Reviewerly/exHarmony huggingface dataset:
Authentication of Bi–Level QR Code with Dynamic Pattern Generation

This paper illustrates about a new Quick Response (QR) code to enhance data security with importance given to its wide use in information storage and authentication. The Bi-Level QR code add new abilities to standard QR Code which comprise of strengthening contemplating degree, which doesn't upset a similar old QR code examining procedure and development in privateers of primer QR code. The outstanding QR code utilized as first level of our Bi – Level QR code remains completely functional. It may be read with the aid of all standard application, without any restrict. Bi – Level QR code ensures privacy of records stored inside the 2d degree through a physical separation, between stages and reader utility skills.
Web Security Aware by using Naive Baye’s ML Technique

Web applications support many of our daily activities, but they often have security issues, and their accessibility makes them easy to use. This paper presents an analysis for finding vulnerabilities that directly address weak or absent of input validation. We present the techniques for finding security vulnerabilities in Web applications. We implement our proposed system with a machine learning technique (ML technique) to measure the accuracy and provide an extensive evaluation that finds all vulnerabilities in web applications. SQL injection, Cross-Site Scripting (XSS), HTTP and command inj1ection vulnerabilities are addressed in the proposed system and also Naive Bayes ML technique is used to calculate the accurateness. The experimental result shows the technique is more efficient and accurate.
Sequent Systems

After giving preliminary remarks and a brief explanation of the scope of this book in Sect. 1.1, we will introduce two sequent systems $$\mathbf {LK}$$ and $$\mathbf {LJ}$$ for classical logic and intuitionistic logic, respectively.
ON EFFICIENTLY CREATING TEMPORALLY BIASED SAMPLES OVER DATA STREAMS: BEYOND MEMORYLESS FUNCTIONS

When creating a sample over a data stream, we sometimes need to give a priority to the recent past, over the distant one.This can be achieved if the probability of each stream element to exist in the sample decays as time passes, according to a bias-function.In order to obtain such a sample, one has to access some elements of the sample when a stream element arrives.For the exponential bias-function, according to which the probability of a sample-element to be deleted at each time-point, is the same for all sample elements, the number of needed accesses is O(1).For any other bias-function, one needs to access O(n) elements per incoming stream element, where n is the sample-size.In this paper we present an algorithm that achieves O(1) amortized accesses for any bias-function such that at each time-point, the probability of a sample-element to be deleted increases over time.
Advanced Instance Management

In the last chapter, we learned how to create a Virtual Private Cloud (VPC) and specify our network topology. In this chapter, we are going to build on the VPC concepts, discussing how we can configure our instances in a VPC.
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Ace systems must work. In this position paper, we disconfirm the reenactment of 802.11b. In this position paper, we dishonor not simply that superpages can be made extensible, secure, and transformative, yet that the equivalent is legitimate for the territory character split [3].
Prediction of Solutions of Arithmetic and Logical Operations on the Basis of the Mathematical Model of Cognitive Digital Automata

An approach to the problem of solution prediction of arithmetic and logical operations on the basis of the mathematical model of cognitive digital automata (CDA) is proposed. A particular advantage of the proposed approach is that the training procedure can be performed on limited (minimum) training sets. Prediction or generation of solutions is performed on the basis of the mathematical model of CDA which is formed in the course of training. As a testbed for the approach, the modeling of an n-bit parallel adder was implemented. The mathematical model of the adder was formed, which made it possible to reproduce the entire truth table for the n-bit parallel adder. The results obtained could be useful as an alternative solution to a number of problems known for conventional feed-forward neural networks, e.g. on-the-fly learning and catastrophic forgetting.
INTERDISCIPLINARY META-LEVEL UNIFICATION SCHEME-INTERPRETATIONISM AND SOME PROBLEMS OF SYSTEMS, MODELS AND INSTRUMENTAL HERMENEUTICS

Overall, the current reality and the scientifically represented world are much more than hitherto characterized by problems of cross-disciplinary or “inter-areal” connections of diverse topical research fields within complex and highly interconnected real, model, and quasi artificial systems. Here, we will address the respected problem of the humanities and social sciences somewhat by the rather old-fashioned contrast to the natural sciences. In the main part, I shall sketch my own approach that I call (scheme-) interpretations or methodological interpretative constructivism. This methodological concept seems to me to make possible a bridge between the disciplines – at least by going higher level in one’s methodology. The next part will turn to the historical and methodological questions of text-interpretations or methodological hermeneutics thus amounting to a prominent special case of the scheme-interpretations approach. Finally, the last part draws some methodological conclusions regarding abstract interpretations of “reunification” between the different scientific disciplines and everyday understanding.
The Perceptions of Students, The Hospitality and Tourism Industry, and Tourism Polytechnics on Internships during the COVID-19 Pandemic

The Perceptions of Students, The Hospitality and Tourism Industry, and Tourism Polytechnics on Internships during the COVID-19 Pandemic 의 이용 수, 등재여부, 발행기관, 저자, 초록, 목차, 참고문헌 등 논문에 관한 다양한 정보 및 관련논문 목록과 논문의 분야별 BEST, NEW 논문 목록을 확인 하실 수 있습니다.
Keynote: Sensing human activities: challenges and opportunities

The ability to acquire and understand context is a key requirement to build intelligent, adaptive, and personalised services and environments. In the last decades we have experienced the impact of spatio-temporal awareness enabled by the localisation technologies of mobile computing. Pervasive computing has a major role in exploiting additional sensing modalities obtaining large streams of sensor data that, processed with AI techniques, can take context awareness to a new level, enabling human-centered applications that could not be considered before. This talk reviews the advances in this topic and the lessons learned. In particular, it considers sensor-based human activity recognition as a task to derive high level context from low level context data and sensors’ signals. The talk identifies open challenges and new research opportunities. Among the challenges there is the scarcity of annotated data as required by deep learning methods, the need to explain the activity prediction process, and the privacy threats involved in releasing personal data. Among the opportunities, some of the limits of purely statistical approaches could be effectively improved by combining them with reasoning on common knowledge in terms of ontologies or knowledge graphs. Data scarcity and privacy concerns may be also mitigated by a combination of techniques including active learning and distributed approaches like federated learning.
Local Random Feature Approximations of the Gaussian Kernel

A fundamental drawback of kernel-based statistical models is their limited scalability to large data sets, which requires resorting to approximations. In this work, we focus on the popular Gaussian kernel and on techniques to linearize kernel-based models by means of random feature approximations. In particular, we do so by studying a less explored random feature approximation based on Maclaurin expansions and polynomial sketches. We show that such approaches yield poor results when modelling high-frequency data, and we propose a novel localization scheme that improves kernel approximations and downstream performance significantly in this regime. We demonstrate these gains on a number of experiments involving the application of Gaussian process regression to synthetic and real-world data of different data sizes and dimensions.
Incidence of Malaria among the Pediatric Age Group Attending to MGM Hospital Warangal

Abstract Background: In India, malaria persist throughout the year and usually the incidence is more during rainy season. This necessitates the use of other laboratory methods to clinch the diagnosis, which is important in view of frequent relapses reported in vivax malaria and hence there is need for radical treatment. Considering all the above factors, a need is felt to evaluate the available lab diagnostic procedures for malaria, and also to know the trend of malaria epidemic at Warangal. Methods: It was a prospective study, conducted in the department of Microbiology, KMC Warangal. Paediatric age group with fever with chills and rigors followed by sweating were included in this research. Blood sample was collected thick and thin blood smears were prepared, stained by JSB or Leishmans or Giemsa. Parasight 'F' test and OptiMAL tests were used to detect the antigen. Chi square test was used to find the correlation between the parameters. Results: Total 300 blood samples were collected, malaria positivity was 24%, male female ratio was 1.05, statistically there was no significant difference. In area wise malaria positivity also statistically there was no significant difference. Age wise, maximum cases were diagnosed in 6 – 10 years group. Parasight F test was identified to be highly sensitive in the diagnosis of malaria among the paediatric age group. Conclusion: Falciparum malaria is identified to be the commonest malaria in this area which effect all the paediatric age groups and gender. Parasight F test was found to be the better technique in malaria diagnosis.
Detection of Malicious Social Boats Using Learning Automata with URL Feature

Bots have made an impact on a variety of social media platforms. Twitter has been hit particularly hard, with bots accounting for a sizable amount of its user base. These bots have been used for nefarious purposes like distributing fake information about politicians and increasing celebrities' perceived popularity. These bots have the ability to alter the outcomes of standard social media analysis. Malicious social bots have also been employed to spread incorrect information (for example, emailing fraudulent urls), which can have real-world effects. To detect such hostile behaviors, the suggested systems employ machine learning methods such as Naive Bayes and RF.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Single Anonymous • Conference submission management system : Morressier • Number of submissions received : 47 • Number of submissions sent for review : 47 • Number of submissions accepted : 22 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 46.8 • Average number of reviews per paper : 2 • Total number of reviewers involved : 17 • Contact person for queries : Name : Dr Geetha Ganesan Email : geetha@advancedcomputingresearchsociety.org Affiliation : Advanced Computing Research Society
The Movie Recommendation System using Content Based Filtering with TF-IDF¬¬-Vectorization and Levenshtein Distance

In this busy life people like to do things to make their mind calm and watching movies is one of the thing but due to large data of a movie exist in the world it is very difficult for the user to select a movie. They have to spend a lot of time in searching and selecting movie. This procedure is time consuming and difficult. So recommendation system make the things easy. Recommendation engines are trained to produce fast and accurate suggestions to users. This paper describes a movie recommendation system using content based filtering and data is processed using Term-frequency Inverse document frequency technique (TF-IDF) for vectorization. Cosine similarity method is used for similarity measure. The system is presented to the user through a web-hosted user-interface which offers a system architecture by considering the initial problem usually faced by recommendation systems, namely the cold start problem. The problem of lack of user preferences data is trying to be overcome by utilizing movies data. The raw data is processed using the TF-IDF algorithm and Vector Space Model to generate a data model. Then levenshtien distance with cosine similarity will improve the performance of existing system. Advantages of the system include efficient recommendations, correct suggestions. Future enhancements include user profiling, documentations and data acquirement through web scraping.
A Study on the Effective Pedagogy of Chinese Ba Construction for Korean Learners, Based on Construction Grammar Approach

The purpose of this study is to discuss the effective pedagogy for teaching Chinese Ba construction for Korean learners. Chinese Ba construction is used in several limited situations both syntactically and semantically as a so-called ‘disposal construction. Many Korean learners often make error sentences, and even advanced learners make errors as well, since Ba construction sometimes behave like a Korean object construction, so learners are confused easily. In order to help Korean learners acquire the exact meaning and correct usage of Chinese Ba construction, this study compares the difference between Korean object construction and Chinese Ba construction. As an effective teaching method, construction grammar approach is used, which points out the importance of scene, network, and prototypical verb in foreign language education. The pedagogy and materials presented in detail in this paper can be used in actual Chinese classes, and also the discussion in this paper will support the result of the previous studies with a new teaching and learning target, namely Korean learners of Chinese.
Crime Prediction Using Machine Learning Algorithms

The most serious security challenges we face in these turbulent times are terrorist attacks and the transmission of disease. length and breadth are measured in hundredths of a centimetre. On a daily basis, we see the most minor offences committed by ordinary citizens. Details of breaches and recurring cases of items should be applied to files to ensure that they are up to date. When it is known that a crime has been committed, people believe that disciplinary action will be taken, even if there is no means of knowing which one. The study of criminology helps to broaden our understanding of who is likely to become a suspect. In the midst of his attempts to identify and deter alleged criminals from reoffending the legal system, he is incorporating both computer science and deep learning. Anyone interested in learning more about the workings of the Chicago Police Force should visit "The Chicago Police Department Site." The Crime Timeline will keep track of all criminal activity as well as the time and date of any incident that occurs. The data collection and modelling have been completed; all that remains is on-line modelling and compilation. To address this question, we must first determine if the case history of K-grooming and other related methods will help with criminal prediction. The invention is typically used as a testing tool, but it can also be used in conjunction with other technologies. Based on internal or external metrics, an algorithm can estimate how easily law enforcement authorities may be able to track, anticipate, and cope with, or preempt, risks, such as the ratio of those sentenced to those arrested, with a life sentence to those awaiting the risk of life imprisonment.
STDC‐MA network for semantic segmentation

Semantic segmentation is applied extensively in autonomous driving and intelligent transportation with methods that highly demand spatial and semantic information. Here, an STDC-MA network is proposed to meet these demands. First, the STDC-Seg structure is employed in STDC-MA to ensure a lightweight and efficient structure. Subsequently, the feature alignment module is applied to understand the offset between high-level and low-level features, solving the problem of pixel offset related to upsampling on the high-level feature map. The approach implements the effective fusion between high-level features and low-level features. A hierarchical multiscale attention mechanism is adopted to reveal the relationship among attention regions from two different input sizes of one image. Through this relationship, regions receiving much attention are integrated into the segmentation results, thereby reducing the unfocused regions of the input image and improving the effective utilisation of multiscale information. STDC-MA maintains the segmentation speed as the STDC-Seg network while improving the segmentation accuracy of small objects. STDC-MA was verified on the validation dataset of Cityscapes. The segmentation result of STDC-MA attained 78.32% mIOU with the input of 0.5× scale, 4.92% higher than STDC-Seg.
Effective Plans during Earthquake Emergencies for Medical Treatment

The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted Convolution Neural Network (CNN) models for extracting earthquake detection, damaged building information and compared their performance. A sample dataset of damaged buildings, earthquake detection, was constructed by using multiple disaster images retrieved from our dataset. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.
Consistency-based Semi-supervised Learning Framework for Power Line Insulators Detection

Abstract It is commonly believed that fast and accurate object detection is the prerequisite for power line insulators automatic fault diagnosis. While the insulators detection task requires a huge number of labeled images to guarantee its performance, placing bounding boxes for every object in each image is time-consuming and requires professional knowledge. To alleviate this problem, we propose CASD, a two-stage semi-supervised learning framework for power line insulators detection based on consistency regularization and data augmentation. Firstly, we train a teacher model on labeled images, then generate pseudo labels for unlabeled images using the trained model, and the confidence-based thresholding is introduced to control the quality of pseudo labels. In the second stage, we apply strong data augmentation to unlabeled images and use both the labeled and unlabeled images to train the CASD. Consistency regularization is introduced in the calculation of unsupervised loss to improve the accuracy and robustness of the model. Finally, we compare CASD with supervised learning, data augmentation and self-training methods on insulators aerial image dataset. The results show that the proposed method performs better than all the three baseline methods, and the detection accuracy of CASD is far better than the supervised learning method in the case of less available images. The comparison results further prove the efficacy of the proposed method.
Growing threat of deepfakes in financial services

iProov's Aarti Samani describes the multiple deepfake fraud attacks now being directed at financial services firms, and how biometrics can help combat them.
Why zero trust security is better than biometrics and passwords

Passwords are notoriously insecure, and even biometric security has its flaws. Jonas Iggbom of Curity looks at the alternative of using a zero trust authentication approach, and what this involves in practice.
An upper bound on binomial coefficients in the de Moivre – Laplace form

We provide an upper bound on binomial coefficients that holds over the entire parameter range an whose form repeats the form of the de Moivre – Laplace approximation of the symmetric binomial distribution. Using the bound, we estimate the number of continuations of a given Boolean function to bent functions, investigate dependencies into the Walsh – Hadamard spectra, obtain restrictions on the number of representations as sums of squares of integers bounded in magnitude.
A quantitative assessment of uncertainty in the measurement of violin impact response

It is a commonly stated belief among violin players and luthiers that new violins require a period of “playing in” for the tone to develop. Several studies have worked towards an answer to this question, such as measuring the change in tone according to the human ear or the vibrational response of stimulated wood. As the effects of sustained excitation on the mechanical response of violins will likely be subtle, it is necessary to create a consistent method of measurement and to quantify the expected range of deviation among repeated measurements. We measure admittance (velocity/force) by tapping the bridge with a small modal impact hammer and recording the velocity response of the top plate near the opposite side of the bridge using a laser doppler vibrometer. The acoustic response is also measured in an anechoic chamber. Since measurements of the same violin on different days do not produce identical response curves, several methods of characterizing the deviation were developed and compared. These uncertainty metrics will be used in the second phase of the experiment to determine the significance of the results, and ultimately work towards a better understanding of the effects of breaking in violins.
Using agile methods to improve inclusion and team diversity / Utilização de métodos ágeis para melhorar a inclusão e a diversidade da equipe

Agile Methodologies have brought insights into how to develop software by focusing on individuals and valuing diversity in the teams. Some studies show that diversity builds better teams and delivers better results. However, there is a known lack of representativeness when discussing diversity for people with disabilities. In this context, this paper aims at identifying the leading prac- tices/techniques that allow an open concept of social inclusion and diversity in agile software development teams and propose any necessary adaptations to help wide diversity. We performed a literature review and raised a set of techniques that were presented to experts in two contexts: (i) within the software development industry and working under agile methodologies and (ii) with disabilities or people who coordinate teams with people with disabilities. The results show that some agile techniques can promote inclusion but is necessary for some adaptations. We verify that to encourage this diversity for people with disabilities, specific tools, knowledge of sign language and transformations in the environment is vital for supporting broad diversity.