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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.

The NIST selection process for standardizing Post-Quantum Cryptography Mechanisms is currently running. Many papers already studied their theoretical security, but the resistance in deployed device has not been much investigated so far. In particular, fault attack is a serious threat for algorithms implemented in embedded devices. One particularly powerful technique is to use safe-error attacks. Such attacks exploit the fact that a specific fault may or may not lead to a faulty output depending on a secret value. In this paper, we investigate the resistance of various Post-Quantum candidates algorithms against such attacks.

The industry of disposables has undergone a huge revolution in this century owing to the busy lifestyles and technological advancements in its production and manufacturing process. Paper cups are one of the widely used products in disposable industry. It has multi utility options that is it can be used to drink water as well as beverages, soups, liquids etc. This paper entirely focuses on increasing the quality factor of production and manufacturing process of these paper cups by adding additional circuitry to the existing machinery to reduce waste of raw materials as well as the per unit cost involved in wastage of electricity. This research work is based on using proximity sensors to sense the materials and molded paper cups infront of machinery and reduce the electric wastage through a relay Mechanism assembly. This is a PLC based live project example of effectively increasing the overall efficiency of the manufacturing plant

For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.

Electromagnetic Fault Injection (EMFI) is a well known method of introducing faults for security analysis of digital devices. Such faults can be seen as analogous to the faults which are known to naturally occur in digital devices, a known problem with designing safety-critical systems.Numerous standards have been developed for safety-critical systems, including the development of standards for increasing the rate of naturally occurring faults using particle sources. In this work, we demonstrate that desktop EMFI tooling can be used to accomplish similar testing, but with more control, effectively speeding up the evaluation process.We demonstrate using EMFI tooling for safety evaluation to recreate a highly publicized safety issue present in an automotive ECU – one that could not easily be recreated with other techniques.

Because the traditional insulation paper state detection method has some shortcomings, this paper proposes a non-destructive detection method, which uses the texture feature value of insulation paper image to establish a relationship with the degree of polymerization (DP) of insulation paper state, and then indirectly estimates the aging state of insulation paper. Firstly, the insulation paper images in different aging states are collected, and the texture feature values of the images are extracted by gray histogram, gray level co-occurrence matrix and gray level run-length matrix; then, the texture feature values are selected by correlation; then, the validity of the selected texture feature values to represent the insulation paper state is verified by support vector machine (SVM); finally, the multiple linear regression method is used. The relationship between the degree of polymerization and the texture feature value is established and verified by the actual results. The results show that the texture feature value has a good fitting relationship with the degree of polymerization, which verifies the effectiveness of this method in evaluating the state of insulating paper.

The ageing status of the oil-paper insulation is closely related to the operation of the transformers. Recently, near infrared spectroscopy (NIRS) is used to identify the ageing condition of oil-paper insulation for it is a more rapid and nondestructive detection than the traditional viscometry method. However, the identification accuracy of existing quantitative analysis method of NIRS are still insufficient. This paper proposed an improved Linear discriminant analysis (LDA) method to classify the spectra and to identify the ageing condition of the insulating paper. Based on DP measurement, the insulating paper specimens prepared in laboratory are divided into varying ageing groups. The principle of LDA method is introduced. The classic LDA method has been applied to identify the ageing status of specimens respectively from 4 ageing groups and 3 ageing groups. The results show that LDA has a better performance in classifying four groups than three groups. To achieve an accurate ageing condition identification with balanced accuracy of the model and enough categories of ageing status, the improved LDA method is proposed. The identification results show that the improved method has satisfactory performance and indicates great application potentials in the ageing assessment of the oil-paper impregnated insulation.

Prediction and detection of skin diseases have always been a challenging and critical task for health care professionals. There are many clinics and skin practitioners who are offering their services at exorbitant costs. At the same time, skin condition prevails in our country in every part. In the current scenario majority of the skincare practitioners are using traditional methods to diagnose the disease which may take a considerable amount of time. Skin Diseases are serious problems in recent times as it is a matter of environmental factors, socioeconomic factors, lack of full diet, etc. This paper is a comparative study about understanding various skin diseases related to normal skin issues as well as cosmetology. A comparison between oily, dry, and normal skin is also projected in this study. A survey of different papers is done on basis of technologies used, results with accuracy, ethical conduct, number of diseases diagnosed, datasets, etc are done.

This paper describes approaches to the use of wearables and related monitoring and diagnostic tools to glean and present data to patients and their caregivers in the form of Personal Health Dashboard. Raw data from devices such as fitness watches, blood glucose and blood pressure monitors can be analyzed by machine learning algorithms ensembles over the cloud or at the edge, and the resulting patterns can be presented to allow for actionable responses. The advent of low-cost computational, sensing, monitoring and diagnostic tools allow for cost-effective solutions that can have wide adaptability. The paper draws from prior and ongoing research conducted by the team to present these positions in the advancement of healthcare.

Cryptography is the methodology in which the information is cast into a form that forestalls the abuse of it. In this advanced time of the wireless, it is imperative to guarantee the safe conveyance of the pertinent information which are otherwise exposed to disgusting goals. The procedure of transformation of plaintext to encode text is known as encryption and its converse is known as decryption. The three kinds of cryptography methods, viz., Asymmetric key, Symmetric-key, and Hash Functions. A huge segment of this contemporary method is the key; which is a specific component of extreme significance, as this is what is utilized while scrambling or descrambling the desired information. In this paper, we have compared the two techniques. Incipiently, Particle Swarm Optimization, a strategy of delivering keys to design a stream figure for text encryption. The tale methodology named Particle Swarm Optimization Key Generation Algorithm (PKGA) uses a character code table for encoding the keys. The essential ideal situation of this procedure is that it diminishes the quantity of keys to be fused and coursed. On the other hand is Ant Colony Optimization Techniques to produce keys for the encryption of the information. This entire philosophy depends on the conduct of the ants in their characteristic inquiry of food. The drawback of using Ant Colony Optimization is to downscale the number of keys to be utilized during encipherment when contrasted with different strategies of cryptography in presence. ACO would warrant the generation of such keys with utmost proficiency.

In this work, we describe the application of the ‘steerable pyramid’ (Portilla and Simoncelli, 2000), a high-dimensional texture descriptor, to the discrimination and classification of a large collection of photographic paper textures. This work is part of a broader effort to bring precise physical measurements to the characterization of photographic papers, and our intended audience includes photograph conservators, curators, and collectors. For our purposes, it is important both to enable fine-grained local comparisons of very similar textures and to identify salient groupings across the entire landscape of photographic papers. The steerable pyramid was developed with the explicit goal of synthesizing textures; as such, it is an overcomplete representation. Its high native dimensionality is an advantage for highly precise local comparisons of texture, as in nearest neighbor search; it is, however, a disadvantage for grouping tasks, which require a well-structured global topology. For grouping tasks, we describe a number of compression and grouping approaches designed to meet a variety of analytical needs. Finally, we validate the steerable pyramid descriptor numerically using ‘one versus all’ classification on an augmented data set that includes multiple samples per paper type.

This paper presents the main disadvantages of receipts printed on thermal paper, underlining the impact that BPA (component of thermal paper) has on the environment and human health. The conceptual solution proposes to use the near-field communication (NFC) protocol available in smartphones, integrating a virtual receipt printing device (called e-receipt) at the end of the contactless payment chain.

Paper-based digital microfluidic biochip (PB-DMFB) technology provides a promising solution to many biochemical applications. However, PB-DMFB manufacturing process may suffer from potential security threats. For example, both Trojan insertion and man-in-the-middle attack may affect the functionality of PB-DMFBs. To ensure the correct functionality of PB-DMFBs, we propose a watermarking scheme to hides information in the PB-DMFB layout, which allows users to check design integrity and authenticate the source of the PB-DMFB design. As a result, it serves as a first countermeasure against both Trojan insertion and man-in-the-middle attacks for PB-DMFBs.

The last decade has witnessed an impressive paradigm shift in practically all networking fields: from hardware-based appliances to software-based infrastructures. Network functions traditionally hard-coded in the network devices have been redesigned in software and deployed in virtualized networking scenarios, with impressive advantages in terms of management flexibility and upgradable capability. Still, at a time in which we strive for more and more network processing and computing resources, such a softwarization process risks to bring about a severe toll in terms of performance and energy consumption. We believe, and posit, that programmable hardware architectures tailored for supporting high performance network processing tasks may come to the rescue. With this position paper, after motivating our position, we further briefly review the past and present of programmable hardware for networking, and we discuss its possible evolution towards more comprehensive "higher layer" HW-accelerated processing tasks.

We propose the original automated information technology for transformation of paper archives of drawing and design documentation into electronic 3D model of an object for CALS and BIM ideologies of designing and manufacturing of an object. The technology assumes the full automated cycle of processing of projective kinds from initial paper drawings to electronic frame 3D model of an object. Original models of representation and algorithms of solving the given problem are proposed.

Basic repair and maintenance tasks of industrial machines are usually performed by local service personnel at the location of the component to be maintained. Complex maintenance tasks require an expert who travels to the customer to solve a specific problem. This process is time and cost intensive. Augmented Reality (AR) Remote Assistance offers means to support newcomers on site by guiding personnel through the maintenance process with novel visualizations. This paper describes an Augmented Reality based remote assistance tool for industrial purposes. We present the technology and our implementation approach, as well as the challenges and limitations we have encountered. The potential of the tool was evaluated and analyzed in an industry oriented use case for repairing an industrial PC. The results were compared with a maintenance procedure that was carried out using standard paper repair instructions. Our approach enabled the users to perform complex tasks faster and with fewer errors. Overall, the error rate decreased significantly due to the AR-based remote support. We also found that paper-based instructions are still more efficient in solving simple tasks.

Prologue: This paper is written after numerous focus-group meetings with several types of people/experts of many genders. Statements given under are not meant to be insulting any gender, but these are focus-group observations. If you do not like any such statements, we are sorry in that you have missed the points we are trying to convey. This paper addresses various perceptions on genders that concerns abstract entities such as various disciplines of studies. This paper defines genders for disciplines through historical biases and perceptions – e.g., birth, religion, language, region, brought up, to say a few; these were obtained thru’ several focus-group discussions. However, technologies, such as computers, the World Wide Web (WWW), Moodle, sensor networks, Internet of Things (IoT), Virtualization and Immersion technologies are constantly changing the nature of education and learning. As a consequence, and through a series of arguments, we establish that the genders of many popular disciplines are becoming feminine in nature. We also define two terms, namely, Gender Capital and Gender Capitalization, through which respectively one could possibly define and analyze ways and means by which the changing nature of genders of disciplines can be profitably exploited for the global goodness.

The security of software systems relies so heavily on the use of secrets, ranging from credentials, API keys, and tokens to secret keys for cryptographic security. This paper examines approaches for managing and handling secrets in software development life cycle, focusing on mobile applications where software must be distributed to devices and operate in an untrusted environment. This paper points out the shortcomings in activities related to secrets management and handling in mobile application development life cycle and outlines future directions in this area of research.

Microfluidics allows analyzing small volumes of samples, which makes it an ideal technology to build a point-of-care device. Microfluidic paper-based analytical devices (μPADs) have been used on multiple applications. Paper is promising since colorimetric assays could be read with the naked eyed or a cell phone camera. Here we systematically tested several possible readers for bio-assays that use as a base a μPADs biosensors. Specifically, we tested digital imaging of an assay that produces a change of colors, imaging of a fluorescence-based assay, and an automatic scanning based on an in-house Multispectral-image Reconstruction System for colorimetric assays. We used commercial tests, both chemical and enzyme-based tests; as well as an in house test that used the emerging technology of DNA aptamers. Similarly, we evaluated the advantages and disadvantages of each setup and demonstrate how adaptable, fast, and versatile μPADs analysis can be. s. Finally, we suggest points of consideration to develop commercial point-of-care applications based on μPADs.

The paper presents the research begin modelling and control of the paper drying. The researches have been generated by the investigations of the industrial paper drying plant. The paper is structured in four parts. In first part they are analysed the references of the drying process, modelling process and control process. The second part presents a detailed description of the drying process and industrial paper drying plant. In this part there are analysed the major operating problems of the plant. The third part represents a author's contribution of paper drying process modelling. The last part is destined to design of paper quality control system.

The purpose of this work is to explore the applicability and effectiveness of deep learning methods for the task------moves recognition in abstract of research paper. We firstly build a large corpus for moves recognition. Then we choose the traditional machine learning method SVM as a benchmark, and develop four moves recognition methods based on DNN, LSTM, Attention-BiLSTM and BERT. Finally, we design two groups of experiments with sample size 10,000 and 50,000 and then compare experimental results. The results show that most of the deep learning methods outperform the traditional machine learning method SVM especially in large-scale sample experiments, in which the BERT with a re-pre-trained model achieves the best results in both groups of experiments. Deep learning methods are proved applicable and effective for moves recognition in research paper abstracts.

Machine learning (ML) has increasingly been adopted in safety-critical systems such as autonomous vehicles (AVs) and industrial robotics. In these domains, reliability and safety are important considerations, and hence it is critical to ensure the resilience of ML systems to faults and errors. On the other hand, soft errors are becoming more frequent in commodity computer systems due to the effects of technology scaling and reduced supply voltages. Further, traditional solutions for masking hardware faults such as Triple-Modular Redundancy (TMR) are prohibitively expensive in terms of their energy and performance overheads. Therefore, there is a compelling need to ensure the resilience of ML applications to soft errors on commodity hardware platforms.We first experimentally assess the resilience of safety-critical ML applications to soft errors. We demonstrate through fault injection experiments that even a single bit flip due to a soft error can lead to misclassification in Deep Neural Network (DNN) applications deployed in AVs, leading to safety violations. However, not all the errors in an DNN will result in serve consequences such as safety violations, and hence it is sufficient to protect the DNN from the ones that do. Unfortunately, finding all possible errors that result in safety violations is a very compute intensive task. We propose BinFI, a fault injection approach that efficiently injects critical faults that are highly likely to result in safety violations, based on the unique properties of DNNs. Finally, we propose Ranger, an approach to protect DNNs from critical faults with minimal performance overheads and no accuracy loss. We will conclude by presenting some of our ongoing work, and the future challenges in this area.

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.

This paper describes a user identification method conducted across online social networks (OSNs) using information regarding friends’ locations, in contrast to a conventional method based on the similarity of two display names. This latter method encounters the problem of decreased identification accuracy if a user registers different display names across OSNs. Our proposed method aims at addressing this issue by utilizing information on friends’ locations. We convert location information extracted from an OSN to that of such geographic administrative units as country, state and city, and calculate the weighted occurrence frequency of a location pair on the basis of distance between the pair. This is based on the hypothesis that a friend list relatively rarely includes an account pair whose locations are extremely distant from one another. We calculate a similarity score between two accounts’ weighted occurrence frequencies for each geographic administrative unit. Finally, we identify a user on the basis of individual similarity scores or weighted average scores for a given geographic administrative unit. Evaluation experiments show that our proposed method improves performance over that of a conventional method (accuracy: 83.1%$\rightarrow$91.2%).

International audience; Internet of Things (IoT) is a network where several and various interconnected, smart objects continuously generate alarge amount of data transmitted over Internet. Several efforts and research works in IoT focused on hardware constraints, lowcost, application design, as well as good communication technologies. However, due to the intrinsic characteristics of diversity,heterogeneity, large-scale, dynamic and large volume of data generated and various IoT applications needs, using traditionaldatabase management mechanisms and analytics architectures is not generally suitable. Then, it is challenging to provideefficient IoT data storage and query processing mechanisms for satisfying IoT application needs. This paper identifies the mainspecifications and mechanisms of data and query management for IoT and classifies them. Moreover, this work presentsadvances on data and query management mechanisms on IoT, shows their advantages and limits and discusses the challengingopen research issues that need to be addressed for providing guidelines for further contributions.

UmpleOnline is a widely used modeling tool that allows practitioners to create class diagrams, state diagrams and several other model types textually, with the option to edit class diagrams graphically. In this paper we propose how UmpleOnline can be used as a tool to conduct modeling empirical studies. UmpleOnline would be instrumented to allow any of its 180,000 annual visitors to volunteer to participate in one of many studies that would be pre-loaded by any member of the modeling research community. Studies could focus on research questions relating to model quality, modeling strategies, modeling errors or many other things. UmpleOnline would be an ideal vehicle for such studies since it is zero-footprint and has demonstrated high usability.

In this article we are presenting a state of the art in the Artificial Intelligence (AI) and Robotics fields, so as to address the problem of autonomy and integration of autonomous heterogeneous systems for decision making autonomy. This state of the art is followed by the presentation of some avenues addressing the issue in order to lead the autonomy of heterogeneous robotic systems in the naval defence environment possible.

The industry of disposables has undergone an astronomically immense revolution in this century owing to the diligent lifestyles and technological advancements in its engenderment and manufacturing process. Paper cups are one of the widely used products in disposable industry. It has multi utility options that is it can be acclimated to imbibe water as well as beverages, soups, liquids etc. This paper entirely fixates on incrementing the quality factor of engenderment and manufacturing process of these paper cups by integrating adscititious circuitry to the subsisting machinery to truncate waste of raw materials as well as the per unit cost involved in wastage of electricity. There is a necessity to improvise the quality of overall production parameters using automation techniques. Replacing the whole machinery is a very high costing implementation hence economic feasibility of automation can be achieved by adding an additional circuitry and improving the overall perfoemance. This research work is predicated on utilizing proximity sensors to sense the materials and molded paper cups in front of machinery and minimize the electric wastage through a relay Mechanism assembly. This is a PLC predicated live project example of efficaciously incrementing the overall efficiency of the manufacturing plant

Facebook, Twitter, Instagram, and LinkedIn are all popular online social media sites these days. Everyone uses different social media platforms, from children to adults. The use of these social media applications is increasing, which leads to a rise in social media crime. Here is where the word “fake profiles” comes into play; these fake profiles are responsible for distributing misleading details about a particular user or attempting fraudulent activities with bad intentions. Many researchers have been carried in response to the issue of fake profiles that exist in any social media application. There is research available in different directions to get a real problem solution. This paper summarizes all of the research on fake profile identification in social media platforms. This paper gives survey of work, indicating whether the user profile is fake or real.

Paper dating analysis is an important research direction of document inspection, which is widely used in the inspection and identification of cultural relics and ancient books. This article focuses on the analysis of paper dating in ancient books, and proposes a non-destructive inspection and analysis method based on paper fiber texture images. Aiming at the global stacking morphology of paper fibers and the local features of specific types of fiber morphology, we propose a neural network-based hybrid texture feature extraction and representation method: On the one hand, we use convolutional networks to obtain global features of fiber texture; at the same time, we designed a method of extracting and representing local fiber morphological features based on the attention mechanism. By mixing the above two types of features, we realize the extraction and representation of paper fiber features. Furthermore, we use the GRU(Gate Recurrent Unit) model to establish a paper dating time series model and design a new loss function. In order to verify the method, this paper selects 36 domestic books published from 1950 to 2000, and uses the document checker VSC 6000 to collect paper texture images as a dataset, and verifies the effectiveness of the proposed method on this dataset. Experiments prove that the method proposed in this paper has achieved ideal results in paper dating analysis.

The dirty paper wiretap channel, also referred to as the Gaussian wiretap channel with noncausal channel state at the transmitter, plays an important role in wireless physical layer security. In this paper, the dirty paper wiretap channel is revisited by considering actions on the channel state, i.e., the transmitter generates an action sequence that affects the formation of the channel state. Lower and upper bounds on the secrecy capacity of this model are proposed, and numerical results indicate that the actions on the state help to increase the achievable secrecy rate of the dirty paper wiretap channel. The study of this paper offers a new option for enhancing the secrecy rates of the state-dependent wiretap channel models.

Paper-based digital microfluidic biochips (PB-DMFBs) provide a promising solution for microfluidic bioassays. Due to the low-cost substrate material and low demand for complicated manufacturing equipment, PB-DMFBs can be fabricated without foundry. On the flip side, convenience of fabrication allows PB-DMFBs to be fabricated everywhere, which makes it is difficult to manage production and distribution of IP (bioassays). As a result, PB-DMFBs are vulnerable to security threats. IP and its creator, the biocoders, may suffer from infringement. To ensure IP protection, in this paper, we proposed the first Digital Rights Management (DRM) scheme to protect IPs of PB-DMFBs from security threats. A chip-level synthesis algorithm is also presented to realize not only complex biochemical operations but also the demand of DRM.

Currently, racial oil blotting papers are counted by hand. However, it takes a long time and this is hard work. Hence, an automatic counting system is necessary. In order to achieve this, we developed a cheap system using a webcam. This system firstly captures one image of arranged papers by shifting. Secondly, the paper boundaries are obtained as straight lines by utilizing the proposed image processing. By counting the acquired lines, the system outputs the number of papers. However, the captured image has noises such as the change of illumination and shadows. They make the extraction of the accurate boundaries difficult. For the accurate extraction, optimal parameters of the image processing must be set. Since the prediction of the optimal parameters in advance is difficult, the proposed method adopts genetic algorithm (GA). Also, Hough transformation for the straight line detection is improved. By applying the proposed method, the accurate paper counting is possible. For the experiment, 14 images were captured in various environments using a general webcam. Then, the proposed method was compared to a latest related work. As a result, F-measure of each method was 0.995 and 0.945.

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.

Smart infrastructure demands resilient data storage, and emerging applications execute queries on this data over time. Typically, time-series databases serve these queries; however, cloud-based time-series storage can be prohibitively expensive. As smart devices proliferate, the amount of computing power and memory available in our connected infrastructure provides the opportunity to move resilient time-series data storage and analytics to the edge. This paper proposes time-series storage in a Distributed Hash Table (DHT), and a novel key-generation technique that provides time-indexed reads and writes for key-value pairs. Experimental results show this technique meets demands for smart infrastructure situations.

Virtual Reality (VR) and 360 film have caught the attention of audiences and content creators and emerged as a new media, however, the market penetration of VR and head mounted hardware has remained slow despite the availability of more affordable mobile options. This has resulted in some audiences turning to VR cinemas, festivals and out-of-home exhibitions. Creating affordable, scalable VR cinemas presents a number of challenges and many of the decisions taken in both developing and facilitating these curated exhibitions directly impact audience's reception of spatial audio soundtracks. This workshop position paper looks to discuss the potential issues and future solutions in the use of current synchronous exhibition applications, the competing formats, standardised Head Related Transfer Functions, headphone build/colourisation, and the on-boarding process.

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 order to realize smart factory which is appeared in the concepts of Society 5.0 and Industry 4.0, wireless Internet of Things (IoT) becomes a key technology. Inside of the factory, there are heavy DATA traffics exist between moving/movable machine tools/robots/automated guided vehicle (AGV) with wireless access point (AP) and high speed moving/rotating objects/materials with wireless sensor node (SN). Currently, wireless systems used for wireless IoT are limited such as wireless-LAN/Bluetooth/RFID. Since these system’s frequency bands are limited and narrow, the interference between these systems becomes severe. In this paper, we propose a simplified beam forming wireless IoT communication using novel Wi-Fi backscatter system to notify the existence of SN who wants to send its DATA to AP. For Wi-Fi AP with beamforming function, knowing the numbers of existing SNs in each beam area will be a help for efficient beam control. This paper describes the Wi-Fi backscatter system in which the SNs backscatter the Wi-Fi signal transmitted from the AP then the AP detects the backscatter signals from SNs and know the existing SN’s number in its beam area. Since this backscatter system uses Wi-Fi modulated signal, the separation of SNs’ backscatter signals from AP’s transmitting Wi-Fi signal becomes complicated. This paper also describes the transceiver architecture of AP which reduces the complexity of signal processing and improves the dynamic range of reception.

Anstract- While wireless technologies are common solutions to the last mile problem for IoT systems, challenges exist with devices that are in remote locations, sparse deployments, have power and cost constraints, and low data applications as the cost of wireless and the supporting infrastructure presents barriers to entry. This work presents a last mile solution relying on smartphones to bridge the gap between the data generation device and the internet. A unique, low-power solution is presented based on QR codes and e-paper displays that allows a user's smartphone to carry the power and processing burden for data transmission. Utilizing the persistent nature of e-paper displays with low power requirements, QR codes can efficiently communicate data with minimal power impacts to a device's power budget and create large communication windows for user data pickup, leveraging the power of the smartphone without the need for extensive user training and costly infrastructure.

In the last several years, we published several papers to address the problem of Face Identification. The techniques employed in those articles were implemented in transform domains. The Discrete Cosine (DCT) and the Discrete Wavelet (DWT) Transforms were utilized, either combined or individually, to extract features which form the final model for each participant in a given dataset. In this paper, we highlight significant parts of our previous works in order to give a fair comparison among all approaches. The results included here are for the following datasets: ORL, YALE, FERET, FEI, Georgia Tech, and Cropped AR. Features are DWT, DCT, energy-based selected DCT-DWT, and combined DCT-DWT coefficients while the classifier is Euclidean distance, either squared or with power of one.

Nowadays, the usage of smartphones keeps increasing day by day and it is also can be used effectively to increase the computational power and the security along with search and rescue. To maintain the attendance records, several organizations are depending on paper-based attendance. Manually recorded attendance for the employees has produced problems that related to information accuracy and workforce performance efficiency. In this paper, reviewed and discussed various methods, applications and systems that can be used by the employer to monitor their staff attendance using fingerprint, Iris, RFID and GPS. The outcome of this survey is getting knowledge on various techniques that used to record and monitor staff’s attendance.

Even current OCR technology has made significant progress with the help of deep learning, rapid identification algorithms for specific information in documents still need elaborate design. In this paper, a lightweight operating framework based on deep neural network is established to deal with the extraction of date and time information from paper worksheets used in transformer station. In the framework, YOLOv4-Tiny is employed to detect the locations of the dates and the time in document images which are mixed with printing and handwritten fonts. Since the handwritten dates in pictures are difficult to segment, a novel lightweight CNN named MobileDateNet is proposed that can recognize the date serially without segmentation. The experimental results show that the identification accuracy of the whole framework reaches 92.1% at the average running speed of 36.5 FPS on an NVIDIA 3090 GPU, ensuring the recognition accuracy and the operating efficiency.

Cloud computing is a new paradigm. In recent years, cloud computing has been a popular model for hosting and providing services, as it has gained great popularity in providing multiple services such as storage and cloud hosting. Moreover, the cloud provides a powerful computing platform that enables many individuals and organizations to perform many tasks such as: use of online storage space, approval on business applications, developing custom computer software, and creating a virtual network environment. In previous years, the number of people using cloud services increased dramatically and a lot of data was stored in cloud computing environments. Meanwhile, data breaches of cloud services are also increasing every year due to hackers who are always trying to exploit security holes in the cloud architecture, so the security problems in these systems are horrific because they store a lot of private and confidential data. In this paper, the authors review security and privacy issues, bottlenecks associated with cloud computing, and the specific security issues in cloud computing layers and vulnerabilities, and how to avoid and control them. This paper helps the reader understand the problems related to cloud computing.

China’s packaging industry has formed a packaging big data system from three aspects: implementing information engineering, building a collaborative symbiosis network platform of packaging information resources, and building an industrial chain knowledge map centered on product chain. Packaging big data visualization design uses big data information acquisition, hierarchical division and management, problem and audience, visual transformation and other thinking modes, and adopts text information visualization, multi-dimensional information visualization, hierarchical relationship visualization and other technologies to realize the visual presentation of packaging industry data information. This paper analyzes the network data, extracts the keywords related to the key elements, and then analyzes the construction method of product paper packaging container automation system based on the keywords extraction algorithm. On the one hand, it emphasizes the importance of big data in online shopping commodity packaging design; on the other hand, it explores new ideas and methods of online shopping commodity packaging design through the application of big data in online shopping commodity packaging design.

As a material, cellulose/paper has attracted significant attention for its fibrous and bendable properties. Paper is a renewable resource with the most common forms coming from trees. This brief article describes efforts to create electronics "on" and "in" paper. Examples of electronics on paper include touch sensors and cold plasma-based sanitizers. The skin-like touch sensors use capacitance and patterned resistive networks for passive, scalable sensing with a reduced number of interconnects. When touched or wetted with water, the interdigitated electrodes in the resistive networks detect significant changes in electrical impedance. The plasma generators with layered and patterned sheets of paper use a simple and flexible format for dielectric barrier discharge to create atmospheric plasma without an applied vacuum. Examples of electronics in paper include sheets with tunable electrical conductivity and piezoresistive force sensors, along with functionalized paper-based electrodes capable of detecting small proteins and clinically relevant biomarkers. These papertronic devices have the potential to supply multi-functionality to new forms of smart bandages, skin-like sensing, food packaging, and robotic sensing and actuation.

Industrial control systems have strict requirements for time-sensitive applications and clock synchronization services. Performance of such applications is adversely impacted by packet delays and jitters. The impact is especially critical in process industries due to harsh environmental conditions. In this paper, we analyze delays and jitters to assess the performance of time-sensitive applications. To this end, we captured and analyzed round trip delay data retrieved from a paper factory. Analysis shows that a sub-millisecond level average delays and the jitters derived from the observed data are sufficient to meet the minimum 10ms update frequency required for most critical control applications. Moreover, the filtered delay variations at the end devices are less than the recommended 150us, which guarantees an adequate time synchronization accuracy in the factory network. Besides, this analysis can provide significant insights into performance bottlenecks for factory applications.