Reviewerly/exHarmony huggingface dataset:
An Evaluation of the Effects of a Virtual Museum on Users’ Attitudes towards Cultural Heritage

(1) Background: Several opportunities have appeared for the dissemination of culture and heritage thanks in part to the widespread use of information and communications technologies. Virtual museums have appeared as innovative technological products but often lack an evaluation of the impact that they have and their success in achieving their purpose. In this sense, this work seeks to evaluate the impact of a virtual museum on users’ attitudes toward cultural heritage. (2) Methods: We used a factorial design of 2 (direction of thoughts: positive vs. negative) × 2 (presence level: high vs. low) × 2 (virtual museum vs. interactive website). (3) Results: Attitudes toward heritage can change as a function of a multimedia experience, thought favorability, and presence. In a virtual museum, when general attitudes are evaluated, a sense of high psychological presence reduces the effect that thoughts (especially when negative) have on attitudes. However, in the case of visiting an interactive website, the effect of the direction of thoughts on attitudes occurred regardless of conditions of high or low presence. Similar tendencies are observed for specific attitudinal objects. (4) Conclusion: A virtual museum can have different effects depending on the interaction of important variables from the virtual reality literature and not only the classic main effects. Recommendations for interventions and future practical and theoretical work are presented.
Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model's weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks' outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model's performance. The experimental results established the proposed model's superiority over recently developed approaches.
Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey

Nowadays Convolutional Neural Networks (CNNs) are being employed in a wide range of industrial technologies for a variety of sectors, such as medical, automotive, aviation, agriculture, space, etc. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way. Layer-based details of CNNs along with parameter and floating-point operation number calculations are outlined. Using an evolutionary approach, the majority of the outstanding image classification CNNs, published in the open literature, is introduced with a focus on their accuracy performance, parameter number, model size, and inference speed, highlighting the progressive developments in convolutional operations. Results of a novel investigation of the convolution types and operations commonly used in CNNs are presented, including a timing analysis aimed at assessing their impact on CNN performance. This extensive experimental study provides new insight into the behaviour of each convolution type in terms of training time, inference time, and layer level decomposition. Building blocks for CNN-based object detection are also discussed, such as backbone networks and baseline types, and then representative state-of-the-art designs are outlined. Experimental results from the literature are summarised for each of the reviewed models. This is followed by an overview of recent ADSs related works and current industry activities, aiming to bridge academic research and industry practice on CNNs and ADSs. Design approaches targeted at solving problems of automakers in achieving real-time implementations are also proposed based on a discussion of design constraints, human vs. machine evaluations and trade-off analysis of accuracy vs. size. Current technologies, promising directions, and expectations from the literature on ADSs are introduced including a comprehensive trade-off analysis from a human-machine perspective.
Approaches on the augmented reality application in Japanese language learning for future language teachers

The article is devoted to the problem, approaches, and features of usage of augmented reality applications in Japanese language learning for future language teachers. We analyzed the usage of augmented reality for supporting and organizing Japanese language learning for future Japanese language teachers and identification of the main approaches to the usage of augmented reality in Japanese language learning. It is concluded that augmented reality tools provide a new paradigm of teaching materials, which has a positive impact on the formation of basic and professional competencies of future Japanese language teachers; it can be effective when used in blended learning that combines distance, online, traditional, and self-directed learning of Oriental languages. Prospects for further research is the creation of guidelines for the usage of augmented reality tools for teaching Oriental languages at different levels of the training of future Oriental languages teachers.
NPC: <u>N</u> euron <u>P</u> ath <u>C</u> overage via Characterizing Decision Logic of Deep Neural Networks

Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns in the practical operational environment, which calls for systematic testing, especially in safety-critical scenarios. Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs. However, due to the blackbox nature of DNN, the existing structural coverage criteria are difficult to interpret, making it hard to understand the underlying principles of these criteria. The relationship between the structural coverage and the decision logic of DNNs is unknown. Moreover, recent studies have further revealed the non-existence of correlation between the structural coverage and DNN defect detection, which further posts concerns on what a suitable DNN testing criterion should be. In this article, we propose the interpretable coverage criteria through constructing the decision structure of a DNN. Mirroring the control flow graph of the traditional program, we first extract a decision graph from a DNN based on its interpretation, where a path of the decision graph represents a decision logic of the DNN. Based on the control flow and data flow of the decision graph, we propose two variants of path coverage to measure the adequacy of the test cases in exercising the decision logic. The higher the path coverage, the more diverse decision logic the DNN is expected to be explored. Our large-scale evaluation results demonstrate that: The path in the decision graph is effective in characterizing the decision of the DNN, and the proposed coverage criteria are also sensitive with errors, including natural errors and adversarial examples, and strongly correlate with the output impartiality.
Explaining Machine Learning Decisions

Abstract The operations of deep networks are widely acknowledged to be inscrutable. The growing field of Explainable AI (XAI) has emerged in direct response to this problem. However, owing to the nature of the opacity in question, XAI has been forced to prioritise interpretability at the expense of completeness, and even realism, so that its explanations are frequently interpretable without being underpinned by more comprehensive explanations faithful to the way a network computes its predictions. While this has been taken to be a shortcoming of the field of XAI, I argue that it is broadly the right approach to the problem.
Library social networking sites for fostering startup business globalization through strategic partnerships

University libraries can be powerful cornerstones of an open innovation ecosystem for entrepreneurs. They are a source of worldwide market research for entrepreneurs looking to expand outside national borders with their existing enterprises. Social networking sites (SNS), which have been widely adopted by libraries as a means of engaging with students, are increasingly being utilized to provide market information to business communities. SNS can assist startups in forming and maintaining strong strategic ties with libraries to obtain ongoing support for globalization efforts. This column discusses the numerous benefits and some problems of using social media to develop such collaborations, particularly in terms of giving useful market insights to entrepreneurs (implementing partnerships). The issues of SNS adaptation for developing such relationships can be addressed with prototyping technologies. To address the needs of global startups, libraries must take advantage of social network access, overcoming the need for meetings on library grounds. To do so, thorough integration of SNS with library business activities is required, which is, regrettably, only observable in a few circumstances. University libraries are boosting their use of SNS to provide more online support for the business community.
Hybrid differential privacy based federated learning for Internet of Things

Wireless sensor networks have been widely used to achieve fine-grained information collection. However, numerous data acquisition and processing of sensors bring some privacy issues. Federated learning is a promising and privacy-friendly framework that trains a model across multiple devices or edge nodes holding local data samples without transferring their data to the server. It is not enough to protect privacy only by maintaining data locality, so differential privacy technology is often used to protect privacy in federated learning. However, different users have different privacy requirements, so it is inappropriate to use the same privacy protection scheme, assuming that all users trust or distrust the server. The former has poor accuracy, while the latter has poor privacy. This paper proposes a secure and reliable federated learning algorithm by integrating hybrid differential privacy into federated learning. We divide users into two categories according to their different privacy needs. In addition, we analyze the convergence and privacy bounds of the proposed algorithm and propose an adaptive gradient clip scheme and improved composition method to reduce the effects of noise and clip, respectively. The validity of the algorithm is verified by theoretical analysis and experimental evaluation on real-world datasets.
A Contrastive learning-based Task Adaptation model for few-shot intent recognition

Few-shot intent recognition aims to identify user’s intent from the utterance with limited training data. A considerable number of existing methods mainly rely on the generic knowledge acquired on the base classes to identify the novel classes. Such methods typically ignore the characteristics of each meta task itself, resulting in the inability to make full use of limited given samples when classifying unseen classes. To deal with such issues, we propose a Contrastive learning-based Task Adaptation model (CTA) for few-shot intent recognition. In detail, we leverage contrastive learning to help achieve task adaptation and make full use of the limited samples of novel classes. First, a self-attention layer is employed in the task adaptation module, which aims to establish interactions between samples of different categories so that new representations are task-specific rather than relying entirely on the base classes. Then, the contrastive-based loss functions and the semantics of the label name are respectively used for reducing the similarity between sample representations in different categories while increasing it in the same categories. Experimental results on a public dataset OOS verify the effectiveness of our proposal by beating the competitive baselines in terms of accuracy. Besides, we conduct the cross-domain experiments on three datasets, i.e., OOS, SNIPS as well as ATIS. We find that CTA gains obvious improvements in terms of accuracy in all cross-domain experiments, indicating that it has a better generalization ability than other competitive baselines in both cross-domain and single-domain settings.
Toward Energy-Efficient Distributed Federated Learning for 6G Networks

The provision of communication services via portable and mobile devices, such as aerial base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally, IoT/edge devices need to transmit data directly to the base station for training the model using machine learning techniques. The data transmission introduces privacy issues that might lead to security concerns and monetary losses. Recently, federated learning was proposed to partially solve privacy issues via model sharing with the base station. However, the centralized nature of federated learning only allows the devices within the vicinity of base stations to share trained models. Furthermore, the long-range communication compels the devices to increase transmission power, which raises energy efficiency concerns. In this work, we propose the distributed federated learning (DBFL) framework that overcomes the connectivity and energy efficiency issues for distant devices. The DBFL framework is compatible with mobile edge computing architecture that connects the devices in a distributed manner using clustering protocols. Experimental results show that the framework increases the classification performance by 7.4 percent in comparison to conventional federated learning while reducing the energy consumption.
Secure, Efficient, and Weighted Access Control for Cloud-Assisted Industrial IoT

In the cloud-assisted Industrial Internet of Things (IIoT), ciphertext-policy attribute-based encryption (CP-ABE) could help the data owner (DO) share his sensitive data via the cloud under self-defined access structures. Among general CP-ABE schemes, the decryption overhead, the key generation cost, and the ciphertext length increase with the number of involved attributes. Additionally, only regular attributes are taking into consideration rather than weighted attributes. In this article, we proposed a secure, efficient, and weighted access control scheme (SEWAC) for cloud-assisted IIoT applications. SEWAC enables the DO to formulate any fine-grained access structure over weighted attributes without making it more complicated. Furthermore, such weighted attributes would not add the length of ciphertext. SEWAC also supports online/offline key generation to alleviate the computational cost of the authority from answering mass key requests in the online phase, while most computational tasks are executed in the offline phase. The heavy decryption overhead is offloaded to the cloud. To ensure the cloud to honestly execute the process of outsourced decryption, we design an efficient batch verification method, which allows the user to spend only three bilinear pairing operations in checking the correctness of batch results. We also give the formal security proof of the proposed scheme. Comprehensive comparisons and implementation results indicate that SEWAC can better achieve weighted access control, compressed ciphertext length, efficient key generation, and the assurance of the outsourced decryption result.
To be or not to be focus adverbials?

The goal of this study is to cast new light on one of the main defining properties of focus adverbials, namely their interaction with the focus structure of the sentence. Following the framework known as Language into Act Theory, we describe the interaction between It. anche and focal information, considered in relation to the accomplishment of a speech act. The results of a corpus-driven study of spontaneous spoken Italian allow proposing a more detailed definition of focus adverbials, which takes into account two layers of information structure: the semantico-presuppositional layer and the pragmatic-illocutionary layer. We claim that association with focus is a stable feature of focus adverbials only in relation to the first layer of information structure.
Augmented Reality Application Selection Framework Using Spherical Fuzzy COPRAS Multi Criteria Decision Making

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Overview of Social Engineering Attacks on Social Networks

Social networks have become a trusted communication medium for both personal and professional communication. However, hackers regularly exploit the trust of the users of social networks for their own gain. This is often done by using phishing attacks. Phishing emails are both a scam and a business. Many companies, governments and individuals have been affected by these attacks. The most powerful tool an attacker can use to access this knowledge is Social Engineering by manipulating a person into giving information to the social engineer. It is superior to most other forms of hacking in that it can breach even the most secure systems, as the users themselves are the most vulnerable part of the system. Research has shown that Social Engineering can be easily automated in many cases and can therefore be performed on a large scale. Social Engineering has become an emerging threat in virtual communities. Information security is key to any business’s growth. This paper gives an overview of Social Engineering attacks on social networks, Social Engineering principles and types.
Modulation Recognition of Underwater Acoustic Signals Using Deep Hybrid Neural Networks

It is a huge challenge for the receiver to correctly identify the modulation types due to the complex underwater channel environment and severe noise interference. Additionally, the real-time communications have strict requirements in terms of time. In order to solve this well-known issue, in this work, we combine the automatic feature extraction and learning ability of recurrent neural network (RNN) and convolutional neural network (CNN) for designing a modulation recognition model for underwater acoustic signals. The proposed model is based on deep hybrid neural networks called recurrent and convolutional neural network (R&CNN). As compared with the traditional modulation recognition techniques, this method achieves higher recognition accuracy without manual feature extraction. The experimental results show that the validation accuracy of the proposed R&CNN’s on the Trestle data set is 98.21%. Similarly, the validation accuracy of the proposed R&CNN’s on the South China Sea data set is 99.38%. The average recognition time is 7.164ms. As compared with the conventional deep learning methods, the proposed R&CNN not only has a higher recognition accuracy, but also greatly reduces the recognition time.
Multi-Objective Genetic Algorithm and CNN-Based Deep Learning Architectural Scheme for effective spam detection

E-mail has traditionally been regarded as the most powerful medium in online social networks, where users can discuss, connect, and share links with other online social media users. In particular, Twitter, in particular, has been determined to be the most popular social network that serves as the best communication channel for its users to share current news, ideas, thoughts, comments, and beliefs with other online social media users. Despite the efforts put in to combat spam operations on the online social network, Twitter spam has a new type of functionality that is limited to 140 characters. It is not only the major cause of annoyance for day-to-day users, but also responsible for the majority of computer security issues that cost billions of dollars in terms of productivity losses. In this paper, we propose a Multi-Objective Genetic Algorithm and a CNN-based Deep Learning Architectural Scheme (MOGA–CNN–DLAS) for the predominant Twitter spam detection process. The experimental details and results discussions of the proposed MOGA-CNN-DLAS are evaluated in terms of accuracy, precision, recall, F-Score, RMSE, and MAE by varying the ratio of training data under the utilization of three real datasets, such as the Twitter 100k dataset and the ASU dataset.
A Decentralized Location-Based Reputation Management System in the IoT Using Blockchain

The Internet of Things (IoT) allows an object to connect to the Internet and observe or interact with a physical phenomenon. The communication technologies allow one IoT device to discover and communicate with another in order to exchange services, in a similar way to what humans do in their social networks. Knowing the reputation of another device is important to consider whether it is trustworthy before establishing a new connection and thus, avoid possible unexpected behaviors as a consequence. Trustworthiness, as a property of a device, can be affected by different factors including its geographical location. Hence, this research work proposes an architecture to manage reputation values of end devices in an IoT system based on the area where they are located. A cloud–fog–edge architecture is proposed, where the fog layer uses the Blockchain technology to keep the reputation management system consistent and fault tolerant across different nodes. The location-based part of the system was done by storing geographical areas in smart contracts (coined as geospatial smart contracts) and making the reputation values subject to different regions depending on the geographical location of the device. To reduce the complexity of the spatial computation, the geographical data are geocoded by either one of two different spatial indexing techniques. This work also introduced two different structures for storing geocoded areas based on either cell list or tree structure. Finally, three experiments to test the proposed architecture are presented, to deploy the architecture in IoT devices, and to compare the two geocoding techniques in smart contracts.