nils-herrmann/clean_scirepeval huggingface dataset:
OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation

If we know most of Smith’s friends are from Boston, what can we say about the rest of Smith’s friends? In this paper, we focus on the node classification problem on networks, which is one of the most important topics in AI and Web communities. Our proposed algorithm which is referred to as OMNIProp has the following properties: (a) seamless and accurate; it works well on any label correlations (i.e., homophily, heterophily, and mixture of them) (b) fast; it is efficient and guaranteed to converge on arbitrary graphs (c) quasi-parameter free; it has just one well-interpretable parameter with heuristic default value of 1. We also prove the theoretical connections of our algorithm to the semi-supervised learning (SSL) algorithms and to random-walks. Experiments on four real, different network datasets demonstrate the benefits of the proposed algorithm, where OMNI-Prop outperforms the top competitors.
Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach

It is well known that post-secondary science and engineering programs attract fewer female students. In this paper, we analyze gender differences through text mining of over 30,000 applications to the engineering faculty of a large North American university. We use syntactic and semantic analysis methods to highlight differences in motivation, interests and background. Our analysis leads to three main findings. First, female applicants demonstrate a wider breadth of experience, whereas male applicants put a greater emphasis on technical depth. Second, more female applicants demonstrate a greater desire to serve society. Third, female applicants are more likely to mention personal influences for studying engineering.
Distinguishing Between Natural and Computer-Generated Images Using Convolutional Neural Networks

Distinguishing between natural images (NIs) and computer-generated (CG) images by naked human eyes is difficult. In this paper, we propose an effective method based on a convolutional neural network (CNN) for this fundamental image forensic problem. Having observed the rather limited performance of training existing CCNs from scratch or fine-tuning pre-trained network, we design and implement a new and appropriate network with two cascaded convolutional layers at the bottom of a CNN. Our network can be easily adjusted to accommodate different sizes of input image patches while maintaining a fixed depth, a stable structure of CNN, and a good forensic performance. Considering the complexity of training CNNs and the specific requirement of image forensics, we introduce the so-called local-to-global strategy in our proposed network. Our CNN derives a forensic decision on local patches, and a global decision on a full-sized image can be easily obtained via simple majority voting. This strategy can also be used to improve the performance of existing methods that are based on hand-crafted features. Experimental results show that our method outperforms existing methods, especially in a challenging forensic scenario with NIs and CG images of heterogeneous origins. Our method also has good robustness against typical post-processing operations, such as resizing and JPEG compression. Unlike previous attempts to use CNNs for image forensics, we try to understand what our CNN has learned about the differences between NIs and CG images with the aid of adequate and advanced visualization tools.
When Ignorance is Bliss : Information , Fairness , and Bargaining Efficiency

Most theories of legal discovery assume that sharing information among disputing parties will lead to convergence of expectations and will facilitate settlement. However, psychological research shows that shared information, if it is open to multiple interpretations, is likely to be interpreted egocentrically by the disputants, which can cause beliefs to diverge rather than converge. We present results from a bargaining experiment which shows that information sharing leads to divergence of expectations, and to settlement delays, when the information exchanged is amenable to multiple interpretations. By contrast, when there is only one obvious interpretation, information sharing leads to convergence of expectations and speeds settlement. We show, further, that information sharing moderates the relationship between size of the bargaining zone and the prospects for settlement. Information and efficiency 3 When Ignorance is Bliss: Information, Fairness, and Bargaining Efficiency
Approximate Steepest Coordinate Descent

We propose a new selection rule for the coordinate selection in coordinate descent methods for huge-scale optimization. The efficiency of this novel scheme is provably better than the efficiency of uniformly random selection, and can reach the efficiency of steepest coordinate descent (SCD), enabling an acceleration of a factor of up to n, the number of coordinates. In many practical applications, our scheme can be implemented at no extra cost and computational efficiency very close to the faster uniform selection. Numerical experiments with Lasso and Ridge regression show promising improvements, in line with our theoretical guarantees.
A machine learning approach for medication adherence monitoring using body-worn sensors

One of the most important challenges in chronic disease self-management is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these approaches are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists.
Trust and Consequences: A Visual Perspective

User interface (UI) composition and information presentation can impact human trust behavior. Trust is a complex concept studied by disciplines like psychology, sociology, economics, and computer science. Definitions of trust vary depending on the context, but are typically based on the core concept of “reliance on another person or entity”. Trust is a critical concept since the presence or absence of the right level of trust can affect user behavior, and ultimately, the overall system performance. In this paper, we look across four studies to explore the relationship between UI elements and human trust behavior. Results indicate that UI composition and information presentation can impact human trust behavior. While further research is required to corroborate and generalize these results, we hope that this paper will provide a reference point for future studies by identifying UI elements that are likely to influence human trust.
When static media promote active learning: annotated illustrations versus narrated animations in multimedia instruction.

In 4 experiments, students received a lesson consisting of computer-based animation and narration or a lesson consisting of paper-based static diagrams and text. The lessons used the same words and graphics in the paper-based and computer-based versions to explain the process of lightning formation (Experiment 1), how a toilet tank works (Experiment 2), how ocean waves work (Experiment 3), and how a car's braking system works (Experiment 4). On subsequent retention and transfer tests, the paper group performed significantly better than the computer group on 4 of 8 comparisons, and there was no significant difference on the rest. These results support the static media hypothesis, in which static illustrations with printed text reduce extraneous processing and promote germane processing as compared with narrated animations.
Sentiment based Food Classification for Restaurant Business

In this paper, we address the problem of classifying the restaurant reviews into different meal courses based on the review sentiments. Based on our survey, we observe that there are no works which focus on classifying the food items into different meal courses with top $\boldsymbol{n}$ food items under each course using restaurant reviews. Most of the works in literature address the issues like predicting the ratings of the restaurant or restaurant business strategies by considering various attributes of the restaurant. Thus, in this paper, we propose a sentiment based food classification framework consisting of two tasks, namely, sentiment classification and four-course meal classification. In sentiment classification, we classify the reviews into positive and negative categories based on the sentiments of the reviews. In four-course meal classification, we categorize the reviews into four courses, namely, soups and salads, appetizers, main course and desserts. We list the top $\boldsymbol{n}$ food items liked by most of the customers in each of these courses. In order to select the suitable classification technique for addressing the identified problem, we analyze the learning curves based on the bias-variance trade-off. We observe that support vector machines (SVM) classification technique outperforms other techniques. The performance analysis of the proposed framework is carried out on the standard Yelp dataset. The top 5 food items obtained in each of the categories are soups and salads: salad, corn, coffee, cocktail, tea; appetizers: burger, pizza, bread, cheese, frie; main course: steak, meal, chicken, meat, beef; and desserts: doughnut, cream, cake, chocolate, flan.
BLE-Backscatter: Ultralow-Power IoT Nodes Compatible With Bluetooth 4.0 Low Energy (BLE) Smartphones and Tablets

Backscatter communication promises significant power and complexity advantages for Internet of Things devices such as radio frequency identification (RFID) tags and wireless sensor nodes. One perceived disadvantage of backscatter communication has been the requirement for specialized hardware such as RFID readers to receive backscatter signals. In this paper, we show how backscatter signals can be designed for compatibility with the Bluetooth 4.0 low energy (BLE) chipsets already present in billions of smart phones and tablets. We present a prototype microcontroller-based “BLE-Backscatter” tag that produces bandpass frequency-shift keying modulation at 1 Mb/s, enabling compatibility with conventional BLE advertising channels. Using a +23-dBm equivalent isotropically radiated power continuous wave (CW) carrier source, we demonstrate a range of up to 13 m between the tag and an unmodified Apple iPad Mini as well as a PC with the Nordic Semiconductor nRF51822 chipset. With the tag 1 m from the receiver, we demonstrate a range of up to 30 m between the CW carrier source and the tag. In both cases, the existing Bluetooth stack was used, with no modifications whatsoever to hardware, firmware, or software. The backscatter tag consumes only 1.56 nJ/b, over  $6\times $  less than the lowest power commercial Bluetooth transmitters.
Kissing Nevus of the Penis

Kissing or divided nevi are similar in shape to congenital melanocytic nevi located on an adjacent part of the body that are separated during embryogenesis. Kissing nevi of the upper and lower eyelids have been reported infrequently since the first report in 1908. Kissing nevi of the penis are very rare, with only 12 cases being reported until now, and this is the first case report in the Korean dermatological literature. A previously healthy 27-year-old man presented with asymptomatic black colored patches, which were detected 10 years ago, on the glans penis and the prepuce with growth in size. We report here a case of kissing nevus of the penis, which showed an obvious mirror-image symmetry relative to the coronal sulcus.
Climbing the "Stairway to Heaven" -- A Mulitiple-Case Study Exploring Barriers in the Transition from Agile Development towards Continuous Deployment of Software

Agile software development is well-known for its focus on close customer collaboration and customer feedback. In emphasizing flexibility, efficiency and speed, agile practices have lead to a paradigm shift in how software is developed. However, while agile practices have succeeded in involving the customer in the development cycle, there is an urgent need to learn from customer usage of software also after delivering and deployment of the software product. The concept of continuous deployment, i.e. the ability to deliver software functionality frequently to customers and subsequently, the ability to continuously learn from real-time customer usage of software, has become attractive to companies realizing the potential in having even shorter feedback loops. However, the transition towards continuous deployment involves a number of barriers. This paper presents a multiple-case study in which we explore barriers associated with the transition towards continuous deployment. Based on interviews at four different software development companies we present key barriers in this transition as well as actions that need to be taken to address these.
Towards large-scale twitter mining for drug-related adverse events

Drug-related adverse events pose substantial risks to patients who consume post-market or Drug-related adverse events pose substantial risks to patients who consume post-market or investigational drugs. Early detection of adverse events benefits not only the drug regulators, but also the manufacturers for pharmacovigilance. Existing methods rely on patients' "spontaneous" self-reports that attest problems. The increasing popularity of social media platforms like the Twitter presents us a new information source for finding potential adverse events. Given the high frequency of user updates, mining Twitter messages can lead us to real-time pharmacovigilance. In this paper, we describe an approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers. Due to the size nature of the dataset (i.e., 2 billion Tweets), the experiments were conducted on a High Performance Computing (HPC) platform using MapReduce, which exhibits the trend of big data analytics. The results suggest that daily-life social networking data could help early detection of important patient safety issues.
0 EEG Signal Processing for Epilepsy

At the present moment, three methods can record functional and physiological changes within the brain with high temporal resolution of neuronal interactions at the network level: the electroencephalogram (EEG), the magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI); each of these has advantages and shortcomings. MEG is not practical for experimental work when subjects may move freely, because of the large size of magnetic sensors. For image sequences, fMRI has a time resolution very low and many types of EEG activities, brain disorders and neurodegenerative diseases cannot be recorded. On the other hand the spatial resolution of the EEG is limited to the number of electrodes, as described in Ebersole & Pedley (2003); Sanei & Chambers (2007).
Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health.

In the present paper, we describe a model of neurovisceral integration in which a set of neural structures involved in cognitive, affective, and autonomic regulation are related to heart rate variability (HRV) and cognitive performance.



We detail the pathways involved in the neural regulation of the cardiovascular system and provide pharmacological and neuroimaging data in support of the neural structures linking the central nervous system to HRV in humans. We review a number of studies from our group showing that individual differences in HRV are related to performance on tasks associated with executive function and prefrontal cortical activity. These studies include comparisons of executive- and nonexecutive-function tasks in healthy participants, in both threatening and nonthreatening conditions. In addition, we show that manipulating resting HRV levels is associated with changes in performance on executive-function tasks. We also examine the relationship between HRV and cognitive performance in ecologically valid situations using a police shooting simulation and a naval navigation simulation. Finally, we review our studies in anxiety patients, as well as studies examining psychopathy.



These findings in total suggest an important relationship among cognitive performance, HRV, and prefrontal neural function that has important implications for both physical and mental health. Future studies are needed to determine exactly which executive functions are associated with individual differences in HRV in a wider range of situations and populations.
Emergency situation awareness from twitter for crisis management

This paper describes ongoing work with the Australian Government to detect, assess, summarise, and report messages of interest for crisis coordination published by Twitter. The developed platform and client tools, collectively termed the Emergency Situation Awareness - Automated Web Text Mining (ESA-AWTM) system, demonstrate how relevant Twitter messages can be identified and utilised to inform the situation awareness of an emergency incident as it unfolds.
 A description of the ESA-AWTM platform is presented detailing how it may be used for real life emergency management scenarios. These scenarios are focused on general use cases to provide: evidence of pre-incident activity; near-real-time notification of an incident occurring; first-hand reports of incident impacts; and gauging the community response to an emergency warning. Our tools have recently been deployed in a trial for use by crisis coordinators.
Web 2 . 0 Social Networks : The Role of Trust

Online social networks (OSNs) have gained enormous popularity in recent years. Hundreds of millions of social network users reveal great amounts of personal information in the Web 2.0 environment that is largely devoid of security standards and practices. The central question in this article is why so many social network users are being so trusting. The focus is on theory-building on trust as a critical issue in OSNs. A theoretical framework is developed, which facilitates a multi-level and multi-dimensional analysis of research problems related to trust in OSNs. First, the structural and relational underpinnings of trust in OSNs are investigated from a governance perspective that integrates concepts of social network theory, social capital and the role of value in relational exchanges. Subsequently, the focus moves to the individual’s decision to trust and to processes through which trust actually emerges. Different types and sources of trust from the trust literature and their importance for trust-related decisions and behaviours in OSNs are discussed. Several research propositions are presented, which contribute to a better understanding of the role of trust and the relevance of facets of trust and social capital
Substrate-Integrated Millimeter-Wave and Terahertz Antenna Technology

Significant advances in the development of millimeter-wave and terahertz (30-10 000 GHz) technologies have been made to cope with the increasing interest in this still not fully explored electromagnetic spectrum. The nature of electromagnetic waves over this frequency range is well suited for the development of high-resolution imaging applications, molecular-sensitive spectroscopic devices, and ultrabroadband wireless communications. In this paper, millimeter-wave and terahertz antenna technologies are overviewed including the conventional and nonconventional planar/nonplanar antenna structures based on different platforms. As a promising technological platform, substrate-integrated circuits (SICs) attract more and more attention. Various substrate-integrated waveguide (SIW) schemes and other synthesized guide techniques have been widely employed in the design of antennas and arrays. Different types of substrate-integrated antennas and beamforming networks are discussed with respect to theoretical and experimental results in connection with electrical and mechanical performances.
ERP implementation: a compilation and analysis of critical success factors

To explore the current literature base of critical success factors (CSFs) of ERP implementations, prepare a compilation, and identify any gaps that might exist.  Hundreds of journals were searched using key terms identified in a preliminary literature review. Successive rounds of article abstract reviews resulted in 45 articles being selected for the compilation. CSF constructs were then identified using content analysis methodology and an inductive coding technique. A subsequent critical analysis identified gaps in the literature base.  The most significant finding is the lack of research that has focused on the identification of CSFs from the perspectives of key stakeholders. Additionally, there appears to be much variance with respect to what exactly is encompassed by change management, one of the most widely cited CSFs, and little detail of specific implementation tactics.  There is a need to focus future research efforts on the study of CSFs as they apply to the perspectives of key stakeholders and to ensure that this stakeholder approach is also comprehensive in its coverage of CSFs. As well, there is need to conduct more in-depth research into the concept of change management. One key limitation of this research is the occurrence of duplication in the frequency analysis of the success factors. This is attributed to secondary research being the main methodology for a large number of the articles cited.  This research provides a comprehensive compilation of all previously identified ERP implementation success factors, through a clearly structured methodological approach.
Data Wrangling: Making data useful again

Data analysis has become an everyday business and advancements of data management routines open up new opportunities. Nevertheless, transforming and assembling newly acquired data into a suitable form remains tedious. It is often stated, that data cleaning is a critical part of the overall process, but also consumes sublime amounts of time and resources.  Wrangling is not only about transforming and cleaning procedures. Many other aspects like data quality, merging of different sources, reproducible processes, and managing data provenance have to be considered. Although various tools designed for specific tasks are available, software solutions accompanying the whole process are still rare. In this paper, some aspects of this first phase of most data driven projects, also known as data wrangling, data munging or janitorial work are described. Beginning with an overview on the topic and current problems, concrete common tasks as well as selected software solutions and techniques are discussed.