Reviewerly/exHarmony huggingface dataset:
Write and Paint: Generative Vision-Language Models are Unified Modal Learners

Recent advances in vision-language pre-training have pushed the state-of-the-art on various vision-language tasks, making machines more capable of multi-modal writing (image-to-text generation) and painting (text-to-image generation). However, few studies investigate if these two essential capabilities can be learned together and boost each other, making a versatile and powerful multi-modal foundation model. In this work, we disclose the potential of symmetric generative vision-language pre-training in learning to write and paint concurrently, and propose a new unified modal model, named DaVinci, trained with prefix language modeling and prefix image modeling, a simple generative self-supervised objective on image-text pairs. Thanks to the proposed prefix multi-modal modeling framework, DaVinci is simple to train, scalable to huge data, adaptable to both writing and painting tasks, and also strong on other vision, text, and multi-modal understanding tasks. DaVinci achieves competitive performance on a wide range of 27 generation/understanding tasks and demonstrates the superiority of combining vision/language generative pre-training. Furthermore, we carefully benchmark the performance of different vision-language pre-training objectives on different scales of pre-training datasets on a heterogeneous and broad distribution coverage. Our results demonstrate the potential of exploiting self-supervision in both language and vision inputs, and establish new, stronger baselines for future comparisons at different data scales. The code and pre-trained models are available at https://github.com/shizhediao/DaVinci.
Summarizing Labeled Multi-Graphs

Real-world graphs can be difficult to interpret and visualize beyond a certain size. To address this issue, graph summarization aims to simplify and shrink a graph, while maintaining its high-level structure and characteristics. Most summarization methods are designed for homogeneous, undirected, simple graphs; however, many real-world graphs are ornate; with characteristics including node labels, directed edges, edge multiplicities, and self-loops. In this paper we propose LM-Gsum, a versatile yet rigorous graph summarization model that (to the best of our knowledge, for the first time) can handle graphs with all the aforementioned characteristics (and any combination thereof). Moreover, our proposed model captures basic sub-structures that are prevalent in real-world graphs, such as cliques, stars, etc. LM-Gsum compactly quantifies the information content of a complex graph using a novel encoding scheme, where it seeks to minimize the total number of bits required to encode (i) the summary graph, as well as (ii) the corrections required for reconstructing the input graph losslessly. To accelerate the summary construction, it creates super-nodes efficiently by merging nodes in groups. Experiments demonstrate that LM-Gsum facilitates the visualization of real-world complex graphs, revealing interpretable structures and high- level relationships. Furthermore, LM-Gsum achieves better trade-off between compression rate and running time, relative to existing methods (only) on comparable settings.
Being With The Waves: An Ultrasonic Art Installation Enabling Rich Interaction Without Sensors

To the naked ear, the installation Being With The Waves appears silent, but a hidden composition of voices, instrumental tones, and maritime sounds is revealed through wearing modified headphones. The installation consists of an array of tweeters emitting a multi-channel ultrasonic composition that sounds physically in the space. Ultrasonic phenomena present at the listenerâs ears are captured by microphones embedded on the outside of headphone earcups, shifted into audibility, and output to the headphones. The amplitude demodulation of ultrasonic material results in exaggerated Doppler effects and listeners hear the music bend and shift precisely with their movement. There are no movement sensors, mappings, or feedback loops, yet the installation is perceived as interactive due to the close entanglement of the listener with sound phenomena. The dynamic quality of interaction emerges solely through the listening faculties of the visitor, as an embodied sensory experience determined by their orientation to sounds, physical movement, and perceptual behaviour. This paper describes key influences on the installation, its ultrasonic technology, the design of modified headphones, and the compositional approach.
The Composite Incompossible: Forbidden symmetries

This work is a collaboration between Steve Tromans and Heidi Schmidt, and operates in the fold between sound art and philosophy. Both researchers recorded improvised monologues on themes of folding, the tesseract, space and time, and Tromans mixed these together in a composition that utilises time and pitch stretching, stereo-field panning, delay and echo-bounce effects, to provide for the listener an experience of “incompossibility” (cf. Deleuze 1988). The piece’s composite incompossibility is created through the superposition, unfolding, and enfolding of the voices – transforming their original space and time into a “forbidden” zone: the event of the piece’s tesseract. The final composition happens in the listening, rendering the listener a co-composer / compositor.
Analisis Kepuasan Mahasiswa Terhadap Penggunaan Aplikasi Media Pembelajaran Online (Studi Pada Perguruan Tinggi Swasta Kota Palembang)

This study aims to measure the level of student satisfaction with the use of online learning media applications, using the End User Computing Satisfaction (EUCS) method which consists of 5 variables, namely content, accuracy, format, ease of use. use) and timeliness. The population and samples used in this study were Bina Darma University, Muhammadiyah University and STISIPOL Candradimuka. The results of the calculation of the total score of respondents' answers on the level of student satisfaction with the use of online learning media applications obtained a score of 94%, which means that user responses strongly agree or are very good. The results of the F test in this study indicate that there is an influence between all variables on user satisfaction (Y) which is carried out together. The T test proves that the format and timeliness variables have no effect on user satisfaction while the content, accuracy and ease of use variables have an influence on user satisfaction variables.
MORPHOLOGY OF THE LANGUAGES: OVERVIEW OF THE EXEMPLIFICATIONS OF ENGLISH, INDONESIAN, IBAN, AND KANAYATN

This study is an overview of morphological words and their formation process based on the exemplifications of the English, Indonesian, Iban, and Kanayatn. The objective of the article is to describe the form or morph and meaning of morphological word elements. The data for this article is secondary, that is, ready-to-use data, taken from examples of morphological words of the four languages. The technique for data analysis is comparative. It compares the morphological word elements and formation process of the languages. Since the three languages are Austronesian, therefore, the comparison is basically between the English and Austronesian, comprising Indonesian, Iban, and Kanayatn. The data taken from the morphological word examples reveal that the morphological word elements or word-formation of the languages are similar, that is, root or stem and bound morpheme. At the same time, the process is derivational affixation and inflection. For English, the inflection involves the process of suffixation, an internal modification, and the replacement order of the lexemes. Although the example is very limited and shown as a case or indication, the Austronesian, especially Indonesian, also has the inflectional system. It applies by infixation to signify plurality.
Immersion Metrics for Virtual Reality

Technological advances in recent years have promoted the development of virtual reality systems that have a wide variety of hardware and software characteristics, providing varying degrees of immersion. Immersion is an objective property of the virtual reality system that depends on both its hardware and software characteristics. Virtual reality systems are currently attempting to improve immersion as much as possible. However, there is no metric to measure the level of immersion of a virtual reality system based on its characteristics. To date, the influence of these hardware and software variables on immersion has only been considered individually or in small groups. The way these system variables simultaneously affect immersion has not been analyzed either. In this paper, we propose immersion metrics for virtual reality systems based on their hardware and software variables, as well as the development process that led to their formulation. From the conducted experiment and the obtained data, we followed a methodology to find immersion models based on the variables of the system. The immersion metrics presented in this work offer a useful tool in the area of virtual reality and immersive technologies, not only to measure the immersion of any virtual reality system but also to analyze the relationship and importance of the variables of these systems.
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models

Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this problem, recent methods consider zero-shot settings with no manual annotation of visual question-answer. In particular, a promising approach adapts frozen autoregressive language models pretrained on Web-scale text-only data to multi-modal inputs. In contrast, we here build on frozen bidirectional language models (BiLM) and show that such an approach provides a stronger and cheaper alternative for zero-shot VideoQA. In particular, (i) we combine visual inputs with the frozen BiLM using light trainable modules, (ii) we train such modules using Web-scraped multi-modal data, and finally (iii) we perform zero-shot VideoQA inference through masked language modeling, where the masked text is the answer to a given question. Our proposed approach, FrozenBiLM, outperforms the state of the art in zero-shot VideoQA by a significant margin on a variety of datasets, including LSMDC-FiB, iVQA, MSRVTT-QA, MSVD-QA, ActivityNet-QA, TGIF-FrameQA, How2QA and TVQA. It also demonstrates competitive performance in the few-shot and fully-supervised setting. Our code and models are publicly available at https://github.com/antoyang/FrozenBiLM.
Block Chain Based Data Storage with Privacy and Authentication

Abstract— The project is entitled Block Chain Based Data Storage's privacy and ensures that blockchain-based framework integrated with authentication and privacy schemes are enhanced by secure connections to wireless nerve networks. In this set of program programs transmit the information collected to BS. As a result, BS records all key parameters in a distributed blockchain and large data is transferred to the cloud for storage. Damaged certificates for all malicious nodes are removed from the blockchain by Base Station. Although, the performance of WSNs are specific requests in terms of area of interest and method of delivery, but the purpose is to maintain the monitoring, hearing, dissemination and processing of the information collected. However, the amount of information is huge at an extraordinary rate. When an enemy attacks a network and deliberately threatens nodes, network security becomes a threat. Therefore, it is necessary for WSNs to isolate and remove malicious nodes from a network before it can be active. Adjusted outcomes, comparative analysis and security assurance support the height of the proposed solution than existing methods. Index Terms- wireless sensor, WSN, network security
Modified PPP Procedure in Teaching Conditional Sentences Through Whatsapp for Vocational School Students

The aims of the study are to find out whether there is a significant difference in students' achievement of conditional sentences between those who are taught using modified PPP procedure through WhatsApp and those who are taught using original PPP procedure in limited offline learning. The population was the students of SMKN 1 Metro. This study is quasi-experimental, and the sampling was taken in two classes. The experimental class was given the treatment using a modified PPP procedure through WhatsApp. In contrast, the control class was given the treatment using the original PPP procedure in limited offline learning. The findings showed a significant difference in students' achievement of conditional sentences between those two procedures, which is in line with the difference in students' perception of the learning process between those two classes. Modified one was more effective in improving students' achievement of conditional sentences, especially in pandemic Covid 19, where online learning is necessary. The conclusion is that the modification of the PPP procedure through WhatsApp is very effective to use even after the pandemic, because there are more opportunities for students to discuss using WhatsApp which makes them gain a deeper understanding and better achievement of the conditional sentence material.
Efficient Decoder-free Object Detection with Transformers

Vision transformers (ViTs) are changing the landscape of object detection approaches. A natural usage of ViTs in detection is to replace the CNN-based backbone with a transformer-based backbone, which is straightforward and effective, with the price of bringing considerable computation burden for inference. More subtle usage is the DETR family, which eliminates the need for many hand-designed components in object detection but introduces a decoder demanding an extra-long time to converge. As a result, transformer-based object detection can not prevail in large-scale applications. To overcome these issues, we propose a novel decoder-free fully transformer-based (DFFT) object detector, achieving high efficiency in both training and inference stages, for the first time. We simplify objection detection into an encoder-only single-level anchor-based dense prediction problem by centering around two entry points: 1) Eliminate the training-inefficient decoder and leverage two strong encoders to preserve the accuracy of single-level feature map prediction; 2) Explore low-level semantic features for the detection task with limited computational resources. In particular, we design a novel lightweight detection-oriented transformer backbone that efficiently captures low-level features with rich semantics based on a well-conceived ablation study. Extensive experiments on the MS COCO benchmark demonstrate that DFFT_SMALL outperforms DETR by 2.5% AP with 28% computation cost reduction and more than $10$x fewer training epochs. Compared with the cutting-edge anchor-based detector RetinaNet, DFFT_SMALL obtains over 5.5% AP gain while cutting down 70% computation cost.
Federated Data Analytics: A Study on Linear Models

As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge compute resources are exploited to process more of the data locally. This regime of analytics is coined as federated data analytics (FDA). In spite of the recent success stories of FDA, most literature focuses exclusively on deep neural networks. In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression. Our treatment is built upon hierarchical modeling that allows borrowing strength across multiple groups. To this end, we propose two federated hierarchical model structures that provide a shared representation across devices to facilitate information sharing. Notably, our proposed frameworks are capable of providing uncertainty quantification, variable selection, hypothesis testing and fast adaptation to new unseen data. We validate our methods on a range of real-life applications including condition monitoring for aircraft engines. The results show that our FDA treatment for linear models can serve as a competing benchmark model for future development of federated algorithms.
Analysis of Big Data Methodology for Pattern Recognition

Abstract: Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed.
Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm

Quantum computing is anticipated to offer immense computational capabilities which could provide efficient solutions to many data science problems. However, the current generation of quantum devices are small and noisy, which makes it difficult to process large data sets relevant for practical problems. Coreset selection aims to circumvent this problem by reducing the size of input data without compromising the accuracy. Recent work has shown that coreset selection can help to implement quantum K-Means clustering problem. However, the impact of coreset selection on the performance of quantum K-Means clustering has not been explored. In this work, we compare the relative performance of two coreset techniques (BFL16 and ONESHOT), and the size of coreset construction in each case, with respect to a variety of data sets and layout the advantages and limitations of coreset selection in implementing quantum algorithms. We also investigated the effect of depolarisation quantum noise and bit-flip error, and implemented the Quantum AutoEncoder technique for surpassing the noise effect. Our work provides useful insights for future implementation of data science algorithms on near-term quantum devices where problem size has been reduced by coreset selection.
HCI Based Virtual Controlling System

Abstract: Researchers around the globe are working on making our devices more interactive and making them function with minimal physical contact in this research project. The proposed system is an interactive computer system that can operate without a physical keyboard or mouse. This system will benefit everyone, particularly immobilized people with special needs operating a physical keyboard and mouse. So in the system, they have developed an interface that uses visual hand-gesture analysis. These gestures are used to assist those who are having trouble controlling or operating computers or gadgets. The model is being developed in such a way that it can assist us in recognizing and implementing it. Regarding hand gestures, our interface uses OpenCV, Python, and computer vision algorithms that can detect different finger orientations, distinguish the user's hand from the background, and distinguish significant hand movements from unwanted hand movements. Keywords: Machine Learning, Computer Vision, Image Processing, OpenCV, Human-Computer Interaction (HCI)
Introduction

This article provides an overview over the legal-theoretical context, the subject matter, aim and content of the book The Law of Global Digitality. Reviewing 25 years of cyberlaw theory, it posits that the debate concerning a proper description and an adequate theory of internet-related “cyberlaw” or, more generally, digital law, is far from over or settled. The book’s purpose accordingly is to improve our understanding of the interplay between digitality-the practical use of digital technologies (hardware, software, applications)-on the one hand and law on the other. The distinctive feature of the book to achieve this aim is that it brings together studies of six very different areas of law, namely intellectual property, data protection/privacy, consumer contracts, media law, financial market regulation and criminal law. By comparing how these very different areas of law have reacted to and at the same time shaped global digitality, structural regulatory patterns occurring across all fields might be identifiable. If a certain type of regulation, a substantive principle or another legal aspect could be observed in many or even all of these very diverse fields, it is plausible to assume that the recurring feature represents a specific characteristic of digital law and not just a variant of pre-digital law (aka “law of the horse”).
TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models

Transferring knowledge from one domain to another is of practical importance for many tasks in natural language processing, especially when the amount of available data in the target domain is limited. In this work, we propose a novel few-shot approach to domain adaptation in the context of Named Entity Recognition (NER). We propose a two-step approach consisting of a variable base module and a template module that leverages the knowledge captured in pre-trained language models with the help of simple descriptive patterns. Our approach is simple yet versatile and can be applied in few-shot and zero-shot settings. Evaluating our lightweight approach across a number of different datasets shows that it can boost the performance of state-of-the-art baselines by 2-5% F1-score.
UNGRAMATICAL NEGATION CONTRUCTIONS ON THE STUDENTS’ INDONESIA-ENGLISH TRANSLATION

This study was to investigate negation constructions in STAKat Negeri Pontianak students’ Indonesia-English translation. The participants were 78 students of semester 2, 4 and 6 in STAKat Negeri Pontianak in academic year 2021. The descriptive method was applied in this study in order to describe and analyze the constructions in negation. The tool of data collection was translation tes by preparing sentences in Indonesia and asking the students to translate them into English. There were 5 for each sentence in Present tense, Past tense, Modal, Progressive tense and Perpect tense. Based on finding, the students faced difficulties in demontrating the formula of each tense. Many of them still make ungramatical negation. The percentages of those are 80% in present perfect tense, 60.8% in past tense (60.8%), 50% in present progressive tense, 40.8% in present tense, and 29% occurs in modal. The highest ungrammatical constructions of singular case are tenses. The tenses mistake in present tense is 26.3% out of 40.8%, past tense is 55.9% out of 60.8%, present perfect tense is 70% out of 80%, present progressive tense is 47% out of 50%, and modal is 23.7% out of 29% ungrammatical negation construction.