Reviewerly/exHarmony huggingface dataset:
An AI-based Decision Support System for Predicting Mental Health Disorders

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.
Integration of blockchain and edge computing in internet of things: A survey

As an important technology to ensure data security, consistency, traceability, etc., blockchain has been increasingly used in Internet of Things (IoT) applications. The integration of blockchain and edge computing (IBEC) can further improve the resource utilization in terms of network, computing, storage, and security. This paper aims to present a survey on the IBEC. In particular, we first give an overview of blockchain and edge computing respectively. We then present a general architecture of an IBEC system. We next study the various applications of the IBEC in IoT. We also discuss the optimizations of the IBEC system and solutions from perspectives of resource management and performance improvement. Finally, we analyze and summarize the existing challenges posed by the IBEC system and the potential solutions in the future.
The effects of FDI on economic growth in Africa

This paper investigates the growth effects of FDI in Africa for the period 1990–2016 using a dynamically common correlated effect approach for an error-correction model. It uses an analytical classification of African economies, with each being fragile, factor-driven or investment-driven. It also accounts for interaction effects and the problem of cross-sectional dependence that previous studies overlooked. While the long-run effect of FDI on growth is significantly positive in investment- and factor-driven economies, its short-run effect is insignificant in the latter type of economies. The effect of FDI on growth is insignificant in the fragile category both in the short-run and long-run, however.
A new RGB color image encryption scheme based on cross-channel pixel and bit scrambling using chaos

A new RGB color image encryption algorithm is presented in this paper. The hyperchaotic two-dimensional logistic-sine-coupling map (2D-LSCM), which was previously suggested in the literature, is used in this study to generate the necessary parameters for the cross-channel pixel and bit scrambling, and the diffusion stages. The SHA-512 hash value of the plain color image is used to calculate the initial values of the map. In the pixel-level scrambling stage, a novel cross-channel scrambling method is implemented. The pixels in each channel are transformed by exchanging rows and columns between different channels according to the random sequences generated by 2D-LSCM. The four most significant bits of each channel’s pixels are concatenated and circularly shifted horizontally and vertically in the cross-channel bit scrambling stage. Finally, in the diffusion stage, each channel’s scrambled pixels are diffused using the diffusion sequences that are controlled by the control sequences. To assess the security performance of the presented method, several analyses such as key sensitivity analysis, statistical analysis, information entropy analysis, differential attack analysis, and noise & data loss analysis are performed. The experimental analyses and comparisons with other methods show that the proposed method can be used to efficiently encrypt color images.
Plain language summaries: A systematic review of theory, guidelines and empirical research

Plain language summaries (PLSs) have been introduced to communicate research in an understandable way to a nonexpert audience. Guidelines for writing PLSs have been developed and empirical research on PLSs has been conducted, but terminology and research approaches in this comparatively young field vary considerably. This prompted us to review the current state of the art of the theoretical and empirical literature on PLSs. The two main objectives of this review were to develop a conceptual framework for PLS theory, and to synthesize empirical evidence on PLS criteria. We began by searching Web of Science, PubMed, PsycInfo and PSYNDEX (last search 07/2021). In our review, we included empirical investigations of PLSs, reports on PLS development, PLS guidelines, and theoretical articles referring to PLSs. A conceptual framework was developed through content analysis. Empirical studies investigating effects of PLS criteria on defined outcomes were narratively synthesized. We identified 7,714 records, of which 90 articles met the inclusion criteria. All articles were used to develop a conceptual framework for PLSs which comprises 12 categories: six of PLS aims and six of PLS characteristics. Thirty-three articles empirically investigated effects of PLSs on several outcomes, but study designs were too heterogeneous to identify definite criteria for high-quality PLSs. Few studies identified effects of various criteria on accessibility, understanding, knowledge, communication of research, and empowerment. We did not find empirical evidence to support most of the criteria we identified in the PLS writing guidelines. We conclude that although considerable work on establishing and investigating PLSs is available, empirical evidence on criteria for high-quality PLSs remains scarce. The conceptual framework developed in this review may provide a valuable starting point for future guideline developers and PLS researchers.
A Study on the Psychology of Social Engineering-Based Cyberattacks and Existing Countermeasures

As cybersecurity strategies become more robust and challenging, cybercriminals are mutating cyberattacks to be more evasive. Recent studies have highlighted the use of social engineering by criminals to exploit the human factor in an organization’s security architecture. Social engineering attacks exploit specific human attributes and psychology to bypass technical security measures for malicious acts. Social engineering is becoming a pervasive approach used for compromising individuals and organizations (is relatively more convenient to compromise a human compared to discovering a vulnerability in the security system). Social engineering-based cyberattacks are extremely difficult to counter as they do not follow specific patterns or approaches for conducting an attack, making them highly effective, efficient, easy, and obscure approaches for compromising any organization. To counter such attacks, a better understanding of the attack tactics is highly essential. Hence, this paper provides an in-depth analysis of the approaches used to conduct social engineering-based cyberattacks. This study discusses human vulnerabilities employed by criminals in recent security breaches. Further, the paper highlights the existing approaches, including machine learning-based methods, to counter social engineering-based cyberattacks.
Locality preserving projection with symmetric graph embedding for unsupervised dimensionality reduction

Preserving the intrinsic structure of data is very important for unsupervised dimensionality reduction. For structure preserving, graph embedding technique is widely considered. However, most of the existing unsupervised graph embedding based methods cannot effectively preserve the intrinsic structure of data since these methods either use the constant graph or only explore the geometric structure based on the distance information or representation information. To solve this problem, a novel method, called locality preserving projection with symmetric graph embedding (LPP_SGE), is proposed. LPP_SGE introduces a novel adaptive graph learning model and can obtain the intrinsic graph and projection in a unified framework by fully exploring the representation information and distance information of the original data. Different from the existing works which generally introduce no less than two constraints to capture the representation information and distance information, LPP_SGE can simultaneously capture the above two kinds of structure information in one term. Moreover, LPP_SGE introduces an ‘l2,1’ norm based projection constraint to select the most discriminative features from the complex data for dimensionality reduction, such that the robustness is enhanced. Experimental results on four databases and two kinds of noisy databases show that LPP_SGE performs better than many well-known methods.
Developments and Implications of Central Bank Digital Currency: The Case of China e‐CNY

China has been both active and cautious in developing a central bank digital currency (CBDC). China CBDC has been in research and development since 2014. The process speeded up in 2019. It is currently at the stage of expanding real field experiments. Residents in 11 areas can open e‐wallets linked to nine major banks. It is centralized digital cash designed to gradually replace traditional paper cash and coins (M0). It will be supported by the traditional double‐layer banking system. It is not blockchain based at issuance but is technology neutral in distribution. Internet and technology companies may join commercial banks in distributing the China CBDC. In the short run, the China CBDC will help improve domestic financial monitoring and policy implementation. In the long run, it may play a role in the RMB's internationalization or even the international monetary system's evolution.
What Does it Mean for a Language Model to Preserve Privacy?

Natural language reflects our private lives and identities, making its privacy concerns as broad as those of real life. Language models lack the ability to understand the context and sensitivity of text, and tend to memorize phrases present in their training sets. An adversary can exploit this tendency to extract training data. Depending on the nature of the content and the context in which this data was collected, this could violate expectations of privacy. Thus, there is a growing interest in techniques for training language models that preserve privacy. In this paper, we discuss the mismatch between the narrow assumptions made by popular data protection techniques (data sanitization and differential privacy), and the broadness of natural language and of privacy as a social norm. We argue that existing protection methods cannot guarantee a generic and meaningful notion of privacy for language models. We conclude that language models should be trained on text data which was explicitly produced for public use.
UK National Screening Committee's approach to reviewing evidence on artificial intelligence in breast cancer screening

Artificial intelligence (AI) could have the potential to accurately classify mammograms according to the presence or absence of radiological signs of breast cancer, replacing or supplementing human readers (radiologists). The UK National Screening Committee's assessments of the use of AI systems to examine screening mammograms continues to focus on maximising benefits and minimising harms to women screened, when deciding whether to recommend the implementation of AI into the Breast Screening Programme in the UK. Maintaining or improving programme specificity is important to minimise anxiety from false positive results. When considering cancer detection, AI test sensitivity alone is not sufficiently informative, and additional information on the spectrum of disease detected and interval cancers is crucial to better understand the benefits and harms of screening. Although large retrospective studies might provide useful evidence by directly comparing test accuracy and spectrum of disease detected between different AI systems and by population subgroup, most retrospective studies are biased due to differential verification (ie, the use of different reference standards to verify the target condition among study participants). Enriched, multiple-reader, multiple-case, test set laboratory studies are also biased due to the laboratory effect (ie, radiologists' performance in retrospective, laboratory, observer studies is substantially different to their performance in a clinical environment). Therefore, assessment of the effect of incorporating any AI system into the breast screening pathway in prospective studies is required as it will provide key evidence for the effect of the interaction of medical staff with AI, and the impact on women's outcomes.
A universal method for constructing non-degenerate hyperchaotic systems with any desired number of positive Lyapunov exponents

Due to the limited machine word length of hardware devices, the dynamics of digital chaotic systems will degenerate. To combat this issue, we proposed a universal method that is based on singular value decomposition (SVD), which can reversely construct non-degenerate hyperchaotic systems with any desired number of positive Lyapunov exponents by controlling pre-specified singular values. To assess the practicability and effectiveness of the method, we construct a 6-dimensional non-degenerate hyperchaotic system as an example. Furthermore, based on the hyperchaotic system, a pseudorandom number generator (PRNG) with desirable statistical characteristics is designed for image encryption. Numerical simulations were performed to evaluate the security of the image encryption algorithm in terms of histogram, information entropy, differential attack test, etc. The proposed non-degenerate hyperchaotic system can be effectively applied in the field of multimedia data encryption and information security.
Investigating the impact of pandemic job stress and transformational leadership on innovative work behavior: The mediating and moderating role of knowledge sharing

Only a few studies have been conducted on job stress and transformational leadership (TL), particularly in the environment of the COVID-19 pandemic. This study aims to overcome this gap and attempts to explore the impact of pandemic job stress (PJS) and TL on employees' innovative work behavior (IWB) through knowledge sharing (KNS), while focusing on the importance of innovations for organizational survival and growth. The data were collected from 357 faculty members of higher education institutions in Pakistan and analyzed using the partial least squares estimation, a structural equation modeling (PLS-SEM) technique. The results demonstrate that PJS positively impacts employees' IWB, negating the negative relationship between job stress and IWB found in previous studies. Moreover, this study found a positive impact of TL and KNS on IWB. KNS also moderates the relationship between PJS and IWB while partially mediating the relationship between TL and IWB. Lastly, the theoretical and practical implications are discussed.
Research on prediction of multi-class theft crimes by an optimized decomposition and fusion method based on XGBoost

The number of theft cases is much higher than that of other criminal cases, which frequently occurs in daily life and is seriously destructive to social order. Studying the law of theft cases has a positive impact on social governance and optimizing police deployment. Therefore, based on the data of theft cases in H city, this study proposes an optimized decomposition and fusion method based on XGBoost, and establishes two multi-classification prediction models, such as OVR-XGBoost and OVO-XGBoost. As the theft data is a datasets with unbalanced class distribution, this paper uses SMOTENN algorithm to process it into a datasets with balanced distribution, which effectively improves the effect of the model. Experiments show that the prediction accuracy of OVR-XGBoost and OVO-XGBoost models is higher than that of baseline XGBoost models. For categories with few samples, the classification effect of OVO-XGBoost is better than that of baseline XGBoost and OVO-XGBoost models. Compared with baseline XGBoost model, the average overall classification accuracy of OVO-XGBoost model is improved by more than 7%, and the MacroR accuracy is also improved by more than 15%. The model proposed in this study has a good effect on the classification and prediction of theft types, and is of great significance for the prevention of theft cases.
A Study on the Relationship Between Cloud Computing and Data Mining in Business Organizations

In order to enhance information services quality, data extraction is a method through which potentially helpful data may be extracted. Integrated with cloud computing, data mining technology enables customers to obtain valuable information from a data warehouse that lowers infrastructure and storage cost. In recent years the data mining sector and society as a whole have become quite attractive large data availability and urgent need for the transformation of such information into valuable expertise and data Market research, fraud prevention and retention of customers, manufacturing control and scientific research. The natural development of information technology may be seen as a consequence of data mining. Security and privacy of user data is of major issue in the usage of cloud computing in data mining. In the context of discovering new, legitimate, usable, and comprehensible data formats, data mining is regarded to be an essential activity. The integration of data mining techniques with cloud computing offers a flexible and scalable architecture that can be utilized to efficiently extract large quantities of information from near-connection data sources in order to provide valuable, decision-making knowledge. This article offers a general explanation of the necessity for data mining to be integrated into cloud computing, to provide its customers effective and secured services and to decrease infrastructure and operating requirements in business.
Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into continuous vector spaces. Such embedding methods simplify the operations of conducting various in-KG tasks (e.g., link prediction) and out-of-KG tasks (e.g., question answering). They can be viewed as general solutions for representing KGs. However, existing KGE methods are not applicable to inductive settings, where a model trained on source KGs will be tested on target KGs with entities unseen during model training. Existing works focusing on KGs in inductive settings can only solve the inductive relation prediction task. They can not handle other out-of-KG tasks as general as KGE methods since they don't produce embeddings for entities. In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings. Such meta-knowledge is modeled by entity-independent modules and learned by meta-learning. Experimental results show that our model significantly outperforms corresponding baselines for in-KG and out-of-KG tasks in inductive settings.
The Role of Digital Technology in Curbing COVID-19

Introduction: Using digital technology to provide support, medical consultations, healthcare services, and to track the spread of the coronavirus has been identified as an important solution to curb the transmission of the virus. This research paper aims to (1) summarize the digital technologies used during the COVID-19 pandemic to mitigate the transmission of the COVID-19; (2) establish the extent to which digital technology applications have facilitated mitigation of the spread of COVID-19; and (3) explore the facilitators and barriers that impact the usability of digital technologies throughout the pandemic. Methods: A rapid electronic search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted of available records up to June 2022 on the medical databases PubMed, Ovid, Embase, CINHAIL, the Cochrane Library, Web of Science, and Google Scholar. Results: An increasing number and variety of digital health applications have been available throughout the pandemic, such as telehealth, smartphone mobile health apps, machine learning, and artificial intelligence. Each technology has played a particular role in curbing COVID-19 transmission. Different users have gained benefits from using digital technology during the COVID-19 pandemic and different determinants have contributed to accelerating the wheel of digital technology implementation during the pandemic. Conclusion: Digital health during the COVID-19 pandemic has evolved very rapidly, with different applications and roles aimed at curbing the pandemic.
Implementation of bio-inspired hybrid algorithm with mutation operator for robotic path planning

Path planning is an NP-hard problem that is aimed to satisfy multi-constraint optimization requirements. In autonomous robotics applications, path planning together with collision avoidance presents a challenging task. It necessitates the generation of possible search directions from a designated point to a fixed varying destination location satisfying spatial constraints. This paper presents a framework for the design of an intelligent multi-objective robotic path planning algorithm. The algorithm relies on the generation of way-points by hybridizing two meta-heuristics techniques, namely Grey Wolf Algorithm (GWO) and Particle Swarm Optimization (PSO). A frequency-based modification in GWO search operators is introduced to fasten the search process. An improvised search strategy is employed for collision detection and avoidance, which converts non-desired points into the desired point. Sensors are deployed around the robot vicinity for search optimization. Mutation operators are then introduced to improve path length by smoothing out the trajectory. The proposed algorithm's effectivity is then validated through extensive simulations, in which different condition environments are simulated. To validate the effectiveness of the proposed methodology, the results are compared with contemporary algorithms namely Minimum Angle Artificial Bee Colony (MAABC) algorithms, Hybrid Cuckoo Search-Bat Algorithm (BA-CSA), Bacterial Bolony (BC) and Genetic Algorithm (GA) algorithms. The results conclusively demonstrated that the proposed algorithm ensures effective performance in path smoothness and safety under a wide range of conditions. • A novel intelligent multi-objective framework for robotic path planning algorithm. • A hybridizing two meta-heuristics techniques to solve the path planning. • An improvised search strategy is employed for collision detection and avoidance. • The proposed algorithm's effectively validated through extensive simulations.