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3D city models are mainly viewed on computer screens, but many municipalities also use 3D printing to make urban planning tangible. Since 3D color printing is still comparatively expensive and the colors often fade over time, many of these models are monochrome. Here, color textured paper models offer an inexpensive and under-appreciated alternative. In this paper, a greedy algorithm adapted to CityGML building models is presented, which creates print templates for such paper models. These 2D layouts consist of cut edges and fold edges that bound polygons of a building. The polygons can be textured or left blank depending on the existence of CityGML textures. Glue tabs are attached to cut edges. In addition to the haptic 3D visualization, the quality of the 3D models can sometimes be better assessed on the basis of the print templates than from a perspective projection. The unfolding procedure was applied to parts of the freely available CityGML model of Berlin as well as to parts of models of the cities of Dortmund and Krefeld.

In order to clarify which types of research is more conducive to improving innovation performance, we use the panel data of key universities in China from 2009 to 2017 for empirical research. The r...

In the general framework of knowledge discovery, different techniques were used for information extraction from multi-label documents. As the world is currently facing COVID-19, it has made it more important than ever to have such knowledge extraction from previous documents. Therefore, Natural Language Processing (NLP) can be an essential model for tackling such an issue. By taking into consideration that having such a model plays an essential role to generate new insights in support of the ongoing fight against this infectious disease. This work introduces a sophisticated model that is able to read data from various articles about COVID-19, and finally give the most appropriate answer to the questions asked in order to gain insight information automatically. The model is applied to COVID-19 open research dataset challenge (CORD-19) that's has caught the attention of many researchers and it contains over 400,000 scholarly articles. The result of the proposed model has shown a good achievement, as it is explained in the result section. It was found that NLP is a good choice for tackling this global pandemic for information extraction and it contribute a new insight in support of the ongoing fight against this infectious disease. ©2021 Seventh Sense Research Group.

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

At non-secular places like temples, gurdwaras, and lots of others, devotees gift cash within the donation box. It's a giant deal to sight the truth of the given cash. Similarly, cash numeration has been a giant task because it is allotted manually. Mistakes on numeration happen most of the time because of several reasons like eyes temporary state, losing focus, etc. typically fiddle of cash happens at the time of numeration money. To prevail over these issues Automatic Coin Sorter for Temples using IOT is meant. During this system, the truth of the coin is detected employing a dimension standardization method. Then the coins when some delay area unit sorted and area unit inserted into the various assigned box with the assistance of the motor. All the data regarding the coin is distributed to the Associate in Nursing digital display wherever the worth, reality, and count of the coin area unit are displayed. The overall count of the noon and full-day is distributed to the various head person through the applying. The advantage of this review paper for the reader is to achieve information about numerous technologies used for creating this method.

Now a day many illegal activities like crossing borders through forest regions, planting landmines, etc to keep an eye on such activities there is a need for some spying device that can spy on the restricted areas or some forest or territorial region where a human cannot go due to risk. In this paper, we have described such type of spy robot that is ‘long range spy robot with night vision camera’. This system Long Range Spy Robot with Metal Detection is very innovative as this system allows operating a robot irrespective of the distance. To drive the system, the user has to formulate a call to the phone coupled to this robot and once the call is received, the user will have to make use of the keypad to transmit data commands to the robot. The data commands will make the robot either go in forward, then backward, left, or right direction. The system makes use of a night vision-enabled spy vision-enabled help of which the user can view the area captured by this spy camera which will be mounted on this robot. The user will be able to view the area captured by this spy camera not only during the daytime but also during the night. All the areas captured by this spy camera can be viewed in a mobile application or PC.

PURPOSE Generalizability, reproducibility and objectivity are critical elements that need to be considered when translating machine learning models into clinical practice. While a large body of literature has been published on machine learning methods for segmentation of brain tumors, a systematic evaluation of paper quality and reproducibility has not been done. We investigated the use of “Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis” (TRIPOD) items, among papers published in this relatively new and growing field. METHODS According to PRISMA a literature review was performed on four databases, Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection first in October 2020 and a second time in February 2021. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. The publications were assessed in order to the TRIPOD items. RESULTS 37 publications from our database search were screened in TRIPOD and yielded an average score of 12.08 with the maximum score being 16 and the minimum score 7. The best scoring item was interpretation (item 19) where all papers scored a point. The lowest scoring items were the title, the abstract, risk groups and the model performance (items number 1, 2, 11 and 16), where no paper scored a point. Less than 1% of the papers discussed the problem of missing data (item 9) and the funding of research (item 22). CONCLUSION TRIPOD analysis showed that a majority of the papers do not score high on critical elements that allow reproducibility, translation, and objectivity of research. An average score of 12.08 (40%) indicates that the publications usually achieve a relatively low score. The categories that were consistently poorly described include the ML network description, measuring model performance, title details and inclusion of information into the abstract.

Catalogue of billions of phrases from 107 million papers could ease computerized searching of the literature. Catalogue of billions of phrases from 107 million papers could ease computerized searching of the literature.

This companion paper is to support the replication of paper "Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding", which was presented at ACM Multimedia 2020. The supported paper's main purpose was to provide a photogrammetry point cloud-based dataset with hierarchical multilabels to facilitate the area of 3D deep learning. Based on this provided dataset and source code, in this work, we build a complete package to reimplement the proposed methods and experiments (i.e., the hierarchical learning framework and the benchmarks of the hierarchical semantic segmentation task). Specifically, this paper contains the technical details of the package, including file structure, dataset preparation, installation package, and the conduction of the experiment. We also present the replicated experiment results and indicate our contributions to the original implementation.

In our MM'20 paper,, we presented a Kalman filter-based approach for prediction of head motion in 6DoF. The proposed approach was employed in our cloud-based volumetric video streaming system to reduce the interaction latency experienced by the user. In this companion paper, we present the dataset collected for our experiments and our simulation framework that reproduces the obtained experimental results. Our implementation is freely available on Github to facilitate further research.

In this paper we reproduce experimental results presented in our earlier work titled "Describing Subjective Experiment Consistency by $p$-Value P-P Plot" that was presented in the course of the 28th ACM International Conference on Multimedia. The paper aims at verifying the soundness of our prior results and helping others understand our software framework. We present artifacts that help reproduce tables, figures and all the data derived from raw subjective responses that were included in our earlier work. Using the artifacts we show that our results are reproducible. We invite everyone to use our software framework for subjective responses analyses going beyond reproducibility efforts., Comment: Please refer to the original publication: https://dl.acm.org/doi/10.1145/3474085.3477935 Related paper: https://dl.acm.org/doi/abs/10.1145/3394171.3413749 or arXiv:2009.13372

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

This chapter focuses on cost control by analyzing the intelligent control system of waste paper deinking production line, and finally designs a solution. In the aspect of control index, machine learning has designed a solution that pays less attention to the benefit of a single control loop and focuses on maximizing the overall benefit of the production line. In the aspect of control demand, according to the complexity of waste paper deinking production line, a distributed control solution is designed Finally, according to the dynamic fuzziness in machine learning system, the basic concept of dynamic fuzzy machine learning is constructed by using dynamic fuzzy set The dynamic fuzzy machine learning model is described and the related learning algorithm is given. Finally, a simulation example is given, and satisfactory results are obtained by comparing with the elastic BP algorithm RPROP of neural network.

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 benefits of many breakthroughs in biotechnology, health, and medicine have often been restricted by the high cost and the limited availability of tests and application devices. There is a need for low-cost bioassays for biotechnological applications and health diagnostics. To facilitate quality health care to resource-limited regions, the point-of-care devices needs to be affordable, sensitive, user-friendly, rapid, and robust. Paper is an ideal ingredient to develop disposable bioassay devices. Bioactive papers can be used to develop paper-based analytical devices for clinical diagnosis, assessing organ dysfunction, and detecting infectious diseases and microorganisms. Bioactive papers can also be used as antibacterial papers in healthcare and food packaging. At present the paper diagnostics is a multibillion-dollar market. Considering the product range and applications in health sector the market potential of bioactive papers is very high. Synchronized scientific and industrial research is needed to develop high quality and consistent commercial products from laboratory proof of concept.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

Our life depends more and more on intelligent machines that help us carry out our work or have completely taken our place in tasks which used to be wholly human. This also applies to the field of care and assistance for the sick and fragile: indeed, carebots are increasingly present by the patient’s bed and can collaborate in care work. However carebots are not objects like others that we use in our everyday life: unlike the tools through which we do things, robots have (and will more and more have) some level of autonomy. We intend to consider the type of relationship it is right to build with these devices and ask whether intelligent robots deserve some moral and legal relevance. The robot may be seen as a slave or an entity with which we can form friendships or loving relationships: we will maintain that it is our responsibility to prepare for future scenarios in which increasingly intelligent, autonomous machines will be not mere tools, but significant life companions.

In this project our goal is to build a tweet sentimental analysis in which user can search and analyse thousands of tweets in one go. For backend we will use python and Django web framework and for front end we are going to use HTML, Css and bootstrap. It will help to analyse any hashtag and companies about their product feedback reviews. This project contains only the one user side. It is like a tweets search analyser. The design of this project is pretty simple so that the user won’t find any difficulties while working on it.

Containers are a mainstream virtualization technique commonly used to run stateful workloads over persistent storage. In multi-tenant hosts with high utilization, resource contention at the system kernel often leads to inefficient handling of the container I/O. Assuming a distributed storage architecture for scalability, resource sharing is particularly problematic at the client hosts serving the applications of competing tenants. Although increasing the scalability of a system kernel can improve resource efficiency, it is highly challenging to refactor the kernel for fair access to system services. As a realistic alternative, we isolate the storage I/O paths of different tenants by serving them with distinct clients running at user level. We introduce the Danaus client architecture to let each tenant access the container root and application filesystems over a private host path. We developed a Danaus prototype that integrates a union filesystem with a Ceph distributed filesystem client and a configurable shared cache. Across different host configurations, workloads and systems, Danaus achieves improved performance stability because it handles I/O with reserved per-tenant resources and avoids intensive kernel locking. Danaus offers up to 14.4x higher throughput than a popular kernel-based client under conditions of I/O contention. In comparison to a FUSE-based user-level client, Danaus also reduces by 14.2x the time to start 256 high-performance webservers. Based on our extensive experience from building and evaluating Danaus, we share several valuable lessons that we learned about resource contention, file management, service separation and performance stability.

Function-as-a-Service (FaaS) has raised a growing interest in how to "tame" serverless to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems have been implemented for training ML models. Certainly, these research articles are significant steps in the correct direction. However, they do not completely answer the nagging question of when serverless ML training can be more cost-effective compared to traditional "serverful" computing. To help in this task, we propose MLLess, a FaaS-based ML training prototype built atop IBM Cloud Functions. To boost cost-efficiency, MLLess implements two key optimizations: a significance filter and a scale-in auto-tuner, and leverages them to specialize model training to the FaaS model. Our results certify that MLLess can be 15X faster than serverful ML systems [24] at a lower cost for ML models (such as sparse logistic regression and matrix factorization) that exhibit fast convergence.

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.

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.

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 entire scientific and academic community has been mobilized to gain a better understanding of the COVID-19 disease and its impact on humanity. Most research related to COVID-19 needs to analyze large amounts of data in very little time. This urgency has made Big Data Analysis, and related questions around the privacy and security of the data, an extremely important part of research in the COVID-19 era. The White House OSTP has, for example, released a large dataset of papers related to COVID research from which the research community can extract knowledge and information. We show an example system with a machine learning-based knowledge extractor which draws out key medical information from COVID-19 related academic research papers. We represent this knowledge in a Knowledge Graph that uses the Unified Medical Language System (UMLS). However, publicly available studies rely on dataset that might have sensitive data. Extracting information from academic papers can potentially leak sensitive data, and protecting the security and privacy of this data is equally important. In this paper, we address the key challenges around the privacy and security of such information extraction and analysis systems. Policy regulations like HIPAA have updated the guidelines to access data, specifically, data related to COVID-19, securely. In the US, healthcare providers must also comply with the Office of Civil Rights (OCR) rules to protect data integrity in matters like plasma donation, media access to health care data, telehealth communications, etc. Privacy policies are typically short and unstructured HTML or PDF documents. We have created a framework to extract relevant knowledge from the health centers’ policy documents and also represent these as a knowledge graph. Our framework helps to understand the extent to which individual provider policies comply with regulations and define access control policies that enforce the regulation rules on data in the knowledge graph extracted from COVID-related papers. Along with being compliant, privacy policies must also be transparent and easily understood by the clients. We analyze the relative readability of healthcare privacy policies and discuss the impact. In this paper, we develop a framework for access control decisions that uses policy compliance information to securely retrieve COVID data. We show how policy compliance information can be used to restrict access to COVID-19 data and information extracted from research papers.

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.

In the process of knowing and understanding the world, the traditional media of books and paper have always played a very important role. However, with the emergence of new media and the skilled use of digital media technology, the traditional medium of book and paper media has gradually faded away in the eyes of mankind. Stereoscopic paper media is a three-dimensional paper media that can display three-dimensional virtual scenes on the display device based on the traditional paper books by using the camera of the display device to identify the logo card or the real scene, and then collect the virtual information and the real scene. In this paper, under Windows 10 operating system, the program is written by VC++ and ARtoolKit software development kit development tool, and based on augmented reality technology, virtual 3D scenes are built through 3D model modeling to achieve matching and integration with real scenes of paper books and realize 3D interaction. Based on this, a 3D interactive augmented reality system with a high degree of realism is realized. The main research contents include: customizing the paper media markers, reducing the system's misjudgment rate of the markers by combining the single-response matrix and calculating the image matching value with an optimization algorithm, realizing and tracking the recognition of the markers in real time; and optimizing the image segmentation method of the ARtoolKit software development kit, using the marker card to rotate 45° relative to the camera's position to simulate the situation when the scene has changed significantly. We use the minimum error method to calculate the gaps in real time to realize augmented reality; build a 3D model with animation effects based on the ARtoolKit software development tool and Unity development platform; accurately align the recognized markers with the virtual 3D model; add audio to the 3D model to match the content and add interactive functions to realize the control of 3D animation. Stereoscopic paper media breaks the traditional paper book book reading method, no longer just read the content of paper books, you can see and content-related virtual information on the display device of the mobile terminal, such as three-dimensional scenes, videos and so on. Stereoscopic paper media improves the reader's interest in reading and gives the reader a vivid and realistic reading experience.

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.

The paper titled “Are Automated Debugging Techniques Actually Helping Programmers?” was published in the proceedings of the International Symposium on Software Testing and Analysis (ISSTA) in 2011, and has been selected to receive the ISSTA 2021 Impact Paper Award. The paper investigated, through two user studies, how developers used and benefited from popular automated debugging techniques. The results of the studies provided (1) evidence that several assumptions made by automated debugging techniques did not hold in practice and (2) insights on limitations of existing approaches and how these limitations could be addressed. In this talk, we revisit the original paper and the work that led to it. We then assess the impact of that research by reviewing how the area of automated debugging has evolved since the paper was published. Finally, we conclude the talk by reflecting on the current state of the art in this area and discussing open issues and potential directions for future work.

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.

Our eventual goal regarding this study is to support students with developing paper-writing skill. In order to achieve this goal, we have been trying to find characteristic features of good papers through analyzing educational and other kinds of data. We take conference papers as target data and suppose full/regular papers are good because they are chosen as reviewers evaluate them more valuable to be presented in the conference than other papers. In our series of study, we have been surveying the differences of full and short papers. In this paper, we aim to investigate further differences of them by taking different analysis method. We have been using the numbers of occurrences of each word in full/short papers as the data for analysis. In this paper we use the numbers of full/short papers that contain the word instead of the total numbers of occurrences of words. We define a new index of a word which shows how likely it is used in full or short papers. We discuss its effectiveness by applying it to an experiment of distinguishing full and short papers.

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.

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.

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.

Storm and flood disasters such as typhoons and torrential rains are becoming more intense and frequent. The national government and municipalities must respond to such natural disasters as soon as possible. When the scale of damage is large; however, it takes much time to investigate the severity of damage, and the initial response can be delayed. If we could precisely and rapidly estimate the severity of damage for each city at an early stage, the national government would be able to better support the municipalities, and consequently respond quickly to help citizens. In this paper, we propose a novel approach to estimate the severity of disaster damage within a short time period after a disaster occurs by exploiting real-time population data generated from cellular networks. First, we investigate the relationship between real-time population data and the severity of damage. Then, we design a Graph Convolutional Networks for Disaster Damage Estimation, called D2E-GCN, which fully exploits the directed and weighted characteristics of human mobility graph. We conduct an offline evaluation on real-world datasets including two typhoons that hit Japan. The evaluation results show that the proposed method outperforms baseline methods which do not consider the graph structure of cities, and the proposed method can estimate the severity of damage approximately 48 hours after typhoons passed. Moreover, we find the experimental insight that the estimation performance can be significantly affected by the graph construction method for GCN models.

In this paper, we demonstrate how to conduct OD analysis based on big taxi trajectory data with XStar in an efficient manner. XStar, originally developed by the first author, is a standalone software system dedicated to trajectory-data users with little programming skills and affordable computing devices. Since its release in Jan. 2019, it has received downloads of over 4000 by May 2021.

Due to the non-linear and highly volatile nature of the Stock market, it has become a very challenging task for researchers to make accurate predictions. Improving the efficiency of predictions has become the main goal of many researchers. From the traditional approach of working with historical dossiers to using the latest machine learning and deep learning techniques, researchers are busy finding out the best possible ways of accurate predictions. Many new models are suggested that can make good estimations of stock prices. Investors are interested in knowing both the immediate next-day prices and as well as future share prices in the long run. This paper inspects the algorithms and techniques that are useful for making accurate predictions.

Blind natural video quality assessment (BVQA), also known as no-reference video quality assessment, is a highly active research topic. In our recent contribution titled "Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features" published in ACM Multimedia 2020, we proposed a two-level video quality model employing statistical temporal features and spatial features extracted by a deep convolutional neural network (CNN) for this purpose. At the time of publishing, the proposed model (CNN-TLVQM) achieved state-of-the-art results in BVQA. In this paper, we describe the process of reproducing the published results by using CNN-TLVQM on two publicly available natural video quality datasets.

VLDB is a premier annual international forum for data management, and it targets database researchers, users, vendors, and application developers. Each year the conference covers current issues in data management and database systems, which remain key technological cornerstones of emerging applications of the twenty-first century. VLDB 2013 took place at the picturesque town of Riva del Garda in Italy. We received a total of 559 research paper submissions, and of these 127 submissions were accepted