Query: Implementation of flash flood monitoring system based on wireless sensor network in Bangladesh

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Real-time flood monitoring and warning system <=SEP=> Flooding is one of the major disasters occurring in various parts of the world. The system for real-time monitoring of water conditions: water level; flow; and precipitation level, was developed to be employed in monitoring flood in Nakhon Si Thammarat, a southern province in Thailand. The two main objectives of the developed system is to serve 1) as information channel for flooding between the involved authorities and experts to enhance their responsibilities and collaboration and 2) as a web based information source for the public, responding to their need for information on water condition and flooding. The developed system is composed of three major components: sensor network, processing/transmission unit, and database/ application server. These real-time data of water condition can be monitored remotely by utilizing wireless sensors network that utilizes the mobile General Packet Radio Service (GPRS) communication in order to transmit measured data to the application server. We implemented a so-called VirtualCOM, a middleware that enables application server to communicate with the remote sensors connected to a GPRS data unit (GDU). With VirtualCOM, a GDU behaves as if it is a cable directly connected the remote sensors to the application server. The application server is a web-based system implemented using PHP and JAVA as the web application and MySQL as its relational database. Users can view real-time water condition as well as the forecasting of the water condition directly from the web via web browser or via WAP. The developed system has demonstrated the applicability of today’s sensors in wirelessly monitor real-time water conditions.

Design of a Water Environment Monitoring System Based on Wireless Sensor Networks <=SEP=> A water environmental monitoring system based on a wireless sensor network is proposed. It consists of three parts: data monitoring nodes, data base station and remote monitoring center. This system is suitable for the complex and large-scale water environment monitoring, such as for reservoirs, lakes, rivers, swamps, and shallow or deep groundwaters. This paper is devoted to the explanation and illustration for our new water environment monitoring system design. The system had successfully accomplished the online auto-monitoring of the water temperature and pH value environment of an artificial lake. The system's measurement capacity ranges from 0 to 80 °C for water temperature, with an accuracy of ±0.5 °C; from 0 to 14 on pH value, with an accuracy of ±0.05 pH units. Sensors applicable to different water quality scenarios should be installed at the nodes to meet the monitoring demands for a variety of water environments and to obtain different parameters. The monitoring system thus promises broad applicability prospects.

Routing in multi-radio, multi-hop wireless mesh networks <=SEP=> We present a new metric for routing in multi-radio, multi-hop wireless networks. We focus on wireless networks with stationary nodes, such as community wireless networks.The goal of the metric is to choose a high-throughput path between a source and a destination. Our metric assigns weights to individual links based on the Expected Transmission Time (ETT) of a packet over the link. The ETT is a function of the loss rate and the bandwidth of the link. The individual link weights are combined into a path metric called Weighted Cumulative ETT (WCETT) that explicitly accounts for the interference among links that use the same channel. The WCETT metric is incorporated into a routing protocol that we call Multi-Radio Link-Quality Source Routing.We studied the performance of our metric by implementing it in a wireless testbed consisting of 23 nodes, each equipped with two 802.11 wireless cards. We find that in a multi-radio environment, our metric significantly outperforms previously-proposed routing metrics by making judicious use of the second radio.

A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends <=SEP=> The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.

From content delivery today to information centric networking <=SEP=> Today, content delivery is a heterogeneous ecosystem composed by various independent infrastructures. The ever increasing growth of Internet traffic has encouraged the proliferation of different architectures to serve content provider needs and user demand. Despite the differences among the technology, their low level implementation can be characterized in a few fundamental building blocks: network storage, request routing, and data transfer. Existing solutions are inefficient because they try to build an information centric service model over a network infrastructure which was designed to support host-to-host communications. Information-Centric Networking (ICN) paradigm has been proposed as a possible solution to this mismatch. ICN integrates content delivery as a native network feature. The rationale is to architect a network that automatically interprets, processes, and delivers content (information) independently of its location. This paper makes the following contributions: 1) it identifies a set of building blocks for content delivery, 2) it surveys the most popular approaches to realize the above building blocks, 3) it compares content delivery solutions relying on the current Internet infrastructure with novel ICN approaches.

Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification <=SEP=> This paper proposes a knowledge-guided fashion network to solve the problem of visual fashion analysis, e.g., fashion landmark localization and clothing category classification. The suggested fashion model is leveraged with high-level human knowledge in this domain. We propose two important fashion grammars: (i) dependency grammar capturing kinematics-like relation, and (ii) symmetry grammar accounting for the bilateral symmetry of clothes. We introduce Bidirectional Convolutional Recurrent Neural Networks (BCRNNs) for efficiently approaching message passing over grammar topologies, and producing regularized landmark layouts. For enhancing clothing category classification, our fashion network is encoded with two novel attention mechanisms, i.e., landmark-aware attention and category-driven attention. The former enforces our network to focus on the functional parts of clothes, and learns domain-knowledge centered representations, leading to a supervised attention mechanism. The latter is goal-driven, which directly enhances task-related features and can be learned in an implicit, top-down manner. Experimental results on large-scale fashion datasets demonstrate the superior performance of our fashion grammar network.

An Ontology For Specifying Spatiotemporal Scopes in Life Cycle Assessment <=SEP=> Life Cycle Assessment (LCA) evaluates the environmental impact of a product through its entire life cycle, from material extraction to final disposal or recycling. The environmental impacts of an activity depend on both the activity’s direct emissions to the environment as well as indirect emissions caused by activities elsewhere in the supply chain. Both the impacts of direct emissions and the provisioning of supply chain inputs to an activity depend on the activity’s spatiotemporal scope. When accounting for spatiotemporal dynamics, LCA often faces significant data interoperability challenges. Ontologies and Semantic technologies can foster interoperability between diverse data sets from a variety of domains. Thus, this paper presents an ontology for modeling spatiotemporal scopes, i.e., the contexts in which impact estimates are valid. We discuss selected axioms and illustrate the use of the ontology by providing an example from LCA practice. The ontology enables practitioners to address key competency questions regarding the effect of spatiotemporal scopes on environmental impact estimation.

RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images <=SEP=> This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of ℓ1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments on both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions.

Semantic image retrieval system based on object relationships <=SEP=> Semantic-based image retrieval has recently become popular as an avenue to improve retrieval accuracy. The “semantic gap” between the visual features and the high-level semantic features could be narrowed down by utilizing this kind of retrieval method. However, most of the current methods of semantic-based image retrieval utilize visual semantic features and do not consider spatial relationships. We build a system for content-based image retrieval from image collections on the web and tackle the challenges of distinguishing between images that contain similar objects, in order to capture the semantic meaning of a search query. In order to do so, we utilize a combination of segmentation into objects as well as the relationships of these objects with each other.

Development of HILS simulator for steering control in hot strip finishing mill <=SEP=> In this paper, hardware-in-the loop-simulation(HILS) simulator for steering control is developed. The simulator describes lateral movement of the strip in hot strip finishing mill. The simulator include strip dynamics, mill model, mill motor and AGC controllers. The factors considered for strip dynamics are initial off-center and wedge difference. The wedge is realized using the expansion of deformation process. To test validation of a simulator, PID controller is tested.

Critical period regulation. <=SEP=> Neuronal circuits are shaped by experience during critical periods of early postnatal life. The ability to control the timing, duration, and closure of these heightened levels of brain plasticity has recently become experimentally accessible, especially in the developing visual system. This review summarizes our current understanding of known critical periods across several systems and species. It delineates a number of emerging principles: functional competition between inputs, role for electrical activity, structural consolidation, regulation by experience (not simply age), special role for inhibition in the CNS, potent influence of attention and motivation, unique timing and duration, as well as use of distinct molecular mechanisms across brain regions and the potential for reactivation in adulthood. A deeper understanding of critical periods will open new avenues to "nurture the brain"-from international efforts to link brain science and education to improving recovery from injury and devising new strategies for therapy and lifelong learning.

Towards a holistic analysis of mobile payments: A multiple perspectives approach <=SEP=> As the mobile technologies and services are in constant evolution, many speculate on whether or not mobile payments will be a killer application for mobile commerce. To have a better understanding of the market, there is a need to analyze not only the technology but also the different actors that are involved. For this purpose, we propose to conduct two disruption analyses to draw the disruptiveness profile of mobile payment solutions compared to other payment instruments. Then, we try to discover what factors have hindered the technical and commercial development by using a DSS based on a multi-criteria decision making method called Electre I.

Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images <=SEP=> Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pascal 3D+ benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method.

The Impact of Information Technology Investment Announcements on the Market Value of the Firm <=SEP=> Determining whether investments in information technology (IT) have an impact on firm performance has been and continues to be a major problem for information systems researchers and practitioners. Financial theory suggests that managers should make investment'decisions that maximize the value of the firm. Using event-study methodology, we provide empirical evidence on the effect of announcements of IT investments on the market value of the firm for a sample of 97 IT investments from the finance and manufacturing industries from 1981 to 1988. Over the announcement period, we find no excess returns for either the full sample or for any one of the industry subsamples. However, cross-sectional analysis reveals that the market reacts differently to announcements of innovative IT investments than to foUowup, or noninnovative investments in IT. Innovative IT investments increase firm value, while noninnovative investments do not. Furthermore, the market's reaction to announcements of innovative and noninnovative IT investments is independent of industry classification. These results indicate that, on average, IT investments are zero net present value (NPV) investments; they are worth as much as they cost. Innovative IT investments, however, increase the value of the firm.

Fingerprint matching by thin-plate spline modelling of elastic deformations <=SEP=> This paper presents a novel minutiae matching method that describes elastic distortions in 4ngerprints by means of a thin-plate spline model, which is estimated using a local and a global matching stage. After registration of the 4ngerprints according to the estimated model, the number of matching minutiae can be counted using very tight matching thresholds. For deformed 4ngerprints, the algorithm gives considerably higher matching scores compared to rigid matching algorithms, while only taking 100 ms on a 1 GHz P-III machine. Furthermore, it is shown that the observed deformations are di6erent from those described by theoretical models proposed in the literature. ? 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

1 Mobile Landscapes : using location data from cellphones for urban analysis <=SEP=> The technology for determining the geographic location of cell phones and other hand-held devices is becoming increasingly available. It is opening the way to a wide range of applications, collectively referred to as Location Based Services (LBS), that are primarily aimed at individual users. However, if deployed to retrieve aggregated data in cities, LBS could become a powerful tool for urban analysis. This paper aims to review and introduce the potential of this technology to the urban planning community. In addition, it presents the ‘Mobile Landscapes’ project: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day. The results enable a graphic representation of the intensity of urban activities and their evolution through space and time. Finally, a number of future applications are discussed and their potential for urban studies and planning is assessed.

Comparison of SQL, NoSQL and NewSQL databases for internet of things <=SEP=> The Internet of Things (IoT) in all its essentiality is a collection of sensors bridged together tightly. The present day development in the technology industry has thus elevated the emphasis of large amounts of data. IoT is expected to generate and collect an enormous amount of data from varied locations very quickly. The concerns for features like storage and performance factors are coercing the databases to outperform themselves. Traditional relational databases have time and again proved to be efficient. NoSQL and more recently NewSQL databases having gained an impetus are asserted to perform better than the classic SQL counterpart. This paper compares the performance of three databases each from the said technologies; SQL (MySQL), NoSQL (MongoDB) and NewSQL (VoltDB) respectively for sensor readings. The sensor data handled ranges in vast (MB to GB) size and tested against: single write, single read, single delete and multi write operations.

Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System <=SEP=> This paper investigates the problem of bootstrapping a statistical dialogue manager without access to training data and proposes a new probabilistic agenda-based method for simulating user behaviour. In experiments with a statistical POMDP dialogue system, the simulator was realistic enough to successfully test the prototype system and train a dialogue policy. An extensive study with human subjects showed that the learned policy was highly competitive, with task completion rates above 90%. 1 Background and Introduction 1.1 Bootstrapping Statistical Dialogue Managers One of the key advantages of a statistical approach to Dialogue Manager (DM) design is the ability to formalise design criteria as objective reward functions and to learn an optimal dialogue policy from real dialogue data. In cases where a system is designed from scratch, however, it is often the case that no suitable in-domain data is available for training the DM. Collecting dialogue data without a working prototype is problematic, leaving the developer with a classic chicken-and-egg problem. Wizard-of-Oz (WoZ) experiments can be carried out to record dialogues, but they are often time-consuming and the recorded data may show characteristics of humanhuman conversation rather than typical human-computer dialogue. Alternatively, human-computer dialogues can be recorded with a handcrafted DM prototype but neither of these two methods enables the system designer to test the implementation of the statistical DM and the learning algorithm. Moreover, the size of the recorded corpus (typically 10 dialogues) usually falls short of the requirements for training a statistical DM (typically 10 dialogues). 1.2 User Simulation-Based Training In recent years, a number of research groups have investigated the use of a two-stage simulation-based setup. A statistical user model is first trained on a limited amount of dialogue data and the model is then used to simulate dialogues with the interactively learning DM (see Schatzmann et al. (2006) for a literature review). The simulation-based approach assumes the presence of a small corpus of suitably annotated in-domain dialogues or out-of-domain dialogues with a matching dialogue format (Lemon et al., 2006). In cases when no such data is available, handcrafted values can be assigned to the model parameters given that the model is sufficiently simple (Levin et al., 2000; Pietquin and Dutoit, 2005) but the performance of dialogue policies learned this way has not been evaluated using real users.

A tomographic formulation of spotlight-mode synthetic aperture radar <=SEP=> Spotlight-mode synthetic aperture radar (spotlight-mode SAR) synthesizes high-resolution terrain maps using data gathered from multiple observation angles. This paper shows that spotlight-mode SAR can be interpreted as a tomographic reeonstrution problem and analyzed using the projection-slice theorem from computer-aided tomograpy (CAT). The signal recorded at each SAR transmission point is modeled as a portion of the Fourier transform of a central projection of the imaged ground area. Reconstruction of a SAR image may then be accomplished using algorithms from CAT. This model permits a simple understanding of SAR imaging, not based on Doppler shifts. Resolution, sampling rates, waveform curvature, the Doppler effect, and other issues are also discussed within the context of this interpretation of SAR.

An Accelerator for High Efficient Vision Processing <=SEP=> In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency and performance of such accelerators remain limited by memory accesses. In this paper, we focus on image applications, arguably the most important category among recognition and mining applications. The neural networks which are state-of-the-art for these applications are convolutional neural networks (CNNs), and they have an important property: weights are shared among many neurons, considerably reducing the neural network memory footprint. This property allows to entirely map a CNN within an SRAM, eliminating all DRAM accesses for weights. By further hoisting this accelerator next to the image sensor, it is possible to eliminate all remaining DRAM accesses, i.e., for inputs and outputs. In this paper, we propose such a CNN accelerator, placed next to a CMOS or CCD sensor. The absence of DRAM accesses combined with a careful exploitation of the specific data access patterns within CNNs allows us to design an accelerator which is highly energy-efficient. We present a single-core implementation down to the layout at 65 nm, with a modest footprint of 5.94mm $^{\boldsymbol {2}}$  and consuming only 336mW, but still about  $\boldsymbol {30\times }$  faster than high-end GPUs. For visual processing with higher resolution and frame-rate requirements, we further present a multicore implementation with elevated performance.

Adaptive Control of KNTU Planar Cable-Driven Parallel Robot with Uncertainties in Dynamic and Kinematic Parameters <=SEP=> This paper addresses the design and implementation of adaptive control on a planar cable-driven parallel robot with uncertainties in dynamic and kinematic parameters. To develop the idea, firstly, adaptation is performed on dynamic parameters and it is shown that the controller is stable despite the kinematic uncertainties. Then, internal force term is linearly separated into a regressor matrix in addition to a kinematic parameter vector that contains estimation error. In the next step to improve the controller performance, adaptation is performed on both the dynamic and kinematic parameters. It is shown that the performance of the proposed controller is improved by correction in the internal forces. The proposed controller not only keeps all cables in tension for the whole workspace of the robot, it is computationally simple and it does not require measurement of the end-effector acceleration as well. Finally, the effectiveness of the proposed control algorithm is examined through some experiments on KNTU planar cable-driven parallel robot and it is shown that the proposed control algorithm is able to provide suitable performance in practice.

Integration of Text and Audio Features for Genre Classification in Music Information Retrieval <=SEP=> Multimedia content can be described in versatile ways as its essence is not limited to one view. For music data these multiple views could be a song’s audio features as well as its lyrics. Both of these modalities have their advantages as text may be easier to search in and could cover more of the ‘content semantics’ of a song, while omitting other types of semantic categorisation. (Psycho)acoustic feature sets, on the other hand, provide the means to identify tracks that ‘sound similar’ while less supporting other kinds of semantic categorisation. Those discerning characteristics of different feature sets meet users’ differing information needs. We will explain the nature of text and audio feature sets which describe the same audio tracks. Moreover, we will propose the use of textual data on top of low level audio features for music genre classification. Further, we will show the impact of different combinations of audio features and textual features based on content words.

Extraction of high-resolution frames from video sequences <=SEP=> The human visual system appears to be capable of temporally integrating information in a video sequence in such a way that the perceived spatial resolution of a sequence appears much higher than the spatial resolution of an individual frame. While the mechanisms in the human visual system that do this are unknown, the effect is not too surprising given that temporally adjacent frames in a video sequence contain slightly different, but unique, information. This paper addresses the use of both the spatial and temporal information present in a short image sequence to create a single high-resolution video frame. A novel observation model based on motion compensated subsampling is proposed for a video sequence. Since the reconstruction problem is ill-posed, Bayesian restoration with a discontinuity-preserving prior image model is used to extract a high-resolution video still given a short low-resolution sequence. Estimates computed from a low-resolution image sequence containing a subpixel camera pan show dramatic visual and quantitative improvements over bilinear, cubic B-spline, and Bayesian single frame interpolations. Visual and quantitative improvements are also shown for an image sequence containing objects moving with independent trajectories. Finally, the video frame extraction algorithm is used for the motion-compensated scan conversion of interlaced video data, with a visual comparison to the resolution enhancement obtained from progressively scanned frames.

The Big Bang: Facial Trauma Caused by Recreational Fireworks. <=SEP=> In the Netherlands, it is a tradition of setting off fireworks to celebrate the turn of the year. In our medical facility, each year patients with severe skeletal maxillofacial trauma inflicted by recreational fireworks are encountered. We present two cases of patients with severe blast injury to the face, caused by direct impact of rockets, and thereby try to contribute to the limited literature on facial blast injuries, their treatment, and clinical outcome. These patients require multidisciplinary treatment, involving multiple reconstructive surgeries, and the overall recovery process is long. The severity of these traumas raises questions about the firework traditions and legislations not only in the Netherlands but also worldwide. Therefore, the authors support restrictive laws on personal use of fireworks in the Netherlands.

A State-Space Approach to Dynamic Nonnegative Matrix Factorization <=SEP=> Nonnegative matrix factorization (NMF) has been actively investigated and used in a wide range of problems in the past decade. A significant amount of attention has been given to develop NMF algorithms that are suitable to model time series with strong temporal dependencies. In this paper, we propose a novel state-space approach to perform dynamic NMF (D-NMF). In the proposed probabilistic framework, the NMF coefficients act as the state variables and their dynamics are modeled using a multi-lag nonnegative vector autoregressive (N-VAR) model within the process equation. We use expectation maximization and propose a maximum-likelihood estimation framework to estimate the basis matrix and the N-VAR model parameters. Interestingly, the N-VAR model parameters are obtained by simply applying NMF. Moreover, we derive a maximum a posteriori estimate of the state variables (i.e., the NMF coefficients) that is based on a prediction step and an update step, similarly to the Kalman filter. We illustrate the benefits of the proposed approach using different numerical simulations where D-NMF significantly outperforms its static counterpart. Experimental results for three different applications show that the proposed approach outperforms two state-of-the-art NMF approaches that exploit temporal dependencies, namely a nonnegative hidden Markov model and a frame stacking approach, while it requires less memory and computational power.

Standalone OPC UA Wrapper for Industrial Monitoring and Control Systems <=SEP=> OPC unified architecture (UA), a communication standard for the manufacturing industry, enables exchanging control and management data among distributed entities in industrial automation systems. OPC UA wrapper is a migration strategy that provides UA clients with seamless access to legacy servers having OPC classic interfaces. This paper presents the design of a standalone OPC UA wrapper and discusses its performance through extensive experiments using a prototype implementation. The wrapper consists of two main components, i.e., UA server and classic client, which communicate with each other via shared memory and semaphore. One important feature of the design is that it employs a distributed component object model runtime library implemented in Java for platform independence. This makes it possible to build a cost-competitive wrapper system by using commercial off-the-shelf non-Windows solutions with low-cost microprocessors. Another key feature is the event-driven update interface between the UA and classic components, which we propose as an alternative to the sampling-based mechanism for the reduced delay. Through experiments using workloads from an industrial monitoring system, we present a systematic approach of identifying the system parameters having a direct impact on the wrapper performance and eventually tuning them such that the read and subscription services of OPC UA exhibit the best performance.

Real-time Action Recognition with Dissimilarity-based Training of Specialized Module Networks <=SEP=> This paper addresses the problem of real-time action recognition in trimmed videos, for which deep neural networks have defined the state-of-the-art performance in the recent literature. For attaining higher recognition accuracies with efficient computations, researchers have addressed the various aspects of limitations in the recognition pipeline. This includes network architecture, the number of input streams (where additional streams augment the color information), the cost function to be optimized, in addition to others. The literature has always aimed, though, at assigning the adopted network (or networks, in case of multiple streams) the task of recognizing the whole number of action classes in the dataset at hand. We propose to train multiple specialized module networks instead. Each module is trained to recognize a subset of the action classes. Towards this goal, we present a dissimilarity-based optimized procedure for distributing the action classes over the modules, which can be trained simultaneously offline. On two standard datasets–UCF-101 and HMDB-51–the proposed method demonstrates a comparable performance, that is superior in some aspects, to the state-of-the-art, and that satisfies the real-time constraint. We achieved 72.5% accuracy on the challenging HMDB-51 dataset. By assigning fewer and unalike classes to each module network, this research paves the way to benefit from light-weight architectures without compromising recognition accuracy1.

Edge Computing and IoT Based Research for Building Safe Smart Cities Resistant to Disasters <=SEP=> Recently, several researches concerning with smart and connected communities have been studied. Soon the 4G / 5G technology becomes popular, and cellular base stations will be located densely in the urban space. They may offer intelligent services for autonomous driving, urban environment improvement, disaster mitigation, elderly/disabled people support and so on. Such infrastructure might function as edge servers for disaster support base. In this paper, we enumerate several research issues to be developed in the ICDCS community in the next decade in order for building safe, smart cities resistant to disasters. In particular, we focus on (A) up-to-date urban crowd mobility prediction and (B) resilient disaster information gathering mechanisms based on the edge computing paradigm. We investigate recent related works and projects, and introduce our on-going research work and insight for disaster mitigation.

Predicting clicks: estimating the click-through rate for new ads <=SEP=> Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.