Miracle-dz/UniFAR-FTUs huggingface dataset:
Making Application-Level Crash Consistency Practical on Flash Storage

We present the design, implementation, and evaluation of a new file system, called <italic>ACCFS</italic>, supporting application-level crash consistency as its first-class citizen functionality. With <italic>ACCFS</italic>, application data can be correctly recovered in the event of system crashes without any complex update protocol at the application level. With the help of the <italic>SHARE</italic> interface supporting atomic address remapping at the flash storage layer, <italic>ACCFS</italic> can easily and efficiently achieve crash consistency as well as single-write journaling. We prototyped <italic>ACCFS</italic> by slightly modifying the full data journal mode in ext4, implemented the <italic>SHARE</italic> interface as firmware in a commercial SSD available in the market, and carried out various experiments by running <italic>ACCFS</italic> on top of the SSD. Our preliminary experimental results are very promising. For instance, the performance of an OLTP benchmark using MySQL/InnoDB engine can be boosted by more than 2–6x by offloading the responsibility of guaranteeing the atomic write of MySQL data pages from the InnoDB engine's own journaling mechanism to <italic>ACCFS</italic>. This impressive performance gain is in part due to the single-write journaling in <italic>ACCFS</italic> and in part comes from the fact that the frequent <monospace>fsync()</monospace> calls caused by the complex update protocol at the application level can be avoided. <italic>ACCFS</italic> is a practical solution for the crash consistency problem in that (1) the <italic>SHARE</italic> interface can be, like the TRIM command, easily supported by commercial SSDs, (2) it can be embodied with a minor modification on the existing ext4 file system, and (3) the existing applications can be made crash consistent simply by opening files in <monospace>O_ATOMIC</monospace> mode while the legacy applications can be run without any change.
Distinguish Markov Equivalence Classes from Large-Scale Linear Non-Gaussian Data

In the problem of causal discovery, conditional independence (CI) tests are generally used to detect the causal relationships among observed data. Due to the curse of dimensionality and the limitation of causal direction learning based on <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula>-structure learning, it is difficult for constraint-based methods to distinguish the actual graph from a set of Markov equivalence classes. To alleviate this problem, in this work, a novel regression-based method to test CIs over linear Non-Gaussian data is proposed. The main purpose of this proposal is to relax the CI test of <inline-formula> <tex-math notation="LaTeX">$x\bot y|Z$ </tex-math></inline-formula> to two unconditional independence tests <inline-formula> <tex-math notation="LaTeX">$x-f\left ({Z }\right) \bot y-g\left ({Z }\right) +\varSigma H\left ({Z }\right)$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$x-f\left ({Z }\right) +\varSigma H\left ({Z }\right) \bot y-g\left ({Z }\right)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula> can be estimated by linear regression independently. In addition, we further show that <inline-formula> <tex-math notation="LaTeX">$x-f\left ({Z }\right) \bot y-g\left ({Z }\right) +\varSigma H\left ({Z }\right)$ </tex-math></inline-formula> (or <inline-formula> <tex-math notation="LaTeX">$x-f\left ({Z }\right) +\varSigma H\left ({Z }\right) \bot y-g\left ({Z }\right)$ </tex-math></inline-formula>) can lead to <inline-formula> <tex-math notation="LaTeX">$x\gets Z$ </tex-math></inline-formula> (or <inline-formula> <tex-math notation="LaTeX">$y\gets Z$ </tex-math></inline-formula>). According to this regression-based method, we design a causal structure learning algorithm to learn the actual graph instead of a set of Markov equivalence classes over the observed data. Experiments indicate that our method can detect much more causal relationships than other existing methods, especially in large-scale cases.
e-Service Quality of Online Transportation in Indonesia: A Preliminary Finding

The purpose of this study is knowing the condition of E-Service Quality, Trust, Customer Satisfaction, Loyalty Customer and in the Online Transportation competition. Knowing the effect of E-Service Quality on Customer Satisfaction. Knowing the effect of E-Service Quality on Trust. Knowing the effect of E-Service Quality on Customer Loyalty. Knowing the influence of Trust on Customer Satisfaction. Knowing the effect of Customer Satisfaction on Customer Loyalty. Knowing the Effect of E-Service Quality on Customer Satisfaction through Trust. The method used is Structural Equation Modeling (SEM) by collecting data using a questionnaire. The results of the study state that E-Service Quality has a positive effect on Customer Loyalty with the mediation of Customer Satisfaction on Online Transportation users in Indonesia.
Forecasting medium-term electricity demand in Thailand: comparison of ANN, SVM, DBN, and their ensembles

Electricity demand forecasting is an important research area, most of the research focuses on forecasting the electricity consumption that is the critical process for planning the electric utilities to avoid a blackout in peak time. This paper focuses on forecasting the medium term (1-month ahead and 1-year ahead) of electricity peak demand in Thailand by using three machine learnings and ensemble method. The machine learnings include artificial neural network (ANN), support vector machine (SVM), and deep belief network (DBN). For the comparative performance between each model, mean absolute percentage error (MAPE) is used as the measurement. The result implies that the ensemble model of ANN and DBN is the best method for 1-month ahead with MAPE 1.44%, and ANN is the best method for 1-year ahead forecasting with MAPE 1.47%.
Sequential Association Rule Mining for Autonomously Extracting Hierarchical Task Structures in Reinforcement Learning

Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces or in environments with sparse rewards. The decomposition of tasks into a hierarchical structure holds the potential to significantly speed up learning, generalization, and transfer learning. However, the current task decomposition techniques often cannot extract hierarchical task structures without relying on high-level knowledge provided by an expert (e.g., using dynamic Bayesian networks (DBNs) in factored Markov decision processes), which is not necessarily available in autonomous systems. In this paper, we propose a novel method based on Sequential Association Rule Mining that can extract Hierarchical Structure of Tasks in Reinforcement Learning (SARM-HSTRL) in an autonomous manner for both Markov decision processes (MDPs) and factored MDPs. The proposed method leverages association rule mining to discover the causal and temporal relationships among states in different trajectories and extracts a task hierarchy that captures these relationships among sub-goals as termination conditions of different sub-tasks. We prove that the extracted hierarchical policy offers a hierarchically optimal policy in MDPs and factored MDPs. It should be noted that SARM-HSTRL extracts this hierarchical optimal policy without having dynamic Bayesian networks in scenarios with a single task trajectory and also with multiple tasks’ trajectories. Furthermore, we show theoretically and empirically that the extracted hierarchical task structure is consistent with trajectories and provides the most efficient, reliable, and compact structure under appropriate assumptions. The numerical results compare the performance of the proposed SARM-HSTRL method with conventional HRL algorithms in terms of the accuracy in detecting the sub-goals, the validity of the extracted hierarchies, and the speed of learning in several testbeds. The key capabilities of SARM-HSTRL including handling multiple tasks and autonomous hierarchical task extraction can lead to the application of this HRL method in reusing, transferring, and generalization of knowledge in different domains.
A 300mV-Supply, 2nW-Power, 80pF-Load CMOS Digital-Based OTA for IoT Interfaces

This paper presents a power-efficient Ultra Low Voltage (ULV) Digital-Based Operational Transconductance Amplifier (DB-OTA), which uses static logic gates and processes digitally the analog input signal. Post-layout simulations in 180nm CMOS technology show that at 300mV supply voltage the circuit consumes just 2nW while driving a capacitive load of 80pF with Total Harmonic Distortion lower than 5% at 100mV input signal swing. The total silicon area is $1,426\ \mu \mathrm{m}^{2}$. The maximum energy efficiency supply for the DB-OTA and its scalability to 40nm CMOS technology node are also demonstrated.
Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

This article proposes a hybrid intelligent-classic control approach for reconstruction and compensation of cyber attacks launched on inputs of nonlinear cyber-physical systems (CPS) and industrial Internet of Things systems, which work through shared communication networks. In this article, a class of n-order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel. An intelligent-classic control system is developed to compensate cyber-attacks. Neural network (NN) is designed as an intelligent estimator for attack estimation and a classic nonlinear control system based on the variable structure control method is designed to compensate the effect of attacks and control the system performance in tracking applications. In the proposed strategy, nonlinear control theory is applied to guarantee the stability of the system when attacks happen. In this strategy, a Gaussian radial basis function NN is used for online estimation and reconstruction of cyber-attacks launched on the networked system. An adaptation law of the intelligent estimator is derived from a Lyapunov function. Simulation results demonstrate the validity and feasibility of the proposed strategy in car cruise control application as the testbed.
4q-QAM Complementary Sequence Sets with Non-Power-of-Two Length*

Complementary sequences with quadrature amplitude modulation (QAM) are proposed to deal with the high peakto- mean envelope power ratio (PMEPR) problem in Orthogonal frequency-division multiplexing (OFDM) systems. There have been many constructions of 4q-QAM Golay complementary sequences of length 2m. This paper presents a construction of 4q-QAM complementary sequence sets of size 4 with non-power-of-two lengths, whose PMEPRs are upper bounded by 4. The number of 4q-QAM complementary sequences generated from this construction is also calculated in this paper.
Circuits of Resistors, Diodes and Sources: Topological Conditions Sufficient to Define the State of a Diode

Let $N$ be a circuit composed of a finite number of positive linear resistors, ideal diodes, and independent voltage and current sources with positive values. In this paper, we consider the problem to determine, without any numerical computation, the state of a diode of $N$. We propose two set of topological conditions – those of the second set being more intuitive and less cumbersome, but also less powerful, than those of the first set – such that, when verified by a diode of $N$, the state of the diode is determined and also independent of the positive values of the resistances and sources. Our results may simplify, sometimes dramatically, the usual trial-and-error procedure to find the solutions of $N$.
Improving Service Quality of Metro Systems—A Case Study in the Beijing Metro

In this study, we propose a method that combines Bayesian network, structural equation modeling, and importance-performance analyses to evaluate and improve the service quality of crowded metros from the point of service components. First, service components that influence the service satisfaction of crowded metros are investigated, and 7 service components and 27 factors are collected from the perspective of metro daily operation. Correspondingly, 8,011 sample questioners are collected using both online (i.e. microblog and WeChat) and face-to-face survey methods. The usage of these service components is more practical for metro managers to improve service quality than the factor analysis methods used in previous studies. Second, a hybrid method combining Bayesian network and structural equation modeling is proposed to establish the relationship between service quality and these service components and factors. This hybrid method can learn the uncertain relationship directly from the data, explain the psychological element, and pinpoint the crucial service component. It is concluded that security check and waiting for boarding have the greatest effect on the overall satisfaction. With the help of importance-performance analyses, some practical suggestions (e.g., theme stations, women-only carriages, and one-day ticket) are proposed to optimize bottleneck service components and thus improve the service quality of daily metro operation. Further, heterogeneity of service quality satisfaction for various groups of respondents is discussed, which contributes to effective strategies for satisfaction improvement.
Dynamic Alterations of EEG Oscillations and Networks from Resting to Deep Meditation

Meditation is a progressive process in which meditators may experience different stages to finally reach a deep meditation state. However, the temporal-dynamic process of meditation remains unclear. In this study, we recruited 38 experienced Tibetan Buddhism meditators to explore the dynamic process from resting to deep meditation from the aspects of EEG oscillations and networks. We found that the delta power was reduced and the transitivity of the delta network was increased immediately after the start of meditation. Then, the beta and gamma power increased, and the clustering coefficient and global efficiency of the gamma network increased at the deep meditation substage. Our results suggested that the participants entered into a wakeful and non-judgmental state immediately after the start of meditation, and then achieved a more efficient information processing and transferring only at a deep meditation state. Our study showed that meditation was a time-varying process, deep meditation was a different substage dominated by high frequency EEG oscillations and more small-world like brain networks.
Joint Selection of Central and Extremal Prototypes Based on Kernel Minimum Enclosing Balls

We present a simple, two step procedure that selects central and extremal prototypes from a given set of data. The key idea is to identify minima of the function that characterizes the interior of a kernel minimum enclosing ball of the data. We discuss how to efficiently compute kernel minimim enclosing balls using the Frank-Wolfe algorithm and show that, for Gaussian kernels, the sought after prototypes can be naturally found via a variant of the mean shift procedure. Practical results demonstrate that prototypes found this way are descriptive, meaningful, and interpretable.
The Advantages of Grouping Method for Repeated Requests in Coded Caching

Recently, coded caching has been widely studied due to its ability of significantly reducing the amount of transmissions (i.e., rate) during peak-traffic time. While to achieve this rate, each file is divided into a number of packets, which is even exponentially increasing with users. In practice, we prefer to design a coded caching scheme to achieve smaller rate with lower packet number. In order to reduce the packet number, the well known grouping method has been proposed by Shanmugam et al. in [1] and generated by Cheng et al. in [2]. However, the rate of the scheme obtained by grouping method is very large since there is no multicasting gain generated between different user groups. Thus, many other schemes have been investigated from other perspective. In this paper, we will show that when some files are requested more than once, the scheme obtained by grouping method has a smaller rate and smaller packet numbers than that of some other improved schemes.
Improved Answer Selection For Factoid Questions

In recent years, question and answer systems and information retrieval have been widely used by web users. The purpose of these systems is to find answers to users' questions. These systems consist of several components that the most essential of which is the Answer Selection, which finds the most relevant answer. In related works, the proposed models used lexical features to measure the similarity of sentences, but in recent works, the line of research has changed. They used deep neural networks. In the deep neural networks, early, recurrent neural networks were used due to the sequencing structure of the text, but in state of the art works, convolutional neural networks are used. We represent a new method based on deep neural network algorithms in this research. This method attempts to find the correct answer to a given question from the pool of responses. Our proposed method uses wide convolution instead of narrow convolution, concatenates sparse features vector into feature vector and uses dropout in order to rank candidate answers of the user’s question semantically. The results show a 1.01% improvement at the MAP and a 0.2% improvement at the MRR metrics than the best previous model. The experiments show using context-sensitive interactions between input sentences is useful for finding the best answer.
Crossbar Memory Architecture Performing Memristor Overwrite Logic

The non-volatile resistive state of memristor and the ease of controlling this state have triggered its usage in the design of non-volatile memories as well as logic design applications. The memristor based material implication (IMPLY) and memristor aided logic (MAGIC) have been recently presented as new methods to achieve logic computations inside memristive memory architectures. However, these methods are still surrounded by several design constraints. In this context, we present new design for memristive memories with an added computational capabilities. These capabilities are exclusively based on our proposed logic design style, namely Memristor Overwrite Logic (MOL) which is fully digital and tolerant against memristor technology variations.
Edge-Detection Based Fast Intra-Mode Selection for Depth Map Coding in 3D-HEVC

3D-High Efficiency Video Coding (3D-HEVC) contains more encoding viewpoints than traditional HEVC, resulting in a significant increase of coding complexity. In this paper, we propose a low complexity intra-mode selection algorithm to reduce the number of intra-modes by detecting the flat area and texture direction of the depth map. The corresponding intra-prediction modes are skipped when the flat region condition is satisfied. Otherwise, the direction of the edge is detected to decrease the number of angle modes in RMD, which can reduce the intra-coding complexity and coding time cost. Experimental results demonstrate that the proposed algorithm achieves on average 36.48% time saving with negligible degradation of coding performance.
A CNN-based Cleanliness Evaluation for Bowel Preparation in Colonoscopy

Colonoscopy is an effective measure for detecting early colorectal lesions. To obtain high-quality pictures during colonoscopy examination, the bowel preparation, or cleansing, is considered as the first and critical step. To date, the evaluation of bowel preparation quality is performed by colonoscopists in most of gastroenterology departments. This manual labeling is time-consuming, uneconomical and sometimes unreliable subject to expertise of physicians. In this work, we propose an automatic evaluation model that labels the cleanliness for bowel preparation in accordance with the Boston Bowel Preparation Scale (BBPS). A compact convolutional neural network (CNN) with 2 Densenet layers which have feature reuse mechanism embedded before the softmax classifier is utilized. The proposed model achieves promising evaluation performance on our dataset as well as on a public dataset named Nerthus where is better than the existing research results. The average correct evaluation ratio is up to 89.58% in 20 individual cases (573 images in total), where each case includes 16 images at least. The method proposed in this paper is considered to be effective and feasible by professional colonoscopists and can be used for clinical auxiliary diagnosis.
Pakistani Media Fake News Classification using Machine Learning Classifiers

Identification of Fake News is import now a days because it is affecting our social life and opinions. Public misinformation detection is complicated task especially Pakistani media Fake News classification. We have seen Fake News in every aspect of life like politics, sports, business, entertainment and many more. For identification of fake news, we have done popular news websites scrap and develop our corpus of 344 News articles and labeled it manually Fake or True. We have investigated two feature extraction techniques like Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF). Seven different supervised Machine Learning (ML) classification algorithms are used and their results comparison have done. Best performance classifier K Nearest Neighbors (KNN) gives 70% accuracy and logistic regression gives 69% accuracy. Results can improved further by increasing number of articles in corpus.
A Digital Watermarking Encryption Technique Based on FPGA Cloud Accelerator

Digital watermarking has the properties such as invisibility and anti-aggression, so the digital watermarking technology has been widely used in copyright protection, information hiding. The watermarking technology takes into account the invisibility and robustness of the watermark by controlling the embedding intensity and position of the watermark mainly in the transformation domain. In this paper, discrete cosine transform (DCT) is adopted to transform the given image from spatial domain to frequency domain for adding watermark information. In order to meet the demands of image watermarking batch processing and cloud processing in the future, this paper optimized the DCT algorithm and the data precision, and successfully deployed the designed accelerator kernel on the FPGA cloud platform to speed up the processing of watermarking. The implementation of data processing based on cloud platform is the development trend of big data era. The cloud platform adopted in this paper is based on the OpenCL heterogeneous architecture combining CPU and FPGA. The cloud-based implementation makes digital watermarking application highly extensible, widely shareable, and more secure. The whole system implements a series of complete cloud processes including image decoding, image preprocessing, watermark embedding, and watermarked image encoding. The watermarking algorithm is accelerated by the efficient parallel computing capabilities of FPGA. It can be seen that the result of acceleration is remarkable, providing the state-of-the-art throughput of 1.676 GBps and the highest processing speed of 937 FPS for $800\times800$ sized colorimage.