Reviewerly/exHarmony huggingface dataset:
Identification the Modulation Type in Cognitive Radio Network Based on Alexnet Architecture

Automatic Modulation Recognition (AMR) plays a major role in telecommunications and in Software Define Radio (SDR), due to its importance in the field of signal classification, as it is involved in many civil, cognitive Radio (CR) and military applications, including in the intelligence field. Many algorithms were used to classify the signals and then developed to enter deep learning (DL) applications on a large scale in this field to detect and classify radio frequency (RF) signals. The reason for adopting these techniques is that it determines the presence of signals without the need for complete information, as well as, it can classify non-contact waveforms, such as radar signals. Alexnet system which is part of Deep Neural Network (DNN) was adopted in our work to distinguish between signals generated in Radio ML2016a, and the goal is to get good accuracy if we compare it with other algorithms. We have implemented the work on an emulator (Google colaboratory), which simulates the Python program. We obtained an accuracy ranging from 79% to 91.3% for positive SNR values ranging from 2 dB to 18 dB. The above ratio is considered good with the proposed system.
Redactable Blockchain with Fine-Grained Autonomy and Transaction Rollback

Redactable blockchain is an innovative but controversial research field emerging in recent years, aiming at revising the blockchain data in a safe and controlled manner without eroding the nature of the blockchain. In this paper, we weigh the conflict between the immutability and editing of blockchain, present a redactable consortium blockchain with fine-grained autonomy and transaction rollback, supporting user data self-management, mandatory deletion of harmful information and recovery of wrong transfers within a specific time. In order to achieve the first two requirements, we put forward a double trapdoor key-exposure free chameleon hash function (DTCH), ensuring the dual needs of data modification and chain governance. In addition, we leverage the concept of temporary accounts to realize transaction rollback.
DOT-M: A Dual Offline Transaction Scheme of Central Bank Digital Currency for Trusted Mobile Devices

In recent years, many major economies have paid close attention to central bank digital currency (CBDC). As an optional attribute of CBDC, dual offline transaction is considered to have great practical value under the circumstances for payment without network connection. However, there is no public report or paper on how to securely design or implement the dual offline transaction function specifically for CBDC. In this paper, we propose DOT-M, a practical dual offline transaction scheme designed for the mobile device user as either a payer or a payee. Precisely, adopting secure element (SE) and trusted execution environment (TEE), the architecture of trusted mobile device is constructed to protect security-sensitive keys and execution of the transaction protocol. According to the trusted architecture, the data structure for offline transaction is designed as well. On this basis, we describe the core procedures of DOT-M in detail, including registration, account synchronization, dual offline transaction, and online data updating. A prototype system is implemented and finally tested with possible parameters. The security analysis and experimental results indicate that our scheme could meet the practical requirement of CBDC offline transaction for mobile users from both aspects of security and efficiency.
Intelligent Software Agent for Solving Choice Problems under Uncertainty

The intelligent software agent is designed to solve multinomial choice problems with visual uncertainty in automated information systems, in which it is required to select the right solutions that match the search conditions. An algorithm for making decisions of multinomial choice based on intelligent methods of data analysis has been developed. An exact solution of a series of perceptual-cognitive tasks was obtained in automatic mode. The CAPTCHA-E program is used in scientific research and education for modeling, analyzing the results of solving a series of test tasks, as well as in developing and analyzing new methods based on the visual form of information presentation, which focuses on the integration of human sensory and cognitive abilities in cyber-physical systems.
Social literacy formation among younger schoolchildren in extracurricular activities

The modern system of general education is focused on the positive socialization of the individual and the development of functional literacy of students, an integrative component of which is social literacy. Being a holistic, system-based, dynamic education of the individual, social literacy ensures the readiness of younger students for successful social adaptation and positive socialization in the constantly changing conditions of the modern social environment. Extracurricular activities contain a high potential for the social development of younger students through the involvement of cultural institutions, additional education for children, public organizations, the participation of younger students in socially significant activities, socially oriented programs and projects that expand the social space of students, increase their social contacts, enrichment social experience and education of socially significant personality traits. At the same time, the potential of extracurricular activities is not fully used by primary school teachers. The proposed model for the formation of social literacy of younger schoolchildren in extracurricular activities is based on a systemic, activity-based, axiological, person-oriented, competence-based, dialogic and reflexive approaches, includes methodological, targeted, meaningful, organizational and evaluative-effective structural blocks. We emphasize the need to organize a system of interaction between an elementary school and social partners, their inclusion in various joint activities (projects, social actions, etc.), development and implementation of methodological support for extracurricular activities with the participation of social partners, contributing to the deep integration of basic and additional education, improving pedagogical competencies of participants, clear coordination of joint actions.
Therapist-patient interaction in virtual reality at the level of the upper limbs

This paper describes upper extremity interaction system implemented in collaborative virtual reality. Main focus of the paper is to the description of the functionality of animation settings within the parameters of interaction/rehabilitation, visualization and functionalization of the system. With this system, the patient can be stimulated under the supervision of a therapist to practice some rehabilitation/training procedures. Due to the use of collaborative virtual reality, this process can be also applied in a distance form and thus it expands the therapist-patient interaction.
Co-Design of an Augmented Reality Maintenance Tool for Gas Pressure Regulation Stations

While augmented reality (AR) gets established in many use cases in the broad field of industrial, education, and entertainment applications, so far, AR has not been used in gas pressure regulation stations (GPRS). In an iterative co-design process, we have developed a first AR prototype to support maintenance work in such GPRS environments. Together with different stakeholders, in particular the technical staff as potential end-users, we discussed and gathered the requirements, observed workers interacting with the AR prototype on-site, and collected feedback, but also identified problems and limitations. Eventually, we deduced two challenging use cases, (i) displaying expandable 3D models of the components built into the station, and (ii) digitizing an inspection form in AR to facilitate the inspection task. We describe the co-design process, its findings, and introduce lessons-learned for the resulting AR prototype.
A Cascaded Cross-Modal Network for Semantic Segmentation from High-Resolution Aerial Imagery and RAW Lidar Data

As various sensors appear, extracting information from multimodal data becomes a prominent topic. Current multimodal approaches for image and LiDAR normally discard the point-to-point topology relationship of the latter to keep the dimension matched. To tackle this task, we propose a cascaded cross-modal network (CCMN) to extract the joint-features from high-resolution aerial imagery and LiDAR point directly, instead of their abridged derivatives. Firstly, point-wise features are extract from raw LiDAR data by a forepart 3D extractor. Subsequently, the LiDAR-derived features are executed spatial reference conversion to project and align to the imagery coordinate space. Finally, the cross-modal compounds containing the obtained feature maps and the corresponding images are placed into a U-shape structure to generate segmentation results. The experiment results indicate that our strategy surpasses the popular multimodal method by 6% on mIoU.
Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-Identification

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.
Polynomial-Time Reachability for LTI Systems With Two-Level Lattice Neural Network Controllers

In this letter, we consider the computational complexity of bounding the reachable set of a Linear Time-Invariant (LTI) system controlled by a Rectified Linear Unit (ReLU) Two-Level Lattice (TLL) Neural Network (NN) controller. In particular, we show that for such a system and controller, it is possible to compute the exact one-step reachable set in polynomial time in the size of the TLL NN controller (number of neurons). Additionally, we show that a tight bounding box of the reachable set is computable via two polynomial-time methods: one with polynomial complexity in the size of the TLL and the other with polynomial complexity in the Lipschitz constant of the controller and other problem parameters. Finally, we propose a pragmatic algorithm that adaptively combines the benefits of (semi-)exact reachability and approximate reachability, which we call L-TLLBox. We evaluate L-TLLBox with an empirical comparison to a state-of-the-art NN controller reachability tool. In our experiments, L-TLLBox completed reachability analysis as much as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5000\times$ </tex-math></inline-formula> faster than this tool on the same network/system, while producing reach boxes that were from 0.08 to 1.42 times the area.
Classification of High-Resolution Satellite Image with Content Based Image Retrieval and Local Binary Pattern

The framework of content-based image retrieval (CBIR) is based on the visual interpretation of contents that are present in the query image, which can deal with problems like image texture classification and image texture visualization. This study uses texture features to create a CBIR system for satellite image datebase exclusively for high-resolution satellite pictures. The discussed technique makes use of a block-based scheme and the local binary pattern texture feature. In order to extract LBP histograms, the query and database pictures are separated into equal-sized blocks. The Chi-square distance is then applied to compare the block histograms. According to experimental data, the local binary pattern (LBP) formulation is a potent tool for retrieving high-resolution satellite images.
Inferring Articulated Rigid Body Dynamics from RGBD Video

Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain. Despite recent progress, significant human effort is needed to configure simulators to accurately reproduce real-world behavior. We introduce a pipeline that combines inverse rendering with differentiable simulation to create digital twins of real-world articulated mechanisms from depth or RGB videos. Our approach automatically discovers joint types and estimates their kinematic parameters, while the dynamic properties of the overall mechanism are tuned to attain physically accurate simulations. Control policies optimized in our derived simulation transfer successfully back to the original system, as we demonstrate on a simulated system. Further, our approach accurately reconstructs the kinematic tree of an articulated mechanism being manipulated by a robot, and highly nonlinear dynamics of a real-world coupled pendulum mechanism. Website: https://eric-heiden.github.io/video2sim
Learning Relation Models to Detect Important People in Still Images

Important people detection aims to identify the most important people ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the people who play the main roles in scenes) in images, which is challenging since people's importance in images depends not only on their appearance but also on their interactions with others ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , relations among people) and their roles in the scene ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , relations between people and underlying events). In this work, we propose the People Relation Network (PRN) to solve this problem. PRN consists of three modules ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the feature representation, relation and classification modules) to extract visual features, model relations and estimate people's importance, respectively. The relation module contains two submodules to model two types of relations, namely, the person-person relation submodule and the person-event relation submodule. The person-person relation submodule infers the relations among people from the interaction graph and the person-event relation submodule models the relations between people and events by considering the spatial correspondence between features. With the help of them, PRN can effectively distinguish important people from other individuals. Extensive experiments on the Multi-Scene Important People (MS) and NCAA Basketball Image (NCAA) datasets show that PRN achieves state-of-the-art performance and generalizes well when available data is limited.
Text Pre-Processing Methods on Cross Language Information Retrieval

Cross Language Information Retrieval (CLIR), is the process of retrieving relevant documents, where in the language of the given query is different from the language of the retrieved documents. CLIR systems allow the users to search and access documents in the language different from the language of the search query. CLIR systems have been divided into Monolingual CLIR, Bi-lingual CLIR, and Multilingual CLIR based on different languages of query and documents. The first step of the Cross Language Information Retrieval system is the text pre-processing of given text documents in to useful representations. Pre-processing is the set of tasks that convert the given text documents into a suitable format for any higher-level text related applications. This technique can be used to reduce the computational process, noise data, and irrelevant information from the given text documents. This paper discusses in detail the different pre-processing techniques such as dataset creation, tokenization, noise removal, stop word removal, stemming, lemmatization and finally term weighting of two languages dataset (i.e., Tamil and Malayalam), which is manually collected from BBC online website. Finally, the study investigates feature extraction techniques of Term Frequency- Inverse Document Frequency (TF-IDF). These techniques will help to design and model CLIR systems with high performance.
A DNS-based Data Exfiltration Traffic Detection Method for Unknown Samples

The advanced persistent threat (APT) is one of the most serious threats to cyberspace security. Posting back of exfiltrated data by way of DNS covert channels has become increasingly popular among APT attackers. Early detection techniques were mainly based on rule matching, whose accuracy may be affected by the subjectivity of the researchers. The rise of machine learning technology solves this problem. However, the current DNS traffic detection models based on machine learning lack the open-source datasets for training and they will lose detection accuracy for unknown malicious traffic whose abnormal points are different from the observed samples. As for the problem of insufficient data set, we propose a sample set enhancement method that simulating DNS attacks in the cyber range and using the captured flow as the training set. Regarding the detection of unknown malicious traffic, we put forward four new features (domain readability, domain structure, second-level domain phishing and IP discreteness) based on the principles for constructing malicious traffic. We use the decision tree algorithm to implement a detection model. In the unknown DNS data leakage traffic tests, our model achieved an average detection rate of 99.925%. After applying our sample set and feature set enhanced schemes to the existing work, the experimental results show that the enhanced detection model can detect unknown DNS data leakage traffic that could not be detected previously.
Object Identification and Localization of Visual Explanation for Weapon Detection

Weapon detection is a difficult task that requires accurate identification of weapon objects in images. The object localization approach is mostly used because it combines a gradient with a convolutional layer to create a map of key locations on the image. This paper presents an Eigen-CAM method to localize and detect objects in an image for a Faster Region-based Convolutional Neural Network (Faster R-CNN) residual neural network (ResNet 50) model, giving a visual explanation. The Internet Movie Firearms Database (IMFDB) was used to train a deep learning model with the Faster R-CNN ResNet 50 model of the pre-trained PyTorch framework. Experimental results indicated that the Faster R-CNN ResNet 50 model achieved the highest mAP of 0.497 with 0.5 IoU. The Eigen-CAM method performed effectively for visual image representation.
Coverage Path Planning for Autonomous Road Sweepers in Obstacle-cluttered Environments

Autonomous road sweepers are an enabling technology for addressing cleaning tasks in public open spaces. In this paper, we investigate the sweeping of an obstacle-cluttered environment (e.g., a parking lot) using autonomous road sweepers. The challenge of this problem lies in the fact that the sweeper usually has a large turning radius and a small coverage radius. With a small coverage radius, the sweeper must operate near obstacles to maximize its coverage, making the coverage path sensitive to the mapping uncertainty. Due to a large turning radius, enough buffer should be left around obstacles, and the planned coverage paths should be dynamically feasible. To this end, we propose a novel coverage path planning (CPP) algorithm that explicitly accounts for the mapping uncertainty and vehicle dynamics, which includes three steps. First, the map is post-processed with morphological operations and convexification to reduce uncertainty in the map. Based on the post-processed map, the boustrophedon cellular decomposition is then modified to generate path segments that cover the sweeping area considering vehicle dynamics. Finally, a generalized traveling salesman problem is formulated and solved to connect the path segments for a CPP path with the minimum length. Through experiments in a parking lot at Isuzu Technical Center of America using an Isuzu VL22 truck, the effectiveness of the proposed approach is verified.