Provisioning of energy consumption information for mobile ads

Advertisement-embedded mobile applications have been reported to consume a non-trivial amount of energy. Although a few studies have focused on the energy consumption of mobile advertisements (ads), no previous work has addressed the mobile ad ecosystem, which consists of users, application developers, and ad providers. In this paper, we define the advertisement energy information (AEI) required for the mobile ad ecosystem, and we propose a set of application programming interfaces (APIs) to provide AEI that considers various requirements of the underlying ecosystem. To realize the APIs, we developed a system service in Android to collect the AEI accurately and with low overhead. The experiment results show the validity of the proposed scheme, and the case studies demonstrate the usefulness of the proposed APIs.     

Development of spatial similarity‐based modelling to improve integrated lake water quality management in Malaysia

This study describes similarity‐based modelling techniques used to develop a spatially based water quality prediction model to facilitate sustainable lake water quality management. The lentic nature of lakes allows them to slowly absorb pollutants over a long period of time with readily noticeable signs, causing symptoms to appear only when the water quality has significantly degraded, meaning the risk of improper water quality management can be very high. Thus, failure to establish sustainable planning at the watershed scale was found to be a major threat of water quality degradation, from extemporary approaches often practised in lake management. Accordingly, the developed model is tailored for lakes facing moderate to serious water quality data limitation. The geodatabase integrates the identified driving factors of physical, social and water quality with significant influences on the status of lake water quality. A 1 km buffer radius with percentages of built‐up area, population, lake surface area and rainfall is measured. Calculation of a water quality index was then quantified on the basis of a similarity‐modelling technique. Lake Putrajaya was chosen as a control point for developing the indicator index. A total of 93 recreational lakes within Selangor and Kuala Lumpur were selected as modelling points. The results of this study indicated the similarity technique of spatial modelling is sufficiently reliable to be applied as an early assessment indicator. From the 93 lakes in this study, none feel in the category of either “bad” or “excellent,” with the majority being in class 3 (medium water quality status) and only four considered as having a good water quality condition. The balance of 35 lakes was considered to exhibit poor water quality. The model output is an indicator index, providing classification guidelines for the water quality status of the assessed lake as an early assessment tools.     

Efficient Data Reporting in Intelligent Transportation Systems

Recently, intelligent transportation systems (ITSs) have emerged. These systems can improve traditional transportation systems and provide traffic information to travelers. In the area of transportation, wireless sensor networks (WSNs) can replace the existing wired sensors and expensive traffic monitoring systems to mitigate the time and costs of installing such systems. However, accurate and on-time traffic information delivery is a major challenge, considering the energy constraints of sensor nodes. In this paper, we propose a two-tier architecture that includes a network of mobile objects (vehicles) in the upper layer and a hierarchical WSN in the bottom layer. Using this approach, a portion of loads on the low-power static sensor nodes can be transferred to mobile objects, such as powerful mobile devices. Moreover, to provide accurate and timely traffic information, a QoS-aware link cost function has been proposed and used for data transmission between the static sensor nodes. In addition, due to the mobility of the objects and the probability of losing packets in the mobile object tier, a reliable data forwarding mechanism has been proposed for this tier. In this mechanism, data packets are forwarded to the neighbors, which enhance the probability of the packets’ being received. The performance evaluation results indicate the effectiveness of the proposed architecture and data reporting mechanism for use in ITS applications.     

Quantum information approach to the azurite mineral frustrated quantum magnet

Quantum correlations are almost impossible to address in bulk systems. Quantum measures extended only to a few number of parties can be discussed in practice. In the present work, we study nonlocality for a cluster of spins belonging to a mineral whose structure is that of a quantum magnet. We reproduce at a much smaller scale the experimental outcomes, and then, we study the role of quantum correlations there. A macroscopic entanglement witness has been introduced in order to reveal nonlocal quantum correlations between individual constituents of the azurite mineral at nonzero temperatures. The critical point beyond which entanglement is zero is found at \(T_c < 1\,\mathrm{K}\).     

Do multipartite correlations speed up adiabatic quantum computation or quantum annealing?

Quantum correlations are thought to be the reason why certain quantum algorithms overcome their classical counterparts. Since the nature of this resource is still not fully understood, we shall investigate how multipartite entanglement and non-locality among qubits vary as the quantum computation runs. We shall encounter that quantum measures on the whole system cannot account for their corresponding speedup.     

RePizer: a framework for prioritization of software requirements

The standard software development life cycle heavily depends on requirements elicited from stakeholders. Based on those requirements, software development is planned and managed from its inception phase to closure. Due to time and resource constraints, it is imperative to identify the high-priority requirements that need to be considered first during the software development process. Moreover, existing prioritization frameworks lack a store of historical data useful for selecting the most suitable prioritization technique of any similar project domain. In this paper, we propose a framework for prioritization of software requirements, called RePizer, to be used in conjunction with a selected prioritization technique to rank software requirements based on defined criteria such as implementation cost. RePizer assists requirements engineers in a decision-making process by retrieving historical data from a requirements repository. RePizer also provides a panoramic view of the entire project to ensure the judicious use of software development resources. We compared the performance of RePizer in terms of expected accuracy and ease of use while separately adopting two different prioritization techniques, planning game (PG) and analytical hierarchy process (AHP). The results showed that RePizer performed better when used in conjunction with the PG technique.     

Locality-constrained nonnegative robust shape interaction subspace clustering and its applications

In this paper, we present a locality-constrained nonnegative robust shape interaction (LNRSI) subspace clustering method. LNRSI integrates the local manifold structure of data into the robust shape interaction (RSI) in a unified formulation, which guarantees the locality and the low-rank property of the optimal affinity graph. Compared with traditional low-rank representation (LRR) learning method, LNRSI can not only pursuit the global structure of data space by low-rank regularization, but also keep the locality manifold, which leads to a sparse and low-rank affinity graph. Due to the clear block-diagonal effect of the affinity graph, LNRSI is robust to noise and occlusions, and achieves a higher rate of correct clustering. The theoretical analysis of the clustering effect is also discussed. An efficient solution based on linearized alternating direction method with adaptive penalty (LADMAP) is built for our method. Finally, we evaluate the performance of LNRSI on both synthetic data and real computer vision tasks, i.e., motion segmentation and handwritten digit clustering. The experimental results show that our LNRSI outperforms several state-of-the-art algorithms.     

Exploring putative inhibitors of Death Associated Protein Kinase 1 (DAPK1) via targeting Gly-Glu-Leu (GEL) and Pro-Glu-Asn (PEN) substrate recognition motifs

Recently, a new signaling complex Death Associated Protein Kinase 1 (DAPK1) ̶ N-methyl-D-aspartate receptor subtype 2B (NMDAR2B or NR2B) engaged in the neuronal death cascade was identified and it was found that after stroke injury, N-methyl-D-aspartate glutamate (NMDA) receptors interact with DAPK1 through NR2B subunit and lead to excitotoxicity via over-activation of NMDA receptors. An acute brain injury, such as stroke, is a serious life-threatening medical condition which occurs due to poor blood supply to the brain and further leads to neuronal cell death. During a stroke, activated DAPK1 migrates towards the extra-synaptic site and binds to NR2B subunit of NMDA receptor. It is this DAPK1-NR2B interaction that arbitrates the pathological processes like apoptosis, necrosis, and autophagy of neuronal cells observed in stroke injury, hence we aimed to inhibit this vital interaction to prevent neuronal damage. In the present study, using PubChem database, we applied an integrative approach of virtual screening and molecular dynamic simulations and identified a potential lead compound 11 that interrupts DAPK1-NR2B interaction by competing with both ATP and substrate for their binding sites on DAPK1. This inhibitor was found potent and considerably selective to DAPK1 as it made direct contact with the ATP binding sites as well as substrate recognition motifs: Gly-Glu-Leu (GEL) and Pro-Glu-Asn (PEN). Further in vitro and in vivo experiments are demanded to validate the efficacy of compound 11 nevertheless, it can be considered as suitable starting point for designing DAPK1 inhibitors.     

Incremental Clustering-Based Facial Feature Tracking Using Bayesian ART

Person-independent, emotion specific facial feature tracking have been of interest in the machine vision society for decades. Among various methods, the constrained local model (CLM) has shown significant results in person-independent feature tracking. In 63this paper, we propose an automatic, efficient, and robust method for emotion specific facial feature detection and tracking from image sequences. Considering a 17-point feature model on the frontal face region, the proposed tracking framework incorporates CLM with two incremental clustering algorithms to increase robustness and minimize tracking error during feature tracking. The Patch Clustering algorithm is applied to build an appearance model of face frames by organizing previously encountered similar patches into clusters while the shape Clustering algorithm is applied to build a structure model of face shapes by organizing previously encountered similar shapes into clusters followed by Bayesian adaptive resonance theory (ART). Both models are used to explore the similar features/shapes in the successive images. The clusters in each model are built and updated incrementally and online, controlled by amount of facial muscle movement. The overall performance of the proposed incremental clustering-based facial feature tracking (ICFFT) is evaluated using the FGnet database and the extended Cohn-Kanade (CK+) database. ICFFT demonstrates better results than baseline-method CLM and provides robust tracking as well as improved localization accuracy of emotion specific facial features tracking.     

A scrambling framework for block transform compressed image

In this work, we propose a scrambling framework for block transform compressed image. First, three attacks are proposed to sketch the outline of the original image directly from its scrambled counterpart by exploiting information deduced from the transformed components. Based on the proposed sketch attacks, a scrambling framework aiming to minimize the bitstream size overhead and prevent the leakage of visual information is put forward. In particular, the DC components are manipulated within each non-overlapping region to achieve the scrambling while simultaneously reducing the bitstream size overhead. The non-DC components are shuffled and substituted to generate a completely distorted image while preventing information leakage. The ideas are implemented in JPEG to verify its performance and compare to that of the conventional JPEG based scrambling methods. Results indicate that the proposed methods exhibit stable performance in terms of the bitstream size overhead when using different quality factors, and it is able to withstand the proposed sketch attacks as well as the classical cryptographic attacks.