Miracle-dz/UniFAR-FTUs huggingface dataset:
A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry

The bin packing problem is one of the most fundamental optimization problems. Owing to its hardness as a combinatorial optimization problem class and its wide range of applications in different domains, different variations of the problem are emerged and many heuristics have been proposed for obtaining approximate solutions. In this paper, we solve a Multi-Level Bin Packing (MLBP) problem in the real make-to-order industry scenario. Existing solutions are not applicable to the problem due to: 1. the final packing may consist multiple levels of sub-packings; 2. the geometry shapes of objects as well as the packing constraints may be unknown. We design an automatic packing framework which extracts the packing knowledge from historical records to support packing without geometry shape and constraint information. Furthermore, we propose a dynamic programming approach to find the optimal solution for normal size problems; and a heuristic multi-level fuzzy-matching algorithm for large size problems. An inverted index is used to accelerate strategy search. The proposed auto packing framework has been deployed in Huawei Process & Engineering System to assist the packing engineers. It achieves a performance of accelerating the execution time of processing 5,000 packing orders to about $8$ minutes with an average successful packing rate as $80.54%$, which releases at least $30%$ workloads of packing workers.
Integrating Discovery and Composition of Semantic Web Services Based on Description Logic

A problem that has become one of the recent critical issues is automated composition of Web services. A number of approaches have been proposed to resolve the problem. However, discovery and composition of Web services have been neglected by majority of those approaches. In this article, an approach based on Description Logic is proposed. First of all, the architecture of the proposed approach which shows all components of the proposed approach is described. Moreover, a case study is designed to test some components of the proposed approach. The preliminary results of the test demonstrate that the approach provides an efficient solution.
A Single-Inductor Triple-Source Quad-Mode Energy-Harvesting Interface With Automatic Source Selection and Reversely Polarized Energy Recycling

This paper presents a single-inductor triple-source quad-mode (SITSQM) energy-harvesting interface in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The proposed reversely polarized energy recycling (RPER) technique improves not only the conversion efficiency at low input voltage but also the system’s output power range. The interface employs the buck–boost topology to convert energy from photovoltaic (PV) cells and a thermoelectric generator (TEG) to a regulated 1.2-V output. The proposed converter features four different operating modes, namely, harvesting, recycling, storing, and backup. The operating mode is automatically selected according to the input and load conditions using the automatic source selection mechanism. The experimental results demonstrate 25.3% efficiency improvement and 10<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> output power range extension from the proposed RPER technique. The proposed SITSQM converter automatically manages three harvesting sources with 82.1% peak conversion efficiency.
New efficient designs for XOR and XNOR functions on the transistor level

Two new methods are proposed to implement the exclusive-OR and exclusive-NOR functions on the transistor level. The first method uses non-complementary signal inputs and the least number of transistors. The other one improves the performance of the prior method but two more transistors are utilized. Both of them have been fully simulated by HSPICE on a SUN SPARC 2 workstation. >
A LATTICE BOLTZMANN 3D-GPU-IMPLEMENTATION ON NON-UNIFORM GRIDS

Since the NVIDIA Compute Unified Device Architecture (CUDA) SDK has been released, multiple publications have demonstrated that simulations in the field of Computational Fluid Dynamics (CFD) may substantially benefit from the utilization of General Purpose Graphics Processing Units (GPGPU), which offer a high performance vs. price ratio compared to classical CPUs typically used in PC clusters. The lattice Boltzmann equation for fluid dynamics is derived from the continuous Boltzmann equation, which describes the probability of finding a fluid particle in a certain velocity state at a certain position and at a certain time. The classical Lattice Boltzmann Method (LBM) is structurally restricted to run on uniform Cartesian grids with a single global time step only. It is obviously desirable to change the resolution of the method from fine grids and short time steps in regions of high interest to coarse grids and long time steps in regions of low interest, typically the peripheral regions of the flow field. Grid refinement approaches mainly have been developed for CPU implementations and do not automatically lead to good performance on GPGPUs. A local grid refinement with low data transfer, a compact memory access pattern and little exceptions at the edges and vertices of the interface is desirable. We present a three dimensional multi scale lattice Boltzmann implementation based on a uniform GPGPU code [1]. For the grid refinement, we use the method of [2] which basically preserves the locality of the LBM and does not lead to a performance degradation on GPGPUs. Our study will thus also address the performance of the multi scale implementation in relation to the uniform grid code.
Doppler-only target tracking for a multistatic radar exploiting FM band illuminators of opportunity

Commensal Radar (CR), defined as a subclass of Passive Radar (PR), is a receive only radar that exploits non-cooperative illuminators of opportunity for target detection, location and subsequent tracking. The objective of this thesis is to evaluate the feasibility of using a Frequency Modulation (FM) Broadcast band CR system as a cost effective solution for Air Traffic Control (ATC). An inherent complication by exploiting FM is the low range resolution due to the low bandwidth of FM radio signals. However, due to typical long integration times associated with CR, the frequency domain resolution is typically very good. As a result, measurements of the target’s Doppler shift are highly accurate and could potentially make FM illuminators a viable source for ATC purposes. Accordingly, this thesis aims to obtain a comprehensive understanding of using high resolution Doppler measurements to accurately track the position of a target. This objective have been addressed by performing a comprehensive mathematical analysis for a Doppler only tracking CR system. The analysis is verified with a tracking simulation, in which the Recursive Gauss Newton Filter (RGNF) is used and lastly, a field experiment was conducted to produce tracking results based on real measurement data. Results demonstrated that Doppler only target tracking from real measurement data is possible, even when the initial target state vector is initialised from real measurement data. A good degree of correlation is achieved between the theoretical, simulated and measured results, hence verifying the theoretical findings of this thesis. Ensuring that the observation matrix is properly conditioned in Doppler only tracking applications is important, as failure to do so results in
Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

Article history: Received 12 March 2019 Received in revised form 3 June 2019 Accepted 4 June 2019 Posted reviews on the relevant webpages about a product not only motivate the company to enhance quality but also it helps users to decide in favor of (or against) purchasing the product. These reviews are classified by different researchers through subjectivity based, entity based, or aspect based to find the polarity using the supervised or unsupervised technique. However, classification based on interrogatives and non-interrogatives is not handled yet. Datasets of interrogatives are analyzed as identifying Answer Seeking questions from Arabic tweets, question conveying and not conveying Information, Rhetorical Questions while here classifying the sentences into interrogatives and non-interrogatives is the preliminary step, which is a core contribution of proposed work. If detected questions are answered and moreover real time, it could not only motivate a user positively to buy the product but also users feel full duplex communication. In this work, we formulated this problem proposing linguistic and heuristic rules that automatically senses the interrogative and answer promptly based on the aforementioned aspect. If there is no aspect in an asked question, then LSI (Latent Semantic Indexing) generate answer using classified noninterrogatives. LSI is an efficient information retrieval algorithm, which finds the closest document to a given query. Experimental results using two publically available datasets show a precision of 95% and 96% which has 10% increased performance than alternatives machine learning methods Meta Filtered Classifier and Naive Bayes.
The Generative Image

simply valorize the digital or even the generative The generative arts have invented a new artistic pedesis and laid the ground for a new materialist aesthetics of the image. The chapter discusses the various forms that the generative arts have taken, including the ordered and disordered. In particular, disordered forms of contemporary images discussed here are the visual arts, literary, plastic, and sonic forms. The argument is not that the generative arts are the only or best forms of art. Their function has been to blaze a trail that shows us what all the arts have already been doing, to one degree or another. The challenge now is to unfold the consequences of this discovery in the arts, to draw our attention to what the contemporary image shows us about the image more generally and what it is capable of today.
Re-parametrising Cost Matrices for Tuning Model Predictive Controllers

In control systems engineering, the selection of controller parameters play an important role in obtaining optimal controller performance. However, it is often not possible to obtain closed-form relationships between the parameters and performance, making the selection process difficult. This paper presents an automated tuning strategy for Model Predictive Controllers (MPC) whereby a meta-cost function is introduced to penalise undesirable behaviour, and subsequently optimised over using black-box search algorithms. To this end, we propose a method of re-parametrising the cost matrices in MPC. This approach results in a box-constrained parametrisation for the matrices, as well as a reduction in the search dimension. The procedure is demonstrated on a diesel engine case study, where we compare the tuning of MPC using the proposed parametrisation to an unbounded parametrisation on a test suite of optimisation algorithms: Simulated Annealing (SA), Particle Swarm Optimisation (PSO), Genetic Algorithms (GA), Nesterov’s gradient-free algorithm (NGF) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We find that the proposed parametrisation provides a statistically significant advantage on all algorithms tested except CMA-ES, for which the performance was similar. We discuss this latter empirical result in relation to the theoretical invariance properties of CMA-ES.
Sentinel-1 GRD Preprocessing Workflow

The Copernicus Programme has become the world’s largest space data provider, providing complete, free and open access to satellite data, mainly acquired by Sentinel satellites. Sentinel-1 Synthetic Aperture Radar (SAR) data have improved spatial resolution and high revisit frequency, making them useful for a wide range of applications. While few research applications need Sentinel-1 Ground Range Detected (GRD) data with few corrections applied, a wider range of users needs products with a standard set of corrections applied. In order to facilitate the exploitation of Sentinel-1 GRD products, there is the need to standardise procedures to preprocess SAR data to a higher processing level. A standard generic workflow to preprocess Copernicus Sentinel-1 GRD data is presented here. The workflow aims to apply a series of standard corrections, and to apply a precise orbit of acquisition, remove thermal and image border noise, perform radiometric calibration, and apply range Doppler and terrain correction. Additionally, the workflow allows spatially snapping of Sentinel-1 GRD products to Sentinel-2 MSI data grids, in order to promote the use of satellite virtual constellations by means of data fusion techniques. The presented workflow allows the production of a set of preprocessed Sentinel-1 GRD data, offering a benchmark for the development of new products and operational down-streaming services based on consistent Copernicus Sentinel-1 GRD datasets, with the aim of providing reliable information of interest to a wide range of communities.
Low complexity phase noise mitigation for linear modulation in DVB-RCS NG standard

This paper suggests the practical approach to mitigate phase noise by using simplified CBC(Colavolpe, Barbieri, and Caire) scheme for linear modulation in DVB-RCS NG standard. The proposed scheme illustrates in detail to implement robust DVB-RCS NG receiver compared to previous study and shows the comparable performance with the results that RCS study group released in DVB-RCS NG standard activities .
An Emphasized Dual Similarity Measure Integration for Online Image Retrieval System Using SimRank

In the real world scenario the use of image grows rapidly, the image rich network is the one that comprises of billions of images. The social media websites, such as Picasa, Flickr and Facebook comprises billions of end user posted images along with their annotations. Similarly the electronic commerce website such as Flipkart, Myntra and Amazon are also furnished with product related images. In this paper, we introduce how to perform efficient and optimum information retrieval in online image rich system. We propose a Mok-SimRank to compute link-based similarity and a dual similarity integration algorithm for both link and content based similarity. Experimental results on online electronic commerce site show that our approach is significantly better than traditional methods in terms of relevance.
Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension

Comprehending multi-turn spoken conversations is an emerging research area, presenting challenges different from reading comprehension of passages due to the interactive nature of information exchange from at least two speakers. Unlike passages, where sentences are often the default semantic modeling unit, in multi-turn conversations, a turn is a topically coherent unit embodied with immediately relevant context, making it a linguistically intuitive segment for computationally modeling verbal interactions. Therefore, in this work, we propose a hierarchical attention neural network architecture, combining turn-level and word-level attention mechanisms, to improve spoken dialogue comprehension performance. Experiments are conducted on a multi-turn conversation dataset, where nurses inquire and discuss symptom information with patients. We empirically show that the proposed approach outperforms standard attention baselines, achieves more efficient learning outcomes, and is more robust to lengthy and out-of-distribution test samples.
Self-Healing Capabilities in Polymeric Materials: An Introduction to Techniques

How to cite this paper: Avinash Pratap Singh | Kuldeep Singh | Aman Jain | Arun Kapoor | Ankit Kumar | Abhishek Pandey "Self-Healing Capabilities in Polymeric Materials: An Introduction to Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 24566470, Volume-3 | Issue-4, June 2019, pp.735-738, URL: https://www.ijtsrd. c om/papers/ijtsrd23 886.pdf
What's in this Collection Dataset? Semantic Annotation with GATE

Semantic annotations of datasets are very useful to support quality assurance, discovery, interpretability, linking and integration of datasets. However, providing such annotations manually is often a time-consuming task . If the process is to be at least partially automated and still provide good semantic annotations, precise information extraction is needed. The recognition of entity names (e.g., person, organization, location) from textual resources is the first step before linking the identified term or phrase to other semantic resources such as concepts in ontologies. A multitude of tools and techniques have been developed for information extraction. One of the big players is the text mining framework GATE (Cunningham et al. 2013) that supports annotation rules, semantic techniques and machine learning approaches. We will run GATE's default ANNIE pipeline on collection datasets to automatically detect persons, locations and time. We will also present extensions to extract organisms (Naderi et al. 2011), environmental terms, data parameters and biological processes and how to link them to ontologies and LOD resources, e.g., DBPedia (Sateli and Witte 2015). We would like to discuss the results with the conference participants and welcome comments and feedbacks on the current solution. The audience is also welcome to provide their own datasets in preparation for this session.
Impact of fixed power allocation in wireless energy harvesting NOMA networks

The time switching‐based relaying (TSR) scheme is considered in energy harvesting protocol to implement with its advantage to nonorthogonal multiple access (NOMA) system. In particular, decode‐and‐forward (DF) mode is proposed to employ in relay to forward signal to serve two far NOMA users. There are two main metrics including outage probability and ergodic rate, which are derived in exact expressions with respect to varying performance under impacts of energy harvesting fractions. To evaluate system performance, outage event and related capacity are illustrated, and we tailor performance gap among two NOMA users and such gap can be controlled by selecting of appropriate power allocation factors assigned for each user to obtain optimal performance. By examining node arrangement, target rates and varying transmit signal to noise ratio (SNR), it can be further achieved performance in several situations of such NOMA. As important result, the considered NOMA system outperforms than the conventional multiple access scheme, and this expected result is confirmed in numerical result and theoretical results. We also explore impacts of transmit power at source, noise power, the other key parameters of energy harvesting scheme to exhibit outage, and ergodic performance. Simulation results are presented to corroborate the proposed methodology.
Representation of the Articulation between Spheres of Life

Our paper aims to improve the understanding of the articulation between spheres of life among Tunisian managers, by mobilizing a mixed theoretical framework. In this context, we develop a model of representation of spheres of life simultaneously explaining the structural relationships between its different areas. This conceptual model is validated on data collected from the Word Values Survey. However, the model obtained leads to a certain hierarchy of the effects of the spheres of life.
Python parallel programming cookbook : master efficient parallel programming to build powerful applications using Python

Master efficient parallel programming to build powerful applications using Python About This Book * Design and implement efficient parallel software * Master new programming techniques to address and solve complex programming problems * Explore the world of parallel programming with this book, which is a go-to resource for different kinds of parallel computing tasks in Python, using examples and topics covered in great depth Who This Book Is For Python Parallel Programming Cookbook is intended for software developers who are well versed with Python and want to use parallel programming techniques to write powerful and efficient code. This book will help you master the basics and the advanced of parallel computing. What You Will Learn * Synchronize multiple threads and processes to manage parallel tasks * Implement message passing communication between processes to build parallel applications * Program your own GPU cards to address complex problems * Manage computing entities to execute distributed computational tasks * Write efficient programs by adopting the event-driven programming model * Explore the cloud technology with DJango and Google App Engine * Apply parallel programming techniques that can lead to performance improvements In Detail This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations. Style and approach A step-by-step guide to parallel programming using Python, with recipes accompanied by one or more programming examples. It is a practically oriented book and has all the necessary underlying parallel computing concepts.
Performance Enhancement for Effective Pattern Discovery & Concept Formation in Text Mining

Due to rapid growth of digital data usages in recent database applications it is required to utilize effective text mining method for efficient performance. Text mining is research area because in many applications database content is not only in form of numerical data but also textual data form. To exploit hidden information from non- structured to semi-structured data form text mining is applicable. The challenging issue in text mining is to extract user required information in effectual manner. To perform this task text mining methods are used. Text mining methods categorization is based on how text document are analyzed. In these methods text document analyzed on the basis of term, phrase, concept and pattern. The pattern based approach can improve the accuracy of system for evaluating term weights because discovered patterns are more specific than whole documents. Objective is to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Proposed system increases efficiency of pattern discovery using different data mining algorithms with pattern deploying and pattern evolving method to solve misinterpretation and low frequency problem. Performance enhancement is achieved by applying multithreaded approach and concept formation from effectively discovered patterns.