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27th International Joint conference on Artificial Intelligence (IJCAI)

作者： Ma, Weizhi Zhang, Min Wang, Chenyang Luo, Cheng Liu, Yiqun Ma, Shaoping Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9780999241127

Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users' preferences in the target domain Thought to be unrelated, users' off-topic content information (such as user tweets) is usually omitted. However, the off-topic content information also helps to indicate the similarity of users on their tastes, interests, and opinions, which matches the underlying assumption of Collaborative Filtering (CF) algorithms. In this paper, we propose a framework to capture the features from user's off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms. The framework is easy to understand and flexible in different embedding approaches and MF based algorithms. To the best of our knowledge, there is no previous study in which user's off-topic content in other platforms is taken into consideration. By capturing the cross-platform content including both on-topic and offtopic information, multiple algorithms with several embedding learning approaches have achieved significant improvements in rating prediction on three datasets. Especially in cold start scenarios, we observe greater enhancement. The results confirm our suggestion that off-topic cross-media information also contributes to the recommendation.

关键词： Embeddings

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A Permissioned Distributed Ledger for Monitoring Web content

A Permissioned Distributed Ledger for Monitoring Web Content

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International conference on Parallel and Distributed systems (ICPADS)

作者： Michalis Konstantopoulos Nikos Chondros Mema Roussopoulos University of Athens Athens Greece

ISBN: (纸本)9781728125848

Online publishing of news and information enables important content to reach a much larger audience than traditional paper publishing, but there is no guarantee of long-term, reliable, and persistent access to the content. Over time, links "decay" because they are not reliably updated when content changes location, or even worse, content is deliberately altered from its original published form or deleted altogether. We present the design of Watchdog, a permissioned distributed ledger that securely and reliably monitors and preserves dynamic web content such as government and news sites. Unlike prior preservation systems, our system design allows nodes to be dispersed across multiple administrative domains, thus eliminating single points of trust and at the same time, is the first to monitor how content changes both over time and across geographical location. Watchdog achieves Byzantine fault-tolerance via a novel interactive consistency algorithm that offers a twist on the traditional definition and use of interactive consistency; the algorithm enables a set of mutually suspicious nodes, with arbitrary-sized, potentially overlapping data collections, to efficiently agree on the exact data collection in possession by each node. We demonstrate how our algorithm enables Watchdog nodes to exchange and agree upon the content each has observed individually, in a fault-tolerant, tamper-proof manner.

关键词： Distributed ledger Web monitoring Consistent broadcast Interactive consistency Byzantine fault tolerance Web page content Fault tolerance Data Collection Methods single turnout news Ganglia Publishing algorithms

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LST-MAC: A Low-Latency Hybrid MAC Protocol for LEO Satellite Supported IoT

LST-MAC: A Low-Latency Hybrid MAC Protocol for LEO Satellite...

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International conference on Parallel and Distributed systems (ICPADS)

作者： Chaoyu Wang Liang Liu Huadong Ma Dan Xia Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728125848

IoT device are usually built as edge networks in smaller areas, but with the development of technology, more and more areas without infrastructure need to be covered. LEO satellites, due to its full coverage of the Earth and relatively low latency, can provide network access for IoT system. In this paper, we propose a hybrid TDMA MAC protocol LST-MAC for IoT network based on LEO satellite, which can reduce network delay and improve the system scalability. Specifically, we design a geographic priority time slot allocation strategy which can ensure optimal load and delay, besides, we also design a continuous forward time slot allocation strategy, which guarantees real-time data while reducing the utilization of satellite resources. Then we design two algorithms based on both strategies, which can effectively reduce network delay and balance the satellite resources. The simulation experimental results are consistent with the theoretical results, which embodies the effectiveness of the algorithm.

关键词： Low latency Time slot scheduling Satellite network Heterogeneous network SATELLITE NETWORKS close-orbit satellite Heterogeneous networks network delay MAC protocol Methotrexate-Actinomycin-Chlorambucil Regimen time slot assignment optimal load network access real-time data

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Toward Wi-Fi AP-Assisted content Prefetching for an On-Demand TV Series: A Learning-Based Approach

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2018年第7期28卷 1665-1676页

作者： Hu, Wen Jin, Yichao Wen, Yonggang Wang, Zhi Sun, Lifeng Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China

The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a largescale measurement study on users' AP connection and TV series watching patterns using real traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov decision process. The objective is to strike a balance between the increased prefetching and storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first we investigate the performance lower bound by using a random algorithm and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm.

关键词： Wi-Fi AP content prefetching learning-based approach

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Intelligent Digital Signage System Based on Gender Identification

Intelligent Digital Signage System Based on Gender Identific...

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Symposium-B on Intelligent Embedded systems / 1st International conference on NextGen Electronic Technologies - Silicon to Software (ICNETS2)

作者： Abraham, Riya Elizabeth Kennedy, M. Robert VIT Univ Sch Elect Engn Chennai Tamil Nadu India Qmax Syst India P Ltd Chennai Tamil Nadu India

ISBN: (纸本)9789811085758;9789811085741

This paper proposes an intelligent transformation of digital signage systems by making it more audience interactive. The increase in flexibility and enhancement of digital signage display system can be done by providing optimized information and appearance attractive multimedia content through the signage system. This emphasizes more on the advertisement industry especially in public spaces like hotels, airports. The system has been designed to broadcast the advertisement on the signage display system based on the demographic features like gender of the observer. Real-time computer vision algorithms are applied to provide an observer-specific advertisement broadcast on the display system.

关键词： FERET database Demographic features content management Prediction model

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content-Based multimedia Analytics: US and NATO Research 6

Content-Based Multimedia Analytics: US and NATO Research

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conference on Next-Generation Analyst VI

作者： Bowman, Elizabeth K. Burghouts, Gertj An Overher, Lasse Kase, Sue E. Zimmerman, Randal J. Oggero, Serena Army Res Lab 321 Johnson St Aberdeen Proving Ground MD 21005 USA TNO Intelligent Imaging Oude Waalsdorperweg 63 NL-2597 AK The Hague Netherlands FFI Inst Veien 20 N-2007 Kjeller Norway Zimmerman Consulting Grp LLC Leavenworth KS USA

ISBN: (数字)9781510618183

ISBN: (纸本)9781510618183

The US and nations of the NATO Alliance are increasingly threatened by the global spread of terrorism, humanitarian crises/disaster response, and public health emergencies. These threats are influenced by the unprecedented rise of information sharing technologies and practices, where mobile access to social networking sites is ubiquitous. In this new information environment, agile data algorithms, machine learning software, and threat alert mechanisms must be developed to automatically create alerts and drive quick response. US science and technology investments in Artificial Intelligence and Machine Learning (AI/ML) and Human Agent Teaming (HAT) are increasingly focused on developing capabilities toward that end. A critical foundation of these technologies is the awareness of the underlying context to accurately interpret machine-processed warnings and recommendations. In this sense, context can be a dynamic characteristic of the operating environment and demands a multi-analytic approach In this paper, we describe US doctrine that formulates capability requirements for operations in the information environment. We then describe a promising social computing approach that brings together information retrieval strategies using multimedia sources that include text, video, and imagery. Social computing is used in this case to increase awareness of societal dynamics at various scales that influence and impact military operations in both the physical and information domains Our focus, content based information retrieval and multimedia analytics, involves the exploitation of multiple data sources to deliver timely and accurate synopses of data that can be combined with human intuition and understanding to develop a comprehensive worldview.

关键词： social computing multimedia analytics information environment text and video analytics

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The vehicle classification based on neural networks in intelligent transport systems

The vehicle classification based on neural networks in intel...

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International conference on Emerging eLearning Technologies and Applications (ICETA)

作者： M. Frniak P. Kamencay M. Markovic J. Dubovan M. Dado Department of multimedia and information-communication technologies University of Zilina Zilina Slovakia

ISBN: (数字)9781728149677

ISBN: (纸本)9781728149684

This article is aimed to new methods of measurement, transfer and processing of data from complex system of FibreBragg Grating (FBG) sensors. State of the art technologies and algorithms for vehicle recognition and categorisation in university campus are used. content of the Article is divided into two parts. The first part is devoted to fibre optic FBG sensors and their basic properties. The second part ofarticle is dedicated to visual data processing by neural networks for visual vehicle categorisation as visual confirmation of data from the fibre Bragg grating sensor arrays. Experimental results show that using AlexNet neural network can be achieved an accuracy for categorization of vehicles of 87.1%. Research is done under the grant project dealing with coexistence of photonic sensor systems and photonic networks within the Internet of Things.

关键词： Sensor arrays Visualization Optical fiber sensors Optical fibers Temperature measurement

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content Abstract Classification Using Naive Bayes 2

Content Abstract Classification Using Naive Bayes

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2nd International conference on Science (ICOS)

作者： Latif, Syukriyanto Suwardoyo, Untung Sanadi, Edwin Aldrin Wihelmus Hasanuddin Univ Dept Elect Engn Makassar Indonesia

This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at ***. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (multimedia). systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.

关键词： Data mining

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New Error Measures for Evaluating algorithms that Estimate the Motion of a Range Camera 13

New Error Measures for Evaluating Algorithms that Estimate t...

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13th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / International conference on Computer Vision Theory and Applications (VISAPP)

作者： Bogaerts, Boris Penne, Rudi Ribbens, Bart Sels, Seppe Vanlanduit, Steve Univ Antwerp Fac Appl Engn Groenenborgerlaan 171 B-2020 Antwerp Belgium Univ Antwerp Dept Math Middelheimlaan 1 B-2020 Antwerp Belgium Karel de Grote Univ Coll Multimedia & Commun Technol Salesianenlaan 90 B-2660 Antwerp Belgium

ISBN: (纸本)9789897583063

We compare the classical point-based algorithms for the extrinsic calibration of a range camera to the recent plane-based method. This method does not require any feature detection, and appears to perform well using a small number of planes (minimally 3). In order to evaluate the accuracy of the computed rigid motion we propose two new error metrics that get direct access to the ground truth provided by a mechanism with reliable motion control. Furthermore, these error metrics do not depend on an additional hand-eye calibration between the mechanism and the sensor. By means of our objective measures, we demonstrate that the planebased method outperforms the point-based methods that operate on 3-D or 2-D point correspondences. In our experiments we used two types of TOF cameras attached to a robot arm, but our evaluation tool applies to other sensors and moving systems.

关键词： Range Camera Extrinsic Calibration Motion Estimation Conjugacy Invariants

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Efficient Processing and Delivery of multimedia Data

Efficient Processing and Delivery of Multimedia Data

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作者： Tang, Linpeng Princeton University

学位级别：Ph.D.

The explosion of multimedia data on the Internet in recent years has greatly enriched people's online experience. However, it also poses a great challenge to analyze and process, and then deliver such content to the worldwide audience. This dissertation presents novel approaches to improve the overall efficiency of the stack by tailoring software design to hardware properties, as well as optimize systems by exploiting workload characteristics using learning-based approaches. First, to improve the caching performance of the flash-memory caches for content delivery network, this thesis proposes RIPQ, a framework for efficient and advanced caching with flash memory. Traditional implementations of these algorithms generate random writes that perform poorly on flash devices, decreasing the device's performance and lifespan. RIPQ overcomes this issue by aggregating small writes, colocating items with similar priorities, and perform lazy updates to achieve low over- head. By providing a priority queue interface, it allows a variety of caching algorithms to be easily implemented. Second, this thesis proposes Chess, which uses popularity prediction for higher quality video streaming. Although better encodings improve video streaming, they are also compute-intensive, and it is infeasible to encode all videos uploaded to Face- book with the highest quality codec. However, because the accesses to videos are highly skewed, we may obtain most of the benefit by only running the compute- intensive encoding on a small portion of popular videos, and the challenge lies in how to accurately and scalably run popularity prediction to detect those videos before- hand. Chess meets this demand by designing an approximate but fast base predictor with the access history information, and using an online learning method to combine multiple such predictors as well as the social signals to boost accuracy. Lastly, this thesis investigates how to accelerate deep learning models on many-core CPUs. D

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