nils-herrmann/clean_scirepeval huggingface dataset:
Diversifying trending topic discovery via Semidefinite Programming

Discovering trending topics from the Web has attracted much attention in recent years, due to users' increasing need for time-sensitive information. A large body of the existing research focuses on detecting trends by examining search traffic fluctuations, and the queries with large traffic increments would be detected as trends. The weakness of this approach is that the trends are dominated by popular fields such as celebrities, as those related queries have large search traffic. Consequently, other topics such as travel and shopping are rarely regarded as trends. In this paper, we present a scalable diversified trending topic discovery system with a MapReduce implementation. The trending topics are discovered based on three criteria: diversity, representativeness, and popularity. We explicitly model these three factors in our objective function and propose an efficient Semidefinite Programming algorithm to solve the corresponding optimization problem. To the best of our knowledge, no prior work in the literature tackles trending topic diversification. We conduct a comprehensive set of experiments with case studies to demonstrate the effectiveness of our approach. The proposed system has been successfully tested in the real-world operational environment, yielding significant improvement in traffic over the existing production system.
Computationally efficient low-pass FIR filter design using Cuckoo Search with adaptive Levy step size

This paper looks into efficient implementation of one dimensional low pass Finite Impulse Response filters using certain commonly used and state-of-the-art optimization techniques.  Parks-McClellan (PM) equiripple design, Quantum-behaved Particle Swarm Optimization (QPSO) and Cuckoo Search Algorithm (CSA) with Levy Flight are employed and overall performance is further improved by hybridization and adaptive step size update. Various performance metrics are analyzed with a focus on increasing convergence speed to reach global optima faster. It is seen that the improved search methods used in this work, i.e., Simulated Annealing based Weighted Mean Best QPSO (SAWQPSO) and Adaptive CSA (ACSA) effect significant reductions in convergence time with ACSA proving to be the faster one. The designed filter is used in the receiver stage of a Frequency Modulated Radio Transmission model using a Quadrature-Phase Shift Keyed (QPSK) Modulator and Demodulator. Its efficiency is validated by obtaining near perfect correlation between the message and recovered signals.
A Behavioral Model of Rational Choice

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Unified Active Filter and Energy Storage System for an MW Electric Vehicle Charging Station

A network of fast-charging stations is of great importance for widespread adoption of electric vehicles (EV) if the so-called range anxiety issue is to be resolved. As with petrol stations, we expect that multiple chargers will be co-located to form charging stations. This layout allows for the fast-charging station to make use of a common rectifier stage and several dc/dc stages to charge multiple EVs. This paper builds on our previous work where we proposed a novel dc-side filter for the 12-pulse rectifier and investigated the power profile for a MW fast-charging station. In this paper, we propose a novel control approach for the filter, based on the virtual resistor injection, which results in further reduction in dc ripple, ac-side harmonics, and filter VA ratings. We also demonstrate that, with the proposed topology and control, the filter stage can be used as dc-side energy storage system.
Acquisition of word-object associations by 14-month-old infants.

The following experiments were designed to determine the age at which infants can first readily learn word--object pairings with only minimal exposure and without social or contextual support. To address this question, 8- to 14-month-old infants were tested on their ability to form word--object associations in a "switch" design. Infants were habituated to 2 word--object pairings and then tested with 1 trial that maintained a familiar word--object pairing and 1 that involved a familiar word and object in a new combination. Across 6 experiments, only 14-month-old infants formed word--object associations under these controlled testing conditions but appeared to do so only when the objects were moving. Although 8- to 12-month-olds did not form the associations, they appeared to process both the word and the object information. These studies provide strong evidence that 14-month-old infants can rapidly learn arbitrary associations between words and objects, that this ability appears to develop at about 14 months of age, and that the Switch design is a useful method for assessing word--object learning in infancy.
Privacy as a fuzzy concept: A new conceptualization of privacy for practitioners

Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain.
An Ontology based Dialog Interface to Database

In this paper, we extend the state-of-the-art NLIDB system and present a dialog interface to relational databases. Dialog interface enables users to automatically exploit the semantic context of the conversation while asking natural language queries over RDBMS, thereby making it simpler to express complex questions in a natural, piece-wise manner. We propose novel ontology-driven techniques for addressing each of the dialog-specific challenges such as co-reference resolution, ellipsis resolution, and query disambiguation, and use them in determining the overall intent of the user query. We demonstrate the applicability and usefulness of dialog interface over two different domains viz. finance and healthcare.
Method of invariant manifold for chemical kinetics

In this paper, we review the construction of low-dimensional manifolds of reduced description for equations of chemical kinetics from the standpoint of the method of invariant manifold (MIM). The MIM is based on a formulation of the condition of invariance as an equation, and its solution by Newton iterations. A review of existing alternative methods is extended by a thermodynamically consistent version of the method of intrinsic low-dimensional manifolds. A grid-based version of the MIM is developed, and model extensions of low-dimensional dynamics are described. Generalizations to open systems are suggested. The set of methods covered makes it possible to e9ectively reduce description in chemical kinetics. ? 2003 Elsevier Ltd. All rights reserved.
A framework for efficient network anomaly intrusion detection with features selection

An intrusion Detection System (IDS) provides alarts against intrusion attacks where a traditional firewall fails. Machine learning algorithms aim to detect anomalies using supervised and unsupervised approaches. Features selection techniques identify important features and remove irrelevant and redundant attributes to reduce the dimensionality of feature space. This paper presents a features selection framework for efficient network anomaly detection using different machine learning classifiers. The framework applies different strategies by using filter and wrapper features selection methodologies. The aim of this framework is to select the minimum number of features that achieve the highest accuracy. UNSW-NB15 dataset is used in the experimental results to evaluate the proposed framework. The results show that by using 18 features from one of the filter ranking methods and applying J48 as a classifier, an accuracy of 88% is achieved.
Design and Optimization of Printed Spiral Coils for Efficient Transcutaneous Inductive Power Transmission

The next generation of implantable high-power neuroprosthetic devices such as visual prostheses and brain computer interfaces are going to be powered by transcutaneous inductive power links formed between a pair of printed spiral coils (PSC) that are batch-fabricated using micromachining technology. Optimizing the power efficiency of the wireless link is imperative to minimize the size of the external energy source, heating dissipation in the tissue, and interference with other devices. Previous design methodologies for coils made of 1-D filaments are not comprehensive and accurate enough to consider all geometrical aspects of PSCs with planar 3-D conductors as well as design constraints imposed by implantable device application and fabrication technology. We have outlined the theoretical foundation of optimal power transmission efficiency in an inductive link, and combined it with semi-empirical models to predict parasitic components in PSCs. We have used this foundation to devise an iterative PSC design methodology that starts with a set of realistic design constraints and ends with the optimal PSC pair geometries. We have executed this procedure on two design examples at 1 and 5 MHz achieving power transmission efficiencies of 41.2% and 85.8%, respectively, at 10-mm spacing. All results are verified with simulations using a commercial field solver (HFSS) as well as measurements using PSCs fabricated on printed circuit boards.
Diffusion MRI visualization.

Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.
From Social Media to GeoSocial Intelligence: Crowdsourcing Civic Co-management for Flood Response in Jakarta, Indonesia

Here we present a review of PetaJakarta.org, a system designed to harness social media use in Jakarta for the purpose of relaying information about flood locations from citizen to citizen and from citizens and the city’s emergency management agency. The project aimed to produce an open, real-time situational overview of flood conditions and provide decision support for the management agency, as well as offering the government a data source for post-event analysis. As such, the platform was designed as a socio-technological system and developed as a civic co-management tool to enable climate adaptation and community resilience in Jakarta, a delta megacity suffering enormous infrastructural instability due to a troubled confluence of environmental factors—the city’s rapid urbanization, its unique geographic limitations, and increasing sea-levels and monsoon rainfalls resulting from climate change. The chapter concludes with a discussion of future research in open source platform and their role in infrastructure and disaster management. From now on there is an interconnection, an intertwining, even a symbiosis of technologies, exchanges, movements, which makes it so that a flood—for instance—wherever it may occur, must necessarily involve relationships with any number of technical, social, economic, political intricacies that keep us from regarding it simply as a misadventure or a misfortune whose consequences can be more or less easily circumscribed. — Jean-Luc Nancy (2015, 3–4) * E. Turpin & T. Holderness SMART Infrastructure Facility, University of Wollongong, Australia e-mail: eturpin@uow.edu.au | tomas@uow.edu.au Chapter 6 in Social Media for Government Services, Springer 2016 (
A Comprehensive Study for Software Testing and Test Cases Generation Paradigms

Software testing is accounted to be an essential part in software development life cycle in terms of cost and manpower, where its total cost is considerable high. Consequently, many studies [48] have been conducted to minimize the associated cost and human effort to fix bugs and errors, and to improve the quality of testing process by automatically generating test cases. Test cases can be generated from different phases (requirement phase, design phase and after development phase). Though, test case generation at early stages is more effective rather than that after development, where time and effort used for finding and fixing errors and bugs is less than that after development. At later stage, fixing errors results in enormous code correction, consuming a lot of time and effort. In this paper, we study the different paradigms of testing techniques for generating test cases, where we investigate their coverage and associated capabilities. We finally propose a preliminary model for a generic automated test cases generation.
The level and nature of autistic intelligence.

Autistics are presumed to be characterized by cognitive impairment, and their cognitive strengths (e.g., in Block Design performance) are frequently interpreted as low-level by-products of high-level deficits, not as direct manifestations of intelligence. Recent attempts to identify the neuroanatomical and neurofunctional signature of autism have been positioned on this universal, but untested, assumption. We therefore assessed a broad sample of 38 autistic children on the preeminent test of fluid intelligence, Raven's Progressive Matrices. Their scores were, on average, 30 percentile points, and in some cases more than 70 percentile points, higher than their scores on the Wechsler scales of intelligence. Typically developing control children showed no such discrepancy, and a similar contrast was observed when a sample of autistic adults was compared with a sample of nonautistic adults. We conclude that intelligence has been underestimated in autistics.
Comparison of mother, father, and teacher reports of ADHD core symptoms in a sample of child psychiatric outpatients.

To explore the significance of adding father ratings to mother and teacher ratings in the assessment of ADHD symptoms in children.



The ADHD Rating Scale-IV, the Child Behavior Checklist, and the Teacher Report Form were filled out by all three informants for a sample of 48 clinically referred children (79% boys) aged 6 to 15 (M = 10.1) years.



Correspondence between father and teacher reports on ADHD-specific symptoms (intraclass correlation coefficient [ICC] = .38) exceeded that between mothers and teachers (ICC = .23). Fathers rated their children as having fewer problems than did mothers and teachers on Total scale scores and the Inattention subscale of the ADHD Rating Scale-IV. Mother ratings were more sensitive to an ADHD diagnosis, whereas father ratings better predicted an ADHD diagnosis requiring the two-setting criterion.



The choice of parent informant and informant combination had a considerable impact on parent-teacher concordance and estimates of ADHD symptoms and subtypes in the child.
Reference Architecture for Cryptocurrency in Banking

The Internet has now become an environment and source for innovation development that has impacted a wide range of industries. The platform has enabled entrepreneurs to create groundbreaking solutions that have had a disruptive and transformative effect on the lives of many people. While banking had dealt with several innovations such as internet payment providers, more recently a virtual alternative to cash has begun to emerge. Although the notion of true electronic cash has been considered for some time, the availability of a practical electronic currency has only recently gained actual adoption that is forcing many financial institutions to reconsider the potential long-term impact. We provide some insight into the latest trends in cryptocurrency and propose a reference architecture for adopting this form of financial asset in a central banking scenario; i.e. a Fiat based cryptocurrency. The presently used cryptocurrency are based on a peer to peer scheme and hence we outline an IT solution that will accommodate a centrally issued electronic currency. cryptocurrency; electronic cash; fiat money;
Improving Web Application Firewalls to detect advanced SQL injection attacks

Injections flaws which include SQL injection are the most prevalent security threats affecting Web applications[1]. To mitigate these attacks, Web Application Firewalls (WAFs) apply security rules in order to both inspect HTTP data streams and detect malicious HTTP transactions. Nevertheless, attackers can bypass WAF's rules by using sophisticated SQL injection techniques. In this paper, we introduce a novel approach to dissect the HTTP traffic and inspect complex SQL injection attacks. Our model is a hybrid Injection Prevention System (HIPS) which uses both a machine learning classifier and a pattern matching inspection engine based on reduced sets of security rules. Our Web Application Firewall architecture aims to optimize detection performances by using a prediction module that excludes legitimate requests from the inspection process.
Triangular Tile Pasting P Systems for Pattern Generation

-In the area of membrane computing, a new computability model, called P system, which is a highly distributed, parallel theoretical computing model, was introduced by G.H. Păun , inspired from the cell structure and its function. There are several application areas of these P systems. Among these one area deals with the problem of picture generation. Ceterachi et al.  began a study on linking the two areas of membrane computing and picture grammars, which were not very much linked before, by relating P system and array-rewriting grammars generating picture languages and proposing array rewriting P systems. Iso-Triangular picture languages were introduced in , which can also generate rectangular and hexagonal picture languages. Tile pasting P system model for pattern generation was introduced in . In this paper we propose a theoretical model of a P system using triangular tiles called Triangular tile pasting P system, for generating two dimensional patterns that are generated by gluing triangular tiles and study some of its properties. Triangular Tiles, Array Grammars, Pasting System, Tile Pasting P Systems
BRAND EQUITY , BRAND LOYALTY AND CONSUMER SATISFACTION

This study aims to investigate the mediating effects of consumer satisfaction on the relationship between consumer-based brand equity and brand loyalty in the hotel and restaurant industry. Based on a sample of 378 customers and using structural equation modelling approach, the five dimensions of brand equity—physical quality, staff behaviour, ideal selfcongruence, brand identification and lifestyle-congruence—are found to have positive effects on consumer satisfaction. The findings of the study suggest that consumer satisfaction partially mediates the effects of staff behaviour, ideal self-congruence and brand identification on brand loyalty. The effects of physical quality and lifestyle-congruence on brand loyalty are fully mediated by consumer satisfaction.
Dendrites, deep learning, and sequences in the hippocampus.

The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.
Categorize the Quality of Cotton Seeds Based on the Different Germination of the Cotton Using Machine Knowledge Approach

A study was made in those region that expose major area of machine learning techniques which available in data mining. There are lots of Research work is analysized in agriculture field. It is a smart techniques is needed to find the resolution over the modern method. Cotton is an significant beneficial harvest and extensively traded commodity across the world. Its yield is insightful to weather, soil as well as organization practices. This study presents a concise idea of several of the broadly capitalized machine learning technique over agricultural domain. Among the algorithms like Navie bayes, j48 ,MLP,Randomforest,Random tree, I have founded J48 gives better result over the other algorithms using cotton seed quality accuracy is best.
Proposed system for estimating intrinsic value of stock using Monte Carlo simulation

Stock market prediction has been the bane and goal for investors, and one of the biggest challenges for artificial intelligence (AI) community. National economies have a great impact by the behaviour of their stock markets. Markets have become a more accessible investment tool. Attribute that all stock markets have in common is uncertainty. This uncertainty associated with stock value in short and long term future investments are undesirable. Stock market prediction is instrumental in process of investment. Drawbacks of existing system faces technical difficulties such as estimating share of dividends, state vector while implementing stochastic modelling for risk analysis. To overcome drawbacks of existing system, proposed system makes an effort of generating a combination of more than one lakh seventy thousand scenarios to find intrinsic value of company, displaying results in graphical visualization. A large scenario generation of distinct intrinsic stock value done by the system will provide intrinsic stock value for each scenario. A large set of values for all the input parameters for example high growth value, declining growth value, terminal growth value, return on equity needs to be created, so that possible intrinsic value can be generated by system. The system will calculate statistical indicators like mean, median, mode, skewness and kurtosis of large data set consisting intrinsic value of company. Comparing statistically calculated intrinsic value and current market price, system will be able to add a robust statistical reasoning for investment decision. This reasoning will have no human or emotional biases as there will be no human intervention involved for arriving to the final intrinsic value of stock. Monte Carlo simulation is best suited solution for generating random scenarios that fall in line with Brownian walk motion of stock prices.