Reviewerly/exHarmony huggingface dataset:
Reversible Data Hiding in Encrypted Images Using Cipher-Feedback Secret Sharing

Reversible data hiding in encrypted images (RDH-EIs) has attracted increasing attention since it can protect the privacy of original images while exactly extracting the embedded data. In this paper, we propose an RDH-EI scheme with multiple data hiders. First, we introduce a cipher-feedback secret sharing (CFSS) technique using the cipher-feedback strategy of the Advanced Encryption Standard. Then, using the CFSS technique, we devise a new <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r,n)$ </tex-math></inline-formula> -threshold ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r\leq n$ </tex-math></inline-formula> ) RDH-EI scheme with multiple data hiders called CFSS-RDHEI. It can encrypt an original image into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> encrypted images with reduced size using an encryption key and sends each encrypted image to one data hider. Each data hider can independently embed secret data into the encrypted image to obtain a marked encrypted image. The embedded data can be extracted from each marked encrypted image using the data hiding key, and the original image can be completely recovered from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> marked encrypted images using the encryption key. Performance evaluations show that our CFSS-RDHEI scheme has a higher embedding rate and that its generated encrypted images are much smaller, while still being well protected, compared to existing secret sharing-based RDH-EI schemes.
Mapping of Hydrothermal Alteration Zones in the Kelâat M’Gouna Region Using Airborne Gamma-Ray Spectrometry and Remote Sensing Data: Mining Implications (Eastern Anti-Atlas, Morocco)

The mapping of hydrothermal alteration zones associated with mineralization is of paramount importance in searching for metal deposits. For this purpose, targeting alteration zones by analyzing airborne geophysical and satellite imagery provides accurate and reliable results. In the Kelâat M’Gouna inlier, located in the Saghro Massif of the Moroccan Anti Atlas, natural gamma-ray spectrometry and ASTER satellite data were used to map hydrothermal alteration zones. Natural gamma-ray spectrometry data were processed to produce maps of Potassium (K in %), Uranium (eU in ppm), Thorium (eTh in ppm) and ratios of K/eTh and K/eU. In addition, four-band ratios were computed, on ASTER data, to map the distribution of clay minerals, phyllitic minerals, propylitic minerals, and iron oxides. The combined results obtained from geophysical and satellite data were further exploited by fuzzy logic modelling using the Geographic Information System (GIS) to generate a mineral prospectivity map. Seven hydrothermal alteration zones likely to be favorable for mineralization have been identified. They show a spatial correlation with (i) known surface prospects and mineral occurrences, (ii) the granite-encasing contact zone, and (iii) the fault zones (Sidi Flah and Tagmout faults). This research therefore provides important information on the prospecting of mineral potential in the study area.
Towards Convergence of AI and IoT for Smart Policing

With the fast growth of IoT and AI techniques, AIoT’s potential in creating and capturing business value is being increasingly acknowledged. AIoT is the practice of combining AI with IoT-based hardware to proactively predict what might happen and what action needs to be taken. However, research on this issue has been limited and what the key mechanisms for designing AIoT based context-aware services are in the real field is still underexplored. In this article, we bridge this gap by studying a case study of AIoT based context-aware system in law enforcement in terms of information system artifacts to extend our understating of the AIoT based system’s mechanisms for business applications. Our case study reveals a conceptual model that can ensure AIoT empowered context-aware services in a police field unit. The implications, limitations, and future applications of the development of AIoT empowered context-aware services are discussed.
An Analytical Model of Page Dissemination for Efficient Big Data Transmission of C-ITS

With the rapid development of Cooperative Intelligent Transportation System (C-ITS), it becomes an urgent problem to effectively evaluate the data transmission efficiency of code dissemination protocols with network coding in the Dedicated Transportation Sensor Network (DTSN). First, this paper builds the overall structure of DTSN to remotely monitor the railway infrastructure in the C-ITS. Second, we propose a time model of page dissemination to reduce the deviation between the predictive data of existing models and the real-world data of code dissemination. Third, a Firefly Algorithm is designed to further improve the data transmission efficiency. The algorithm makes the feasibility rules to handle constraints, and it searches the optimal page granularity to minimize the time of page dissemination through iteration. Experiments show that the simulation results are consistent with the prediction results of proposed model, and code images can be distributed quickly and efficiently in the DTSN.
A new optimization algorithm inspired by the quest for the evolution of human society: Human felicity algorithm

The present paper introduces a new evolutionary algorithm to solve optimization issues called the human felicity algorithm (HFA). The main idea of the HFA has been inspired the efforts of human society to become felicity. Because the change in human felicity is possible, only through a change in human thought, in the proposed algorithm, the objective function is human felicity in society and the search space optimization is the human thought in society. Like other optimization algorithms, the human felicity algorithm starts solving the problem by an initial random population. Then the population is divided into three categories of elites, disciples, and ordinary people. Afterward, people in human society change their thoughts by 3 ways: 1- being influenced by elites 2- personal experiences 3- sudden and drastic changes of thoughts for various reasons, such as war and famine. To test the capability of the proposed algorithm, the standard test functions of IEEE (Institute of Electrical and Electronics Engineers) Congress on Evolutionary Computation (CEC) 2014, CEC 2019, and CEC2020 with different dimensions have been used. In addition, to evaluate the ability of the proposed algorithm to solve various real-world complex engineering problems, 4 standard classic engineering optimization problems are examined and compared. To test the ability of the proposed algorithm in hyper-parameters tuning, the five-database classification was simulated using the Support Vector Machines (SVM). The simulation of the proposed algorithm on different test functions shows its high ability to solve complex problems with many local optimal points. For statistical analysis, powerful nonparametric methods such as Friedman and Nemenyi post-Hoc test for statistical analysis have been used. Comparing the HFA with some other optimization algorithms demonstrates its better performance in solving complicated multidimensional problems with the lower iteration.
Institutional Quality and Education Quality in Developing Countries: Effects and Transmission Channels

The main objective of this study is to analyze the effect of institutional quality on education quality in developing countries. The literature review explores the channels through which institutional quality transits to affect education quality. The empirical analyses cover a sample of 82 developing countries. The main results obtained using ordinary least squares and two-stage least squares estimators show that institutional quality has a positive effect on student achievement and school completion, and a negative effect on educational failure. Concerning the role of transmission channels, the results show that a deterioration in institutional quality, characterized in particular by the presence of corruption, political instability, or the deterioration of government effectiveness, reduces the effectiveness of public spending on education and the quality of teaching through unethical behavior of teachers and the recruitment of untrained or less trained persons to carry out teaching tasks.
Interpretability in the medical field: A systematic mapping and review study

Recently, the machine learning (ML) field has been rapidly growing, mainly owing to the availability of historical datasets and advanced computational power. This growth is still facing a set of challenges, such as the interpretability of ML models. In particular, in the medical field, interpretability is a real bottleneck to the use of ML by physicians. Therefore, numerous interpretability techniques have been proposed and evaluated to help ML gain the trust of its users. This review was carried out according to the well-known systematic map and review process to analyze the literature on interpretability techniques when applied in the medical field with regard to different aspects: publication venues and publication year, contribution and empirical types, medical and ML disciplines and objectives, ML black-box techniques interpreted, interpretability techniques investigated, their performance and the best performing techniques, and lastly, the datasets used when evaluating interpretability techniques. A total of 179 articles (1994–2020) were selected from six digital libraries: ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, Wiley, and Google Scholar. The results showed that the number of studies dealing with interpretability increased over the years with a dominance of solution proposals and experiment-based empirical type. Diagnosis, oncology, and classification were the most frequent medical task, discipline, and ML objective studied, respectively. Artificial neural networks were the most widely used ML black-box techniques investigated for interpretability. Additionally, global interpretability techniques focusing on a specific black-box model, such as rules, were the dominant explanation types, and most of the metrics used to evaluate interpretability were accuracy, fidelity, and number of rules. Moreover, the variety of the techniques used by the selected papers did not allow categorization at the technique level, and the high number of the sum of evaluations (671) of the articles raised a suspicion of subjectivity. Datasets that contained numerical and categorical attributes were the most frequently used in the selected studies. Further effort is needed in disciplines other than diagnosis and classification. Global techniques such as rules are the most used because of their comprehensibility to doctors, but new local techniques should be explored more in the medical field to gain more insights into the model’s behavior. More experiments and comparisons against existing techniques are encouraged to determine the best performing techniques. Lastly, quantitative evaluation of interpretability and physicians’ implications in interpretability techniques evaluation is highly recommended to evaluate how the techniques will perform in real-world scenarios. It can ensure the soundness of the techniques and help gain trust in black-box models in medical environments.
Application of IoT technology in ecology (on the example of the Aral Sea region)

The article discusses the depth of wells and salinity levels to take the results using IoT technology in Nukus of the Republic of Karakalpakstan, located in the Aral Sea region.
A fault detection algorithm for pipeline insulation layer based on immune neural network

Images of the pipeline can be easily captured by the infrared thermal camera. However, factors such as uneven lighting or the complex and diverse outdoor environment can affect the pipeline infrared image. Under these circumstances, detection of the abnormally high-temperature areas of pipe insulation becomes very difficult because the contrast between the image target and background is reduced. This work aims to overcome these issues and presents an immune neural network algorithm. The existing artificial immune algorithm only reflects the function and characteristics of adaptive immunity, without considering the holistic nature of the biological immune system. This method draws on the synergy mechanism of the innate immune network and the adaptive immune network in the biological immune system and combines the holistic immune system with the neural network. The method can effectively detect abnormally high-temperature areas in the pipeline infrared images caused by insulation faults and has higher detection accuracy compared with other image detection algorithms.
Towards shared prosperity in sub‐Saharan Africa: How does the effect of economic integration compare to social equity policies?

Abstract This study extends the inclusive growth literature on sub‐Saharan Africa (SSA) by addressing two conspicuous gaps. First, the study examines the effects of social equity policies and economic integration on inclusive growth. Second, the study investigates the joint effects of economic integration and resource allocation on inclusive growth. Using data on 43 SSA countries for the period 1980–2019, we provide robust evidence from the GMM estimator to show that relative to economic integration, social equity policies are rather remarkable in enhancing inclusive growth. The results further reveal that, although economic integration induces inclusive growth, the effect is pronounced in the presence of productive resource allocation. Policy recommendations are provided in line with the African Continental Free Trade Area (AfCFTA) agreement and the reversals of welfare gains due to the coronavirus pandemic.
Realistic Motion Avatars are the Future for Social Interaction in Virtual Reality

This study evaluated participant self-reported appraisal of social interactions with another person in virtual reality (VR) where their conversational partner was represented by a realistic motion avatar. We use the term realistic motion avatar because: 1. The avatar was modelled to look like the conversational partner it represented, and 2. Full face and body motion capture was utilised so that the avatar mimicked the facial and body language of the conversational partner in real-time. We compared social interaction in VR with face-to-face interaction across two communicative contexts: 1. Getting acquainted conversation, and 2. A structured interview where the participant engaged in self-disclosure about positive and negative experiences. Overall, participants largely indicated they preferred face-to-face over VR communication. However, some participants did indicate a preference for VR communication. Additionally, an analysis of post-conversation ratings indicated no significant difference for rated enjoyment, understanding, self-disclosure, comfort, and awkwardness between communication modes. The only ratings where face-to-face was found to be superior was for perceived closeness across both types of communication, and for feeling understood specifically when disclosing negative experiences. Most participants perceived frequent eye contact in both face-to-face and VR interaction, but typically more eye contact when face-to-face. Eye contact was positively associated with rated enjoyment, closeness, and comfort. Overall, our findings suggest that harnessing full face and body motion capture can make social interaction in VR very similar to face-to-face interaction. We anticipate that VR social interaction is poised to become the next major technological evolution for human computer mediated communication and suggest avenues for further research.
Social Contexts, Agency, and Conflicts: Exploring Critical Aspects of Design for Future Smart Home Technologies

Smart devices are increasingly being designed for, and adopted in, the home environment. Prior scholarship has investigated the challenges that users face as they take up these devices in their homes. However, little is known about when and how users or potential users would prefer future domestic Internet of Things (IoT) to support their activities in home settings. To fill this gap, we conducted two co-design workshops, an in-home activity between the two sessions, and pre- and post-study interviews with 18 adult participants, who had diverse levels of prior experience of IoT use. Our findings contribute new insights into how smart home devices could adapt their behavior based on social contexts; how to re-imagine agency and support useful intelligibility; and how to resolve user-driven conflict by providing appropriate information about those with whom devices are shared. Finally, based on these findings, we discuss the implications of our work and provide a set of design considerations from which designers of future smart home technologies can benefit.
Inter-Server Collaborative Federated Learning for Ultra-Dense Edge Computing

Increasingly serious data security and privacy protection issues make federated learning (FL) gradually evolve to be an important technology in the field of artificial intelligence (AI). Meanwhile, in consideration of the huge demands for network access and computing resources from massive IoT devices, ultra-dense edge computing (UDEC), which integrates mobile edge computing (MEC) and ultra-dense network (UDN), has turned out to be a promising network architecture in the era of 5G and even 6G. Facing requirements on ultra-low processing latency, performing FL for UDEC confronts many challenges, one of which is how to relieve the barrel effect caused by the difference in computing power of local devices while ensuring overall FL efficiency. Nevertheless, little work can be found in this area. Toward this end, the paper takes the lead in studying FL for UDEC, and proposes an inter-server collaborative federated learning method by grouping the servers and clients. Theoretical analysis and numerical results corroborate that our proposed inter-server collaborative method can significantly reduce the waiting time during local training without reducing the learning accuracy, thus improving the overall efficiency.
ComGCN: Community-Driven Graph Convolutional Network for Link Prediction in Dynamic Networks

Recent advances in deep learning have tremendously leveraged the performance of network representation learning (NRL). Multiple deep learning-based NRL models have been proposed recently to effectively handling primitive tasks of information network analysis and mining (INAM) domain, including link prediction (LP). LP is considered as an important one due to its multiple applications in many disciplines. In the recent few years, LP in dynamic networks has attracted a lot of attention from researchers to propose novel algorithms for better capturing both rich structural and evolutional information of complex information networks (INs). However, recent models are mainly concentrated on preserving the sequential representations of a given network over time. They have largely ignored other important structural features, such as: intracommunity which contributes to the creation of links between network nodes. In this article, we propose a novel community-driven dynamic NRL technique upon the RNN+GCN framework, called: ComGCN. Specifically, the ComGCN model is a combination of microscopic (node embedding-based) and mesoscopic (intracommunity-based) dynamic network embedding approach which enable effectively handling the LP problem in context of dynamism. Extensive experiments on real-world dynamic networks demonstrated the effectiveness of the proposed model compared with recent state-of-the-art baselines.
Credit Card Fraud Detection Using Support Vector Machine

Credit card fraud has without hesitation an expression of criminal deception. Fraud identification seems to be a complicated problem that requires a significant amount of skill until throwing algorithms regarding machine learning into it. However, it is an implementation for both the better of machine learning as well as artificial intelligence, ensuring that perhaps the funds of both the customer seems to be secure and therefore not manipulated. The whole research article addressed an effective system of identifying fraud depending on machine learning methodologies, with such a feedback system. Its feedback process relates to enhancing the classifier's detection rate as well as effectiveness. The state of art approaches failed to detect the frauds through credit card transactions. Thus, to solve these drawbacks, the proposed system is implemented with the Support vector machine (SVM) classification to detect the frauds. The simulation results show that the proposed method gives the better classification accuracy compared to the state of art approaches.
Cultural heritage preservation by using blockchain technologies

Abstract Ubiquitous digitization enables promising options for cultural heritage preservation. Therefore, a new approach is presented that considers deployment scenarios by linking heritage science to tourism. Such an approach is necessary because neither technology nor society views can be treated separately to obtain deployable solutions of a wider social, and even national importance. Clearly, while the traditional approaches to cultural heritage preservation will remain a gold standard, they will be increasingly complemented by digital preservation techniques. Thus, based on practical implementations and lessons learnt in other areas, this multidisciplinary framework paper analyses existing disruptive information technologies deployments. In line with the findings it presents a novel technological architecture tailored to the needs of cultural heritage preservation that deploys an open blockchain architecture. The architecture preserves the advantages of traditional blockchains, which made this technology so important, while enabling energy efficient implementations that can be deployed in mobile applications. By additionally using the contribution-ware principle it links it to tourism, where the identification of users focused incentives and business models play a central role. It is obvious that tourism is a good candidate in such preservation efforts due to the organic links between it and cultural heritage and can support further developments in the heritage preservation domain.