Reviewerly/exHarmony huggingface dataset:
Open-World Graph Active Learning for Node Classification

The great power of Graph Neural Networks (GNNs) relies on a large number of labeled training data, but obtaining the labels can be costly in many cases. Graph Active Learning (GAL) is proposed to reduce such annotation costs, but the existing methods mainly focus on improving labeling efficiency with fixed classes, and are limited to handle the emergence of novel classes. We term the problem as Open-World Graph Active Learning (OWGAL) and propose a framework of the same name. The key is to recognize novel-class as well as informative nodes in a unified framework. Instead of a fully connected neural network classifier, OWGAL employs prototype learning and label propagation to assign high uncertainty scores to the targeted nodes in the representation and topology space, respectively. Weighted sampling further suppresses the impact of unimportant classes by weighing both the node and class importance. Experimental results on four large-scale datasets demonstrate that our framework achieves a substantial improvement of 5.97% to 16.57% on Macro-F1 over state-of-the-art methods.
Individual-based predominance of visual input in multisensorial integration for balance is correlated with proprioceptive drift in rubber hand illusion

Rubber hand illusion (RHI) is a traditional task that examines multisensory integration. The visual capture of tactile stimulus given to the seen rubber hand was considered to predominate the sensory processing and interfere with the bottom-up proprioceptive and tactile inputs received from the unseen real hand that results in mislocalization of participants hand towards rubber hand, namely proprioceptive drift (PD). Another task that requires multisensorial integration and shows a predominance of visual input is the maintenance of body posture. However, if the predominance of visual input in one task is generalizable to another task is yet to be elucidated. We aimed to examine if individual dependency on visual inputs in multisensorial integration in balance correlated with PD in RHI. Twenty healthy participants were recruited for the study and completed the RHI task. The contribution of visual inputs to the static body balance was measured with the instrumented clinical test of sensory interaction for balance and indexed with Romberg Quotient (RQ). We found a moderate positive correlation between PD and RQ. Individuals with more dependence on visual information in maintaining body posture had higher PD in RHI. Our results indicate that there can be an individual-based dependence on particular domains of sensory input preserved during different tasks of multisensorial integration. Future studies must clarify whether this tendency relates to certain physical or physiological traits.
A Technological Smartness All Over the Place: Small‐Scale Thing‐Power Experiments With Wider Inclusive Ambitions

We live in a societal realm where robotics, artificial intelligence, and digitalization are strongly reshaping our futures. Technological progress has created multiple possibilities. However, the unequally divided impact of technological progress reminds us of the danger of an uncontrolled detonation of technological smartness in society. Some of its experienced and anticipated effects are most likely undesirable. In this thematic issue, we present a compilation of small‐scale experiments that help us think through the multiple challenges of a fast‐evolving techno‐mediated society. It sits on the cross‐road between resisting technology or insisting on it in order to create a more socially inclusive sustainable society. The technological “smartification” of our society reshapes our notion of what it means to be human in the complex assemblage with non‐human and other‐than‐human agents we are currently involved in. But it is also a catalyst for intelligent acts of human creativity that will strongly shape our collective future.
Identifying Incorrect Postures While Performing Sun Salutation Using MoveNet

Sun salutation or salute to the Sun is a sequence of about twelve poses that are gently linked to one another. The basic movement pattern entails rising to your feet, striking the downward and upward dog postures, and then rising to your feet again. Sun salutation stimulates the parasympathetic nervous system, which helps you to feel relaxed. Yoga necessitates expert coaching since it consists of asanas that must be performed correctly for the best results. COVID has taught us that we can perform many activities online like exercises. For the better experience in the online mode, there is a need to have a system that will not only check correct posture but also alert users if the pose is performed wrongly. This paper addresses this issue by creating a supervised system that detects a yoga pose using MoveNet architecture and neural networks and transfer learning for pose estimation. Twelve Sun salutation poses are examined to train the system and gage its prediction accuracy. The most recent, cutting-edge human posture estimation architecture, MoveNet model, developed by Google’s TensorFlow team, is employed. An effective use of this model’s KeyPoints regression field function was made on the input image frames to locate 17 KeyPoints on the human body. As a result of which, the paper offers a system having accuracy of 96% in identifying users’ yoga posture. The system is capable of verifying posture and sending buzzers to alert the users when they perform erroneous postures.
A Multi-Path Inpainting Forensics Network Based on Frequency Attention and Boundary Guidance

With the continuous advancement of image-editing technologies, it is particularly important to develop image forensics methods for digital information security. In this study, a deep neural network called multi-path inpainting forensics network (MPIF-Net) was developed to locate the inpainted regions in an image. The interaction of shallow and deep features between different paths was established, which not only preserved detailed information but also allowed for the further mining of deep features. Meanwhile, an improved residual dense block was employed as the deep feature extraction module of each path, which can enhance the feature extraction ability of the model by introducing a frequency domain attention mechanism. In addition, a boundary guidance module was constructed to alleviate the prediction distortion in the boundaries of the inpainted region. Finally, extensive experimental results regarding various deep inpainting datasets demonstrated that the proposed network can accurately locate inpainted regions, exhibit excellent generalization and robustness, and verify the effectiveness of the designed module.
Intelligent Personal Assistants in practice. Situational agencies and the multiple forms of cooperation without consensus

There are at least two perspectives concerning the role of digital and connected media systems in relation to individual agency. One suggests that individuals have a gain in agency while using digital devices and services while other approaches see that users become dependent on tech companies that use data flows to analyse and manipulate user behaviour. In our paper, we want to examine empirically how those two descriptions of agency work out and relate to each other. We use conversation analysis of face-to-interface interactions with smart speakers and intelligent personal assistants to examine the agency in front of the device and we rely on interviews with smart-speaker users to understand the users’ strategies to curtail the potentially unwanted effects of smart and connected devices and services. With reference to concepts of ‘co-operative work’ and ‘cooperation without consensus’ and a discussion of media and data practices, this paper elucidates how the two agencies of users and of device and service providers are intertwined and distributed.
Bayesian Multivariate Analysis of Mixed Data

Graphical models provide an effective tool to represent conditional independences among variables. While this class of models has been extensively studied in the Gaussian and categorical settings separately, state-of-the-art literature which jointly models the two types of data is narrow. However, mixed data are widespread in many applications where both continuous and categorical measurements are available, such as genomics or industrial machine processes. In this paper, we propose a Bayesian framework for the analysis of mixed data. Specifically, we define a likelihood function for n observations following a conditional Gaussian distribution and assign suitable priors to model parameters. We develop an MCMC scheme for approximate posterior inference in two alternative model parameterizations and a structure learning algorithm for related undirected graph models.
Color Image Encryption Using Hybrid Three-Scroll Unified Chaotic Attractor and 6D Hyperchaotic System

This paper proposes a hybrid encryption algorithm using the three-scroll unified chaotic attractor (TSUCA) and 6D hyperchaotic systems.With the help of a 32-character key, six highly sensitive initial conditions have been generated. Out of these six initial conditions, the first three have been used in TSUCA, and all six initial conditions have been used in the 6D hyperchaotic system along with the image information for generating the chaotic sequences. The proposed algorithm involves pixel confusion and pixel shuffling to acquire a high security level. Two-level encryption using chaotic sequences generated from TUSCA and 6D hyperchaotic systems are used in the encryption algorithm. To check the efficacy of the suggested algorithm, standard security tests like key space and key sensitivity, histogram analysis, correlation analysis, NPCR, UACI, entropy, noise effect, etc., have been performed. The suggested cryptosystem has shown promising results compared to other methods, as mentioned in this paper.
A Data Feature Extraction Method Based on the NOTEARS Causal Inference Algorithm

Extracting effective features from high-dimensional datasets is crucial for determining the accuracy of regression and classification models. Model predictions based on causality are known for their robustness. Thus, this paper introduces causality into feature selection and utilizes Feature Selection based on NOTEARS causal discovery (FSNT) for effective feature extraction. This method transforms the structural learning algorithm into a numerical optimization problem, enabling the rapid identification of the globally optimal causality diagram between features and the target variable. To assess the effectiveness of the FSNT algorithm, this paper evaluates its performance by employing 10 regression algorithms and 8 classification algorithms for regression and classification predictions on six real datasets from diverse fields. These results are then compared with three mainstream feature selection algorithms. The results indicate a significant average decline of 54.02% in regression prediction achieved by the FSNT algorithm. Furthermore, the algorithm exhibits exceptional performance in classification prediction, leading to an enhancement in the precision value. These findings highlight the effectiveness of FSNT in eliminating redundant features and significantly improving the accuracy of model predictions.
Classification Confidence in Exploratory Learning: A User’s Guide

This paper investigates the post-hoc calibration of confidence for “exploratory” machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the “one-versus-all” approach (top-label calibration) must be used rather than the “calibrate-the-full-response-matrix” approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation using only the test set and the final model. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) as well as the classic MNIST benchmark. Finally, our analysis argues that post-hoc calibration should always be performed, may be performed using only the test dataset, and should be sanity-checked visually.
Depth Information Precise Completion-GAN: A Precisely Guided Method for Completing Ill Regions in Depth Maps

In the depth map obtained through binocular stereo matching, there are many ill regions due to reasons such as lighting or occlusion. These ill regions cannot be accurately obtained due to the lack of information required for matching. Since the completion model based on Gan generates random results, it cannot accurately complete the depth map. Therefore, it is necessary to accurately complete the depth map according to reality. To address this issue, this paper proposes a depth information precise completion GAN (DIPC-GAN) that effectively uses the Guid layer normalization (GuidLN) module to guide the model for precise completion by utilizing depth edges. GuidLN flexibly adjusts the weights of the guiding conditions based on intermediate results, allowing modules to accurately and effectively incorporate the guiding information. The model employs multiscale discriminators to discriminate results of different resolutions at different generator stages, enhancing the generator’s grasp of overall image and detail information. Additionally, this paper proposes Attention-ResBlock, which enables all ResBlocks in each task module of the GAN-based multitask model to focus on their own task by sharing a mask. Even when the ill regions are large, the model can effectively complement the missing details in these regions. Additionally, the multiscale discriminator in the model enhances the generator’s robustness. Finally, the proposed task-specific residual module can effectively focus different subnetworks of a multitask model on their respective tasks. The model has shown good repair results on datasets, including artificial, real, and remote sensing images. The final experimental results showed that the model’s REL and RMSE decreased by 9.3% and 9.7%, respectively, compared to RDFGan.
Amplitude damping error on QKD: Effect and a probable bypass

Quantum cryptography was proposed as a counter to the capacity of quantum computers to break classical cryptosystems. A broad subclass of quantum cryptography, called quantum key distribution (QKD), relies on quantum mechanical process for secure distribution of the keys. Quantum channels are inherently noisy, and therefore these protocols will be susceptible to noise as well. In this paper, we study the performance of two QKD protocols — BB84 and E91 under amplitude damping error. We show that while E91 protocol loses its security immediately due to loss of entanglement, the performance of BB84 protocol reduces to random guessing with increasing time. Next, we consider the action of an Eve on the BB84 protocol under amplitude damping noise, who is restricted to guessing the outcome of the protocol only. Under this restriction, we show that Eve can still do better than random guessing when equipped with the characteristics of the noisy channel. Finally, we propose a modification of the BB84 protocol which retains the security of the original protocol, but ensures that Eve cannot get any advantage in guessing the outcome, even with complete channel information.
Modified Siamese Network Based on Feature Enhancement and Dynamic Template for Low-Light Object Tracking in UAV Videos

Unmanned aerial vehicles (UAVs) visual object tracking under low-light conditions serves as a crucial component for applications, such as night surveillance, indoor searches, night combat, and all-weather tracking. However, the majority of the existing tracking algorithms are designed for optimal lighting conditions. In low-light environments, images captured by UAV typically exhibit reduced contrast, brightness, and a signal-to-noise ratio, which hampers the extraction of target features. Moreover, the target’s appearance in low-light UAV video sequences often changes rapidly, rendering traditional fixed template tracking mechanisms inadequate, and resulting in poor tracker accuracy and robustness. This study introduces a low-light UAV object tracking algorithm (SiamLT) that leverages image feature enhancement and a dynamic template-updating Siamese network. Initially, the algorithm employs an iterative noise filtering framework-enhanced low-light enhancer to boost the features of low-light images prior to feature extraction. This ensures that the extracted features possess more critical target characteristics and minimal background interference information. Subsequently, the fixed template tracking mechanism, which lacks adaptability, is enhanced by dynamically updating the tracking template through the fusion of the reference and base templates. This improves the algorithm’s capacity to address challenges associated with feature changes. Furthermore, the Average Peak-to-Correlation Energy (APCE) is utilized to filter the templates, mitigating interference from low-quality templates. Performance tests were conducted on various low-light UAV video datasets, including UAVDark135, UAVDark70, DarkTrack2021, NAT2021, and NAT2021L. The experimental outcomes substantiate the efficacy of the proposed algorithm in low-light UAV object-tracking tasks.
FPGA Implementation of Image Registration Using Accelerated CNN

Accurate and fast image registration (IR) is critical during surgical interventions where the ultrasound (US) modality is used for image-guided intervention. Convolutional neural network (CNN)-based IR methods have resulted in applications that respond faster than traditional iterative IR methods. However, general-purpose processors are unable to operate at the maximum speed possible for real-time CNN algorithms. Due to its reconfigurable structure and low power consumption, the field programmable gate array (FPGA) has gained prominence for accelerating the inference phase of CNN applications.This study proposes an FPGA-based ultrasound IR CNN (FUIR-CNN) to regress three rigid registration parameters from image pairs. To speed up the estimation process, the proposed design makes use of fixed-point data and parallel operations carried out by unrolling and pipelining techniques. Experiments were performed on three US datasets in real time using the xc7z020, and the xcku5p was also used during implementation.The FUIR-CNN produced results for the inference phase 139 times faster than the software-based network while retaining a negligible drop in regression performance of under 200 MHz clock frequency.Comprehensive experimental results demonstrate that the proposed end-to-end FPGA-based accelerated CNN achieves a negligible loss, a high speed for registration parameters, less power when compared to the CPU, and the potential for real-time medical imaging.
The CKKS FHE Scheme

In this chapter, we describe the CKKS FHE scheme in detail. The CKKS scheme supports operations on real numbers, and thus, is the preferred scheme for implementing privacy-preserving machine learning applications. We would like to note that the original CKKS scheme (Cheon et al. 2017) supports homomorphic computations over the real numbers, but its implementation could not employ core optimization techniques based on the Residue Number System (RNS) decomposition (see Section 2.2.1) and the Number Theoretic Transform (NTT) (see Section 2.2.3). Consequently, Cheon et al. (2019) proposed a full RNS variant of the CKKS FHE scheme that is optimal for implementation on a standard computer system. They introduced a new structure of the ciphertext modulus which allows using both the RNS decomposition of cyclotomic polynomials and the NTT conversion on each of the RNS components.
The Evaluation Technology of Manufacturer Intelligence Regarding the Selection of the Decision Support System of Smart Manufacturing Technologies: Analysis of China–South Africa Relations

With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking with the best-in-class manufacturers in each country. In this research, an evaluation technology of manufacturer intelligence regarding the selection of decision support system (DSS) of smart manufacturing technologies, analyzing China–South Africa relations, is described. Firstly, the three keys aspects that enable the technologies of DSS are discussed in detail. Then, one key technology, the manufacturers’ intelligent evaluation system with 15 indexes, was built. The indexes and their measurements are also proposed. Finally, a fusion method based on boosting with multi-kernel function (online sequential extreme learning machine based on boosting, Boosting-OSELM) is introduced. The purpose of Boosting-OSKELM is to combine several weak learners into a strong learner (lower mean square error, MSE) through an acceptable time delay. Finally, the case study is presented to demonstrate the improvement on the MSE and process time, showing a relative MSE improvement of 96.19% and a relative time delay ratio of 31.46%. Totally, the largest contribution of the proposed evaluation method in this study is the conversion of the history data saved by the manual scoring method into knowledge in accessible MES and resealable time delay, which will free up the expert workforce in the entire process. We expect this paper will help future research in this field.
Image Enhancement of Underwater Images Using Deep Learning Techniques

In the present era enhancing the under image quality play a major role for research purpose and ocean exploration. The market for high-quality underwater images is expanding exponentially. Despite this, there are a number of issues including blurriness, dim lighting, poor contrast, and colour fidelity. Light attenuation, absorption, and scattering lower the quality. As a result, it is essential to enhance underwater images, and different image processing techniques are used for enhancement. The deep learning methodology used in this work to improves image quality and produce the best outcomes when compared to conventional image enhancing techniques. The architecture used is GAN with the combination of Image processing algorithms such as histogram equalization as a preprocessing step for implementing deep learning models. The algorithms include histogram equalization and Dark Channel Prior. These methods normalize the contrast of images and removes the hazing or dark parts of the images. The dataset used in this work is EUVP (Paired) which contains about 2186 real-world underwater images. Three parameters, UICM, MSE, and PSNR are used to assess quality of enhanced images. The optimum technique for improving the images is determined by comparing the performance of the models after the dataset has been trained through them.