Reviewerly/exHarmony huggingface dataset:
Let it Snow: On the Synthesis of Adverse Weather Image Data

Camera systems of automated vehicles capture images from the surrounding environment and process these datastreams with algorithms to detect and classify objects. A lot of research has been devoted to improve object detection algorithms in order to provide highly accurate detection results in real time. However, these algorithms show a strong drop in performance as soon as they are exposed to adverse weather. Poor weather conditions such as rain, fog or snow lead to a reduction in visibility and thus objects are more difficult to recognize or not visible at all. This leads to a high degree of uncertainty for an automotive camera system. To enable automated driving, camera systems must be able to cope with adverse weather and the associated high uncertainty. Including more weather image data when training the algorithms can improve object detection in bad visibility conditions. However, weather image data is difficult to collect in reality and thus only available to a limited extent. In this work, we evaluate the possibility of using Generative Adversarial Networks to create synthetic weather image data. For this purpose, we compare the generated images of different network architectures trained on a diverse weather dataset collected from Flickr. The resulting data is evaluated qualitatively and quantitatively with respect to its realism and suggests that our approach is capable of generating realistic weather images.
Copula-based convolution for fast point-mass prediction

This paper deals with the state estimation of the nonlinear stochastic dynamic discrete-in-time models by a numerical solution to the Bayesian recursive relations represented by the point-mass filter (PMF). In particular, emphasis is placed on the development of the fast convolution, which reduces computational complexity of the PMF prediction step by the orders of magnitude for models with a diagonal form of the dynamic equation. The copula-based convolution decomposes the joint conditional density into the marginal densities (allowing efficient prediction) and an easy-to-calculate copula density function. As a consequence, it has the linear growth of its computational complexity with the state dimension, which is in a contrast with the exponential growth of the standard convolution complexity in PMF methods. The proposed fast convolution is analysed and illustrated in a numerical study for a static example and a dynamic terrain-aided navigation scenario. An exemplary implementation of the proposed convolution is provided along with the paper.
Seq2Bubbles

User behavior sequences contain rich information about user interests and are exploited to predict user's future clicking in sequential recommendation. Existing approaches, especially recently proposed deep learning models, often embed a sequence of clicked items into a single vector, i.e., a point in vector space, which suffer from limited expressiveness for complex distributions of user interests with multi-modality and heterogeneous concentration. In this paper, we propose a new representation model, named as Seq2Bubbles, for sequential user behaviors via embedding an input sequence into a set of bubbles each of which is represented by a center vector and a radius vector in embedding space. The bubble embedding can effectively identify and accommodate multi-modal user interests and diverse concentration levels. Furthermore, we design an efficient scheme to compute distance between a target item and the bubble embedding of a user sequence to achieve next-item recommendation. We also develop a self-supervised contrastive loss based on our bubble embeddings as an effective regularization approach. Extensive experiments on four benchmark datasets demonstrate that our bubble embedding can consistently outperform state-of-the-art sequential recommendation models.
Vector Semiotic Model for Visual Question Answering

In this paper, we propose a Vector Semiotic Model as a possible solution to the symbol grounding problem in the context of Visual Question Answering. The Vector Semiotic Model combines the advantages of a Semiotic Approach implemented in the Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model represents information about a scene depicted on an input image in a structured way and grounds abstract objects in an agent’s sensory input. We use the Vector Symbolic Architecture to represent the elements of the Sign-Based World Model on a computational level. Properties of a high-dimensional space and operations defined for high-dimensional vectors allow encoding the whole scene into a high-dimensional vector with the preservation of the structure. That leads to the ability to apply explainable reasoning to answer an input question. We conducted experiments are on a CLEVR dataset and show results comparable to the state of the art. The proposed combination of approaches, first, leads to the possible solution of the symbol-grounding problem and, second, allows expanding current results to other intelligent tasks (collaborative robotics, embodied intellectual assistance, etc.).
Self-Supervised Human Detection and Segmentation via Background Inpainting

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively expensive, we introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving camera. At the heart of our approach lies the observation that object segmentation and background reconstruction are linked tasks, and that, for structured scenes, background regions can be re-synthesized from their surroundings, whereas regions depicting the moving object cannot. We encode this intuition into a self-supervised loss function that we exploit to train a proposal-based segmentation network. To account for the discrete nature of the proposals, we develop a Monte Carlo-based training strategy that allows the algorithm to explore the large space of object proposals. We apply our method to human detection and segmentation in images that visually depart from those of standard benchmarks and outperform existing self-supervised methods.
Real-Time Age Estimation from Facial Images Using YOLO and EfficientNet

Automatic age estimation from facial images is attracting increasing interest due to its many potential applications. Several deep learning-based methods have been proposed to tackle this task; however, they usually require prohibitive resources to run in real-time. In this work, we propose a fully automated system based on YOLOv5 and EfficientNet to perform face detection and subsequent age estimation in real-time. Also, to make the model more robust, EfficientNet was trained on the new MIVIA Age Dataset, released as part of a challenge. The results obtained in the contest are promising, and are strengthened by the lightness of the overall system which in fact is not only effective but also efficient.
BCT: An Efficient and Fault Tolerance Blockchain Consensus Transform Mechanism for IoT

With the vigorous development of 5G communication technology, massive Internet of Things (IoT) devices generate data incrementally. Different data owners control different private domains of the IoT through edge devices and hope to achieve credible data sharing. Most of the existing solutions are based on blockchain to realize cross-domain IoT data sharing. However, incremental IoT data sharing has dual requirements for the consensus mechanism to be efficient and Byzantine fault tolerant. The independent use of the existing consensus mechanism cannot meet the above requirements simultaneously. Therefore, we propose an efficient and fault-tolerant blockchain consensus transform (BCT) mechanism for IoT. In addition, we design two consensus algorithms, namely, detectable RAFT (DRAFT) and double-layer parallel BFT (DPBFT), to improve the efficiency and fault tolerance of the data-sharing process. Extensive experiments have been conducted to show the efficiency and tolerance of our BCT mechanism.
Blockchain &amp; IoT based Drugs Traceability for Pharma Industry

The delivery of medicine directly from the manufacturer to the end-user is the highest priority of today&#x0027;s healthcare system. Several stakeholders, including distributors, suppliers, wholesalers, and consumers, are involved in the pharmaceutical supply chain. However, verifying the source of raw materials and maintaining visibility of goods and merchandise as they move across the supply chain network has become increasingly difficult thus, forming opportunities for counterfeits to enter the authentic supply chain. This paper proposes using the Internet of Things (IoT) and Blockchain to improve traceability in the pharmaceutical supply chain. Blockchain-based on the IoT is a type of distributed ledger (DLT) that keeps an immutable record of all transaction data that cannot be falsified and is transparent to all stakeholders. Implementing an IoT-based blockchain system will give pharmaceutical industry the resources it needs to enhance drug traceability across the supply chain, resulting in more effective and safe healthcare.
About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving

Although data augmentation is considered an important step in the training strategy of 3D object detectors on point clouds to increase the overall performance and robustness, in almost all publications the topic of augmentation and the choice of the individual augmentation methods used are only addressed very briefly with reference to previous work and are not backed up with sufficient experiments. The question therefore arises as to the impact and the transferability of different augmentation policies. Through a series of elaborate experiments with four networks on two datasets, this paper shows that the positive effects of different data augmentation methods are not so clear-cut and instead depend strongly on the network architecture and the dataset.
Sentiment Analysis on Zomato Reviews

The impact of online reviews on restaurants has reached to unprecedented level where vast number of people are checking posted opinions/reviews prior to ordering their food deliveries. The two main concepts used in the online reviews are sentiment analysis and exploratory data analysis (EDA). The goal of sentimental analysis is to determine whether the given data is positive, negative or neutral. It can help brands to determine how their product is perceived by their clientele. Sentiment analysis, otherwise known as opinion mining, works thanks to natural language processing and machine learning algorithms, to automatically determine the emotional tone behind online conversations. Sentiment analysis mainly relies on the keywords. The overall analysis is made on the data that has been reviewed on Zomato. Most restaurants available on the applications are established ones, hence we get a good idea regarding the restaurants of Hyderabad. Exploratory data analysis (EDA) is a term for certain kinds of initial analysis and findings done with data sets, usually early in an analytical process.
Software Design and Experimental Evaluation of a Reduced AES for IoT Applications

IoT devices include RFID tags, microprocessors, sensors, readers, and actuators. Their main characteristics are their limited resources and computing capabilities, which pose critical challenges to the reliability and security of their applications. Encryption is necessary for security when using these limited-resource devices, but conventional cryptographic algorithms are too heavyweight and resource-demanding to run on IoT infrastructures. This paper presents a lightweight version of AES (called LAES), which provides competitive results in terms of randomness levels and processing time, operating on GF(24). Detailed mathematical operations and proofs are presented concerning LAES rounds design fundamentals. The proposed LAES algorithm is evaluated based on its randomness, performance, and power consumption; it is then compared to other cryptographic algorithm variants, namely Present, Clefia, and AES. The design of the randomness and performance analysis is based on six measures developed with the help of the NIST test statistical suite of cryptographic applications. The performance and power consumption of LAES on a low-power, 8-bit microcontroller unit were evaluated using an Arduino Uno board. LAES was found to have competitive randomness levels, processing times, and power consumption compared to Present, Clefia, and AES.
Quick Block Transport System for Scalable Hyperledger Fabric Blockchain Over D2D-Assisted 5G Networks

This paper presents a quick block transport system for scalable Hyperledger Fabric (HLF) blockchain over device-to-device (D2D)-assisted 5G networks. The objective of the proposed system is to increase the transaction throughput and further enable the application of the blockchain to delay-sensitive 5G services. To this end, the system rapidly disseminates the block data to all the peers on the blockchain network. The blockchain network is organized with mobile edge cloud (MEC) server and mobile peers in the 5G network. To ensure a scalable and reliable blockchain architecture, the core functionalities of the HLF blockchain are appropriately distributed among the mobile edge cloud and each mobile peer. The MEC server handles core functionalities for the blockchain network, such as block generation and membership management. In addition, it is responsible for gathering the network status of all peers and organizing the overlay structure to be utilized to rapidly propagate the block with a low network cost. On the other hand, the mobile peers handle the endorsing and validating functionalities to prevent block data tampering in the blockchain network. Furthermore, locality-aware device-to-device networking is utilized in 5G network coverage to achieve faster block dissemination. The proposed system is designed to work seamlessly in an existing HLF blockchain framework with consideration of scalability and compatibility. It is fully implemented using the open-source HLF and OpenAirInterface. To verify the effectiveness of the proposed block transport system, it was evaluated in an actual 5G network as well as 802.11 network. The experimental results verified its feasibility.
Social Media and Steganography: Use, Risks and Current Status

Steganography or data hiding is used to protect the privacy of information in the transit; it has been observed that the information that flows through Online Social Networks (OSN) is very much unsafe. Therefore, people hesitate to communicate their sensitive data on social media.. Most of the information on the online social network is not useful to users and appears to disregard such details. People’s actions provided a possibility for digital Steganography through the Internet.. TCPIP covert channels were used for steganography until the last decade. People began to utilize social media as a covert conduit to communicate hidden messages to targeted users as social media grew in popularity. There are numerous Online Social Networks accessible nowadays, ranging from Facebook to the more contemporary Twitter and Instagram. All of them may be utilized as covert channels without the general public noticing. The primary characteristic of steganography is the protection of information privacy; nonetheless, it has been utilized more for illicit message transmission, which is a source of concern. To make matters worse, adversaries are using steganalysis techniques to mess with the concealed data. In this article, we examine the different social media steganography techniques, such as those used on Facebook, WhatsApp, and Twitter, as well as the difficulties that these approaches raise. The positive and negative consequences of social media, as well as its current state, are discussed in this study. This paper discusses how the performances of Steganography methods may be assessed using the Entropy value of the Stego object. A look of the three features of steganography. It has been given with undetectability, robustness, and payload capacity. Finally, the paper’s concept’s future scope is explored.
Neural Network-Oriented Big Data Model for Yoga Movement Recognition

The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region-Convolutional Neural Network (MR-CNN)- based method for yoga movement recognition based on the image task of yoga movement recognition. The improved MR-CNN model is based on the framework and structure of the region-convolutional network, which proposes a certain number of candidate regions for the image by feature extraction and classifies them, then outputs these regions as detected bounding boxes, and does mask prediction for the candidate regions using segmentation branches. The improved MR-CNN model uses an improved deep residual network as the backbone network for feature extraction, bilinear interpolation of the extracted candidate regions using Region of Interest (RoI) Align, followed by target classification and detection, and segmentation of the image using the segmentation branch. The model improves the convolution part in the segmentation branch by replacing the original standard convolution with a depth-separable convolution to improve the network efficiency. Experimentally constructed polygon-labeled datasets are simulated using the algorithm. The deepening of the network and the use of depth-separable network improve the accuracy of detection while maintaining the reliability of the network and validate the effectiveness of the improved MR-CNN.
Fired bullet signature correlation using the finite ridgelet transform (FRIT) and the gray level co-occurrence matrix (GLCM) methods

When a bullet is fired from a barrel, micro striation marks caused by the sliding motion of the bullet through the rifled barrel are one of the foremost factors in automated ballistic identification. This paper focuses on 3D topography images of land engraved areas (LEA) and proposes a bullet identification method incorporating the finite ridgelet transform (FRIT) and gray level co-occurrence matrix (GLCM) algorithms. The FRIT extracts the striation marks from the 3D micro image and the GLCM generates a linearly weighted weight corresponding to the texture features for 2D average profile calculation. The entire striation marks image is divided into several cells and a cell with valid correlation areas is assigned a large weight, but the one with invalid correlation areas is assigned a small weight along the vertical direction. The visible results show that the valid correlation areas are effectively identified and the negative effects of invalid correlation areas are suppressed. Tests were performed on a control set and an unknown set, giving a total of 35 bullet samples fired from pistols with 10 consecutively manufactured slides. The results included no false identifications or false exclusions and a clear separation between the matching index of the matching and non-matching LEA profiles, demonstrating excellent performance in striation mark capture and valid correlation areas extraction of FRIT and GLCM algorithms. The proposed method is capable of correctly matching toolmarked surfaces to the barrel used.
Protecting ECG Signals with Hybrid Swarm Intelligence Algorithm

Protecting biomedical data on the internet has become utmost important. A proper scheme is required for the security of the biomedical data, ignoring data security can cause serious harm to the patient data. In recent times the hybrid swarm intelligence algorithms have gained popularity in the field of data security, owing to their efficient performance. In this Chapter, to resolve the issue of medical data security, a hybrid PSO (particle swarm optimization) based watermarking mechanism is investigated. The hybrid PSO algorithm is combination of Firefly algorithm and PSO algorithm (HFPSO). The HFPSO combine best of both the algorithms removing the flaws. The patient's confidential information is embedded in the ECG signal. The main aim of this work is to identify the optimal embedding factor to embed the patient's information in an ECG signal without disturbing the medical information of the ECG signal. The optimal embedding allows the successful extraction of embedded information even after various intentional and unintentional attacks. The patient's data is converted to QR code. The patient QR code is embedded in ECG signal using QR decomposition and Discrete Cosine Transform (DCT). The ECG signal is decomposed using DCT before data embedding. The Hybrid Firefly-Particle Swarm Optimization algorithm is used to adaptively detect the best value for embedding factor for the given ECG signal.
DIUcerts DApp: A Blockchain-based Solution for Verification of Educational Certificates

Educational certificate verification is the process of checking and verifying the certificate legitimacy of graduate students. It is a costly, lengthy, and time-consuming procedure as university authorities invest millions of dollars in maintaining the entire process each year. The employer also takes plenty of time to verify the authenticity of the applicant&#x0027;s certificate. The current certification system provides traditional certificates to the candidates. That&#x0027;s why certificates can be tampered with and lost at any time. Moreover, counterfeiting the certificates by scammers and issued by many illegal institutions makes the process hazardous. People frequently lie about their degrees and qualifications by counterfeiting certificates. A fake certificate generated by skillful scammers is always tough to identify and address as the original one. Therefore, there is a crucial need to upgrade the certification and verification process. This paper introduced a Blockchain-based decentralized DIUcerts platform that offers an easy way to issue, check, and verify educational certificates. Additionally, in DIUcerts, data doesn&#x0027;t have to be stored in one place as each certificate&#x0027;s information is kept in an individual file; entire issuance and verifications are done through the Ethereum platform. With this infrastructure, the cost of maintaining a Blockchain-based certificate verification system could be highly minimized as compared to building a similar application on a centralized database. As a result, DIUcerts can lead to better security, cost savings, and a time-saving platform for educational certificate verification.