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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.

The exchange of academic marks between HEIs (Higher Education Institutions) is mandatory in every student mobility programs (i.e. the EU Erasmus Program) but that process remains to present date with insufficient technological support and the absence of a comprehensive reference model that allows the integration of potential technological solutions for the exchange of academic data with existing Academic Information Systems seems to limit greatly the possibility of adopting solutions of this type referred to in the existing literature. This work addresses this issue, conducting an initial bibliographic review aimed at the identification of the fundamental requirements of such an architecture as well as explores some of the technologies that are showing potential for usage in the safe exchange of academic results between partner HEIs, with particular interest in blockchain technology applied in an educational context. info:eu-repo/semantics/publishedVersion

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

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.

The modern HPC scientific software ecosystem is instrumental to the practice of science. However, software can only fulfill that role if it is readily usable. In this position paper, we discuss usability in the context of scientific software development, how usability engineering can be incorporated into current practice, and how software engineering research can help satisfy that objective.

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.

The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.

Replications are rare in visualization research, but if they were more common, it is not unreasonable to believe that they would show a similar rate of unreproducible results as in the psychological and social sciences. While a replication crisis in visualization research would be a helpful wake-up call, examining and correcting the underlying problems in many studies is ultimately more productive.In this paper, we survey the state of replication in visualization research. We examine six threats to the validity of studies in visualization and suggest ways to address them. Finally, we describe possible models for publishing replications that satisfy the novelty criterion that can keep replications from being accepted.

There has been an increase in the interest in blockchain technologies. While the most popular application of this technology is in the cryptocurrency sector in the developed world, recent studies have shown that they hold promise toward deployment in many sectors such as digital contracts and healthcare in the developing world. This article discusses the various use cases of the blockchain technology, particularly from the point of view of developing countries in general and for India in particular.

Artificial Intelligence (AI), and digital technology in general, has the potential to transform healthcare in every setting, and even more in the developing world, where the current healthcare system simply cannot scale to the growing masses, often in more remote locations. Digital technology— mobile phones, remote sensing, and the Internet—can help improve quality of healthcare delivery, reduce cost of healthcare systems, and improve accessibility. However, in addition to AI, there is the need for an appropriate business model for solving healthcare problems. Digital technologies also have the potential to deliver preventative care, focusing on wellness, in addition to personalized disease treatment. In that context, mobile health platforms are more versatile because they can monitor events at a finer granularity and are often used by “healthy” individuals to track their daily fitness-centric activities. Further, molecular data is available at unprecedented levels and encompass different aspects of the “being”, be in genomic, epigenomic, proteomic, or metagenomic datasets, in other words offering a holistic view of the individual. This can then be integrated with fine-grained physiological signals obtained through wearables. Thus, there are volumes of datasets that need to be annotated, often in multiple formats, offering a multi-dimensional view of the healthy or diseased human. In fact, the human reference genome should have multiple representations based on the race, ethnicity, and the general well-being of the human. This calls for monitoring healthy people at scale, not just diseased people so we get a better understanding of the “ground truth” for the healthy human being. Overall, AI can therefore be thought of as augmented intelligence that can leverage both better observational and interventional capabilities for delivering precision healthcare and augmented monitoring capabilities to the masses at scale.

The formulation of good academic paper titles in English is challenging for intermediate English authors (particularly students). This is because such authors are not aware of the type of titles that are generally in use. We aim to realize a support system for formulating more effective English titles for intermediate English and beginner authors. This study develops an extractive title generation system that formulates titles from keywords extracted from an abstract. Moreover, we realize a title evaluation model that can evaluate the appropriateness of paper titles. We train the model with titles of top-conference papers by using BERT. This paper describes the training data, implementation, and experimental results. The results show that our evaluation model can identify top-conference titles more effectively than intermediate English and beginner students., 6 pages, 3 figures, 4 tables, 2 algorithms

Our understanding of COVID-19 pandemic epidemiology has many gaps, with many challenges arising on a global scale. This paper looks at the problem at a smaller geographical scale, the extent of the campus of a large organization. Equipped with an asymptomatic testing program and rough location data from the campus wireless network, we make the case that epidemiological models may be informed from this new source of data, which offers fidelity at the temporal resolution of seconds and spatial resolution of a Wi-Fi cell size, in particular for the tasks of pinpointing clusters of cases and contexts of infection transmission. We sketch the design of a system that fuses the two foregoing information streams and explain how the result can be incorporated into standard epidemiological models of communicable disease, both for better parameter estimation in elementary models, as well as for providing spatial inputs into more sophisticated models. We conclude with logistical and privacy considerations we have encountered in an associated ongoing study, to inform similar efforts at other organizations.

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.

This paper focuses on the potential and practice of alternative forms of assessment to the conventional written exam. Readers will be interested in issues such as why would a staff member want to replace the written exam and how can I be sure that the replacement is equally effective at capturing student attainment. The paper summarises a trial at the University of Sheffield where the written exam was replaced by a computer quiz which was marked automatically.

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

In the era of emerging Smart Built Environments (SBEs), a smart house, unlike regular houses with static "components", consists of numerous interconnected and often actuated devices, capable of executing tasks independent of user supervision. Living in such a SBE, where for example, the furniture can rearrange itself, and the doors open and close of their own volition, may be difficult and unpredictable. Furthermore, cyber-security attacks and intrusion could allow attackers to assume control of the SBE, damage its components and to potentially harm its inhabitants. Such novel characteristics of SBEs present developers with several unique challenges with regards to implementing the needed safety and security measures and protocols that go along with them. With such environments, therefore, there is a need for a system that is capable of monitoring user activities in real-time, identifying the safety and security hazards to users in their immediate local context, warning users of these hazards, and perhaps even taking preventative and mitigative action against the hazards that it identified. In this paper, we survey some of these challenges and explore the design and implementation of a system designed around the safety and security of SBE inhabitants. We propose an approach to modeling SBE safety that combines the three laws of robotics and the swarm behavior model. We also present a preliminary prototype and discuss a case study.

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.

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. Preprint: https://arxiv.org/abs/2102.06251. Data and materials: https://doi.org/10.5281/zenodo.3996696.

The Panama Papers is a collection of 11.5 million leaked records that contain information for more than 214,488 offshore entities. This collection is growing rapidly as more leaked records become available online. In this paper, we present a work in progress on a web browser plugin that detects company names from the Panama Papers and alerts the user by means of unobtrusive visual cues. We matched a random sample of company names from the Public Works and Government Services Canada registry against the Panama Papers using three different string matching techniques. Monge-Elkan is found to provide the best matching results but at increased computational cost. Levenshtein-based approach is found to provide the best tradeoff between matching and computational cost, while Jacquard index like approach is found to be less sensitive to slight textual change.

Microfluidic engineering technology has been widely used for implementing lab-on-chip (LOC) type point of care (POC) devices since its origins in the 1990s. LOC devices offer some distinct advantages such as the possibility of reducing the quantity of valuable samples or reagents needed and the shortening of the detection times, both of which result from their compact structure [1]. Paper-based microfluidic devices, which are regarded as a low-cost alternative to conventional POC diagnostics tools, have also been popularly studied in the last few decades, and a huge number of advantages have been explored, such as low-cost, mass production, disposability, being equipment free etc. [2] However, some disadvantages or limitations are also apparent, such as issues with the control of flow rate, multiplexed detection on the same device footprint and further reduction of size [2]. As a result, 3D microfluidic paper analytical devices, as an alternative solution, have been proposed in recent years, which enable distribution of fluids in both lateral and vertical directions.

Development of dependable multi-/many-core systems requires assurance that the system is operable in a range of conditions, subjected to both functional and non-functional requirements. To achieve this, tools need to be implemented that can enable exploration of design options and be able to detect deficiencies earlier to avoid costly system re-design. In this work we discuss the challenges of design of multi-core realtime systems with timing assurance and discuss what are the requirements for modelling, testing and analysis tools. Digital Twin-based predictive modelling and fast design space evaluation are studied that work toward addressing these challenges.

The recognition of the development stage of creepage discharge of oil-paper insulation under AC-DC combined voltage is the basis of fault monitoring and diagnosis of converter transformers, and there are few related studies. In this paper, the AC-DC combined voltage with a ratio of 1:1 was used to study the development stage recognition method of creepage discharge of oil-paper insulation under the cylinder-plate electrode structure. Firstly, the pulse current method was used to collect the discharge signals in the process of creepage discharge development. Finally, based on the online sequential extreme learning machine (OS-ELM), the above characteristic parameters were used to recognize the development stage of oilpaper insulation creepage discharge. The research results show that when the size of the sample training set in the OS-ELM algorithm is closed to the number of hidden layer neurons, higher recognition accuracy can be obtained, and the type of activation function has less influence on it. Based on the OS-ELM algorithm, the developmental stage of the creepage discharge is recognized. The development process of the creepage discharge is recognized as four stages, which is the same as the trend of the characteristic parameters of the whole creepage discharge development process, and the recognition accuracy is 91.4%. The algorithm has fast rate, high accuracy and batch training data characteristics, which can be widely used in the field of online monitoring and evaluation of electrical equipment status.

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time., 12 pages, 3 figures

We consider the problem of communicating over a channel that randomly "tears" the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\rm Geometric}(p_n)$, we characterize the capacity as $C = e^{-\alpha}$, where $\alpha = \lim_{n\to\infty} p_n \log n$. Our results show that the case of ${\rm Geometric}(p_n)$-length fragments and the case of deterministic length-$(1/p_n)$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity.

Distributed smart and connected community applications of the present and future will continuously sense perishable data on which they make decisions. We say the data are perishable when they represent information sensed at a past point in time, possibly at a distant part of the distributed application, and for which the ground truth could have since changed. This paper discusses actions the sensing source, the network, and the controller can take to make the best possible decisions given that we know only a delayed version of the truth. Some of these actions to preserve freshness of data are based on known real-time techniques such as Time Sensitive Networking. To these we add Information Theory applied on a message-by-message basis and cross-layer coordination between content and network handling. Both the unexpectedness (surprisal) and timeliness (freshness) properties of perishable information can be encoded using a Shannon information entropy framework. Examples of smart community systems of the future for which these concepts are relevant are given.

Literature analysis facilitates researchers better understanding the development of science and technology. The conventional literature analysis focuses on the topics, authors, abstracts, keywords, references, etc., and rarely pays attention to the content of papers. In the field of machine learning, the involved methods (M) and datasets (D) are key information in papers. The extraction and mining of M and D are useful for discipline analysis and algorithm recommendation. In this paper, we propose a novel entity recognition model, called MDER, and constructe datasets from the papers of the PAKDD conferences (2009-2019). Some preliminary experiments are conducted to assess the extraction performance and the mining results are visualized.

This paper considers a cell-free massive MIMO system with multiple-antenna access points and single-antenna users. The APs use conjugate beamforming to beamform the data to all users in the network. Total energy efficiency maximization is investigated. This optimization problem is nonconvex and thus difficult to solve. Existing solutions are based on second-order optimization methods in connection with convex approximations. These methods have been shown to perform very well but their complexity does not scale favorably with the network size. To tackle this issue, in this paper we propose to use a first-order method for nonconvex programming to our energy efficiency problem. Compared to the second-order methods, the proposed method achieves the same performance, while its run time is much faster. Thus, it is considered as a feasible solution for resource allocation in cell-free massive MIMO systems.

Gamification is one of the trends in education that is best suited to the current learning environment. Putra BINGO is a transformative assessment tool that uses gamification techniques. This research starts by elaborating the concept of Putra BINGO and its implementation using traditional method with pen and paper. The feedbacks gathered from the implementation are analyzed and applied to develop a multiplayer educational online game using Unity Game Engine. This paper describes details of the development for its two components; the database and the digital online game. Putra Bingo is available on PC and Android devices. It required additional plugins such as Photon Networking to handle game communication between multiple players. This is crucial to accommodate its test case application as an alternative assessment for teamwork skills using Rubrics and questionnaires. Further research will be conducted to enhance its functionality and user experience after the initial development of the digital online version is completed.

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

Privacy of the individual data, especially in the Health data, is very sensitive and important. Privacy preserving Machine learning is emerging as one of the solutions for the security of data with the utility to create knowledge. In this paper, we have proposed a differential private artificial neural network (DP-ANN) and shows its application to predict the spread and the peak number of COVID-19 cases. We proposed a differential private artificial neural network (DP-ANN) in which laplacian noise has been introduced at activation function level and it has been compared with existing privacy ideas at error function and weights level of ANN. Results show that DP-ANN model with the private activation function produces the result similar to the base ANN model.

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly available via commercial cloud providers, which currently allocate devices on a per-user basis. As the underlying hardware is often underutilized, several proposals for multi-tenant use of FPGA resources have been brought forth, along with some initial work on security attacks in this setting. Simultaneously, high-end FPGAs are being produced with 2.5D integration of multiple distinct dies, called Super Logic Regions (SLRs), onto the same chip. Although one might expect that physical separation of logic onto separate dies could prevent multi-tenant attacks, this paper demonstrates for the first time that cross-SLR information leaks based on sensing voltage changes within the FPGA chip are possible, without physical access to or modification of the boards. The cross-SLR covert channel is characterized analytically and experimentally on five Xilinx Virtex UltraScale+ FPGAs, both locally and on the Amazon and Huawei clouds. Several configurations of the source transmitters and the sink receivers are tested, including their locations, types, and sizes. The power-based channel is shown to have a bandwidth upwards of 4.6 Mbps and accuracy of over 97.6%. Consequently, as physical separation of tenants onto separate dies (SLRs) is an insufficient countermeasure against information leaks, hardware-level architectural improvements are necessary to make secure multi-tenant FPGAs on shared clouds a reality.

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation.

In this paper we propose a statistical approach for the optimization of the phased codes used in radar. By considering each code as a realization of a random variable we show that we can build good codes if the distribution of the random variable is suitably chosen. We then consider sequences generated by the skew tent map and we show that for some values of the bifurcation parameter the invariant probability density coincides with the desired one. To confirm this idea we compute the maximal peak of the ambiguity function outside zero relative to the bifurcation parameter of the skew tent map.

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

We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning. In particular, we report the first experimental demonstration of a BRNN auto-encoder, highlighting the performance improvement achieved with recurrent processing for communication over dispersive nonlinear channels.

Visible light communication (VLC) is considered a breakthrough wireless communication technology that has been proven capable of achieving very high data rates. This is a key advantage in indoor communication scenarios, since the vast majority of wireless traffic is witnessed in indoor communications. In every wireless system, the trade-off between achievable throughput, transmit signal power and corresponding error rate performance is largely dependent upon the considered modulation format. This is also the case in VLC systems, which are typically characterized by stringent performance requirements. Motivated by this, in the present contribution we introduce the space shift keying (SSK) modulation scheme in the context of non-orthogonal-multiple-access (NOMA) communications, which have been shown to be a performance enhancer of indoor based VLC systems. Based on this, all network users in the considered set up receive the same superimposed signal of all NOMA users, which is transmitted from the activated transmitters corresponding to the multiplexed SSK users information. Based on this and assuming a unique maximum likelihood detection, we quantity the system performance in terms of the corresponding bit error rate (BER) performance at each receiver. This analysis leads to the development of useful insights of theoretical and practical interest, which are expected to be useful in the effective design, implementation and deployment of SSK in NOMA based VLC systems. acceptedVersion

A general dynamic resource allocation scheme is introduced, which iteratively reconfigures an elastic optical network according to traffic dynamism and service diversity. As an application example, the scheme is employed for impairment-aware power-efficient resource allocation in a short-haul metro elastic optical network to demonstrate a typical 5% power plus service penalty for compensating bandwidth limitations compared with the corresponding limitless counterpart.

Cyber Physical Systems (CPSs) refer to a new generation of embedded ICT systems (PCs, smartphones, sensors, actuators, etc.) that are interconnected and collaborating to provide users with a wide range of innovative applications and services. Many application domains, e.g., Emergency management, factories of the future, personalized healthcare, just to name a few, require the definition, design and development of systems able to carry out complex processes that coordinate the services offered by the CPS in the "physical" real world. The physical world, however, is not entirely predictable, and such processes must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this paper, we tackle the above issue and we propose an approach and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events.

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.

This study utilizes citation analysis and automated topic analysis of papers published in International Conference on Agile Software Development (XP) from 2002 to 2018. We collected data from Scopus database, finding 789 XP papers. We performed topic and trend analysis with R/RStudio utilizing the text mining approach, and used MS Excel for the quantitative analysis of the data. The results show that the first five years of XP conference cover nearly 40% of papers published until now and almost 62% of the XP papers are cited at least once. Mining of XP conference paper titles and abstracts result in these hot research topics: “Coordination”, “Technical Debt”, “Teamwork”, “Startups” and “Agile Practices”, thus strongly focusing on practical issues. The results also highlight the most influential researchers and institutions. The approach applied in this study can be extended to other software engineering venues and applied to large-scale studies.

In an ongoing discussion comparing the security properties of electronic and paper voting, decreased privacy is often presented as an argument against remote Internet voting. We contribute to this discussion by presenting a side-channel attack against the physical environment of traditional paper-based elections. More precisely, we build a device based on an Arduino development board and cheap electret microphones, capable of triangulating the locations of marks made on wooden tables with high precision. In the best configuration, we are able to determine the correct cell having dimensions 4×5 cm with more than 90% accuracy. This will allow breaching privacy of ballot sheet designs that rely on the voter marking her choice(s) between a potentially high number of candidates printed on one large sheet. We complement our attack with a study on various aspects of deployment of facial recognition. This gives rise to the setup where the attacker installs cameras in the polling stations, aiming at automated detection of people leaving the voting booths. Combining the two approaches, we will have a completely automated (and hence relatively well scalable) attack against the privacy of paper-based voting.