Miracle-dz/UniFAR-FTUs huggingface dataset:
A Casing Damage Prediction Method Based on Principal Component Analysis and Gradient Boosting Decision Tree Algorithm

The examination and prevention of casing damage is an important work in oil and gas field development projects. Casing damage will directly influence the production of oil and gas, the effect of water injection and the life cycle of oil, gas and water well. With the number of casing damage wells increasing year by year in each oil field, inspections and precautions of casing damage status have become more and more important during development of the filed. With the development of big data technologies, we can predict the casing damage based on historical and real-time data, and optimize the maintenance intervals of casing. In this paper, we proposed a casing damage prediction method based on principal component analysis (PCA) and gradient boosting decision tree (GBDT) algorithm. Building a three-nodes Spark big data platform, we tested our proposed method on a dataset in an oil-field in mid-east China. Firstly, based on data analysis and expertise, selected 10 parameters which affect the casing damage most, including casing outside diameter, wall thickness, perforation density etc. Secondly, using PCA to reduce the dimension of parameters. Thirdly, building the algorithm model of casing damage risk assessment by GBDT, training and optimizing the model parameters. Fourthly, using the proposed model for casing damage prediction. Using precision and AUC(Area Under ROC Curve) to evaluate the proposed method, and compared with traditional methods, including decision tree, logistic regression and Naïve Bayes, the experiment results show that compared with traditional methods, the proposed method gain 86.3% precision and AUC is 1, both higher than traditional methods, which means it has better prediction precision and performance. The proposed method also can apply to predict the probability of a normal well becoming a casing damage well, and finally can provide the decision support for on-site operations. The proposed method using data-driven idea to provide the decision support for oilfield operations, and lay the foundation for the construction of intelligence oilfield.
Comparative Assessment of Optical Techniques for Semi-Invasive SRAM Data Read-out on an MSP430 Microcontroller

This paper compares the three major semi-invasive optical approaches, Photon Emission (PE), Thermal Laser Stimulation (TLS) and Electro-Optical Frequency Mapping (EOFM) for contactless static random access memory (SRAM) content read-out on a commercial microcontroller. Advantages and disadvantages of these techniques are evaluated by applying those techniques on a 1 KB SRAM in an MSP430 microcontroller. It is demonstrated that successful read out depends strongly on the core voltage parameters for each technique. For PE, better SNR and shorter integration time are to be achieved by using the highest nominal core voltage. In TLS measurements, the core voltage needs to be externally applied via a current amplifier with a bias voltage slightly above nominal. EOFM can use nominal core voltages again; however, a modulation needs to be applied. The amplitude of the modulated supply voltage signal has a strong effect on the quality of the signal. Semi-invasive read out of the memory content is necessary in order to remotely understand the organization of memory, which finds applications in hardware and software security evaluation, reverse engineering, defect localization, failure analysis, chip testing and debugging.
A Unidirectional Beam-Scanning Antenna Excited by Corrugated Metal–Insulator–Metal Ground Supported Spoof Surface Plasmon Polaritons

A unidirectional beam-scanning antenna is proposed. The antenna is constructed by placing a series of circular radiating elements on a 2-mm dielectric slab backed by a corrugated metal–insulator–metal (CMIM) ground. The CMIM structure is introduced not only as a ground but also as a waveguide for spoof surface plasmon polaritons (SSPPs). The beam-scanning function is realized through the phase difference of SSPPs fed to the radiating elements. In addition, the CMIM ground reflects downward radiation waves onto upward space, leading to a unidirectional pattern and enhanced radiation gain. Both the numerical simulation and experiment results demonstrate the good performance of the proposed antenna. They validate that the antenna achieves unidirectional radiation and scanning angle of 57° when the frequency varies from 6.2 to 9 GHz with the frequency sensitivity of 20.4°/GHz. The realized gains are higher than 8.11 dBi over the entire band. Compared with the published works, this paper provides a novel feed technology, a simpler structure, and higher frequency sensitivity. The high-frequency sensitivity is beneficial to reduce the bandwidth burden of the entire system. The antenna has potential applications in satellite communication.
A Calibration-Free 12-bit 50-MS/s Full-Analog SAR ADC With Feedback Zero-Crossing Detectors

This paper presents a calibration-free 12-bit 50-MS/s full-analog SAR analog-to-digital (A-to-D) converter (ADC) in 40-nm CMOS, which integrates the functions of comparator, SAR logic, and digital-to-analog converter (DAC) switches into multiple feedback zero-crossing detectors (FB-ZCDs). By eliminating all the digital circuits, the full-analog bit conversions operate with the asynchronous amplification-to-regeneration (A-to-R) operation that highly relaxes the requirements of the analog circuits and reference generator. This paper is implemented without any calibration, where the non-ideal effects (offset mismatch, insufficient residue, and noise) are mitigated and eliminated with the proposed techniques as follows. The offset mismatches among the FB-ZCDs are covered by the redundancies of the sub-radix DAC arrangement in the coarse ADC. The bottom-plate coupling compensators (BPCCs) are proposed to correct the consecutive SAR decision errors as quantizing the insufficient residue. The shared FB-ZCD with capacitor coupling decision (CCD) network is developed to obtain the identical offset in the fine ADC. Amplification extension (AE) technique is proposed to enhance the precision of the shared FB-ZCD by reconfiguring as a complementary inverter-based amplifier with the extended amplification period to enhance the output SNR. This paper occupies an active area of 0.01 mm2 and achieves the Nyquist-rate signal-to-noise-and-distortion ratio (SNDR), spurious-free dynamic range (SFDR), Walden figure-of-merit (FoMW), and Schreier FoM (FoMS) of 64.1, 75.6 dB, 5.3 fJ/conversion step, and 172.8 dB, respectively.
An Image Forgery Detection Solution based on DCT Coefficient Analysis

JPEG compression and double JPEG compression introduces systematically some particular characteristics in the Discrete Cosine Transform (DCT) domain. In this paper, we propose a description of these characteristics. We also describe how to exploit these characteristics to introduce a new and efficient solution for estimating the quantization steps used in the first compression of a double-compressed image. We also introduce a method for detecting forgery in compressed images. Rapid, having a simple implementation and there is no need for training to be functional, the proposed solution gives, however, a performance auspicious. Its performance is demonstrated on simulated images and images retrieved from several public databases.
Multi-Service Provider Network Traffic Scheduling Method Based on Application Perception1

Multi-service provider network traffic scheduling is an important research topic. Previous scheduling methods based on DNS resolves the issues of high latency and package loss due to cross-ISP access. However, there are still some problems like large bandwidth fluctuations and low bandwidth utilization in existing methods due to low scheduling accuracy. In this article, we propose a scheduling method based on application perception to solve these issues. Our scheduling method consists three components: application perception, traffic prediction of uplinks and network traffic scheduling based on DNS. It performs scheduling for each elephant flow application to improve scheduling accuracy. The experiment result shows that the application perception based method improves scheduling performance from both scheduling stability and bandwidth utilization.
Application of EEMD-HHT Method in Fault Signal Analysis of Electric Power System in LNG Carriers

The unit capacity of the LNG carriers propulsion motor is almost equal to that of the electric generator, and random changes of this kind of high power load are tend to cause faults and system crashes. Therefore, the effective extraction of transient signal feature information is the core of fault diagnosis in electric propelling ship power system. Based on the Ensemble Empirical Mode Decomposition (EEMD), the method of Hilbert-Huang Transform (HHT) has solved the mode mixing problem which exists when the method of Empirical Mode Decomposition (EMD) is used during the process of fault signal diagnosis in electric power system, and HHT can successfully get the accurate position and feature information extraction of the fault time. Digital simulation analysis indicates that the method is correct and effective.
Efficient and Tamper-Resilient Architectures for Pairing Based Cryptography

Identity based cryptography was first proposed by Shamir [36] in 1984. Rather than deriving a public key from private information, which would be the case in traditional public key encryption schemes, in identity based schemes a user’s identity plays the role of the public key. This reduces the amount of computations required for authentication, and simplifies key-management. Efficient and strong implementations of identity based schemes are based around easily computable bilinear mappings of two points on an elliptic curve onto a multiplicative subgroup of a field, also called pairing. The idea of utilizing the identity of the user simplifies the public key infrastructure. However, since pairing computations are expensive for both area and timing, the proposed identity based cryptosystem are hard to implement. In order to be able to efficiently utilize the idea of identity based cryptography, there is a strong need for an efficient pairing implementations. Pairing computations could be realized in multiple fields. Since the main building block and the bottleneck of the algorithm is multiplication, we focused our research on building a fast and small arithmetic core that can work on multiple fields. This would allow a single piece of hardware to realize a wide spectrum of cryptographic algorithms, including pairings, with minimal amount of software coding. We present a novel unified core design which is extended to realize Montgomery multiplication in the fields GF (2), GF (3), and GF (p). Our unified design supports RSA and elliptic curve schemes, as well as identity based encryption which requires a pairing computation on an elliptic curve. The architec-
Model Predictive Direct Power Control With Fixed Switching Frequency and Computational Amount Reduction

The model predictive direct power control (MPDPC) has attracted significant attention due to its outstanding dynamic response and high power factor. However, the variable switching frequency makes the design of alternating current (ac) filter more challenging, and the heavy computational burden limits the application of MPDPC. This paper proposed a new cost function and four steps’ MPDPC (FSMPDPC) scheme for T-type inverters. The proposed cost function can reduce the number of division operations and does not require calculating the duty cycles of all vectors. Meanwhile, the four steps’ calculation process is divided into four steps to reduce the number of cycle calculations. The first three steps are assigned to adjust the active and reactive powers, and the neutral-point (NP) voltage is balanced in the fourth step. An experimental platform of a T-type inverter is established to demonstrate the superiorities of the proposed FSMPDPC. The results show that FSMPDPC improves the steady-state performance of the T-type inverter with lower current total harmonic distortion (THD) and lower ripples in the active and reactive powers. In addition, the proposed algorithm eases the computational burden of the digital signal processor (DSP).
The LiquidWebWorker API for Horizontal Offloading of Stateless Computations

As most users access the Web from multiple devices with different characteristics, ranging from powerful desktops or laptops to tablets, mobile phones or watches and cars, liquid Web applications seamlessly ﬂow across multiple Web-enabled devices and adapt their distributed user interface to the set of devices simultaneously accessing the application. In this paper we focus on the business logic layer of rich Web applications and explore the opportunity to reduce the execution time of CPU-intensive tasks or limit their energy consumption by ofﬂoading them among nearby devices running the same liquid Web application. We extend the standard HTML5 WebWorker API with the concept of liquid WebWorkers, so that developers can transparently ofﬂoad parallel execution of stateless tasks by managing the necessary device selection and direct peer-to-peer data transfer. By introducing the liquid WebWorkerAPI into our Liquid.js framework, we present how to create a pool of devices sharing their CPU processing capabilities according to different policies.
Process Mining in Frail Elderly Care: A Literature Review

Process mining has proved to be a valuable technique for extracting process knowledge from data within information systems. Much work has been conducted in applying process mining to domains such as logistics, banking, transportation and many areas of the government, including healthcare. Frail elderly people who have an increased risk of adverse outcomes are amongst the main users of healthcare services and understanding healthcare processes for the frail elderly is challenging because of their diverse and complex needs combined with an often high number of co-morbidities. This paper aims to provide an overview of work applying process mining techniques to improving the care of frail elderly people. We conducted a literature search using broad criteria to identify 1,047 potential papers followed by a review of titles, abstract and content which identified eight papers where process mining techniques have been successfully applied to the care of frail elderly people. Our review shows that, to date, there has been limited application of process mining to support this important segment of the population. We summarise the results based on five themes that emerged: types of source data and process; geographical location; analysis methodology; medical domain; and challenges. Our paper concludes with a discussion on the issues and opportunities for process mining to improve the care pathways for frail elderly people.
Deterministic System Analysis to Guarantee Worst Case Performance for Optimal ESS and PV Sizing

In this paper, we propose a deterministic system analysis that guarantees a profit by developing a system calculus with deterministic bounds. A system analysis model is required to guarantee a minimum profit and ensure stable investments in the energy storage system (ESS) and photovoltaic (PV) by the third-party operators. The deterministic system analysis guarantees the worst case performance and determines the optimal ESS and PV capacity with an optimization problem through a smart grid system analysis. The proposed approach can be used as an analytical tool to determine the investment cost for a third-party operator and guarantee investment-based return by presenting the optimal ESS and PV capacity in a smart grid system.
Application of deep learning algorithms for automated detection of arrhythmias with ECG beats

I Acknowledgement . IV List of Figures . IV List of Tables . V 1 Chapter One – Introduction . 1 1.1 Motivations & Scope of Research . 1 1.2 Arrhythmias. 2 1.3 Aims of Research . 5 2 Chapter Two – Literature Review . 6 3 Chapter Three – Cardiac System & Electrocardiogram . 20 3.1 Anatomy & Physiology of Human Heart . 20 3.2 Conduction System of the Heart . 21 3.3 Electrocardiogram & Characteristics of Arrhythmic Signals . 23 3.3.1 Bundle Branch Block . 24 3.3.2 Atrial Premature Beats . 25 3.3.3 Premature Ventricular Contraction . 26 4 Chapter Four – Deep Neural Network . 27 4.1 Artificial Neural Network & Deep Network . 27 4.1.1 Convolutional Neural Network (CNN) . 29 4.1.2 Recurrent Neural Networks (RNN) and Long short-term memory (LSTM) . 31 4.1.3 U-Net . 32
Privacy Preservation of Social Network Users Against Attribute Inference Attacks via Malicious Data Mining

Online social networks (OSNs) are currently a popular platform for social interactions among people. Usually, OSN users upload various contents including personal information on their profiles. The ability to infer users’ hidden information or information that has not been even uploaded (i.e. private/sensitive information) by an unauthorised agent is commonly known as attribute inference problem. In this paper, we propose 3LP+, a privacy-preserving technique, to protect users’ sensitive information leakage. We apply 3LP+ on a synthetically generated OSN data set and demonstrate the superiority of 3LP+ over an existing privacy-preserving
Pattern Matching in Discrete Models for Ecosystem Ecology

In this paper we consider discrete qualitative models of ecosystems viewed as collections of interacting living (animals, plants. .) and nonliving entities (air, water, soil. .), whose conditions of appearance/disappearance are controlled by a set of formal rules (i.e., processes). We present here a rule-based method allowing to compare ecosystems. The method relies on a measure of similarity and on an optimization algorithm. In addition, the proposed method allows to detect patterns (i.e., ecological processes or sets of processes) in ecosystems. We have validated the method by applying it against a set of models and patterns provided by ecologists.
Data Multiplexing Through Animated Texture Orientation and Color

Multidimensional data visualization is still a challenge for complex data exploration. Usually, each data dimension might be mapped to one available visual variable such as position, shape or color. While spatial and color data mappings have been previously intensively explored, animated encodings have been far less investigated. However, such techniques are widely used in existing visualizations. In this paper, we propose to assess the visual assets of direction and orientation of directed animated textures to encode data. We present a user study that compares three animated textures and a static representation. The results suggest that the animated techniques can be as effective as the static representation in terms of accuracy and data retrieval time. Finally, we present some design guidelines to efficiently use animated particle visualizations .
Emotions detection scheme using facial skin temperature and heart rate variability

Technology nowadays is aiming to provide a better life quality for people, schools and universities are working for the convenient of the students as well as ensuring a high quality of education is attained. Emotions detections system can be a solution for better education results and may also be used as a part of human-computer interaction applications such as robotics, games, and intelligent tutoring system, This study shows potentials method of detecting emotions using mobile computing to recognize and identify emotions (Relax, Fear, Sadness, and Joy) based on facial skin temperature, more specifically 5 spots on the face, Nose, Glabellar line (between the eyes and eyebrows) right\lift cheeks and the chin, in addition to the Heart Rate Variability (HRV). An experiment was conducted with 20 healthy subjects (10 females and 10 males, 20 to 31 years old), Both visual and auditory media were used to induce these emotions in the experiment. By the end of this paper, the output data will be anglicized by an Artificial neural network (ANN) The Multilayer Perceptron (MLP) was selected as a classifier with a result of 88.75 % accuracy. This mechanism proves that human`s emotions can easily identify without physical interaction with the subject and with high reliability with only 0.11 misprediction rate
Robust Updating Classification Rule with applications in Food Authenticity Studies

Food authenticity studies deal with the detection of products that are not what they claim to be. We introduce a robust semi-supervised classification rule in which a potential illegal sub-sample is detected by selecting observations with the lowest contributions to the overall likelihood using impartial trimming. Experiments on an artificially adulterated dataset are provided to underline the benefits of the proposed method.
Analysis of the Effects of Appearances of Avatars on User's Self-evaluation of Extroversion

Proteus effect is known as a phenomenon in which the personality and behavior of an individual, within online virtual worlds, is changed by the appearances of their avatar. This paper reports whether Proteus effect is applicable to accompanying avatars with whom a user's avatar interacts in a virtual world. The results indicated that Proteus effect was applicable to those who perceived the avatar was their alter-ego, while those who did not was not affected by the appearance of the avatar. The results suggest the importance of considering avatar’s and agent’s appearances and user’s personality in serious games, online virtual worlds, and avatar-mediated online communications.
A Community Based Reliable Trusted Framework for Collaborative Filtering

Recommender Systems are a primary component of online service providers, formulating plenty of information produced by users’ histories (e.g., their procurements, ratings of products, activities, browsing patterns). Recommendation algorithms use this historical information and their contextual data to offer a list of likely items for each user. Traditional recommender algorithms are built on the similarity between items or users. (e.g., a user may purchase the identical items as his nearest user). In the process of reducing limitations of traditional approaches and to improve the quality of recommender systems, a reliability based community method is introduced.This method comprises of three steps: The first step identifies the trusted relations of the current user by allowing trust propagation in the trust network. In next step, the ratings of selected trusted neighborhood are used for predicting the unrated item of current user. The prediction relies only on items that belong to candidate items’ community. Finally the reliability metric is computed to assess the worth of prediction rating. Experimental results confirmed that the proposed framework attained higher accuracy matched to state-of-the-art recommender system approaches.