Reviewerly/exHarmony huggingface dataset:
Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning

Inspired by the curvature of space-time (Einstein, 1921), we introduce Curved Contrastive Learning (CCL), a novel representation learning technique for learning the relative turn distance between utterance pairs in multi-turn dialogues. The resulting bi-encoder models can guide transformers as a response ranking model towards a goal in a zero-shot fashion by projecting the goal utterance and the corresponding reply candidates into a latent space. Here the cosine similarity indicates the distance/reachability of a candidate utterance toward the corresponding goal. Furthermore, we explore how these forward-entailing language representations can be utilized for assessing the likelihood of sequences by the entailment strength i.e. through the cosine similarity of its individual members (encoded separately) as an emergent property in the curved space. These non-local properties allow us to imagine the likelihood of future patterns in dialogues, specifically by ordering/identifying future goal utterances that are multiple turns away, given a dialogue context. As part of our analysis, we investigate characteristics that make conversations (un)plannable and find strong evidence of planning capability over multiple turns (in 61.56% over 3 turns) in conversations from the DailyDialog (Li et al., 2017) dataset. Finally, we show how we achieve higher efficiency in sequence modeling tasks compared to previous work thanks to our relativistic approach, where only the last utterance needs to be encoded and computed during inference.
Ray-based Interference Free Workspace Analysis and Path Planning for Cable-Driven Robots

This thesis studies the interference-free workspace (IFW) of arbitrary cable-driven robots (CDRs) for both cables and obstacles using the ray-based method. Continuing from this, the point-to-point path planning and verification technique to ensure that the resulting motion is within the workspace is also studied.
NEWS HEADLINE CONFIGURATION AND NEWSPAPER SELECTION IN UYO URBAN, NIGERIA

This article examined the extent to which news headline configuration constitutes a factor in newspaper selection by readers in Uyo Urban of AkwaIbom State of Nigeria. Specifically, the study aimed to: examine the influence of headline deck on the readers’ selection of newspapers; ascertain the role of headline grammar in newspaper selection among readers; examine the influence of headline sentence structure on readers’ selection of newspapers; and find out whether headline font sizes play a role in respondents’ newspaper selection. The survey method was adopted in the study and the population comprised all the newspaper readers in Uyo, Nigeria, numbering 472,534 people. The sample size was 383 and the questionnaire was the research instrument. Findings showed that the readers: selected newspapers whose news headlines were arranged in one or two-deck formation; were attracted to newspapers whose headlines obeyed the rules of concord; selected newspapers whose headlines were set in the subject-predicate structure; and were attracted to headlines with conspicuous font sizes. It was therefore recommended that editors should always be mindful of grammatical, editorial and stylistic issues while casting newspaper headlines. Keywords:-Headlines, Configuration, News, Structure, Format, Deck, Grammar.
Reform and Practice of Practical Teaching of Applied Undergraduate Courses Based on “Internet +”: Taking the Course “Application of Engineering Costing Software” as an Example

The reform of teaching methods is the core of classroom revolution. With the rapid advancement of information technology, the technical means are constantly being updated. This study takes the practical course “Engineering Costing Software Application” of engineering costing majors as an example and implements a “classroom revolution” by means of “Internet + education,” creating a new mode of teaching and forming a hybrid teaching mode based on “Internet +,” which organically integrates online teaching and physical classroom. Through practical exploration, this teaching method can stimulate students’ interest in learning; thus, it is worth promoting.
Efficient Data Loading for Deep Neural Network Training

Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
The 21st Century Classrooms in India

The twenty-first century is a time of innovation and cataclysmic change.(Puranik 2020).India has a tremendous need for employable workforce and the New Education Policy (2020) is focusing on providing the industry specific skills to the students. Skills like critical thinking, problem solving, teamwork, innovation, digital literacy, and flexibility (Herodotou et al, 2019) need to be developed through innovative teaching methods. The nurturing of these employable abilities requires a shift from fact based traditional teaching to interactive learning. Each and every teacher tries to bring out the best in their students through creative teaching and evaluation techniques. Keeping in mind the pace of learners, the teachers use variety of tools in the classroom (physical or online). The pandemic has forced us to move from normal physical classrooms to Google classrooms and adapt hybrid style of learning. (ISR Report 2022).To enhance the necessary abilities of the teachers and students, the most effective techniques for teaching and learning must be found. The paper discusses the techniques and tools used by teachers for making classrooms more engaging. It is said that in order to develop fresh concepts or a method to unearth something new, teachers and students must be proactive in their research, exploration, and usage of a variety of tactics.(Sinha). The researcher focuses on bringing out the widely used and popular ICT applications, use of new methods and evaluation techniques for the benefit of the students.
Printed Ottoman Text Recognition Using Synthetic Data and Data Augmentation

Abstract Ottoman script is an Arabic alphabet-basedscript as well. It was a writing system of theTurkish language for several centuries until it was replaced with the modern Turkish script,which is based on the Latin alphabet, in 1928. With the ever increasing digitization campaigns, millions of Ottoman documents are coming to light. But, their contents are not directly accessible, nor they are digitally editable and searchable. OCR and text recognition technologies can bea solution to this problem in the form of auto-mated and semi-automated conversion systems. This study presents a DL-based characterrecognition system for the printed Ottoman script. We first generate a synthetic text image dataset from a text corpus, and then augment it using some image processing methods. We develop a hybrid Convolutional Neural Network-Bidirectional Long Short Term Memory recognizer and train it with the original and the augmented datasets. Finally we apply a Transfer Learning procedure for adapting the system to real image data. The proposed system obtains 0.16 CER on a test set containing line images from a historical printed Ottoman book.
Students’ Perspectives Toward Grammar Teaching Methods in English Language Education Study Program

The purpose of this research is to elucidate students' perspectives on grammar teaching methods. It observed 5th-semester students from the 2019 academic year, in English Language Education Study Program. Five students were chosen at random from each class, so the total sample for this study was 25. The descriptive quantitative research method was applied to this study. The information was gathered using a questionnaire that represented two grammar teaching methods: explicit and implicit. Each one consists of six statements, with several options for students to choose from, including strongly agree, agree, neutral, disagree, and strongly disagree. The findings revealed that each student has a different perspective on what method is required. The questionnaire results showed that approximately 80% of students agreed with every statement in the explicit method, and the majority of them chose neutral to the statements in the implicit method, with an average percentage of 37%-70%. As a result, students can be taught using both implicit and explicit methods, as both have advantages, and it is determined by the needs of each student.
Wasiilah Google Sites Fii tadriis Maharah Al-Qira’ah As Samitha Fii Al-Madrasah Al-Aliyah Al-Hukumiyah 2 Kuantan Singingi

The main problem in this research is “does media Google Sites influence in learning silent reading skills on student eleventh graders of Madrasah Aliyah Negeri 2 Kuantan Singingi?”. This research is very broad, the author limits the first is the ability to silent reading skills before using media Google Sites. second is the using of media Google Sites in learning silent reading skills. third is the influence of media Google Sites in learning silent reading skills. The aims of this research is to describe ability silent reading skills before using media Goole Sites, to describe using media Google Sites, to describe influence media Google Sites in improving ability silent reading skills on student eleventh graders. The research method used is quantitative study approach that is quasi eksperimen mothod and analytic data use SPSS. The result in this research is first the ability before use media Google Sites in control class is 69, and eksperimental class is 68,33. second is the using media Google Sites in three steps. first is opening, second is mian activity its five steps observing, asking, trying, associating, and communicating, third is closing. third is the influence media Google Sites in eksperimental class is 81,66 and control class is 71,33. and based on result of Paired Sample T test, T table 3,460 bigger than T count 2,043. and sig(2-Tailed) 0,005 bigger than 0,000. its mean Median oogle Sites is influence to ability silent reading skills on student eleventh graders of Madrasah Aliyah Negeri 2 Kuantan Singingi
A Transitivity Analysis of some Compounds in Computer Networking Jargon

There have been numerous attempts by linguists to investigate into the ideational function of language. However, most researches concerning transitivity analysis base themselves only on the clause. Scant attention has been paid to compounds, as linguistic devices that can be used to see how the experience of computer networking is represented. This study uses Hallidayan transitivity to analyze some compounds found in computer networking jargon, as most of the words in it are compound nouns. The study aims to identify how language is manipulated by computer networking persons to construe the experience of computer networking. To conduct this study, a sample of 166 was drawn from the population of 262 compounds that were collected from different books on computer networking. The collected compounds were those expressing technical terms of computer networking. The findings reveal that mental processes are not used to construe the experience of computer networking. By contrast, material processes and relational processes are used to construe the experience of computer networking. These processes are realized in different configurations.
Introduction to R-statistics

R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical and graphical techniques, and is highly extensible. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. In a nutshell, R is an open-source software environment for statistical computing that is rapidly becoming the tool of choice for data analysis in the life sciences and elsewhere. R is an integrated suite of software facilities for data manipulation, calculation and graphical display.
Adaptive Multilevel Coloring and Significant Texture Selecting for Automatic Deep Learning Image Transfer

This paper proposes an image style transfer technique based on target image color and style, which improves the limitations of previous studies that only consider inter-image color transfer and use only deep learning for style transfer. First, an adaptive multilevel cut is made based on the luminance distribution of the two image pixels, and then a color transfer is applied to each region. Next, deep learning is used to select effective features for the target image, and the convolutional layer determines the extent of effective features by using the structural similarity index (SSIM) and black blocks. Selecting a convolutional layer with more effective features can reduce the limitations of the deep learning style transfer that requires artificial control parameters. The proposed method improves image quality by automatically simulating the color and style of the target image and controlling the parameters without human intervention. By evaluating the similarity between the result image and the target image, the proposed method can reduce the gap of variance by more than two times, and the result image can display a balance between the color and style of the target image.
Effective Audio Encryption Using Pseudo Noise Generation Architecture

Encryption of audio signal is performed in this work. Pseudo random noise signal is used to encrypt the audio signal. Generation of pseudo random numbers are done by two interconnected Linear Feedback Shift Registers, the first of which controls the second. Different formulae are presented depending on the seed word, and therefore appropriate random numbers are generated. The usage of two LFSR increases the randomness of the random number generated and also increases the periodicity of generated random numbers making it more secure. The random numbers are generated on Xilinx. The suggested method has high frequency, less delay and more randomization as well as user set modes.&#x0D; For real-time applications, an audio signal is considered, which is encrypted by performing an xor operation with the key to obtain an encrypted audio signal. Decryption is performed by performing an xor operation on the encrypted audio signal with the same key to obtain the original audio signal, demonstrated in Matlab. The encrypted histogram shows better signal distribution like white noise which ensures more security.
Effective Communication Between Differently Abled And Normal People Using Speech To Sign Translation System

There are very less way of communicating between a differently abled person i.e. deaf and dumb also called as hearing impaired and normal people. Communications from deaf and dumb to normal people are done in many ways but from normal people to hearing impaired people have not been much. What we say cannot be understood by the deaf and dumb people so a system should be made so that a deaf and dumb person can understand the voice of normal people. The voice will be converted to sign language so that deaf and dumb people can easily understand. Machine learning techniques will be used to implement this with the help of Google API for voice recognition and then the Natural Language Processing is used. The voice will be broken into sentences or words and then the context of natural language processing will come into picture. The text will be compared with the gifs generated, if found the gifs will be shown as output or else letter by letter mapping will be done so the hearing impaired people can understand the voice through sign language. This is very important for the hearing impaired people. This helps hearing impaired people a lot because they can easily understand what the normal people are telling and for their daily life it will be very helpful. The system is tested with the number of data samples. As data samples increases, the accuracy of the model is said to have gone up as well.
Text Files Protection using Chaotic Logistic Keys

Text file some times contains secret or personal data which requires for protection from being hacked by unauthorized data hackers. In this research paper a simple, efficient and highly secure method of text file cryptography will be introduced. The text file will be divided into partitions with small sizes to increase the cryptography process throughput. Each partition will be encrypted-decrypted using its associated secret key. Each secret key will be generated from a chaotic logistic map by running a chaotic logistic map model with selected values of chaotic parameters. The chaotic keys will be converted to indices keys to form the secret keys. The secret keys will be used as a lockup tables to apply text file encryption-decryption. The characters of the text file will be rearranged based on the secret key indexes to form the encrypted text file. The characters of the encrypted file will be retrieved by the sequence of the indexes in the secret key. The contents of the decrypted file will be very sensitive to any changes in the chaotic parameters. The PK will have a complicated structure and it will contain the chaotic parameters values and the partition size. The partition size will be a percentage from the total text file size; this complicated structure will increase the key space, making it capable to resist any attack. The proposed method will be implemented using various in size text files, the obtained results will be analyzed to prove the quality, security and efficiency of the proposed method.
Virtual Conference Cannot Replace Physical Meeting: Lessons from COVID-19 Pandemic Online Transformation

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Contour Loss for Instance Segmentation via k-step Distance Transformation Image

Instance segmentation aims to locate targets in the image and segment each target area at pixel level, which is one of the most important tasks in computer vision. Mask R-CNN is a classic method of instance segmentation, but we find that its predicted masks are unclear and inaccurate near contours. To cope with this problem, we draw on the idea of contour matching based on distance transformation image and propose a novel loss function, called contour loss. Contour loss is designed to specifically optimize the contour parts of the predicted masks, thus can assure more accurate instance segmentation. In order to make the proposed contour loss to be jointly trained under modern neural network frameworks, we design a differentiable k-step distance transformation image calculation module, which can approximately compute truncated distance transformation images of the predicted mask and corresponding ground-truth mask online. The proposed contour loss can be integrated into existing instance segmentation methods such as Mask R-CNN, and combined with their original loss functions without modification of the inference network structures, thus has strong versatility. Experimental results on COCO show that contour loss is effective, which can further improve instance segmentation performances.
Shape of unmanned submersible hull taken as classification feature

Modern research in the field of oceanology in the Arctic region, studying the ice conditions, environmental and climatic monitoring, prospecting and hydrographic works, monitoring of extended bottom structures, oceano-graphic measurements, search for sunken objects, chemical and physical measurements of the aquatic environment, research of underwater objects and bottom topography cannot be imagined without the use of autonomous unmanned submersibles. Underwater robotics is rapidly developing. In the course of technological progress the scope of applications of autonomous unmanned submersible crafts has significantly expanded. The priority areas of robotics involve developing the intelligent methods and models to control operation of the autonomous submersibles in the extreme and uncertain environmental conditions, developing the architecture of autonomous submersibles, and solving problems of navigation and communication. Each class of autonomous submersible crafts has not only the advantages, but also the disadvantages that limit the scope of their application. Analysis and comparison of autonomous submersibles are carried out, the trends in their development are determined. The alternative classification of autonomous unmanned underwater crafts by the shape of the hull. Various versions of autonomous underwater vehicles and examples of underwater robotics are illustrated. Autonomous unmanned submersible crafts having a bluff shape, well-streamlined cylindrical shape, well-streamlined non-cylindrical shape, well-streamlined bionic shape are considered in detail, the system of control over the submersibles is analyzed.
Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease and does not consider clinical relations among diseases in a visit. Many machine learning approaches assume disease representations are static in different visits of a patient. However, in real practice, multiple diseases that are frequently diagnosed at the same time reflect hidden patterns that are conducive to prognosis. Moreover, the development of a disease is not static since some diseases can emerge or disappear and show various symptoms in different visits of a patient. To effectively utilize this combinational disease information and explore the dynamics of diseases, we propose a novel context-aware learning framework using transition functions on dynamic disease graphs. Specifically, we construct a global disease co-occurrence graph with multiple node properties for disease combinations. We design dynamic subgraphs for each patient's visit to leverage global and local contexts. We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes. Experimental results on two real-world EHR datasets show that the proposed model outperforms state of the art in predicting health events.
Possibilities for futurecasting: designing a digital map of trends

This article suggests a new approach to developing instruments for futurecasting, presenting the methodology for creating a digital map of trends based on the discipline of design. To achieve this goal, the article proposes a set of processes that lead to establishing the elements, components, and interactions of a trend visualization system. The result of the application of the described set of processes is the creation of an automatic interactive visual map of trends, which helps users to interpret and predict changes in society, and thus becomes a tool offering solutions to complex problems and generating opportunities for innovation. In addition, this paper presents the tangible results of the described methodology, detailing a specific case: the digital map of trends known as Deflexor.