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This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects x and y through the compression ratio to compress x with y (’s dictionary). Specifically, we focus on the known framework called PRDC, which represents an object x as a compression-ratio vector (CV) that lines up the compression ratios after x is compressed with multiple different dictionaries. By representing an object x as a CV, PRDC makes it possible to apply vector-based pattern recognition techniques to the compression-based pattern recognition. For PRDC, the dimensions, i.e., the dictionaries determine the quality of the CV space. This paper presents a practical technique to modify the chosen dictionaries in order to improve the performance of pattern recognition substantially: First, in order to make the dictionaries independent from each other, our method leaves any word shared by multiple dictionaries in only one dictionary and assures that any pair of dictionaries have no common words. Next, we transfer words among the dictionaries, so that all the dictionaries may keep roughly the same number of words and acquire the descriptive power evenly. The application to real image classification shows that our method increases classification accuracy by up to 8% compared with the case without our method, which demonstrates that our approach to keep the dictionaries independent is effective. Full article

Nowadays, the rapid growth of technology delivers many new concepts and notations that aim to increase the efficiency and comfort of human life. One of these techniques is the Internet of Things (IoT). The IoT has been used to achieve efficient operation management, cost-effective operations, better business opportunities, etc. However, there are many challenges facing implementing an IoT smart environment. The most critical challenge is protecting the IoT smart environment from different attacks. The IoT Botnet attacks are considered a serious challenge. The danger of this attack lies in that it could be used for several threatening commands. Therefore, the Botnet attacks could be implemented to perform the DDoS attacks, phishing attacks, spamming, and other attack scenarios. This paper has introduced a detection approach against the IoT Botnet attacks using the interpolation reasoning method. The suggested detection approach was implemented using the interpolation reasoning method instead of the classical reasoning methods to handle the knowledge base issues and reduce the size of the detection fuzzy rules. The suggested detection approach was designed, tested, and evaluated using an open-source benchmark IoT Botnet attacks dataset. The implemented experiments show that the suggested detection approach was able to detect the IoT Botnet attacks effectively with a 96.4% detection rate. Furthermore, the obtained results were compared with other literature results; the accomplished comparison showed that the suggested method is a rivalry with other methods, and it effectively reduced the false positive rate and interpolated the IoT Botnet attacks alerts even in case of a sparse rule base. Full article

This paper is based on the box office data of films released in China in the past, which was collected from ENDATA on 30 November 2021, providing 5683 pieces of movie data, and enabling the selection of the top 2000 pieces of movie data to be used as the box office prediction dataset. In this paper, some types of Chinese micro-data are used, and a Baidu search of the index data of movie names 30 days before and after the release date, coronavirus disease 2019 (COVID-19) data in China, and other characteristics are introduced, and the stacking algorithm is optimized by adopting a two-layer model architecture. The first layer base learners adopt Extreme Gradient Boosting (XGBoost), the Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), the Gradient Boosting Decision Tree (GBDT), random forest (RF), and support vector regression (SVR), and the second layer meta-learner adopts a multiple linear regression model, to establish a box office prediction model with a prediction error, Mean Absolute Percentage Error (MAPE), of 14.49%. In addition, in order to study the impact of the COVID-19 epidemic on the movie box office, based on the data of 187 movies released from January 2020 to November 2021, and combined with a number of data features introduced earlier, this paper uses LightGBM to establish a model. By checking the importance of model features, it is found that the situation of the COVID-19 epidemic at the time of movie release had a certain related impact on the movie box office. Full article

We survey SoTA open-domain conversational AI models with the objective of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models, while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main takeaway is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are (1) the identification of prevailing challenges in SoTA open-domain conversational AI, (2) the rarely held discussion on open-domain conversational AI for low-resource languages, and (3) the discussion about the ethics surrounding the gender of conversational AI. Full article

Fashion influencers are a new phenomenon and profession to which many young individuals may currently aspire; such is its impact in the digital and online world. Hence, the article serves an upcoming group of fashion-influencers-to-be, as well as firms that seek the help of such professionals. This study aimed to test the mediating role of the attitude toward influencers in the relation between, on the one hand, perceived credibility, trustworthiness, perceived expertise, likeability, similarity, familiarity, and attractiveness, and, on the other hand, purchase intention. Path analysis was used to test a conceptual model in which attitude toward influencers mediates the relation between perceived credibility, trustworthiness, perceived expertise, likeability, similarity, familiarity, attractiveness, and purchase intention. Among the seven components, the association between perceived credibility, trustworthiness, perceived expertise, similarity, and familiarity, on the one hand, and purchase intention, on the other, was completely and significantly mediated through attitudes toward influencers. It was found that the attitude toward the influencer determines the purchase intent; this attitude is, in turn, conditioned by the competence, the resemblance, and the proximity that the consumer perceives in the influencer. Thus, to lead the consumer to buy a certain product, influencers must pay attention to perceived credibility, trustworthiness, perceived expertise, similarity, and familiarity with the product (or service). Full article

Text vectorization is the basic work of natural language processing tasks. High-quality vector representation with rich feature information can guarantee the quality of entity recognition and other downstream tasks in the field of traditional Chinese medicine (TCM). The existing word representation models mainly include the shallow models with relatively independent word vectors and the deep pre-training models with strong contextual correlation. Shallow models have simple structures but insufficient extraction of semantic and syntactic information, and deep pre-training models have strong feature extraction ability, but the models have complex structures and large parameter scales. In order to construct a lightweight word representation model with rich contextual semantic information, this paper enhances the shallow word representation model with weak contextual relevance at three levels: the part-of-speech (POS) of the predicted target words, the word order of the text, and the synonymy, antonymy and analogy semantics. In this study, we conducted several experiments in both intrinsic similarity analysis and extrinsic quantitative comparison. The results show that the proposed model achieves state-of-the-art performance compared to the baseline models. In the entity recognition task, the F1 value improved by 4.66% compared to the traditional continuous bag-of-words model (CBOW). The model is a lightweight word representation model, which reduces the training time by 51% compared to the pre-training language model BERT and reduces 89% in terms of memory usage. Full article

The digital transformation of core marketing activities substantially impacts relations between consumers and companies. Novel technologies are usually complex, making their underlying functionality as well as the desirable and undesirable implications hard to grasp for ordinary consumers. Cryptocurrencies are a prominent yet controversial and poorly understood example of an innovation that may transform companies’ future marketing activities. In this study, we investigate how easily consumers’ attitudes toward cryptocurrencies can be shaped by splitting a convenience sample of 100 consumers into two equal groups and exposing them to true, but biased, information about cryptocurrencies (including market forecasts), respectively, highlighting either the advantages or disadvantages of the technology. We subsequently found a significant difference in the trust, security and risk perceptions between the two groups; specifically, more positive attitudes pertaining to trust, security, risk and financial gains prevailed in the group exposed to positively-skewed information, while perceptions regarding trust, risk and the sustainability of cryptocurrencies were weaker among the group exposed to negatively-skewed information. These findings reveal some important insights into how easily consumer attitudes toward new technologies can be shaped through the presentation of lopsided information and call for further in-depth research in this important yet under-researched field. Full article

Neural Machine Translation (NMT) has improved performance in several tasks up to human parity. However, many companies still use Computer-Assisted Translation (CAT) tools to achieve perfect translation, as well as other tools. Among these tools, we find Interactive-Predictive Neural Machine Translation (IPNMT) systems, whose main feature is facilitating machine–human interactions. In the most conventional systems, the human user fixes a translation error by typing the correct word, sending this feedback to the machine which generates a new translation that satisfies it. In this article, we remove the necessity of typing to correct translations by using the bandit feedback obtained from the cursor position when the user performs a Mouse Action (MA). Our system generates a new translation that fixes the error using only the error position. The user can perform multiple MAs at the same position if the error is not fixed, each of which increases the correction probability. One of the main objectives in the IPNMT field is reducing the required human effort, in order to optimize the translation time. With the proposed technique, an 84% reduction in the number of keystrokes performed can be achieved, while still generating perfect translations. For this reason, we recommend the use of this technique in IPNMT systems. Full article

To further improve the accuracy of multilingual off-line handwritten signature verification, this paper studies the off-line handwritten signature verification of monolingual and multilingual mixture and proposes an improved verification network (IDN), which adopts user-independent (WI) handwritten signature verification, to determine the true signature or false signature. The IDN model contains four neural network streams with shared weights, of which two receiving the original signature images are the discriminative streams, and the other two streams are the reverse stream of the gray inversion image. The enhanced spatial attention models connect the discriminative streams and reverse flow to realize message propagation. The IDN model uses the channel attention mechanism (SE) and the improved spatial attention module (ESA) to propose the effective feature information of signature verification. Since there is no suitable multilingual signature data set, this paper collects two language data sets (Chinese and Uyghur), including 100,000 signatures of 200 people. Our method is tested on the self-built data set and the public data sets of Bengali (BHsig-B) and Hindi (BHsig-H). The method proposed in this paper has the highest discrimination rate of FRR of 10.5%, FAR of 2.06%, and ACC of 96.33% for the mixture of two languages. Full article

The Fourth Industrial Revolution (4IR) offers optimum productivity and efficiency via automation, expert systems, and artificial intelligence. The Fourth Industrial Revolution deploys smart sensors, Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Services (IoS), big data and analytics, Augmented Reality (AR), autonomous robots, additive manufacturing (3D Printing), and cloud computing for optimization purposes. However, the impact of 4IR has brought various changes to digital humanities, mainly in the occupations of people, but also in ethical compliance. It still requires the redefining of the roles of knowledge management (KM) as one of the tools to assist in organization growth, especially in negotiating tasks between machines and people in an organization. Knowledge management is crucial in the development of new digital skills that are governed by the ethical obligations that are necessary in the Fourth Industrial Revolution. The purpose of the study is to examine the role of KM strategies in responding to the emergence of 4IR, its impact on and challenges to the labor market, and employment. This paper also analyzes and further discusses how 4IR and employment issues are being viewed in the context of ethical dilemmas. Full article

Consumer adoption of electric vehicles is essentially related to product quality factors, such as safety, performance and compatibility; however, the relationship between product quality standards and consumer behavior is not clear. Based on Multi-Attribute Utility Theory (MAUT) and Prospect Theory, we distinguish claimed quality attributes, intrinsic quality attributes, measured quality attributes and perceived quality attributes and establish a conceptional model using System Dynamics (SD) simulation from the perspective of a Standard-Information-Behavior framework to explore the heterogeneous impacts of technical standards on consumers’ willingness to adopt electric vehicles. Based on the theory model and simulation, we try to explain the heterogeneous effects of three different standards: safety, performance and compatibility. We find that safety standards affect adoption through a market access mechanism, perceived performance of risk standards positively impacts customers’ perceived quality, and compatibility standards influence consumers’ perceived network value. The perceived risk, perceived quality and perceived network value influence consumer adoption willingness and behavior. The study contributes to the theory of innovation diffusion and consumer adoption behavior, and offers insights for standardizing activity, innovation diffusion and marketing product information for electric vehicles. Full article

Composing the representation of a sentence from the tokens that it comprises is difficult, because such a representation needs to account for how the words present relate to each other. The Transformer architecture does this by iteratively changing token representations with respect to one another. This has the drawback of requiring computation that grows quadratically with respect to the number of tokens. Furthermore, the scalar attention mechanism used by Transformers requires multiple sets of parameters to operate over different features. The present paper proposes a lighter algorithm for sentence representation with complexity linear in sequence length. This algorithm begins with a presumably erroneous value of a context vector and adjusts this value with respect to the tokens at hand. In order to achieve this, representations of words are built combining their symbolic embedding with a positional encoding into single vectors. The algorithm then iteratively weighs and aggregates these vectors using a second-order attention mechanism, which allows different feature pairs to interact with each other separately. Our models report strong results in several well-known text classification tasks. Full article

Augmented reality (AR) has found application in online games, social media, interior design, and other services since the success of the smartphone game Pokémon Go in 2016. With recent news on the metaverse and the AR cloud, the contexts in which the technology is used become more and more ubiquitous. This is problematic, since AR requires various different sensors gathering real-time, context-specific personal information about the users, causing more severe and new privacy threats compared to other technologies. These threats can have adverse consequences on information self-determination and the freedom of choice and, thus, need to be investigated as long as AR is still shapeable. This communication paper takes on a bird’s eye perspective and considers the ethical concept of autonomy as the core principle to derive recommendations and measures to ensure autonomy. These principles are supposed to guide future work on AR suggested in this article, which is strongly needed in order to end up with privacy-friendly AR technologies in the future. Full article

In recent years, the need for robotic fleets in large warehouse environments has constantly increased. The customers require faster services concerning the delivery of their products, making the use of systems such as robots and order-management software more than essential. Numerous researchers have studied the problem of robot routing in a warehouse environment, aiming to suggest an efficient model concerning the robotic fleet’s management. In this research work, a methodology is proposed, providing feasible solutions for optimal pathfinding. A novel algorithm is proposed, which combines Dijkstra’s and Kuhn–Munkers algorithms efficiently. The proposed system considers the factor of energy consumption and chooses the optimal route. Moreover, the algorithm decides when a robot must head to a charging station. Finally, a software tool to visualize the movements of the robotic fleet and the real-time updates of the warehouse environment was developed. Full article

In recent years, researchers from the fields of computer vision, language, graphics, and robotics have tackled Embodied AI research. Embodied AI can learn through interaction with the real world and virtual environments and can perform various tasks in virtual environments using virtual robots. However, many of these are one-way tasks in which the interaction is interrupted only by answering questions or requests to the user. In this research, we aim to develop a two-way interactive navigation system by introducing knowledge-based reasoning to Embodied AI research. Specifically, the system obtains guidance candidates that are difficult to identify with existing common-sense reasoning alone by reasoning with the constructed home ontology. Then, we develop a two-way interactive navigation system in which the virtual robot can guide the user to the location in the virtual home environment that the user needs while repeating multiple conversations with the user. We evaluated whether the proposed system was able to present appropriate guidance locations as candidates based on users’ speech input about their home environment. For the evaluation, we extracted the speech data from the corpus of daily conversation, the speech data created by the subject, and the correct answer data for each data and calculated the precision, recall, and F-value. As a result, the F-value was 0.47 for the evaluation data extracted from the daily conversation corpus, and the F-value was 0.49 for the evaluation data created by the subject. Full article

This study proposes a digital twin (DT) application framework that integrates deep reinforcement learning (DRL) algorithms for the dynamic scheduling of crane transportation in workshops. DT is used to construct the connection between the workshop service system, logical simulation environment, 3D visualization model and physical workshop, and DRL is used to support the core decision in scheduling. First, the dynamic scheduling problem of crane transportation is constructed as a Markov decision process (MDP), and the corresponding double deep Q-network (DDQN) is designed to interact with the logic simulation environment to complete the offline training of the algorithm. Second, the trained DDQN is embedded into the DT framework, and then connected with the physical workshop and the workshop service system to realize online dynamic crane scheduling based on the real-time states of the workshop. Finally, case studies of crane scheduling under dynamic job arrival and equipment failure scenarios are presented to demonstrate the effectiveness of the proposed framework. The numerical analysis shows that the proposed method is superior to the traditional dynamic scheduling method, and it is also suitable for large-scale problems. Full article

Content and metadata concerning a specialized field such as Art and Cultural Heritage are often scattered throughout the World Wide Web, making it hard for end-users to find, especially amid the vast and often commercialized general content of the Web. This paper presents the process of designing and developing a Federated Search Engine (FSE) that collects such content from multiple credible sources of the world of Art and Culture and presents it to the user in a unified user-oriented manner, enhancing it with added functionality. The study focuses on the challenges such an endeavor presents and the technological tools, design decisions and methodology that lead to a fully functional, Web-based platform. This implemented search engine was evaluated by a group of stakeholders from the wider fields of art, culture and media during a closed test and the insights and feedback gained by these tests are herein analyzed and presented. These insights contain both the quantitative metrics of user engagement during the testing period and the qualitative information presented by the stakeholders through interviews. The above findings are thoroughly discussed and lead to conclusions regarding the usefulness and viability of Web applications in the aggregation and diffusion of Art and Cultural Heritage related content. Full article

Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection. Full article

Multi-view ensemble learning exploits the information of data views. To test its efficiency for full text classification, a technique has been implemented where the views correspond to the document sections. For classification and prediction, we use a stacking generalization based on the idea that different learning algorithms provide complementary explanations of the data. The present study implements the stacking approach using support vector machine algorithms as the baseline and a C4.5 implementation as the meta-learner. Views are created with OHSUMED biomedical full text documents. Experimental results lead to the sustained conclusion that the application of multi-view techniques to full texts significantly improves the task of text classification, providing a significant contribution for the biomedical text mining research. We also have evidence to conclude that enriched datasets with text from certain sections are better than using only titles and abstracts. Full article