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The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

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

The clinical importance of blood group (BG) antigens is related to their ability to induce immune antibodies that can cause hemolysis. Yet, ABO and D (Rh) are still considered to be the key antigens for healthy blood transfusion and secondary antigens are the next priority. Serological typing is the most widely used typing method. Rapid and accurate blood grouping plays an important role in some clinical conditions, rather than conventional techniques. Hence, developing a simple and economical model for rapid blood grouping would facilitate these tests. In recent decades, paper-based microfluidics such as μPADs has gained much interest in wide application areas such as point-of-care diagnostic. In this study, we evaluated μPADs that are performed for blood grouping and its recent progress. A comprehensive literature search was performed using databases including PUBMED, SCOPUS, Web of Science and Google Scholar. Keywords were blood grouping or typing, paper analytical device, rapid test, etc. After investigation of search results, 16 papers from 2010 to 2020 were included. Further information in detail was classified in Table 1. Generally, two principles for blood typing μPADs are introduced. The lateral chromatographic flow method and the vertical flow-through method that detects BG in a visual-based manner. To detect results with acceptable clarity many factors and challenges like paper, blood sample, buffer, Ab and RBC interaction and also μPADs stability need to be considered, which are discussed. In conclusion, the simplicity, stability, cheapness, portability and biocompatibility of μPADs for blood grouping confirming its utility and also they have the capability to robust, universal blood-grouping platform. Table 1 Summary of blood grouping tests using paper-based analytical devices Antigens Type of diagnosis Validation method Sample No Accuracy Action time Paper type Stability Sample dilution Buffer Ref A, B, Rh Forward volunteers records 5 - - Whatman No. 4 - 1/2 PBS* (Khan et al. 2010) A, B, Rh Forward gel assay test and conventional slide test 100 100% 1 min Whatman No. 4 and Kleeenex paper towel 7 Days in 4 °C 1/1 NSS (Al-Tamimi et al. 2012) A, B, Rh Forward gel card assay 99 100% 20 Sec + Washing Kleeenex paper towel - 1/1 NSS (Li et al. 2012) A, B, Rh Forward - - - - Kleeenex paper towel - 45/100 PSS (Li et al. 2013) A, B, Rh Forward gel card assay 98 100% 1.5 min Kleeenex paper towel - 85/100 PBS (Guan et al. 2014b) C, E, c, e, K, Jka, Jkb, M, N, S, P1, and Lea Forward gel card assay 266 100% - Kleeenex paper towel - 1/1 NSS (Li et al. 2014b) A, B, Rh Forward and Reverse conventional slide test 96 ≈ 91% 10 min Whatman No. 1 21 Days in 4 °C 1/2 NSS (Noiphung et al. 2015) C, c, E, e, K, k, Fya, Fyb, Jka, Jkb, M, N, S and s, P1, Lea and Leb Forward - 478 - - Kleeenex paper towel - 1/1 NSS, PBS (Then et al. 2015) A, B Forward and Reverse conventional slide test 76 100% 5-8 min Whatman No. 4 38 Days in 4 °C 1/4, 1/1 NSS (Songjaroen and Laiwattanapaisal 2016) D, K Forward volunteers records 210 - 7.5 min Kleenex paper towel - 1/1 NSS (Yeow et al. 2016) A, B, c, e, D, C, E, M, N, S, s, P1, Jka, Jkb, Lea, Leb, Fya, and Fyb Forward and Reverse gel card assay 3550 ≈100% 30 s Fiber glass and cotton linter 180 Days in 25 °C 45/100, 1/1 PBS (Zhang et al. 2017) A, B Forward conventional slide test 598 100% 3 min Whatman No. 113 14 Day in 4 °C 1/1 NSS (Songjaroen et al. 2018) A, B, Rh Forward conventional slide test - - 30 Sec + Washing Unrefined sisal paper - 1/2 NSS (Casals-Terré et al. 2019) A, B, Rh Forward - - - - Whatman No.1 - 1/1 NSS (Ansari et al. 2020) ABORh Forward and Reverse conventional slide test - 100% Unrefined Eucalyptus papers - 1/2 NSS, PBS (Casals-Terré et al. 2020) A, B, Rh Forward - - - 30 Sec + Washing Whatman No. 4 modified with chitosan ≥ 100 days in 25 °C 1/1 NSS (Parween et al. 2020)

Due to the increasing use of the Internet and other online resources, there is tremendous growth in the data of text documents. It is not possible to manage this huge data manually. This has led to the growth of fields like text mining and text summarization. This paper presents a title prediction model for research papers using Recursive Recurrent Neural Network (Recursive RNN) and evaluates its performance by comparing it with sequence-to-sequence models. Research papers published between 2018 and 2020 were obtained from a standard repository, viz. Kaggle, to train the title prediction model. The performance of different versions of Recursive RNN and Seq2Seq was evaluated in terms of training and hold-out loss. The experimental results show that Recursive RNN models perform significantly better than the other models.

International audience; This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.

Foam forming technology has attracted much attention during the past few years in the paper industry. Its advantages compared to conventional water forming are a new product portfolio and increased process efficiency. To support the paper industry in pushing foam forming technology forward, process simulation is needed to provide supporting data for strategic decision-making and as a basis for equipment dimensioning. This study examined the conversion of an existing wallpaper machine from water to foam forming technology using process simulation. To determine the required process configuration and parameter changes in the existing process, both published and unpublished data on the foam forming process were collected. This paper also describes modeling of the foam phase in the selected simulation software. The suitability of existing paper process equipment for foam was analyzed. Simulations revealed that undisturbed operation with foam requires some equipment modifications and re-arrangements in water circuits. With foam forming, the water balance in both short and long circulation changes remarkably compared to conventional water forming, leading to a large increase in the long circulation volume flows.

Headline generation is a challenging subtask of abstractive text summarization, which its output should be a summary, shorter than one sentence. It would be precious to develop a dataset for the evaluation of abstractive summarization methods on this task in the Persian language. There are several datasets for headline generation in Persian, most of which are not large enough to be used by more sophisticated methods of text summarization, such as deep learning models. Moreover, all of these datasets are focused on daily news and there is no dataset for summarizing scientific Persian papers. In this article, we present “ElmNet,” a headline generation dataset of about 400,000 abstract/headline pairs of scientific papers, gathered from six major publishers for scientific articles in Persian. We, moreover, evaluate the performance of the most important deep learning-based headline generation methods, on the proposed dataset. The results prove the comparability of the performance of the state-of-the-art methods on this task, to their results on the existing English datasets.

Text summarization is one problem in natural language processing that generates a brief version of the original document. This research took attention for some researchers in this last decade and growing fast, including Indonesia language. This paper aims to recap summarization text research especially in Indonesia language. As usual, this paper discusses two summarization approaches, extractive and abstractive. In fact, the number of research of extractive is more than abstractive. This paper investigates some methods such as Statistical Based Approach, Graph Based Approach, Machine Learning Approach, Fuzzy Logic Approach, Algebraic Approach, and Hybrid Approach. This paper shows some methods details and summarize the results. Keywords— Text summarization, extractive summary, abstractive summary, natural language processing

Many Indonesian spelling errors occur in research papers published to the public, closely related to academics in all institutions such as research institutions, government, schools, and universities. The spelling errors usually writing punctuation, writing letters, writing words, writing words originating from foreign or regional languages (uptake words), using affixed words, and writing ineffective sentences. The mistakes made by the academics then become a cycle in the academic environment. They usually provide guidance for writing an undergraduate thesis, thesis, dissertations to students, or the other forms of documents and scientific papers. Therefore, the research proposed the application to facilitate all authors of scientific papers in producing quality scientific works based on the General Guidelines for Indonesian Spelling published by the Agency for Development and Language Development. The application is named KEBI 1.0 Checker (Indonesian Spelling Error 1.0 Checker), a web-based application with a built-in algorithm to detect and correct Indonesian Spelling in scientific papers. The experiment result shows that the application has given the best accuracy performance to correct the non-standard words, and typographical errors reached 100% and 55,52%, respectively. The application also has been detected 209 meaningless words. The application processing time is relatively low, the average time needed to correct non-standard words is 0.016 seconds, and typo words are 14.58 seconds. KEBI 1.0 Checker is helpful for the end-user in academics but needs to improve the vocabulary of the large corpus in various fields of science for correcting typo words.

Internet of Things (IoT) has drawn important attention in recent years since it has made revolutionary changes in human life. The IoT enables the exchange of information or data in a wide variety of applications such as smart buildings, smart health, smart transport, and so on. As billions of connected things communicate with each other and can interchange sensitive information that may be revealed. Hence, strengthening IoT’s security and preserving users’ privacy is a crucial challenge. The aim of this paper is to provide a comprehensive study of the IoT security. Several IoT security attacks are examined and a taxonomy of the security requirements based on the attacks purposes is proposed. Furthermore, recent security solutions are described and classified based on their application area. Ultimately, open research directions and security challenges are discussed.

Due to the growth of e-commerce, most of the banking transactions are made on the online platform and all these transactions are made on the websites provided by the merchant or the payable apps and because of this the vulnerability of attacks has increased and there are also chances of using fraudulent websites and apps by the attackers though there are many high-security algorithms are been used for safeguarding against vulnerabilities. The way of judging the relation of trust on the online platform has become a major issue, so a proper trust model also needs to be maintained. In this paper, we reviewed various security algorithms which involve cryptographic algorithms, machine learning, anonymization, and masking techniques, blockchain, distributed networks, and many more to provide integrity, privacy, reliability, authentication, security, and risk-less e-commerce platform.

This study explored the relationship between syntactic complexity and rhetorical functions and the relationship between syntactic complexity and level of move/step realization in learner-generated research paper introductions. We created a corpus of 79 introductions written by undergraduate L2 students at intermediate to high-intermediate levels. We annotated the introductions for moves and steps, measured their syntactic complexity at the global, clausal, and phrasal levels, and rated their level of move/step realization across four levels (no, low, intermediate, and high). We conducted one-way MANOVA, one-way ANOVA, Pearson correlation, and stepwise multiple linear regression analyses. We found sentences realizing Reviewing items of previous research function to be significantly longer than other steps in Move1 and to contain significantly more clausal and phrasal complexity features. Sentences performing Claiming centrality function also demonstrated significantly more phrasal complexity features as measured by complex nominals per clause. We also found a higher level of phrasal complexity in introductions with a higher level of move/step realization, which can be attributed to introductions including Step1 and Step3, rather than containing all steps. Our findings imply that there is value in developing explicit instructions on how to use complex structures in performing rhetorical steps in research paper introductions. © 2021 Elsevier Ltd

With the arrival of the Internet and social media, at the same time as masses of humans have benefitted from the full-size reassets of records available, there was an full-size boom with inside the upward push of cyber-crimes, mainly targeted closer to women. According to a 2019 file with inside the Economics Times, India has witnessed a 457% upward push in cybercrime with inside the 5 years span among 2011 and 2016. Most speculate that that is because of effect of social media inclusive of Facebook, Instagram and Twitter on our day by day lives. While those simply assist in growing a legitimate social network, advent of consumer debts in those websites normally desires simply an email-id. A actual lifestyles man or woman can create more than one fake IDs and for this reason impostors can effortlessly be made. Unlike the actual international state of affairs in which more than one policies and guidelines are imposed to become aware of oneself in a completely unique manner (as an instance at the same time as issuing one’s passport or driver’s license), with inside the digital international of social media, admission does now no longer require this kind of checks. In this paper, we study the one-of-a-kind debts of Instagram, specifically and try and verify an account as fake or actual the use of Machine Learning strategies specifically Logistic Regression and Random Forest Algorithm.

Heart disease has been one of the ruling causes for death for quite some time now. About 31% of all deaths every year in the world take place as a result of cardiovascular diseases [1]. A majority of the patients remain uninformed of their symptoms until quite late while others find it difficult to minimise the effects of risk factors that cause heart diseases. Machine Learning Algorithms have been quite efficacious in producing results with a high level of correctness thereby preventing the onset of heart diseases in many patients and reducing the impact in the ones that are already affected by such diseases. It has helped medical researchers and doctors all over the world in recognising patterns in the patients resulting in early detections of heart diseases.

Formulaic expressions, such as 'in this paper we propose', are helpful for authors of scholarly papers because they convey communicative functions; in the above, it is showing the aim of this paper'. Thus, resources of formulaic expressions, such as a dictionary, that could be looked up easily would be useful. However, forms of formulaic expressions can often vary to a great extent. For example, 'in this paper we propose', 'in this study we propose' and 'in this paper we propose a new method to' are all regarded as formulaic expressions. Such a diversity of spans and forms causes problems in both extraction and evaluation of formulaic expressions. In this paper, we propose a new approach that is robust to variation of spans and forms of formulaic expressions. Our approach regards a sentence as consisting of a formulaic part and non-formulaic part. Then, instead of trying to extract formulaic expressions from a whole corpus, by extracting them from each sentence, different forms can be dealt with at once. Based on this formulation, to avoid the diversity problem, we propose evaluating extraction methods by how much they convey specific communicative functions rather than by comparing extracted expressions to an existing lexicon. We also propose a new extraction method that utilises named entities and dependency structures to remove the non-formulaic part from a sentence. Experimental results show that the proposed extraction method achieved the best performance compared to other existing methods., 21 pages, 11 figures

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.

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 this companion paper, we provide the details of the reproducibility artifacts of the paper "Visual Relation of Interest Detection" presented at MM'20. Visual Relation of Interest Detection (VROID) aims to detect visual relations that are important for conveying the main content of an image. In this paper, we explain the file structure of the source code and publish the details of our ViROI dataset, which can be used to retrain the model with custom parameters. We also detail the scripts for component analysis and comparison with other methods and list the parameters that can be modified for custom training and inference.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

The pervasive use of software systems and current threat environment demand that software systems not only survive cyberattacks, but also bounce back better, stronger, and faster. However, what constitutes a modern software system? Where should the security and resilience mechanisms be-in the application software or in the cloud environment where it runs? In this concept paper, we set up a context to pose these questions and present a roadmap to answer them. We describe challenges to achieving resilience and beyond, and outline potential research directions to stimulate discussion in the workshop.

The author claimed for the artifact associated with his paper the following ACM Reproducibility badges:(1) Artifact Available,(2) Artifact Evaluated-Functional,(3) Results Reproduced. After an in-depth review process, we agree to assign all the requested badges as we found it to meet the following requirements:i) it is uploaded on a persistent repository, accessible via a DOI; ii) it is well documented, consistent with the presented data, complete of all the necessary software sources and packages, and exercisable; iii) it is exhaustive in the reproduction of all the relevant data of the paper. Some curves in some reproduced plots are truncated, due to the computational limits imposed by the short-term deadline of the review process. Nevertheless, the overall trends are respected, and the curves are supporting the paper's claims.

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

Critical flaws continue to exist at the level of domain, requirement, and/or design specification, and specification verification (i.e. to check whether a specification has desirable properties) is still one of the most important challenges in software/system engineering. CafeOBJ is an executable algebraic specification language system and domain/requirement/design engineers can write proof scores in CafeOBJ for improving quality of specifications by the specification verification. This paper describes advances of the proof scores for the specification verification in CafeOBJ from the author’s point of view.

In this paper, we propose a data augmentation framework for Optical Character Recognition (OCR). The proposed framework is able to synthesize new viewing angles and illumination scenarios, effectively enriching any available OCR dataset. Its modular structure allows to be modified to match individual user requirements. The framework enables to comfortably scale the enlargement factor of the available dataset. Furthermore, the proposed method is not restricted to single frame OCR but can also be applied to video OCR. We demonstrate the performance of our framework by augmenting a 15% subset of the common Brno Mobile OCR dataset. Our proposed framework is capable of leveraging the performance of OCR applications especially for small datasets. Applying the proposed method, improvements of up to 2.79 percentage points in terms of Character Error Rate (CER), and up to 7.88 percentage points in terms of Word Error Rate (WER) are achieved on the subset. Especially the recognition of challenging text lines can be improved. The CER may be decreased by up to 14.92 percentage points and the WER by up to 18.19 percentage points for this class. Moreover, we are able to achieve smaller error rates when training on the 15% subset augmented with the proposed method than on the original nonaugmented full dataset.

Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time -- where each individual key-point may be critical in the targeted application -- and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining., to appear in ISSTA 2021

The emerging of information technology (IT)and operational technology (OT) convergence has driven the smart grid technology adoption in the European (EU) energy system for better visibility and automated controllability. On the other hand, the energy system infrastructures can be threaten by the cyber attacks due to the increasing of information and communication technology integration. The cyber vulnerabilities are caused by the increasing of internet connection and the application complexities. It is crucial to identify essential skills in the field of cyber security protection and defense for the students. This paper presents the outcome of a literature review and a workshop with stakeholders from industry and academia about the state of the art and trends in the education of cyber security in smart grids.

Due to the increasingly growing demand for user identification on cell phones, PCs, laptops, and so on, face anti-spoofing has risen to significance and is an active research area in academia and industry. The detection of the real face then recognize it present an important challenge regarding the techniques that can be used to spoof any recognition system like masks, printed photos. This paper we present an anti-spoofing face method to solve the real-world scenario that learns the target domain classifier based on samples used for training in a particular source domain. Specifically, with the conventional regression CNN, the Spatial/Channel-wise Attention Modules were introduced. Two modules, namely the Spatial-wise Attention Module and the Channel-wise Attention Module, were used at spatial and channel levels to improve local features and ignore the irrelevant features. Extensive experiments on current collections with benchmarks datasets verifies that the recommended solution will significantly benefit from the two modules and better generalization capability by providing significantly improved results in anti-spoofing.

Drowsiness can be dangerous when performing tasks that require constant attention, such as driving a vehicle. Sleepiness is correlated with a variety of physiological variables, such as eye closing, head movements, pulse rate, eye twitch rate, etc. Also, the yawn can be considered as an accurate indicator of drowsiness and fatigue. Yawning detection is very important for the safety purpose of drivers as it will let the driver know if he/she is getting drowsy. Driving at that moment may not be safe. Several automatic yawning detection techniques have been developed for driver's drowsiness monitoring system. Nevertheless, correctly detecting the yawning of the driver and predicting exhaustion in real-time situations is still a crucial challenge. In this paper, we will be reviewing various existing machine learning approaches for driver's yawning detection. In previous approaches, various classical machine learning algorithms such as viola-Jones, contour activation algorithm and SVM have been used for yawning detection, but these approaches failed to predict yawning in realtime situations. Using Deep learning techniques, we can make a real-time yawn detection system with high accuracy. We find that some precious Deep learning algorithms like CNN, RNN, LSTM, Bi-LSTM can detect the patterns with high accuracy. After the comparison of various algorithms and techniques, we find that with the help of Deep learning algorithms the yawning can be detected in real time with high accuracy.

Human recogntion technologies are gaining significant research attention, where the model can be trained to be more precise to recognize the poses performed by objects. Activity identification is a kind of problem, which needs more research consideration and improvement. There is also an increasing need to detect different poses of objects. To tackle the issue, different sensors like Gyroscope and Accelerometer are required to classify the data in the form of images using machine learning algorithms like SVM and CNN. These approaches help us in implementing real-time applicaions such as health monitoring, tracking sports activity and security. The paper also discusses about its benefits, limitations and prominent approach for human activity recognition.

With the upgrade of ICT infrastructure at a rocketed pace, diversification of applications and involvement of the Internet have increased. This evolution has led to the production, development, and implementation of various smart software/hardware solutions in multiple dimensions to make the process faster, smooth, accessible, and inclusive. The story of IoT (Internet of things) is a game-changer in several fields at different levels, starting from an office to home and industries. The term IoT was coined in 1999 by Kevin Ashton, since then it has seen an exponential growth, and now it has become ubiquitous. IoT may be summarized as the group of interrelated/interconnected devices embedded with sensors, software, actuator, and technology over the network for exchanging the data over the Internet without human involvement. Like any other technology, IoT has its pros and cons. In the last decade, cyber criminals have exploited many attack vectors, several of which can be used to exploit and launch attacks on IoT devices too. IoT attacks have increased substantially over the years, and there has been a jump of 900% in such attacks in 2019. Due to various constraints, IoT solutions don’t possess traditional security solutions or mechanisms to identify anomalies. Multiple security issues in IoT devices are persistent because of the limitation of computational power, hardware, and storage. These limitations make IoT devices more prone to cyber-attacks. IoT cyber-attacks are ranging from DDoS, MITM, brute-forcing, eavesdropping, privilege scaling to more sophisticated ransomware attacks and many more. This chapter will discuss multiple cyber-attacks, their mechanisms and TTPs, along with their impact on IoT infrastructure. This will also include security flaws that were exploited and related challenges in a holistic way at a single place. Statistical data leading to trend analysis along with shares of various attack campaigns shall also be highlighted in this study to give the readers more insight. In order to overcome these issues, remedial measures and mitigation policies will also be discussed.

Over 100 fatalities and more than 8000 injuries are reported on average every day in the US caused by motor vehicle accidents. In order to provide drivers a safer travel plan, we present a machine learning powered risk profiler for road segments using geo-spatial data. We built an end-to-end pipeline to extract static road features from map data and combined them with other data such as weather and traffic patterns. Our approach proposes novel methods for data pre-processing and feature engineering using statistical and clustering methods. Our model achieves significant performance improvement for risk prediction using hyper-parameter optimization (HPO) and the open source AutoGluon library to optimize the ML model. Finally, an enduser visualization interface is developed in the form of interactive maps. The results indicate 31% improvement in model performance compared to baseline when model is applied to a new geo location. We tested this approach on six major cities in the US. The findings of this research will provide users a tool to quantitatively assess accident risk at road segment level.

Trusted execution as offered by Intel's Software Guard Extensions (SGX) is considered as an enabler to protect the integrity and confidentiality of stateful workloads such as key-value stores and databases in untrusted environments. These systems are typically long running and require extension mechanisms built on top of dynamic loading as well as hot-patching to avoid downtimes and apply security updates faster. However, such essential mechanisms are currently neglected or even missing in combination with trusted execution. We present sgx-dl, a lean framework that enables dynamic loading of enclave code at the function level and hot-patching of dynamically loaded code. Additionally, sgx-dl is the first framework to utilize the new SGX version 2 features and also provides a versioning mechanism for dynamically loaded code. Our evaluation shows that sgx-dl introduces a performance overhead of less than 5% and shrinks application downtime by an order of magnitude in the case of a database system.

Attention to the environment and its problems has undergone unprecedented growth in recent years [...]