Reviewerly/exHarmony huggingface dataset:
Clustering-Based Granular Representation of Time Series With Application to Collective Anomaly Detection

Efficient anomaly detection is at the forefront of ensuring optimized operations and system safety, especially in the field of engineering. It is deemed indispensable for the enhancement of work efficiency, maximization of resource utilization, and proactive addressal of potential risks. From this practical perspective, a new method for granular representation of time series data is proposed in this study and is validated through a collective anomaly detection task. The density peak clustering (DPC) algorithm is employed to evaluate clusters in two-dimensional (2-D) data, which are created from the original dataset and its variations. Using the principle of justifiable granularity, each cluster is depicted, allowing the time series to be converted into rectangular information granules. A distinctive similarity measure, designed to assess the similarity among subsequences in this format, is then introduced, with subsequences having reduced similarity being pinpointed as anomalies. Extensive testing on various datasets confirms that the effectiveness and applicability of the proposed method are evident in real-world scenarios. This approach not only enhances the understanding of time series patterns but is also recognized for its prominence in engineering applications where precise anomaly detection is demanded. Due to its ability to capture the inherent structure of time series and analyze the correlation among data, superior effectiveness is consistently exhibited by the proposed approach when compared to other existing methods.
Grasping Lightweight Objects With Chat-PM: A Rotorwash-Aware Motion Planning Method

Grasping lightweight objects with aerial manipulators remains challenging due to the rotorwash interference caused by spinning rotor blades. This letter details a rotorwash-aware motion planning method for Chat-PM, a hybrid aerial-terrestrial vehicle equipped with a robotic arm. With proposed method's adjusting the vehicle's thrust and pose on flat surfaces timely, the rotorwash can be reduced and directed away from targets. To achieve this, we first model the rotorwash velocity distribution of Chat-PM in aerial and surface modes with computational fluid dynamics (CFD) simulations. The model further functions as an optimization objective of nonlinear model predictive control (NMPC) method, alongside other objectives including aerial-surface reference tracking and target grasping. An aerial-surface path searching method for the vehicle is then introduced, which takes into consideration both rotorwash velocity and kinodynamic feasibility to generate a high-quality reference for NMPC to track. Experimental results showcase the effectiveness of our proposed planning approach for aerial-surface grasping with rotorwash optimization.
Predicate Abstraction, Informally

Over the course of history, modal logic has earned a reputation for being difficult and confusing. To be sure, modal logic is more complex than classical logic, and to ease the way into the subject we have emphasized thinking in terms of possible worlds throughout this book. In this chapter, however, we will address some of the sources of difficulty. One of the main reasons for confusion is that the way in which we ordinarily express modal claims can frequently be interpreted—very naturally—in more than one way. This ambiguity is often hidden from view, and that is how even the most gifted of logicians can get tripped up. Accordingly we are going to spend a considerable amount of time in the current chapter identifying the most famous of these ambiguities, and explaining the notation that we have chosen to ensure that it is free from these problems.
First-Order Modal Tableaus

In Sects. 7.1 and 7.2 we gave prefixed tableau rules for propositional modal logics in what we called the Lesser Modal Cube. Now we extend these to deal with quantifiers. But we have considered two versions of quantifier semantics: varying domain and constant domain. Not surprisingly, these correspond to different versions of tableau rules for quantifiers. Also not surprisingly, the rules corresponding to constant domain semantics are simpler, so we will start with them.
Tableaus for Predicate Abstraction

There are several propositional modal logics that we have looked at.
Adaptive Momentum-Based Loss Rebalancing for Monocular Depth Estimation

Monocular depth estimation in outdoor scenes presents significant challenges due to ambiguity from occlusions and structural variations. One important challenge lies in effectively incorporating loss functions while considering the distribution of ground truth pixels and structural variations of the scene. The utilization of conventional loss functions, such as scale-invariant loss and gradient loss without considering contribution of each loss in relation to the structural variation of the scene may lead to suboptimal outcomes. To solve this problem, we propose an Adaptive Momentum-based Loss Rebalancing (AMLR) to balance loss functions for monocular depth estimation in outdoor scenes. Our method utilizes the scale-invariant loss and gradient loss, with the proposed balancing term inspired by traditional weight optimizer, Adam. By dynamically updating the loss weights using momentum and considering the increase and decrease of individual losses, we facilitate convergence of the total loss and consequently obtain more accurate results. We observed the gradient loss with an appropriate weight serves the role of assistant to the overall loss convergence. Experimental results on the KITTI benchmark demonstrate that our approach achieves performance comparable to state-of-the-art, achieving an absolute relative difference of 0.049. This work contributes to advancing the field of monocular depth estimation in challenging outdoor scenes.
The COVID-19 Pandemic and Italian Constitutional Law—Some Reflections on the Sources of Law

The massive body of legislation adopted during the first few months of the Covid pandemic in order to contain its effects has represented a new, extraordinarily serious and pervasive phenomenon, which has had a profound impact both on fundamental rights and on individuals’ lives, standing in open contradiction to the constitutional order of the sources of law. The paper provides some reflections on the relationship between the pandemic and sources of law.
A New Panorama for Intellectual Property: The Benefits and Challenges of Blockchain

Intellectual property (IP) plays an essential role in protecting the output of human creativity and is a tool for transforming intangible assets into innovation. The increased use of digital technologies and the transition to a knowledge-based economy have brought unexpected challenges to the IP system, which needs more robust tools. Blockchain presents itself as a technology with disruptive potential because of its revolutionary nature and potential to break several paradigms imposed by the regulation of the IP system. This article aims to introduce to readers of both areas which challenges blockchain can solve within the IP ecosystem, as well as to understand how these proposals are understood and worked on an international scale, primarily through the actions of global organizations, performing a diagnosis of how it is possible to make blockchain bring tangible benefits to the IP system.
Propositional Modal Axiom Systems

Historically, most of the best-known modal logics had axiomatic characterizations long before either tableau systems or semantical approaches were available. While early modal axiom systems were somewhat circuitous by today’s standards, a natural and elegant system for $$\mathbf {S4}$$ was given in Gödel (1933), and this has become the paradigm for axiomatizing modal logics ever since. It is how we do things here.
Propositional Modal Logic

We now start the formal treatment of (propositional) modal logic. And we begin with syntax. The language of propositional modal logic extends that of classical propositional logic, Definition 1.2.1 . Two new unary symbols are added, $$\square $$ (necessarily) and $$\lozenge $$ (possibly).
Background: Propositional Language

Logics are generally specified in two fundamentally different ways, using proofs and using models. Both are important, and the two are intimately connected.
Health State of Emergency and Management of the Pandemic—The Italian Experience

The Italian Constitution does not contain a section or title specifically dedicated to the regulation of states of emergency or institutional crisis, internal or external, unlike what happens in the cases of the German or Spanish Constitution.
Propositional Modal Tableaus

We discussed propositional classical tableaus in Chap. 3 , and now this is extended to take modal operators into account. The extension retains the pleasant advantages that classical tableaus had. A tableau proof generally only uses subformulas and negations of subformulas of the formula being proved, and hence the search for a proof is easier than axiomatically.
Quantified Modal Logic

We have the same propositional connectives that we had in Chap. 5 , and the same modal operators.
Manipulation Through Design: Unconscious Movement Towards Loss of Autonomy and Privacy Indifference

As technology is evolving, the mode of interaction between technology and its users is rapidly changing. The architectures in cyberspace are encoded with designs, including features and tools, which enable users to easily navigate through the internet. With enhancing user accessibility being one of the goals, these designs are often personalised for users, depending on various factors such as their online browsing habits. In this data driven market where grabbing and holding on to the attention of a user is the challenge, user interface (“UI”) designs have gained immense importance.Dark patterns are deceptive designs on UIs which interfere with the users’ decision-making process. Prior research has introduced different taxonomies of dark patterns and the tactics undertaken to influence the users. It is essential to note that deceptive designs take into consideration the inherent cognitive abilities and limitations of the users. Such interference leads to a loss of user autonomy, loss of privacy and personal data, which violates the fundamental rights of the users in the European Union (“EU”). Such practices are also against the very essence of consumer protection, which is a fundamental right of users in the EU. There is therefore a dire need to regulate the architectures in cyberspace while balancing the tension between the right of private players to conduct business versus the users’ fundamental rights.
The Role of Blockchain in Public Administration in the Field of Economic Activity

Nowadays, almost every mention of the word “blockchain” is associated with cryptocurrencies in one way or another. This way of thinking is ingrained in people’s minds because of the most high-profile and discussed cases of fraud or the cryptocurrency market crash, which have hurt many investors in an extremely negative way. However, if we abstract away from preconceptions and take apart the mechanics of blockchain technology, the situation is not so straightforward. In creating a model for improving the efficiency of public administration in the field of economic activity, it is necessary to study blockchain as a concept, as well as its practical applicability in public administration, using real-life examples from various jurisdictions. The key means and ways to achieve the goal is a comprehensive study of the advantages of blockchain technologies that will be useful in the field of public administration of the economy, using special legal, specific historical, formal dogmatic, and comparative legal methods and the method of legal forecasting. The paper raises the issues associated with the introduction of blockchain into the economic sector and analyses the blockchain’s benefits that will be useful in public administration when it is used. It analyses the integration of blockchain into public administration. The role of blockchain in such areas of the economy as finance, economic development, digitalization, agriculture, and others is analyzed. Based on this research, the benefits of blockchain in the economic sector, including the development of the entrepreneurial activity, are formulated. The paper identifies trends indicating that the continued implementation of good governance is not conceivable without the introduction of groundbreaking blockchain technology. The study of the introduction of blockchain in different areas of public administration has led to the conclusion that the legislator should pay special attention to the financial sector because proposals aimed at improving this area of public administration will improve the standard of living of the population of Russia.
Benchmark Models and Technologies in Marketing Management

The paper describes the opportunities provided by validating the authors’ innovation—a benchmark model of the work of the university’s enrolment board, which is relevant for contemporary educational institutions that already use the marketing management concept and those intending to implement it. The authors describe the following stages: planning, analysis of the application results, and marketing management prospects aimed at the growth of intermediate and final state metrics; the first is represented by functional sections and aggregated assessment model, and the latter includes the conversion of customer flows based on the results of the activities of the enrolment board. Additionally, these stages are aimed at improving the results of a specific university’s marketing activity in the education market. The model was tested in the higher education market of the Belgorod Region in 2019–2020, which provided the authors with new applied results, allowing the authors to determine and assess high-potential marketing management opportunities connected with an enhancement of tactical marketing functions of contemporary universities.
Sign Language Recognition System based on Jetson TX2 and Yolov5

With the continuous enhancement of China's comprehensive national strength and the rising sense of well-being of our citizens, reasonable concerns about the hearing impaired in China should also be considered. Yolov5, as the latest work of Yolo series, has shocked the world with its readable code, super high image processing efficiency and simple structure since it came out in 2020. The Jetson TX2 equipped with embedded GPU has the ability to realize more complex and deep neural networks. This paper uses Jetson TX2 and yolov5 algorithm as the core, uses high-definition camera to capture sign language information in real time, and then carries out target extraction and gesture recognition on video frames. The image recognition rate reaches 0.05 seconds per frame, and the average gesture detection accuracy reaches 98%. The experimental results show that the sign language recognition system based on Jetson TX2 and Yolov5 has a high recognition rate and accuracy, which is sufficient to meet the basic sign language communication of hearing impaired people in daily life.
Method for Chunking and Managing Large Urban Road 3D Point Cloud Maps based on Voronoi Graph

The urban road scenario is one of the main scenarios for autonomous driving. The chunking and managing of large 3D point cloud maps in this scenario are essential to solving the memory occupation and memory fluctuation problems caused by map loading in the process of autonomous driving localization, sensing, and planning. This paper proposes a method for chunking large urban road 3D point cloud maps based on Voronoi graph, which achieves uniform and efficient chunking of the maps in this scenario and effectively preserves the semantic integrity of the maps at roads and junctions. At the same time, this paper presents an efficient and applicable map dynamic management method for the autonomous driving localization process, significantly reducing the memory footprint and fluctuations during map loading. We experimented our method extensively on the KITTI dataset. It achieved uniform chunking of the point cloud coverage area and the number of point clouds on a large urban road laser point cloud map covering an area of 3.5km*3.5km, respectively. The memory footprint was reduced by 16% compared to existing map dynamic loading experiment methods.
PortalEyes: A Portal Identification Method via Noisy Video Traffic

As a new mode and new concept of modern city operation and governance, smart city has long-term significance for optimizing urban resource scheduling, improving urban operating efficiency, and improving the quality of life of citizens. As more and more citizens rely on video platforms(such as YouTube) for entertainment and information acquisition, media construction centered on video platforms has become an indispensable and important part of smart city, so the improvement of video service quality is becoming increasingly important. However, in the process of watching the video, the video portal and the video resource are transmitted separately, and it is difficult for the network providers to infer the portal (that is, the user's access intention) through the video resource. This paper abstracts the problem into the association of video resources and portals, and proposes PortalEyes, a portal identification method that combines co-occurrence relationship mining and order embedding, which quickly and roughly screens a large amount of traffic generated by users then sorts the screened results to further improve the accuracy. We conducted extensive experiments and compared the baseline and classic sorting algorithms. Our method makes the average position of the portal to be 1.2, which is the best compared with these algorithms. The experimental results show that our method is helpful for user intent discovery. It is of great significance to help Internet providers improve the quality of video services and improve the efficiency of information acquisition in smart cities.
Where and What We Should Look at: A Cross-Modal Fusion Method with Attention for Object Counting

Object counting is a challenging computer vision task. Recent studies find that incorporating multi-modal information can help find the hard points. But the cross-modal representation learning, which aims to comprehend and represent the information in various modalities, is still far from being fully explored in this area. The current works fail to model the dynamic complementary relationship between different modalities, leading to incomplete information extraction. To make up for that, we propose an attention mechanism to facilitate information fusion. Our work adopts a three-stream structure as the framework, consisting of two modality-specific branches and one modality-shared branch. The proposed two attention modules aggregate contextual statistics along the channel and spatial dimensions to generate attention maps that reflect the features' importance in various modalities. In this way, the complementary relationship between different modalities would be exposed to our model precisely. Besides, we share the convolution filters of the attentions modules between the RGB branch and modality-shared branch. The modality-common statistic can be captured through parameter sharing, exposing the informative features. To validate our ideas, we implement extensive experiments on the RGBT -CC benchmark. Consequently, we outperform the state-of-the-art results by a large margin. The performance strongly supports the effectiveness of our method.
Racing with Vision Transformer Architecture

Vision-based reinforcement learning (RL) holds great potential for addressing complex decision-making problems and has benefited numerous research domains, such as game intelligence, medical diagnosis, and autonomous driving. In previous works, deep reinforcement learning (DRL) frameworks combining CNNs and value-based algorithms were applied to Atari games and achieved significant performance. However, exploring these applications further using recent advanced deep models remains an open problem. In this work, we introduce the Vision Transformer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\text{ViT})$</tex> model as a feature extraction network in comparison to the traditional CNN model to explore the potential of ViT in RL. The performance of both models is tested in the CarRacing-v0 pixel environment and analyzed in terms of sampling efficiency and algorithmic stability. The results show that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{ViT}$</tex> brings innovation by utilizing its self-attention mechanism for processing images, which provides evidence that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{ViT}$</tex> is a promising solution for RL and opens up new avenues for future work.
A Trend of 2D Human Pose Estimation Base on Deep Learning

The research on Human pose estimation remains the most fundamental and challenging problem in computer vision. In this context, computer vision-based automobile safety-assisted driving technology has received comprehensive attention and is widely used in automobile-assisted driving systems. Powered by advanced artificial intelligence, the scale of data to be learned by deep learning networks is increasing, the processing tasks are becoming more and more complex, and the model parameters are also increasing. In the field of distributed parallel computing research has also received extensive attention. In this work, we focus on the challenge of human posture estimation in two dimensions, using both conventional and deep learning techniques, with an emphasis on its study's potential use in traffic-related settings. Recent approaches for estimating human poses are introduced, and a summary of how to extract bone joint points using deep learning techniques is provided. In conclusion, it elaborates on the present state of going beyond the bounds of single-computer computing resources and merging distributed architecture with deep learning to address the challenges and opportunities in human pose estimation. Human posture estimation has several technological applications, including the detection of driving behaviour and the analysis of fitness data.
A Study on an Intelligent Drug Delivery Robot Based on ROS

When public health emergency like epidemic breaks out, there is always shortage of medical staff in isolation wards and the risk of infection is high. As for the laborious drug delivery task and the working environment where cross-infection happens easily, a solution is proposed — an intelligent robot car. This paper presents the overall plan and mechanical structure design of the robot: The hardware system is composed of STM32 as the main controller, Raspberry Pi, Iidar, camera and other devices; the robot operating software system is built based on ROS and Raspberry Pi Ubuntu environment; functions include indoor map construction, autonomous path planning, obstacle avoidance and navigation, drug delivery are implemented. The robot runs smoothly and has various and flexible functions, which can provide reference for the development of medical service robots.