Reviewerly/exHarmony huggingface dataset:
Enhancement of Sensor-based User Identification using Data Augmentation Techniques

Mobile devices such as smartphones are increasingly being used to record personal, delicate, and security information such as images, emails, and payment information due to the growth of wearable computing. It is becoming more vital to employ smartphone sensor-based identification to safeguard this kind of information from unwanted parties. In this study, we propose a sensor-based user identification approach based on individual walking patterns and use the sensors that are pervasively embedded into smartphones to accomplish this. Individuals were identified using a convolutional neural network (CNN). Four data augmentation methods were utilized to produce synthetically more data. These approaches included jittering, scaling, and time-warping. We evaluate the proposed identification model's accuracy, precision, recall, F1-score, FAR, and FRR utilizing a publicly accessible dataset named the UIWADS dataset. As shown by the experiment findings, the CNN with the time-warping approach operates with very high accuracy in user identification, with the lowest false positive rate of 8.80 % and the most incredible accuracy of 92.7%.
Introducing Visual Studio Code

Visual Studio Code is not just another evolved Notepad with syntax colorization and automatic indentation. Instead, it is a very powerful code-focused development environment expressly designed to make it easier to write web, mobile, and cloud applications using languages that are available to different development platforms and to support the application development life cycle with a built-in debugger and integrated support for the popular Git version control engine.
On Worst-Case Learning in Relativized Heuristica

A PAC learning model involves two worst-case requirements: a learner must learn all functions in a class on all example distributions. However, basing the hardness of learning on NP-hardness has remained a key challenge for decades. In fact, recent progress in computational complexity suggests the possibility that a weaker assumption might be sufficient for worst-case learning than the feasibility of worst-case algorithms for NP problems. In this study, we investigate whether these worst-case re-quirements for learning are satisfied on the basis of only average-case assumptions in order to understand the nature of learning. First, we construct a strong worst-case learner based on the assumption that DistNP ⊆ AvgP, i.e., in Heuristica. Our learner agnostically learns all polynomial-size circuits on all unknown P/ poly-samplable distributions in polynomial time, where the complexity of learning depends on the complexity of sampling examples. Second, we study the limitation of relativizing constructions of learners based on average-case heuristic algorithms. Specifically, we construct a powerful oracle such that DistPH ⊆ AvgP, i.e., every problem in PH is easy on average, whereas UP ∩ coUP and PAC learning on almost-uniform distributions are hard even for 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n/w(1og n)</sup> - time algorithms in the relativized world, which improves the oracle separation presented by Impagliazzo (CCC 2011). The core concept of our improvements is the consideration of a switching lemma on a large alphabet, which may be of independent interest. The lower bound on the time complexity is nearly optimal because Hirahara (STOC 2021) showed that DistPH ⊆ AvgP implies that PH can be solved in time 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(n/ log n)</sup> under any relativized world. The full version of this paper is available on ECCC [1].
Teacher Identity of Non-native Korean Student Teachers

The present study investigates the teacher identity of non-native Korean student teachers. Data were collected from international students majoring in Korean language education through a survey and interviews. The results showed: 1) the results of the survey indicate that their perception of competence as a Korean language teacher showed the lowest average while efforts to improve teacher professionalism showed the highest of the four variables of teacher identities; 2) the doctorate students showed the strongest teacher identities while the undergraduate students showed the lowest; 3) the students’ teacher role identities were mainly formed by their personal experiences; 4) the perception and terms of employment of Korean language teacher at home country had a great influence on deciding whether to become a Korean teacher or not; 5) the students’ identities as non-native teachers cause anxiety in teaching despite recognition of their strengths as bilingual and Korean learners; and 6) the student teachers had both Korean learner and teacher identities, and the two identities varied in degree over the program of Korean language education. (Chonnam National University)
Compact Multiple Attribute-Based Signatures With Key Aggregation and Its Application

In blockchain-enabled applications, it is difficult to preserve privacy completely because of the openness of each signer’s public key. In this article, an efficient multiattribute-based signature scheme with key aggregation (M-ABS-KA) is presented that supports a flexible threshold predicate. This construction aggregates the public keys and provides a compact signature that is verified as a group, which realizes faster batch verification than confirming them individually. Furthermore, both the multisignature scale and the public key length are independent of the number of signers and attributes in this scheme. To the best of our knowledge, this M-ABS-KA scheme is the first multi-ABS protocol with an aggregated public key and fast batch verification. Based on the hardness of the computational Diffie–Hellman problem, it is formally proven that the proposal achieves unforgeability in the random oracle model and enjoys signer privacy. Additionally, the performance is evaluated in theoretical and experimental analyses with other related ABS schemes, and the results show that it is feasible and efficient in storage, bandwidth, and computational cost. Finally, as an illustrative application, we design a framework of the blockchain-enabled electronic health records assessment system based on this presented M-ABS-KA scheme.
Challenges Faced by Industries and Their Potential Solutions in Deploying Machine Learning Applications

Across all sectors, organizations attempt to make efficiency savings and performance improvements by incorporating machine learning (ML) into commercial application services. However, in comparison to traditional software applications, design, deployment, and maintenance of ML applications is more complicated. In particular, ML introduces new challenges of data availability, concept drift, scalability, and technical debt. In this paper, we introduce some of the practical challenges that arise when deploying ML applications, and describe potential solutions. Our analysis is based on experience designing and deploying a commercial spend classification service.
Big Data: Towards a Collaborative Security System at the Service of Data Quality

Big Data often refers to a set of technologies that process large volumes of heterogeneous data. “Data Security” and “Data Quality” are two essential aspects of any data-centric project. On the one hand, Data Security System aims to protect the Confidentiality, Integrity and Availability of data. On the other hand, Data Quality Management System ensures the monitoring, evaluation and improvement of many data characteristics such as Accuracy, Consistency, Completeness and Freshness. Much research has pointed out that these two systems, Security and Quality, can be mutually blocked. Assessing or improving data quality may require extended read and/or write access to the data which may not be permitted by the security policies. In this paper, we demonstrate that it is possible to use security systems to improve data quality thanks to a dynamic access control framework dedicated to collaborative and distributed environments. Our solution consists in extending the PolyOrBAC framework, initially designed to control access in collaborative platforms, to make it a dynamic data distribution platform. Thanks to this new architecture, it will be possible to eliminate duplicates in distributed and big data environments, reduce the amount of data and improve its quality.
Fusing Global Gabor Feature and Local Binary Pattern for Texture Image Recognition

Texture is a fundamental visual feature in human vision, and texture image recognition is an important part of artificial intelligence. However, traditional texture recognition methods can’t effectively utilize global and local texture features simultaneously, which restricts the recognition performance of traditional methods. Therefore, this work combines global Gabor feature and local binary pattern to further improve the performance of texture image recognition. Firstly, a multi-scale image pyramid space is generated to reflect the scale variation of texture image. Secondly, Gabor filtering is used in the image pyramid space to extract the global Gabor feature, which is used as the global texture feature. Thirdly, the completed local binary count algorithm with multiple radii is applied to original image to extract the local binary pattern, which is used as the local texture feature. Finally, the extracted global and local texture features are fused to recognize the texture image by the nearest subspace classifier (NSC). The experimental results show that the proposed method can achieve state-of-the-art recognition accuracy with high efficiency. In addition, the proposed method is robust to scale variation and the number of training samples in texture recognition task.
Exposing Video Frame Removal via Deep Features

In recent years, with the rapid increase of video data, its security problems have gradually been exposed, and people have begun to pay attention to it. Among them, video frame deletion is a common video forgery attack. In this paper, we propose a video frame deletion detection method using deep features. We first use the deep features of AlexNet pre-trained on the ImageNet dataset to calculate the inter-frame distortion level. After that, a feature enhancement method based on the local average distortion level is proposed to eliminate the fluctuation of the video content and obtain the relative distortion level. Finally, the generalized ESD test is used to detect the frame deletion points in the video. We created two datasets containing large-scale complex content scenarios for testing the proposed method. The experimental results show that the F1 score of the proposed method reaches 90.26%, which is significantly better than the existing methods on the created dataset.
A study of technical support for artificial intelligence systems applied to knowledge management systems

Under the current international competitive situation, knowledge is considered as a strategic resource that can generate sustainable competitive advantage and the most value, and an enterprise is not only a simple economy, but also a knowledge body rich in various scientific and technological theories. When knowledge becomes the core competitive knowledge of an enterprise, knowledge management requires more information technology provided by artificial intelligence to operate and share knowledge. Knowledge management can be effectively carried out through the assistance of artificial intelligence systems. AI systems in knowledge management systems are mainly divided into knowledge-based expert systems, neural networks and case-based reasoning systems in terms of function, and exploring the due of AI technology in knowledge management systems not only helps to improve the efficiency of knowledge management, but also provides exploration ideas for AI system research.
Integrating a General Search Agent into an Imperative Programming Language

Search is one of the more common strategies used by problem-solving agents. For many hard problems, a backtracking search can be the most effective approach for finding a solution. Logic programming languages provide, as a core feature of the language, the mechanics for the search behind the scenes without requiring the user to specify the algorithm. However, this functionality has been generally left out of the core features of imperative programming languages. In this work we propose integrating a search agent into an imperative programming language through a search construct in the syntax. By doing so, our intention is that the user may apply a model-based approach to problem solving. We extend the Go programming language with this search construct and develop an agent to perform the search. The algorithm behind the search is implicitly handled by the agent. We demonstrate the effectiveness of this search construct through solutions to an exhaustive search over a file-system, the NQueens problem, and the monkey and the bananas problem.
UIRNet: Facial Landmarks Detection Model with Symmetric Encoder-Decoder

One of the challenging problems for facial landmarks detection is learning important features from faces that contain different deformation of face shapes and pose. These important features include eye centres, jawline points, nose points, mouth corners etc that are helpful in various computer vision-related applications. The detection of facial landmarks is difficult when faces have a lot of variation in different conditions. These conditions could be various imaging conditions such as illumination, occlusion, or head poses. In this paper, we propose a deep learning-based facial landmarks detection model called Unet-Inception-ResNet (UIRNet) to predict distinct feature points. The model predicts 68-point landmarks from the detected faces from digital images or video.
Hand Gesture Recognition System for Games

Video games are among the most popular forms of entertainment in the modern world. However, many gamers with physical disabilities are impeded by traditional controllers. While accessories such as foot pedals and enlarged buttons exist, many accessible gaming setups can end up costing hundreds of dollars. The solution to this problem must be obtainable by gamers of all demographics, and ideally, incorporate items most people already own. The Hand Gesture Recognition System for Games comes to the rescue. Most laptop computers and many desktops come equipped with a webcam, so naturally, that would be the starting point. Users would be able to perform various hand gestures, with each being mapped to a set of button combinations on a virtual gamepad. As gesture detection would have to be done in real-time, fast Computer Vision libraries such as OpenCV are needed to process images. This establishes the basis for the research, a program that can be deployed on any computer with a webcam to instantly create a gamepad out of thin air. The final program features an intuitive user interface with customizable game profiles that can be saved to or loaded from storage. The program captures webcam input 60 times per second, performing multiple levels of processing on each image. Using techniques such as thresholding, gaussian blurring, and grayscale conversion, an ideal image is fed to OpenCV&#x2019;s contour detection algorithm. By calculating the angles between contours, the number of fingers held up can be determined. When a gesture is detected, the program communicates with a kernel-mode driver to send controller inputs directly to games. The result is a low latency controller with real-world applications. This research work is designed to enabling user customization allows for any Xinput compatible game to be controlled. This allowed for the implementation of not just racing games and driving simulators, but also first-person shoots and side-scrolling platformers.
A Review on Machine Learning Methods for Credit Card Fraud Classification

Since the liberalization of the Indian economy in 1991, the banking sector has expanded and transformed significantly. Despite the fact that the banking industry is generally well-regulated and overseen, it faces its own set of issues in terms of ethical behavior, financial crisis, and corporate governance. Credit card is also major factor in which different kinds of frauds occurred. In this review will discuss about types of credit card fraud and its classification based on different machine learning methods. Also gives the future direction for cash-out fraud detection method.
Vehicle Speed Detection Using Kalman Filter and DBSCAN

Many traffic accidents occur across the planet as a result of rash driving. Because there&#x0027;s no efficient way to regulate or monitor the speed of cars on the road, the population of traffic has risen dramatically. This technique aids within the monitoring of car speeds so as to stop collisions. The footage taken with the help of a camera is employed to determine the vehicle&#x0027;s speed. However, as technology advances, several regulatory organizations are requesting some form of computerized technology to tackle the issues of excessive speeding. In this instance, we design a scheme that can identify vehicles that are exceeding the maximum speed restriction set by the individual roads or highways. The project is divided into three sections: speed detection, picture capturing and transmission, and image analysis. The microwave Doppler radar sensor utilized in the speed detecting gadget operates on the idea of the Doppler effect. The speed is compared to the predetermined threshold, and if the speed restriction is exceeded, the camera is activated. The data is depicted in the form of a clip, and is captured with a camera with a 1280x1024 screen size. Four videos of identical resolution are taken for the data base. After foreground detection and DBS can with the help of bounding boxes speed of particular vehicle is detected. The motion of the vehicle is detected using Kalman filter. We can also theoretically estimate the average velocity using number of video frames.
Information Design for Augmented Reality Challenges and Concerns of Interdisciplinary Practice

New media paradigms, supported by the information society and new technologies, pose challenges to information design that integrates new practices and knowledge in its activity. Augmented Reality focuses on the user’s natural view field enrichment by consistent integration of text, images, symbols, and interactive three-dimensional objects in real-time. Augmented Reality presents singular characteristics that admit new concepts and conventions for the information design field. In this work, we are interested in reflecting on the inter and transdisciplinary character of information design and the extent to which its historical trajectory reflects these characteristics and the adaptation to new media such as the case of Augmented Reality. We are also interested in understanding the state-of-the-art concerning visualization of information using Augmented Reality, which topics are most studied, and which terms most used? Finally, we will analyze the challenges and concerns that Augmented Reality poses to design and information designers.
A Lacunarity Based Index for Spatial Heterogeneity

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Earth and Space Science. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]A Lacunarity Based Index for Spatial HeterogeneityAuthorsRyanScottiDHawwaKadumiDGiuliaSalmasoMarcCalafRaul BayoanCalSee all authors Ryan ScottiDCorresponding Author• Submitting AuthorPortland State UniversityiDhttps://orcid.org/0000-0003-2810-7574view email addressThe email was not providedcopy email addressHawwa KadumiDKarlsruhe Institute of TechnologyiDhttps://orcid.org/0000-0002-4082-6363view email addressThe email was not providedcopy email addressGiulia SalmasoUniversity of Utahview email addressThe email was not providedcopy email addressMarc CalafUniversity of Utahview email addressThe email was not providedcopy email addressRaul Bayoan CalPortland State Universityview email addressThe email was not providedcopy email address
Classification of Text Data in Healthcare Systems – A Comparative Study

The number of text classification applications is increasing in parallel with the rise of artificial intelligence applications in the healthcare domain. It is vital to collect, classify, disseminate and manage information for applications used in healthcare systems. However, the process of classifying text data is of particular importance. In this chapter, we present several text classification techniques and algorithms used in healthcare systems. Also, we present a novel methodology to tune the text classification process. Further, we provide the results of a comparative study utilizing presented techniques and classifiers on a public healthcare data set.
Increasing Patron’s Outreach and Engagement through Relationship Marketing: A Case Study

Increased diversity, demographics, and population shifts in higher education have led many academic libraries to reexamine their marketing efforts and adopt strategies that promote relationships and inclusivity. This requirement has become more prominent among libraries serving a highly diverse user community, particularly in a more racially and ethnically diverse campus. Adopting a marketing model that cultivates more meaningful long-term relationships with customers ensures long-term satisfaction and loyalty to the library brand. Using marketing and promotional examples from the James White Library, this article addresses the academic library’s relationship marketing strategy in an increasingly multicultural higher education environment.
ACTIVE LEARNING: EXPERIENCES AND EDUCATIONAL PERSPECTIVES OF THE APPLICATION OF AUGMENTED REALITY IN EDUCATION AND TRAINING OF CADETS

J. Tuta1, L. Luić 1, D. Boras2, D. Radovac2 1University North (CROATIA) 2University of Zagreb (CROATIA)
Fault aware intelligent resource allocation using Big Bang-Big Crunch trained neural network for cloud infrastructure

This Ongoing COVID-19 epidemic situation, which has resulted in the loss of lives and economics. In this scenario, social distancing is the only way to prevent ourselves. In such a scenario to boost the economy, a globally large number of industries and businesses have shifted their system to cloud-like education, shipping, training and many more globally. To support this transition cloud services are the only solution to provide reliable and secure services to the user to sustain their business. Due to this, the load over the existing cloud infrastructure has drastically increased. So it is the responsibility of the cloud to manage the load over the existing infrastructure to maintain reliability and serve high-quality services to the user. Task allocation in the cloud is one of the key features to optimize the performance of cloud infrastructure. In this work, we have proposed a prediction-based technique using a pre-trained neural network to find a reliable resource for a task based on previous training and history of cloud and its performance to optimize the performance under the overloaded and under loaded situation. The main aim of this work is to reduce the fault and provide high performance by reducing scheduling time, execution time and network load. The proposed model uses the Big Bang Big Crunch algorithm to generated huge datasets for training our neural model. The accuracy of the BB-BC-ANN model is improved with 98% accuracy.