Reviewerly/exHarmony huggingface dataset:
Cryptocurrency Price Prediction Based on ARIMA, Random Forest and LSTM Algorithm

Price prediction of cryptocurrencies is bound to get more opportunities for investors engaged in digital currency-related industries in order to earn more revenue. In the traditional forecasting methods, the problem of the high volatility of bitcoin price needs to be effectively solved, making the forecasting accuracy become low and ineffective. Due to the rapid development of artificial intelligence technology, more and more relevant algorithms were used for cryptocurrency price research. This study would compare and analyze the prediction effect of the ARIMA time-series model, the Random Forest algorithm of machine learning, and the LSTM algorithm of deep learning algorithm on cryptocurrencies price prediction to assist investors in making investment decisions. In this paper, five years of time-series data of Bitcoin, Ether, and Dogecoin is obtained from 2018 to 2022. Then, the training set and testing set are separated with 0.8:0.2 to test ARIMA, Random Forest, and LSTM algorithms. To evaluate the model, the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and decidability coefficient (R2) are chosen as metrics to measure the prediction of each prediction model precision. Comparing the experimental results, the prediction accuracy of LSTM is better than that of Random Forest, and the prediction accuracy of Random Forest is better than ARIMA model. These results shed light on guiding further exploration of significant to cryptocurrency industry practitioners and visitors.
Stylized image denoising via noise style transfer and Quasi Siamese network

Neural style transfer has attracted wide attention for the surprising quality of synthetic images. However, the visual quality of existing methods usually suffers from style transfer noise (STN). Unlike the real-world noises contaminated in natural image capturing, the STN results from the stylized image synthesizing process. This difference leads to two major challenges for stylized image denoising: (1) Synthesized stylized images have no corresponding ground-truth images, which makes it challenging to obtain noisy-clean sample pairs for training denoising networks. (2) STNs are usually color noises or accompanied by color distortions, so color correction should be considered in denoising. To tackle the first challenge, we propose a novel strategy called noise style transfer to produce noisy-clean sample pairs. Specifically, the noise style transfer takes noises as special style textures and transfers them from the noisy stylized image onto the clean style image. To tackle the second challenge, we design a Quasi Siamese denoising network, which takes the noisy sample and its high saturation (HS) version as inputs of two network branches in training. Through mutual supervision between the two branches, the “photo developer” effect of HS images on color noise is conducive to simultaneous noise removal and color correction. Extensive experimental results on various stylized images substantially demonstrate the superior performance of our approach. With great adaptiveness to different style transfer methods, our approach successfully removes distinctive STNs while keeping the artistic texture and achieves a further reduction in style loss of up to 57.7%.
Enhance cloud security and effectiveness using improved RSA-based RBAC with XACML technique

In the current era, numerous products are accessible online from anywhere and every minute. This work used encryption with access management because authenticities, anonymity, and security over accessibility are mandatory. The proposed design introduced a significantly better encryption strategy to ensure better protection of overall resource admissions. This proposed work uses Improved RSA-based role-based access control (RBAC) with extendable access connectivity markup language (XACML) to encrypt information and maintain privileges. This approach enables storing information within the online computer using cryptographic ideas and information available via a basic admission management mechanism. To ensure the overall protection of sensitive information, the encryption method is employed that merged the conventional homogeneous encryption procedure with unstable information distribution method. This hybrid technique provides user to get advantage from retrieved information in a protected manner. The overall execution of proposed work is considerably quicker than other existing encryption methods.
Usability Evaluation with Eye Tracking: The Case of a Mobile Augmented Reality Application with Historical Images for Urban Cultural Heritage

Eye-tracking technologies have matured significantly in recent years and have become more affordable and easier to use. We investigated how eye-tracking technology can be applied to evaluate the usability of mobile augmented reality applications with historical images for urban cultural heritage. The experiment involved a series of complex user evaluation sessions, combining semi-structured interviews, observations, think-aloud protocol, SUS questionnaire, and product reaction cards, complemented by eye tracking, to gather insights on the Spotlight Timisoara AR mobile application, part of a digital storytelling multiplatform for the city of Timisoara (Romania), soon to be European Capital of Culture in 2023. The results indicate strong and weak aspects of the application, both as expressed by the participants and as derived from analyzing the eye-tracking data. The paper also lists the main challenges we identified in using eye-tracking equipment to evaluate the usability of such mobile augmented reality applications for urban outdoor heritage.
SequenceSense: A Tool for Designing Usable Foot-Based Gestures Using a Sequence-Based Gesture Recognizer

Foot-based gestures enable people to interact with mobile and wearable devices when their hands are unavailable for interaction. For the foot gestures to be truly usable, the gestures should be recognizable by the system without being confused by daily activities and still be easy to perform. However, designing such gestures often requires multiple iterations of gesture design, model training, and evaluation. In this paper, we present SequenceSense, a tool developed to help designers efficiently design a usable gesture set using inertial sensors, which eliminates the need for multiple data collection studies to evaluate the gestures' usability through gesture modification by sequencing atomic actions and instant false positive analysis, and instead requires only the initial gesture sample collection. Unlike gesture recognizers using complete gestures to train a model, SequenceSense segments gesture into a sequence of atomic actions. For example, a foot tap to the right may have (1) lift the foot, (2) move the foot to the right, and (3) land the foot. SequenceSense also compares the gesture sequence with the sequence database created from the daily activities to identify possible conflicts. This allows gesture designers to build easily usable foot-based gestures without the need for recollecting and evaluating gestures. We validated SequenceSense's efficacy in designing usable gestures with low false positives through a user study with nine gesture designers.
CHARLES: A C++ fixed-point library for Photonic-Aware Neural Networks

In this paper we present CHARLES (C++ pHotonic Aware neuRaL nEtworkS), a C++ library aimed at providing a flexible tool to simulate the behavior of Photonic-Aware Neural Network (PANN). PANNs are neural network architectures aware of the constraints due to the underlying photonic hardware, mostly in terms of low equivalent precision of the computations. For this reason, CHARLES exploits fixed-point computations for inference, while it supports both floating-point and fixed-point numerical formats for training. In this way, we can compare the effects due to the quantization in the inference phase when the training phase is performed on a classical floating-point model and on a model exploiting high-precision fixed-point numbers. To validate CHARLES and identify the most suited numerical format for PANN training, we report the simulation results obtained considering three datasets: Iris, MNIST, and Fashion-MNIST. Fixed-training is shown to outperform floating-training when executing inference on bitwidths suitable for photonic implementation. Indeed, performing the training phase in the floating-point domain and then quantizing to lower bitwidths results in a very high accuracy loss. Instead, when fixed-point numbers are exploited in the training phase, the accuracy loss due to quantization to lower bitwidths is significantly reduced. In particular, we show that for Iris dataset, fixed-training achieves a performance similar to floating-training. Fixed-training allows to obtain an accuracy of 90.4% and 68.1% with the MNIST and Fashion-MNIST datasets using only 6 bits, while the floating-training reaches an accuracy of just 25.4% and 50.0% when exploiting the same bitwidths.
A Graph Neural Network Social Recommendation Algorithm Integrating the Multi-Head Attention Mechanism

Collaborative filtering recommendation systems are facing the data sparsity problem associated with interaction data, and social recommendations introduce user social information to alleviate this problem. Existing social recommendation methods cannot express the user interaction interest and social influence deeply, which limits the recommendation performance of the system. To address this problem, in this paper we propose a graph neural network social recommendation algorithm integrating multi-head attention mechanism. First, based on the user-item interaction graph and social network graph, the graph neural network is used to learn the high-order relationship between users and items and deeply extract the latent features of users and items. In the process of learning user embedding vector representation based on the social network graph, the multi-head attention mechanism is introduced to increase the importance of friends with high influence. Then, we make rating predictions for the target users according to the learned user embedding vector representation and item embedding vector. The experimental results on the Epinions dataset show that the proposed method outperforms the existing methods in terms of both Recall and Normalized Discounted Cumulative Gain.
GraphCS: Graph-based client selection for heterogeneity in federated learning

Federated Learning coordinates many mobile devices to train an artificial intelligence model while preserving data privacy collaboratively. Mobile devices are usually equipped with totally different hardware configurations, leading to various training capabilities. At the same time, the distribution of the local training data is highly heterogeneous across different clients. Randomly selecting the clients to participate in the training process results in poor model performance and low system efficiency. In this paper, we propose GraphCS, a graph-based client selection framework for heterogeneity in Federated Learning. GraphCS first measures the distribution coupling across the clients via the model gradients. After that, it divides the clients into different groups according to the diversity of the local datasets. At the same time, it well estimates the runtime training capability of each client by jointly considering the hardware configuration and resource contention caused by the concurrently running apps. With the distribution coupling information and runtime training capability, GraphCS selects the best clients in order to well balance the model accuracy and overall training progress. We evaluate the performance of GraphCS with mobile devices with different hardware configurations on various datasets. The experiment results show that our approach improves model accuracy up to 45.69%. Meanwhile, it reduces communication and computation overhead 87.35% and 89.48% at best, respectively. Furthermore, GraphCS accelerates the overall training process up to 35×.
Serial-parallel multi-scale feature fusion for anatomy-oriented hand joint detection

Accurate hand joints detection from images is a fundamental topic that is essential for many applications in computer vision and human–computer interaction. This paper presents a two-stage network for hand joints detection from a single unmarked image by using serial-parallel multi-scale feature fusion. In stage I, the hand regions are located by an encoder-decoder network, and the features of each detected hand region are extracted by a shallow spatial hand features representation module. The extracted hand features are then fed into stage II, which consists of serially connected feature extraction modules with similar structures, called "multi-scale feature fusion" (MSFF). An MSFF contains parallel multi-scale feature extraction branches, which generate initial hand joint heatmaps. The initial heatmaps are then mutually reinforced by the anatomic relationship between hand joints. The hand joint detection accuracy shows that the proposed network overperforms the state-of-the-art methods on current datasets, 1) RHD, 2) HS, 3) MPII & NZSL, 4) DCD8-6000, with the [email protected] of 0.94, 0.92, 0.84, 0.97. Meanwhile, one hand in the image takes between 24 and 37 ms to process, which is adequate for supporting many real-time applications.
Hierarchical disentangling network for object representation learning

An object can be described as the combination of primary visual attributes. Disentangling such underlying primitives is the long-term objective of representation learning. It is observed that categories have natural hierarchical characteristics, i.e., any two objects can share some common primitives at a particular category level while possess unique traits at another. However, previous works usually operate in a flat manner (i.e., at a particular level) to disentangle the representations of objects. Even though they may obtain the primitives to constitute objects as the categories at that level, their results are obviously not efficient and complete. In this paper, we propose a Hierarchical Disentangling Network (HDN) to exploit the rich hierarchical characteristics among categories to divide the disentangling process in a coarse-to-fine manner (i.e., level-wise), such that each level only focuses on learning the specific representations and finally the common and unique representations at all levels jointly constitute the raw object. Specifically, HDN is designed based on an encoder-decoder architecture. To simultaneously ensure the level-wise disentanglement and interpretability of the encoded representations, a novel hierarchical Generative Adversarial Network (GAN) is introduced. Quantitative and qualitative evaluations on popular object datasets validate the effectiveness of our method.
Enhanced abbreviation–expansion pair detection for glossary term extraction

Providing precise definitions of all project specific terms is a crucial task in requirements engineering. In order to support the glossary building process, many previous tools rely on the assumption that the requirements set has a certain level of quality. Yet, the parallel detection and correction of quality weaknesses in the context of glossary terms is beneficial to requirements definition. In this paper, we focus on detection of uncontrolled usage of abbreviations by identification of abbreviation–expansion pair (AEP) candidates. We compare our feature-based approach (ILLOD+) to other similarity measures to detect AEPs and propose how to extend the glossary term extraction (GTE) and synonym clustering with AEP-specific methods. It shows that feature-based methods are more accurate for AEPs than syntactic and semantic similarity measures. Experiments with PURE data-sets extended with uncontrolled abbreviations show that ILLOD+ is able to extract abbreviations as well as match their expansions viably in a real-world setting and is well suited to augment previous synonym clusters with clusters that combine AEP candidates. AEP clusters generated with ILLOD+ are generally smaller than those based on syntactic or semantic similarity measures and have a higher recall. In this paper, we present ILLOD+, an extended feature-based approach to AEP detection and propose a workflow for its integration to clustering of glossary term candidates to enhance term consolidation in evolving requirements.
Event-Driven Serverless Pipelines for Video Coding and Quality Metrics

Abstract Nowadays, the majority of Internet traffic is multimedia content. Video streaming services are in high demand by end users and use HTTP Adaptive Streaming (HAS) as transmission protocol. HAS splits the video into non-overlapping chunks and each video chunk can be encoded independently using different representations. Therefore, these encode tasks can be parallelized and Cloud computing can be used for this. However, in the most extended solutions, the infrastructure must be configured and provisioned in advance. Recently, serverless platforms have made posible to deploy functions that can scale from zero to a configurable maximum. This work presents and analyses the behavior of event-driven serverless functions to encode video chunks and to compute, optionally, the quality of the encoded videos. These functions have been implemented using an adapted version of embedded Tomcat to deal with CloudEvents. We have deployed these event-driven serverless pipelines for video coding and quality metrics on an on-premises serverless platform based on Knative on one master node and eight worker nodes. We have tested the scalability and resource consumption of the proposed solution using two video codecs: x264 and AV1, varying the maximum number of replicas and the resources allocated to them (fat and slim function replicas). We have encoded different 4K videos to generate multiple representations per function call and we show how it is possible to create pipelines of serverless media functions. The results of the different tests carried out show the good performance of the serverless functions proposed. The system scales the replicas and distributes the jobs evenly across all the replicas. The overall encoding time is reduced by 18% using slim replicas but fat replicas are more adequate in live video streaming as the encoding time per chunk is reduced. Finally, the results of the pipeline test show an appropriate distribution and chaining among the available replicas of each function type.
RRN: A differential private approach to preserve privacy in image classification

Abstract The wide application of image classification has given rise to many intelligent systems, such as face recognition systems, which makes our life more convenient. However, the ensuing privacy leakage problem has become increasingly serious. The training of a deep neural network requires lots of data, which may contain sensitive information of users and may be exploited by data collectors. A perturbation algorithm named RRN is proposed for image data based on local differential privacy, which provides a rigorous privacy guarantee. Existing solutions have low accuracy due to the high sensitivity of an image; the authors' method combines the Randomized Response mechanism with the Laplace mechanism to solve this problem. Experiments were conducted on the MNIST and CIFAR‐10 datasets to show the effectiveness of the algorithm. Experimental results shows that the model is better than baseline models. The algorithm was also implemented on the commonly used model in deep learning, the VGG model, which can achieve 96.4% accuracy in the non‐private version on the CIFAR‐10 dataset. The accuracy of the differential private VGG model based on the RRN algorithm is 83% when , which is still excellent. The experimental results show that the RRN algorithm can both preserve privacy and data utility.
Steganography in IoT: Information Hiding with Joystick and Touch Sensors

This paper describes a multi-secret steganographic system for the Internet-of-Things. It uses two user-friendly sensors for data input: thumb joystick and touch sensor. These devices are not only easy to use, but also allow hidden data entry. The system conceals multiple messages into the same container, but with different algorithms. The embedding is realized with two methods of video steganography that work on mp4 files, namely, videostego and metastego. These methods were chosen because of their low complexity so that they may operate smoothly in environments with limited resources. It is possible to replace the suggested sensors with others that offer similar functionality.
Visible-Image-Assisted Nonuniformity Correction of Infrared Images Using the GAN with SEBlock

Aiming at reducing image detail loss and edge blur in the existing nonuniformity correction (NUC) methods, a new visible-image-assisted NUC algorithm based on a dual-discriminator generative adversarial network (GAN) with SEBlock (VIA-NUC) is proposed. The algorithm uses the visible image as a reference for better uniformity. The generative model downsamples the infrared and visible images separately for multiscale feature extraction. Then, image reconstruction is achieved by decoding the infrared feature maps with the assistance of the visible features at the same scale. During decoding, SEBlock, a channel attention mechanism, and skip connection are used to ensure that more distinctive channel and spatial features are extracted from the visible features. Two discriminators based on vision transformer (Vit) and discrete wavelet transform (DWT) were designed, which perform global and local judgments on the generated image from the texture features and frequency domain features of the model, respectively. The results are then fed back to the generator for adversarial learning. This approach can effectively remove nonuniform noise while preserving the texture. The performance of the proposed method was validated using public datasets. The average structural similarity (SSIM) and average peak signal-to-noise ratio (PSNR) of the corrected images exceeded 0.97 and 37.11 dB, respectively. The experimental results show that the proposed method improves the metric evaluation by more than 3%.
A Reference Architecture for Enabling Interoperability and Data Sovereignty in the Agricultural Data Space

Agriculture is one of the major sectors of the global economy and also a software-intensive domain. The digital landscape of agriculture is composed of multiple digital ecosystems, which together constitute an agricultural domain ecosystem, also referred to as the “Agricultural Data Space’’ (ADS). As the domain is so huge, there are several sub-domains and specialized solutions, and each of them poses challenges to interoperability. Additionally, farmers have increasing concerns about data sovereignty. In the context of the research project COGNAC, we elicited architecture drivers for interoperability and data sovereignty in agriculture and designed a reference architecture of a platform that aims to address these qualities in the ADS. In this paper, we present the solution concepts and design decisions that characterize the reference architecture. Early prototypes have been developed and made available to support the validation of the concept.