nils-herrmann/clean_scirepeval huggingface dataset:
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data

Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose we introduce an infinite Gaussian mixture model coupled with (bi-directional) generative adversarial networks, IGMM-GAN, that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.
The classroom as a dashboard: co-designing wearable cognitive augmentation for K-12 teachers

When used in classrooms, personalized learning software allows students to work at their own pace, while freeing up the teacher to spend more time working one-on-one with students. Yet such personalized classrooms also pose unique challenges for teachers, who are tasked with monitoring classes working on divergent activities, and prioritizing help-giving in the face of limited time. This paper reports on the co-design, implementation, and evaluation of a wearable classroom orchestration tool for K-12 teachers: mixed-reality smart glasses that augment teachers' realtime perceptions of their students' learning, metacognition, and behavior, while students work with personalized learning software. The main contributions are: (1) the first exploration of the use of smart glasses to support orchestration of personalized classrooms, yielding design findings that may inform future work on real-time orchestration tools; (2) Replay Enactments: a new prototyping method for real-time orchestration tools; and (3) an in-lab evaluation and classroom pilot using a prototype of teacher smart glasses (Lumilo), with early findings suggesting that Lumilo can direct teachers' time to students who may need it most.
Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review.

Emotion-related disturbances, such as depression and anxiety, have been linked to relative right-sided resting frontal electroencephalograph (EEG) asymmetry among adults and infants of afflicted mothers. However, a somewhat inconsistent pattern of findings has emerged. A meta-analysis was undertaken to (a) evaluate the magnitude of effects across EEG studies of resting frontal asymmetry and depression, anxiety, and comorbid depression and anxiety and (b) determine whether certain moderator variables could help reconcile inconsistent findings. Moderate effects of similar magnitude were obtained for the depression and anxiety studies, whereas a smaller effect emerged for comorbid studies. Three moderating variables predicted effect sizes: (a) Shorter EEG recording periods were associated with larger effects among adults, (b) different operationalizations of depression yielded effects of marginally different magnitudes, and (c) younger infant samples showed larger effects than older ones. The current data support a link between resting frontal EEG asymmetry and depression and anxiety and provide a partial account of inconsistent findings across studies.
A two-stage intelligent search algorithm for the two-dimensional strip packing problem

This paper presents a two-stage intelligent search algorithm for a two-dimensional strip packing problem without guillotine constraint. In the first stage, a heuristic algorithm is proposed, which is based on a simple scoring rule that selects one rectangle from all rectangles to be packed, for a given space. In the second stage, a local search and a simulated annealing algorithm are combined to improve solutions of the problem. In particular, a multi-start strategy is designed to enhance the search capability of the simulated annealing algorithm. Extensive computational experiments on a wide range of benchmark problems from zero-waste to non-zero-waste instances are implemented. Computational results obtained in less than 60 seconds of computation time show that the proposed algorithm outperforms the supposedly excellent algorithms reported recently, on average. It performs particularly better for large instances.
Low-Power 2.4 GHz Wake-Up Radio for Wireless Sensor Networks

Power consumption is a critical issue in many wireless sensor network scenarios where network life expectancy is measured in months or years. Communication protocols typically rely on duty-cycle mechanisms to reduce the power usage at the cost of decreased network responsiveness and increased communication latency. A low-power radio-triggered device can be used to continuously monitor the channel and activate the node for incoming communications, allowing purely asynchronous operations. To be effective, the power consumption of this wake-up receiver must be on the order of tens of microwatts since this device is always active. This paper presents the ongoing efforts to design such a low-power receiver. Initial results indicate an average power consumption below 20 uW.
Advanced Analytics for Train Delay Prediction Systems by Including Exogenous Weather Data

State-of-the-art train delay prediction systems neither exploit historical data about train movements, nor exogenous data about phenomena that can affect railway operations. They rely, instead, on static rules built by experts of the railway infrastructure based on classical univariate statistics. The purpose of this paper is to build a data-driven train delay prediction system that exploits the most recent analytics tools. The train delay prediction problem has been mapped into a multivariate regression problem and the performance of kernel methods, ensemble methods and feed-forward neural networks have been compared. Firstly, it is shown that it is possible to build a reliable and robust data-driven model based only on the historical data about the train movements. Additionally, the model can be further improved by including data coming from exogenous sources, in particular the weather information provided by national weather services. Results on real world data coming from the Italian railway network show that the proposal of this paper is able to remarkably improve the current state-of-the-art train delay prediction systems. Moreover, the performed simulations show that the inclusion of weather data into the model has a significant positive impact on its performance.
The psychological roots of anti-vaccination attitudes: A 24-nation investigation.

Strengthening of antivaccination movements in recent decades has coincided with unprecedented increases in the incidence of some communicable diseases. Many intervention programs work from a deficit model of science communication, presuming that vaccination skeptics lack the ability to access or understand evidence. However, interventions focusing on evidence and the debunking of vaccine-related myths have proven to be either nonproductive or counterproductive. Working from a motivated reasoning perspective, we examine the psychological factors that might motivate people to reject scientific consensus around vaccination. To assist with international generalizability, we examine this question in 24 countries.



We sampled 5,323 participants in 24 countries, and measured their antivaccination attitudes. We also measured their belief in conspiracy theories, reactance (the tendency for people to have a low tolerance for impingements on their freedoms), disgust sensitivity toward blood and needles, and individualistic/hierarchical worldviews (i.e., people's beliefs about how much control society should have over individuals, and whether hierarchies are desirable).



In order of magnitude, antivaccination attitudes were highest among those who (a) were high in conspiratorial thinking, (b) were high in reactance, (c) reported high levels of disgust toward blood and needles, and (d) had strong individualistic/hierarchical worldviews. In contrast, demographic variables (including education) accounted for nonsignificant or trivial levels of variance.



These data help identify the "attitude roots" that may motivate and sustain vaccine skepticism. In so doing, they help shed light on why repetition of evidence can be nonproductive, and suggest communication solutions to that problem.
When moderation is mediated and mediation is moderated.

Procedures for examining whether treatment effects on an outcome are mediated and/or moderated have been well developed and are routinely applied. The mediation question focuses on the intervening mechanism that produces the treatment effect. The moderation question focuses on factors that affect the magnitude of the treatment effect. It is important to note that these two processes may be combined in informative ways, such that moderation is mediated or mediation is moderated. Although some prior literature has discussed these possibilities, their exact definitions and analytic procedures have not been completely articulated. The purpose of this article is to define precisely both mediated moderation and moderated mediation and provide analytic strategies for assessing each.
HUmanoid Robotic Leg via pneumatic muscle actuators : implementation and control

In this article, a HUmanoid Robotic Leg (HURL) via the utilization of pneumatic muscle actuators (PMAs) is presented. PMAs are a pneumatic form of actuation possessing crucial attributes for the implementation of a design that mimics the motion characteristics of a human ankle. HURL acts as a feasibility study in the conceptual goal of developing a 10 degree-of-freedom (DoF) lower-limb humanoid for compliance and postural control, while serving as a knowledge basis for its future alternative use in prosthetic robotics. HURL’s design properties are described in detail, while its 2-DoF motion capabilities (dorsiflexion–plantar flexion, eversion–inversion) are experimentally evaluated via an advanced nonlinear PID-based control algorithm.
Hand gesture recognition using Bag-of-features and multi-class Support Vector Machine

This paper discusses the use of the Scale Invariance Feature Transform (SIFT) features for bare hand gesture recognition. In the training stage, we can not use SIFT keypoints of training images directly with a multi-class Support Vector Machine (SVM) to build a training classifier model, because of the space incompatibility of the SIFT keypoints for every training image that contains the hand gesture only. Therefore, the Bag-of-features model was introduced. After extracting the keypoints for every training image using the SIFT algorithm, a vector quantization technique is used to unify them. The quantization will map keypoints extracted from every training image into a unified dimensional histogram vector (Bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multi-class SVM to build the training classifier model. In the testing stage, the keypoints are extracted from every image captured from the webcam and fed into the cluster model to map them with one (Bag-of-words) vector, which is finally fed into the multi-class SVM training classifier model to recognize the hand gesture.
Application of Machine Learning Algorithms to an online Recruitment System

In this work, we present a novel approach for evaluating job applicants in online recruitment systems, leveraging machine learning algorithms to solve the candidate ranking problem. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants’ LinkedIn profile, and infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters; thus, it can be trusted for the automation of applicant ranking and
The timing of puberty (oocyte quality and management)

This review aims at giving an overview on the physiological events leading to puberty onset in mammals and more specifically in cattle. Puberty is an important developmental milestone in mammals involving numerous changes in various physiological regulations and behaviors. It is a physiological unique event integrating several important central regulations at the crossroad of adaptation to environment: reproductive axis, feeding behavior and nutritional controls, growth, seasonal rhythm and stress. Puberty onset is also an important economic parameter in replacement heifer program and in genomic selection (genomic bulls). The quest for advanced puberty onset should be carefully balanced by its impact on physiological parameters of the animal and its offspring. Thus one has to carefully consider each step leading to puberty onset and set up a strategy that will lead to early puberty without being detrimental in the long term. In this review, major contributions in the understanding of puberty process obtained in rodents, primates and farm animals such as sheep and cattle are discussed. In the first part we will detail the endocrine events leading to puberty onset with a special focus on the regulation of GnRH secretion. In the second part we will describe the neural mechanisms involved in silencing and reactivating the GnRH neuronal network. These central mechanisms are at the crossroad of the integration of environmental factors such as the nutritional status, the stress and the photoperiod that will be discussed in the third part. In the fourth part, we will discuss the genetic determinants of puberty onset and more particularly in humans, where several pathologies are associated with puberty delay or advance and in cattle where several groups have now identified genomic regions or gene networks associated with puberty traits. Last but not least, in the last part we will focus on the embryologist point of view, how to get good oocytes for in vitro fertilization and embryo development from younger animals.
Continual Match Based Training in Pommerman: Technical Report

Continual learning is the ability of agents to improve their capacities throughout multiple tasks continually. While recent works in the literature of continual learning mostly focused on developing either particular loss functions or specialized structures of neural network explaining the episodic memory or neural plasticity, we study continual learning from the perspective of the training mechanism. Specifically, we propose a COnitnual Match BAsed Training (COMBAT) framework for training a population of advantage-actor-critic (A2C) agents in Pommerman, a partially observable multi-agent environment with no communication. Following the COMBAT framework, we trained an agent, namely, Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018 Pommerman Competition. Two critical features of our agent are worth mentioning. Firstly, our agent did not learn from any demonstrations. Secondly, our agent is highly reproducible. As a technical report, we articulate the design of state space, action space, reward, and most importantly, the COMBAT framework for our Pommerman agent. We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones. Finally, the result in the Pommerman Competition verifies the robustness of our agent when competing with various opponents.
Miniature Continuous Coverage Antenna Array for GNSS Receivers

This letter presents a miniature conformal array that provides continuous coverage and good axial ratio from 1100-1600 MHz. Concurrently, it maintains greater than 1.5 dBic RHCP gain and return loss less than -10 dB. The four element array is comprised of two-arm slot spirals with lightweight polymer substrate dielectric loading and a novel termination resistor topology. Multiple elements provide the capability to suppress interfering signals common in GNSS applications. The array, including feeding network, is 3.5" times 3.5" times 0.8" in size and fits into the FRPA-3 footprint and radome.
Coping through emotional approach: scale construction and validation.

Four studies demonstrate the psychometric adequacy and validity of scales designed to assess coping through emotional approach. In separate undergraduate samples, exploratory and confirmatory factor analyses of dispositional (Study 1) and situational (Study 3) coping item sets yielded 2 distinct emotional approach coping factors: emotional processing (i.e., active attempts to acknowledge and understand emotions) and emotional expression. The 2 scales yielded high internal consistency and test-retest reliability, as well as convergent and discriminant validity. A study (Study 2) of young adults and their parents established the scales' interjudge reliabilities. Longitudinal (Study 3) and experimental (Study 4) research supported the predictive validity of the emotional approach coping scales with regard to adjustment to stressful encounters. Findings highlight the utility of functionalist theories of emotion as applied to coping theory.
Topological structural analysis of digitized binary images by border following

Two border following algorithms are proposed for the topological analysis of digitized binary images. The first one determines the surroundness relations among the borders of a binary image. Since the outer borders and the hole borders have a one-to-one correspondence to the connected components of l-pixels and to the holes, respectively, the proposed algorithm yields a representation of a binary image, from which one can extract some sort of features without reconstructing the image. The second algorithm, which is a modified version of the first, follows only the outermost borders (i.e., the outer borders which are not surrounded by holes). These algorithms can be effectively used in component counting, shrinking, and topological structural analysis of binary images, when a sequential digital computer is used.
Assessing online collaborative learning: process and product

The assessment of online collaborative study presents new opportunities and challenges, both in terms of separating the process and product of collaboration, and in the support of skills development. The purpose of this paper is to explore the role of assessment with respect to the processes and products of online collaborative study. It describes a qualitative case study of staff and students perspectives on two UK Open University courses which have used a variety of models of online collaborative assessment. The findings underline the importance of assessment in ensuring online participation, and in supporting the practice and development of online collaborative learning. They have led to a number of recommendations for the assessment of online collaborative learning.
New sensorless control for brushless DC motors using disturbance observers and adaptive velocity estimations

A brushless dc motor has been represented by a nonlinear equation. Therefore, it has been difficult to apply the linear control theory to brushless dc motor systems. In this paper, to apply the linear control theory to brushless dc motor systems, we propose considering the nonlinear term of the equation as a disturbance and to realize a sensorless control of the brushless dc motor using both the disturbance observer and the adaptive velocity estimation. With proper pole locations of the disturbance observer, stabiity of the position estimation is guaranteed, and stability of the adaptive velocity estimation is also guaranteed by Popov’s hyperstability theory. The experimental results show that the proposed method is very useful.
Sampling-based algorithms for optimal motion planning with deterministic μ-calculus specifications

Automatic generation of control programs that satisfy complex task specifications given in high-level specification languages such as temporal logics has been studied extensively. However, optimality of such control programs, for instance with respect to a cost function, has received relatively little attention. In this paper, we study the problem of optimal trajectory synthesis for a large class of specifications, given in the form of deterministic mu-calculus. We propose a sampling-based algorithm, based on the Rapidly-exploring Random Graphs (RRGs), that solves this problem with probabilistic completeness and asymptotic optimality guarantees. We evaluate our algorithm in a simulation studies involving a curvature constrained car. Our simulation results show that in this scenario the algorithm quickly discovers a trajectory that satisfies the specification, and improves this trajectory towards an optimal one if allowed more computation time. We also point out connections to (optimal) memoryless winning strategies in infinite parity games, which may be of independent interest.
Re-conceptualizing Information System Success: The IS-Impact Measurement Model

This paper re-conceptualizes 'information system success' as a formative, multidimensional index. A validated and widely accepted such index would facilitate cumulative research on the impacts of IS, while at the same time providing a benchmark for organizations to track their IS performance. The proposed IS-Impact measurement model represents the stream of net benefits from an Information System (IS), to date and anticipated, as perceived by all key-user-groups. Model measures are formulated to be robust, economical and simple, yielding results that are comparable across diverse systems and contexts, and from multiple user perspectives. The model includes 4 dimensions in two halves. The 'impact' half measures benefits to date, or Individual-and Organizational-Impact; the 'quality' half, uses System-Quality and Information-Quality as proxies for probable future impacts. Study findings evidence the necessity, additivity and completeness of these four dimensions. The validation study involved three separate surveys, including exploratory and confirmatory phases preceded by an identification-survey. Content analysis of 485 qualitative 'impacts' cited by 137 respondents from across 27 Australian Government Agencies that implemented SAP Financials in the late 90's, identified salient dimensions and measures. The resultant a-priori model ('pool' of 37 measures) was operationalized in the subsequent specification-survey, yielding 310 responses across the same 27 agencies. The confirmation-survey, employing 27 validated measures from the specification-survey, was next conducted in a large university that had implemented ORACLE Financials. Confirmatory analysis of the 153 responses provides further strong evidence of model validity.  The authors acknowledge the extensive and conscientious efforts of