Pendekatan Multi Attributes Decision Making dengan Metode Topsis dalam Pemilihan Lokasi Perakitan Studi Kasus PT. Hartono Istana Teknologi

The titled of this research is Multi Attributes Decision Making with Using TOPSIS Method for Assembly Site Selection at PT Hartono Istana Teknologi. PT. Hartono Istana Teknologi will move the sub-assembly or assembly location for the components of the refrigerator, but the location has not been determined. Company has a three alternatif locations to be used as the location of the new assembly. Decision support method is very useful if applied in this company to determine the best alternatif of several alternatifs. There are several methods of making keptusan that can be used, one of them is with Multi Attributess Decision Making (MADM) by using TOPSIS method. This is because the TOPSIS method has a simple concept, easy to understand, have efficient computing with the ability to measure the relative performance of alternatifs decision in the form of mathematically simple, which in this problem, companies must choose one of three alternatifs with taking into account several criteria or the attributes that has ditentukant. The end result of this is seen MADM TOPSIS method and determined based on the value of the highest preference reckoned end, which in this case, the selected alternatif is the first alternatif, with a preference value of 0.58

Multi-task CNN Model for Attribute Prediction

This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model's parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape

Deep Attributes Driven Multi-Camera Person Re-identification

The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations. And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely \emph{deep attributes} exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple metric learning modular further boosts our method, making it significantly outperform many recent works.Comment: Person Re-identification; 17 pages; 5 figures; In IEEE ECCV 201

CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.Comment: 8 pages, Submitted in CCIA 201

Stability of the WTP measurements with successive use of choice experiments method and multiple programmes method

This paper is part of an investigation to evaluate the benefits of landscape policies. Such policies are, within a specific area (here the Monts d’Arrée in Brittany), favouring some landscape attributes. We test out a procedure based on a double device. The first one relies on the choice experiments method and focuses on each attribute. Without prior information about the presence of substitution and complementarity effects between attributes, we work on the basis of scenarios built to ensure the independence of attributes. The important question of the impact of an attribute variation on the aesthetic value of another one, when these attributes are jointly perceived, is tackled by use of the multi-programme method. The two surveys were launched after an interval of one year, sampling among the same population. The WTP results obtained from each method are not statistically different.Valuation; choice modelling; multi-attributes choice set; multi-programme method; choice experiments; landscape; Monts d’Arrée

Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in computer vision community due to it plays an important role in video surveillance. Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attributes recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criterion. Thirdly, we analyse the concept of multi-task learning and multi-label learning, and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have widely applied in the deep learning community. Fourthly, we analyse popular solutions for this task, such as attributes group, part-based, \emph{etc}. Fifthly, we shown some applications which takes pedestrian attributes into consideration and achieve better performance. Finally, we summarized this paper and give several possible research directions for pedestrian attributes recognition. The project page of this paper can be found from the following website: \url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey: https://sites.google.com/view/ahu-pedestrianattributes

Attracting Convention and Exhibition Attendance to Complex Mice Venues: Emerging Data From Macao

This study presents an importance-performance analysis of multi-level attributes (event, facility and destination) evaluated by delegates attending an exhibition event in a ‘complex meetings, incentive, convention or exhibition (MICE) venue’ in greater China (mainland China, Hong Kong, Macao and Taiwan). The study's findings expound the relevance of various attributes in light of the emergence of complex MICE venues and destination resorts and, in particular, emphasizes the relative importance of destination – vis-à-vis facility – and core event-related attributes towards determining exhibition attendance