Reviewerly/exHarmony huggingface dataset:
A survey of the estimation and fusion methods for battlefield situation awareness

Constructing an accurate battlefield situation is indispensable to accomplish the combat mission. It's difficult to build the closed-loop battlefield combat chain in the complex battlefield environment by relying on single sensing information. Thus it has been urgent for solving the problem of how to use the available multi-sensor information to complete the task of battlefield situation construction under multi-constraint and high dynamic conditions. Based on the battlefield combat mission, this paper analyzes the filtering estimation and fusion methods in battlefield situation awareness, then summarizes and prospects the development of them considering the actual constraints that exist in battlefield situation awareness network.
Privacy-Preserving Classification Method for Neural-Biomarkers using Homomorphic Residue Number System CNN: HoRNS-CNN

The increasing generation of MRI dataset and the recent cloud deployment of deep learning (DL) algorithms have enabled timely remote classifications of discrepancies in neural-biomarkers of critical health conditions such as dyslexia. Using these untrusted platforms to implement a secure DL algorithm will identify and resolve potential security attacks or patient data theft, hence, the need for a privacy-preserving method. However, existing homomorphic (FHE) privacy-preserving methods are still inefficient in terms of accuracy, latency, and feature extraction time with significantly large cipher-image expansion problem. This study proposes homomorphic residue number system-CNN (HoRNS-CNN) model for the privacy-preserving classification of dyslexia neural-biomarkers. The HoRNS-CNN architecture is composed of the RNS-FHE scheme and pre-trained CNN models. The RNS-FHE scheme was used to design an encryption module for each pixel in the dataset, while pre-trained CNNs were applied directly to the encrypted data in the cloud after adapting their activation layers to homomorphic computations. Results from proposed HoRNS-CNN model demonstrated an improved performance.
Identifying Common Errors in Open-Ended Machine Learning Projects

Machine learning (ML) is one of the fastest growing subfields in Computer Science, and it is important to identify ways to improve ML education. A key way to do so is by understanding the common errors that students make when writing ML programs, so they can be addressed. Prior work investigating ML errors has focused on an instructor perspective, but has not looked at student programming artifacts, such as projects and code submissions to understand how these errors occur and which are most common. To address this, we qualitatively coded over 2,500 cells of code from 19 final team projects (63 students) in an upper-division machine learning course. By isolating and codifying common errors and misconceptions across projects, we can identify what ML errors students struggle with. In our results, we found that library usage, hyperparameter tuning, and misusing test data were among the most common errors, and we give examples of how and when they occur. We then provide suggestions on why these misconceptions may occur, and how instructors and software designers can possibly mitigate these errors.
Design and Evaluation of "The Missing CS Class," a Student-led Undergraduate Course to Reduce the Academia-industry Gap

One notable part of the academia-industry gap is the deficiency in computing ecosystem literacy, which may result in college graduates exhibiting little technical knowledge of software development tools and practices commonly used in industry. This paper presents our experience developing and teaching "The Missing CS Class," the student-led 1-unit course that we created at our university to address computing ecosystem literacy. This course primarily targets lower-division students and, based on our observations as peer tutors, covers four common but crucial gaps in technical knowledge: (1) Unix-like command-line environments and tools, (2) Software testing and debugging, (3) Scripting, and (4) Version control. Based on the collected feedback from two consecutive offerings of this course during the winter and spring quarters of 2021, most surveyed students reported having increased their self-efficacy on all course topics and incorporated them into their software development workflow.
Reproducibility Challenges and Their Impacts on Technical Q&amp;A Websites: The Practitioners’ Perspectives

Software developers often submit questions to technical Q&A sites like Stack Overflow (SO) to resolve their code-level problems. Usually, they include example code segments with their questions to explain the programming issues. When users of SO attempt to answer the questions, they prefer to reproduce the issues reported in questions using the given code segments. However, such code segments could not always reproduce the issues due to several unmet challenges (e.g., too short code segment) that might prevent questions from receiving appropriate and prompt solutions. A previous study produced a catalog of potential challenges that hinder the reproducibility of issues reported at SO questions. However, it is unknown how the practitioners (i.e., developers) perceive the challenge catalog. Understanding the developers’ perspective is inevitable to introduce interactive tool support that promotes reproducibility. We thus attempt to understand developers’ perspectives by surveying 53 users of SO. In particular, we attempt to – (1) see developers’ viewpoints on the agreement to those challenges, (2) find the potential impact of those challenges, (3) see how developers address them, and (4) determine and prioritize tool support needs. Survey results show that about 90% of participants agree to the already exposed challenges. However, they report some additional challenges (e.g., error log missing) that might prevent reproducibility. According to the participants, too short code segment and absence of required Class/Interface/Method from code segments severely prevent reproducibility, followed by missing important part of code. To promote reproducibility, participants strongly recommend introducing tool support that interacts with question submitters with suggestions for improving the code segments if the given code segments fail to reproduce the issues.
A SAR Image Denoising Method for Target Shadow Tracking Task

research-article Share on A SAR Image Denoising Method for Target Shadow Tracking Task Authors: Yankun Huang National Laboratory of Radar Signal Processing, Xidian University, China National Laboratory of Radar Signal Processing, Xidian University, ChinaSearch about this author , Guangcai Sun National Laboratory of Radar Signal Processing, Xidian University, China National Laboratory of Radar Signal Processing, Xidian University, ChinaSearch about this author , Mengdao Xing National Laboratory of Radar Signal Processing, Xidian University, China National Laboratory of Radar Signal Processing, Xidian University, ChinaSearch about this author Authors Info & Claims ICDSP '22: Proceedings of the 6th International Conference on Digital Signal ProcessingFebruary 2022 Pages 164–169https://doi.org/10.1145/3529570.3529598Online:29 June 2022Publication History 0citation8DownloadsMetricsTotal Citations0Total Downloads8Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improving Quantization Matrices for Image Coding by Machine Learning

We investigate the generation of quantization matrices for image coding in the scenario to balance compression ratio and quality. We make use of machine learning to train and determine those quantization matrices that can achieve the best compression ratio while reaching the quality settings. By introducing the trainable parameters and considering the impact of the quantization module on task performance and compression ratio, the DCT and quantization modules are jointly optimized to minimize the total coding cost. We evaluate the well-trained quantization matrices under various quality settings of JPEG. The results indicate that the proposed scheme can be combined with quality settings to consistently achieve better compression performance.
Deepfake forensics: Cross-manipulation robustness of feedforward- and recurrent convolutional forgery detection methods

Recently, deep-learning based video-manipulation techniques, so-called deepfakes, have gained a lot of traction in popular media. Deepfakes are predominantly used to photo-realistically alter the identity of actors recorded on video, and misuse of this new technology has the potential to harm individuals, companies, or to inflict damage in a societal context. The accelerated pace, at which deepfake technology is improved, challenges contemporary research to provide the urgently required detection methods. A major obstacle to reliable deepfake detection in the field is the impaired generalizability across specific instances of deepfake models, i.e. detection performance of previously not encountered manipulation models. The present work aims to establish a better understanding of the critical factors that guide cross-manipulation detection performance. Recent studies have indicated that the properties of video-based modeling may be leveraged to enhance the detection of unknown deepfake manipulations. Therefore, we compare multiple image- and video-based detection models and evaluate their performance on instances both generated by known, and unknown manipulation models. Furthermore we attempt to replicate research that successfully improved cross-manipulation detection by using image-perturbation methods to degrade training data. Using multiple data-sources to emulate in- and out-sample detection performance, we demonstrate that none of the two model types show universally superior performance. Also, our results confirm that cross-manipulation evaluation results in problematic detection accuracy for all models, regardless of whether we apply image-perturbation techniques during training. Additionally, the detection performance on a set of manually selected, high-quality deepfake videos indicates that the current state-of-the-art detection models are not yet fully equipped for real-world applications.
PUF-Based Post-Quantum CAN-FD Framework for Vehicular Security

The Controller Area Network (CAN) is a bus protocol widely used in Electronic control Units (ECUs) to communicate between various subsystems in vehicles. Insecure CAN networks can allow attackers to control information between vital vehicular subsystems. As vehicles can have lifespans of multiple decades, post-quantum cryptosystems are essential for protecting the vehicle communication systems from quantum attacks. However, standard CAN’s efficiency and payload sizes are too small for post-quantum cryptography. The Controller Area Network Flexible Data-Rate (CAN-FD) is an updated protocol for CAN that increases transmission speeds and maximum payload size. With CAN-FD, higher security standards, such as post-quantum, can be utilized without severely impacting performance. In this paper, we propose PUF-Based Post-Quantum Cryptographic CAN-FD Framework, or PUF-PQC-CANFD. Our framework provides post-quantum security to the CAN network while transmitting and storing less information than other existing pre-quantum and post-quantum CAN frameworks. Our proposal protects against most cryptographic-based attacks while transmitting (at up to 100 ECUs) 25–94% less messages than existing pre-quantum frameworks and 99% less messages than existing post-quantum frameworks. PUF-PQC-CANFD is optimized for smaller post-quantum key sizes, storage requirements, and transmitted information to minimize the impact on resource-restricted ECUs.
Investigation of Deep Learning Methodologies in Satellite Image based Ship Detection

Maritime surveillance is one of the major phases of security concern of borders to track the illegal movement across the borders. Over the past decade, deep learning proves in majority of applications for an efficient object detection and classification. This research article gives the detailed overview of various deep learning based architecture for an efficient ship detection using satellite images.
AI CHATBOT using Web Speech API and Node.Js

Numerous cell phone clients use voice associates like Siri and Cortana, and the utilization of voice orders is turning out to be more normal today as gadgets, for example, Amazon Echo and Google Home are attacking our rooms. Discourse acknowledgment programming permits clients to make voice orders. Presently our program presents the Speech API, which permits clients to incorporate voice data into a web application. With the assistance of the present-day web, users can highly depend on an assortment of UI capacities to speak with clients. With the Web Speech API, the voice orders can be utilized to foster rich sites with local clients and little web clients. There is a wide range of ways for visiting, and this study particularly attempts to utilize chatbots that remain valuable for providing instruction. This permits numerous applications to be utilizedon rich sites. Furthermore, the API can empower online applications to assist individuals with physical or mental handicaps or wounds.
Fake Reviews Detection using Support Vector Machine

One of the fastest expanding business categories in the world today is internet shopping. People nowadays buy a lot of things from internet shopping sites. Customers can buy a better quality products based on the reviews given by previous buyers of the products. Reviews includes text reviews, ratings and smileys. On a product review there are hundreds of reviews in which some of the reviews would be fake reviews. Opinion mining from natural languages is a difficult method for evaluating customers&#x0027; sentiments, but sentiment analysis provides the best answer. It provides crucial data for decision-making in a variety of fields. So, we propose a fake reviews detection system using support vector machine which detect the fake reviews of the products. The primary goal is to suggest higher-quality products to the user. We use the support vector machine algorithm to classify the reviews into positive and negative groups. Finally fake reviews are predicted which are posted by the users. The reviews are grouped as negative, positive and neutral. In this system, only purchased users can post the reviews and duplicates are verified based on user id and booking id. Genuine reviews are considered for product recommendation
Fast Evolutionary Algorithm based Identifying Surgically Distorted Face for Surveillance Application

This research, which employs granular computing and hybrid spatial feature extraction, presents face identification with surgically changed characteristics. The increased popularity of cosmetic surgery and its influence on automatic face recognition has sparked the curiosity of researchers. The face detection module generates face pictures with normalised intensity, homogenous size and shape, and just the face region. The Difference of Gaussian (DOG) structures will then be generated from successive iterations of Gaussian photographs. Granulation divides face pictures into different resolutions and provides blurriness, edge information, and noise in a face image. The Gabor filter bank is then used to extract characteristics from face regions in order to differentiate between illumination variations. These characteristics are merged in order to reliably identify a huge samples, and they will be matched with stored real face samples to identify them. The simulated results of detecting surgically changed face photographs show that utilising granulation and hybrid spatial and spectral descriptors has greater selectivity and recognition accuracy.
Monitoring of IoT Systems at the Edges with Transformer-based Graph Convolutional Neural Networks

Internet of Things (IoT) systems are complex, and consist of distributed and interdependent components, making the modeling, prediction and monitoring of their behavior a critical challenge. This challenge is specifically exacerbated by the need to develop precise models that inherently consider the domain knowledge about the underlying physical structure, and the causal interdependencies between the various components of IoT systems. In this paper, we present an approach that is capable of leading to improved modeling and monitoring at the edges. To this end, we consider an IoT system as a network that can be represented by a graph. Then, we develop an approach that combines a semantic knowledge graph with a Transformer-based neural networks, under a Graph Convolutional Neural Networks (GCNN). Our GCNN produces features that are subsequently used with the Transformer to learn the parameters of an IoT model. We exploit the parameters of the model for monitoring and anomaly detection. We validate our approach using a real robot workcell for anomaly detection during a pick and place process, and we demonstrate that our approach outperforms other competitive techniques.
DeepCAD: A Stand-alone Deep Neural Network-based Framework for Classification and Anomaly Detection in Smart Healthcare Systems

Contemporary smart healthcare systems (SHSs) frequently use wireless body sensor devices (WBSDs) for vital sign monitoring and the internet of medical things (IoMT) network for rapid communication with a cloud-based controller. The SHS controllers generate required control decisions based on the patient status to enable real-time patient medication/treatment. Hence, the correct medical delivery primarily depends on accurately identifying the patient's status. Accordingly, SHSs mostly leverage deep neural network (DNN)-based machine learning (ML) models for patient status classification due to their prediction accuracy and complex relation capturing capability. Nevertheless, the open IoMT network is prone to several cyberattacks, including adversarial ML-based attacks, which can exploit DNN models and create a life-threatening event in a safety-critical SHS. Existing solutions usually propose outlier detection or transfer learning-based ML models on top of the patient status classification model to deal with SHS security issues. However, incorporating a separate anomaly detection model increases the model complexity and raises feasibility issues for real-time deployment. This work presents a novel framework, DeepCAD, that considers training a stand-alone DNN model integrated with anomaly detection rules for classification and anomaly detection in SHS. The proposed framework is verified on the Pima Indians Diabetes and Parkinson datasets.
Continuous Authentication for UAV Delivery Systems Under Zero-Trust Security Framework

In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used in smart logistics to address the last-mile delivery challenge. To complete the delivery tasks, UAVs usually need to store or have access to some sensitive data such as the private information about the parcel and its receiver during their flight, and only the authenticated people or devices are authorised to access the UAV's data. However, there are many potential risks such as injection attacks and spoofing attacks that can cause data leakage. Therefore, how to preserve users' sensitive data and authenticate the identities of people and devices in the UAV delivery system has become an essential requirement under the emerging zero-trust security framework. In this paper, to address such an issue, we propose a continuous authentication scheme for UAV delivery systems that is featured with continuous authentication, multi-factor authentication (MFA), and digital authentication using the blockchain-based Self-Sovereign Identity (SSI) technique. Specifically, continuous authentication is applied to both users and UAVs in the system. The evaluation results demonstrate that our proposed continuous authentication scheme can effectively prevent various attacks and provide security analysis for UAV delivery systems.
vTPM-SM: An Application Scheme of SM2/SM3/SM4 Algorithms Based on Trusted Computing in Cloud Environment

Numbers of applications and businesses are hosted on cloud computing platforms, and it is essential for cloud tenants to protect their data through encryption or other methods. When tenants use encryption algorithms provided by software, they are bound to face the defect that keys are not protected by hardware. Trusted computing technology can securely store the key in the hardware device. However, the hardware TPM cannot provide services for multiple VMs simultaneously. The virtual trusted computing technology virtualizes the TPM and can assign vTPM to each VM. Currently, vTPM only supports RSA, ECDSA, SHA256, and AES algorithms, et al. Relevant studies have shown that SM2/SM3/SM4 algorithms are more secure than ECDSA/SHA256/AES. In order to cope with the limitations of the cryptographic algorithms supported by vTPM, we design the vTPM-SM scheme to provide a secure and reliable SM2/SM3/SM4 algorithm application method for cloud environments. Experiments show that vTPM-SM can effectively realize the VM using Chinese commercial cryptographic algorithms through vTPM. Compared with the existing scheme, using SM2/SM3/SM4 algorithm reduces the time overhead by about 31.6%, 83.3% and 15.5%, respectively.
Detection of total iron content based on improved extreme learning machine

The average grade of iron ore is low in China, and it is significant for the effective utilization of iron ore to obtain grades. In the paper, the high-precision model was constructed based on the spectrum of hematite and the multiple hidden layers extreme learning machine with random values and dense connection, which took the output of all previous layers as the input of each hidden layer. It has achieved high precision during the detection of the total iron content for hematite, which meets the requirements for the construction and development of new green and intelligent mines.
PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Service composition is a mainstream paradigm for rapidly constructing large-scale distributed applications. QoS-aware service composition, i.e., selection of the optimal execution plan that maximizes the composition’s end-to-end QoS properties, is an active area of research and development endeavors in service composition. In this paper, we propose PPDRL, a pretraining-and-policy-based deep reinforcement learning approach, to solve the QoS-aware service composition problem. Its significant feature is to incorporate a maximum likelihood estimate and a policy scoring mechanism into a deep reinforcement learning framework. As a result, our approach can balance the exploitation and exploration efforts adaptively and can search for the solution space in a robust and efficient manner. We have executed our approach to solve 6 randomly generated QoS-aware service composition problems with different sizes and structures based on QWS data set including 2,507 real Web services classified into 233 categories. The results indicate that our approach can find near-optimal solutions within moderate numbers of iteration and has performance superiority in comparison with five state-of-the-art algorithms.