nils-herrmann/clean_scirepeval huggingface dataset:
A survey of results on mobile phone datasets analysis

In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.
TRIPILLAR: a miniature magnetic caterpillar climbing robot with plane transition ability

In this paper, we describe a miniature climbing robot, 96 x 46 x 64 [mm], able to climb ferromagnetic surfaces and to make inner plane to plane transition using only two degrees of freedom. Our robot, named TRIPILLAR, combines magnetic caterpillars and magnets to climb planar ferromagnetic surfaces. Two triangular tracks are mounted in a differential drive mode, which allows squid steering and on spot turning. Exploiting the particular geometry and magnetic properties of this arrangement, TRIPILLAR is able to transit between intersecting surfaces. The intersection angle ranges from -10° to 90° on the pitch angle of the coordinate system of the robot regardless of the orientation of gravity. A possible path is to move from ground to ceiling and back. This achievement opens new avenues for mobile robotics inspection of ferromagnetic industrial structure with stringent size restriction, like the one encountered in power plants.
Unknown malware detection using network traffic classification

We present an end-to-end supervised based system for detecting malware by analyzing network traffic. The proposed method extracts 972 behavioral features across different protocols and network layers, and refers to different observation resolutions (transaction, session, flow and conversation windows). A feature selection method is then used to identify the most meaningful features and to reduce the data dimensionality to a tractable size. Finally, various supervised methods are evaluated to indicate whether traffic in the network is malicious, to attribute it to known malware “families” and to discover new threats. A comparative experimental study using real network traffic from various environments indicates that the proposed system outperforms existing state-of-the-art rule-based systems, such as Snort and Suricata. In particular, our chronological evaluation shows that many unknown malware incidents could have been detected at least a month before their static rules were introduced to either the Snort or Suricata systems.
Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition

This paper introduces the end-to-end embedding of a CNN into a HMM, while interpreting the outputs of the CNN in a Bayesian fashion. The hybrid CNN-HMM combines strong discriminative abilities of CNNs with sequence modeling capabilities of HMMs. Most current approaches in the field of gesture and sign language recognition disregard the necessity of dealing with sequence data both for training and evaluation. With our presented end-to-end embedding we are able to improve over the state-of-the-art on three challenging benchmark continuous sign language recognition tasks by between 15% & 38% relative & up to 13.3% absolute.
Cooperative localization and mapping of MAVs using RGB-D sensors

The fusion of IMU and RGB-D sensors presents an interesting combination of information to achieve autonomous localization and mapping using robotic platforms such as ground robots and flying vehicles. In this paper, we present a software framework for cooperative localization and mapping while simultaneously using multiple aerial platforms. We employ a monocular visual odometry algorithm to solve the localization task, where the depth data flow associated to the RGB image is used to estimate the scale factor associated with the visual information. The current framework enables autonomous onboard control of each vehicle with cooperative localization and mapping. We present a methodology that provides both a sparse map generated by the monocular SLAM and a multiple resolution dense map generated by the associated depth. The localization algorithm and both 3D mapping algorithms work in parallel improving the system real-time reliability. We present experimental results to show the effectiveness of the proposed approach using two quadrotors platforms.
Multiple Player Tracking in Sports Video: A Dual-Mode Two-Way Bayesian Inference Approach With Progressive Observation Modeling

Multiple object tracking (MOT) is a very challenging task yet of fundamental importance for many practical applications. In this paper, we focus on the problem of tracking multiple players in sports video which is even more difficult due to the abrupt movements of players and their complex interactions. To handle the difficulties in this problem, we present a new MOT algorithm which contributes both in the observation modeling level and in the tracking strategy level. For the observation modeling, we develop a progressive observation modeling process that is able to provide strong tracking observations and greatly facilitate the tracking task. For the tracking strategy, we propose a dual-mode two-way Bayesian inference approach which dynamically switches between an offline general model and an online dedicated model to deal with single isolated object tracking and multiple occluded object tracking integrally by forward filtering and backward smoothing. Extensive experiments on different kinds of sports videos, including football, basketball, as well as hockey, demonstrate the effectiveness and efficiency of the proposed method.
Pay, working conditions, and teacher quality.

Eric Hanushek and Steven Rivkin examine how salary and working conditions affect the quality of instruction in the classroom. The wages of teachers relative to those of other college graduates have fallen steadily since 1940. Today, average wages differ little, however, between urban and suburban districts. In some metropolitan areas urban districts pay more, while in others, suburban districts pay more. But working conditions in urban and suburban districts differ substantially, with urban teachers reporting far less administrator and parental support, worse materials, and greater student problems. Difficult working conditions may drive much of the difference in turnover of teachers and the transfer of teachers across schools. Using rich data from Texas public schools, the authors describe in detail what happens when teachers move from school to school. They examine how salaries and student characteristics change when teachers move and also whether turnover affects teacher quality and student achievement. They note that both wages and student characteristics affect teachers' choices and result in a sorting of teachers across schools, but they find little evidence that teacher transitions are detrimental to student learning. The extent to which variations in salaries and working conditions translate into differences in the quality of instruction depends importantly on the effectiveness of school personnel policies in hiring and retaining the most effective teachers and on constraints on both entry into the profession and the firing of low performers. The authors conclude that overall salary increases for teachers would be both expensive and ineffective. The best way to improve the quality of instruction would be to lower barriers to becoming a teacher, such as certification, and to link compensation and career advancement more closely with teachers' ability to raise student performance.
Adapting to User Interest Drift for POI Recommendation

Point-of-Interest recommendation is an essential means to help people discover attractive locations, especially when people travel out of town or to unfamiliar regions. While a growing line of research has focused on modeling user geographical preferences for POI recommendation, they ignore the phenomenon of user interest drift across geographical regions, i.e., users tend to have different interests when they travel in different regions, which discounts the recommendation quality of existing methods, especially for out-of-town users. In this paper, we propose a latent class probabilistic generative model Spatial-Temporal LDA (ST-LDA) to learn region-dependent personal interests according to the contents of their checked-in POIs at each region. As the users' check-in records left in the out-of-town regions are extremely sparse, ST-LDA incorporates the crowd's preferences by considering the public's visiting behaviors at the target region. To further alleviate the issue of data sparsity, a social-spatial collective inference framework is built on ST-LDA to enhance the inference of region-dependent personal interests by effectively exploiting the social and spatial correlation information. Besides, based on ST-LDA, we design an effective attribute pruning (AP) algorithm to overcome the curse of dimensionality and support fast online recommendation for large-scale POI data. Extensive experiments have been conducted to evaluate the performance of our ST-LDA model on two real-world and large-scale datasets. The experimental results demonstrate the superiority of ST-LDA and AP, compared with the state-of-the-art competing methods, by making more effective and efficient mobile recommendations.
Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks

Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output problems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input. In this paper we focus on the case where the input also has a rich structure and the input and output structures are somehow related. We describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip description, and speech recognition. All these systems are based on a shared set of building blocks: gated recurrent neural networks and convolutional neural networks, along with trained attention mechanisms. We report on experimental results with these systems, showing impressively good performance and the advantage of the attention mechanism.
"Highly Recommended!" The Content Characteristics and Perceived Usefulness of Online Consumer Reviews

The aim of the present study was to gain a better understanding of the content characteristics that make online consumer reviews a useful source of consumer information. To this end, we content analyzed reviews of experience and search products posted on Amazon.com (N = 400). The insights derived from this content analysis were linked with the proportion of ‘useful’ votes that reviews received from fellow consumers. The results show that content characteristics are paramount to understanding the perceived usefulness of reviews. Specifically, argumentation (density and diversity) served as a significant predictor of perceived usefulness, as did review valence although this latter effect was contingent on the type of product (search or experience) being evaluated in reviews. The presence of expertise claims appeared to be weakly related to the perceived usefulness of reviews. The broader theoretical, methodological and practical implications of these findings are discussed.
Improving Distant Supervision for Information Extraction Using Label Propagation Through Lists

Because of polysemy, distant labeling for information extraction leads to noisy training data. We describe a procedure for reducing this noise by using label propagation on a graph in which the nodes are entity mentions, and mentions are coupled when they occur in coordinate list structures. We show that this labeling approach leads to good performance even when off-the-shelf classifiers are used on the distantly-labeled data.
A System for Converting PDF Documents into Structured XML Format

We present in this paper a system for converting PDF legacy documents into structured XML format. This conversion system first extracts the different streams contained in PDF files (text, bitmap and vectorial images) and then applies different components in order to express in XML the logically structured documents. Some of these components are traditional in Document Analysis, other more specific to PDF. We also present a graphical user interface in order to check, correct and validate the analysis of the components. We eventually report on two real user cases where this system was applied on.
I ’ m not a human : Breaking the Google reCAPTCHA

Since their inception, captchas have been widely used for preventing fraudsters from performing illicit actions. Nevertheless, economic incentives have resulted in an arms race, where fraudsters develop automated solvers and, in turn, captcha services tweak their design to break the solvers. Recent work, however, presented a generic attack that can be applied to any text-based captcha scheme. Fittingly, Google recently unveiled the latest version of reCaptcha. The goal of their new system is twofold; to minimize the effort for legitimate users, while requiring tasks that are more challenging to computers than text recognition. ReCaptcha is driven by an “advanced risk analysis system” that evaluates requests and selects the difficulty of the captcha that will be returned. Users may be required to click in a checkbox, or solve a challenge by identifying images with similar content. In this paper, we conduct a comprehensive study of reCaptcha, and explore how the risk analysis process is influenced by each aspect of the request. Through extensive experimentation, we identify flaws that allow adversaries to effortlessly influence the risk analysis, bypass restrictions, and deploy large-scale attacks. Subsequently, we design a novel low-cost attack that leverages deep learning technologies for the semantic annotation of images. Our system is extremely effective, automatically solving 70.78% of the image reCaptcha challenges, while requiring only 19 seconds per challenge. We also apply our attack to the Facebook image captcha and achieve an accuracy of 83.5%. Based on our experimental findings, we propose a series of safeguards and modifications for impacting the scalability and accuracy of our attacks. Overall, while our study focuses on reCaptcha, our findings have wide implications; as the semantic information conveyed via images is increasingly within the realm of automated reasoning, the future of captchas relies on the exploration of novel directions.
Software Development for the Kinematic Analysis of a Lynx 6 Robot Arm

The kinematics of manipulators is a central problem in the automatic control of robot manipulators. Theoretical background for the analysis of the 5 Dof Lynx-6 educational Robot Arm kinematics is presented in this paper. The kinematics problem is defined as the transformation from the Cartesian space to the joint space and vice versa. The Denavit-Harbenterg (D-H) model of representation is used to model robot links and joints in this study. Both forward and inverse kinematics solutions for this educational manipulator are presented, An effective method is suggested to decrease multiple solutions in inverse kinematics. A visual software package, named MSG, is also developed for testing Motional Characteristics of the Lynx-6 Robot arm. The kinematics solutions of the software package were found to be identical with the robot arm’s physical motional behaviors. Lynx 6, robot arm, forward kinematics, inverse kinematics, software, DH parameters, 5 DOF ,SSC-32 , simulator.
Face Recognition Based GLOH Descriptor and Integration of Local Features

In order to reduce the computational complexity of high-dimensional feature descriptor and improve the accuracy of recognition algorithm, the paper proposes a face recognition algorithm based on GLOH descriptor, with scale and geometric invariant. In the paper, face image is divided into four separate sub-regions and clusters the feature points extracted from the every region. In order to describe the feature operator and feature matching more effectively, the different region is to give different weight values according to the distinctiveness. The method of the whole combining with local clustering sub-region is employed for face recognition. The effectiveness of the algorithm is verified by experiments on the ORL face image database, which demonstrates good stability and robustness especially under the conditions of some confounding factors such as different facial expressions, postures and so on.
Unseen facial and bodily expressions trigger fast emotional reactions.

The spontaneous tendency to synchronize our facial expressions with those of others is often termed emotional contagion. It is unclear, however, whether emotional contagion depends on visual awareness of the eliciting stimulus and which processes underlie the unfolding of expressive reactions in the observer. It has been suggested either that emotional contagion is driven by motor imitation (i.e., mimicry), or that it is one observable aspect of the emotional state arising when we see the corresponding emotion in others. Emotional contagion reactions to different classes of consciously seen and "unseen" stimuli were compared by presenting pictures of facial or bodily expressions either to the intact or blind visual field of two patients with unilateral destruction of the visual cortex and ensuing phenomenal blindness. Facial reactions were recorded using electromyography, and arousal responses were measured with pupil dilatation. Passive exposure to unseen expressions evoked faster facial reactions and higher arousal compared with seen stimuli, therefore indicating that emotional contagion occurs also when the triggering stimulus cannot be consciously perceived because of cortical blindness. Furthermore, stimuli that are very different in their visual characteristics, such as facial and bodily gestures, induced highly similar expressive responses. This shows that the patients did not simply imitate the motor pattern observed in the stimuli, but resonated to their affective meaning. Emotional contagion thus represents an instance of truly affective reactions that may be mediated by visual pathways of old evolutionary origin bypassing cortical vision while still providing a cornerstone for emotion communication and affect sharing.
Visible embodiment: gestures as simulated action.

Spontaneous gestures that accompany speech are related to both verbal and spatial processes. We argue that gestures emerge from perceptual and motor simulations that underlie embodied language and mental imagery. We first review current thinking about embodied cognition, embodied language, and embodied mental imagery. We then provide evidence that gestures stem from spatial representations and mental images. We then propose the gestures-as-simulated-action framework to explain how gestures might arise from an embodied cognitive system. Finally, we compare this framework with other current models of gesture production, and we briefly outline predictions that derive from the framework.
Using Commutative Assessments to Compare Conceptual Understanding in Blocks-based and Text-based Programs

Blocks-based programming environments are becoming increasingly common in introductory programming courses, but to date, little comparative work has been done to understand if and how this approach affects students' emerging understanding of fundamental programming concepts. In an effort to understand how tools like Scratch and Blockly differ from more conventional text-based introductory programming languages with respect to conceptual understanding, we developed a set of "commutative" assessments. Each multiple-choice question on the assessment includes a short program that can be displayed in either a blocks- based or text-based form. The set of potential answers for each question includes the correct answer along with choices informed by prior research on novice programming misconceptions. In this paper we introduce the Commutative Assessment, discuss the theoretical and practical motivations for the assessment, and present findings from a study that used the assessment. The study had 90 high school students take the assessment at three points over the course of the first ten weeks of an introduction to programming course, alternating the modality (blocks vs. text) for each question over the course of the three administrations of the assessment. Our analysis reveals differences on performance between blocks-based and text-based questions as well as differences in the frequency of misconceptions based on the modality. Future work, potential implications, and limitations of these findings are also discussed.
Context-Dependent Word Representation for Neural Machine Translation

We first observe a potential weakness of continuous vector representations of symbols in neural machine translation. That is, the continuous vector representation, or a word embedding vector, of a symbol encodes multiple dimensions of similarity, equivalent to encoding more than one meaning of the word. This has the consequence that the encoder and decoder recurrent networks in neural machine translation need to spend substantial amount of their capacity in disambiguating source and target words based on the context which is defined by a source sentence. Based on this observation, in this paper we propose to contextualize the word embedding vectors using a nonlinear bag-of-words representation of the source sentence. Additionally, we propose to represent special tokens (such as numbers, proper nouns and acronyms) with typed symbols to facilitate translating those words that are not well-suited to be translated via continuous vectors. The experiments on En-Fr and En-De reveal that the proposed approaches of contextualization and symbolization improves the translation quality of neural machine translation systems significantly.
A Common Grounded Z-Source DC–DC Converter With High Voltage Gain

In this paper, a common grounded Z-source dc-dc converter with high voltage gain is proposed for photovoltaic (PV) applications, which require a relatively high output-input voltage conversion ratio. Compared with the traditional Z-source dc-dc converter, the proposed converter, which employs a conventional Z-source network, can obtain higher voltage gain and provide the common ground for the input and output without any additional components, which results in low cost and small size. Moreover, the proposed converter features low voltage stresses of the switch and diodes. Therefore, the efficiency and reliability of the proposed converter can be improved. The operating principle, parameters design, and comparison with other converters are analyzed. Simulation and experimental results are given to verify the aforementioned characteristics and theoretical analysis of the proposed converter.