nils-herrmann/clean_scirepeval huggingface dataset:
Depression, anxiety, and stress and their association with khat use: a cross-sectional study among Jazan University students, Saudi Arabia

Depression, anxiety, and stress levels are considered important indicators for mental health. Khat chewing habit is prevalent among all segments of Jazan population in Saudi Arabia. Few studies have been conducted to evaluate depression, anxiety, and stress among Jazan University students, and information about the correlation between khat use and these disorders is scarce. Thus, this study aims to evaluate the prevalence of depression, anxiety, and stress and their correlation with khat chewing and other risk factors among Jazan University students.



A cross-sectional study was conducted on 642 students from Jazan University. Multistage sampling was used, with probability proportional to size-sampling technique. The Depression, Anxiety, and Stress Scale 21 questionnaire was used to collect the data, which were analyzed using SPSS Version 20.0 software.



Moderate depression was prevalent among 53.6% of the sample, anxiety was found among 65.7%, while 34.3% of the students suffered from stress. Female gender was strongly associated with higher mean scores for symptoms of depression, anxiety, and stress, with P-values <0.05 for all. Moreover, anxiety symptoms scores were statistically associated with grade point average and caffeine consumption. Khat use was statistically associated with higher mean scores of anxiety among males and a higher mean score of depression and anxiety among females.



The results indicate a high rate of symptoms of depression, anxiety, and stress among Jazan University students. Khat use was associated with anxiety, and a higher rate of symptoms of depression, anxiety, and stress was indicated among female students. Therefore, strategy for the prevention and management of depression, anxiety, and stress is highly recommended to minimize the impact of these serious disorders.
Analysing the Relationship between IT Governance and Business/IT Alignment Maturity

IT governance is one of these concepts that suddenly emerged and became an important issue in the information technology area. Some organisations started with the implementation of IT governance in order to achieve a better alignment between business and IT. This paper interprets important existing theories, models and practices in the IT governance domain and derives research questions from it. Next, multiple research strategies are triangulated in order to understand how organisations are implementing IT governance in practice and to analyse the relationship between these implementations and business/IT alignment. Major finding is that organisations with more mature IT governance practices likely obtain a higher degree of business/IT alignment maturity.
The Relationship Between Subthreshold Autistic Traits, Ambiguous Figure Perception and Divergent Thinking.

This research investigates the paradox of creativity in autism. That is, whether people with subclinical autistic traits have cognitive styles conducive to creativity or whether they are disadvantaged by the implied cognitive and behavioural rigidity of the autism phenotype. The relationship between divergent thinking (a cognitive component of creativity), perception of ambiguous figures, and self-reported autistic traits was evaluated in 312 individuals in a non-clinical sample. High levels of autistic traits were significantly associated with lower fluency scores on the divergent thinking tasks. However autistic traits were associated with high numbers of unusual responses on the divergent thinking tasks. Generation of novel ideas is a prerequisite for creative problem solving and may be an adaptive advantage associated with autistic traits.
QNoC: QoS architecture and design process for network on chip

We define Quality of Service (QoS) and cost model for communications in Systems on Chip (SoC), and derive related Network on Chip (NoC) architecture and design process. SoC inter-module communication traffic is classified into four classes of service: signaling (for inter-module control signals); real-time (representing delay-constrained bit streams); RD/WR (modeling short data access) and block-transfer (handling large data bursts). Communication traffic of the target SoC is analyzed (by means of analytic calculations and simulations), and QoS requirements (delay and throughput) for each service class are derived. A customized Quality-of-Service NoC (QNoC) architecture is derived by modifying a generic network architecture. The customization process minimizes the network cost (in area and power) while maintaining the required QoS. The generic network is based on a two-dimensional planar mesh and fixed shortest path (X–Y based) multi-class wormhole routing. Once communication requirements of the target SoC are identified, the network is customized as follows: The SoC modules are placed so as to minimize spatial traffic density, unnecessary mesh links and switching nodes are removed, and bandwidth is allocated to the remaining links and switches according to their relative load so that link utilization is balanced. The result is a low cost customized QNoC for the target SoC which guarantees that QoS requirements are met.   Network on chip; QoS architecture; Wormhole switching; QNoC design process; QNoC
Context Ontology Implementation for Smart Home

Context awareness is one of the important fields in ubiquitous computing. Smart Home, a specific instance of ubiquitous computing, provides every family with opportunities to enjoy the power of hi-tech home living. Discovering that relationship among user, activity and context data in home environment is semantic, therefore, we apply ontology to model these relationships and then reason them as the semantic information. In this paper, we present the realization of smart home’s context-aware system based on ontology. We discuss the current challenges in realizing the ontology context base. These challenges can be listed as collecting context information from heterogeneous sources, such as devices, agents, sensors into ontology, ontology management, ontology querying, and the issue related to environment database explosion.
Fear and Fitness : An Evolutionary Analysis of Anxiety

This article reviews the evolutionary origins and functions of the capacity for anxiety, and relevant clinical and research issues. Normal anxiety is an emotion that helps organisms defend against a wide variety of threats. There is a general capacity for normal defensive arousal, and subtypes of normal anxiety protect against particular kinds of threats. These normal subtypes correspond somewhat to mild forms of various anxiety disorders. Anxiety disorders arise from dysregulation of normal defensive responses, raising the possibility of a bypophobic disorder (too little anxiety). If a drug were discovered that abolished all defensive anxiety, it could do harm as well as good. Factors that have shaped anxiety-regulation mechanisms can explain prepotent and prepared tendencies to associate anxiety more quickly with certain cues than with others. These tendencies lead to excess fear of largely archaic dangers, like snakes, and too little fear of new threats, like cars. An understanding of the evolutionary origins, functions, and mechanisms of anxiety suggests new questions about anxiety disorders.
Machine learning in computational docking

The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has gained much popularity and interest over the last few years. Central to this methodology is the notion of computational docking which is the process of predicting the best pose (orientation + conformation) of a small molecule (drug candidate) when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule. In computational docking, a large number of binding poses are evaluated and ranked using a scoring function. The scoring function is a mathematical predictive model that produces a score that represents the binding free energy, and hence the stability, of the resulting complex molecule. Generally, such a function should produce a set of plausible ligands ranked according to their binding stability along with their binding poses. In more practical terms, an effective scoring function should produce promising drug candidates which can then be synthesized and physically screened using high throughput screening process. Therefore, the key to computer-aided drug design is the design of an efficient highly accurate scoring function (using ML techniques).



The methods presented in this paper are specifically based on ML techniques. Despite many traditional techniques have been proposed, the performance was generally poor. Only in the last few years started the application of the ML technology in the design of scoring functions; and the results have been very promising.



The ML-based techniques are based on various molecular features extracted from the abundance of protein-ligand information in the public molecular databases, e.g., protein data bank bind (PDBbind).



In this paper, we present this paradigm shift elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area. For instance, the best random forest (RF)-based scoring function on PDBbind v2007 achieves a Pearson correlation coefficient between the predicted and experimentally determined binding affinities of 0.803 while the best conventional scoring function achieves 0.644. The best RF-based ranking power ranks the ligands correctly based on their experimentally determined binding affinities with accuracy 62.5% and identifies the top binding ligand with accuracy 78.1%.



We conclude with open questions and potential future research directions that can be pursued in smart computational docking; using molecular features of different nature (geometrical, energy terms, pharmacophore), advanced ML techniques (e.g., deep learning), combining more than one ML models.
Chinese Poetry Generation with a Salient-Clue Mechanism

As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has been made in this area benefiting from the development of neural networks. However, the coherence in meaning, theme or even artistic conception for a generated poem as a whole still remains a big problem. In this paper, we propose a novel Salient-Clue mechanism for Chinese poetry generation. Different from previous work which tried to exploit all the context information, our model selects the most salient characters automatically from each so-far generated line to gradually form a salient clue, which is utilized to guide successive poem generation process so as to eliminate interruptions and improve coherence. Besides, our model can be flexibly extended to control the generated poem in different aspects, for example, poetry style, which further enhances the coherence. Experimental results show that our model is very effective, outperforming three strong baselines.
Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach

As online social networking sites become more and more popular, they have also attracted the attentions of the spammers. In this paper, Twitter, a popular micro-blogging service, is studied as an example of spam bots detection in online social networking sites. A machine learning approach is proposed to distinguish the spam bots from normal ones. To facilitate the spam bots detection, three graph-based features, such as the number of friends and the number of followers, are extracted to explore the unique follower and friend relationships among users on Twitter. Three content-based features are also extracted from user’s most recent 20 tweets. A real data set is collected from Twitter’s public available information using two different methods. Evaluation experiments show that the detection system is efficient and accurate to identify spam bots in Twitter.
Comparison of Water Flosser and Interdental Brush on Plaque Removal: A Single-Use Pilot Study.

The purpose of this randomized, single-use, single-blind, two-group, parallel pilot study was to compare the plaque removal ability of a water flosser and interdental brush when used in combination with a manual toothbrush.



Twenty-eight (28) subjects completed the study. Subjects were randomly assigned to one of two groups: Waterpik®Water Flosser (WF) plus manual tooth brushing or interdental brushes (IDB) plus manual tooth brushing. Pre-cleaning plaque scores were obtained using the Rustogi Modification of the Navy Plaque Index (RMNPI). Subjects were instructed on the use of their interdental product. Post-cleaning scores were obtained after a supervised brushing and use of the interdental device. Scores were recorded for whole mouth, marginal, approximal, facial, and lingual regions for each subject.



There were no differences between the groups for pre-cleaning plaque scores for whole mouth, marginal, approximal, facial, or lingual regions. Both groups showed significant reductions in plaque from baseline for all areas measured (p < 0.001). The WF group was significantly more effective than the IDB group for removing plaque from all areas measured. Specifically, the WF was 18% more effective for whole mouth and marginal areas, 20% for approximal areas, 11% for facial areas, and 29% for lingual areas.



The Waterpik Water Flosser and manual toothbrush removes significantly more plaque from tooth surfaces (whole mouth, marginal, approximal, facial, and lingual) than interdental brushes and a manual toothbrush after a single use.
Putting Your Best Face Forward : The Accuracy of Online Dating Photographs

This study examines the accuracy of 54 online dating photographs posted by heterosexual daters. We report data on (a1) online daters’ self-reported accuracy, (b) independent judges’ perceptions of accuracy, and (c) inconsistencies in the profile photograph identified by trained coders. While online daters rated their photos as relatively accurate, independent judges rated approximately 1/3 of the photographs as not accurate. Female photographs were judged as less accurate than male photographs, and were more likely to be older, to be retouched or taken by a professional photographer, and to contain inconsistencies, including changes in hair style and skin quality. The findings are discussed in terms of the tensions experienced by online daters to (a) enhance their physical attractiveness and (b) present a photograph that would not be judged deceptive in subsequent face-to-face meetings. The paper extends the theoretical concept of selective self-presentation to online photographs, and discusses issues of self-deception and social desirability bias.
Real-Time 6D Object Pose Estimation on CPU

We propose a fast and accurate 6D object pose estimation from a RGB-D image. Our proposed method is template matching based and consists of three main technical components, PCOF-MOD (multimodal PCOF), balanced pose tree (BPT) and optimum memory rearrangement for a coarse-to-fine search. Our model templates on densely sampled viewpoints and PCOF-MOD which explicitly handles a certain range of 3D object pose improve the robustness against background clutters. BPT which is an efficient tree-based data structures for a large number of templates and template matching on rearranged feature maps where nearby features are linearly aligned accelerate the pose estimation. The experimental evaluation on tabletop and binpicking dataset showed that our method achieved higher accuracy and faster speed in comparison with state-of-the-art techniques including recent CNN based approaches. Moreover, our model templates can be trained only from 3D CAD in a few minutes and the pose estimation run in near realtime (23 fps) on CPU. These features are suitable for any real applications.
The kaleidoscope of effective gamification: deconstructing gamification in business applications

Developers of gamified business applications face the challenge of creating motivating gameplay strategies and creative design techniques to deliver subject matter not typically associated with games in a playful way. We currently have limited models that frame what makes gamification effective (i.e., engaging people with a business application). Thus, we propose a design-centric model and analysis tool for gamification: The kaleidoscope of effective gamification. We take a look at current models of game design, self-determination theory and the principles of systems design to deconstruct the gamification layer in the design of these applications. Based on the layers of our model, we provide design guidelines for effective gamification of business applications.
AN INFORMATION SECURITY AWARENESS CAPABILITY MODEL ( ISACM )

A lack of information security awareness within some parts of society as well as some organisations continues to exist today. Whilst we have emerged from the threats of late 1990s of viruses such as Code Red and Melissa, through to the phishing emails of the mid 2000’s and the financial damage some such as the Nigerian scam caused, we continue to react poorly to new threats such as demanding money via SMS with a promise of death to those who won’t pay. So is this lack of awareness translating into problems within the workforce? There is often a lack of knowledge as to what is an appropriate level of awareness for information security controls across an organisation. This paper presents the development of a theoretical framework and model that combines aspects of information security best practice standards as presented in ISO/IEC 27002 with theories of Situation Awareness. The resultant model is an information security awareness capability model (ISACM). A preliminary survey is being used to develop the Awareness Importance element of the model and will leverage the opinions of information security professionals. A subsequent survey is also being developed to measure the Awareness Capability element of the model. This will present scenarios that test Level 1 situation awareness (perception), Level 2 situation awareness (comprehension) and finally Level 3 situation awareness (projection). Is it time for awareness of information security to now hit the mainstream of society, governments and organisations?
A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification

Text summarization and sentiment classification, in NLP, are two main tasks implemented on text analysis, focusing on extracting the major idea of a text at different levels. Based on the characteristics of both, sentiment classification can be regarded as a more abstractive summarization task. According to the scheme, a Self-Attentive Hierarchical model for jointly improving text Summarization and Sentiment Classification (SAHSSC) is proposed in this paper. This model jointly performs abstractive text summarization and sentiment classification within a hierarchical end-to-end neural framework, in which the sentiment classification layer on top of the summarization layer predicts the sentiment label in the light of the text and the generated summary. Furthermore, a self-attention layer is also proposed in the hierarchical framework, which is the bridge that connects the summarization layer and the sentiment classification layer and aims at capturing emotional information at text-level as well as summary-level. The proposed model can generate a more relevant summary and lead to a more accurate summary-aware sentiment prediction. Experimental results evaluated on SNAP amazon online review datasets show that our model outperforms the state-of-the-art baselines on both abstractive text summarization and sentiment classification by a considerable margin.
Critical Review of the Blended Learning Models Based on Maslow's and Vygotsky's Educational Theory

Blended learning involves the combination of two fields of concern: education and educational technology. To gain the scholarly recognition from educationists, it is necessary to revisit its models and educational theory underpinned. This paper respond to this issue by reviewing models related to blended learning based on two prominent educational theorists, Maslow’s and Vygotsky’s view. Four models were chosen due to their holistic ideas or vast citations related to blended learning: (1) E-Moderation Model emerging from Open University of UK; (2) Learning Ecology Model by Sun Microsoft System; (3) Blended Learning Continuum in University of Glamorgan; and (4) Inquirybased Framework by Garrison and Vaughan. The discussion of each model concerning pedagogical impact to learning and teaching are made. Critical review of the models in accordance to Maslow or Vygotsky is argued. Such review is concluded with several key principles for the design and practice in
Patient Privacy in Paralinguistic Tasks

Recent developments in cryptography and, in particular in Fully Homomorphic Encryption (FHE), have allowed for the development of new privacy preserving machine learning schemes. In this paper, we show how these schemes can be applied to the automatic assessment of speech affected by medical conditions, allowing for patient privacy in diagnosis and monitoring scenarios. More specifically, we present results for the assessment of the degree of Parkinsons Disease, the detection of a Cold, and both the detection and assessment of the degree of Depression. To this end, we use a neural network in which all operations are performed in an FHE context. This implies replacing the activation functions by linear and second degree polynomials, as only additions and multiplications are viable. Furthermore, to guarantee that the inputs of these activation functions fall within the convergence interval of the approximation, a batch normalization layer is introduced before each activation function. After training the network with unencrypted data, the resulting model is then employed in an encrypted version of the network, to produce encrypted predictions. Our tests show that the use of this framework yields results with little to no performance degradation, in comparison to the baselines produced for the same datasets.
Energy comparison of MPPT techniques for PV Systems

Many maximum power point tracking techniques for photovoltaic systems have been developed to maximize the produced energy and a lot of these are well established in the literature. These techniques vary in many aspects as: simplicity, convergence speed, digital or analogical implementation, sensors required, cost, range of effectiveness, and in other aspects. This paper presents a comparative study of ten widely-adopted MPPT algorithms; their performance is evaluated on the energy point of view, by using the simulation tool Simulink®, considering different solar irradiance variations.  Maximum power point (MPP), maximum power point tracking (MPPT), photovoltaic (PV), comparative study, PV Converter.