Reviewerly/exHarmony huggingface dataset:
Analysis of Smart Cities Security: Challenges and Advancements

Smart cities are made up of various components that are interconnected. These components exchange data on an ongoing basis and they facilitate the lives of citizens. Its use of Information and Communication Technology (ICT) was a key factor in its sustainable development. Nonetheless, this development contributed to a rise of safety threats, criminal use of information and several other security and privacy challenges. As a result, security and privacy concerns have emerged as a significant problem for smart cities. Safety factors for smart cities have become a concern for all those involved in this field. In this study, we deeply examine and review and the concept of smart cities and the challenges it faces at first. In a second phase, we mainly address the research gap of security in smart cities and present an analysis of associated security challenges. In the last section, we introduce our approach that aims to: (i) capture the processes of penetration attempts, alterations and cyber attacks; (ii) truck malicious behaviors and locate their sources; and (iii) finally setup controls to repel and prevent them. To illustrate the applicability and efficiency of our solution, We refer to a case of study to demonstrate the efficacy of our detection method, using different machine learning algorithms and the dataset CICIDS2017.
Regularized Generalized Identification Algorithm in the Problem of Modeling of Weakly Formalizable Systems

The analysis of convergence of adaptive regularized generalized identification algorithm under measurement noise is presented. The application of the regularized generalized identification algorithm provides and improves the computational stability of the procedure for estimating the parameters of neuro-fuzzy models.
Development of a Secure Data Sharing Mechanism for IoT Application Systems

Research on Internet of Things (IoT) application systems, which store data collected by various sensor devices to a cloud and utilize the data through statistical analysis and machine learning, has been conducted extensively. Several platforms have been proposed to support the development of IoT application systems, which enable system administrators to easily manage edge hardware and group sensors. However, conventional development support platforms do not provide functions for secure data management, such as encryption processing when handling confidential information and flexible access control among members of a research group. In this paper, we propose a mechanism for securely and easily encrypting data and sharing data using a Secure Configuration Server. We have been developing "SINETStream," an IoT application development support tool provided by National Institute of Informatics, Japan. The secure configuration server can co-work with the SINETStream-based IoT systems. This mechanism enables easy sharing of confidential information by securely managing data encryption keys within the system, which has been complicated with conventional methods. In addition, our mechanism provides users with confidential information in an encrypted form, so that only authorized users can view the data.
Research on Small Face Detection in UAV Command and Control System

Small face detection technology was introduced and researched in depth to meet the needs of crowd monitoring in specific scenes and corresponding decision-making in UAV command and control systems. At present, the focus of small face detection research is mainly on general deep neural network object detection algorithms, but there is a lack of relevant research on small face detection in UAV command and control systems. In this study, we analyze the current challenges of UAV small face detection and discuss the research ideas and optimization schemes in the direction of data enhancement and feature enhancement. The cutting-edge algorithm is followed up and the experimental results are verified.
Research on Multi-Robot Cooperative Handling and Obstacle Avoidance Algorithm

In order to improve the efficiency and stability of multi-robot handling, inspired by stretcher handling, this paper is mainly aimed at obstacle avoidance and trajectory tracking in multi-robot handling of deformable sheet materials. When facing the obstacle avoidance problem in multi-robot handling, under the premise of ensuring stability, obstacle avoidance is achieved by improving the geometric obstacle avoidance control method based on the leader-follower formation control method while taking advantage of the characteristics of the deformable material. By improving the sliding mode variable structure control, the tracking of the respective target trajectory by each mobile robot is achieved, and the tracking error converges to zero. The simulation results show that the designed multi-robot cooperative handling algorithm can effectively achieve formation handline and obstacle avoidance.
An Overview of Correlation-Filter and Deep-Learning Based Single Object Tracking

Single object tracking is the key technology in computer vision. It can search, extract, correlate, match, represent and predict the characteristic information of the target in the sequence. It has irreplaceable value in modern military fields such as video retrieval, intelligent monitoring, intelligent interaction, automatic driving, navigation guidance and so on. In 2010 and 2012, correlation-filter and deep-learning technologies were introduced into visual tracking respectively. Since then, both of them have gradually developed into the mainstream. Taking single object tracking as the main task, this paper introduces the principle, process and difficulties of correlation filtering and deep learning tracking technologies, summarizes the classical single target tracking methods based on the above two technologies in recent years, and summarizes and analyzes their basic implementation principles, advantages and disadvantages. The development trend and optimization direction of tracking algorithms in the future are considered and prospected.
Plane path planning and driving behavior control based on obstacle avoidance and driving safety

Path planning plays an important role in unmanned driving. At present, most of the existing path planning is based on the orderly road with lanes, but few people research on the path planning based on plane driving. In this paper, aiming at the plane based unmanned driving, a new function is designed in “celestial potential field method” and it is used for the plane unmanned driving path planning by referring to the artificial potential field method and the universal gravitation formula. Furthermore a “speed decision system” based on safety considerations is designed for the dynamic path planning and driving behavior control when dynamic vehicles or other obstacles are appear around. Finally, the static, dynamic and mixed obstacle road conditions are simulated and the results shows that it can avoid obstacle well by pro-planning. Also it can keep driving safety by controlling the driving behavior(angle and acceleration).
Using meta-reasoning for incremental repairs in multi-object robot manipulation tasks

Robots tasked with object assembly by manipulation of parts require not only a high-level plan for order of placement of parts but also detailed low-level information on how to place and pick the part based on its state. This is a complex multi-level problem prone to failures at various levels. This paper employs meta reasoning architecture along with robotics principles and proposes dual encoding of state expectations during the progression of task to ground nominal scenarios. We present our results on table-top scenario using perceptual expectations based in the concept of occupancy grids and key point representations. Our results in a constrained manipulation setting suggest using low-level information or high-level expectations alone the system performs worse than if the architecture uses them both. We then outline a complete architecture and system which tackles this problem for repairing more generic assembly plans with objects moving in spaces with 6 degrees of freedom.
Strategic Alliances with University Libraries

As sources of market research, university libraries could be important collaborators for startups. Libraries may be able to assist entrepreneurs in gathering information about global and domestic markets, allowing them to reinvent their business models. Their assistance may be critical for startups to overcome the limitations of newness and smallness, allowing them to extend their operations abroad. By utilizing strong social proximities with its social media users and availability of competitor information across their social accounts, social networking technologies could be effective for libraries to acquire worldwide market knowledge for startups. The constant and long-term support of libraries will be the key to market success as the business environment for startups changes so quickly and there is a need to be very responsive to such changes. Using social networking technology, a strategic partnership between libraries and startups might be established and maintained. This chapter discusses the significance of social networking technologies in building strategic partnerships (particularly in addressing business needs for market research), as well as the obstacles and solutions that may be encountered. Prototyping technologies can help with SNS adaption for the development of such partnerships. During a pandemic, libraries provide more online support to the business community, as well as increasing usage of social media. The libraries’ pricing model for offering their services could be classified into four categories: free, freemium, and subscription model and revenue sharing model. These models depend on the priorities of the country, the sectors in which the startup operates, and how well they support social innovations. As this field matures, new models are anticipated to appear. This chapter is based on real-world consulting experience with a Spanish company and the library of a top American university in Madrid. This chapter is also based on practical experiences with various businesses, particularly during pandemics.
Conco-ERNIE: Complex User Intent Detect Model for Smart Healthcare Cognitive Bot

The outbreak of Covid-19 has exposed the lack of medical resources, especially the lack of medical personnel. This results in time and space restrictions for medical services, and patients cannot obtain health information all the time and everywhere. Based on the medical knowledge graph, healthcare bots alleviate this burden effectively by providing patients with diagnosis guidance, pre-diagnosis, and post-diagnosis consultation services in the way of human-machine dialogue. However, the medical utterance is more complicated in language structure, and there are complex intention phenomena in semantics. It is a challenge to detect the single intent, multi-intent, and implicit intent of a patient’s utterance. To this end, we create a high-quality annotated Chinese Medical query (utterance) dataset, CMedQ (about 16.8k queries in medical domain which includes single, multiple, and implicit intents). It is hard to detect intent on such a complex dataset through traditional text classification models. Thus, we propose a novel detect model Conco-ERNIE , using concept co-occurrence patterns to enhance the representation of pre-trained model ERNIE. These patterns are mined using Apriori algorithm and will be embedded via Node2Vec. Their features will be aggregated with semantic features into Conco-ERNIE by using an attention module, which can catch user explicit intents and also predict user implicit intents. Experiments on CMedQ demonstrates that Conco-ERNIE achieves outstanding performance over baseline. Based on Conco-ERNIE, we develop an intelligent healthcare bot, MedicalBot . To provide knowledge support for MedicalBot , we also build a Chinese medical graph, CMedKG (about 45k entities and 283k relationships).
Performance analysis of various denoising filters on intravascular ultrasound coronary artery images

Abstract Accurate diagnosis of atherosclerotic coronary artery stenosis largely depends on intravascular ultrasound (IVUS) image quality. Coherent sources present during IVUS acquisition generates speckle noise and obstructs the clear view of the artery. Denoising aims to remove speckle noise while preserving edges and other important features. Accordingly, speckle noise suppression serves as an important preprocessing step for IVUS image analysis. This paper presents a performance analysis of various despeckling filters such as Median, Wiener, Speckle Reducing Anisotropic Diffusion (SRAD) filter, Wavelet, Non‐local means (NLM) and curvelet based non‐local means (CNLM) for IVUS images. To evaluate the efficacy, each filter is evaluated for three categories of cross‐sectional IVUS images, that is, healthy, mild calcification, and dense calcification of real pullbacks from four patients. As it is challenging to denoise the IVUS images with complex lesions, that is, calcified arc &gt;180°, in this performance analysis, the denoising performance for different denoising filters is considered not only for the healthy IVUS but also for complex lesions. Various measures such as peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), and expert evaluation are used to compare these approaches. The results depict that the CNLM denoising filter outperforms others in terms of PSNR and FSIM for speckle noise suppression for IVUS images while preserving the essential edge and feature details for better image visualization and diagnosis.
A Case Study on a Self-directed Task in a College English Class: Focusing on Student Reflection

This study aims to contribute to research on enhancing college students’ self-directness by sharing a case of a self-directed task activity conducted in a college English class in South Korea. For twelve weeks, one hundred two undergraduates participated in the self-directed task to build a positive habit. Participants‘ written reflections on the process and the result were analyzed. Further analysis was followed on whether there was a relationship between goal achievement and self-reflection patterns. The results revealed that the participants experienced different self-regulation through the task process. The differences were rooted in personalized motivation, a clear goal, self-understanding, and self-reflection. The group with weak motivation and a vague goal had an unspecified plan and lacked self-understanding as well as self-reflection. Meanwhile, the group with strong motivation and a solid personalized goal deeply understood themselves and was active in self-reflection. Their successful experience of self-regulation strengthened their positive self-image and helped them to have a continuance will. The study concludes with educational implications and suggestions for implementing the task in classes.
A Study of Plans to Supplement the Operations of Curricular and Extracurricular Writing Programs in University: Focusing on Writing Center in University

The purpose of this study is to explore the directions of writing education in University with a focus on the roles of a writing center. The researcher believes that it is necessary to reinforce writing education further to help college students cultivate their communicative or writing abilities. Here, the management of a writing center will be essential which serves as a pivot to take full charge of writing education and promote its operation in each college and university. It is critical to ensure integrated management of writing curriculums and extra-curriculums to reinforce the cross-curricular nature of writing education and establish a beginning point of a continuous writing curriculum. It is also needed to introduce education on writing across the curriculum(WAC) to improve student's communicative abilities and help them transfer their writing abilities to their fields of specialization through writing education. In addition, there is a need to vitalize an online writing feedback system to guarantee the easiness of education and offer the continuity of writing education to students.
What’s Wrong with Trolling?

Abstract It is clear what is wrong with online trolling when it involves behavior—such as harassment, bullying, and libel—against which there are already established norms in the offline world. It is less clear what is wrong with trolling when it involves behavior that we do not usually encounter offline. Examples of such behaviour—which I call low-key trolling—include joining and disrupting other people’s conversations, name-calling, and deliberately annoying others. Drawing on Joel Feinberg’s work on offence, and on the literature on silencing and epistemic injustice, I sketch an ethical framework according to which we can evaluate and critique low-key trolling. I argue that low-key trolling is wrong because of its effects on the people trolled; effects which are disproportionately felt by people from vulnerable and oppressed groups.
Investigation of expressive power of a neural network architecture suited for optical neural networks

In recent years, the concept of a fully optical neural network (ONN) has attracted attention for its potential to reach significantly higher speed and lower energy consumption compared to semiconductor electronics-based artificial neural network (ANN). Although the technology promises significant improvements through the utilization of light instead of digital signals, an ONN also requires physical fabrication on a planar substrate that introduces new limitations and constraints to the network when compared to the conventional NN. Limitations on node connectivity, choices of possible nonlinear neuron activation functions, as well as constraints on parameters in the case of a passive-circuit ONN are among the few peculiar challenges that are faced by the ONNs. In this study, we aim to understand the effect of ONNs’ limitations and constraints on their fitting ability when compared to the unconstrained NN. We use digital NNs that simulate the properties of ONNs to investigate the potential and limitations of the ONNs and might be useful as a practical way to pre-calibrate the ONNs’ parameters before their physical production. The ONNs performance and potential are evaluated and compared with the conventional NN by comparing their fitting ability on different datasets. Furthermore, the use-case for ONNs’ application is built and demonstrated on the material and financial datasets.
Continuous Variable Based Quantum Random Number Generator with Observable Assumptions

Abstract This paper presents a semi-device independent protocol for quantum randomness generation constructed on the prepare-and-measure scenario based on the on-off-keying encoding scheme and homodyne and heterodyne detection schemes. Additionally, we introduce a straightforward optical implementation utilizing commercial off-the-shelf components.
Invisible Women and Intangible Property

Abstract Recognizing the significant gender disparities promulgated by intellectual property laws requires a feminist gaze. Gender disparities in the intellectual property law areas of patents, copyrights, and trademarks negatively affect women’s economic and social status. Accurately mapping these disparities can be thwarted by bad data sets that amplify bias, suggesting that we need better, more inclusive data as well as feminist insights. This chapter focuses on the ways that gender and feminism intersect with intellectual property laws and policies, asserting two basic claims: first, that gender itself can be property, and is exploited as such in several intellectual property contexts, and second, that gender can affect the creation, distribution and consumption of works protected by intellectual property laws. The chapter then turns to issues of gender inequality in three major areas of intellectual property: patents, copyrights and trademarks. The final section discusses two legal areas adjacent to intellectual property: naming rights and artificial intelligence in the form of robots.
A Novel Strategic Aircraft Track Planning Method Considering Conflict Probability

Generally, air track planning is conducted in real time and takes modified track distance minimization as objective. Next-generation air transport systems provide aircrafts with more flexibility in track planning and more responsibilities in self-separation, which present a great challenge for aircraft optimal track planning, especially in some high-density airspaces and some complex conflict scenarios. This paper proposes a novel aircraft track planning method by taking aircraft conflict probability into consideration. First, the concepts of aircraft potential motion space and the estimation method for aircraft conflict probability is introduced. Then, taking conflict probability minimization as the objective, the classical ant colony algorithm (ACA) algorithm is improved to solve the model. Finally, an experimental study is conducted to illustrate the proposed method. Results show that the proposed method is able to provide a scientific and effective track planning approach considering the potential conflict probability of aircrafts, which is able to provide fundamental to the safety of entire air transport system.
Multiscale attention dynamic aware network for fine‐grained visual categorization

Abstract Fine-grained visual categorization (FGVC) is a challenging task, facing the issues such as inter-class similarities, large intra-class variances, scale variation, and angle variation. To address these issues, the authors propose a novel multiscale attention dynamic aware network (MADA-Net). The core of network consists of three parallel sub-networks, which learn features from different scales. Each sub-network is composed of three serial sub-modules: (1) A self-attention module (SAM) locates objects according to relative importance scattered throughout feature map. (2) A multiscale feature extractor (MFE) learns the non-linear features of objects. (3) A dynamic aware module (DAM) enhances the learning capability of spatial deformation of the network to generate high-quality feature map. In addition, the authors propose a multiscale adjusted loss (MA-Loss) to improve the performance of network. Experiments on three prevailing benchmark datasets demonstrate that our method can achieve state-of-the-art performance.