Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Server Crash Prediction in Cloud Service Data Center

In recent years, Cloud Service has gradually been adopted by more and more end customers. Large amounts of applications from various businesses has been migrated to Cloud. Availability is one of the key considerations for end customers when adopting Cloud Service, so CSPs (Cloud Service Providers) are pursuing ever higher standard of SLA (Service-Level Agreement) to accommodate the need. Especially when considering VM (Virtual Machine) based Cloud Service, where resources in one physical server are virtualized and shared among multiple tenants, a server crash would be a huge impact to tenants' business. One solution is to establish an effective and accurate method to predict server crash in advance, so that workloads can be migrated to a healthy server before impacting the service. It is extremely challenging to deliver accurate prediction, since server crash occurs due to all kinds of failures with most of them occurring randomly and suddenly.This paper proposes a smart server crash prediction method for triggering early warning and migration in Cloud Service data center. The proposed server crash perdition is developed based on hardware, firmware and software system information collected from low-level hardware indicators and kernel status to upper-level system logs in OS (Operation System). Machine learning algorithms are adopted in logs analysis and failure prediction. Random Forests algorithm is chosen upon all providing the best precision. The final proposed method is deployed and evaluated in Baidu's data center, and it achieved 93.33% and 87.33% precision in providing Minutes-level and Hours-level ahead-of-time warning in server crash prediction.
Axon diameter and fluctuation in excitability.

A nerve fiber responds with an action potential in a fraction of all trials, if stimulated with identical electrical stimuli at threshold intensity and at a frequency such that the interval between the stimuli is larger than the recovery period (BLAIR and ERLANGER, 1932, 1933; PECHER, 1936). PECHER (1937, 1939) showed that this phenomenon is an inherent property of the nerve fiber. It is not due to variations in external circumstances, viz, stimulus intensity, hence the name, fluctuation in excitability. The sequence of these responses is a haphazard one; the probability of response has the same value each time and is independent of the preceding reactions (VERVEEN, 1960). The probability of response depends on stimulus intensity. PECHER (1939) suggested already the existence of a Gaussian relationship between response probability and stimulus intensity. In previous studies on nerve fibers of frog and of crag-fish (VERVEEN, 1960, 1961) it was shown that these relations approximate the Gaussian
Towards Wideband Signal Acquisition Systems

Towards Wideband Signal Acquisition Systems by Nima Baniasadi Master of Science, Plan II in Electrical Engineering and Computer Sciences University of California, Berkeley Professor Ali M. Niknejad, Chair Deep scaling of CMOS technology has provided an exceptional opportunity for wideband high data-rate digital communication. However, since the medium between transceivers accept only analog signals, use of analog to digital converters is inevitable. Currently, ADCs are the bottleneck of communication links, where speed trades o↵ with resolution. Moreover, all digital equalization is desirable but necessitates higher resolution and higher bandwidth ADCs. Several methods for increasing the speed of ADCs are considered in this thesis. An extensive analysis, confirmed by simulations, on the jitter-induced error shows that proper system architecture can improve the total SNDR of ADCs.
Low-Loss Thin Film Lithium Niobate Bonded on Silicon Nitride Waveguides

We demonstrate heterogeneous integration of thin film LiNbO<inf>3</inf> on Si<inf>3</inf>N<inf>4</inf> photonic integrated circuits with SiO<inf>2</inf>, Al<inf>2</inf>O<inf>3</inf> and direct bonding. We report record low losses in bonded ring resonators of 0.4 dB/cm (intrinsic Q = 8.19×10<sup>5</sup>).
Live multimedia streaming multicast in local area social networking environment of MANETs using SLIM+ Protocol

Evolution of multicast routing protocols for MANET are becoming the center of attraction for scholars of these days. This is due to varying applications areas, which in turn requires appropriate routing schemes for them. In the environment of mobile ad hoc networks, multicast communication efficiently saves the network resources. Adapting MANET as local area social networks allows new nodes to keep arriving and existing nodes departing the network as and when they like. Multicast routing protocols need to be readdressed to introduce the openness of the node set in such environments. The research paper focuses on evaluating the performance of routing scheme used by SLIM+in multicast of live video or audio streaming to the freely interested devices or nodes, coming in the antenna rage. Performance metric used is Packet Delivery Ratio and End-to-End-Delay; by comparing it with its competitive and most commonly used tree based and mesh based multicast routing protocols. It then observed that SLIM+ remained successful in utmost delivery of data packets to the destinations thus took a little bit increased but insignificant time to advertise for live streaming. Whereas its counterpart MAODV failed in successful delivery of the data packets to the desired destinations and become free in less time. Whereas SLIM+ with its tree based structure competed with mesh based structured PUMA.
Mechanism Analysis on Radio Frequency Radiation in IC/Package with Bonding Wires

IC/package radiation from a self-designed inverter chip bonded to a PCB was measured, and the radiation mechanism was investigated. Digital signals from ICs can have wide-spread frequency spectrums, and radiated signals in the radio frequency range can be picked up by other radio receivers, which causes radio-frequency interference problems. First, an equivalent radiation source for an inverter chip bonded to a PCB is reconstructed based on near-field patterns measured above the chip. The validity of the reconstructed source is proved by comparing the coupled voltage measured on a victim antenna and the voltage calculated based on the reconstructed radiation source. Then, the radiation mechanism is investigated by analyzing the equivalent radiation source as well as the signal and return current paths in full-wave simulations. Then, the critical current paths responsible for the radiation are identified. Finally, a simplified model is proposed to calculate the equivalent radiation source, with which the errors between the simplified model and full-wave simulation results were found to be within 5%.
A Voxelized Fractal Descriptor for 3D Object Recognition

Currently, state-of-the-art methods for 3D object recognition rely in a deep learning-pipeline. Nonetheless, these methods require a large amount of data that is not easy to obtain. In addition to that, the majority of them exploit features of the datasets, like the fact of being CAD models to create rendered representation which will not work in real life because the 3D sensors provide point clouds. We propose a novel global descriptor for point clouds which takes advantage of the fractal dimension of the objects. Our approach introduces many benefits, such as being agnostic to the density of points of the sample, number of points in the input cloud, sensor of choice, and noise up to a level, and it works on real life point cloud data provided by commercial sensors. We tested our descriptor for 3D object recognition using ModelNet, which is a well-known dataset for that task. Our approach achieves 92.84% accuracy on the ModelNet10, and 88.74% accuracy on the ModelNet40.
Effects of water temperature on the critical swimming speed and metabolic scope of juvenile Parabramis pekinensis

In order to understand the effects of water temperature on the aerobic locomotor capacity and its relationships with the energetic metabolism of juvenile Parabramis pekinensis, the juveniles were put into the waters at different temperature (10, 18, 26, and 34 ℃) to measure their resting metabolic rate (RMR), critical swimming speed (Ucrit), and maximal metabolic rate (MMR) during swimming, and the differences of MMR-RMR at different water temperature were used to calculate the metabolic scope (MS) of the juveniles. Both the Ucrit and the RMR increased significantly with increasing water temperature (P0.05). The Ucrit was increased from 6.01±0.32 BL·s-1 to 8.82 ±0.27 BL·s-1 when the water temperature increased from 10 to 34 ℃, and the MMR increased significantly when the temperature increased from 10 and 26 ℃, and remained unchanged with the further increase of temperature. The RMR and MMR were 161.7 ±28.81 mg O2·kg-1·h-1 and 292.95 ±27.59 mg O2·kg-1·h-1 at 10 ℃, and 558.55±20.41 mg O2·kg-1·h-1 and 1278.74 ± 57.40 mg O2·kg-1·h-1 at 34 ℃, respectively. The MS increased significantly when the temperature increased from 10 to 26 ℃ (P0.05), but decreased significantly when the temperature further increased from 26 to 34 ℃. It was suggested that the different reaction patterns of the MS and Ucrit to the changes of water temperature were possibly due to the high swimming efficiency as the results of the decreased water viscosity coefficient and the high anaerobic metabolism capacity of the fish at high water temperature.
MaskDGA: An Evasion Attack Against DGA Classifiers and Adversarial Defenses

Domain generation algorithms (DGAs) are commonly used by botnets to generate domain names that bots can use to establish communication channels with their command and control servers. Recent publications presented deep learning classifiers that detect algorithmically generated domain (AGD) names in real time with high accuracy and thus significantly reduce the effectiveness of DGAs for botnet communication. In this paper, we present MaskDGA, an evasion technique that uses adversarial learning to modify AGD names in order to evade inline DGA classifiers, without the need for the attacker to possess any knowledge about the DGA classifier’s architecture or parameters. MaskDGA was evaluated on four state-of-the-art DGA classifiers and outperformed the recently proposed CharBot and DeepDGA evasion techniques. We also evaluated MaskDGA on enhanced versions of the same classifiers equipped with common adversarial defenses (distillation and adversarial retraining). While the results show that adversarial retraining has some limited effectiveness against the evasion technique, it is clear that a more resilient detection mechanism is required. We also propose an extension to MaskDGA that allows an attacker to omit a subset of the modified AGD names based on the classification results of the attacker’s trained model, in order to achieve a desired evasion rate.
From Nano-Communications to Body Area Networks: A Perspective on Truly Personal Communications

This article presents an overview of future truly personal communications, ranging from networking inside the human body to the exchange of data with external wireless devices in the surrounding environment. At the nano- and micro-scales, communications can be realized with the aid of molecular mechanisms, Förster resonance energy transfer phenomenon, electromagnetic or ultrasound waves. At a larger scale, in the domain of Body Area Networks, a wide range of communication mechanisms is available, including smart-textiles, inductive- and body-couplings, ultrasounds, optical and wireless radio transmissions, a number of mature technologies existing already. The main goal of this article is to identify the potential mechanisms that can be exploited to provide interfaces in between nano- and micro-scale systems and Body Area Networks. These interfaces have to bridge the existing gap between the two worlds, in order to allow for truly personal communication systems to become a reality. The extraordinary applications of such systems are also discussed, as they are strong drivers of the research in this area.
Medicare Fraud Detection using CatBoost

In this study we investigate the performance of CatBoost in the task of identifying Medicare fraud. The Medicare claims data we use as input for CatBoost contain a number of categorical features. Some of these features, such as the procedure code and provider zip code, have thousands of possible values. One contribution we make in this study is to show how we use CatBoost to eliminate some data pre-processing steps that authors of related works take. A second contribution we make is to show improvements in CatBoost’s performance in terms of Area Under the Receiver Operating Characteristic Curve (AUC), when we include another one of the categorical features (provider state) as input to CatBoost. We show that CatBoost attains better performance than XGBoost in the task of Medicare fraud detection with respect to the AUC metric. At a 99% confidence level (with p-value 0) our experiments show that XGBoost obtains a mean AUC value of 0.7615 while CatBoost obtains a mean AUC value of 0.7851, validating the significance of CatBoost’s performance improvement over XGBoost. Moreover, when we include an additional categorical feature (healthcare provider state) in our data analysis, CatBoost yields a mean AUC value of 0.8902, which is also statistically signficant at a 99% confidence interval level (with p-value 0). Our empirical evidence clearly indicates CatBoost is a better alternative to XGBoost for Medicare fraud detection, especially when dealing with categorical features.
Modulation Techniques for a Modified Three-Phase Quasi-Switched Boost Inverter With Common-Mode Voltage Reduction

In a single-stage buck-boost quasi-switched boost inverter (qSBI), the shoot-through state insertion causes high amplitude common-mode voltage. Consequently, the qSBI becomes less attractive in transformerless photovoltaic (PV) systems. In this paper, a novel space vector pulse-width modulation for a modified qSBI is introduced to reduce the magnitude of common-mode voltage and push the modulation index up to 1. By properly choosing the shoot-through interval time, shoot-through states are considered to be inserted for boosting voltage and also reducing the THD value of the output voltage. The mathematical analysis and operating principles of the converter are discussed and verified through PSIM simulations. Finally, an experimental prototype is validated based on a TMS320F28335 DSP microcontroller and a DE0-Nano FPGA digital control platform.
A Highly Stable and Robust 7T SRAM Cell using Memristor

The ever-growing needs for processing on system-on-chip (SoC) demands us to scale down the Static RAM (SRAM) cell which occupies around 65-75% area of SoCs. Continued scaling procedure have decreased the read and write stability of SRAMs cell with increased delay. Emerging technologies like Memristor is used for smaller area consumption than a standard CMOS design without compromising the device's functionality. A seven transistor(7T) cell at 7nm feature size is proposed to accomplish improvements in noise margins. In the proposed architecture 7nm model card used is taken from Predictive Technology Model (PTM) and all the simulations are done in Cadence Virtuoso tool. The Static Noise Margin (SNM), Read Noise Margin (RNM), Write Noise Margin (WNM) of the proposed architecture are 210mv, 210mv, 145mv respectively. The read time of 1-bit cell is 5ps and write time is 17.6ps with an estimated area of 0. 0291µm2.
A New Approach for Predicting an Important User on a Topic on Twitter

Twitter is an online social networking service with millions of users and an impressive flow of messages that are published and spread daily through interactions among users. There are different types of users on Twitter; therefore, determining the most important users in each topic is highly challenging. Hence, it is necessary to define efficient computed measures to classify users according to the criteria of relevance and the possibility of representing reality. Although several studies have considered identifying the user influence, user popularity, or user activity in a social network, relatively less focus has been on measuring and predicting important users in case of a topic. In this study, we have proposed a method to determine an important user based on the activities related to each topic on Twitter by combining the measures related to user influence, user activity, and user popularity. The results verified the effectiveness of our proposed approach for the identification of important users in each topic.
Intersubband Transitions in GaN/Al0.5Ga0.5N Quantum Wells on a-Plane and m-Plane GaN Substrates

We experimentally characterize mid-infrared intersubband transitions in identical Al0.5Ga0. 5N/GaN heterostructures grown on a- and m-plane GaN substrates. The absorption peaks of the m-plane samples are 10 to 40% narrower than that of the a-plane samples.
Design and Research on a Nonlinear 2DOF Electromagnetic Energy Harvester With Velocity Amplification

In order to power the microelectronic devices of the Internet of Things in the low-frequency broadband vibration environment, a novel nonlinear 2DOF electromagnetic energy harvester was proposed based on the magnetic levitation structure and velocity amplification mechanism. The electromechanical coupling model was established and numerically simulated to analyze the generating characteristics. A prototype was fabricated and tested to verify the theoretical model. The influences of the key parameters, including the gap between the suspended magnet and the lower spring, excitation acceleration, spring stiffness, mass of copper cylinder, on the generating characteristics were studied. When it was subjected to a harmonic excitation with acceleration amplitude of <inline-formula> <tex-math notation="LaTeX">$0.8g$ </tex-math></inline-formula>, the measured open-circuit voltage reached peaks of 3.13 V at 8 Hz and 2.93 V at 13.5 Hz, respectively. The optimal output power at 8 Hz was 10.18 mW with the matched load resistance of <inline-formula> <tex-math notation="LaTeX">$250~\Omega $ </tex-math></inline-formula>. Both the experimental and theoretical results demonstrated that this novel electromagnetic energy harvester had obvious advantages in increasing the output power and widening the bandwidth.
ReARM: A Reconfigurable Approximate Rounding-Based Multiplier for Image Processing

This paper aims to present a reconfigurable rounding based multiplier with different accuracy levels. It is based on a divide and conquer approach for applications in image processing. Our proposed approach divides the multiplicand and multiplier into two halves. Each half is multiplied with the other and our architecture is made accuracy-configurable. Further, due to rounding based approach, our approximate multiplication technique generates results faster. Based on our experiments, we observe that our proposed multiplier is 26.3% more accurate on an average compared to other state-of-the-art approximate multipliers for 8-bit operations.
A reliable and efficient forwarding strategy in vehicular named data networking

Vehicular named data networking (VNDN) has the data-centric and in-network caching advantages that are in line with the requirements of multi-hop content retrieval in vehicular ad hoc networks (VANETs). Some current restrictive forwarding strategies utilize single-path forwarding to mitigate the Interest broadcast storm in VNDN. However, these strategies do not provide any reliable mechanism for Interest and Data forwarding to address the problem of low performance caused by the dynamic and unreliable VANETs communication environment. In this paper, we propose a packet forwarding strategy based on optimal and backup (PFOB) for VNDN. For Interest, PFOB establishes a single-path forwarding with high reliability and reachability by increasing the signal strength and node-degree criterion and retransmission mechanism. For Data, PFOB provides a reliable multi-path backup forwarding mechanism without additional network overhead. Simulation results show that PFOB can effectively improve the Interest satisfaction ratio, reduce the number of application retransmissions, and reduce the Interest satisfaction delay compared to the current strategies.
The study on T lymphocyte subpopulation influence of Glehnia littoralis Schmidt on the C57BL/6J immunosuppressive mice

Objective: To study the T lymphocyte subpopulation influence of Glehnia littoralis Schmidt SFE-CO2 extract on the C57BL/6J immunosuppressive mice.Methods: Thirty-two normal C57BL/6J female mice were divided randomly into the blank group,model group,low-Glehnia littoralis Schmidt SFE-CO2 extract-dose group and high-Glehnia littoralis Schmidt SFE-CO2 extract-dose group,(each n=8). All the mice were killed and the orbit venous sampling was done at the seventh day.EDTA was used as an anticoagulant.WBC,RBC,PLT were tested by globulimeter automatically.The absolute quantity of T lymphocyte subpopulation was tested and calculated by flow cytometry.Results: The Glehnia littoralis Schmidt SFE-CO2 extract obviously improved the absolute quantity of WBC,PLT,CD3+T,CD4+T and CD8+T cells in the peripheral blood of C57BL/6J immunosuppressive mice.Conclusion: The Glehnia littoralis Schmidt SFE-CO2 extract has special rehabilitation for the peripheral immune system of the C57BL/6J immunosuppressive mice.
A 0.8-V, 1.54-pJ/940-MHz Dual-Mode Logic-Based 16×16-b Booth Multiplier in 16-nm FinFET

The dual-mode logic (DML) defines runtime adapted digital architectures that switch to either improved performance or lower energy consumption as a function of the actual computational workload. This flexibility is demonstrated for the first time by silicon measurements on a $16\times 16$ -b Booth multiplier fabricated as a part of an ultralow-power digital signal processing (DSP) architecture for 16-nm FinFET technology. When running in the full-speed mode, the DML multiplier can achieve a performance boost of 19.5% as compared to the equivalent standard CMOS design. The same circuit saves precious energy (−27%, on average) when the energy-efficient mode is enabled, while occupying 13% less silicon area.
Defect Detection and Identification of Point-Focusing Shear-Horizontal EMAT for Plate Inspection

As a kind of nondestructive testing (NDT) method, shear-horizontal (SH)-guided wave detection technology is widely used on an electromagnetic acoustic transducer (EMAT). Although ultrasonic-guided waves perform well in defect location, it is difficult to obtain detailed information about defects, and the low efficiency of EMAT energy conversion still reduces the EMAT’s performance. Therefore, in this work, the defect detection method of different shapes and sizes by point-focusing shear-horizontal (PFSH)-guided wave EMAT with the use of periodic permanent magnet (PPM) is investigated through simulation and experiment. For the purpose of defect classification and quantification, the extraction principles of defect features are obtained through simulation based on the circumferential scatter diagrams, and the neural network (NN) is used to process the features extracted from the experimental data. The results show that by extracting effective defect features from the scatter diagram, high-accuracy classification and high-precision quantification of defects under the influence of the focusing transducer can be achieved.
Embedded vision system for monitoring arc welding with thermal imaging and deep learning

We develop a novel embedded vision system for online monitoring of arc welding with thermal imaging. The thermal images are able to provide clear information of the melt pool and surrounding areas during the welding process. We propose a deep learning processing pipeline with a CNNLSTM architecture for the detection and classification of defects based on video sequences. The experimental results show that the CNN-LSTM architecture is able to model the complex dynamics of the welding process and detect and classify defects with high accuracy. In addition, the embedded vision system implements an OPC-UA server, enabling an easy vertical and horizontal integration in Industry 4.0.