nils-herrmann/clean_scirepeval huggingface dataset:
Cognitive presence in asynchronous online learning: a comparison of four discussion strategies

Some scholars argue that students do not achieve higher level learning, or cognitive presence, in online courses. Online discussion has been proposed to bridge this gap between online and face-to-face learning environments. However, the literature indicates that the conventional approach to online discussion – asking probing questions – does not necessarily advance the discussion through the phases of cognitive presence: triggering events, exploration, integration and resolution, which are crucial for deep knowledge construction. Using mixed methods, we examined the contribution of four scenario-based online discussion strategies – structured, scaffolded, debate and role play – to the learners’ cognitive presence, the outcome of the discussion. Learners’ discussion postings within each strategy were segmented and categorized according to the four phases. The discussion strategies, each using the same authentic scenario, were then compared in terms of the number of segments representing these phases. We found that the structured strategy, while highly associated with triggering events, produced no discussion pertaining to the resolution phase. The scaffolded strategy, on the other hand, showed a strong association with the resolution phase. The debate and role-play strategies were highly associated with exploration and integration phases. We concluded that discussion strategies requiring learners to take a perspective in an authentic scenario facilitate cognitive presence, and thus critical thinking and higher levels of learning. We suggest a heuristic for sequencing a series of discussion forums and recommend areas for further related research.
Crowdsourcing Narrative Intelligence

Narrative intelligence is an important part of human cognition, especially in sensemaking and communicating with people. Humans draw on a lifetime of relevant experiences to explain stories, to tell stories, and to help choose the most appropriate actions in real-life settings. Manual authoring the required knowledge presents a significant bottleneck in the creation of systems demonstrating narrative intelligence. In this paper, we describe a novel technique for automatically learning script-like narrative knowledge from crowdsourcing. By leveraging human workers’ collective understanding of social and procedural constructs, we can learn a potentially unlimited range of scripts regarding how real-world situations unfold. We present quantitative evaluations of the learned primitive events and the temporal ordering of events, which suggest we can identify orderings between events with high accuracy.
Ultrasound texture-based CAD system for detecting neuromuscular diseases

Diagnosis of neuromuscular diseases in ultrasonography is a challenging task since experts are often unable to discriminate between healthy and pathological cases. A computer-aided diagnosis (CAD) system for skeletal muscle ultrasonography was developed and tested for myositis detection in ultrasound images of biceps brachii. Several types of features were extracted from rectangular and polygonal image regions-of-interest (ROIs), including first-order statistics, wavelet-based features, and Haralick’s features. Features were chosen that are sensitive to the change in contrast and structure for pathological ultrasound images of neuromuscular diseases. The number of features was reduced by applying different sequential feature selection strategies followed by a supervised principal component analysis. For classification, two linear approaches were investigated: Fisher’s classifier and the linear support vector machine (SVM) as well as the nonlinear $$k$$ k -nearest neighbor approach. The CAD system was benchmarked on datasets of 18 subjects, seven of which were healthy, while 11 were affected by myositis. Three expert radiologists provided pre-classification and testing interpretations. Leave-one-out cross-validation on the training data revealed that the linear SVM was best suited for discriminating healthy and pathological muscle tissue, achieving 85/87 % accuracy, 90 % sensitivity, and 83/85 % specificity, depending on the radiologist. A muscle ultrasonography CAD system was developed, allowing a classification of an ultrasound image by one-click positioning of rectangular ROIs with minimal user effort. The applicability of the system was demonstrated with the challenging example of myositis detection, showing highly accurate results that were robust to imprecise user input.
Cardiac coherence, self-regulation, autonomic stability, and psychosocial well-being

The ability to alter one's emotional responses is central to overall well-being and to effectively meeting the demands of life. One of the chief symptoms of events such as trauma, that overwhelm our capacities to successfully handle and adapt to them, is a shift in our internal baseline reference such that there ensues a repetitive activation of the traumatic event. This can result in high vigilance and over-sensitivity to environmental signals which are reflected in inappropriate emotional responses and autonomic nervous system dynamics. In this article we discuss the perspective that one's ability to self-regulate the quality of feeling and emotion of one's moment-to-moment experience is intimately tied to our physiology, and the reciprocal interactions among physiological, cognitive, and emotional systems. These interactions form the basis of information processing networks in which communication between systems occurs through the generation and transmission of rhythms and patterns of activity. Our discussion emphasizes the communication pathways between the heart and brain, as well as how these are related to cognitive and emotional function and self-regulatory capacity. We discuss the hypothesis that self-induced positive emotions increase the coherence in bodily processes, which is reflected in the pattern of the heart's rhythm. This shift in the heart rhythm in turn plays an important role in facilitating higher cognitive functions, creating emotional stability and facilitating states of calm. Over time, this establishes a new inner-baseline reference, a type of implicit memory that organizes perception, feelings, and behavior. Without establishing a new baseline reference, people are at risk of getting "stuck" in familiar, yet unhealthy emotional and behavioral patterns and living their lives through the automatic filters of past familiar or traumatic experience.
Computer Security Incident Response Team Development and Evolution

When computer security incidents occur, it's critical that organizations be able to handle them in a timely manner. The speed with which an organization can recognize, analyze, and respond to an incident will affect the damage and lower recovery costs. Organized incident management requires defined, repeatable processes and the ability to learn from incidents that threaten the confidentiality, availability, and integrity of critical systems and data. Some organizations assign responsibility for incident management to a defined group of people or a designated unit, such as a computer security incident response team. This article looks at the development, purpose, and evolution of such specialized teams; the evolving nature of attacks they must deal with; and methods to evaluate the performance of such teams as well as the emergence of information sharing as a core service.
Key Challenges in Defending Against Malicious Socialbots

Popular Internet sites are under attack all the time from phishers, fraudsters, and spammers. They aim to steal user information and expose users to unwanted spam. The attackers have vast resources at their disposal. They are well-funded, with full-time skilled labor, control over compromised and infected accounts, and access to global botnets. Protecting our users is a challenging adversarial learning problem with extreme scale and load requirements. Over the past several years we have built and deployed a coherent, scalable, and extensible realtime system to protect our users and the social graph. This Immune System performs realtime checks and classifications on every read and write action. As of March 2011, this is 25B checks per day, reaching 650K per second at peak. The system also generates signals for use as feedback in classifiers and other components. We believe this system has contributed to making Facebook the safest place on the Internet for people and their information. This paper outlines the design of the Facebook Immune System, the challenges we have faced and overcome, and the challenges we continue to face.
High gain low profile slotted SIW cavity antenna for 5G applications

High gain low profile slotted cavity with substrate integrated waveguide (SIW) is presented using TE440 high order mode. The proposed antenna is implemented to achieve 16.4 dBi high gain at 28 GHz with high radiation efficiency of 98%. Furthermore, the proposed antenna has a good radiation pattern. Simulated results using CST and HFSS software are presented and discussed. Several advantages such as low profile, low cost, light weight, small size, and easy implementation make the proposed antenna suitable for millimeter-wave wireless communications.
Ongoing Evolution of Visual SLAM from Geometry to Deep Learning: Challenges and Opportunities

Visual simultaneous localization and mapping (SLAM) has been investigated in the robotics community for decades. Significant progress and achievements on visual SLAM have been made, with geometric model-based techniques becoming increasingly mature and accurate. However, they tend to be fragile under challenging environments. Recently, there is a trend to develop data-driven approaches, e.g., deep learning, for visual SLAM problems with more robust performance. This paper aims to witness the ongoing evolution of visual SLAM techniques from geometric model-based to data-driven approaches by providing a comprehensive technical review. Our contribution is not only just a compilation of state-of-the-art end-to-end deep learning SLAM work, but also an insight into the underlying mechanism of deep learning SLAM. For such a purpose, we provide a concise overview of geometric model-based approaches first. Next, we identify visual depth estimation using deep learning is a starting point of the evolution. It is from depth estimation that ego-motion or pose estimation techniques using deep learning flourish rapidly. In addition, we strive to link semantic segmentation using deep learning with emergent semantic SLAM techniques to shed light on simultaneous estimation of ego-motion and high-level understanding. Finally, we visualize some further opportunities in this research direction.
A review on artificial intelligence techniques for developing intelligent honeypot

Honeypot is a closely monitored computer resource that emulates behaviors of production host within a network in order to lure and attract the attackers. The workability and effectiveness of a deployed honeypot depends on its technical configuration. Since honeypot is a resource that is intentionally made attractive to the attackers, it is crucial to make it intelligent and self-manageable. This research reviews at artificial intelligence techniques such as expert system and case-based reasoning, in order to build an intelligent honeypot.
Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment

To operate reliably in real-world traffic, an autonomous car must evaluate the consequences of its potential actions by anticipating the uncertain intentions of other traffic participants. This paper presents an integrated behavioral inference and decision-making approach that models vehicle behavior for both our vehicle and nearby vehicles as a discrete set of closedloop policies that react to the actions of other agents. Each policy captures a distinct high-level behavior and intention, such as driving along a lane or turning at an intersection. We first employ Bayesian changepoint detection on the observed history of states of nearby cars to estimate the distribution over potential policies that each nearby car might be executing. We then sample policies from these distributions to obtain high-likelihood actions for each participating vehicle. Through closed-loop forward simulation of these samples, we can evaluate the outcomes of the interaction of our vehicle with other participants (e.g., a merging vehicle accelerates and we slow down to make room for it, or the vehicle in front of ours suddenly slows down and we decide to pass it). Based on those samples, our vehicle then executes the policy with the maximum expected reward value. Thus, our system is able to make decisions based on coupled interactions between cars in a tractable manner. This work extends our previous multipolicy system [11] by incorporating behavioral anticipation into decision-making to evaluate sampled potential vehicle interactions. We evaluate our approach using real-world traffic-tracking data from our autonomous vehicle platform, and present decision-making results in simulation involving highway traffic scenarios.
Remote water quality monitoring system using wireless sensors

It is found that the current manual water quality monitoring entails tedious process and is time consuming. To alleviate the problems caused by the manual monitoring and the lack of effective system for prawn farming, a remote water quality monitoring for prawn farming pond is proposed. The proposed system is leveraging on wireless sensors in detecting the water quality and Short Message Service (SMS) technology in delivering alert to the farmers upon detection of degradation of the water quality. Three water quality parameters that are critical to the prawn health are monitored, which are pH, temperature and dissolved oxygen. In this paper, the details of system design and implementation are presented. The results obtained in the preliminary survey study served as the basis for the development of the system prototype. Meanwhile, the results acquired through the usability testing imply that the system is able to meet the users’ needs.  Remote monitoring, Water Quality, Wireless sensors
Semantics-aware detection of targeted attacks: a survey

In today’s interconnected digital world, targeted attacks have become a serious threat to conventional computer systems and critical infrastructure alike. Many researchers contribute to the fight against network intrusions or malicious software by proposing novel detection systems or analysis methods. However, few of these solutions have a particular focus on Advanced Persistent Threats or similarly sophisticated multi-stage attacks. This turns finding domain-appropriate methodologies or developing new approaches into a major research challenge. To overcome these obstacles, we present a structured review of semantics-aware works that have a high potential for contributing to the analysis or detection of targeted attacks. We introduce a detailed literature evaluation schema in addition to a highly granular model for article categorization. Out of 123 identified papers, 60 were found to be relevant in the context of this study. The selected articles are comprehensively reviewed and assessed in accordance to Kitchenham’s guidelines for systematic literature reviews. In conclusion, we combine new insights and the status quo of current research into the concept of an ideal systemic approach capable of semantically processing and evaluating information from different observation points.
Unsupervised Representation Adversarial Learning Network: from Reconstruction to Generation

A good representation for arbitrarily complicated data should have the capability of semantic generation, clustering and reconstruction. Previous research has already achieved impressive performance on either one. This paper aims at learning a disentangled representation effective for all of them in an unsupervised way. To achieve all the three tasks together, we learn the forward and inverse mapping between data and representation on the basis of a symmetric adversarial process. In theory, we minimize the upper bound of the two conditional entropy loss between the latent variables and the observations together to achieve the cycle consistency. The newly proposed RepGAN is tested on MNIST, fashionMNIST, CelebA, and SVHN datasets to perform unsupervised or semi-supervised classification, generation and reconstruction tasks. The result demonstrates that RepGAN is able to learn a useful and competitive representation. To the author’s knowledge, our work is the first one to achieve both a high unsupervised classification accuracy and low reconstruction error on MNIST.
Fast and automatic video object segmentation and tracking for content-based applications

The new video-coding standard MPEG-4 enables content-based functionality, as well as high coding efficiency, by taking into account shape information of moving objects. A novel algorithm for segmentation of moving objects in video sequences and extraction of video object planes (VOPs) is proposed in this paper. For the case of multiple video objects in a scene, the extraction of a specific single video object (VO) based on connected components analysis and smoothness of VO displacement in successive frames is also discussed. Our algorithm begins with a robust double-edge map derived from the difference between two successive frames. After removing edge points which belong to the previous frame, the remaining edge map, moving edge (ME), is used to extract the VOP. The proposed algorithm is evaluated on an indoor sequence captured by a low-end camera as well as MPEG-4 test sequences and produces promising results.
Sentiment Informed Cyberbullying Detection in Social Media

Cyberbullying is a phenomenon which negatively affects the individuals, the victims suffer from various mental issues, ranging from depression, loneliness, anxiety to low self-esteem. In parallel with the pervasive use of social media, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include the use of standards and guidelines, human moderators, and blacklists based on the profane words. However, these mechanisms fall short in social media and cannot scale well. Therefore, it is necessary to develop a principled learning framework to automatically detect cyberbullying behaviors. However, it is a challenging task due to short, noisy and unstructured content information and intentional obfuscation of the abusive words or phrases by social media users. Motivated by sociological and psychological findings on bullying behaviors and the correlation with emotions, we propose to leverage sentiment information to detect cyberbullying behaviors in social media by proposing a sentiment informed cyberbullying detection framework. Experimental results on two realworld, publicly available social media datasets show the superiority of the proposed framework. Further studies validate the effectiveness of leveraging sentiment information for cyberbullying detection.
Printed circuit board implementation of small inductors for domestic induction heating applications using a planar litz wire structure

In this paper, the design and implementation of small inductors in printed circuit board (PCB) for domestic induction heating applications is presented. With this purpose, we have developed both a manufacturing technique and an electromagnetic model of the system based on finite-element method (FEM) simulations. The inductor arrangement consists of a stack of printed circuit boards in which a planar litz wire structure is implemented. The developed PCB litz wire structure minimizes the losses in a similar way to the conventional multi-stranded litz wires; whereas the stack of PCBs allows increasing the power transferred to the pot. Different prototypes of the proposed PCB inductor have been measured at low signal levels. Finally, a PCB inductor has been integrated in an electronic stage to test at high signal levels, i.e. in the similar working conditions to the commercial application.
Assessment of degree of risk from sources of microbial contamination in cleanrooms ; 2 : Surfaces and liquids Introduction

The requirements for minimising microbial contamination in pharmaceutical cleanrooms are outlined in regulatory documents published by authorities that include the European Commission1 and the Food and Drug Administration in the USA2. These authorities also suggest the use of risk management and assessment techniques to identify and control sources of microbial contamination3,4. Risk assessment and management methods have been investigated by the authors of this article5–9 and other approaches are discussed by Mollah et al10. Risk assessment methods are used to calculate the degree of risk to the product from microbial sources in a cleanroom. Factors that influence risk are determined and assigned descriptors of risk, which are of the ‘high’, ‘medium’, and ‘low’ type that act as surrogates for actual numerical values. Numerical scores are assigned to these descriptors and the scores combined, usually by multiplication, to obtain a risk assessment for each source of contamination. However, a risk assessment carried out in this manner may not be accurate, for the following reasons.
Region-based Activity Recognition Using Conditional GAN

We present a method for activity recognition that first estimates the activity performer's location and uses it with input data for activity recognition. Existing approaches directly take video frames or entire video for feature extraction and recognition, and treat the classifier as a black box. Our method first locates the activities in each input video frame by generating an activity mask using a conditional generative adversarial network (cGAN). The generated mask is appended to color channels of input images and fed into a VGG-LSTM network for activity recognition. To test our system, we produced two datasets with manually created masks, one containing Olympic sports activities and the other containing trauma resuscitation activities. Our system makes activity prediction for each video frame and achieves performance comparable to the state-of-the-art systems while simultaneously outlining the location of the activity. We show how the generated masks facilitate the learning of features that are representative of the activity rather than accidental surrounding information.
Residual Policy Learning

We present Residual Policy Learning (RPL): a simple method for improving nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in complex robotic manipulation tasks where good but imperfect controllers are available. In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning a residual on top of the initial controller can yield substantial improvement. We study RPL in five challenging MuJoCo tasks involving partial observability, sensor noise, model misspecification, and controller miscalibration. By combining learning with control algorithms, RPL can perform long-horizon, sparse-reward tasks for which reinforcement learning alone fails. Moreover, we find that RPL consistently and substantially improves on the initial controllers. We argue that RPL is a promising approach for combining the complementary strengths of deep reinforcement learning and robotic control, pushing the boundaries of what either can achieve independently.