The C-OAR-SE procedure for scale development in marketing

Construct definition, Object classification, Attribute classification, Rater identification, Scale formation, and Enumeration and reporting (C-OAR-SE) is proposed as a new procedure for the development of scales to measure marketing constructs. COAR- SE is based on content validity, established by expert agreement after pre-interviews with target raters. In C-OAR-SE, constructs are defined in terms of Object, Attribute, and Rater Entity. The Object classification and Attribute classification steps in C-OAR-SE produce a framework (six types of scales) indicating when to use single-item vs. multiple-item scales and, for multiple-item scales, when to use an index of essential items rather than selecting unidimensional items with a high coefficient alpha. The Rater Entity type largely determines reliability, which is a precision-of-score estimate for a particular application of the scale

Functional Dependencies for Object Databases: Motivation and Axiomatization

Object identification by abstract identifiers should be considered as a modeling and not as a database concept. This means that object identifiers are not appropriate for the access to specific objects using a database language. In this paper we discuss how the relational concept of a functional dependency can be adapted to object databases in order to get more convenient ways of accessing objects. Graph based object functional dependencies are proposed as a means to specify constraints between attributes and object types of an object schema. Value based identification criteria can be defined using a special type of object functional dependencies. Different definitions of satisfaction are given for these constraints, based on a so-called validation relation, and their relationships are investigated. These definitions are related to different forms of identification. Using the strongest notion of satisfaction, inference rules for the derivation of new dependencies are discussed with emphasis on the characteristics of rules combining two dependencies, like the transitivity rule. In addition to generalized relational rules further rules are needed, mainly concerned with transition from the object type level to the attribute level and vice versa

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

Video analytics system for surveillance videos

Developing an intelligent inspection system that can enhance the public safety is challenging. An efficient video analytics system can help monitor unusual events and mitigate possible damage or loss. This thesis aims to analyze surveillance video data, report abnormal activities and retrieve corresponding video clips. The surveillance video dataset used in this thesis is derived from ALERT Dataset, a collection of surveillance videos at airport security checkpoints. The video analytics system in this thesis can be thought as a pipelined process. The system takes the surveillance video as input, and passes it through a series of processing such as object detection, multi-object tracking, person-bin association and re-identification. In the end, we can obtain trajectories of passengers and baggage in the surveillance videos. Abnormal events like taking away other's belongings will be detected and trigger the alarm automatically. The system could also retrieve the corresponding video clips based on user-defined query