nils-herrmann/clean_scirepeval huggingface dataset:
Roadside unit deployment for information dissemination in a VANET: an evolutionary approach

A VANET is a network where each node represents a vehicle equipped with wireless communication technology. This type of network enhances road safety, traffic efficiency, Internet access and many others applications to minimize environmental impact and in general maximize the benefits for the road users. This paper studies a relevant problem in VANETs, known as the deployment of RSUs. A RSU is an access points, used together with the vehicles, to allow information dissemination in the roads. Knowing where to place these RSUs so that a maximum number of vehicles circulating is covered is a challenge. We model the problem as a Maximum Coverage with Time Threshold Problem (MCTTP), and use a genetic algorithm to solve it. The algorithm is tested in four real-world datasets, and compared to a greedy approach previously proposed in the literature. The results show that our approach finds better results than the greedy in all scenarios, with gains up to 11 percentage points.
A Template-based Methodology for Disaster Management Information Systems

Timely and accurate processing of crisis information and effective communication have been documented as critical elements of disaster relief operations. Despite the lessons learned from previous crises, preparing information for humanitarian assistance and ineffective information sharing remain a recurring and almost impossible task for relief agencies. The purpose of this paper is to propose a template-based methodology to archive past disaster relief operations, create “descriptive” templates for advanced preparedness, and design “normative” templates for fast execution of assistance operations, while reducing miscommunications among aid agencies.
Game Theory of Mind

This paper introduces a model of 'theory of mind', namely, how we represent the intentions and goals of others to optimise our mutual interactions. We draw on ideas from optimum control and game theory to provide a 'game theory of mind'. First, we consider the representations of goals in terms of value functions that are prescribed by utility or rewards. Critically, the joint value functions and ensuing behaviour are optimised recursively, under the assumption that I represent your value function, your representation of mine, your representation of my representation of yours, and so on ad infinitum. However, if we assume that the degree of recursion is bounded, then players need to estimate the opponent's degree of recursion (i.e., sophistication) to respond optimally. This induces a problem of inferring the opponent's sophistication, given behavioural exchanges. We show it is possible to deduce whether players make inferences about each other and quantify their sophistication on the basis of choices in sequential games. This rests on comparing generative models of choices with, and without, inference. Model comparison is demonstrated using simulated and real data from a 'stag-hunt'. Finally, we note that exactly the same sophisticated behaviour can be achieved by optimising the utility function itself (through prosocial utility), producing unsophisticated but apparently altruistic agents. This may be relevant ethologically in hierarchal game theory and coevolution.
Bayesian analogy with relational transformations.

How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuous-valued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought.
Reliability evaluation on low k wafer level packages

Wafer Level Package (WLP) technology has seen tremendous advances in recent years and is rapidly being adopted at the 65nm Low-K silicon node. For a true WLP, the package size is same as the die (silicon) size and the package is usually mounted directly on to the Printed Circuit Board (PCB). Board level reliability (BLR) is a bigger challenge on WLPs than the package level due to a larger CTE mismatch and difference in stiffness between silicon and the PCB . The BLR performance of the devices with Low-K dielectric silicon becomes even more challenging due to their fragile nature and lower mechanical strength. A post fab re-distribution layer (RDL) with polymer stack up provides a stress buffer resulting in an improved board level reliability performance. Drop shock (DS) and temperature cycling test (TCT) are the most commonly run tests in the industry to gauge the BLR performance of WLPs. While a superior drop performance is required for devices targeting mobile handset applications, achieving acceptable TCT performance on WLPs can become challenging at times. BLR performance of WLP is sensitive to design features such as die size, die aspect ratio, ball pattern and ball density etc. In this paper, 65nm WLPs with a post fab Cu RDL have been studied for package and board level reliability. Standard JEDEC conditions are applied during the reliability testing. Here, we present a detailed reliability evaluation on multiple WLP sizes and varying ball patterns. Die size ranging from 10 mm2 to 25 mm2 were studied along with variation in design features such as die aspect ratio and the ball density (fully populated and de-populated ball pattern). All test vehicles used the aforementioned 65nm fab node.
Flexible printed circuit board (FPC) antennas for mobile phone operation

In this paper suggestion FPC for antenna design not only easy to control the antenna pattern on desired direction and radiation magnitude but also effective and quickly solved the complex tuning parameters of antenna application. Flexible printer circuit (FPC) board serves planar wireless antenna design. Effectively to supersede the antenna based on the traditional fixed printer circuit board (PCB) implement, and then provide free dimension of antenna and wireless system integration to designer. This paper proposes a new antenna technique for FPC printed monopole antenna as a radiator. This branch lines FPC monopole antenna based on asymmetric meander lines with flexible structure exhibits good performances. Branch lines monopole radiator has the merits to improve antenna bandwidth with fine frequency tuning. The PDA phone for CDMA2000 cellular, PCS, BT/WiFi, and GPS applications is well demonstrated with the integration and measurement of the co-designed.
A Wideband Low-Power CMOS LNA With Positive–Negative Feedback for Noise, Gain, and Linearity Optimization

A wideband common-gate (CG) low-noise amplifier (LNA) utilizing positive-negative-feedback technique is presented. The positive-negative-feedback technique boosts effective transconductance (Gm) and output impedance, which leads to an LNA with higher gain and lower noise figure (NF) over the previously reported amplifiers. In addition, this approach provides high linearity with an aid of third harmonic cancellation, and it breaks transconductance (gm) constriction for input matching in CG amplifiers. In this paper, linearity and output impedance improvement through the proposed technique are fully analyzed. An LNA prototype is implemented in 0.18- μm CMOS technology occupying a total area of 0.33 mm2. The implemented LNA delivers a maximum voltage gain of 21 dB, a minimum NF of 2 dB, an third-order intermodulation intercept point of -3.2 dBm, and 3.6 mW of power consumption in 300-920 MHz of 3-dB bandwidth with input matching (S11 <; -10 dB).
Applications of Path Compression on Balanced Trees

Several fast algorithms are presented for computing functions defined on paths in trees under various assumpuons. The algorithms are based on tree mampulatton methods first used to efficiently represent equivalence relations. The algorithms have O((m + n)a(m + n, n)) running tunes, where m and n are measures of the problem size and a Is a functional reverse of Ackermann's function By usmg one or more of these algorithms m combination with other techniques, it is possible to solve the followmg graph problems m O(ma(m, n)) tnne, where m Is the number of edges and n Is the number of vertices m the problem graph A Venfymg a minimum spanning tree m an undirected graph (Best previously known time bound O(m log log n).) B Flndmg dominators in a flow graph (Best previously known tune bound O(n log n + m).) C Solvmg a path problem on a reducible flow graph. (Best previously known time bound. O(m log n) ) Application A is discussed
Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact. Suction grasp planners often target planar surfaces on point clouds near the estimated centroid of an object. In this paper, we propose a compliant suction contact model that computes the quality of the seal between the suction cup and local target surface and a measure of the ability of the suction grasp to resist an external gravity wrench. To characterize grasps, we estimate robustness to perturbations in end-effector and object pose, material properties, and external wrenches. We analyze grasps across 1,500 3D object models to generate Dex-Net 3.0, a dataset of 2.8 million point clouds, suction grasps, and grasp robustness labels. We use Dex-Net 3.0 to train a Grasp Quality Convolutional Neural Network (GQ-CNN) to classify robust suction targets in point clouds containing a single object. We evaluate the resulting system in 350 physical trials on an ABB YuMi fitted with a pneumatic suction gripper. When evaluated on novel objects that we categorize as Basic (prismatic or cylindrical), Typical (more complex geometry), and Adversarial (with few available suction-grasp points) Dex-Net 3.0 achieves success rates of 98%, 82%, and 58% respectively, improving to 81% in the latter case when the training set includes only adversarial objects. Code, datasets, and supplemental material can be found at http://berkeleyautomation.github.io/dex-net.
A review of sex differences in peer relationship processes: potential trade-offs for the emotional and behavioral development of girls and boys.

Theory and research on sex differences in adjustment focus largely on parental, societal, and biological influences. However, it also is important to consider how peers contribute to girls' and boys' development. This article provides a critical review of sex differences in several peer relationship processes, including behavioral and social-cognitive styles, stress and coping, and relationship provisions. The authors present a speculative peer-socialization model based on this review in which the implications of these sex differences for girls' and boys' emotional and behavioral development are considered. Central to this model is the idea that sex-linked relationship processes have costs and benefits for girls' and boys' adjustment. Finally, the authors present recent research testing certain model components and propose approaches for testing understudied aspects of the model.
On the Use of "Deep" Features for Online Image Sharing

Online image sharing in social networking sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. Despite that social networking sites allow users to set their privacy preferences, this can be cumbersome for the vast majority of users. In this paper, we explore privacy prediction models for social media that can automatically identify private (or sensitive) content from images, before they are shared online, in order to help protect users’ privacy in social media. More precisely, we study “deep” visual features that are extracted from various layers of a pre-trained deep Convolutional Neural Network (CNN) as well as “deep” image tags generated from the CNN. Experimental results on a Flickr dataset of thousands of images show that the deep visual features and deep image tags can successfully identify images’ private content and substantially outperform previous models for this task.
3D Shock Wave Visualization on Unstructured Grids

A critical issue in understanding high speed flows is the study of shock waves. This paper summarizes our research on techniques for the detection and visualization of shock waves occuring in simulations of threedimensional flows on unstructured grids. Detection algorithms based on Mach number, density gradient and directional derivatives are compared using a data set from calculations of a transonic flow with a weak double shock around an airfoil. Both surface and volume rendering techniques are used to display the shocks. The issues in this research area are very much like those occurring in medical imaging. Since the data themselves (in this case the results of the fluid dynamics simulation) are intrinsically low resolution and noisy, properly extracting and visualizing the shock is very difficult. In this environment blurry, low-resolution techniques, like the splatting volume rendering, seem to do rather well. More complex schemes, using sophisticated numerical shock detectors coupled with polygon rendering, produce visually sharper shocks, but a,lso introduce “graphics artifacts,” which complicate understanding of the flow physics. On the other hand, visualization results produced with techniques like splatting are, in effect, relying more on the human visual system to compensate for limited resolution in the simulation.
Predicting online e-marketplace sales performances: A big data approach

To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical
Exploiting self-similarity in geometry for voxel based solid modeling

Voxel-based modeling techniques are known for their robustness and flexibility. However, they have three major shortcomings: (1) Memory intensive, since a large number of voxels are needed to represent high-resolution models (2) Computationally expensive, since a large number of voxels need to be visited (3) Computationally expensive isosurface extraction is needed to visualize the results. We describe techniques which alleviate these by taking advantage of self-similarity in the data making voxel-techniques practical and attractive. We describe algorithms for MEMS process emulation, isosurface extraction and visualization which utilize these techniques.
Investigating LSTMs for Joint Extraction of Opinion Entities and Relations

We investigate the use of deep bidirectional LSTMs for joint extraction of opinion entities and the IS-FROM and ISABOUT relations that connect them — the first such attempt using a deep learning approach. Perhaps surprisingly, we find that standard LSTMs are not competitive with a state-of-the-art CRF+ILP joint inference approach (Yang and Cardie, 2013) to opinion entities extraction, performing below even the standalone sequencetagging CRF. Incorporating sentence-level and a novel relation-level optimization, however, allows the LSTM to identify opinion relations and to perform within 1– 3% of the state-of-the-art joint model for opinion entities and the IS-FROM relation; and to perform as well as the state-of-theart for the IS-ABOUT relation — all without access to opinion lexicons, parsers and other preprocessing components required for the feature-rich CRF+ILP approach.
A Complex Event Processing Toolkit for Detecting Technical Chart Patterns

With the advent of large high volume data, we have seen need for real time analytic techniques like Complex Event Processing. This paper extends a Complex Event Processing Engine to support real time identification of technical chart patterns from streaming data. Technical chart patterns are known interesting recurring patterns on time series data, and they are used by experts in time series data analysis domains such as stock market and currency exchange rates. Yet the automated identification of these patterns is challenging due to the high volatility and noise of data. The paper focuses on identifying suitable technique to filter out volatility and a set of algorithms to query the data streams continuously and to identify patterns. The resulting solution is a toolkit for chart pattern recognition which is a composition of a set of complex CEP queries and a Kernel regression smoother applied on moving windows. Same toolkit can be used to detect chart patterns in other domains like Gold and Oil prices etc as well.
A review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT)

The current prominence and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT) are extensively reviewed and a summary survey report is presented. The analysis clearly distinguishes between IoT and IoE which are wrongly considered to be the same by many people. Upon examining the current advancement in the fields of IoT, IoE and IoNT, the paper presents scenarios for the possible future expansion of their applications.
Detecting Offensive Language in Tweets Using Deep Learning

This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with userrelated information, such as the users’ tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.
Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

Hyperspectral imagery typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image; however, when used in statistical pattern-classification tasks, the resulting high-dimensional feature spaces often tend to result in ill-conditioned formulations. Popular dimensionality-reduction techniques such as principal component analysis, linear discriminant analysis, and their variants typically assume a Gaussian distribution. The quadratic maximum-likelihood classifier commonly employed for hyperspectral analysis also assumes single-Gaussian class-conditional distributions. Departing from this single-Gaussian assumption, a classification paradigm designed to exploit the rich statistical structure of the data is proposed. The proposed framework employs local Fisher's discriminant analysis to reduce the dimensionality of the data while preserving its multimodal structure, while a subsequent Gaussian mixture model or support vector machine provides effective classification of the reduced-dimension multimodal data. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed approach significantly outperforms several traditional alternatives.
Diagnosis of Alzheimer's disease from EEG signals: where are we standing?

This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety of sophisticated computational approaches has been proposed to detect those subtle perturbations in the EEG of AD patients. The paper ﬁrst describes methods that try to detect slowing of the EEG. Next the paper deals with several measures for EEG complexity, and explains how those measures have been used to study ﬂuctuations in EEG complexity in AD patients. Then various measures of EEG synchrony are considered in the context of AD diagnosis. Also the issue of EEG pre-processing is briefly addressed. Before one can analyze EEG, it is necessary to remove artifacts due to for example head and eye movement or interference from electronic equipment. Pre-processing of EEG has in recent years received much attention. In this paper, several state-of-the-art pre-processing tech- niques are outlined, for example, based on blind source separation and other non-linear ﬁltering paradigms. In addition, the paper outlines opportunities and limitations of computational approaches for diagnosing AD based on EEG. At last, future challenges and open problems are discussed.