Reviewerly/exHarmony huggingface dataset:
Towards Rotation Invariance in Object Detection

Rotation augmentations generally improve a model’s in-variance/equivariance to rotation - except in object detection. In object detection the shape is not known, therefore rotation creates a label ambiguity. We show that the de-facto method for bounding box label rotation, the Largest Box Method, creates very large labels, leading to poor performance and in many cases worse performance than using no rotation at all. We propose a new method of rotation augmentation that can be implemented in a few lines of code. First, we create a differentiable approximation of label accuracy and show that axis-aligning the bounding box around an ellipse is optimal. We then introduce Rotation Uncertainty (RU) Loss, allowing the model to adapt to the uncertainty of the labels. On five different datasets (including COCO, PascalVOC, and Transparent Object Bin Picking), this approach improves the rotational invariance of both one-stage and two-stage architectures when measured with AP, AP50, and AP75.
Reading fiction with an e-book or in print

Abstract Empirical research on the differences between digital and print reading has recently increased, mainly concentrating on informational texts while disregarding literary texts. Concerning narrative fiction, the existing quantitative studies have found no or very few differences between reading printed books and e-books. In our focus group study, we amplify the perspective on digital and print book reading through a largely explorative approach. The results gained by interviewing 34 habitual readers of e-books in six groups show that e-books complement rather than replace printed books. Crucial differences can be found in the dimensions of the reading situation, genre selection, purpose of reading, as well as literary quality and status of the text. Furthermore, our results shed new light on the importance of the printed book as an individual material object, with its own specific iconicity and with notable consequences for intellectual possession, memory, and remembrance of read books and lived reading experiences.
A Car-like Mobile Manipulator with an n-link Prismatic Arm

In this research, the Lyapunov based Control Scheme (LbCS) is used to solve the motion planning and control problem of a car-like mobile robot with a long extendible prismatic arm comprising $n \in\mathrm{N}$ links. The prismatic arm consists of a base revolute joint and $n \in\mathrm{N}$ translational joints, and is mounted on the wheeled car-like mobile platform. The kinematic model of the manipulator is developed, and velocity based algorithms are utilized to firstly, move the car-like base from an initial position to its pseudo-target and secondly, maneuver the end-effector to its designated target, taking into account the restrictions and limitations of the prismatic links and the steering control laws of the system. Computer simulations are presented to illustrate the effectiveness of the proposed control laws.
An integrated approach for eye centre localization using deep networks and rectangular-intensity-gradient technique

Eye centre localization plays a crucial role in computer vision applications like face recognition, gaze estimation, driver fatigue detection, liveness detection, etc. However, it is difficult to localize the eye centre due to the variations in pose, occlusion, illumination, specular reflection, rotation, scale, etc. This work proposes an integrated approach robust to the variations mentioned above and localizes the eye centre precisely. This integrated approach consists of (i) Faster RCNN deep learning model to detect the face and eyes; (ii) AlexNet is utilized for eye openness detection using transfer learning; (iii) Rectangular-intensity-gradient (RIG) is proposed for eye centre localization. Experimentations were performed on benchmark databases like GI4E and BioID. An accuracy of 97.50% and 94.25% for Nerr ≤ 0.05, 98.75% and 98.40% for Nerr ≤ 0.10, 99.64% and 99.45% for Nerr ≤ 0.25 was achieved across GI4E and BioID databases respectively. In addition, the robustness of the proposed approach was tested on AR and CAS-PEAL databases. The proposed approach of eye centre localization performs relatively better than existing state-of-the-art methods in terms of accuracy and computational time.
An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

Abstract Recently, automatic modulation classification (AMC) has extensively and commonly been utilized in several modern wireless communication systems as a significant tool of signal detection for civilian and military applications and cognitive radio systems. Although several methods have been established to identify the modulation scheme of a received signal, they show a difficulty of learning radio characteristics for most conventional machine learning algorithms. This article focuses on the deep learning (DL) classification technique to solve these problems. To improve the classification accuracy of a communication signal modulation type, we apply a new model that combines Gabor filtering and thresholding with the help of convolution filters implemented in DL. A basic convolutional neural network, AlexNet, and a residual neural network are used for being compatible with constellation diagrams in order to achieve a superior classification performance. Moreover, the Gabor filter can effectively extract spatial information, including edges and textures. In terms of classification accuracy, the proposed AMC system improves the signal modulation classification accuracy significantly, and achieves competitive results. We use seven modulation types over the range of signal‐noise ratio (SNR) values from −10 to 30 dB. The performed experiments reveal that the proposal guarantees a remarkable classification accuracy of approximately 100% at a 10 dB SNR over AWGN and Rayleigh fading channels. Therefore, to prove the functional viability of our proposed method, it can be applied in adaptive modulators that can be used in many devices in applications such as Internet‐of‐Things (IoT).
Correction model of linear structured light sensor in underwater environment

Underwater 3D reconstruction using linear structured light sensor is crucial for many applications. This paper presents a correction model of a linear structured light sensor and a calibration method for such a model. The model takes into account the distortion introduced to the system by the flat sealed glass and the water medium. This model takes the linear structured light sensor as a whole to analyze the influence of underwater multi-medium refraction on it, instead of considering the influence of underwater refraction on camera imaging alone. The correction model can be modeled knowing the thickness of the flat window, the refractive index of the air, the refractive index of the flat widow, the refractive index of the water, the coordinates of the camera pinhole center, and the distance between the sensor coordinate system and the flat glass interface. The present study reports results obtained using the real sensor in a water tank to reconstruct objects to assess the sensor's accuracy.
Vehicle-based secure location clustering for IoT-equipped building and facility management in smart city

Vehicles equipped with various sensing devices have the strong ability to generate location information, which is beneficial to a lot of applications in smart city. In special, it is important to take full advantage of the traffic information to manage the buildings and facilities with special services, including charging/gas stations, car parks and leisure centers. However, location data are outsourced to the third party (e.g., vehicles and the vehicular cloud), which causes high security risks. In this paper, we first define a personalized local differential privacy criterion for location data, which offers a strong private guarantee to drivers. Then, we propose a density-based location clustering mechanism, i.e., PLPP-CLIQUE, which not only satisfies the proposed privacy criterion but also maximizes the utility of vehicles for location-based services. Moreover, we design a more efficient mechanism, i.e., Improved-PLPP-CLIQUE, to increase the utility of location clustering for vehicular clouds under the same privacy requirement. We demonstrate that our mechanisms satisfy the privacy criterion and have the high utility by theoretical analysis. Finally, we perform extensive experiments to show the good capability of our mechanisms.
Lyapunov-based Controllers of an n-link Prismatic Robot Arm

This research provides a generalized stabilizing velocity controllers for planer robot arm with a base rotational joint and $n\in \mathbb{N}$ translation joint for navigation. The end-effector of the planer robot arm has to navigate from an initial to a final configuration space in an environment, which cluttered with obstacles. The velocity controllers are developed from a Lyapunov function, total potentials, designed via Lyapunov-based control scheme (LbCS) falling under the classical approach of artificial potential fields method. The effectiveness of the controllers is validated through computer simulations.
INFLUENCE OF BLOCKCHAIN AND SMART CONTRACTS ON PARTNERS’ TRUST, VISIBILITY, COMPETITIVENESS, AND ENVIRONMENTAL PERFORMANCE IN MANUFACTURING SUPPLY CHAINS

This study investigates the influence of blockchain and smart contracts on partners’ trust, increasing visibility, competitiveness, and environmental performance when implemented in manufacturing supply chains. Partial least squares (PLS) was the main method used to test and verify the research model and hypotheses. According to the test results, blockchain and smart contracts positively influence partners’ trust. Increasing trust produces a positive effect on increasing visibility, further promoting competitiveness and environmental performance. However, the vulnerability of smart contracts still causes information security concerns. If smart contracts are adopted for an extended time, partners will worry about problems of information vulnerability stemming from the smart contract, further reducing trust and affecting visibility. Regarding academic implications, this study not only proves the relationship between blockchain, smart contracts, partners’ trust, increasing visibility, competitiveness, and environmental performance. In addition, we also proved the vulnerability of smart contracts; our test results fill these gaps. Regarding practical implications, with extended contract periods, partners may expand their doubt in the smart contract and reduce information sharing, further affecting visibility. Manufacturers are reminded that smart contracts may carry hidden trouble that disrupts the relationship between partners’ trust and visibility.
Detection of Deep-Morphed Deepfake Images to Make Robust Automatic Facial Recognition Systems

Face Morphing has emerged as a pervasive attack of Facial Recognition Systems. The rapid growth of Generative Adversarial Networks takes it to a complete new level. Deepfake or deep neural network based face morphing, a.k.a deep-morph attack, presents a significant threat to Facial Recognition System. In this paper, we propose a novel Convolutional Neural Network based detection method of deep morphed deepfake images which is suitable for IoT environments in smart cities. A high accuracy of 94.83% has been achieved for the DeepfakeTIMIT HQ dataset. This lightweight and fast network is a natural choice for IoT environments.
Adaptive Cost Volume Representation for Unsupervised High-Resolution Stereo Matching

Learning-based stereomatching methods have produced remarkable results in recent years. However, typical supervised learning-based methods always suffer from the non-negligible problem of costly and time-consuming depth annotations. To mitigate this issue, in this work, a multi-stage unsupervised stereo matching method based on the cascaded Siamese network is proposed. To obtain a better performance on depth annotations, the improvements of this work are as follows. Firstly, sparse costs are constructed to predict the coarse disparity, and an adaptive sampling strategy is developed to dynamically adjust the sampling interval and effectively narrow the disparity search range. The proposed cost sparse and sampling strategy can certainly guarantee the accuracy of disparity estimation under the limited memory requirements. Then, geometric constraints with left and right semantic features are integrated into the loss function to learn the inherent matching correspondences. Next, information entropy of the probability volume is used to measure the quality of estimated disparity and designed as weighted guidance for the photometric loss. Finally, a pixel-wise disparity refinement module is designed to achieve high-resolution disparity estimation at the final stage. Experimental results on the datasets, including SceneFlow and KITTI, show the effectiveness and practicability of our method with limited memory consumption and running time.
Smart Recon: Network Traffic Fingerprinting for IoT Device Identification

The ubiquity of IoT devices in both public and private networks has increased dramatically in the last few years with billions of network connected devices appearing in every sector. In many cases these devices provide low-power and low-cost solutions to multiple problems, but this convenience comes with a price. Low-powered devices often lack the computational capacity to support encryption or other means of protection. In addition, devices are often optimized for easy connection out-of-the-box, potentially leaving them vulnerable due to default configurations. Accounting for IoT devices within a network can be problematic as spoofing of IP and MAC addresses is trivial, but device identification is fundamental to network security. Much work has been done in recent times to address the problem of identification by fingerprinting network traffic using various machine learning techniques, allowing network administrators to track device membership and detect anomalous behavior. While a high degree of accuracy has been achieved, effective feature extraction and acceptable computational overhead continue to be an issue. In addition, machine leaning models must be frequently modified and updated to remain effective. We apply a combination of locality sensitive hashing and neural networks to identify specific devices based on their network traffic, eliminating the need for complex feature engineering and model retraining. Our approach achieves an accuracy identifying known devices as high as 98 % using only a single packet sniffed from the network, allowing for real-time device identification. In addition, our approach eliminates the need for feature extraction providing a significant improvement over other approaches.
Perspective—Longitudinal Sleep Monitoring for All: Payoffs, Challenges and Outlook

Longitudinal tracking of sleep metrics is important for detecting and managing various diseases, spanning cardiorespiratory disorders to dementia. However, at present, sleep monitoring primarily occurs in specialized medical facilities that are not conducive to long-term studies. In-home solutions either compromise user comfort or signal accuracy in tracking sleep variables and have not yet provided reliable longitudinal data. Here, we survey the current state of sleep trackers and highlight key shortcomings to provide guiding principles for improved sensor system design. We believe that human-centered design of multimodal, low-form-factor, comfortable sensing systems is needed for this increasingly-important area of human health monitoring.
MAC: An Open and Free Moroccan Arabic Corpus for Sentiment Analysis

The proliferation of social media has allowed Internet users to post their views and opinions online. This generated a vast amount of raw data in informal ways. For many organizations and individuals, this data is vital for providing insight into future decisions. Preprocessed corpora are considered as the basic requirement for the development and evaluation of opinion mining (OM) systems. However, the vast majority of corpora intended for OM research are not large and free for the researchers' community. This lack of free and large OM corpora represents a major obstacle for promoting research on sentiment analysis systems, especially for rich and complex languages as the Moroccan Arabic (MA) one. To overcome this gap, this paper presents a new contribution to the MA resources. A free and large Moroccan Arabic corpus consisting of 18000 manually labeled tweets resulting in a lexicon-dictionary of 30000 words labeled as positive, negative and neutral. To the best of our knowledge, MAC (Moroccan Arabic Corpus) is the first open and largest MA corpus for sentiment analysis. It is pioneer by its size, its quality given by the consistency of the native annotators (IAA = 0.9), and its accessibility to the research community. The MAC is benchmarked for forthcoming works through an exploratory data analysis carried out using the two-sentiment analysis approaches for polarity classification as well as language identification. In addition, the MAC corpus along with the necessary code to explore it have been released.
Region or Global A Principle for Negative Sampling in Graph-based Recommendation

Graph-based recommendation systems are blossoming recently, which models user-item interactions as a user-item graph and utilizes graph neural networks (GNNs) to learn the embeddings for users and items. A fundamental challenge of graph-based recommendation is that there only exists observed positive user-item pairs in the user-item graph. Negative sampling is a vital technique to solve the one-class problem and is widely used in many recommendation methods. However, the previous works only focus on the design of negative sampling distribution but ignore the sampled region for negative sampling. In this work, we propose the Three-Region Principle to guide negative sampling, which suggests that we should negatively sample more items at an intermediate region and less adjacent and distant items. In light of this principle, we present the RecNS method, which is a general negative sampling method designed with two sampling strategies: positive-assisted sampling and exposure-augmented sampling. Instead of sampling existing negative items from graph data, we merge these two strategies in embedding space to generate negative item embeddings. Extensive experiments demonstrate that our proposed RecNS method significantly outperforms all negative sampling baselines, e.g., 10.47% for PinSage, 6.02% for NGCF, and 8.20% for LightGCN in terms of Recall@20 on the Alibaba dataset.
Novel Encoding method for Quantum Error Correction

Quantum error correction plays a vital role in the Quantum information process. Nowadays, the research has been increased in quantum technology and is being applied in various applications like secure communications, finance, machine learning, drug analysis and etc. But the quantum information process is difficult compared to the classical due to the challenges in quantum technology like no-cloning theorem, decoherence, and the difficulty in quantum states measurement. Thus the error rate is high and it is not possible to build a quantum computer without an error detection and correction mechanism. In this paper, we are discussing various error correction methods and proposing a novel encoding method for quantum error correction.
Rotation‐Invariant Self‐Similarity Descriptor for Multi‐Temporal Remote Sensing Image Registration

Abstract In this paper, a novel approach for the registration of multi‐sensor remote sensing images with substantial time differences is proposed. The proposed method consists of four main steps. First, robust image features are extracted using the well‐known UR‐SURF (uniform robust‐speeded up robust features) algorithm. Second, the feature descriptors are generated using a novel method based on self‐similarity measure, named RISS (rotation invariant self‐similarity). The RISS descriptor is an inherent rotation‐invariant descriptor based on the gradient orientation histogram of correlation values and is very resistant against illumination differences. Third, the outlier rejection process is performed based on a simple improvement of graph transform matching, named LWGTM (localized weighted graph transformation matching). Finally, the estimation of the transformation model and the rectification process are done using TPS (thin‐plate spline) model and the bilinear interpolation method. Five multi‐sensor remote sensing image pairs with relatively long years of time difference are used for evaluation. The results indicate the capability of the proposed method for reliable remote sensing image registration. The average recall, precision, the number of extracted matched points and the average registration accuracy of the proposed method are about 31.6, 39.5, 4940, and 1.8 pixels, respectively.
Automatically recognizing four-legged animal behaviors to enhance welfare using spatial temporal graph convolutional networks

Automatically recognizing animal behaviors in zoos and in national natural reserves can provide valuable insight into their welfare for facilitating scientific decision-making processes in animal management. Due to the difficulty of capturing massive amounts of animal video footage, a few existing methods have identified the behaviors of several different animal species in static images, but little is known about video-based animal behavior recognition. An animal's behavior is carried out in consecutive frames rather than in a single image; thus, image-based animal behavior recognition methods have low recognition accuracy. To address this dilemma, we not only construct the first skeleton-based dynamic multispecies dataset (Animal-Skeleton) but also propose a novel scheme that automatically designs the best spatial-temporal graph convolutional network (GCN) architecture with neural architecture search (NAS) to perform animal behavior recognition, named Animal-Nas for short. This is the first time that GCNs with NAS have been introduced into the animal behavior recognition task. To alleviate the trial-and-error cost of manually designing the network structure, we turn to NAS and design a novel search space with graph-based cells. Furthermore, we adopt a differentiable architecture search strategy to automatically search the cost-efficient spatial-temporal graph convolutional network structure. To evaluate the performance of the proposed model, we conduct extensive experiments on Animal-Skeleton datasets from three perspectives: model accuracy, parameter amount and stability. The results show that our model can achieve state-of the-art performance with fewer parameters.
Distribution and quantification of remotely generated Wigner negativity

Abstract Wigner negativity, as a well-known indicator of nonclassicality, plays an essential role in quantum computing and simulation using continuous-variable systems. The conditional preparation of Wigner-negative states through appropriate non-Gaussian operations on an auxiliary mode is common procedure in quantum optics experiments. Motivated by the demand of real-world quantum network, here we investigate the remote creation and distribution of Wigner negativity in the multipartite scenario from a quantitative perspective. By establishing a monogamy relation akin to the generalized Coffman-Kundu-Wootters inequality, we show that the amount of Wigner negativity cannot be freely distributed among different modes. Moreover, for photon subtraction—one of the main experimentally realized non-Gaussian operations—we provide an intuitive method to quantify remotely generated Wigner negativity. Our results pave the way for exploiting Wigner negativity as a valuable resource for numerous quantum information protocols based on non-Gaussian scenario.