paper tells the story of how Sraffa's interpretation of classical economics, particularly Ricardo, closely follows the developments of his own theoretical project that culminated in his book, Production of Commodities by Means of Commodities, in 1960. It highlights some early movements in Sraffa's position on classical economics and then a major shift in his position on Ricardo in the 'Introduction' to Ricardo's Principles in 1951. It brings forward archival evidence to suggest that Sraffa's new interpretation of Ricardo was perhaps influenced by his attempt to prove his 'Hypothesis' that the output/capital ratio of a given economic system remains constant with respect to changes in the rate of profits. This hypothesis as such, however, had nothing to do with Ricardo's problem of the 'invariable measure of value' The paper concludes by pointing out that as early as 1927 28, Sraffa had set himself a task of constructing a homogenous understanding of classical economics in terms of modern terminology that would be helpful in understanding their scope from the point of view of his own theoretical project. Our story told here fits well with the task Sraffa had set himself at the beginning of his project.
Spatial temporal convolutional neural networks for anomaly detection and localization in crowded scenes
Venue: Signal Process. Image Commun.

Abnormal behavior detection in crowded scenes is extremely challenging in the field of computer vision due to severe inter object occlusions, varying crowd densities and the complex mechanics of a human crowd. We propose a method for detecting and locating anomalous activities in video sequences of crowded scenes. The key novelty of our method is the coupling of anomaly detection with a spatial temporal Convolutional Neural Networks (CNN) which to the best of our knowledge has not been previously done. This architecture allows us to capture features from both spatial and temporal dimensions by performing spatial temporal convolutions, thereby, both the appearance and motion information encoded in continuous frames are extracted. The spatial temporal convolutions are only performed within spatial temporal volumes of moving pixels to ensure robustness to local noise, and increase detection accuracy. We experimentally evaluate our model on benchmark datasets containing various situations with human crowds, and the results demonstrate that the proposed approach surpass state of the art methods.  mechanism for localizing dynamic regions in crowded scenes is proposed.A spatial temporal Convolutional Neural Network is designed to automatically extract spatial temporal features of the crowd.The performance of anomaly detection is improved when the analysis is concentrated on the dynamic regions only.The anomaly events that take place in small regions are effectively detected and localized by the spatial temporal Convolutional Neural Network.
Learning Natural Language Inference using Bidirectional LSTM model and Inner Attention
Venue: ArXiv

In this paper, we proposed a sentence encoding based model for recognizing text entailment. In our approach, the encoding of sentence is a two stage process. Firstly, average pooling was used over word level bidirectional LSTM (biLSTM) to generate a first stage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence's first stage representation to attend words appeared in itself, which is called "Inner Attention" in our paper Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of "Inner Attention" mechanism. With less number of parameters, our model outperformed the existing best sentence encoding based approach by a large margin.