Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust fault tolerant control for a class second-order nonlinear systems using an adaptive third-order sliding mode control

—Conventional sliding mode control (SMC) has been extensively developed for design of fault tolerant control (FTC) systems. However, the used of conventional SMC has several disadvantages such as large transient state error, less robustness and large chattering, that limit its application for real application. In order to enhance the performance, a novel passive fault tolerant control (AFTC) based on a chattering-free adaptive third-order sliding mode control (ATOSMC), which integrates a novel third-order sliding mode surface (TOSMS) with a continuous strategy and an adaptation law, is proposed. Compared to other state-of-the-art approaches, the proposed controller has a great fault-tolerant capability to accommodate several types of actuator faults with an enhancing on robustness, precision, chattering reduction and time of convergence. The proposed analytical results are then applied to the attitude control of a spacecraft. Simulation results demonstrate the superior performance of the proposed algorithm.
Robust fault tolerant control for a class second-order nonlinear systems using an adaptive third-order sliding mode control

Conventional sliding mode control (SMC) has been extensively developed for design of fault tolerant control (FTC) systems. However, the used of conventional SMC has several disadvantages such as large transient state error, less robustness and large chattering, that limit its application for real application. In order to enhance the performance, a novel passive fault tolerant control (AFTC) based on a chattering-free adaptive third-order sliding mode control (ATOSMC), which integrates a novel third-order sliding mode surface (TOSMS) with a continuous strategy and an adaptation law, is proposed. Compared to other state-of-the-art approaches, the proposed controller has a great fault-tolerant capability to accommodate several types of actuator faults with an enhancing on robustness, precision, chattering reduction and time of convergence. The proposed analytical results are then applied to the attitude control of a spacecraft. Simulation results demonstrate the superior performance of the proposed algorithm.
A Survey on: Routing Techniques for VANET

Vehicular Ad-Hoc Networks(VANET) , are a particular kind of Mobile Ad-Hoc Networks(MANET), in which vehicles act as nodes and each vehicle is equipped with transmission capabilities which are interconnected to form a network. The topology created by vehicles is very dynamic and nonuniformly distributed. Routing is the process of selecting best paths in networks. Routing Protocols should be localized, loopfree, free from stale routes, must optimally use scarce resources like bandwidth, battery power etc., must able to provide a certain level of Quality Of Service( QoS). The routing principal criterion of succesful routing in VANET is correctness but it is not only the criteron. Preference is to take the most direct , reliable , scenic and the least expensive route. The main objective of this survey paper is to study and analyse various routing techniques for VANET, and features provided by these techniques. Keywords— Adaptive Position Update, Dead-End Reduction, Zone of Relevance, Global Positioning system, End Systems.
ShareABEL : Secure Sharing of mHealth Data through Cryptographically-Enforced Access Control

Owners of mobile-health apps and devices often want to share their mHealth data with others, such as physicians, therapists, coaches, and caregivers. For privacy reasons, however, they typically want to share a limited subset of their information with each recipient according to their preferences. In this paper, we introduce ShareABEL, a scalable, usable, and practical system that allows mHealth-data owners to specify access-control policies and to cryptographically enforce those policies so that only parties with the proper corresponding permissions are able to decrypt data. The design (and prototype implementation) of this system makes three contributions: (1) it applies cryptographically-enforced access-control measures to wearable healthcare data, which pose different challenges than Electronic Medical Records (EMRs), (2) it recognizes the temporal nature of mHealth data streams and supports revocation of access to part or all of a data stream, and (3) it departs from the vendorand device-specific silos of mHealth data by implementing a secure end-to-end system that can be applied to data collected from a variety of mHealth apps and devices.
Novelty and diversity enhancement and evaluation in Recommender Systems

Recommender Systems have become a pervasive technology in a wide spectrum of everyday applications, and can be said to be familiar to the general public. In situations where there is an information overload, such as e-commerce, streaming platforms or social networks, providing personalized recommendations has proven to be a major source of enhanced functionality, user satisfaction, and revenue improvements. The development of recommendation algorithms and technologies has typically focused on maximizing the prediction accuracy of the user? s interests. However, there is an increasing awareness in the field that there are other properties that have an impact on user satisfaction and business performance. In particular, there are many cases where applying some degree of novelty or diversity may be beneficial for both the users that receive the recommendations and the business that provides them. In this thesis we develop a principled approach to the evaluation and enhancement of novelty and diversity in Recommender Systems. We consider that the improvement of such fundamental dimensions of the usefulness of recommendations has to take into account how users explore and perceive recommendations, what are the problems that novelty and diversity solve and the causes of such problems. We propose in our first contribution a unified framework for the evaluation and enhancement of novelty and diversity in recommendations that generalizes and enhances many of the proposals previously studied in the state of the art under a common basis. Special emphasis is done in the study of the diversity within recommendations lists, for which two different contributions are presented. On the one hand, an adaptation of search result diversification metrics and techniques from Information Retrieval is explored to cope with the ambiguity of user interests and tastes. On the other hand, a domain-specific solution for assessing and optimizing the diversity of recommendations is proposed to address the need of users for varied recommendations when genre information about the recommendation domain is available. Finally, we address diversity as an overall quality from the system point of view, and we propose solutions for the problem in this perspective by turning the recommendation task around and recommending users to items. Our proposals are tested on a common experimental design that considers three different datasets for movie and music recommendation and four well-known baseline recommendation algorithms. The results of our experiments support the validity of our contributions and allow the analysis and further insights on their behavior when applied to different settings.
Reducing UK-Means to K-Means

This paper proposes an optimisation to the UK-means algorithm, which generalises the k-means algorithm to han- dle objects whose locations are uncertain. The location of each object is described by a probability density function (pdf). The UK-means algorithm needs to compute expected distances (EDs) between each object and the cluster repre- sentatives. The evaluation of ED from first principles is very costly operation, because the pdf 's are different and arbi- trary. But UK-means needs to evaluate a lot of EDs. This is a major performance burden of the algorithm. In this pa- per, we derive a formula for evaluating EDs efficiently. This tremendously reduces the execution time of UK-means, as demonstrated by our preliminary experiments. We also il- lustrate that this optimised formula effectively reduces the UK-means problem to the traditional clustering algorithm addressed by the k-means algorithm.
Rotation Invariant Texture Classification using BRINT and GLCM with SVM Classifier

: Texture classification is one of the four problem domains in the field of texture analysis for the development of effective features to extract from a given textured image. This paper proposes a Binary Rotation Invariant and Noise Tolerant (BRINT) which is a very fast, compact and also more accurate while illumination variations, noise and rotation changes. Here Gray Level Co-occurrence matrix algorithm is used along with BRINT for feature extraction. Texture classification is performed with the SVM classification. The proposed method is compared with the existing K Nearest Neighbourhood algorithm. In our proposed work, snake texture has been taken for processing and the proposed method is quantified with various performance metrics like accuracy, sensitivity, specificity and PSNR performance and found to be greater compared with the existing method.
Forçage d'un profil par des tourbillons de Von Kármán provenant d'un profil amont

Une etude experimentale de l’interaction entre deux profils, correspondant a une configuration d’avion a empennage en T, est conduite en soufflerie pour une gamme de nombres de Reynolds moyens. La mesure des forces aerodynamiques est effectuee par des balances a jauges de deformation et le champ de vitesse est obtenu par velocimetrie par images de particules. L’etude se concentre sur les configurations a grands angles d’incidence, correspondant a des ecoulements decolles sur les profils et la presence d’une allee de tourbillons alternes de Von Karman. Des moyennes de phase des champs de vitesse sont obtenues afin d’observer l’evolution temporelle de l’ecoulement. On constate une modulation de la portance, qui depend des parametres geometriques des trajectoires des tourbillons.
Studying MOOC completion at scale using the MOOC replication framework

Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. In the proof of concept presented here, we use MORF to attempt to replicate 15 previously published findings across 29 iterations of 17 MOOCs. The findings indicate that 12 of the 15 findings replicated significantly across the data sets. Results contradicting previously published findings were found in two cases. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.
“ Knowing About ” Knowledge : A Framework for Knowledgeable Agents

We present the QITEKAT Question-Answering system based on the conceptual theory of Knowing About Knowledge, which adopts an agent-based approach to extract information from suitable corpora. The components of the QITEKAT system entered by the School of Informatics, University of Wales, Bangor, in the 2003 Text Retrieval Conference are described in detail. We describe PPM compression techniques for Named Entity classification; distributed agent technologies for developing a Knowledgeable Framework and Knowledge Grid; and a Search Engine corroboration system for generating confidence estimates for Question Answering. We present favourable results for certain question types in the TREC Question Answering Track, and discuss future directions for the QITEKAT
Topographic Analysis of Wetlandscapes: Fractal Dimension and Scaling Properties

Wetlands are ubiquitous topographic depressions on landscapes and form critical elements of the mosaic of aquatic habitats. The role of wetlands in the global hydrological and biogeochemical cycles is intimately tied to their geometric characteristics. We used DEM analysis and local search algorithms to identify wetland attributes (maximum stage, surface area and storage volume) in four wetlandscapes across the United States. We then derived the exceedance cumulative density functions (cdfs) of these attributes for the identified wetlands, applied the concept of fractal dimension to investigate the variability in wetland’ shapes. Exponentially tempered Pareto distributions were fitted to DEM derived wetland attributes. In particular, the scaling exponents appear to remain constant through the progressive water-filling of the landscapes, suggesting self-similarity of wetland geometrical attributes. This tendency is also reproduced by the fractal dimension (D) of wetland shorelines, which remains constant across different water-filling levels. In addition, the variability in D is constrained within a narrow range (1 <D < 1.33) in all the four wetlandscapes. Finally, the comparison between wetlands identified by the DEM-based model are consistent with actual data. Engineering EPiC Series in Engineering Volume 3, 2018, Pages 217–226 HIC 2018. 13th International Conference on Hydroinformatics G. La Loggia, G. Freni, V. Puleo and M. De Marchis (eds.), HIC 2018 (EPiC Series in Engineering, vol. 3),
A Fast Multi-Dimensional Algorithm for Drawing Large Graphs?

We present a novel hierarchical force-directed method for drawing large graphs. The algorithm produces a graph embedding in an Euclidean space E of any dimension. A two or three dimensional drawing of the graph is then obtained by projecting a higher-dimensional embedding into a two or three dimensional subspace of E. Projecting high-dimensional drawings onto two or three dimensions often results in drawings that are “smoother” and more symmetric. Among the other notable features of our approach are the utilization of a maximal independent set filtration of the set of vertices of a graph, a fast energy function minimization strategy, efficient memory management, and an intelligent initial placement of vertices. Our implementation of the algorithm can draw graphs with tens of thousands of vertices using a negligible amount of memory in less than one minute on a mid-range PC.
A New Laboratory Short Encapsulation Pull Test for Investigating Load Transfer Behavior of Fully Grouted Cable Bolts

A new laboratory short-encapsulation pull test was developed to better understand load transfer behavior of fully grouted cable bolts that are commonly used in underground mines. Within the embedment section, a series of tests was performed to examine the effect of confining medium diameter on the maximum bearing capacity of cable bolts. A bearing plate appropriate for the investigation of bond failure at both cable/grout and grout/rock interfaces was designed. To prevent a cable bolt from rotating during the pull-out process, a system of locking keys and nuts was integrated into the design to couple together the holder tube, anchor tube, and bearing plate while still allowing axial movement of the cable bolt. Three experiments were conducted. First, different termination methods were used and compared to evaluate the effect of termination instruments on the performance of cable bolts. Then, the confining medium was subjected to a range of preconfinement pressures to determine its effect on cable bolt performance. And finally, the effect of borehole roughness on the pull-out behavior of plain and modified cable bolts was investigated. Once the testing environment was determined, pull-out tests were undertaken on plain and modified cable bolts in a medium-strength material.
M USIC I NFORMATION R ETRIEVAL WITH T EMPORAL F EATURES AND T IMBRE

KEITH J. BELL. When I first met Dr. Tzacheva, I found out she was involved with computers and music. I have a background in both computers and music and had wanted to combine the two disciplines. For a long time I had wanted to do a project on Music and Neural Networks, but had not been able to find a sponsor-so I jumped at the chance. The project we embarked on was an extension of musical information retrieval that uses temporal features and timbre by applying the automatic classification of musical features using data mining techniques (including applied Neural Networks.) The experience was both educational and rewarding. While the research we conducted is basic, I hope that eventually it will lead to innovative applications. I would like to thank Dr. Tzacheva for her guidance and direction on this project. I currently work in information systems as a vocation (mostly piecework.). I co-authored a paper with Dr. DR. ANGELINA TZACHEVA is an Assistant Professor of Informatics and has been at USC Upstate since 2005. Her research interests include data mining and knowledge discovery, distributed databases, multimedia databases, biomedical image processing, and human factors in computing.
Precision is in the Eye of the Beholder: Application of Eye Fixation-Related Potentials to Information Systems Research

Recommended Citation Léger, Pierre-Majorique; Sénecal, Sylvain; Courtemanche, François; Ortiz de Guinea, Ana; Titah, Ryad; Fredette, Marc; and Labonte-LeMoyne, Élise (2014) "Precision is in the Eye of the Beholder: Application of Eye Fixation-Related Potentials to Information Systems Research," Journal of the Association for Information Systems, 15(10), . DOI: 10.17705/1jais.00376 Available at: https://aisel.aisnet. org/jais/vol15/iss10/3
ZU Scholars ZU Scholars

Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination of an ANN model and a set of global feature descriptors. Our experimental results show that the proposed solution presents very promising results in support for next-generation situational awareness and cooperative collision avoidance systems. Hence it can potentially contribute towards safer driving conditions in the presence of fog. ©
How to Extract More Information with Less Burden: Fundus Image Classification and Retinal Disease Localization with Ophthalmologist Intervention

Image classification using deep convolutional neural networks (DCNN) has a competitive performance as compare to other state-of-the-art methods. Here, attention can be visualized as a heatmap to improve the explainability of DCNN. We generated the initial heatmaps by using gradient-based classification activation map (Grad-CAM). We firstly assume that these Grad-CAM heatmaps can reveal the lesion regions well, then apply the attention mining on these heatmaps. Another, we assume that these Grad-CAM heatmaps can't reveal the lesion regions well then apply the dissimilarity loss on these Grad-CAM heatmaps. In this study, we asked the ophthalmologists to select 30% of the heatmaps. Furthermore, we design a knowledge preservation (KP) loss to minimize the discrepancy between heatmaps generated from the updated network and the selected heatmaps. Experiments revealed that our method improved accuracy from 90.1% to 96.2%. We also found that the attention regions are closer to the GT lesion regions.
Privacy-Preserving Face Recognition with Outsourced Computation

Face recognition is one of the most important biometrics pattern recognitions, which has been widely applied in a variety of enterprise, civilian and law enforcement. The privacy of biometrics data raises important concerns, in particular if computations over biometric data is performed at untrusted servers. In previous work of privacy-preserving face recognition, in order to protect individuals’ privacy, face recognition is performed over encrypted face images. However, these results increase the computation cost of the client and the face database owners, which may enable face recognition cannot be efficiently executed. Consequently, it would be desirable to reduce computation over sensitive biometric data in such environments. Currently, no secure techniques for outsourcing face biometric recognition is readily available. In this paper, we propose a privacy-preserving face recognition protocol with outsourced computation for the first time, which efficiently protects individuals’ privacy. Our protocol substantially improves the previous works in terms of the online computation cost by outsourcing large computation task to a cloud server who has large computing power. In particular, the overall online computation cost of the client and the database owner in our protocol is at most 1/2 C. Xiang College of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China E-mail: xiangcan1987@sina.com C. Tang College of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong Higher Education Institutes, Guangzhou University, Guangzhou 510006, China State Key Laboratory of Information Security, Beijing 100093, China E-mail: ctang@gzhu.edu. cn of the corresponding protocol in the state of the art algorithms. In addition, the client requires the decryption operations with only O(1) independent of M , where M is the size of the face database. Furthermore, the client can verify the correction of the recognition result.
Designing high performance and scalable distributed datacenter services over modern interconnects

Modern interconnects like InfiniBand and 10 Gigabit Ethernet have introduced a range of novel features while delivering excellent performance. Due to their high performance to cost ratios, increasing number of datacenters are being deployed in clusters and cluster-of-cluster scenarios connected with these modern interconnects. However, the extent to which the current deployments manage to benefit from these interconnects is often far below the achievable levels. In order to extract the possible benefits that the capabilities of modern interconnects can deliver, selective redesigning of performance critical components needs to be done. Such redesigning needs to take the characteristics of datacenter applications into account. Further, performance critical operations common to multiple datacenter applications like caching, resource management, etc. need to be identified and redesigned utilizing the features of modern interconnects. These operations can be designed and implemented as system services such that they can be provided in a consolidated platform for all other datacenter applications and services to utilize. In this thesis we explore various techniques to leverage the advanced features of modern interconnects to design these distributed system services. We identify a set of services with high performance requirements that have wide utility in datacenters scenarios. In particular, we identify distributed lock management, global memory aggregation and large scale data-transfers as performance critical operations that need to be carefully redesigned in order to maximize the benefits using of modern interconnects. We further explore the use of these primitive operations and RDMA capabilities of modern interconnects to design highly efficient caching schemes which are critical to most datacenters. We present these components in a layered framework such that applications can leverage the benefits of these services through pre-defined interfaces. We present detailed experimental results demonstrating the benefits of our approaches.