Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervisory Control And Data Acquisition ( SCADA ) Laboratory for Research & Training in Distribution Automation Mini

This paper reports a unique SCADA Laboratory facility for power systems at Jamia Millia Islamia, New Delhi. It has been designed to function as a research and training center for utilities, faculty members & students. The paper covers the functioning of the SCADA/EMS laboratory facility, based on Distributed-processing technology. The SCADA laboratory is currently providing hands on experience to students and practicing engineers and is giving them an insight into the contemporary SCADA systems. This lab is the first of its kind to be functional in India and may be unique.
Foreign exchange rate forecasting with a virtual-expert partial-consensus fuzzy-neural approach for semiconductor manufacturers in Taiwan

Accurately forecasting the foreign exchange rate is very important to export-oriented enterprises, like semiconductor manufacturers in Taiwan. To this end, a virtual-expert partial-consensus fuzzy-neural approach is proposed in this study. In the proposed methodology, instead of calling a number of experts in the field, a committee of virtual experts is formed, and then they are asked to provide views on fuzzy forecasts. For each virtual expert, the corresponding fuzzy linear regression (FLR) equation is constructed to predict the foreign exchange rate. Each FLR equation can be fitted by solving two equivalent non-linear programming problems, based on the virtual experts' views. To aggregate these fuzzy foreign exchange rate forecasts, a two-step aggregation mechanism is applied. To evaluate the effectiveness of the proposed methodology, the real case of forecasting the foreign exchange rate of NTD for USD is used.
Use of Digital Signature and Rijndael Encryption Algorithm to Enhanced Security of data in Cloud Computing Services

Cloud computing is an amazing technology above all other types of computing. Cloud computing implies sharing of computing resources rather than having local servers or personal individual devices to handle the applications. The goal of cloud computing is to increase the speed of computations per second by applying traditional supercomputing, or high-performance computing power. It is used in consumer-oriented applications such as recruitments, smart staffing and medical image retrieval etc. The cloud computing however, has some problems associated with it . Among them security and privacy are the main concerns. This paper focus on both security and privacy issues of cloud computing. In this paper we have proposed to use Rijndael algorithm for encryption of data on the fly to provide data security in cloud . We have also proposed to make use of digital signature to ensure privacy and authentication of client’s data
Logic programming in the context of multiparadigm programming: the Oz experience

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This paper has two goals: to give a tutorial of logic programming in Oz; and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. We give examples that can be run interactively on the Mozart system, which implements Oz. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Furthermore, as consequences of its multiparadigm nature, the model supports new abilities such as first-class top levels, deep guards, active objects, and sophisticated control of the search process. Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We give a brief history of Oz that traces the development of its main ideas and we summarize the lessons learned from this work. Finally, we give many entry points into the Oz literature.
J un 2 01 8 Tilted shear-free axially symmetric fluids : silent universes or deaf observers ?

We carry on a systematic study of the physical properties of axially symmetric fluid distributions, which appear to be geodesic, shear–free, irrotational, non–dissipative and purely electric, for the comoving congruence of observers, from the point of view of the tilted congruence. The vanishing of the magnetic part of the Weyl tensor for the comoving congruence of observers, suggests that no gravitational radiation is produced during the evolution of the system. Instead, the magnetic part of the Weyl tensor as measured by tilted observers is non vanishing (as well as the shear, the four–acceleration, the vorticity and the dissipation), giving rise to a flux of gravitational radiation that can be characterized through the super–Poynting vector. This result strengthens further the relevance of the role of observers in the description of a physical system. An explanation of this dual interpretation in terms of the information theory, is provided.
Exploration of Language Ecological Niche

Traditionally, the research of teaching Chinese as a foreign language emphasizes classroom teaching more and less emphasizes the learning of outside classroom. In addition to that, second language acquisition theory can’t completely explain the effective acquisition of learners under the target language environment or the cultivation of the learners’ second language acquisition ability. Based on the beneficial achievement of “ecological niche”, this paper puts forward the theory of “language ecological niche” in the perspective of linguistics and applies it to second language acquisition under target language environment, which makes detailed explanation to effectiveness of second language acquisition under target language environment and the cultivation of the learners’ second language acquisition ability. This paper also describes the components of language ecological niche in detail and proves that the exploration of language ecological niche contributes to improving efficiency of second language acquisition. 11 students in the elective senior oral Chinese language course are randomly drawn as investigation objects. This paper divides language ecological niches according to language function, sticks to the studentcentered, teacher-guiding, and cultivating students’ active learning ability. This research have contributed to understanding the definition and the components of the language ecological niche, the relationship of language ecological niche and second language acquisition, helps to explain how learners acquire language in target language environment from multidiscipline perspective based on the basic language communication system. Keywords—Chinese; language niche; second language acquisition; interaction; active learning ability
Model-Based Clustering and Data Transformations for Gene Expression Data

A wide range of clustering algorithms have been proposed to analyze gene expression data, including hierarchical clustering [6], self-organizing maps [13], k-means [14], graphtheoretic approaches (for example, Ben-Dor and Yakhini [2] and Hartuv et al. [9]), and support vector machines [3]. Success in applications has been reported for many clustering approaches, but so far no single method has emerged as the method of choice in the gene expression analysis community. Most of the proposed clustering algorithms are largely
ANCF: An Adaptive Aspect Attention Model for Rating Prediction

Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems assume that for an individual, this vector is the same for all items. However, this assumption is often invalid, especially when considering a user’s interactions with items of diverse characteristics. To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named ANCF, which can capture the varying aspect attentions that a user pays to different items. Specifically, we design a new topic model to extract user preferences and item characteristics from review texts. They are then used to 1) guide the representation learning of users and items, and 2) capture a user’s special attention on each aspect of the targeted item with an attention network. Through extensive experiments on several largescale datasets, we demonstrate that our model outperforms the state-of-the-art review-aware recommender systems in the rating prediction task.
Investigating the presumed cognitive advantage of aspiring interpreters

In complex tasks such as interpreting, the importance of a well-functioning working memory can hardly be overestimated. However, empirical studies have failed to produce consistent results with regard to an interpreter advantage in working memory. Recent studies tend to focus on the executive component of working memory. To our knowledge, no such study has compared the possible cognitive advantage of aspiring interpreters relative to other multilinguals before training takes place, in spite of the fact that excellent cognitive abilities are considered important in many interpreter selection procedures. In this study, we compared the working memory capacity and executive functions of a group of 20 student interpreters with two other groups of advanced language users who were all at the start of their Master’s training. Data were collected on three executive control functions: inhibition, shifting and updating. A forward and a backward digit span task for measuring the participants’ working memory capacity was also included in this study. Results revealed only negligible differences between the three groups at onset of training. The presumed cognitive advantage of aspiring interpreters with regard to executive control was not found.
Final Report : Hausdorff Dimension of the Brownian Earthworm

The Minkowski dimension (sometimes called Minkwski-Bouligand dimension) is often referred to simply as box-counting dimension. As a consequence of the finite capacity of computing power, one often uses the box-counting dimension as an approximation of the Hausdorff dimension. A more thorough treatise on box-counting and Hausdorff dimension, as well as other notions of dimension, can be found in [1].
From Context to Distance: Learning Dissimilarity for Categorical Data Clustering

Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical at- tributes, it is difﬁcult to deﬁne a distance between pairs of values of a categorical attribute, since the values are not ordered. In this paper, we propose a framework to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute A i can be determined by the way in which the values of the other attributes A j are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of A i a low value of distance is obtained. We propose also a solution to the critical point of the choice of the attributes A j . We validate our approach by embedding our distance learning framework in a hi- erarchical clustering algorithm. We applied it on various real world and synthetic datasets, both low and high-dimensional. Experimental results show that our method is competitive w.r. t. the state of the art of categorical data clustering approaches. We also show that our approach is scalable and has a low impact on the overall computational time of a clustering task.
BIOINFORMATICS ORIGINAL

Motivation: Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden. Results: Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive ﬁelds as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman–Wunsch and Smith–Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identiﬁed at the cost of 13% false positive detections. Availability: An open-source reference implementation is available in the Supplementary information. Contact
Fair Morse Functions for Extracting the Topological Structure of a Surface Mesh

Modern techniques for interrogating the topology (e.g. genus) of a single meshed surface find handles via a breadth-first search for cycles that do not separate the mesh into two disjoint components. Morse theory can find handles with a simple search for the minima, maxima and saddles of a real function evaluated at the vertices. Bad choices for this function can generate numerous critical vertices that complicate the search for non-separating cycles. We show that the limit of the iteration of a constrained Laplacian on a mesh yields a “fair” Morse function with a single minumum and maximum, and two critical points for every handle. We then describe an algorithm that uses this function and its critical points to find a bouquet of non-separating cycles that cut the mesh into a flattenable domain homeomorphic to a disk. These cycles can be further optimized for applications in texturing, parameterization, geometry images and filtering topological noise.
Launch Traverse Shade Ray Generation Program Miss Program Closest Hit Program Selector Visit Program Intersection Program Any Hit Program Acceleration Traversal Node Graph Traversal rtContextLaunch rtTrace Exception Program

The NVIDIA® OptiXTM ray tracing engine is a programmable system designed for NVIDIA GPUs and other highly parallel architectures. The OptiX engine builds on the key observation that most ray tracing algorithms can be implemented using a small set of programmable operations. Consequently, the core of OptiX is a domain-specific just-in-time compiler that generates custom ray tracing kernels by combining user-supplied programs for ray generation, material shading, object intersection, and scene traversal. This enables the implementation of a highly diverse set of ray tracing-based algorithms and applications, including interactive rendering, offline rendering, collision detection systems, artificial intelligence queries, and scientific simulations such as sound propagation. OptiX achieves high performance through a compact object model and application of several ray tracing-specific compiler optimizations. For ease of use it exposes a single-ray programming model with full support for recursion and a dynamic dispatch mechanism similar to virtual function calls. CR Categories: I.3. 7 [Computer Graphics]: Three-Dimensional Graphics and Realism; D.2. 11 [Software Architectures]: Domainspecific architectures; I.3. 1 [Computer Graphics]: Hardware Architectures—;
AN INTRODUCTION TO II1 FACTORS

1. Finite von Neumann algebras 2 1.1. Basics on von Neumann algebras 2 1.2. Finite von Neumann algebras 3 1.3. Orbit equivalence relations 6 2. Hilbert bimodules. Completely positive maps 13 2.1. Generalities 13 2.2. Dictionary between Hilbert bimodules and u.c. p. maps 15 2.3. Popa’s intertwining techniques 17 3. Approximation properties 22 3.1. Amenability 22 3.2. Property Gamma 27 3.3. Haagerup property 30 4. Rigidity of II1 factors 32 4.1. Rigid inclusions of von Neumann algebras 32 4.2. Applications to rigidity of II1 factors 34 4.3. Uniqueness of Cartan subalgebras 37 Appendix A. Polar decomposition of a vector 39 Appendix B. Von Neumann’s dimension theory 39 Appendix C. Ultraproducts 40 Appendix D. On the geometry of two projections 41 References 42
Performance Assessment of Ensemble Decision Trees-based Fault Detection System in a Chemical Process

This paper presents an ensemble decision trees-based approach for fault detection and isolation in a chemical process. This is one of the well-known machine learning techniques, which is also known as random forest (RF) classifier. RF has some advantages over other classification techniques like It can deal with substantial number of variables effectively without over-fitting, It is quick and simple to execute, provides good results even with large number of classes, parameter tuning is not a problem and identification of the important variables in classification. By considering every one of these characteristics of RF was tested on benchmark Tennessee Eastman (TE) process. Results conclude that it can be proved one of the potential tools for fault detection and isolation in industrial systems.
Advance Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud

Cloud Computing means more than simply saving on IT implementation costs. One of the most fundamental services offered by cloud providers is data storage. A company allows its staffs in the same group or department to store and share files in the cloud. By utilizing the cloud, the staffs can be completely released from the troublesome local data storage and maintenance. However, it also poses a significant risk to the confidentiality of those stored files. Cloud offers enormous opportunity for new innovation, and even disruption of entire industries. Cloud computing is the long dreamed vision of computing as a utility, where data owners can remotely store their data in the cloud to enjoy on demand high-quality applications and services from a shared pool of configurable computing resources. Identity privacy is one of the most significant obstacles for the wide deployment of cloud computing. Without the guarantee of identity privacy, users may be unwilling to join in cloud computing systems because their real identities could be easily disclosed to cloud providers and attackers.
Approximate Holistic Aggregation in Wireless Sensor Networks

Holistic aggregations are popular queries for users to obtain detailed summary information from Wireless Sensor Networks. An aggregation operation is holistic if there is no constant bound on the size of the storage needed to describe a sub-aggregation. Since holistic aggregation cannot be distributable, it requires that all the sensory data should be sent to the sink in order to obtain the exact holistic aggregation results, which costs lots of energy. However, in most applications, exact holistic aggregation results are not necessary; instead, approximate results are acceptable. To save energy as much as possible, we study the approximated holistic aggregation algorithms based on uniform sampling. In this article, four holistic aggregation operations, frequency, distinct-count, rank, and quantile, are investigated. The mathematical methods to construct their estimators and determine optional sample size are proposed, and the correctness of these methods are proved. Four corresponding distributed holistic algorithms to derive (ε, δ)-approximate aggregation results are given. The solid theoretical analysis and extensive simulation results show that all the proposed algorithms have high performance on the aspects of accuracy and energy consumption.
Granular Computing for Web Intelligence and Brain Informatics

In this talk, we examine the basic principles, ideas, and strategies of granular computing, and look at the potential applications and implications of granular computing to the studies of Web Intelligence (WI) and Brain Informatics (BI). We argue that granular computing may provide the necessary theory for designing and implementing new types of web-based information processing systems and developing a conceptual model of the human brain.
Securing Internet of Things devices in home networks

Modern households are deploying Internet of Things (IoT) devices at a fast pace. The heterogeneity of these devices, which range from low-end sensors to smart TVs, make securing home IoT particularly challenging. To make matters worse, many consumerIoT devices are hard or impossible to secure because device manufacturers fail to adopt security best practices (e.g., regular software patches). Vulnerable IoT devices make home networks open to attacks or privacy leaks and make the Internet subject to large-scale Distributed Denial of Service (DDoS) attacks such as the Dyn Attack by the Mirai botnet. Securing home networks is particularly challenging. IoT devices are more diverse than those in a managed, conventional network, in terms of both the devices themselves, their introduction to the home network, and the software they run. In an enterprise network, for instance, network administrators often enforce device and software homogeneity to ease management and maintenance. Home users may connect a broad array of devices, applications, and services. In conventional networks, expert administrators are responsible for managing the network. For example, they take steps to secure end hosts such as applying security patches. In contrast, most home networks do not have a technically skilled network administrator.
Source MetaModel Target MetaModel Matching MetaModel Source Model Target Model MTBE Engine links Transformation rules Candidate Matching Model Validated Matching Model ( Transformation Example ) Source MetaModel Target MetaModel Matching MetaModel Source Model Target Model MTBE Engine Transformation

Model Based Transformation Example (MTBE) is a recent track of research aiming at learning a transformation from examples. In most MTBE processes, a transformation example is given in the form of a source model, a transformed model and links between source elements and the corresponding transformed elements. Building the links is done manually, which is a tedious task, while in many cases, they can be deduced from the examination of the source and transformed models, by using relevant attributes, like names or identi ers. We exploit this characteristic by proposing a semi-automatic matching operation, suitable for discovering matches between the source model and the transformed model. Our technique is inspired by and extends the Anchor-Prompt approach, and is based on the automatic discovery of pairs of anchors (pairs of elements for which there is a strong assumption of matching) to support the whole matching discovery. An implementation of the approach is provided for validation on a case study.