Miracle-dz/UniFAR-FTUs huggingface dataset:
Actuator fault detection and isolation for robot manipulator using higher order sliding mode observers

Fault detection and isolation (FDI) approach for actuator faults on a robot manipulator using a model-based FDI fault, which can occur on a specific component of the system. To detect actuator faults, higher order sliding mode Unknown Input Observers (UIO) are proposed to make analytical redundancy. The observers input laws are designed according to the so-called Super-Twisting Second Order Sliding Mode Control (SOSMC) approach and they are proved to be capable of guaranteeing the exponential convergence of the fault estimate to the actual fault signal. The proposed approach is verified in simulation on a robot manipulator.
Repairing Reed-Solomon Codes Over <inline-formula> <tex-math notation="LaTeX">$GF(2^\ell)$ </tex-math></inline-formula>

We provide a detailed algorithm to repair a single node failure in an <inline-formula> <tex-math notation="LaTeX">$(n,k)$ </tex-math></inline-formula> Reed-Solomon code over <inline-formula> <tex-math notation="LaTeX">$ {\textit{GF}}(2^\ell)$ </tex-math></inline-formula> with repair bandwidth <inline-formula> <tex-math notation="LaTeX">$\frac {\ell }{\textit {a}}(\textit {n}-1)( {\textit a-s})$ </tex-math></inline-formula>, for any integers <inline-formula> <tex-math notation="LaTeX">$a,s$ </tex-math></inline-formula> such that <inline-formula> <tex-math notation="LaTeX">$ {a}|\ell, 2^{ {a}} \ge {n}+1, 2^{ {s}}\leq {\textit n-k}$ </tex-math></inline-formula>. We present the constructions of necessary lookup tables for the repair. The storage overhead and the repair complexity of our algorithm are also analyzed. The algorithm can be applied to the <inline-formula> <tex-math notation="LaTeX">$(14,10)$ </tex-math></inline-formula> Reed-Solomon codes over <inline-formula> <tex-math notation="LaTeX">${\textit{GF}}(2^{8})$ </tex-math></inline-formula>, which is a modification of the code in Facebook’s f4 system, and reaches the lowest repair bandwidth among the existing schemes to the best of our knowledge. The algorithm can be generalized to other codes, including the ones based on Yahoo Object Store and Baidu’s Atlas.
Decision trees using local support vector regression models for large datasets

ABSTRACT Our proposed decision trees using local support vector regression models (tSVR, rtSVR) aim to efficiently handle the regression task for large datasets. The learning algorithm tSVR of regression models is done by two main steps. The first one is to construct a decision tree regressor for partitioning the full training dataset into k terminal-nodes (subsets), followed which the second one is to learn the SVR model from each terminal-node to predict the data locally in a parallel way on multi-core computers. The algorithm rtSVR learns the random forest of decision trees with local SVR models for improving the prediction correctness against the tSVR model alone. The performance analysis shows that our algorithms tSVR, rtSVR are efficient in terms of the algorithmic complexity and the generalization ability compared to the classical SVR. The experimental results on five large datasets from UCI repository showed that proposed tSVR and rtSVR algorithms are faster than the standard SVR in training the non-linear regression model from large datasets while achieving the high correctness in the prediction. Typically, the average training time of tSVR and rtSVR are 1282.66 and 482.29 times faster than the standard SVR; Furthermore, tSVR and rtSVR improve 59.43%, 63.70% of the relative prediction correctness compared to the standard SVR.
Mininet on steroids: exploiting the cloud for Mininet performance

Networks have become complex systems that combine various concepts, techniques, and technologies. As a consequence, modelling or simulating them is now extremely complicated and researchers massively resort to prototyping techniques. Among other tools, Mininet is the most popular when it comes to evaluate SDN propositions. It allows to emulate SDN networks on a single computer. However, under certain circumstances experiments (e.g., resource intensive ones) may overload the host running Mininet. To tackle this issue, we propose Distrinet, a way to distribute Mininet over multiple hosts. Distrinet uses the same API than Mininet, meaning that it is compatible with Mininet programs. Distrinet is generic and can deploy experiments in Linux clusters or in the Amazon EC2 cloud. Thanks to optimization techniques, Distrinet minimizes the number of hosts required to perform an experiment given the capabilities of the hosting infrastructure, meaning that the experiment is run in a single host (as Mininet) if possible. Otherwise, it is automatically deployed on a platform using a minimum amount of resources in a Linux cluster or with a minimum cost in Amazon EC2.
Ultra-low latency LTE uplink frame structure

Various aspects described herein relate to communicating in a wireless network. An uplink resource grant can be received from a network entity for communicating in the wireless network. A transmission time interval (TTI) for an uplink transmission within a subframe based on the uplink resource grant can be determined, wherein the TTI comprises one or more symbols which are a subset of a plurality of symbols in the subframe. Communications can be transmitted to the network entity over resources specified in the uplink resource grant during the TTI.
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing

This paper presents the system used in our submission to the CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing. Our system is a graph-based parser which combines an extended pointer-generator network that generates nodes and a second-order mean field variational inference module that predicts edges. Our system achieved 1st and 2nd place for the DM and PSD frameworks respectively on the in-framework ranks and achieved 3rd place for the DM framework on the cross-framework ranks.
Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this article, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices’ transmission schedule over time, while ensuring the reliability of data collection and required 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.
Online eigenvector transformation reflecting concept drift for improving network intrusion detection

Currently, large data streams are constantly being generated in diverse environments, and continuous storage of the data and periodic batch‐type principal component analysis (PCA) are becoming increasingly difficult. Various online PCA algorithms have been proposed to solve this problem. In this study, we propose an online PCA methodology based on online eigenvector transformation with the moving average of the data stream that can reflect concept drift. We compared the network intrusion detection performance based on online transformation of eigenvectors with that of offline methods by applying three machine learning algorithms. Both online and offline methods demonstrated excellent performance in terms of precision. However, in terms of the recall ratio, the performance of the proposed methodology with integrated online eigenvector transformation was better; thus, the F1‐measure also indicated better performance. The visualization of the principal component score shows the effectiveness of our method.
Dependency-Based Self-Attention for Transformer NMT

In this paper, we propose a new Transformer neural machine translation (NMT) model that incorporates dependency relations into self-attention on both source and target sides, dependency-based self-attention. The dependency-based self-attention is trained to attend to the modifiee for each token under constraints based on the dependency relations, inspired by Linguistically-Informed Self-Attention (LISA). While LISA is originally proposed for Transformer encoder for semantic role labeling, this paper extends LISA to Transformer NMT by masking future information on words in the decoder-side dependency-based self-attention. Additionally, our dependency-based self-attention operates at sub-word units created by byte pair encoding. The experiments show that our model improves 1.0 BLEU points over the baseline model on the WAT’18 Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.
Monitoring tool wear using wavelet package decomposition and a novel gravitational search algorithm–least square support vector machine model

Monitoring tool wear has drawn much attention recently since tool failure will make it hard to guarantee the surface integrity of workpieces and the stability of manufacturing process. In this paper, the integrated approach that combines wavelet package decomposition, least square support vector machine, and the gravitational search algorithm is proposed for monitoring the tool wear in turning process. Firstly, the wavelet package decomposition is utilized to decompose the original cutting force signals into multiple sub-bands. Root mean square of the wavelet packet coefficients in each sub-band are extracted as the monitoring features. Then, the gravitational search algorithm–least square support vector machine model is constructed by using the extracted wavelet–domain features so as to identify the tool wear states. Eight sets of cutting experiments are conducted to prove the superiority of the proposed integrated approach. The experimental results show that the wavelet–domain features can help to ameliorate the performance of the gravitational search algorithm–least square support vector machine model. Besides, gravitational search algorithm–least square support vector machine performs better than gravitational search algorithm–support vector machine in prediction accuracy of tool wear states even in the case of small-sized training data set and the time consumption of parameters optimization in gravitational search algorithm–least square support vector machine is less than that of gravitational search algorithm–support vector machine under large-sized training data set. What's more, the gravitational search algorithm–least square support vector machine model outperforms some other related methods for tool wear estimation, such as k-NN, feedforward neural network, classification and regression tree, and linear discriminant analysis.
Improving the Construction Industry Quality Using the Seven Basic Quality Control Tools

The organizations used quality tools to develop their processes and gain satisfaction from the customers. The main objective of this study is to develop levels of quality in the construction industry through the use of the seven basic quality control tools. Such tools are extremely crucial tools which are used worldwide in the industries for continual improvement. The seven basic quality tools are Check Sheet, Histogram, Pareto Chart, Fishbone Diagram, Control Chart, Flowchart and Scatter Diagram. They were implemented in various steps of the process in order to define the problems, measure its impacts, find out its root causes and solve these problems to ensure the production of non-defective items. The study shows how the seven basic tools of quality are very useful and effective in identifying and removal of defects from the manufacturing process. These tools are helpful in every stage of the defect removal process. This study was conducted on Cleopatra Group Company. This company succeeded to serve the public and private projects in the Egyptian construction sectors.
Improved Session-Based Recommendations Using Recurrent Neural Networks for Music Discovery

The number of songs on the music platform is large while users just have access to a fraction of songs. Recommendation systems are created to solve this problem and collaborative filtering is the most widely used method. But collaborative filtering methods are overly dependent on user ratings of projects. New recommendation methods, Deep Learning techniques, are proposed especially for items’ feature extraction and for session-based recommendation with RNN. In this paper, we considered a LSTM-based approach for session-based recommendation and built architecture for music discovery. This architecture is composed of four modules: MDM (Music Data Modeling), NSP (Next Song Prediction Using LSTM), MLB (Music Library Building) and R4U (Recommendation for Users). Improved session-parallel mini-batches and ranking loss function are leveraged by the architecture to modify the basic LSTM. Through the experiment, we obtained a relative improvement in ranking metrics (MRR@N and Recall@N) over the session-based algorithms baselines.
The key for success in enterprise information systems projects: development of a novel ERP readiness assessment method and a case study

ABSTRACT ERP projects have a significant cost-share with high failure-rates. When the failure factors of ERP projects are evaluated, it is noteworthy that enterprises do not pay enough attention to ERP Readiness and moved directly to implementation. The literature lacks to explain ‘how and with which method’ an ERP Readiness assessment of an enterprise is made. Therefore, this study develops a novel ERP Readiness Assessment Model and Measurement Tool. The developed eight-staged methodology gives the ability for a holistic assessment of companies in transition to ERP systems. Besides, the results are in alignment with two MCDM methods for case company evaluation.
Performance Analysis and Design of an IoT-Friendly DAG-based Distributed Ledger System

Distributed ledgers provide many advantages over centralized solutions in IoT projects including but not limited to improved security, transparency and fault tolerance. However, in order to leverage them at scale, their well-known limitations, i.e., scalability and performance, should be adequately addressed. DAG-based distributed ledgers have been proposed to tackle the performance and scalability issues by design. The first among them, IOTA, has shown promising signs in terms of scalability and performability. In this thesis, we first conduct a comprehensive literature review on both distributed ledger technology applications in IoT and the performance evaluation of such decentralized systems. Then we present a detailed technical overview of IOTA, following a contractive review of different DAG-based distributed ledger technologies. Next, we propose a scalable transactive smart homes infrastructure by leveraging IOTA protocol and following the separation of concerns (SOC) design principle. Based on the proposed solution, an experiment with 40 home nodes is conducted to prove the concept at large scale in a cloud environment. The results show that our solution provides a high transaction speed and scalability, as well as good performance on micropayment which is important in IoT initiatives. We conduct an analysis and discuss how the new system breaks out the Blockchain Trilemma, which claims that it is almost impossible for a ii blockchain platform to simultaneously reach decentralization, scalability and security. Based on our findings on scalability and performance, we conclude that the proposed DAG-based distributed ledger is an effective solution for building an IoT infrastructure for smart communities, in which local residents can freely and securely transfer values. Finally, we rigorously study the performance of the ledger to examine its applicability for IoT projects in which a high throughput is required. More specifically, we investigate the IOTA system to answer two key research questions: 1) what is the confirmation rate in the system given the design parameters and 2) what will be the optimal waiting time for a user to resend its previously submitted but not yet confirmed transaction to the ledger? In order to answer these vital questions, we perform real experimentation, simulation and analytical modeling. Our findings reveal the impact of arrival rate of transactions, consensus algorithm, randomness of the weighted random walk for tip selection and network delay on the confirmation rate. By decomposing the transaction confirmations in each graph layer, we build an analytical layered model. Thanks to the analytical modeling, we shed some light on the distribution of confirmation process, which is leveraged to calculate the optimal time for resending the unconfirmed transaction to the distributed ledger. Our performance model can be used by IoT project designers to perform whatif analysis and capacity planning in advance of the real deployments, with high level of accuracy.
Performance evaluation of different clustering algorithms for data streams

: Many internet-based applications generate data streams, among others, the financial markets, computer network, phone conversations, biological and medical applications. Streaming data presents challenges in different level that cannot be handled by traditional database management systems. Reliability, scalability, durability, lack of structure, response time are some of them. Motivated by the industry’s need to obtain useful knowledge from data streams on the fly, several clustering methods have been proposed. In particular, in this paper, we compare experimentally 5 state of the art stream clustering algorithms: StreamKM++, CluStream, DenStream, ClusTree and ClusCTA-MEWMA. We assess their robustness in the presence of noisy data. We conduct experiments based on synthetic datasets. The results show that ClusCTA-EWMA has better performance than the other algorithms in datasets with noise.
S.O.B (Save Our Budget) - A Simulation-Based Method for Prediction of Acquisition Costs of Constituents of a System-of-Systems

Software economics, acquisition, and pricing are important con- cerns for Systems-of-Systems (SoS). SoS are alliances of independent software- intensive systems combined to offer holistic functionalities as a result of the con- stituents interoperability. SoS engineering involves separately acquiring con- stituents and combining them to form the SoS. Despite the existence of cost pre- diction techniques, predicting SoS acquisition costs at design-time should also include the analysis of different suppliers of constituents, their respective prices and quality. However, known methods cover only two out of these three param- eters. The main contribution of this article is to present the S.O. B. (Save Our Budget) method, a novel simulation-based method to predict, at design-time, the acquisition cost of constituents, while still considering quality attributes and different suppliers. Results of a case study in the Smart Building domain re- vealed that S.O. B. method supports a precise prediction of acquisition cost of constituents to build a SoS for that domain. Furthermore, it also contributes to estimate the cost based on a pre-established quality attribute (functional suit- ability), as well as to support the selection of coalition that exhibits better results through the analysis of cost-benefit ratio.
Run-Time Adaptation of Modalities of Interaction and Context of Use in Web MobileApps

Several approaches have emerged to support the development of mobile web applications (apps). Front-end frameworks (FeF) have emerged to support developers in the construction of responsive mobile web apps. However, these frameworks do not provide resources to handle easily variables of the context of use and to deal with diﬀerent modalities of interaction. Considering this gap, we proposed the HyMobWeb, an approach that assists developers in working with these aspects. By grasping the popularity of the FeFs, HyMobWeb proposes a ﬂexible and reusable approach based on FeF structure. It works with a hybrid approach that treats the adaptation in two phases. The static one, performed in the development time, allows developers to implement the resources of adaptation. The dynamic one performs the adaptation during the run-time. In this article, we present the HyMobWeb dynamicadaptationapproach. Asend-usersarethereceiversofthedynamicadaptationourevaluation was carried out in the perspective of this audience. The results showed that the adaptations regarding the context of use were the ones that presented more impact on the user experience. We concluded that the HyMobWeb dynamic adaptation provides ways to enhance user interaction in mobile web apps when compared with RWD resources.
Proposed Intelligent Software System for Early Fault Detection

The major challenge in designing an Intelligent Information Software System for fault detection is to detect faults at an early stage unless it becomes a failure. This can be achieved by using feature selection and effective classification applied on failure datasets. Support Vector Machines (SVM) are used for efficient and accurate feature selection by finding unknown model parameters using local and global kernel parameter optimization. Research shows that previously many attempts were made using classical classifiers as decision tree, naïve bayes, and k-NN for software fault prediction. In earlier research, class imbalance problems in software fault datasets were not addressed. In this paper, we propose an intelligent hybrid algorithm that is based on feature selection hybrid kernel function SVM and entropy-based bagging for efficient classification to reduce the class imbalance problem. The proposed model is compared with traditional approaches. The improved hybrid algorithm based on entropy-based bagging and mixed kernel SVM can effectively improve the classification accuracy of NASA Metric Data Program (MDP) faulty datasets. This paper presents an empirical study on using the proposed hybrid algorithm and results showed that our proposed approach enhances the classification accuracy when compared with existing methods.