nils-herrmann/clean_scirepeval huggingface dataset:
Nocturnal Wakefulness as a Previously Unrecognized Risk Factor for Suicide.

Suicide is a major public health problem and the 10th leading cause of death in the United States. The identification of modifiable risk factors is essential for reducing the prevalence of suicide. Recently, it has been shown that insomnia and nightmares significantly increase the risk for suicidal ideation, attempted suicide, and death by suicide. While both forms of sleep disturbance may independently confer risk, and potentially be modifiable risk factors, it is also possible that simply being awake at night represents a specific vulnerability for suicide. The present analysis evaluates the frequency of completed suicide per hour while taking into account the percentage of individuals awake at each hour.



Archival analyses were conducted estimating the time of fatal injury using the National Violent Death Reporting System for 2003-2010 and the proportion of the American population awake per hour across the 24-hour day using the American Time Use Survey.



The mean ± SD incident rate from 06:00-23:59 was 2.2% ± 0.7%, while the mean ± SD incident rate from 00:00-05:59 was 10.3% ± 4.9%. The maximum incident rate was from 02:00-02:59 (16.3%). Hour-by-hour observed values differed from those that would be expected by chance (P < .001), and when 6-hour blocks were examined, the observed frequency at night was 3.6 times higher than would be expected by chance (P < .001).



Being awake at night confers greater risk for suicide than being awake at other times of the day, suggesting that disturbances of sleep or circadian neurobiology may potentiate suicide risk.
MQTT based secured home automation system

In this paper we discuss and create a MQTT based Secured home automation system, by using mentioned sensors and using Raspberry pi B+ model as the network gateway, here we have implemented MQTT Protocol for transferring & receiving sensor data and finally getting access to those sensor data, also we have implemented ACL (access control list) to provide encryption method for the data and finally monitoring those data on webpage or any network devices. R-pi has been used as a gateway or the main server in the whole system, which has various sensor connected to it via wired or wireless communication.
Password Guessing Based on LSTM Recurrent Neural Networks

Passwords are frequently used in data encryption and user authentication. Since people incline to choose meaningful words or numbers as their passwords, lots of passwords are easy to guess. This paper introduces a password guessing method based on Long Short-Term Memory recurrent neural networks. After training our LSTM neural network with 30 million passwords from leaked Rockyou dataset, the generated 3.35 billion passwords could cover 81.52% of the remaining Rockyou dataset. Compared with PCFG and Markov methods, this method shows higher coverage rate.
Learning outcomes and instructional objectives: is there a difference?

Learning outcomes are broad statements of what is achieved and assessed at the end of a course of study. The concept of learning outcomes and outcome-based education is high on today's education agenda. The idea has features in common with the move to instructional objectives which became fashionable in the 1960s, but which never had the impact on education practice that it merited. Five important differences between learning outcomes and instructional objectives can be recognized: (1) Learning outcomes, if set out appropriately, are intuitive and user friendly. They can be used easily in curriculum planning, in teaching and learning and in assessment. (2) Learning outcomes are broad statements and are usually designed round a framework of 8-12 higher order outcomes. (3) The outcomes recognize the authentic interaction and integration in clinical practice of knowledge, skills and attitudes and the artificiality of separating these. (4) Learning outcomes represent what is achieved and assessed at the end of a course of study and not only the aspirations or what is intended to be achieved.  A design-down approach encourages ownership of the outcomes by teachers and students.
Layrub: layer-centric GPU memory reuse and data migration in extreme-scale deep learning systems

Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements of those models make it difficult to execute the training process in one GPU. To address it, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities of memory reuse on both intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that cannot be run on one single GPU.
Time, Consciousness, and Mind Uploading

The function of the brain is intricately woven into the fabric of time. Functions such as (1) storing and accessing past memories, (2) dealing with immediate sensorimotor needs in the present, and (3) projecting into the future for goal-directed behavior are good examples of how key brain processes are integrated into time. Moreover, it can even seem that the brain generates time (in the psychological sense, not in the physical sense) since, without the brain, a living organism cannot have the notion of past nor future. When combined with an evolutionary perspective, this seemingly straightforward idea that the brain enables the conceptualization of past and future can lead to deeper insights into the principles of brain function, including that of consciousness. In this paper, we systematically investigate, through simulated evolution of artificial neural networks, conditions for the emergence of past and future in simple neural architectures, and discuss the implications of our findings for consciousness and mind uploading.
A Comprehensive IoT Attacks Survey based on a Building-blocked Reference Model

Internet of Things (IoT) has not yet reached a distinctive definition. A generic understanding of IoT is that it offers numerous services in many domains, utilizing conventional internet infrastructure by enabling different communication patterns such as human-to-object, object-to-objects, and object-to-object. Integrating IoT objects into the standard Internet, however, has unlocked several security challenges, as most internet technologies and connectivity protocols have been specifically designed for unconstrained objects. Moreover, IoT objects have their own limitations in terms of computation power, memory and bandwidth. IoT vision, therefore, has suffered from unprecedented attacks targeting not only individuals but also enterprises, some examples of these attacks are loss of privacy, organized crime, mental suffering, and the probability of jeopardizing human lives. Hence, providing a comprehensive classification of IoT attacks and their available countermeasures is an indispensable requirement. In this paper, we propose a novel four-layered IoT reference model based on building blocks strategy, in which we develop a comprehensive IoT attack model composed of four key phases. First, we have proposed IoT asset-based attack surface, which consists of four main components: 1) physical objects, 2) protocols covering whole IoT stack, 3) data, and 4) software. Second, we describe a set of IoT security goals. Third, we identify IoT attack taxonomy for each asset. Finally, we show the relationship between each attack and its violated security goals, and identify a set of countermeasures to protect each asset as well. To the best of our knowledge, this is the first paper that attempts to provide a comprehensive IoT attacks model based on a building-blocked reference model. Internet of Things (IoT); building block; security and privacy; reference model
Mm-Wave Antenna in Package (AiP) Design Applied to 5th Generation (5G) Cellular User Equipment Using Unbalanced Substrate

The mm-Wave bands defined as the new radio in the fifth generation (5G) mobile networks would decrease the dimension of the antenna into the scale of package level. In this study, a patch antenna array with stacked patches was designed for a wider operation frequency band than a typical patch. By considering a better electrical performance of the antenna in package (AiP), an unbalanced substrate of 4-layer metal stack-up within the processing capacity is proposed in this paper. The proposed unbalanced substrate structure is more elegant than the conventional substrate structure because of fewer substrate layers. The electrical and dimensional data are collected and analyzed. The designed patch antenna in this paper shows good correlations between simulations and measurements. The measured results show that the 1×4 patch array achieves a bandwidth of about 15.4 % with -10 dB return loss and gain of 10.8 dBi.
#FailedRevolutions: Using Twitter to Study the Antecedents of ISIS Support

Within a fairly short amount of time, the Islamic State of Iraq and Syria (ISIS) has managed to put large swaths of land in Syria and Iraq under their control. To many observers, the sheer speed at which this “state” was established was dumbfounding. To better understand the roots of this organization and its supporters we present a study using data from Twitter. We start by collecting large amounts of Arabic tweets referring to ISIS and classify them into pro-ISIS and anti-ISIS. This classification turns out to be easily done simply using the name variants used to refer to the organization: the full name and the description as “state” is associated with support, whereas abbreviations usually indicate opposition. We then “go back in time” by analyzing the historic timelines of both users supporting and opposing and look at their pre-ISIS period to gain insights into the antecedents of support. To achieve this, we build a classifier using pre-ISIS data to “predict”, in retrospect, who will support or oppose the group. The key story that emerges is one of frustration with failed Arab Spring revolutions. ISIS supporters largely differ from ISIS opposition in that they refer a lot more to Arab Spring uprisings that failed. We also find temporal patterns in the support and opposition which seems to be linked to major news, such as reported territorial gains, reports on gruesome acts of violence, and reports on airstrikes and foreign intervention.
Predicting Academic Performance of Students in UAE Using Data Mining Techniques

In this paper, we implemented an approach to predict final exam scores from early course assessments of the students during the semester. We used a linear regression model to check which part of the evaluation of the course assessment affects final exam score the most. In addition, we explained the origins of data mining and data mining in education. After preprocessing and preparing data for the task in hand, we implemented the linear regression model. The results of our work show that quizzes are most accurate predictors of final exam scores compared to other kinds of assessments.
Inductance calculation and layout optimization for planar spiral inductors

The inductance calculation and the layout optimization for spiral inductors still are research topics of actuality and very interesting especially in radio frequency integrated circuits. Our research work is fixed on the vast topics of this research area. In this effect we create a software program dedicate to dc inductance calculation and to layout optimization for spiral inductors. We use a wide range of inductors in the application made with our program; we compare our applications results with measurements results existing in the literature and with three-dimensional commercial field solver results in order to validate it. Our program is accurate enough, has a very friendly interface, is very easy to use and its running time is very sort compared with other similar programs. Since spiral inductors tolerance is generally on the order of several percent, a more accurate program is not needed in practice. The program is very useful for the spiral inductor design because it calculates the inductance of spiral inductors with a very good accuracy and also for the spiral inductor optimization, because it optimize the spiral inductor layouts in terms of technological restrictions and/or in terms of the designers' needs.
Design of Efficient Microstrip Linear Antenna Array for 5G Communications Systems

This paper presents design of an efficient microstrip antenna array for 5G communication systems. The antenna operates at 28 GHz and it is formed of 16 elements of rectangular patches arranged in a linear configuration. The single element has gap-coupled feeder and the antenna is designed on low-loss Teflon based RT/duroid 5880 substrate with dielectric constant of 2.2 and substrate thickness of 0.381 mm. A defected ground structure (DGS) in the form of rectangular slots is employed in the array design to reduce the mutual coupling between adjacent elements. The antenna is simulated using CST microwave studio and the results show that the 16-element array has a 10-dB bandwidth greater than 2 GHz and a maximum gain of 17.4 dBi at 28.4 GHz. The performance of the proposed antenna satisfies the requirements of 5G communications systems in terms of high gain, high radiation efficiency and adequate bandwidth.
Female and male perceptions of female physical attractiveness in front-view and profile.

Two important cues to female physical attractiveness are body mass index (BMI) and shape. In front view, it seems that BMI may be more important than shape; however, is it true in profile where shape cues may be stronger? There is also the question of whether men and women have the same perception of female physical attractiveness. Some studies have suggested that they do not, but this runs contrary to mate selection theory. This predicts that women will have the same perception of female attractiveness as men do. This allows them to judge their own relative value, with respect to their peer group, and match this value with the value of a prospective mate. To clarify these issues we asked 40 male and 40 female undergraduates to rate a set of pictures of real women (50 in front-view and 50 in profile) for attractiveness. BMI was the primary predictor of attractiveness in both front and profile, and the putative visual cues to BMI showed a higher degree of view-invariance than shape cues such as the waist-hip ratio (WHR). Consistent with mate selection theory, there were no significant differences in the rating of attractiveness by male and female raters.
Mixture of topic model for multi-document summarization

Based on LDA(Latent Dirichlet Allocation) topic model, a generative model for multi-document summarization, namely Titled-LDA that simultaneously models the content of documents and the titles of document is proposed. This generative model represents each document with a mixture of topics, and extends these approaches to title modeling by allowing the mixture weights for topics to be determined by the titles of the document. In the mixing stage, the algorithm can learn the weight in an adaptive asymmetric learning way based on two kinds of information entropies. In this way, the final model incorporated the title information and the content information appropriately, which helped the performance of summarization. The experiments showed that the proposed algorithm achieved better performance compared the other state-of-the-art algorithms on DUC2002 corpus.
Understanding Participatory Hashtag Practices on Instagram: A Case Study of Weekend Hashtag Project

Instagram, a popular global mobile photo-sharing platform, involves various user interactions centered on posting images accompanied by hashtags. Participatory hashtagging, one of these diverse tagging practices, has great potential to be a communication channel for various organizations and corporations that would like to interact with users on social media. In this paper, we aim to characterize participatory hashtagging behaviors on Instagram by conducting a case study of its representative hashtagging practice, the Weekend Hashtag Project, or #WHP. By conducting a user study using both quantitative and qualitative methods, we analyzed the way Instagram users respond to participation calls and identified factors that motivate users to take part in the project. Based on these findings, we provide design strategies for any interested parties to interact with users on social media.
PHYSICAL LAYER SECURITY USING ARTIFICIAL NOISE

The candidate confirms that the work submitted is his/her own and that appropriate credit has been given where reference has been made to the work of others. i ACKNOWLEDGMENT I am truly indebted and thankful to my scholarship sponsor ―National Information Technology Development Agency (NITDA), Nigeria‖ for giving me the rare privilege to study at the University of Leeds. I am sincerely and heartily grateful to my supervisor Dr. Des McLernon for his valuable support, patience and guidance throughout the course of this dissertation. I am sure it would not have been possible without his help. I would like to express my deep gratitude to Romero-Zurita Nabil for his enthusiastic encouragement, useful critique, recommendation and providing me with great information resources. I also acknowledge my colleague Frempong Kwadwo for his invaluable suggestions and discussion. Finally, I would like to appreciate my parents for their support and encouragement throughout my study at Leeds. Above all, special thanks to God Almighty for the gift of life.  This thesis is dedicated to family especially; to my parents for inculcating the importance of hardwork and higher education to Omobolanle for being a caring and loving sister. to Abimbola for believing in me.
Don't Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text

We describe an efficient neural network method to automatically learn sentiment lexicons without relying on any manual resources. The method takes inspiration from the NRC method, which gives the best results in SemEval13 by leveraging emoticons in large tweets, using the PMI between words and tweet sentiments to define the sentiment attributes of words. We show that better lexicons can be learned by using them to predict the tweet sentiment labels. By using a very simple neural network, our method is fast and can take advantage of the same data volume as the NRC method. Experiments show that our lexicons give significantly better accuracies on multiple languages compared to the current best methods.
A study of transgender adults and their non-transgender siblings on demographic characteristics, social support, and experiences of violence.

A national sample of 295 transgender adults and their nontransgender siblings were surveyed about demographics, perceptions of social support, and violence, harassment, and discrimination. Transwomen were older than the other 4 groups. Transwomen, transmen, and genderqueers were more highly educated than nontransgender sisters and nontransgender brothers, but did not have a corresponding higher income. Other demographic differences between groups were found in religion, geographic mobility, relationship status, and sexual orientation. Transgender people were more likely to experience harassment and discrimination than nontransgender sisters and nontransgender brothers. All transgender people perceived less social support from family than nontransgender sisters. This is the first study to compare trans people to nontrans siblings as a comparison group.
Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud

Cloud computing provides new opportunities to deploy scalable application in an efficient way, allowing enterprise applications to dynamically adjust their computing resources on demand. In this paper we analyze and test the microservice architecture pattern, used during the last years by large Internet companies like Amazon, Netflix and LinkedIn to deploy large applications in the cloud as a set of small services that can be developed, tested, deployed, scaled, operated and upgraded independently, allowing these companies to gain agility, reduce complexity and scale their applications in the cloud in a more efficient way. We present a case study where an enterprise application was developed and deployed in the cloud using a monolithic approach and a microservice architecture using the Play web framework. We show the results of performance tests executed on both applications, and we describe the benefits and challenges that existing enterprises can get and face when they implement microservices in their applications.
Research and development of a 5-axis hybrid kinematic CNC machine

A 5-axis hybrid computer numerical controlled machine was developed using a 2 degree of freedom spherical parallel mechanism known as the Agile Eye. The hybrid machine design consisted of a 3-axis serial gantry type structure, with the Agile Eye being placed at the end of the Z-Axis to allow for machining on inclined planes. A control system was designed that controlled two kinematic systems, the 3-axis serial kinematic system and the 2-axis parallel kinematic system. This paper details the design of the agile eye, its kinematic models and the integration of the agile eye mechanism to create a functional hybrid machine.
The Effects of Organizational Learning Culture and Job Satisfaction on Motivation to Transfer Learning and Turnover Intention

Although organizational learning theory and practice have been clarified by practitioners and scholars over the past several years, there is much to be explored regarding interactions between organizational learning culture and employee learning and performance outcomes. This study examined the relationship of organizational learning culture, job satisfaction, and organizational outcome variables with a sample of information technology (IT) employees in the United States. It found that learning organizational culture is associated with IT employee job satisfaction and motivation to transfer learning. Turnover intention was found to be negatively influenced by organizational learning culture and job satisfaction. Suggestions for future study of learning organizational culture in association with job satisfaction and performance-related outcomes are discussed.
A scalable verification solution for blockchains

Bitcoin and Ethereum, whose miners arguably collectively comprise the most powerful computational resource in the history of mankind, offer no more power for processing and verifying transactions than a typical smart phone. The system described herein bypasses this bottleneck and brings scalable computation to Ethereum. Our new system consists of a financial incentive layer atop a dispute resolution layer where the latter takes form of a versatile “verification game.” In addition to secure outsourced computation, immediate applications include decentralized mining pools whose operator is an Ethereum smart contract, a cryptocurrency with scalable transaction throughput, and a trustless means for transferring currency between disjoint cryptocurrency systems.