nils-herrmann/clean_scirepeval huggingface dataset:
Improved Graph Clustering

Graph clustering involves the task of dividing nodes into clusters, so that the edge density is higher within clusters as opposed to across clusters. A natural, classic, and popular statistical setting for evaluating solutions to this problem is the stochastic block model, also referred to as the planted partition model. In this paper, we present a new algorithm-a convexified version of maximum likelihood-for graph clustering. We show that, in the classic stochastic block model setting, it outperforms existing methods by polynomial factors when the cluster size is allowed to have general scalings. In fact, it is within logarithmic factors of known lower bounds for spectral methods, and there is evidence suggesting that no polynomial time algorithm would do significantly better. We then show that this guarantee carries over to a more general extension of the stochastic block model. Our method can handle the settings of semirandom graphs, heterogeneous degree distributions, unequal cluster sizes, unaffiliated nodes, partially observed graphs, planted clique/coloring, and so on. In particular, our results provide the best exact recovery guarantees to date for the planted partition, planted k-disjoint-cliques and planted noisy coloring models with general cluster sizes; in other settings, we match the best existing results up to logarithmic factors.
On the analysis and design of software for reinforcement learning, with a survey of existing systems

Reinforcement Learning (RL) is a very complex domain and software for RL is correspondingly complex. We analyse the scope, requirements, and potential for RL software, discuss relevant design issues, survey existing software, and make recommendations for designers. We argue that broad and flexible libraries of reusable software components are valuable from a scientific, as well as practical, perspective, as they allow precise control over experimental conditions, encourage comparison of alternative methods, and allow a fuller exploration of the RL domain.
Simulation of dynamics and realistic contact forces for manipulators and legged robots with high joint elasticity

In this paper, multibody system dynamics simulation for manipulators and legged robots with high joint elasticities, particularly with focus on collision modeling, is addressed. We present the architecture of a newly developed toolbox in conjunction with a detailed discussion of a realistic contact, friction and stiction model, which is validated with real measurement data of a bouncing ball. The work presented is driven and inspired by two concrete robot developments in the authors' group: the manipulator BioRob and the biped BioBiped. The libraries are used to develop kinematic and kinetic models of these bio-inspired and highly elastic robots. Models and simulation of both robots are discussed, as well as occurring forces during collisions of the BioRob-X4 arm with the ground. We are also able to demonstrate good agreement of ground contact forces measured during slow jogging motion of a human subject with simulation results obtained with BioBiped1.
Temporal Networks

A great variety of systems in nature, society and technology—from the web of sexual contacts to the Internet, from the nervous system to power grids—can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names—temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered, but does not attempt to unify related terminology—rather, we want to make papers readable across disciplines.
Examining the Effects of Classroom Discussion on Students ’ Comprehension of Text : A Meta-Analysis

The role of classroom discussions in comprehension and learning has been the focus of investigations since the early 1960s. Despite this long history, no syntheses have quantitatively reviewed the vast body of literature on classroom discussions for their effects on students’ comprehension and learning. This comprehensive meta-analysis of empirical studies was conducted to examine evidence of the effects of classroom discussion on measures of teacher and student talk and on individual student comprehension and critical-thinking and reasoning outcomes. Results revealed that several discussion approaches produced strong increases in the amount of student talk and concomitant reductions in teacher talk, as well as substantial improvements in text comprehension. Few approaches to discussion were effective at increasing students’ literal or inferential comprehension and critical thinking and reasoning. Effects were moderated by study design, the nature of the outcome measure, and student academic ability. While the range of ages of participants in the reviewed studies was large, a majority of studies were conducted with students in 4th through 6th grades. Implications for research and practice are discussed.
A Millimeter-Wave Filtering Monopulse Antenna Array Based on Substrate Integrated Waveguide Technology

A millimeter-wave filtering monopulse antenna array based on substrate integrated waveguide (SIW) technology is proposed, manufactured, and tested in this communication. The proposed antenna array consists of a filter, a monopulse comparator, a feed network, and four antennas. A square dual-mode SIW cavity is designed to realize the monopulse comparator, in which internal coupling slots are located at its diagonal lines for the purpose of meeting the internal coupling coefficiencies in both sum and difference channels. Then, a four-output filter including the monopulse comparator is synthesized efficiently by modifying the coupling matrix of a single-ended filter. Finally, each SIW resonator coupled with those four outputs of the filter is replaced by a cavity-backed slot antenna so as to form the proposed filtering antenna array. A prototype is demonstrated at Ka band with a center frequency of 29.25 GHz and fractional bandwidth of 1.2%. Our measurement shows that, for the H-plane, the sidelobe levels of the sum pattern are less than -15 dB and the null depths of the difference pattern are less than -28 dB. The maximum measured gain of the sum beam at the center operating frequency is 8.1 dBi.
A New Generic Taxonomy on Hybrid Malware Detection Technique

Malware is a type of malicious program that replicate from host machine and propagate through network. It has been considered as one type of computer attack and intrusion that can do a variety of malicious activity on a computer. This paper addresses the current trend of malware detection techniques and identifies the significant criteria in each technique to improve malware detection in Intrusion Detection System (IDS). Several existing techniques are analyzing from 48 various researches and the capability criteria of malware detection technique have been reviewed. From the analysis, a new generic taxonomy of malware detection technique have been proposed named Hybrid-Malware Detection Technique (Hybrid-MDT) which consists of HybridSignature and Anomaly detection technique and HybridSpecification based and Anomaly detection technique to complement the weaknesses of the existing malware detection technique in detecting known and unknown attack as well as reducing false alert before and during the intrusion occur.
Principles of Light Field Imaging: Briefly revisiting 25 years of research

Light field imaging offers powerful new capabilities through sophisticated digital processing techniques that are tightly merged with unconventional optical designs. This combination of imaging technology and computation necessitates a fundamentally different view of the optical properties of imaging systems and poses new challenges for the traditional signal and image processing domains. In this article, we aim to provide a comprehensive review of the considerations involved and the difficulties encountered in working with light field data.
Robust Online Visual Tracking with a Single Convolutional Neural Network

Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they require very long training time and a large number of training samples. In this work, we present an efficient and very robust online tracking algorithm using a single Convolutional Neural Network (CNN) for learning effective feature representations of the target object over time. Our contributions are multifold: First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation, thus drift, by accommodating the uncertainty of the model output. Second, we enhance the ordinary Stochastic Gradient Descent approach in CNN training with a temporal selection mechanism, which generates positive and negative samples within different time periods. Finally, we propose to update the CNN model in a “lazy” style to speed-up the training stage, where the network is updated only when a significant appearance change occurs on the object, without sacrificing tracking accuracy. The CNN tracker outperforms all compared state-ofthe-art methods in our extensive evaluations that involve 18 well-known benchmark video sequences.
Toward a Principled Framework to Design Dynamic Adaptive Streaming Algorithms over HTTP

Client-side bitrate adaptation algorithms play a critical role in delivering a good quality of experience for Internet video. Many studies have shown that current solutions perform suboptimally, and despite the proliferation of several proposals in this space, both from commercial providers and researchers, there is still a distinct lack of clarity and consensus w.r.t. several natural questions: (1) What objectives does/should such an algorithm optimize? (2) What environment signals such as buffer occupancy or throughput estimates should an algorithm use in its control loop? (3) How sensitive is an algorithm to operating conditions (e.g., bandwidth stability, buffer size, available bitrates)? This work attempts to bring clarity to this discussion by casting adaptive bitrate streaming as a model-based predictive control problem. We demonstrate the initial promise of shedding light on these questions using this control-theoretic abstraction.
Fast point cloud segmentation for an intelligent vehicle using sweeping 2D laser scanners

The previously developed radially bounded nearest neighbor (RBNN) algorithm have been shown a good performance for 3D point cloud segmentation in indoor scenarios. In outdoor scenarios however it is hard to adapt the original RBNN to an intelligent vehicle directly due to several drawbacks. In this paper, drawbacks of RBNN are addressed and we propose an enhanced RBNN for an intelligent vehicle operating in urban environments by proposing the ground elimination and the distance-varying radius. After the ground removal, objects can be remained to segment without merging the ground and objects, whereas the original RBNN with the fixed radius induced over-segmentation or under-segmentation. We design the distance-varying radius which is varied properly from the distance between a laser scanner and scanning objects. The proposed distance-varying radius is successfully induced to segment objects without over or under segmentation. In the experimental results, we have shown that the enhance RBNN is preferable to segment urban structures in terms of time consumption, and even segmentation rates.
Basic emotions are associated with distinct patterns of cardiorespiratory activity.

The existence of specific somatic states associated with different emotions remains controversial. In this study, we investigated the profile of cardiorespiratory activity during the experience of fear, anger, sadness and happiness. ECG and respiratory activity was recorded in 43 healthy volunteers during the recall and experiential reliving of one or two potent emotional autobiographical episodes and a neutral episode. Univariate statistics indicated that the four emotions differed from each other and from the neutral control condition on several linear and spectral indices of cardiorespiratory activity. Dependent variables were further reduced to five physiologically meaningful factors using an exploratory principal component analysis (PCA). Multivariate analyses of variance and effect size estimates calculated on those factors confirmed the differences between the four emotion conditions. A stepwise discriminant analyses predicting emotions using the PCA factors led to a classification rate of 65.3% for the four emotions (chance=25%; p=0.001) and of 72.0-83.3% for pair-wise discrimination (chance=50%; p's<0.05). These findings may be considered preliminary in view of the small sample on which the multivariate approach has been applied. However, this study emphasizes the need to better characterize the multidimensional factors involved in cardio-respiratory regulation during emotion. These results are consistent with the notion that distinct patterns of peripheral physiological activity are associated with different emotions.
Trust in the Sharing Economy: An Experimental Framework

Peer-to-peer platforms in the realm of the sharing economy, such as Airbnb or BlaBlaCar, have heavily rattled the electronic commerce landscape and are expected to further impact consumer behavior in the future. While trust between the parties involved is of utmost importance in such platform economies, experimental research on this aspect is scarce. In this conceptual paper, we first present an experimental framework for targeting trust in the sharing economy based on experimental economics and the trust game in particular. In doing so, we sketch out a path to complement existing Information Systems research on the sharing economy by experimental methods. Second, we apply the framework to a specific use case, by developing a research model and experimental design to explore the role of user representation for trust on sharing economy platforms. We therefore set the stage for controlled (laboratory) experiments to enrich research on trust in the sharing economy.
Low-Profile Substrate-Integrated Lens Antenna Using Metamaterials

A low-profile substrate-integrated lens antenna is designed using planar metamaterials for a broadband operation. The lens antenna is based on embedding a Vivaldi antenna source inside a parallel-plate waveguide to illuminate a half Maxwell fish-eye (HMFE) lens operating in X-band. The focusing condition of the lens, requiring a gradient refractive index is achieved through the use of complementary nonresonant metamaterial structures. Numerical simulations are performed to determine the suitable unit cells geometry with respect to the wave launcher inserted into the parallel-plate waveguide. The electric field distribution inside the antenna system has also been explored numerically. Far-field radiation patterns have been measured on a fabricated prototype in an anechoic chamber. It has been shown from both near- and far-field plots that the proposed planar antenna presents good focusing properties.
Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher Education Institutions

Educational data mining is the process of applying data mining tools and techniques to analyze data at educational institutions. In this paper, educational data mining was used to predict enrollment of students in Science, Technology, Engineering and Mathematics (STEM) courses in higher educational institutions. The study examined the extent to which individual, sociodemographic and school-level contextual factors help in pre-identifying successful and unsuccessful students in enrollment in STEM disciplines in Higher Education Institutions in Kenya. The Cross Industry Standard Process for Data Mining framework was applied to a dataset drawn from the first, second and third year undergraduate female students enrolled in STEM disciplines in one University in Kenya to model student enrollment. Feature selection was used to rank the predictor variables by their importance for further analysis. Various predictive algorithms were evaluated in predicting enrollment of students in STEM courses. Empirical results showed the following: (i) the most important factors separating successful from unsuccessful students are: High School final grade, teacher inspiration, career flexibility, pre-university awareness and mathematics grade. (ii) among classification algorithms for prediction, decision tree (CART) was the most successful classifier with an overall percentage of correct classification of 85.2%. This paper showcases the importance of Prediction and Classification based data mining algorithms in the field of education and also presents some promising future lines.
Assessing the Wearability of Wearable Computers

In terms of mounting a computer on the body, the computer's weight, size, shape, placement and method of attachment can elicit a number of effects. Inappropriate design may mean that the wearer is unable to perform specific tasks or achieve goals. Excessive stress on the body may result in perceptions of discomfort, which may in turn affect task performance, but ultimately raises issues of health and safety. This paper proposes a methodology for assessing the affects of wearing a computer in terms of physiological energy expenditure, the biomechanical effects due to changes in movement patterns, posture and perceptions of localised pain and discomfort due to musculoskeletal loading, and perceptions of well- being through comfort assessment. From ratings of these effects the paper proposes 5 levels to determine the wearability of a computer.
A new class of high speed disconnect switch based on piezoelectric actuators

With the adoption of power electronic converters in shipboard power systems and associated novel fault management concepts, the ability to isolate electric faults quickly from the power system is becoming more important than breaking high magnitude fault currents and the corresponding arcing between opening contacts within a switch. This allows for the design of substantially faster, as well as potentially lighter and more compact, mechanical disconnect switches. Herein, we are proposing a new class of mechanical disconnect switches that utilize piezoelectric actuators to isolate within less than one millisecond. This technology may become a key enabler for future all-electric ships.
Bag-of-Audio-Words Approach for Multimedia Event Classification

With the popularity of online multimedia videos, there has been much interest in recent years in acoustic event detection and classification for the improvement of online video search. The audio component of a video has the potential to contribute significantly to multimedia event classification. Recent research in audio document classification has drawn parallels to text and image document retrieval by employing what is referred to as the bag-of-audio words (BoAW) method. Compared to supervised approaches where audio concept detectors are trained using annotated data and extracted labels are used as lowlevel features for multimedia event classification. The BoAW approach extracts audio concepts in an unsupervised fashion. Hence this method has the advantage that it can be employed easily for a new set of audio concepts in multimedia videos without going through a laborious annotation effort. In this paper, we explore variations of the BoAW method and present results on NIST 2011 multimedia event detection (MED) dataset.
ChemSpot: a hybrid system for chemical named entity recognition

The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be supported by computational tools.



We present ChemSpot, a named entity recognition (NER) tool for identifying mentions of chemicals in natural language texts, including trivial names, drugs, abbreviations, molecular formulas and International Union of Pure and Applied Chemistry entities. Since the different classes of relevant entities have rather different naming characteristics, ChemSpot uses a hybrid approach combining a Conditional Random Field with a dictionary. It achieves an F(1) measure of 68.1% on the SCAI corpus, outperforming the only other freely available chemical NER tool, OSCAR4, by 10.8 percentage points.


AVAILABILITY
ChemSpot is freely available at: http://www.informatik.hu-berlin.de/wbi/resources.
Gaussian Processes for Rumour Stance Classification in Social Media

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a conversation around a rumour as either supporting, denying or questioning the rumour. Using a Gaussian Process classifier, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will show both ordinary users of Twitter and professional news practitioners how others orient to the disputed veracity of a rumour, with the final aim of establishing its actual truth value.