Reviewerly/exHarmony huggingface dataset:
Improved Coherent One-Way Quantum key Distribution for High-Loss Channels

Coherent one-way quantum key distribution (COW-QKD) is a popular cryptography protocol, due to its fairly simple implementation and passive measurement device. Its security analysis is challenging, though, and extra assumptions or modifications are typically employed. Here the authors propose minor modifications to COW-QKD's prepared states while retaining the original, fully passive measurement device, and generalize the ``universal squashing'' framework to analyze their system's security. The results suggest that performance should be comparable to upper bounds from the literature, making this protocol a viable alternative to one based on phase-modulated coherent states.
Text-Image Matching for Cross-Modal Remote Sensing Image Retrieval via Graph Neural Network

The rapid development of remote sensing (RS) technology has produced massive images, which makes it difficult to obtain interpretation results by manual screening. Therefore, researchers began to develop automatic retrieval method of RS images. In recent years, cross-modal RS image retrieval based on query text has attracted many researchers because of its flexible and has become a new research trend. However, the primary problem faced is that the information of query text and RS image is not aligned. For example, RS images often have the attributes of multiscale and multiobjective, and the amount of information is rich, while the query text contains only a few words, and the information is scarce. Recently, graph neural network (GNN) has shown its potential in many tasks with its powerful feature representation ability. Therefore, based on GNN, this article proposes a new cross-modal RS feature matching network, which can avoid the degradation of retrieval performance caused by information misalignment by learning the feature interaction in query text and RS image, respectively, and modeling the feature association between the two modes. Specifically, to fuse the within-modal features, the text and RS image graph modules are designed based on GNN. In addition, in order to effectively match the query text and RS image, combined with the multihead attention mechanism, an image-text association module is constructed to focus on the parts related to RS image in the text. The experiments on two public standard datasets verify the competitive performance of the proposed model.
StyleSwap: Style-Based Generator Empowers Robust Face Swapping

Numerous attempts have been made to the task of person-agnostic face swapping given its wide applications. While existing methods mostly rely on tedious network and loss designs, they still struggle in the information balancing between the source and target faces, and tend to produce visible artifacts. In this work, we introduce a concise and effective framework named StyleSwap. Our core idea is to leverage a style-based generator to empower high-fidelity and robust face swapping, thus the generator’s advantage can be adopted for optimizing identity similarity. We identify that with only minimal modifications, a StyleGAN2 architecture can successfully handle the desired information from both source and target. Additionally, inspired by the ToRGB layers, a Swapping-Driven Mask Branch is further devised to improve information blending. Furthermore, the advantage of StyleGAN inversion can be adopted. Particularly, a Swapping-Guided ID Inversion strategy is proposed to optimize identity similarity. Extensive experiments validate that our framework generates high-quality face swapping results that outperform state-of-the-art methods both qualitatively and quantitatively.
GIMO: Gaze-Informed Human Motion Prediction in Context

Predicting human motion is critical for assistive robots and AR/VR applications, where the interaction with humans needs to be safe and comfortable. Meanwhile, an accurate prediction depends on understanding both the scene context and human intentions. Even though many works study scene-aware human motion prediction, the latter is largely underexplored due to the lack of ego-centric views that disclose human intent and the limited diversity in motion and scenes. To reduce the gap, we propose a large-scale human motion dataset that delivers high-quality body pose sequences, scene scans, as well as ego-centric views with the eye gaze that serves as a surrogate for inferring human intent. By employing inertial sensors for motion capture, our data collection is not tied to specific scenes, which further boosts the motion dynamics observed from our subjects. We perform an extensive study of the benefits of leveraging the eye gaze for ego-centric human motion prediction with various state-of-the-art architectures. Moreover, to realize the full potential of the gaze, we propose a novel network architecture that enables bidirectional communication between the gaze and motion branches. Our network achieves the top performance in human motion prediction on the proposed dataset, thanks to the intent information from eye gaze and the denoised gaze feature modulated by the motion. Code and data can be found at https://github.com/y-zheng18/GIMO .
Making Smart Contracts Predict and Scale

The machine learning algorithms can predict the events based on the trained models and datasets. However, a reliable prediction requires the model to be trusted and tamper-resistant. Blockchain technology provides trusted output with consensus-based transactions and an immutable distributed ledger. The machine learning algorithms can be trained on blockchain smart contracts to produce trusted models for reliable prediction. But most smart contracts in the blockchain do not support floating-point data type, limiting computations for classification, which can affect the prediction accuracy. In this work, we propose a novel method to produce floating-point equivalent probability estimation to classify labels on-chain with a Naive Bayes algorithm. We derive a mathematical model with Taylor series expansion to compute the ratio of the posterior probability of classes to classify labels using integers. Moreover, we implemented our solution in Ethereum blockchain smart-contract with the Solidity programming language, where we achieved a prediction accuracy comparable to the scikit-learn library in Python. Our derived method is platform-agnostic and can be supported in any blockchain network. Furthermore, machine learning and deep-learning algorithms can borrow the derived method.
Geometric Representation Learning for Document Image Rectification

In document image rectification, there exist rich geometric constraints between the distorted image and the ground truth one. However, such geometric constraints are largely ignored in existing advanced solutions, which limits the rectification performance. To this end, we present DocGeoNet for document image rectification by introducing explicit geometric representation. Technically, two typical attributes of the document image are involved in the proposed geometric representation learning, i.e., 3D shape and textlines. Our motivation raises from the insight that 3D shape provides global unwarping cues for rectifying a distorted document image, while overlooking the local structure. On the other hand, textlines complementarily provide explicit geometric constraints for local patterns. The learned geometric representation effectively bridges the distorted image and the ground truth one. Extensive experiments show the effectiveness of our framework and demonstrate the superiority of our DocGeoNet over state-of-the-art methods on both the DocUNet Benchmark dataset and our proposed DIR300 test set.
Cloud Data Centre Energy Utilization Estimation

Due to the growth of the internet and internet-based software applications, cloud data center demand has increased. Cloud data centers have thousands of servers that are 24×7 working for users; it is the strong witness of enormous energy consumption for the operation of the cloud data center. However, server utilization is not remaining the same all the time, so, from an economic feasibility point of view, energy management is an essential activity for cloud resource management. Some well-known energy management techniques for cloud data centers generally used are dynamic voltage and frequency scaling (DVFS), dynamic power management (DPM), and task scheduling-based techniques. The present work is based on an analytical approach to integrating resource provisioning with sophisticated task scheduling; the authors estimate energy utilization by cloud data centers using iDR cloud simulator. The work is intended to optimize power consumption in the cloud data center.
Identifying similar-bicliques in bipartite graphs

Bipartite graphs have been widely used to model the relationship between entities of different types, where vertices are partitioned into two disjoint sets/sides. Finding dense subgraphs in a bipartite graph is of great significance and encompasses many applications. However, none of the existing dense bipartite subgraph models consider similarity between vertices from the same side, and as a result, the identified results may include vertices that are not similar to each other. In this paper, we formulate the notion of similar-biclique which is a special kind of biclique where all vertices from a designated side are similar to each other, and aim to enumerate all similar-bicliques. The naive approach of first enumerating all maximal bicliques and then extracting all maximal similar-bicliques from them is inefficient, as enumerating maximal bicliques is time consuming. We propose a backtracking algorithm MSBE to directly enumerate maximal similar-bicliques, and power it by vertex reduction and optimization techniques. Furthermore, we design a novel index structure to speed up a time-critical operation of MSBE, as well as to speed up vertex reduction. Efficient index construction algorithms are also developed. Extensive experiments on 17 bipartite graphs as well as case studies are conducted to demonstrate the effectiveness and efficiency of our model and algorithms.
Blockchain implications for auditing: a systematic literature review and bibliometric analysis

Blockchain technology, smart contracts, and asset tokenization have relevant implications for the auditing environment. This paper evaluates the current stage of blockchain application in auditing, analyzing scientific publications and identifying the impact of what is already a reality and the potential effects of its improvements in audit professionals’ activities performance. The article considers the proposals and suggestions on the leading research indexed by the Scopus and Web of Science databases. We analyzed 374 papers on the topic of blockchain and provide a summary and analysis of the current state of auditing research. The bibliometric analysis was performed using the Bibliometrix R Package and the VOSviewer software. After a systematic study of abstracts and a general review of the papers to only include those directly related to our work’s objectives, we found 78 papers. The work results in a framework of potential and effective implications of blockchain technology for auditing, pointing out several new challenges in terms of skills and knowledge needed in this new reality of audit professionals.
Demo: Real-Time Semantic Communications with a Vision Transformer

Semantic communications are expected to enable the more effective delivery of meaning rather than a precise transfer of symbols. In this paper, we propose an end-to-end deep neural network-based architecture for image transmission and demonstrate its feasibility in a real-time wireless channel by implementing a prototype based on a field-programmable gate array (FPGA). We demonstrate that this system outperforms the traditional 256-quadrature amplitude modulation system in the low signal-to-noise ratio regime with the popular CIFAR-10 dataset. To the best of our knowledge, this is the first work that implements and investigates real-time semantic communications with a vision transformer.
HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields

We propose high dynamic range (HDR) radiance fields, HDR-Plenoxels, that learn a plenoptic function of 3D HDR radiance fields, geometry information, and varying camera settings inherent in 2D low dynamic range (LDR) images. Our voxel-based volume rendering pipeline reconstructs HDR radiance fields with only multi-view LDR images taken from varying camera settings in an end-to-end manner and has a fast convergence speed. To deal with various cameras in real-world scenario, we introduce a tone mapping module that models the digital in-camera imaging pipeline (ISP) and disentangles radiometric settings. Our tone mapping module allows us to render by controlling the radiometric settings of each novel view. Finally, we build a multi-view dataset with varying camera conditions, which fits our problem setting. Our experiments show that HDR-Plenoxels can express detail and high-quality HDR novel views from only LDR images with various cameras.
A reading engagement-promoting strategy to facilitate EFL students' mobile learning achievement, behaviour and engagement

Mobile learning (M-learning) refers to the use of mobile and wireless communication technologies to enable students to access learning materials in an interactive manner, regardless of location and time constraints. However, without a proper learning design or the provision of learning guidance, most students might fail to plan their learning schedule to effectively read and comprehend the learning materials on their own, which could significantly affect their learning engagement and outcomes. In this study, a Reading Engagement-Promotion Strategy (REPS) was proposed to tackle this issue. A quasi-experiment was designed to compare the students using the REPS approach and those using the conventional ones in terms of learning achievement, behaviours (i.e., frequency of use of markers, the number of words, and usage patterns), and engagement (i.e., time spent reading and learning). The results showed that the REPS approach could improve students' learning achievement and facilitate the high achievers' engagement.
HDNet: Multi-Modality Hierarchy-Aware Decision Network for RGB-D Salient Object Detection

RGB-D Salient object detection (SOD) is a pixel-level dense prediction task, which can highlight the prominent object in the scene. Recently, Convolution Neural Network (CNN) is widely applied in SOD to generate multi-level features, which are complementary to each other. However, most methods ignore the unique characteristics of multi-level features (high-level and low-level features). Given the effective employment of multi-level features, we propose a novel multi-modality hierarchy-aware decision network (HDNet) by embedding a Swin Transformer as an encoder. The proposed HDNet contains three primary designs: (1) a Swin Transformer encoder is employed instead of a CNN to learn long-range dependencies; (2) a hierarchy-aware feature decision mechanism (HFDM) is proposed to exploit effective local detail cues of low-level features and global semantic information of high-level features, which consists of two sub-modules, namely low-hierarchy edge module (LEM) and high-hierarchy region module (HRM); (3) a decision-based fusion module (DFM) is designed to fuse RGB and depth features under the attribute of multi-level features generated from HFDM. Experiments on five public benchmarks verify that our framework has better performance than the other 18 state-of-the-art algorithms.
Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image

Abstract Mineralized indicator minerals are an important geological and mineral exploration indicator. Rapid extraction of mineralized indicator minerals from hyperspectral remote sensing images using ensemble learning model has important geological significance for mineral resources exploration. In this study, two mineralized indicator minerals, limonite and chlorite, exposed at the surface of Qinghai Gouli area were used as the research objects. Sparrow search algorithm (SSA) was combined with random forest (RF) and gradient boosting decision tree (GBDT) ensemble learning models, respectively, to construct hyperspectral mineralized indicative mineral information extraction models in the study area. Youden index (YD) and ore deposit coincidence (ODC) were applied to evaluate the performance of different models in the mineral information extraction. The results indicate that the optimization of SSA parameter algorithm is obvious, and the accuracy of both the integrated learning models after parameter search has been improved substantially, among which the SSA-GBDT model has the best performance, and the YD and the ODC can reach 0.661 and 0.727, respectively. Compared with traditional machine learning model, integrated learning model has higher reliability and stronger generalization performance in hyperspectral mineral information extraction and application, with YD greater than 0.6. In addition, the distribution of mineralized indicative minerals extracted by the ensemble learning model after parameter optimization is basically consistent with the distribution pattern of the fracture tectonic spreading characteristics and known deposits (points) in the area, which is in line with the geological characteristics of mineralization in the study area. Therefore, the classification and extraction model of minerals based on hyperspectral remote sensing technology, combined with the SSA optimization algorithm and ensemble learning model, is an efficient mineral exploration method.
Improving fine-tuning of self-supervised models with Contrastive Initialization

Self-supervised learning (SSL) has achieved remarkable performance in pre-training the models that can be further used in downstream tasks via fine-tuning. However, these self-supervised models may not capture meaningful semantic information since the images belonging to the same class are often regarded as negative pairs in the contrastive loss. Consequently, the images of the same class are often located far away from each other in the learned feature space, which would inevitably hamper the fine-tuning process. To address this issue, we seek to explicitly enhance the semantic relation among instances on the targeted downstream task and provide a better initialization for the subsequent fine-tuning. To this end, we propose a Contrastive Initialization (COIN) method that breaks the standard fine-tuning pipeline by introducing an extra class-aware initialization stage before fine-tuning. Specifically, we exploit a supervised contrastive loss to increase inter-class discrepancy and intra-class compactness of features on the target dataset. In this way, self-supervised models can be easily trained to discriminate instances of different classes during the final fine-tuning stage. Extensive experiments show that, with the enriched semantics, our COIN significantly outperforms existing methods without introducing extra training cost and sets new state-of-the-arts on multiple downstream tasks. For example, compared with the baseline method, our COIN improves the accuracy by 5% on ImageNet-20 and 2.57% on CIFAR100, respectively.
Anomaly Detection in Social Media Using Text-Mining and Emotion Classification with Emotion Detection

Anomaly detection in online social networks identifies abnormal activity the most as illegal behavior. Anomalous behavior identifies malicious activities, including spammers, and online fraudsters. Social networks like Twitter, Facebook users share opinions to the world primarily in text form, such as Micro-blogs. Anomaly detection in social media depends on text mining analytics. Text mining analytics derives quality information from the text corpus. Text mining is a prominent mining technique that is still under research for further development. Many researchers have proposed anomaly detection techniques using various text mining processes. Each comment or tweetis updated in informal human writings, using NLPT Natural Language Processing techniques unstructured texts are normalized into a standard format to apply ML algorithms. Opinions shared in social media are classified by emotions, emotion classification classified with the different emotions like happy, sad, fear, disgust, anger, surprise, trust. This paper aimsto analyze anomaly detection in social media with micro-blogs. The study deepens with text mining and emotion classification techniques from different authors.
Risk Analysis Automation Process in IT Security for Cloud Applications

Modern Secure Development Life Cycles recognize that there is a need to (i) perform a risk assessment to identify the threats that a system is facing and (ii) a risk rating procedure to prioritize the development and maintenance activities. However, such processes are hardly applicable in the development of Cloud-based applications, due to the cost (money and time) that such procedures imply. This article aims at addressing such an issue by proposing a technique, compatible with the Security-By-Design development methodologies, that automates the threat modeling and risk evaluation of a system, reducing the costs and requiring the developers with just a limited set of security skills. Through the proposed approach, the software system is analyzed to identify the threats that affect the system assets, ranking the level of risk associated with each threat and suggesting a set of countermeasures in standard terms; the process requires minimal user interaction. The proposed technique was implemented through a dedicated tool and validated against a simple case study.
Forecasting with Using Quasilinear Recurrence Equation

We developed a new approach to the analysis of time series based on the use of quasi-linear recurrence relations. Unlike neural networks, this approach makes it possible to explicitly obtain high-quality quasi-linear difference equations (adequately describing the considered process). Currently, we developed and tested methods for identifying the parameters of a single equation. The research considers the identification algorithm for parameters of quasilinear recurrence equation. We use it to solve the problem of regression analysis with mutually dependent observable variables, which allows to implement the generalized last deviations method (GLDM). Using this model we held the computational experiment. The model using the identified parameters allows to obtain the long-time forecast.
Federated Learning-Based IoT Intrusion Detection on Non-IID Data

Federated learning allows multiple parties to jointly train and update machine learning models without shared data to a central server, which is particularly suitable for intrusion detection in IoT environments. However, data on each node in the IoT scenario are usually not independently and identically distributed (IID), which poses an additional challenge to the convergence and speed of federated learning. To address this problem, there have been proposals to have nodes share a portion of their data centrally, which can still raise privacy concerns. In this paper we propose a strategy to improve the training of non-IID data by allowing mutually trusted nodes to self-aggregate into clusters of trust that will participate in federated learning as peers. We experiment with an up-to-date IoT dataset, Aposemat IoT-23 (IoT-23 for short) and show that using this strategy is considerably more accurate federated learning, comparably accurate to proposals that envisage central sharing a portion of node data, and comparable to centralised machine learning accuracy.