Reviewerly/exHarmony huggingface dataset:
Minerals Yearbook, volume III, Area Reports — International

For additional information, contact: Director, National Minerals Information Center U.S. Geological Survey 12201 Sunrise Valley Drive 988 National Center Reston, VA 20192 Email: nmicrecordsmgt@usgs.gov The U.S. Geological Survey (USGS) Minerals Yearbook discusses the performance of the worldwide minerals and materials industries and provides background information to assist in interpreting that performance. Content of the individual Minerals Yearbook volumes follows:Volume I, Metals and Minerals, contains chapters about virtually all metallic and industrial mineral commodities important to the U.S. economy. Chapters on survey methods, summary statistics for domestic nonfuel minerals, and trends in mining and quarrying in the metals and industrial mineral industries in the United States are also included.Volume II, Area Reports: Domestic, contains a chapter on the mineral industry of each of the 50 States and Puerto Rico and the Administered Islands. This volume also has chapters on survey methods and summary statistics of domestic nonfuel minerals.Volume III, Area Reports: International, is published as four separate reports. These regional reports contain the latest available minerals data on more than 180 foreign countries and discuss the importance of minerals to the economies of these nations and the United States. Each report begins with an overview of the region’s mineral industries during the year. It continues with individual country chapters that examine the mining, refining, processing, and use of minerals in each country of the region and how each country’s mineral industry relates to U.S. industry. Most chapters include production tables and industry structure tables, information about Government policies and programs that affect the country’s mineral industry, and an outlook section.The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the Minerals Yearbook are welcome.
Contrastive Analysis of the Interlanguages of Unaccompanied Foreign Minors Learning L2 Italian

This paper proposes a contrastive analysis of the interlanguage of L2 Italian learners belonging to the category of "unaccompanied foreign minorsˮ, with the same L1 but different levels of education.The profile of unaccompanied foreign minors is complex and sociolinguistically very peculiar, since it is characterized by the frequent coexistence of a plurilingual ability and a (very) low, or zero, level of education.The study is based on oral productions collected through video interviews and studied with the support of conversation analysis.Focusing on the acquisition of verb inflectional morphology, the comparison of learners' varieties aims to show to what extent several differences depend on learners' profile and, particularly, on the only parameter that differentiates them, namely literacy vs. illiteracy in L1.
Security Enhancement of Online Accounting Data from Cyber Attacks

The growing use of digital technology among businesses has highlighted the significance and function of cybersecurity as a fresh dimension of risk management, not least because cyber threats and hazards have drawn considerable public attention. Users typically do not understand the precise place of their information or the other jointly recorded information sources with theirs. The exchange of data on cybersecurity lists a comprehensive list of prospective advantages for government and private sector organizations. Cloud Accounting (CA) plays a predominant role in corporate finance. CA is a type of lease based accounting services. Client access the accounting package anywhere in the world. The major issue in Accounting is to secure accounting data. The aim of this is to provide a deep understanding of security vulnerabilities and solutions in online accounting with specific reference to cloud accounting. The proposed efficient double secured accounting environment for business using bio-metric based Iris, Rivest Shamir Adleman (RSA) and Advanced Encryption Standard (AES) algorithms provides the double standard highest security for online accounting applications. The author designing a prototype model to solve the issues related to security in the cloud accounting problem, this model is used to tackle the intruders from data hijacking. The results suggested that the proposed system gives an enhanced security mechanism in terms of high privacy and confidentiality. The major contribution of the study is the use of protecting valuable data from intruders.
Shuddhi -A Cleaning Agent

Cleaning activities are considered as mundane tasks. These tasks though time-consuming and unpleasant, are essential for a hygienic lifestyle. This issue has been tackled before but they have come at a price, whether in the form of overconsumption of energy resources or prices of such products. Also, they only try to do either dust removal in the form of vacuum cleaning or mopping the floor, but not both. Reducing energy consumption and lowering expenses will help in the widespread usage of such automated alternatives. Combining the two forms of cleaning will help in increasing the versatility of the product. This paper outlines a solution to these issues through the development of a robot. This robot consists of a brush in the front which helps in the removal of dust and a set of mops that cleans the floor using disinfectant water. It can operate in an autonomous mode where it navigates through the room using ultrasonic sensors. It can alternatively be operated manually through the user's smartphone.
Enhanced Web Log Data Mining using Probability Density Based Fuzzy C Means Clustering

The World Wide the net is very great place where things are stored and it is growing exponentially. It has in it sizeable amount of news given which is growing and bringing up to the current state quickly. Different organizations, institutes, government agencies and support centers bring up to the current state their news given regularly. The World Wide the net provides its services to the ranges of the net users. The net users may have different interests, needs and back knowledge. Clustering into groups is one of the most important tasks in the action-bound areas of the net record mining. It says without any doubt to grip the trouble of news given over-weight on the net while many users are connected on the meeting thing by which something is done. Clustering into groups is made use of for grouping news given into by comparison way in design for making discovery of person for whom one does work interest. There are two bad points of FCM algorithm, firstly the requirements of no. of clusters C and secondly giving to the first relation matrix. Because of, in relation to these two bad points the FCM algorithm is hard to come to a decision about the right no. of mass, group and this algorithm is unsafe. The strong decision of desirable first stage mass, group is an important hard question, therefore a new expert way called PDFCM algorithm is made, was moving in.
Isophote-based Low-Computing-Power Eye-Detection Embedded-System

The eye tracking field is an area highly studied by different researchers. In addition, there are companies that offer commercial eye trackers, but they are usually economically restrictive. However, current technology allows to design embedded eye tracking systems, capable of running on low-cost hardware. This paper presents the implementation of a real-time eye-detection method that uses the properties of “isophotes”, to achieve robustness against changes in illumination, eye rotation and pupil size. The method is implemented in a portable platform, reduced both in computing power and in RAM memory. The performance is evaluated using different eye tracking methods implemented by the authors on the same platform. For this purpose, a database generated on this hardware is used. This database is composed of a set of low-resolution near-infrared eyes-images created with the dark pupil technique, using a lightweight glasses head unit. Results show the robustness of the method and a significant accuracy improvement over the methods analyzed.
Research on Path Planning of Cloud Robot in Dynamic Environment Based on Improved DDPG Algorithm

Aiming at the problems of low success rate and slow learning speed of DDPG algorithm in dynamic environment path planning, an improved DDPG algorithm is designed. In this paper, radam algorithm is used to replace the neural network optimization algorithm of ddpg algorithm, and priority experience replay is added to improve the success rate and convergence speed. Then introduces transfer learning enhancement training based on the improved algorithm. In order to solve the problem of limited storage space and insufficient local computing ability caused by the increased complexity of the improved algorithm in the path planning of traditional mobile robots in complex dynamic environment, a cloud robot path planning system is proposed. Among them, robots are responsible for transmitting environment and motion information to the local cloud, and the cloud is responsible for complex computing services. Through ROS robot operating system and Gazebo simulation software, the simulation environment is established, and the contrast experiment between improved DDPG algorithm and DDPG algorithm is carried out. The simulation experiment of dynamic environment path planning is carried out in the cloud robot path planning system. The results show that the improved DDPG algorithm improves the convergence speed by 18% and the success rate by 35% compared with the original DDPG algorithm. Compared with the original system, the cloud robot path planning system effectively saves 37.2% CPU utilization and 23.5% memory utilization. For cloud robot with continuous action space, it has a good effect in dynamic environment path planning.
A comprehensive framework for designing and evaluating vocabulary learning apps from multiple perspectives

The integration of emerging mobile technology into the education domain has become increasingly ubiquitous. This research reviewed the advantages and challenges of mobile learning, discussed the key factors that promote effective vocabulary learning and conducted a survey to investigate and identify the ten most popular word learning apps. Based on the results, we analysed the key features of the popular apps and summarised the elements that play crucial roles in leading to successful learning. Based on such analyses, we also developed a comprehensive framework for designing and evaluating vocabulary learning apps, covering dimensions, such as content quality, multimodal presentation, engagement, personalisation, repetition and usability. A popular app, Vocabulary.com, was also assessed utilising the proposed framework. Users, evaluators, designers and developers of word-learning apps are expected to benefit from this research by being provided with a comprehensive understanding of app-facilitated vocabulary learning and essential features of effective word learning apps.
Design and Research of an Intelligent Ironing Machine

As a good helper in human life, the ironing machine has the function of fast ironing clothes. Unlike the traditional hand-held hanging ironing machine and electric iron, this paper creatively proposes an intelligent ironing machine that can automatically identify the types of clothes and iron them. The machine is equipped with various sensors and controlled by Arduino single-chip microcomputer. It has intelligent ironing functions such as intelligent temperature control and timing.
Information Leakage Tracking Algorithms in Online Social Networks

Aim: In order to explore the study on information leakage tracking algorithms in online social networks, solve the problem of information leakage in the current online social network. a deterministic leaker tracking algorithm based on digital fingerprints is proposed Background: : First, the basic working principle of the algorithm is that the platform uses plug-ins to embed a unique user-identifying information before users try to obtain digital media such as images and videos shared by others on the platform. Objective: Secondly, because the scale of users in social networks is extremely large and dynamic, while ensuring the uniqueness of digital fingerprints, it is also necessary to ensure the coding efficiency and scalability of digital fingerprint code words. Methods: Simulation experiments show that: 10 experiments are performed on 10,000 to 100,000 nodes, the Hamming distance threshold d is set to be 3, and the length of the hash code and the binary random sequence code are both 64 bits. Results: Compared with the traditional linear search, the proposed digital fingerprint fast detection scheme has better performance Conclusion: It is proved that an index table based on hash code and user ID is established and combines with community structure, to improve the detection efficiency of digital fingerprints
Supply chain operation evaluation and management decision by fuzzy cognitive map model

Abstract The components of the logistics and supply chain are analysed to meet the needs of supply chain management and decision support, aiming at the complexity and uncertainty of the supply chain system. First, the characteristics of logistics demand and logistics service types of some enterprises are analysed. The factors affecting supplier selection are obtained through the questionnaire survey of some enterprises. Second, the mutual feedback relationship between the influencing factors of logistics service demand is analysed, and a fuzzy cognitive graph model of the selection process of enterprise logistics service providers is established. Finally, the fuzzy cognitive map (FCM) model is used to conduct an empirical study on the choice of logistics service providers. The results show that the ranking results of the three candidate logistics service companies based on FCM are WL1 (0.7233) &gt; WL3 (0.6823) &gt; WL2 (0.6764). When each node of the concept does not affect each other, the ranking result of the three candidate logistics service companies based on analytic hierarchy process (AHP) is WL1 (0.7265) &gt; WL2 (0.6949) &gt; WL3 (0.6920). The final evaluation result will be affected by the mutual influence relationship between the concept nodes. Overall, the lowest evaluation score is WL2, and the highest is WL1. The most suitable logistics service provider for the enterprise is WL1. The FCM model selected by the manufacturing enterprise logistics service provider is reasonable and feasible. The mutual feedback between evaluation factors affects the selection of logistics service providers in a company. The FCM can get a more accurate evaluation weight value and can realize the optimal choice of logistics suppliers. It provides reference and decision support for other enterprises.
Trajectory Planning and Execution of Delta Robot

&lt;div&gt;&lt;p class="4"&gt;The main purpose of this study is to plan and execute effective trajectory on the Delta robot through graphical user interface (GUI) and appropriate electronic circuits, and optimize the integration with the mechanical prototype designed and implemented by the robot laboratory of Nueva Granada Military University, while considering and respecting its different characteristics, such as mechanical structure, workspace, kinematics, dynamics and motion singularity allow the correct development and implementation of algorithms that can effectively describe and control the planned trajectory.&lt;/p&gt;&lt;/div&gt;
Multiscale anti-deformation network for target tracking in UAV aerial videos

With the popularity of unmanned aerial vehicle (UAV) technology, remote sensing target tracking in aerial videos from UAVs has drawn much attention. However, various problems occur, such as obvious target scale changes and frequent target shape updates in UAV aerial videos, and typical tracking algorithms have difficulties in solving these challenges. Therefore, we propose a remote sensing target tracking method for UAV videos based on a multiscale antideformation network (MSADN). This method uses the fully convolutional Siamese network (SiameseFC) as its basic architecture. First, in the target feature extraction stage, we use a receptive field block module to change the single convolutional layer of the network. And the multibranch structure can improve the algorithm’s adaptability to target scale changes. Then, in the tracking stage, to solve the problem of the lack of template updates, we integrate a template dynamic update module into the SiameseFC architecture. This module uses long short-term memory to generate control signals to dynamically update target shape information, and it will improve the algorithm’s ability to cope with frequent target shape updates. Finally, compared to state-of-the-art trackers, experimental results show that the MSADN algorithm can obtain better performance (75.9% Prec, 56.4% Succ) while ensuring higher efficiency (60 fps).
Lessons from a breast cell annotation competition series for school pupils

Abstract Due to COVID-19 outbreaks, most school pupils have had to be home-schooled for long periods of time. Two editions of a web-based competition “Beat the Pathologists” for school age participants in the UK ran to fill up pupils’ spare time after home-schooling and evaluate their ability on contributing to AI annotation. The two editions asked the participants to annotate different types of cells on Ki67 stained breast cancer images. The Main competition was at four levels with different level of complexity. We obtained annotations of four kinds of cells entered by school pupils and ground truth from expert pathologists. In this paper, we analyse school pupils’ performance on differentiating different kinds of cells and compare their performance with two neural networks (AlexNet and VGG16). It was observed that children tend to get very good performance in tumour cell annotation with the best F1 measure 0.81 which is a metrics taking both false positives and false negatives into account. Low accuracy was achieved with F1 score 0.75 on positive non-tumour cells and 0.59 on negative non-tumour cells. Superior performance on non-tumour cell detection was achieved by neural networks. VGG16 with training from scratch achieved an F1 score over 0.70 in all cell categories and 0.92 in tumour cell detection. We conclude that non-experts like school pupils have the potential to contribute to large-scale labelling for AI algorithm development if sufficient training activities are organised. We hope that competitions like this can promote public interest in pathology and encourage participation by more non-experts for annotation.
An automatic kriging machine learning method to calibrate meta-heuristic algorithms for solving optimization problems

For years, meta-heuristic algorithms have been widely studied and many improved versions have been developed: from the evolution of the swarm topologies of the Particle Swarm Optimization algorithm, to the using of machine learning to Differential Evolutionary algorithms. However, the tuning of the fundamental meta-heuristic parameters has been less studied, but may lead to significant improvements on the convergence accuracy of these algorithms. This paper aims at developing an automated methodology to calibrate the parameters of population-based meta-heuristic algorithms for optimization problems. Based on the kriging estimation of the best combination of parameters, the Automated parameter tuning of Meta-heuristics (AptM) methodology gives the optimal algorithm setup for each considered problem in order to lead to a better convergence accuracy. The proposed AptM methodology is used to tune three different meta-heuristic algorithms, each applied to twelve mathematical unimodal or multimodal objective functions. AptM methodology performance is assessed by comparison of classical setups usually used in the literature. The numerical results show that the AptM methodology allows a significant improvement of the convergence accuracy of meta-heuristics with an average improvement of 62.02%, 69.12% and 64.94% on optimization problems defined in dimensions 10, 30 and 50 respectively. An experimental criterion is defined based on the convergence accuracy of the AptM methodology over the classical setups, assessing the AptM performances. The previous experimental criterion allows to compare the AptM methodology over the base-set. The AptM methodology shows a significant improvement of the algorithms performance on 97.2% of the tested problems.
In Defense of Logical Minds

According to the received view in the psychology of reasoning, Piaget’s view that ℱ Humans naturally develop a context-free deductive reasoning scheme at the level of elementary first-order logic. has been overthrown by the poor performance of educated adult subjects on specific logic problems (e.g., Wason’s selection task). I propose that Piaget’s ℱ (or at least a variant) is alive and well, because the subjects in question are simply victims of a defective education. With a modicum of the right sort of logic training, humans reason deductively on logic problems well enough to vindicate Piaget.
Internet of Things based Fall Prediction and Alerting Device

Mostly, the older people are living their own life alone or being separated from their younger ones due to many factors. As they live alone, they tend to fall. This fall leads to various health related problem and even lead to death. In this work, an Internet of Things (IoT) based fall prediction and alerting device plays a major role in helping these older peoples, as it is difficult to look after continuously. Further, the proposed device is designed in a way that whenever the patient is about to fall, the buzzer gives alarming sound by sensing the value from the accelerometer and gyroscope sensor and comparing with the threshold value in order to indicate the patient. Also, the device sends the alerting messages to their guardian once they fell. Result demonstrates that the message alerts the patient guardian and makes the patient to get help in time. By this way, the special care is ensured to take care of those people and can avoid these problems.
Blood cell quantification on dry blood samples: toward patient-centric complete blood counts

Background: Performing complete blood counts from patients' homes could have a transformative impact on e-based healthcare. Blood microsampling and sample drying are enabling elements for patient-centric healthcare. The aim of this study was to investigate the potential of dry blood samples for image-based cell quantification of red and white blood cells. Methods: A manual sample preparation method was developed and tested for image-based red and white blood cell counting. Results & conclusion: Dry blood samples enable image-based cell counting of red and white blood cells with a good correlation to gold standard hematology analyzer data (average coefficient of variation <6.5%; R2 >0.8) and resolve the basic morphology of white blood cell nuclei. The presented proof-of-principle study is a first step toward patient-centric complete blood counts.