nils-herrmann/clean_scirepeval huggingface dataset:
Sensor Applications and Physiological Features in Drivers’ Drowsiness Detection: A Review

Drowsiness in drivers has become a serious cause of concern due to the occurrences of a large number of fatalities on the road each year. Lives of pedestrians and passengers are put to risk as drivers tend to fall asleep at the steering wheel. In the recent past, many researchers have paid attention to the problem of drowsiness detection since safe roads and safe driving are of paramount concern to all societies. This paper has led to the development of several novel and effective methods in detecting drivers’ drowsiness. These include: 1) Vehicle based methods; 2) Behavioral methods; and 3) Physiological methods. Since wake-sleep is an intermediate state between two physiologically dissimilar states, physiological signals can define this transition more accurately when compared with approaches that fall in other categories. This paper focuses on the role of physiological signals in detecting driver’s drowsiness level. The proposed methods measure the physiological signals by means of various sensors, which monitor the driver’s physiological parameters on a continual basis. Multiple sensors can be embedded on the driver or in the vicinity of the driver to capture vital signs indicating the onset of drowsiness. The aim here is to provide an insightful review of all such key approaches that fall in this category. This paper conducts a detailed study in which key physiological parameters that relate to drowsiness are identified, described, and analyzed. Furthermore, the overall advantages and limitations of these physiological based schemes are also highlighted.
Blockchain Queueing Theory

Blockchain has many benefits including decentralization, availability, persistency, consistency, anonymity, auditability and accountability, and it also covers a wide spectrum of applications ranging from cryptocurrency, financial services, reputation system, Internet of Things, sharing economy to public and social services. Not only may blockchain be regarded as a by-product of Bitcoin cryptocurrency systems, but also it is a type of distributed ledger technology through using a trustworthy, decentralized log of totally ordered transactions. By summarizing the literature of blockchain, it is found that more papers focus on engineering implementation and realization, while little work has been done on basic theory, for example, mathematical models (Markov processes, queueing theory and game models), performance analysis and optimization of blockchain systems. In this paper, we develop queueing theory of blockchain systems and provide system performance evaluation. To do this, we design a Markovian batch-service queueing system with two different service stages, while the two stages are suitable to well express the mining process in the miners pool and the building of a new blockchain. By using the matrix-geometric solution, we obtain a system stable condition and express three key performance measures: (a) The number of transactions in the queue, (b) the number of transactions in a block, and (c) the transaction-confirmation time. Finally, We use numerical examples to verify computability of our theoretical results. Although our queueing model is simple under exponential or Poisson assumptions, our analytic method will open a series of potentially promising research in queueing theory of blockchain systems.
Studying Irony Detection Beyond Ironic Criticism: Let's Include Ironic Praise

Studies of irony detection have commonly used ironic criticisms (i.e., mock positive evaluation of negative circumstances) as stimulus materials. Another basic type of verbal irony, ironic praise (i.e., mock negative evaluation of positive circumstances) is largely absent from studies on individuals' aptitude to detect verbal irony. However, it can be argued that ironic praise needs to be considered in order to investigate the detection of irony in the variety of its facets. To explore whether the detection ironic praise has a benefit beyond ironic criticism, three studies were conducted. In Study 1, an instrument (Test of Verbal Irony Detection Aptitude; TOVIDA) was constructed and its factorial structure was tested using N = 311 subjects. The TOVIDA contains 26 scenario-based items and contains two scales for the detection of ironic criticism vs. ironic praise. To validate the measurement method, the two scales of the TOVIDA were experimentally evaluated with N = 154 subjects in Study 2. In Study 3, N = 183 subjects were tested to explore personality and ability correlates of the two TOVIDA scales. Results indicate that the co-variance between the ironic TOVIDA items was organized by two inter-correlated but distinct factors: one representing ironic praise detection aptitude and one representing ironic criticism detection aptitude. Experimental validation showed that the TOVIDA items truly contain irony and that item scores reflect irony detection. Trait bad mood and benevolent humor (as a facet of the sense of humor) were found as joint correlates for both ironic criticism and ironic praise detection scores. In contrast, intelligence, trait cheerfulness, and corrective humor were found as unique correlates of ironic praise detection scores, even when statistically controlling for the aptitude to detect ironic criticism. Our results indicate that the aptitude to detect ironic praise can be seen as distinct from the aptitude to detect ironic criticism. Generating unique variance in irony detection, ironic praise can be postulated as worthwhile to include in future studies-especially when studying the role of mental ability, personality, and humor in irony detection.
Where you Instagram?: Associating Your Instagram Photos with Points of Interest

Instagram, an online photo-sharing platform, has gained increasing popularity. It allows users to take photos, apply digital filters and share them with friends instantaneously by using mobile devices.Instagram provides users with the functionality to associate their photos with points of interest, and it thus becomes feasible to study the association between points of interest and Instagram photos. However, no previous work studies the association. In this paper, we propose to study the problem of mapping Instagram photos to points of interest. To understand the problem, we analyze Instagram datasets, and report our findings, which also characterize the challenges of the problem. To address the challenges, we propose to model the mapping problem as a ranking problem, and develop a method to learn a ranking function by exploiting the textual, visual and user information of photos. To maximize the prediction effectiveness for textual and visual information, and incorporate the users' visiting preferences, we propose three subobjectives for learning the parameters of the proposed ranking function. Experimental results on two sets of Instagram data show that the proposed method substantially outperforms existing methods that are adapted to handle the problem.
Implementing the Flipped Classroom in Teacher Education: Evidence from Turkey

The flipped classroom, a form of blended learning, is an emerging instructional strategy reversing a traditional lecture-based teaching model to improve the quality and efficiency of the teaching and learning process. The present article reports a study that focused on the implementation of the flipped approach in a higher education institution in Turkey. For this pretest-posttest quasi-experimental study, a classroom management course in a pre-service English teacher education program was flipped and its effectiveness was measured against a traditionally taught class. Quantitative and qualitative data came from 62 preservice teachers (PTs) in two intact classes randomly assigned as the experimental and the control groups. Findings revealed a higher level of self-efficacy beliefs and better learning outcomes for the experimental group PTs in the flipped classroom compared to the control group PTs in the traditional classroom. PTs’ perceptions of the flipped classroom were also positive.
A Lagrangian Perspective on Latent Variable Generative Models

A large number of objectives have been proposed to train latent variable generative models. We show that many of them are Lagrangian dual functions of the same primal optimization problem. The primal problem optimizes the mutual information between latent and visible variables, subject to the constraints of accurately modeling the data distribution and performing correct amortized inference. By choosing to maximize or minimize mutual information, and choosing different Lagrange multipliers, we obtain different objectives including InfoGAN, ALI/BiGAN, ALICE, CycleGAN, beta-VAE, adversarial autoencoders, AVB, AS-VAE and InfoVAE. Based on this observation, we provide an exhaustive characterization of the statistical and computational trade-offs made by all the training objectives in this class of Lagrangian duals. Next, we propose a dual optimization method where we optimize model parameters as well as the Lagrange multipliers. This method achieves Pareto optimal solutions in terms of optimizing information and satisfying the constraints.
Classification of Book Genres By Cover and Title

This paper discusses the classification of books purely based on cover image and title, without prior knowledge or context of author and origin. Several methods were implemented to assess the ability to distinguish books based on only these two characteristics. First we used a color-based distribution approach. Then we implemented transfer learning with convolutional neural networks on the cover image along with natural language processing on the title text. We found that image and text modalities yielded similar accuracy which indicate that we have reached a certain threshold in distinguishing between the genres that we have defined. This was confirmed by the accuracy being quite close to the human oracle accuracy.
Mental health morbidity among people subject to immigration detention in the UK: a feasibility study.

The UK has one of the largest systems of immigration detention in Europe.. Those detained include asylum-seekers and foreign national prisoners, groups with a higher prevalence of mental health vulnerabilities compared with the general population. In light of little published research on the mental health status of detainees in immigration removal centres (IRCs), the primary aim of this study was to explore whether it was feasible to conduct psychiatric research in such a setting. A secondary aim was to compare the mental health of those seeking asylum with the rest of the detainees.



Cross-sectional study with simple random sampling followed by opportunistic sampling. Exclusion criteria included inadequate knowledge of English and European Union nationality. Six validated tools were used to screen for mental health disorders including developmental disorders like Personality Disorder, Attention Deficit Hyperactivity Disorder (ADHD), Autistic Spectrum Disorder (ASD) and Intellectual Disability, as well as for needs assessment. These were the MINI v6, SAPAS, AQ-10, ASRS, LDSQ and CANFOR. Demographic data were obtained using a participant demographic sheet. Researchers were trained in the use of the screening battery and inter-rater reliability assessed by joint ratings.



A total of 101 subjects were interviewed. Overall response rate was 39%. The most prevalent screened mental disorder was depression (52.5%), followed by personality disorder (34.7%) and post-traumatic stress disorder (20.8%). 21.8% were at moderate to high suicidal risk. 14.9 and 13.9% screened positive for ASD and ADHD, respectively. The greatest unmet needs were in the areas of intimate relationships (76.2%), psychological distress (72.3%) and sexual expression (71.3%). Overall presence of mental disorder was comparable with levels found in prisons. The numbers in each group were too small to carry out any further analysis.



It is feasible to undertake a psychiatric morbidity survey in an IRC. Limitations of the study include potential selection bias, use of screening tools, use of single-site study, high refusal rates, the lack of interpreters and lack of women and children in study sample. Future studies should involve the in-reach team to recruit participants and should be run by a steering group consisting of clinicians from the IRC as well as academics.
Interventions to reduce stress in university students: a review and meta-analysis.

Recent research has revealed concerning rates of anxiety and depression among university students. Nevertheless, only a small percentage of these students receive treatment from university health services. Universities are thus challenged with instituting preventative programs that address student stress and reduce resultant anxiety and depression.



A systematic review of the literature and meta-analysis was conducted to examine the effectiveness of interventions aimed at reducing stress in university students. Studies were eligible for inclusion if the assignment of study participants to experimental or control groups was by random allocation or parallel cohort design.



Retrieved studies represented a variety of intervention approaches with students in a broad range of programs and disciplines. Twenty-four studies, involving 1431 students were included in the meta-analysis. Cognitive, behavioral and mindfulness interventions were associated with decreased symptoms of anxiety. Secondary outcomes included lower levels of depression and cortisol.



Included studies were limited to those published in peer reviewed journals. These studies over-represent interventions with female students in Western countries. Studies on some types of interventions such as psycho-educational and arts based interventions did not have sufficient data for inclusion in the meta-analysis.



This review provides evidence that cognitive, behavioral, and mindfulness interventions are effective in reducing stress in university students. Universities are encouraged to make such programs widely available to students. In addition however, future work should focus on developing stress reduction programs that attract male students and address their needs.
abits and beliefs that guide self-regulated learning : Do they vary ith mindset ?

Prior research by Kornell and Bjork (2007) and Hartwig and Dunlosky (2012) has demonstrated that college students tend to employ study strategies that are far from optimal. We examined whether individuals in the broader—and typically older—population might hold different beliefs about how best to study and learn, given their more extensive experience outside of formal coursework and deadlines. Via a web-based survey, however, we found striking similarities: Learners’ study decisions tend to be driven by deadlines, and the benefits of activities such as self-testing and reviewing studied materials are elf-regulated learning etacognition indset tudy strategies mostly unappreciated. We also found evidence, however, that one’s mindset with respect to intelligence is related to one’s habits and beliefs: Individuals who believe that intelligence can be increased through effort were more likely to value the pedagogical benefits of self-testing, to restudy, and to be intrinsically motivated to learn, compared to individuals who believe that intelligence is fixed.  Many recent findings, however—both survey-based and experimental—have revealed that learners continue to study in non-optimal ways. Learners do not appear, for example, to understand two of the most robust effects from the cognitive psychology literature—namely, the testing effect (that practicing retrieval leads to better long-term retention, compared even to re-reading; e.g., Roediger & Karpicke, 2006a) and the spacing effect (that spacing repeated study sessions leads to better long-term retention than does massing repetitions; e.g., Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006;  1988). A survey of 472 undergraduate students by Kornell and Bjork (2007)—which was replicated by Hartwig and Dunlosky (2012)—showed that students underappreciate the learning benefits of testing. Similarly, Karpicke, Butler, and Roediger (2009) surveyed students’ study strategies and found that re-reading was by far the most popular study strategy and that self-testing tended to be used only to assess whether some level of learning had been achieved, not to enhance subsequent recall. Even when students have some appreciation of effective strategies they often do not implement those strategies. Susser and McCabe (2013), for example, showed that even though students reported understanding the benefits of spaced learning over massed learning, they often do not space their study sessions on a given topic, particularly if their upcoming test is going to have a that guide self-regulated learning: Do they vary with mindset? oi.org/10.1016/j.jarmac.2014.04.003 multiple-choice format, or if they think the material is relatively easy, or if they are simply too busy. In fact, Kornell and Bjork’s (2007) survey showed that students’ study decisions tended to be driven by impending deadlines, rather than by learning goals, Elsevier Inc. All rights reserved.
ACCELEROMETRIC GLOVE FOR HEARING IMPAIRED

In this paper we propose a human interface device that converts the mechanism of hand sign language into alphanumerical characters. This device is in the form of a portable right hand glove. We propose this device in concurrence with assistive engineering to help the underprivileged. Our main goal is to identify 26 alphabets and 10 numbers of American Sign Language and display it on the LCD. Once the text is obtained on the LCD, text to speech conversion operation is carried out and a voice output is obtained. Further, the text obtained can also be viewed on a PC or any portable hand held device. People with hearing disability find it difficult to communicate with others using their Universal Sign Language, as a normal person doesn't understand these sign languages. Our main objective is to set an interface between the Deaf/Dumb and normal person to improve the communication capabilities so that they can communicate easily with others. We mount dual axis accelerometers on the glove and propose and efficient methodology to convert these sign languages.
Modeling Time Series Similarity with Siamese Recurrent Networks

Traditional techniques for measuring similarities between time series are based on handcrafted similarity measures, whereas more recent learning-based approaches cannot exploit external supervision. We combine ideas from timeseries modeling and metric learning, and study siamese recurrent networks (SRNs) that minimize a classification loss to learn a good similarity measure between time series. Specifically, our approach learns a vectorial representation for each time series in such a way that similar time series are modeled by similar representations, and dissimilar time series by dissimilar representations. Because it is a similarity prediction models, SRNs are particularly well-suited to challenging scenarios such as signature recognition, in which each person is a separate class and very few examples per class are available. We demonstrate the potential merits of SRNs in withindomain and out-of-domain classification experiments and in one-shot learning experiments on tasks such as signature, voice, and sign language recognition.
Biodegradable Materials for Bone Repair and Tissue Engineering Applications

This review discusses and summarizes the recent developments and advances in the use of biodegradable materials for bone repair purposes. The choice between using degradable and non-degradable devices for orthopedic and maxillofacial applications must be carefully weighed. Traditional biodegradable devices for osteosynthesis have been successful in low or mild load bearing applications. However, continuing research and recent developments in the field of material science has resulted in development of biomaterials with improved strength and mechanical properties. For this purpose, biodegradable materials, including polymers, ceramics and magnesium alloys have attracted much attention for osteologic repair and applications. The next generation of biodegradable materials would benefit from recent knowledge gained regarding cell material interactions, with better control of interfacing between the material and the surrounding bone tissue. The next generations of biodegradable materials for bone repair and regeneration applications require better control of interfacing between the material and the surrounding bone tissue. Also, the mechanical properties and degradation/resorption profiles of these materials require further improvement to broaden their use and achieve better clinical results.
Margin maximization for robust classification using deep learning

Deep neural networks have achieved significant success for image recognition problems. Despite the wide success, recent experiments demonstrated that neural networks are sensitive to small input perturbations, or adversarial noise. The lack of robustness is intuitively undesirable and limits neural networks applications in adversarial settings, and for image search and retrieval problems. Current approaches consider augmenting training dataset using adversarial examples to improve robustness. However, when using data augmentation, the model fails to anticipate changes in an adversary. In this paper, we consider maximizing the geometric margin of the classifier. Intuitively, a large margin relates to classifier robustness. We introduce novel margin maximization objective for deep neural networks. We theoretically show that the proposed objective is equivalent to the robust optimization problem for a neural network. Our work seamlessly generalizes SVM margin objective to deep neural networks. In the experiments, we extensively verify the effectiveness of the proposed margin maximization objective to improve neural network robustness and to reduce overfitting on MNIST and CIFAR-10 dataset.
SmartSync : An Integrated Real-Time Structural Health Monitoring and Structural Identi fi cation System for Tall Buildings

This study introduces a unique prototype system for structural health monitoring (SHM), SmartSync, which uses the building’s existing Internet backbone as a system of virtual instrumentation cables to permit modular and largely plug-and-play deployments. Within this framework, data streams from distributed heterogeneous sensors are pushed through network interfaces in real time and seamlessly synchronized and aggregated by a centralized server, which performs basic data acquisition, event triggering, and database management while also providing an interface for data visualization and analysis that can be securely accessed. The system enables a scalable approach to monitoring tall and complex structures that can readily interface a variety of sensors and data formats (analog and digital) and can even accommodate variable sampling rates. This study overviews the SmartSync system, its installation/operation in theworld’s tallest building, Burj Khalifa, and proof-of-concept in triggering under dual excitations (wind and earthquake).DOI: 10.1061/(ASCE)ST.1943-541X.0000560.  High-rise buildings; Structural health monitoring; Wind loads; Earthquakes.  Tall buildings; Structural health monitoring; System identification.
A3T: Adversarially Augmented Adversarial Training

Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier. In this work, we investigate a procedure to improve adversarial robustness of deep neural networks through enforcing representation invariance. The idea is to train the classifier jointly with a discriminator attached to one of its hidden layer and trained to filter the adversarial noise. We perform preliminary experiments to test the viability of the approach and to compare it to other standard adversarial training methods.