Reviewerly/exHarmony huggingface dataset:
Handwriting Transformers

We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local style patterns. The proposed HWT captures the long and short range relationships within the style examples through a self-attention mechanism, thereby encoding both global and local style patterns. Further, the proposed transformer-based HWT comprises an encoder-decoder attention that enables style-content entanglement by gathering the style features of each query character. To the best of our knowledge, we are the first to introduce a transformer-based network for styled handwritten text generation.Our proposed HWT generates realistic styled handwritten text images and outperforms the state-of-the-art demonstrated through extensive qualitative, quantitative and human-based evaluations. The proposed HWT can handle arbitrary length of text and any desired writing style in a few-shot setting. Further, our HWT generalizes well to the challenging scenario where both words and writing style are unseen during training, generating realistic styled handwritten text images. Code is available at: https://github.com/ankanbhunia/Handwriting-Transformers
An Empirical Evaluation of Customers’ Adoption of Drone Food Delivery Services: An Extended Technology Acceptance Model

A single technological advancement in the business sector tremendously changed customers’ lifestyles and consumption behavior. Drone technology is one of the main revolutions that increase business efficiency at a lower cost. However, the acceptance of emerging technologies is not rapid in developing markets. Therefore, this study aims to evaluate customers’ adoption of drone technology in the context of food delivery services. This study has used an extended technology acceptance model (TAM) to assess customers’ behavior. Product processing innovativeness, information processing innovativeness, and subjective norms have been added as additional constructs into TAM. The data of 354 customers from five different cities of Pakistan have been collected and analyzed through partial least square structural equation modeling (PLS-SEM). The results of the study revealed that all proposed hypotheses, except the positive influence of perceived ease of use on perceived usefulness, were accepted. Further, the results depict that perceived usefulness, subjective norms, and attitude were the major predictors of customers’ adoption of drone food delivery services. In addition to this, customers’ word of mouth has a greater influence and reach than other forms of marketing communication. Therefore, practitioners and marketers may consider hosting competition programs to experiment with drone food delivery systems to enhance the acceptance of this technology among the masses.
Image Synthesis via Semantic Composition

In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts. It hypothesizes that for objects with similar appearance, they share similar representation. Our method establishes dependencies between regions according to their appearance correlation, yielding both spatially variant and associated representations. Conditioning on these features, we propose a dynamic weighted network constructed by spatially conditional computation (with both convolution and normalization). More than preserving semantic distinctions, the given dynamic network strengthens semantic relevance, benefiting global structure and detail synthesis. We demonstrate that our method gives the compelling generation performance qualitatively and quantitatively with extensive experiments on benchmarks.
Key escrow-free attribute based encryption with user revocation

There exists a key escrow issue in ciphertext-policy attribute-based encryption (CP-ABE). The key generator center issues all users’ secret keys and can decrypt each ciphertext by calculating the corresponding secret key. Besides, applying CP-ABE in data sharing environment also brings another challenging problem which is users’ revocation. To resolve the above issues, we provide a key escrow-free CP-ABE scheme with the user revocation, which withstands collusion attack between malicious users and revoked users. In our scheme, a secret key is calculated utilizing a secure key issuing protocol between key authority (KA) and data user (DU). KA is unable to obtain DU’s secret value and generate the complete secret key independently, which solves the key escrow issue. When a user revokes from the system, the secret keys of the unrevoked users require to be updated. We introduce a group manager (GM) to update the unrevoked users’ group secret keys and generate a re-encryption key. The re-encryption technology is applied to prevent the revoked users from decrypting ciphertexts. Moreover, the decryption cloud server provider (D-CSP) executes most of decryption operations to decrease computation costs. The performance analysis indicates that our scheme is practical and efficient. The security of the presented scheme is reduced to divisible computable Diffie-Hellman (DCDH) assumption.
Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone.
Multi-view graph convolutional networks with attention mechanism

Recent advances in graph convolutional networks (GCNs), which mainly focus on how to exploit information from different hops of neighbors in an efficient way, have brought substantial improvement to many graph data modeling tasks. Most of the existing GCN-based models however are built on the basis of a fixed adjacency matrix, i.e., a single view topology of the underlying graph. That inherently limits the expressive power of the developed models especially when the raw graphs are often noisy or even incomplete due to the inevitably error-prone data measurement or collection. In this paper, we propose a novel framework, termed Multi-View Graph Convolutional Networks with Attention Mechanism (MAGCN), by incorporating multiple views of topology and an attention-based feature aggregation strategy into the computation of graph convolution. As an advanced variant of GCNs, MAGCN is fed with multiple "trustable" topologies, which already exist for a given task or are empirically generated by some classical graph construction methods, which has good potential to produce a better learning representation for downstream tasks. Furthermore, we present some theoretical analysis about the expressive power and flexibility of MAGCN, which provides a general explanation as to why multi-view based methods can potentially outperform those relying on a single view. Our experimental study demonstrates the state-of-the-art accuracies of MAGCN on Cora, Citeseer, and Pubmed datasets. Robustness analysis is also undertaken to show the advantage of MAGCN in handling some uncertainty issues in node classification tasks.
A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting the trajectories of player or ball, recognizing the teams strategies, classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researchers views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been provided. This work reviews a detailed discussion on some of the artificial intelligence(AI)applications in sports vision, GPU-based work stations, and embedded platforms. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.
Electronically Reconfigurable Photonic Switches Incorporating Plasmonic Structures and Phase Change Materials

The ever-increasing demands for data processing and storage will require seamless monolithic co-integration of electronics and photonics. Phase-change materials are uniquely suited to fulfill this function due to their dual electro-optical sensitivity, nonvolatile retention properties, and fast switching dynamics. The extreme size disparity however between CMOS electronics and dielectric photonics inhibits the realization of efficient and compact electrically driven photonic switches, logic and routing elements. Here, the authors achieve an important milestone in harmonizing the two domains by demonstrating an electrically reconfigurable, ultra-compact and nonvolatile memory that is optically accessible. The platform relies on localized heat, generated within a plasmonic structure; this uniquely allows for both optical and electrical readout signals to be interlocked with the material state of the PCM while still ensuring that the writing operation is electrically decoupled. Importantly, by miniaturization and effective thermal engineering, the authors achieve unprecedented energy efficiency, opening up a path towards low-energy optoelectronic hardware for neuromorphic and in-memory computing.
What makes multi-class imbalanced problems difficult? An experimental study

Multi-class imbalanced classification is more difficult and less frequently studied than its binary counterpart. Moreover, research on the causes of the difficulty of multi-class imbalanced data is quite limited and insufficient. Therefore, we experimentally study the impact of various multi-class imbalanced difficulty factors on the performance of three popular classifiers. The results demonstrated a strong influence of the class overlapping with the extent of its impact related to the types of overlapped classes. In particular, overlapping between minority and majority classes was more difficult than the others. The type of the class size configuration turned out to be another important factor, highlighting the special role of the configurations with classes of intermediate sizes. The obtained results could support studying the nature of the multi-class imbalanced data as well as the development of new methods for improving classifiers.
Conceptualizing Cybercrime: Definitions, Typologies and Taxonomies

Cybercrime is becoming ever more pervasive and yet the lack of consensus surrounding what constitutes a cybercrime has a significant impact on society, legal and policy response, and academic research. Difficulties in understanding cybercrime begin with the variability in terminology and lack of consistency in cybercrime legislation across jurisdictions. In this review, using a structured literature review methodology, key cybercrime definitions, typologies and taxonomies were identified across a range of academic and non-academic (grey literature) sources. The findings of this review were consolidated and presented in the form of a new classification framework to understand cybercrime and cyberdeviance. Existing definitions, typologies and taxonomies were evaluated, and key challenges were identified. Whilst conceptualizing cybercrime will likely remain a challenge, this review provides recommendations for future work to advance towards a universal understanding of cybercrime phenomena as well as a robust and comprehensive classification system.
Nexus among blockchain visibility, supply chain integration and supply chain performance in the digital transformation era

Purpose Digital transformation (DT) in the semiconductor industry goes beyond traditional business operations and supply chain management (OSCM) to the digital world. Despite significant developments in recent years, blockchain implementations for OSCM remain relatively underdeveloped in the semiconductor industry. Therefore, this research aims to examine the relationships between blockchain visibility, supply chain integration (SCI) and supply chain performance (SCP) in the era of DT in Malaysia's semiconductor industry to shed light on this emerging area. Design/methodology/approach A convenience sampling of 71 operations and supply chain managers attached to semiconductor manufacturing firms in Malaysia were invited to participate in a survey. In assessing blockchain visibility within the industry, key terms namely business intelligence gathering, information exchange, information technology (IT) and knowledge of asset status, were conceptualised from the literature review. The questionnaires developed to collect data were validated by industry and academic experts. Findings The results from the analysis confirmed that SCI mediates the link between blockchain visibility (information exchange, business intelligence gathering and knowledge asset status) and SCP. Likewise, the importance-performance matrix analysis (IPMA) outcomes revealed that IT played a minor role. The results suggested that semiconductor manufacturers should pay less attention to IT since this was identified as having the least priority towards improvement. Practical implications The outcomes from this research enable policymakers to strategise and integrate blockchain technology in the era of DT to ensure sustainable SCM in the semiconductor industry in Malaysia. Originality/value The research bridge the knowledge gap by revealing the value that blockchain visibility can facilitate SCP and explore SCI as the prevailing factor and demonstrates how Resource-Based Theory and Network Theory can be applied in this study.
Assessment of Hybrid Cryptographic Algorithm for Secure Sharing of Textual and Pictorial Content

Nowadays, networks have become global in scope, and information has assumed a digital shape. Due to the volume of data transmitted over the internet, data security has become a significant problem. Today, both symmetric and asymmetric cryptography methods are utilized to conceal any sensitive information from an intruder. Cryptography ensures that secret data maintains its integrity, secrecy, non-repudiation, and validity. A hybrid cryptography is a combination of asymmetric and symmetric cryptography that focuses on enhancing the security level by combining the capabilities of asymmetric cryptography with the efficacy of symmetric encryption. The hybrid approach described here combines a symmetric algorithm (AES), an asymmetric algorithm (ECC), and a hash function (SHA256). SHA-256 is a mathematical formula that is used to digital data in this case. It is used to ensure the data’s integrity. In reference to an existing technique, the suggested hybrid solution is said to be similar to one that use the AES algorithm to encrypt both text and pictures. The proposed technique is more efficient when compared to the approaches discussed before, since it is more efficient when it comes to text encryption. The suggested technique is less efficient at image encryption than the current method. The time required to encrypt and decode a picture may be decreased in future development.
Cryptocurrency as an Investment: The Malaysian Context

Cryptocurrency is gaining popularity worldwide, with some countries already starting to regulate and accept cryptocurrency in their financial services. Malaysia’s Securities Commission (SC) announced in October 2021 that over MYR 16 billion (USD 3.85 billion) involving digital assets and cryptocurrencies were traded between August 2020 and September 2021. Since cryptocurrencies are issued by private corporations and are technically beyond the federal government’s control, criminals may use them for illegal reasons such as money laundering and terrorist funding. Consequently, it is vital to examine why investors are engaged in cryptocurrency in the first place. This study aims to provide insight into Malaysian investors’ perceptions by evaluating the influence of perceived risk and perceived value on their cryptocurrency adoption decision. The retail investors’ demographic characteristics (gender, age, education, income, and investment experience) were analyzed as control variables. Data were gathered using purposive sampling, and responses from 211 respondents from various cities in Malaysia were used in the final analysis. Data were examined using Smart PLS Structural Equation Modelling (PLS-SEM). Based on the finding’s, perceived value was found to have a significant influence on cryptocurrency adoption. Meanwhile, perceived risk had no significant influence on the adoption of cryptocurrency among the Malaysian investors.
Generalized Type-2 Fuzzy Parameter Adaptation in the Marine Predator Algorithm for Fuzzy Controller Parameterization in Mobile Robots

This article is oriented to the application of generalized type-2 fuzzy systems in the dynamic adjustment of the parameters of a recent metaheuristic based on nature that follows the rules of the best feeding strategies of predators and prey in ecosystems. This metaheuristic is called fuzzy marine predator algorithm (FMPA) and is presented as an improved variant of the original marine predator algorithm (MPA). The FMPA balances the degree of exploration and exploitation through its iterations according to the advancement of the predator. In the state of the art, it has been shown that type-2 fuzzy increases metaheuristic performance when adapting parameters, although there is also an increase in the execution time. The FMPA with generalized type-2 and interval type-2 parameter adaptations was applied to a group of benchmark functions introduced in the competition on evolutionary computation (CEC2017); the results show that generalized FMPA provides better solutions. A second case for FMPA is also presented, which is the optimal fuzzy control design, in the search for the optimal membership function parameters. A symmetrical distribution of these functions is assumed for reducing complexity in the search process for optimal parameters. Simulations were carried out considering different degrees of noise when analyzing the performance when simulating each of the used fuzzy methods.
Path Language Modeling over Knowledge Graphsfor Explainable Recommendation

To facilitate human decisions with credible suggestions, personalized recommender systems should have the ability to generate corresponding explanations while making recommendations. Knowledge graphs (KG), which contain comprehensive information about users and products, are widely used to enable this. By reasoning over a KG in a node-by-node manner, existing explainable models provide a KG-grounded path for each user-recommended item. Such paths serve as an explanation and reflect the historical behavior pattern of the user. However, not all items can be reached following the connections within the constructed KG under finite hops. Hence, previous approaches are constrained by a recall bias in terms of existing connectivity of KG structures. To overcome this, we propose a novel Path Language Modeling Recommendation (PLM-Rec) framework, learning a language model over KG paths consisting of entities and edges. Through path sequence decoding, PLM-Rec unifies recommendation and explanation in a single step and fulfills them simultaneously. As a result, PLM-Rec not only captures the user behaviors but also eliminates the restriction to pre-existing KG connections, thereby alleviating the aforementioned recall bias. Moreover, the proposed technique makes it possible to conduct explainable recommendation even when the KG is sparse or possesses a large number of relations. Experiments and extensive ablation studies on three Amazon e-commerce datasets demonstrate the effectiveness and explainability of the PLM-Rec framework.
CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space

Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session. However, session embedding learned by a non-linear encoder is usually not in the same representation space as item embeddings, resulting in the inconsistent prediction issue while recommending items. To address this issue, we propose a simple and effective framework named CORE, which can unify the representation space for both the encoding and decoding processes. Firstly, we design a representation-consistent encoder that takes the linear combination of input item embeddings as session embedding, guaranteeing that sessions and items are in the same representation space. Besides, we propose a robust distance measuring method to prevent overfitting of embeddings in the consistent representation space. Extensive experiments conducted on five public real-world datasets demonstrate the effectiveness and efficiency of the proposed method. The code is available at: https://github.com/RUCAIBox/CORE.
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of blood cancer prevalent in children and teenagers, characterized by the rapid proliferation of immature white blood cells (WBCs). These atypical cells can overwhelm healthy cells, leading to severe health consequences. Early and accurate detection of ALL is vital for effective treatment and improving survival rates. Traditional diagnostic methods are time-consuming, costly, and prone to errors. The paper proposes an automated detection approach using computer-aided diagnostic (CAD) models, leveraging deep learning techniques to enhance the accuracy and efficiency of leukemia diagnosis. The study utilizes various transfer learning models like ResNet101V2, VGG19, InceptionV3, and InceptionResNetV2 for classifying ALL. The methodology includes using the Local Interpretable Model-Agnostic Explanations (LIME) for ensuring the validity and reliability of the AI system's predictions. This approach is critical for overcoming the "black box" nature of AI, where decisions made by models are often opaque and unaccountable. The paper highlights that the proposed method using the InceptionV3 model achieved an impressive 98.38% accuracy, outperforming other tested models. The results, verified by the LIME algorithm, showcase the potential of this method in accurately identifying ALL, providing a valuable tool for medical practitioners. The research underscores the impact of explainable artificial intelligence (XAI) in medical diagnostics, paving the way for more transparent and trustworthy AI applications in healthcare.