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Many fire situations have represented the loss of lives and material costs due to the lack of early fire detection through smoke or gas sensing, which can become complex and critical. Meanwhile, engineers worldwide develop and test multiple systems for smoke and gas detection, commonly based on sensor networks, digital image processing, or computer vision. Furthermore, the detection system must work thoroughly with alarms and warnings that aware of a risk situation for prompt evacuation of the population in the surroundings based on a reliable data network topology with adequate device deployments that will let us know the moment a fire outbreak. This paper presents a low-cost Internet of Things (IoT) prototype for fire detection in outdoor environments based on sensors and Low Power Wide Area Network (LPWAN), focused on the accuracy in the temperature and gas measurement at the moment a fire starts. For its achievement, we integrated wireless components, development boards, and electronic devices, following the management of information updates through a database schema for the alarm settings based on the data gathered from the sensors.

This Community Service activities are carried out using the substantive developing database approach to assertions - accounting management assertions of intangible assets recorded in the State - Owned Financial Management Information System (SIMAK BMN) of the Public Service Agency of Universitas Negeri Jakarta. The substantive developing database carried out includes assertions - occurrence assertions, completeness, valuation, right and obligation and disclosure. The results of the substantive developing will be used as follow-up material from the recommendations of the findings of the Republic of Indonesia Supreme Audit Agency to the Republic of Indonesia Ministry of Technology and Higher Education where the Public Service Agency of Universitas Negeri Jakarta is one of the entities that sampling BPK RI. The Community Service activity is also an alternative discussion to be a guideline for the existence of a research gap in the implementation of the PSAK (Statement of Financial Accounting Standards) Number 19 concerning intangible assets that adopt the basis of IFRS (International Financial Reporting Standards) where financial reports on assets become reliable, must be assessed based on fair value. PSAK 19 regulates the appraisal time to be carried out at the latest 3 years and at the latest every 5 years. This is where a phenomenon occurs, where if the appraisal is carried out in the 5th year while the amortization period of the intangible asset has been completed before 5 years, then the audit cannot be carried out based on the audit management assertions and make the asset the subject of audit findings potentially affecting audit opinion if material.Participants were given training on tips for managing a procurement of intangible fixed assets, both in the context of procurement by the university or by other units in certain circumstances, and systematically recording the SIMAK BMN UNJ after first carrying out asset reconciliation regular with the accounting and reporting section.

This paper provides a survey of the state-of-the-art and future directions of one of the most importantemerging technologies within Business Analytics(BA), namely Prescriptive Analytics (PSA). BA focuseson data-driven decision making and consists ofthree phases: Descriptive, Predictive, and PrescriptiveAnalytics. While Descriptive and Predictive Analyticsallow us to analyze past and predict future events, respectively,these activities do not provide any directsupport for decision making. Here, PSA lls the gapbetween data and decisions. We have observed an increasinginterest for in-DBMS PSA systems in both researchand industry. Thus, this paper aims to providea foundation for PSA as a separate eld of study. Todo this, we rst describe the dierent phases of BA.We then survey classical analytics systems and identifytheir main limitations for supporting PSA, basedon which we introduce the criteria and methodologyused in our analysis. We next survey, categorize, anddiscuss the state-of-the-art within emerging, so-calledPSA+, systems, followed by a presentation of the mainchallenges and opportunities for next generation PSAsystems. Finally, the main ndings are discussed anddirections for future research are outlined. This paper provides a survey of the state-of-the-art and future directions of one of the most important emerging technologies within business analytics (BA), namely prescriptive analytics (PSA). BA focuses on data-driven decision-making and consists of three phases: descriptive, predictive, and prescriptive analytics. While descriptive and predictive analytics allow us to analyze past and predict future events, respectively, these activities do not provide any direct support for decision-making. Here, PSA fills the gap between data and decisions. We have observed an increasing interest for in-DBMS PSA systems in both research and industry. Thus, this paper aims to provide a foundation for PSA as a separate field of study. To do this, we first describe the different phases of BA. We then survey classical analytics systems and identify their main limitations for supporting PSA, based on which we introduce the criteria and methodology used in our analysis. We next survey, categorize, and discuss the state-of-the-art within emerging, so-called PSA + , systems, followed by a presentation of the main challenges and opportunities for next-generation PSA systems. Finally, the main findings are discussed and directions for future research are outlined.

This paper reports on the experiences of assessing a large postgraduate module in Database Systems using an on-line MCQ test under COVID-19 conditions with no remote proctoring. While the technology and the “higher order thinking” assessment worked well, analysis of response patterns suggested that, despite the use of randomised questions, time pressure and an honour pledge, a substantial number of students formed mini-networks during the test and colluded with each other to discuss the answers. No differences by gender or age were observed as regards tendency to engage in this behaviour, and domestic students were just as likely as international students to do it. Notably, domestic and international students seemed to stay apart, colluding almost exclusively with others of their own nationality. non-peer-reviewed

Objective: Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Methods: In this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of false alarms. This CNN architecture consists of an encoder block and a corresponding decoder block followed by a sample-wise classification layer to construct the 1D segmentation map of R-peaks from the input ECG signal. Once the proposed model has been trained, it can solely be used to detect R-peaks possibly in a single channel ECG data stream quickly and accurately, or alternatively, such a solution can be conveniently employed for real-time monitoring on a lightweight portable device. Results: The model is tested on two open-access ECG databases: The China Physiological Signal Challenge (2020) database (CPSC-DB) with more than one million beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB). Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the best R-peak detection performance ever achieved. Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision. Significance: Compared to all competing methods, the proposed approach can reduce the false-positives and false-negatives in Holter ECG signals by more than 54% and 82%, respectively. Conclusion: Finally, the simple and invariant nature of the parameters leads to a highly generic system and therefore applicable to any ECG dataset. publishedVersion

International audience; Non-Volatile Memory (NVM) is a promising development for Database Management Systems (DBMS), offering abundant and fast storage to complement traditional disk and main memory architectures. NVM introduces additional data migration possibilities to the traditional buffer pool (BP) managers used by the DBMS. Hence, the efficient use of this new technology requires a redesign of the BP manager. For the cloud Database-as-a-Service products, this need for a redesign is further complicated by the traditional cloud providers' goal to minimize the Service Level Agreement (SLA) violation penalties paid to their tenants. Unfortunately, current research in the area does not provide a comprehensive picture of the components constituting a multi-tenant persistent memory aware BP manager for a cloud database that makes use of NVM. Furthermore, researchers lack the software tools needed to quickly prototype and estimate the effectiveness of novel data management policies guiding those components. In this paper, we attempt to remedy both issues, first, by proposing a generalized framework that defines the purpose and the abstract interfaces of various multi-tenant persistent memory-aware BP manager components, and second, by developing and demonstrating a simulator algorithm that is shown to aid in quick testing of different implementations of those BP manager components.

Research interests spanning numerous domains increasingly rely upon computational systems which can store and process a large volume of variable data that is stored at high velocity –representing a big data problem. This is particularly notable within the domain of ubiquitous and pervasive computing. This domain increasingly relies on storage and retrieval of sensor data to enable outcomes such as predictive analytics and activity recognition. Several current big data platforms exist; however, they have a range of deficiencies including lack of generic interoperability with agnostic sensors and an absence of features supporting academic research. Due to these deficiencies a custom, research oriented, high performance, and big data platform was devised and implemented. This platform is called SensorCentral and is presented within this paper. SensorCentral provides a framework which enables interoperability with a large range of agnostic sensor devices whilst simultaneously providing features which support research. Research supporting features include; facility to define experiments, ability to annotate experimental instances via purpose-built mobile applications, integrated machine learning functionality, facility to export data sets, rule-based classification and an extensible platform. The flagship implementation of this platform has been in operation for over 28 months within a University research group and has been successfully integrated with a range of sensors from a variety of manufacturers. This implementation currently stores over 850 million records and has been central to several research and industrial projects. Future work will integrate this platform into the open data initiative enabling collaboration with the international community of researchers.

We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c-consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c-consensus meets problem. CC BY 4.0Also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 12276)Partial support of the project Swedish Research Council (grant number VR 2016-03346) is acknowledged.DRIAT

Technological development and pressure to meet customer requirements through high productivity and low costs forces companies to be extremely flexible and efficient. For this purpose, many organizations have been adopting continuous improvement strategy which promotes constant insertion of small incremental improvements and better results in regard of efficiency and quality. The aim of this paper is to show how a company has progressed its continuous improvement of management processes. This was successfully achieved through new tools developed to solve different problems in distinct dimensions regarding continuous improvement. The developed tools were database systems and it was possible to realise their positive impact in continuous improvement of management processes in the focused company were they were implemented, since they established access to consistent data and did enable to reduce redundant information spread across multiple files and software tools, and thus eliminating waste. Moreover, these new tools developed allowed to pass from traditional and manual recorded methods to computerized systems, making information more accessible and always updated. Thus connection between information systems and management of continuous improvement processes is essential for companies’ evolution and success., This work has been supported by FCT Fundação para a Ciência e Tecnologia, under the Project: PEst2015-2020

When exploring big amounts of data without a clear target, providing an interactive experience becomes really dif cult, since this tentative inspection usually defeats any early decision on data structures or indexing strategies. This is also true in the physics domain, speci cally in high-energy physics, where the huge volume of data generated by the detectors are normally explored via C++ code using batch processing, which introduces a considerable latency. An interactive tool, when integrated into the existing data management systems, can add a great value to the usability of these platforms. Here, we intend to review the current state-of-the-art of interactive data exploration, aiming at satisfying three requirements: access to raw data les, stored in a distributed environment, and with a reasonably low latency. This paper follows the guidelines for systematic mapping studies, which is well suited for gathering and classifying available studies.We summarize the results after classifying the 242 papers that passed our inclusion criteria. While there are many proposed solutions that tackle the problem in different manners, there is little evidence available about their implementation in practice. Almost all of the solutions found by this paper cover a subset of our requirements, with only one partially satisfying the three. The solutions for data exploration abound. It is an active research area and, considering the continuous growth of data volume and variety, is only to become harder. There is a niche for research on a solution that covers our requirements, and the required building blocks are there.

Within the framework of the research, the information-educational environment 'Practice' has been developed: functional requirements have been identified, software and development tools have been selected, a database has been designed, a client-server web-application for its access has been implemented. The created information educational environment will effectively organize the process of students' practical training in terms of increasing the level of formation of the most important 'XXI century skills': bachelors' ICT-competence, communicative reflection, critical thinking. © 2019 IEEE.

The digital data stored in the cloud requires much space due to copy of the same data. It can be reduced by dedupilcation, eliminating the copy of the repeated data in the cloud provided services. Identifying common checkoff data both files storing them only once. Deduplication can yield cost savings by increasing the utility of a given amount of storage. Unfortunately, deduplication has many security problems so more than one encryption is required to authenticate data. We have developed a solution that provides both data security and space efficiency in server storage and distributed content checksum storage systems. Here we adopt a method called interactive Message-Locked Encryption with Convergent Encryption (iMLEwCE). In this iMLEwCE the data is encrypted firstly then the cipher text is again encrypted. Block-level deduplication is used to reduce the storage space. Encryption keys are generated in a consistent configuration of data dependency from the chunk data. The identical chunks will always encrypt to the same cipher text. The keys configuration cannot be deduced by the hacker from the encrypted chunk data. So the information is protected from cloud server. This paper focuses on reducing the storage space and providing security in online cloud deduplication.

Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from theusers. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interactionprocedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.

Graphics processing units have proved its capability for general purpose computing in many research areas. In this paper, we propose the mechanism and implementation of a database system that encrypts and decrypts data by using GPU. The proposed mechanism is mainly designed for a database system that requires data encryption and decryption to support high security level or ODBS. By exploiting the computation capability of GPU, we achieve not only a fast encryption and decryption time per operation but also a higher overall performance of a database system by offioading computation to GPU. Moreover, the proposed system includes a mechanism which can decide whether to offload computation to GPU or not for more performance gain. We implemented the AES algorithm based on CUDA framework and integrate with MySQL, a commodity database system. Our evaluation demonstrates that the encryption and decryption on GPU shows eight times better performance compared that on CPU when the data size is 16 MB. We also show that the proposed system alleviates the utilization of CPU and the overall performance of database system is improved by offioading heavy encrypting and decrypting computation to GPU.

Traditional Database Management System (DBMS) software relies on hard disks for storing relational data. Hard disks are cheap, persistent, and offer huge storage capacities. However, data retrieval latency for hard disks is extremely high. To hide this latency, DRAM is used as an intermediate storage. DRAM is significantly faster than disk, but deployed in smaller capacities due to cost and power constraints, and without the necessary persistency feature that disks have. Non-Volatile Memory (NVM) is an emerging storage class technology which promises the best of both worlds. It can offer large storage capacities, due to better scaling and cost metrics than DRAM, and is non-volatile (persistent) like hard disks. At the same time, its data retrieval time is much lower than that of hard disks and it is also byte-addressable like DRAM. In this paper, we explore the implications of employing NVM as primary storage for DBMS. In other words, we investigate the modifications necessary to be applied on a traditional relational DBMS to take advantage of NVM features. As a case study, we have modified the storage engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the necessary changes and challenges such modifications entail and evaluate them using a comprehensive emulation platform. Results indicate that our modified SE reduces query execution time by up to 40% and 14.4% when compared to disk and NVM storage, with average reductions of 20.5% and 4.5%, respectively. The research leading to these results has received funding from the European Union’s 7th Framework Programme under grant agreement number 318633, the Ministry of Science and Technology of Spain under contract TIN2015-65316-P, and a HiPEAC collaboration grant awarded to Naveed Ul Mustafa.

Conference of 4th ACM International Workshop on Security in Cloud Computing, SCC 2016 ; Conference Date: 30 May 2016; Conference Code:121977; International audience; The wide use of cloud computing and of data outsourcing rises important concerns with regards to data security resulting thus in the necessity of protection mechanisms such as encryption of sensitive data. The recent major theoretical breakthrough of finding the Holy Grail of encryption, i.e. fully homomorphic encryption guarantees the privacy of queries and their results on encrypted data. However, there are only a few studies proposing a practical performance evaluation of the use of homomorphic encryption schemes in order to perform database queries. In this paper, we propose and analyse in the context of a secure framework for a generic database query interpreter two different methods in which client requests are dynamically executed on homomorphically encrypted data. Dynamic compilation of the requests allows to take advantage of the different optimizations performed during an off-line step on an intermediate code representation, taking the form of boolean circuits, and, moreover, to specialize the execution using runtime information. Also, for the returned encrypted results, we assess the complexity and the efficiency of the different protocols proposed in the literature in terms of overall execution time, accuracy and communication overhead.

Over the past decade, the development of web-based Geographic Information Systems (GIS) for health has grown quite rapidly due to an increased need of data integration and spatial visualization. One GIS growth area in health is the construction of map-based applications that provide information on health care resources. Such applications are typically used as standard tools by public health departments, public health policy and research organizations, hospitals and health insurance organizations to provide public access to health care resources. This paper presents the design and development process of Clinic Finder (CliFin) - an open-access web-based GIS application relying on the Google Maps technology and providing access to a database with point of care facilities across the Province of New Brunswick, Canada. The uniqueness of CliFin consists in the implementation of a time-frame dependent search and results trimming approach, which allows users to identify clinics and hospitals open at any given time. The users are also encouraged to contribute with schedule updates and new point of care information to further develop CliFin's database and its accuracy. The combination of GIS visualization capabilities, database management, user involvement in database update and the time-frame dependence of search results, confers CliFin increased practicality, especially in situations of crisis such as natural disasters. Copyright © 2012, IGI Global.

In this paper, a workflow is proposed to detect 3D building changes in urban and sub-urban areas using high-resolution stereoscopic satellite images of different epochs and a GIS database. Semi-global matching (SGM) is used to derive Digital Surface Models (DSM) and subsequently normalised digital surface models (nDSM, the difference of a DSM and a digital elevation model (DEM)), from the stereo pairs at each epoch. Large differences between the two DSMs are assumed to represent height changes. In order to reduce the effect of matching errors, heights in the nDSM of at least one epoch must also lie above a certain threshold in order to be considered as candidates for building change. A GIS database is used to check the existence of buildings at epoch 1. As a result of geometric discrepancies during data acquisition caused by different view directions and illumination conditions, the outlines of existing buildings do not necessarily match even in non-changed areas. Consequently, in the change map, there are streaking-shaped structures along the building outlines which do not correspond to actual changes. To eliminate these effects morphologic filtering is applied. The mask we use operates as a threshold on the shape and size of detected new blobs and effectively removes small objects such as cars, small trees and salt and pepper noise. The results of the proposed algorithm using IKONOS and GeoEye images demonstrate its performance for detecting 3D building changes and to extract building boundaries.

Decision support systems are computer based programs which assists decision makers in effective and efficient decision making. Outliers are the data sets which are highly irrelevant with respect to main data sets. Spatial outliers are the spatial objects with distinct features from their surrounding neighbours. In this paper we have proposed a decision support system for strategic decision makers to establish an industry. We have shown how spatial outlier detection techniques may be used to aid decision makers. Each site is evaluated based upon multiple decision variables. Here, we use two different outlier detection algorithms to detect a site which is most unsuitable to establish an industry. We also perform an experiment on a synthetic dataset of fifteen different sites to locate ambiguous sites. We found that decision making process for site selection may be improved by using spatial outlier detection techniques.

A software application for automatic descriptive feature extraction from image-objects, FETEX 2.0, is presented and described in this paper. The input data include a multispectral high resolution digital image and a vector file in shapefile format containing the polygons or objects, usually extracted from a geospatial database. The design of the available descriptive features or attributes has been mainly focused on the description of agricultural parcels, providing a variety of information: spectral information from the different image bands; textural descriptors of the distribution of the intensity values based on the grey level co-occurrence matrix, the wavelet transform and a factor of edgeness; structural features describing the spatial arrangement of the elements inside the objects, based on the semivariogram curve and the Hough transform; and several descriptors of the object shape. The output file is a table that can be produced in four alternative formats, containing a vector of features for every object processed. This table of numeric values describing the objects from different points of view can be externally used as input data for any classification software. Additionally, several types of graphs and images describing the feature extraction procedure are produced, useful for interpretation and understanding the process. A test of the processing times is included, as well as an application of the program in a real parcel-based classification problem, providing some results and analyzing the applicability, the future improvement of the methodologies, and the use of additional types of data sets. This software is intended to be a dynamic tool, integrating further data and feature extraction algorithms for the progressive improvement of land use/land cover database classification and agricultural database updating processes. © 2011 Elsevier B.V., The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the FEDER in the framework of the Project CGL2009-14220 and CGL2010-19591/BTE, the Spanish Institut Geografico Nacional (IGN), Institut Cartografico Valenciano (ICV), Institut Murciano de Investigacion y Desarrollo Agrario y Alimentario (IMIDA) and Banco de Terras de Galicia (Bantegal).

Fifty-one organisations network in SARNET (Severe Accident Research NETwork of Excellence) their research capacities in order to resolve the most important pending issues for enhancing, with regard to Severe Accidents (SA), the safety of existing and future Nuclear Power Plants (NPPs). This project, co-funded by the European Commission (EC) under the 6th Framework Programme, has been defined in order to optimise the use of the available means and to constitute sustainable research groups in the European Union. SARNET tackles the fragmentation that may exist between the different national R&D programmes, in defining common research programmes and developing common computer tools and methodologies for safety assessment. SARNET comprises most of the organisations involved in SA research in Europe, plus Canada. To reach these objectives, all the organisations networked in SARNET contributed to a Joint Programme of Activities, which consisted of: Implementation of an advanced communication tool for accessing all project information, fostering exchange of information, and managing documents; Harmonization and re-orientation of the research programmes, and definition of new ones; Analysis of the experimental results provided by research programmes in order to elaborate a common understanding of relevant phenomena; Development of the ASTEC code (integral computer code used to predict the NPP behaviour during a postulated SA), which capitalizes in terms of physical models the knowledge produced within SARNET; Development of Scientific Databases in which all the results of research programmes are stored in a common format (DATANET); Development of a common methodology for Probabilistic Safety Assessment of NPPs; Development of short courses and writing a text book on Severe Accidents for students and researchers; Promotion of personnel mobility amongst various European organisations. This paper presents the major achievements after four and a half years of operation of the network, in terms of knowledge gained, of improvement of the ASTEC reference code, of dissemination of results and of integration of the research programmes conducted by the various partners. After this first period (2004-2008), co-funded by the EC, a further contract SARNET2 with the EC for the next four years started in April 2009 as part of the 7th Framework Programme. During this period, the networking activities will focus mainly on the remaining pending issues as determined during the first period, experimental activities will be directly included in the common work and the network will evolve toward complete self-sustainability. The bases for such an evolution are presented in the last part of the paper., JRC.F.5-Safety of present nuclear reactors

Carbon dioxide Capture and Storage (CCS) is a promising contribution to reduce further increase of atmospheric CO2 emissions from fossil fuels. The CCS concept anticipates that large amounts of CO2 are going to be stored in the subsurface for the long term. Since CCS is a rather new technology, uncertainties related with the future evolution of a CO2 storage system should be addressed in a systematic framework. Performance and risk assessment frameworks are being created for the evaluation of the safeness and effectiveness of a CCS project. The recommended first step of this framework is a qualitative analysis, which encompasses the scenario analysis. The main goal of this step is to identify CO2 release scenarios, which can be supported by using a database of Features, Events and Processes (FEP), i.e. the elements that describe the characteristics and behaviour of the geological system. A qualitative FEP database tool assists to describe the storage concept and the factors which might influence the future behaviour of the storage reservoir and its surroundings. Scenario analysis using a FEP database is a crucial part of the risk assessment of a CCS project and has been applied to define release scenarios for a number of storage sites. However, further improvement is needed to facilitate a faster and more coherent analysis. For this purpose, a novel scenario analysis framework has been created, called Carbon Storage Scenario Identification Framework (CASSIF) (Yavuz et al., 2008 [1]). It is based on the three major CO2 release scenarios (well, fault and seal) from where the relevant events and processes are identified and modelled. Expert opinion is a key value within this framework. A questionnaire to be filled in by experts has been designed for the initial screening and ranking of events and processes. Features (static entities) have been separated from the events and process (the dynamic characteristics) so the focus in the expert elicitation will be on the latter. The new interface facilitates the online use of the FEP database. Together with real-time scenario formation, these improvements will greatly enhance the speed, transparency and comprehe nsiveness in the creation of subsurface CO2 release scenarios. © 2009 Elsevier Ltd. All rights reserved.

The product generation from hyperspectral sensor data has high requirements on the processing infrastructure, both hardware and software. The Airborne Prism Experiment (APEX) processing and archiving facility has been set up to provide for the automated generation of level-1 calibrated data and user-configurable on-demand product generation for higher processing levels. The system offers full reproducibility of user orders and processing parameters by employing a relational database. The flexible workflow software allows for the quick integration of novel algorithms or the definition of new processing sequences. Reprocessing of data is supported by the archiving approach. Configuration management based on the database enables the control over different versions of processing modules to be applied. The system is described with a focus on the APEX instrument; however, its generic design allows adaptation to other sensor systems.

The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10 Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13% for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.

In this paper, we propose a signature verification system based on Dynamic Time Warping (DTW). The method works by extracting the vertical projection feature from signature images and by comparing reference and probe feature templates using elastic matching. Modifications are made to the basic DTW algorithm to account for the stability of the various components of a signature. The basic DTW and the modified DTW methods are tested on a signature database of 100 people. The modified DTW algorithm, which incorporates stability, has an equal-error-rate of only 2% in comparison to 29% for the basic DTW method. � 2007 Elsevier B.V. All rights reserved.

In the present paper, we investigate the validity and reliability of de-facto evaluation standards, defined for measuring or predicting the quality of the interaction with spoken dialogue systems. Two experiments have been carried out with a dialogue system for controlling domestic devices. During these experiments, subjective judgments of quality have been collected by two questionnaire methods (ITU-T Rec. P.851 and SASSI), and parameters describing the interaction have been logged and annotated. Both metrics served the derivation of prediction models according to the PARADISE approach. Although the limited database allows only tentative conclusions to be drawn, the results suggest that both questionnaire methods provide valid measurements of a large number of different quality aspects; most of the perceptive dimensions underlying the subjective judgments can also be measured with a high reliability. The extracted parameters mainly describe quality aspects which are directly linked to the system, environmental and task characteristics. Used as an input to prediction models, the parameters provide helpful information for system design and optimization, but not general predictions of system usability and acceptability. © 2005 Elsevier Ltd. All rights reserved.

Zipf's law is used to model the uneveness of customers and telecommunications traffic distribution among the geographical areas covered by a nationwide network. Three datasets, relative to the traffic intensity in the telephone network and to the number of Internet customers as recorded on an Italian network, are used to assess the validity of the law. The estimated Zipf parameter is lower than 1 (typical of town population) in all three cases but larger than those observed for population over the same administrative groupings (code areas and provinces). Therefore the largest code areas and provinces account for a larger share of the overall traffic than what their population share would suggest, but far lower than that expected from the popularised 80/20 Pareto rule. Copyright (C) 2006 AEIT.

With the growth in the popularity of the World Wide Web (WWW), there is a need to provide education and hands-on training to students in preparing materials for presentation on the Web, developing client and server-side software, and building Web-based applications. Employers seek candidates who are proficient in computer languages and tools used on the WWW. Courses on Web programming are in great demand. This paper presents the details of a course the author has developed on Web languages and Web-based applications. Key features of the course are 1) teaching of diverse Web languages and technologies using a program completion strategy, 2) discussion of Web-based applications involving database access and electrical/mechanical measuring instruments, and 3) long individual student projects and seminars.

Though the designers of Flexible Manufacturing Systems (FMS) strive to ensure the maximum flexibility in the system, in practice, after the implementation of such systems the operational executives often find it hard to accommodate frequent variations in the part designs of incoming jobs. This difficulty can very well be overcome by scheduling the variety of incoming parts into the system efficiently. In this work an appropriate scheduling mechanism is designed to generate a nearer-to-optimum schedule using Genetic Algorithm (GA) with two different GA Coding Schemes. Two contradictory objectives of the system were achieved simultaneously by the scheduling mechanism. The results are compared with those obtained by different scheduling rules and conclusions are presented.

In this paper, we present the concept of speaker-specific mapping for the task of speaker recognition. The speaker-specific mapping is realized using a multilayer feedforward neural network. In the mapping approach, the aim is to capture the speaker-specific information by mapping a set of parameter vectors specific to linguistic information in the speech, to a set of parameter vectors having linguistic and speaker information. In this study, parameter vectors suitable for speaker-specific mapping are explored. Background normalization for score comparison and network error criterion for frame selection are proposed to improve the performance of the basic system. It is shown that removing the high frequency components of speech results in loss of performance of the speaker verification system. For all the 630 speakers of the TIMIT database, an equal error rate (EER) of 0.5% and 100% identification is achieved by the mapping approach. On a set of 38 speakers of the dialect region "dr1" of NTIMIT database, an EER of 6.6% is obtained. ? 2002 Elsevier Science B.V. All rights reserved.

Cataloged from PDF version of article. Using one's hands in human-computer interaction increases both the effectiveness of computer usage and the speed of interaction. One way of accomplishing this goal is to utilize computer vision techniques to develop hand-gesture-based interfaces. A video database system is one application where a hand-gesture-based interface is useful, because it provides a way to specify certain queries more easily. We present a hand-gesture-based interface for a video database system to specify motion and spatiotemporal object queries. We use a regular, low-cost camera to monitor the movements and configurations of the user's hands and translate them to video queries. We conducted a user study to compare our gesture-based interface with a mouse-based interface on various types of video queries. The users evaluated the two interfaces in terms of different usability parameters, including the ease of learning, ease of use, ease of remembering (memory), naturalness, comfortable use, satisfaction, and enjoyment. The user study showed that querying video databases is a promising application area for hand-gesture-based interfaces, especially for queries involving motion and spatiotemporal relations. © 2014 Springer-Verlag London.

We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and very few tunable parameters, the method has strong potential for applications requiring frequent retraining or online training. The approach is characterized by (a) convolutional filters based on biologically inspired visual processing filters, (b) randomly-valued classifier-stage input weights, (c) use of least squares regression to train the classifier output weights in a single batch, and (d) linear classifier-stage output units. We demonstrate the efficacy of the method by applying it to image classification. Our results match existing state-of-the-art results on the MNIST (0.37% error) and NORB-small (2.2% error) image classification databases, but with very fast training times compared to standard deep network approaches. The network's performance on the Google Street View House Number (SVHN) (4% error) database is also competitive with state-of-the art methods., Comment: 7 pages, 2 figures, Paper at IJCNN 2015 (International Joint Conference on Neural Networks, 2015)

Cataloged from PDF version of article. Mobile computers can be equipped with wireless communication devices that enable users to access data services from any location. In wireless communication, the server-to-client (downlink) communication bandwidth is much higher than the client-to-server (uplink) communication bandwidth. This asymmetry makes the dissemination of data to client machines a desirable approach. However, dissemination of data by broadcasting may induce high access latency in case the number of broadcast data items is large. In this paper, we propose two methods aiming to reduce client access latency of broadcast data. Our methods are based on analyzing the broadcast history (i.e., the chronological sequence of items that have been requested by clients) using data mining techniques. With the first method, the data items in the broadcast disk are organized in such a way that the items requested subsequently are placed close to each other. The second method focuses on improving the cache hit ratio to be able to decrease the access latency. It enables clients to prefetch the data from the broadcast disk based on the rules extracted from previous data request patterns. The proposed methods are implemented on a Web log to estimate their effectiveness. It is shown through performance experiments that the proposed rule-based methods are effective in improving the system performance in terms of the average latency as well as the cache hit ratio of mobile clients.

In this paper, a neural network based feature recognition approach which is capable of extracting information from design database is proposed to automate the integration of the design and applications following design. CAD data base is converted to feature based model information which can be used by CAM applications. Multilayer perceptron neural network is provided with Boundary representation (B-rep) information to recognise simple and complex features. B-rep structure is used to process the face-score values in terms of geometry and topology of the solid model. The effectiveness of proposed approach is demonstrated with experimental results which show the validity of this method to recognise complex shape features.

This paper presents an expert system for differential diagnosis of erythemato-squamous diseases incorporating decisions made by three classification algorithms: nearest neighbor classifier, naive Bayesian classifier and voting feature intervals-5. This tool enables doctors to differentiate six types of erythemato-squamous diseases using clinical and histopathological parameters obtained from a patient. The program also gives explanations for the classifications of each classifier. The patient records are also maintained in a database for further references. (C) 2000 Elsevier Science Ltd. All rights reserved.

For signature files we propose a new false drop estimation method for databases with varying record lengths. Our approach provides more accurate estimation of the number of false drops by considering the lengths of individual records instead of using the average number of terms per record. In signature file processing, accurate estimation of the number of false drops is essential to obtain a more accurate signature file and therefore to obtain a better (query) response time. With a formal proof we show that under certain conditions the number of false drops estimated by considering the average record length is less than or equal to the precise 'expected' estimation which is based on the individual record lengths. The experiments with real data show that the proposed method accurately estimates the number of false drops and the actual response time. Depending on the space overhead, our approach obtains up to 33% and 20% response time improvements for the conventional sequential and new efficient multiframe signature file methods, respectively.

India, with a vast coastal front, witnesses a wide range of coastal processes and activities. As the country's economic growth depends largely on effective utilization of the coast and coastal resources, management strategies have gained significance in recent years. Though both the central and state governments have taken initiatives in the implementation of various schemes for better utilization and management of the coast, certain external factors have decelerated the progress. For successful management, all important issues related to coastal zone should be considered prior to implementation. In addition, participation of beneficiaries in the preparation of coastal management schemes would fetch good results, and the concept of public involvement in the exercise is gaining momentum. This article highlights various coastal zone processes and activities along the east coast of India, especially along the coast of Tamil Nadu. Policies of both the central and state governments with regard to coastal zone ...

In this paper, a workflow is proposed to delineate building footprints from high resolution satellite stereo images through integration of a Digital Surface Model (DSM), 3D edge matching technique and GIS building polygons. First, Digital Surface Models (DSM) and a normalised DSM are derived by traditional image matching. Three different removal masks are employed to reduce the effects of matching errors, roads and vegetation. We then compare all resulting blobs with the GIS 2D building layer. For blobs without a partner in the database, building outlines are extracted based on 3D edge matching. To do so, edges on epipolar images are first detected individually on each image using the Canny operator. After removing short edges and extracting straight lines, we find the best corresponding lines using various constraints. Finally, the topological relationship of the derived 3D edges is employed to reconstruct the shape of the building and the building footprints using a box-fitting approach. Our experiments on GeoEye-1 stereo images show promising results if buildings are large enough, have simple shape and show good contrast compared to the background.

et al., The process of accurately gauging lifetime improvements in organic photovoltaics (OPVs) or other similar emerging technologies, such as perovskites solar cells is still a major challenge. The presented work is part of a larger effort of developing a worldwide database of lifetimes that can help establishing reference baselines of stability performance for OPVs and other emerging PV technologies, which can then be utilized for pass-fail testing standards and predicting tools. The study constitutes scanning of literature articles related to stability data of OPVs, reported until mid-2015 and collecting the reported data into a database. A generic lifetime marker is utilized for rating the stability of various reported devices. The collected data is combined with an earlier developed and reported database, which was based on articles reported until mid-2013. The extended database is utilized for establishing the baselines of lifetime for OPVs tested under different conditions. The work also provides the recent progress in stability of unencapsulated OPVs with different architectures, as well as presents the updated diagram of the reported record lifetimes of OPVs. The presented work is another step forward towards the development of pass-fail testing standards and lifetime prediction tools for emerging PV technologies., This work has been supported by the European Commission's StableNextSol COST Action MP1307. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 609788 (CHEETAH) and n° 290022 (ESTABLIS). ICN2 acknowledges the support of the Spanish MINECO through the Severo Ochoa Centers of Excellence Program under Grant Nos. SEV-2013-0295 and ENE2013-48816-C5-4-R. Thanks goes to Agencia de Gestio d'Ajuts Universitaris i de Recerca for the support through the Xarxa de Referencia en Materials Avançats per a l'Energia (XaRMAE) and consolidated research group No. 2014SGR-1212. This work has been carried out under the Materials Science Ph.D. Degree for A.M. of the Universitat Autònoma de Barcelona. R.R. and H.H. are grateful for financial support from BMBF (grant number 03EK3502) and ESF. F.P. received support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 659747. L.S. and M.R. received supported from the Portuguese Foundation for Science and Technology in the framework of the Strategic Program UID/FIS/04650/2013.

International audience; In the long run, your data matters more than your code. It's worth investing some effort to keep your data in good shape for years to come. Data is at the heart of science. A scientist is expected to be able to back up all published conclusions with data. Data management should thus be a priority in science. Scientists can't afford to lose data, be uncertain of what it means, or not know where it came from. In the experimental sciences, there's a long tradition of writing down all experimental setups, parameters, and results meticulously in a lab notebook. Unfortunately, computational science is much less rigorous about data handling, although there are clear signs of improvement. Here, we'll consider what you can do to better prepare and manage your data.

The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management. © 2011 IEEE. 21 3 1211 1222 Cited By :37

Gaussian mixture models are an important tool in Bayesian decision theory. In this study, we focus on building such models over statistical database protected under differential privacy. Our approach involves querying necessary statistics from a database and building a Bayesian classifier over the noise added responses generated according to differential privacy. We formally analyze the sensitivity of our query set. Since there are multiple methods to query a statistic, either directly or indirectly, we analyze the sensitivities for different querying methods. Furthermore we establish theoretical bounds for the Bayes error for the univariate (one dimensional) case. We study the Bayes error for the multivariate (high dimensional) case in experiments with both simulated data and real life data. We discover that adding Laplace noise to a statistic under certain constraint is problematic. For example variance-covariance matrix is no longer positive definite after noise addition. We propose a heuristic method to fix the noise added variance-covariance matrix. Publisher's Version

Technical report TR10-03. Top-k queries are a popular type of query in Wireless Sensor Networks. Typical solutions rely on coordinated root-to-nodes and nodes-to-root messages and on maintaining filters at the nodes, aiming at suppressing unnecessary messages, hence saving energy and furthering the network's lifetime. In this paper, we exploit the capability of a sensor node to cache a few recently observed values in order to determine \"trends\" for the observed values. Those trends can be used to further restrict the number of messages that need to be exchanged in the network, thus ultimately extending the network's lifetime. We compare our approach to the most recently proposed solutions in the literature using real and synthetic datasets, and we show that our approach is robust with respect to a variety of parameters and is able to improve the network's lifetime by up to 28% without any loss in the quality of the answer. | TRID-ID TR10-03

BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.

Date of Conference: 26-29 Sept. 2010 A feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. The concept of one-dimensional (1D) mel-cepstrum which is widely used in speech recognition is extended to 2D in this article. Feature matrices resulting from the 2D mel-cepstrum, Fourier LDA, 2D PCA and original image matrices are converted to feature vectors and individually applied to a Support Vector Machine (SVM) classification engine for comparison. The AR face database, ORL database, Yale database and FRGC version 2 database are used in experimental studies, which indicate that recognition rates obtained by the 2D mel-cepstrum method is superior to the recognition rates obtained using Fourier LDA, 2D PCA and ordinary image matrix based face recognition. This indicates that 2D mel-cepstral analysis can be used in image feature extraction problems. © 2010 IEEE.

Technical report TR08-13. The vast number of on-line biological and medical databases available can be a great resource for medical researchers. However, the different types of data and interfaces available can be overwhelming for many medical researchers to learn. Moreover, the available resources lack needed integration. Here we focus on an important task in medical research: to provide researchers with promoter analysis for a given gene. PROM-OOGLE is a web based data mining tool that provides a means for researchers to take a gene name of interest and obtain its promoter sequence in return after automatic integration of text databases. Additionally, the program is capable of returning multiple promoters from different genes allowing researchers to study how promoters regulate genes. This tool facilitates the process of acquiring information on a promoter and may lead to interesting discoveries. | TRID-ID TR08-13

In this paper we present the results of a project, named ASIt, which provides linguists with a crucial test bed for formal hypotheses concerning human language. In particular, ASIt aims to capture cross-linguistic variants of grammatical structures within a sample of about 200 Italian Dialects. Since dialects are rarely recognized as official languages, first of all linguists need a dedicated digital library system providing the tools for the unambiguous identification of each dialect on the basis of geographical, administrative and geo-linguistic parameters. Secondly, the information access component of the digital library system needs to be designed to allow users to search the occurrences of a specific grammatical structure (e.g. a relative clause or a particular word order) rather than a specific word. Thirdly, since ASIt has been specifically geared to the needs of linguists, user-friendly graphical interfaces need to be created to give easy access to and make the building of the language resource easier and distributed. The paper reports on the ways these three main aims have been achieved.

Date of Conference: 23-26 Aug. 2010 In this paper, a feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. Feature matrices resulting from the 2D mel-cepstrum, Fourier LDA approach and original image matrices are individually applied to the Common Matrix Approach (CMA) based face recognition system. For each of these feature extraction methods, recognition rates are obtained in the AR face database, ORL database and Yale database. Experimental results indicate that recognition rates obtained by the 2D mel-cepstrum method is superior to the recognition rates obtained using Fourier LDA approach and raw image matrices. This indicates that 2D mel-cepstral analysis can be used in image feature extraction problems. © 2010 IEEE.

The design of a scalable system for holding spatial data in general and Digital Elevation Models (DEMs) in specific has to account for the characteristics of data from various application fields. The data can be heterogeneous in coverage, as well as in resolution, information content and quality. A database aiming at the representation of world-wide DEMs has to consider these differences in the design of the system with respect to the structure and the algorithms. The database system W42, which is presented in the work at hand, is a scalable spatial database system capable of holding, extracting, mosaicking, and fusing spatial data represented in raster- as well as in vector-format. Design aspects for this task can be specified as holding spatial data in unique data structures and providing unique access functions to the data. These are subject of this work as well as first experiences gained from the implementation of part of the extensions made for the TanDEM-X mission.

Date of Conference: 23-26 June, 2008 Conference name: IEEE International Conference on Multimedia and Expo, 2008 We describe a method to automatically extract important video objects for object-based indexing. Most of the existing salient object detection approaches detect visually conspicuous structures in images, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in terms of color and texture. Then, we extract a set of regional and inter-regional color, shape, texture and motion features for all regions, which are classified as being important or not using SVMs trained on a few hundreds of example regions. Finally, each important region is tracked within each shot for trajectory generation and consistency check. Experimental results from news video sequences show that the proposed approach is effective. © 2008 IEEE.