Reviewerly/exHarmony huggingface dataset:
Mask Cross-Modal Hashing Networks

Due to the rapid development of deep learning, cross-modal retrieval has achieved significant progress in recent years. Moreover, cross-modal hashing has recently attracted considerable attention to multi-modal retrieval applications due to its advantages of low storage costs and fast retrieval speed. However, it is still a challenging problem due to an existing semantic heterogeneity gap between different modalities. In order to further narrow the gap and obtain more effective hash codes, we put forward a novel mask deep cross-modal hashing (MDCH) approach to explore the similarity between inter-modal instances. The main contributions of this paper are that: (1) we attempt to introduce semantic mask information into cross-modal hashing retrieval, (2) we alternately train intra-modal and inter-modal networks to fully mine the semantic relationship between different modalities. The semantic mask can improve the semantic information of the image feature. While inter-modal similarity, explored by inter-modal networks, focuses on enforcing images and their corresponding text tags to have similar hash codes, intra-modal similarity, explored by intra-modal networks, can retain local structural information embedded in each modality to achieve internal similarity. A large number of experiments conducted on three datasets demonstrate that our proposed MDCH approach is superior to several state-of-the-art cross-modal hashing approaches.
Self-Adaptation in Nonelitist Evolutionary Algorithms on Discrete Problems With Unknown Structure

A key challenge to make effective use of evolutionary algorithms is to choose appropriate settings for their parameters.However, the appropriate parameter setting generally depends on the structure of the optimisation problem, which is often unknown to the user.Non-deterministic parameter control mechanisms adjust parameters using information obtained from the evolutionary process.Self-adaptation -where parameter settings are encoded in the chromosomes of individuals and evolve through mutation and crossover -is a popular parameter control mechanism in evolutionary strategies.However, there is little theoretical evidence that self-adaptation is effective, and self-adaptation has largely been ignored by the discrete evolutionary computation community.Here we show through a theoretical runtime analysis that a non-elitist, discrete evolutionary algorithm which self-adapts its mutation rate not only outperforms EAs which use static mutation rates on LeadingOnes k , but also improves asymptotically on an EA using a state-of-the-art control mechanism.The structure of this problem depends on a parameter k, which is a priori unknown to the algorithm, and which is needed to appropriately set a fixed mutation rate.The self-adaptive EA achieves the same asymptotic runtime as if this parameter was known to the algorithm beforehand, which is an asymptotic speedup for this problem compared to all other EAs previously studied.An experimental study of 1 how the mutation-rates evolve show that they respond adequately to a diverse range of problem structures.These results suggest that self-adaptation should be adopted more broadly as a parameter control mechanism in discrete, non-elitist evolutionary algorithms.
A New Robust Kalman Filter With Adaptive Estimate of Time-Varying Measurement Bias

To better model the non-Gaussian heavy-tailed measurement noise with unknown and time-varying bias, a new Student's t-inverse-Wishart (STIW) distribution is presented. The STIW distribution is firstly written as a Gaussian, inverse-Wishart and normal-Gamma hierarchical form, from which a new robust Kalman filter is then derived based on the variational Bayesian method. Simulation results illustrate the potentials of the new derived robust Kalman filter for addressing the above measurement noise.
Convolutional Neural Networks for User Identification Based on Motion Sensors Represented as Images

In this paper, we propose a deep learning approach for smartphone user identification based on analyzing motion signals recorded by the accelerometer and the gyroscope, during a single tap gesture performed by the user on the screen. We transform the discrete 3-axis signals from the motion sensors into a gray-scale image representation which is provided as input to a convolutional neural network (CNN) that is pre-trained for multi-class user classification. In the pre-training stage, we benefit from different users and multiple samples per user. After pre-training, we use our CNN as feature extractor, generating an embedding associated to each single tap on the screen. The resulting embeddings are used to train a binary Support Vector Machines (SVM) model in a few-shot user identification setting, i.e. requiring only 20 taps on the screen during the registration phase. We compare our identification system based on CNN features with two baseline systems, one that employs handcrafted features and another that employs recurrent neural networks (RNN) features. All systems are based on the same classifier, namely SVM. To pre-train the CNN and the RNN models for multi-class user classification, we use a different set of users than the set used for few-shot user identification, ensuring a realistic scenario. The empirical results demonstrate that our CNN model yields a top accuracy of 90.75% in multi-class user classification and a top accuracy of 96.72% in few-shot user identification. We thus believe that our system is ready for practical use, having a better generalization capacity than both baselines. We also conduct experiments showing that the binary SVM provides better results than the one-class SVM, although the negative samples added during training do not belong to the attackers (known only at test time).
Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge-desire-intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users' beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.
A Robust Deep Neural Network Based Breast Cancer Detection and Classification

The exponential upward push in breast cancer cases across the globe has alarmed academia-industries to obtain certain more effect and strong Breast cancer laptop Aided prognosis (BC-CAD) device for breast most cancers detection. Some of techniques have been evolved with focus on case centric segmentation, feature extraction and class of breast cancer Histopathological photos. However, rising complexity and accuracy regularly demands more sturdy answer. Recently, Convolutional Neural community (CNN) has emerged as one of the maximum efferent techniques for medical records evaluation and diverse picture classification issues. On this paper, a notably strong and green BC-CAD solution has been proposed. Our proposed gadget consists of pre-processing, more suitable adaptive learning based totally Gaussian aggregate model (GMM), connected element analysis based vicinity of interest localization, and AlexNet-DNN primarily based characteristic extraction. The precept factor analysis (PCA) and Linear Discriminant analysis (LDA) primarily based on characteristic selection that's used as dimensional discount. One of the blessings of the proposed method is that not one of the current dimensional reduction algorithms hired with SVM to perform breast most cancers detection and class. The overall results acquired signify that the AlexNet-DNN based capabilities at completely connected layer; FC6 together with LDA dimensional discount and SVM-based totally classification outperforms other country-of-artwork techniques for breast cancer detection. The proposed method completed 96.20 for AlexNet-FC6 and 96.70 for AlexNet-FC7 in term of assessment measures.
Assessing the Scope of Generalized Countermeasures for Anti-Spoofing

Most of the research on anti-spoofing countermeasures are specific to a type of spoofing attacks, where models are trained on data of a particular nature, either synthetic or replay. However, one does not have such leverage as there is no prior knowledge about the kind of spoofing attack in practice. Therefore, there is a requirement to assess the scope of generalized countermeasures for anti-spoofing. The ASVspoof 2019 challenge covers both synthetic as well as replay attacks, which makes the database suitable for such study. In this work, we consider widely popular constant-Q cepstral coefficient features along with two other promising front-ends that capture long-term signal characteristics to assess their scope as generalized countermeasures. Additionally, a comprehensive study is made across different editions of ASVspoof corpora to highlight the need of robust generalized countermeasures in unseen conditions.
Quantifying and Detecting Collective Motion in Crowd Scenes

People in crowd scenes always exhibit consistent behaviors and form collective motions. The analysis of collective motion has motivated a surge of interest in computer vision. Nevertheless, the effort is hampered by the complex nature of collective motions. Considering the fact that collective motions are formed by individuals, this paper proposes a new framework for both quantifying and detecting collective motion by investigating the spatio-temporal behavior of individuals. The main contributions of this work are threefold: 1) an intention-aware model is built to fully capture the intrinsic dynamics of individuals; 2) a structure-based collectiveness measurement is developed to accurately quantify the collective properties of crowds; 3) a multistage clustering strategy is formulated to detect both the local and global behavior consistency in crowd scenes. Experiments on real world data sets show that our method is able to handle crowds with various structures and time-varying dynamics. Especially, the proposed method shows nearly 10% improvement over the competitors in terms of NMI, Purity and RI. Its applicability is illustrated in the context of anomaly detection and semantic scene segmentation.
Enable faster and smoother spatio-temporal trajectory planning for autonomous vehicles in constrained dynamic environment

Trajectory planning is of vital importance to decision-making for autonomous vehicles. Currently, there are three popular classes of cost-based trajectory planning methods: sampling-based, graph-search-based, and optimization-based. However, each of them has its own shortcomings, for example, high computational expense for sampling-based methods, low resolution for graph-search-based methods, and lack of global awareness for optimization-based methods. It leads to one of the challenges for trajectory planning for autonomous vehicles, which is improving planning efficiency while guaranteeing model feasibility. Therefore, this paper proposes a hybrid planning framework composed of two modules, which preserves the strength of both graph-search-based methods and optimization-based methods, thus enabling faster and smoother spatio-temporal trajectory planning in constrained dynamic environment. The proposed method first constructs spatio-temporal driving space based on directed acyclic graph and efficiently searches a spatio-temporal trajectory using the improved A* algorithm. Then taking the search result as reference, locally convex feasible driving area is designed and model predictive control is applied to further optimize the trajectory with a comprehensive consideration of vehicle kinematics and moving obstacles. Results simulated in four different scenarios all demonstrated feasible trajectories without emergency stop or abrupt steering change, which is kinematic-smooth to follow. Moreover, the average planning time was 31 ms, which only took 59.05%, 18.87%, and 0.69%, respectively, of that consumed by other state-of-the-art trajectory planning methods, namely, maximum interaction defensive policy, sampling-based method with iterative optimizations, and Graph-search-based method with Dynamic Programming.
Speaker Augmentation for Low Resource Speech Recognition

Text-to-speech synthesis (TTS) has been used as a data augmentation approach for automatic speech recognition (ASR), leveraging additional texts for ASR training. However, in low resource tasks, usually only a limited number of speakers are available, leading to the lack of speaker variations in synthetic speech. In this paper, we propose a novel speaker augmentation approach which can synthesize data with sufficient speaker and text diversity. Here, an end-to-end TTS system is trained with speaker representations from a variational auto-encoder (VAE), which enables TTS to synthesize speech from unseen new speakers via sampling from the trained latent distribution. As a new type of data augmentation approach, speaker augmentation can be combined with traditional feature augmentation approaches, such as SpecAugment. Experiments on a switchboard task show that, given 50 hours of data, the proposed speaker augmentation with SpecAugment significantly reduces word error rate (WER) by 30% relative compared to the system without any data augmentation, and about 18% relative compared to the system with SpecAugment.
Gun Detection System Using Yolov3

Based on current situation around the world, there is major need of automated visual surveillance for security to detect handgun. The objective of this paper is to visually detect the handgun in real time videos. The proposed method is using YOLO-V3 algorithm and comparing the number of false positive and false negative with Faster RCNN algorithm. To improve the result, we have created our own dataset of handguns with all possible angles and merged it with ImageNet dataset. The merged data was trained using YOLO-V3 algorithm. We have used four different videos to validate the results of YOLO-V3 compared to Faster RCNN. The detector performed very well to detect handgun in different scenes with different rotations, scales and shapes. The results showed that YOLO-V3 can be used as an alternative of Faster RCNN. It provides much faster speed, nearly identical accuracy and can be used in a real time environment.
Partial AUC Optimization Based Deep Speaker Embeddings with Class-Center Learning for Text-Independent Speaker Verification

Deep embedding based text-independent speaker verification has demonstrated superior performance to traditional methods in many challenging scenarios. Its loss functions can be generally categorized into two classes, i.e., verification and identification. The verification loss functions match the pipeline of speaker verification, but their implementations are difficult. Thus, most state-of-the-art deep embedding methods use the identification loss functions with softmax output units or their variants. In this paper, we propose a verification loss function, named the maximization of partial area under the Receiver-operating-characteristic (ROC) curve (pAUC), for deep embedding based text-independent speaker verification. We also propose a class-center based training trial construction method to improve the training efficiency, which is critical for the proposed loss function to be comparable to the identification loss in performance. Experiments on the Speaker in the Wild (SITW) and NIST SRE 2016 datasets show that the proposed pAUC loss function is highly competitive with the state-of-the-art identification loss functions.
Predicting Invasive Ductal Carcinoma in breast histology images using Convolutional Neural Network

Over the past ten years, there has been a rise in using deep learning for medical image analysis such as CNN. Deep learning is used extensively in the field of healthcare to identify patterns, classify and segment tumors and so on. The classification of breast cancer is a well-known problem that attracts the attention of many researchers in the field of healthcare because breast cancer is the second major cause of cancer-related deaths in women. The most common subtype of all breast cancers is the Invasive Ductal Carcinoma (IDC). There are many ways to identify this type of breast cancer such as a biopsy where tissue is removed from patient and studied under microscope. The biopsy is followed by a diagnosis which is based on the qualification of the pathologists, who will look for abnormal cells. The next task for pathologists is to assign an aggressiveness grade to a whole mount sample. To do this, pathologists focus on the region of interest which contain the IDC. Therefore, one of the popular pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide. In this paper, we have experimentally tested two CNN models using depthwise separable convolution and standard convolution to enhance the accuracy of the convolutional neural network. We tested different types of activation functions such as ReLU, Sigmoid, and Tanh. As well as applying Gaussian noise to test the robustness of the two models. The results show convolutional neural networks outperformed the softmax classifier, with standard convolution and ReLU where we achieved ~87.5% classification accuracy, ~93.5% sensitivity, and ~71.5% specificity.
Anonymous decentralized attribute-based access control for cloud-assisted IoT

Attribute-Based Encryption (ABE) has emerged as powerful cryptographic tools to bring fine-grained access control with widespread applications such as Cloud-assisted IoT data sharing. Subsequently, decentralized ABE with untrusted attribute authorities is proposed to remove the online Trusted Authority (TA). In the decentralized architecture, a user as a data customer (e.g., IoT-device) submits his attributes to the untrusted authorities to get the private keys. In the architecture, user’s privacy, against the untrusted authorities, is a significant challenge that must be ensured (e.g., E-health Cloud application). In this paper, we address the privacy issue in the decentralized ABE and propose a novel anonymous and decentralized attribute-based encryption in the standard model. It preserves the user’s anonymity against the authorities in an efficient manner. In our solution, we use cryptographic accumulators to verify the user’s attributes anonymously. Then, we include the accumulator in the ciphertext to ensure the ABE access control against unauthorized users. Moreover, in some applications, access structures (encryption/decryption policy) include sensitive information and should be obfuscated from everyone minus the users whose secret key attributes meet the access structures. To ensure the hidden policy, we propose an efficient and decentralized policy obfuscation technique to preserve the privacy of the policy against the Public Cloud Server (PCS). It is exciting for a decentralized environment where the authorities are untrusted and may collude with the PCS. To be applicable for IoT resource-constrained devices, we outsource the expensive decryption computation over powerful Cloud servers. Then, we formally analyze the security properties of the proposed scheme and conduct experimental results to show its efficiency. Finally, we briefly explain how the features of the proposal meet the requirements of some real-world applications.
VisuaLint: Sketchy In Situ Annotations of Chart Construction Errors

Abstract Chart construction errors, such as truncated axes or inexpressive visual encodings, can hinder reading a visualization, or worse, imply misleading facts about the underlying data. These errors can be caught by critical readings of visualizations, but readers must have a high level of data and design literacy and must be paying close attention. To address this issue, we introduce VisuaLint: a technique for surfacing chart construction errors in situ. Inspired by the ubiquitous red wavy underline that indicates spelling mistakes, visualization elements that contain errors (e.g., axes and legends) are sketchily rendered and accompanied by a concise annotation. VisuaLint is unobtrusive — it does not interfere with reading a visualization — and its direct display establishes a close mapping between erroneous elements and the expression of error. We demonstrate five examples of VisualLint and present the results of a crowdsourced evaluation (N = 62) of its efficacy. These results contribute an empirical baseline proficiency for recognizing chart construction errors, and indicate near‐universal difficulty in error identification. We find that people more reliably identify chart construction errors after being shown examples of VisuaLint, and prefer more verbose explanations for unfamiliar or less obvious flaws.
High-Capacity Data Hiding for ABTC-EQ Based Compressed Image

We present a new data hiding method based on Adaptive BTC Edge Quantization (ABTC-EQ) using an optimal pixel adjustment process (OPAP) to optimize two quantization levels. The reason we choose ABTC-EQ as a cover media is that it is superior to AMBTC in maintaining a high-quality image after encoding is executed. ABTC-EQ is represented by a form of t r i o ( Q 1 , Q 2 , [ Q 3 ] , BM) where Q is quantization levels ( Q 1 ≤ Q 2 ≤ Q 3 ) , and BM is a bitmap). The number of quantization levels are two or three, depending on whether the cover image has an edge or not. Before embedding secret bits in every block, we categorize every block into smooth block or complex block by a threshold. In case a block size is 4x4, the sixteen secret bits are replaced by a bitmap of the smooth block for embedding a message directly. On the other hand, OPAP method conceals 1 bit into LSB and 2LSB respectively, and maintains the quality of an image as a way of minimizing the errors which occur in the embedding procedure. The sufficient experimental results demonsrate that the performance of our proposed scheme is satisfactory in terms of the embedding capacity and quality of an image.