nils-herrmann/clean_scirepeval huggingface dataset:
Leveraging Careful Microblog Users for Spammer Detection

Microblogging websites, e.g. Twitter and Sina Weibo, have become a popular platform for socializing and sharing information in recent years. Spammers have also discovered this new opportunity to unfairly overpower normal users with unsolicited content, namely social spams. While it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users seeking for spammers on purpose is also observed. We regard this behavior as a useful information for spammer detection. In this paper, we approach the problem of spammer detection by leveraging the "carefulness" of users, which indicates how careful a user is when she is about to follow a potential spammer. We propose a framework to measure the carefulness, and develop a supervised learning algorithm to estimate it based on known spammers and legitimate users. We then illustrate how spammer detection can be improved in the aid of the proposed measure. Evaluation on a real dataset with millions of users and an online testing are performed on Sina Weibo. The results show that our approach indeed capture the carefulness, and it is effective to detect spammers. In addition, we find that the proposed measure is also beneficial for other applications, e.g. link prediction.
Dietary intake of phosphorus flame retardants (PFRs) using Swedish food market basket estimations.

The occurrence of eight phosphorus flame retardants (PFRs) was investigated in 53 composite food samples from 12 food categories, collected in 2015 for a Swedish food market basket study. 2-ethylhexyl diphenyl phosphate (EHDPHP), detected in most food categories, had the highest median concentrations (9 ng/g ww, pastries). It was followed by triphenyl phosphate (TPHP) (2.6 ng/g ww, fats/oils), tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) (1.0 ng/g ww, fats/oils), tris(2-chloroethyl) phosphate (TCEP) (1.0 ng/g ww, fats/oils), and tris(1-chloro-2-propyl) phosphate (TCIPP) (0.80 ng/g ww, pastries). Tris(2-ethylhexyl) phosphate (TEHP), tri-n-butyl phosphate (TNBP), and tris(2-butoxyethyl) phosphate (TBOEP) were not detected in the analyzed food samples. The major contributor to the total dietary intake was EHDPHP (57%), and the food categories which contributed the most to the total intake of PFRs were processed food, such as cereals (26%), pastries (10%), sugar/sweets (11%), and beverages (17%). The daily per capita intake of PFRs (TCEP, TPHP, EHDPHP, TDCIPP, TCIPP) from food ranged from 406 to 3266 ng/day (or 6-49 ng/kg bw/day), lower than the health-based reference doses. This is the first study reporting PFR intakes from other food categories than fish (here accounting for 3%). Our results suggest that the estimated human dietary exposure to PFRs may be equally important to the ingestion of dust.
Systematic Planning for ICT Integration in Topic Learning

Integrating Information and Communication Technology (ICT) into teaching and learning is a growing area that has attracted many educators’ efforts in recent years. Based on the scope of content covered, ICT integration can happen in three different areas: curriculum, topic, and lesson. This paper elaborates upon the concept of ICT integration, and presents a systematic planning model for guiding ICT integration in the topic area. A sample of an ICT integration plan is described in this paper to demonstrate how this model can be applied in practice.
Adaptive meshless centres and RBF stencils for Poisson equation

We consider adaptive meshless discretisation of the Dirichlet problem for Poisson equation based on numerical differentiation stencils obtained with the help of radial basis functions. New meshless stencil selection and adaptive refinement algorithms are proposed in 2D. Numerical experiments show that the accuracy of the solution is comparable with, and often better than that achieved by the mesh-based adaptive finite element method.
Trajectory probability hypothesis density filter

This paper presents the probability hypothesis density (PHD) filter for sets of trajectories: the trajectory probability density (TPHD) filter. The TPHD filter is capable of estimating trajectories in a principled way without requiring to evaluate all measurement-to-target association hypotheses. The TPHD filter is based on recursively obtaining the best Poisson approximation to the multitrajectory filtering density in the sense of minimising the Kullback-Leibler divergence. We also propose a Gaussian mixture implementation of the TPHD recursion. Finally, we include simulation results to show the performance of the proposed algorithm.
Star-EDT: Deterministic On-Chip Scheme Using Compressed Test Patterns

This paper presents Star-EDT—a novel deterministic test compression scheme. The proposed solution seamlessly integrates with EDT-based compression and takes advantage of two key observations: 1) there exist clusters of test vectors that can detect many random-resistant faults with a cluster comprising a parent pattern and its derivatives obtained through simple transformations and 2) a significant majority of specified positions of ATPG-produced test cubes are typically clustered within a single or, at most, a few scan chains. The Star-EDT approach elevates compression ratios to values typically unachievable through conventional reseeding-based solutions. Experimental results obtained for large industrial designs, including those with a new class of test points aware of ATPG-induced conflicts, illustrate feasibility of the proposed deterministic test scheme and are reported herein. In particular, they confirm that the Star-EDT can act as a valuable form of deterministic BIST.
A Gap Analysis Methodology for Product Lifecycle Management Assessment

Product lifecycle management (PLM) is a strategic and holistic approach for the management of information, processes, and resources supporting the entire product lifecycle from concept to disposal. Several systems are available to manage data and information during the lifecycle. A PLM system does not have to be considered as a unique solution, but as a set of software supporting different perspectives and activities related to the lifecycle management. A full comprehensive implementation of PLM systems is rare. Since PLM reflects the peculiarities of processes and data structures, implementation differences are evidently observable among companies. The design of methods and tools supporting an assessment of PLM implementation inside a company can enable a correct definition of PLM strategies and goals. Based on these premises, the paper aims to specify a methodology for carrying out a gap analysis of PLM with the scope of discovering existing technological and methodological gaps, and planning actions for improvement. The proposed methodology implements a visual and lean reference model, and an assessment questionnaire for data collection. Lesson learned and feedback from three industrial applications are also described and discussed.
Outliers detection methods in wireless sensor networks

Detection and accommodation of outliers are crucial in a number of contexts, in which collected data from a given environment is subsequently used for assessing its running conditions or for data-based decision-making. Although a significant number of studies on this subject can be found in literature, a comprehensive empirical assessment in the context of local online detection in wireless sensor networks is still missing. The present work aims at filling this gap by offering an empirical evaluation of two state-of-the-art online detection methods. The first methodology is based on a Least Squares-Support Vector Machine technique, along with a sliding window-based learning algorithm, while the second approach relies on Principal Component Analysis and on the robust orthonormal projection approximation subspace tracking with rank-1 modification. The performance and implementability of these methods are evaluated using a generated non-stationary time-series and a test-bed consisting of a benchmark three-tank system and a wireless sensor network, where deployed algorithms are implemented under a multi-agent framework.
Jeffrey's rule of conditioning generalized to belief functions

Jeffrey's rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models based on belief functions. We show that several forms of Jeffrey's conditionings can be defined that correspond to the geometrical rule of conditioning and to Dempster's rule of conditioning, respectively. 1. Jeffrey's rule in probability theory. In probability theory conditioning on an event . is classically obtained by the application of Bayes' rule. Let (Q, � , P) be a probability space where P(A) is the probability of the event Ae � where� is a Boolean algebra defined on a finite2 set n. P(A) quantified the degree of belief or the objective probability, depending on the interpretation given to the probability measure, that a particular arbitrary element m of n which is not a priori located in any of the sets of� belongs to a particular set Ae�. Suppose it is known that m belongs to Be� and P(B)>O. The probability measure P must be updated into PB that quantifies the same event as previously but after taking in due consideration the know ledge that me B. PB is obtained by Bayes' rule of conditioning: This rule can be obtained by requiring that: 81: VBE�. PB(B) = 1 82: VBe�, VX,Ye� such that X.Y�B. and PJ3(X) _ P(X) PB(Y)P(Y) PB(Y) = 0 ifP(Y)>O
Enhancement of Spectral Tilt in Synthesized Speech

The research in statistical parametric speech synthesis is towards improving naturalness and intelligibility. In this work, the deviation in spectral tilt of the natural and synthesized speech is analyzed and observed a large gap between the two. Furthermore, the same is analyzed for different classes of sounds, namely low-vowels, mid-vowels, high-vowels, semi-vowels, nasals, and found to be varying with category of sound units. Based on variation, a novel method for spectral tilt enhancement is proposed, where the amount of enhancement introduced is different for different classes of sound units. The proposed method yields improvement in terms of intelligibility, naturalness, and speaker similarity of the synthesized speech.
Searching for Learner-Centered , Constructivist , and Sociocultural Components of Collaborative Educational Learning Tools

strategies and tools must be based on some theory of learning and cognition. Of course, crafting well-articulated views that clearly answer the major epistemological questions of human learning has exercised psychologists and educators for centuries. What is a mind? What does it mean to know something? How is our knowledge represented and manifested? Many educators prefer an eclectic approach, selecting “principles and techniques from the many theoretical perspectives in much the same way we might select international dishes from a smorgasbord, choosing those we like best and ending up with a meal which represents no nationality exclusively and a design technology based on no single theoretical base” (Bednar et al., 1995, p. 100). It is certainly the case that research within collaborative educational learning tools has drawn upon behavioral, cognitive information processing, humanistic, and sociocultural theory, among others, for inspiration and justification. Problems arise, however, when tools developed in the service of one epistemology, say cognitive information processing, are integrated within instructional systems designed to promote learning goals inconsistent with it. When concepts, strategies, and tools are abstracted from the theoretical viewpoint that spawned them, they are too often stripped of meaning and utility. In this chapter, we embed our discussion in learner-centered, constructivist, and sociocultural perspectives on collaborative technology, with a bias toward the third. The principles of these perspectives, in fact, provide the theoretical rationale for much of the research and ideas presented in this book.
Frequency domain complex permittivity measurements at microwave frequencies

Overview of frequency domain measurement techniques of the complex permittivity at microwave frequencies is presented. The methods are divided into two categories: resonant and non-resonant ones. In the first category several methods are discussed such as cavity resonator techniques, dielectric resonator techniques, open resonator techniques and resonators for non-destructive testing. The general theory of measurements of different materials in resonant structures is presented showing mathematical background, sources of uncertainties and theoretical and experimental limits. Methods of measurement of anisotropic materials are presented. In the second category, transmission–reflection techniques are overviewed including transmission line cells as well as free-space techniques.
Using the taxonomy of cognitive learning to model online searching

In this research, we investigated whether a learning process has unique information searching characteristics. The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels. In a laboratory experiment, we studied the searching characteristics of 72 participants engaged in 426 searching tasks. We classified the searching tasks according to Anderson and Krathwohl’s taxonomy of the cognitive learning domain. Research results indicate that applying and analyzing, the middle two of the six categories, generally take the most searching effort in terms of queries per session, topics searched per session, and total time searching. Interestingly, the lowest two learning categories, remembering and understanding, exhibit searching characteristics similar to the highest order learning categories of evaluating and creating. Our results suggest the view of Web searchers having simple information needs may be incorrect. Instead, we discovered that users applied simple searching expressions to support their higher-level information needs. It appears that searchers rely primarily on their internal knowledge for evaluating and creating information needs, using search primarily for fact checking and verification. Overall, results indicate that a learning theory may better describe the information searching process than more commonly used paradigms of decision making or problem solving. The learning style of the searcher does have some moderating effect on exhibited searching characteristics. The implication of this research is that rather than solely addressing a searcher’s expressed information need, searching systems can also address the underlying learning need of the user.
ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user’s home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd’s preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.
Spinal joint compliance and actuation in a simulated bounding quadruped robot

Spine movements play an important role in quadrupedal locomotion, yet their potential benefits in locomotion of quadruped robots have not been systematically explored. In this work, we investigate the role of spinal joint actuation and compliance on the bounding performance of a simulated compliant quadruped robot. We designed and conducted extensive simulation experiments, to compare the benefits of different spine designs, and in particular, we compared the bounding performance when (i) using actuated versus passive spinal joint, (ii) changing the stiffness of the spinal joint and (iii) altering joint actuation profiles. We used a detailed rigid body dynamics modeling to capture the main dynamical features of the robot. We applied a set of analytic tools to evaluate the bounding gait characteristics including periodicity, stability, and cost of transport. A stochastic optimization method called particle swarm optimization was implemented to perform a global search over the parameter space, and extract a pool of diverse gait solutions. Our results show improvements in bounding speed for decreasing spine stiffness, both in the passive and the actuated case. The results also suggests that for the passive spine configuration at low stiffness values, periodic solutions are hard to realize. Overall, passive spine solutions were more energy Electronic supplementary material The online version of this article (doi:10.1007/s10514-015-9540-2) contains supplementary material, which is available to authorized users. B Soha Pouya soha.pouya@epfl.ch 1 Biorobotics Laboratory, Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne (EPFL), 1015 Lausanne, Switzerland efficient and self-stable than actuated ones, but they basically exist in limited regions of parameter space. Applying more complex joint control profiles reduced the dependency of the robot’s speed to its chosen spine stiffness. In average, active spine control decreased energy efficiency and self-stability behavior, in comparison to a passive compliant spine setup.
Investigating Siamese LSTM networks for text categorization

Recently, deep learning and deep neural networks have attracted considerable attention and emerged as one predominant field of research in the artificial intelligence community. The developed techniques have also gained widespread use in various domains with good success, such as automatic speech recognition, information retrieval and text classification, etc. Among them, long short-term memory (LSTM) networks are well suited to such tasks, which can capture long-range dependencies among words efficiently, meanwhile alleviating the gradient vanishing or exploding problem during training effectively. Following this line of research, in this paper we explore a novel use of a Siamese LSTM based method to learn more accurate document representation for text categorization. Such a network architecture takes a pair of documents with variable lengths as the input and utilizes pairwise learning to generate distributed representations of documents that can more precisely render the semantic distance between any pair of documents. In doing so, documents associated with the same semantic or topic label could be mapped to similar representations having a relatively higher semantic similarity. Experiments conducted on two benchmark text categorization tasks, viz. IMDB and 20Newsgroups, show that using a three-layer deep neural network based classifier that takes a document representation learned from the Siamese LSTM sub-networks as the input can achieve competitive performance in relation to several state-of-the-art methods.
Set-Membership-Based Fault Detection and Isolation for Robotic Assembly of Electrical Connectors

This paper addresses the fault detection and isolation (FDI) problem for robotic assembly of electrical connectors in the framework of set-membership. Both the fault-free and faulty cases of assembly are modeled by different switched linear models with known switching sequences, bounded parameters, and external disturbances. The locations of switching points of each model are assumed to be inside some areas but the accurate positions are not clear. Given current input/output data, the feasible parameter set of fault-free switched linear model is obtained by sequentially calculating an optimal ellipsoid. If the pair of data is not consistent with any possible submodel, a fault is then detected. The isolation of fault is realized by checking the consistency between the data sequence and each possible fault model one by one. The robustness of the proposed FDI algorithms is proved. The effectiveness of these algorithms is verified by the robotic assembly experiments of mating electrical connectors.Note to Practitioners—In modern robotic assembly tasks, the industrial robots often need to manipulate tiny objects with complex structure. Electrical connectors are a typical kind of these objects and widely used in many industrial fields. To avoid damaging the fragile connectors and accelerate the assembly process, it is required to promptly detect and isolate the certain assembly fault in real time so that the robot can immediately implement an error recovery procedure according to the identified fault. The proposed set-membership-based fault detection and isolation (FDI) methodology satisfies both the timing and fault-isolation requirements for this kind of robotic assembly task. In terms of the set-membership theory, no false alarm will occur if there are sufficient training data for the proposed method. In addition, it turns out that the proposed method can signal an alarm faster than conventional residual-based FDI method from plentiful experiments. Although only the robotic assembly of electrical connectors is investigated, our FDI method can also be applied in the assembly task of other small and complex parts. This is especially useful for increasing the productivity and promoting the automation level of electronic industries.
Kinematic Analysis and Motion Control of Wheeled Mobile Robots in Cylindrical Workspaces

Wheeled mobile robots (WMRs) are often used for maintenance of round pipes or ducts, which can typically be represented as a cylindrical workspace. Working in round pipes or ducts, kinematic models of WMRs are different from those applying on a plane and thus pose significant challenges in terms of kinematic analysis and motion control. To address these challenges, the kinematic properties of WMRs in a cylindrical workspace are analyzed in this paper. First, we discuss the kinematic properties of a single wheel in a cylindrical workspace. Then, we analyze the geometric constraints of WMRs in round pipes or ducts with analytical geometry. Based on these analyses, kinematic properties of WMRs in cylindrical workspaces are discussed with screw theory. A control law based on biaxial clinometer information is proposed, and it enables the robot to move horizontally in round pipes or ducts. Finally, the motion of a single wheel purely rolling in a cylindrical workspace is simulated. Experiments using a car-like mobile robot moving in round ducts are carried out to show the feasibility of the proposed algorithm.