Reviewerly/exHarmony huggingface dataset:
Geochemical Artificial Intelligence Tool for Enhanced Water Management

Abstract Produced water from crude oil and gas producing zones is geochemically heterogeneous due to the input from diverse underground sources and/or operation-related fluids. The present work targets developing and implementing an innovative numerical tool to automatically predict the type and provenance of recovered produced water from exploration and production. Machine learning-based tools was assessed for the automated classification of produced water types in oilfields with a differentiation of formation water from operation-related fluids. Multiple algorithms in machine learning were tested on hundreds of training and testing datasets of produced water samples to identify methods with the highest performance rates. A combination of the supervised XGBoost algorithm with semi-supervised self-training methods resulted in the two best-performing algorithms for all metrics (accuracy, precision, recall) with an accuracy above 90%. The developed software allows the automated recognition of representative formation water samples, and its differentiation from drilling or operation-related fluids, such as mud filtrate, completion brine, supply water, condensate water, and mixed water types. The algorithm was successfully tested for the classification of hundreds of produced water samples from different fields and reservoirs to define accurate formation water properties for several wells and lithological units. In general, the majority of produced water samples resulted in being post-testimonies from drilling, workover, and production-related activities. Enhanced water management by processed geochemical data can directly contribute to a potential reduction in drilling lost-time, well cleanup time, and reducing the risk of drilling dry holes. Additionally, well control can be optimized by identifying water inflow zones, and the amount of required bottomhole samples can be reduced by identifying nonrepresentative sampling intervals. Furthermore, geochemical fingerprinting of formation water as dominant reservoir fluid can provide analogous clues on the source rock, migration, trapping, and alteration of hydrocarbons.
Study of Excellent English Teachers' Classroom Feedback in Junior High School under the New Curriculum Reform

In this paper, the video lessons of three excellent junior high school English teachers from the National Education Resources Public Service Platform (NERSP) were selected for the study, and the teacher feedback in their classroom discourse was collected and quantitatively analyzed through classroom observation. The results showed that, in general, the excellent English teachers tended to use positive affirmative feedback in classroom feedback, and tended to choose to guide students' responses and thinking when giving positive negative feedback, and negative feedback was almost never used. It is hoped that these findings will provide positive implications for English teachers' effective use of classroom feedback.
Foundations for Human-AI teaming for self-regulated learning with explainable AI (XAI)

This discussion takes a human-centred perspective of the contributions of the collection. Its papers explore diverse, new uses of AI with rich, multimedia sensor data towards new ways to measure and understand self-regulated learning. This work can contribute to the learning sciences. It can also provide a foundation for future personalised teaching and learning systems with explainable AI (XAI) and learner control. I will discuss the papers from that perspective with a focus on an important form of XAI in education – the Open Learner Models (OLM). When suitably designed, OLMs can empower a learner to: (1) contribute data about themself and their self-regulated learning processes, complementing conventional and multimedia data; (2) scrutinise and control learner data collection and use in AI-based systems and (3) be the controlling partner in AI-teaming that scaffolds their self-regulated learning processes.
Action recognition for sports combined training based on wearable sensor technology and SVM prediction

In the field of sports, coaches have mainly relied on observing the performance of athletes on the spot to formulate suitable training plans for athletes, which has extremely high requirements for the professionalism of coaches. Based on the above requirements, this paper designs a sports action recognition system for sports enthusiasts based on the SVM algorithm optimization model, and for the purpose of verifying the applicability of the system to different sports fields, experiments are carried out on basketball actions and race walking actions. The system uses wearable sensors to capture the motion data of the user, and then analyzes and identifies the user's actions through the SVM algorithm optimization model. By standardizing the user's sports combination training under the system algorithm, the user can improve their training efficiency and reduce the risk of injury. To establish the human body motion model, this paper divides the human skeleton model into five motion branches. The rotation freedom constraints and joint rotation angle range limits are added to the model to ensure the accuracy of the motion analysis. Combining the forward kinematics of the robot and the homogeneous coordinate transformation, the human body joint rotation motion model and the human bone position and posture model are established. In the end, the user can standardize the sports combination training under the system algorithm. In this paper, through the research of wearable sensor technology and sports combined training action recognition, and apply it to practical life, it aims to promote its development and application.
Explore unsupervised exposure correction via illumination component divided guidance

The capturing of images with poor exposures remains a major source of errors in camera-based photography. Under- or over-exposure problems greatly reduce contrast and naturalness, and the disappearance of detailed content affects the understanding of an image. While recent works have built a great deal of learning frameworks to address this problem, they mainly focus on low-light or general image enhancement. Towards this end, we explore an unsupervised exposure correction framework. Our model disentangles the exposure correction into two easier sub-tasks. Different branches focus on different missions, making it easy for the network to solve coupling correction problems. Additionally, a novel attention-based pixel-wise brightness estimation makes it possible for coarse-to-fine mapping adjustment and correction. Experiments on various widely used benchmark datasets show that, by relying on illumination perception and component divided guidance, the proposed network can outperform previous state-of-the-art methods, especially when recovering fine detailed contents.
ABC: Aligning binary centers for single-stage monocular 3D object detection

Precisely perceiving the environment through a 3D perspective is essential and challenging for autonomous navigation and driving. Existing techniques rely on depth from LiDAR data or disparity in stereo vision to solve the poorly presented problem of detecting far-off and occluded objects. This increases structure complexity and computation burden, especially for single-stage systems. We argue that existing well-established detectors have the intrinsic potential to detect full-scene objects, but the extrinsic capabilities are limited by the structure form and optimization. Hence, we propose a double-branch single-stage monocular 3D object detection framework that aligns binary centers of object. Structurally, we construct two symmetrical and independent detectors, respectively using different prediction manners for 3D box parameters. Functionally, two detection heads have different sensitivities for the same object due to disentangling alignment. During the training, the detection heads were trained separately to obtain specific ability and aligned to promote the convergence. At inference, predictions of two branches are filtered via depth-aware non-maximal suppression (NMS) to acquire comprehensive detection results. Extensive experiments demonstrate that the proposed method achieves the state-of-the-art performance in monocular 3D detection on the KITTI-3D benchmark.
Immersion, Presence and Transference: Bringing Story Thinking to Simulation Design

Simulation has been used for training since the 1970's. The ability to use skills gained in one simulation outside of that simulation is called transference. Immersion is an important element of transference. Traditional psychological and simulation research defines immersion via fidelity. Gaming literature approaches immersion from a storytelling perspective, and thus focuses on emotional responses which can be reliably measured by physiological signals. Leveraging this game research enhances the simulation researcher's ability to make performance predictions based on simulated results. In this paper, researchers designed an intentionally stressful simulation environment and then tested different display configurations within both stressful and non-stressful environments. Researchers saw a significant difference in responses to display configurations when tested in a stressful environment.
Evaluation of Edge Detection Algorithms Performance

Analysis and Interpretation of contents and environments is one of the main objectives of the computer vision field. This task is very necessary for various application domains, especially object recognition and its features extraction, and its motion analysis in a 3D environment. For that, the purpose of image edge detection is very essential to separate the object in the image and read its contents. One of these visual data is a combination of edges. Edge extraction is an important step in all processes of identifying and locating objects. In this work, five edge extraction algorithms are studied to find the best and worst performance of these techniques on the digital images of type. tiff and. jpg.
Data Augmentation for Convolutional Neural Network DeepFake Image Detection

We need to develop a technique for better identifying deepfakes because they can distort our perception of reality. This study offers a brand-new forensic technique for spotting falsified facial photos. We made advantage of the Kaggle- provided “real-and - fake- facial-detection” dataset. We are able to distinguish between probable facial alterations based on CNN's design. Thanks to data augmentation approaches, the results exhibit performances that are equivalent to those of previous works. The proposed approach fared better for this binary categorization into fake or real faces than the other cutting-edge studies. Our accuracy is close to 99 percent.
Review of Recent Systems for Detecting and Diagnosing Pronunciation Errors

Nowadays, Computer Aided Language Learning (CALL) frameworks have attracted a lot of attention because of their capability, adaptability, and flexibility in improving people's language skills. The field of Mispronunciation Detection and Diagnosis (MDD) is considered one of the applications of these frameworks and has recently benefited from the rapid innovation spawned in deep learning models and different acoustic, phonetic, and linguistic features. The various manner of combinations of different features and deep learning architectures for different MDD systems require huge amount of annotated textual data to be pretrained and hence achieved good performances. However, providing a large amount of annotated textual data for pre-training is a tedious and time-consuming operation. For this, a pre-trained acoustic model from unannotated data can be a good alternative. In this article, we present different MDD systems categorized according to the pre-training mode: pre-trained approaches without annotated text data and pre-trained approaches with annotated text data. Experimental results, advantages, disadvantages and additional improvements for each method explored for each category are presented and discussed.
Hybrid Smartwatch Multi-factor Authentication

We propose Hybrid Smartwatch Multi-Factor Authenticator (HS-MFA), a system for leveraging a smartwatch as an additional form of user authentication. HS-MFA examines both inherent and subtle intentional gestures as a means of appropriately identifying a user. The system leverages a custom-developed Android Wear OS smartwatch app that records accelerometer sensor data for both user keystrokes and touchscreen interactions using a smartwatch paired with a smartphone. Observed authentication methods include username and password entry on a keyboard, pattern unlocking for smartphones, and PIN entry with a smartphone. Five different pattern matching methods were examined for a total of 96,880 genuine and impostor comparison tests by processing data from 246 unique user samples. The two best performing analysis methods achieved Equal Error Rate (EER) values between 0 and 67%, with an average of 28%, across the observed three axes of accelerometer sensor data captured through smartwatch. With notable accuracy and ease of use, this method would be a novel and intuitive multifactor authentication system for regular users as well as severely vision-impaired users to provide security for their digital assets in cyberspace.
Elderly People Care Support System Under COVID-19 Environment Using Advanced ICT/AI Data Science and Its Field Trials

During the pandemic due to COVID-19, healthcare management for elderly people in care centers has been facing difficulty in judgment acceptance of center users suffering from COVID-19 or not because of a limited number of caregivers in a cost-effective manner. This paper introduces a developed system for dependable healthcare support during the pandemic using advanced ICT and AI data science such as an integrated ICT platform among variable vital sensors connected with a wireless body area network(BAN) and data server via a cloud network, and data analysis for the judgement based on AI data science using a sophisticated machine learning algorithm. This paper investigates feasibility of the developed system by various field trials in practical healthcare service centers sponsored by medical organization and IT service company.
Co-design of Transimpedance Amplifiers and Photonic Quantum Random Number Generators

We investigate the photonic and electronic elements of a quantum random number generator based on vacuum state homodyne detection. We account for photodiode responsivity, optical beam splitting ratio, and optical path length in balancing the detector's photocurrents. Finally, the quantum to classical noise ratio is increased through the design of an operational amplifier, including its common mode rejection ratio, bandwidth, gain, and classical noise. A quantum to classical noise ratio of. 064 is achieved and the result is analyzed for improvement in future work.
Vulnerability of Prime Based Cryptosystem

It is practically impossible to break a cryptosystem like RSA if the key is large enough with the prime numbers are randomly distributed. Recent research shows prime numbers show a certain distribution pattern. This makes it practically possible to break such systems by solving the factoring problem. By computing the distribution probabilities and finite field incremental changes of connected prime numbers, we have validated Robert J. Lemke Oliver and Kannan Soundararajan's study findings on trailing prime numbers. We proposed and developed ranked selection algorithm on prime number distribution. We developed a number of applications, including proposing one for fast factoring the product of two large prime numbers, also known as the factoring problem.
Fastlane to quantum advantage through co‐design

Abstract Quantum computers promise to solve certain advanced computational tasks with higher computational speed, higher accuracy, and less energy consumption. To be useful for real‐world applications, traditional quantum algorithms typically require significantly more sophisticated quantum computers than are available today. Co‐designing application‐specific quantum integrated circuits (ASICs) is one avenue being explored by quantum computing scientists and engineers to achieve quantum advantage in the near future. Currently, the co‐design paradigm is most prominently pursued in the field of superconducting quantum systems, which operate in the microwave regime. Nevertheless, due to the universality of the underlying light‐matter coupling mechanisms, insights taken from the field of photonics could provide valuable contributions to the advancement of co‐designed quantum computers.
Hybrid Authentification System of Face Mask Detection Using CNN-based on Lightweight Cryptography and Blockchain Technology

COVID-19 is currently wreaking havoc around the world. A severe acute respiratory pattern causes acute COVID-19 disease (SARS-CoV-2). Face masks have become very popular in our daily life. These face shields have raised concerns about the accuracy of facial recognition technology used to check hand/student attendance and unlock phones. Many companies use facial recognition as an identification system and have created internal databases to implement such a system. Security and authentication are critical in these situations, as an untrusted hostile device can compromise an IoT system. In this study, we proposed a new method based on a fusion algorithm that merges password and image features to increase the randomness and security of the pattern structure and developed a combination of cryptography and blockchain technology. This technique ensures that the image verification system is protected when accepting standards for confidentiality and security information by transferring the model from the Acess point to the destination.
Detection and Recognition of Objects in Real Time Using an Intelligent Camera by a Delta Robot

Developing robots and making them autonomous based on artificial intelligence through computer vision to distinguish objects and make decisions in real time is one of the important things in the field of industry to improve the efficiency of industrial robots and make them smarter. In this article, we have developed an intelligent algorithm in Python language that recognizes 2D objects and sends the information to the robot. This data concerns the size, color, shape and position in the surface. The program can recognize multiple shapes in a single image. All the experiments carried out in this research have given very good results with an error percentage of 0.2 % and thanks to the smart camera connected to the Delta robot. The experimental results obtained show the effectiveness and validity of our work. Numerous experiments demonstrate the effectiveness of the model. The video is available here: https://sites.google.com/view/dalta-smart-camera/accueil
A scrolling performance model based on two-dimensional touch peephole interactions

In multi-touch interfaces, scrolling is a common interactive task. Scrolling performance can help users search and browse content initially off-screen. To evaluate this interaction task by performing analyses and making model assumptions about the interaction process, we divided it into two phases: the search phase and the pointing phase. As a result, quantitative modelling was developed, and two scrolling modes with and without distance feedback were considered. The results of the controlled experiments verified our four proposed mathematical hypotheses of the interaction process and indicated that our model achieved a good fit with and without distance feedback. Our work provides a theoretical foundation for modelling sophisticated scrolling actions and suggestions for user interface designs related to scrolling performance.
Sequential unsharp measurement of photon polarization

We propose a general experimental scheme based on binary trees of partially polarizing beam splitters (PPBSs) for realizing sequential unsharp measurements of photon polarization. The sharpnesses and the bases of the particular photon polarization measurements can be chosen arbitrarily by using corresponding PPBSs and phase plates in the setup. In the limit of low sharpnesses the scheme can realize sequential weak measurements, too. We develop a general formalism for describing sequential unsharp measurements of photon polarization in which the particular unsharp measurements are characterized by appropriate measurement operators. We show that a straightforward experimental realization of this model is the proposed scheme. In this formalism the output polarization states after the sequential measurement and any correlation functions characterizing the measurement results can be easily calculated. Our model can be used for analyzing the consequences of applying postselection and reselection in the measurement. We derive the anomalous mean value for an unsharp polarization measurement with postselection and the anomalous second-order correlation function for the sequential unsharp measurement of photon polarization with reselection. We show that these anomalies can be easily measured using the proposed scheme.
Using a Digital Twin of Hybrid Energy Complexes to Optimize Their Parameters and Operation Modes

The aim of the work is to develop a universal technique that will make it possible to determine -- both quickly and with high accuracy -- the optimal composition, parameters and operation modes of hybrid energy complexes containing power plants that use renewable energy sources and energy storage systems from the viewpoint of ensuring safe operation and guaranteed electricity and heat supply to isolated consumers and hard-to-reach areas. The article shows the effectiveness of research and development of recommendations on the choice of parameters and operation modes of hybrid energy complexes with a heterogeneous composition of generating plants and a high dependence on natural uncertain factors at all stages of the life cycle using the developed universal digital twin, which was verified on the example of real energy complexes with miscellaneous composition of equipment.
Impact of incidental visualizations on primary tasks

Incidental visualizations are meant to be seen at-a-glance, on-the-go, and during short exposure times. They will always appear side-by-side with an ongoing primary task while providing ancillary information relevant to those tasks. They differ from glanceable visualizations because looking at them is never their major focus, and they differ from ambient visualizations because they are not embedded in the environment, but appear when needed. However, unlike glanceable and ambient visualizations that have been studied in the past, incidental visualizations have yet to be explored in-depth. In particular, it is still not clear what is their impact on the users’ performance of primary tasks. Therefore, we conducted an empirical online between-subjects user study where participants had to play a maze game as their primary task. Their goal was to complete several mazes as quickly as possible to maximize their score. This game was chosen to be a cognitively demanding task, bound to be significantly affected if incidental visualizations have a meaningful impact. At the same time, they had to answer a question that appeared while playing, regarding the path followed so far. Then, for half the participants, an incidental visualization was shown for a short period while playing, containing information useful for answering the question. We analyzed various metrics to understand how the maze performance was impacted by the incidental visualization. Additionally, we aimed to understand if working memory would influence how the maze was played and how visualizations were perceived. We concluded that incidental visualizations of the type used in this study do not disrupt people while they played the maze as their primary task. Furthermore, our results strongly suggested that the information conveyed by the visualization improved their performance in answering the question. Finally, working memory had no impact on the participants’ results.