Pre-defined Attributes Research Articles

Modeling Dynamic Consumer Preferences in Product Attributes for Social Media-Based Product Improvement Planning

Consumer preferences for products change over time. The changes in consumer preferences are not just related to the level of importance of the attribute but also dynamics in the attribute itself. Previous studies generally employed the pre-determine attribute to measure the level of importance of the attribute. However, the pre-defined attributes approach has a problem in that new consumer attributes are not captured, resulting in misleading product development. Therefore, this study proposes a method to identify dynamic attributes and the level of importance of the attribute for product development without pre-determined attributes and unsupervised. A case study on the airline industry demonstrates the proposed method. Attribute changes are assessed using latent Dirichlet allocation to identify topics related to product attributes and sentiment analysis to evaluate satisfaction levels. Furthermore, the association rule approach is used to label unsupervised product topics. A total of 222,959 tweets related to the airline industry from 2015 to 2018 were collected and analyzed to demonstrate changes in consumer preferences over time. The results indicate that the proposed method effectively explores the dynamic of product attributes and critical attributes and provides valuable strategies for developing product or service improvement. The proposed method has also been validated and is in line with the successful innovation strategies of an Indonesian airline.

Semantic-visual shared knowledge graph for zero-shot learning.

Almost all existing zero-shot learning methods work only on benchmark datasets (e.g., CUB, SUN, AwA, FLO and aPY) which have already provided pre-defined attributes for all the classes. These methods thus are hard to apply on real-world datasets (like ImageNet) since there are no such pre-defined attributes in the data environment. The latest works have explored to use semantic-rich knowledge graphs (such as WordNet) to substitute pre-defined attributes. However, these methods encounter a serious "role="presentation">domain shift" problem because such a knowledge graph cannot provide detailed enough semantics to describe fine-grained information. To this end, we propose a semantic-visual shared knowledge graph (SVKG) to enhance the detailed information for zero-shot learning. SVKG represents high-level information by using semantic embedding but describes fine-grained information by using visual features. These visual features can be directly extracted from real-world images to substitute pre-defined attributes. A multi-modals graph convolution network is also proposed to transfer SVKG into graph representations that can be used for downstream zero-shot learning tasks. Experimental results on the real-world datasets without pre-defined attributes demonstrate the effectiveness of our method and show the benefits of the proposed. Our method obtains a +2.8%, +0.5%, and +0.2% increase compared with the state-of-the-art in 2-hops, 3-hops, and all divisions relatively.

Evaluating citizens' satisfaction on the urban environmental management through a multi-criteria approach: An application experience in Sicily

City appearance is accepted worldwide to be highly determinant of citizens' well-being and quality of life. This is the main reason why it has become one key objective that urban managers use as the baseline criterion to design and maintain cities.In this paper, starting from a set of pre-defined attributes, we investigated the applicability of the Multicriteria Satisfaction Analysis (MUSA) method to evaluate the satisfaction level of citizens towards the way urban development and management is carried out in the city of Catania, Italy. To this aim, we submitted a questionnaire, composed of items on the satisfaction about ten sectors of urban environmental management, to a sample of academic students. The analysis is performed at two different levels: on the one hand, we study how satisfaction in the different sectors affects global satisfaction with urban environmental management; on the other hand, we link the level of satisfaction in each sector to the different aspects on which the same sectors are evaluated.The study made it possible for us to state that, in the considered sample, citizens are quite demanding, which means that the expectations of the citizens are fulfilled only if they express the highest satisfaction level. Moreover, we documented socio-and health-care as the sector containing aspects that are quite relevant for the citizens, and so need to be improved to increase their global satisfaction.Overall, the results obtained proved the feasibility of MUSA application in the field of citizens' satisfaction with urban management and allowed us to highlight some peculiar characteristics and potentialities related to the method used.

Sistem Pendukung Keputusan Penilaian Siswa Menggunakan Metode Simple Additive Weighting Berbasis Framework Laravel

The assessment of student learning outcomes is a mandatory process in educational institutions, whether they are formal or non-formal, with the aim of monitoring and evaluating student progress during the learning process. With the advancement of technology, there are many processes that can be integrated with information technology to simplify existing procedures. The purpose of this research is to facilitate schools in making decisions regarding the results of the learning assessment process by using predefined attributes. The attributes used as reference for consideration are assignments, mid-term exams (UTS), and final exams (UAS). Therefore, this study is built using the SAW method and the PHP programming language supported by the Laravel framework. The output of this research is an information system that supports decision-making in assessing student learning outcomes in the form of subject rankings and provides transparency and convenience for students to access their grades or the final results of the student learning assessment process.

Creation of an OBE Model PO Attainment Tool

This paper is focused on the strategy of outcome-based education (OBE) approach to boost the quality of education. This approach emphasizes on pre-defined attributes required in a graduate engineer. Based on evaluation of OBE, the attainment process of programme outcome (PO) is defined for qualitative education. To show that an outcome has been met, the OBE requires evidence of evaluation. An evaluation framework has been designed and developed by considering the engineering stream students. The tools of attainment are built by considering different parameters of the respective curriculum to measure the attainment of POs. In the OBE system, there is no single specified technique of teaching and assessment to achieve the specified outcomes..

Analysis of sentiment expressions for user-centered design

Devising intelligent systems capable of identifying the idiosyncratic needs of users at scale and translating them into attribute-level design feedback and recommendations is a key prerequisite for successful user-centered design processes. Recent studies show that 49% of design firms lack systems and tools for monitoring external platforms, and only 8% have adopted digital, data-driven approaches for new product development despite acknowledging them as a high priority. The state-of-the-art attribute-level sentiment analysis approaches based on deep learning have achieved promising results; however, these methods pose strict preconditions, require manually labeled data for training and pre-defined attributes by experts, and only classify sentiments intro predefined categories which have limited implications for designers. This article develops a rule-based methodology for extracting and analyzing the sentiment expressions of users on a large scale, from myriad reviews available on social media and e-commerce platforms. The methodology further advances current unsupervised attribute-level sentiment analysis approaches by enabling efficient identification and mapping of sentiment expressions of individual users onto their respective attributes. Experiments on a large dataset scraped from a major e-commerce retail store for apparel and indicate 74.3%–93.8% precision in extracting attribute-level sentiment expressions of users and demonstrate the feasibility and potentials of the developed methodology for large-scale need finding from user reviews.

Multi-criteria decision analysis to prioritize the introduction of new vaccines in Indonesia by using the framework of the strategic multi-attribute ranking tool for vaccines (SMART vaccines)

ABSTRACT Background: Decision-making processes regarding new vaccine prioritizations are complex. The objective of this study was to prioritize the introduction of new vaccines in Indonesia. Methods: A multi-criteria decision analysis (MCDA) was applied in this study. A preliminary data collection form was developed to collect country-specific data in relation to 30 pre-defined attributes. In particular, an open-ended questionnaire was conducted among targeted respondents from global level, national level and vaccine manufacturers, which were involved in the financial flows of new vaccine procurement in Indonesia. For setting new vaccines priorities, targeted respondents were asked to assign weight on 10 selected criteria. Results: Top 3 attributes with the highest weight from respondents were premature deaths averted per year, incident cases prevented per year, and cost-effectiveness. Applying criteria scores and weight assessment, the result showed that PCV, rotavirus, HPV, and JE would be on the 1st, 2nd, 3rd, and 4th rank for setting new vaccine priority in Indonesia. There was a significant difference score (p value <0.05) between all these vaccines. Conclusions: PCV, rotavirus and HPV vaccines should be more prioritized than JE vaccine. This ranking is in line with the WHO’s priority list, which potentially illustrates the validity and usefulness of our MCDA-approach.

Ranking of commercial photodiodes in radiation detection using multiple-attribute decision making approach

Recent studies have shown that silicon PIN photodiodes can be used in the area of detecting ionizing radiation. Selection of proper photodiode as ionizing radiation detector is crucial. In this paper the SMART technique as a subset of multiple-attribute decision making (MADM) approaches is employed in order to rank detectors. Using this method leads to the optimized initial selection of detector. The process of ranking is based on pre-defined attributes such as active area, breakdown voltage, minimum wavelength in the spectral range, wavelength with maximum response in spectral range, dark current, junction capacitance, and power dissipation of photodiodes. Evaluation results of a set of photodiodes show that PS100-6 and S1223-01 are the best and second-best alternatives, respectively. Considering commercial contemplations, cost could also be added to the aforementioned attributes. In this case, BPV10 and BPW34 are found as the best and second-best alternatives.

PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.

Generating Character Descriptions for Automatic Summarization of Fiction

Summaries of fictional stories allow readers to quickly decide whether or not a story catches their interest. A major challenge in automatic summarization of fiction is the lack of standardized evaluation methodology or high-quality datasets for experimentation. In this work, we take a bottomup approach to this problem by assuming that story authors are uniquely qualified to inform such decisions. We collect a dataset of one million fiction stories with accompanying author-written summaries from Wattpad, an online story sharing platform. We identify commonly occurring summary components, of which a description of the main characters is the most frequent, and elicit descriptions of main characters directly from the authors for a sample of the stories. We propose two approaches to generate character descriptions, one based on ranking attributes found in the story text, the other based on classifying into a list of pre-defined attributes. We find that the classification-based approach performs the best in predicting character descriptions.

The effect of social influence and social interactions on the adoption of a new technology: The use of bike sharing in a student population

The present study investigates how social influence and social interactions can affect the adoption of new technologies, using stated preference (SP) survey data combined with an “accelerated reality” experience of social interaction among the respondents. Specifically, the intention to use a pro-environmental transport mode (the bike sharing) during a public transport strike within a cohort of students has been analysed. Previous studies have modelled social influence effects using SP data by providing a hypothetical scenario with simulated interactions or information about social conformity processes (i.e. social adoption) during the survey. In our paper, in addition to the impact of assumed social norms, the effect of live/real social interactions is included in the survey. SP survey is developed to investigate the effect of Level-of-Service attributes on the hypothetical choices in the scenario of a public transport strike. Besides the pre-defined attributes characterising the alternatives in the SP design, the survey includes techniques to acquire information on conformity and social interactions. Specifically, the interviewees undertake a before and after stated preference experiment (SP1 and SP2), with a period of group discussion in between the two parts. This SP experiment involves different cognitive and interpersonal mechanisms, such as the functional information exchange on benefits and drawbacks of cycling and bike sharing. The aim is to establish whether hypothetical scenarios of social conformity are different from real/live social interactions and whether these social influence processes actually affect the individuals' mode choice. A joint SP1/SP2 mixed logit (ML) model has been estimated to explore the choice behaviour of individuals and allows us to incorporate the inertia/propensity to change behaviour between SP1 and SP2. Moreover, considering the “Reflexive Layers of Influence” (RLI) framework, the processes generated by social interactions (diffusion, translation and reflexivity) are measured and incorporated in the model. We finally show the effect of these social influence variables on the goodness-of-fit of the models and choice simulation for prediction. We also draw conclusions about the value of such enhanced choice models in understanding and predicting the impacts of social interactions on choice behaviour in the context of new transport technologies.

The evaluation of a brand association density metric

PurposeThe purpose of this paper is to propose a new brand association density metric and evaluate its performance in terms of correlations with recall, consideration, brand equity and market share and to compare different data collection methodologies to identify brand associations.Design/methodology/approachThe authors present results from two studies covering three product categories. The authors use an open free association question and associations to a set of pre-defined brand attributes. The responses to the open free format question are text-mined prior to further analysis.FindingsThe authors find that the brand association density metric performs better than a metric that only uses the number of distinct associations. The authors also find that these metrics work best when derived from open free association data.Practical implicationsFirst, in addition to focusing on trying to build specific brand associations in consumers’ minds, it may be equally important, if not more important, to manage the number and inter-connectedness of the brand’s associations. Second, firms should complement their existing survey approaches with open-ended free association questions.Originality/valueThe brand association density concept presented is believed to be new. The empirical comparison between the use of free association to pre-defined attributes is also new.

Broad Attribute Prediction Model With Enhanced Attribute and Feature

For the zero-shot image classification without intersection between training and testing sets, the high-quality representation of image attributes and features plays a key role to improve the classification performance. In order to overcome the limitations related to insufficient attribute and feature expression in zero-shot image classification, we propose a broad attribute prediction model with enhanced attribute and feature (EAF-BAP) based on broad learning and elastic net constraint. Firstly, the EAF-BAP enhances pre-defined attributes by elastic net constraint to obtain hybrid attributes, which effectively improves the finiteness of semantic attributes. Secondly, the enhanced features are constructed by broad learning to increase the discrimination ability of features in different classes. Meanwhile, the broad learning is employed to train multiple attribute classifiers synchronously, which is more efficient compared to traditional support vector machines. Finally, the similarity between predicted attributes and hybrid attributes in testing classes is calculated by Manhattan distance, which is further used to implement image classification. Experiments on both AwA and Shoes datasets show that the proposed EAF-BAP model is capable of improving the accuracy of zero-shot image classification efficiently.

Sketch-Based 3D Model Retrieval Using Attributes

With the rapid growth of available 3D models on the Internet, how to retrieve 3D models based on hand-drawn sketch retrieval are becoming increasingly important. This article proposes a new sketch-based 3D model retrieval approach. This approach is different from current methods that make use of low-level visual features to capture the search intention of users. The proposed method uses two kinds of semantic attributes, including pre-defined attributes and latent attributes. Specifically, pre-defined attributes are defined manually which can provide prior knowledge about different sketch categories and latent-attributes are more discriminative which can differentiate sketch categories at a finer level. Therefore, these semantic attributes can provide a more descriptive and discriminative meaningful representation than low-level feature descriptors. The experiment results demonstrate that this proposed method can achieve superior performance over previously proposed sketch-based 3D model retrieval methods.

Anomaly detection in heterogeneous bibliographic information networks using co-evolution pattern mining

Detecting evolution-based anomalies have emerged as an effective research topic in many domains, such as social and information networks, bioinformatics, and diverse security applications. However, the majority of research has focused on detecting anomalies using evolutionary behavior among objects in a network. The real-world networks are omnipresent, and heterogeneous in nature, while, in these networks, multiple types of objects co-evolve together with their attributes. To understand the anomalous co-evolution of multi-typed objects in a heterogeneous information network (HIN), we need an effective technique that can capture abnormal co-evolution of multi-typed objects. For example, detecting co-evolution-based anomalies in the heterogeneous bibliographic information network (HBIN) can depict better the object-oriented semantics than just scrutinizing the co-author or citation network alone. In this paper, we introduce the novel notion of a co-evolutionary anomaly in the HBIN, detect anomalies using co-evolution pattern mining (CPM), and study how multi-typed objects influence each other in their anomalous declaration by following a special type of HIN called star networks. The influence of three pre-defined attributes namely paper-count, co-author, and venue over target objects is measured to detect co-evolutionary anomalies in HBIN. The anomaly scores are calculated for each 510 target objects and individual influence of attributes is measured for two top target objects in case-studies. It is observed that venue has the most influence on the target objects discussed as case studies, however, about the rest of anomalies in the list, the most anomalous influential attribute could be rather different than the venue. Indeed, the CABIN algorithm constructs the way to find out the most influential attributes in co-evolutionary anomaly detection. Experiments on bibliographic dataset validate the effectiveness of the model and dominance of the algorithm. The proposed technique can be applied on various HINs such as Facebook, Twitter, Delicious to detect co-evolutionary anomalies.

Knowledge Acquisition for Electronic Health Records on cloud

Every Electronic Health Record (EHR) System has functionalities which protect patient’s data and drug related information prescribed at the time of medical diagnosis. Decision support system capabilities of the system provide necessary paperless handling support for the health information of a large number of patients. The extensive search on the practice of medical data indicates that there is a steep trend in the electronic health record usage. There is quite a number of EHR software available which are used as a Service globally, but many practitioners are not aware of selecting the right EHR SaaS product. Hence, there is a need to identify the best EHR SaaS from a pool of Software service products. To enhance the search of getting the best possible EHR SaaS product in this paper, researchers have adapted the novel way of using predefined attributes of these products and finding the relationship between them. Further, they are grouped into clusters depending on the rating, customer reviews, record of attested customers, eligible user professionals are guided in selecting a particular EHR SaaS.

Post-Miocene faults in Auckland: insights from borehole and topographic analysis

Although experiencing some seismicity, the Auckland region is generally considered to be one of New Zealand's most tectonically stable areas. There is a distinct lack of mapped faults in the central Auckland area, yet regional trends indicated by several mapped faults surrounding it suggest that these faults should continue through Auckland. The overprint of Auckland Volcanic Field (AVF) deposits may mask any potential fault traces in the area. We carried out a detailed study of the topography of Auckland concealed beneath the AVF deposits, using borehole and geophysical data, and combined this with structural information gleaned from current topography and exposed geology to identify significant concealed fault offsets. The reliability of each potential fault displacement was determined using pre-defined attributes. The resulting block faulting model for Auckland supports the proposed connection between the position of the Junction Magnetic Anomaly and Auckland volcanism. The identification of several possible faults in the area covered by the AVF might indicate more structural control on volcanism than previously thought.

A flow-based approach to datagram security

Datagram services provide a simple, flexible, robust, and scalable communication abstraction; their usefulness has been well demonstrated by the success of IP, UDP, and RPC. Yet, the overwhelming majority of network security protocols that have been proposed are geared towards connection-oriented communications. The few that do cater to datagram communications tend to either rely on long term host-pair keying or impose a session-oriented (i.e., requiring connection setup) semantics.Separately, the concept of flows has received a great deal of attention recently, especially in the context of routing and QoS. A flow characterizes a sequence of datagrams sharing some pre-defined attributes. In this paper, we advocate the use of flows as a basis for structuring secure datagram communications. We support this by proposing a novel protocol for datagram security based on flows. Our protocol achieves zero-message keying, thus preserving the connectionless nature of datagram, and makes use of soft state, thus providing the per-packet processing efficiency of session-oriented schemes. We have implemented an instantiation for IP in the 4.4BSD kernel, and we provide a description of our implementation along with performance results.

Workers' health in Brazilian state constitutions: a comparative analysis

This article briefly analyzes the legal measures related to workers' health in the various Brazilian state constitutions. It makes a qualitative and quantitative comparison of the different provisions based on the presence, weight, and relevance ascribed to the issue, using predefined attributes: statement of guidelines; production, control, and utilization of information; labor intervention; guarantee of rights. The analysis showed legislators' intent to allocate power to the Unified Health System in the field of workers' health so as to allow for new practices in this area.