Reviewerly/exHarmony huggingface dataset:
Face Detection and Recognition System using Digital Image Processing

While recognizing any individual, the most important attribute is face. It serves as an individual identity of everyone and therefore face recognition helps in authenticating any person's identity using his personal characteristics. The whole procedure for authenticating any face data is sub-divided into two phases, in the first phase, the face detection is done quickly except for those cases in which the object is placed quite far, followed by this the second phase is initiated in which the face is recognized as an individual. Then the whole process is repeated thereby helping in developing a face recognition model which is considered to be one of the most extremely deliberated biometric technology. Basically, there are two type of techniques that are currently being followed in face recognition pattern that is, the Eigenface method and the Fisherface method. The Eigenface method basically make use of the PCA (Principal Component Analysis) to minimize the face dimensional space of the facial features. The area of concern of this paper is using the digital image processing to develop a face recognition system.
Machine Learning–Driven Language Assessment

We describe a method for rapidly creating language proficiency assessments, and provide experimental evidence that such tests can be valid, reliable, and secure. Our approach is the first to use machine learning and natural language processing to induce proficiency scales based on a given standard, and then use linguistic models to estimate item difficulty directly for computer-adaptive testing. This alleviates the need for expensive pilot testing with human subjects. We used these methods to develop an online proficiency exam called the Duolingo English Test, and demonstrate that its scores align significantly with other high-stakes English assessments. Furthermore, our approach produces test scores that are highly reliable, while generating item banks large enough to satisfy security requirements.
Benchmarking one-shot distillation in general quantum resource theories

Developing a unified formalism to describe distillation in a general resource-theoretic setting is challenging. Here, a comprehensive set of tools applicable to broad classes of resource theories is developed, revealing the similarities among many of them and establishing universal limitations on the distillation of such resources in practical settings.
TouchPass

With increasing private and sensitive data stored in mobile devices, secure and effective mobile-based user authentication schemes are desired. As the most natural way to contact with mobile devices, finger touches have shown potentials for user authentication. Most existing approaches utilize finger touches as behavioral biometrics for identifying individuals, which are vulnerable to spoofer attacks. To resist attacks for on-touch user authentication on mobile devices, this paper exploits physical characters of touching fingers by investigating active vibration signal transmission through fingers, and we find that physical characters of touching fingers present unique patterns on active vibration signals for different individuals. Based on the observation, we propose a behavior-irrelevant on-touch user authentication system, TouchPass, which leverages active vibration signals on smartphones to extract only physical characters of touching fingers for user identification. TouchPass first extracts features that mix physical characters of touching fingers and behavior biometrics of touching behaviors from vibration signals generated and received by smartphones. Then, we design a Siamese network-based architecture with a specific training sample selection strategy to reconstruct the extracted signal features to behavior-irrelevant features and further build a behavior-irrelevant on-touch user authentication scheme leveraging knowledge distillation. Our extensive experiments validate that TouchPass can accurately authenticate users and defend various attacks.
Case study

This paper describes a verification case study on an autonomous racing car with a neural network (NN) controller. Although several verification approaches have been recently proposed, they have only been evaluated on low-dimensional systems or systems with constrained environments. To explore the limits of existing approaches, we present a challenging benchmark in which the NN takes raw LiDAR measurements as input and outputs steering for the car. We train a dozen NNs using reinforcement learning (RL) and show that the state of the art in verification can handle systems with around 40 LiDAR rays. Furthermore, we perform real experiments to investigate the benefits and limitations of verification with respect to the sim2real gap, i.e., the difference between a system's modeled and real performance. We identify cases, similar to the modeled environment, in which verification is strongly correlated with safe behavior. Finally, we illustrate LiDAR fault patterns that can be used to develop robust and safe RL algorithms.
A Tutorial and Future Research for Building a Blockchain-Based Secure Communication Scheme for Internet of Intelligent Things

The Internet of Intelligent Things (IoIT) communication environment can be utilized in various types of applications (for example, intelligent battlefields, smart healthcare systems, the industrial internet, home automation, and many more).Communications that happen in such environments can have different types of security and privacy issues, which can be resolved through the utilization of blockchain.In this paper, we propose a tutorial that aims in desiging a generalized blockchain-based secure authentication key management scheme for the IoIT environment.Moreover, some issues with using blockchain for a communication environment are discussed as future research directions.The details of different types of blockchain are also provided.Some of the widely-accepted consensus algorithms are then discussed.Next, we discuss different types of applications in blockchain-based IoIT communication environments.The details of the associated system models are provided, such as, the network and attack models for the blockchain-based IoIT communication environment, which are helpful in designing a security protocol for such an environment.A practical demonstration of the proposed generalized scheme is provided in order to measure the impact of the scheme on the performance of the essential parameters.Finally, some of the future research challenges in the blockchain-based IoIT communication environment are highlighted, which will also be helpful to the researchers.
Neural Sign Language Synthesis: Words Are Our Glosses

This paper deals with a text-to-video sign language synthesis. Instead of direct video production, we focused on skeletal models production. Our main goal in this paper was to design a fully end-to-end automatic sign language synthesis system trained only on available free data (daily TV broadcasting). Thus, we excluded any manual video annotation. Furthermore, our designed approach even do not rely on any video segmentation. A proposed feed-forward transformer and recurrent transformer were investigated. To improve the performance of our sequence-to-sequence transformer, soft non-monotonic attention was employed in our training process. A benefit of character-level features was compared with word-level features. We focused our experiments on a weather forecasting dataset in the Czech Sign Language.
Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders. The strain this places on services means providing 24-hour monitoring is not sustainable. No solution exists to non-intrusively monitor the wellbeing of patients with dementia, resulting in delayed intervention. Using machine learning and signal processing, domestic energy supplies can be disaggregated to detect appliance usage. This enables Activities of Daily Living (ADLs) to be assessed. The aim is to facilitate early intervention and enable patients to stay in their homes for longer. A Support Vector Machine (SVM) and Random Decision Forest classifier are modelled using data from three test homes. The trained models are then used to monitor two patients with dementia during a six-month clinical trial undertaken in partnership with Mersey Care NHS Foundation Trust. In the case of load disaggregation, the SVM achieved (AUC = 0.86074, Sen = 0.756 and Spec = 0.92838). While the Decision Forest achieved (AUC = 0.9429, Sen = 0.9634 and Spec = 0.9634). ADLs are also analysed to identify the behavioural patterns of the occupant while detecting alterations in routine. The approach is sensitive in identifying behavioural routines and detecting anomalies in patient behaviour.
A robust density peaks clustering algorithm with density-sensitive similarity

Density peaks clustering (DPC) algorithm is proposed to identify the cluster centers quickly by drawing a decision-graph without any prior knowledge. Meanwhile, DPC obtains arbitrary clusters with fewer parameters and no iteration. However, DPC has some shortcomings to be addressed before it is widely applied. Firstly, DPC is not suitable for manifold datasets because these datasets have multiple density peaks in one cluster. Secondly, the cut-off distance parameter has a great influence on the algorithm, especially on small-scale datasets. Thirdly, the method of decision-graph will cause uncertain cluster centers, which leads to wrong clustering. To address these issues, we propose a robust density peaks clustering algorithm with density-sensitive similarity (RDPC-DSS) to find accurate cluster centers on the manifold datasets. With density-sensitive similarity, the influence of the parameters on the clustering results is reduced. In addition, a novel density clustering index (DCI) instead of the decision-graph is designed to automatically determine the number of cluster centers. Extensive experimental results show that RDPC-DSS outperforms DPC and other state-of-the-art algorithms on the manifold datasets.
SAR Image Despeckling Employing a Recursive Deep CNN Prior

Synthetic aperture radar (SAR) images are inherently affected by speckle noise, for which deep learning-based methods have shown good potential. However, the deep learning-based methods proposed until now directly map low-quality images to high-quality images, and they are unable to characterize the priors for all the kinds of speckle images. The variational method is a classic model optimization approach that establishes the relationship between the clean and noisy images from the perspective of a probability distribution. Therefore, in this article, we propose the recursive deep convolutional neural network (CNN) prior model for SAR image despeckling (SAR-RDCP). First, the data-fitting term and regularization term of the SAR variational model are decoupled into two subproblems, i.e., a data-fitting block and a deep CNN prior block. The gradient descent algorithm is then used to solve the data-fitting block, and a predenoising residual channel attention network based on dilated convolution is used for the deep CNN prior block, which combines an end-to-end iterative optimization training. In the experiments undertaken in this study, the proposed model was compared with several state-of-the-art despeckling methods, obtaining better results in both the quantitative and qualitative evaluations.
Improved Long Short-Term Memory based anomaly detection with concept drift adaptive method for supporting IoT services

The rapid rising of artificial intelligence (AI) and Internet of Things technologies leads to the accumulation of abundant communication data without being processed on time, which causes potential threat for smart city. How to effectively leverage these data for anomaly detection has become an increasingly popular research field as it is a fundamental aspect of cyber security for smart city services. The existing methods often focus on either static data for anomaly detection or streaming data without considering the influence of poor detection accuracy caused by concept drift phenomenon. In this paper, we concentrate on the anomaly detection problem of smart city services and distinguish different anomalies of communication in an effective way, which is aimed to protect data privacy of users. Then we propose an innovative concept drift adaptive method to improve the accuracy of anomaly detection, which fully considers time influence to change the sample distribution along timeline. Furthermore, we present an AI based Improved Long Short-Term Memory (I-LSTM) neural network that adds time factor and employs a novel smooth activation function, which can enhance the performance of multi-classification for anomaly detection. Finally, our proposed methods are evaluated with a real communication dataset. Extensive experimental results indicate that I-LSTM achieves the highest values on all indicators. This demonstrates the effectiveness of our proposed methods that can offer excellent quality of service for smart city, which is a perfect fusion of artificial intelligence and communication security.
Online Anomalous Trajectory Detection with Deep Generative Sequence Modeling

Detecting anomalous trajectory has become an important and fundamental concern in many real-world applications. However, most of the existing studies 1) cannot handle the complexity and variety of trajectory data and 2) do not support efficient anomaly detection in an online manner. To this end, we propose a novel model, namely Gaussian Mixture Variational Sequence AutoEncoder (GM-VSAE), to tackle these challenges. Our GM-VSAE model is able to (1) capture complex sequential information enclosed in trajectories, (2) discover different types of normal routes from trajectories and represent them in a continuous latent space, and (3) support efficient online detection via trajectory generation. Our experiments on two real-world datasets demonstrate that GM-VSAE is more effective than the state-of-the-art baselines and is efficient for online anomalous trajectory detection.
Empathy Is All You Need: How a Conversational Agent Should Respond to Verbal Abuse

With the popularity of AI-infused systems, conversational agents (CAs) are becoming essential in diverse areas, offering new functionality and convenience, but simultaneously, suffering misuse and verbal abuse. We examine whether conversational agents' response styles under varying abuse types influence those emotions found to mitigate peoples' aggressive behaviors, involving three verbal abuse types (Insult, Threat, Swearing) and three response styles (Avoidance, Empathy, Counterattacking). Ninety-eight participants were assigned to one of the abuse type conditions, interacted with the three spoken (voice-based) CAs in turn, and reported their feelings about guiltiness, anger, and shame after each session. The results show that the agent's response style has a significant effect on user emotions. Participants were less angry and more guilty with the empathy agent than the other two agents. Furthermore, we investigated the current status of commercial CAs' responses to verbal abuse. Our study findings have direct implications for the design of conversational agents.
Improving the Healthcare Effectiveness: The Possible Role of EHR, IoMT and Blockchain

New types of patient health records aim to help physicians shift from a medical practice, often based on their personal experience, towards one of evidence based medicine, thus improving the communication among patients and care providers and increasing the availability of personal medical information. These new records, allowing patients and care providers to share medical data and clinical information, and access them whenever they need, can be considered enabling Ambient Assisted Living technologies. Furthermore, new personal disease monitoring tools support specialists in their tasks, as an example allowing acquisition, transmission and analysis of medical images. The growing interest around these new technologies poses serious questions regarding data integrity and transaction security. The huge amount of sensitive data stored in these new records surely attracts the interest of malicious hackers, therefore it is necessary to guarantee the integrity and the maximum security of servers and transactions. Blockchain technology can be an important turning point in the development of personal health records. This paper discusses some issues regarding the management and protection of health data exchanged through new medical or diagnostic devices.
The Rise of the Quantum Internet

The Internet just turned 50 five decades that shaped the world we live in. But what comes next, the so-called Quantum Internet, will be even more revolutionary, likely in ways we can't imagine yet.
Augmented Reality for Early Language Learning: A Systematic Review of Augmented Reality Application Design, Instructional Strategies, and Evaluation Outcomes

In this article, we present a systematic review of literature on augmented reality (AR) supported for early language learning. We analyzed a total of 53 papers from 2010 to 2019 using qualitative analysis with complementary descriptive quantitative analysis. Our findings revealed three main AR learning activities: word spelling games, word knowledge activities, and location-based word activities. Our findings also uncovered five main design strategies: three-dimensional multimedia content, hands-on interaction with physical learning materials, gamification, spatial mappings, and location-based features. Several combinations of design and instructional strategies tended to be effective: Learning gains were enhanced by using three-dimensional multimedia with advanced organizers (presentation strategy) and/or using location-based content with learners’ self-exploration (discovery strategy); and motivation was enhanced by using game mechanisms with discovery strategy. We suggest that future designers of AR early language applications should move beyond these basic approaches and consider how unique benefits of AR may be applied to support key activities in early language learning while also considering how to support sociotechnical factors such as collaboration between teachers and learners and different learning contexts. We conclude with a discussion of future directions for research in this emerging space.
Show, Edit and Tell: A Framework for Editing Image Captions

Most image captioning frameworks generate captions directly from images, learning a mapping from visual features to natural language. However, editing existing captions can be easier than generating new ones from scratch. Intuitively, when editing captions, a model is not required to learn information that is already present in the caption (i.e. sentence structure), enabling it to focus on fixing details (e.g. replacing repetitive words). This paper proposes a novel approach to image captioning based on iterative adaptive refinement of an existing caption. Specifically, our caption-editing model consisting of two sub-modules: (1) EditNet, a language module with an adaptive copy mechanism (Copy-LSTM) and a Selective Copy Memory Attention mechanism (SCMA), and (2) DCNet, an LSTM-based denoising auto-encoder. These components enable our model to directly copy from and modify existing captions. Experiments demonstrate that our new approach achieves state of-art performance on the MS COCO dataset both with and without sequence-level training.
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection

Generating explanations for neural networks has become crucial for their applications in real-world with respect to reliability and trustworthiness.In natural language processing, existing methods usually provide important features which are words or phrases selected from an input text as an explanation, but ignore the interactions between them.It poses challenges for humans to interpret an explanation and connect it to model prediction.In this work, we build hierarchical explanations by detecting feature interactions.Such explanations visualize how words and phrases are combined at different levels of the hierarchy, which can help users understand the decision-making of blackbox models.The proposed method is evaluated with three neural text classifiers (LSTM, CNN, and BERT) on two benchmark datasets, via both automatic and human evaluations.Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models and interpretable to humans.
Learning Invariant Representation for Unsupervised Image Restoration

Recently, cross domain transfer has been applied for unsupervised image restoration tasks. However, directly applying existing frameworks would lead to domain-shift problems in translated images due to lack of effective supervision. Instead, we propose an unsupervised learning method that explicitly learns invariant presentation from noisy data and reconstructs clear observations. To do so, we introduce discrete disentangling representation and adversarial domain adaption into general domain transfer framework, aided by extra self-supervised modules including background and semantic consistency constraints, learning robust representation under dual domain constraints, such as feature and image domains. Experiments on synthetic and real noise removal tasks show the proposed method achieves comparable performance with other stateof-the-art supervised and unsupervised methods, while having faster and stable convergence than other domain adaption methods.
An End-to-End Edge Aggregation Network for Moving Object Segmentation

Moving object segmentation in videos (MOS) is a highly demanding task for security-based applications like automated outdoor video surveillance. Most of the existing techniques proposed for MOS are highly depend on fine-tuning a model on the first frame(s) of test sequence or complicated training procedure, which leads to limited practical serviceability of the algorithm. In this paper, the inherent correlation learning-based edge extraction mechanism (EEM) and dense residual block (DRB) are proposed for the discriminative foreground representation. The multi-scale EEM module provides the efficient foreground edge related information (with the help of encoder) to the decoder through skip connection at subsequent scale. Further, the response of the optical flow encoder stream and the last EEM module are embedded in the bridge network. The bridge network comprises of multi-scale residual blocks with dense connections to learn the effective and efficient foreground relevant features. Finally, to generate accurate and consistent foreground object maps, a decoder block is proposed with skip connections from respective multi-scale EEM module feature maps and the subsequent down-sampled response of previous frame output. Specifically, the proposed network does not require any pre-trained models or fine-tuning of the parameters with the initial frame(s) of the test video. The performance of the proposed network is evaluated with different configurations like disjoint, cross-data, and global training-testing techniques. The ablation study is conducted to analyse each model of the proposed network. To demonstrate the effectiveness of the proposed framework, a comprehensive analysis on four benchmark video datasets is conducted. Experimental results show that the proposed approach outperforms the state-of-the-art methods for MOS