Reviewerly/exHarmony huggingface dataset:
Are We Indeed So Illuded? Recency and Frequency Illusions in Dutch Prescriptivism

In 2005, Arnold Zwicky posited two misapprehensions about language: the Recency Illusion, or the false idea that certain language variation is new, and the Frequency Illusion, the erroneous belief that a particular word or phrase occurs often. Since their conception, these concepts have received widespread attention in popular scientific linguistics, but quantitative research investigating their application is scarce. The purpose of this paper is to provide an empirical investigation of Zwicky’s proposed illusions. It does so by collecting statements about recency (‘this word is new’) and frequency (‘this construction occurs often’) from a database of Dutch prescriptive publications (1900–2018). I assessed their accuracy by comparing them to linguistic sources, including dictionaries, and usage corpora and other data. Our research showed that recency statements were rare, but that frequency statements, especially using high frequency terms such as vaak (‘often’), were commonplace. Compared to usage, most prescriptive recency and frequency statements for both lexis and grammar indeed constituted Zwickian illusions. This seems partly due to genuine erroneous or unsupported beliefs by authors, but also partly to prescriptive genre conventions and rhetorical choices. Our explorative research highlights the complex usage–prescriptivism interface, and argues for more research into this aspect of language perceptions.
Digital Literacy Skills Among Black Pre-Service Florida Teachers

Digital literacy and the practical application of technology are essential skills for pre-service teacher (PST) candidates. PSTs’ abilities to effectively implement and foster digital literacy among their students could be influenced by the degree to which digital literacy is emphasized in their teacher education preparation programs. Such disparity factors as age and race have been noted between digital natives and digital immigrants. This study uses a two-phase qualitative design to explore how Black PST candidates perceive (a) digital literacy, (b) digital literacy instruction during their programs at Florida colleges and universities, and (c) the degree of importance of teacher education faculty placed on digital literacy. Preliminary data was collected using a qualitative questionnaire and focus groups. Findings revealed four themes: (1) computer savvy is not the same as digital literacy, (2) personal uses do not transfer to professional skills, (3) more significant need for explicit instruction, and (4) Generation Alpha is on top.
Pre-computation Based Real-time Reference Image Selection Algorithm

To realize roaming by Image-Based Rendering (IBR) technology in large-scale complex virtual scenes, it is necessary to sample a large number of reference images in the scene. It will bring problems such as storing and scheduling a large number of reference images, and the real-time generation of virtual views. This paper proposes a real-time reference image selection algorithm based on pre-computation. Several viewpoints and view directions are preset in the scene, then the corresponding reference images are calculated and stored. According to the viewpoint and perspective of the user during roaming, the optimal reference image sets can be obtained directly by querying the lookup table; in addition, considering that the moving range of the viewpoint is small, the same reference image will be loaded repeatedly. To improve the efficiency of loading images, this paper proposes an optimal reference image selection algorithm and image caching mechanism. The experimental results show that the reference image selection algorithm based on pre-computation improves the quality by 4%-10%, and the efficiency is also improved.
Attention based on Pseudo 3D Convolution Residual Network for Action Recognition of Earth-Moving Machinery

Action recognition of earth-moving machinery plays an important part in the field of industry automation and unmanned monitoring. However, current action recognition methods suffer from the low accuracy of action recognition by complex background, especially when lots of irrelevant targets move in background. To solve this problem, an action video database on earth-moving machinery, which contains 476 videos, is built. Furthermore, attention based on pseudo 3D convolutional residual network is proposed. Specifically, this network first performs spatial two-dimensional convolution on the input video, and then performs one-dimensional convolution on time, and then integrates the channel attention mechanism into the network. The experiment results validate that our proposed network outperforms the state-of-the-art action recognition algorithms.
Expert System to Diagnose Dengue Fever

Expert system is a system that emulates experts to aid in decision making. This system can be applied in various categories such as diagnosis, prediction, interpretation, and others. Expert System to Diagnose Dengue Fever is a web-based system which is integrated with prolog language in order to provide rules for dengue fever detection. The aims of this research are to study dengue fever symptoms and other illnesses related to the fever, to design an inference engine, and to build an expert system. The challenges faced while developing this expert system were the complexity of prolog codes and their integration with the web development. In this system, rules were developed by prolog language which define dengue fever and accuracy based on input from the user. This system is expected to aid users in self-detecting early symptoms of dengue fever before seeing the doctors.
Current Research on Theory and Practice of Digital Libraries: Best Papers from TPDL 2019 &amp; 2020

This volume presents a special issue on selected papers from the 2019 & 2020 editions of the International Conference on Theory and Practice of Digital Libraries (TPDL). They cover different research areas within Digital Libraries, from Ontology and Linked Data to quality in Web Archives and Topic Detection. We first provide a brief overview of both TPDL editions, and we introduce the selected papers.
Design and Implementation of English Speech Scoring Data System Based on Neural Network Algorithm

With the improvement of the performance of the Internet and other mobile terminals, English speech scoring technology based on neural network algorithms has become more and more favored by business development and daily life. Therefore, how to provide a voice scoring data system with high recognition performance and strong robustness has become more and more obvious, and it is also particularly important. The purpose of this paper is to study the design of English speech scoring data system based on neural network algorithm. This article makes full use of the freedom of neural network learning and the adaptability of multiple data domains. This paper has developed a set of data system for scoring spoken English. Students can use the system to simulate film dubbing, and the system can evaluate the multi-index data in the recordings submitted by the students. Experimental research shows that the improved BP algorithm used in this paper has a higher oral English recognition rate than the traditional BP algorithm by about 10%, and the optimized algorithm training performance and recognition rate are significantly better than the traditional algorithm.
End-to-end Characterization of Game Streaming Applications on Mobile Platforms

With the advent of 5G, supporting high-quality game streaming applications on edge devices has become a reality. This is evidenced by a recent surge in cloud gaming applications on mobile devices. In contrast to video streaming applications, interactive games require much more compute power for supporting improved rendering (such as 4K streaming) with the stipulated frames-per second (FPS) constraints. This in turn consumes more battery power in a power-constrained mobile device. Thus, the state-of-the-art gaming applications suffer from lower video quality (QoS) and/or energy efficiency. While there has been a plethora of recent works on optimizing game streaming applications, to our knowledge, there is no study that systematically investigates the &lt;QoS, Energy&gt; design pairs on the end-to-end game streaming pipeline across the cloud, network, and edge devices to understand the individual contributions of the different stages of the pipeline for improving the overall QoS and energy efficiency. In this context, this paper presents a comprehensive performance and power analysis of the entire game streaming pipeline consisting of the server/cloud side, network, and edge. Through extensive measurements with a high-end workstation mimicking the cloud end, an open-source platform (Moonlight-GameStreaming) emulating the edge device/mobile platform, and two network settings (WiFi and 5G) we conduct a detailed measurement-based study with seven representative games with different characteristics. We characterize the performance in terms of frame latency, QoS, bitrate, and energy consumption for different stages of the gaming pipeline. Our study shows that the rendering stage and the encoding stage at the cloud end are the bottlenecks to support 4K streaming. While 5G is certainly more suitable for supporting enhanced video quality with 4K streaming, it is more expensive in terms of power consumption compared to WiFi. Further, fluctuations in 5G network quality can lead to huge frame drops thus affecting QoS, which needs to be addressed by a coordinated design between the edge device and the server. Finally, the network interface and the decoder units in a mobile platform need more energy-efficient design to support high quality games at a lower cost. These observations should help in designing more cost-effective future cloud gaming platforms.
Detecting Credit Card Frauds Using Isolation Forest And Local Outlier Factor - Analytical Insights

The usage of credit cards for online shopping and regular purchases is exponentially increasing and the fraud is related with it. A large number of fraudulent transactions are made every day. Moreover, it is become one of the rising issues. Therefore, it is basic that credit card organizations are skilled to perceive false transactions so clients are not charge for things which they didn't buy. Machine learning is one of the techniques considered as most successful to identify fraud. This paper offers proposed system for detection of fraud transactions with named FDS (Fraud Detection System) which can be used in a bank and in a organization where credit cards are used for transactions. Additionally, the paper illustrates various methods for fraud identification, compare all of them in terms of performance measures such as precision, and accuracy, to analyze the previous records of buyers and extract behavioral patterns. The authors have concentrated on the data pre-processing data sets just as sending of numerous anomaly identification algorithms such as Isolation Forest and Local Outlier Factor for calculation on the given PCA transformed exchange data that gives better result compare to usual classification algorithms.
Forensic Analysis of the Bumble Dating App for Android

Mobile applications that facilitate interaction between people have grown in popularity and, as a result, the number of e-dating applications have expanded. In these types of applications, there is usually a trade-off between user privacy and safety. On one hand, users want to keep their data as private as possible, on the other hand, user identification forces accountability, which, hopefully, will foster the development of responsible behaviors and minimize abuses. The Bumble e-dating app has been growing in popularity and differs from other apps by giving women the power to make the first contact after a match. Their main goal is to prevent women’s harassment. In this work, we study the digital artifacts that can be found after the use of the Bumble app on Android devices. Despite applying many measures to ensure data protection, it was possible to obtain information that identifies users and exchanged messages. The data structure stored on the device is described, as well as the artifacts with forensic value for an investigation. Moreover, a script was created to parse and visualize the main forensic artifacts of the Bumble app.
NER Based on feed-forward depth neural network

The named entity recognition technology refers to the identification of human names, place names, institutions and other nouns from the text, is the key basic work of the information extraction, question-and-answer system, machine translation and other applications, its research is in full swing. In this paper, based on feed-forward depth neural networks, in the method of window, the named entities in text are identified and a series of comparative experiments are carried out. The experiments show that in the named entity recognition the reasonable settings for the window, the replacement processing of low-frequency words and the use of BIO tags can improve the entity recognition performance of the feed-forward depth neural network model. The data comparison basis is obtained for subsequent research on the identification of named entities based on LSTM depth neural network.
Edutainment with a Mixed reality pop-up book for children

In this paper, I present an interactive digital pop-up book for children. The book has been designed with the aim of improving the mode of storytelling for children. It allows children to search for a story in a fun and interactive way. Through this digital pop-up book, I hope to bring out the dexterity of flipping through a pop-up book together with the technology..
Few-Shot Visual Relationship Co-Localization

In this paper, given a small bag of images, each containing a common but latent predicate, we are interested in localizing visual subject-object pairs connected via the common predicate in each of the images. We refer to this novel problem as visual relationship co-localization or VRC as an abbreviation. VRC is a challenging task, even more so than the well-studied object co-localization task. This becomes further challenging when using just a few images, the model has to learn to co-localize visual subject-object pairs connected via unseen predicates. To solve VRC, we propose an optimization framework to select a common visual relationship in each image of the bag. The goal of the optimization framework is to find the optimal solution by learning visual relationship similarity across images in a few-shot setting. To obtain robust visual relationship representation, we utilize a simple yet effective technique that learns relationship embedding as a translation vector from visual subject to visual object in a shared space. Further, to learn visual relationship similarity, we utilize a proven meta-learning technique commonly used for few-shot classification tasks. Finally, to tackle the combinatorial complexity challenge arising from an exponential number of feasible solutions, we use a greedy approximation inference algorithm that selects approximately the best solution.We extensively evaluate our proposed framework on variations of bag sizes obtained from two challenging public datasets, namely VrR-VG and VG-150, and achieve impressive visual co-localization performance.
Improving the Model-Based Systems Engineering Process

Abstract Modern embedded software systems are becoming more and more complex. Engineering embedded systems raise specific challenges that are rarely present in other software engineering disciplines due to the systems’ steady interactions with their environment. Research and industry often describe embedded systems as component and connector models (C&amp;C). C&amp;C models describe the logical architecture by focusing on software features and their logical communications. In C&amp;C models, hierarchical decomposed components encapsulate features, and connectors model the data flow between components via typed ports. As extra-functional properties, for example, safety and security, are also key features of embedded systems, C&amp;C models are mostly enriched with them. However, the process to develop, understand, validate, and maintain large C&amp;C models for complex embedded software is onerous, time consuming, and cost intensive. Hence, the aim of this chapter is to support the automotive software engineer with: (i) automatic consistency checks of large C&amp;C models, (ii) automatic verification of C&amp;C models against design decisions, (iii) tracing and navigating between design and implementation models, (iv) finding structural inconsistencies during model evolution, (v) presenting a flexible approach to define different extra-functional properties for C&amp;C models, and (vi) providing a framework to formalize constraints on C&amp;C models for extra-functional properties for automatic consistency checks.
Effectively Leveraging Attributes for Visual Similarity

Measuring similarity between two images often requires performing complex reasoning along different axes (e.g., color, texture, or shape). Insights into what might be important for measuring similarity can be provided by annotated attributes. Prior work tends to view these annotations as complete, resulting in them using a simplistic approach of predicting attributes on single images, which are, in turn, used to measure similarity. However, it is impractical for a dataset to fully annotate every attribute that may be important. Thus, only representing images based on these incomplete annotations may miss out on key information. To address this issue, we propose the Pairwise Attribute-informed similarity Network (PAN), which breaks similarity learning into capturing similarity conditions and relevance scores from a joint representation of two images. This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e.g., due to fine-grained differences between them) and ignored for measuring similarity between the two images. Notably, while prior methods of using attribute annotations are often unable to outperform prior art, PAN obtains a 4-9% improvement on compatibility prediction between clothing items on Polyvore Outfits, a 5% gain on few shot classification of images using Caltech-UCSD Birds (CUB), and over 1% boost to Recall@1 on In-Shop Clothes Retrieval. Implementation available at https://github.com/samarth4149/PAN
A Genetic-based Fusion Approach of Persian and Universal Phonetic Results for Spoken Language Identification

Automatic Spoken language identification (LID) refers to the automatic process of identifying languages spoken in the audio files. Pure acoustic approaches have shown great potential in LID. Since acoustic approaches have become more and more popular, phonetic information has been largely overlooked. In this paper, we present a genetic-based fusion approach based on the score probabilities of two phonetic LID systems. There are two SVM classifiers trained on perplexities as their feature vectors which are obtained from phone language models of different phone recognizers. Two phone recognizers are here utilized; one decodes the speech file to a sequence of IPA alphabet, as a universal phone recognizer, and the other is a Farsi phone recognizer which is trained on FARSDAT databases. With the help of the genetic-based fusion approach, we will extract 54 weights. We have 27 languages in our database and 2 individual phonetic LID systems; therefore, we will achieve 54 weights for our fusion. The first 27 weights correspond to our system using a universal phone recognizer and the second 27 weights are related to our system with the Farsi phone recognizer. In the end, we use these weights to combine the results of each of our individual phonetic LID systems. The experimental results conducted on 27 languages within the NIST-LRE09 corpus demonstrated that the proposed fusion approach could greatly increase the classification accuracy of target languages. It should also be noted that we separate the files of each speaker and place them only in one set (train set, development set, or test set) to prevent speaker-related biases.
The Purpose of the Use Classroom Language

Currently, there are still many who cannot use English. The results of previous studies also stated that the cause of students not being able to speak English was because students thought learning English was complicated. English in everyday life is still rare, so students have difficulty understanding English. This study aimed to analyze the classroom language used by the English teacher and to analyze the purpose of using the classroom language during the learning process. This study uses a case study using descriptive qualitative research. The object of this research is 1 English teacher. Data were collected by observation and interviews. The technique used to analyze the data is descriptive qualitative and quantitative analysis. The results of this study indicate that first, the English teacher uses language based on the situations encountered during class. Second, the teacher chooses the class language based on the teacher's goals from the students during the learning process. This study categorizes three purposes of classroom language use: motivating, instructing, and guiding. Each plan has a different frequency in one meeting. The analysis results are supported by the teacher's statements obtained through interviews regarding how the situation requires certain types of language objectives. The use of classroom language has different purposes according to the current situation in the learning range.
Two-Fold Improved Poor Rich Optimization Algorithm based De-centralized Information Flow Control for Cloud Virtual Machines: An Algorithmic Analysis

Today, cloud computing is being employed in many of organizations, owing to its higher computational efficiency, cost-effectiveness and flexibility. Nevertheless, security is being the major obstacle that hinders the success of the cloud computing platform. Therefore, the de-centralized Information Flow Control (DIFC) has been suggested as an appropriate solution for overcoming the security issues in the cloud. In the DIFC, the Conventional access control and encryption technologies weren'table to control the propagation of the tenant's private data efficiently in the system. Therefore, a novel DIFC framework with a hybrid AFS-ECC encryption model has been introduced. Within the hybrid AES-ECC encryption model, the optimal key selection has been carried out with the newly introduced Two-Fold Improved Poor Rich Optimization Algorithm (TF-IPRO). In this research work, an algorithmic evaluation has been carried out to validate the efficiency of the proposed TF-IPRO that has been utilized for optimal key selection during the de-centralized Information Flow Control for cloud Virtual Machines. The proposed TF-IPRO model has been validated by fixing the random value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> as 0.2, 0.4, 0.6 and 0.8, respectively.
High-level axioms for graphical linear algebra

We focus on a modular, graphical language—graphical linear algebra—and use it as high-level language for calculational reasoning. We propose a minimal framework of axioms that highlight the dualities and symmetries of linear algebra, and showcase the resulting diagrammatic calculus. Our work develops a relational approach to linear algebra, closely connected to classical relational algebra. With the symmetrical high-level axioms we are able to provide a fully diagrammatic proof that a fragment of Graphical Linear Algebra is equivalent to matrix algebra.