Reviewerly/exHarmony huggingface dataset:
Data-driven traffic dynamic understanding and safety monitoring applications

In this work, a novel framework is proposed to improve air traffic safety from the perspective of human factors by detecting possible risks from air-ground speech communication. In the proposed framework, the automatic speech recognition and the spoken instruction understanding techniques serve as an interface to sense real-time traffic dynamics of airspace, which are achieved by deep learning-based data-driven models. Based on the obtained dynamics, several applications are designed to detect safety-critical risks of air-ground communication, including the normative instruction check, readback confirmation, trajectory conformance, and potential aircraft conflict detection. A prototype is developed to implement the proposed framework. To validate the proposed framework and prototype, we deploy them in the Chengdu area control center, Changsha aerodrome and approach, China. The data statistics collected from the real-world environment demonstrate that the proposed framework has the ability to improve air traffic safety and reduce the workload of air traffic controllers by understanding the traffic dynamics automatically. In addition, the performance of the core techniques is also confirmed by extensive data investigation.
A novel robust minimum error entropy Kalman filter in the presence of measurement packet dropping

In a practical system, the simultaneous presence of packet dropping and impulsive noise can render the state estimation problem complicated and its solution challenging. Kalman-type filter algorithms based on the minimum error entropy may involve instabilities in numerical calculations. To efficiently process the impulse noise in the system, this paper proposes a novel robust minimum error entropy Kalman filter algorithm in the presence of packet dropping, named RMEEPDKF. The augmented model of the packet dropping system is constructed by designing a coefficient matrix containing the packet dropping variable. In addition, the posterior state estimation is updated based on the fixed-point iterative algorithm with the robust minimum error entropy criterion. Furthermore, we extend RMEEPDKF to the suboptimal Kalman filter with packet dropping to obtain a estimator named RMEEPDSKF. The simulation results demonstrate that RMEEPDKF and RMEEPDSKF can achieve the highest estimation accuracy when packet dropping and impulse noise coexist in the system, which implies that the introduction of a small amount of computational burden may be acceptable to achieve a high estimation accuracy.
IoT-Fog: A Blockchain-based Middleware Framework for Communication Security in the Internet of Things

The fog computing is the emerging technology to compute, store, control and connecting smart devices with each other using cloud computing. The Internet of Things (IoT) is an architecture of uniquely identified interrelated physical things, these physical things are able to communicate with each other and can transmit and receive information. This research presents a framework of the combination of the Internet of Things (IoT) and Fog computing. The blockchain is also the emerging technology that provides a hyper, distributed, public, authentic ledger to record the transactions. Blockchains technology is a secured technology that can be a boon for the next generation computing. The combination of fog, blockchains, and IoT creates a new opportunity in this area. In this research, the author presents a middleware framework based on the blockchain, fog, and IoT. The framework is implemented and tested. The results are found positive.
Design of Voice based Virtual Assistant using Internet of Things

Virtual assistance is one of the most important features in everyday life. People nowadays rely on the enhanced features of virtual assistance. The proposed system is used for the propulsion of the technology that is been in need. The virtual assistance in the process is costly and needs more manpower to build them. The proposed system embellishes the cost-effective nature and the maintenance of the system will be very efficient the hardware used is made to give a good life balance and they will not get perished as time goes over. The Proposed system recognizes the voice that has been recorded and analyses them for command retrieval. The user can add as many as many voices as they can. The Process of adding the voices are user friendly and the method of making the commands reliable and commendable depend on the performance ratio of the hardware. The most important feature in the proposed system is the hardware construction. In this system Raspberry Pi is used for the construction of the CPU and the voice control unit is designed with voice recognition sensors. The correctness and the effective time being are processed by the microphones connected to the system. The sheerness of the system relies on the microphone only. The microphone plays a major role in the proposed system as such the microphone helps us to recognize the voice and repeat the commands that have been commenced by the user. The Virtual used here is manual assistance through the Linux platform. It is a new venture in the phenomenal wave.
Investigation, Detection and Prevention of Online Child Sexual Abuse Materials: A Comprehensive Survey

Child sexual abuse inflicts lifelong devastating consequences for victims and is an ongoing social concern. In most countries, child sexual abuse material (CSAM) distribution is illegal. As a result, there are many research papers in the literature which proposed technologies to detect and investigate CSAM. In this survey, a comprehensive search of the peer-reviewed journal and conference paper databases (including preprints) is conducted to identify high-quality literature. We use the PRISMA methodology to refine our search space to 2,761 papers published by Springer, Elsevier, IEEE and ACM. After iterative reviews of title, abstract and full text for relevance to our topics, 43 papers are included for full review. Our paper provides a comprehensive synthesis of the tasks of the current research and how the papers use techniques and datasets to solve their tasks and evaluate their models. To the best of our knowledge, we are the first to focus exclusively on online CSAM detection and prevention with no geographic boundaries, and the first survey to review papers published after 2018. It can be used by researchers to identify gaps in knowledge and relevant publicly available datasets that may be useful for their research.
Inter-Feature-Map Differential Coding of Surveillance Video

In Collaborative Intelligence, a deep neural network (DNN) is partitioned and deployed at the edge and the cloud for bandwidth saving and system optimization. When a model input is an image, it has been confirmed that the intermediate feature map, the output from the edge, can be smaller than the input data size. However, its effectiveness has not been reported when the input is a video. In this study, we propose a method to compress the feature map of surveillance videos by applying inter-feature-map differential coding (IFMDC). IFMDC shows a compression ratio comparable to, or better than, HEVC to the input video in the case of small accuracy reduction. Our method is especially effective for videos that are sensitive to image quality degradation when HEVC is applied.
Speed-Robust Keyword Spotting Via Soft Self-Attention on Multi-Scale Features

In this work, we focus on the robustness of keyword spotting (KWS) at various speech speeds. First, to enable small-footprint KWS, we graft a depthwise separable convolution and a dilated temporal convolution to build our basic model block. Second, to make KWS desensitized to speech rate, a simple yet effective soft self-attention is proposed to operate between different hierarchical features, offering our model the ability to be aware of the speaker's speech rate and to dynamically integrate multi-scale features from varying sizes of receptive fields. Besides, we construct two frame-level annotated Chinese intelligent in-car speech commands datasets, termed Car-C1 and Car-C2, for model evaluation. Experimental results show that our model achieves the highest accuracy on both datasets with a comparable number of model parameters and computational cost. Meanwhile, the sensitivity analysis suggests that our model outperforms baselines when tested on a fast corpus and a slow corpus.
A Network-on-Chip-Based Annealing Processing Architecture for Large-Scale Fully Connected Ising Model

Combinatorial optimization problems are prevalent in many different fields. Most of these problems are NP-hard and challenging for computers with conventional Von-Neumann architecture. Ising machines with a number of spins have the potential to solve these problems by emulating the natural annealing process of solid matter. Recent research has explored some hardware implementation methods of Ising machines to accelerate the convergence process of such problems at room temperature. However, most of them are suffering from low scalability and low parallel processing capability due to the huge hardware cost and high complexity. In this paper, a novel network-on-chip-based annealing processing architecture (NoCAPA) for a large-scale Ising processor is described to address these issues with a NoC computing paradigm, a distributed storage scheme, and a fully pipelined structure design. Several techniques are developed to further increase convergence speed and reduce hardware resource consumption, including a dynamic multithread parallel update algorithm, a router with merge and deflection abilities, and a unique multiply-accumulate operation. The prototype is implemented in FPGA with the maximum operation frequency of 200MHz, achieving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$120.5\times $ </tex-math></inline-formula> faster than conventional simulated annealing method when solving the max-cut problem while supporting high scalability.
New MGCAMB tests of gravity with CosmoMC and Cobaya

We present a new version of MGCAMB, a patch for the Einstein-Boltzmann solver CAMB for cosmological tests of gravity. New features include a new cubic-spline parameterization allowing for a simultaneous reconstruction of $\mu$, $\Sigma$ and the dark energy density fraction $\Omega_X$ as functions of redshift, the option to work with a direct implementation of $\mu$, $\Sigma$ (instead of converting to $\mu$, $\gamma$ first), along with the option to test models with a scalar field coupled only to dark matter, and the option to include dark energy perturbations when working with $w\ne -1$ backgrounds, to restore consistency with CAMB in the GR limit. This version of MGCAMB comes with a Python wrapper to run it directly from the Python interface, an implementation in the latest version of CosmoMC, and can be used with Cobaya.
Contextualized Embeddings from Transformers for Sentiment Analysis on Code-Mixed Hinglish Data: An Expanded Approach with Explainable Artificial Intelligence

Transformer-based models have gained traction for giving breakthrough performance on various Natural Language Processing (NLP) tasks in recent years. A number of studies have been conducted to understand the type of information learned by the model and its performance on different tasks. YouTube comments can serve as a rich source for multilingual data, which can be used to train state-of-the-art models. In this study, two transformer-based models, multilingual Bidirectional Encoder Representations from Transformers (mBERT) and RoBERTa, are fine-tuned and evaluated on code-mixed ‘Hinglish’ data. The representations learned by the intermediate layers of the models are also studied by using them as features for machine learning classifiers. The results show a significant improvement compared to the baseline for both datasets using the feature-based method, with the highest accuracy of 92.73% for Kabita Kitchen’s channel and 87.42% for Nisha Madhulika’s channel. Explanations of the model predictions using the Local Interpretable Model-Agnostic Explanations (LIME) technique show that the model is using significant features for classification and can be trusted.
Lightweight encryption mechanism with discrete-time chaotic maps for Internet of Robotic Things

Chaotic systems have different characteristics that can be utilized for the purpose of cyber security. These features result from the erratic structure of images, making it difficult for systems to comprehend those images. Therefore, chaotic systems can serve as an effective means of securely storing and concealing images by implementing them in the IoRT for data encryption. In this study, a new lightweight encryption mechanism for IoRT based robots is proposed. A novel chaotic encryption mechanism is designed with discrete-time chaotic maps (Cubic Map and Ricker’s Population Model Map) for more efficiently encrypting large-sized data, such as images and videos, in robots. The test of the developed mechanism was conducted on the NVIDIA Jetson Orin development kit, which is utilized in artificial intelligence-supported robots and boasts high performance and energy efficiency. The study’s most important finding is that the established mechanism is suitable for parallel processing, making it possible to accomplish a high rate of image encryption per second utilizing the proposed method. In order to encrypt large files for IoRT, firstly, random number generation was performed and statistical tests were carried out. Then, encryption operations were performed with the developed encryption algorithm and security analyses were realized. The performances of the proposed mechanism will be compared with some studies in the literature. Based on the obtained image analysis and encryption performance, it has been shown that the developed mechanism can be easily used with high security for IoRT.
Is <scp>ORCID</scp> your <scp>ID</scp>? A case study at the Faculty of Arts and Humanities of the University of Porto

Abstract Author disambiguation has been a key component of scientific communication since the mid‐19th century, and now more than ever. This paper discusses the use of ORCID as a digital identity platform in Social Sciences and Humanities, by analysing the adoption of ORCID in the Faculty of Arts and Humanities of the University of Porto using a manual‐qualitative method. The results show a discrepancy between the use of ORCID as an ID and as an author record. Even though 90.4% of the sample studied had an ORCID iD, the records were found to be incomplete for disambiguation purposes. The ‘Also known as’ field was used by only 31% of the 170 profiles analysed, less than half of the profiles had completed the ‘Country’ field and the ‘Peer review’ field is hardly used. An important finding is the inconsistency in affiliation information recorded in the ‘Employment’ field. We verified that keeping profiles updated and complete requires interoperability and the role of ORCID‐trusted organizations, such as the FCT in Portugal. In conclusion, it was found that a university's institutional strategy, the organizational culture and a mediation process will help improve the correct adoption of ORCID.
Security enhancement of the access control scheme in IoMT applications based on fuzzy logic processing and lightweight encryption

Abstract Security of Internet-of-Medical-Things (IoMT) networks has evolved as a vital issue in recent years. The IoMT networks are designed to link patients with caregivers. All reports, data, and medical signals are transferred over these networks. Hence, these networks require robust and secure access strategies for patients to send their vital data or reports. Hence, hacking of these networks may lead to harmful effects on patients. One of the vulnerable points to hacking in these networks is the access point. Access to these networks could be performed with biometrics. The popular biometric traits for this purpose are biomedical signals such as Electrocardiogram (ECG) signals, as they are continuously monitored and measured for patients. A common thread between all biometric authentication systems is the possibility of losing the biometric traits forever if hacking attempts manage to concur the biometric template storage. A new trend in the field of biometric authentication is to avoid the utilization of original biometrics in access control processes. A possible alternative is to use cancelable biometrics instead. Cancelable biometrics can be generated through encryption schemes or non-invertible transforms. This paper adopts both strategies in a unified framework for cancelable ECG signal recognition that can be used in the access step of IoMT networks. The proposed framework begins with applying a non-invertible transformation on the ECG signals through fuzzy logic to change the dynamic range of the signals. As this process is non-invertible in nature, it prevents the recovery of the original ECG signals from the processed versions, which is the main target of cancelable biometric systems. After that, lightweight encryption through XOR operation with user-specific patterns is implemented. Here, the high complexity of full encryption schemes that need a large processing burden is eliminated. The addition of the encryption stage enhances the security of cancelable biometric traits, allowing a hybrid nature of the proposed cancelable biometric framework through the merging of non-invertible transforms and encryption algorithms. Moreover, an FPGA hardware implementation is introduced for real implementation of the proposed ECG-based cancelable biometric recognition framework. This hardware can accompany the user to allow access of the IoMT network when requested. Experimental results show a promising performance of the proposed framework with a large Area under the Receiver Operating Characteristic curve (AROC) of 99.5% and an Equal Error Rate (EER) of 0.058%.
Variational Disentangled Attention and Regularization for Visual Dialog

One of the most important challenges in a visual dialog is to effectively extract the information from a given image and its historical conversation which are related to the current question. Many studies adopt the soft attention mechanism in different information sources due to its simplicity and ease of optimization. However, some of visual dialogs are observed in a single round. This implies that there is no substantial correlation between individual rounds of questions and answers. This paper presents a unified approach to disentangled attention to deal with context-free visual dialogs. The question is disentangled in latent representation. In particular, an informative regularization is imposed to strengthen the dependence between vision and language by pretraining on the visual question answering before transferring to visual dialog. Importantly, a novel variational attention mechanism is developed and implemented by a local reparameterization trick which carries out a discrete attention to identify the relevant conversations in a visual dialog. A set of experiments are evaluated to illustrate the merits of the proposed attention and regularization schemes for visual dialogs.
PARTICUL: Part Identification with Confidence Measure Using Unsupervised Learning

In this paper, we present PARTICUL, a novel algorithm for unsupervised learning of part detectors from datasets used in fine-grained recognition. It exploits the macro-similarities of all images in the training set in order to mine for recurring patterns in the feature space of a pre-trained convolutional neural network. We propose new objective functions enforcing the locality and unicity of the detected parts. Additionally, we embed our detectors with a confidence measure based on correlation scores, allowing the system to estimate the visibility of each part. We apply our method on two public fine-grained datasets (Caltech-UCSD Bird 200 and Stanford Cars) and show that our detectors can consistently highlight parts of the object while providing a good measure of the confidence in their prediction. We also demonstrate that these detectors can be directly used to build part-based fine-grained classifiers that provide a good compromise between the transparency of prototype-based approaches and the performance of non-interpretable methods.
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools

Methods for model explainability have become increasingly critical for testing the fairness and soundness of deep learning. Concept-based interpretability techniques, which use a small set of human-interpretable concept exemplars in order to measure the influence of a concept on a model’s internal representation of input, are an important thread in this line of research. In this work we show that these explainability methods can suffer the same vulnerability to adversarial attacks as the models they are meant to analyze. We demonstrate this phenomenon on two well-known concept-based interpretability methods: TCAV and faceted feature visualization. We show that by leveraging the geometry of the problem and carefully perturbing the examples of the concept that is being investigated, we can radically change the output of the interpretability method. The attacks that we propose can either induce positive interpretations (polka dots are an important concept for a model when classifying zebras) or negative interpretations (stripes are not an important factor in identifying images of a zebra). Our work highlights the fact that in safety-critical applications, there is need for security around not only the machine learning pipeline but also the model interpretation process.
Quantum Deep Q-Learning with Distributed Prioritized Experience Replay

This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. The framework incorporates prioritized experience replay, asynchronous training and novel matrix loss into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline distributed quantum Q-learning with the same model architecture. The proposed framework holds potential for more complex tasks while maintaining training efficiency.
Methods for data validation using QR codes

Abstract This article aims to elaborate on a few strategies designed for validating data encoded as a QR code. As the use-cases for such validation schemes vary widely as the demand for such practices is continuously increasing, the present article will represent a comparative study of checksum, hashing, as well as asymmetric encryption algorithms for the offline validation of claims transmitted through the payload of the exchanged data. Results are evaluated based on different criteria impacting the reliability, such as the size of the QR code and the resulting security.