Reviewerly/exHarmony huggingface dataset:
A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution

Natural language provides an accessible and expressive interface to specify long-term tasks for robotic agents. However, non-experts are likely to specify such tasks with high-level instructions, which abstract over specific robot actions through several layers of abstraction. We propose that key to bridging this gap between language and robot actions over long execution horizons are persistent representations. We propose a persistent spatial semantic representation method, and show how it enables building an agent that performs hierarchical reasoning to effectively execute long-term tasks. We evaluate our approach on the ALFRED benchmark and achieve state-of-the-art results, despite completely avoiding the commonly used step-by-step instructions.
Weighted entropy and modified MDL for compression and denoising data in smart grid

Denoising and compression of power system data from the measurement and monitoring instruments in smart grid is an important topic. Compression is essential for the transmission and storage of a big amount of smart grid data through the communication channels and denoising is essential as the noise produces erroneous results in further analysis of the power system data. This paper presents a technique for denoising and lossy compression of data in smart grid communication using wavelet packet transform (WPT). The paper proposes weighted entropy to calculate the best basis of a signal from the complete WPT. Then the paper presents the application of the proposed best basis algorithm for the denoising and compression of the signal. A modified minimum description length (MDL) algorithm is proposed that allows to adjust the threshold for the denoising that does not require the use of a noise estimation formula. A set of real power system data recorded by various measurement and monitoring instruments in smart grid is utilized to assess the effectiveness of the proposed technique. Both the denoising and compression performance on the simulation show promising results such that the proposed algorithm could be considered as a potential technique for the real time noise removal and compression application. Results from the comparison are presented with Shannon entropy - MDL based WPT, wavelet transform (WT) based method, fuzzy transform and Matlab’s state of art technique.
Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm

With k-medoids algorithm, it often takes many iterations to cluster a large dataset, that is, the k-medoids algorithm cannot achieve the optimal performance.Based on cluster validity, this paper tries to optimize the clustering performance of k-medoids algorithm, using the purity algorithm.Specifically, the medoids value was determined by the purity value, and cluster validity was measured with the Davies Bouldin Index (DBI) on the Iris Dataset and the Death/Birth Rate Dataset.The results show that the cluster validity of the proposed purity k-medoids algorithm was better than the conventional k-medoids algorithm.The conventional k-medoids converged in an average of 8.7 iterations on the Death Birth Rate Dataset and 13.2 on the Iris Dataset.By contrast, the purity k-medoids algorithm only needed 2 iterations on either dataset.Therefore, the purity k-medoids algorithm can effectively minimize the number of iterations in the clustering of large datasets.
Privacy methods and zero-knowledge poof for corporate blockchain

Nowadays distributed ledger technology or blockchain is widely used in the corporate sector for various industries. Although implementation issues (coding practice, lack of capabilities, etc.) are not among the major barriers for the technology adaption, there are still some informational security challenges to adjust and scale blockchain networks for corporate usage. One of them is to provide functionality for private transactions stored in a blockchain. Some methods including mix networks, ring signatures, and off-chain protocols were applied to meet the privacy requirements. However, these methods have some limitations associated with the key blockchain characteristics such as decentralized storing system and immutability verification of private data. This article examines zero-knowledge proof (ZKP) methods for corporate blockchain networks. The article provides the review of existing methods for private transactions, discovers the implementation of ZKP methods, also performance and scalability issues are discussed.
Twitter Dataset and Evaluation of Transformers for Turkish Sentiment Analysis

Sentiment analysis is one of the key topics in Natural Language Processing which helps several applications from social media analysis to stock market prediction. Sentiment analysis datasets are generally collected by semi-supervision through shopping or review websites. These datasets are constructed by mapping users' text reviews to the given scores by users. However, these datasets might contain errors due to automatic mapping, and generally they don't have the characteristic features of social media texts such as emojis, slangs, and typos. To address these problems, one of the first manually annotated Turkish Sentiment Analysis datasets from Twitter is proposed. The BounTi dataset contains Turkish tweets about specific universities at Turkey. Furthermore, the performance of multilingual and Turkish transformer models such as MBERT, XLM-Roberta, and BERTurk are analyzed for this dataset. The best proposed model https://github.com/boun-tabi/BounTi-Turkish-Sentiment-Analysis is based on BERTurk and achieves 0.729 macro-averaged recall score on the test set. Finally, a social media analysis demonstration with the best model is performed on Turkish tweets about a food brand. BounTi dataset, finetuned models, and related scripts are publicly released.
A Comparative Study of Meta-Heuristic-Based Task Scheduling in Cloud Computing

The massive advances in web, mobile and computer technologies and its users are exponentially growing. Now the era has come where every user is very much conscious about the word “cloud computing”. The users are rolling in cloud services (storage space, computational power and standalone applications). Hence, the cloud service providers (CSP) are concerned about the quality of service (QoS) to their clients. To make it into action, task scheduling was introduced. The principle goal of task scheduling is to carry out successfully the objectives of both server and its clients. As the traditional task scheduling is not enough to attain the best performance. So, meta-heuristic techniques are required, which can produce a solution close to optimal. This optimal solution decides the mapping of tasks on resources and comes up with results that match the desirable objectives. This paper presented a comparative investigation of meta-heuristic centric task scheduling algorithms, such as ant colony optimization (ACO), particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA) and flower pollination algorithm (FPA) which are being used by many researchers for developing new techniques from last decade.
Double bayesian pairwise learning for one-class collaborative filtering

Recommender systems have become an indispensable tool for real-world applications. Only one-class feedback can be obtained in many applications. Therefore, the one-class recommendation problem has attracted much attention. Pairwise ranking methods are popular for dealing with the one-class problem. Bayesian Personalized Ranking (BPR) is one of the most popular pairwise methods, assuming users prefer the observed item to the unobserved item. The parameters in BPR are learned based on stochastic gradient descent (SGD). However, the previous work has shown that existing the vanishing gradient problem in the learning process when the preference difference between the observed item and the unobserved item is very large. In this paper, we propose a novel algorithm called Double Bayesian Pairwise Learning (DBPL). In the learning process of DBPL, the preference difference between the observed item and the unobserved item can be reduced by fusing a relatively smaller preference difference between another pair of items. Moreover, we calculate potential preference scores between users and items based on user–item interactions to measure preference differences between unobserved items of each user. Experimental results on three real-world datasets show the effectiveness of the DBPL algorithm.
Natural resources and wealth inequality: a cross-country analysis

Purpose This study investigates the impact of natural resources on wealth inequality as a first attempt on a panel of 45 developed and developing countries. Design/methodology/approach Using the generalized method of moments (GMM), the results provide strong evidence that natural resources increase wealth inequality within a linear empirical framework. Findings These results are robust to the use of alternative natural resources and wealth inequality measures. Additionally, a nonlinear analysis provides evidence of an inverted U shaped relationship between natural resources and wealth inequality. The net effect of enhancing natural resources on wealth inequality is positive and building on the corresponding conditional negative effect, the attendant natural resource thresholds for inclusive development are provided. It follows that while natural resources increase wealth inequality, some critical levels of natural resources are needed for natural resources to reduce wealth inequality. Originality/value To the best of knowledge, this is the first study to assess how enhancing natural resources affect wealth inequality.
PDGraph: A Large-Scale Empirical Study on Project Dependency of Security Vulnerabilities

The reuse of libraries in software development has become prevalent for improving development efficiency and software quality. However, security vulnerabilities of reused libraries propagated through software project dependency pose a severe security threat, but they have not yet been well studied. In this paper, we present the first large-scale empirical study of project dependencies with respect to security vulnerabilities. We developed PDGraph, an innovative approach for analyzing publicly known security vulnerabilities among numerous project dependencies, which provides a new perspective for assessing security risks in the wild. As a large-scale software collection in dependency, we find 337,415 projects and 1,385,338 dependency relations. In particular, PDGraph generates a project dependency graph, where each node is a project, and each edge indicates a dependency relationship. We conducted experiments to validate the efficacy of PDGraph and characterized its features for security analysis. We revealed that 1,014 projects have publicly disclosed vulnerabilities, and more than 67,806 projects are directly dependent on them. Among these, 42,441 projects still manifest 67,581 insecure dependency relationships, indicating that they are built on vulnerable versions of reused libraries even though their vulnerabilities are publicly known. During our eight-month observation period, only 1,266 insecure edges were fixed, and corresponding vulnerable libraries were updated to secure versions. Furthermore, we uncovered four underlying dependency risks that can significantly reduce the difficulty of compromising systems. We conducted a quantitative analysis of dependency risks on the PDGraph.
Response to a phishing attack: persuasion and protection motivation in an organizational context

Purpose This study aims to examine the effect of cybersecurity threat and efficacy upon click-through, response to a phishing attack: persuasion and protection motivation in an organizational context. Design/methodology/approach In a simulated field trial conducted in a financial institute, via PhishMe, employees were randomly sent one of five possible emails using a set persuasion strategy. Participants were then invited to complete an online survey to identify possible protective factors associated with clicking and reporting behavior ( N = 2,918). The items of interest included perceived threat severity, threat susceptibility, response efficacy and personal efficacy. Findings The results indicate that response behaviors vary significantly across different persuasion strategies. Perceptions of threat susceptibility increased the likelihood of reporting behavior beyond clicking behavior. Threat susceptibility and organizational response efficacy were also associated with increased odds of not responding to the simulated phishing email attack. Practical implications This study again highlights human susceptibility to phishing attacks in the presence of social engineering strategies. The results suggest heightened awareness of phishing threats and responsibility to personal cybersecurity are key to ensuring secure business environments. Originality/value The authors extend existing phishing literature by investigating not only click-through behavior, but also no-response and reporting behaviors. Furthermore, the authors observed the relative effectiveness of persuasion strategies used in phishing emails as they compete to manipulate unsafe email behavior.
Copyright protection for AI-generated outputs: The experience from China

Artificial intelligence (AI) is involved more frequently in the creative process nowadays, which raises debates associated with copyright protection for its outputs across the globe, China included. On 25 April 2019, the Beijing Internet Court released the first decision in relation to the copyrightability of the output automatically generated by computer software in China. In this case, the Beijing Internet Court held that copyrightable works should be created by natural persons, and therefore denied copyright protection for the output intelligently generated by computer software although it possessed originality. In another case decided on 24 December 2019, the Nanshan District Court of Shenzhen approved that the output automatically generated by computer software was copyrightable, holding that the review generated by an intelligent writing software conformed to the formal requirements of written works and it could be granted copyright protection. This article analyses these two cases in detail and describes the experience of China in copyright protection for AI-generated outputs. As the first two cases about copyrightability of AI-generated outputs in China, the two cases play a significant role in future copyright protection of such outputs nationally and internationally. The two cases indicate that some of AI-generated outputs are eligible for copyright protection in China. Instead of challenging the existing doctrines of modern copyright regime, the two decisions provide a mechanism for copyright protection of AI-generated outputs within the current human-centered copyright law realm.
Aircraft Inspection by Multirotor UAV Using Coverage Path Planning

All military and commercial aircraft must undergo frequent visual inspections in order to identify damage that could pose a danger to safety of flight. Currently, these inspections are primarily conducted by maintenance personnel. Inspectors must scrutinize the aircraft's surface to find and document defects such as dents, hail damage, broken fasteners, etc.; this is a time consuming, tedious, and hazardous process. The goal of this work is to develop a visual inspection system which can be used by an Unmanned Aerial Vehicle (UAV), and to test the feasibility of this system on military aircraft. Using an autonomous system in place of trained personnel will improve the safety and efficiency of the inspection process. Open-source software for coverage path planning (CPP) is modified and used to create a path from which the UAV can view the entire top surface of the aircraft. Simulated and experimental flight testing is conducted to validate the generated paths by collecting imagery, flight data, and coverage estimates. Simulation is also used to predict UAV performance for an inspection of a full-size aircraft. Analysis shows that multirotor UAVs are a viable inspection platform for military aircraft.
High-performance implementation of evolutionary privacy-preserving algorithm for big data using GPU platform

Privacy protection has become a predominant concern in big data analysis. Privacy-Preserving Association Rule Mining (PPARM) is a process in which original data is transformed into a sanitized version to remove sensitive patterns. Although evolutionary PPARM approaches were developed, they are inefficient because the fitness function is overwhelmingly expensive. In this paper, an efficient algorithm, GPU-based Evolutionary Privacy-Preserving (GEPP), for improving the performance of PPARM is presented. We introduce two strategies for this reason: the first one is to develop a parallel indexing machine to generate retrieval index lists by parallelizing dataset scanning between GPU blocks, and the second strategy is the parallelization of the index lists for fitness function computation using CPU/GPU platform. In the second strategy, search mechanism and fitness function steps are performed on CPU and GPU, respectively. The scalability of GEPP is evaluated in terms of GPU characteristics, e.g., blocks and threads, data characteristics, e.g., the number of transactions, items, and density ratio, and patterns characteristics, e.g., the ratio of sensitive patterns and support thresholds. Experimental results show that our parallel implementation of the proposed approach obtains a speedup up to 36.6x, on average, compared to the CPU implementation using real and synthetic large-scale transaction datasets.
Self-Weighted Unsupervised LDA

As a hot topic in unsupervised learning, clustering methods have been greatly developed. However, the model becomes more and more complex, and the number of parameters becomes more and more with the continuous development of clustering methods. And parameter-tuning in most methods is a laborious work due to its complexity and unpredictability. How to propose a concise and beautiful model in which the parameters can be learned adaptively becomes a very meaningful problem. Aim at tackling this problem, we develop a novel self-weighted unsupervised linear discriminative analysis method, namely SWULDA. The proposed method not only avoids adjusting parameters but also explains the link between k -means and linear discriminant analysis (LDA). To obtain superior structural performance, the idea of minimizing the within-class scatter matrix and maximizing the between-class scatter matrix is embedded in the unsupervised model. Moreover, equipped with the proposed quadratic weighted optimization framework, the parameter can be adaptively learned. The extensive experiments on several datasets are conducted to validate the effectiveness of our method.
The Mechanisms Forming the Five–Floor Zonation of Quartz Veins: A Case Study in the Piaotang Tungsten–Tin Deposit, Southern China

It is common among many vein–type tungsten deposits in southern China that the thickness of ore veins increases from &lt;1 cm to &gt;1 m with increasing depth. A five–floor zonation model for the vertical trend of vein morphology was proposed in the 1960s and has been widely applied for predicting ore bodies at deeper levels, but the causative mechanisms for such a zonation remain poorly understood. The Piaotang tungsten–tin deposit, one of the birthplaces of the five–floor zonation model, is chosen as a case study for deciphering the mechanisms forming its morphological zonation of quartz veins. The vertical trend of vein morphology and its link to the W–Sn mineralization in Piaotang was quantified by statistical distributions (Weibull distribution and power law distribution) of vein thickness and ore grade data (WO3 and Sn) from the levels of 676 m to 328 m. Then, the micro–scale growth history of quartz veins was reconstructed by scanning electron microscope–cathodoluminescence (SEM–CL) imaging and in situ trace element analysis. The Weibull modulus α of vein thickness increases with increasing depth, and the fractal dimensions of both vein thickness and ore grade data (WO3 and Sn) decrease with increasing depth. Their vertical changes indicate that the fractures that bear the thick veins were well connected, facilitating fluid focusing and mineralization in mechanically stronger host rocks. Three generations (Q1–Q3) of quartz were identified from CL images, and the CL intensity of quartz is possibly controlled by the concentrations of Al and temperature. From the relative abundance of the Q1–Q3 quartz at different levels, the vertical trend of vein morphology in Piaotang was initially produced during the hydrothermal event represented by Q1 and altered by later hydrothermal events represented by Q2 and Q3. Statistical distributions of vein thickness combined with SEM–CL imaging of quartz could be combined to evaluate the mineralization potential at deeper levels.
Pixel and Object-Based Machine Learning Classification Schemes for Lithological Mapping Enhancement of Semi-Arid Regions Using Sentinel-2A Imagery: A Case Study of the Southern Moroccan Meseta

Mapping lithological units of an area using remote sensing data can be broadly grouped into pixel-based (PBIA), sub-pixel based (SPBIA) and object-based (GEOBIA) image analysis approaches. Since it is not only the datasets adequacy but also the correct classification selection that influences the lithological mapping. This research is intended to analyze and evaluate the efficiency of these three approaches for lithological mapping in semi-arid areas, by using Sentinel-2A data and many algorithms for image enhancement and spectral analysis, in particular two specialized Band Ratio (BR) and the Independent component analysis (ICA), for that reason the Paleozoic Massif of Skhour Rehamna, situated in the western Moroccan Meseta was chosen. In this study, the support vector machine (SVM) that is theoretically more efficient machine learning algorithm (MLA) in geological mapping is used in PBIA and GEOBIA approaches. The evaluation and comparison of the performance of these different methods showed that SVM-GEOBIA approach gives the highest overall classification accuracy (OA≈93%) and kappa coefficient (K) of 0,89, while SPBIA classification showed OA of approximately 89% and kappa coefficient of 0,84, whereas the lithological maps resulted from SVM-PBIA method exhibit salt and pepper noise, with a lower OA of 87% and kappa coefficient of 0,80 comparing them with the other classification approaches. From the results of this comparative study, we can conclude that the SVM-GEOBIA classification approach is the most suitable technique for lithological mapping in semi-arid regions, where outcrops are often inaccessible, which complicates classic cartographic work.