Reviewerly/exHarmony huggingface dataset:
How children speak with their voice assistant Sila depends on what they think about her

Children encounter artificial intelligence, e.g., voice assistants, at an increasingly younger age. However, we know far too little about children's communicative behavior with a voice-only interaction partner and whether and how their assumptions about the interaction partner predict how they communicate. To address these aspects, we ran a treasure hunt game with 50 5-6-year-olds who were guided through the hunt by a voice-only interaction partner "Sila" – acted by a human and connected via loudspeakers. Crucially, Sila was introduced either as a human or a voice assistant. We employed easy communication trials in which communication went smoothly and challenging communication trials that contained staged misunderstandings. Results showed that at the group level, children differed in the way they adjusted their speech across experimental phases – both in quantity (fewer grounding turns and fewer syllables in speech directed to voice assistants) and quality (reduced increase in f0 and intensity in speech directed to voice assistants). Notably, the communicative behavior was predicted by children's individual parasocial relationship with their voice-only interaction partner. We discuss the relevance of our findings for developmental science at the interface with communication science and phonetics, and the direct practical implications we can infer for speech technology.
SRS-Net: Training object detectors from scratch for remote sensing images without pretraining

Most of the current object detection algorithms use pretrained models that are trained on ImageNet and then fine-tuned in the network, which can achieve good performance in terms of general object detectors. However, in the field of remote sensing image object detection, as pretrained models are significantly different from remote sensing data, it is meaningful to explore a train-from-scratch technique for remote sensing images. This paper proposes an object detection framework trained from scratch, SRS-Net, and describes the design of a densely connected backbone network to provide integrated hidden layer supervision for the convolution module. Then, two necessary improvement principles are proposed: studying the role of normalization in the network structure, and improving data augmentation methods for remote sensing images. To evaluate the proposed framework, we performed many ablation experiments on the DIOR, DOTA, and AS datasets. The results show that whether using the improved backbone network, the normalization method or training data enhancement strategy, the performance of the object detection network trained from scratch increased. These principles compensate for the lack of pretrained models. Furthermore, we found that SRS-Net could achieve similar to or slightly better performance than baseline methods, and surpassed most advanced general detectors.
SedSVD: Statement-level software vulnerability detection based on Relational Graph Convolutional Network with subgraph embedding

Current deep-learning based vulnerability detection methods have been proven more automatic and correct to a certain extent, nonetheless, they are limited to detect at function-level or file-level, which can hinder software developers from acquiring more detailed information and conducting more targeted repairs. Graph-based detection methods have shown dominant performance over others. Unfortunately, the information they reveal has not been fully utilized. We design SedSVD (Subgraph embedding driven Statement-level Vulnerability Detection) with two objectives: (i) to better utilize the information the code-related graphs can reflect; (ii) to detect vulnerabilities at a finer-grained level. In our work, we propose a novel graph-based detection framework that embeds graphs at subgraph-level to realize statement-level detection. It first leverages Code Property Graph (CPG) to learn both semantic and syntactic information from source code, and then selects several center nodes (code elements) in CPG to build their subgraphs. After embedding each subgraph with its nodes and edges, we apply Relational Graph Convolutional Network (RGCN) to process different edges differently. A Multi-Layer Perceptron (MLP) layer is further added to ensure its prediction performance. We conduct our experiments on C/C++ projects from NVD and SARD. Experimental results show that SedSVD achieves 95.15% in F1-measure which proves our work to be more effective. Our work detects at a finer-grained level and achieves higher F1-measure than existing state-of-art vulnerability detection techniques. Besides, we provide a more detailed detection report pointing the specific error code elements within statements.
Using Domain-Augmented Federated Learning to Model Thermostatically Controlled Loads

Optimization of thermostatically controlled loads, such as heat pumps, using data-driven models can significantly reduce domestic energy consumption besides providing critical grid services.However, these data-driven models often require a prohibitive amount of data before reaching sufficient accuracy for individual devices.Centralized or collaborative learning schemes, which aggregate data from many devices, can lower data requirements from individual devices, but at the cost of loss of user privacy (or data leakage).In this paper, we explore federated learning as a modelling alternative to address these concerns, and compare its accuracy against collaborative learning approaches using a real-world dataset.Some important insights emerge from this work.Notably, we show that federated learning, on its own, suffers from several drawbacks when compared against collaborative approaches; including poor convergence in low data availability regimes, as well as a failure to learn causal links even asymptotically.We explore the reasons for these shortcomings, and demonstrate that these issues can be resolved by incorporating domain-informed data augmentation in the learning process, allowing it to converge to a solution that is very close to the baseline collaborative model in terms of both accuracy and interpretability.
RAHG: A Role-Aware Hypergraph Neural Network for Node Classification in Graphs

Graph neural networks have been widely studied and applied to node classification in graph-format datasets in recent years. Traditional graph neural networks mainly consider the adjacency characteristics of nodes and fail to learn rich role representations of nodes. Existing role representations methods of nodes are mostly in unsupervised approaches, resulting in unsatisfactory performance in downstream tasks. A graph can be reorganized as a hypergraph, in which the role characteristics of nodes are more intuitively represented. Based on this, we propose a role-aware hypergraph neural network (RAHG) that utilizes hypergraphs and an attention mechanism to fuse nodes' role and adjacency representations. A residual network is also applied to relieve the smoothing problem between layers in the model. The model adjusts the weights on the role and adjacency representations according to the characteristics of the graphs. RAHG significantly improves the prediction performance compared with existing graph neural networks on seven datasets, with accuracy increased by up to 12.1% on the node classification task in graphs.
Jacobi polynomials and design theory I

In this paper, we introduce the notion of Jacobi polynomials of a code with multiple reference vectors, and give the MacWilliams type identity for it. Moreover, we derive a formula to obtain the Jacobi polynomials using the Aronhold polarization operator. Finally, we describe some facts obtained from Type III and Type IV codes that interpret the relation between the Jacobi polynomials and designs.
A privacy-preserving cross-media retrieval on encrypted data in cloud computing

Frequent cloud data breaches cause irreparable damage to cloud users and providers. Cross-media retrieval can better leverage the value of data, but existing cross-media retrievals are conducted on plaintext data with limited security. In this paper, we propose a privacy-preserving cross-media retrieval on encrypted data in cloud computing (PPCMR). First, the user encrypts the data and uploads it to the cloud, which enhances cloud data security. Secondly, a two-branch feature extraction network based on a convolutional neural network is designed, which is used to extract cross-media features on encrypted data in a cloud environment. And the cosine distance between cross-media features is calculated for similarity metric to solve the 'semantic gap' problem of cross-media encrypted data. Finally, the user decrypts the encrypted retrieval results from the cloud to get the cross-media plaintext results. The scheme shifts complex computing operations from the client to the cloud server, with little client-side computation. We analytically evaluate PPCMR on real-world datasets: PPCMR outperforms plaintext retrieval schemes in terms of security and supports efficient cross-media encrypted retrieval on large-scale, dynamically updated cloud databases and mobile devices (lightweight).
Qos-Aware Video Streaming based Admission Control and Scheduling for Video Transcoding in Cloud Computing

Power and other computing resources can be stored and processed in cloud computing environments. Depending on the features of the clients' devices, video streams, whether they are live or on-demand, often need to be transcoded or converted (such as supported formats, bandwidth, and spatial resolution, for example). Currently, streaming service providers maintain multiple transcoded versions of the same video to serve various client devices because transcoding is a computationally expensive and time-consuming process. To eliminate jitters in accepted streams while transcoding, a task scheduling mechanism is included. To ensure that the consumer receives continuous video content delivery, this technique involves cutting a tiny number of video frames from a video segment. In this evaluation, admission control and scheduling based on QoS -aware video streams is the new task scheduling method for video recording that is suggested. With the help of this framework, streaming platform services have made efficient use of cloud resources while following to the Quality of Service (QoS) standards for video transmissions. The technologies are advancing a scheduling technique that is QoS -aware to effectively map video streams to cloud resources in order to deliver high QoS. The performance of this analysis is calculated on different aspects such as Accuracy, Qos and Recall. In this approach the QoS -aware video streaming based admission control and scheduling for video transcoding in cloud computing will give the best outcomes.
Privacy Preserving Encryption with Optimal Key Generation Technique on Deduplication for Cloud Computing Environment

Cloud computing performs a significant part in sharing resources and data with other devices via data outsourcing. The data collaboration services, as a potential service given by the cloud service provider (CSP), is to assist the consistency and availability of the shared data amongst users. At the time of sharing resources, it is a complicated process for providing secure writing and access control operations. This study develops a Privacy Preserving Encryption with Optimal Key Generation Technique (PPE-OKGT) for CC environment. The presented PPE-OKGT technique secures the data prior to storing in the cloud sever via encryption process. For accomplishing this, the presented PPE-OKGT technique employs data encryption technology to secure the input data into a hidden format. Besides, in order to improve secrecy, the presented PPE-OKGT technique designs a chaotic search and rescue optimization (CSRO) algorithm for optimal generation of keys. The promising performance of the PPE-OKGT technique can be verified using a set of experimentations. A comprehensive comparison study reported the enhancements of the PPE-OKGT technique over other models.
Detection of Face Wearing Mask Based on AdaBoost and YCrCb

At the beginning of 2020, COVID-19 broke out and swept the world. Wearing masks remains an important means of preventing epidemics. Many scholars have developed and studied mask wearing detection based on YOLO algorithm, and have made some achievements. AdaBoost algorithm has the advantages of high precision and low complexity, and is also suitable for solving this problem. This paper uses OpenCV to propose a face detection algorithm based on AdaBoost. This algorithm is based on face detection, including initialization of background estimation example, background subtraction preprocessing, obtaining eye position, face detection and other steps. LBP features are used as the training basis of the classifier. The trained classifier is generated and used as a function in the mask detection algorithm. At present, there are two problems in the research of mask wearing detection: first, only consider whether the tested object wears a mask, but not analyze the non-standard wearing of masks; Secondly, due to the influence of light and other external environments, the real-time detection effect of targets in complex scenes changes greatly. In view of the above problems, this paper adopts the following methods to solve them: pre-processing the image to reduce noise, light spots and other external environmental interference; For the case that the mask is not standardized, the condition that the mask covers the nose and mouth shall be detected. Finally, the Adaboost algorithm for facial mask wearing detection is obtained. Experiments show that the algorithm has high adaptability, robustness and accuracy, and can be used to promote the development of epidemic prevention.
Exploring the high-throughput and low-delay hardware design of SM4 on FPGA

SM4 (or SMS4) is a 32-round unbalanced Feistel block cipher. This paper explores the hardware design method of SM4 for different scenarios on FPGA. Targeting the real-time high speed communication system on FPGA, we construct the pipeline across the unrolled rounds to gain balanced pipeline stages with online and offline key expansion schemeS. Meanwhile, for the low-delay application scenario, various SM4 structures are implemented to show the trade-off relationship between the number of unrolled rounds and the performance metrics. The experimental results show that in the high-throughput application scenario, our optimization method can achieve a throughput of 118. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$19\text{Gbps}$</tex> with relatively higher efficiency <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\text{Mbps}/\text{area})$</tex> ; in the low-delay application scenario, the delay <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\times$</tex> area can be minimized to 177.18 ns <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\times(\text{FF}+ \text{LUT})$</tex> when unfolding 2 rounds of operations.
Text Localization and Recognition from Natural Scene Images using AI

In computer vision systems, text detection and recognition (TDR) in natural scene images can be used for things like license plate recognition, automated street sign interpretation, and assisting blind people. Accordingly, finding text within an image is a time-based challenge in the field of computer vision. Because of factors like cluttered backgrounds, image blurring, partially obscured text, various fonts, noise, and fluctuating lighting, text identification in natural scenes has become a significant task with the increase in the use of actual vision systems. Images and videos with accompanying textual data can be leveraged for automatic annotation. This study provides a system for automatically identifying the text from images, and it discusses the methodology behind locating and recognizing text in images of natural scenes. This article handles the scene text recognition challenge from start to finish, breaking it down into text localization and recognition. The Maximally Stable Extremal Regions (MSER) technique is used to detect text and non-text regions in images for localization purposes. Convolutional Neural Networks (CNNs) and convolutional recurrent neural networks (CRNNs) are utilized for text recognition. By evaluating the accuracy, precision, and F1 score, the CRNN is determined to be the best.
A Decentralized Identity System for Accelerating Medical Communications within Rare Disease Communities

This paper presents a blockchain-based architecture for facilitating clinical communications within the historically underfunded rare disease community. Due to the complex nature of diagnosing and treating these conditions, patients with rare diseases are often referred to multiple care providers and specialists. Each care provider instructs patients to provide identification information to onboard them to their in-house medical record system. An effective identity management service or platform is needed to avoid repetitive steps and to identify patients in a scalable, collaborative, and sustainable way. This work proposes a decentralized, blockchain-based identity management infrastructure driven by interoperability across data management systems to accurately identify unique patients within rare disease communities and provide a supporting platform that facilitates the management and exchanges of information about identities. The proposed system identifies and represents participants with specific roles using a domain-specific digital identifier model. Once a patient's information is certified by a verifier, a Unique Digital Identifier is created for them that is linked to a persistent document holding their “cyber” identity. This allows the patient to identify themselves by presenting only the digital proof of their Unique Digital Identifier ownership during the subsequent medical encounters. Overall, we present a conceptual framework that allows for the fast identification of patients in the rare disease community that minimizes duplication of entries. It also enables providers and researchers to receive updates and exchange knowledge about rare diseases in an unambiguous way.
Wheat Head Detection using YOLO: A Comparative Study

Classical object detection techniques such as RetinaNet, Fast R-CNN, and Single-Shot MultiBox Detector (SSD) are unable to locate objects in a single iteration. These approaches have solved the modelling and data scarcity issues in object detection. The YOLO algorithm has gained popularity because it performs better than the above-mentioned object recognition approaches. YOLO achieves cutting-edge findings and significantly outperforms prior real-time object identification algorithms by approaching object detection in a novel way. YOLO object detection algorithms advance from YOLOv1 [1] in 2016 attaining 63.4mAP on the Pascal VOC dataset to YOLOR in 2021 with 73.3 mAP on the far more difficult MS COCO dataset. This study assess the efficacy of the several YOLO algorithm iterations, from versions 3 all the way to version 7 and how well versions 3, 4, 5, 6 and 7 of the YOLO algorithms perform at identifying the wheat head from the provided object image.
Comparative Stratification of Steganalysis Techniques to Interpret &amp; Target Anomalies

The art of detecting hidden messages in media is known as steganalysis. Statistically and visually data is hidden in the media. Using deep learning techniques is evidently favorable as they are efficient in learning hierarchical data. Through this research, we tend to investigate and compare various steganalysis techniques and implement classifiers to compare the results achieved. Neural networks, Clustering Algorithms, and other Tools are used to design a model for the investigation of Experimental Findings. Classifiers like the SVM classifier, K-NN classifier, Random Forests classifier, MLP NN classifier, and Naive Bayes classifier are trained and tested to detect images undergoing steganography. The results acquired are compared and analyzed.
MCR: Multilayer cross‐fusion with reconstructor for multimodal abstractive summarisation

Abstract Multimodal abstractive summarisation (MAS) aims to generate a textual summary from multimodal data collection, such as video‐text pairs. Despite the success of recent work, the existing methods lack a thorough analysis for consistency across multimodal data. Besides, previous work relies on the fusion method to extract multimodal semantics, neglecting the constraints for complementary semantics of each modality. To address those issues, a multilayer cross‐fusion model with the reconstructor for the MAS task is proposed. Their model could thoroughly conduct cross‐fusion for each modality via layers of cross‐modal transformer blocks, resulting in cross‐modal fusion representations with consistency across modalities. Then the reconstructor is employed to reproduce source modalities based on cross‐modal fusion representations. The reconstruction process constrains the fusion representations with the complementary semantics of each modality. Comprehensive comparison and ablation experiments on the open domain multimodal dataset How2 are proposed. The results empirically verify the effectiveness of the multilayer cross‐fusion with the reconstructor structure on the proposed model.
Performance Analysis of Gold- and Fiat-Backed Cryptocurrencies: Risk-Based Choice for a Portfolio

This study aims to investigate the performance and behavior of fiat- and gold-backed cryptocurrencies to support stakeholders through the preparation of a portfolio from 1 January 2021 to 30 June 2022. Moreover, while searching for a hedge or a diversifier to construct a less risky portfolio with handsome returns, the prices of fiat-backed cryptocurrencies report high fluctuation during the sample period. ARIMA-EGARCH models have been employed to examine the volatile behavior of these cryptocurrencies. The empirical results are mixed as Bitcoin has been highly volatile during the economic recession. Due to its volatility, investors seek a safe haven. Ripple, on the other hand, shows low risk compared to Bitcoin. The results further reveal that PAX gold is more volatile than PM gold, while Bitcoin, being a highly traded cryptocurrency, is significantly correlated to other cryptocurrencies. The implications of this research showing the volatility of gold- and fiat-backed cryptocurrencies are equally important to stakeholders, such as investors, and policymakers.