Reviewerly/exHarmony huggingface dataset:
Fluid Inclusions and Stable Isotope Geochemistry of Gold Mineralization Associated with Fine-Grained Granite: A Case Study of the Xiawolong Gold Deposit, Jiaodong Peninsula, China

The Xiawolong gold deposit, located in the Muping–Rushan gold metallogenic belt (eastern Jiaodong Peninsula), is a newly discovered deposit that developed in the late Early Cretaceous as fine-grained granite. Gold mineralization, which mainly occurs in the middle of fresh fine-grained granite dikes, consists of stockwork-style and disseminated ores. They are characterized by middle-high-temperature mineral assemblages, such as molybdenite and magnetite, associated with gold-bearing pyrite. Four types of primary fluid inclusions, contained within the quartz grains from the gold-bearing disseminated and stockwork-style fine-grained granitic ores, were identified based on microthermometry and Raman spectroscopy. The types identified were type 1 aqueous inclusions with middle-high temperature (201 to 480 °C) and middle-low salinity of 0.18 to 17.00 wt.% NaCl equiv.; type 2 H2O–CO2 inclusions, which show middle-high temperatures (218 to 385 °C), middle-low salinities (1.23 to 13.26 wt.% equiv. NaCl), and variable XCO2 (0.031 to 0.044); type 3 daughter mineral-bearing inclusions with high temperature (416 to 446 °C) and relatively constant and high salinity (28.59 to 32.87 wt.% NaCl equiv.); and type 4 CO2 fluid inclusions, which possess a bulk density of 0.405 to 0.758 g/cm3 and a constant XCO2 (0.952 to 0.990) (according to the decreasing abundance of fluid inclusions). The δ18Owater range is between 3.4 and 5.9‰, and the range of the δD is from −97.1 to −77.4‰, which indicates that the ore-forming process is of a magmatic water origin. The δ34S values possess a narrow range between 4.5 and 9.3‰, indicating the source of the Mesozoic Kunyushan granitoids. The Pb isotopic compositions of pyrite show that the Mesozoic Kunyushan granitoids are the main lead source for pyrites. Types 1, 2, and 3 fluid inclusions coexist in the same view field of the quartz grain, which are suggested to occur as the result of fluid immiscibility because of the boiling of a single homogeneous NaCl-CaCl2-KCl-CO2-H2O system. The fluid immiscibility, rather the fluid mixing and wall-rock sulfidation, is the mechanism of gold precipitation in the Xiawolong deposit. Compared with both the “Linglong-type” and “Jiaojia-type” gold deposits in the Jiaodong Peninsula in terms of geological–petrographic evidence and all of the available geochemical data, it can be concluded the Xiawolong gold deposit is of magmatic hydrothermal origin, having a genetic relation to the fine-grained granite.
Effective mutation rate adaptation through group elite selection

Evolutionary algorithms are sensitive to the mutation rate (MR); no single value of this parameter works well across domains. Self-adaptive MR approaches have been proposed but they tend to be brittle: Sometimes they decay the MR to zero, thus halting evolution. To make self-adaptive MR robust, this paper introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm. GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions. The resulting best mutational change in the group, instead of average mutational change, is used for MR selection during evolution, thus avoiding the vanishing MR problem. With the same number of function evaluations and with almost no overhead, GESMR converges faster and to better solutions than previous approaches on a wide range of continuous test optimization problems. GESMR also scales well to high-dimensional neuroevolution for supervised image-classification tasks and for reinforcement learning control tasks. Remarkably, GESMR produces MRs that are optimal in the long-term, as demonstrated through a comprehensive look-ahead grid search. Thus, GESMR and its theoretical and empirical analysis demonstrate how self-adaptation can be harnessed to improve performance in several applications of evolutionary computation.
Local optima organize into lattices under recombination

Local optima networks (LONs) model the global distribution and connectivity pattern of local optima under given search operators. Recent research has looked at how recombination operators can jump from a pair of parents that are locally optimal to a new child that is either a local optimum, or is guaranteed to be in a new basin of attraction. Recombination can therefore also induce a local optima network which maps how crossover moves between local optima. In this paper, we prove that recombination induces a LON which is actually a network of overlapping hypercube lattices. Given two or more samples from any lattice, we can also infer the existence of additional local optima that have not previously been reached by sampling. We prove that these lattices can be exponentially large. Finally, we prove that there exists TSP instances can be solved in polynomial time by exploiting Partition Crossover; these same instances are not solved by local search.
Upgraded trajectory planning method deployed in autonomous exploration for unmanned aerial vehicle

Autonomous exploration is grounded on target decision and trajectory planning, which is widely deployed on unmanned aerial vehicles. However, existing methods generally only focus on the exploration effect of target decision but neglect the environment information gained with trajectory planning during flight, resulting in redundant exploration trajectories and low exploration efficiency. This article proposes an upgraded method of trajectory planning for autonomous exploration work. We design a fresh cost term considering the frontier information in the part of trajectory optimization. Besides, yaw angles are planned independently to catch more environment information during flight. We present extensive simulations and real-world tests. The results show that our proposed method reduces the exploration cost time by 10–15% compared with the previous one.
Minimising line segments in linear diagrams is NP-hard

Linear diagrams have been shown to be an effective method of representing set-based data. Moreover, a number of guidelines have been proven to improve the efficacy of linear diagrams. One of these guidelines is to minimise the number of line segments appearing in a diagram. We show this problem to be NP-hard.
Ordinal classification with a spectrum of information sources

The amount of labeled data (referred to as absolute information) obtained from experts has an obvious effect on the performance of an ordinal classifier. In order to make up for a lack of sufficient labeled data, a possibility is to access other sources of information. Inspired by the popularity of public crowdsourcing platforms, an effective way is to invite (numerous) novices for expressing their preference orders for given couples of examples, resulting in frequentist relative information. In this work, we extend a recently introduced nearest neighbor method (with or without an associated learned distance metric) that is able to deal with both absolute and relative information to the case of such frequentist relative information. In particular, we design a new labeling rule and propose a distance metric learning method specifically designed to deal with the combination of absolute and frequentist relative information. We test the proposed method(s) in several problem settings on a variety of benchmark datasets. The experimental results confirm that incorporating frequentist relative information helps to improve the ordinal classification performance.
Implementation and Analysis of Different POS Tagger in Khasi Language

Part-of-speech tagging is a process of assigning each word of a sentence to a part of speech based on its context and definition. POS tagging is a prerequisite tool for many NLP tasks like word sense disambiguity, name entity information extraction, etc. Unfortunately, very little work has been done so far in this line for Khasi Language. The main difficulty lies with the unavailability of an annotated corpus. Hence, a small corpus is created which consists of 778 sentences with 34,873 words, out of which 3,942 are distinct words and a tagset of 52 tags. In this chapter, three methods for POS tagging, namely Brill's tagger, hidden Markov model (HMM)-based tagger, and bidirectional long short-term memory recurrent neural network (Bi-LSTM), have been implemented. Then a comparative analysis is performed, and it is observed that Bi-LSTM performs better in terms of accuracy.
An Empirical Analysis of Code Smelling and Code Restructuring in Python

Code smellings are not bugs or errors; rather, they are a fundamental deviation in software design that lowers code quality. Code smells don't always mean the software won't work; it will still provide a result, but it can slow down processing, increase the risk of failure and errors, and make the programme more vulnerable to future flaws. The conceptual theory behind code smell and its various kinds are discussed in this chapter. Identifying and eradicating code smells is a time-consuming and endless process with no guarantee that the software will be smell-free. Also, because it's very hard to uncover and eliminate all smells by hand, adopting automated code review techniques that can detect smells becomes essential. Code refactoring is one method of restructuring written code to reduce the effects of bad code smell on generated software code. A novel three-phase code refactoring framework has been proposed in this study. The effectiveness of Python code smell detection using Pysmell tool and refactoring using the rope automation tool are also studied in this chapter.
Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage

Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosis. Unfortunately, cerebral ventricle segmentation in complex clinical cases, such as in the coexistence with other lesions/hemorrhages, remains unexplored. In this paper, we, for the first time, focus on cerebral ventricle segmentation with the presence of intra-ventricular hemorrhages (IVH). To overcome the occlusions formed by IVH, we propose a symmetry-aware deep learning approach inspired by contrastive self-supervised learning. Specifically, for each slice, we jointly employ the raw slice and the horizontally flipped slice as inputs and penalize the consistency loss between the corresponding segmentation maps in addition to their segmentation losses. In this way, the symmetry of cerebral ventricles is enforced to eliminate the occlusions brought by IVH. Extensive experimental results show that the proposed symmetry-aware deep learning approach achieves consistent performance improvements for ventricle segmentation in both normal (i.e. without IVH) and challenging cases (i.e. with IVH). Through evaluation of multiple backbone networks, we demonstrate the architecture-independence of the proposed approach for performance improvements. Moreover, we re-design an end-to-end version of symmetry-aware deep learning, making it more extendable to other approaches for brain-related analysis.
Deep Reinforcement Learning

In this chapter, we will introduce and discuss the concept of deep reinforcement learning (DRL) which is an exciting and rapidly growing area of ML [1]. DRL is primarily used to learn from actions enacted in an environment. This is like how humans learn from experience. This area is seeing rapid development in a broad range of disciplines which include driverless cars, simulation, and gameplay.
Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining

Economic security is a core theoretical issue in economics. In modern economic conditions, the ups and downs caused by economic instability in any economic system will affect the stability of the financial market, bring huge losses to the economy, and affect the development of the whole national economy. Therefore, research on the regularity of economic security and economic fluctuations is one of the important contents to ensure economic stability and scientific development. Accurate monitoring and forecasting of economic security are an indispensable link in economic system regulation, and it is also an important reference factor for any economic organization to make decisions. This article focuses on the construction of an economic security early warning system as the main research content. It integrates cloud computing and data mining technologies and is supported by CNN-SVM algorithm and designs an early warning model that can adaptively evaluate and warn the economic security state. Experiments show that when the CNN network in the model uses ReLU activation function and SVM uses RBF function, the prediction accuracy can reach 0.98, and the prediction effect is the best. The data set is verified, and the output Q province's 2018 economic security early warning comprehensive index is 0.893. The 2019 economic security early warning index is 0.829, which is consistent with the actual situation.
The Descriptive Data Analysis for the Adoption of Community Cloud in Saudi HEI-Based Factor Adoption

Due to its increased reliability, adaptability, scalability, availability, and processing capacity, cloud computing is rapidly becoming a popular trend around the world. One of the major issues with cloud computing is making informed decision about adoption of community cloud (CC) computing (ACCC). To date, there are various technology acceptance theories and models to validate perspective of ACCC at both organizational and individual levels. However, no experimental studies have been carried out to provide a comprehensive assessment of the factors of ACCC, specifically in the area of the Saudi Higher Education (HEI) Institution. Thus, this research was aimed at exploring the factors of ACCC and the relationship to the experiences of the employees. The analysis of the employee context was driven by the success factors of technological, organizational, environmental, human, security, and advantage contexts on community cloud computing adoption in HEI. The data collection was a questionnaire-based survey based on 106 responses. We present findings based on descriptive analysis in identifying the significant component that contributed to the effective implementation of ACCC. Security concerns are a significant influencing element in the adoption of community cloud technology.
Kinship identification using age transformation and Siamese network

Facial images are used for kinship verification. Traditional convolutional neural networks and transfer learning-based approaches are presently used for kinship identification. The transfer-learning approach is useful in many fields. However, it does not perform well in the identification of humans' kinship because transfer-learning models are trained on a different type of data that is significantly different as compared to human face image data, a technique which may be able for kinship identification by comparing images of parents and their children with transformed age instead of comparing their actual images is required. In this article, a technique for kinship identification using a Siamese neural network and age transformation algorithm is proposed. The results are satisfactory as an overall accuracy of 76.38% has been achieved. Further work can be carried out to improve the accuracy by improving the Life Span Age Transformation (LAT) algorithm for kinship identification using facial images.
Use of Resources of the Educational Environment in the Context of Remote Studying of Foreigners in Ukraine

This article examines the essence of the educational environment concept, іndividual approaches of scientists to determining the content and structural components, means and forms of its main tasks within the format of remote studying of foreign citizens in Ukrainian higher educational facilities, which is determined and adjusted by time, society, forms of the educational process. The peculiarities of the organization of the virtual educational environment through the lens of language training of foreigners in Ukrainian higher educational facilities, content, purpose and level of efficiency of its resources for solving educational tasks of an educational facility, are revealed. External resources of the educational environment are examined, and internal interrelated components that ensure its effectiveness and efficiency are highlighted: technological (using normative methods of teaching Ukrainian as a foreign language and achieving planned results), material-technical, and subject-resource (implementing equal partnership between the subjects of the educational environment). It is determined that at the initial stage of education, the lack of language and cultural environment, which contributes to foreigners’ formation of speech skills and abilities, determines their communication needs and motivates them to learn the language, negatively affects the quality of education. Daily verbal communication with the tutor in the format of videoconferencing, involvement of authentic videos, fragments of television programs, films or cartoons, educational videos, virtual tours, phonograms in the educational process provides an opportunity to optimize the educational process and achieve planned studying outcomes.
Multiplierless Design of Very Large Constant Multiplications in Cryptography

This brief addresses the problem of implementing very large constant multiplications by a single variable under the shift-adds architecture using a minimum number of adders/subtractors. Due to the intrinsic complexity of the problem, we introduce an approximate algorithm, called TÕLL, which partitions the very large constants into smaller ones. To reduce the number of operations, TÕLL incorporates graph-based and common subexpression elimination methods proposed for the shift-adds design of constant multiplications. It can also consider the delay of a multiplierless design defined in terms of the maximum number of operations in series, i.e., the number of adder-steps, while reducing the number of operations. High-level experimental results show that the adder-steps of a shift-adds design can be reduced significantly with a little overhead in the number of operations. Gate-level experimental results indicate that while the shift-adds design can lead to a 36.6% reduction in gate-level area with respect to a design using a multiplier, the delay-aware optimization can yield a 48.3% reduction in minimum achievable delay of the shift-adds design when compared to the area-aware optimization.
Path-Tree Optimization in Discrete Partially Observable Environments Using Rapidly-Exploring Belief-Space Graphs

Robots often need to solve path planning problems where essential and discrete aspects of the environment are partially observable. This introduces a multi-modality, where the robot must be able to observe and infer the state of its environment. To tackle this problem, we introduce the Path-Tree Optimization (PTO) algorithm which plans a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path-tree</i> in belief-space. A path-tree is a tree-like motion with branching points where the robot receives an observation leading to a belief-state update. The robot takes different branches depending on the observation received. The algorithm assumes a deterministic observation model and is composed of three main steps. First, a rapidly-exploring random graph (RRG) on the state space is grown. Second, the RRG is expanded to a belief-space graph by querying the observation model. In a third step, dynamic programming is performed on the belief-space graph to extract a path-tree. The resulting path-tree combines exploration with exploitation i.e. it balances the need for gaining knowledge about the environment with the need for reaching the goal. We demonstrate the algorithm capabilities on navigation and mobile manipulation tasks, and show its advantage over a baseline using a task and motion planning approach (TAMP) both in terms of optimality and runtime.
Enabling Manageable and Secure Hybrid P2P-CDN Video-on-Demand Streaming Services Through Coordinating Blockchain and Zero Knowledge

Video-on-Demand (VoD) streaming services on the hybrid P2P-CDN architecture nicely balances the high reliability contributed by the CDN, and the great scalability provided by P2P. However, the unmanageable and trustless feature of the P2P network can cause content piracy and security threats to the copyright holders and users. To date, there has not been an adequate scheme based on P2P-CDN providing VoD streaming services in the literature that resolves the content protection and secure delivery while keeping up the efficiency of P2P streaming performance. In this work, a manageable and secure VoD streaming delivery scheme is proposed for P2P-CDN, which marries the requisite requirements with the blockchain and zero-knowledge. The experimental results show that our proposed scheme offers a superior VoD streaming service both on the performance metrics and security compared with the most widely used and mature system for P2P-CDN nowadays, even under a large-scale P2P network.
An Intelligent System for Detecting Abnormal Behavior in Students Based on the Human Skeleton and Deep Learning

With the use of an intelligent video system, this research provides a method for detecting abnormal behavior based on the human skeleton and deep learning. To begin with, the spatiotemporal features of human bones are extracted through iterative training using the OpenPose deep learning network and the redundant information of human bone facial features is reduced in the feature extraction process, effectively reducing the time it takes to identify and analyze abnormal behavior. The collected human skeleton features are then classified using a graph convolution neural network to reduce the computational complexity of the behavior identification algorithm, and the sliding window voting method is used to further improve the accuracy of the behavior classification in practical application, resulting in the diagnosis and classification of abnormal behavior of students under video surveillance. Finally, using the self-built student trajectory data set and the INRIA data set, simulation analysis is performed, and the practicality and superiority of the proposed method for abnormal behavior detection is confirmed by comparing it to the existing abnormal behavior recognition methods. The proposed method for detecting anomalous behavior in a self-built database and INRIA data set has a high accuracy of more than 99.50 percent and a high processing efficiency rate.
Enhancing Text Generation via Parse Tree Embedding

Natural language generation (NLG) is a core component of machine translation, dialogue systems, speech recognition, summarization, and so forth. The existing text generation methods tend to be based on recurrent neural language models (NLMs), which generate sentences from encoding vector. However, most of these models lack explicit structured representation for text generation. In this work, we introduce a new generative model for NLG, called Tree-VAE. First it samples a sentence from the training corpus and then generates a new sentence based on the corresponding parse tree embedding vector. Tree-LSTM is used in collaboration with the Stanford Parser to retrieve sentence construction data, which is then used to train a conditional discretization autoencoder generator based on the embeddings of sentence patterns. The proposed model is extensively evaluated on three different datasets. The experimental results proved that the proposed model can generate substantially more diverse and coherent text than existing baseline methods.