nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Pedestrian Detection in Urban Traffic Scenes

Pedestrians are important participants in urban traffic environments, and thus act as an interesting category of objects for autonomous cars. Automatic pedestrian detection is an essential task for protecting pedestrians from collision. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance.
Cellular Automata Models of Road Traffic

In this paper, we give an elaborate and understandable revie w of traffic cellular automata (TCA) models, which are a class of computationally efficient microscopic traffic flow models. TCA models arise from the physics discipline of statist ical mechanics, having the goal of reproducing the correct macroscopic behaviour based on a minimal description of microscopic interactions. After giving an overview of cellul ar automata (CA) models, their background and physical setup, we introduce the mathematic al notations, show how to perform measurements on a TCA model’s lattice of cells, as well a s how to convert these quantities into real-world units and vice versa. The majori ty of this paper then relays an extensive account of the behavioural aspects of several TCA models encountered in literature. Already, several reviews of TCA models exist, but n one of them consider all the models exclusively from the behavioural point of view. In th is respect, our overview fills this void, as it focusses on the behaviour of the TCA models, b y means of time-space and phase-space diagrams, and histograms showing the distribu tions of vehicles’ speeds, space, and time gaps. In the report, we subsequently give a concise o verview of TCA models that are employed in a multi-lane setting, and some of the TCA mode ls used to describe city traffic as a two-dimensional grid of cells, or as a road networ k with explicitly modelled intersections. The final part of the paper illustrates some o f the more common analytical approximations to single-cell TCA models.
(In-)Secure messaging with the Silent Circle instant messaging protocol

Silent Text, the instant messaging application by the company Silent Circle, provides its users with end-to-end encrypted communication on the Blackphone and other smartphones. The underlying protocol, SCimp, has received many extensions during the update to version 2, but has not been subjected to critical review from the cryptographic community. In this paper, we analyze both the design and implementation of SCimp by inspection of the documentation (to the extent it exists) and code. Many of the security properties of SCimp version 1 are found to be secure, however many of the extensions contain vulnerabilities and the implementation contains bugs that affect the overall security. These problems were fed back to the SCimp maintainers and some bugs were fixed in the code base. In September 2015, Silent Circle replaced SCimp with a new protocol based on the Signal Protocol.
Authorship categorization of public domain literature

We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature - all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naïve Bayes Classifier, and Support Vector Machine with our features. We found that certain features, such as punctuation and various suffixes result in a higher accuracy. In addition, the Support Vector Machine classifier produces repeatedly higher accuracies than other classifiers and seems to be a far superior method of classification in terms of authorship categorization.
GlobeSen: an open interconnection framework based on named sensory date for IoT

How to efficiently interconnect ubiquitous wireless sensor networks (WSNs) and the Internet becomes an important challenge of Internet of Things. In this paper, we explore a route of information-centric networking (ICN) to solve this challenge and propose an open interconnection framework named GlobeSen. To overcome the problem that traditional ICN solution (such as NDN) is not suitable for resource-constrained WSNs, we present a new implementation of NDN, NDNs, for WSNs. Specifically, by extracting the spatio-temporal information and data type information of interest, we construct a globally unique name structure, and exploit the spatio-temporal relation operation as the matching method. Based on the new naming strategy and matching method, we further design packet forwarding and routing schemes. Moreover, we also develop a gateway system, NDNt, for protocol translating and an application, SenBrowser, to provide a user-friendly interface for generating interests and illustrating the returned sensory data. We implement a proof-of-concept prototype based on TelosB sensor nodes and an ARM development board, and conduct a series of experiments to evaluate the performance of GlobeSen.
Early sensitivity to arguments: how preschoolers weight circular arguments.

Observational studies suggest that children as young as 2 years can evaluate some of the arguments people offer them. However, experimental studies of sensitivity to different arguments have not yet targeted children younger than 5 years. The current study aimed at bridging this gap by testing the ability of preschoolers (3-, 4-, and 5-year-olds) to weight arguments. To do so, it focused on a common type of fallacy-circularity-to which 5-year-olds are sensitive. The current experiment asked children-and, as a group control, adults-to choose between the contradictory opinions of two speakers. In the first task, participants of all age groups favored an opinion supported by a strong argument over an opinion supported by a circular argument. In the second task, 4- and 5-year-olds, but not 3-year-olds or adults, favored the opinion supported by a circular argument over an unsupported opinion. We suggest that the results of these tasks in 3- to 5-year-olds are best interpreted as resulting from the combination of two mechanisms: (a) basic skills of argument evaluations that process the content of arguments, allowing children as young as 3 years to favor non-circular arguments over circular arguments, and (b) a heuristic that leads older children (4- and 5-year-olds) to give some weight to circular arguments, possibly by interpreting these arguments as a cue to speaker dominance.
Algorithms for Visualizing Large Networks

Graphs are often used to encapsulate relationship between objects. Graph drawing enables visualization of such relationships. The usefulness of this visual representation is dependent on whether the drawing is aesthetic. While there are no strict criteria for aesthetics of a drawing, it is generally agreed, for example, that such a drawing has minimal edge crossing, with vertices evenly distributed in the space, connected vertices close to each other, and with symmetry that may exist in the graph preserved. One of the earliest example of graph drawing is probably the Mill (also known as Morris) game boards, found in the 13th century The Book of Games, produced under the direction of Alfonso X (1221-1284), King of Castile (Spain). Other examples of early graph drawing are drawings of family trees, and trees of virtues and vices. In 1735, Euler presented and later published his famous paper [13] on the Seven Bridges of Königsberg. The problem was to find a walk through the city of Königsberg, which include two islands connected to the mainland by seven bridges . The walk must cross each bridge once and only once. Euler proved that such a walk is not possible. Euler’s paper laid the foundation of graph theory. Interestingly, however, Euler did not provide a drawing of the graph representation of the problem itself in the paper. In the 18th century, the Irish physicist, astronomer, and mathematician, William Hamilton, invented the Iconsian game. To play the game, one has to visit every node of a dodecahedral graph once, and back to the starting node. Every edge can be passed at most once. In 1857 he sold the game to a London game dealer for 25 Pounds (about 3000 US dollars in today’s money after taking into account of inflation). The dealer made two versions; unfortunately, neither sold well, probably because the game was so simple that even a child could succeed. Around the same time, British mathematician Arthur Cayley wrote a paper on the enumeration of tree diagrams. In the paper he pioneered the
Post-Punching Behavior of Flat Slabs by

Reinforced concrete flat slabs are a common structural system for cast-in-place concrete slabs. Failures in punching shear near the column regions are typically governing at ultimate. In case no punching shear or integrity reinforcement is placed, failures in punching develop normally in a brittle manner with almost no warning signs. Furthermore, the residual strength after punching is, in general, significantly lower than the punching load. Thus, punching of a single column of a flat slab overloads adjacent columns and can potentially lead to their failure on punching, thus triggering the progressive collapse of the structure. Over the past decades, several collapses have been reported due to punching shear failures, resulting in human casualties and extensive damage. Other than placing conventional punching shear reinforcement, the deformation capacity and residual strength after punching can also be enhanced by placing integrity reinforcement to avoid progressive collapses of flat slabs. This paper presents the main results of an extensive experimental campaign performed at the Ecole Polytechnique Fédérale de Lausanne (EPFL) on the role of integrity reinforcement by means of 20 slabs with dimensions of 1500 x 1500 x 125 mm (≈5 ft x 5 ft x 5 in.) and various integrity reinforcement layouts. The performance and robustness of the various solutions is investigated to obtain physical explanations and a consistent design model for the load-carrying mechanisms and strength after punching failures.  Over the past decades, several collapses due to punching shear failure have been reported in Europe and America,
Jubatus : An Open Source Platform for Distributed Online Machine Learning

Distributed computing is essential for handling very large datasets. Online learning is also promising for learning from rapid data streams. However, it is still an unresolved problem how to combine them for scalable learning and prediction on big data streams. We propose a general computational framework called loose model sharing for online and distributed machine learning. The key is to share only models rather than data between distributed servers. We also introduce Jubatus, an open source software platform based on the framework. Finally, we describe the details of implementing classifier and nearest neighbor algorithms, and discuss our experimental evaluations.
A Hierarchical Game Framework for Resource Management in Fog Computing

Supporting real-time and mobile data services, fog computing has been considered as a promising technology to overcome long and unpredicted delay in cloud computing. However, as resources in FNs are owned by independent users or infrastructure providers, the ADSSs cannot connect and access data services from the FNs directly, but can only request data service from the DSOs in the cloud. Accordingly, in fog computing, the DSOs are required to communicate with FNs and allocate resources from the FNs to the ADSSs. The DSOs provide virtualized data services to the ADSSs, and the FNs, motivated by the DSOs, provide data services in the physical network. Nevertheless, with fog computing added as the intermediate layer between the cloud and users, there are challenges such as the resource allocation in the virtualized network between the DSOs and ADSSs, the asymmetric information problem between DSOs and ADSSs, and the resource matching from the FNs to the ADSSs in the physical network. In this article, we propose a three-layer hierarchical game framework to solve the challenges in fog computing networks. In the proposed framework, we apply the Stackelberg sub-game for the interaction between DSOs and ADSSs, moral hazard modeling for the interaction between DSOs and FNs, and the student project allocation matching sub-game for the interaction between FNs and ADSSs. The purpose is to obtain stable and optimal utilities for each DSO, FN, and ADSS in a distributed fashion.
Single-layered complex-valued neural network for real-valued classification problems

This paper presents two models of complex-valued neurons (CVNs) for real-valued classification problems, incorporating two newly-proposed activation functions, and presents their abilities as well as differences between them on benchmark problems. In both models, each real-valued input is encoded into a phase between 0 and  of a complex number of unity magnitude, and multiplied by a complex-valued weight. The weighted sum of inputs is fed to an activation function. Activation functions of both models map complex values into real values, and their role is to divide the net-input (weighted sum) space into multiple regions representing the classes of input patterns. The gradient-based learning rule is derived for each of the activation functions. Ability of such CVNs are discussed and tested with two-class problems, such as two and three input Boolean problems, and symmetry detection in binary sequences. We exhibit here that both the models can form proper boundaries for these linear and nonlinear problems. For solving n-class problems, a complex-valued neural network (CVNN) consisting of n CVNs is also considered in this paper. We tested such single-layered CVNNs on several real world benchmark problems. The results show that the classification and generalization abilities of single-layered CVNNs are comparable to the conventional real-valued neural networks (RVNNs) having one hidden layer. Moreover, convergence of CVNNs is much faster than that of RVNNs in most of the cases.
Ontology based classification system for online job offers

The significance of employment in a setup of society is quite evident. Publishing jobs online opened the research opportunities to study different methods for the automation of job offers retrieval — from the internet. We need a classification mechanism, from machine learning or some other discipline, to automate job offers retrieval. In this paper, we devised an ontology-based classifier for job offers classification. More than 5000 job offers were collected from different job offers websites. The ontology-based classification works in three stages, concepts are extracted from job offers text description as feature vectors, the minimum threshold for the classification is calculated, and a classification model is developed. Rather using machine learning algorithms, we used an ontology for classification. We evaluated this classifier according to machine learning evaluation model, training, and test dataset. Our classifier showed more than 0.9 accuracy, precision, and recall for both training and test dataset. Our finding paves the way to automate the job offers classification and retrieval.
Software defined satellite networks: Benefits and challenges

To date, traditional satellite systems are contained-designed and inflexible for configuration update, space systems interconnections and providing fine-grained services. In this paper, we propose a novel satellite network architecture: software defined satellite networks (SDSN) to solve these problems. The new architecture is based on the central control patten of software defined networks (SDN), and takes full use of the inter-satellite links (ISLs) forwarding and GEO broadcasting for rapid network deployment. With global network view and central control ability, SDSN can (1) balance the flexibility and controllability of space dynamic routing algorithms, (2) rapidly deploy flexible and fine-grained network management strategies, (3) reduce the system cost, and (4) improve the collaboration between satellites and the compatibility of heterogeneous space systems. Meanwhile, the challenges are also introduced in designing and deploying the new architecture. Finally, the performance of the new SDSN architecture is validated in this paper.
Iterative Graph-Based Filtering for Image Abstraction and Stylization

In this brief, motivated by the recent advances in graph signal processing, we address the problem of image abstraction and stylization. A novel unified graph-based multi-layer framework is proposed to perform iterative filtering without requiring any weight updates. The proposed graph-based filtering approach is shown to be superior to other existing methods due to iteratively using the filtered Laplacian in order to enhance the smoothened image signal at each layer. In order to render real images into painterly style ones and create a simple stylized format from color images, the low-contrast regions of an image are first smoothened using the proposed iterative graph filters in either vertex or spectral domains. The abstracted image is then quantized and sharpened using the proposed iterative highpass graph filter. The effectiveness of the graph-based image stylization method is verified through several experiments. It is shown that the proposed method can yield significantly improved visual quality for stylized images as compared to other existing methods.
Artificial Intelligence and Legal Liability

A recent issue of a popular computing journal asked which laws would apply if a self-driving car killed a pedestrian. This paper considers the question of legal liability for artificially intelligent computer systems. It discusses whether criminal liability could ever apply; to whom it might apply; and, under civil law, whether an AI program is a product that is subject to product design legislation or a service to which the tort of negligence applies. The issue of sales warranties is also considered. A discussion of some of the practical limitations that AI systems are subject to is also included.
Stuttering in relation to anxiety, temperament, and personality: review and analysis with focus on causality.

Anxiety and emotional reactions have a central role in many theories of stuttering, for example that persons who stutter would tend to have an emotionally sensitive temperament. The possible relation between stuttering and certain traits of temperament or personality were reviewed and analyzed, with focus on temporal relations (i.e., what comes first). It was consistently found that preschool children who stutter (as a group) do not show any tendencies toward elevated temperamental traits of shyness or social anxiety compared with children who do not stutter. Significant group differences were, however, repeatedly reported for traits associated with inattention and hyperactivity/impulsivity, which is likely to reflect a subgroup of children who stutter. Available data is not consistent with the proposal that the risk for persistent stuttering is increased by an emotionally reactive temperament in children who stutter. Speech-related social anxiety develops in many cases of stuttering, before adulthood. Reduction of social anxiety in adults who stutter does not in itself appear to result in significant improvement of speech fluency. Studies have not revealed any relation between the severity of the motor symptoms of stuttering and temperamental traits. It is proposed that situational variability of stuttering, related to social complexity, is an effect of interference from social cognition and not directly from the emotions of social anxiety. In summary, the studies in this review provide strong evidence that persons who stutter are not characterized by constitutional traits of anxiety or similar constructs.



This paper provides a review and analysis of studies of anxiety, temperament, and personality, organized with the objective to clarify cause and effect relations. Readers will be able to (a) understand the importance of effect size and distribution of data for interpretation of group differences; (b) understand the role of temporal relations for interpretation of cause and effect; (c) discuss the results of studies of anxiety, temperament and personality in relation to stuttering; and (d) discuss situational variations of stuttering and the possible role of social cognition.