As a harmless detection method, terahertz has become a new trend in security detection. However, there are inherent problems such as the low quality of the images collected by terahertz equipment and the insufficient detection accuracy of dangerous goods. This work advances BiFPN at the neck of YOLOv5 of the deep learning model as a mechanism to improve low resolution. We also perform transfer learning, thereby fine-tuning the pre-training weight of the backbone for migration learning in our model. Results from experimental analysis reveal that mAP@0.5 and mAP@0.5:0.95 values witness a percentage increase of 0.2% and 1.7%, respectively, attesting to the superiority of the proposed model to YOLOv5, which is the state-of-the-art model in object detection.

The cocoa tree is prone to diverse diseases such as stem borer, stem canker, swollen shot, and root rot disease which impedes high yield. Early disease detection is a critical component of diverse management processes that are implemented throughout the life cycle of cocoa plants. Consequently, several studies on the application of detection techniques to recognize diseases have been proposed by several researchers. This study proposes the YOLOv5m network for cocoa tree disease detection. The development of cocoa disease detection systems will aid farmers in early identification prompt response, and efficient management of related cocoa tree diseases which will ultimately increase yield and sustainability. To improve the performance of the YOLOv5m network, a Swin Transformer (Swin-T) was added to the backbone network to improve cocoa tree disease detection accuracy. By obtaining global information, the K-means++ algorithm was added to modify the choice of initial clustering locations, and Efficient Intersection over Union Loss (EIoU) was used as a bounding box regression loss function to speed up the bounding box regression rate, resulting in higher precision of the YOLOv5m network. The experimental assessment outcome of this study showed that the proposed method YOLOv5m (Swin-T, K-means++, EIoU) achieved 96% precision, mAP of 92%, and recall of 94%. Compared to the original YOLOv5m, precision improved by 5%, mAP improved by 6%, and recall by 5%. Comparing the proposed method to the conventional YOLOv5m, the latter showed improved performance and better accuracy with a high detection speed and compactness. This improvement offers a useful and effective method for detecting diseases related to cocoa trees.

Blockchain technology unarguably has over a decade gained widespread attention owing to its often-tagged disruptive nature and remarkable features of decentralization, immutability and transparency among others. However, the technology comes bundled with challenges. At center-stage of these challenges is privacy-preservation which has massively been researched with diverse solutions proposed geared towards privacy protection for transaction initiators, recipients and transaction data. Dual-key stealth address protocol for IoT (DkSAP-IoT) is one of such solutions aimed at privacy protection for transaction recipients. Induced by the need to reuse locally stored data, the current implementation of DkSAP-IoT is deficient in the realms of data confidentiality, integrity and availability consequently defeating the core essence of the protocol in the event of unauthorized access, disclosure or data tampering emanating from a hack and theft or loss of the device. Data unavailability and other security-related data breaches in effect render the existing protocol inoperable. In this paper, we propose and implement solutions to augment data confidentiality, integrity and availability in DkSAP-IoT in accordance with the tenets of information security using symmetric encryption and data storage leveraging decentralized storage architecture consequently providing data integrity. Experimental results show that our solution provides content confidentiality consequently strengthening privacy owing to the encryption utilized. We make the full code of our solution publicly available on GitHub.

Recently, blockchain technology has garnered support. However, an attenuating factor to its global adoption in certain use cases is privacy-preservation owing to its inherent transparency. A widely explored cryptographic option to address this challenge has been ring signature which aside its privacy guarantee must be double spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attack in a Lightweight Ring Signature scheme and proceed to construct a new, fortified commitment scheme using the signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secured and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as Kovan testnet along with performance analysis attesting to efficiency and usability and make the code publicly available on GitHub.