Reviewerly/exHarmony huggingface dataset:
CEOS Analysis Ready Data and the Private Sector: Early Progress and the Way Forward

This paper has been developed to address one deliverable in the CEOS ARD Strategy [1] and provides an initial evaluation of the views from the private sector on how the CEOS community needs to engage with the broader community to successfully advance the CEOS Analysis Ready Data (CARD) initiative to the next level. Further, the paper offers an overview of the next steps that the CEOS community should consider to ensure all three main stakeholder groups; a) Earth Observation (EO) data providers; b) Big Data hosts and c) aggregators and data users are actively involved in the CARD initiative. The full version of the paper can be found at the CEOS webpage [2]. Methods of evaluation included consultation with a small group from the private sector through an initial teleconference, follow-up emails and informal face-to-face discussions. Outcomes from this initial engagement show that the private sector recognises the CARD work as an important initiative and is open for future collaboration with the CEOS community. The next steps proposed in this paper include: –Share Information on CEOS Analysis Ready Data to build engagement support, –Understand the Industry Perspective, –Engage Industry in CARD Specifications, –Move CEOS specifications into the broader community.
Using Future Work Sentences to Explore Research Trends of Different Tasks in a Special Domain

Abstract Research trend detection is an important topic for scientific researchers. Future work sentences (FWS), as direct descriptions of future research, aren't fully utilized in research trend detection. Therefore, this article uses FWS to investigate research trends of different tasks in a particular domain. Taking the conference papers in the natural language processing (NLP) field as our research objects, we obtain the FWS in each paper to build the corpus and classified them into 6 main types. After that, the task of each paper is annotated, and a task system with 29 categories is constructed to compare the FWS in different tasks. The results show that the proportion of method mentioned in FWS is the highest, and different tasks focus on different FWS types: emerging tasks need more resources , while mature tasks prefer method and application . This study provides researchers a reference to understand the research trend of specific tasks and is helpful to compare different tasks.
Diversified Paraphrase Generation with Commonsense Knowledge Graph

Paraphrases refer to text with different expressions conveying the same meaning, which is usually modeled as a sequence-to-sequence (Seq2Seq) learning problem. Traditional Seq2Seq models mainly concentrate on fidelity while ignoring the diversity of paraphrases. Although recent studies begin to focus on the diversity of generated paraphrases, they either adopt inflexible control mechanisms or restrict to synonyms and topic knowledge. In this paper, we propose KnowledgE-Enhanced Paraphraser (KEEP) for diversified paraphrase generation, which leverages a commonsense knowledge graph to explicitly enrich the expressions of paraphrases. Specifically, KEEP retrieves word-level and phrase-level knowledge from an external knowledge graph, and learns to choose more related ones using graph attention mechanism. Extensive experiments on benchmarks of paraphrase generation show the strengths especially in the diversity of our proposed model compared with several strong baselines.
Accurate Uncalibrated Rectification Using Feature Matching Re-Selection

Rectification is a core programming process that involves multiple views. In this study, we focus on uncalibrated cases that neglect intrinsic and extrinsic camera information. Existing uncalibrated rectification methods use feature-matching techniques to form relationships between different views and use those features to estimate optimized homography matrices. However, outliers are inevitable in feature matching. Using these outliers in a rectification process produces vertical disparity errors and unwanted geometric distortion. To tackle the problem, we propose a novel method that can learn from the rectification results, re-select the matching pairs, and find the superior solutions. The proposed method introduces a novel workflow for uncalibrated rectification that incorporates three cores: field of view (FoV) neutralization, rectification, and feature matching re-selection (FMR). While the FoV neutralization module handles FoV differences among views, a combination of the rectification module and FMR module results in the optimal homography matrices. The rectification module takes neutral correspondences and estimates the optimized rectified matrices. Applying the results from the rectification module, the FMR module optimizes the correspondences and return them to the rectification module. For the rectification module, we apply adaptive geometric constraints and our updated optimization strategy to secure the satisfied vertical disparity errors while maintaining low distortion levels. Multidisciplinary experiments are performed to analyze the capabilities of the proposed method. We combine existing datasets with extra samples, including various outdoor environments, to gauge the performance better. Compared with existing methods, the proposed method produces fewer rectification errors and keeps the rectified images under low distortion rates.
A New Defogging Algorithm for Underwater Images

Images taken underwater will appear greenish or blueish due to the scattering and attenuation of light, resulting in color distortion of underwater images. This paper proposes a new underwater image defogging algorithm, which can effectively remove image noise, improve the color saturation and contrast of underwater images, and restore clear underwater images without fog. The algorithm introduces guided filtering, which increases speed and work efficiency. The linear enhancement of the saturation component can effectively enhance the color saturation of the underwater image. Compensation for local brightness can better show image details. Through experimental comparison with other algorithms, our algorithm has achieved good advantages in terms of defogging, image structural information integrity, and resistance to color attenuation.
Rule-Based System Against Reinforcement Learning*

Reinforcement Learning is becoming an increasingly popular type of machine learning. In it, artificial intelligence learns what the best action in each given situation is and over time optimizes the decisions it makes. On the other hand, provided that sufficiently good rules are created, a Rule-Based System is a program task that can support very complex behavior. The aim of the authors in this study is to analyze and compare the behavior of two agents, realized on the basis of these two approaches.
Modified Differential Evolution Variants with Local Search Operator to Solve Combinatorial Optimization Problems

Differential Evolution (DE) is a global optimization algorithm, which is known for its simplicity, robustness, and efficiency. Though DE was designed to solve optimization problems in continuous domain, it has made remarkable efficiency in discrete domain also by incorporating relevant mapping and local search approaches. However, still DE is not free from the issues of stagnation and premature convergence. This paper presents a set of modified DE variants incorporating changes to its mutation component and adding the 2-OPT local search approach for updating the population. This approach is an attempt to improve the exploration and exploitation capabilities of DE algorithm without any additional evaluation. Three different variants of discrete DE algorithms were proposed and experimented. One best variant out of these three proposed variants was used to extend the study to compare the DE with proposed modified mutation logic with state-of-the-art classical optimization algorithms. The proposed DE variant presented in this paper is an attempt to study and analyze the exploration and exploitation nature of DE on solving the combinatorial optimization problems. The classical traveling salesman problem (TSP) was taken for experiments to evaluate the performance of the proposed DE variants. The empirical and the statistical results obtained are presented in this paper.
CoPaint: Guiding Sketch Painting with Consistent Color and Coherent Generative Adversarial Networks

Art design plays an important role in attracting users. Thro- ugh art design, some sketches are more in line with aesthetics. Traditionally, we need to artificially color many series of black-and-white sketches using the same color, which is time-consuming and difficult for art designers. In addition, coherent sketch painting is challenging to automate. We propose a GAN-based approach CoPaint for sketch colorization. Our neural network takes as its input two black-and-white sketches with different rotation angles and produces a series of high-quality colored images of consistent color. We present an approach to generate a coherent sketch painting dataset. We also propose a paired generator network with shared weights that consists of convolutional layers and batch-normal layers. In addition, we propose a similarity loss that makes the images produced by the generator more similar. The provided experiments demonstrate the effectiveness of our approach.
An Analysis of Automated Code Inspection Tools for PHP Available on Github Marketplace

Code Inspection is a validation process widely used to improve the quality of software. To streamline this process and decrease the possibility of human error, improving the reliability of inspection results, it is possible to use specialized automated code inspection tools. Thus, this article proposes to analyze code inspection tools for PHP programming language, freely available on Github Marketplace. To achieve this goal, the GLPI system was chosen to be inspected, in addition, four code inspection tools were selected, out of twenty-eight available. Criteria were used for the tools selection, consistent with the sytem profile to be inspected and that do not have limitations on the inspection result. To classify the results obtained, the Common Weakness Enumeration (CWE) was used, a list of software and hardware weaknesses developed by numerous renowned companies, such as Microsoft, Apple and IBM. As a result of the inspection work, we found more than ten thousand failures divided into thirty-four different CWEs and from these we analyzed the individual feedback of each tool, as each one of them had unique advantages and disadvantages.
Procedural Image Generation Using Markov Wave Function Collapse

Wave Function Collapse (WFC) is an iterative constraint solving algorithm that performs texture synthesis on input samples to generate procedural outputs. The two commonly used WFC implementations are the Overlapping WFC (OWFC) and Tiling WFC (TWFC) implementations. OWFC incurs a performance cost in identifying constraints whereas TWFC receives pre-determined constraints in the form of meta-data. Pre-determining the meta-data, however, is a non-trivial process and requires substantial design time before the execution of the algorithm. The proposed Markov WFC (MkWFC) implementation aims to reduce the time involved during the meta-data design stage while maintaining the performance benefits of the TWFC implementation. This is achieved by using Markov random fields to determine constraints from a set of input samples and introduces a pre-processing step to the TWFC implementation. By automating constraint identification, the MkWFC implementation reduces both the meta-data generation time as well as the scope for human error. This paper compares TWFC against MkWFC on a set of 6 different procedural image generation problems using 3 sizes for inputs and outputs in a total of 720 trials. When comparing the MkWFC implementation against TWFC, there is an increase in the number of identified and used constraints from input and output respectively, which increases with image size. The performance of TWFC and MkWFC was compared by measuring their respective execution times for all trials. MkWFC was able to identify over 1.5 times more constraints for a 5 × 5 tiled image in 1.03 ± 0.09 ms(α = 0.05) and almost 3 times more constraints in 25 × 25 tiled image in 28.19 ± 2.58 ms(α = 0.05). This is substantially faster than the TWFC methodology where these constraints have to be manually identified and entered into the meta-data file. The automated meta-data generation and nominal increase in execution time allows for MkWFC to scale where TWFC cannot.
Modern Cognitive Solutions for Advanced Information Processing

This paper will discuss modern cognitive solutions dedicated to information processing. Cognitive processes, as the most individual methods of information processing, are characteristic of every human being, and thus transferred to the ground of machine solutions, they offer the possibility of deep, meaningful data analysis. Their distinguishing feature is that they are based on a semantic interpretation based on a linguistic description. The possibility of using such solutions in information processing will allow to show their universality and usefulness.
Dynamic Interconnection Approach With BLX-Based Search Applied to Multi-Swarm Optimizer: An Empirical Analysis to Real Constrained Optimization

The Multi-Swarm approach allows the use of multiple configurations between two or more populations of particles, where each one can present different approaches (e.g. lbest, gbest, Unified, Guaranteed-Convergence) directed towards improving the optimization process. This article presents a proposal for local/global stochastic interconnection applied to the context of the Multi-Swarm algorithm, as well as for incrementing a local search method for refining previously obtained solutions. Two proposals are introduced for this new Multi-Swarm PSO (MSO). The first one is the inclusion of “counterpart particles”, which establishes a sub-topology between inter-swarm particles, accessed by migration and evaluability rules. The other involves using customized crossover operators and is based on the BLX scheme (Blend Crossover) with direction information used as a reference for establish a subspace search around the particles. Performance and robustness of the new approaches were assessed by ten constrained engineering design optimization problems (COPs), as is compared to other solutions already published in the scientific literature. Results indicate significant performance improvements for all 10 COPs when compared to concurrent-based MSOs. By making available new references from other swarms, the counterpart particles approach tends to improve the optimization process in the search space, while an intermediate layer of local search based on a modified directed BLX crossover should provide an extra search around the particle, and thus, refining previously obtained solutions.
Encryption and Decryption of Image for Secured Data Transmission

This work combines Arnold transform method with the spatial domain to encrypt a covered image and also recovers the covered image by the application of inverse Arnold transform. First, transform a cover image into subparts which consists of eight binary images by decimal value to eight-digit binary operation. Then, transform eight binary images into sub-blocks of eight binary scrambled images by the Arnold transform, respectively. Further, recombine the sequence of the eight binary scrambled matrices into a scrambled matrix with 256 Gy levels according to the specific club. Discrete wavelet transform (DWT) is used to perform image compression on the input image and secretly hidden image which is done using alpha blending. Finally, derive an encrypted image from the scrambled image by the Hartley transform. Second, decode the encrypted image using inverse Arnold Transform. Inverse DWT is performed to regain the compressed images. Simulations indicate that the hybrid new technique has more security, more robustness counter to occlusion and attacks about with high degree of image scrambling.
Transfer mechanism of data decryption authority in JointCloud computing

Data security sharing in the Jointcloud has always been a difficult problem for researchers. Some cloud data sharing solutions have a series of problems, such as high communication overhead, difficult key management, data tampering, single cloud failure, etc. We propose a data-sharing mechanism based on blockchain and threshold proxy re-encryption to solve the problems listed above. The proxy re-encryption algorithm we adopted is improved based on the Conditional Proxy Broadcast Re-Encryption. By combining the secret sharing scheme to realize trust decentralization. The re-encryption computing is delivered to multiple cloud service providers, which reduces the centralization problem caused by single cloud processing. At the same time, blockchain is used to manage metadata information and data access control rights to solve data loss and tampering problems. Experimental results show that our mechanism achieves low re-encryption computing overhead.
An outlier detection algorithm based on maximum and minimum distance

Outlier detection is a very important type of data mining, which is extensively used in application areas. The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data, but also uses lots of machine resources, which results in the imbalance of the machine load. This paper presents an outlier detection algorithm based on maximum and minimum distance. These experiments show that the improved algorithm is able to effectively improve the efficiency of the outlier detection as well as the accuracy.
Exploring the Level of Grade 7 Students' Reading Proficiency in Comparison to the Norm

Reading comprehension requires specific skills, and improving it entails the collective efforts of the school, teachers, and students. Schools use various assessments to measure students' reading skills to ensure that students are at par with their grade level. This study ascertains the reading proficiency level of 232 Grade 7 students in an international school in Cambodia. The researchers used a quantitative method, and data from the Measure of Academic Progress Test were gathered through the Rasch Unit Scale. The participants are the whole population of grade 7. Furthermore, data were analyzed through arithmetic mean and standard deviation to assess students' proficiency level and identify the goal that should be prioritized for improvement. The results reflected that the majority of the students did not achieve the norm grade level mean Rasch Unit. Moreover, from the five goals, Literary Text: Language, Craft, and Structure should be improved the most. The findings of this study suggest improving all five reading goals with Literary Text: Language, Craft, and Structure as the main priority. Consequently, the results provided significant implications to the school in developing enrichment programs tailored to address the reading goals, teachers in terms of teaching strategies and differentiation, and students preparing for their Advanced Placement course.
SparrowHawk: Memory Safety Flaw Detection via Data-Driven Source Code Annotation

Detecting code flaws in programs is a vital aspect of software maintenance and security. Classic code flaw detection techniques rely on program analysis to check whether the code logic violates certain pre-define rules. In many cases, however, program analysis falls short of understanding the semantics of a function (e.g., the functionality of an API), and thus is difficult to judge whether the function and its related behaviors would lead to a security bug. In response, we propose an automated data-driven annotation strategy to enhance the understanding of the semantics of functions during flaw detection. Our designed SparrowHawk source code analysis system utilizes a programming language aware text similarity comparison to efficiently annotate the attributes of functions. With the annotation results, SparrowHawk makes use of the Clang static analyzer to guide security analyses.To evaluate the performance of SparrowHawk, we tested SparrowHawk for memory corruption detection, which relies on the annotation of customized memory allocation/release functions. The experiment results show that by introducing function annotation to the original source code analysis, SparrowHawk achieves more effective and efficient flaw detection, and successfully discovers 51 new memory corruption vulnerabilities in popular open source projects such as FFmpeg and kernel of OpenHarmony IoT operating system.
Social critique as a scientific virtue: an external scientific ethos in the making

The article discusses the possibility of using the external ethics of science to formulate a new social contract between science and the state (society). To do this, it is necessary to re-think the value thesaurus inherited from the cold war and the arms race, when the state gave scientists a social order, concentrated resources and allowed the scientists them-selves to distribute them on the basis of anonymous (secret) expert reviewing and refereeing. The resulting model of relationships within the scientific community can be called Pareto-competition, in which the winner re-ceives everything and the vanquished are screened to the periphery. The current situation of Big Science and Distributed Knowledge puts on the agenda the question of transition to a different relationship in the style of Pare-to-collaboration. In it, both victory and defeat are common cause, each group is prescribed its share of obligations and advantages, and all scientists have a chance to move in the sys-tem of epistemic virtues and sins. The new state of the scientific community, described by the term “full constituency” (S. Fuller), not only leads to internal democratic consensus, but also allows for social criticism. Its desirable result is such a restructuring of the whole society, in which cognitive and moral values come to the fore.
A Variant with the Variable-Sharing Property of Brady’s 4-Valued Implicative Expansion BN4 of Anderson and Belnap’s Logic FDE

A logic L has the “variable-sharing property” (VSP) if in all L-theorems of conditional form antecedent and consequent share at least a propositional variable. Anderson and Belnap consider the VSP as a necessary property any relevance logic has to fulfil. Now, among relevance logicians, Brady’s logic BN4 is widely viewed as the adequate implicative 4-valued logic. But BN4 does not have the VSP. The aim of this paper is to define a variant of BN4 having, in addition to the VSP, some properties that do not support its consideration as a mere artificial construct.