nils-herrmann/clean_scirepeval huggingface dataset:
Thermal management consumption and its effect on remaining range estimation of electric vehicles

This paper investigates the significance of thermal management energy consumption on the range of electric vehicles. It is necessary to combine consumption models of powertrain components such as the electric machine, power electronics and high voltage battery that take into account trip-based information containing a predicted speed profile and elevation data with a thermal management system that has the ability to incorporate predicted thermal losses of the powertrain. The thermal management system refers to the sum of all components within the refrigeration cycle of the vehicle as well as the associated control strategy. Based on the results of a real world drive study in an electric vehicle, it is demonstrated, that the range uncertainties introduced by the consumption of the thermal management are significant and should be considered when developing predictive range estimation algorithms. These uncertainties are further investigated by an energy flow based thermal system model. In order to minimize range uncertainties, a system architecture for the prediction of thermal management consumption is proposed.
AGE STANDARDIZATION OF RATES : A NEW WHO STANDARD

2  A recent WHO analysis has revealed the need for a new world standard population (see attached table). This has become particularly pertinent given the rapid and continued declines in age-specific mortality rates among the oldest old, and the increasing availability of epidemiological data for higher age groups. There is clearly no conceptual justification for choosing one standard over another, hence the choice is arbitrary. However, choosing a standard population with higher proportions in the younger age groups tends to weight events at these ages disproportionately. Similarly, choosing an older standard does the opposite. Hence, rather than selecting a standard to match the current age-structure of some population(s), the WHO adopted a standard based on the average age-structure of those populations to be compared (the world) over the likely period of time that a new standard will be used (some 25-30 years), using the latest UN assessment for 1998 (UN Population Division, 1998). From these estimates, an average world population age-structure was constructed for the period 2000-2025. The use of an average world population, as well as a time series of observations, removes the effects of historical events such as wars and famine on population age composition. The terminal age group in the new WHO standard population has been extended out to 100 years and over, rather than the 85 and over as is the current practice.  It is also notably younger than the European standard. It is important to note, however, that the age standardized death rates based on the new standard are not comparable to previous estimates that are based on some earlier standard(s). However, to facilitate comparative analyses, WHO will disseminate trend analyses of the " complete " historical mortality data using on the new WHO World Standard Population in future editions of the World Health Statistics Annual.
Detecting and Tracking Political Abuse in Social Media

We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation. We present promising preliminary results with better than 96% accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections.
Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques

Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS. Data mining, K-means, road traffic accidents, Waze, Weka.
Hierarchical Text Classification and Evaluation

Hierarchical Classification refers to assigning of one or more suitable categories from a hierarchical category space to a document. While previous work in hierarchical classification focused on virtual category trees where documents are assigned only to the leaf categories, we propose a topdown level-based classification method that can classify documents to both leaf and internal categories. As the standard performance measures assume independence between categories, they have not considered the documents incorrectly classified into categories that are similar or not far from the correct ones in the category tree. We therefore propose the Category-Similarity Measures and DistanceBased Measures to consider the degree of misclassification in measuring the classification performance. An experiment has been carried out to measure the performance of our proposed hierarchical classification method. The results showed that our method performs well for Reuters text collection when enough training documents are given and the new measures have indeed considered the contributions of misclassified documents.
Control of Mechanical Systems With Rolling Constraints: Application to Dynamic Control of Mobile Robots

There are many examples of mechanical systems which require rolling contacts between two or more rigid bodies. Rolling contacts engender nonholonomic constraints in an otherwise holonomic system. In this paper, we develop a unified approach to the control of mechanical systems subject to both holonomic and nonholonomic constraints. We first present a state space realization of a constrained system and show that it is not input-state linearizable. We then discuss the input-output linearization and zero dynamics of the system. This approach is applied to the dynamic control of mobile robots. Two types of control algorithms for mobile robots are investigated: (a) trajectory tracking, and (b) path following. In each case, a smooth nonlinear feedback is obtained to achieve asymptotical input-output stability, and Lagrange stability of the overall system. Simulation results are presented to demonstrate the effectiveness of the control algorithms and to compare the performance of trajectory tracking and path following algorithms. Disciplines Mechanical Engineering Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-92-44. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/504 Control of Mechanical Systems with Rolling Constraints: Application to Dynamic Control of Mobile Robots MS-CIS-92-44 GRASP LAB 320 Nilanjan Sarkar Xiaoping Yun Vijay Kuillar University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department
Blockchain and Artificial Intelligence

It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that “a blockchain is decentralized and therefore no one controls it”. But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop

We present a new neural text to speech (TTS) method that is able to transform text to speech in voices that are sampled in the wild. Unlike other systems, our solution is able to deal with unconstrained voice samples and without requiring aligned phonemes or linguistic features. The network architecture is simpler than those in the existing literature and is based on a novel shifting buffer working memory. The same buffer is used for estimating the attention, computing the output audio, and for updating the buffer itself. The input sentence is encoded using a context-free lookup table that contains one entry per character or phoneme. The speakers are similarly represented by a short vector that can also be fitted to new identities, even with only a few samples. Variability in the generated speech is achieved by priming the buffer prior to generating the audio. Experimental results on several datasets demonstrate convincing capabilities, making TTS accessible to a wider range of applications. In order to promote reproducibility, we release our source code and models1.
Fatigue in soccer: a brief review.

This review describes when fatigue may develop during soccer games and the potential physiological mechanisms that cause fatigue in soccer. According to time-motion analyses and performance measures during match-play, fatigue or reduced performance seems to occur at three different stages in the game: (1) after short-term intense periods in both halves; (2) in the initial phase of the second half; and (3) towards the end of the game. Temporary fatigue after periods of intense exercise in the game does not appear to be linked directly to muscle glycogen concentration, lactate accumulation, acidity or the breakdown of creatine phosphate. Instead, it may be related to disturbances in muscle ion homeostasis and an impaired excitation of the sarcolemma. Soccer players' ability to perform maximally is inhibited in the initial phase of the second half, which may be due to lower muscle temperatures compared with the end of the first half. Thus, when players perform low-intensity activities in the interval between the two halves, both muscle temperature and performance are preserved. Several studies have shown that fatigue sets in towards the end of a game, which may be caused by low glycogen concentrations in a considerable number of individual muscle fibres. In a hot and humid environment, dehydration and a reduced cerebral function may also contribute to the deterioration in performance. In conclusion, fatigue or impaired performance in soccer occurs during various phases in a game, and different physiological mechanisms appear to operate in different periods of a game.
Masquerade Attack Detection Using a Search-Behavior Modeling Approach

Masquerade attacks are unfortunately a familiar security problem that is a consequence of identity theft. Detecting masqueraders is very hard. Prior work has focused on user command modeling to identify abnormal behavior indicative of impersonation. This paper extends prior work by presenting one-class Hellinger distance-based and one-class SVM modeling techniques that use a set of novel features to reveal user intent. The specific objective is to model user search profiles and detect deviations indicating a masquerade attack. We hypothesize that each individual user knows their own file system well enough to search in a limited, targeted and unique fashion in order to find information germane to their current task. Masqueraders, on the other hand, will likely not know the file system and layout of another user’s desktop, and would likely search more extensively and broadly in a manner that is different than the victim user being impersonated. We extend prior research that uses UNIX command sequences issued by users as the audit source by relying upon an abstraction of commands. We devise taxonomies of UNIX commands and Windows applications that are used to abstract sequences of user commands and actions. We also gathered our own normal and masquerader data sets captured in a Windows environment for evaluation. The datasets are publicly available for other researchers who wish to study masquerade attack rather than author identification as in much of the prior reported work. The experimental results show that modeling search behavior reliably detects all masqueraders with a very low false positive rate of 0.1%, far better than prior published results. The limited set of features used for search behavior modeling also results in huge performance gains over the same modeling techniques that use larger sets of features.
TopicPanorama: A Full Picture of Relevant Topics

This paper presents a visual analytics approach to analyzing a full picture of relevant topics discussed in multiple sources, such as news, blogs, or micro-blogs. The full picture consists of a number of common topics covered by multiple sources, as well as distinctive topics from each source. Our approach models each textual corpus as a topic graph. These graphs are then matched using a consistent graph matching method. Next, we develop a level-of-detail (LOD) visualization that balances both readability and stability. Accordingly, the resulting visualization enhances the ability of users to understand and analyze the matched graph from multiple perspectives. By incorporating metric learning and feature selection into the graph matching algorithm, we allow users to interactively modify the graph matching result based on their information needs. We have applied our approach to various types of data, including news articles, tweets, and blog data. Quantitative evaluation and real-world case studies demonstrate the promise of our approach, especially in support of examining a topic-graph-based full picture at different levels of detail.
Automatic parking path optimization based on Bezier curve fitting

The autonomous parking system is an intelligent technology to park a car into a small space. More and more studies have addressed this issue. However, the planned path in these studies is not smooth enough. Based on Ackermann steering geometry, the car kinematic model is established in this paper, the collision possibility between car body and obstacles in the parking space are analyzed, and then the bound of start point and collision-free space are presented. Based on this, the parking trajectory is planned and Bezier curve is used to smooth the trajectory which is generated on multi arcs of circumference, finally, the generation of continuous curvature path is realized. In the end, we build the environment of the simulation in MATLAB and the Fuzzy PID control method is used to track the trajectory which is planned in advance. The simulation results show that the car can not only avoid the obstacles effectively, but also move smoothly on turning points for using the Bezier curve. The design can meet the continuity of the parking requirements very well.
The Magnetoelectric Dipole—A Wideband Antenna for Base Stations in Mobile Communications

In this paper, stringent requirements imposed on the design of base station antennas for mobile communications are summarized. Conventional techniques for implementing base station antennas are reviewed. The complementary antenna concept of combining an electric dipole with a magnetic dipole is reconsidered. Recently, this kind of antenna has been commonly called a “Huygen's source.” The purpose is to develop wideband unidirectional antennas with stable frequency characteristics and low back radiation. Based on this concept, the magnetoelectric dipole was invented by integrating an electric dipole with an -probe fed shorted quarter-wave patch antenna. A number of magnetoelectric dipoles with different radiation patterns and different polarizations have been developed in recent years. An overview of the characteristics of this new class of complementary antennas is presented. Major design challenges are explained. Finally, a new magnetoelectric dipole that is low in profile and robust in structure is presented. The magnetic dipole part of this antenna is realized by a triangular-shaped loop antenna. The antenna is inherently direct current (dc) grounded, which satisfies the requirement for outdoor applications.
Ranking outlier nodes in subspaces of attributed graphs

Outlier analysis is an important data mining task that aims to detect unexpected, rare, and suspicious objects. Outlier ranking enables enhanced outlier exploration, which assists the user-driven outlier analysis. It overcomes the binary detection of outliers vs. regular objects, which is not adequate for many applications. Traditional outlier ranking techniques focus on either vector data or on graph structures. However, many of today's databases store both, multi dimensional numeric information and relations between objects in attributed graphs. An open challenge is how outlier ranking should cope with these different data types in a unified fashion. In this work, we propose a first approach for outlier ranking in subspaces of attributed graphs. We rank graph nodes according to their degree of deviation in both graph and attribute properties. We describe novel challenges induced by this combination of data types and propose subspace analysis as important method for outlier ranking on attributed graphs. Subspace clustering provides a selected subset of nodes and its relevant attributes in which deviation of nodes can be observed. Our graph outlier ranking (GOutRank) introduces scoring functions based on these selected subgraphs and subspaces. In addition to this technical contribution, we provide an attributed graph extracted from the Amazon marketplace. It includes a ground truth of real outliers labeled in a user experiment. In order to enable sustainable and comparable research results, we publish this database on our website1 as benchmark for future publications. Our experiments on this graph demonstrate the potential and the capabilities of outlier ranking in subspaces of attributed graphs.
The possibility of predicting learning performance using features of note taking activities and instructions in a blended learning environment

A formative assessment was introduced to a blended learning course in order to predict participant’s learning performance using measurements of their note taking activity and metrics of their attitudes. The lexical metrics were created by analyzing the contents of student’s notes from every class, and by measuring their characteristics. In the results of two types of regression analysis of these measurements, features of note taking activities were a significant source of information for predicting the scores of final exams. In regards to temporal changes in prediction performance during the course, the progress of learning and the typical changes in features of note taking activity were discussed. During the analyses, the effectiveness of the lecturer’s note taking instructions was also confirmed.
Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces

Real-time robot motion planning using rasterizing computer graphics hardware. In Proc. OY82] C. O'D unlaing and C.K. Yap. A retraction method for planning the motion of a disc. A local approach for path planning of manipulators with a high number of degrees of freedom. a path generation algorithm into oo-line programming of airbus panels. Motion planning for many degrees of freedom-random reeections at c-space obstacles.  One rst question is: How should a roadmap computed for a given workspace be updated if a few obstacles are removed or added? Answering this question would be useful to apply our method to scenes subject to small incremental changes. Such changes occur in many manufacturing (e.g., assembly) cells; while most of the geometry of such a cell is permanent and stationary, a few objects (e.g., xtures) are added or removed between any two consecutive manufacturing operations. Similar incremental changes also occur in automatic graphic animation. A second question is: How should the learning and query phase be modiied if some obstacles are moving along known trajectories? An answer to this question might consist of applying our roadmap method in the conngurationtime space of the robot Lat91]. The roadmap would then have to be built as a directed graph, since local paths between any two nodes must monotonically progress along the time axis, with possibly additional constraints on their slope and curvature to reeect bounds on the robot's velocity and acceleration. A motion planner with performance proportional to task diiculty. 30 In KL94a, KL94b, O S94] prior versions of the method have been applied to a great variety of holonomic robots including planar and spatial articulated robots with revolute, prismatic, and/or spherical joints, xed or free base, and single or multiple kinematic chains. In Sve93, SO94] a variation of the method (essentially one with a diierent general local planner) was also run successfully on examples involving nonholonomic car-like robots. Experimental results show that our method can eeciently solve problems which are beyond the capabilities of other existing methods. For example, for planar articulated robots with many dofs, the customized implementation of Section 5 is much more consistent than the Randomized Path Planner (RPP) of BL91]. Indeed, the latter can be very fast on some diicult problems, but it …
Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining

Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer's interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.
Estimating the Maximum Expected Value: An Analysis of (Nested) Cross Validation and the Maximum Sample Average

We investigate the accuracy of the two most common estimators for the maximum expected value of a general set of random variables: a generalization of the maximum sample average, and cross validation. No unbiased estimator exists and we show that it is non-trivial to select a good estimator without knowledge about the distributions of the random variables. We investigate and bound the bias and variance of the aforementioned estimators and prove consistency. The variance of cross validation can be significantly reduced, but not without risking a large bias. The bias and variance of different variants of cross validation are shown to be very problem-dependent, and a wrong choice can lead to very inaccurate estimates.