Reviewerly/exHarmony huggingface dataset:
Distortion Disentanglement and Knowledge Distillation for Satellite Image Restoration

Satellite images are typically subject to multiple distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun illumination, viewing geometries etc., limiting its application to downstream tasks. In supervised networks, the availability of paired datasets is a strong assumption. Consequently, many unsupervised algorithms have been proposed to address this problem. These methods synthetically generate a large dataset of degraded images using image formation models. A neural network is then trained with an adversarial loss to discriminate between images from distorted and clean domains. However, these methods yield suboptimal performance when tested on real images that do not necessarily conform to the generation mechanism. Also, they require a large amount of training data and are rendered unsuitable when only a few images are available. We propose a distortion disentanglement and knowledge distillation framework for satellite image restoration to address these important issues. Our algorithm requires only two images: the distorted satellite image to be restored and a reference image with similar semantics. Specifically, we first propose a mechanism to disentangle distortion. This enables us to generate images with varying degrees of distortion using the disentangled distortion and the reference image. We then propose the use of knowledge distillation to train a restoration network using the generated image pairs. As a final step, the distorted image is passed through the restoration network to get the final output. Ablation studies show that our proposed mechanism successfully disentangles distortion. Exhaustive experiments on different timestamps of Google-Earth images and publicly available datasets, LEVIR-CD and SZTAKI, show that our proposed mechanism can tackle a variety of distortions and outperforms existing state-of-the-art restoration methods visually as well as on quantitative metrics.
Multi-Level Cascade Sparse Representation Learning for Small Data Classification

Deep learning (DL) methods have recently captured much attention for image classification. However, such methods may lead to a suboptimal solution for small-scale data since the lack of training samples. Sparse representation stands out with its efficiency and interpretability, but its precision is not so competitive. We develop a Multi-Level Cascade Sparse Representation (ML-CSR) learning method to combine both advantages when processing small-scale data. ML-CSR is proposed using a pyramid structure to expand the training data size. It adopts two core modules, the Error-To-Feature (ETF) module, and the Generate-Adaptive-Weight (GAW) module, to further improve the precision. ML-CSR calculates the inter-layer differences by the ETF module to increase the diversity of samples and obtains adaptive weights based on the layer accuracy in the GAW module. This helps ML-CSR learn more discriminative features. State-of-the-art results on the benchmark face databases validate the effectiveness of the proposed ML-CSR. Ablation experiments demonstrate that the proposed pyramid structure, ETF, and GAW module can improve the performance of ML-CSR. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Zhongwenyuan98/ML-CSR</uri> .
Development of the Minangkabau Local Language Translation Machine Based on Stemming

Indonesia is an archipelagic country that has hundreds of ethnic groups and regional languages. One of the well-known regional languages is the Minangkabau language (BM) which is dominantly used in several areas in Sumatra which are in the Austronesian family. The habit of the Minangkabau people in their daily life is to always use the Minangkabau language (BM) in communicating. Usually, the Minang tribe always communicates every day using the Minangkabau language, so that it is unique for the people around them, thus creating curiosity to know BM. So this research was conducted to translate BM into Indonesian. The purpose of this research is to translate BM into Indonesian. By using the translation engine of the Minangkabau Language Stemming Algorithm (SBMK). The data processed were 600 basic words in printed dictionaries and sentences in 12 BM documents. The level of accuracy of the translation results from this study is 98.33% for basic words and 94.68% for sentences in the document. The resulting algorithm is very precise to translate and process the basic word spelling checker in BM words and documents into Indonesian.
An Investigation of Entropy and Refactoring in Software Evolution

As software evolves, the source code tends to become more complex and therefore harder to maintain, something that is exacerbated by poor development practice, where a disorderly development process is more likely to yield a disorderly and more complex result. Disorder in source code evolution may be quantified by using source code change entropy, a measure of the scattering pattern of code changes, i.e., how widely spread throughout the codebase are individual changes. Refactoring is an important activity for improving the structure of existing code and reducing its complexity, but it is unclear if refactoring can effectively counteract software entropy. Understanding how or if refactoring can reduce entropy could provide insights on software evolution and help reduce maintenance cost. We empirically investigate how entropy at the system and file level evolves in software systems and how it is influenced by refactoring activities as well as non-refactoring changes. We identify refactorings in the version history of open-source projects through the analysis of source code and change metadata. We observe that system-level entropy fluctuates up and down, but remains relatively stable over time on average. We also observe that file-level entropy tends to decrease over time in response to both refactorings and non-refactoring changes, challenging the findings of previous studies. We observe factors which may lessen the utility of existing entropy metrics and suggest future avenues for exploring this intriguing but little-studied concept in software evolution.
Three Dimensional Experiences Using a Commercially-Integrated Stand-alone Head-Mounted Display and Applying it to Education

In this study, we introduce a plan about the application of various realities for future educational purposes. To start, we provide an overview for three types of realities [Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR)] and clarify their relationships to each other. Then we classify VR tools into three types and focus on the integrated stand-alone type and prepare a plan for its educational application. Finally, we discuss important factors that could contribute to the examinees' educational outcomes in the future.
Graph Data Augmentation for Node Classification

In recent years, Graph Neural Networks (GNNs) have emerged as powerful techniques for graph-structure data, which are essential for a wide range of graph-based tasks like link prediction and node classification. However, over-smoothing and over-fitting are two main challenges that impact negatively on model performance. Data augmentation is a good solution to these two problems, and it is also proven very effective in computer vision and nature language processing. But there is a relatively small body of literature when it comes to graph data augmentation. In this paper, we propose a Graph Data Augmentation (GDA) strategy to optimize the graph topology for node classification tasks. Our GDA approach consists of two operations: edge manipulation based on similarities of node pairs (GDA-E) and new nodes addition to under-informed old nodes (GDA-N). GDA-E is designed to add missing edges and remove noisy edges, while GDA-N is established to help nodes with low degree. Both operations can improve the information-to-noise ratio of the whole graph and lead to better performance of GNNs. The comparative results of experiments on three different datasets show that our GDA approach achieves considerable improvement (11.0% average) over origin graphs, and the ablation study verifies the effectiveness of both GDA-E and GDA-N.
Parallel feature selection based on the trace ratio criterion

The growth of data today poses a challenge in management and inference. While feature extraction methods are capable of reducing the size of the data for inference, they do not help in minimizing the cost of data storage. On the other hand, feature selection helps to remove the redundant features and therefore is helpful not only in inference but also in reducing management costs. This work presents a novel parallel feature selection approach for classification, namely Parallel Feature Selection using Trace criterion (PFST), which scales up to very large datasets. Our method uses trace criterion, a measure of class separability used in Fisher's Discriminant Analysis, to evaluate feature usefulness. We analyzed the criterion's desirable properties theoretically. Based on the criterion, PFST rapidly finds important features out of a set of features for big datasets by first making a forward selection with early removal of seemingly redundant features parallelly. After the most important features are included in the model, we check back their contribution for possible interaction that may improve the fit. Lastly, we make a backward selection to check back possible redundant added by the forward steps. We evaluate our methods via various experiments using Linear Discriminant Analysis as the classifier on selected features. The experiments show that our method can produce a small set of features in a fraction of the amount of time by the other methods under comparison. In addition, the classifier trained on the features selected by PFST not only achieves better accuracy than the ones chosen by other approaches, but can also achieve better accuracy than the classification on all available features.
CGVIZ: A Cesium-Based Visualization System for Multi-Source Geohazards Data

Visualizing the associated data of urban geological disasters can better describe the urban spatial information and provide technical support for analyzing geological disasters and upper-level decision-making. Aiming at the multi-source data with various types and complex structures in the urban space, how to integrate, organize, and visualize them is a critical technical problem for the urban geological disaster big data system. This paper deeply researched and discussed the visualization technology and implementation methods of multiple data types related to urban geological disasters. At the same time, we developed a visualization analysis system for urban geological disasters, which realized the integrated visualization of multi-source data in urban space and simulation visualization of geological disasters process. In addition, it can also provide users with related analysis functions of urban geological disasters based on the visualization system.
Adoption of Blockchain Technology for Storage and Verification of Educational Documents

The importance of creating a reliable storage system for electronic documents in the field of education is a well-known reality. The general characteristics of the key methods of protection against counterfeiting in the field of education were analyzed. The example of a prototype of the information system named UPADHI is presented. The basis of the system is blockchain technology. This technology can be applied in a number of educational institutions to ensure the security and legality of documents. Using the IDEF methodology, the basic information flows in the system are studied. The functional blocks of the developed prototype are described and highlighted. In addition, an algorithm is proposed to validate a transaction in a socially oriented system by voting for participants who do not have equal rights.
Super-resolution Reconstruction of Airborne Remote Sensing Images based on Multi-scale Fusion

To extract more detailed features of airborne remote sensing images to obtain more information, super-resolution reconstruction is performed on them. However, the existing super-resolution reconstruction algorithms of airborne remote sensing images have poor feature extraction capabilities, and smooth image edges, and are difficult to restore high-frequency information effectively. In this paper, the residual features of different residual modules are densely connected to form a dense group (DG), which combines different residual features to reduce the redundancy of features and ensure the effective transmission of high-frequency residual features. Further, the residual features of DG are densely connected to realize the reuse of information, and combined with multi-scale fusion, a two-branch lightweight multi-scale fusion super-resolution reconstruction network is proposed. The experimental results show that the algorithm has good performance and is lightweight, and can obtain a better reconstruction effect.
Boosting Source-free Domain Adaptation via Confidence-based Subsets Feature Alignment

Source-free Domain Adaptation (SFDA) aims to adapt a model trained on a given (source) environment to the new (target) environment, without directly accessing the source data. Due to the lack of labeled source data, it is often difficult for SFDA methods to provide reliable class representations for the target data. To overcome this issue, we propose the idea of Confidence-based Subsets Feature Alignment (CSFA). CSFA divides the target data into two subsets: confident subset that consists of samples having low entropy class predictions from the source model, and non-confident subset with samples that do not. By using the pseudo-labels from the confident subset, we can frame the original SFDA problem as a Universal Domain Adaptation (UniDA) problem, and provide reliable class representations for the target data by aligning feature distributions of the two subsets. Specifically, we propose a multi-task framework that simultaneously applies a standard SFDA algorithm in combination with a UniDA-inspired algorithm, which further infuses class representations into the adaption process. We evaluate the proposed method on a wide range of cross-domain object recognition tasks and achieve higher or comparable accuracy compared to existing SFDA methods. Ablation studies are conducted to verify the effectiveness of the proposed method.
Modeling Sparse Store-and-Forward Bulk Data Transfers in Inter-Datacenter Networks With Multiple Congested Links

The increasing demand of bulk data transfer imposes significant challenges in inter-datacenter networks that span multiple domains. Storing data temporarily at domain borders and forward it at later off-peak hours (SnF) can reduce congestion and improve network utilization. However, performance gains from SnF transfer still requires further quantitative study. In this paper, we model deadline-constrained SnF transfers in networks with congested inter-domain links, where traffic has slightly varying peak hour and diurnal patterns. A closed-form estimation for the two-links case is derived to quantify the performance gain of intermediate storage (IS) and edge storage (ES). Our study shows that if bulk data transfer requests arrive with equal probability in a day, increase the allowed waiting time will increase the successful transfer probability linearly. Increasing the aggregated network load, defined as the summed duration of the background traffic peak-hour and data transfer, will result in linear decrease in successful transfer probability, when the network is moderately loaded. Our research also shows that IS has significant advantages over ES under heavy network load, and the maximum benefit occurs when the allowed waiting time is two times the aggregated network load.
Anomaly Detection based on Robust Spatial-temporal Modeling for Industrial Control Systems

Industrial Control Systems (ICS) are increasingly facing the threat of False Data Injection (FDI) attacks. As an emerging intrusion detection scheme for ICS, process-based Intrusion Detection Systems (IDS) can effectively detect the anomalies caused by FDI attacks. Specifically, such IDS establishes anomaly detection model which can describe the normal pattern of industrial processes, then perform real-time anomaly detection on industrial process data. However, this method suffers low detection accuracy due to the complexity and instability of industrial processes. That is, the process data inherently contains sophisticated nonlinear spatial-temporal correlations which are hard to be explicitly described by anomaly detection model. In addition, the noise and disturbance in process data prevent the IDS from distinguishing the real anomaly events. In this paper, we propose an Anomaly Detection approach based on Robust Spatial-temporal Modeling (AD-RoSM). Concretely, to explicitly describe the spatial-temporal correlations within the process data, a neural based state estimation model is proposed by utilizing 1D CNN for temporal modeling and multi-head self attention mechanism for spatial modeling. To perform robust anomaly detection in the presence of noise and disturbance, a composite anomaly discrimination model is designed so that the outputs of the state estimation model can be analyzed with a combination of threshold strategy and entropy-based strategy. We conducted extensive experiments on two benchmark ICS security datasets to demonstrate the effectiveness of our approach.
A Comprehensive Approach to Enhance Dark Image Implementing Image Processing Techniques

Overall enhancement for poorly illuminated images is one of the most challenging issues. The degraded images suffer from different aspects of low contrast, color saturation, unnatural noise and non-ideal environment, which are more prominent in dark images. Most image enhancement techniques focus on a specific degradation issue and never lead to the desired enhancement. This paper proposes an efficient dark image enhancement model that addresses essential degradation issues by incorporating gamma correction, gray world, white balance algorithm, denoising and dark channel prior techniques. A Gamma correction technique is implemented to address the low contrast issue of dark images. Accompanying this, we also applied the gray world to balance the colour temperature, Convolution Neural Networks (CNN) to remove the noise and dark channel prior to dehazing. Our experimental results show the outstanding performance of the proposed method in terms of visual and quantitative measures.
A Novel Data Stream Learning Approach to Tackle One-Sided Label Noise From Verification Latency

Many real-world data stream applications suffer from verification latency, where the labels of the training examples arrive with a delay. In binary classification problems, the labeling process frequently involves waiting for a pre-determined period of time to observe an event that assigns the example to a given class. Once this time passes, if such labeling event does not occur, the example is labeled as belonging to the other class. For example, in software defect prediction, one may wait to see if a defect is associated to a software change implemented by a developer, producing a defect-inducing training example. If no defect is found during the waiting time, the training example is labeled as clean. Such verification latency inherently causes label noise associated to insufficient waiting time. For example, a defect may be observed only after the pre-defined waiting time has passed, resulting in a noisy example of the clean class. Due to the nature of the waiting time, such noise is frequently one-sided, meaning that it only occurs to examples of one of the classes. However, no existing work tackles label noise associated to verification latency. This paper proposes a novel data stream learning approach that estimates the confidence in the labels assigned to the training examples and uses this to improve predictive performance in problems with one-sided label noise. Our experiments with 14 real-world datasets from the domain of software defect prediction demonstrate the effectiveness of the proposed approach compared to existing ones.
Cloud computing’s emergence and associated security challenges

Cloud Computing is booming technology in the IT sector. It provides virtual resources, dynamic scalability, software processing, and data storing facilities. Ever since cloud computing came into existence its demand has been increasing day by day. Nowadays, as confidentiality and security of the data is the biggest concern; the rising demand of the cloud platforms has made its providers re-think about the security facilities they are providing to their consumers to assure the data security on their platform. This paper gives a glimpse of the security issues being faced while using the cloud computing platforms and the degree of damage that could be done to the customer's data if not chosen carefully. The article discusses the important services provided by cloud platforms and various ways to avoid or tackle security concerns.
Carambola: Enforcing Relationships Between Values in Value-Sensitive Agent Design

Carambola is a text-based strategy game that operationalizes the Theory of Basic Values (TBV) to model the motivations of its non-player characters (NPC) and the dilemmas it presents to players. The player takes on the role of the Emperor of a nation, making a series of executive decisions while noting the subsequent reactions of their NPC advisors. After a fixed number of rounds in which they choose actions, their NPC advisors vote on whether they should dethrone the player based on the affinity they have with the other subjects of the game. Advisor affinity is affected by the Emperor's actions, which each harm and promote a subset of their values. Our implementation of the TBV is a geometric interpretation that enforces restrictions on the attitudes that agents can have toward the values. We give a brief overview of the theory, and then describe our implementation and our plans for evaluating how this usage of the TBV affects the advisors' believability.
Benchmarking Quantum(-Inspired) Annealing Hardware on Practical Use Cases

Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu Digital Annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in practical use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.