Reviewerly/exHarmony huggingface dataset:
Blind Watermarking/Encryption Schema for Security Medical Image Applying DWT-SVD-AES-Chaos Combination

This paper proposes an effective and imperceptible method to protect medical images using watermarking and encryption. The approach combines DWT, SVD, AES and a serial Turbo code. The method employs a second-level DWT decomposition on the cover image and specifically chooses the LH2 and HL2 sub-bands for watermark embedding. These sub-bands are further divided into 8x8 sub-blocks, which undergo SVD transformation. The watermark is subsequently embedded into the singular values of the chosen DWT sub-bands. This approach effectively utilizes transform domain techniques, the proposed method provides a more reliable and robust solution. Moreover, the modified AES encryption is included, which has two advantages: it conceals the watermarked material and strengthens overall security. The proposed technique has shown to be highly effective, with significant PSNR (45.9001 dB), WPSNR (50.8099 dB) and NC values very close to 1 even in the presence of major attacks.
Bearing Information-Based Trajectory Planning for Window Traversal

This paper presents a novel trajectory planning algorithm for 3- Dimensional window traversal using bearing only information of the window extremities. The key idea behind the proposed method is to use of an angular bisector approach to generate guidance commands comprising a bearing angular bisector component and an elliptical shaping angle function. Numerical simulation case studies validate the effectiveness and accuracy of the proposed guidance algorithm.
A Graph Neural Network Approach for Detecting Smart Contract Anomalies in Collaborative Economy Platforms Based on Blockchain Technology

Blockchain technology provides a promising solution for collaborative economy systems by offering a decentralized, transparent, and secure platform. This is mainly accomplished through smart contracts, which are self-executing computer programs that facilitate, verify, and enforce the negotiation or performance of a contract. Digital tokens, on the other hand, are used to represent assets or currencies in these systems. Despite the benefits of Blockchain-based collaborative economy systems, significant security concerns are associated with them. These include the possibility of fraud, risk assessment, bugs in smart contracts, and cyber-attacks. For instance, attackers can exploit vulnerabilities in smart contracts to perform reentrancy and infinite loop attacks, leading to significant financial losses. To address these security challenges, this paper proposes integrating artificial intelligence models to prevent vulnerabilities in smart contracts and detect anomalies. Specifically, Graph Neural Networks models can be utilized to safeguard Blockchain-based collaborative economy platforms from attacks such as reentrancy and infinite loop attacks. According to the findings, this approach can accurately identify both normal and abnormal traffic and classify specific types of attacks. The framework's performance is further evaluated using various metrics to ensure its effectiveness in detecting anomalies, thereby providing an additional layer of security for Blockchain-based collaborative economy systems.
Securing An Agri - Food Marketplace: An Implementation of a Robust Security Layer with API Gateway Integration

As food safety is undergoing through significant challenges due to recent food scandals, and the consumers demands for products of higher quality is increasing, the need for better knowledge of the food production processes and adoption of data sharing practices in the product and supply chain management are emerging. To address those issues, data sharing platforms have been introduced as essential tools for creating high value from data with secure and mutually beneficial multi-partner data sharing fascilitation. Blockchain technology, through its inhereted distributed nature can help to build trust mechanisms to enhance transparency and security dimension of food chains. In this work we propose a novel security mechanism for proper authentication and authorization when accessing resources through an agrifood data platform. Our proposed methodology aims to deliver sophisticated backbone service capabilities that will enable trusted, secure, automated, robust and controlled data transactions for food certification to all food sector businesses that demand easy, fast, and actionable access to variegating food safety data from multiple devices and in various settings.
A Greedy Method for Coverage Path Planning of Autonomous Heterogeneous UAVs

Coverage Path Planning (CPP) is a well-known problem which objective is to find an optimal plan for a fleet of UAVs covering a certain area of interest in the shortest overall time. CPP has a practical impact even in its static formulation, due to the growing interest in UAVs and their numerous applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. Unfortunately, CPP is NP-hard, so that it is computationally infeasible to obtain an exact solution for a large number of UAVs and regions. Different approaches are available in the literature to face the above problem. In this paper, we propose a novel approach working in the case of autonomous heterogeneous UAVs, based on a greedy technique. The method appears promising for its nice features of computational efficiency and potential adaptivity in the case of dynamic settings.
Best Fit Decreasing Algorithm for Virtual Machine Placement Modeled as a Bin Packing Problem

This paper presents a novel Best Fit Decreasing (BFD) algorithm for virtual machine (VM) placement, formulated as a bin packing problem in cloud data centers. The algorithm focuses on minimizing resource wastage by strategically allocating VMs to physical servers. It operates in two stages: first, sorting the VMs in descending order of resource requirements, and second, searching for the best-fit server based on available resources. Extensive simulations using real-world workload traces demonstrate that the BFD algorithm outperforms popular placement strategies, reducing the number of servers required and achieving higher packing efficiency. Comparative analysis against First Fit Decreasing (FFD) and Best Fit (BF) algorithms reveals consistent superiority in terms of resource utilization and packing efficiency. The proposed BFD algorithm offers an effective solution for VM placement, improving resource utilization and system efficiency in cloud data centers. Its findings can guide the development of practical VM placement algorithms, leading to enhanced resource management and cost savings in cloud computing environments.
Distributed Cooperative Task Planning for Autonomous Mobile Robots in Intralogistics

In this paper, we consider the problem of distributed task allocation for multi robot systems in a manufacturing workshop, where new transportation task may arrive randomly. The tasks of the mobile robot team is to efficiently pick and deliver work pieces according to the production plan. We propose two distributed schemes of receding horizon task allocation (RHTA) algorithm. In our approach, each robot estimates the costs of possible task sequences locally, and resolve task conflicts by assigning priority or constructing a negotiation set based on marginal cost. Monte-Carlo simulations show the performance of the proposed method compared with its centralized counterpart and the popular CBBA method.
ChatGPT vs Sarcasm – How Well do State of the Art Language Models Handle Sarcasm?

The work presented in this paper highlights the ability of state-of-the-art Large Language Models to generate sarcasm. In the recent months, ChatGPT has become increasingly popular and has started a new wave of interest in AI, especially on the non-scientific community. The model has displayed incredible feats, capable of producing human level results in a variety of tasks. To this end, sarcasm generation comes as a natural evaluation of the model's performance, as it was proven to be a highly difficult task, even for humans. As such, ChatGPT was tested in three experimental setups, no instructions, instructed to be sarcastic and instructed to impersonate a sarcastic character. The results generated by the model were benchmarked with a previous version, GPT2. Quantitative and qualitative analyses of the results were conducted, with ChatGPT achieving the highest BLEU score and generating the best results, outclassing its predecessor.
Algorithm and Administrative Law; Challenges and Solutions

In recent years, algorithmic decision-making mechanisms and machine learning have been increasingly used by the private sector and then by state agencies. Despite the advantages such as increasing the speed, accuracy and reducing the cost of decisions, the algorithm has created new challenges for the principles of administrative law, such as jurisdiction and judicial supervision, due process, transparency, impartiality and, non-discrimination. This article tries to analyze the aforementioned challenges considering the necessity to adapt to the new achievements of mankind on the one hand and preserve the ancient achievements such as respect to the rule of law on the other hand. Revision in administrative law, taking into account the continuous development of technology, can finally turn the theory of algorithmic administrative law into a prominent theory in administrative law. The requirements related to upgrading existing technologies and removing their disadvantages to make algorithmic processes more transparent can still preserve the fundamental value of the rule of law and prevent it from turning into the algorithmic rule. Algorithm technology has not been as formidable and destructive as to deplete administrative law of its foundations, nor is it so insignificant that it does not justify the necessity of re-articulating the foundations of this legal branch.
Trajectory Planning for Tethered Robots in Uncertain Environments

The present paper considers the problem of planar motion planning for environments with partially unknown obstacles for a robot with tether connection and missions with multiple target positions. The presence of a tether increases the complexity of the already challenging problem of dynamic motion planning by introducing additional feasibility constraints, i.e. not entangling the cable in any of the obstacles within the working space. For a given mission, the developed algorithm finds a closed trajectory such that after its execution the cable can be retrieved without difficulties. It combines the dynamic path-planning capabilities of the RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sup> (Rapid-exploring Random Tree X) method with a geometric approach that efficiently identifies feasible paths that satisfy all the criteria imposed by the problem. Extensive simulation tests demonstrate the validity and reduced computational complexity of the proposed solution.
Blockchain Service Layer for ERP data interoperability among multiple supply chain stakeholders

Companies in the supply chain recognize Enterprise Resource Planning (ERP) software as an indispensable component of their businesses that significantly helps with planning, decision-making, and cost reductions. However, despite the benefits of ERP, the interaction between supply chain stakeholders with varying and even contradictory security and privacy requirements could be challenging. In addition, incorporating a cross-domain access control might be hard because parties may require different access levels for information maintained outside their own ERP. In this work, we propose a blockchain architecture that acts as a service layer on top of existing ERP systems to achieve fine-grained intra- and cross-domain access control. A private blockchain is combined with four fully functional Smart Contracts to enable access control and trust management services, a data handler service, ensuring data integrity for both insiders and outsiders, and an audit mechanism.
Design Frameworks for Hyper-Connected Social XRI Immersive Metaverse Environments

The metaverse refers to the merger of technologies for providing a digital twin of the real world and the underlying connectivity and interactions for the many kinds of agents within. As this set of technology paradigms-involving artificial intelligence, mixed reality, the Internet of Things, and others-gains in scale, maturity, and utility there are rapidly emerging design challenges and new research opportunities. In particular is the metaverse disconnect problem, the gap in task switching that inevitably occurs when a user engages with multiple virtual and physical environments simultaneously. Addressing this gap remains an open issue that affects the user experience and must be overcome to increase overall utility of the metaverse. This article presents design frameworks that consider how to address the metaverse as a hyper-connected meta-environment that connects and expands multiple user environments, modalities, contexts, and the many objects and relationships within them. This article contributes to: a framing of the metaverse as a social XR-IoT (XRI) concept, design Considerations for XRI metaverse experiences, a design architecture for social multi-user XRI metaverse environments, and descriptive exploration of social interaction scenarios within XRI multi-user metaverses. These contribute a new design framework for metaverse researchers and creators to consider the coming wave of interconnected and immersive smart environments.
Optimizing Efficient Personalized Federated Learning with Hypernetworks at Edge

The recent advances in 5G and mobile edge computing facilitate the rapid development of the Internet of Things (IoT), enabling collective intelligence with data support from a massive number of IoT devices. Meanwhile, federated learning (FL) has emerged as a promising solution for collaborative training while preserving user privacy, which, however, is prone to poor learning performance in large-scale IoT scenarios. On the one hand, due to the task heterogeneity in various IoT scenarios and data heterogeneity (non-IID) across different IoT clients, more than traditional FL models derived from a Uniform FL (UFL) learning architecture are required to satisfy the diversified demands of each client. On the other hand, with the proliferation of IoT devices, achieving low latency and low communication costs with traditional FL architectures becomes even more challenging. In this article, we argue that customizing a specific model for each client is an urgent requirement, calling for the development of UFL to Personalized FL (PFL). In addition, the confluence of PFL and edge computing further provides opportunities for practical implementation in the 5G IoT environment. Accordingly, we propose EdgeFHN, an edge computing-based personalized federated learning framework, which can strike a balance between collaborative FL training among different clients and efficient model personalization for each client. Experiments on image classification demonstrate the superiority of our framework in improving accuracy and reducing communication overhead compared with other state-of-the-art solutions.
Multimodal Cooperative 3D Object Detection Over Connected Vehicles for Autonomous Driving

Having an accurate and comprehensive understanding of surrounding environment is the first step toward autonomous driving. For an autonomous car to plan its actions, for example, to make instant decisions about whether to proceed at an intersection or slow down to wait until a pedestrian crosses the road, it first needs to recognize its surroundings, including but not limited to vehicles and pedestrians. One of the challenges needed to be addressed is how to handle situations where objects are being occluded, especially for pedestrians since they are small and vulnerable. Cooperative perception is a promising approach in addressing this challenge. By sharing perceived information together, the detection range for each individual connected autonomous could be extended. Although existing works focusing on cooperative perception improve the accuracy and extend detection range for vehicles, they fail to consider the effect of small objects. In this article, we investigate cooperative 3D object detection targeting on improving the detection of small objects (i.e., pedestrians), which may be achieved by leveraging shared information extracted from image and LiDAR over connected autonomous vehicles. Evaluation results show that the proposed method outperforms state-of-the-art networks on 3D pedestrian detection.
A Novel Generalized Meta Hierarchical Reinforcement Learning Method for Autonomous Vehicles

Despite the recent successes of deep reinforcement learning, there still remain challenges for its real-world applications, such as autonomous vehicles. There are two main issues: sample inefficiency and poor generalization. In this article, we propose a novel method, called MHRL-I, short for meta hierarchical reinforcement learning with imitation learning, to improve the sample efficiency and learn a more generalized model for autonomous vehicles by reusing low-level policies and reducing the action space of the high-level policy. In addition, we design a new mechanism in MHRL-I, where the high-level policy and the low-level policies are of different timescales, which can increase the speed of convergence and make autonomous driving more stable and comfortable. Simulation results show that MHRL-I can have better sample efficiency and generalization compared with other reinforcement learning methods in autonomous vehicles.
Byzantine-Resilient Online Federated Learning with Applications to Network Traffic Classification

Rapid growth in distributed streaming data at the network edge in many applications has prompted the emergence of online federated learning (OFL), a promising distributed machine learning paradigm where multiple agents cooperate to perform online learning via a central server. Despite its distinctive capability in handling various real-world applications, OFL has yet to be widely adopted in the industry due to its vulnerability to the ubiquitous Byzantine attacks initiated by adversarial agents; these agents can influence the OFL process by sending arbitrary updates to the central server. Byzantine attacks in OFL still falls short of being satisfactorily addressed in research as reflected by current literature. In this work, we propose a Byzantine-resilient OFL algorithm called BROFL. Our theoretical analysis shows that BROFL has a sub-linear regret bound under mild conditions and has a polynomial time-complexity in computation. To validate the performance of BROFL, we conduct extensive evaluations based on the tasks of network anomaly detection and application identification and using several real-world datasets. The evaluation results illustrate that BROFL can approach the performance of an offline optimal classification model and achieve F1 score that is at least 37 percent higher than those of two online baselines, which validates the effectiveness of BROFL in practice.
Communication-Efficient and Byzantine-Robust Federated Learning for Mobile Edge Computing Networks

Federated learning in mobile edge computing allows a larger number of devices to jointly train an accurate machine learning model without collecting local data from edge nodes. However, there are two major challenges to using federated learning for mobile edge computing. The first is that mobile edge computing networks only tolerate a limited communication overhead, that is, communication overhead between edge nodes, edge servers, and cloud servers cannot be excessive. Unfortunately, federated learning clients send a large number of local model update that do not meet realistic requirements. The second is that resource-constrained edge nodes are vulnera-ble to attacks, that is, these edge nodes are highly vulnerable to potential adversary compromise and can be used to launch Byzantine attacks. To address the aforementioned challenges, we propose a communication-efficient and Byzan-tine-robust federated learning for mobile edge computing networks named CBFL. Specifically, edge nodes compress local model updates by taking element-wise signs for local model updates. Then, edge nodes sends it to the edge server for local model aggregation. Finally, the cloud server uses a small evaluation dataset to evaluate the edge servers' local model aggregation results and utilizes the evaluation results for global model aggregation. Moreover, extensive experiments are also conducted to evaluate the performance of the proposed CBFL, and the results show that our CBFL can withstand Byzantine attacks while maintaining communication efficiency.
Analisis Rendahnya Minat Belajar Peserta Didik pada Pembelajaran Bahasa Jawa Kelas IV di Sekolah Dasar

This study, conducted at SD Negeri Ciberem, aims to identify the factors contributing to the low learning interest of students and describe the teacher's efforts in fostering students' interest in the Javanese language lessons for the fourth grade at SD Negeri Ciberem. Based on the researcher's observations in the fourth-grade Javanese language classes at SD Negeri Ciberem, it was found that students displayed a low interest during learning sessions. This study employs a descriptive qualitative research method. Data collection techniques include observation, interviews, and documentation. The study also involves three sources: teachers, students, and the parents of the students. The study results show that:(1) Factors contributing to the low interest of students in Javanese language learning are rooted in physical and psychological aspects, such as intelligence, attention, and interest. Other influencing factors include teachers, parents, and the learning environment. (2) Teachers' efforts to address students' learning interests include peer tutoring activities, ice-breaking sessions, and adopting a cooperative attitude during instruction.&#x0D; &#x0D; Keywords: Learning interest, Javanese Language, Factors of Leaming Interest, Primary School (SD).
Nilai-Nilai Kehidupan dalam Naskah Drama Kibar Bendera Si Sarto di Halaman Rumah Karya Rodli TL (Kajian Pragmatik)

Drama as a genre of literature, has its own characteristics compared to other genres, namely poetry and fiction. Drama is unique from the point of view of using language and conveying its message and useful values ??for its readers. However, often the delivery of messages and values ??through character conversations is often not caught by the reader because dialogue in drama often uses connotative words that have ambiguity or indirectness of expression. The presentation of language in drama literary works is in the form of using behavioral instructions that describe the atmosphere and using the dialogue of the characters. In terms of message content, the playwright tells human life with its various problems. This study discusses the values ??of life that exist in the drama script "Raising the Si Sarto Flag in the Homeyard" by Rodli TL. The method used in this research is descriptive qualitative. The approach used is a pragmatic approach. The purpose of this study is to describe, analyze, and identify pragmatic values, including moral values, social values, cultural values, and nationalism values ??in drama scripts. Keywords: Life values, Pragmatics, Drama script