Reviewerly/exHarmony huggingface dataset:
Denoising of computed tomography using bilateral median based autoencoder network

Abstract Denoising of Computed tomography (CT) images is a critical aspect of image processing that is expected to improve the performance of Computer‐aided diagnosis (CAD) systems. However, the use of complex imaging modalities such as CT imaging to ascertain pancreatic cancer is vulnerable to gaussian and poisson noises, making image denoising an imperative step for the accurate performance of CAD systems. This paper presents a Bilateral median based autoencoder network (BMAuto‐Net) constructed with intermediate batch normalization layers and dropout factors to eliminate gaussian noise from the CT images. The skip connections adjoining the network, prevent performance degradation that generally occurs in most autoencoder architectures. Based on the presented study, BMAuto‐Net is reckoned to outperform other traditional filters and autoencoders. The performance measurement of the proposed architecture is performed using the peak signal‐to‐noise ratio (PSNR), mean squared error (MSE), and structured similarity index (SSIM) metric values. The Cancer imaging archive (TCIA) dataset consisting of 19 000 CT images is used to validate the performance of the architecture with average PSNR values of 30.01, 30.53, and 30.52, MSE values of 98.23, 98.87, and 98.94, and SSIM of values of 0.67, 0.60, and 0.57 for noise factors (NFs) of 0.1, 0.3, and 0.5 respectively.
Knowledge Graph Completion Based on GCN of Multi‐Information Fusion and High‐Dimensional Structure Analysis Weight

Knowledge graph completion (KGC) can solve the problem of data sparsity in the knowledge graph. A large number of models for the KGC task have been proposed in recent years. However, the underutilisation of the structure information around nodes is one of the main problems of the previous KGC model, which leads to relatively single encoding information. To this end, a new KGC model that encodes and decodes the feature information is proposed. First, we adopt the subgraph sampling method to extract node structure. Moreover, the graph convolutional network (GCN) introduced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information. Eventually, the high-dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scoring function. The experimental results show that the model performs well on the datasets used.
A trusted proof mechanism of data source for smart city

In smart city, technologies such as blockchain, Internet of Things (IoT) and edge computing tend to be integrated that promotes horizontal information exchange and multi-party cooperation. Large amounts of superior services can be provided by analyzing the information. However, lots of IoT devices generating the information do not have sufficient resources to resist malicious attacks so that the devices deliberately upload the tampered data, which reduces quality of service. To solve the problem, we propose a data source trusted proof mechanism based on the node security measurement and threshold ring signature method. The mechanism firstly judges whether a node is in a trusted domain based on the proposed Distributed Sub-domain Networking (DSN) method to reduce energy consumption and avoid single point failure. Then, it quantifies security value of the nodes in the trusted domain based on the proposed three-dimension security measurement method. Finally, it confirms authenticity of the nodes using the Chinese Remainder Theorem (CRT)-based threshold ring signature scheme.Simulation results show our proposed mechanism can distinguish all malicious nodes among trusted nodes, the loss rate of trusted nodes is reduced to less than 5% and the false report rate is reduced to less than 10%. • A distributed subdomain networking method to strong user communication and cooperation. • A trust measurement method to monitor the operation status of nodes more comprehensive. • A distributed threshold ring signature scheme to protect users’ privacy. • A mechanism considering the running state of the node itself and its surrounding nodes.
Hierarchical Feature Aggregation and Self-Learning Network for Remote Sensing Image Continuous-Scale Super-Resolution

Conducting research on remote sensing image (RSI) super-resolution (SR) is important, especially in terms of the continuous scale, which is beneficial to the application of RSI, such as RSI object detection and data fusion. Continuous-scale SR aims to use a single model to achieve SR at arbitrary (integer and noninteger) scale factors. Therefore, in this letter, we propose a hierarchical feature aggregation and self-learning network for RSI continuous-scale SR (RSI-HFAS). Our network can magnify the RSI continuously, which is beneficial for extracting the RSI multiscale features. First, we design a hierarchical feature aggregation module (HFAM) that is used for hierarchical feature extraction by placing convolutional layers on different floors and completing global feature fusion, which is crucial for achieving RSI continuous-scale SR with a single model. Second, the proposed network introduces a feedback mechanism, which can refine the hierarchical feature through feature feedback and enrich the texture parts of the RSI step by step. Finally, we design a self-learning upscaling structure to dynamically predict the number and weights of the upsampling filters, which can achieve RSI continuous-scale SR. Compared to the meta-learning based on enhanced deep SR (META-EDSR) method, our experimental results show a nearly 0.2-dB improvement on the metrics of the peak signal-to-noise ratio (PSNR).
QoS prediction for service selection and recommendation with a deep latent features autoencoder

The number of services on the Internet has increased rapidly in recent years. This makes it increasingly difficult for users to find the right services from a large number of the functionally equivalent candidate. In many cases, the number of services invoked by a user is quite limited, resulting in a large number of missing QoS values and sparseness of data. Consequently, predicting QoS values of the services is important for users to find the exact service among many functionally similar services. However, improving the accuracy of QoS prediction is still a problem. Despite the successful results of the proposed QoS prediction methods, there are still a set of issues that should be addressed, such as Sparsity and Overfitting. To address these issues and improve prediction accuracy. In this paper, we propose a novel framework for predicting QoS values and reduce prediction error. This framework named auto-encoder for neighbor features (Auto-NF) consists of three steps. In the first step, we propose an extended similarity computation method based on Euclidean distance to compute the similarity between users and find similar neighbors. In the second step, we form clusters of similar neighbors and partition the initial matrix into sub-matrices based on these clusters to reduce the data sparsity problem. In the third step, we propose a simple neural network autoencoder that can learn deep features and select an ideal number of latent factors to reduce the overfitting phenomenon. To validate and evaluate our method, we conduct a series of experiments use a real QoS dataset with different data densities. The experimental results demonstrate that our method achieves higher prediction accuracy compared to existing methods.
Graph convolutional network meta-learning with multi-granularity POS guidance for video captioning

Video as information carrier has gained overwhelming popularity in city surveillance and social networks, such as WeChat, Weibo, and TikTok. To bridge the semantic gap between video content (e.g., user and landmark building) and textual information (e.g., user location), video captioning has emerged as an attracting technique in recent years. Existing works mostly focus on sentence-level Part-of-Speech (POS) information and use Long Short-Term Memory (LSTM) as encoder, which neglects word or phrase-level POS information and also fails to globally consider long-range temporal relations among video frames. To address the drawbacks, we leverage multi-granularity POS guidance to learn Graph Convolutional Network (GCN) via meta-learning, abbreviated as GMMP (GCN Meta-learning with Multi-granularity POS), for generating high-quality captions for videos. It models temporal dependency by treating frames as nodes in the graph, and captures POS information of words and phrases by multi-granularity POS attention mechanism. We adopt meta-learning to better learn GCN by maximizing the reward of generated caption in a reinforcement task and also the probability of ground-truth caption in a supervised task, simultaneously. Experiments have verified the advantages of our GMMP model on several benchmark data sets.
SoK: Securing Email—A Stakeholder-Based Analysis

While email is the most ubiquitous and interoperable form of online communication today, it was not conceived with strong security guarantees, and the ensuing security enhancements are, by contrast, lacking in both ubiquity and interoperability. This situation motivates our research. We begin by identifying a variety of stakeholders who have an interest in the current email system and in efforts to provide secure solutions. We then use the tussle among stakeholders to explain the evolution of fragmented secure email solutions undertaken by industry, academia, and independent developers, and to draw the conclusion that a one-size-fits-all solution is unlikely. We highlight that vulnerable users are not well served by current solutions. We also account for the failure of PGP, and argue secure messaging, while complementary, is not a fully substitutable technology.
Identification Scheme and Forward-Secure Signature in Identity-Based Setting from Isogenies

Identity-based cryptography (IBC) introduced by Adi Shamir [] has paved the way for authenticating the public key of a user without the use of certificates. In addition to identity-based encryption (IBE), a full-fledged identity-based system would require identity-based authentication, which is where ID-based identification (IBID) and identity-based signature (IBS) comes into picture. Thus the advent of IBID and IBS renews interest in IBE. Since the discovery of IBC, a number of IBID and IBS were proposed in the literature. The overwhelming majority of them, however, substantially rely on discrete logarithm problem and are thus vulnerable to quantum attacks. As a result, developing a quantum-resistant IBID and IBS is the dire need of the hour.
Statistical image watermark decoder using high-order difference coefficients and bounded generalized Gaussian mixtures-based HMT

Imperceptibility, robustness and data payload are three main requirements of any image watermarking systems to guarantee desired functionalities, but there is a tradeoff among them from the information-theoretic perspective. In order to achieve this balance, in this paper, we propose a new statistical image watermarking scheme in nonsubsampled Shearlet transform (NSST) domain, which is based on high-order difference coefficients and bounded generalized Gaussian mixture model (BGGMM) based hidden Markov tree (HMT). In embedding process, NSST is firstly performed on host image and NSST highpass subband is divided into no-overlapping blocks. Then high-order differences of NSST coefficient blocks are computed. And finally, watermark signal is inserted into robust high-order difference coefficients through non-linear multiplicative approach. In extraction phase, robust high-order difference coefficients are firstly modeled by employing BGGMM based HMT, which can capture accurately both marginal distributions and strong dependencies of high-order difference coefficients. Statistical model parameters are then estimated by combining the approach of minimizing the higher bound on data negative log-likelihood function and upward–downward algorithm. And finally, an image watermark decoder is developed using BGGMM based HMT and maximum likelihood decision. Experimental results on some test images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed scheme.
Camera Self-Calibration with GNSS Constrained Bundle Adjustment for Weakly Structured Long Corridor UAV Images

Camera self-calibration determines the precision and robustness of AT (aerial triangulation) for UAV (unmanned aerial vehicle) images. The UAV images collected from long transmission line corridors are critical configurations, which may lead to the “bowl effect” with camera self-calibration. To solve such problems, traditional methods rely on more than three GCPs (ground control points), while this study designs a new self-calibration method with only one GCP. First, existing camera distortion models are grouped into two categories, i.e., physical and mathematical models, and their mathematical formulas are exploited in detail. Second, within an incremental SfM (Structure from Motion) framework, a camera self-calibration method is designed, which combines the strategies for initializing camera distortion parameters and fusing high-precision GNSS (Global Navigation Satellite System) observations. The former is achieved by using an iterative optimization algorithm that progressively optimizes camera parameters; the latter is implemented through inequality constrained BA (bundle adjustment). Finally, by using four UAV datasets collected from two sites with two data acquisition modes, the proposed algorithm is comprehensively analyzed and verified, and the experimental results demonstrate that the proposed method can dramatically alleviate the “bowl effect” of self-calibration for weakly structured long corridor UAV images, and the horizontal and vertical accuracy can reach 0.04 m and 0.05 m, respectively, when using one GCP. In addition, compared with open-source and commercial software, the proposed method achieves competitive or better performance.
From Textual to Verbal Communication: Towards Applying Sentiment Analysis to a Software Project Meeting

Sentiment analysis gets increasing attention in software engineering with new tools emerging from new insights provided by researchers. Existing use cases and tools are meant to be used for textual communication such as comments on collaborative version control systems. While this can already provide useful feedback for development teams, a lot of communication takes place in meetings and is not suited for present tool designs and concepts. In this paper, we present a concept that is capable of processing live meeting audio and classifying transcribed statements into sentiment polarity classes. We combine the latest advances in open source speech recognition with previous research in sentiment analysis. We tested our approach on a student software project meeting to gain proof of concept, showing moderate agreement between the classifications of our tool and a human observer on the meeting audio. Despite the preliminary character of our study, we see promising results motivating future research in sentiment analysis on meetings. For example, the polarity classification can be extended to detect destructive behaviour that can endanger project success.
An improved YOLOv3 model for detecting location information of ovarian cancer from CT images

Ovarian cancer is a malignant tumor that poses a serious threat to women’s lives. Computer-aided diagnosis (CAD) systems can classify the type of ovarian tumors, but few of them can provide exactly the location information of ovarian cancer cells. Recently, deep learning technology becomes hot for automatic detection of cancer cells, particularly for detecting their locations. In this work, we propose a novel end-to-end network YOLO-OC (Ovarian cancer) model, which can extract the characteristics of ovarian cancer more efficiently. In our method, deformable convolution is used to enhance the model’s ability to learn geometric deformation in space. Squeeze-and-Excitation (SE) module is proposed to automatically learn the importance of different channel features. Data experiments are conducted on datasets collected from The Affiliated Hospital of Qingdao University Medical College, China. Experimental results show that our YOLO-OC model achieves 91.83%, 85.66% and 73.82% on mean average precision mAP@.5, mAP@.75 and mAP@[.5,.95], respectively, which performs better than Faster R-CNN, SSD and RetinaNet on both accuracy and efficiency.
Mapping Quantum Chemical Dynamics Problems to Spin-Lattice Simulators

The accurate computational determination of chemical, materials, biological, and atmospheric properties has a critical impact on a wide range of health and environmental problems, but is deeply limited by the computational scaling of quantum mechanical methods. The complexity of quantum chemical studies arises from the steep algebraic scaling of electron correlation methods and the exponential scaling in studying nuclear dynamics and molecular flexibility. To date, efforts to apply quantum hardware to such quantum chemistry problems have focused primarily on electron correlation. Here, we provide a framework that allows for the solution of quantum chemical nuclear dynamics by mapping these to quantum spin-lattice simulators. Using the example case of a short-strong hydrogen-bonded system, we construct the Hamiltonian for the nuclear degrees of freedom on a single Born-Oppenheimer surface and show how it can be transformed to a generalized Ising model Hamiltonian. We then demonstrate a method to determine the local fields and spin-spin couplings needed to identically match the molecular and spin-lattice Hamiltonians. We describe a protocol to determine the on-site and intersite coupling parameters of this Ising Hamiltonian from the Born-Oppenheimer potential and nuclear kinetic energy operator. Our approach represents a paradigm shift in the methods used to study quantum nuclear dynamics, opening the possibility to solve both electronic structure and nuclear dynamics problems using quantum computing systems.
The Effects of Virtual Avatar Visibility on Pointing Interpretation by Observers in 3D Environments

Avatars are often used to provide representations of users in 3D environments, such as desktop games or VR applications. While full-body avatars are often sought to be used in applications, low visibility avatars (i.e., head and hands) are often used in a variety of contexts, either as intentional design choices, for simplicity in contexts where full-body avatars are not needed, or due to external limitations. Avatar style can also vary from more simplistic and abstract to highly realistic depending on application context and user choices. We present the results of two desktop experiments that examine avatar visibility, style, and observer view on accuracy in a pointing interpretation task. Significant effects of visibility were found, with effects varying between horizontal and vertical components of error, and error amounts not always worsening as a result of lowering visibility. Error due to avatar visibility was much smaller than error resulting from avatar style or observer view. Our findings suggest that humans are reasonably able to understand pointing gestures with a limited observable body.
A machine learning-based framework for data mining and optimization of a production system

In the present paper, we performed several decision tree algorithms to classify instances and represent the most efficient policies depicted by a hybrid reinforcement learning algorithm and treat a complex production, maintenance and quality control optimization problem within a degrading manufacturing and remanufacturing system. The constructed decision trees contained of nodes, which represent its independent variables, and leaves that stand for the set of function values. All optimization parameters and optimal policies found by the hybrid reinforcement learning algorithm, used as the training set for the trees algorithms. After the construction of each tree, the resulting rule used to treat the optimization problem and the performance of each rule compared. In addition, for the best performing trees algorithms, further investigation performed for the impact of their parameters to its rule effectivity.
Full Key Recovery Side-Channel Attack Against Ephemeral SIKE on the Cortex-M4

This paper describes the first practical single-trace side-channel power analysis of SIKE. While SIKE is a post-quantum key exchange, the scheme still relies on a secret elliptic curve scalar multiplication which involves a loop of a double-and-add procedure, of which each iteration depends on a single bit of the private key. The attack therefore exploits the nature of elliptic curve point addition formulas which require the same function to be executed multiple times. We show how a single trace of a loop iteration can be segmented into several power traces on which 32-bit words can be hypothesised based on the value of a single private key bit. This segmentation enables a classical correlation power analysis in an extend-and-prune approach. Further error-correction techniques based on depth-search are suggested. The attack is explicitly geared towards and experimentally verified on an STM32F3 featuring a Cortex-M4 microcontroller which runs the SIKEp434 implementation adapted to 32-bit ARM that is part of the official implementations of SIKE. We obtained a resounding 100% success rate recovering the full private key in each experiment. We argue that our attack defeats many countermeasures which were suggested in a previous power analysis of SIKE, and finally show that the well-known countermeasure of projective coordinate randomisation stops the attack with a negligible overhead.
Evaluation Platform for DDM Algorithms With the Usage of Non-Uniform Data Distribution Strategies

Huge amounts of data are collected in numerous independent data storage facilities around the world. However, how the data is distributed between physical locations remains unspecified. Downloading all of the data for the purpose of processing it is undesirable and sometimes even impossible. Various methods have been proposed for performing data mining tasks, but the main problem is the lack of an objective strategy for comparing them. The authors present current research on a novel evaluation platform for distributed data mining (DDM) algorithms. The proposed platform opens up a new field to evaluate algorithms in terms of the quality of the results, transfer used, and speed, but also for the use of a non-uniform data distribution among independent nodes during algorithm evaluation. This work introduces a ‘data partitioning strategy’ term referring to a specific, not necessarily uniform data distribution. A brief evaluation for three clustering algorithms is also reported, showing the usability and simplicity of identifying differences in processing with the use of the platform.