Miracle-dz/UniFAR-FTUs huggingface dataset:
A FPGA implementation for linearly unmixing a hyperspectral image using OpenCL

Hyperspectral imaging systems provide images in which single pixels have information from across the electromagnetic spectrum of the scene under analysis. These systems divide the spectrum into many contiguos channels, which may be even out of the visible part of the spectra. The main advantage of the hyperspectral imaging technology is that certain objects leave unique fingerprints in the electromagnetic spectrum, known as spectral signatures, which allow to distinguish between different materials that may look like the same in a traditional RGB image. Accordingly, the most important hyperspectral imaging applications are related with distinguishing or identifying materials in a particular scene. In hyperspectral imaging applications under real-time constraints, the huge amount of information provided by the hyperspectral sensors has to be rapidly processed and analysed. For such purpose, parallel hardware devices, such as Field Programmable Gate Arrays (FPGAs) are typically used. However, developing hardware applications typically requires expertise in the specific targeted device, as well as in the tools and methodologies which can be used to perform the implementation of the desired algorithms in the specific device. In this scenario, the Open Computing Language (OpenCL) emerges as a very interesting solution in which a single high-level synthesis design language can be used to efficiently develop applications in multiple and different hardware devices. In this work, the Fast Algorithm for Linearly Unmixing Hyperspectral Images (FUN) has been implemented into a Bitware Stratix V Altera FPGA using OpenCL. The obtained results demonstrate the suitability of OpenCL as a viable design methodology for quickly creating efficient FPGAs designs for real-time hyperspectral imaging applications.
MemepiC: Towards a Unified In-Memory Big Data Management System

In-memory data management systems have recently gained a lot of attraction due to cheaper and faster DRAM and other hardware advancement. However, these systems are either pure storage systems with online data query service, or just offline batch processing systems with data analytics functionality. Heavy data movement (e.g., data loading) occurs in order to analyze the data. In this paper, we propose an innovative in-memory data management system—MemepiC, which unifies both online data query and data analytics functionality, allowing low-latency storage service and efficient in-situ data analytics. We also explore the emerging RDMA technique in the context of in-memory data management systems, by designing an RDMA-based communication protocol for message delivery inside MemepiC, and proposing to overlap execution and RDMA communication. Extensive experiments are conducted to show the superior performance of MemepiC in terms of both the storage and the data analytics services, compared against other in-memory systems.
Algorithmic debugging for complex lazy functional programs

An algorithmic debugger finds defects in programs by systematic search. It relies on the programmer to direct the search by answering a series of yes/no questions about the correctness of specific function applications and their results. Existing algorithmic debuggers for a lazy functional language work well for small simple programs but cannot be used to locate defects in complex programs for two reasons: Firstly, to collect the information required for algorithmic debugging existing debuggers use different but complex implementations. Therefore, these debuggers are hard to maintain and do not support all the latest language features. As a consequence, programs with unsupported language features cannot be debugged. Also inclusion of a library using unsupported languages features can make algorithmic debugging unusable even when the programmer is not interested in debugging the library. Secondly, algorithmic debugging breaks down when the size or number of questions is too great for the programmer to handle. This is a pity, because, even though algorithmic debugging is a promising method for locating defects, many real-world programs are too complex for the method to be usuable. I claim that the techniques in in this thesis make algorithmic debugging useable for a much more complex lazy functional programs. I present a novel method for collecting the information required for algorithmically debugging a lazy functional program. The method is non-invasive, uses program annotations in suspected modules only and has a simple implementation. My method supports all of Haskell, including laziness, higher-order functions and exceptions. Future language extensions can be supported without changes, or with minimal changes, to the implementation of the debugger. With my method the programmer can focus on untrusted code -- lots of trusted libraries are unaffected. This makes traces, and hence the amount of questions that needs to be answered, more manageable. I give a type-generic definition to support custom types defined by the programmer. Furthermore, I propose a method that re-uses properties to answer automatically some of the questions arising during algorithmic debugging, and to replace others by simpler questions. Properties may already be present in the code for testing; the programmer can also encode a specification or reference implementation as a property, or add a new property in response to a statement they are asked to judge.
Enhancing Navigability: An Algorithm for Constructing Tag Trees

Abstract Purpose This study introduces an algorithm to construct tag trees that can be used as a user-friendly navigation tool for knowledge sharing and retrieval by solving two issues of previous studies, i.e. semantic drift and structural skew. Design/methodology/approach Inspired by the generality based methods, this study builds tag trees from a co-occurrence tag network and uses the h-degree as a node generality metric. The proposed algorithm is characterized by the following four features: (1) the ancestors should be more representative than the descendants, (2) the semantic meaning along the ancestor-descendant paths needs to be coherent, (3) the children of one parent are collectively exhaustive and mutually exclusive in describing their parent, and (4) tags are roughly evenly distributed to their upper-level parents to avoid structural skew. Findings The proposed algorithm has been compared with a well-established solution Heymann Tag Tree (HTT). The experimental results using a social tag dataset showed that the proposed algorithm with its default condition outperformed HTT in precision based on Open Directory Project (ODP) classification. It has been verified that h-degree can be applied as a better node generality metric compared with degree centrality. Research limitations A thorough investigation into the evaluation methodology is needed, including user studies and a set of metrics for evaluating semantic coherence and navigation performance. Practical implications The algorithm will benefit the use of digital resources by generating a flexible domain knowledge structure that is easy to navigate. It could be used to manage multiple resource collections even without social annotations since tags can be keywords created by authors or experts, as well as automatically extracted from text. Originality/value Few previous studies paid attention to the issue of whether the tagging systems are easy to navigate for users. The contributions of this study are twofold: (1) an algorithm was developed to construct tag trees with consideration given to both semantic coherence and structural balance and (2) the effectiveness of a node generality metric, h-degree, was investigated in a tag co-occurrence network.
A hybrid intelligent control based cyber-physical system for thermal comfort in smart homes

With the fast development of human society, as environmental issues have drawn incomparable attention, energy efficiency is playing a significant role in residential buildings. Meanwhile, spending more time in homes leads people to constantly improve comfort there. Considering the fact that space heating makes a great contribution to residential energy consumption and thermal comfort, this paper presents a novel hybrid intelligent control (HIC) system to manage space heating devices in a smart home with advanced technologies to save energy while to increase thermal comfort level. The approach combines a meta-heuristic algorithm used to compute a setpoint from the Predicted Mean Vote model with a Proportional-Integral-Derivative controller for indoor temperature regulation. In order to validate the system, computer simulations are conducted and analysed. The results indicate the proposed control system can provide better thermal comfort comparing with other conventional and intelligent control methods, and consume less energy when demand response is considered.
Measuring Similarity of Academic Articles with Semantic Profile and Joint Word Embedding

: Long-document semantic measurement has great signiﬁcance in many applications such as semantic searchs, plagiarism detection, and automatic technical surveys. However, research efforts have mainly focused on the semantic similarity of short texts. Document-level semantic measurement remains an open issue due to problems such as the omission of background knowledge and topic transition. In this paper, we propose a novel semantic matching method for long documents in the academic domain. To accurately represent the general meaning of an academic article, we construct a semantic proﬁle in which key semantic elements such as the research purpose, methodology, and domain are included and enriched. As such, we can obtain the overall semantic similarity of two papers by computing the distance between their proﬁles. The distances between the concepts of two different semantic proﬁles are measured by word vectors. To improve the semantic representation quality of word vectors, we propose a joint word-embedding model for incorporating a domain-speciﬁc semantic relation constraint into the traditional context constraint. Our experimental results demonstrate that, in the measurement of document semantic similarity, our approach achieves substantial improvement over state-of-the-art methods, and our joint word-embedding model produces signiﬁcantly better word representations than traditional word-embedding models.
Supplier selection in the sustainable supply chain: The application of analytic hierarchy process and fuzzy data envelopment analysis

The development and management of an effective and efficient supply chain involve the selection of the suppliers. Only economic criteria, including cost and delivery, once used to be considered in the process of supplier selection. But, they do not suffice for the evaluation of suppliers anymore due to the rapidly changing environment, and different perspectives are needed to be considered. The present paper aims to present a hybrid method based on fuzzy data envelopment analysis for sustainable supplier selection. At first, the criteria for sustainable supplier selection are derived from the relevant literature. Then, the hierarchy of the criteria and their preferential interrelations are specified by analytic hierarchy process. Eventually, the performance of the suppliers is evaluated using fuzzy data envelopment analysis. The presented DEA model has been inspired by the concept of ideal and anti-ideal decision-making units (DMUs) in the evaluation of cross-efficiency. According to this concept, a DMU is efficient if it is close to the ideal DMU’s performance and far from the anti-ideal DMU’s performance.
Resource Management in Constrained Dynamic Situations

Resource Management in Constrained Dynamic Situations by Jinwoo Seok Chair: Anouck Girard Resource management is considered in this dissertation for systems with limited resources, possibly combined with other system constraints, in unpredictably dynamic environments. Resources may represent fuel, power, capabilities, energy, and so on. Resource management is important for many practical systems; usually, resources are limited, and their use must be optimized. Furthermore, systems are often constrained, and constraints must be satisfied for safe operation. Simplistic resource management can result in poor use of resources and failure of the system. Furthermore, many real-world situations involve dynamic environments. Many traditional problems are formulated based on the assumptions of given probabilities or perfect knowledge of future events. However, in many cases, the future is completely unknown, and information on or probabilities about future events are not available. In other words, we operate in unpredictably dynamic situations. Thus, a method is needed to handle dynamic situations without knowledge of the future, but few formal methods have been developed to address them. Thus, the goal is to design resource management methods for constrained systems, with limited resources, in unpredictably dynamic environments.
Scalable Reasoning and Querying for the Semantic Web

Ontologies, in the OWL language, form the basis of the Semantic Web: they define concepts and relationships, and are thus the fundamental model for encoding information. As the Semantic Web has been more widely adopted, extremely large ontologies have begun to emerge, particularly in the life sciences. Reasoning about such ontologies requires scalability beyond that of current Description Logic-based reasoning tools. We argue that in many practical settings, the solution to the scalability problem is to exploit relational database systems. Relational queries (views), like Description Logics, are based on a subset of first-order logic. Many OWL class definitions can be cleanly converted into relational view definitions, which, when supplemented with an external operation that recomputes multiple views until fixpoint is achieved, can perform inference. We implement this approach in a system called OWLDB, which utilizes the power of relational database query optimization and indexing to process large volumes of data. We evaluate the presented algorithms by comparing their reasoning performance on ontologies from dierent domains, including bio-informatics, with Description Logic reasoners for terminology reasoning, and with highly specialized RDF instance stores for instance reasoning.
Fixed layer Convolutional Neural Network

Fully trained convolutional neural networks are being used nowadays in various applications. But, can we get an understanding on how they actually work? A small step in that direction is taken in this paper. The filters of the first layers seem to be fairly straightforward and mostly are known and recognizable. If one makes a network with fixed weights on the filters, how is its performance compared with a fully trained one? And how is the training time influenced? This paper, answers this question by experimenting in a verification style CNN put in three different situations: first a fixed layer network, second a set layer network and third a fully trained CNN. It is shown that although similar results can be achieved with the three networks, a fully trained one still has superior performance; however, training time is increased as the number of fixed layers are increased.
A Kind of Heterogeneous Database Synchronization Mechanism Based on ETL and XML

In the distributed database system, the data stored in the same network is distributed on different nodes. How to synchronize the database information of each node is the key problem to be solved. Unless all the nodes obtain the synchronized information in the database, distributed database may sustain integrity. If the data is not synchronized, the platform would have a negative impact. So that, data synchronization is the thing we must firstly solve in the distributed database system. Framework of ETL building data warehouse is given. Analysis and research of the new synchronization mechanism based on ETL and XML is investigated. Provincial Level solution based on the synchronization mechanism of ETL and XML is also given. This scheme is efficient and reliable and it effectively solves the heterogeneous data synchronization issue.
Discrete Event System Specification-based framework for modeling and simulation of propagation phenomena in social networks: application to the information spreading in a multi-layer social network

The diffusion of information is defined as the communication process by which an idea or information spreads within a social system and impacts the behavior of social actors (individuals). The social interaction plays an important role in studying the propagation of information and how it influences people. When an informational event occurs, it can either die out quickly or have significant impact on a population. The interactions could be supported by physical proximity contact, remote collaboration, any type of social meetings, and some forms of verbal or written communication, depending on the situations. Institutions and firms search to understand and predict the impact of information propagation on individuals. Agent-based modeling is a powerful approach for studying such a collective process. However, existing models oversimplify the cultural attributes, the different types of links, and information content, despite the evidence of their central role in the diffusion process. In this context, great benefits could be derived from the exploitation of an individual’s personality and cultural values in the diffusion models. In this paper, we describe a new architecture for an agent-based model using the DEVS (Discrete Event System Specification) framework and show how this architecture is flexible and can support the simulation of the dissemination process. In more detail, we define a set of models of individuals characterized by a set of state variables to represent the behavior of an individual and the individual’s network within a multi-layer social network. Then, we start by introducing the platform architecture, specifically designed to simulate message propagation in a multi-layer network. Finally, a military scenario of message diffusion during a stabilization phase is used to test our DEVS models on the platform and the relevancy of the simulation results.
Redundancy-Allocation in Neel Metal Products Limited

Objective: In manufacturing setup, limited budget is allocated for each system. In order to increase reliability of the system, redundancy is allocated within given cost constraints. Objective of this paper is to come out with the best optimal solution and increase their liability of a system under cost constraint in a manufacturing plant. Methods: In this paper Heuristic Algorithm (HA) and Constrained Optimization Genetic Algorithm (COGA) are used to optimize constrained Redundancy Allocation Problem (RAP) in a manufacturing plant. These methods are used to allocate the best redundancy strategy for each subsystem with a view to increase the reliability of the system under cost constraints. Best optimal solution is reached by comparing results of CPU time taken by these two methods. Findings: Generally RAP is a NP hard problem and a non-linear integer programming problem, which is difficult to solve. Both methods are applied and comparison between reliability of both the methods is made on the basis of which result obtained by COGA is 0.8632 which is found better against HA which is 0.8380. Application: Results applied in the manufacturing plant which resulted in the increase of reliability by using best redundancy strategy.
Methods of the automated aircraft configuration

Aircraft are great and comprehensive technical systems, which are characterised by the great configuration density. This article presents formation of the geometrical conceptual design of the aircraft on the basis of the formal heuristic procedures within the framework of the infrastructural constraints. In addition, this article includes description of influence of the aerodynamic configuration and the volume-weight configuration upon formation of the geometrical conceptual design of the aircraft, as well as description of other mass/inertia characteristics of the aggregates, which are to be installed within the specific aircraft. This article also states that in the case of the "very strict" infrastructural constraints, it is necessary to solve "the inverse" problem of configuration, that is the problem, where initial data for formation of the geometrical conceptual design of the aircraft are determined by the necessary configuration space, which is determined by the infrastructural constraints. The article presents the project problem (in aggregate) of finding rational values for parameters of the geometrical conceptual design of the aircraft as the problem of the multiple criteria discrete optimisation. This article states that it is possible to formulate this problem as the search of the vector of structural parameters (that is, search of the multitude of the admissible alternatives of the drawing and designing solutions).
Seeing the unseen: Spatial visualization of the Solar System with physical prototypes and Augmented Reality

The presented design case gives examples of designing an Augmented Reality learning scenario about the Solar System with 6th grade. The case connects the physical and virtual worlds in Augmented Reality, like 3D planetary globes floating above the textbook. This way, students can interact with digital information embedded in the physical environment. The stakeholders in the design process consisted of researchers in the role of designers, while teacher and students were co-designers and informants. During the process, several physical and digital prototypes were developed and tested. Through the co-design development process, it became clear that the design affordances of Augmented Reality together with the scholastic content of the Solar System invited design potentials such as visualising far away objects by bringing them near i.e. being able to see what is otherwise difficult to perceive. Through the development process, the design potentials of Augmented Reality acted as a lens to access distant realities; included herein visualisation of planet sizes, their relationships
Implementasi Algoritma Rabin Karp Untuk Pendeteksian Plagiat Dokumen Teks Menggunakan Konsep Similarity

Plagiarism is a crime, because it recognizes the work of others as personal work. Plagiarism is becoming serious problem as the rapid development of technology. Ironically acts of plagiarism is most prevalent among academics such as students and learners. In this case, the most often done is copy-paste-edit text documents. This study aims to analyze the act of plagiarism based on similarity contents of the document, such as copy&paste and disguised plagiarism. We implement strategic approach of Rabin-Karp algorithm, which is a multiple-pattern search algorithm. This algorithm is one of the best in analyzing compatibility between documents. The use of hashing techniques make the matching algorithm more efficient because it will just compare multiple digit of numbers. The final result is the percentage of similarity between documents being tested.