The effect of time on gait recognition performance

Many studies have shown that it is possible to recognize people by the way they walk. However, there are a number of covariate factors that affect recognition performance. The time between capturing the gallery and the probe has been reported to affect recognition the most. To date, no study has shown the isolated effect of time, irrespective of other covariates. Here we present the first principled study that examines the effect of elapsed time on gait recognition. Using empirical evidence we show for the first time that elapsed time does not affect recognition significantly in the short to medium term. By controlling the clothing worn by the subjects and the environment, a Correct Classification Rate (CCR) of 95% has been achieved over 9 months, on a dataset of 2280 gait samples. Our results show that gait can be used as a reliable biometric over time and at a distance. We have created a new multimodal temporal database to enable the research community to investigate various gait and face covariates. We have also investigated the effect of different type of clothes, variations in speed and footwear on the recognition performance. We have demonstrated that clothing drastically affects performance regardless of elapsed time and significantly more than any of the other covariates that we have considered here. The research then suggests a move towards developing appearance invariant recognition algorithms. Thi

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting

Exploring sensor data management

The increasing availability of cheap, small, low-power sensor hardware and the ubiquity of wired and wireless networks has led to the prediction that `smart evironments' will emerge in the near future. The sensors in these environments collect detailed information about the situation people are in, which is used to enhance information-processing applications that are present on their mobile and `ambient' devices.\ud \ud Bridging the gap between sensor data and application information poses new requirements to data management. This report discusses what these requirements are and documents ongoing research that explores ways of thinking about data management suited to these new requirements: a more sophisticated control flow model, data models that incorporate time, and ways to deal with the uncertainty in sensor data

Searching for patterns in political event sequences: Experiments with the KEDs database

This paper presents an empirical study on the possibility of discovering interesting event sequences and sequential rules in a large database of international political events. A data mining algorithm first presented by Mannila and Toivonen (1996), has been implemented and extended, which is able to search for generalized episodes in such event databases. Experiments conducted with this algorithm on the Kansas Event Data System (KEDS) database, an event data set covering interactions between countries in the Persian Gulf region, are described. Some qualitative and quantitative results are reported, and experiences with strategies for reducing the problem complexity and focusing on the search on interesting subsets of events are described

Penghasilan dan penilaian video pembelajaran (CD) bagi mata pelajaran Prinsip Ekonomi (BPA 1013) bertajuk permintaan dan penawaran di KUITTHO

Kajian ini dijaiankan untuk meniiai keberkesanan sebuah video pembeiajaran (CD) mata peiajaran Prinsip Ekonomi (BPA 1013) bertajuk Permintaan dan Penawaran. Bagi tujuan tersebut, sebuah video pembelajaran telah dihasilkan membantu pelajar bagi memahami mata pelajaran berkenan semasa proses pengajaran dan pembelajaran berlaku. Video pembelajaran yang dihasilkan ini kemudian dinilai dari aspek proses pengajaran dan pembelajaran, minat dan persepsi responden terhadap ciri-ciri video (audio dan visual). Seramai 60 orang pelajar semester 2 Sarjana Muda Sains Pengurusan di Kolej Universiti Teknologi Tun Hussein Onn telah dipiih bagi membuat penilaian kebolehgunaan produk ini sebagai alat bantuan mengajar di dalam kelas. Semua data yang diperolehi kemudiannya dikumpulkan bagi dianalisis dengan menggunakan perisian "SrarMfKM/ Pac/rageybr Rocaj/ Sb/'eace " (SPSS). Hasil dapatan kajian yang dilakukan jelas menunjukkan video pengajaran yang dihasilkan dan dinilai ini amat sesuai digunakan bagi tujuan memenuhi keperluan proses pengajaran dan pembelajaran subjek ini di dalam kelas

On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

Information-theoretic temporal Bell inequality and quantum computation

An information-theoretic temporal Bell inequality is formulated to contrast classical and quantum computations. Any classical algorithm satisfies the inequality, while quantum ones can violate it. Therefore, the violation of the inequality is an immediate consequence of the quantumness in the computation. Furthermore, this approach suggests a notion of temporal nonlocality in quantum computation.Comment: v2: 5 pages, refereces added, discussion slightly revised, main result unchanged. v3: typos correcte

Handling Confidential Data on the Untrusted Cloud: An Agent-based Approach

Cloud computing allows shared computer and storage facilities to be used by a multitude of clients. While cloud management is centralized, the information resides in the cloud and information sharing can be implemented via off-the-shelf techniques for multiuser databases. Users, however, are very diffident for not having full control over their sensitive data. Untrusted database-as-a-server techniques are neither readily extendable to the cloud environment nor easily understandable by non-technical users. To solve this problem, we present an approach where agents share reserved data in a secure manner by the use of simple grant-and-revoke permissions on shared data.Comment: 7 pages, 9 figures, Cloud Computing 201