Reviewerly/exHarmony huggingface dataset:
Successful Application of Fishable and Sectional ESS Completion Technology for Sand Control _ A Case History in SHAD Field

Abstract SHAD oilfield, located in one of the fields with the highest onshore production in the Middle East; more than 70% of its production wells are flowing wells, with an average single well production capacity of more than 3000 bbl/d. Its main oil production formation is loose sandstone reservoir. The completion style of its production wells are generally completed casing perforation, and sand production is obvious after a period of exploitation. In the process of production, sand cleaning and sand discharging are often needed in the perforated section of oil well; however, after 3-5 years of production, serious sand burial still forces developers to give up the original production formation. In addition to the problem of sand production, there are many complex layers, some of them are oil-water layer, and the increase of water in the liquid production gradually affects the well flowing. Fishable and sectional expandable sand screen (ESS) completion technology is developed to deal with sand production and the increase of water production. The upper of the original production formation of the old wells is as the new production formation. The new formation is divided into several sections due to different physical properties. Perforation is carried out at the same time for all sections. After this, each the ESS completion string is run into each section. The top sealing hanger and the bottom packer will seal each ends of the annular between ESS and perforating casing, thus to form independent production section. In later process of production, plugging tools is run for sections if needed, so as to realize selective open/ close of different sections. Special fishing tools are developed, combined with the mechanical plastic characteristics of ESS, which can realize the fishing of the completion string. This solves the problem of fishing long string, and provides good conditions for subsequent operations. The research results show that two good results are obtained. One is that arranging several sections of ESS completion strings in the old well bore gives full play to the characteristics of ESS with strong flow capacity, good sand control effect, and is suitable for sand control in high-yield wells. The other is that selective open/close is realized for different sections which meets the sand control and production needs of in SHAD oilfield. The research results have applied to 9 wells in SHAD oilfield, and the success rate is 100%. Among them, one single well is divided into three sections, and the production is stable, the sand production is near to 0, and the sand control effect is significant. The successful application of this technology fills up the gap of multi-stage ESS completion in the Middle East.
OPAC:Catalogue of Modern Library

OPAC stands for “Open Public Access Catalogue”. The online public access catalogue depicts the online library database of resources like books, journals, newspapers, e-books, etc. The students can get access to any books as well as e-content from anywhere &amp; at any time with the OPAC .All they need is to search for keywords such as – name of the book, title, author’s name, volume number, and much more. Thus, OPAC can contribute to students’ success in the long run by helping them carry on their e-learning journey. The purpose of OPAC or the Online Public Access Catalogue to make the digital resources search faster &amp; easier for the students by offering a digital library catalogue
HUBUNGAN KOMPETENSI SOSIAL DENGAN PERILAKU CYBERBULLYING

AbstractCyberbullying behavior is behavior that threatens or oppresses someone who will have a bad impact on the victim through their actions through social media. Social competence is thought to be a factor that influences the occurrence of cyberbullying behavior. The study aims to analyze the relationship between social competence and cyberbullying behavior. The population in this study were students of the Muamalah Study Program at the State Islamic University of Raden Intan Lampung batch 2018 totaling 290 students. Researchers used quantitative research methods with purposive sampling and screening techniques first so that the sample in this study was 73 students. Data collection techniques in this study used a psychological scale including the cyberbullying behavior scale, and social competence. The cyberbullying behavior scale is 38 items (α = 0.963), on social competence there are 31 items (α = 0.901). The analysis technique used is a multiple regression analysis technique assisted by the SPSS 21.0 for windows program. The data analyzed showed the following results: 1. Rx1.2.3-y = 0.911 F = 171.458 with a significance of p = 0.000 (p 0.01). The social competence and parental autonomy support provided an effective contribution of 83% to the dependent variable and 17% generated by other variables outside the study. 2. rx1-y = -0.803 and p = 0.000 (p 0.01) indicates that there is a significant negative relationship between social competence and cyberbullying behavior in adolescents. 3. rX2-y = -0.833. Keywords: Cyberbullying Behavior, Social Competence, Media
Three‐stage auction scheme for computation offloading on mobile blockchain with edge computing

Summary Blockchain has been applied in wide range of fields to guarantee security. However, it has been very challenging for blockchain to flourish in mobile environment with limited resources. Existing studies mainly assume that single mobile user can buy the whole resources from edge servers in mobile blockchain. This paper formulates the problem of maximizing the social welfare for computation offloading in mobile blockchain. A three‐stage auction scheme with approximation ratio of based on group‐buying mechanism is proposed to allocate edge server resources for mobile blockchain applications. In the first stage, the miners are divided into groups, and a Vickrey–Clarke–Groves based auction is proposed to determine the bid of each group for each edge server. In the second stage, a matching algorithm is proposed to match edge servers and Access Points for maximizing the profit of edge servers. In the third stage, the edge server resources are allocated to mobile users for mining base on the results in the above stages. We prove that our auction scheme guarantees truthfulness, individual rationality and budget balance. Simulation results show that, the social welfare of our scheme is improved by 33.78%, 21.84%, 19.69%, and 6.69% for 1000 miners, compared with the existing works.
High-precision visual imaging model and calibration method for multi-depth-of-field targets

In the field of high-precision large-depth 3D reconstruction, some bottlenecks exist in the improvement of visual measurement accuracy. This is because traditional vision measurement methods simplify the imaging model of a fixed-focus camera to a pinhole imaging model with fixed parameters accompanied by various distortion information. In fact, according to the optical properties of camera imaging, the position of the optical center in the pinhole imaging model varies with the depth of the imaging target. In this paper, in order to achieve the objective of improving visual measurement accuracy, a high-precision visual imaging model for multi-depth-of-field targets and the corresponding camera parameters calibration method are proposed based on the optical properties of camera imaging. When using this imaging model for 3D measurements, different pixel points in the image use different camera internal parameters depending on the depth of the imaging target, thus enabling highly accurate 3D reconstruction. The experimental results show that the imaging model and calibration method proposed in this paper greatly improve the accuracy and robustness of visual 3D measurement and have a promising future in the field of large-depth 3D reconstruction.
Self-discriminative cycle generative adversarial networks for face image translation

Face cross-domain translation aims at mapping face images from one image domain to another. Common face image translation tasks include face photo-sketch and face photo-APDrawing cross-domain translation, which can be widely applied in real-world scenarios, such as criminal investigation, movie production, and digital entertainment. However, due to the limited face image pairs and the great gap of color and texture between different domains, face image cross-domain translation still faces many challenges. Existing methods usually produce blurring, artifacts, and structural distortion, leading to poor visualization quality. To tackle this problem, we propose a self-discriminative cycle generative adversarial network, in which the generator adopts an encoder-decoder structure and the corresponding discriminator is the encoder of the other generator in the reverse direction.In the self-discriminative manner, the encoder (i.e., discriminator) cleverly incorporates “True/False" semantic information and the sensitivity to pixel-level information, thereby enhancing the robustness and generalization ability of the generative model.Besides, we propose a novel omni-directional pixel-gradient loss. Thedesigned convolution kernel calculates the gradients of all directions around each pixel to extract the gradient information.Our model is motivated to effectively learn the continuous inter-pixel variation pattern by constraining the gradient information of generated images and ground-truth images to be consistent. The omni-directional pixel-gradient loss can be flexibly applied to other generative models and improve their performance.Extensive experiments show that the proposed framework can produce advanced results on the paired face photo-sketch datasets (CUFS, CUFSF) and the photo-APDrawing dataset (APDrawing). We further demonstrate the strong generalization ability of our model on real-world data and the excellent performance on unpaired face datasets.
Chaff identification method based on Range‐Doppler imaging feature

Chaff is widely used in electronic countermeasures as an effective passive jamming method. This study proposes a chaff identification method that integrates the distribution of distance, Doppler frequency, and power in Range-Doppler imaging. The traditional radar signal processing method estimates the distance and Doppler frequency, and the mean-shift algorithm is combined to complete the clustering after the constant false alarm detection. Then, the target and chaff cloud models are modelled in three dimensions: radar cross-section, range, and Doppler frequency. Using the Neyman–Pearson criterion, three chaff likelihood ratio test detectors are designed based on these assumptions. The three detectors can effectively identify the chaff, and then the theoretical detection probability and influencing factors are analysed. At the same time, the Kullback–Leibler divergence is used to describe the distribution difference between the data to be detected and the theoretical target and chaff. Besides, the different classifiers are used to identify the features of the differences in Kullback–Leibler divergence in three dimensions. Finally, the real-life chaff datasets verified the excellent recognition rate of the authors’ method for the chaff.
A Motion Planning Algorithm for Live Working Manipulator Integrating PSO and Reinforcement Learning Driven by Model and Data

To solve the motion planning of the live working manipulator, this research proposes a hybrid data-model–driven algorithm called the P-SAC algorithm. In the model-driven part, to avoid obstacles and make the trajectory as smooth as possible, we designed the trajectory model of the sextic polynomial and used the PSO algorithm to optimize the parameters of the trajectory model. The data generated by the model-driven part are then passed into the replay buffer to pre-train the agent. Meanwhile, to guide the manipulator in reaching the target point, we propose a reward function design based on region guidance. The experimental results show that the P-SAC algorithm can reduce unnecessary exploration of reinforcement learning and can improve the learning ability of the model-driven algorithm for the environment.
A Scrum-Based New Product Introduction (NPI) in Contract Manufacturing

Recent literature review shows an increasing number of new product introduction (NPI) projects realized in manufacturing sectors. The study implemented NPI using Scrum, a highly regulated team-based framework to complete a project progressively. Scrum is a popular framework adapted from the agile methodology to develop complex products and systems. The case study involved contract manufacturing (CM), and the customer actively participates in NPI and contributes to decision-making. Scrum successfully broke down, prioritized, and performed project tasks in successive cycles. The case study demonstrated the potential of applying Scrum in NPI to ensure the adherence of customer requirements, especially the timely fulfilment of the milestones.
The Study on Applying Cooperative Learning Approach to Analyze the Effectiveness of a Labor Education Course in Higher Education

The study aims to explore the learning effectiveness of labor education courses affected by cooperative learning teaching method in higher education.The target population was students at a university in Taiwan who were enrolled in a course on "Industrial Relations and Workplace Ethics".The study incorporated a cooperative group learning model, collected data on teaching and learning experiences, and conducted a study on teaching and learning in order to construct a labor education program with good teaching quality and learning effectiveness.The results of the study showed that students' learning engagement, motivation and attitude, cooperative skills and peer interaction, teacher-student relationship, and labor knowledge improved significantly after the adoption of cooperative group learning.Therefore, cooperative group learning is a good teaching method for university courses.At the end of this study, we propose specific recommendations based on the study results for teachers, schools, and future research.
Annotating dialogue acts in speech data

Abstract The aims of this paper are to detect the most problematic issues related to dialogue act annotation in speech corpora and to define basic categories of dialogue acts. I critically examine and test generic schemes that represent different lines of dialogue act annotation: AMI, DART, ISO 24617–2 and SWBD-DAMSL. It is found that the most problematic issues regarding dialogue act annotation are related to the distinction between the semantic and pragmatic meanings of utterances, the annotation of metadiscourse, and the adequacy and informativeness of the tagset. The identified basic dialogue act categories are information providing, information seeking, actions, social acts and metadiscourse. The findings help improve dialogue act annotation.
The Efficacy and Real-Time Performance of Refraction Networking

As the global number of internet users grows it becomes an increasingly important medium for communication and the dissemination of information. This has led to a corresponding rise in internet censorship. Most current censorship circumvention methods relay traffic via a host which is susceptible to address-based blocking. Refraction networking seeks to overcome this problem through stations that intercept traffic at the network level. This chapter evaluates current approaches to refraction networking, classifies them according to their architecture, and presents a comparison of their security properties. It finds there are still barriers to wider scale adoption, including performance issues that may impact real-time communication. The analysis shows that more research is needed to determine the viability of refraction networking in a production environment.
High-Speed Chip-based Measurement-Device-Independent Quantum Key Distribution with Double-Scanning Method

Photonic integration offers a practical solution for wider deployment of quantum secure communication. Based on carrier-depletion modulators (CDMs) in silicon photonics, we design and fabricate high-speed transmitter chips and demonstrate its capability to implement measurement-device-independent quantum key distribution (MDI-QKD). In the proof-of-concept experiment, the quantum bit error rate (QBER) is as low as 0.85% in Z basis and 25.5% in X basis, respectively. With a double-scanning method, the optimized system shows a key rate per pulse of 1.65×10 −4 over an emulated attenuation of 10 dB (corresponding to 50-km standard fiber) with 80% single photon detection efficiency.
Gesture Controlled Mechanism of Robot Using Ardiuno

The phrase IoT, or Internet of Things, refers to the ability of real-world objects to be connected to the Internet.Elements are connected to the internet so that they can see the area around them with the least human interaction.The Internet of Goods brings together the digital and physical worlds to make our surroundings smarter and more responsive.The main purpose of this design is to operate the vehicle with simple hand movements.With the use of an accelerometer, the movements of the mortal hand will be tasted.For wireless connectivity, this system uses an additional RF transceiver module.Models can be built and required work can be done.Therefore, the proposed approach will benefit those working in hazardous environments and help them avoid pitfalls.. Robots are increasingly being used and used in industry to replace humans, especially when performing dangerous and dangerous activities.The hand gesture control bus is a type of robot that can perform complex tasks autonomously or under human supervision.They can be used in a variety of areas such as entrepreneurship, research, services and healthcare.Treatment of concentrated toxins, treatment of fatal illnesses such as Ebola and Nimbus, mitigation of disasters, and transportation of large industrial products can be dangerous to the mortal.
The effect of co-opinion on the cocitation-based information retrieval systems’ effectiveness evaluated by semantic similarity

The co-opinionatedness measure, that is, the similarity of cociting documents in their opinions about their cocited articles, has been recently proposed. The present study uses a wider range of baselines and benchmarks to investigate the measure’s effectiveness in retrieval ranking that was previously confirmed in a pilot study. A test collection was built including 30 seed documents and their 4702 cocited articles. Their citances and full-texts were analysed using natural language processing (NLP) and opinion mining techniques. Cocitation values, syntactical similarity and contexts similarity were used as baselines. The distributional semantic similarity and the linear and hierarchical Medical Subject Headings (MeSH) similarities served as benchmarks to evaluate the effect of the co-opinionatedness as a boosting factor on the performance of the baselines. The improvements in the rankings were measured by normalised discounted cumulative gain (nDCG). According to the findings, there existed significant differences between the nDCG mean values obtained before and after weighting the baselines by the co-opinionatedness measures. The results of the generalisability study corroborated the reliability and generalisability of the systems. Accordingly, the similarity in the opinions of the cociting papers towards their cocited articles can explain the cocitation relation in the scientific papers network and can be effectively utilised for improving the results of the cocitation-based retrieval systems.
Model selection of Gaussian mixture process and its application

In this article, new penalized likelihood methods are proposed for model selection of Gaussian mixture process. Our methods integrate functional principal component analysis, kernel density regression, and penalized estimation, which can be carried out by EM algorithms. Component selection and parameter estimation are conducted simultaneously. Monte Carlo simulation shows that our methods require less computation and have accurate estimation even with not well separated data. The methods are further applied to a supermarket customer flow data to reveal some interesting patterns of shopping behavior.
Migrating Data and Code

When you are migrating a database to the cloud, you will want to give careful consideration to the code and data you are migrating. As you are already aware, we pay for everything we use within the cloud, so migrating our technical debt or unneeded components is going to cost, sometimes quite a lot. In fact, it is not uncommon at all for the use of legacy data types, outdated T-SQL, or things like the use of cross-database queries to be the determining factor between migrating to a VM in the cloud (IaaS) or a PaaS offering like Azure SQL or Amazon RDS. That can have a large impact on the ultimate cost of the migration. Last but not least, we also need to be sure that the code we are moving is supported on the service we are migrating to and does not have compatibility issues or is needlessly wasteful with billable resources.
Node classifications with DjCaNE: Disjoint content and network embedding

Machine learning approaches have become a crucial tool in graph analysis. Despite the accurate results of the existing approaches, most of them are not scalable enough to be used in real-world problems. Networks provide two different kinds of information, nodes contents and nodes relations (network structure). Training deep graph neural networks (GNN) over large-scale graphs is challenging due to the limitation of the message passing framework. Graph Convolutional Networks (GCN) work on all node neighbours at once. Furthermore, it is usual to transform node features with a deep neural network before the GC operation. Therefore, the deep transform operation may apply up to hundreds of times for each target node which is heavy computation and hard to batch. This paper presents an abstract framework with two embedding components, the first component embeds node relations, and the second one embeds node contents. The model makes predictions by aggregating these embeddings through a combination component. The presented approach limits the deep transform only to the target node and uses random walk-based embedding instead of the GC operator to reduce the cost. The main goal of the proposed approach is to provide a light framework for the task. To this aim, node relations are embedded based on node neighbourhood structure by a biased variant of the DeepWalk model, called GuidedWalk, and an autoencoder embeds node contents. The experimental results on three well-known datasets show the superiority of the proposed model compared to the state-of-the-art GraphSAGE and TADW models with less computational complexity. On the Citeseer, Cora, and PubMed datasets, the model has achieved 3.23%, 0.88%, and 7.63% improvement in Macro-F1 and 3.25%, 0.7%, and 6.34% improvement in Micro-F1, respectively. Although GNNs are state-of-the-art models, considering node content is their main advantage. This paper shows that even a simple integration of node content to available random walk-based methods improves their performance up to GCNs without increasing the complexity.
Did you make a mistake?

No one likes to talk about their mistakes, especially during a job interview when they are trying to make a good impression. But a question about past mistakes is one of the most commonly asked because it tells the potential employer a lot about you. So, how can you talk about your failures in a way that ensures your success during an interview? Prepare a real example. Since you know this question is likely, you can be prepared. While you don’t want to sidestep the question, you also don’t want to say “Oh, I have so many failures to choose from!” Interviewers know you aren’t perfect, but they want to know that you are qualified, competent, and able to learn and get better over time. Think back on your professional history and find an example of a time when things did not work out as you intended but had a