nils-herrmann/clean_scirepeval huggingface dataset:
Depression, anxiety and substance use in medical students in the Republic of Macedonia.

To determine the prevalence of depressive and high trait anxiety symptoms and substance use, including alcohol and nicotine, in first-year and second-year medical students in Skopje University Medical School, Republic of Macedonia.



It is important to investigate medical students because they are under significant pressure during early years of medical education, a period during which the attitudes and behaviors of physicians develop.



A cross-sectional survey in classroom settings, using an anonymous self-administered questionnaire, was performed in 354 participants (181 first-year, 118 females and 63 males and 173 second-year medical students, 116 females and 57 males) aged 18 to 23 years. The Beck Depression Inventory (BDI) and Taylor Manifest Anxiety Scale (TMAS) were used to determine depressive and high trait anxiety symptoms. BDI scores 17 or higher were categorized as depressive and TMAS scores 16 or higher as high anxiety symptoms. A Student t-test was used for continuous data analysis.



Out of all participants 10.4% had BDI score 17 or higher and 65.5% had TMAS score 16 or higher. Alcohol was the most frequently used substance in both groups. Smoking prevalence was 25%. Benzodiazepines (diazepam, alprazolam) use was 13.1%. Illicit drug use was rare (1.1% in freshmen and 3.6% in juniors) in both groups.



High frequency of manifest high anxiety symptoms and depressive symptoms and benzodiazepine use among Macedonian junior medical students should be taken seriously and a student counseling service offering mental health assistance is necessary (.  Text (Free, PDF) www.bmj.sk.
Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis.

This paper aims to describe different approaches for studying the overall diet with advantages and limitations. Studies of the overall diet have emerged because the relationship between dietary intake and health is very complex with all kinds of interactions. These cannot be captured well by studying single dietary components. Three main approaches to study the overall diet can be distinguished. The first method is researcher-defined scores or indices of diet quality. These are usually based on guidelines for a healthy diet or on diets known to be healthy. The second approach, using principal component or cluster analysis, is driven by the underlying dietary data. In principal component analysis, scales are derived based on the underlying relationships between food groups, whereas in cluster analysis, subgroups of the population are created with people that cluster together based on their dietary intake. A third approach includes methods that are driven by a combination of biological pathways and the underlying dietary data. Reduced rank regression defines linear combinations of food intakes that maximally explain nutrient intakes or intermediate markers of disease. Decision tree analysis identifies subgroups of a population whose members share dietary characteristics that influence (intermediate markers of) disease. It is concluded that all approaches have advantages and limitations and essentially answer different questions. The third approach is still more in an exploration phase, but seems to have great potential with complementary value. More insight into the utility of conducting studies on the overall diet can be gained if more attention is given to methodological issues.
Changes in Self-Definition Impede Recovery From Rejection.

Previous research highlights how adept people are at emotional recovery after rejection, but less research has examined factors that can prevent full recovery. In five studies, we investigate how changing one's self-definition in response to rejection causes more lasting damage. We demonstrate that people who endorse an entity theory of personality (i.e., personality cannot be changed) report alterations in their self-definitions when reflecting on past rejections (Studies 1, 2, and 3) or imagining novel rejection experiences (Studies 4 and 5). Further, these changes in self-definition hinder post-rejection recovery, causing individuals to feel haunted by their past, that is, to fear the recurrence of rejection and to experience lingering negative affect from the rejection. Thus, beliefs that prompt people to tie experiences of rejection to self-definition cause rejection's impact to linger.
Multiple Vehicle Detection and Tracking from Surveillance Camera with Collision Prediction

This paper describes a system for detection and tracking of multiple vehicles from a surveillance camera with collision detection and prediction. Accurate vehicles’ contour is obtained in the detection phase, and object centroids are calculated. Each detected vehicle is assigned to the specific lane and tracked separately. Calculated centroids are used for object tracking using a contour-based algorithm with movement prediction which provides sufficient amount of information to predict vehicle movement. A rectangle constructed around the ground part of the vehicle is used for vehicle collision prediction. Experimental results show a success rate of 71 % when constructing the ground rectangles, which are key for collision prediction.
Paraphrase-focused learning to rank for domain-specific frequently asked questions retrieval

A frequently asked questions (FAQ) retrieval system improves the access to information by allowing users to pose natural language queries over an FAQ collection. From an information retrieval perspective, FAQ retrieval is a challenging task, mainly because of the lexical gap that exists between a query and an FAQ pair, both of which are typically very short. In this work, we explore the use of supervised learning to rank to improve the performance of domain-specific FAQ retrieval. While supervised learning-to-rank models have been shown to yield effective retrieval performance, they require costly human-labeled training data in the form of document relevance judgments or question paraphrases. We investigate how this labeling effort can be reduced using a labeling strategy geared toward the manual creation of query paraphrases rather than the more time-consuming relevance judgments. In particular, we investigate two such strategies, and test them by applying supervised ranking models to two domain-specific FAQ retrieval data sets, showcasing typical FAQ retrieval scenarios. Our experiments show that supervised ranking models can yield significant improvements in the precision-at-rank-5 measure compared to unsupervised baselines. Furthermore, we show that a supervised model trained using data labeled via a low-effort paraphrase-focused strategy has the same performance as that of the same model trained using fully labeled data, indicating that the strategy is effective at reducing the labeling effort while retaining the performance gains of the supervised approach. To encourage further research on FAQ retrieval we make our FAQ retrieval data set publicly available.
Fast Scene Understanding for Autonomous Driving

Most approaches for instance-aware semantic labeling traditionally focus on accuracy. Other aspects like runtime and memory footprint are arguably as important for realtime applications such as autonomous driving. Motivated by this observation and inspired by recent works that tackle multiple tasks with a single integrated architecture , [20], [22], in this paper we present a real-time efficient implementation based on ENet [18] that solves three autonomous driving related tasks at once: semantic scene segmentation, instance segmentation and monocular depth estimation. Our approach builds upon a branched ENet architecture with a shared encoder but different decoder branches for each of the three tasks. The presented method can run at 21 fps at a resolution of 1024x512 on the Cityscapes dataset without sacrificing accuracy compared to running each task separately.
Authorship verification for short messages using stylometry

Authorship verification can be checked using stylometric techniques through the analysis of linguistic styles and writing characteristics of the authors. Stylometry is a behavioral feature that a person exhibits during writing and can be extracted and used potentially to check the identity of the author of online documents. Although stylometric techniques can achieve high accuracy rates for long documents, it is still challenging to identify an author for short documents, in particular when dealing with large authors populations. These hurdles must be addressed for stylometry to be usable in checking authorship of online messages such as emails, text messages, or twitter feeds. In this paper, we pose some steps toward achieving that goal by proposing a supervised learning technique combined with n-gram analysis for authorship verification in short texts. Experimental evaluation based on the Enron email dataset involving 87 authors yields very promising results consisting of an Equal Error Rate (EER) of 14.35% for message blocks of 500 characters.
In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions.

The importance of recognizing early melanoma is generally accepted. Because not all pigmented skin lesions can be diagnosed correctly by their clinical appearance, additional criteria are required for the clinical diagnosis of such lesions. In vivo epiluminescence microscopy provides for a more detailed inspection of the surface of pigmented skin lesions, and, by using the oil immersion technic, which renders the epidermis translucent, opens a new dimension of skin morphology by including the dermoepidermal junction into the macroscopic evaluation of a lesion. In an epiluminescence microscopy study of more than 3000 pigmented skin lesions we have defined morphologic criteria that are not readily apparent to the naked eye but that are detected easily by epiluminescence microscopy and represent relatively reliable markers of benign and malignant pigmented skin lesions. These features include specific patterns, colors, and intensities of pigmentation, as well as the configuration, regularity, and other characteristics of both the margin and the surface of pigmented skin lesions. Pattern analysis of these features permits a distinction between different types of pigmented skin lesions and, in particular, between benign and malignant growth patterns. Epiluminescence microscopy is thus a valuable addition to the diagnostic armamentarium of pigmented skin lesions at a clinical level.
OntoLearn Reloaded: A Graph-Based Algorithm for Taxonomy Induction

In 2004 we published in this journal an article describing OntoLearn, one of the first systems to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn has continued to be an active area of research in our group and has become a reference work within the community. In this paper we describe our next-generation taxonomy learning methodology, which we name OntoLearn Reloaded. Unlike many taxonomy learning approaches in the literature, our novel algorithm learns both concepts and relations entirely from scratch via the automated extraction of terms, definitions, and hypernyms. This results in a very dense, cyclic and potentially disconnected hypernym graph. The algorithm then induces a taxonomy from this graph via optimal branching and a novel weighting policy. Our experiments show that we obtain high-quality results, both when building brand-new taxonomies and when reconstructing sub-hierarchies of existing taxonomies.
Effect of urban design on microclimate and thermal comfort outdoors in warm-humid Dar es Salaam, Tanzania

Due to the complexity of built environment, urban design patterns considerably affect the microclimate and outdoor thermal comfort in a given urban morphology. Variables such as building heights and orientations, spaces between buildings, plot coverage alter solar access, wind speed and direction at street level. To improve microclimate and comfort conditions urban design elements including vegetation and shading devices can be used. In warm-humid Dar es Salaam, the climate consideration in urban design has received little attention although the urban planning authorities try to develop the quality of planning and design. The main aim of this study is to investigate the relationship between urban design, urban microclimate, and outdoor comfort in four built-up areas with different morphologies including low-, medium-, and high-rise buildings. The study mainly concentrates on the warm season but a comparison with the thermal comfort conditions in the cool season is made for one of the areas. Air temperature, wind speed, mean radiant temperature (MRT), and the physiologically equivalent temperature (PET) are simulated using ENVI-met to highlight the strengths and weaknesses of the existing urban design. An analysis of the distribution of MRT in the areas showed that the area with low-rise buildings had the highest frequency of high MRTs and the lowest frequency of low MRTs. The study illustrates that areas with low-rise buildings lead to more stressful urban spaces than areas with high-rise buildings. It is also shown that the use of dense trees helps to enhance the thermal comfort conditions, i.e., reduce heat stress. However, vegetation might negatively affect the wind ventilation. Nevertheless, a sensitivity analysis shows that the provision of shade is a more efficient way to reduce PET than increases in wind speed, given the prevailing sun and wind conditions in Dar es Salaam. To mitigate heat stress in Dar es Salaam, a set of recommendations and guidelines on how to develop the existing situation from microclimate and thermal comfort perspectives is outlined. Such recommendations will help architects and urban designers to increase the quality of the outdoor environment and demonstrate the need to create better urban spaces in harmony with microclimate and thermal comfort.
Intercultural Communication

1.1 Terminology Intercultural communication or communication between people of different cultural backgrounds has always been and will probably remain an important precondition of human co-existance on earth. The purpose of this paper is to provide a framework of factors thatare important in intercultural communication within a general model of human, primarily linguistic, communication. The term intercultural is chosen over the largely synonymousterm cross-cultural because it is linked to language use such as “interdisciplinary”, that is cooperation between people with different scientific backgrounds. Perhaps the term also has somewhat fewer connotations than crosscultural. It is not cultures that communicate, whatever that might imply, but people (and possibly social institutions) with different cultural backgrounds that do. In general, the term”cross-cultural” is probably best used for comparisons between cultures (”crosscultural comparison”).
Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization— a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a “predicate-argument” form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).
Social and occupational justice barriers in the transition from foster care to independent adulthood.

The professional discourse on social justice suggests that more critical work is needed to sufficiently address the societal issues that affect occupational therapy practitioners' ability to advocate for and with clients. Occupational therapy offers unique opportunities for the scholarly discussion of social justice and for clinical practice to address these issues. This article discusses the importance of incorporating a social justice perspective into occupational therapy by using an example from the author's research program. The experiences of adolescents in foster care were documented in an ongoing qualitative participatory study. An overview of adolescents' (N = 40) perceived independent living and vocational service needs is provided, and several barriers that affect adolescents' ability to develop the skills needed to achieve independent adulthood are described. The article concludes with a discussion of social justice implications as they relate to the myriad issues in the foster care system, occupational therapy research, and practice.
Neural Graph Learning: Training Neural Networks Using Graphs

Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training framework with a graph-regularised objective, namely Neural Graph Machines, that can combine the power of neural networks and label propagation. This work generalises previous literature on graph-augmented training of neural networks, enabling it to be applied to multiple neural architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a)~allowing the network to train using labeled data as in the supervised setting, (b)~biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs, with a runtime that is linear in the number of edges. The proposed joint training approach convincingly outperforms many existing methods on a wide range of tasks (multi-label classification on social graphs, news categorization, document classification and semantic intent classification), with multiple forms of graph inputs (including graphs with and without node-level features) and using different types of neural networks.
Private Sector Cyber Security Investment: An Empirical Analysis

Organizations typically use very robust analysis techniques to determine how best to spend resources in order to increase revenue and decrease costs or losses. However, few organizations attempt such analysis processes to determine the level and type of cyber security mechanisms in which they invest and which they maintain. Key performance and evaluation metrics are not available, so those organizations that use quantitative analysis techniques typically have well developed internal tracking systems and have spent considerable time analyzing their internal data. Using a case study approach, we conducted a series of interviews with large organizations in a variety of sectors in order to understand their investment and implementation strategies, particularly focusing on the factors which drive the level of security they maintain and the information resources they rely on for planning and resource allocation. Here we present a qualitative discussion of some of our findings and introduce a conceptual approach to consider the trade-offs between various investment and implementation strategies and some public policy options. This paper is based on an ongoing study funded by the U.S. Department of Homeland Security.
Using Retweet Information as a Feature to Classify Messages Contents

We investigate the use of machine learning algorithms to classify the topic of messages published in Online Social Networks using as input solely user interaction data, instead of the actual message content. During a period of six months, we monitored and gathered data from users interacting with news messages on Twitter, creating thousands of information diffusion processes. The data set presented regular patterns on how messages were spread over the network by users, depending on its content, so we could build classifiers to predict the topic of a message using as input only the information of which users shared such message. Thus, we demonstrate the explanatory power of user behavior data on identifying content present in Social Networks, proposing techniques for topic classification that can be used to assist traditional content identification strategies (such as natural language or image processing) in challenging contexts, or be applied in scenarios with limited information access.
Neural Citation Network for Context-Aware Citation Recommendation

The accelerating rate of scientific publications makes it difficult to find relevant citations or related work. Context-aware citation recommendation aims to solve this problem by providing a curated list of high-quality candidates given a short passage of text. Existing literature adopts bag-of-word representations leading to the loss of valuable semantics and lacks the ability to integrate metadata or generalize to unseen manuscripts in the training set. We propose a flexible encoder-decoder architecture called Neural Citation Network (NCN), embodying a robust representation of the citation context with a max time delay neural network, further augmented with an attention mechanism and author networks. The recurrent neural network decoder consults this representation when determining the optimal paper to recommend based solely on its title. Quantitative results on the large-scale CiteSeer dataset reveal NCN cultivates a significant improvement over competitive baselines. Qualitative evidence highlights the effectiveness of the proposed end-to-end neural network revealing a promising research direction for citation recommendation.
Security challenges in internet of things: survey

The Internet of Things is part of our everyday life, which applies to all aspects of human life; from smart phones and environmental sensors to smart devices used in the industry. Although the Internet of Things has many advantages, there are risks and dangers as well that need to be addressed. The information used and transmitted on Internet of Things contain important info about the daily lives of people, banking information, location and geographical information, environmental and medical information, together with many other sensitive data. Therefore, it is critical to identify and address the security issues and challenges of Internet of Things. In this article, considering the broad scope of this field and its literature, we are going to express some comprehensive information on security challenges of the Internet of Things.