Reviewerly/exHarmony huggingface dataset:
Objective Domain Boundaries Detection in New Caledonian Nickel Laterite from Spectra Using Quadrant Scan

Modelling of 3D domain boundaries using information from drill holes is a standard procedure in mineral exploration and mining. Manual logging of drill holes can be difficult to exploit as the results may not be comparable between holes due to the subjective nature of geological logging. Exploration and mining companies commonly collect geochemical or mineralogical data from diamond drill core or drill chips; however, manual interpretation of multivariate data can be slow and challenging; therefore, automation of any of the steps in the interpretation process would be valuable. Hyperspectral analysis of drill chips provides a relatively inexpensive method of collecting very detailed information rapidly and consistently. However, the challenge of such data is the high dimensionality of the data’s variables in comparison to the number of samples. Hyperspectral data is usually processed to produce mineral abundances generally involving a range of assumptions. This paper presents the results of testing a new fast and objective methodology to identify the lithological boundaries from high dimensional hyperspectral data. This method applies a quadrant scan analysis to recurrence plots. The results, applied to nickel laterite deposits from New Caledonia, demonstrate that this method can identify transitions in the downhole data. These are interpreted as reflecting mineralogical changes that can be used as an aid in geological logging to improve boundary detection.
Assessment Methods for Evaluation of Recommender Systems: A Survey

Abstract The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.
Measurement of Employees on Human Resources with Fuzzy Logic

Artificial intelligence, which is the indispensable technology of our age, has started to gain a place in many institutions. Institutions give great importance to human resources management because hiring the right employee for the job will increase productivity within the organization. When recruiting personnel for the position, human resources face difficulties such as measuring the success levels of applicants and deciding whether they are suitable. In this study, in order to provide solutions to the difficulties encountered, a decision-making mechanism is created by using the fuzzy logic method, which is one of the artificial intelligence techniques. This decision-making mechanism measures the performance of people applying for recruitment. While measuring performance, all applications are taken into consideration, and a rule base is formed according to graduation status and experience. The system, which is based on this rule base, evaluates people according to the inputs and finds out their success levels in return. According to the results, it is decided whether the persons are suitable for the position sought. When human resources departments in corporations are combined with artificial intelligence technologies, an advantage will be achieved in the competitive environment between corporations.
VR Supermarket: a Virtual Reality Online Shopping Platform with a Dynamic Recommendation System

In this paper, VR Supermarket, a Virtual Reality (VR) online shopping platform is proposed to provide an immersive shopping experience for users. The system consists of four components – the VR application, front-end interfaces, the external databases and the recommendation system. The front-end interfaces handle the user input in a 360° rendered showroom where users can explore and buy products. Structured Query Languages (SQL) and Not only SQL (NoSQL) databases are integrated with the application to store inventory, customer information and billing information. An integrated recommendation system provides users a personalized shopping experience by recommending relevant products. The recommendations are based on the purchase history, which makes the VR supermarket a dynamic, adaptive and user-oriented system. The paper proposes a Neural Collaborative Filtering (NCF) recommendation system augmented by Convolutional Neural Networks (CNN) and Attention layers. The VR supermarket provides a realistic user-friendly shopping experience where users are able to select from a variety of products and place orders. Seamless integration with databases enables extensive storage of supermarket data and the potential to scale up the application into an enterprise-level application that will add a new dimension to the statement “Shop at home”.
MANIPULATIVE SPEECH: A THEORETICAL OVERVIEW

Manipulation is one of the concepts that needs a more in-depth theoretical and practical examination. It is employed in many areas of life, including personal and social matters, intellectual and professional pursuits, and, of course, politics. This paper is an approach, the purpose of which is to study the concept of manipulation as a psychological and linguistic phenomenon where the central emphasis is on the manipulative techniques and tactics that are utilized for various reasons such as illegitimate domination and social abuse. At this level of investigation we find it essential to illustrate different approaches to the study of this phenomenon as well as certain dictionary definitions to be able to fully perceive and comprehend the properties of manipulation.&#x0D;
A Signal Demodulation Algorithm Based on Generative Adversarial Networks

In recent years, extensive research has been done to obtain better demodulation performance by combining signal demodulation with deep learning algorithms, such as convolutional neural networks (CNN). However, the demodulation performance of CNN is not much improved compared with traditional coherent demodulation algorithms. This paper proposes a signal demodulation algorithm based on generative adversarial networks (GAN). The proposed GAN-based demodulation network uses the confrontation characteristics between the generator and the discriminator in GAN to greatly improve the demodulation ability of the signal. Numerical results show that the demodulation performance is better than the coherent demodulation algorithm and the CNN-based demodulation algorithm.
Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices

The usage and amount of information available on the internet increase over the past decade. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional knowledge sources. Such systems are designed to cater the most prominent answer from this giant knowledge source to the user&amp;rsquo;s query using natural language understanding (NLU) and thus eminently depends on the Question-answering(QA) field. Question answering involves but not limited to the steps like mapping of user&amp;rsquo;s question to pertinent query, retrieval of relevant information, finding the best suitable answer from the retrieved information etc. The current improvement of deep learning models evince compelling performance improvement in all these tasks. In this review work, the research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailing followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language. In the end, a survey of available datasets and evaluation measures is presented.
Application of Analytic Hierarchy Process and Weighted Product Methods in Determining the Best Employees

&lt;p&gt;&lt;strong&gt;A&lt;/strong&gt;&lt;strong&gt;bstract&lt;/strong&gt;&lt;strong&gt;.&lt;/strong&gt; Employees are one of the company's assets that must be managed properly. Therefore the selection of the best employees is now needed. The problem faced in determining the best and qualified employees is that there are still no standards in assessing only one person subjectively in determining the best employee, which consequently lacks appropriate or objective results. To provide rewards for the best employees, we need a system to support the decisions of the best employees who deserve to receive rewards to be on target. The purpose of this research is to design and build a decision support system application in determining the best employees using the analytic hierarchy process and weighted product methods. Stages of software development of the Software Development Life Cycle (SDLC) uses a waterfall, that is data analysis, system design, construction, coding, testing and implementation. The results of this process are in the form of calculation applications that have been obtained from the analytic hierarchy process and weighted product methods in determining the best employee. The result gives an accuracy rate of 82.3%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;analytic hierarchy process, weighted product, decision support system, employees&lt;/p&gt;
Maternal and Child Healthcare Service by Portable Health Clinic System Using a Triage Protocol

The number of deaths of a mother and child caused by maternal and child healthcare (MCH) issues has been greatly decreased recently, but still, the number is extremely high especially in developing countries. Although the governments have been given a priority in this issue, the lack of financial and human resources brings a limit. Thus, the use of low-cost but appropriate technology is required. Portable Health Clinic (PHC), a telemedicine system developed for providing primary healthcare, is such a technology. This study aimed to address this MCH issue with the aid of a low-cost PHC service involving a continuum-of-care protocol to the rural communities of Bangladesh. Moreover, this study introduces a triage protocol to distinguish high-risk patients from the early stage of the continuum of care who need special care and refer to specialized physicians to prevent unwanted deaths.
Validity and Practically of Learning Resources Flipbook Identification of Basidiomycota Mushrooms for Senior High School Students

The aim of this research is to describe validity flipbook type e-book in terms of the feasibility content, language, and display; and describe the practicality of an e-book in terms of an implementation observation. The method used in this study is a modified research and development method and was carried out at the Postgraduate Science Education, the State University of Surabaya in February 2020 - August 2021. The data collection technique was carried out using the validation method and the implementation observation method. The assessment instrument uses a validation sheet and an implementation observation sheet. The validity of the flipbook type e-book is reviewed from the feasibility of the content, the feasibility of the language, and the feasibility of the display. The validity of the flipbook-type e-book was assessed by three validators which included two biology lecturers and one biology teacher. For practicality, use the implementation observation sheet in terms of the use of flipbooks during the learning process. The observation sheet contains statements that will be filled out by four observers. Data analysis was carried out quantitatively. The developed e-book was tested on 20 students of class X Senior High School students in August 2021. The validation results of the flipbook type e-book by three validators obtained an average score of 3.84 with a very valid category. Based on practicality in terms of implementation, flipbooks get an average percentage of 87.2%. Thus, the flipbook-type e-book on the identification of Basidiomycota fungi is declared valid and practical.
Curating Cyberbullying Datasets: a Human-AI Collaborative Approach

Cyberbullying is the use of digital communication tools and spaces to inflict physical, mental, or emotional distress. This serious form of aggression is frequently targeted at, but not limited to, vulnerable populations. A common problem when creating machine learning models to identify cyberbullying is the availability of accurately annotated, reliable, relevant, and diverse datasets. Datasets intended to train models for cyberbullying detection are typically annotated by human participants, which can introduce the following issues: (1) annotator bias, (2) incorrect annotation due to language and cultural barriers, and (3) the inherent subjectivity of the task can naturally create multiple valid labels for a given comment. The result can be a potentially inadequate dataset with one or more of these overlapping issues. We propose two machine learning approaches to identify and filter unambiguous comments in a cyberbullying dataset of roughly 19,000 comments collected from YouTube that was initially annotated using Amazon Mechanical Turk (AMT). Using consensus filtering methods, comments were classified as unambiguous when an agreement occurred between the AMT workers' majority label and the unanimous algorithmic filtering label. Comments identified as unambiguous were extracted and used to curate new datasets. We then used an artificial neural network to test for performance on these datasets. Compared to the original dataset, the classifier exhibits a large improvement in performance on modified versions of the dataset and can yield insight into the type of data that is consistently classified as bullying or non-bullying. This annotation approach can be expanded from cyberbullying datasets onto any classification corpus that has a similar complexity in scope.
Open set task augmentation facilitates generalization of deep neural networks trained on small data sets

Abstract Many application scenarios for image recognition require learning of deep networks from small sample sizes in the order of a few hundred samples per class. Then, avoiding overfitting is critical. Common techniques to address overfitting are transfer learning, reduction of model complexity and artificial enrichment of the available data by, e.g., data augmentation. A key idea proposed in this paper is to incorporate additional samples into the training that do not belong to the classes of the target task. This can be accomplished by formulating the original classification task as an open set classification task. While the original closed set classification task is not altered at inference time, the recast as open set classification task enables the inclusion of additional data during training. Hence, the original closed set classification task is augmented with an open set task during training. We therefore call the proposed approach open set task augmentation. In order to integrate additional task-unrelated samples into the training, we employ the entropic open set loss originally proposed for open set classification tasks and also show that similar results can be obtained with a modified sum of squared errors loss function. Learning with the proposed approach benefits from the integration of additional “unknown” samples, which are often available, e.g., from open data sets, and can then be easily integrated into the learning process. We show that this open set task augmentation can improve model performance even when these additional samples are rather few or far from the domain of the target task. The proposed approach is demonstrated on two exemplary scenarios based on subsets of the ImageNet and Food-101 data sets as well as with several network architectures and two loss functions. We further shed light on the impact of the entropic open set loss on the internal representations formed by the networks. Open set task augmentation is particularly valuable when no additional data from the target classes are available—a scenario often faced in practice.
Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique

Recently, in computer vision and video surveillance applications, moving object recognition and tracking have become more popular and are hard research issues. When an item is left unattended in a video surveillance system for an extended period of time, it is considered abandoned. Detecting abandoned or removed things from complex surveillance recordings is challenging owing to various variables, including occlusion, rapid illumination changes, and so forth. Background subtraction used in conjunction with object tracking are often used in an automated abandoned item identification system, to check for certain pre-set patterns of activity that occur when an item is abandoned. An upgraded form of image processing is used in the preprocessing stage to remove foreground items. In subsequent frames with extended duration periods, static items are recognized by utilizing the contour characteristics of foreground objects. The edge-based object identification approach is used to classify the identified static items into human and nonhuman things. An alert is activated at a specific distance from the item, depending on the analysis of the stationary object. There is evidence that the suggested system has a fast reaction time and is useful for monitoring in real time. The aim of this study is to discover abandoned items in public settings in a timely manner.
Production Deployment of Machine-Learned Rotorcraft Surrogate Models on HPC

We explore how to optimally deploy several different types of machine-learned surrogate models used in rotorcraft aerodynamics on HPC. We first developed three different rotorcraft models at three different orders of magnitude (2M, 44M, and 212M trainable parameters) to use as test models. Then we developed a benchmark, which we call “smiBench”, that uses synthetic data to test a wide range of alternative configurations to study optimal deployment scenarios. We discovered several different types of optimal deployment scenarios depending on the model size and inference frequency. For most cases, it makes sense to use multiple inference servers, each bound to a GPU with a load balancer distributing the requests across multiple GPUs. We tested three different types of inference server deployments: (1) a custom Flask-based HTTP inference server, (2) TensorFlow Serving with gRPC protocol, and (3) RedisAI server with SmartRedis clients using the RESP protocol. We also tested three different types of load balancing techniques for multi-GPU inferencing: (1) Python concurrent.futures thread pool, (2) HAProxy, and (3) mpi4py. We investigated deployments on both DoD HPCMP’s SCOUT and DoE OLCF’s Summit POWER9 supercomputers, demonstrated the ability to inference a million samples per second using 192 GPUs, and studied multiple scenarios on both Nvidia T4 and V100 GPUs. Moreover, we studied a range of concurrency levels, both on the client-side and the server-side, and provide optimal configuration advice based on the type of deployment. Finally, we provide a simple Python-based framework for benchmarking machine-learned surrogate models using the various inference servers.
The Role of Cloud Computing in Education: Saudi Arabian Study

Education is a crucial factor in ensuring sustainable progress, In particular, for countries with growing economies. Due to resource virtualization, Cloud computing has both the Internet and complex scalability. Both public and private learning institutions should take advantage of the potential benefit of cloud infrastructure to ensure high-quality service regardless of the minimum resources available. Because of its numerous advantages, cloud computing is taking center stage in academia. This research will seek to establish the benefits associated with the use of cloud computing in education. Cloud technology solutions ensure research and development as well as teaching, and its more sustainable and efficient, thereby having a positive impact on the quality of teaching and learning in educational institutions. The goal of this research is to identify the benefits of cloud computing usage in education. Cloud technology solutions make research and development as well as teaching more competitive and effective, thereby having a positive impact on the quality of education.
Computer Image Recognition and Recovery Method for Distorted Underwater Images by Structural Light

Abstract When imaging through water surface, the random fluctuation of sea surface will cause the distortion of the target scene image, so the distorted image needs to be corrected and reconstructed. At present, distortion compensation mainly adopts iterative registration strategy based on image sequences which is difficult to satisfy the real-time observation. This paper presents a correction method based on active imaging of structured light for underwater image. Experimental results show that compared with the traditional iterative algorithm, the proposed algorithm cannot only improve the restoration accuracy, but also greatly shorten the processing time. Experimental test results demonstrate that the proposed algorithm has good recovery results.
Depth estimation from single-shot monocular endoscope image using image domain adaptation and edge-aware depth estimation

We propose a depth estimation method from a single-shot monocular endoscopic image using Lambertian surface translation by domain adaptation and depth estimation using multi-scale edge loss. We employ a two-step estimation process including Lambertian surface translation from unpaired data and depth estimation. The texture and specular reflection on the surface of an organ reduce the accuracy of depth estimations. We apply Lambertian surface translation to an endoscopic image to remove these texture and reflections. Then, we estimate the depth by using a fully convolutional network (FCN). During the training of the FCN, improvement of the object edge similarity between an estimated image and a ground truth depth image is important for getting better results. We introduced a muti-scale edge loss function to improve the accuracy of depth estimation. We quantitatively evaluated the proposed method using real colonoscopic images. The estimated depth values were proportional to the real depth values. Furthermore, we applied the estimated depth images to automated anatomical location identification of colonoscopic images using a convolutional neural network. The identification accuracy of the network improved from 69.2% to 74.1% by using the estimated depth images.