Reviewerly/exHarmony huggingface dataset:
Blockchain Anomaly Transaction Detection: An Overview, Challenges, and Open Issues

In recent years, the rapid development of blockchain technology has attracted a great deal of attention from academia and industry, as it can be applied to a variety of traditional financial and non-financial domains. Blockchain provides decentralized, tamper-evident, and traceable characteristics that enhance the security of these domains. Recent researches have revealed, however, that there are some security abnormalities in the blockchain transaction process, and in order to solve these issues, the detection of the behavior of anomaly transaction is required. In this paper, we initially explore typical and abnormal transactions in blockchain technology before delving deeply into the integration of anomaly transaction detection algorithms in blockchain applications. Then, we examine conventional approaches for anomaly identification and blockchain-based techniques for transactional anomaly detection. Then, a thorough analysis of blockchain anomaly detection models in the financial domain and its application in non-financial domains was presented. Finally, based on the results of the survey, we conclude with future research directions and challenges.
A Blockchain-Based Encrypted Data Retrieval Scheme for Smart Grid

With the popularization of smart grid, the power industry has gradually attracted people's attention. Among them, grid data is an important data that contains personal privacy. Once these data are stolen by attackers, it may be inferred from the private information of grid users, such as work schedule, income. In order to protect the private data in smart grid, this paper proposes a blockchain-based data retrieval scheme for smart grid. Firstly, private data is symmetrically encrypted and then uploaded to a cloud service. For the purpose of retrieve encrypted data on cloud services, we use searchable encryption combined with inverted index technology. Data users holding retrieval tokens can directly retrieve ciphertext data from cloud services. Secondly, to ensure the transparency and traceability of data access, we propose to take the alliance chain composed of several sub-institutions under the same power system organization as the network basis. And all the privacy data access records will be recorded on the chain for the convenience of later access traceability. Finally, we simulated our scheme in a realistic environment. The safety analysis and simulation experiment prove that our scheme is safe and feasible.
Detection Method of Insulation Gloves Wearing in Complex Scenes Based on Improved YOLOX

Supervision of wearing insulation gloves during electrical equipment inspection is the top priority of safety protection in indoor and outdoor high-voltage power areas. However, in the actual scene, there are usually incomplete features of monitoring targets and insufficient feature information of small-scale targets caused by factors such as occlusion and distance, which leads to low accuracy of inspection personnel wearing insulation gloves. In view of the above situation, this paper proposes an improved testing model for insulation gloves. Firstly, SCAM attention mechanism and M-MHSA module were integrated into the feature extraction network to improve the ability of the model to extract global information and target channel features combined with multi-head attention mechanism. Adding small target detection layer and using weighted bidirectional feature pyramid network (BiFPN) to carry out multi-scale feature fusion can improve the detection ability of the model on different scale targets. The experimental results show that the improved algorithm achieves 93.27% average accuracy and 32frame/s detection speed in indoor and outdoor high-voltage electricity usage scenarios, which has good performance in the detection task of insulation gloves.
What Happens When a Chatbot Gives Detrimental Advice? Who’s Responsible?

Many online ChatbotsChatbot provide disclaimers letting the public know that it is possible to get misinformationMisinformation or just incorrect information from the ChatbotChatbot. Additionally, businesses are already rolling out training and ChatbotChatbot rules/policies internally because of the potential risks and liabilities of wrong information created and used by persons within the company using a ChatbotChatbot for work-related efforts and/or the dangers of misuse and misunderstanding by users of a company’s products and related data artifacts. However, providers of these disclaimers are still in the minority of ChatbotChatbot producers. We will discuss what happens when incorrect information is provided to the user by a ChatbotChatbot and what ramifications are there for both the user and the company who provided the ChatbotChatbot.
Categories of Chatbots

Over the last 60+ years, many different ChatbotsChatbot have been created for a variety of purposes. Even though each may be different, ChatbotsChatbot generally group into major categories. Here in this chapter, we will discuss the 6 categories of ChatbotsChatbot and how they are similar and the differences that exist between them. We will also introduce a new category, looking to the future of truly cognitive, AI-driven ChatbotsChatbot.
Chatbots for Casual Interactions

Can ChatbotsChatbot be merely a form of casual entertainment without the person becoming attached, without ascribing a gender or personality? Is it possible to create a ChatbotChatbot without the notion of gender or personality. Is it possible to remove all bias from a ChatbotChatbot? We will discuss these, and other topics related to the use of ChatbotsChatbot for casual/entertainment use.
Chatbots: Do They Fill a Need in Society?

In this chapter deals with role(s) ChatbotsChatbot fill in today’s society (if any). What roles might they fill in the future? Here, we will explore these questions considering people's activities like work, play, entertainment, sports, and other parts of society like the adult entertainment industryEntertainment industry.
Introduction

The world is at a crossroads on its use of Artificial IntelligenceArtificial Intelligence (or what passes for Artificial IntelligenceArtificial Intelligence). The use of ChatbotsChatbot in our everyday lives is exponentially increasing with every passing year with widely varied results. What are the overall effects (both good and bad) of turning over more and more of our work to ChatbotsChatbot acting as our “digital assistantsDigital assistant?” Here we will explore these questions and many more as we delve into the human experience and our need to communicate with truly impartial, emotion challenged, digital entities (ChatbotsChatbot). There are no easy answers to the possible benefits and dangers of the proliferation of ChatbotsChatbot, but here we present information to get the dialog going.
What Does the Future of Chatbots Look Like?

There has always been a push for fully autonomous AI entities. From self-driving cars, self-piloted plans, trains. There are plans for caregiver robotsRobot for the infirm and/or the elderly. Can we have fully autonomous AI entities and ChatbotsChatbot loose on society and what constraints/controls are needed to ensure physical, emotional, and psychological safety? Also, the question must be asked, “will ChatbotsChatbot kill human creativity or enhance it?”.
Chatbots and the Need for Connectivity

The rise of ChatbotsChatbot in today’s society highlights the human need for connectivity and how that drives our involvement in and possibly obsession of online ChatbotChatbot entities. This includes how we would ascribe male/female/non-binary and/or other gender designations to an online ChatbotsChatbot. Can we create a ChatbotChatbot that has no gender designations of biases? In this chapter, we will explore this question and why ChatbotsChatbot are gaining such a global following.
Examples of Issues with Chatbots Over the Last Few Years

There have been many incidents over the last few years that brought ChatbotsChatbot to the forefront of the media (and not in a good way). Here we will discuss what happens when a ChatbotChatbot learns things it should not have learned. We will present and discuss concepts like “emergent behaviorEmergent behavior” and how these always have the potential for an intelligent ChatbotChatbot to evolve in ways their creator and programmer never intended.
Chatbots Like Siri: Your Digital Assistant

ChatbotChatbot assistants like SiriSiri, AlexaAlexa, etc., have permeated over 90,000 homes across the US and are forecasted to exceed 8.4 billion units by 2024. We have looked at the origins of the ChatbotChatbot as well as their categories and the roles they play in everyday life now. Here, we discuss the features of such devices and how they might be used for n advantageous and worthy purposes. We will take a close look at ChatbotsChatbot that act as personal assistants, whether at home or at work, and how they enhance our experience and interface with the world around us.
Chatbots That Analyze Data and Provide Valuable Information

In many cases, ChatbotsChatbot are needed just to process and present information based on sensor readings. This applies to pilots, drivers, equipment operators, and other professions. Here we will explore the use of ChatbotsChatbot as a digital assistantDigital assistant and how they have the potential to greatly enhance concepts like situational awareness, environmental control, and cyber security.
Microsoft’s 2016 Chatbot: What Went Wrong?

Tay was an online ChatbotChatbot that had to be pulled in 24 h because it became caustic and offensive. The issues Microsoft had with Tay drive home the problem of letting AI-based entities learn from interacting with people. In this chapter, we will explore and discuss the problems associated with Human Interaction Learning (HIL) or learning from experiencing interactions with people.
CCMFRNet: A Real-Time Semantic Segmentation Network with Context Cascade and Multi-scale Feature Refinement

At present, many of the popular semantic segmentation networks focus on accuracy and require a lot of computational overhead, which results in a very slow inference speed and is difficult to deploy in practical application scenarios. On the other hand, many works sacrifice the performance of segmentation networks in pursuit of real-time inference speed. Therefore, in semantic segmentation, balancing accuracy and real-time performance becomes a formidable challenge. For this challenge, we propose a lightweight semantic segmentation network that takes into account both accuracy and real-time performance, named CCMFRNet. The core components of CCMFRNet are the Context Cascade Module (CCM) and the Multi-scale Feature Refinement Module (MFRM). CCM consists of three Dense Cascade Dilated Convolution Modules (DCDM), which are cascaded in a short-term dense cascade, aiming to obtain rich multi-scale context information to enhance information representation. MFRM adopts the attention mechanism to realize deep features to guide the captured shallow multi-scale spatial features. It aims to capture high-quality and multi-scale shallow features to enrich the feature space and more effectively refine the spatial details information. The proposed method achieves an accuracy of 72.6% MIoU at speed of 32 fps on Cityscapes test datasets.
A Survey of Low-Resource Named Entity Recognition

Named Entity Recognition, as one of the typical tasks of information extraction, has a wide range of applications. However, the difficulty of data collection and the lack of data annotation are common problems in real scenarios. It is difficult for classical named entity recognition methods to fully obtain hidden information when the dataset and data labels are insufficient. In this case, the recognition accuracy will drop significantly. In resource-poor scenarios, the use of multi-task learning, data augmentation, and transfer learning methods can effectively utilize limited data resources or introduce data from other fields to optimize model performance. In this survey, firstly, we introduce the reader to the state of research on named entity recognition, and outline the reasons for the survey and the contribution of this paper. Then, we comprehensively describe the datasets and evaluation methods included in named entity recognition, and propose a Classification of named entity recognition in the case of lack of resources. Finally, we analyze the existing problems on the basis of the above, and further look forward to the future development direction.
Adversarial network for multi-input image restoration under strong turbulence

Turbulence generated by random ups and downs in the refractive index of the atmosphere produces varying degrees of distortion and blurring of images in the camera. Traditional methods ignore the effect of strong turbulence on the image. This paper proposes a deep neural network to enhance image clarity under strong turbulence to handle this problem. This network is divided into two sub-networks, the generator and the discriminator, whose functions are to mitigate the effects of turbulence on the image and to determine the authenticity of the recovered image. After extensive experiments, it is proven that the present network plays a role in mitigating the image degradation problem caused by atmospheric turbulence.
Overview of Non-Fungible Token Eco-Regulation, Traceability and Data Right Confirmation Security

As a new technology, Non-Fungible Token (NFT) has sparked a surge in asset digitization. NFT, which is unique, irreplaceable, and indivisible, can be used for investment, traceability, data storage, and so on. However, the application of NFT is not solid—risks such as wash trading and broken links abound, and data cannot be confirmed. In response to the aforementioned issues, previous authors proposed some policy solutions; however, few academic studies on NFT risks have been published. First, this paper examines the current transaction risks of the NFT ecology before proposing a regulatory approach to financial market fraud. Then we investigate two common applications of NFT—supply chain traceability and data right confirmation—and propose methods to improve security based on existing research risks. Finally, we summarize the work of this paper and provide an outlook on NFT. To the best of our knowledge, we have conducted the first exploration of NFT ecological regulation approaches, as well as studied the security risks of two major application scenarios: traceability and data right confirmation.
Pixel Diffuser: Practical Interactive Medical Image Segmentation without Ground Truth

Medical image segmentation is essential for doctors to diagnose diseases and manage patient status. While deep learning has demonstrated potential in addressing segmentation challenges within the medical domain, obtaining a substantial amount of data with accurate ground truth for training high-performance segmentation models is both time-consuming and demands careful attention. While interactive segmentation methods can reduce the costs of acquiring segmentation labels for training supervised models, they often still necessitate considerable amounts of ground truth data. Moreover, achieving precise segmentation during the refinement phase results in increased interactions. In this work, we propose an interactive medical segmentation method called PixelDiffuser that requires no medical segmentation ground truth data and only a few clicks to obtain high-quality segmentation using a VGG19-based autoencoder. As the name suggests, PixelDiffuser starts with a small area upon the initial click and gradually detects the target segmentation region. Specifically, we segment the image by creating a distortion in the image and repeating it during the process of encoding and decoding the image through an autoencoder. Consequently, PixelDiffuser enables the user to click a part of the organ they wish to segment, allowing the segmented region to expand to nearby areas with pixel values similar to the chosen organ. To evaluate the performance of PixelDiffuser, we employed the dice score, based on the number of clicks, to compare the ground truth image with the inferred segment. For validation of our method's performance, we leveraged the BTCV dataset, containing CT images of various organs, and the CHAOS dataset, which encompasses both CT and MRI images of the liver, kidneys and spleen. Our proposed model is an efficient and effective tool for medical image segmentation, achieving competitive performance compared to previous work in less than five clicks and with very low memory consumption without additional training.
Implementation of Collision Free Path Following Maneuver for WMR Embedding Fuzzy Logic Driven Artificial Intelligence Framework

Autonomous mobile robots are being used for wide range of practical applications ranging from conventional household requirements to space domains. Everywhere, autonomous robots need to maneuver along a predefined path or perform autonomous motion based on given initial and final pose. Depending on the types of demanded applications, sometimes it is necessary to move along the path, e.g., contour welding and in other cases reaching destination might be the soul important. However, in both the scenarios, it is important to perform obstacle avoidance, if static or dynamic obstacles are present along the path-way. Considering the aforementioned environmental situation, it is absolutely necessary to develop a strategy towards intelligent maneuver of the robot along the closed formed path and its subsequent implementation. In this research article, a path-following controller embedded with fuzzy logic driven artificial intelligence framework (FLDAIF) is designed and implemented for a nonholonomic wheeled mobile robot (WMR). WMR usually maneuver along a predefined path, however, it is capable of avoiding obstacles or agents (static or dynamic in nature) in its path at all times during operation. Feedback linearization based path-following control law enforces the WMR asymptotically to the predefined path. In contrast, FLDAIF based collision avoidance control law judiciously acts in association with path-following control law to deviate the WMR from the predefined path in an obstacle-prone working environment. Further, the proposed controller reinforces the WMR to the original path immediately after successful avoidance of obstacles and subsequent collisions. Asymptotic stability of the hybrid controller is also established. The efficiency of the developed control technique in achieving the dual objectives of path-following and simultaneous collision avoidance is demonstrated with a commercial WMR (Pioneer: P3-DX).
A Lightweight Deep Learning-based Weapon Detection Model for Mobile Robots

As mobile robotics continues to advance, the need for adequate surveillance in robotic environments is becoming increasingly important. Detecting suspicious objects in sensitive areas using mobile robots is challenging due to the limited computational resources available on these devices. This paper describes a new system for automatically detecting weapons in real-time video footage designed for low-computing devices in mobile robots. We present a novel weapon detection model that aims to balance the trade-off between inference time and detection accuracy, making it a lightweight model compared to existing models. The proposed model is trained and tested on existing benchmark datasets. The model is compared to existing lightweight weapon detection models to determine its suitability for low-computing devices. We obtain the mAP of 90.3%, 85.13% and 92.38% for the IITP_W, Handgun and Sohas datasets, respectively. The results outperforming the well-known PicoDet model. We envisage that the proposed model could be a useful tool for surveillance using mobile robots during events such as riots and anti-terrorist operations.
Motion Planning using Reinforcement Learning for Serial Manipulators

Object manipulation is a primary function expected of a robotic manipulator. The solution to the inverse kinematic problem is of significance for accomplishment of such tasks. There are inherent bottlenecks associated with arriving at these solutions. This is particularly true in the context of human-robot interaction scenarios, wherein the dynamics makes it challenging for traditional motion planning algorithms to define safe, collision-free path. Deep Reinforcement Learning is being increasingly applied to circumvent the bottlenecks associated with solution of inverse kinematics. In this paper, we enriched Deep Deterministic Policy Gradient with Hindsight Experience Replay to teach a robot to autonomously manipulate objects by trial and error interactions with the environment. To assess our proposal, we simulate a 4-DOF DOBOT robot manipulator via a physics simulation backed by MuJoCo to perform object manipulation tasks including push, pick and place, and slide. Experimental evaluation shows that the desired tasks can be satisfactorily achieved using the aforementioned policies.
A privacy-preserved blockchain for vehicles in smart societies

Data collection from the mobile vehicle is the most vulnerable task. In an open network of vehicles, device identities must be protected. Our work in this paper describes a proof-of-authentication-based blockchain to collect and store data from vehicular devices with privacy preservation in the blockchain. We are using Ed25519 for the identity generation for devices and vehicles, so we can protect the real identity of the vehicles by generating hash-based wallets. Providing privacy for the vehicle’s real identity is required due to the immutable property of the blockchain. Since blockchain stores each transaction, if any personal information or private information is committed to the ledger, then the data provider can be in a vulnerable situation. To provide protection from this kind of situation, the data privacy of the vehicle and owner is required. We are dividing the areas of the smart city into different data collection nodes named CRA (Certification and Registration Authority). This CRA acts as the node for the proposed blockchain. The novelty of our work is that device and vehicle identities must be preserved while collecting data from mobile vehicles in the smart city scenario. The CRA is selected only to be a miner in the proposed system because the vehicles do not have large storage and computation power. The blockchain is implemented using a custom Python-based blockchain. Our proposed work involves collecting data from the devices embedded in the vehicles and storing it in the python-based, lightweight, consensus-enabled blockchain. Our proposed work improves the secure connection between devices and CRA. The proposed system is fast, secure, and privacy-preserving.
Investigating the Efficacy of a ROS-Plugin Based RRT Path Planner: An Empirical Parametric Analysis and Recovery Behavior Study

This paper presents the development of a ROS plugin based on the Rapidly Exploring Random Tree (RRT). RRT is a well-established probabilistic global path planning technique utilized for generating obstacle-free trajectories for mobile robots operating in uncertain and dynamic environments. The algorithm is implemented on the mobile robot named Turtlebot3 in the ROS environment Gazebo. Additionally, the paper also incorporates novel empirical parametric analysis to determine the appropriate parameters for achieving optimal planner performance employing a newly defined metric termed path quality alongside other metrics. The ROS framework is used to interface with the robot and provide the necessary infrastructure for implementing the RRT algorithm through the created plugin. The implemented plugin is subjected to rigorous testing in diverse scenarios, including obstacle navigation and the generation of smooth paths. The simulations validate the successful working of the plugin. The robustness and adaptability of the proposed plugin were also assessed by introducing random obstacles in the generated path. A novel rigorous parametric study is done on the paths obtained by the algorithm, and the effects of parameter variance are documented to choose the optimal parameter.