nils-herrmann/clean_scirepeval huggingface dataset:
Gender and website design in e-business

Although online shopping has increased exponentially in recent years, very little is known about the difference in male and female perceptions of this experience. This investigation is focused on examining how perceptions between the genders differ concerning transaction security, website design elements, and how the experience of online shopping results in perceptions of website trust, website satisfaction, and e-loyalty. Results of the research indicate that transaction security was not as large a concern as expected, and perceptions about it did not differ between men and women. Alternately, in support of the research hypotheses, there are significant differences in perceptions of website design and website satisfaction between the genders, but not for e-loyalty.
A High-Efficiency Broadband Rectenna for Ambient Wireless Energy Harvesting

This paper presents a novel broadband rectenna for ambient wireless energy harvesting over the frequency band from 1.8 to 2.5 GHz. First of all, the characteristics of the ambient radio-frequency energy are studied. The results are then used to aid the design of a new rectenna. A novel two-branch impedance matching circuit is introduced to enhance the performance and efficiency of the rectenna at a relatively low ambient input power level. A novel broadband dual-polarized cross-dipole antenna is proposed which has embedded harmonic rejection property and can reject the second and third harmonics to further improve the rectenna efficiency. The measured power sensitivity of this design is down to -35 dBm and the conversion efficiency reaches 55% when the input power to the rectifier is -10 dBm. It is demonstrated that the output power from the proposed rectenna is higher than the other published designs with a similar antenna size under the same ambient condition. The proposed broadband rectenna could be used to power many low-power electronic devices and sensors and found a range of potential applications.
Emotion recognition from expressions in face, voice, and body: the Multimodal Emotion Recognition Test (MERT).

Emotion recognition ability has been identified as a central component of emotional competence. We describe the development of an instrument that objectively measures this ability on the basis of actor portrayals of dynamic expressions of 10 emotions (2 variants each for 5 emotion families), operationalized as recognition accuracy in 4 presentation modes combining the visual and auditory sense modalities (audio/video, audio only, video only, still picture). Data from a large validation study, including construct validation using related tests (Profile of Nonverbal Sensitivity; Rosenthal, Hall, DiMatteo, Rogers, & Archer, 1979; Japanese and Caucasian Facial Expressions of Emotion; Biehl et al., 1997; Diagnostic Analysis of Nonverbal Accuracy; Nowicki & Duke, 1994; Emotion Recognition Index; Scherer & Scherer, 2008), are reported. The results show the utility of a test designed to measure both coarse and fine-grained emotion differentiation and modality-specific skills. Factor analysis of the data suggests 2 separate abilities, visual and auditory recognition, which seem to be largely independent of personality dispositions.
Understanding Cybercrime

Existing cybercrime research in the information systems (IS) field has focused on a subset of corporate incidents (e.g., fraud, hacking intrusions), and emphasized solutions designed to repel attacks or to minimize their aftermath (e.g., barrier technologies, enhanced security procedures). This focused, defensive, and pragmatic posture is valuable and necessary as an immediate triage response, to "stop the bleeding" and provide protection from imminent harm. However, the extant work has not painted a sufficiently broad picture of the scope of cybercriminal activity, nor paid adequate attention to its root causes. This paper presents a different view. It analyzes 113 U.S. Department of Justice federal cybercrime cases from 2008 and 2009, categorizes these cases using an applied criminal offense framework developed by the FBI, considers philosophical explanations for criminal motives, and then identifies the apparent motive(s) that led to the commission of each crime. This paper seeks to contribute to an improved understanding of what cybercrime is, and why it is occurring at the individual level, in order to develop more proactive and effective solutions.
Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention

Semantic relation classification remains a challenge in natural language processing. In this paper, we introduce a hierarchical recurrent neural network that is capable of extracting information from raw sentences for relation classification. Our model has several distinctive features: (1) Each sentence is divided into three context subsequences according to two annotated nominals, which allows the model to encode each context subsequence independently so as to selectively focus as on the important context information; (2) The hierarchical model consists of two recurrent neural networks (RNNs): the first one learns context representations of the three context subsequences respectively, and the second one computes semantic composition of these three representations and produces a sentence representation for the relationship classification of the two nominals. (3) The attention mechanism is adopted in both RNNs to encourage the model to concentrate on the important information when learning the sentence representations. Experimental results on the SemEval-2010 Task 8 dataset demonstrate that our model is comparable to the state-of-the-art without using any hand-crafted features.
Graph Convolutional Networks for Named Entity Recognition

In this paper we investigate the role of the dependency tree in a named entity recognizer upon using a set of Graph Convolutional Networks (GCNs). We perform a comparison among different Named Entity Recognition (NER) architectures and show that the grammar of a sentence positively influences the results. Experiments on the OntoNotes 5.0 dataset demonstrate consistent performance improvements, without requiring heavy feature engineering nor additional language-specific knowledge.
Linking obesity and activity level with children's television and video game use.

This study examined the links between childhood obesity, activity participation and television and video game use in a nationally representative sample of children (N = 2831) ages 1-12 using age-normed body mass index (BMI) ratings. Results indicated that while television use was not related to children's weight status, video game use was. Children with higher weight status played moderate amounts of electronic games, while children with lower weight status played either very little or a lot of electronic games. Interaction analyses revealed that this curvilinear relationship applied to children under age 8 and that girls, but not boys, with higher weight status played more video games. Children ages 9-12 with lower weight status used the computer (non-game) for moderate amounts of time, while those with higher weight status used the computer either very little or a lot. This was also true for the relationship between print use and weight status for children of all ages. Results also indicated that children with higher weight status spent more time in sedentary activities than those with lower weight status.
Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie.
Why phishing still works: User strategies for combating phishing attacks

We have conducted a user study to assess whether improved browser security indicators and increased awareness of phishing have led to users’ improved ability to protect themselves against such attacks. Participants were shown a series of websites and asked to identify the phishing websites. We use eye tracking to obtain objective quantitative data on which visual cues draw users’ attention as they determine the legitimacy of websites. Our results show that users successfully detected only 53% of phishing websites even when primed to identify them and that they generally spend very little time gazing at security indicators compared to website content when making assessments. However, we found that gaze time on browser chrome elements does correlate to increased ability to detect phishing. Interestingly, users’ general technical proficiency does not correlate with improved detection scores.
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis

Recent work using auxiliary prediction task classifiers to investigate the properties of LSTM representations has begun to shed light on why pretrained representations, like ELMo (Peters et al., 2018) and CoVe (McCann et al., 2017), are so beneficial for neural language understanding models. We still, though, do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn. With this in mind, we compare four objectives—language modeling, translation, skip-thought, and autoencoding—on their ability to induce syntactic and part-of-speech information. We make a fair comparison between the tasks by holding constant the quantity and genre of the training data, as well as the LSTM architecture. We find that representations from language models consistently perform best on our syntactic auxiliary prediction tasks, even when trained on relatively small amounts of data. These results suggest that language modeling may be the best data-rich pretraining task for transfer learning applications requiring syntactic information. We also find that the representations from randomly-initialized, frozen LSTMs perform strikingly well on our syntactic auxiliary tasks, but this effect disappears when the amount of training data for the auxiliary tasks is reduced.
Cross-Modal Subspace Learning via Pairwise Constraints

In multimedia applications, the text and image components in a web document form a pairwise constraint that potentially indicates the same semantic concept. This paper studies cross-modal learning via the pairwise constraint and aims to find the common structure hidden in different modalities. We first propose a compound regularization framework to address the pairwise constraint, which can be used as a general platform for developing cross-modal algorithms. For unsupervised learning, we propose a multi-modal subspace clustering method to learn a common structure for different modalities. For supervised learning, to reduce the semantic gap and the outliers in pairwise constraints, we propose a cross-modal matching method based on compound ℓ21 regularization. Extensive experiments demonstrate the benefits of joint text and image modeling with semantically induced pairwise constraints, and they show that the proposed cross-modal methods can further reduce the semantic gap between different modalities and improve the clustering/matching accuracy.
Using Coloured Petri Nets in Penetration Testing

Network penetration testing is a well-known approach used for security testing. Penetrating testing can be a laborious task which relies much on human knowledge and expertise, with various techniques employed, and an extensive amount of tools used in the process. A methodical approach to penetration testing is therefore recommended. The flaw hypothesis methodology, used in this thesis, represent one of the most used models for penetration testing and have great similarities in other penetration testing methodologies and standards used today. Petri nets represent a graph based mathematically sound modelling technique for concurrent systems, and provide a graphically intuitive approach for modelling, simulation and execution. A coloured Petri net is a high level Petri net that provides a significant increase in the expressiveness and compactness of Petri net models. The flaw hypothesis methodology used together with coloured Petri net attack models is presented in the thesis. The use of coloured Petri nets is described and analysed through case studies elucidating several properties of Petri net variants and their suitability to modelling attacks in penetration testing. Advantages of modelling attacks with coloured Petri nets have been explored and are described. Coloured Petri nets have been found to have many usefull mechanisms for modelling, analysing, and automatically executing penetration attempts, e.g. through their ability to model states, transitions, concurrency, and timing. The overall topic of the thesis is the technical aspects of penetration testing, what it is, and a methodical approach to it.
Are you ready? Towards the engineering of forensic-ready systems

As security incidents continue to impact organisations, there is a growing demand for systems to be ‘forensic-ready’ - to maximise the potential use of evidence whilst minimising the costs of an investigation. Researchers have supported organisational forensic readiness efforts by proposing the use of policies and processes, aligning systems with forensics objectives and training employees. However, recent work has also proposed an alternative strategy for implementing forensic readiness called forensic-by-design. This is an approach that involves integrating requirements for forensics into relevant phases of the systems development lifecycle with the aim of engineering forensic-ready systems. While this alternative forensic readiness strategy has been discussed in the literature, no previous research has examined the extent to which organisations actually use this approach for implementing forensic readiness. Hence, we investigate the extent to which organisations consider requirements for forensics during systems development. We first assessed existing research to identify the various perspectives of implementing forensic readiness, and then undertook an online survey to investigate the consideration of requirements for forensics during systems development lifecycles. Our findings provide an initial assessment of the extent to which requirements for forensics are considered within organisations. We then use our findings, coupled with the literature, to identify a number of research challenges regarding the engineering of forensic-ready systems.
Challenges Internet of Things Maturity : From Experiments to Industrialization About the

The Internet of Things (IoT) is expected to be the next revolution following the World Wide Web. It will provide new bridges between real life and the virtual world. The Internet will no longer be merely a network of 'human brain', but will integrate real-life objects, sensors and physical activities. The associated technologies: wireless, minia-turization, batteries, networking, etc. are now mature and cost effective enough to enable objects to become connected and usher in this new era. The opportunities are huge and so are the challenges: " One trillion nanoscale sensors and actuators will need the equivalent of 1000 Internets: the next huge demand for computing! " Peter Hartwell (HP).
Object Detection with Deep Learning: A Review

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.
Time Reversal as Self-Supervision

A longstanding challenge in robot learning for manipulation tasks has been the ability to generalize to varying initial conditions, diverse objects, and changing objectives. Learning based approaches have shown promise in producing robust policies, but require heavy supervision to efficiently learn precise control, especially from visual inputs. We propose a novel self-supervision technique that uses time-reversal to learn goals and provide a high level plan to reach them. In particular, we introduce the time-reversal model (TRM), a self-supervised model which explores outward from a set of goal states and learns to predict these trajectories in reverse. This provides a high level plan towards goals, allowing us to learn complex manipulation tasks with no demonstrations or exploration at test time. We test our method on the domain of assembly, specifically the mating of tetris-style block pairs. Using our method operating atop visual model predictive control, we are able to assemble tetris blocks on a physical robot using only uncalibrated RGB camera input, and generalize to unseen block pairs. Project’spage: https://sites.google.com/corp/view/time-reversal
Deep recurrent neural networks for predicting intraoperative and postoperative outcomes and trends

Intraoperative vital signals convey a wealth of complex temporal information that can provide significant insights into a patient's physiological status during the surgery, as well as outcomes after the surgery. Our study involves the use of a deep recurrent neural network architecture to predict patient's outcomes after the surgery, as well as to predict the immediate changes in the intraoperative signals during the surgery. More specifically, we will use a Long Short-Term Memory (LSTM) model which is a gated deep recurrent neural network architecture. We have performed two experiments on a large intraoperative dataset of 12,036 surgeries containing information on 7 intraoperative signals including body temperature, respiratory rate, heart rate, diastolic blood pressure, systolic blood pressure, fraction of inspired O2 and end-tidal CO2. We first evaluated the capability of LSTM in predicting the immediate changes in intraoperative signals, and then we evaluated its performance on predicting each patient's length of stay outcome. Our experiments show the effectiveness of LSTM with promising results on both tasks compared to the traditional models.
Learning Testing a Conception of How School Leadership Influences Student

This article describes and reports the results of testing a new conception of how leadership influences student learning (“The Four Paths”). Framework: Leadership influence is conceptualized as flowing along four paths (Rational, Emotions, Organizational, and Family) toward student learning. Each path is populated by multiple variables with more or less powerful effects on student learning. Leaders increase student learning by improving the condition or status of selected variables on the Paths.  Evidence includes teacher responses to an online survey (1,445 responses) measuring distributed leadership practices in their schools (N = 199) and variables mediating leaders’ effects on students. Grade 3 and 6 math and literacy achievement data were provided by the province’s annual testing program. The 2006 Canadian Census data provided a composite measure of school socioeconomic status. Path modeling techniques were used to test six hypotheses.  The Four Paths model as a whole explains 43% of the variation in student achievement. Variables on the Rational, Emotions, and Family Paths explain similarly significant amounts of that variation. Variables on the Organizational Path were unrelated to student achievement. Leadership had its greatest influence on the Organizational Path and least influence Article at Ebsco Electronic Journals Service (EJS) on June 20, 2012 eaq.sagepub.com Downloaded from 672 Educational Administration Quarterly 46(5) on the Family Path.  School leaders and leadership researchers should be guided much more directly by existing evidence about school, classroom, and family variables with powerful effects on student learning as they make their school improvement and research design decisions.