nils-herrmann/clean_scirepeval huggingface dataset:
Survey on security challenges in VANET

Recent advances in development of Wireless Communication in Vehicular Adhoc Network (VANET) has provided emerging platform for industrialists and researchers. Vehicular adhoc networks are multihop networks with no fixed infrastructure. It comprises of moving vehicles communicating with each other. One of the main challenge in VANET is to route the data efficiently from source to destination. Designing an efficient routing protocol for VANET is tedious task. Also because of wireless medium it is vulnerable to several attacks. Since attacks mislead the network operations, security is mandatory for successful deployment of such technology. This survey paper gives brief overview of different routing protocols. Also attempt has been made to identify major security issues and challenges associated with different routing protocols. .
"Killing Spree": Exploring the Connection Between Competitive Game Play and Aggressive Cognition

Although scholars have repeatedly linked video games to aggression, little research has investigated how specific game characteristics might generate such effects. In this study, we consider how game mode—cooperative, competitive, or solo—shapes aggressive cognition. Using experimental data, we find partial support for the idea that cooperative play modes prompt less aggressive cognition. Further analysis of potential mediating variables along with the influence of gender suggests the effect is primarily explained by social learning rather than frustration.
Examining the effect of user satisfaction on system usage and individual performance with business intelligence systems: An empirical study of Taiwan's electronics industry

The advent of new information technology has radically changed the end-user computing environment over the past decade. To enhance their management decision-making capability, many organizations have made significant investments in business intelligence (BI) systems. The realization of business benefits from BI investments depends on supporting effective use of BI systems and satisfying their end user requirements. Even though a lot of attention has been paid to the decision-making benefits of BI systems in practice, there is still a limited amount of empirical research that explores the nature of enduser satisfaction with BI systems. End-user satisfaction and system usage have been recognized by many researchers as critical determinants of the success of information systems (IS). As an increasing number of companies have adopted BI systems, there is a need to understand their impact on an individual end-user’s performance. In recent years, researchers have considered assessing individual performance effects from IS use as a key area of concern. Therefore, this study aims to empirically test a framework identifying the relationships between end-user computing satisfaction (EUCS), system usage, and individual performance. Data gathered from 330 end users of BI systems in the Taiwanese electronics industry were used to test the relationships proposed in the framework using the structural equation modeling approach. The results provide strong support for our model. Our results indicate that higher levels of EUCS can lead to increased BI system usage and improved individual performance, and that higher levels of BI system usage will lead to higher levels of individual performance. In addition, this study’s findings, consistent with DeLone and McLean’s IS success model, confirm that there exists a significant positive relationship between EUCS and system usage. Theoretical and practical implications of the findings are discussed.
A CMOS low-dropout regulator with 3.3 μA quiescent current independent of off-chip capacitor

A CMOS low-dropout regulator (LDO) with 3.3 V output voltage and 100 mA output current for system-on-chip applications is presented. The proposed LDO is independent of off-chip capacitor, thus the board space and external pins are reduced. By utilizing dynamic slew-rate enhancement (SRE) circuit and nested Miller compensation (NMC) on LDO structure, the proposed LDO provides high stability during line and load regulation without off-chip load capacitor. The overshot voltage has been limited within 550 mV and settling time is less than 50 mus when load current reducing from 100 mA to 1 mA. By using 30 nA reference current, the quiescent current is 3.3 muA. The experiment results agree with the simulation results. The proposed design is implemented by CSMC 0.5 mum mixed-signal process.
Deep Sequential Recommendation for Personalized Adaptive User Interfaces

Adaptive user-interfaces (AUIs) can enhance the usability of complex software by providing real-time contextual adaptation and assistance. Ideally, AUIs should be personalized and versatile, i.e., able to adapt to each user who may perform a variety of complex tasks. But this is difficult to achieve with many interaction elements when data-per-user is sparse. In this paper, we propose an architecture for personalized AUIs that leverages upon developments in (1) deep learning, particularly gated recurrent units, to efficiently learn user interaction patterns, (2) collaborative filtering techniques that enable sharing of data among users, and (3) fast approximate nearest-neighbor methods in Euclidean spaces for quick UI control and/or content recommendations. Specifically, interaction histories are embedded in a learned space along with users and interaction elements; this allows the AUI to query and recommend likely next actions based on similar usage patterns across the user base. In a comparative evaluation on user-interface, web-browsing and e-learning datasets, the deep recurrent neural-network (DRNN) outperforms state-of-the-art tensor-factorization and metric embedding methods.
5.6 Adaptive clocking system for improved power efficiency in a 28nm x86-64 microprocessor

In high-performance microprocessor cores, the on-die supply voltage seen by the transistors is non-ideal and exhibits significant fluctuations. These supply fluctuations are caused by sudden changes in the current consumed by the microprocessor in response to variations in workloads. This non-ideal supply can cause performance degradation or functional failures. Therefore, a significant amount of margin (10-15%) needs to be added to the ideal voltage (if there were no AC voltage variations) to ensure that the processor always executes correctly at the committed voltage-frequency points. This excess voltage wastes power proportional to the square of the voltage increase.
Camera Calibration with Distortion Models and Accuracy Evaluation

The objective of stereo camera calibration is to estimate the internal and external parameters of each camera. Using these parameters, the 3-D position of a point in the scene, which is identified and matched in two stereo images, can be determined by the method of triangulation. In this paper, we present a camera model that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortions. The proposed calibration procedure consists of two steps. In the first step, the calibration parameters are estimated using a closed-form solution based on a distortion-free camera model. In the second step, the parameters estimated in the first step are improved iteratively through a nonlinear optimization, taking into account camera distortions. According to minimum variance estimation, the objective function to be minimized is the mean-square discrepancy between the observed image points and their inferred image projections computed with the estimated calibration parameters. We introduce a type of measure that can be used to directly evaluate the performance of calibration and compare calibrations among different systems. The validity and performance of our calibration procedure are tested with both synthetic data and real images taken by teleand wide-angle lenses. The results consistently show significant improvements over less complete camera models.
Information Systems Strategy: Reconceptualization, Measurement, and Implications

Term Used Definition Provided Source I/T strategy None provided Henderson and Venkatraman 1999 Information Management Strategy " A long-term precept for directing, implementing and supervising information management " (information management left undefined) Reponen 1994 (p. 30) " Deals with management of the entire information systems function, " referring to Earl (1989, p. 117): " the management framework which guides how the organization should run IS/IT activities " Ragu-Nathan et al. 2001 (p. 269)
Direction Maps for Cooperative Pathfinding

Cooperative behavior is a desired trait in many fields from computer games to robotics. Yet, achieving cooperative behavior is often difficult, as maintaining shared information about the dynamics of agents in the world can be complex. We focus on the specific task of cooperative pathfinding and introduce a new approach based on the idea of “direction maps” that learns about the movement of agents in the world. This learned data then is used to produce implicit cooperation between agents. This approach is less expensive and has better performance than several existing cooperative algorithms.
Non-invasive glucose measurement technologies: an update from 1999 to the dawn of the new millennium.

There are three main issues in non-invasive (NI) glucose measurements: namely, specificity, compartmentalization of glucose values, and calibration. There has been progress in the use of near-infrared and mid-infrared spectroscopy. Recently new glucose measurement methods have been developed, exploiting the effect of glucose on erythrocyte scattering, new photoacoustic phenomenon, optical coherence tomography, thermo-optical studies on human skin, Raman spectroscopy studies, fluorescence measurements, and use of photonic crystals. In addition to optical methods, in vivo electrical impedance results have been reported. Some of these methods measure intrinsic properties of glucose; others deal with its effect on tissue or blood properties. Recent studies on skin from individuals with diabetes and its response to stimuli, skin thermo-optical response, peripheral blood flow, and red blood cell rheology in diabetes shed new light on physical and physiological changes resulting from the disease that can affect NI glucose measurements. There have been advances in understanding compartmentalization of glucose values by targeting certain regions of human tissue. Calibration of NI measurements and devices is still an open question. More studies are needed to understand the specific glucose signals and signals that are due to the effect of glucose on blood and tissue properties. These studies should be performed under normal physiological conditions and in the presence of other co-morbidities.
Grandiose and vulnerable narcissism: a nomological network analysis.

Evidence has accrued to suggest that there are 2 distinct dimensions of narcissism, which are often labeled grandiose and vulnerable narcissism. Although individuals high on either of these dimensions interact with others in an antagonistic manner, they differ on other central constructs (e.g., Neuroticism, Extraversion). In the current study, we conducted an exploratory factor analysis of 3 prominent self-report measures of narcissism (N=858) to examine the convergent and discriminant validity of the resultant factors. A 2-factor structure was found, which supported the notion that these scales include content consistent with 2 relatively distinct constructs: grandiose and vulnerable narcissism. We then compared the similarity of the nomological networks of these dimensions in relation to indices of personality, interpersonal behavior, and psychopathology in a sample of undergraduates (n=238). Overall, the nomological networks of vulnerable and grandiose narcissism were unrelated. The current results support the need for a more explicit parsing of the narcissism construct at the level of conceptualization and assessment.
Temporal expert finding through generalized time topic modeling

Please cite this article in press as: A. Daud et al., j.knosys.2010.04.008 This paper addresses the problem of semantics-based temporal expert finding, which means identifying a person with given expertise for different time periods. For example, many real world applications like reviewer matching for papers and finding hot topics in newswire articles need to consider time dynamics. Intuitively there will be different reviewers and reporters for different topics during different time periods. Traditional approaches used graph-based link structure by using keywords based matching and ignored semantic information, while topic modeling considered semantics-based information without conferences influence (richer text semantics and relationships between authors) and time information simultaneously. Consequently they result in not finding appropriate experts for different time periods. We propose a novel Temporal-Expert-Topic (TET) approach based on Semantics and Temporal Information based Expert Search (STMS) for temporal expert finding, which simultaneously models conferences influence and time information. Consequently, topics (semantically related probabilistic clusters of words) occurrence and correlations change over time, while the meaning of a particular topic almost remains unchanged. By using Bayes Theorem we can obtain topically related experts for different time periods and show how experts’ interests and relationships change over time. Experimental results on scientific literature dataset show that the proposed generalized time topic modeling approach significantly outperformed the non-generalized time topic modeling approaches, due to simultaneously capturing conferences influence with time information.
Critical Hyper-Parameters: No Random, No Cry

The selection of hyper-parameters is critical in Deep Learning. Because of the long training time of complex models and the availability of compute resources in the cloud, “one-shot” optimization schemes – where the sets of hyper-parameters are selected in advance (e.g. on a grid or in a random manner) and the training is executed in parallel – are commonly used.  show that grid search is sub-optimal, especially when only a few critical parameters matter, and suggest to use random search instead. Yet, random search can be “unlucky” and produce sets of values that leave some part of the domain unexplored. Quasi-random methods, such as Low Discrepancy Sequences (LDS) avoid these issues. We show that such methods have theoretical properties that make them appealing for performing hyperparameter search, and demonstrate that, when applied to the selection of hyperparameters of complex Deep Learning models (such as state-of-the-art LSTM language models and image classification models), they yield suitable hyperparameters values with much fewer runs than random search. We propose a particularly simple LDS method which can be used as a drop-in replacement for grid/random search in any Deep Learning pipeline, both as a fully one-shot hyperparameter search or as an initializer in iterative batch optimization.
Neural-network classifiers for recognizing totally unconstrained handwritten numerals

Artificial neural networks have been recognized as a powerful tool for pattern classification problems, but a number of researchers have also suggested that straightforward neural-network approaches to pattern recognition are largely inadequate for difficult problems such as handwritten numeral recognition. In this paper, we present three sophisticated neural-network classifiers to solve complex pattern recognition problems: multiple multilayer perceptron (MLP) classifier, hidden Markov model (HMM)/MLP hybrid classifier, and structure-adaptive self-organizing map (SOM) classifier. In order to verify the superiority of the proposed classifiers, experiments were performed with the unconstrained handwritten numeral database of Concordia University, Montreal, Canada. The three methods have produced 97.35%, 96.55%, and 96.05% of the recognition rates, respectively, which are better than those of several previous methods reported in the literature on the same database.
A Neural Autoregressive Topic Model

We describe a new model for learning meaningful representations of text documents from an unlabeled collection of documents. This model is inspired by the recently proposed Replicated Softmax, an undirected graphical model of word counts that was shown to learn a better generative model and more meaningful document representations. Specifically, we take inspiration from the conditional mean-field recursive equations of the Replicated Softmax in order to define a neural network architecture that estimates the probability of observing a new word in a given document given the previously observed words. This paradigm also allows us to replace the expensive softmax distribution over words with a hierarchical distribution over paths in a binary tree of words. The end result is a model whose training complexity scales logarithmically with the vocabulary size instead of linearly as in the Replicated Softmax. Our experiments show that our model is competitive both as a generative model of documents and as a document representation learning algorithm.
Extended-Connectivity Fingerprints

Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure-activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.
Data quality: The other face of Big Data

In our Big Data era, data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Recent studies have shown that poor quality data is prevalent in large databases and on the Web. Since poor quality data can have serious consequences on the results of data analyses, the importance of veracity, the fourth `V' of big data is increasingly being recognized. In this tutorial, we highlight the substantial challenges that the first three `V's, volume, velocity and variety, bring to dealing with veracity in big data. Due to the sheer volume and velocity of data, one needs to understand and (possibly) repair erroneous data in a scalable and timely manner. With the variety of data, often from a diversity of sources, data quality rules cannot be specified a priori; one needs to let the “data to speak for itself” in order to discover the semantics of the data. This tutorial presents recent results that are relevant to big data quality management, focusing on the two major dimensions of (i) discovering quality issues from the data itself, and (ii) trading-off accuracy vs efficiency, and identifies a range of open problems for the community.
Voxel-based fabrication through material property mapping: A design method for bitmap printing

Wepresent a bitmap printingmethod and digital workflow usingmulti-material high resolution Additive Manufacturing (AM). Material composition is defined based on voxel resolution and used to fabricate a design object with locally varying material stiffness, aiming to satisfy the design objective. In this workflowvoxel resolution is set by theprinter’s native resolution, eliminating theneed for slicing andpath planning. Controlling geometry and material property variation at the resolution of the printer provides significantly greater control over structure–property–function relationships. To demonstrate the utility of the bitmap printing approach we apply it to the design of a customized prosthetic socket. Pressuresensing elements are concurrently fabricated with the socket, providing possibilities for evaluation of the socket’s fit. The level of control demonstrated in this study cannot be achieved using traditional CAD tools and volume-based AM workflows, implying that new CAD workflows must be developed in order to enable designers to harvest the capabilities of AM.
Deep adaptive feature embedding with local sample distributions for person re-identification

Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance system. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range are robust to guide deep embedding against uncontrolled variations, which however, cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable positives (i.e., intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample’s local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize ∗Corresponding author. Email addresses: lin.wu@uq.edu.au (Lin Wu ), wangy@cse.unsw.edu.au (Yang Wang), junbin.gao@sydney.edu.au (Junbin Gao), xueli@itee.uq.edu.au (Xue Li)  6. 03 16 0v 2 [ cs .C V ] 7 S ep 2 01 7 similarity metric learning, local positive mining and robust deep embedding. This yields local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.
Fraud detection in international calls using fuzzy logic

Fraud is a problem that affects operators and telecommunication companies all around the world. One of the most known fraud is the bypass fraud, which is used in international calls in order to avoid access charges and making profits. This kind of fraud causes severe losses in revenue to operators with other negative side effects. In this paper a new fraud detection technique is proposed. The proposed technique depends on user profiling and using fuzzy logic. Fuzzy logic was used in decision making process by utilizing fuzzy logic membership function. A real database from mobile operator company Almadar Aljadid in Libya is used in this investigation. Five features are extracted and employed as detection patterns in the proposed technique. The five features or detection patterns are, subscriber's mobility, incoming to outgoing calls ratio, suspicious cell activity, irregular calls, and service type.