Miracle-dz/UniFAR-FTUs huggingface dataset:
Explicit accounting of electronic effects on the Hugoniot of porous materials

A generalized enthalpy based equation of state, which includes thermal electron excitations explicitly, is formulated from simple considerations. Its application to obtain Hugoniot of materials needs simultaneous evaluation of pressure-volume curve and temperature, the latter requiring solution of a differential equation. The errors involved in two recent papers [Huayun et al., J. Appl. Phys. 92, 5917 (2002); 92, 5924 (2002)], which employed this approach, are brought out and discussed. In addition to developing the correct set of equations, the present work also provides a numerical method to implement this approach. Constant pressure specific heat of ions and electrons and ionic enthalpy parameter, needed for applications, are calculated using a three component equation of state. The method is applied to porous Cu with different initial porosities. Comparison of results with experimental data shows good agreement. It is found that temperatures along the Hugoniot of porous materials are significantly mod.
Bayesian change points analysis for earthquakes body wave magnitude

ABSTRACT Recently, the world has experienced an increased number of major earthquakes. The Zagros belt is among the most seismically active mountain ranges in the world. Due to Kuwait's location in the southwest of the Zagros belt, it is affected by relative tectonic movements in the neighboring region. It is vital to assess the Zagros seismic risks in Kuwait using recent data and coordinate with the competent authorities to reduce those risks. Using the body wave magnitude (Mb) data collected in Kuwait, we want to assess the recent changes in the magnitude of earthquakes and its variations in Kuwait's vicinity. We built a change point model to detect the significant changes in its parameters. This paper applies a hierarchical Bayesian technique and derives the marginal posterior density function for the Mb. Our interest lies in identifying a shift in the mean of a single or multiple change points as well as the changes in the variation. Building upon the model and its parameters for the 2002–2003 data, we detected three change points. The first, second and third change points occurred in September 2002, April 2003 and August 2003, respectively.
Power analysis for cluster randomized trials with binary outcomes modeled by generalized linear mixed-effects models

Power analysis for cluster randomized control trials is difficult to perform when a binary response is modeled using the generalized linear mixed-effects model (GLMM). Although methods for clustered binary responses exist such as the generalized estimating equations, they do not apply to the context of GLMM. Also, because popular statistical packages such as R and SAS do not provide correct estimates of parameters for the GLMM for binary responses, Monte Carlo simulation, a popular ad-hoc method for estimating power when the power function is too complex to evaluate analytically or numerically, fails to provide correct power estimates within the current context as well. In this paper, a new approach is developed to estimate power for cluster randomized control trials when a binary response is modeled by the GLMM. The approach is easy to implement and seems to work quite well, as assessed by simulation studies. The approach is illustrated with a real intervention study to reduce suicide reattempt rates among US Veterans.
Enhancing the fidelity of two-qubit gates by measurements

Dynamical decoupling techniques are the method of choice for increasing gate fidelities. While these methods have produced very impressive results in terms of decreasing local noise and increasing the fidelities of single qubit operations, dealing with the noise of two qubit gates has proven more challenging. The main obstacle is that the noise time scale is shorter than the two qubit gate itself so that refocusing methods do not work. We present a measurement and feedback based method to refocus two qubit gates which cannot be refocused by conventional methods. We analyze in detail this method for an error model which is relevant for trapped ions quantum information.
NICFS: a file system based on persistent memory and SmartNIC

Emergence of new hardware, including persistent memory and smart network interface card (SmartNIC), has brought new opportunities to file system design. In this paper, we design and implement a new file system named NICFS based on persistent memory and SmartNIC. We divide the file system into two parts: the front end and the back end. In the front end, data writes are appended to the persistent memory in a log-structured way, leveraging the fast persistence advantage of persistent memory. In the back end, the data in logs are fetched, processed, and patched to files in the background, leveraging the processing capacity of SmartNIC. Evaluation results show that NICFS outperforms Ext4 by about 21%/10% and about 19%/50% on large and small reads/writes, respectively.
Hybrid arithmetic

Kaufmann's (1986) formulation of hybrid numbers, which simultaneously express fuzzy and probabilistic uncertainty, allows addition and subtraction, but offers no obvious way to do multiplication, division or other operations. We describe another, more comprehensive formulation for hybrid numbers that allows the full suite of arithmetic operations, permitting them to be incorporated into complex mathematical calculations. There are two complementary approaches to computing with these hybrid numbers. The first is extremely efficient and yields theoretically optimal results in many circumstances. The second more general approach is based on Monte Carlo simulation using intervals or fuzzy numbers rather than scalar numbers.
NEHASH: high-concurrency extendible hashing for non-volatile memory

Extendible hashing is an effective way to manage increasingly large file system metadata, but it suffers from low concurrency and lack of optimization for non-volatile memory (NVM). In this paper, a multilevel hash directory based on lazy expansion is designed to improve the concurrency and efficiency of extendible hashing, and a hash bucket management algorithm based on groups is presented to improve the efficiency of hash key management by reducing the size of the hash bucket, thereby improving the performance of extendible hashing. Meanwhile, a hierarchical storage strategy of extendible hashing for NVM is given to take advantage of dynamic random access memory (DRAM) and NVM. Furthermore, on the basis of the device driver for Intel Optane DC Persistent Memory, the prototype of high-concurrency extendible hashing named NEHASH is implemented. Yahoo cloud serving benchmark (YCSB) is used to test and compare with CCEH, level hashing, and cuckoo hashing. The results show that NEHASH can improve read throughput by up to 16.5% and write throughput by 19.3%.
Method of Color Image Retrieval Based on Quantified Color Space

In HSV color space that is the best visual identity of people,color images can be quantified and quantitative handle again,so the impact of the continuity of the color is overcome in retrieval.the Euclidean distance is used in the retrieval of a Color image database.Because the Euclidean distance in retrieval process does not take into account similarities between the color limitations,and so the introduction of a similar color,matching similar matrix,using histogram quadratic distance formula as a similar measure to achieve a more satisfactory results.
The resolvent average : an expansive analysis of firmly nonexpansive mappings and maximally monotone operators

Monotone operators and firmly nonexpansive mappings are essential to modern optimization and fixed point theory. Minty first discovered the link between these two classes of operators; every resolvent of a monotone operator is firmly nonexpansive and every firmly nonexpansive mapping is a resolvent of a monotone operator. This thesis provides an in-depth study of the relationship between firmly nonexpansive mappings and maximally monotone operators. First, corresponding properties between maximally monotone operators and their resolvents are collected. Then a new method of averaging monotone operators is presented, called the resolvent average, which is based on the convex combination of the resolvents of monotone operators. Several new results are given concerning the asymptotic regularity of compositions and convex combinations of firmly nonexpansive mappings. Finally, the resolvent average is studied with respect to which properties the average inherits from the averaged operators.
Rice's Theorem

Rice’s Theorem is a very general and powerful result. Roughly speaking, it states that it every nontrivial property of recognizable languages is undecidable. As immediate corollaries we have that it is impossible to decide if, given a Turing machine, its language is regular, or is context-free, or is empty, or is finite, or contains the string 0100, or contains only strings of odd length, and so on. To state the theorem we must define what we mean by “property of recognizable languages”. We use a very general and simple definition: A property P of recognizable languages is any set of recognizable languages. (Intuitively, if we think of the property P as a predicate that a language satisfies or not, the set in question is precisely the set of languages that satisfy the predicate.) Property P is non-trivial if it is neither empty nor the set of all recognizable languages: In other words, some recognizable languages have property P and some don’t. Do not confuse the property P = ∅ with the property P ′ = {∅}. The former is trivial (no language has it) while the second is non-trivial (exactly one language has it, namely the language that contains no strings).
A Demodulator for UWOC-OAM-SK Systems Based on Convolutional Neural Networks

In order to identify the different modes of vortex beams in the underwater OAM shift keying (OAM-SK) system more conveniently and accurately, a deep learning technique is introduced, and an improved convolutional neural networks (CNN) model is proposed as the demodulator of the communication system. Based on the vortex beam transmission characteristics and the fundamental theory of ocean turbulence, a random phase screen is generated by the power spectrum inversion method with low frequency compensation to simulate the transmission of vortex beam in different ocean turbulence channels. The recognition rate of this CNN model as a system demodulator for the damaged light intensity distribution maps of LG beams, BG beams and superposed state LG beams (S-LG) after passing through ocean turbulence is investigated. The experimental results show that the CNN model has strong generalization ability and can effectively identify different kinds of vortex beams and its modes. The recognition rate can reach more than 97% for superposition state LG beams after 100m transmission in high intensity turbulent environment. The model has some reference value for the research design of OAMSK system.
Long Range Detection of Emanation from HDMI Cables Using CNN and Transfer Learning

The transition of data and clock signals between high and low states in electronic devices creates electromagnetic radiation according to Maxwell's equations. These unintentional emissions, called emanation, may have a significant correlation with the original information-carrying signal and form an information leakage source, bypassing secure cryptographic methods at both hardware and software levels. Information extraction exploiting compromising emanations poses a major threat to information security. Shielding the devices and cables along with setting a control perimeter for a sensitive facility are the most commonly used preventive measures. These countermeasures raise the research need for the longest detection range of exploitable emanation and the efficacy of commercial shielding. In this work, using data collected from 3 types of commercial HDMI cables (unshielded, single-shielded, and double-shielded) in an office environment, we have shown that the CNN-based detection method outperforms the traditional threshold-based detection method and improves the detection range from 4 m to 22.5 m for an iso-accuracy of ~ 95%. Also, for an iso-distance of 16 m, the CNN-based method provides ~ 100% accuracy, compared to ~ 88.5% using the threshold-based method. The significant performance boost is achieved by treating the FFT plots as images and training a residual neural network (ResNet) with the data so that it learns to identify the impulse-like emanation peaks even in the presence of other interfering signals. A comparison has been made among the emanation power from the 3 types of HDMI cables to judge the efficacy of multi-layer shielding. Finally, a distinction has been made between monitor contents, i.e., still image vs video, with an accuracy of 91.7% at a distance of 16 m. This distinction bridges the gap between emanation-based image and video reconstruction algorithms.
An Ultra-Low-Power Serial Implementation for Sigmoid and Tanh Using CORDIC Algorithm

Activation functions (AFs) such as sigmoid and tanh play an important role in neural networks (NNs). Their efficient implementation is critical for always-on edge devices. In this work, we propose a serial-arithmetic architecture for AFs in edge audio applications using the CORDIC algorithm. The design enables to dynamically trade-off throughput/latency and accuracy, and pos-sesses higher area and power efficiency compared to conventional methods such as look-up table (LUT) and piece-wise linear (PWL)-based methods. Considering the throughput difference among the designs, we evaluate average power consumption taking into account active and idle working cycles for same applications. Synthesis results in a $\mathbf{22}\mathbf{nm}$ process show that our CORDIC-based design has an area of 545.77 $\boldsymbol{\mu} \mathbf{m}^{2}$ and an average power of 0.69 $\boldsymbol{\mu} \mathbf{W}$ for a keyword spotting task, achieving a reduction of 36.92% and 71.72% in average power consumption compared to LUT and PWL-based implementations, respectively.
FastIoTBot: Identifying IoT Bots by Fast Detecting Anomalous Domain Queries with Long Short-Term Memory Networks

Along with the progression in technology, Internet of Things (IoT) has been dramatically developed in recent ten years. It connects physical world and digital world, which makes people's life more convenient. However, IoT devices have bring great vulnerability to Internet security since they usually under weak protection, which makes them easy to be exploited by criminals to launch multiple attacks. In fact, IoT devices have been a crucial part of botnets that launch horrible Distributed Denial of Service (DDoS) with explosive traffic. Unfortunately, traditional detection works have limited effectiveness face IoT botnets because of the restricted resources of IoT devices and unprecedented huge scale of IoT botnets. To mitigate the threat of IoT botnets, in this paper, we propose a lightweight system, named FastIoTBot, to discover compromised IoT devices in a fast way. FastIoTBot can distinguish compromised IoT devices instantly and prevent potential malicious behaviors by examining domain query activities. Specifically, FastIoTBot monitors the DNS query for a device and generates its NXDOMAIN query sequence. Then, for each domain in the sequence, FastIoTBot takes the domain name string as input and calculates its malicious score using long short-term memory (LSTM) model. Finally, FastIoTBot identifies compromised IoT devices through analyzing NXDOMAIN sequences with internal domains' malicious score leveraging threshold random walk (TRW) algorithm. The effectiveness of FastIoTBot is evaluate with real world DNS data of two large ISP networks. The results show that FastIoTBot perform well with over 99% accuracy.
A Review of Research on Virtual Reality Technology Based on Human-Computer Interaction in Military

Recent research shows that virtual reality is starting to be used in various field around the world. Virtual reality also has great application value in the civil industry, entertainment and education industries. However, compares to augmented reality, virtual reality has came into a period of bottleneck. Although virtual reality is developing rapidly, the effectiveness and using situation of virtual reality in practical application is still missing. Military operations are also seeking new developments and changes of virtual reality. By applying virtual reality into combat training, decision-making simulation and soldier training can reduce injury risk efficiently. This article classifies and summarizes current virtual reality technology applied in the military field, and looks forward to the virtual reality in military training, learning and actual warfares practical applications.
Bayesian Network Structure Learning: A Review

A Bayesian network (BN) is a probabilistic graphical model used in artificial intelligence research and application, providing a theoretical framework for machine learning. Based on Bayes’ theorem, BN is a directed acyclic graph (DAG) that connects variables using conditional probability. In recent years, significant progress has been made in this subject. By reviewing current relevant literature, this paper summarizes the most recent status of BN structure learning research. BN structure learning techniques are classified into three types: constraint-based learning methods, score-based learning methods, and hybrid learning methods. Furthermore, this paper summarizes and examines the BN structure learning technique with missing data. Finally, further research directions and topics are proposed.
TURBULENCE: Complexity-effective Out-of-order Execution on GPU with Distance-based ISA

A graphic processing unit (GPU) is a processor that achieves high throughput by exploiting data parallelism. We found that many GPU workloads also contain instruction-level parallelism, which can be extracted through out-of-order execution to provide additional performance improvement opportunities. We propose the TURBULENCE architecture for very low-cost out-of-order execution on GPUs. TURBULENCE consists of 1) a novel ISA that introduces the concept of referencing operands by inter-instruction distance instead of register numbers and 2) a novel microarchitecture that executes the novel ISA. Our proposed ISA and microarchitecture enable cost-effective out-of-order execution on GPUs without introducing expensive hardware.
ANN based Quick and Accurate Estimate of Channel Availability by using Hybrid M1-MMSE Equalizer

MIMO (multiple-input multiple-output) systems with hybrid precoding using millimeter-wave (mmWave) channel require channel estimation. However, accurate channel estimation is a challenging task, since there are a large number of antennas, but very few RF chains. To overcome this issue, this study proposes a novel pilot phase shift scheduling algorithm based quick and accurate estimation of channel availability by using a hybrid ML-MMS E equalizer. The Channel estimation is essential for mmWave MIMO systems with hybrid precoding. For an uplink (UL) massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system an effective channel estimation process is carried out. ANN and wireless channel are combined to represent the received signal using a real-valued model. Hilbert transform is a simple technique and using this the complexity of the overall process is minimized. For the accurate estimation of channel availability an efficient hybrid minimum mean square error (ML-MMSE) equalizer is utilized. According to experimental findings, the proposed technique outperforms the existing model.
Machine Learning based Fault Classification using Stray Flux and Stator Current in Induction Motor

Induction motors have wide range of applications in many industrial processes. Fault detection and classification is an important subject for the sake of safe and reliable operation of induction motors. In this work, ANSYS Maxwell-based simulations are performed for four different loading conditions (25%, 50%, 75%, and 100%) of the induction motor to obtain the stator current and stray flux data under normal and faulty conditions (BRB1, BRB2, BRB3, FPP, and SE). A deep neural network (DNN) machine learning (ML) algorithm is then proposed and compared with support vector machine (SVM) and random forest classifiers (RFC) for the detection and classification of various faults in induction motors using stray flux and stator current. The proposed deep neural network algorithm has shown better accuracy for stray flux compared with SVM and RFC on 100% loading conditions, however, it could not perform well on stator current. The results indicate that the overall performance of all machine learning algorithms is less efficient for stator current than that of stray flux.
ACRE: An Advanced CAN Signal Reverse Engineering Scheme

As modern vehicles become more exposed to the outside world, many efforts have been made to enhance the security of in-vehicle Controller Area Network(CAN). These efforts are facing significant challenges, because the CAN signals that store the driving status of vehicle are encoded into specific format, thus preventing researchers from understanding its meaning. Unfortunately, the Database CAN(DBC) file that contain these encoding patterns are kept strictly confidential by the car manufacturer. Currently, many work has been conducted to reverse engineer CAN signals. However, through our experiments, these studies have shown highly inaccuracy in identifying the boundary of CAN signals. In this paper, we propose ACRE, an advanced CAN signal reverse engineering scheme based on a novel metric of adjacent-bit changing rate (ACR). With ACR, ACRE automatically segments CAN data field. Inspired by ACR, we further present Diagonal-bit Changing Rate (DCR) to determine the endianness of CAN signal, thus accomplishing signal extraction and signedness discrimination. Verified by real-world dataset and corresponding DBC, ACRE achieves 100% coverage, and its vehicle-series compatibility have been proven by extensive experiments on 4 datasets of real car.
Speech Emotion Recognition using Channel Attention Mechanism

In order to improve the accuracy of speech emotion recognition, this paper proposes a speech emotion recognition method based on the channel attention mechanism. Firstly, Mel Frequency Ceptral Coefficient(MFCC), speech spectrograms and spectral envelopes are selected as the initial input features; then, multiple depth network models are used to extract feature maps from different angles in parallel; then, weights are assigned and fused to the feature maps output from each sub-depth network model by the channel attention mechanism; finally, the fused feature maps are used to predict emotion categories. The experimental results on CASIA, Emo-DB, and SAVEE emotion datasets show that the method achieves 88.3%, 85.1%, and 64.5% recognition accuracy, respectively, with better recognition performance compared to recent comparative literature models.
Assessment of Asthma BAL Cytokines using Machine Learning Techniques

Asthma is a chronic respiratory disorder characterised by airway inflammation and constriction, leading to difficulty in breathing and recurrent attacks of wheezing, coughing, and shortness of breath. In asthma, various cytokines, including interleukins (IL-4, IL-5, and IL-13) and tumor necrosis factoralpha (TNF-alpha), have been found to be increased in the airways of individuals. These cytokines are involved in the recruitment and activation of immune cells, such as eosinophils and T-lymphocytes, which contribute to the inflammation and airway hyperresponsiveness. Dysregulation of cytokine production and signaling has been implicated in the pathogenesis of asthma and may be targeted by therapies to alleviate symptoms and improve outcomes in individuals with this disease. We propose a predictive binary and multi-class machine learning model analysis that efficiently classify the asthma and healthy control patients by detecting cytokines in bronchoalveolar lavage (BAL) fluid which achieved better F1-score than existing approaches.