Miracle-dz/UniFAR-FTUs huggingface dataset:
Entity Relation Extraction in Equipment Maintenance Support Based on Dual Attention Mechanism

Aiming at the lack of labeled training data in the field of equipment maintenance support, a training instance set of entity relation extraction of equipment maintenance support is constructed by distant supervision method. Furthermore, aiming at the problem of noise data in the training instance set automatically constructed, an entity relation extraction model based on dual attention mechanism is proposed in this paper. The model adds character-level attention mechanism and instance-level attention mechanism after bidirectional gated recurrent unit (BI-GRU) network, which enables the model to obtain the bidirectional context semantic information of training instances through BI-GRU network, and focus on the key semantic information in each training instance through character-level attention mechanism. At the same time, the instance-level attention mechanism is introduced to calculate the correlation between the instance and the corresponding relation among multiple instances to dynamically reduce the weight of wrong label instances, so as to reduce the impact of noise data. The experiments on Chinese equipment maintenance support training instance set show that the model can effectively utilize the semantic information contained in instances, reduce the impact of the wrong label instances and improve the precision of relation extraction.
A Spatiotemporal Content-Based CU Size Decision Algorithm for HEVC

The high efficiency video coding (HEVC) standard provides superior efficiency for encoding and improves the compression ratio by almost 50% compared with previous video coding standards, such as advanced video coding (AVC). However, more intensive computation complexity is introduced by implementing the flexible quad tree-structured coding model. In a typical HEVC encoder, coding units (CUs) in a coding tree unit (CTU) that is built as a quad-tree structure are recursively traversed each depth level (CU size) to select the optimal coding configuration. Therefore, most of the encoding time is spent searching for the optimal coding configuration. In this paper, an efficient and fast CU size decision algorithm is proposed to reduce HEVC encoder complexity by the spatiotemporal features. First, an adaptive depth-range prediction method minimizes the possible range depth level by observing previous frames and proximal CTUs. Second, an early termination method based on the boundary examination from the de-blocking filter (DBF) prevents unnecessary calculation on small CU sizes. Furthermore, according to the sum of absolution difference (SAD), a smooth area detection mechanism is triggered when the predictive depth range excludes the largest CU size. This mechanism increase the bitrate of the CU, which contains static objects with complex textures. Compared with the HM 16, the experimental results revealed that the proposed algorithm can achieve an average 59.73% and 64.98% reduction in encoding time along with a 0.68% and 1.27% Bjontegaard Delta bitrate (BDBR) penalty for various test videos under low-delay P and random-access conditions, respectively.
Multi-band Microstrip Filter Based on Sierpinski Triangle Fractal

A multi-band filter based on Sherpinski fractal is presented. The filter has certain advantages in size and frequency band characteristics, and the working frequency band covers the L band, the S band and the C band. It consisted by stepped appearance and triangular patches. Through simulation analysis, the S-parameters of no fractal, first-order fractal and second-order fractal are compared in turn, and the effect of fractal structure on the filter can be seen. A fractal structure is introduced and ultra-wideband, multi-band and miniaturization can be achieved, which can improve insertion loss, return loss, bandwidth and reduce resonant frequency. After the fractal is improved, the center frequencies of the three bands are shifted to the low frequency band, and the passband’s reflection coefficient is reduced from −28dB to −37 dB, which optimizes system performance. The proposed filter has good crossover characteristics from 1.44 to 7.83 GHz, with the pass band range is 3.42 to 7.08 GHz, and the −3dB FBW is 70%, which is ideal for WLAN requirements. At the same time, the stopband characteristics can also be applied to WIMAX to filter out interference signals. Another major feature after the use of the fractal structure is that the filter becomes more compact, the effective area is greatly reduced, and it is advantageous for miniaturization and integration. Based on the previous research, the fractal structure filter effectively implements multiple requirements such as multi-band, miniaturization, resonance frequency reduction and frequency band selection. The physical and actual measurements of the filter are performed, and the results of the vector network analyzer measurement filter are compared with the simulation results, and the measurement data agrees well with the simulation data.
Conditional Patch-based Generative Adversarial Network for Retinal Vessel Segmentation

In various medical applications, retinal vessel segmentation is a key step to diagnose diseases in fundoscopic images. This research propose a deep learning base patch-based Generative Adversarial Network (GAN) to map complex boundaries of retinal vessels to a probability masks. The proposed conditional patch GAN which utilizes the generator and patch discriminator conditioned on the sampled ground truth distribution to segment out the thick and thin retinal vessels and robust to the inadequate brightness and contrast regions and distinguish the vessels and background. Moreover, the generator network is combined with a patch based discriminator where conditional distribution is imposed on both the generator and discriminator to learn the features from conditional data. Finally, model generates a binary map of vessel segmentation and achieves comparable accuracy on benchmark datasets.
Modeling Method for Interference Analysis between IMT-2020 and Satellite in the mmWave Band

We propose a modeling method to calculate the interference that existing satellite services receive from fifth-generation mobile communication systems (IMT-2020). This study is based on the modeling and parameter document proposed for spectrum sharing research provided in the ITU- recommendation for IMT-2020 services in the millimeter-wave band. To complement previous research, we expand the coverage area from the footprint area to entire area by considering side lobe of satellites. In addition, we obtain various results depending on the location and main lobe direction of the satellite by using terrain data. Furthermore, we use this modeling method to calculate the interference that the Fixed-Satellite Service (FSS) satellite and Earth Exploration- Satellite Service (EESS) passive sensor receive in the co-channel and adjacent channel and compare that with protection criteria to present the possibility of frequency coexistence.
A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays

Fault diagnosis in photovoltaic (PV) arrays is essential in enhancing power output as well as the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high impedance faults, low location mismatch, and the presence of Maximum Power Point Tracking (MPPT) make fault detection challenging in harsh environmental conditions. In this regard, there have been several attempts made by various researchers to identify PV array faults. However, most of the previous work has focused on fault detection and classification in only a few faulty scenarios. This paper presents a novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Networks (CNN) to extract features from 2-D scalograms generated from PV system data in order to effectively detect and classify PV system faults. An in-depth quantitative evaluation of the proposed approach is presented and compared with previous classification methods for PV array faults – both classical machine learning based and deep learning based. Unlike contemporary work, five different faulty cases (including faults in PS – on which no work has been done before in the machine learning domain) have been considered in our study, along with the incorporation of MPPT. We generate a consistent dataset over which to compare ours and previous approaches, to make for the first (to the best of our knowledge) comprehensive and meaningful comparative evaluation of fault diagnosis. It is observed that the proposed method involving fine-tuned pre-trained CNN outperforms existing techniques, achieving a high fault detection accuracy of 73.53%. Our study also highlights the importance of representative and discriminative features to classify faults (as opposed to the use of raw data), especially in the noisy scenario, where our method achieves the best performance of 70.45%. We believe that our work will serve to guide future research in PV system fault diagnosis.
Corporate Governance, Opaque Bank Activities, and Risk/Return Efficiency: Pre- and Post-Crisis Evidence from Turkey

Does better corporate governance unambiguously improve the risk/return efficiency of banks? Or does either a re-orientation of banks’ revenue mix towards more opaque products, an economic downturn, or tighter supervision create off-setting or reinforcing effects? The authors relate bank efficiency to shortfalls from a stochastic risk/return frontier. They analyze how internal governance mechanisms (CEO duality, board experience, political connections, and education profile) and external governance mechanisms (discipline exerted by shareholders, depositors, or skilled employees) determine efficiency in a sample of Turkish banks. The 2000 financial crisis was a wake-up call for bank efficiency and corporate governance. As a result, better corporate governance mechanisms have been able to improve risk/return efficiency when the economic, regulatory, and supervisory environments are more stable and bank products are more complex.
Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review

Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection methods are often not enough, and machine learning poses as a potential solution. Adversarial machine learning is a research area that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively studied specifically in the area of image recognition, where minor modifications are performed on images that cause a classifier to produce incorrect predictions. However, in other fields, such as intrusion and malware detection, the exploration of such methods is still growing. The aim of this survey is to explore works that apply adversarial machine learning concepts to intrusion and malware detection scenarios. We concluded that a wide variety of attacks were tested and proven effective in malware and intrusion detection, although their practicality was not tested in intrusion scenarios. Adversarial defenses were substantially less explored, although their effectiveness was also proven at resisting adversarial attacks. We also concluded that, contrarily to malware scenarios, the variety of datasets in intrusion scenarios is still very small, with the most used dataset being greatly outdated.
Longitudinal study of cognitive and functional brain changes in ageing and cerebrovascular disease, using proton magnetic resonance spectroscopy

VIII LIST OF ABBREVIATIONS IX CHAPTER 1. RATIONALE AND HYPOTHESES 1 1.1 DESIGN 2 1.2 MAIN AIM 2 1.2.1 Subsidiary Aims 2 1.3 HYPOTHESES 2 CHAPTER 2. PROTON MAGNETIC RESONANCE SPECTROSCOPY 4 2.1 METABOLITES IN H-MRS 5 2.1.1 N-acetyl groups 5 2.1.2 Creatine-Phosphocreatine 7 2.1.3 Choline-containing compounds 7 2.1. 4 Myo-Inositol 8 2.1. 5 Water 8 CHAPTER 3. H-MRS AND COGNITION 9 3.1 H-MRS AND NORMAL COGNITION 9 3.1.1 Studies in Children 9 3.1.2 Studies in Adults 11 3.2. 3 Studies in the Elderly 12 3.2 THE ROLE OF NAA 12 3.3 ENERGY METABOLISM 13 3.4 MEMBRANE FUNCTION 14 3.5 INOSITOL CHANGES 15 3.6 CONCLUSIONS 15 CHAPTER 4. THE AGEING BRAIN 17 4.1 COGNITIVE CHANGES WITH AGE 17 4.1.1 Attention/concentration 18 4.1.2 Memory 19 4.1.3 Executive Function 19 4.1. 4 Abstract reasoning 19 4.1. 5 Working Memory 20 4.1. 6 Language 20
ANALISIS FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN OUTCOME PASIEN CEDERA KEPALA DI IGD RSUD Prof. Dr. MARGONO SOEKARDJO PURWOKERTO

Trauma is one of the biggest causes of death in the world. Thousands of people died from trauma each years. Many trauma occur in developing countries or countries with low incomes. The survey carried out showed 90% of trauma occur in developing countries. Outcome head injury patients determined from the initial condition when the patient entered in the ER (Emergency Room). Analysis of the patient's condition will determine the appropriate nursing actions that affect patient outcome. The purpose of this study was to analyze factors associated with outcome head injury patients in ER Prof. Dr. Margono Soekardjo Purwokerto Hospital. This study is a prospective with observational analytic design. The sample in this study amounted to 56 people. The results of Spearman and coefficient contingency indicates that there is a relationship between initial GCS score (p = 0.000) and systolic blood pressure (p = 0.000)with a outcomes of head injury patient. There is no correlation between age (p = 0.478)respiratory rate (p = 0.956) and pulse (.318) with a outcomes of head injury patient. Analysis of logistic regression shows that systolic blood pressure (RR = 6.768) is the dominant factor associated with outcomes of head injury patient. Therefore, the need to improve the management hemodynamic of the patient's, especially blood pressure to prevent bad outcomes. Keywords: outcome, head injury, Emergency Room
Improving Security of THz Communication with Intelligent Reflecting Surface

Intelligent Reflecting Surface (IRS)-assisted beamforming is an emerging paradigm proposed to enhanced signal coverage and multiplexing in terahertz (THz) communications with low hardware cost and reduced power consumption. This paper investigates the beamformer and IRS design to enhance security of downlink THz communication in multi-input single-output (MISO) wiretap channel, where an Alice-Bob pair wishes to communicate secretly with the assist of IRS, in the presence of Eve. Our goal is to maximize the secrecy rate by designing the active beamformer at the base station (BS) and the passive reflecting phase shifters (PSs) at the IRS, which turns out to be an intractable mixed integer non-convex optimization problem. To tackle it, we respectively propose a low-computational- complexity successive design and a high-secure- performance joint design, which both attain high- quality suboptimal solutions. Specifically, in the successive design, we first design the PSs under an introduced principle, followed by that the beamformer is optimally derived, both in closed form. In the joint design, an alternating manner is proposed to optimize the PSs and the beamformer one by one with each other being fixed in each iteration until both reach the convergence. Numerical results show that the proposed designs achieve better secrecy performance than existing IRS-assisted design and traditional non-IRS- assisted secure design.
Multimodule DC-DC Converters for High-Voltage High-Power Renewable Energy Sources

High-voltage high-power DC-DC converters are an essential requirement for constructing HVDC networks since voltage levels, as well as the network configuration, are not standardized. Accordingly, this paper focuses on multimodule DC-DC converter-based Dual Active Bridge (DAB) for high-voltage high-power renewable energy sources such as PV plant and wind farms. The modularity concept is applied to the entire stage and not only limited to the power electronic stage. In addition, higher switching frequency in the AC link can be achieved, which would result in significant weight and size reduction. To study the dynamic behavior of such converters, the small-signal analysis for a four-module Input-Series Input-Parallel Output-Series (ISIPOS) DC-DC converter along with its frequency response is delivered. In addition, sensitivity analysis for the ISIPOS converter is presented to study the behavior of the converter when its parameters change. Moreover, different multimodule DC-DC converter-based DAB scenarios for different purposes are presented. To clarify, the main aim of this paper is studying the small-signal analysis of a four-module ISIPOS DC-DC converter as well as presenting different scenarios for multimodule DC-DC converters. The two studied scenarios can be summarized in: connecting a high-voltage high-power PV park to a high-voltage transmission and connecting two HVDC transmission with different voltage levels.
Domain Flux-based DGA Botnet Detection Using Feedforward Neural Network

Botnets have been a major area of concern in the field of cybersecurity. There have been a lot of research works for detection of botnets. However, everyday cybercriminals are coming up with new ideas to counter the well-known detection methods. One such popular method is domain flux-based botnets in which a large number of domain names are produced using domain generation algorithm. In this paper, we have proposed a robust way of detecting DGA-based botnets using few novel features covering both syntactic and semantic viewpoints. We have used Area under ROC curve as our performance metric since it provides comprehensive information about the performance of binary classifiers at various thresholds. Results show that our approach performs significantly better than the baseline approach. Our proposed method can help in detecting established DGA bots (equipped with extensive features) as well as prospective advanced DGA bots imitating real-world domain names.
All-Digital Mismatched Calibrator and Compensator for SR Latch-Based Variable-Gain Time Amplifier

Two major mismatches in SR latch-based time amplifiers (TA) include input skew, which causes gain imbalance, and loading mismatch, which reduces gain accuracy. Accordingly, we propose an all-digital mismatched self-calibrator and compensator for an SR latch-based variable-gain TA. Tunable matching cells and variable capacitors are built into TAs to compensate for input skew (gain imbalance) and loading mismatch (gain inaccuracy). To ensure that the proposed calibration works efficiently and accurately, the TA must provide at least high and low gains where the low gain calibrates the most significant bit (MSB) and the high gain calibrates the least significant bit (LSB). This self-calibrator costs 4375 gates, and the power consumption is 2.8 mA for the TA gain with 2 and 3.2 mA for the TA gain with 16 at a sampling rate of 10 MHz.
The Biology and Therapeutic Approach to BRAF -Mutant Cutaneous Melanoma

Introduction Melanoma is the fifth and seventh most common cancer in American men and women, respectively. Its incidence continues to rise dramatically, and the approach to therapy has changed dramatically in recent years. Chemotherapy was previously considered the standard of care for these patients, but was associated with no overall survival (OS) benefit. Advancements in immunotherapy and targeted therapy agents have now provided multiple options with known OS benefit. In the treatment of BRAF-mutated metastatic melanoma, the possible algorithms for treatment are especially complex and an optimal approach is still being investigated. BRAF, a proto-oncogene belonging to the raf/mil family of serine/threonine protein kinases, is the most common gene associated with mutations in cutaneous melanoma, occurring in 40% to 50% of patients. The resulting activation of mitogen activated protein kinase (MAPK) pathway leads to oncogenesis through stimulation of cellular growth and inhibition of apoptosis. Clinical trials have demonstrated a median progression-free survival of approximately 12 months with combining a targeted BRAF inhibitor with a MEK inhibitor. Simultaneously, trials with immunotherapy agents have demonstrated long-term disease control in a subset of patients. This article will review the biology of BRAF-mutated melanoma, discuss current evidence and challenges for approaching therapy for these patients, and explore future clinical investigations.
Reliability-Aware Design Space Exploration for Fully Integrated RF CMOS PA

A design for reliability approach is proposed for fully integrated RF CMOS class A-to-C power amplifiers. Reliability hazards like time dependent dielectric breakdown and hot carrier injection are mapped into the design space, including the expected parametric degradation of the circuit, by fitting widely accepted models to experimental degradation results on the target technology. A prototype amplifier was used to validate RF degradation models and their impact on the output power degradation under accelerated stress. The methodology is based on a design space exploration of the highest efficiency designs attainable in a target technology. Electrical characteristics of passives and transistors are considered through look-up tables. The proposed approach allows to reduce the full universe of available designs to those that are specification and reliability compliant, avoiding a simulator-in-the-loop approach. A test case for a 3 dBm output power amplifier from the design space shows good agreement between predictions and SPICE simulations, including projected parametric degradation due to hot carrier injection.
An Explicit MDD-Based Method for Common-Cause Failure Analysis in Phased-Mission Systems

In this paper, an explicit approach based on multivalued decision diagrams is suggested for reliability analysis of phased-mission systems undergoing common-cause failures. The approach addresses dynamic system structure functions, changing component failure behaviors, and multiple correlated component failures that take place at the same time due to a shared external cause. In addition, a hybrid binary and multivalued decision diagram approach is suggested to reduce the model complexity. An illustrative example is provided. The correctness and efficiency of the suggested approach is verified through comparison to an existing approach.
Improving Restore Performance of Packed Datasets in Deduplication Systems via Reducing Persistent Fragmented Chunks

Data deduplication, though being efficient for redundancy elimination in storage systems, introduces chunk fragmentation which severely decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Typically, the backup software aggregates files into larger “tar” type files for storage. We observe that, in tar type datasets, a large number of Persistent Fragmented Chunks (PFCs) are repeatedly rewritten by state-of-the-art rewriting algorithms in every backup, which severely impacts restore performance. We found that the existence of PFCs is due to the traditional strategy of storing PFCs along with other chunks in the containers to preserve the stream locality, rendering them always stored in the containers with low utilization. We propose DePFC to reduce PFCs. DePFC identifies and removes PFCs from the containers preserving the stream locality, and groups them together, to increase the utilization of containers holding them for the subsequent backup, thus preventing them from being rewritten again. We further propose an FC Buffer to avoid mistaken rewrites of PFCs and grouping PFCs that cause restore cache thrashing together. Experimental results demonstrate that DePFC improves restore performance of state-of-the-art rewriting algorithms by 44.24-89.42 percent, while attaining comparable deduplication efficiency, and FC Buffer further improves restore performance.