Reviewerly/exHarmony huggingface dataset:
Advances in anti-spoofing: from the perspective of ASVspoof challenges

Abstract In recent years, automatic speaker verification (ASV) is used extensively for voice biometrics. This leads to an increased interest to secure these voice biometric systems for real-world applications. The ASV systems are vulnerable to various kinds of spoofing attacks, namely, synthetic speech (SS), voice conversion (VC), replay, twins, and impersonation. This paper provides the literature review of ASV spoof detection, novel acoustic feature representations, deep learning, end-to-end systems, etc. Furthermore, the paper also summaries previous studies of spoofing attacks with emphasis on SS, VC, and replay along with recent efforts to develop countermeasures for spoof speech detection (SSD) task. The limitations and challenges of SSD task are also presented. While several countermeasures were reported in the literature, they are mostly validated on a particular database, furthermore, their performance is far from perfect. The security of voice biometrics systems against spoofing attacks remains a challenging topic. This paper is based on a tutorial presented at APSIPA Annual Summit and Conference 2017 to serve as a quick start for those interested in the topic.
MedGlasses: A Wearable Smart-Glasses-Based Drug Pill Recognition System Using Deep Learning for Visually Impaired Chronic Patients

Today, with the arrival of an aging society, the average age of the population is rising. It is known that the physiology of a person degrades with age. There are approximately 285 million visually impaired people in the world, of whom 140 million are elderly people over the age of 50, and 110 million of these visually impaired elderly people suffer from multiple chronic diseases. In the case of multiple medication usage, these 110 million vulnerable people will be more likely to take the wrong medicines or forget to take their medication. To solve this problem, this paper proposes a wearable smart-glasses-based drug pill recognition system using deep learning, named MedGlasses, for visually impaired people to improve their medication-use safety. The proposed MedGlasses system consists of a pair of wearable smart glasses, an artificial intelligence (AI)-based intelligent drug pill recognition box, a mobile device app, and a cloud-based information management platform. Experimental results show that a recognition accuracy of up to 95.1% can be achieved. Therefore, the proposed MedGlasses system can effectively mitigate the problem of drug interactions caused by taking incorrect drugs, thereby reducing the cost of medical treatment and providing visually impaired chronic patients with a safe medication environment.
A hybrid algorithm based on particle filter and genetic algorithm for target tracking

The particle filter (PF) is an influential instrument for visual tracking; it relies on the Monte Carlo Chain Framework and Bayesian probability that is of tremendous importance for smart monitoring systems. The current study introduces a particle filter based upon genetic resampling. In the suggested method called Reduced Particle Filter based upon Genetic Algorithm (RPFGA), particles with the highest weights are chosen and go through evolution using a GA in the resampling phase of PF algorithm. Moreover, this study aims to introduce the ideas of marking (marking the target by user (observer) in the first frame of a video sequence) and decreasing image size. Applying both ideas leads to reduced number of particles, the processing time of each frame, and the total tracking time. Additionally, the performance of the offered RPFGA method to tackle the occlusion problem is enhanced by the marking idea. According to the results obtained in challenges, such as Occlusions (OCC), deformation (DEF), low resolution (LR), scale variations(SV), Fast Motions (FM), In-Plane Rotation (IPR), Out-Of-Plane Rotation (OPR), Motion Blur (MB), Illumination Variation (IV) and color similarity between the target and the background, and regarding precision and tracking time, the recommended hybrid approach only with a few particles overtakes the generic particle filter, Particle Swarm Optimization particle filter (PSO-PF) and the particle filter based upon improved cuckoo search (ICS-PF). The suggested method can be applied for real time video objects tracking.
Using Channel-Wise Attention for Deep CNN Based Real-Time Semantic Segmentation With Class-Aware Edge Information

Advanced Driver Assistance Systems (ADAS) consists of two basic functions. One is the object detection for preventing vehicles from hitting pedestrians or other obstacles. The other is image segmentation for recognizing drivable areas and guiding the vehicle forward. For the latter, unlike those traditional image segmentation methods, image semantic segmentation based on deep learning architecture can handle the irregularly shaped road areas better, guiding a vehicle to drive in a more complex environment. With the popularity of Convolution Neural Networks (CNNs) in recent year, the traditional hand-crafted features methods have shown to be outperformed. However, deep CNN models are difficult to implement on vehicle application because the severe cost of time for complex processing. Although some proposed methods, such as Efficient neural network (Enet), achieved higher speed by removing some layers, it also led to the decrease of segmentation accuracy. In this research work, we propose a novel semantic segmentation network, Edgenet, which contains a class-aware edge loss module and a channel-wise attention mechanism, aiming to improve the accuracy with no harm to inference speed. We evaluate Edgenet on Cityscapes dataset, which is the most challenging and authoritative on-road semantic segmentation dataset. The results show that our proposed method can achieve over 70% mean IOU on Cityscapes test set and run at over 30 FPS in a single GTX Titan X (Maxwell) GPU.
I am a blockchain too: How does the market respond to companies’ interest in blockchain?

We investigate the price reaction of listed companies in response to blockchain-related announcements. The average abnormal return based on a global sample of 713 firm announcements is approximately 5% on the announcement day, with significantly higher returns for U.S. firms, smaller firms and announcements in late 2017 and early 2018. We show that abnormal returns are linked to the performance of bitcoin. Additionally, speculative announcements exhibit higher returns than non-speculative announcements, and blockchain-related Form 8-K disclosures have negligible difference in performance compared to their U.S. peers. Whilst we acknowledge the possibility of a latent variable that affects both the abnormal returns and the performance of bitcoin, we hypothesise that investors have confused bitcoin and blockchain, and used the performance of bitcoin as an indicator of the expected success of the blockchain technology.
Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Abstract In the last years, there have been rapid developments in social robotics, which bring about the prospect of their application as persuasive robots to support behavior change. In order to guide related developments and pave the way for their adoption, it is important to understand the factors that influence the acceptance of social robots as persuasive agents. This study extends the technology acceptance model by including measures of social responses. The social responses include trusting belief, compliance, liking, and psychological reactance. Using the Wizard of Oz method, a laboratory experiment was conducted to evaluate user acceptance and social responses towards a social robot called SociBot. This robot was used as a persuasive agent in making decisions in donating to charities. Using partial least squares method, results showed that trusting beliefs and liking towards the robot significantly add the predictive power of the acceptance model of persuasive robots. However, due to the limitations of the study design, psychological reactance and compliance were not found to contribute to the prediction of persuasive robots’ acceptance. Implications for the development of persuasive robots are discussed.
Construction of force haptic reappearance system based on Geomagic Touch haptic device

Force haptic reappearance technology is considered to be one of the top ten technologies that can change human life in the future. It has broad application prospects and market demand. Most of the existing medical robots, especially the remote diagnosis and treatment robots, lack haptic feedback, or the calculation of feedback force is insufficient. Haptic reappearance technology is an effective method to solve the problem of haptic presence and improve the practicability of medical robot. The ultimate goal of the force haptic reappearance system is to let the operator feel the haptic feedback when interacting with the soft tissue model in the virtual environment in real time. Haptic device is the necessary condition to realize force haptic reappearance, and it is an essential part of the system. Its important role is to introduce the external force imposed by the operator into the virtual environment, and let the operator feel the force in the virtual environment, which effectively guarantees the operator's sense of reality and immersion when interacting with the virtual environment. Therefore, starting with the key technology of force and haptic reappearance system, this paper studies the construction of force and haptic reappearance system. Soft tissue surface model is drawn by OpenGL, and hand model is drawn by 3Ds Max. The haptic reappearance and visual feedback of soft tissue model of hand palpation are realized. The quality of feedback is evaluated. The haptic reappearance is stable and realistic, and the visual feedback is smooth. This indicates that the system has a certain application value and is worth to promote to the public.
Deep and joint learning of longitudinal data for Alzheimer's disease prediction

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. The close AD monitoring of this disease is essential for the patient treatment plan adjustment. For AD monitoring, clinical score prediction via neuroimaging data is highly desirable since it is able to reveal the disease status, adequately. For this task, most previous studies are focused on a single time point without considering relationship between neuroimaging data (e.g., Magnetic Resonance Imaging (MRI)) and clinical scores at multiple time points. Differing from these studies, we propose to build a framework based on longitudinal multiple time points data to predict clinical scores. Specifically, the proposed framework consists of three parts, feature selection based on correntropy regularized joint learning, feature encoding based on deep polynomial network, and ensemble learning for regression via the support vector regression method. Two scenarios are designed for scores prediction. Namely, scenario 1 uses the baseline data to achieve the longitudinal scores prediction, while scenario 2 utilizes all the previous time points data to obtain the predicted scores at the next time point, which can improve the score prediction's accuracy. Meanwhile, the missing clinical scores at longitudinal multiple time points are imputated to solve the incompleteness of the data. Extensive experiments on the public database of Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the state-of-the-art methods in scores prediction.
Deep Learning Local Descriptor for Image Splicing Detection and Localization

In this paper, a novel image splicing detection and localization scheme is proposed based on the local feature descriptor which is learned by deep convolutional neural network (CNN).A two-branch CNN, which serves as an expressive local descriptor is presented and applied to automatically learn hierarchical representations from the input RGB color or grayscale test images.The first layer of the proposed CNN model is used to suppress the effects of image contents and extract the diverse and expressive residual features, which is deliberately designed for image splicing detection applications.In specific, the kernels of the first convolutional layer are initialized with an optimized combination of the 30 linear high-pass filters used in calculation of residual maps in spatial rich model (SRM), and is fine-tuned through a constrained learning strategy to retain the high-pass filtering properties for the learned kernels.Both the contrastive loss and cross entropy loss are utilized to jointly improve the generalization ability of the proposed CNN model.With the block-wise dense features for a test image extracted by the pre-trained CNN-based local descriptor, an effective feature fusion strategy, known as block pooling, is adopted to obtain the final discriminative features for image splicing detection with SVM.Based on the pre-trained CNN model, an image splicing localization scheme is further developed by incorporating the fully connected conditional random field (CRF).Extensive experimental results on several public datasets show that the proposed CNN based scheme outperforms some state-of-the-art methods not only in image splicing detection and localization performance, but also in robustness against JPEG compression.
An Optimized Consortium Blockchain for Medical Information Sharing

Medical information is private, and medical data are valuable for medical research. Thus, medical information sharing is challenging because the data might be manipulated improperly and revealed during the operational process. The accuracy and integrity of medical information should be guaranteed throughout the sharing process. Medical institutions require shared information for scientific research and development; however, the issue of privacy inhibits the sharing process. In this article, we propose a new business process and a blockchain-based platform for medical information sharing. Our method exploits the advantages of blockchain in medical information recording and sharing. Information can be stored, shared, and credibly verified among parties in the distributed network. In addition, we propose a new consensus algorithm and a universal anonymous sharing model. These methods improve the efficiency and security of medical information sharing between users. In this way, both the information and the traces of the transaction can be stored in a distributed manner to prevent manipulation and fraud. Consequently, the value of medical information can be fully utilized.
Establishment and application of intelligent city building information model based on BP neural network model

The construction of smart cities in our country has received extensive attention. Under the situation that smart cities are vigorously promoted nowadays, compared with traditional construction and operation and maintenance methods, building information model (BIM) technology is more suitable to serve as an important foundation for intelligent management in the whole process of construction projects. BIM is an abbreviation for building information model. BIM relies on a variety of digital technologies, which can be used to realize information modeling of urban buildings and infrastructure. The efficiency of information exchange in the process of intelligence construction ensures the integrity and accuracy of information data exchange and maintains the consistency of information data exchange. Data and information have objectivity, applicability, transferability, and sharing. Geographic data is a digital representation of various geographical features and phenomena and their relationships. BIM is a digital representation of physical and functional characteristics of a facility. It can It is used as a shared knowledge resource for facility information. It becomes a reliable basis for facility life-cycle decision-making. Input BP neural network, and then learn and train by BP neural network.
Reasoning on the Relation: Enhancing Visual Representation for Visual Question Answering and Cross-Modal Retrieval

Cross-modal analysis has become a promising direction for artificial intelligence. Visual representation is crucial for various cross-modal analysis tasks that require visual content understanding. Visual features which contain semantical information can disentangle the underlying correlation between different modalities, thus benefiting the downstream tasks. In this paper, we propose a Visual Reasoning and Attention Network (VRANet) as a plug-and-play module to capture rich visual semantics and help to enhance the visual representation for improving cross-modal analysis. Our proposed VRANet is built based on the bilinear visual attention module which identifies the critical objects. We propose a novel Visual Relational Reasoning (VRR) module to reason about pair-wise and inner-group visual relationships among objects guided by the textual information. The two modules enhance the visual features at both relation level and object level. We demonstrate the effectiveness of the proposed VRANet by applying it to both Visual Question Answering (VQA) and Cross-Modal Information Retrieval (CMIR) tasks. Extensive experiments conducted on VQA 2.0, CLEVR, CMPlaces, and MS-COCO datasets indicate superior performance comparing with state-of-the-art work.
Silicon Photonics Codesign for Deep Learning

Deep learning is revolutionizing many aspects of our society, addressing a wide variety of decision-making tasks, from image classification to autonomous vehicle control. Matrix multiplication is an essential and computationally intensive step of deep-learning calculations. The computational complexity of deep neural networks requires dedicated hardware accelerators for additional processing throughput and improved energy efficiency in order to enable scaling to larger networks in the upcoming applications. Silicon photonics is a promising platform for hardware acceleration due to recent advances in CMOS-compatible manufacturing capabilities, which enable efficient exploitation of the inherent parallelism of optics. This article provides a detailed description of recent implementations in the relatively new and promising platform of silicon photonics for deep learning. Opportunities for multiwavelength microring silicon photonic architectures codesigned with field-programmable gate array (FPGA) for pre- and postprocessing are presented. The detailed analysis of a silicon photonic integrated circuit shows that a codesigned implementation based on the decomposition of large matrix-vector multiplication into smaller instances and the use of nonnegative weights could significantly simplify the photonic implementation of the matrix multiplier and allow increased scalability. We conclude this article by presenting an overview and a detailed analysis of design parameters. Insights for ways forward are explored.
Investigation of Chaotic Image Encryption in Spatial and FrFT Domains for Cybersecurity Applications

The need for cybersecurity increases to protect the exchange of information for improving the data privacy.This paper presents an investigation of the encryption efficiency of the chaotic-based image block ciphering in the spatial and Fractional Fourier Transform (FrFT) domains.The main aim of this investigation is to examine the efficiency of different chaotic maps, while considering the parameters of the FrFT as additional keys for encryption and achieving reliable cybersecurity for robust image communication.In this paper, Cat, Baker, and Logistic map confusion approaches are applied in the spatial and FrFT domains to study and analyze the cybersecurity and ciphering efficiency of chaos-based image cryptosystems.The confusion features of the chaotic maps in spatial and FrFT domains are investigated using information entropy, differential analysis, histograms, visual observation, attack analysis, impact of noise, and encryption quality tests.Simulation results prove that the chaotic-based image encryption in the FrFT domain increases the efficiency of the confusion process and achieves a high nonlinear relation between the plainimage and the cipherimage in a symmetric ciphering approach.Moreover, the results demonstrate that the Cat-FrFT scheme is more susceptible to channel noise attacks than the Baker-FrFT and the Logistic-FrFT schemes.Hence, they can be implemented efficiently in the scenarios of noisy channels due to their high robustness to channel noise.
Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.
Key Issues in Healthcare Data Integrity: Analysis and Recommendations

Managing data integrity is a challenging task for any expert or a researcher. This study attempts to collate a Systematic Literature Review of the research efforts done in the domain of healthcare data integrity. The paper highlights the criticalness of data integrity issues in healthcare through attack statistics in the first section. The second section of the paper systematically reviews the previous studies discussing the healthcare related Systematic literature reviews and data integrity techniques in healthcare sector. The third section of this study examines the collated literature through various analysis methodologies and discusses about the most prioritized technique as well as its challenges in healthcare data security. The fourth section illustrates about the various challenges and future directions to take while constructing a roadmap for the future research endeavors in healthcare data integrity management techniques. The concluding segment of the paper presents an objective assessment and sensitivity analysis for finding the implications and difficulties in the studies while outlining feasible solutions. Furthermore, this research endeavour also conducts a Scientometric analysis of all the studies for better understanding of the literature reviewed. Ranking or the Priority analysis part of the paper is totally dedicated to the previously used techniques in healthcare. The paper also discusses about data integrity techniques and postulates that the most prioritized data integrity technique is the blockchain.