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Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%.

The accelerating rate of scientific publications makes it extremely difficult for researchers to find out the relevant papers and related works. Recommender systems that aim at solving the information overload problem have attracted lots of attention. However, existing paper recommendation works generally rely on the simple citation-ships between papers, which ignore the heterogeneity of the academic graphs. In this paper, we solve the personalized paper recommendation problem in the setting of heterogeneous information networks. A heterogeneous graph representation based recommendation method named HGRec is proposed. First, the author and paper profiles are constructed based on the extracted contents information. Second, we initialize the node vectors by employing the word-embedding technique. Third, we jointly update the node embeddings in the heterogeneous graph by proposing two meta-path based proximity measures. Finally, the paper recommendation is completed by calculating the similarity of the generated author and paper feature vectors. We present experiments on a real academic network, the DBLP network. The comparative results demonstrate the effectiveness of the proposed personalized recommendation approach compared to state-of-the-art methods.

During the almost 27 years of its existence, the business ecosystem research has developed a substantial level of ambiguity and multifacetedness. Due to the technological advancements that promote interconnectedness and value co-creation, the field has consequently spun off into more domain specific branches, such as the arenas of digital business ecosystems or business ecosystems driven by IoT. Nonetheless, despite the efforts to mend the theoretical foundations and close the gap between academia and empirical practice, the absolute majority of IoT driven business ecosystem literature follows the trend of conceptual randomness while expanding the volume of publications exponentially. Therefore, in order to tackle this conceptual inflation, the author encourages other researchers to make extended effort and increase the external validity of their researches, as well as the relevance of the research stream as a whole, by bounding the IoT driven business ecosystems on a rigorous basis and deploying the extant theory in an careful and appropriate manner. Overall, this position paper contributes to the research area of IoT driven business ecosystems by unfolding the importance of focusing on developing conceptual consistency within the discussed emerging field of interest.

© 2013 IEEE. With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.

With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers. In this paper, we focus on the problem of recommending conference papers to the conference attendees. While most of the related existing methods depend on the content-based filtering, we propose a unified conference paper recommendation method named $CPRec$ , which exploits both the contents and the authorship information of the papers. In particular, besides the contents, we exploit the relationships between a user and the authors of a paper for recommendation. In our method, we extract several features for a user-paper pair from the citation network, the coauthor network, and the contents, respectively. In addition, we derive a user’s pairwise preference towards the conference papers from the user’s bookmarked papers in each conference. Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user’s preference towards a paper based on the extracted features. We conduct a recommendation performance evaluation using real-world data and the experimental results demonstrate the effectiveness of our proposed method.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially under practical PAPC. The existing solution to the considered problem is computationally inefficient due to employing high-complexity interior-point method. In this study, we propose two new low-complexity approaches to this important problem. More specifically, the first algorithm achieves the optimal solution by transforming the original problem in the broadcast channel into an equivalent problem in the multiple access channel, then the resulting problem is solved by alternating optimization together with successive convex approximation. We also derive a suboptimal solution based on machine learning to which simple linear regressions are applicable. The approaches are analyzed and validated extensively to demonstrate their superiors over the existing approach., Comment: 5 pages, 4 figures

International audience; As part of the Historic Photographic Paper Classification Challenge, a multitude of approaches to quantifying paper texture similarity have been developed. These approaches have yielded encouraging results when applied to very controlled datasets containing photomicrographs of familiar specimens. In this paper, we report on the k-nearest neighbors classification performance of two multiscale analysis-based texture similarity approaches when applied to a much larger reference collection of silver gelatin photographic papers. The clusters for this data set were derived from a visual sorting experiment conducted by art conservators and paper experts later extended through crowd-sourcing. The results show that these texture similarity approaches, when combined with a simple k-nearest neighbors classification algorithm, yield workable performances with accuracy of up to 69%. We discuss this outcome in the context of available data and the cross-validation procedure used, then provide suggestions for improvement.

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.

Usability and the use of automated static analysis tools in the software development process have been an evolving subject of research in the last decades. Several studies shed light on issues like high false positive rates and low comprehensibility, which hinder tool adoption for even software engineers. Yet, the tools' perceived usefulness and ease of use play a much larger role when it comes to untrained software developers, as is usually the case in scientific software development. In this paper, we outline a multi-stage interview study to learn more about how scientists come to accept and use static analysis tools.

In this paper, we work on analyzing old paper advertisement (Ad). An Ad usually contains various types of textual and non-textual objects, which may also be in different orientations. We attempt to detect such objects from an early Indian-print paper Ad database comprising 1500 Ad images. The past major object detectors did not perform well on this database. We propose a deep reinforcement learning-based orientation-aware object detector. Our system learns by itself where to look and what to look of an Ad image. Therefore, it can bypass the impeding zone due to degraded image quality. To find the looking spot, we come up with a foveal transformation. In reinforcement learning, we present a scheme for shaping an internal reward with a top-up. For oriented object detection, we also propose a generic loss function. Our system obtained encouraging results from the experiments performed on the Ad database.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

Visible light communication (VLC) is a promising solution to the current congestion in radio frequency (RF) spectrum. It achieves that by exploiting the huge unregulated visible light portion of the electromagnetic spectrum in order to enable high-speed short range wireless communications, as well as, providing an sufficient lighting. This new solution is envisioned to provide a considerably wider bandwidth that can accommodate ubiquitous broadband connectivity to indoor users and further offload data traffic from overloaded cellular networks. However, VLC suffers from several limitations, such as the limited modulation bandwidth of light-emitting diodes (LEDs) that degrades the overall system spectral efficiency. In this respect, several interesting solutions have been proposed in the recent literature to overcome this limitation, such as the implementation of efficient optical modulation and multipleinput-multiple-output (MIMO) schemes. In this paper, we investigate the performance of multiple active spatial modulation (MASM) integrated with orthogonal space time block codes (STBC) for indoor VLC systems. Additionally, in ordered to reduce the receiver complexity, a simplified version of the joint maximum likelihood (ML) detector is proposed which has a linear complexity with respect to the number of transmit LEDs and the constellation size. Extensive computer simulations demonstrate that STBC-MASM improves the overall system performance compared to MASM with a considerably simplified detection. acceptedVersion

With the increasing popularity of optimization algorithms in electromagnetic engineering, it is clear that mixed-variable design problems prevail. This paper shows that the Cross-Entropy (CE) optimization method is intrinsically versatile to handle these and other types of problems. We provide implementation details of two antenna examples optimized by the CE method to demonstrate its elegance and efficiency.

J.A. Moral-Munoz holds a FPU scholarship (AP2012-1789) from the Spanish Ministry of Education., Citation classics offer an outlook on those papers that have attracted great and historical interest by a research community and that could be also considered the basis of the research field. A new approach, which is called H-Classics, has been developed to identify such highly cited papers. It is based on the H-index and is sensitive to both the own characteristics of the corresponding research discipline and its evolution. The present study provides a useful insight into the development of the intelligent transport systems research field, revealing those scientific actors (authors, countries, and institutions) that have made the biggest research contribution to its development., Spanish Government AP2012-1789

This manuscript considers a secondary system sharing the spectrum with the primary system at the same time and on teh same frequency. We propose a cognitive overlay system in which the secondary system relays the primary signal and piggybacks its own data on it. Furthermore, the secondary system uses the channel coding based on the dirty paper coding (DPC). The result of the analysis shows that the proposed scheme allows the secondary system to communicate at the same speed as the primary system without any harm to it.

International audience; Texture characterization of photographic papers is likely to provide scholars with valuable information regarding artistic practices. Currently, texture assessment remains mostly based on visual and manual inspections, implying long repetitive tasks prone to inter- and even intra-observer variability. Automated texture characterization and classification procedures are thus important tasks in historical studies of large databases of photographic papers, likely to provide quantitative and reproducible assessments of texture matches. Such procedures may, for instance, produce vital information on photographic prints of uncertain origins. The hyperbolic wavelet transform, because it relies on the use of different dilation factor along the horizontal and vertical axes, permits to construct robust and meaningful multiscale and anisotropic representation of textures. In the present contribution, we explore how unsupervised clustering strategies can be complemented both to assess the significance of extracted clusters and the strength of the contribution of each texture to its associated cluster. Graph based filterbank strategies are notably investigated with the aim to produce small size significant clusters. These tools are illustrated at work on a large database of about 2500 exposed and non exposed photographic papers carefully assembled and documented by the MoMA and P. Messier's foundation. Results are commented and interpreted.

The F composite fading model was recently proposed as an accurate and tractable statistical model for the characterization of the simultaneous occurrence of multipath fading and shadowing conditions encountered in realistic wireless communication scenarios. In the present contribution we capitalize on the distinct properties of this composite model to derive the achievable capacity over F composite fading channels assuming fixed rate quality of service requirements. To this end, novel exact and tractable analytic expressions are derived for both the exact and the truncated channel inversion strategies. This also enables the derivation of additional simplified approximate and asymptotic expressions for these cases, which provide meaningful insights on the effect of fading conditions on the overall system performance. This is particularly useful in the context of numerous emerging wireless applications of interest that exhibit stringent fixed rate requirements such as vehicular communications, body area networks and telemedicine, among others. acceptedVersion

Software packages that support data analysis in a scientific research can be understood as a mean to assisting the researcher in the treatment, organization and analysis phases, with the main objective of collaborate to the development of research or projects. These tools stand out and are sought by allowing data analysis to be done in a variety of formats (audio, video, image, text) providing collaborative work (two or more research working on the same research project) and the searched data. Considering this reality this article aims to carry out a bibliographic survey in journals evaluated with the Qualis A1, about which software packages the scientific publications – especially in the Brazilian scenario - have chosen to explore in their scientific analyzes in the field of Education. This research presents itself as a qualitative study with its bibliographical and documentary analysis. The dimensions of this work are delimited in the understanding of the reality from the use of software that the researcher chooses to work in i) qualitative, quantitative or mixed, and ii) which software packages could be identified in a researched universe of 121 journals were consulted of which 13 were selected for this study. published

A novel approach is here presented to embed a low-priority data stream into a high-priority data stream for OFDM wireless communications. The main improvement with respect to conventional hierarchical modulations is that the low-priority data stream can be decoded from the received subcarrier symbols without any previous equalization in case of multipath fading channel. As a consequence, the expected performance is independent of the channel estimation method used to equalize the channel. The low-priority signal is superimposed on the high-priority symbols using a dirty paper coding technique. As a result of the analysis of the proposed method, several improvements to reduce the interference on the high-priority symbols are introduced. The performance of our scheme has been compared with that of a conventional hierarchical modulation using DVB-T system parameters, showing a considerable improvement for multipath fading channels.

© 2018 IEEE. During the secure boot process for a trusted execution environment, the processor must provide a chain of certificates to the remote client demonstrating that their secure container was established as specified. This certificate chain is rooted at the hardware manufacturer who is responsible for constructing chips according to the correct specification and provisioning them with key material. We consider a semi-honest manufacturer who is assumed to construct chips correctly, but may attempt to obtain knowledge of client private keys during the process. Using the RISC-V Rocket chip architecture as a base, we design, document, and implement an attested execution processor that does not require secure non-volatile memory, nor a private key explicitly assigned by the manufacturer. Instead, the processor derives its cryptographic identity from manufacturing variation measured by a Physical Unclonable Function (PUF). Software executed by a bootloader built into the processor transforms the PUF output into an elliptic curve key pair. The (re)generated private key is used to sign trusted portions of the boot image, and is immediately destroyed. The platform can therefore provide attestations about its state to remote clients. Reliability and security of PUF keys are ensured through the use of a trapdoor computational fuzzy extractor. We present detailed evaluation results for secure boot and attestation by a client of a Rocket chip implementation on a Xilinx Zynq 7000 FPGA.

Digital technology can make cities smart. City managements can make use of information that can be extracted from databases in which data is collected about energy consumption, traffic behavior, waste management, human behavior in public environments and opinions of the general public, for example as they can be collected from social media. But can this digital technology and the data collected from it help to make living in a city more enjoyable? How can digital technology and information provided by digital technology make cities playful, allow citizens to engage in playful and entertaining activities that help to enjoy their daily and sometimes boring activities such as travelling, working, child care, house-keeping? This paper investigates how sensors and actuators in an urban environment can be introduced and used to design playful applications. We observe how ideas about playable cities emerge and develop. Interactive digital installations can provide fun, urban games can provide fun, but the experiences obtained from them can as well provide urban designers, architects, gamification and public media designers about public behavior and preferences.

International audience; Worst-case delay analysis of real-time networks is mandatory, since distributed real-time applications require bounded end-to-end delays. Switched Ethernet technologies have become popular solutions in the context of real-time systems. Several approaches, based on Network Calculus, trajectories, ..., have been proposed for the worst-case analysis of such technologies. They compute pessimistic upper bounds of end-toend delays. Since this pessimism leads to an over-dimensioning of the network, it is important to quantify the pessimism of the computed upper bounds. In this paper, we propose such a pessimism analysis, based on Network Calculus. In a first step we focus on avionics switched Ethernet network (AFDX) with Fixed Priority/First In First Out (FP/FIFO) scheduling.

An institutional repository is a system for archiving research outputs such as scholarly papers. In order to increase the number of archived papers in institutional repositories, the registration process controlled by repository managers should be improved. The authors of this paper conducted an investigation on the efforts involved in paper registration with institutional repositories in several universities. The paper shows the result as a formalized workflow of the process. The investigation showed that one of the hardest tasks for repository managers is confirmation of the copyright policy of publishers. The authors developed a system that automatically searches the copyright policy on external databases.

This paper introduces a system that realizes a simultaneous registration of scholarly papers for both a researcher database and a repository. An institutional repository is a method to realize free access to research outputs. However, the number of scholarly papers archived in institutional repositories is extremely small compared to the papers practically produced by researchers. The authors considered that an obstacle to increase the number is the efforts of researchers to register their papers to institutional repositories, and they developed a system that realizes a simultaneous registration of papers to the researcher database and the institutional repository in their university. This paper introduces the main idea of the system.

We consider the problem of matching the topics of a scientific paper with those of possible publication venues for that paper. While every researcher knows the few top-level venues for his specific fields of interest, a venue recommendation system may be a significant aid when starting to explore a new research field. We propose a venue recommendation system which requires only title and abstract, differently from previous works which require full-text and reference list: hence, our system can be used even in the early stages of the authoring process and greatly simplifies the building and maintenance of the knowledge base necessary for generating meaningful recommendations. We assessed our proposal using a standard metric on a dataset of more than 58000 papers: the results show that our method provides recommendations whose quality is aligned with previous works, while requiring much less information from both the paper and the knowledge base.

The typical pricing policy, offered by service providers, is on a pay-as-you-go basis, which is proportional to the resources consumed by services. However, providers do not expose the specification of the resource consumption of services. Thus, providers offer cost pre-calculators that are service-agnostic. Such cost pre-calculators take into account upper bounds of provided facilities, instead of an estimation of the resource consumption of a service. To help service providers in specifying the resource consumption of services, we propose an initial version of an automated approach that captures the resource consumption of services. To make it in an independent way of the used measurement units and of the infrastructure over which a service is hosted, our approach builds for a service a mathematical model of computation, estimating its computational performance that is directly related to its resource consumption. Our approach moves beyond the state-of-the-art, since it automatically builds a model of computation based on the interface of a service, instead of its source code, and based on the monitoring of the execution of a service by using its executable files, instead of the monitoring or the theoretical analysis of its source code. The evaluation of our approach in a set of basic case studies shows promising results.

The paper introduces a Web system which supports registration of scholarly papers to institutional repositories. Institutional repository is an important service for academic societies, however the number of archived papers is not enough in most of institutions. The authors of the paper developed a system that supports authors of papers (that is, researchers) and managers of repositories in registration of papers. By connecting with external databases of published research papers, the system urges researchers register their papers to repository. Additionally, the system automatically searches the copyright policy for each paper also using external databases. This paper makes clear the problem of current institutional repositories, and then describes the detailed idea of the developed system as a solution. The system is expected to decrease the efforts of researchers and managers for paper registration, and increase the number of archived papers in institutional repositories thereby.

Digital pen and paper technologies provide the basis for linking digital content and services to printed materials in the form of interactive paper publications. To realise the potential of these technologies, it is important to develop platforms and tools that can support the large-scale publishing of interactive paper documents. We show how an extensible content management system that was developed to support context-aware publishing was used for the production of interactive paper documents. The publishing process consists of two phases and requires one channel to support the production of the document together with cross-media link definitions and a second channel to support interaction with the document.

In this paper the selection of suitable content-based features for the reconstruction of shredded notebook paper is examined. An algorithm for the detection of squared paper is proposed, based on the Hough transform. Experimental results show a good performance of the algorithm.

Scalable video coding is an important mechanism to provide different types of end-user devices with different versions of the same encoded bitstream. However, scalable video encoding remains a computationally expensive operation. To decrease the complexity we propose generic techniques. These techniques can also be combined with existing fast mode decision modes. We show that extending these existing techniques yield an average complexity reduction of 87%.

Real-time applications, hard or soft, are raising the challenge of unpredictability. This is an extremely difficult problem in the context of modern, dynamic, multiprocessor platforms which, while providing potentially high performance, make the task of timing prediction extremely difficult. Also, with the growing software content in embedded systems and the diffusion of highly programmable and re-configurable platforms, software is given an unprecedented degree of control on resource utilization. The 2PARMA project aims at overcoming the lack of parallel programming models and run-time resource management techniques to exploit the features of many-core processor architectures. The main goals of the 2PARMA project are: the definition of a parallel programming model combining component-based and single-instruction multiple-thread approaches, instruction set virtualisation based on portable byte-code, run-time resource management policies and mechanisms as well as design space exploration methodologies for Many-core computing architectures.

Published version

The main purpose of this paper is to present the methodology for calculating the electromagnetic behaviour of real integrated circuit (IC) parts using a no-mesh local Finite Differential Method (FDM). Furthermore, the comparison of computational results and measurements is presented. All considerations are based on typical long transmission lines (TLs) in modern ICs. The obtained results have been analysed in detail and compared with measured values. The measurement data are de-embedded using the test board model. This problem is illustrated in this paper based on a practical example of the Multi-Conductor Transmission Lines test structure whose electrical responses to various excitations are presented and analysed in detail.

Delivering confidential messages in wireless network is gaining continuous attraction as a fundamental building block for the Internet of things (IoT). This paper overviews the confidential message transmission techniques in broadcast channel (BC) where a transmitter serves multiple users by delivering confidential messages to their intended users. Each confidential message must be prevented from extracting its information by any unintended users. First, we consider a basic network consisting of two users. In the two-user BC, the secret dirty paper coding (S-DPC) achieves the secrecy capacity region. However, since the DPC requires a high computational complexity for characterizing the capacity region, two sub-optimal transmission techniques with low complexities are explained. Second, we extend our discussion to the network including more than two users. For $K$ -user BC with confidential messages, the optimal transmission scheme and the secrecy capacity region have not yet been derived. Instead of presenting the optimal scheme, we explain two sub-optimal transmission techniques and compare their features and performances. In conclusion, we discuss the open problems in the confidential message transmission for networks with more than two users.

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.