Reviewerly/exHarmony huggingface dataset:
Blockchain Supercharges Digital Well Program towards Cyber-Physical Drilling System

Abstract The oil and gas industry is transforming itself into a data-intensive industry with the addition of artificial engineering intelligence, cognitive computing, machine learning, internet of things and robotics. With the growth and expansion, various exponential technologies can carry the energy industry's transformation forward in the new era of cyber-automation and more so on the cyber-physical -automation. Blockchain technology can drastically streamline the current complex matrix of activities from seismic to production and abonnement. The existing integration and management of various disciplines is very expensive, requires several disciplines that are sometimes disjointed and siloed, and is exceedingly complex in the course of the digital well program and as we receive more and more data. With blockchain as a common technology, firms may more easily handle the many digital procedures, operational tasks, diverse vendors, production schedules, and supply chains that affect how the digital well program and schedules are made. In this paper, the full lifecycle system—which can serve as the lifecycle's template—is discussed. It can be housed on a permissioned blockchain. Blockchain has been discovered to streamline and facilitate open collaboration because to its inherent qualities of trustworthiness and transparent execution of transactions involving numerous parties. By making asset transactions transparent and flexible, it facilitates data sharing among several businesses, providers, and partners, making it simple to connect the real world to the digital one through the use of edge devices and cloud computing. This approach has been discovered to offer crucial characteristics of privacy and auditability while limiting ledger size, in addition to transparency and efficiency. In order to balance the competing demands of privacy and openness, peer identities are concealed from one another while they agree on transaction outcomes. Additionally, data will never be exchanged among blockchain peers without the owners' express permission. Blockchain is a decentralized peer-to-peer digital ledger technology that has a lot of promise for the digital well program. A local peer communicates with an associated anchor peer inside the global network and a common database like Open Subsurface Data Universe in the system, which is built for both intra- and inter-organizational transactions like Open Subsurface Data Universe (OSDU).
Yolov4-Sensitive: Feature sensitive multiscale object detection network

In this paper, we propose the YOLOv4-Sensitive algorithm based on the YOLOv4 algorithm. Firstly, the residual cell structure in the backbone feature extraction network CSPDarknet53 is reconstructed, and a new feature extraction network U-CSPDarknet53 is designed to extract and retain small target feature information at finer granularity. Secondly, MFENet (Multi-receptive Field Extraction Network) network is designed to extract the contextual information of small targets using parallel expansion convolutional branches to alleviate the feature loss problem of SPP network due to pooling operation. Finally, CA attention mechanism is introduced to design a multi-scale feature fusion network CA-PANet to integrate location information into the feature aggregation network and enhance the feature description capability. The algorithm is validated on the PASCAL VOC dataset. the improvement of YOLOv4-Sensitive for small target detection accuracy is more obvious, with an improvement of about 4.02 percentage points. In addition, the inference speed of the algorithm in this paper is 42 frames/second, which improves the small target detection accuracy without losing the inference speed.
Securing Data Over Network with Blockchain and AI

The research proposes a framework called SecNet for ensuring the safety of data over its lifecycle via the Internet at scale.The widespread dispersion of data across digital spaces has rendered authentication and authorization of its usage difficult.The proposed architecture is comprised of three primary elements: In order to generate authentic big data, blockchainbased data exchange with ownership guarantee is necessary.Blockchain technology makes it possible to verify the authenticity of the data and trace it back to its original creator.Data encryption enabled by artificial intelligence: This system has the potential to create a more secure and trustworthy digital ecosystem by developing more nuanced security rules.When used to cyber security, AI has the potential to discover new vulnerabilities and flaws in the system.The effectiveness of AI may be improved if individuals were incentivized to share their data via the establishment of a trustworthy system for exchanging monies in exchange for the supply of a security service.This article compares and contrasts the typical SecNet implementation with some of its alternatives.We also analyze the repercussions on network security and revenue generation.Overcoming the challenges of allowing data interchange through the internet is central to the proposed architecture, which aims to boost AI performance utilizing real-world huge data.
Recognition of handwritten Tibetan numerals based on IoT devices

Internet of Things (IoT) and AI are correlating with each other closer and closer. By combining these two techniques, most of the tasks can be done efficiently. In this circumstance, however, most of the recognition tasks of handwritten Tibetan is still fulfilled manually or using methods like convolutional neural network (CNN), which lowers the efficiency because of their long training time. In order to solve those problems, in this paper, we use portable IoT devices and seek for a traditional method with not only high accuracy but also short training time on the tasks. We use methods that are commonly used in handwriting recognition to recognize handwritten Tibetan numerals and compare not only accuracy but also training time of those methods. Furthermore, we adjust the best method and find the most appropriate setting for handwriting recognition task. We combine AI and IoT and provide an efficient way of recognizing handwritten Tibetan numerals.
Research on similar handwritten character feature extraction algorithm by introducing deformable convolution

Handwritten character recognition belongs to the image feature classification problem. To address the problems that convolutional neural network-based handwritten character feature extraction algorithms have complex structures, long training time and recognition accuracy of similar characters is much lower than the average recognition accuracy, we improve the VGG16 and propose a new character recognition algorithm, which effectively improves the recognition accuracy of similar characters. The improved model reduces the complexity and accelerates the model convergence by simplifying the original convolutional structure, adding Ghost convolution, and batch normalization layer. In addition, we introduce an attention mechanism and deformable convolution to solve the problem of false recognition caused by local feature differences and stroke deformation of similar characters. Experimental results on Chinese character datasets and self-built datasets show that our proposed approach achieves over 97% accuracy in general and 92.82% in similar characters. Compared with the traditional approaches, the training speed is faster, and the similar character recognition effect is improved. The model size is reduced by 80%, achieving lightweight and having important application value for improving the performance of character recognition.
SkillsLab+ - Proof of Concept for Medical Augmented Reality Teaching Application with Haptic Feedback

The classic SkillsLab is a training environment in which medical students can train procedures, examinations, hand movements, therapy, and treatment on anatomically correct models or simulated body parts. This training takes place in a stress-free environment, as there is no responsibility, risk, or time pressure. In addition, detailed explanations and instructions are provided by medical experts. SkillsLab+ is a digitalized version of the classic SkillsLab and currently implemented as proof of concept. The course concept is transferred into an augmented reality application, which currently includes nine stations of approaches to medical teaching. These include, among others, the injection into an arm and clinical abdominal exam. The purpose of this application is to allow medical students to work on the learning content independently at their own pace and even from home. Furthermore, the additional integration of haptic gloves allows students to feel and touch virtual objects. A distinction between different surface structures such as hard bones or soft skin can be made via vibrational and force feedback. The evaluation of a user questionnaire shows that a majority of the students felt supported in the preparation and follow-up of the medical content and can imagine using SkillsLab+ in distance learning and in medical teaching to deepen their knowledge in the medical application field.
The Analysis on the Use of Past Tense Found in the Students' Recount Writing

Errors are common problems faced by foreign language learners during the learning process. The errors are affected by the mother tongue and other conditions. In this case, the problems occurred at the English department students at STKIP PGRI Pacitan. Therefore, the solution should be found by the lecturers by employing qualitative research-type error analysis to reveal the problems. The procedures of this research were: 1) preparation (formulating the research problem; 2) formulating the title, extensive literature survey, and preparing the research design; 3) determining sample design; 4) collecting the data, execution of the project); 5) processing (analysis of data, conclusion), report writing. Moreover, there were 40 students as the subject of this research. To get the data, the researcher took the answer sheets of essay writing of recount text to be analyzed qualitatively. In conformity with the findings, errors based on the use of past form are as the following: past simple 422 errors or 96%, past perfect 13 errors or 2,98%, past continues 1 error or 1% and past perfect continues 0 or 0%. It means that the frequent error made by the students was in using simple past tense and the absence of errors in using past perfect, past continuous, and past perfect continuous. Henceforth, the lecturers should provide more appropriate approaches and methods to solve the problematic errors.
Ghostwriting-Federal Learning Key Technology Research for Big Data Privacy Protection

The research on the key technology of federation learning for big data privacy protection is proposed. Using federal learning algorithm, the attack noise parameters of big data are analyzed to improve the generalization ability of the model. The privacy-preserving data release model is constructed by using anonymization means to cut off the association between sensitive information and individual identity and to improve data encryption. In the experiments, the proposed model is tested for its data protection capability. The analysis of the experimental results shows that the privacy-preserving data distribution model constructed using the proposed method has a high data protection capability.
Neologisms Recognition Technology Based on a variety of Statistical Indicators

In the era of big data, new technical vocabulary is constantly emerging. The construction of a knowledge graph in the recruitment domain requires accurate identification of these neologisms. However, manually constructing a new vocabulary is resource-intensive. Based on the existing research, this paper proposes a neologism discovery technique. Through the frequency, mutual information, left and right information entropy and inverse document frequency four statistical indicators. Build a screening index system to screen out neologisms that meet the rules. Automate computer discovery of neologisms and the construction of vocabulary. Finally, through the recruitment text of the ‘‘ 51job’’ platform, the effectiveness and feasibility of neologism discovery technology are verified.
The reality of school management's practice of transformational leadership dimensions from the teachers' point of view in Al-Mahra Governorate

Abstract: This study aimed to identify the degree of school administration’s practice of transformational leadership dimensions: (ideal influence, inspiring influence, intellectual influence, individual consideration, and empowerment). The researchers used the descriptive approach, by designing a questionnaire that was distributed to a random sample of (162) teachers from Basic Education Schools in Al-Mahra governorate during the academic year 2019-2020 AD. The results revealed that the degree of school administration practice of the five transformational leadership dimensions was high, with the average response of the sample on the questionnaire as a whole (4.06), and a percentage (81.26%). The sample response has varied on the dimensions of transformational leadership, where the inspirational influence dimension came first, with an average (4.12), followed by the intellectual influence dimension, with an average (4.11), then the ideal influence dimension, with an average (4.07), followed by the individual consideration dimension, with an average (4.02). ), And in the last order came the empowerment dimension, with an average of (3.83). Thus there were no statistically significant differences between the sample's response averages on the transformational leadership dimensions due to variables (gender, academic qualification, and years of experience). Based on the results of the study, the researchers presented a set of recommendations and suggestions to activate the school administration's practice of transformational leadership dimensions in Al-Mahra governorate and all public education schools in the Republic of Yemen. ملخص: هدفت الدراسة إلى التعرف على درجة ممارسة الإدارة المدرسية لأبعاد القيادة التحويلية: (التأثير المثالي، التأثير الإلهامي، التأثير الفكري، الاعتبار الفردي، التمكين) واستخدم الباحثون المنهج الوصفي، من خلال تصميم استبانة تم توزيعها على عينة عشوائية بلغ عددها (162) معلماً ومعلمة من مدارس التعليم الأساسي بمحافظة المهرة خلال العام الدراسي 2019-2020م، وكشفت النتائج أن درجة ممارسة الإدارة المدرسية لأبعاد القيادة التحويلية الخمسة كانت بدرجة عالية، حيث بلغ متوسط استجابة العينة على الاستبانة ككل (4.06)، وبنسبة مئوية (%81.26)، وقد تفاوتت استجابة العينة على أبعاد القيادة التحويلية، حيث حل في الترتيب الأول بُعْدُ التأثير الإلهامي، بمتوسط (4.12)، يليه في الترتيب الثاني بُعْدُ التأثير الفكري، بمتوسط (4.11)، ثم بُعْدُ التأثير المثالي، بمتوسط (4.07)، يليه بُعْدُ الاعتبار الفردي، بمتوسط (4.02)، وفي الترتيب الأخير جاء بُعْدُ التمكين، بمتوسط (3.83)، كما لم يكن هناك فروق ذات دلالة إحصائية بين متوسطات استجابة العينة على أبعاد القيادة التحويلية تعزى لمتغيرات (الجنس، والمؤهل الدراسي، وسنوات الخبرة). واستنادا لنتائج الدراسة قدم الباحثون جملة من التوصيات والمقترحات لتفعيل ممارسة الإدارة المدرسية لأبعاد القيادة التحويلية في محافظة المهرة وعموم مدارس الجمهورية اليمنية.
Future-guided offline imitation learning for long action sequences via video interpolation and future-trajectory prediction

Imitation learning is the task of optimizing a policy network to imitate demonstrations. The policy sequentially predicts each time-step action based on observations. The observations are videos in this study. Offline imitation learning only uses a pre-collected demonstration dataset to train the policy and thus is applicable even in scenarios where interaction with the environment, or exploration, is impractical in the training stage. One of the problems of offline imitation learning is error accumulation. Since the prediction error is accumulated along time-steps, the distribution of future observations is gradually shifted from the pre-collected dataset. One of the solutions is utilizing a forward dynamics model that predicts the observation of the shifted distribution from past observations and actions. This forward dynamics model enables the optimization of the policy to minimize the accumulated error. However, there are still the following two problems: (1) Since predicting future observations is an extrapolation problem, it is difficult to predict observations correctly because of the uncertainty. (2) Since the policy and the forward dynamics model are used recurrently, optimizing the policy with long sequences takes a vast amount of memory. In this paper, we resolve these problems using (1) a new forward dynamics model that predicts the observation as interpolation and (2) auxiliary learning of future-trajectory images that makes a policy learn the ideal future state without recurrent learning. We demonstrate that our proposed methods reduce the accumulated error and improve the task success rate in various robot manipulation tasks in a simulator.
Artificial Intelligence and Morality: A Social Responsibility

Both the globe and technology are growing more quickly than ever. Artificial intelligence's design and algorithm are being called into question as its deployment becomes more widespread, raising moral and ethical issues. We use artificial intelligence in a variety of industries to improve skill, service, and performance. Hence, it has both proponents and opponents. AI uses a given collection of data to derive action or knowledge. There is therefore always a chance that it will contain some inaccurate information. Since artificial intelligence is created by scientists and engineers, it will always present issues with accountability, responsibility, and system reliability. There is great potential for economic development, societal advancement, and improved human security and safety thanks to artificial intelligence.
Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model

Dynamic magnetic resonance imaging has emerged as a powerful modality for investigating upper-airway function during speech production. Analyzing the changes in the vocal tract airspace, including the position of soft-tissue articulators (e.g., the tongue and velum), enhances our understanding of speech production. The advent of various fast speech MRI protocols based on sparse sampling and constrained reconstruction has led to the creation of dynamic speech MRI datasets on the order of 80-100 image frames/second. In this paper, we propose a stacked transfer learning U-NET model to segment the deforming vocal tract in 2D mid-sagittal slices of dynamic speech MRI. Our approach leverages (a) low- and mid-level features and (b) high-level features. The low- and mid-level features are derived from models pre-trained on labeled open-source brain tumor MR and lung CT datasets, and an in-house airway labeled dataset. The high-level features are derived from labeled protocol-specific MR images. The applicability of our approach to segmenting dynamic datasets is demonstrated in data acquired from three fast speech MRI protocols: Protocol 1: 3 T-based radial acquisition scheme coupled with a non-linear temporal regularizer, where speakers were producing French speech tokens; Protocol 2: 1.5 T-based uniform density spiral acquisition scheme coupled with a temporal finite difference (FD) sparsity regularization, where speakers were producing fluent speech tokens in English, and Protocol 3: 3 T-based variable density spiral acquisition scheme coupled with manifold regularization, where speakers were producing various speech tokens from the International Phonetic Alphabetic (IPA). Segments from our approach were compared to those from an expert human user (a vocologist), and the conventional U-NET model without transfer learning. Segmentations from a second expert human user (a radiologist) were used as ground truth. Evaluations were performed using the quantitative DICE similarity metric, the Hausdorff distance metric, and segmentation count metric. This approach was successfully adapted to different speech MRI protocols with only a handful of protocol-specific images (e.g., of the order of 20 images), and provided accurate segmentations similar to those of an expert human.
Pineal cyst-related sleep disorders – a narrative review

The pineal gland is an endocrine gland, which is responsible for the human circadian rhythm, through the regulation of its hormone – melatonin. The most common pathology of a pineal gland is pineal cyst – its population incidence is estimated as 1-23%, depending on the publication. The most common symptoms of a pineal cyst include headache (87%), visual field defects (54%), nausea/vomiting (34%), and dizziness/vertigo (31%). Among the majority of patients with the asymptomatic pineal cyst, the wait-and-see strategy is proper.&#x0D; According to the newest meta-analyze, the prevalence of sleep disturbances among patients with pineal cyst estimates to be 17%. The results of direct melatonin level measurements in pineal cysts remain unclear. The study focused on sleep disturbances in pediatric patients with pineal cysts and showed a significantly higher score on Sleep Disturbance Scale for Children (SDSC) in the domains of disorders of excessive sleepiness and disorders of initiating and maintaining sleep than the control groups. The impact of lesion on sleep quality correlated with its size.&#x0D; The pineal cyst may be considered a rare, and potentially reversible, cause of sleep disorders. Nevertheless, according to the present reports (often opposite to each other), this thesis and the mechanism of its occurrence need to be further researched.
An effective evaluation measurement for image stitching based on SIFT algorithm

Image stitching is widely used in computer vision where it is often necessary to first manually specify the “left to right” or “right to left” transformation. To solve the problem of evaluating which stitching mode is better, on the basis of studying of SIFT algorithm’s image stitching, this paper calculates the polygons after perspective transformation by analyzing two methods of homography matrices and proposes an measurement that refers to the degree of blurring caused by the oblique angle on the long side of the transformed polygon rectangle as stretching degree. It was used as one of the evaluation index for the quality of image stitching. The experimental results show that this measure can more automatic and robust to choose the better mode of image stitching.
Research on abnormal detection of gas load based on LSTM-WGAN

Traditional anomaly detection methods cannot take into account the influence of external factors. Therefore, it is difficult to obtain an accurate detection effect when performing gas anomaly detection. In this paper, we propose an anomaly detection model based on LSTM-WGAN. The multi-layer LSTM network captures temporal dependencies and introduces a self-attention layer to integrate embedded weather, holidays and other influencing factor data. Embed different features into the WGAN framework and make the detection results more accurate by fused models. This paper conducts experiments on the real gas load data set. The experimental results show that the model in this paper has obvious performance advantages in precision, recall and F1 value compared with traditional time series anomaly detection methods.