Reviewerly/exHarmony huggingface dataset:
Adaptive HDR Image Blind Watermarking Approach Based on Redundant Discrete Wavelet Transform

Remarkable success has been recorded in the usage of digital watermarking which is aimed at protecting the intellectual property of multimedia content. In this paper, a new tone mapping attack-resistant high dynamic range (HDR) image zero-watermarking algorithm is proposed. In this algorithm extraction of stable and invariant features are extracted for efficient zero-watermarking through the application of the redundant discrete wavelet transform (RDWT) to the HDR image. The first step involves transforming the HDR image to HVS color space, and RDWT is implemented using the V-channel so that the LL sub-band which contains the strong structure contents of the image is obtained. The second step involves dividing the LL sub-band into non-overlapping blocks, which are afterwards subjected to the process of transformation through the use of the singular value decomposition (SVD) so that the U matrix can be extracted. Third, the use of an Auto-Regressive (AR) prediction technique was employed in generating a local relationship model and comparison is done to facilitate the production of a binary feature mask. In the fourth process, hybrid chaotic mapping (HCM) is used to generate blended watermark so that the security of the watermark can be fortified. Lastly, the computation of an effective zero-watermark is achieved through the implementation of an exclusive-or operation on the blended watermark and the binary feature mask. Based on the results, the approach presented in this study demonstrated superior performance in terms of withstanding TM attacks and other attacks associated with image processing.
International Law and Digital Disease Surveillance in Pandemics: On the Margins of Regulation

Abstract The COVID-19 pandemic elicited a surge in the use of digital tools to replace “classic” manual disease tracking and contact tracing across individuals. The main technical reason is based on the disease surveillance needs imposed by the magnitude of the spread of the SARS-CoV-2 virus since 2020, particularly how these needs overwhelmed governments around the world. Such developments led to stark variations across countries in terms of legal approaches towards the use of digital tools, including self-reporting software and mobile phone apps, for both disease tracking and contact tracing. Against this backdrop, in this article I highlight some of the normative challenges posed by the digitalization of disease surveillance, underscoring its almost non-existent regulation under international law. I look back at the historical emergence of the epidemiological principles underlying this procedure, by referring to John Snow’s trailblazing work in cholera control. I emphasize how the COVID-19 pandemic prompted both technical and normative shifts related to the digitalization of these procedures. Furthermore, I refer to some of the overarching obstacles for deploying international law to tackle future tensions between the public health rationale for digitalized disease tracking and contact tracing, on the one hand, and normative concerns directly related to their legality, on the other hand. Lastly, I put forward conclusions in light of the current juncture of international health law reforms, and how they so far display limited potential to herald structural changes concerning the legality of the use of digital tools in disease surveillance.
Channel-agnostic Radio Frequency Fingerprint Identification Using Spectral Quotient Constellation Errors

Radio frequency fingerprint identification (RFFI) is a physical layer security methodology to recognize individual devices by leveraging hardware imperfections inevitably induced in the manufacturing process. However, the performance degradation caused by the time-varying channel impacts and interferences has severely restricted the development of RFFI. To this end, we present a channel-agnostic RFFI system, which consists of three modules, i.e., signal preprocessing module, feature extraction module, and classification module. In the signal preprocessing module, we first propose a novel approach, referred to as limiter-based spectral circular shift bidirectional division (LB-SCSBD), to generate two parallel spectral quotient (SQ) sequences. Then, we define the spectral quotient constellation (SQC) symbols according to different modulation formats, and thereby transform the SQ sequences into four magnitude-based sequences in terms of two channel-robust signal representations, i.e., the SQ magnitude (SQM) and SQC error vector magnitude (SQC-EVM). In the feature extraction module, we present a moment-based statistical feature extractor (MB-SFE) to extract the device-specific information from the above four sequences. In the classification module, the extracted statistics are fed into the multi-class support vector machine (SVM) for training and testing. We take WiFi as a case study and evaluate the performance of the proposed RFFI system by classifying eight simulated device models and six universal software radio peripheral (USRP) transmitter radios. Experimental results show that (i) the proposed method achieves the accuracies of 99.84% and 98.26% with eight devices in QPSK and 16QAM cases, as well as the accuracy of 92.42% with six USRP devices (ii) the proposed method exhibits superior classification performance in comparison to some existing RFFI methods, leading to a significant accuracy improvement of at least 38.33%.
Developing Chatbot Conversational Systems &amp; the Future Generation Enterprise Systems

Conversational technology has recently emerged effectively; it helps people in communicating with smart devices such as smartphones by using human language. When they emerged, they enabled and assisted users to perform various functions such as gathering information, conducting transactions, having general conversations, and easily navigating web services and entertainment. They not only have an impact on people in general by improving customer service as they can provide answers to any inquiries but also facilitated navigation by assisting people with disabilities by interacting with a system that deals with voices or any other human language. In addition, recently they started to play a significant role in enterprise systems by supporting employees as they are assisted in learning the newly implemented system. As virtual assistants, they contribute to better accessibility, and acceptance, and also reduce the costs associated with customer service. This paper provides a chatbot system that helps the employees to learn how to deal with the newly installed ERP system flexibly and easily, which helps in solving one of the common problems that occur when switching to the ERP system.
A Multi-Purpose Realistic Haze Benchmark With Quantifiable Haze Levels and Ground Truth

Imagery collected from outdoor visual environments is often degraded due to the presence of dense smoke or haze. A key challenge for research in scene understanding in these degraded visual environments (DVE) is the lack of representative benchmark datasets. These datasets are required to evaluate state-of-the-art object recognition and other computer vision algorithms in degraded settings. In this paper, we address some of these limitations by introducing the first realistic haze image benchmark, from both aerial and ground view, with paired haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene, and consists of images captured from the perspective of both an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). We also evaluate a set of representative state-of-the-art dehazing approaches as well as object detectors on the dataset. The full dataset presented in this paper, including the ground truth object classification bounding boxes and haze density measurements, is provided for the community to evaluate their algorithms at: https://a2i2-archangel.vision. A subset of this dataset has been used for the “Object Detection in Haze” Track of CVPR UG2 2022 challenge at http://cvpr2022.ug2challenge. org/track1.html.
Dynamic Group Management to Improve the Scalability of PBFT

블록체인의 신뢰도와 성능을 결정하는 합의 알고리즘은 합의에 참여하는 노드들의 동일한 의사 결정을 위하여 사용된다. 그중 투표기반 합의 알고리즘인 PBFT는 O(n2)의 시간복잡도를 갖기 때문에 확장성이 떨어진다. 본 논문에서는 PBFT(Practical Byzantine Falut Tolerance) 기반 블록체인 네트워크에 참여하는 노드를 그룹화하고 각 그룹을 계층화하여 동적으로 관리하는 기법을 제안한다. 또한 제안기법의 예상 합의 소요시간을 추정하기 위한 수학적 모델을 제안한다. 이후 소요시간 추정 모델에 기반한 그룹과 계층을 동적으로 조절하는 동적 합의 알고리즘을 제안한다. 실험결과 제안 기법은 계층 구조가 존재하지 않은 그룹 기반 PBFT와 비교할 때 합의 소요시간 성능이 평균 약 97% 향상되었다. A consensus algorithm that affects the reliability and performance of a blockchain is used for identical decision-making of nodes participating in the consensus. PBFT(Practical Byzantine Falut Tolerance) is a voting-based consensus algorithm with an O(n2) time complexity. The scalability of PBFT is generally poor. This paper proposes a scheme for grouping the nodes participating in a PBFT-based blockchain network and dynamically managing each group by layering it. In addition, we create a mathematical model for estimating the expected time required for consensus of the proposed scheme. Afterwards, we propose a dynamic consensus algorithm for dynamically adjusting the structures of groups and layers based on the model for estimating the expected time of a consensus. As a result of the experiment, the proposed scheme improves the performance of the consensus time by about 97% on average compared to the group-based PBFT without hierarchical structures.
Convolutional neural network and unmanned aerial vehicle‐based public safety framework for human life protection

Summary In this paper, we developed an object detection and identification framework to bolster public safety. Before developing the proposed framework, several existing frameworks were analyzed to bolster public safety. The other models were carefully observed for their strengths and weaknesses based on the machine learning and deep learning algorithms they operate on. All these were kept in mind during the development of the proposed model. The proposed framework consists of an unmanned aerial vehicle (UAV) utilized for data collection that constantly monitors and captures the images of the designated areas. A convolutional neural network (CNN) model is developed to recognize a threat and identifies various handheld objects, such as guns and knives, which facilitate criminals to commit crimes. The proposed CNN model comprises 16 layers with input, convolutional, dense, max‐pool, and flattened layers of different dimensions. For that, a benchmarked dataset, that is, small objects handled similarly to a weapon (SOHAs), a weapon detection dataset is used. It comprises six classes of 8945 images, with 5947 used for training, 1699 used for testing, and 849 used for validation. Once the CNN model accomplishes the object identification and classification, that is, the person is criminal or non‐criminal, the criminal is forwarded to various law enforcement agencies and non‐criminal data are again forwarded to the CNN model for improvising its accuracy rate. As a result, the proposed CNN model outperforms several pre‐trained models with an accuracy of 0.8352 and a validation accuracy of 0.7758. In addition, the proposed model gives a minimal loss of 0.83 with a validation loss of 0.97. The proposed framework decreases the burden on crime‐fighting agencies and increases the accuracy of crime detection. Additionally, it ensures fairness and operates at a meager computational cost compared to similar pre‐trained models.
YOLO V5-MAX: A Multi-object Detection Algorithm in Complex Scenes

The target detection of autonomous ground vehicles (AGVs) has the problem of few and slow object categories, which will cause great safety problems for AGVs. This paper proposes a YOLO v5-MAX algorithm to deal with the problem of a few types of object detection in complex scenes, e.g., city traffic jam, pedestrians crossing the road, running red lights, overtaking, merging, etc. The proposed algorithm consists of two parts. Firstly, the proposed algorithm uses YOLO v5s as the initial network model to train the vehicle detection model, which is used to detect the three categories of cars, buses, and trucks. Secondly, based on the first part, a Neck network and Head output layer are added to the proposed algorithm to detect four categories of person, bike, motor, and rider. In this paper, the most commonly used YOLO v5 object detection network is taken as an example to verify the effectiveness and realizability of our innovation. Of course, our method can also be applied to other object detection models, providing a theoretically feasible method for multi-object detection in the future. Finally, after the proposed algorithm is trained, it is deployed to Jetson TX2 for actual AGVs detection experiments. The experimental results show that the detection types and detection speed of the proposed algorithm have been greatly improved.
Product Rental Web Application using HTML, CSS, BOOTSTRAP, PHP, and SQL

New web technologies, languages, and approaches have aided in creating dynamic apps that represent a new form of cooperation and collaboration among many users. The objective of this study is to develop a website that would help people in reducing their expenditure for products that are temporarily required. In order to do so, an application has been developed in this research, in which people can request their needs (products for temporary use) and offer products to others as well. In this way, people can get products that are required for them temporarily. At the same time, cost can also be saved since the required product is brought only for rent rather than buying. Another advantage is that the other person offering products for rent as well gains some money. This system can solve the problem of people owning non-essential products readily. Hence this application could be very helpful for people in their day-to-day life.
Mid-Air Gesture Based Multi-Finger Control System For Paralyzed Patients Using Leap Motion

Social security, mobile technology, system security, art and culture, sign language recognition, defense technologies, and soon have all recently paid a great deal of attention to image processing, and specifically Air Gesture Recognition (HGR). One of the most important uses of Air movements is in sign language, and this study has implications for resolving issues with this vital communication modality. However, its lack of universal comprehension undermines the entire sign language environment. The computer’s ability to recognise Air gestures is being put to good use in this area. Therefore, it is necessary to develop an adequate Air gesture detection system for use in robotics, gaming and virtual reality, human computer interaction, sharing information contained in historical artefacts, etc. Current methods for recognising Air gestures typically rely on either a sensor based or a Vision-based approach. Sensor-based approaches, such as gloves, Inertial Measurement Unit (IMU), Electromyography (EMG), Wi-Fi, etc., record the location and motion of the Air, while vision-based approaches acquire the Air gesture using a camera, webcam, etc. The sensor based method is more efficient because it eliminates the need for pre-processing and segmentation; nevertheless, it is prohibitively expensive to set up a dedicated laboratory for this purpose, and it can be unpleasant for users to wear bulky sensors for extended periods of time. Because they encode texture and colour aspects of Air gesture for recognition, vision-based techniques are cost-effective. However, there are a number of obstacles that must be overcome in order to create a vision-based Air gesture detection system. The study’s overarching objective is to enhance existing approaches to problem-solving and system efficiency in order to design a system that can successfully navigate these obstacles. Pre-processing, feature Extraction, and classification are the three main building blocks of any Air gesture detection system. In this study, we provide LMS (Leap Motion sensor), a feature-based descriptor that was built from the ground up. The static Air gesture identification problem is addressed by suggesting an Extended Radial mean response pattern. When compared to state-of-the-art Aircrafted methods, the suggested descriptor improves recognition system robustness to noise and light fluctuations.
The Tiny Eye Movement Transformer

In this paper, we evaluate different small neural network models for eye movement classification and show our so far developed improved model architecture. For evaluation, we used a subset (1.5 million sequences) of the TEyeDS annotations since it contains in the wild recordings and has the most eye movement annotations to our knowledge. We classified fixations, saccades, and smooth pursuits with four different network architectures and the proposed model improves the equally weighted accuracy by 3.8% to the best competitor while only using 6% of the amount of learnable weights.
The Development of Augmented Reality (AR) in Anatomy Course

The development of Augmented Reality (AR) media is the goal of this research. The research method used in this research activity is development research. This method is carried out in nine steps. Research data were collected through interviews, observation and questionnaires. The instruments used to measure the quality of teaching materials are assessments from material and media experts and student response questionnaires. Based on the validation results by learning media and material experts, the categories "Good", or 79.05% and "Very Good", or 87.67%, were obtained. In a small-scale trial involving ten students, 81.6% was obtained, while in a large group test involving 30 students, 88.8% was obtained. Student responses also showed satisfaction with the developed Augmented Reality (AR) media, namely the "very interesting" criterion. This research is limited to product revisions, and further research should consider mass products. This research has a novelty in learning media: Augmented Reality (AR) in anatomy courses.
A CNN-SPEED-BASED GNSS/PDR INTEGRATED SYSTEM FOR SMARTWATCH

Abstract. In recent years, wearable devices such as smart bands and smartwatches have gained widespread popularity due to their ability to provide various health and fitness applications by detecting and analyzing the human body and motion information. However, the accuracy of location-based services can be limited, especially in urban areas and indoors. This study proposes a series of smartwatch Pedestrian Dead Reckoning (PDR) improvements based on 9 Degrees of Freedom (DOF) IMU orientation estimation, which includes the heading estimation of human movement and a novel pre-trained velocity regression model. The proposed system holds the potential to enhance positioning accuracy and augment navigation availability for smartwatch users, thus offering potential applications across various fields. This study makes significant contributions to the field of smartwatch navigation by proposing a GNSS/PDR fusion algorithm specifically designed for the consumer-grade IMU, magnetometer, and GNSS receiver built into Apple Watch, tracking varied roll and pitch of the sensor caused by hand swing, and integrating a CNN model to predict the 1-D speed and provide ZUPT information, offering improved accuracy and reliability.
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models

State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Instead, a sequence of data points related to the state are obtained. The linear-Gaussian state-space model is widely used, since it allows for exact inference when all model parameters are known, however this is rarely the case. The estimation of these parameters is a very challenging but essential task to perform inference and prediction. In the linear-Gaussian model, the state dynamics are described via a state transition matrix. This model parameter is known to behard to estimate, since it encodes the relationships between the state elements, which are never observed. In many applications, this transition matrix is sparse since not all state components directly affect all other state components. However, most parameter estimation methods do not exploit this feature. In this work we propose SpaRJ, a fully probabilistic Bayesian approach that obtains sparse samples from the posterior distribution of the transition matrix. Our method explores sparsity by traversing a set of models that exhibit differing sparsity patterns in the transition matrix. Moreover, we also design new effective rules to explore transition matrices within the same level of sparsity. This novel methodology has strong theoretical guarantees, and unveils the latent structure of the data generating process, thereby enhancing interpretability. The performance of SpaRJ is showcased in example with dimension 144 in the parameter space, and in a numerical example with real data.
A Comparative Study of Cloud Computing Platforms

This article offers a comparison and analysis of three of the most widely used cloud computing platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The research focuses on the most important aspects of each platform, such as the virtual machines they offer, the storage choices they provide, the database services they provide, and the serverless computing capabilities they provide. In addition to this, the paper discusses issues like as cost, availability, security, and scalability, as well as some of the positives and negatives associated with using each platform. The literature review that is included in this paper highlights some of the most important findings from recent studies on cloud computing platforms. These findings include the widespread use of Amazon Web Services (AWS), the difficulties that are encountered by organisations in the management of cloud computing resources, and the significance of aspects such as reliability, security, and cost when selecting a cloud computing platform. This article's overarching objective is to offer businesses a comprehensive introduction to AWS, Microsoft Azure, and Google Cloud Platform (GCP), and to assist those businesses in making well-informed choices regarding which cloud platform is best suited to meet their requirements.
Generating Document Summary using Data Mining and Clustering Techniques

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; This paper presents a novel approach to generating document summaries using data mining and clustering techniques, specifically K-means clustering and bisecting K-means clustering algorithms. With the exponential growth of textual data, there is an increasing need for efficient and accurate summarization techniques to aid users in understanding the key information within large collections of documents. This study explores the potential of data mining and clustering methods in extracting salient features from textual data and producing high-quality summaries. By applying K-means clustering and bisecting K-means clustering algorithms to the preprocessed textual data, the proposed approach groups similar sentences together and selects the most representative sentences from each cluster to form the final summary. The performance of the proposed method is evaluated using standard evaluation metrics, such as precision, recall, and F1-score, and compared with existing summarization techniques. The results demonstrate that the combination of data mining and clustering techniques provides a promising solution for generating accurate and concise document summaries, with potential applications in various domains, such as news aggregation, scientific literature summarization, and social media content analysis.&#x0D; &#x0D; &#x0D; &#x0D;