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Everyday there may be a vast stream of research documents give in to conferences, anthologies, journals, newsletters, annual report, daily paper, and different periodicals. Numerous such publications use unbiased and exterior specialists to survey. This methodology is frequently termed peer review, in addition the assessors are named referees. but it is not always possible to pick out the best referee for reviewing. Moreover, the new fields of research are emerging in every sector and the number of research is increasing dramatically. but, to review all of these papers, every journal assign a short team of referees and maybe they are not expert in all areas. For example, a research paper in the communication technology area should be reviewed by the expert from the same field. So, it is a big challenge to efficiently select the best reviewer or referee for a research paperIn this research work, we proposed and implemented a program or software where, we used a new strategy to select the best reviewers of a research work automatically. As we already know that every research paper contains references in the end of the paper and the references are generally from same area as the paper is. In this work, first of all, we collect the references and count the authors who have at least one paper that belong to the reference section. Once, we collect the authors name, automatically browse the web to extract the research topic keywords. Then again search for finding top researchers in the specific topic field and count their h-index, i10-index and citations for first n author. After then, ranked top n authors based on ranking score and automatically browse their homepage to retrieve emails address. We also check their co-authors and colleagues from the web and discard them accordingly from the list. The remaining tops n authors (researcher) generally professor and maybe the best referees for reviewing a research paper.

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.

Several applications can benefit from recording information about the places a mobile entity visits and the length of time it spends there (e.g., shoppers, employees, buses, movable equipment, autonomous robots, etc.). In this paper, we present our approach to recording spatio-temporal information in a secure and inviolable way using a Distributed Ledger Technology. So far in this work, we observe that, although sending data to the blockchain is a heavy process, there are ways that this transaction time does not disrupt the flow of new data such as adding these data to a queue. However, this makes the sending process overall longer. We intend to continue this study by testing different blockchain services and comparing their performances.

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.

The problem of joint information transmission and input signalling estimation over a state-dependent channel is considered. The general single-letter upper and lower bounds of the optimal capacity-distortion trade-offs are provided. For the Gaussian dirty paper channel, we calculate the bounds by choosing parameters carefully, and obtain a computable form eventually. In such a way, we obtain a complete characterization for this problem setting.

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.

This work is centered on the paper making process in an industrial paper mill. There exist plenty of sub-processes in a paper mill for transformation of raw material to finished product. A particular process that is taken up in this research is paper basis weight control process, which is very important for good quality paper production. But the controller design for this process is very challenging due to the involvement of severe dead time value, which in addition to delaying the control output, makes the control system unstable. So, two different approaches are used here, in MATLAB, for controller design, one is the dead time approximation using Pade approximation technique and the other is dead time compensation using Smith predictor technique. Hence, the performance of the designed control systems is compared.

The present work presents a hybrid identification of recursive least squares with a metaheuristic called radial movement optimization (RMO) applied to the joints of a cylindrical robotic manipulator. The main contribution of the research is to consider the covariance matrix with RMO. At the end a comparative analysis will be made with some classic methods. The results of the identifications were tested with the RMSE. A covariance matrix has also been generated from manipulator identifications.

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.

Contrary to former fears, factories will not be deserted in the foreseeable future. The challenge will be the development of digital approaches that assist human beings in manufacturing environments by processing the enormous amount of data available. The proposed approach allows the implementation of manufacturing functionalities based on artificial intelligence. The objective is to design a Cognitive Factory that is able to both reactively and proactively assist humans in complex manufacturing environments. To this purpose, a combination of the Digital Shadow concept and a Service-based Intelligence is presented.

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.

Computing machines, it seems, have been becoming more deeply embedded in our lives. Today, this alliance with computers is taking shape as competition with robots for jobs that are becoming increasingly mechanized, be it on the smart factory floor, or next to hospital beds, or humanoid robots making elderly Japanese laugh, or even cognitive cooking. Should we be concerned at this development or just think of this as another agricultural revolution or Gutenberg’s printing press revolution with a bonanza of benefits in retrospect? What about human-robot cooperation, could that be a happy medium? Or, are machines destined to take over more facets of our lives at work and at play, like the sensors monitoring our sleep patterns and helping to improve sleep quality. Would you consider scenarios like some of the ones mentioned above to be a threat or an entry ramp to utopia? Some questions to think of: What are the jobs where robots can or should have only a marginal role? Where can man and machine work together? How do we guarantee that the machine stays tamed? Does AI pose an existential threat?

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.

This paper will review the upgrade of CMPC Paper Machine #19 at Maule, Chile. The upgrade involved retrofitting the existing control system to remove the obsolete master controllers, updating and standardizing the drive system from different AC drive manufacturers to a single one, and adding a new machine section to improve product quality. The project was designed and executed in four phases that lasted a total of three years. The first phase of the project replaced the original four master controllers with new state-of-the-art controllers, which were compatible with the communication protocols of the existing drive system. The subsequent three phases of the project replaced the existing drives, all from a mix of different manufacturers, with a new modern drive system. In total, 142 drives were installed, commissioned, and tuned during the course of this upgrade. A challenge was presented by continuing to operate with the unique combination of existing drives, from different manufacturers, and new drives during the phases of the project. The staged design of the project helped reduce the outage time impact. This Paper Machine upgrade project was completed ahead of schedule and has exceeded the expectation of a successful upgrade.

Coaters sections are challenging to tune during the commissioning of a paper machine. This paper presents a detailed mechanical-electrical model of a one-coater section which includes a linear, operating point model of the sheet in the different spans, all the drivetrain components, and the vector controlled ac drives. The sheet model considers tail widening as well as the effect of coating and drying on the sheet elastic constant, activation of the torque current control and the beginning of coating. The developed model allows for complete evaluation of the different transient events including paper slack due to load impacts. Results show that the best performance of the drives is obtained operating the spans with even sheet tensions, which reduces and/or eliminates the operation of the drives in regeneration or braking zones.

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.

In this paper, we describe the system of ad hoc search on statistical data based on categorization and metadata augmentation. The documents covered by this paper consist of metadata extracted from the governmental statistical data and the body of the corresponding statistical data. The metadata is characterized by the fact that its document length is short, and the main body of statistical data is almost always composed of numbers, except for titles, headers, and comments. We newly developed the categorical search that narrows the set of documents to be retrieved by category in order to properly capture the scope of the problem domain intended by the given query. In addition, to compensate for the short document length of metadata, we implemented a method of extracting the header information of the table from the main body of statistical data to augment documents to be searched. As a ranking model, we adopted BM25, which can be adjusted with few parameters to take into account term frequency and document length.

The recommendation system can recommend information to users efficaciously, which helps many users to obtain information in different fields. The paper recommendation is a research topic to provide authors with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring the author's interest and structure information of the academic network. In the paper recommendation system, authors and papers and the interaction of their information have a crucial impact on the efficiency and accuracy of the recommendations. However, most recommendation systems are usually designed based only on users. Therefore, we propose a method based on the author's periodic interest and academic graph network structure to obtain as much effective information as possible to recommend papers. Extensive offline experiments on large-scale real data show that our method outperforms the representative baselines.

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.

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.

Cyber networks are extremely non-deterministic, complex systems. To address this, we must develop foundational research protocols to enable reproducible cyber experiments that can systematically uncover deep understanding of a cyber system's security posture. One core tenant of this approach is to have a test environment to enable a space to create and test hypotheses about systems and reason about results. To date, this has generally been done through cyber testbeds or cyber ranges. National infrastructure supported by various government agencies have all created multi-million dollar ranges, and support other national infrastructure such as the National Cyber Range (NCR) and the Regional Service Delivery Points (RSDP). The thrust of this paper was based on multi-year studies, the culmination of which uncovered gaps and challenges associated with using various national infrastructures to represent a multitude of complex heterogeneous systems. Ranges, such as the NCR, have experiment life-cycle processes to take a cyber experiment from inception to analysis. However, our position is that processes used are not sufficient to address gaps and challenges. In this position paper, we review current range experiment methodologies and our observations of other considerations that should require inclusion.

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.

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.

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.

Due to the long-term operation, the transformer oil-paper insulation system will gradually experience aging and degrading, resulting in the insulation failure. And the insufficient research about the accurate diagnosing method of aging condition will severely threaten the security and stability of the transformer. At present, the diagnostic criteria for the aging of transformer insulation is only limited to the degree of polymerization of the insulating paper/pressboard and furfural in the oil without a comprehensive diagnostic scheme. Based on the existing theory, for the oil-paper insulation system after undergoing the accelerated thermal aging test, the degree of polymerization of the insulation paper, frequency domain dielectric properties, furfural, CO, CO 2 were firstly tested in the laboratory. Then, principal component analysis (PCA) was used to integrate the measured multiple parameters and obtain a comprehensive evaluating index of the insulation aging condition, and the establishment of the degree of polymerization and the evaluating index was conducted, which was in good linear relationship. According to the evaluating index and the quantitative relationship with the degree of polymerization, a diagnostic method that can be applied to the aging assessment of transformer insulation in the field is put forward, which makes up for the deficiency of the current diagnosis method only including single assessment factor and is of certain practical significance.

The multi-user multiple-input multiple-output (MU-MIMO) scheme is one of the main methods for improving the frequency-efficiency in wireless communication systems. In MU-MIMO systems, a base station equipped with multiple antennas and a large number of receivers with multiple antennas communicate simultaneously. Conventionally, we adopted the principle of chaos communications to MU-MIMO and proposed a multi-user chaos MIMO (MU-C-MIMO) scheme that has physical layer security between each receiver. In the previous study, we utilized block diagonalization (BD) — a liner precoding method — to remove interference components in MU-C-MIMO. On the one hand, channel gain for each user is not maximized in BD. On the other hand, the gain maximization can be realized using nonlinear precoding, and further improvement of transmission quality is expected in MU-C-MIMO. Therefore, in this paper, we propose a MU-C-MIMO scheme using dirty paper coding (DPC), a nonlinear precoding method, in order to realize the most ideal precoding characteristics. The effectiveness of this scheme is evaluated through numerical simulations with comparisons to a conventional MU-C-MIMO system with BD.

This paper exemplifies the design and analysis of an inexpensive, compact high data capacity chipless RFID tag on flexible paper substrate. The size of the overall tag with integrated antennas is similar to that of a credit card. The designed tag uses polarization diversity to provide a bit capacity of up to 30 bits in the short range UWB band of 22–26.5 GHz. A systematic progression on to the incorporation of increased number of bit capacity is depicted hereby. Along with frequency variation, this paper also proposes the scheme for bit detection using phase difference determination in order to increase robustness. An interrogation process of the proposed tag to extract the encoded information is demonstrated here and the resulting analysis establishes the reliability of the designed tag.

Conventional research trend detection methods require a large number of papers to perform analysis, while the trends detected are usually retrospective and cannot represent the emerging topic which is in embryonic stage, because detection method based on word features or citation relationships have to wait for the trend to come into form and become popular to detect it. Besides, the acquisition of paper full text is usually costly. We employ the paper metadata, which is relatively easy to acquire and process, to detect potentially valuable papers in specific field once the papers are published or their metadata become accessible and extract emerging cutting-edge trend from them. By comparing the result with conventional methods, we prove the effectiveness of the method.

In the process of cigarette, the dynamic splicing of running bobbin paper and spare bobbin paper plays a very important role in the continuous operation of Cigarette Making and Filter Assembling Group. However, at present, the dynamic splicing system of some domestic Cigarette Making and Filter Assembling Group mostly adopts magnetic particle clutch. After using the magnetic particle clutch for a period of time, it will not rotate or rotate too slowly due to wear and tear. It is easy to cause the rotation speed of spare bobbin paper and running bobbin paper to be inconsistent when splicing, resulting in relative speed, resulting in failure of splicing. Therefore, a dynamic splicing system based on Lenze 9400 servo controller is proposed. The system can guarantee the smooth splicing of the running and spare bobbin paper, reduce The Times of maintenance and improve the production efficiency.

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.

The forestry industry all over the world is seeing the need for modernization of its machines toward autonomy. In this paper, we focus on a wheel loader that should operate autonomously in the yard of a pulp-and-paper mill, scooping wood chips from a pile of wood and dropping them into a hopper, which is linked to a conveyor that carries them inside the mill. The modelling of the wheel loader is elaborated first, while taking into account that it is composed of two systems: (i) the vehicle and (ii) the arm carrying the bucket. Notice that the former pertains to the category of articulated vehicles that steer using a different mechanism than the conventional Ackermann steering used in car-like vehicles. As for the latter, it is a 2DOF serial manipulator. The navigation is considered then. Finally, simulation results of the kinematics model are shown in Matlab/Simulink first, then dynamics and 3D animation are added using ROS2/Gazebo. Notice that this work is a first step toward the development of the digital twin of the wheel loader. Later, it will be used as the virtual platform for the validation of the autonomous wheel loader.

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.

The manufacturing business is one of the businesses in Indonesia that continues to show its development from year to year. Like a manufacturing business in general, one of the important efforts made in the printing business is the supply of raw paper materials to produce finished goods. The purpose of this research is making a forecasting of the raw paper material for printing company on 7 different types of 269 historical data with weekly intervals from January 2015 to February 2020 before the Covid19 pandemic season. Forecasting is done using the Long Short Term Memory method with Python language. The model architecture for training and testing is carried out using vanilla LSTM with single input, hidden and output layer with the configuration of 64 neurons in the hidden layer, 150 epoch, 12 batch size and Adam Optimizer (lr = 0.0001) which was repeated 10 times for best result. The test results show the best window size length in the model for each paper raw material differently from 4 to 16. All models was successfully forecasting the test data with an average MAPE of the overall forecast of 21.48%.

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.

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

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.

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

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.

Academic papers is an important means to test students’ mastery of basic knowledge and professional ability. The quality of academic papers, on the one hand, is relected by the innovation and prefessionalism of content. On the other hand, the normalization of documents is an important index. In order to solve the problem of low efficiency that the paper standardization inspection is basically completed by manual work. This paper proposes an automatic detection framework based on document rule tree (DRT) and can solve the problem well. The practical application results show that good results have been achieved in terms of accuracy and query efficiency.

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.

Forgery of paper invoices and certificates negatively causes huge economic loss every year. In the molding process of paper, plant fibres inside will distribute randomly and form the unique microscopic surface. Accordingly, forensic researchers have presented many techniques to build its feature descriptor for paper anti-counterfeiting. Among those methods, normal vector field and reconstructed surface based on multi-view photos captured by mobile camera have indicated incredibly effectiveness and practicality. However, the inefficiency of existing models using mobile camera is the neck in real time detection scenarios. In this paper, we focus on the efficient microstructure orientation estimation of paper surface for authentication. To address this problem, we investigate the reflection characteristics of rough surface and quantify the light attenuation accurately. Then, we model the entire propagation path of light emitted by flash, and exploit single photo captured by mobile camera to reconstruct microscopic inclination based on beam energy loss. Reduction of required pictures significantly enhances forensics efficiency. Through comparison experiments, we demonstrate that our proposed method can be used for paper discrimination effectively with speedy feature extraction.

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.

This paper investigates the reconfigurable intelligent surface (RlS)-aided multiple-input-single-output (MISO) systems with imperfect channel state information (CSI), where RIS-related channels are modeled by Rician fading. Considering the overhead and complexity in practical systems, we employ the low-complexity maximum ratio combining (MRC) at the base station (BS), and configure the phase shifts of the RIS based on long-term statistical CSI. Specifically, we first estimate the overall channel matrix based on the linear minimum mean square error (LMMSE) estimator, and evaluate the performance of mean square error (MSE) and normalized MSE (NMSE). Then, with the estimated channel, we derive the closed-form expressions of the ergodic rate. The derived expressions show that with Rician RIS-related channels, the rate can maintain at a non-zero value when the transmit power is scaled down proportionally to 1 /M or 1 /N2, where M and N are the number of antennas and reflecting elements, respectively. However, if all the RIS-related channels are purely Rayleigh, the transmit power of each user can only be scaled down proportionally to $1/\sqrt M $ or 1/N. Finally, numerical results verify the promising benefits from the RIS to traditional MISO systems.