Reviewerly/exHarmony huggingface dataset:
Accurate Detection of IoT Sensor Behaviors in Legitimate, Faulty and Compromised Scenarios

In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart network's performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Moran's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> index tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.
Noise/fault aware regularization for incremental learning in extreme learning machines

This paper investigates noise/fault tolerant incremental algorithms for the extreme learning machine (ELM) concept. Existing incremental ELM algorithms can be classified into two approaches: non-recomputation and recomputation. This paper first formulates a noise/fault aware objective function for nonlinear regression problems. Instead of developing noise/fault aware algorithms for the two computational approaches in a one-by-one manner, this paper uses two representative incremental algorithms, namely incremental ELM (I-ELM) and error minimized ELM (EM-ELM), to develop two noise/fault aware incremental algorithms. The proposed algorithms are called generalized I-ELM (GI-ELM) and generalized EM-ELM (GEM-ELM). The GI-ELM adds k hidden nodes into the existing network at each incremental step without recomputing the existing weights. To have a fair comparison, we consider a modified version of I-ELM as a comparison algorithm. The simulation demonstrates that the noise/fault tolerance of the proposed GI-ELM is better than that of the modified I-ELM. In the GEM-ELM, k hidden nodes are added into the existing network at each incremental step. Meanwhile, all output weights are recomputed based on a recursive formula. We also consider a modified version of EM-ELM as a comparison algorithm. The simulation demonstrates that the noise/fault tolerance of the proposed GEM-ELM is better than that of the modified EM-ELM. Moreover, we demonstrate that the multiple set concept can further enhance the performance of the two proposed algorithms. Following our research results, one can make some non-noise/fault tolerant incremental algorithms to be noise/fault tolerant.
Faster Dual Lattice Attacks for Solving LWE with Applications to CRYSTALS

Cryptosystems based on the learning with errors (LWE) problem are assigned a security level that relates to the cost of generic algorithms for solving the LWE problem. This includes at least the so-called primal and dual lattice attacks. In this paper, we present an improvement of the dual lattice attack using an idea that can be traced back to work by Bleichenbacher. We present an improved distinguisher that in combination with a guessing step shows a reduction in the overall complexity for the dual attack on all schemes. Our second contribution is a new two-step lattice reduction strategy that allows the new dual lattice attack to exploit two recent techniques in lattice reduction algorithms, i.e., the "dimensions for free" trick and the trick of producing many short vectors in one sieving. Since the incompatibility of these two tricks was believed to be the main reason that dual attacks are less interesting, our new reduction strategy allows more efficient dual approaches than primal attacks, for important cryptographic parameter sets.
A zero-shot learning framework via cluster-prototype matching

Given the descriptions of classes, Zero-Shot Learning (ZSL) aims to recognize unseen samples by learning a projection between the visual features of samples and the semantic descriptions (prototypes) of classes from seen data. However, due to the inherent distribution gap between seen and unseen domains, the learned projection is generally biased to seen classes and may produce misleading relationships between unseen samples and prototypes (sample-prototype relationship). To tackle this problem, we propose a Cluster-Prototype Matching (CPM) framework which exploits the distribution information of samples to explore the cluster structure of samples and then use the robust cluster-prototype relationship to correct the biased sample-prototype relationship. Specifically, we first use an iterative cluster generation module to identify the underlying cluster structure of samples based on their embedding features, which are acquired via a basic ZSL model. Then each identified cluster will be matched with a specific class prototype through the Kuhn-Munkres algorithm, based on which we can export a sharp cluster-prototype similarity. Finally, the cluster-prototype similarity is combined with the sample-prototype similarity to determine the class labels of test samples. We apply CPM to five well-established ZSL methods and the experimental results show that CPM can significantly improve the performance of basic models and enable them achieve or beyond the state-of-the-art.
Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments

This paper proposes a new blind, color image watermarking method using fast Walsh-Hadamard transformation (FWHT) and multi-channel fractional Legendre-Fourier moments (MFrLFMs). The input host color image is first split into 4 × 4 non-interfering blocks, and the MFrLFMs are computed for each block, where proper MFrLFMs coefficients are selected and FWHT is applied on the selected coefficients. The scrambled binary watermark has been inserted in the quantized selected MFrLFMs coefficients. The proposed method is a blind extraction, as the original host image is not required to extract the watermark. The proposed method is evaluated over many visual imperceptibility terms such as peak signal-to-noise ratio (PSNR), normalized correlation (NC), and bit error rate. The robustness of the proposed method is tested over several geometrical attacks such as scaling, rotation, cropping, and translation with different parameter values. The most widely recognized image processing attacks are also considered, e.g., compressing and adding noise attacks. A set of combination attacks are also tested to analyze the robustness of the proposed scheme versus several attacks. The proposed model's experimental and numerical results for invisibility and robustness were superior to the results of similar watermarking methods.
Classification of clothing images based on a parallel convolutional neural network and random vector functional link optimized by the grasshopper optimization algorithm

To improve accuracy in clothing image recognition, this paper proposes a clothing classification method based on a parallel convolutional neural network (PCNN) combined with an optimized random vector functional link (RVFL). The method uses the PCNN model to extract features of clothing images. Then, the structure-intensive and dual-channel convolutional neural network (i.e., the PCNN) is used to solve the problems of traditional convolutional neural networks (e.g., limited data and prone to overfitting). Each convolutional layer is followed by a batch normalization layer, and the leaky rectified linear unit activation function and max-pooling layers are used to improve the performance of the feature extraction. Then, dropout layers and fully connected layers are used to reduce the amount of calculation. The last layer uses the RVFL as optimized by the grasshopper optimization algorithm to replace the SoftMax layer and classify the features, further improving the stability and accuracy of classification. In this study, two aspects of the classification (feature extraction and feature classification) are improved, effectively improving the accuracy. The experimental results show that on the Fashion-Mnist dataset, the accuracy of the algorithm in this study reaches 92.93%. This value is 1.36%, 2.05%, 0.65%, and 3.76% higher than that of the local binary pattern (LBP)-support vector machine (SVM), histogram of oriented gradients (HOG)-SVM, LBP-HOG-SVM, and AlexNet-sparse representation-based classifier algorithms, respectively, effectively demonstrating the classification performance of the algorithm.
CenterPoly: real-time instance segmentation using bounding polygons

We present a novel method, called CenterPoly, for realtime instance segmentation using bounding polygons. We apply it to detect road users in dense urban environments, making it suitable for applications in intelligent transportation systems like automated vehicles. CenterPoly detects objects by their center keypoint while predicting a fixed number of polygon vertices for each object, thus performing detection and segmentation in parallel. Most of the network parameters are shared by the network heads, making it fast and lightweight enough to run at real-time speed. To properly convert mask ground-truth to polygon ground-truth, we designed a vertex selection strategy to facilitate the learning of the polygons. Additionally, to better segment overlapping objects in dense urban scenes, we also train a relative depth branch to determine which instances are closer and which are further, using available weak annotations. We propose several models with different backbones to show the possible speed / accuracy trade-offs. The models were trained and evaluated on Cityscapes, KITTI and IDD and the results are reported on their public benchmark, which are state-of-the-art at real-time speeds. Code is available at https://github.com/hu64/CenterPoly.
Intelligent classification for three-dimensional metal powder particles

The shape of constituent metal particles has a significant influence on the bulk properties of raw powder and the mechanical properties of manufactured parts. In this paper, we provide a method for automatic shape-based classification of metal powder particles. Firstly, X-ray computed tomography was used to obtain three-dimensional volume data of metal powder particles, and a segmentation operation was performed to separate particles from one another. Classifying by particle shape, particles were manually labelled to create training and testing data, separating the powder particles into one of six user-defined categories: 'connected', 'ellipsoidal', 'irregular', 'pear', 'porous' and 'spherical'. The machine learning network PointNet++ was used to classify particles, using 1024 automatically-defined geometric particle features, as well as twelve user-defined features. Our results show that the accuracy of this automatic classification method reaches 93.8%. Finally, an additional test batch of measured powder was used to verify our classification method.
Image preparations of multi-mode quantum image representation and their application on quantum image reproduction

Efficient and general quantum image representation and preparation are the precondition of quantum image processing. The multi-mode quantum image representation is compatible with many important color modes. Studying image preparation and image processing will help to further improve the universality of quantum image on different application fields. In this paper, we improve the multi-mode quantum image representation to reduce the difficulty of entanglement preparation. We give a detailed image preparation process and algorithm based on this proved representation. Then, to reduce the number of Not-gates in preparation circuits, the optimal preparation is proposed. Furthermore, we give the reproduction circuit based on these preparations. The preparation and reproduction experiments are performed on the IBM Quantum lab platform. The circuits and the Bell-state statistic histograms of the measurement collapse are given. The results show that the preparations and reproduction are correct and efficient.
Metro Anomaly Detection Based on Light Strip Inductive Key Frame Extraction and MAGAN Network

Abnormal objects in the risk space between the platform door and train will threaten the safe operation of the metro transportation system. However, selecting the right time to perform anomaly detection during the process of metro in and out station is the primary problem. The trouble of lacking abnormal samples, the high imbalance of abnormal categories, and the defects of abnormal regeneration are not well resolved in most reconstruction-based anomaly detection methods. To handle the aforementioned issues, a method based on light strip inductive key frame extraction and patch-level unsupervised network is proposed. The light strip inductive feature is designed to simulate the status of train door movement, which contributes to extracting key frames for anomaly detection. An unsupervised network, which is named metro anomaly generative adversarial network (MAGAN) and based on dilate fine-grained generator, multipatch discriminator, and local attention reconstruction loss, is proposed for anomaly classification and localization. Our network only utilizes normal samples for training and greatly suppresses the reconstruction of abnormalities by dilate fine-grained generators during testing. The discriminator and local attention reconstruction loss promote the interaction of global and local information in the image and provide a new multipatch-based anomaly localization scheme. The proposed method is tested on the metro risk space anomaly dataset, and the AUCs of anomaly classification and localization achieve 97.2% and 96.4%, respectively, which outperforms those of the state-of-the-art methods.
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation

Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects, which is essential for full scene understanding. Existing SGG methods trained on the entire set of relations fail to acquire complex reasoning about visual and textual correlations due to various biases in training data. Learning on trivial relations that indicate generic spatial configuration like ‘on’ instead of informative relations such as ‘parked on’ does not enforce this complex reasoning, harming generalization. To address this problem, we propose a novel framework for SGG training that exploits relation labels based on their informativeness. Our model-agnostic training procedure imputes missing informative relations for less informative samples in the training data and trains a SGG model on the imputed labels along with existing annotations. We show that this approach can successfully be used in conjunction with state-of-the-art SGG methods and improves their performance significantly in multiple metrics on the standard Visual Genome benchmark. Furthermore, we obtain considerable improvements for unseen triplets in a more challenging zero-shot setting.
Sentiment Analysis in Hindi—A Survey on the State-of-the-art Techniques

Sentiment Analysis (SA) has been a core interest in the field of text mining research, dealing with computational processing of sentiments, views, and subjective nature of the text. Due to the availability of extensive web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. It has become extremely significant to analyze this data and recover valuable and relevant information. Hindi being the first language of the majority of the population in India, SA in Hindi has turned out to be a critical task particularly for companies and government organizations. This research portrays a systematic review specifically in the field of Hindi SA. The major contribution of this article includes the categorization of numerous articles based on techniques that have attracted researchers in performing SA tasks in Hindi language. This survey classifies these state-of-the-art computational intelligence techniques into four major categories namely lexicon-based techniques, machine learning techniques, deep learning techniques, and hybrid techniques. It discusses the importance of these techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures. The research puts forward a comprehensive overview of the majority of the work done in Hindi SA. This study will help researchers in finding out resources such as annotated datasets, linguistic resources, and lexical resources. This survey delivers some significant findings and presents overall future research directions in the field of Hindi SA.
AI based Login System using Facial Recognition

With rapid growth in the application of AI, Access Control Systems are walking in a new technology lane. Powered by deep learning technologies or cognitive analytics, login pages can implement more secure, efficient, and easy to use authentication systems. Face Detection and Recognition is emerging as preferred solution to enable secure verification and authentication in login systems. Moreover, Facial Recognition has been applied in many fields from unlocking smartphones through built in camera of smartphones to identification of suspected people by the law enforcement organizations. The goal of this research paper is to provide an easier authentication system using Face Detection and Recognition instead of using usernames and passwords. This paper mainly analyzes the application of Face detection systems to authenticate and login users It presents the prototype system implemented with the usage of a Flask server, requesting face recognition services from Amazon's Rekognition. The prototype receives images of the user instead of his username and password. The received image is analyzed by AWS's Face recognition tools and the ID of the face is sent as a response along with the confidence level of the algorithm used to analyze the face. The prototype is tested with eight different faces and the system authenticate users with 100% accuracy and navigate them to their respective feeds.
Near-Optimal Covariant Quantum Error-Correcting Codes from Random Unitaries with Symmetries

Quantum error correction and symmetries play central roles in quantum information science and physics. It is known that quantum error-correcting codes that obey (are covariant with respect to) continuous symmetries in a certain sense cannot correct erasure errors perfectly (a well-known result in this regard being the Eastin-Knill theorem in the context of fault-tolerant quantum computing), in contrast to the case without symmetry constraints. Furthermore, several quantitative fundamental limits on the accuracy of such covariant codes for approximate quantum error correction are known. Here, we consider the quantum error correction capability of uniformly random covariant codes. In particular, we analytically study the most essential cases of $U(1)$ and $SU(d)$ symmetries, and show that for both symmetry groups the error of the covariant codes generated by Haar-random symmetric unitaries, i.e., unitaries that commute with the group actions, typically scale as $O(n^{-1})$ in terms of both the average- and worst-case purified distances against erasure noise, saturating the fundamental limits to leading order. We note that the results hold for symmetric variants of unitary 2-designs, and comment on the convergence problem of symmetric random circuits. Our results not only indicate (potentially efficient) randomized constructions of optimal $U(1)$- and $SU(d)$-covariant codes, but also reveal fundamental properties of random symmetric unitaries, which yield important solvable models of complex quantum systems (including black holes and many-body spin systems) that have attracted great recent interest in quantum gravity and condensed matter physics. We expect our construction and analysis to find broad relevance in both physics and quantum computing.
Event-centric multi-modal fusion method for dense video captioning

Dense video captioning aims to automatically describe several events that occur in a given video, which most state-of-the-art models accomplish by locating and describing multiple events in an untrimmed video. Despite much progress in this area, most current approaches only encode visual features in the event location phase and they neglect the relationships between events, which may degrade the consistency of the description in the identical video. Thus, in the present study, we attempted to exploit visual-audio cues to generate event proposals and enhance event-level representations by capturing their temporal and semantic relationships. Furthermore, to compensate for the major limitation of not fully utilizing multimodal information in the description process, we developed an attention-gating mechanism that dynamically fuses and regulates the multi-modal information. In summary, we propose an event-centric multi-modal fusion approach for dense video captioning (EMVC) to capture the relationships between events and effectively fuse multi-modal information. We conducted comprehensive experiments to evaluate the performance of EMVC based on the benchmark ActivityNet Caption and YouCook2 data sets. The experimental results showed that our model achieved impressive performance compared with state-of-the-art methods.
Universal Efficient Variable-Rate Neural Image Compression

Recently, Learning-based image compression has reached comparable performance with traditional image codecs(such as JPEG, BPG, WebP). However, computational complexity and rate flexibility are still two major challenges for its practical deployment. To tackle these problems, this paper proposes two universal modules named Energy-based Channel Gating(ECG) and Bit-rate Modulator(BM), which can be directly embedded into existing end-to-end image compression models. ECG uses dynamic pruning to reduce FLOPs for more than 50% in convolution layers, and a BM pair can modulate the latent representation to control the bit-rate in a channel-wise manner. By implementing these two modules, existing learning-based image codecs can obtain ability to output arbitrary bit-rate with a single model and reduced computation.
SwapInpaint: Identity-Specific Face Inpainting With Identity Swapping

As face editing scenarios have become popular, the face inpainting technique has become a hot topic. Although some existing methods can inpaint faces with preserved identity information, they fail to solve a more flexible inpainting problem that fills the “holes” with identity-specific content from other faces. In this work, we propose a disentangle and subject-agnostic framework that affects both full and partial-face inpainting with the guidance of a reference face image. The framework consists of an identity encoding module, a content inference module and a generative module. The identity encoding module extracts the identity embedding from the reference image, the content inference module learns to predict the content image, and the generative module integrates the content image and the reference identity embedding to generate the identity-specific inpainted result. To minimize the structure and style gap between the incomplete image and inpainted image, we use a double attribute loss to the generative module and a postprocess of blending operation to the swapped result. We compare our method with state-of-the-art works and demonstrate that our method achieves higher identity similarity and better structural correctness.