nils-herrmann/clean_scirepeval huggingface dataset:
Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior

Microblogging services such as Twitter are said to have the potential for increasing political participation. Given the feature of 'retweeting' as a simple yet powerful mechanism for information diffusion, Twitter is an ideal platform for users to spread not only information in general but also political opinions through their networks as Twitter may also be used to publicly agree with, as well as to reinforce, someone's political opinions or thoughts. Besides their content and intended use, Twitter messages ('tweets') also often convey pertinent information about their author's sentiment. In this paper, we seek to examine whether sentiment occurring in politically relevant tweets has an effect on their retweetability (i.e., how often these tweets will be retweeted). Based on a data set of 64,431 political tweets, we find a positive relationship between the quantity of words indicating affective dimensions, including positive and negative emotions associated with certain political parties or politicians, in a tweet and its retweet rate. Furthermore, we investigate how political discussions take place in the Twitter network during periods of political elections with a focus on the most active and most influential users. Finally, we conclude by discussing the implications of our results.
Predicting new molecular targets for known drugs

Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug–target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.
Evolving Culture vs Local Minima

We propose a theory that relates difficulty of learning in deep architectures to culture and language. It is articulated around the following hypotheses: (1) learning in an individual human brain is hampered by the presence of effective local minima; (2) this optimization difficulty is particularly important when it comes to learning higher-level abstractions, i.e., concepts that cover a vast and highly-nonlinear span of sensory configurations; (3) such high-level abstractions are best represented in brains by the composition of many levels of representation, i.e., by deep architectures; (4) a human brain can learn such high-level abstractions if guided by the signals produced by other humans, which act as hints or indirect supervision for these high-level abstractions; and (5), language and the recombination and optimization of mental concepts provide an efficient evolutionary recombination operator, and this gives rise to rapid search in the space of communicable ideas that help humans build up better high-level internal representations of their world. These hypotheses put together imply that human culture and the evolution of ideas have been crucial to counter an optimization difficulty: this optimization difficulty would otherwise make it very difficult for human brains to capture high-level knowledge of the world. The theory is grounded in experimental observations of the difficulties of training deep artificial neural networks. Plausible consequences of this theory for the efficiency of cultural evolution are sketched.
A DATA MINING APPROACH TO IDENTIFY KEY FACTORS OF TRAFFIC INJURY SEVERITY

Seventy percent of the traffic crash fatalities of Iran happen on rural roads, and a significant proportion of the rural roads network of this country is constituted of the main twolane, two-way roads. The purpose of this study is to identify the most important factors which affect injury severity of drivers involved in traffic crashes on these roads, so that by eliminating or controlling such factors an overall safety improvement can be accomplished. Using the Classification and Regression Tree (CART), one of the powerful data mining tools, the crash data pertaining to the last three years (2006-2008) were analyzed. The variable selection procedure was carried out on the basis of Variable Importance Measure (VIM) which is one of the CART method outputs. The results revealed that not using the seat belt, improper overtaking and speeding are the most important factors associated with injury severity.
Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) processing speed scores as measures of noncredible responding: The third generation of embedded performance validity indicators.

Research suggests that select processing speed measures can also serve as embedded validity indicators (EVIs). The present study examined the diagnostic utility of Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests as EVIs in a mixed clinical sample of 205 patients medically referred for neuropsychological assessment (53.3% female, mean age = 45.1). Classification accuracy was calculated against 3 composite measures of performance validity as criterion variables. A PSI ≤79 produced a good combination of sensitivity (.23-.56) and specificity (.92-.98). A Coding scaled score ≤5 resulted in good specificity (.94-1.00), but low and variable sensitivity (.04-.28). A Symbol Search scaled score ≤6 achieved a good balance between sensitivity (.38-.64) and specificity (.88-.93). A Coding-Symbol Search scaled score difference ≥5 produced adequate specificity (.89-.91) but consistently low sensitivity (.08-.12). A 2-tailed cutoff on the Coding/Symbol Search raw score ratio (≤1.41 or ≥3.57) produced acceptable specificity (.87-.93), but low sensitivity (.15-.24). Failing ≥2 of these EVIs produced variable specificity (.81-.93) and sensitivity (.31-.59). Failing ≥3 of these EVIs stabilized specificity (.89-.94) at a small cost to sensitivity (.23-.53). Results suggest that processing speed based EVIs have the potential to provide a cost-effective and expedient method for evaluating the validity of cognitive data. Given their generally low and variable sensitivity, however, they should not be used in isolation to determine the credibility of a given response set. They also produced unacceptably high rates of false positive errors in patients with moderate-to-severe head injury. Combining evidence from multiple EVIs has the potential to improve overall classification accuracy.
A Spectral Framework for Detecting Inconsistency across Multi-source Object Relationships

In this paper, we propose to conduct anomaly detection across multiple sources to identify objects that have inconsistent behavior across these sources. We assume that a set of objects can be described from various perspectives (multiple information sources). The underlying clustering structure of normal objects is usually shared by multiple sources. However, anomalous objects belong to different clusters when considering different aspects. For example, there exist movies that are expected to be liked by kids by genre, but are liked by grown-ups based on user viewing history. To identify such objects, we propose to compute the distance between different eigen decomposition results of the same object with respect to different sources as its anomalous score. We also give interpretations from the perspectives of constrained spectral clustering and random walks over graph. Experimental results on several UCI as well as DBLP and Movie Lens datasets demonstrate the effectiveness of the proposed approach.
Comparison of state of the art multilevel inverters

In this paper, a comparison between existing state of the art multilevel inverter topologies is performed. The topologies examined are the neutral point clamp multilevel inverter (NPCMLI) or diode-clamped multilevel inverter (DCMLI), the flying capacitor multilevel inverter (FCMLI) and the cascaded cell multilevel inverter (CCMLI). The comparison of these inverters is based on the criteria of output voltage quality (Peak value of the fundamental and dominant harmonic components and THD), power circuitry complexity, and implementation cost. The comparison results are based on theoretical results verified by detailed simulation results.
Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering

Cancer tissues in histopathology images exhibit abnormal patterns; it is of great clinical importance to label a histopathology image as having cancerous regions or not and perform the corresponding image segmentation. However, the detailed annotation of cancer cells is often an ambiguous and challenging task. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL), to classify, segment and cluster cancer cells in colon histopathology images. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), pixel-level segmentation (cancer vs. non-cancer tissue), and patch-level clustering (cancer subclasses). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to perform the above three tasks in an integrated framework. Experimental results demonstrate the efficiency and effectiveness of MCIL in analyzing colon cancers.
Remote Moisture Sensing utilizing Ordinary RFID Tags

The paper presents a concept where pairs of ordinary RFID tags are exploited for use as remotely read moisture sensors. The pair of tags is incorporated into one label where one of the tags is embedded in a moisture absorbent material and the other is left open. In a humid environment the moisture concentration is higher in the absorbent material than the surrounding environment which causes degradation to the embedded tag's antenna in terms of dielectric losses and change of input impedance. The level of relative humidity or the amount of water in the absorbent material is determined for a passive RFID system by comparing the difference in RFID reader output power required to power up respectively the open and embedded tag. It is similarly shown how the backscattered signal strength of a semi-active RFID system is proportional to the relative humidity and amount of water in the absorbent material. Typical applications include moisture detection in buildings, especially from leaking water pipe connections hidden beyond walls. Presented solution has a cost comparable to ordinary RFID tags, and the passive system also has infinite life time since no internal power supply is needed. The concept is characterized for two commercial RFID systems, one passive operating at 868 MHz and one semi-active operating at 2.45 GHz.
ZeroN: mid-air tangible interaction enabled by computer controlled magnetic levitation

This paper presents ZeroN, a new tangible interface element that can be levitated and moved freely by computer in a three dimensional space. ZeroN serves as a tangible rep-resentation of a 3D coordinate of the virtual world through which users can see, feel, and control computation. To ac-complish this, we developed a magnetic control system that can levitate and actuate a permanent magnet in a pre-defined 3D volume. This is combined with an optical tracking and display system that projects images on the levitating object. We present applications that explore this new interaction modality. Users are invited to place or move the ZeroN object just as they can place objects on surfaces. For example, users can place the sun above physical objects to cast digital shadows, or place a planet that will start revolving based on simulated physical conditions. We describe the technology and interaction scenarios, discuss initial observations, and outline future development.
An extension of the technology acceptance model in an ERP implementation environment

This paper presents an extension to the technology acceptance model (TAM) and empirically examines it in an enterprise resource planning (ERP) implementation environment. The study evaluated the impact of one belief construct (shared beliefs in the benefits of a technology) and two widely recognized technology implementation success factors (training and communication) on the perceived usefulness and perceived ease of use during technology implementation. Shared beliefs refer to the beliefs that organizational participants share with their peers and superiors on the benefits of the ERP system. Using data gathered from the implementation of an ERP system, we showed that both training and project communication influence the shared beliefs that users form about the benefits of the technology and that the shared beliefs influence the perceived usefulness and ease of use of the technology. Thus, we provided empirical and theoretical support for the use of managerial interventions, such as training and communication, to influence the acceptance of technology, since perceived usefulness and ease of use contribute to behavioral intention to use the technology.
A supervised machine learning link prediction approach for academic collaboration recommendation

In this work we tackle the problem of link prediction in co-authoring network. We apply a topological dyadic supervised machine learning approach for that purpose. A co-authoring network is actually obtained by the projection of a two-mode graph (an authoring graph linking authors to publications they have signed) over the authors set. We show that link prediction performances can be substantially enhanced by analyzing not only the co-authoring network, but also the dual graph obtained by projecting the original two-mode network over the set of publications.
Robustness to lighting variations: An RGB-D indoor visual odometry using line segments

Large lighting variation challenges all visual odometry methods, even with RGB-D cameras. Here we propose a line segment-based RGB-D indoor odometry algorithm robust to lighting variation. We know line segments are abundant indoors and less sensitive to lighting change than point features. However, depth data are often noisy, corrupted or even missing for line segments which are often found on object boundaries where significant depth discontinuities occur. Our algorithm samples depth data along line segments, and uses a random sample consensus approach to identify correct depth and estimate 3D line segments. We analyze 3D line segment uncertainties and estimate camera motion by minimizing the Mahalanobis distance. In experiments we compare our method with two state-of-the-art methods including a keypoint-based approach and a dense visual odometry algorithm, under both constant and varying lighting. Our method demonstrates superior robustness to lighting change by outperforming the competing methods on 6 out of 8 long indoor sequences under varying lighting. Meanwhile our method also achieves improved accuracy even under constant lighting when tested using public data.
Large-Margin Classification in Infinite Neural Networks

We introduce a new family of positive-definite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural networks with one layer of hidden units. We also show how to derive new kernels, by recursive composition, that may be viewed as mapping their inputs through a series of nonlinear feature spaces. These recursively derived kernels mimic the computation in deep networks with multiple hidden layers. We evaluate SVMs with these kernels on problems designed to illustrate the advantages of deep architectures. Compared to previous benchmarks, we find that on some problems, these SVMs yield state-of-the-art results, beating not only other SVMs but also deep belief nets.
HOME FADS: A dedicated fire alert detection system using ZigBee wireless network

This paper describes the development of innovative low-cost home dedicated fire alert detection system (FADS) using ZigBee wireless network. Our home FADS system are consists of an Arduino Uno Microcontroller, Xbee wireless module (Zigbee wireless), Arduino digital temperature sensor, buzzer alarm and X-CTU software. Arduino and wireless ZigBee has advantages in terms of its long battery life and much cheaper compared to the others wireless sensor network. There are several objectives that need to be accomplished in developing this project which are to develop fire alert detection system (FADS) for home user using ZigBee wireless network and to evaluate the effectiveness of the home FADS by testing it in different distances and the functionality of heat sensor. Based from the experiments, the results show that the home FADS could function as expected. It also could detect heat and alarm triggered when temperature is above particular value. Furthermore, this project provides a guideline for implementing and applying home FADS at home and recommendation on future studies for home FADS in monitoring the temperature on the web server.
Collective nostalgia: A group-level emotion that confers unique benefits on the group.

This research established collective nostalgia as a group-level emotion and ascertained the benefits it confers on the group. In Study 1, participants who reflected on a nostalgic event they had experienced together with ingroup members (collective nostalgia) evaluated the ingroup more positively and reported stronger intentions to approach (and not avoid) ingroup members than those who recalled a nostalgic event they had experienced individually (personal nostalgia), those who reflected on a lucky event they had experienced together with ingroup members (collective positive), and those who did not recall an event (no recall). In Study 2, collective (vs. personal) nostalgia strengthened behavioral intentions to support the ingroup more so than did recalling an ordinary collective (vs. personal) event. Increased collective self-esteem mediated this effect. In Study 3, collective nostalgia (compared with recall of an ordinary collective event) led participants to sacrifice money in order to punish a transgression perpetrated against an ingroup member. This effect of collective nostalgia was more pronounced when social identification was high (compared with low). Finally, in Study 4, collective nostalgia converged toward the group average (i.e., was socially shared) when participants thought of themselves in terms of their group membership. The findings underscore the viability of studying nostalgia at multiple levels of analysis and highlight the significance of collective nostalgia for understanding group-level attitudes, global action tendencies, specific behavioral intentions, and behavior.
A Reputation and Knowledge Based Trust Service Platform for Trustworthy Social Internet of Things

The Internet of Things has attracted a plenty of research in this decade and imposed fascinating services where large numbers of heterogeneous-features entities socially collaborate together to solve complex scenarios. However, these entities need to trust each other prior to exchanging data or offering services. In this paper, we briefly present our ongoing project called Trust Service Platform, which offers trust assessment of any two entities in the Social Internet of Things to applications and services. We propose a trust model that incorporates both reputation properties as Recommendation and Reputation trust metrics; and knowledge-based property as Knowledge trust metric. For the trust service platform deployment, we propose a reputation system and a functional architecture with Trust Agent, Trust Broker and Trust Analysis and Management modules along with mechanisms and algorithms to deal with the three trust metrics. We also present a utility theory-based mechanism for trust calculation. To clarify our trust service platform, we describe the trust models and mechanisms in accordance with a trust car-sharing service. We believe this study offers the better understanding of the trust as a service in the platform and will impose many trustrelated research challenges as the future work. Social Internet of Things; Trust as a Service; TaaS; Trust Model; Trust Metric; Trust Management; Recommendation; Reputation; Knowledge; Fuzzy; Utility Theory;
Continuous 3D scan-matching with a spinning 2D laser

Scan-matching is a technique that can be used for building accurate maps and estimating vehicle motion by comparing a sequence of point cloud measurements of the environment taken from a moving sensor. One challenge that arises in mapping applications where the sensor motion is fast relative to the measurement time is that scans become locally distorted and difficult to align. This problem is common when using 3D laser range sensors, which typically require more scanning time than their 2D counterparts. Existing 3D mapping solutions either eliminate sensor motion by taking a “stop-and-scan” approach, or attempt to correct the motion in an open-loop fashion using odometric or inertial sensors. We propose a solution to 3D scan-matching in which a continuous 6DOF sensor trajectory is recovered to correct the point cloud alignments, producing locally accurate maps and allowing for a reliable estimate of the vehicle motion. Our method is applied to data collected from a 3D spinning lidar sensor mounted on a skid-steer loader vehicle to produce quality maps of outdoor scenes and estimates of the vehicle trajectory during the mapping sequences.
Expert system, fuzzy logic, and neural network applications in power electronics and motion control

Artificial intelligence (AI) tools, such as expert system, fuzzy logic, and neural network are expected to usher a new era in power electronics and motion control in the coming decades. Although these technologies have advanced significantly in recent years and have found wide applications, they have hardly touched the power electronics and mackine drives area. The paper describes these Ai tools and their application in the area of power electronics and motion control. The body of the paper is subdivided into three sections which describe, respectively, the principles and applications of expert system, fuzzy logic, and neural network. The theoretical portion of each topic is of direct relevance to the application of power electronics. The example applications in the paper are taken from the published literature. Hopefully, the readers will be able to formulate new applications from these examples.
Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images

This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.