Reviewerly/exHarmony huggingface dataset:
32 Discourse traditions and computational linguistics

The concept of discourse traditions (DTs) has not yet been applied to computational linguistic (CL) analyses, partly because the theoretical approach has not yet gained enough audience. But both approaches share a common scientific object - text -, and this chapter thus reviews several textual dimensions that have already been tackled by CL: the semantic content, its segmentation and articulations within a text, as well as text classification and clustering; the semiotic and linguistic structure of documents and conversational agent modelling; the modelling and description of subjectivity, speech acts and communicative intents; and extra-textual features of discourse. These different approaches of discourse have not yet been unified in a general framework that would allow putting discourse traditions at the center of computational linguistic concerns. However, some key points are emerging that allow to devise a work program aiming towards a detailed analysis of documents and taking into account their clustering into discourse traditions as well as the life cycles of the latter.
A STUDY OF DATA MINING BASED KNOWLEDGE MANAGEMENT

Mr. DAMAN SINGH
DIGITAL LIBRARY: MEETING THE NEEDS OF ACADEMIC FRATERNITY

PREETI; MR. MANISH SHARMA
Digital media flow forecasting system based on scene digitization technology

In order to improve the traffic prediction capability of digital media network video communication, we propose a traffic prediction system based on traffic features. The distributed sensing information collection and multi-dimensional spatial storage structure model is built, and the time series reconstruction is realized by using multi-source information fusion and feature reorganization method. The embedded program is developed and designed for predicting video communication traffic in digital media networks by using wireless ZigBee networking control protocol, and the transmission prediction of communication traffic is realized by traffic feature fusion analysis. The simulation results show that this method can predict the video traffic of digital media network with high accuracy and good transmission reliability.
Image Forgery Detection Using Deep Learning Framework

With the continuous development of digital media and the rapid change of digital image processing equipment, digital images are easy to be tampered with by criminals. In recent years, illegal elements often use tampering images to commit crimes, so it is urgent to study image forensics technology. Blind forensics technology is used to verify the original and authenticity of the image, and then the tampering mode of the image is discriminated and the tampered area is located according to the tampering trace of the image. This paper focuses on the detection of image manipulation and tampering and the localization of tampered areas. The main innovation of this paper is to use the core idea of end-to-end training of u-net network to further optimize the performance of image tampering detection tasks in combination with residual network.
Cloud Computing Forensics

Cloud computing is a relatively new development in computing which has a significant impact on the examination and analysis of digital evidence. There exists a wide range of cloud service providers who themselves provide a range of services to meet business needs. Each provider will have their own architecture and processes by which data is structured and stored. The foundations of digital forensics are rooted in the examination of devices, whether they be computers, mobile phones or other devices. However, these methods do not translate well to forensic examinations on evidence in the cloud and new approaches will need to be taken. In order to provide reliable test results and testimony to courts, examiners who are responsible for performing digital evidence examinations will need to gain new understanding, knowledge and skills. No matter the challenges presented by cloud forensics, the fundamental principles of forensic science, including sound evidential chain of custody, sound analytical methods and robust approaches to verification need to be met.
Payment Cards

Payment cards are often targets of fraud schemes and may also be misused to launder money. This entry describes types of payment cards, intended uses of these cards, payment card-related fraud schemes, and the misuse of payment cards to launder money
SAR Image Super-Resolution Reconstruction Based on Full-Resolution Discrimination

In image super-resolution reconstruction based on generative adversarial networks (GANs), the discrimination of high-resolution (HR) images enriches texture details. However, solely discriminating HR images limits the reconstruction quality, while discriminating other resolution features can improve the texture structures of the reconstructed HR images. Therefore, this paper proposes a SAR image super-resolution reconstruction algorithm based on full-resolution discrimination (FRD). In the suggested architecture, the full-resolution discriminator network is used to extract the high, medium, and low-resolution features, which are then fused into a full-resolution feature. Finally, the full-resolution feature difference between the authentic and fake images is input to the generator, which reduces the inaccuracy of single-resolution feature discrimination. Experimental results on synthetic aperture radar (SAR) images demonstrate that the proposed FRD algorithm performs better than the state-of-the-art super-resolution algorithms in reconstructing the texture structures of the HR SAR images.
Sub-Aperture Feature Adaptation in Single Image Super-Resolution Model for Light Field Imaging

With the availability of commercial Light Field (LF) cameras, LF imaging has emerged as an up-and-coming technology in computational photography. However, the spatial resolution is significantly constrained in commercial micro-lens-based LF cameras because of the inherent multiplexing of spatial and angular information. Therefore, it becomes the main bottleneck for other applications of light field cameras. This paper proposes an adaptation module in a pre-trained Single Image Super-Resolution (SISR) network to leverage the powerful SISR model instead of using highly engineered light field imaging domain-specific Super Resolution models. The adaption module consists of a Sub-aperture Shift block and a fusion block. It is an adaptation in the SISR network to further exploit the spatial and angular information in LF images to improve the super-resolution performance. Experimental validation shows that the proposed method outperforms existing light field super-resolution algorithms. It also achieves PSNR gains of more than 1 dB across all the datasets as compared to the same pre-trained SISR models for scale factor 2, and PSNR gains 0.6 − 1 dB for scale factor 4.
Meta-BNS FOR Adversarial Data-Free Quantization

Data-free quantization has recently been a promising method to perform quantization without access to the original data. However, the drawback of such approaches is the homogenization of synthetic data due to low efficiency for diverse data generation and the performance collapse of the generator. To alleviate the above issue, we propose a novel Meta-BNS for adversarial data-free quantization scheme which consists of Meta-BNS module and adversarial exploration module. Meta-BNS module automatically learns an enhancement coefficient matrix function for BN loss module to provide a suitable constrain on the generator. Adversarial exploration module leverages minimax game between the generator and quantized model via input gradient to encourage the generator to learn high-dimensional and complex real data distribution. The experimental results show that our method achieves state-of-the-art performance for various settings on data-free quantization.
DAT: Domain Adaptive Transformer for Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (UDA) for semantic segmentation aims to predict class annotations on an unlabeled target dataset by training on a rich labeled source dataset. It is crucial in UDA semantic segmentation to decrease the domain gap by learning domain invariant feature representations across both domains. In this paper, we propose a novel transformer-based network, called a domain adaptive transformer (DAT), using a self-training scheme. We introduce domain invariant attention (DIA), which enables the DAT to exploit high-level domain invariant features at the patch level. Moreover, an entropy-based selective pseudo-labeling algorithm provides the DAT with reliable pseudo-labels of target samples for domain adaptive self-training, which corrects the noisy pseudo-labels online. We show that our DAT greatly improves the domain adaptability and achieves state-of-the-art results on the SYNTHIA-to-Cityscapes benchmark.
New Active Learning Approach for Seabed Segmentation

Many fields are interested in mapping and monitoring the sea floor natural structure and biology, for environmental surveys, including identifying macro waste or detection of submerged artifacts such as cars, tires, wrecks, and even military applications, e.g., mine warfare whose detection depends heavily on the seabed structure. In this paper, we propose a new active learning method to improve seabed segmentation by deep learning. We perform segmentation of the sea floor using two data sources, sonar, and bathymetry. We train a network to fuse these two modalities and segment each sea floor pixel into nine fine ecological classes, then into three gross class sets, alive/not alive, and, in two different ways, whether mines can be hunted for or not. Once this training is done, a second stage involving a new active learning method based on network uncertainties greatly improves the performance.
Recognition-Aware Deep Video Compression for Remote Surveillance

Image recognition techniques such as object detection are useful for assisting humans in remote video surveillance tasks. However, compression algorithms used for efficient video transmission are usually tuned for low reconstruction error and not for machine vision, leading to suboptimal recognition accuracies. In this work, we propose convolutional encoder-decoder neural networks for compressing video data intended for object detection. These networks are trained for optimal detection accuracy and bitrate, and make use of a novel stochastic quantization technique. In our experiments, we evaluate our method using publicly available datasets and show that we can substantially reduce bitrate over traditional codecs such as H.265 and also over other deep learning based compression methods at identical object detection accuracy. Moreover, we argue that perceived image quality of our compression method is close to H.265 at similar bitrates.
Semantic 3D Grid Maps for Autonomous Driving

Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is three-dimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.
Rancang Bangun Penilaian Hasil Belajar Anak pada PAUD Soka Indah Berbasis Website

Information technology in a very fast period of time is growing rapidly making iteasier for humans to access all available information, including in the world of earlychildhood education (PAUD). Early childhood education or PAUD is the level ofeducation that a child takes before he enters elementary school or elementary school.Learning from home activities require educators to be better prepared in preparinglearning activities and force educators to utilize various technologies to facilitatelearning activities. This is one of the problems that arise in the implementation oflearning during the current pandemic. So, we need a system that can providefacilities for educators, especially in recapitulating the assessment and learningoutcomes of children. Therefore, this study proposes the implementation ofAssessment and Learning Outcomes of Children at PAUD Soka Indah. Web-Basedis a website that is used to recap the results of the assessment of early childhoodlearning outcomes. After being tested, the proposed application can be implementedproperly to support the assessment of children's learning outcomes.
The role of photon-number pulses in the operation of a simple light diode

One of the challenges faced by optical platforms for quantum technologies is the implementation of (ultimately) a transistor. The functionality that is hard to achieve is rectification: having the beam propagating in one direction transmitted, the other reflected. Here we take up a simple model of such a rectifying device, a.k.a.~optical diode, consisting of two atoms with different detuning interacting with light in a one-dimensional waveguide. In previous studies, it was found that high rectifying efficiencies can be achieved with coherent states, while it was claimed that the device cannot rectify single-photon Fock states. In this paper, we clarify the functioning of this diode. Notably, we show that coherences across the Fock bases in the input state do not play any role, and thence the rectifying properties of the device depend only on its behavior on the Fock states. In the process, we show that some single-photon rectification is predicted when the limit of infinitely-long pulses is not taken.
Handwriting Recognition and Conversion Using Neural Networks

In the current world of automation, everything is getting atomized as reducing manual labor is the key to efficiency. Here, we focus on offline handwriting recognition. Handwriting recognition is the process of extracting text from handwritten scripts, this is also known as offline handwriting recognition. The purpose here is to attempt to improve the accuracy and efficiency of the system using neural networks and datasets used for training the model, as well as detecting and identifying the characters and exporting them in a text format. The proposed system can be used to recognize handwritten characters and convert them into the text from the scanned image of a page.
Internet of Medical Things (IoMT) – Medical Applications and it’s Cyber Security

The Internet of Things (IoT) is a huge deal all around the world. It can be used in a variety of fields, including industrial, healthcare, agriculture, and the environment. Due to a lack of interaction among patients and doctors in the past, correct diagnosis was difficult to come by, and fatality rates were high. They were incapable of dealing with epidemics and pandemics. The Internet of Medical Things (IoMT) was recently developed to better and improve the healthcare profession. IoMT is a medical gadget that allows for increased patient convenience, price healthcare treatments, improved medical therapies, and more customized care. Wearable gadgets have been on the rise, with several benefits in terms of keeping watch of vitals and healthcare, igniting the growth of the Internet of Medical Things (IoMT). It allows electronic technologies to collect, analyze, and send information to cloud systems. This allows you to examine the patient's health status at any time and from anywhere, including body temperature, pulse rate, hearing beats utilizing ECG sensors, temperature, pressure, and observation. Digital gadgets have been designed and are widely used as a result of these systems. Doctors may keep track of a patient's health status in real time and prescribe medicine, vitamins supplements, and healthcare advice to them through. IoMT aids in the detection of body changes such as the growth of irregular masses of undifferentiated cells, the recognition of neural disorders, the checking of diabetic patients' glucose levels, the detection of psychiatric conditions using heartbeat, the real-time tracking of chronic disease symptoms, the screening of internal organ dysfunction, and the monitoring of cardiac blocks that cause heart attacks. IoMT assisted doctors in identifying and diagnosing COVID-19-affected patients and assisting them in providing appropriate therapy remotely. Clinical decisions were made with the help of IoMT as Artificial Intelligence (AI), telemedicine, and sensor technologies advanced. In the context of smart cities, IoMT introduces a new issue in the healthcare sphere. This chapter provides an outline as to how IoMT operates and how it has improved the lives of many patients through the use of modern technologies. This also includes a design and overview of IoMT, as well as numerous activities in the health-care industry, pop-up technologies, and several IoMT case studies in medical applications. It also lays out the IoMT's cybersecurity rules and indicates issues which need to be tackled at the parliamentary and community sectors.
Mobile Web Application Development in Administration Services in Nursing Department Laboratory

Many skills laboratories have been depending on various progress technology information for help and make it accessible in management administration in the skill laboratory, including using computers and software/ applications. Study this use type research and development. The development model in the build system uses SDLC (System Development Life Cycle) with the Waterfall model. This study is quasi-experimental research with one group pretest and posttest designs. Applications created containing loan tools and space laboratory, reporting inventory equipment laboratory, documentation activity practicum and system evaluation and reporting. The sample in a study consists of 40 selected respondents using a purposive sampling technique. Test convenience use application uses a questionnaire consisting of 12 questions with a choice very appropriate, appropriate, sufficient appropriate, no suitable, very no appropriate. Data analysis used paired sample t-test. This mobile website is named SiMAPiN based on nine items of need: user, item, category item, unit goods, officers, submissions loan, submission item, category application and borrowing. The analysis shows a change perception of convenience in using application loan tools and goods laboratory (p-value =&lt;0.001). The average change perception is as much as 13.1 points (33.7 – 46.8). The effectiveness of the application of the results is -5.98, which is enormous (&gt;2.0). We conclude that application loan tools and materials, as well as room, can make it easier for stakeholders and laboratories to do management laboratory. Moment conducted testing application, got results that occur enhancement perception to convenience use application. Future research could focus on integration applications this with procurement goods Required laboratory government.
Research on the Basic Theory of Contemporary Pedagogy: From Background to Recent Advances

Research on the basic theory of contemporary pedagogy from background to recent advances is conducted in this paper. The cultural nature of pedagogy means the existence of cultural hegemony in pedagogy, and cultural colonization directly affects the way of speaking and academic trajectory of comparative education scholars. Pedagogical knowledge, as one of the human knowledge categories, has been accompanied by the development of human society, the improvement of cognitive ability and the three major transformations of human knowledge type. The three major transformations of human knowledge have presented different knowledge beliefs, structures and contents, and thus different knowledge forms and paradigms in various historical periods. and paradigms. Hence, this paper gives the novel suggestions regarding the related studies.
Effective non-adiabatic holonomic quantum computation of cavity modes via invariant-based reverse engineering

In this paper, we propose a protocol to realize non-adiabatic holonomic quantum computation (NHQC) of cavity modes via invariant-based reverse engineering. Coupling cavity modes with an auxiliary atom trapped in a cavity, we derive effective Hamiltonians with the help of laser pulses. Based on the derived Hamiltonians, invariant-based reverse engineering is used to find proper evolution paths for NHQC. Moreover, the systematic-error-sensitivity nullified optimal control method is considered in the parameter selections, making the protocol insensitive to the influence of systematic errors of pulses. We also estimate the imperfections induced by random noise and decoherence. Numerical results show that the protocol holds robustness against these imperfections. Therefore, the protocol may provide useful perspectives to quantum computation with optical qubits in cavity quantum electrodynamics systems. This article is part of the theme issue 'Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives'.
GPU implementations of deflate encoding and decoding

Summary Deflate coding is a very popular lossless data compression method used in zlib, gzip (GNU zip), and zip, which performs the LZSS compression algorithm with Huffman coding. Deflate encoding and decoding involve sequential operations and their parallel acceleration using a GPU is quite hard. The main purpose of this paper is to present GPU implementations for encoding and decoding of Deflate coding. For efficient GPU implementations of Deflate coding, we have used multiple small hash tables for finding matching subsequences in the dictionary by multiple threads in parallel and applied the Single Kernel Soft Synchronization (SKSS) technique to fully utilize GPU computing resources. We have also adopted Huffman coding with gap arrays to accelerate parallel Huffman decoding. We have evaluated the performance of our GPU implementations using an NVIDIA A100 GPU and compared them with parallel/sequential Deflate encoding and decoding on the Intel X86 multicore CPUs using multiple threads/a single thread. Our GPU implementation of Deflate decoding is 1.66x–8.33x faster than the multiple thread implementation and 4.13x–36.56x faster than the single thread implementation.
A Study of Japanese Topic Syntax “X₁wa X₂ga Y da.” : Focusing on Comparison and Contrast with Korean in Untitled Sentences

This study focuses on “untitled sentences” in both Japanese and Korean. This research is based on “象は鼻が長いZō wa hana ga nagai, in “the elephant has a long nose” in Mikami's (1960) influential grammar book. In it, Mikami (1960) says that there is no Japanese word for the ‘subject’. Additionally in the expression ‘象の’ Zō no, elephant's’ in ‘the elephant's trunk is long’ is the subject of ‘象は鼻が長いZō wa hana ga nagai,. When ‘鼻が Hana ga is used then ‘the nose’ becomes the subject, it becomes ‘象の鼻は長い(Zō no hana wa nagai)’. However, if ‘象は鼻が長いZō wa hana ga nagai, is used and ‘코끼리는 코가 길다 Kokkilineun koga gilda, in the Elephant has a long nose’ and have ‘topic syntax’ elements (in Japanese and Korean) and are untitled, they then become ‘象の鼻が長い(Zō no hana ga nagai)’ and ‘코끼리가 코가 길다(Kokkiliga koga gilda)’. Therefore, in this study, we examined why these differences can be seen in ‘untitled sentences’ between Japanese and Korean. As a result, ‘no’ in Japanese has a ‘provisional subject’ function in addition to the ‘ownership/affiliation’ function, whereas ‘ui’ in Korean has only a particle, that has no substantial meaning and aids another word, so the ‘double subject’ is considered to be recognized. From these points of view, it is speculated that there may be differences in the main sentence that make the topic untitled sentences between Japanese and Korean. In the future, clearer definitions of “nominative /subject /topic /double nominative object /double subject /provisional subject /‘no’ of case” etc. are required in both Japanese and Korean.