Miracle-dz/UniFAR-FTUs huggingface dataset:
A genetic algorithm approach to designing finite-precision PID controller structures

The parameters of a digital control design need to be rounded if the controller is implemented with finite precision arithmetic. This often results in degradation of the closed loop performance and reduced stability margins. This paper presents a multiobjective genetic algorithm based approach to designing the structure of a finite-precision PID controller implementation to minimize both the performance degradation and the memory requirements of the implementation. The approach provides a set of solutions that are near Pareto-optimal, and so allows the designer to trade-off the performance degradation against the memory requirements. The method is applied to the PID controller structure for the IFAC93 benchmark problem.
Information system using Dart and MongoDB

An information system is a set of information collection and processing components used for providing information and knowledge. They are seen and used by everyone at everywhere for e.g. banks, restaurants, education portals, ticket reservation centers, etc. Objectory and Mongo.dart are both helper libraries for interacting with MongoDB and manipulating it. While Mongo.dart is helpful for establishing basic connection with the database, Objectory provides a more structured approach towards interacting with the database and realizing them into data models. The information system discussed in this paper is an implementation of a restaurant system built using latest tools for developing modern web based applications. The system is reliable, robust, scalable, flexible, simple and fast.
LC/NACO Authority File in the Library of Congress BIBFRAME Pilots

Abstract The LC/NACO Authority File is a large cooperatively-maintained authority file that also functions as the Library of Congress local name authority file. In the Library of Congress BIBFRAME Pilots, catalogers access the LC/NACO Authority File as Linked Data in MADS/RDF serialization, through the Library of Congress Linked Data Service and the BIBFRAME Editor. This article explores the steps that enable BIBFRAME Pilot participants to complete authority work in a Linked Data environment. It also addresses the limitations, opportunities, and successes encountered by BIBFRAME Pilot participants as they interact with name authority data as Linked Data.
System Design of a Cheetah Robot Toward Ultra-high Speed

High-speed legged locomotion pushes the limits of the most challenging problems of design and development of the mechanism, also the control and the perception method. The cheetah is an existence proof of concept of what we imitate for high-speed running, and provides us lots of inspiration on design. In this paper, a new model of a cheetah-like robot is developed using anatomical analysis and design. Inspired by a biological neural mechanism, we propose a novel control method for controlling the muscles' flexion and extension, and simulations demonstrate good biological properties and leg's trajectory. Next, a cheetah robot prototype is designed and assembled with pneumatic muscles, a musculoskeletal structure, an antagonistic muscle arrangement and a J-type cushioning foot. Finally, experiments of the robot legs swing and kick ground tests demonstrate its natural manner and validate the design of the robot. In the future, we will test the bounding behaviour of a real legged system.
A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation

Cooperative overtaking is believed to have the capability of improving road safety and traffic efficiency by means of the real-time information exchange between traffic participants, including road infrastructures, nearby vehicles, and others. In this paper, we focused on the critical issues of modeling, computation, and analysis of cooperative overtaking and made it playing a key role in the road overtaking area. In detail, for the purpose of extending the awareness of the surrounding environment, the lane markings in front of ego vehicle were detected and modeled with a Bezier curve using an onboard camera. While the nearby vehicle positions were obtained through the vehicle-to-vehicle communication scheme making assure of the accuracy of localization. Then, conflict potential field was proposed to guarantee overtaking safety. To support the proposed method, many experiments were conducted on the human-in-the-loop simulation platform. The results demonstrated that our proposed method achieves better performance, especially in some unpredictable nature road circumstances.
IMPLEMENTASI THRESHOLDING CITRA MENGGUNAKAN ALGORITMA HYBRID OPTIMAL ESTIMATION

Salah satu metode yang sering digunakan dalam pengolahan citra digital atau image processing adalah thresholding citra. Thresholding citra adalah suatu metode yang digunakan untuk memisahkan antara obyek dan backgroundnya. Thresholding adalah teknik yang sederhana tapi efektif untuk segmentasi citra. Proses Thresholding sering disebut dengan proses binerisasi. Dalam proses thresholding terhadap sebuah citra, hasil yang diperoleh tidak selalu memuaskan dan sesuai dengan keinginan. Hal ini dikarenakan faktor penghambat seperti pencahayaan yang tidak merata atau citra yang kabur yang menyebabkan histogram tidak bisa dipartisi dengan baik. Banyak metode thresholding citra yang sudah di kembangkan. Salah satu metode yang akan akan di implementasikan dalam tugas akhir ini adalah metode thresholding citra dengan menggunakan algoritma hybrid optimal estimation. Algoritma ini adalah gabungan dari PSO(particle swam optimization) yang digunakan untuk globak search, dengan EM(expectation maximization) yang digunakan untuk mengupdate particle terbaik. Dalam algoritma ini estimasi parameter dimasukan ke proses EM yang diperoleh dari pencarian global melalui PSO untuk mendapatkan titik awal yang cocok untuk EM dan sesuai dengan GMM. Dalam tugas akhir ini juga akan dilakukan pembandingan antara metode yang dipakai dalam tugas akhir ini dengan metode thresholding citra yang lain.
An Effective Combination Technique for Artificial intelligence based Ensembles for Intrusion detection

Artificial intelligence based techniques capitulate better performance in identifying the intrusions than other conventional approaches used in this field, but no single classifier is capable of identifying intrusions with acceptable accuracy. So, there is a necessity of integrating more than one classifier. This paper mainly focuses on constructing an ensemble of classifiers for classification of network traffic. In ensemble approach numerous machine learning algorithms are pooled. The main inspiration behind combining these classifiers is to take advantage of the powers of each classifier of the ensemble to get an overall more accurate classification. The existing combination techniques do not perform well when base classifiers are used with low accuracy or low diversity. The idea of the paper is to find promising classifiers for each type of network traffic class and combine them by proposing an effective combination technique so as to increase the overall accuracy. This paper proposes a combination technique is based on weighted voting. It uses two weights, one weight is assigned to each traffic class and the second weight is assigned to each classifier for each class. Traffic classes are weighted in order of their error-rate of classification i.e. the class which is hard to classify is given more weight. Classifiers are weighted by their ability to classify the instance of the particular traffic class. The results indicate the superiority of the proposed combination technique over the other combination techniques. In all experiments, the ensemble was able to increase the overall accuracy over every individual classifier used.
Application of Data Warehouse and Data Mining in Construction Management

All construction project are constrained by their schedules, budgets and specifications, and safety and environmental regulations. These constraints made construction management more complex and difficult. At the same time, many historical data that can support the decisions in the future are kept in construction enterprises,. To use the historical data effectively and efficiently, it is essential to apply the data warehouse and data mining technologies. This paper introduces a research which aims to develop a data warehouse system according to the requirements of construction enterprises and use data mining technology to learn useful information and knowledge from the data warehouse system. The design, the development and the application of this system are detailedly introduced in this paper.
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues

Currently, researchers have paid great attention to retrieval-based dialogues in open-domain. In particular, people study the problem by investigating context-response matching for multi-turn response selection based on publicly recognized benchmark data sets. State-of-the-art methods require a response to interact with each utterance in a context from the beginning, but the interaction is performed in a shallow way. In this work, we let utterance-response interaction go deep by proposing an interaction-over-interaction network (IoI). The model performs matching by stacking multiple interaction blocks in which residual information from one time of interaction initiates the interaction process again. Thus, matching information within an utterance-response pair is extracted from the interaction of the pair in an iterative fashion, and the information flows along the chain of the blocks via representations. Evaluation results on three benchmark data sets indicate that IoI can significantly outperform state-of-the-art methods in terms of various matching metrics. Through further analysis, we also unveil how the depth of interaction affects the performance of IoI.
You Only Need Attention to Traverse Trees

In recent NLP research, a topic of interest is universal sentence encoding, sentence representations that can be used in any supervised task. At the word sequence level, fully attention-based models suffer from two problems: a quadratic increase in memory consumption with respect to the sentence length and an inability to capture and use syntactic information. Recursive neural nets can extract very good syntactic information by traversing a tree structure. To this end, we propose Tree Transformer, a model that captures phrase level syntax for constituency trees as well as word-level dependencies for dependency trees by doing recursive traversal only with attention. Evaluation of this model on four tasks gets noteworthy results compared to the standard transformer and LSTM-based models as well as tree-structured LSTMs. Ablation studies to find whether positional information is inherently encoded in the trees and which type of attention is suitable for doing the recursive traversal are provided.
Unmanned aerial system–assisted wilderness search and rescue mission

The success of the wilderness search and rescue missions is highly dependent on the time required to search for the lost person. The use of unmanned aerial systems may enhance search and rescue missions by supplying aerial support of the search process. There are unmanned aerial system–based solutions, which are capable of detecting the lost person using computer vision, infrared sensors, and detection of a mobile phone signal. The most pressing issue is reducing the cost of a search and rescue mission. Thus, to improve the efficiency of the resource utilization in wilderness search scenario, we consider the use of unmanned aerial system for both mobile phone detection and enabling Wi-Fi communication for the ground portion of the search and rescue team. Such an approach does not require specific additional tools (e.g. access point, specific user equipment) for communication, which reduces the cost and improves the scalability and coordination of the search and rescue mission. As a result, the article provides methods of searching the wilderness for a person using beacon signals from a mobile phone for two situations: when the distance to the source of emergency signals is unknown and when the distance is known. In addition, the voice transmission delay and the number of unmanned aircrafts are found to guaranty the quality of a call.
WiFi Fingerprinting Indoor Localization Using Local Feature-Based Deep LSTM

Indoor localization has attracted more and more attention because of its importance in many applications. One of the most popular techniques for indoor localization is the received signal strength indicator (RSSI) based fingerprinting approach. Since RSSI values are very complicated and noisy, conventional machine learning algorithms often suffer from limited performance. Recently developed deep learning algorithms have been shown to be powerful for the analysis of complex data. In this paper, we propose a local feature-based deep long short-term memory (LF-DLSTM) approach for WiFi fingerprinting indoor localization. The local feature extractor attempts to reduce the noise effect and extract robust local features. The DLSTM network is able to encode temporal dependencies and learn high-level representations for the extracted sequential local features. Real experiments have been conducted in two different environments, i.e., a research lab and an office. We also compare the proposed approach with some state-of-the-art methods for indoor localization. The results show that the proposed approach achieves the best localization performance with mean localization errors of 1.48 and 1.75 m under the research lab and office environments, respectively. The improvements of our proposed approach over the state-of-the-art methods range from $\text{18.98}{\%}$ to $\text{53.46}{\%}$.
“Geo-Indoor” Design of an indoor navigation app to help elderly people to navigate in buildings

Some elderly people can experience problems to use new technology having feelings of distrust and indecision because technology is not accessible to their cognitive needs and mobility’s. For example: some of them can have problems of disorientation and it can be difficult to arrive independently to an unknown destination if the paths are not adapted to their mobility needs; additionally, the use of technology is not intuitive in complex interfaces. Depending on the level of mobility of the elderly person, they can use canes, walkers, wheelchairs. Taking into account previous work done in the Altran Research Medic@ team, our proposition is to develop a mobile app, which gives direction in indoor places using the minimum quantity of beacons in the place. The app will be adapted for every kind of cognitive and mobility needs. This work shows the first part of the project in which we have worked on indoor navigation for the pedestrian mode; a data fusion has been programmed in order to compute the position estimation. Tests have been done in a 2D map showing encouraging results with less than 6% of error in all cases.
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text

This paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills. In particular, we first encode the given document, question and options in a context aware way. We then propose a new network to solve the inference problem by decomposing it into a series of attention-based reasoning steps. The result of the previous step acts as the context of next step. To make each step can be directly inferred from the text, we design an operational cell with prior structure. By recursively linking the cells, the inferred results are synthesized together to form the evidence chain for reasoning, where the reasoning direction can be guided by imposing structural constraints to regulate interactions on the cells. Moreover, a termination mechanism is introduced to dynamically determine the uncertain reasoning depth, and the network is trained by reinforcement learning. Experimental results on 3 popular data sets, including MCTest, RACE and MultiRC, demonstrate the effectiveness of our approach.
Using Deep Learning Neural Networks to Control Nonlinear Systems in Real Time

Machine learning is a powerful tool that has the capability to control complex nonlinear systems where traditionally linear approximation would have otherwise been used. This project will aim to use machine learning to apply damping to a simulated pendulum model in real-time which is analogous to the laser interferometers used in the LIGO observatory to detect gravitational waves. In order to develop this solution, a series of increasingly complex models and tests will be developed for the machine learner and it is hoped that by developing the machine learning software it will be capable of controlling the highly sensitive interferometers in a similar fashion. The machine learner will be built using Google Brain’s TensorFlow software which provides the basic framework for a simple machine learning program.
Speckle noise reduction in SAR images using a new morphological filter

Speckle noise is one of the most critical disturbances that alter the quality of Synthetic Aperture Radar (SAR) coherent images. Before using SAR images in automatic target detection and recognition, the first step is to reduce the effect of speckle noise. Several adaptive and non-adaptive filters are widely used for despeckling in SAR images. In this paper, a novel mathematical morphological filter is proposed to reduce the speckle noise in SAR images. The new filter performance is compared with a number of despeckling filters with different parameters. For performance measurements, four parameters were evaluated to test the filter ability to attenuate the speckle noise and keep target information. From experimental results, the new proposed morphological filter gives promising results for significantly suppressing speckle noise and preserving the potential targets.
Poetry to Prose Conversion in Sanskrit as a Linearisation Task: A Case for Low-Resource Languages

The word ordering in a Sanskrit verse is often not aligned with its corresponding prose order. Conversion of the verse to its corresponding prose helps in better comprehension of the construction. Owing to the resource constraints, we formulate this task as a word ordering (linearisation) task. In doing so, we completely ignore the word arrangement at the verse side. kāvya guru, the approach we propose, essentially consists of a pipeline of two pretraining steps followed by a seq2seq model. The first pretraining step learns task-specific token embeddings from pretrained embeddings. In the next step, we generate multiple possible hypotheses for possible word arrangements of the input %using another pretraining step. We then use them as inputs to a neural seq2seq model for the final prediction. We empirically show that the hypotheses generated by our pretraining step result in predictions that consistently outperform predictions based on the original order in the verse. Overall, kāvya guru outperforms current state of the art models in linearisation for the poetry to prose conversion task in Sanskrit.
A multistrategy framework for analyzing system requirements

The identification of user needs and definition of system requirements is essential for the timely and correct implementation of software systems. Incorrect results during the identification of user needs or the definition of system requirements leads to errors that are difficult and expensive to correct, thus it is important to correctly capture the system level requirements, and analyze and refine these, as a prerequisite for the accurate implementation of the system. One of the software development life-cycle phases (definition of requirements) involves a thorough and careful review of the system requirements document (SRD). This document is a systems description of the functionality or performance characteristics of the proposed software or system product. A complete and accurate SRD is essential to the success of any project. Omissions, inconsistencies, ambiguities or contradictions not discovered and corrected during the initial review of the SRD will propagate through the software life cycle and can result in either an improperly functioning system, a low quality system, or an expensive and time consuming redesign. Early detection and correction of faults in the SRD are key to keeping development costs down and to building correct, reliable software. To analyze requirements and systems, and identify potential weaknesses such as these described, mechanisms are needed that assist in the process of requirements analysis. The usefulness of these mechanisms is that they may help the requirements engineer trace requirements, and the requirements maintainer ensure that changing requirements will not introduce conflicts. The objectives of this research are to provide a framework for automated support to tracing software requirements and the gathering together of requirements in sets that are able to more effectively/efficiently assess for potential errors. The research approach is to identify such mechanisms and develop a prototype that incorporates these. The contributions to the field are: the identification of a method for semantically correlating software requirements, the identification of a method for tracing software requirements, and the establishment of foundations for a framework to aid assessment of software requirements. These contributions are enabled through: the identification of a morphosemantic classification scheme (a scheme that depends on both surface and deep features of the language) that can be utilized to analyze requirements, the identification of a discourse structure; the identification of a method for the context based grouping of requirements through the creation of word sequences; the identification of a method for context based retrieval of semantic information from a set of requirements; and a foundation for an environment that helps with the analysis of software requirements.
Hidden POI Ranking with Spatial Crowdsourcing

Exploring Hidden Points of Interest (H-POIs), which are rarely referred in online search and recommendation systems due to insufficient check-in records, benefits business and individuals. In this work, we investigate how to eliminate the hidden feature of H-POIs by enhancing conventional crowdsourced ranking aggregation framework with heterogeneous (i.e., H-POI and Popular Point of Interest (P-POI)) pairwise tasks. We propose a two-phase solution focusing on both effectiveness and efficiency. In offline phase, we substantially narrow down the search space by retrieving a set of geo-textual valid heterogeneous pairs as the initial candidates and develop two practical data-driven strategies to compute worker qualities. In the online phase, we minimize the cost of assessment by introducing an active learning algorithm to jointly select pairs and workers with worker quality, uncertainty of P-POI rankings and uncertainty of the model taken into account. In addition, a (Minimum Spanning) Tree-constrained Skip search strategy is proposed for the purpose of reducing search time cost. Empirical experiments based on real POI datasets verify that the ranking accuracy of H-POIs can be greatly improved with small number of query iterations.
Towards Improving Neural Named Entity Recognition with Gazetteers

Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. This could increase the chance of overfitting since the models cannot access any supervision signal beyond the small amount of annotated data, limiting their power to generalize beyond the annotated entities. In this work, we show that properly utilizing external gazetteers could benefit segmental neural NER models. We add a simple module on the recently proposed hybrid semi-Markov CRF architecture and observe some promising results.