Reviewerly/exHarmony huggingface dataset:
Tampering detection and localization base on sample guidance and individual camera device convolutional neural network features

Abstract This paper proposes an image tampering detection algorithm based on sample guidance and individual camera device's convolutional neural network (CNN) features (SGICD‐CF) to address the challenges in the authenticity and integrity of images. Due to the development of the digital image processing technology, which makes image editing and processing, image tampering and forgery easy and lot simplified, thus solving the problem of image tamper detection, to maintain information security. The principle of SGICD‐CF assumes that pixels of the pristine image come from a single camera device, but on the contrary, if an image to be tested is spliced by multiple images from different cameras, then the pixels from the multiple camera devices will be detected. SGICD‐CF divides the image to be tested into 64 × 64 pixel image patches, extracts the camera‐related features and some camera model‐related information of image patches by source camera identification network (SCI‐Net) which is proposed by us, and obtains the classification confidence degree of the image patch. Furthermore, it determines whether the image patch contains foreign pixels according to the obtained confidence degree and finally determines whether the image was tampered according to the classification results of all the image patches, thus locating the tampered area. However, the experimental results show that SGICD‐CF can detect and locate the tampered area of an image accurately and our methods have a better performance than other existing methods. Our algorithm can achieve an average correct rate of 0.855 on the synthetic data set based on Dresden, which is higher than other existing detection methods.
Security of a High Dimensional Two‐Way Quantum Key Distribution Protocol

Abstract Two‐way quantum key distribution (QKD) protocols utilize bi‐directional quantum communication to establish a shared secret key. Due to the increased attack surface, security analyses remain challenging. Here a high‐dimensional variant of the Ping Pong protocol is investigated and an information theoretic security analysis in the finite‐key setting is performed. The main contribution in this work is to show a new proof methodology for two‐way quantum key distribution protocols based on the quantum sampling framework of Bouman and Fehr introduced in 2010 and also sampling‐based entropic uncertainty relations introduced by the authors in 2019. The Ping Pong protocol is only investigated here, but these methods may be broadly applicable to other QKD protocols, especially those relying on two‐way channels. Along the way, some fascinating benefits to high‐dimensional quantum states applied to two‐way quantum communication are also showed.
Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree

This paper presents a type-2 fuzzy inference tree designed for a differential wheeled mobile robot that navigates in indoor environments. The proposal consists of a controller designed for obstacle avoidance, a controller for path recovery and goal reaching, and a third controller for the real-time selection of behaviors. The system takes as inputs the information provided for a 2D laser range scanner, i.e., the distance of nearby objects to the robot, as well as the robot position in space, calculated from mechanical odometry. The real performance is evaluated through metrics such as clearance, path smoothness, path length, travel time and success rate. The experimental results allow us to demonstrate an appropriate performance of our proposal for the navigation task, with a higher efficiency than the reference methods taken from the state of the art.
Incorporating various perspectives in using instant messages in teaching programming: A critical system thinking perspective

Abstract In this paper, the application of instant messaging (IM) in programming training is argued to be possible and inevitable, especially in the high schools/colleges of poor communities. However, challenges in its application were noted. These challenges of utilising IM and the reluctance of the students in applying it in their programming training acted as a motivation for the study. Our aim is to use IM for a different purpose than its original intention. Critical systems heuristics was used as a multi‐methodological framework for the incorporation of various conditioned realities, where the technological pedagogical content knowledge framework was applied to incorporate the applicable technological, pedagogical, content areas. The guidelines were enhanced from knowledge gained from an action research project with trainee teachers. This iterative process demonstrates the incorporation of both theoretical and empirical context‐based perspectives into the understanding and addressing of a problem environment.
Satellite-based silica mapping as an essential mineral for clean energy transition: Remote sensing mineral exploration as a climate change adaptation approach

One of the most important challenges in facing climate changes is transition to clean energy to reduce the warm gases emissions. The technologies of clean energy transition require more mineral resources than the traditional fossil fuel methods. The present study sheds the light on the role of mineral exploration using remote sensing techniques to fulfill the requirements of the clean energy technologies from mineral resources. In this study, the capabilities of ASTER data in mapping silica (an essential mineral for solar energy technologies) were demonstrated. Two case study areas with different geological environments of silica-rich rock units were studied. The Faiyum area contains sedimentary silica-rich rock units; mainly white sand, sandstone, and sand dunes. While the Higlig-Suwayqat area contains basement silica-rich rock units; mainly quartz plugs and veins included in the granitic rocks. Two remote sensing techniques; Band ratio (BR), and Constrained Energy Minimization (CEM) supervised classification techniques were applied to the ASTER surface reflectance. Several ASTER band ratios were tested; the ratio of b14/b12 was found the most effective band ratio for delineating the silica-rich areas. On the other hand, the CEM technique was applied using the USGS spectral signature of quartz mineral. CEM technique enabled mapping the pixels that have similar signatures to the input quartz signature as silica-rich areas. A field study was conducted in both the studied case areas to validate the remote sensing results; several silica-rich units were observed including; white sand, sandstone, sand dunes, quartz plugs/veins, and sand-rich wadi deposits. Based on the distribution of the silica-rich units mapped by both the adopted techniques as compared to the field observations, it is found that the accuracy of both the techniques is very high with an advantage of the CEM technique over the BR technique. • The study highlights the role of mineral exploration in clean energy transition. • The study presents a silica exploration methodology using remote sensing data. • Both sedimentary and basement rocks-hosted silica were investigated. • Two remote sensing techniques were applied on ASTER Satellite data. • The results of the satellite data were validated by field studies.
No-reference Quality Assessment for Contrast-distorted Images Based on Gray and Color-gray-difference Space

No-reference image quality assessment is a basic and challenging problem in the field of image processing. Among them, contrast distortion has a great impact on the perception of image quality. However, there are relatively few studies on no-reference quality assessment of contrast-distorted images. This article proposes a no-reference quality assessment algorithm for contrast-distorted images based on gray and color-gray-difference (CGD) space. In terms of gray space, we consider the local and global aspects, and use the distribution characteristics of the grayscale histogram to represent global features, while local features are described by the fusion of Local Binary Pattern (LBP) operator and gradient. In terms of CGD space, we first randomly extract patches from the entire image and then extract appropriate quality perception features in the patch’s CGD histogram. Finally, the AdaBoosting back propagation (BP) neural network is used to train the prediction model to predict the quality of the contrast-distorted image. Extensive analysis and cross-validation are carried out on five contrast-related image databases, and the experimental results have proved the superiority of this method compared with recent related algorithms.
Transfer Learning for Low-Resource Multilingual Relation Classification

Relation classification (sometimes called relation extraction ) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challenging for Indian languages, because they are syntactically and morphologically diverse, as well as different from resource-rich languages like English. Despite recent interest in deep generative models for Indian languages, relation classification is still not well served by public datasets. In response, we present IndoRE , a dataset with 21K entity- and relation-tagged gold sentences in three Indian languages (Bengali, Hindi, and Telugu), plus English. We start with a multilingual BERT (mBERT)-based system that captures entity span positions and type information, and provides competitive performance on monolingual relation classification. Using this baseline system, we explore transfer mechanisms between languages and the scope to reduce expensive data annotation while achieving reasonable relation extraction performance. Specifically, we (a) study the accuracy-efficiency trade-off between expensive, manually labeled gold instances vs. automatically translated and aligned silver instances to train a relation extractor, (b) device a simple mechanism for budgeted gold data annotation by intelligently converting distant-supervised silver training instances to gold training instances with human annotators using active learning, and finally (c) propose an ensemble model to provide a performance boost over that achieved via limited gold training instances. We release the dataset for future research. 1
Information Security Risk Management Model for Big Data

In the current society of rapid expansion of information, big data have achieved vigorous development in all walks of life, considerably promoting data transmission and information sharing. Meanwhile, individuals are becoming increasingly reliant on big data and the Internet, but at the same time, the threat of information security posed by big data is becoming increasingly visible. As a result, how to protect the information security of big data has piqued the interest of both government and businesses. The essence of information security management is risk management, which is closely related to each other. Therefore, this study focuses on the following two aspects of research work. On the one hand, most existing risk management models merely describe risk management in the abstract from a macro-level, and they lack research on risk assessment, making them ineffective. This research builds a novel information security risk management model on the basis of existing risk management models based on the concept of multidimensional risk management. To achieve multidimensional dynamic management of big data risks and to keep them within an acceptable range as much as possible, the model is divided into five levels and two dimensions. On the other hand, this research also optimizes and improves the fuzzy mathematical analysis method and proposes a fuzzy comprehensive assessment method as the core algorithm for the risk assessment layer in the model. As a post-event risk assessment method, the advantage of this method is that it can comprehensively consider factors affecting risk and can quantify some assessment factors in the real network to achieve an effective combination of qualitative and quantitative, thereby providing a basis for decision-making in risk analysis and risk control. Finally, the effectiveness of the risk model in the real application is verified by example analysis, and it is intended that the study work would provide assistance and assurance for big data information security management.
Evolutionary ensemble generative adversarial learning for identifying terrorists among high-speed rail passengers

As one of the most salient features of China’s economic development, high-speed rail (HSR) is considered to be an attractive target and travel mode for terrorists. Distinguishing potential terrorists from normal passengers is of critical importance to public security, but very challenging because terrorists constitute only a very small fraction of HSR passengers, especially when they can disguise their attributes and behaviors to deceive the classifiers. For this extremely imbalanced classification problem, we propose a novel evolutionary generative adversarial network (GAN) ensemble method, where each GAN in the ensemble simultaneously trains a discriminator to identify abnormal samples from a large number of passenger profiles and trains a generator to produce abnormal samples that are disguised as normal ones in a subspace of the sample space, and the final classifier combines these GANs using an evolutionary fusion method. Experiments on benchmark problems demonstrate that the proposed method has very competitive performance compared to popular imbalanced classifiers. The successful applications in terrorist identification for China Railway also demonstrate the feasibility and effectiveness of our approach.
Undulate: A framework for data-driven software engineering enabling soft computing

Especially web-facing software systems enable the collection of usage data at a massive scale. At the same time, the scale and scope of software processes have grown substantively. Automated tools are needed to increase the speed and quality of controlling software processes. The usage data has great potential as a driver for software processes. However, research still lacks constructs for collecting, refining and utilising usage data in controlling software processes. The objective of this paper is to introduce a framework for data-driven software engineering. The Undulate framework covers generating, collecting and utilising usage data from software processes and business processes supported by the software produced. In addition, we define the concepts and process of extreme continuous experimentation as an exemplar of a software engineering process. We derive requirements for the framework from the research literature, with a focus on papers inspired by practical problems. In addition, we apply a multilevel modelling language to describe the concepts related to extreme continuous experimentation. We introduce the Undulate framework and give requirements and provide an overview of the processes of collecting usage data, augmenting it with additional dimensional data, aggregating the data along the dimensions and computing different metrics based on the data and other metrics. The paper represents significant steps inspired by previous research and practical insight towards standardised processes for data-driven software engineering, enabling the application of soft computing and other methods based on artificial intelligence.
Semi-Supervised Clustering via Cannot Link Relationship for Multiview Data

Due to the diversity of data modalities, the research interest of multi-view clustering is gradually increasing, in the field of large-data analytics, particularly in clustering. However, the greater part of current multi-view clustering methods is mainly in view of unsupervised learning, which leads to unpredictable results and algorithmic instability. Besides, they ignore the diversity of graphs, which is not desirable in practical applications, because the characteristic properties of each view are different. To solve these problems, inspired by the outstanding performance of semi-supervised learning in machine learning, we propose a valid semi-supervised multi-view spectral clustering algorithm. We use the pre-set labels as prior knowledge to obtain the overall distribution of the remaining unlabeled data. Tensor minimization Schatten <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -norm is utilized to mine the mutual information hidden in multiple views. Meanwhile, we also use the cannot-link as another semi-supervised constraint to update the graph. Our proposed algorithm is generally 5%-10% better than the comparison algorithms in view of the experimental results on five datasets, and our algorithm is relatively fast with the computational complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}({T({n^{2}}\log (n) + {n^{2}} + {u^{2}}l + ulc + uc\log (c))})$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> denotes the number of iterations and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$u$ </tex-math></inline-formula> represent the number of samples, the number of labeled and unlabeled samples, respectively, which shows that our proposed method has broad application prospects.
Hierarchical Dirichlet Process Based Gamma Mixture Modeling for Terahertz Band Wireless Communication Channels

Due to the unique channel characteristics of Terahertz (THz), comprehensive propagation channel modeling is essential to understand the spectrum and develop reliable communication systems in these bands. In this work, we propose the utilization of the hierarchical Dirichlet process Gamma mixture model (DPGMM) to characterize THz channels statistically in the absence of any prior knowledge. DPGMM provides mixture component parameters and the required number of components. A revised expectation-maximization (EM) algorithm is also proposed as a pre-step for DPGMM. Kullback-Leibler Divergence (KL-divergence) is utilized as an error metric to examine the amount of inaccuracy of the EM algorithm and DPGMM when modeling the experimental probability density functions (PDFs). DPGMM and EM algorithm are implemented over the measurements taken at frequencies between 240 GHz and 300 GHz. By comparing the results of the DPGMM and EM algorithms for the measurement datasets, we demonstrate how well the DPGMM fits the target distribution. It is shown that the proposed DPGMM can accurately describe the various THz channels as well as the EM algorithm, and its flexibility allows it to represent more complex distributions better than the EM algorithm. We also demonstrated that DPGMM can be used to model any wireless channel due to its versatility.
Glance and Focus Networks for Dynamic Visual Recognition

Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand. Therefore, static models which process all the pixels with an equal amount of computation result in considerable redundancy in terms of time and space consumption. In this paper, we formulate the image recognition problem as a sequential coarse-to-fine feature learning process, mimicking the human visual system. Specifically, the proposed Glance and Focus Network (GFNet) first extracts a quick global representation of the input image at a low resolution scale, and then strategically attends to a series of salient (small) regions to learn finer features. The sequential process naturally facilitates adaptive inference at test time, as it can be terminated once the model is sufficiently confident about its prediction, avoiding further redundant computation. It is worth noting that the problem of locating discriminant regions in our model is formulated as a reinforcement learning task, thus requiring no additional manual annotations other than classification labels. GFNet is general and flexible as it is compatible with any off-the-shelf backbone models (such as MobileNets, EfficientNets and TSM), which can be conveniently deployed as the feature extractor. Extensive experiments on a variety of image classification and video recognition tasks and with various backbone models demonstrate the remarkable efficiency of our method. For example, it reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 1.3x without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.
3D-Pruning: A Model Compression Framework for Efficient 3D Action Recognition

The existing end-to-end optimized 3D action recognition methods often suffer from high computational costs. Observing that different frames and different points in point cloud sequences often have different importance values for the 3D action recognition task, in this work, we propose a fully automatic model compression framework called 3D-Pruning (3DP) for efficient 3D action recognition. After performing model compression by using our 3DP framework, the compressed model can process different frames and different points in each frame by using different computational complexities based on their importance values, in which both the importance value and computational complexity for each frame/point can be automatically learned. Extensive experiments on five benchmark datasets demonstrate the effectiveness of our 3DP framework for model compression.
Wireless Signal Representation Techniques for Automatic Modulation Classification

In this paper, we present a comprehensive survey and detailed comparison of techniques that have been applied to the problem of identifying the type of modulation contained within received wireless signals. Known as automatic modulation classification (AMC), the problem has been studied for many decades. AMC plays a significant role in both military and civilian scenarios and is the main step in smart receivers. Especially with the development of software-defined radios and automatic communication systems, IoT technology and the spread of 5G technology bring the number of spectrum-using equipment explosion, making the problem of scarce spectrum resources more prominent. Although AMC techniques can be optimized from the classifier&#x2019;s point of view, signal pre-processing also plays a critical role. Relevant data representation approaches include time-frequency analysis, cyclostationary transforms, and hybrid techniques.We provide a taxonomy of common approaches based on order and dimensionality along with an overall analysis of signal pre-processing algorithms for AMC. Furthermore, we reproduce the major existing schemes under uniform conditions, allowing an objective comparison among different methodologies. Finally, we create an open-source Python library to simulate these techniques so the results in this paper are reproducible for future research.