基于网络损伤的流媒体测试与研究

随着宽带网络的普及以及越来越多的宽带视音频应用的需求,各种流媒体技术相继面市。其中最为领先的有microsoft公司的windows media系统、real networks公司的real流媒体系统以及苹果公司的quicktime系统等。每套系统都各有特点,孰优孰劣,说法不一。该文就网络损伤对主流的网络流媒体技术进行分析比较,得出了公正客观的结论,为技术选型及业务发展提供依据。     

Selling safety: the use of celebrities in improving awareness of safety in commercial aviation

The aim of this study was to investigate the influential power of a celebrity to convey key safety messages in commercial aviation using a pre-flight safety briefing video. In addition, the present research sought to examine the effectiveness of subtitles in aiding the recall of these important messages as well as how in-cabin aircraft noise affects recall of this information. A total of 101 participants were randomly divided into four groups (no noise without subtitles, no noise with subtitles, noise without subtitles and noise with subtitles) and following exposure to a pre-recorded pre-flight safety briefing video were tested for recall of key safety messages within that video. Participants who recognised and recalled the name of the celebrity in the safety briefing video recalled significantly more of the messages than participants who did not recognise the celebrity. Subtitles were also found to be effective, however, only in the presence of representative in-cabin aircraft noise.

Practitioner Summary: Passenger attention to pre-flight safety briefings on commercial aircraft is poor. Utilising the celebrity status of a famous person may overcome this problem. Results suggest that celebrities do increase the recall of safety-related information.     

Framework for distributed video surveillance in heterogeneous environment

In order to overcome the disadvantages of traditional centralized video monitoring system or distributed systems based on wired network, we propose a framework for distributed video surveillance in heterogeneous environment. Video flows are compressed with the scalable video encoding standard MPEG-4 and transmitted over lnternet or wireless network. Video surveillance can be performed wherever there is Internet or mobile telephone signal. The feasibility of this framework has been demonstrated with a prototype implementation. The system is cheaper and easier to achieve with simple equipments, so it can be widely used in practice.     

Fragile video watermarking technique by motion field embedding with rate-distortion minimization

In this paper, we proposed a fragile video watermarking technique to authenticate the H.264 video content. Our watermark information is embedded in motion vectors as which have strong fragile nature. The proposed method finds out the best locations of motion vectors to embed the information to achieve the fragility, where are based on the statistical analysis of the motion vector by the H.264/AVC rate-distortion cost function. This scheme does not require the original video sequence for watermark detection. Experimental results show that the proposed watermarking technique can keep the perceptual quality at best effort and still has good fragility.     

Safer chairs for elderly patients: design evaluation using electromyography and force measurement

A vast majority of patient fall events in hospitals involve the elderly. In inpatient care settings, despite the risk of fall, patients are encouraged to leave their bed, move around their room, and sit on their chair to progress in their healing. Despite the vital role of patient chair design in improving recovery, few studies have examined the ergonomic requirements of safe patient chairs. This study examined the impact of manipulating horizontal and vertical positions of armrests in a test chair on required physical effort during Stand-to-Sit-to-Stand (St-Si-St) transitions among 15 elderly women. Physical effort was measured using: (1) surface electromyography (sEMG); (2) force measurement by load cells; (3) video recording. Findings showed non-linear patterns of change in required physical effort due to changes in armrests’ height and distance. It was also found that minimum effort is associated with armrests higher and farther apart than those in typical patient chairs.

Practitioner Summary: Safe chairs are essential for inpatient recovery, yet their ergonomic features are not investigated. Impact of changes in chair armrests on required physical effort was examined using electromyography, force measurement and video recording. Armrests higher and farther apart than those in typical patient chairs may be safer for elderly patients.     

Rapid building detection using machine learning

This work describes algorithms for performing discrete object detection, specifically in the case of buildings, where usually only low quality RGB-only geospatial reflective imagery is available. We utilize new candidate search and feature extraction techniques to reduce the problem to a machine learning (ML) classification task. Here we can harness the complex patterns of contrast features contained in training data to establish a model of buildings. We avoid costly sliding windows to generate candidates; instead we innovatively stitch together well known image processing techniques to produce candidates for building detection that cover 80–85 % of buildings. Reducing the number of possible candidates is important due to the scale of the problem. Each candidate is subjected to classification which, although linear, costs time and prohibits large scale evaluation. We propose a candidate alignment algorithm to boost classification performance to 80–90 % precision with a linear time algorithm and show it has negligible cost. Also, we propose a new concept called a Permutable Haar Mesh (PHM) which we use to form and traverse a search space to recover candidate buildings which were lost in the initial preprocessing phase. All code and datasets from this paper are made available online (http://kdl.cs.umb.edu/w/datasets/ and https://github.com/caitlinkuhlman/ObjectDetectionCLUtility).     

Coarse-to-Fine Video Instance Segmentation With Factorized Conditional Appearance Flows

We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect, segment and track each instance in a video sequence. Differently from current discriminative tracking-by-detection solutions, our proposed hierarchical structural embedding learning can predict more highquality masks with accurate boundary details over spatio-temporal space via the normalizing flows. We formulate the instance inference pr...     

GRAIL: a scalable index for reachability queries in very large graphs

Given a large directed graph, rapidly answering reachability queries between source and target nodes is an important problem. Existing methods for reachability tradeoff indexing time and space versus query time performance. However, the biggest limitation of existing methods is that they do not scale to very large real-world graphs. We present a simple yet scalable reachability index, called GRAIL, that is based on the idea of randomized interval labeling and that can effectively handle very large graphs. Based on an extensive set of experiments, we show that while more sophisticated methods work better on small graphs, GRAIL is the only index that can scale to millions of nodes and edges. GRAIL has linear indexing time and space, and the query time ranges from constant time to being linear in the graph order and size. Our reference C++ implementations are open source and available for download at http://www.code.google.com/p/grail/.     

Design of optimal reduced order filters

This paper shows that the design of a reduced order optimal filter is equivalent to a frequency weighted norm model reduction problem.     

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Accurate estimation of the remaining useful life (RUL) of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage. A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development. However, it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries, as well as dynamic operating conditions in practical applications. Moreover, due to insignificant capacity degradation in early stages, early prediction of battery life with early cycle data can be more difficult. In this paper, we propose a hybrid deep learning model for early prediction of battery RUL. The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction. We also design a non-linear correlation-based method to select effective domain knowledge-based features. Moreover, a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost. Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set, but also generalizes well to the secondary test set having a clearly different distribution with the training set. The PyTorch implementation of our proposed approach is available athttps://github.com/batteryrul/battery_rul_early_prediction.      

LSShare: an efficient multiple query optimization system in the cloud

Multiple query optimization (MQO) in the cloud has become a promising research direction due to the popularity of cloud computing, which runs massive data analysis queries (jobs) routinely. These CPU/IO intensive analysis queries are complex and time-consuming but share common components. It is challenging to detect, share and reuse the common components among thousands of SQL-like queries. Previous solutions to MQO, heuristic or genetic based, are not appropriate for the large growing query set situation. In this paper, we develop a sharing system called LSShare using our proposed Lineage-Signature approach. By LSShare, we can efficiently solve the MQO problem in a recurring query set situation in the cloud. Our system has been prototyped in a distributed system built for massive data analysis based on Alibaba’s cloud computing platform (http://www.alibaba.com/). Experimental results on real data sets demonstrate the efficiency and effectiveness of the proposed approach.     

A Study on the design method of KPI system in Chinese communication enterprises

This paper studies the characteristics of KPIs of Chinese communication enterprises （illustrated by the case of a provincial mobile communication enterprise）, discusses the methods of designing and establishing KPI system in communication enterprises, and summarizes indicator extraction codes and data analysis results. This study provides useful experience for maintenance and operation of KPIs.     

On finding spanning trees with few leaves

The problem of finding a spanning tree with few leaves is motivated by the design of communication networks, where the cost of the devices depends on their routing functionality (ending, forwarding, or routing a connection). Besides this application, the problem has its own theoretical importance as a generalization of the Hamiltonian path problem. Lu and Ravi showed that there is no constant factor approximation for minimizing the number of leaves of a spanning tree, unless P=NP. Thus instead of minimizing the number of leaves, we are going to deal with maximizing the number of non-leaves: we give a linear-time 2-approximation for arbitrary graphs, a -approximation for claw-free graphs, and a -approximation for cubic graphs.     

A subgraph matching algorithm based on subgraph index for knowledge graph

The problem of subgraph matching is one fundamental issue in graph search, which is NP-Complete problem. Recently, subgraph matching has become a popular research topic in the field of knowledge graph analysis, which has a wide range of applications including question answering and semantic search. In this paper, we study the problem of subgraph matching on knowledge graph. Specifically, given a query graph q and a data graph G, the problem of subgraph matching is to conduct all possible subgraph isomorphic mappings of q on G. Knowledge graph is formed as a directed labeled multi-graph having multiple edges between a pair of vertices and it has more dense semantic and structural features than general graph. To accelerate subgraph matching on knowledge graph, we propose a novel subgraph matching algorithm based on subgraph index for knowledge graph, called as F G q_T-Match. The subgraph matching algorithm consists of two key designs. One design is a subgraph index of matching-driven flow graph ( F G q_T), which reduces redundant calculations in advance. Another design is a multi-label weight matrix, which evaluates a near-optimal matching tree for minimizing the intermediate candidates. With the aid of these two key designs, all subgraph isomorphic mappings are quickly conducted only by traversing F G q_T. Extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.     

Domain-specific languages: from design to implementationapplication to video device drivers generation

Domain-specific languages (DSL) have many potential advantages in terms of software engineering, ranging from increased productivity to the application of formal methods. Although they have been used in practice for decades, there has been little study of methodology or implementation tools for the DSL approach. We present our DSL approach and its application to a realistic domain: the generation of video display device drivers. The article focuses on the validation of our proposed framework for domain-specific languages, from design to implementation. The framework leads to a flexible design and structure, and provides automatic generation of efficient implementations of DSL programs. Additionally, we describe an example of a complete DSL for video display adaptors and the benefits of the DSL approach for this application. This demonstrates some of the generally claimed benefits of using DSLs: increased productivity, higher-level abstraction, and easier verification. This DSL has been fully implemented with our approach and is available. Compose project URL: http://www.irisa.fr/compose/gal     

Benchmarking SQL on MapReduce systems using large astronomy databases

In the era of bigdata, with a massive set of digital information of unprecedented volumes being collected and/or produced in several application domains, it becomes more and more difficult to manage and query large data repositories. In the framework of the PetaSky project (http://com.isima.fr/Petasky), we focus on the problem of managing scientific data in the field of cosmology. The data we consider are those of the LSST project (http://www.lsst.org/). The overall size of the database that will be produced is expected to exceed 60 PB (Lsst data challenge handbook, 2012). In order to evaluate the performances of existing SQL On MapReduce data management systems, we conducted extensive experiments by using data and queries from the area of cosmology. The goal of this work is to report on the ability of such systems to support large scale declarative queries. We mainly investigated the impact of data partitioning, indexing and compression on query execution performances.     

Pentagons: A weakly relational abstract domain for the efficient validation of array accesses

We introduce Pentagons (), a weakly relational numerical abstract domain useful for the validation of array accesses in byte-code and intermediate languages (IL). This abstract domain captures properties of the form of . It is more precise than the well known Interval domain, but it is less precise than the Octagon domain.The goal of is to be a lightweight numerical domain useful for adaptive static analysis, where is used to quickly prove the safety of most array accesses, restricting the use of more precise (but also more expensive) domains to only a small fraction of the code.We implemented the abstract domain in , a generic abstract interpreter for.NET assemblies. Using it, we were able to validate 83% of array accesses in the core runtime library in a little bit more than 3 minutes.