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In the short history of smart contracts, substantial losses have occurred due to unaccounted vulnerabilities in the smart contracts loaded onto the blockchain. Vulnerabilities in smart contracts threaten the viability and confidence of blockchain technology. Machine and deep learning architectures have been increasingly proposed to assist with writing smart contracts and detecting vulnerabilities to minimize the risks of successful attacks. This paper presents the findings of deep learning vulnerability detection in smart contracts collated from selected research papers and provides an overview of the vulnerability detection architecture. This paper assessed each component of the overall architecture, individually and in combinations, to infer potential relationships in increasing detection rates in smart contract vulnerability detection tools. This study found that the area of detecting smart contract vulnerabilities is gravitating towards deep learning models, with a particular focus on combining neural networks in serial or parallel to achieve high detection results. Feature extraction of syntax and semantic information greatly determines the detection results of a model, which suggests a strong relationship between the use of source code and neural networks. Despite this relationship, given the inaccessibility and unavailability of smart contract source code, future work should focus on efficiently extracting features and context from opcodes, which are more readily available on the blockchain. Based on these findings, the study offers an in-depth discussion of the matters arising, open issues, and key aspects such as bytecode and opcode in smart contracts, failure of attention mechanisms, the rationale of hybrid models, dataset challenges, computational complexity in a large-scale blockchain environment, optimization strategies, Expunge and interdisciplinary approaches to smart contract vulnerability detection. These discussions enhance the practical relevance of the paper, highlighting the opportunities for scaling deep learning models for real-world blockchain applications.

Syntax is usually studied in the realm of linguistics and refers to the arrangement of words in a sentence. Similarly, an image can be considered as a visual ‘sentence’, with the semantic parts of the image acting as ‘words’. While visual syntactic understanding occurs naturally to humans, it is interesting to explore whether deep neural networks (DNNs) are equipped with such reasoning. To that end, we alter the syntax of natural images (e.g. swapping the eye and nose of a face), referred to as ‘incorrect’ images, to investigate the sensitivity of DNNs to such syntactic anomaly. Through our experiments, we discover an intriguing property of DNNs where we observe that state-of-the-art convolutional neural networks, as well as vision transformers, fail to discriminate between syntactically correct and incorrect images, when trained on only correct ones. To counter this issue and enable visual syntactic understanding with DNNs, we propose a three-stage framework- (i) the ‘words’ (or the sub-features) in the image are detected, (ii) the detected words are sequentially masked and reconstructed using an autoencoder, (iii) the original and reconstructed parts are compared at each location to determine syntactic correctness. The reconstruction module is trained with BERT-like masked autoencoding for images, with the motivation to leverage language model inspired training to better capture the syntax. Note, our proposed approach is unsupervised in the sense that the incorrect images are only used during testing and the correct versus incorrect labels are never used for training. We perform experiments on CelebA, and AFHQ datasets and obtain classification accuracy of 92.10%, and 90.89%, respectively. Notably, the approach generalizes well to ImageNet samples which share common classes with CelebA and AFHQ without explicitly training on them.

The increasing integration of ChatGPT, a Generative Artificial Intelligence (Gen-AI) model, into educational environments has sparked substantial ethical concerns. This paper addresses the crucial question of whether to impose restrictions or legislate the usage of Gen-AI, with ChatGPT as a pivotal case study. Through systematic literature review and frequency of occurrence analysis, 10 ethical concerns were selected for further analysis using the Analytic Hierarchy Process (AHP). The analysis responses of 10 expert panels show that the top concerns, as revealed by their weights, after meeting the consistency requirement, include copyright, legal, and compliance issues (0.1731), privacy and confidentiality (0.1286), academic integrity (0.1206), incorrect reference and citation practices (0.1111), and safety and security concerns (0.1050). Evaluating the impact of these concerns on the policy alternatives (restriction and legislation), the findings revealed that “Restriction” received a higher weight (0.513712) compared to “Legislation” (0.485887). Notably, copyright, legal, and compliance issues, privacy and confidentiality, and academic integrity emerged as crucial factors influencing the decision between restriction and legislation. This study offers valuable insights for educational institutions and policymakers, suggesting the need for inclusive discussions, pilot programs to assess impacts on critical thinking, development of clear guidelines, flexible regulatory frameworks, awareness campaigns, and potential strategies for ethical and responsible use.

Online Impulse Buying Behavior (IBB) significantly increases and contributes to the total S-commerce revenue. Hence, understanding such behavior is critical for an online business to strengthen competitiveness, enhance revenue, and ensure business sustainability. The majority of the prior research used urge-to-buy (UBI) to examine online IBB instead of the actual IBB. Hence, the studies that differentiate between UBI and IBB are limited. Moreover, these studies have concentrated on the impact of consumer characteristics, website, and marketing factors, but they neglected the role of social factors. This study integrated Social Influence and Uses and Gratifications Theories to explore the social factors affecting online IBB. The findings indicate that social influences, entertainment, purposive value, and maintaining interpersonal connectivity influence IBB significantly. UBI also mediates the relationships between social influences and IBB. Impulsiveness moderates the indirect relationships between compliance, internalization, and IBB through UBI. This study has practical implications for social commerce designers, marketers, and managers.

The heart sound signals captured via a digital stethoscope are often distorted by environmental and physiological noise, altering their salient and critical properties. The problem is exacerbated in crowded low-resource hospital settings with high noise levels which degrades the diagnostic performance. In this study, we present a novel deep encoder-decoder-based denoising architecture (LU-Net) to suppress ambient and internal lung sound noises. Training is done using a large benchmark PCG dataset mixed with physiological noise, i.e., breathing sounds. Two different noisy datasets were prepared for experimental evaluation by mixing unseen lung sounds and hospital ambient noises with the clean heart sound recordings. We also used the inherently noisy portion of the PASCAL heart sound dataset for evaluation. The proposed framework showed effective suppression of background noises in both unseen real-world data and synthetically generated noisy heart sound recordings, improving the signal-to-noise ratio (SNR) level by 5.575 dB on an average using only 1.32 M parameters. The proposed model outperforms the current state-of-the-art U-Net model with an average SNR improvement of 5.613 dB and 5.537 dB in the presence of lung sound and unseen hospital noise, respectively. LU-Net also outperformed the state-of-the-art Fully Convolutional Network (FCN) by 1.750 dB and 1.748 dB for lung sound and unseen hospital noise conditions, respectively. In addition, the proposed denoising method model improves classification accuracy by 38.93% in the noisy portion of the PASCAL heart sound dataset. The results presented in the paper indicate that our proposed architecture demonstrated a robust denoising performance on different datasets with diverse levels and characteristics of noise. The proposed deep learning-based PCG denoising approach is a pioneering study that can significantly improve the accuracy of computer-aided auscultation systems for detecting cardiac diseases in noisy, low-resource hospitals and underserved communities.

Trash production and disposal have emerged as serious issues for underdeveloped nations as their populations have swelled. As manual classification can be both time-consuming and potentially dangerous, therefore, nowadays, it is increasingly being replaced by automated methods. Recent advances in AI and deep learning have allowed for significant advancements in trash detection and classification systems. Due to the lack of a comprehensive trash detection dataset tailored to Bangladesh, we set out to collect data that would accurately portray the complexity of Bangladesh’s scenario while also incorporating openlittermap. In this study, we employ a deep learning model known as YOLOv5. Several variants of the YOLOv5 model are used and assessed with both the freshly minted dataset and the already existing benchmark datasets. Simulation results indicate that the finetuned YOLOv5 model outperforms existing models in terms of mean average precision (mAP) and F1-score. On the Bangladeshi dataset, the model shows an mAP of 34.3% and an F1-score of 43.7%. The mAP and F1-score provide a holistic evaluation of YOLOv5’s object recognition accuracy, localization, and precision-recall balance. By incorporating the additional data from openlittermap into the new dataset, the mAP is increased to 45.4%. In addition, for some variants of YOLOv5, the suggested model produces greater mAP than the current literature on both the TACO and PlastOpol datasets. The model also achieves an mAP of 84.4% and an F1-score of 78.2% in single-class detection experiments with the newly created dataset. This is because concentrating on just one class helps eliminate class ambiguity, improves localization accuracy, and mitigates class imbalance.

Although numerous case studies have determined the critical success factors (CSFs) for enterprise resource planning (ERP) during the adoption and implementation stages, empirical investigations of CSFs for ERP in post-implementation stages (after going live) are in scarcity. As such, this study examined the influence of top management support and vendor support as CSFs on the post-implementation stage of ERP systems in small and medium enterprises (SMEs) established in the Kingdom of Saudi Arabia (KSA). A total of 177 end-users of ERP systems from two manufacturing organizations in KSA that had implemented on-premises ERP systems were involved in this study. Data gathered from structured questionnaires were analyzed using SmartPLS3 and SPSS software programs. The regression analysis was performed to assess the correlations among the variables. Out of seven CSFs identified from the literature, the impact of top management support was significant on user training, competency of internal Information Technology (IT) department, and effective communication between departments, but insignificant on continuous vendor support. Meanwhile, continuous vendor support had a significant influence on continuous integration of the system, but was insignificant on user interfaces and custom code. The study outcomes may serve as practical guidance for effective post-implementation in ERP systems. Referring to the proposed research model, ERP post-implementation success in KSA was significantly influenced by top management support, whereas continuous vendor support displayed a substantial impact on the continuous integration of ERP systems.

The world we live in is where a human-machine connection is a barometer, and this tendency is only growing to provide richer and universal human reminiscences. New technologies, as well as the fusion of different classes of current technologies, are used to create such unique solutions. The Internet of Robotic Things (IoRT) is the product of collaboration between robotics and the Internet of Things (IoT), which has opened up incredible potential for localized self-sufficient solutions. Robots can collect accurate data from the environment and provide a comprehensive service instantaneously. Being an amalgamation of two different concepts i.e. IoT and Robotics, the IoRT system is anticipated to benefit both by expanding their potential to innovative heights. This paper gives a concise introduction to the communication architecture of the IoRT system. The taxonomy of various data exchange protocols used in an IoRT system is proposed. The necessary improvements in protocols that are required to enhance communication in an IoRT system are presented. A Review of some data exchange methodologies based on Artificial Intelligence (AI) for secure and reliable communication is also given in this paper. The key motive of this paper is to understand the data exchange within an IoRT system using common communication protocols.

Embedded-chip planar silver-elastomer interconnect technology is developed with flexible substrates and demonstrated for on-skin biophotonic sensor applications. This approach has several benefits and is also consistent with chip-thinning where the chip thickness is 100 microns and less. The key benefits from this approach arise because both the bottom and top sides are now available as flat surfaces for 3D integration of other components. It also results in the lowest electrical parasitics compared to flipchip with adhesives or printed-ramp interconnections with surface-assembled devices. Embedding of chips in flexible carriers was accomplished with direct screen-printed interconnects onto the chip pads in substrate cavities. Silver nanoflake-loaded polyurethane is utilized in the embedded-chip packages to provide the desired lower interconnect resistance and also reliability in flexible packages under deformed configurations. Viscoelastic models were utilized to model the interconnection stresses. Planar interconnects in flexible substrates are developed with conductive silver-loaded elastomer interconnects. This approach is compared to direct chip-on-flex assembly technology for reliability under bending and high-temperature storage. The embedded-chip technology is demonstrated through biophotonic sensor applications where light sources (LEDs) and photodetectors are embedded inside the package. Functional validation in bent configuration at low curvatures is shown by measuring pulse rate and muscle activity with human subjects. By extending this technology to nanowires in elastomers, further enhancement in electrical and reliability performance can be achieved.

The last 5 to 6 years have seen tremendous progress in automatic image captioning using deep learning. Initial research focused on the attribute-to-attribute comparison of image features and texts to describe the image as a sentence, the current research is handling issues related to semantics and correlations. However, current state of art research suffers from insufficient concepts when it comes to positional and geometrical attributes. The majority of research relying on CNN’s (Convolutional Neural Networks) for object feature extractions has no clue about equivariance and rotational invariance which leads towards the orientation-less understanding of objects for captioning along with longer training time, and larger dataset. Furthermore, CNN’s based image captioning encoders also fail to understand the geometrical alignment of object attributes within the image and hence mislabels distorted as correct. To cater to the above issues, we propose ICPS (Image Captioning with Positional and Geometrical semantics) a capsule network-based image captioning technique along with transformer neural networks as the decoder. The proposed ICPS architecture handles various geometrical properties of image objects with the help of parallelized capsules while the object-to-text decoding is done by Transformer Neural Networks. The inclusion of cluster capsules provides better object understanding in terms of position, equivariance, and geometrical orientation with more augmented object understanding over a small dataset in comparatively less time. The extracted image features provide a better understanding of image objects and help the decoding stage to narrate effectively with positional and geometrical details. We trained and tested our ICPS over the Flickr8k dataset and found ourselves to be better at captioning when it comes to describing the positional and geometrical transitions as compared to other current state-of-the-art research.

Sign Language provides the means of conveying messages for hearing impaired people. Effective communication with the masses is a great challenge for the hearing impaired community, as Sign Language is not commonly understood. Many researchers have done numerous works in foreign language datasets like English, French, Japanese, etc. However, for Bangla, one of the most widely spoken languages, much significant work has not been done yet. Most of the works on Bangla Sign Language are executed on small datasets and report satisfactory performance. However, when small datasets are evaluated from the perspective of generalizability, particularly when using deep learning based solutions, these models fail to reproduce to the same performance. Therefore, this paper poses inter-dataset evaluation as the main evaluation criteria and evaluates several deep learning based models. This evaluation is done for Bangla by leveraging two popular datasets of Bangla Sign Language. Unsurprisingly, the inter-dataset performance is inferior, and several approaches to improve are explored and documented, including the use of angular margin based loss functions. The results demonstrate the importance of such an evaluation and also show that one of the proposed approaches shows promising performance, albeit with significant room for improvement. This raises the need for a standardized dataset to overcome this issue of generalization for real-life applications, as well as the need to encourage future works to concentrate on challenging evaluations instead of pursuing deceptively good intra-dataset performance.

Smart contracts are programs that reside within decentralized blockchains and are executed pursuant to triggered instructions. A smart contract acts in a similar way to a traditional agreement but negates the necessity for the involvement of a third party. Smart contracts are capable of initiating their commands automatically, thus eliminating the involvement of a regulatory body. As a consequence of blockchain's immutable feature, smart contracts are developed in a manner that is distinct from traditional software. Once deployed to the blockchain, a smart contract cannot be modified or updated for security patches, thus encouraging developers to implement strong security strategies before deployment in order to avoid potential exploitation at a later time. However, the most recent dreadful attacks and the multifarious existing vulnerabilities which result as a consequence of the absence of security patches have challenged the sustainability of this technology. Attacks such as the Decentralized Autonomous Organization (DAO) attack and the Parity Wallet hack have cost millions of dollars simply as a consequence of naïve bugs in the smart contract code. In this paper, we classify blockchain exploitation techniques into 4 categories based on the attack rationale; attacking consensus protocols, bugs in the smart contract, malware running in the operating system, and fraudulent users. We then focus on smart contract vulnerabilities, analyzing the 7 most important attack techniques to determine the real impact on smart contract technology. We reveal that even adopting the 10 most widely used tools to detect smart contract vulnerabilities, these still contain known vulnerabilities, providing a dangerously false sense of security. We conclude the paper with a discussion about recommendations and future research lines to progress towards a secure smart contract solution.