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  1. Book ; Online: CloudifierNet -- Deep Vision Models for Artificial Image Processing

    Damian, Andrei / Piciu, Laurentiu / Purdila, Alexandru / Tapus, Nicolae

    2019  

    Abstract: Today, more and more, it is necessary that most applications and documents developed in previous or current technologies to be accessible online on cloud-based infrastructures. That is why the migration of legacy systems including their hosts of ... ...

    Abstract Today, more and more, it is necessary that most applications and documents developed in previous or current technologies to be accessible online on cloud-based infrastructures. That is why the migration of legacy systems including their hosts of documents to new technologies and online infrastructures, using modern Artificial Intelligence techniques, is absolutely necessary. With the advancement of Artificial Intelligence and Deep Learning with its multitude of applications, a new area of research is emerging - that of automated systems development and maintenance. The underlying work objective that led to this paper aims to research and develop truly intelligent systems able to analyze user interfaces from various sources and generate real and usable inferences ranging from architecture analysis to actual code generation. One key element of such systems is that of artificial scene detection and analysis based on deep learning computer vision systems. Computer vision models and particularly deep directed acyclic graphs based on convolutional modules are generally constructed and trained based on natural images datasets. Due to this fact, the models will develop during the training process natural image feature detectors apart from the base graph modules that will learn basic primitive features. In the current paper, we will present the base principles of a deep neural pipeline for computer vision applied to artificial scenes (scenes generated by user interfaces or similar). Finally, we will present the conclusions based on experimental development and benchmarking against state-of-the-art transfer-learning implemented deep vision models.

    Comment: ITQM 2019
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 004
    Publishing date 2019-11-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders

    Damian, Andrei / Piciu, Laurentiu / Turlea, Sergiu / Tapus, Nicolae

    2019  

    Abstract: Product recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has enabled various approaches and ... ...

    Abstract Product recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has enabled various approaches and directions of recommender systems advancement. Worth to mention is the fact that in past years multiple traditional "offline" retail business are gearing more and more towards employing inferential and even predictive analytics both to stock-related problems such as predictive replenishment but also to enrich customer interaction experience. One of the most important areas of recommender systems research and development is that of Deep Learning based models which employ representational learning to model consumer behavioral patterns. Current state of the art in Deep Learning based recommender systems uses multiple approaches ranging from already classical methods such as the ones based on learning product representation vector, to recurrent analysis of customer transactional time-series and up to generative models based on adversarial training. Each of these methods has multiple advantages and inherent weaknesses such as inability of understanding the actual user-journey, ability to propose only single product recommendation or top-k product recommendations without prediction of actual next-best-offer. In our work we will present a new and innovative architectural approach of applying state-of-the-art hierarchical multi-module encoder-decoder architecture in order to solve several of current state-of-the-art recommender systems issues. Our approach will also produce by-products such as product need-based segmentation and customer behavioral segmentation - all in an end-to-end trainable approach.

    Comment: 2019 22nd International Conference on Control Systems and Computer Science (CSCS)
    Keywords Computer Science - Information Retrieval ; Computer Science - Machine Learning ; I.2.4
    Subject code 006
    Publishing date 2019-04-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: The Accuracy and Efficiency of Posit Arithmetic

    Ciocirlan, Stefan Dan / Loghin, Dumitrel / Ramapantulu, Lavanya / Tapus, Nicolae / Teo, Yong Meng

    2021  

    Abstract: Motivated by the increasing interest in the posit numeric format, in this paper we evaluate the accuracy and efficiency of posit arithmetic in contrast to the traditional IEEE 754 32-bit floating-point (FP32) arithmetic. We first design and implement a ... ...

    Abstract Motivated by the increasing interest in the posit numeric format, in this paper we evaluate the accuracy and efficiency of posit arithmetic in contrast to the traditional IEEE 754 32-bit floating-point (FP32) arithmetic. We first design and implement a Posit Arithmetic Unit (PAU), called POSAR, with flexible bit-sized arithmetic suitable for applications that can trade accuracy for savings in chip area. Next, we analyze the accuracy and efficiency of POSAR with a series of benchmarks including mathematical computations, ML kernels, NAS Parallel Benchmarks (NPB), and Cifar-10 CNN. This analysis is done on our implementation of POSAR integrated into a RISC-V Rocket Chip core in comparison with the IEEE 754-based Floting Point Unit (FPU) of Rocket Chip. Our analysis shows that POSAR can outperform the FPU, but the results are not spectacular. For NPB, 32-bit posit achieves better accuracy than FP32 and improves the execution by up to 2%. However, POSAR with 32-bit posit needs 30% more FPGA resources compared to the FPU. For classic ML algorithms, we find that 8-bit posits are not suitable to replace FP32 because they exhibit low accuracy leading to wrong results. Instead, 16-bit posit offers the best option in terms of accuracy and efficiency. For example, 16-bit posit achieves the same Top-1 accuracy as FP32 on a Cifar-10 CNN with a speedup of 18%.

    Comment: 12 pages, 5 figures, 7 tables
    Keywords Computer Science - Hardware Architecture ; Computer Science - Performance
    Subject code 629
    Publishing date 2021-09-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Deep recommender engine based on efficient product embeddings neural pipeline

    Piciu, Laurentiu / Damian, Andrei / Tapus, Nicolae / Simion-Constantinescu, Andrei / Dumitrescu, Bogdan

    2019  

    Abstract: Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and ... ...

    Abstract Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and development of the "perfect" recommendation system. In our paper we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking have been done using very large pharma-industry retailer Big Data stream.

    Comment: 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet)
    Keywords Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2019-03-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Algorithmic Solutions for Several Offline Constrained Resource Processing and Data Transfer Multicriteria Optimization Problems

    Andreica, Mugurel Ionut / Tapus, Nicolae

    2010  

    Abstract: In this paper we present novel algorithmic solutions for several resource processing and data transfer multicriteria optimization problems. The results of most of the presented techniques are strategies which solve the considered problems (almost) ... ...

    Abstract In this paper we present novel algorithmic solutions for several resource processing and data transfer multicriteria optimization problems. The results of most of the presented techniques are strategies which solve the considered problems (almost) optimally. Thus, the developed algorithms construct intelligent strategies which can be implemented by agents in specific situations. All the described solutions make use of the properties of the considered problems and, thus, they are not applicable to a very general class of problems. However, by considering the specific details of each problem, we were able to obtain very efficient results.
    Keywords Computer Science - Data Structures and Algorithms ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; 05A05 ; 05C05 ; 05C12 ; 05C38 ; 68M14 ; 68P05 ; 68P10 ; 90C39 ; C.2 ; G.2
    Publishing date 2010-06-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  6. Book ; Online: Practical Range Aggregation, Selection and Set Maintenance Techniques

    Andreica, Mugurel Ionut / Tapus, Nicolae

    2010  

    Abstract: In this paper we present several new and very practical methods and techniques for range aggregation and selection problems in multidimensional data structures and other types of sets of values. We also present some new extensions and applications for ... ...

    Abstract In this paper we present several new and very practical methods and techniques for range aggregation and selection problems in multidimensional data structures and other types of sets of values. We also present some new extensions and applications for some fundamental set maintenance problems.
    Keywords Computer Science - Data Structures and Algorithms ; 68P05 ; E.1
    Publishing date 2010-06-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Book ; Online: Efficient Upload Bandwidth Estimation and Communication Resource Allocation Techniques

    Andreica, Mugurel Ionut / Tapus, Nicolae

    2010  

    Abstract: In this paper we address two problems, for which we present novel, efficient, algorithmic solutions. The first problem is motivated by practical situations and is concerned with the efficient estimation of the upload bandwidth of a machine, particularly ... ...

    Abstract In this paper we address two problems, for which we present novel, efficient, algorithmic solutions. The first problem is motivated by practical situations and is concerned with the efficient estimation of the upload bandwidth of a machine, particularly in the context of a peer-to-peer content sharing and distribution application. The second problem is more of a theoretical nature and considers a constrained communication resource allocation situation.

    Comment: Proceedings of the 9th WSEAS International Conference on Multimedia, Internet & Video Technologies (MIV), Budapest, Hungary, 3-5 September, 2009; ISBN: 978-960-474-114-4 / ISSN: 1790-5109
    Keywords Computer Science - Networking and Internet Architecture ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Data Structures and Algorithms ; C.2.1 ; C.2.2 ; C.2.4 ; G.2.1 ; G.2.2
    Publishing date 2010-01-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  8. Book ; Online: Efficient Offline Algorithmic Techniques for Several Packet Routing Problems in Distributed Systems

    Andreica, Mugurel Ionut / Tapus, Nicolae

    2009  

    Abstract: In this paper we consider several problems concerning packet routing in distributed systems. Each problem is formulated using terms from Graph Theory and for each problem we present efficient, novel, algorithmic techniques for computing optimal solutions. ...

    Abstract In this paper we consider several problems concerning packet routing in distributed systems. Each problem is formulated using terms from Graph Theory and for each problem we present efficient, novel, algorithmic techniques for computing optimal solutions. We address topics like: bottleneck paths (trees), optimal paths with non-linear costs, optimal paths with multiple optimization objectives, maintaining aggregate connectivity information under a sequence of network link failures, and several others.
    Keywords Computer Science - Data Structures and Algorithms ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Discrete Mathematics ; F.2.2 ; G.2.1
    Publishing date 2009-06-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  9. Book ; Online: Offline Algorithmic Techniques for Several Content Delivery Problems in Some Restricted Types of Distributed Systems

    Andreica, Mugurel Ionut / Tapus, Nicolae

    2009  

    Abstract: In this paper we consider several content delivery problems (broadcast and multicast, in particular) in some restricted types of distributed systems (e.g. optical Grids and wireless sensor networks with tree-like topologies). For each problem we provide ... ...

    Abstract In this paper we consider several content delivery problems (broadcast and multicast, in particular) in some restricted types of distributed systems (e.g. optical Grids and wireless sensor networks with tree-like topologies). For each problem we provide efficient algorithmic techniques for computing optimal content delivery strategies. The techniques we present are offline, which means that they can be used only when full information is available and the problem parameters do not fluctuate too much.

    Comment: Proceedings of the International Workshop on High Performance Grid Middleware (HiPerGrid), pp. 65-72, Bucharest, Romania, 21-22 November, 2008. (ISSN: 2065-0701)
    Keywords Computer Science - Data Structures and Algorithms ; Computer Science - Networking and Internet Architecture ; G.2.2 ; G.2.1
    Publishing date 2009-01-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  10. Book ; Online: Practical Algorithmic Techniques for Several String Processing Problems

    Andreica, Mugurel Ionut / Tapus, Nicolae

    2009  

    Abstract: The domains of data mining and knowledge discovery make use of large amounts of textual data, which need to be handled efficiently. Specific problems, like finding the maximum weight ordered common subset of a set of ordered sets or searching for ... ...

    Abstract The domains of data mining and knowledge discovery make use of large amounts of textual data, which need to be handled efficiently. Specific problems, like finding the maximum weight ordered common subset of a set of ordered sets or searching for specific patterns within texts, occur frequently in this context. In this paper we present several novel and practical algorithmic techniques for processing textual data (strings) in order to efficiently solve multiple problems. Our techniques make use of efficient string algorithms and data structures, like KMP, suffix arrays, tries and deterministic finite automata.
    Keywords Computer Science - Data Structures and Algorithms ; Computer Science - Formal Languages and Automata Theory ; F.2.2 ; F.1.1
    Publishing date 2009-12-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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