Reviewerly/exHarmony huggingface dataset:
An Energy-Efficient Heterogeneous Fourier Transform-Based Transformer Accelerator with Frequency-Wise Dynamic Bit-Precision

Recently, transformer models [1] have achieved human-level accuracy and become the mainstream algorithm for diverse Natural Language Processing (NLP) tasks with the self-attention mechanism. The transformer models comprise a series of repeated self-attention layers that find the relationships between tokens and mixing information (TokenMix) and the subsequent feed-forward network (FFN) for capturing complex patterns. The self-attention layer calculates the relevance weights for each input token and employs these weights to a weighted-sum operation for calculating the output token. Therefore, the model can capture contextual relationships by mixing the information in multiple input tokens into output tokens. The Fourier transform-based TokenMix (FT-TokenMix) algorithm [2] replaces the computationally-heavy self-attention algorithm with a light-weight Fourier transform while achieving nearly the same performance. Since Fourier transform also finds the correlation between a different data point and mixes the information of the input token, it can capture the contextual relationships. Also, because the computation complexity of the Fourier transform is much lower than the self-attention layer with the Fast Fourier transform (FFT) algorithm, 99.6% of token mixing computation can be reduced.
Visualizing Source Code as Comics Using Generative AI

The architecture and inner structure of software is often only implicitly available in the form of its source code and thus not tangible and intuitively easy to understand for non-programmers and laymen. Our goal is to create visualizations as automatically as possible, with which such people can neverthe-less understand the software or parts of the software and get a feel for the structure of the software and how its methods work. Especially for newcomers to software projects, for management or even for students and pupils, it can be helpful to get a non-technical insight into the software. We use the concept of visualizing information as comics to present aspects of the software as strikingly as possible, as comics are an effective way to present complex systems and interrelationships for certain target groups. For this purpose, we present a method to generate comics from source code. Our semi-automated process is based on generating a prompt for an LLM from source code, which in turn generates a prompt for a comic image generation using the text-to-image model Stable Diffusion. We show that generative AI methods can be used to rapidly generate human-compatible artistic representations from source code. However, further research is needed to validate the understandability of the results.
Smart Home Healthcare Technologies Based on IoT for Bedridden Patient: A Systematic Literature Review

Smart home healthcare technology has emerged as a promising solution for providing effective and personalized care to bedridden patients. By incorporating various smart devices and sensors into the home environment, healthcare providers can monitor the health status of patients remotely and intervene promptly when necessary. This paper presents a Systematic Literature Review (SLR) on smart home healthcare technology for bedridden patients. A search spanning five database sources (IEEE Xplore, ACM Digital Library, Science@Direct, Scopus, and Web of Science) from 2018 to 2023 was conducted. This review highlights the key findings and trends in smart home healthcare technology for bedridden patients, including the benefits of such technologies, challenges faced, and future directions for research in this area. By promoting SLR methodologies enhances the quality and reliability of evidence in this field, facilitating the development of effective smart home healthcare solutions for bedridden patients.
NBID Dataset: Towards Robust Information Extraction in Official Documents

The Visual Document Understanding (VDU) task is of great interest for a variety of organizations, including banks, governments and schools, all of which would benefit from reliable automatic information extraction from pictures of documents. However, due to the sensitive nature of the data, creating new datasets for official documents, such as identity cards and passports, proves to be very challenging as the data must first be safely anonymized and synthesized. Such a process requires the source images to be modified, which may impact performance on VDU models. In this paper, we propose a new dataset and the synthesizer used for its generation, both made publicly available. We also selected three state-of-the-art VDU models: PICK, StrucTexT, and DocFormer, for evaluation on the dataset, in order to study the impact of the synthetic data on performance. We trained the models using both synthetic-only and synthetic-plus-real data protocols and present the results for both datasets. Our synthesizing process is shown to benefit training when used as an addition to the real data.
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation

Sequence-to-sequence (seq2seq) learning is a popular fashion for large-scale pretraining language models. However, the previous seq2seq pretraining models generally focus on reconstructive objectives on the decoder side and neglect the effect of encoder-side supervision, which we argue may lead to sub-optimal performance. To verify our hypothesis, we first empirically study the functionalities of the encoder and decoder in seq2seq pretrained language models, and find that the encoder takes an important but under-exploitation role than the decoder regarding the downstream performance and neuron activation. Therefore, we propose an encoding-enhanced seq2seq pretraining strategy, namely <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E2S2</small> , which improves the seq2seq models via integrating more efficient self-supervised information into the encoders. Specifically, E2S2 adopts two self-supervised objectives on the encoder side from two aspects: 1) locally denoising the corrupted sentence (denoising objective); and 2) globally learning better sentence representations (contrastive objective). With the help of both objectives, the encoder can effectively distinguish the noise tokens and capture high-level (i.e., syntactic and semantic) knowledge, thus strengthening the ability of seq2seq model to accurately achieve the conditional generation. On a large diversity of downstream natural language understanding and generation tasks, E2S2 dominantly improves the performance of its powerful backbone models, e.g., BART and T5. For example, upon BART backbone, we achieve +1.1% averaged gain on the general language understanding evaluation (GLUE) benchmark and +1.75% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F_{0.5}$</tex-math></inline-formula> score improvement on CoNLL2014 dataset. We also provide in-depth analyses to show the improvement stems from better linguistic representation. We hope that our work will foster future self-supervision research on seq2seq language model pretraining.
Introduction to CKIP’s Language Resources and Their Applications

In this chapter, we will introduce the language resources developed by the Chinese Knowledge Information Processing (CKIP) Group at Academia Sinica in Taiwan over the past 30 years. These include monolingual and bilingual lexical knowledge bases (CKIP lexical knowledge base, Hantology, Chinese WordNet, Sinica BOW, and E-HowNet), Chinese grammar (Information-based Case Grammar), annotated corpora (Sinica Chinese Corpus, Sinica Ancient Chinese Corpus, Sinica Chinese Treebank, and Chinese Sketch Engine), and online Chinese word segmentation and parsing systems. After a brief overview, we will show how some of these resources can be employed to generate natural language processing (NLP) applications using machine learning algorithms.
Bi-Resolution Hash Encoding in Neural Radiance Fields: A Method for Accelerated Pose Optimization and Enhanced Reconstruction Efficiency

NeRF has garnered extensive attention from researchers due to its impressive performance in three-dimensional scene reconstruction and realistic rendering. It is perceived as a potential pivotal technology for scene reconstruction in fields such as virtual reality and augmented reality. However, most NeRF-related research and applications heavily rely on precise pose data. The challenge of effectively reconstructing scenes in situations with inaccurate or missing pose data remains pressing. To address this issue, we examine the relationship between different resolution encodings and pose estimation and introduce BiResNeRF, a scene reconstruction method based on both low and high-resolution hash encoding modules, accompanied by a two-stage training strategy. The training strategy includes setting different learning rates and sampling strategies for different stages, designing stage transition signals, and implementing a smooth warm-up learning rate scheduling strategy after the phase transition. The experimental results indicate that our method not only ensures high synthesis quality but also reduces training time. Compared to other algorithms that jointly optimize pose, our training process is sped up by at least 1.3×. In conclusion, our approach efficiently reconstructs scenes under inaccurate poses and offers fresh perspectives and methodologies for pose optimization research in NeRF.
Enter IBM Quantum: A One-of-a-Kind Platform for Quantum Computing in the Cloud

In this chapter we take a look at quantum computing in the cloud with IBM Q: the first platform of its kind. The chapter starts with an overview of the composer, the web console used to visually create circuits, submit experiments, explore hardware devices, and more. Next, you will learn how to create your first experiment and submit it to the simulator or real quantum device. IBM Quantum features a powerful REST API to control the life cycle of the experiment, and this chapter will show you how with detailed descriptions of the endpoints and request parameters. Finally, the chapter ends with a series of exercises to put your REST API skills to the test. Let’s get started.
Mathematical Foundation: Time to Dust Up That Linear Algebra

Matrices, complex numbers, and tensor products are the holy trinity of quantum computing. During my quantum learning curve on the mathematical background, I wondered as you may: Where is the quantum magic in all this? How can a complex number or a matrix tell me about superposition or entanglement? Where is the spookiness? My immature understanding of superposition was that a system existed in multiple states at the same time: Like the cat in the box, it is dead and alive at once. However that is incorrect, the cat cannot be dead and alive at the same time just like a quantum system cannot exist in two simultaneous states.
Start Your Engines: From Quantum Random Numbers to Teleportation and Super Dense Coding

This chapter takes you on a journey about three remarkable information processing capabilities of quantum systems. We start with one of the simplest procedures by exploring the fundamentally random nature of quantum mechanics as a source of true randomness. Next, the chapter looks at perhaps two exuberant but related procedures called super dense coding and quantum teleportation. In super dense coding, you will learn how it is possible to send two classical bits of information using a single qubit. In quantum teleportation you will learn how the quantum state of a qubit can be recreated by a hybrid classical-quantum information transfer procedure. All algorithms include circuit design for the IBM Quantum Composer as well as Python and QASM code. Results will be gathered for display and analysis, so let’s get started.
Adaptation Via Proxy: Building Instance-Aware Proxy for Unsupervised Domain Adaptive 3D Object Detection

3D detection task plays a crucial role in the perception system of intelligent vehicles. LiDAR-based 3D detectors perform well on particular autonomous driving benchmarks, but may poorly generalize to other domains. Existing 3D domain adaptive detection methods usually require annotation-related statistics or continuous refinement of pseudo-labels. The former is not always feasible for practical applications, while the latter lacks sufficient accurate supervision. In this work, we propose a novel unsupervised domain adaptive framework, namely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptation <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> ia <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> roxy ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AVP</b> ), that explicitly leverages cross-domain relationships to generate adequate high-quality samples, thus mitigating domain shifts for existing LiDAR-based 3D detectors. Specifically, we first train the detector on source domain with the curriculum example mining (CEM) strategy to enhance its generalization capability. Then, we integrate the profitable instance knowledge from the source domain with the contextual information from the target domain, to construct the instance-aware proxy, which is a data collection with diverse training scenes and stronger supervision. Finally, we fine-tune the pre-trained detector on the proxy data for further optimizing the detector to overcome domain gaps. To build the instance-aware proxy, two components are proposed, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the multi-view multi-scale aggregation (MMA) method for producing high-quality pseudo-labels, and the hybrid instance augmentation (HIA) technique for integrating the knowledge from source annotations to enhance supervision. Note that AVP is architecture-agnostic thus it can be easily injected with any LiDAR-based 3D detectors. Extensive experiments on Waymo, nuScenes, KITTI and Lyft demonstrate the superiority of the proposed method over the state-of-the-art approaches for different adaptation scenarios.
Design and Development of an Imitation Detection System for Human Action Recognition Using Deep Learning

Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain. This paper presents a study on the design and development of an imitation detection system using an HAR algorithm based on deep learning. This study explores the use of deep learning models, such as a single-frame convolutional neural network (CNN) and pretrained VGG-16, for the accurate classification of human actions. The proposed models were evaluated using a benchmark dataset, KTH. The performance of these models was compared with that of classical classifiers, including K-Nearest Neighbors, Support Vector Machine, and Random Forest. The results showed that the VGG-16 model achieved higher accuracy than the single-frame CNN, with a 98% accuracy rate.
The role of cyber security in advancing sustainable digitalization: Opportunities and challenges

Sustainable digitalization is a growing trend in which digital technology is used to promote environmental, social, and economic sustainability. Cyber threats can undermine sustainability efforts, resulting in economic and social disruptions. Therefore, it is important to consider cyber security as an integral component of sustainable digitalization. This paper explores the opportunities and challenges that arise from the integration of cyber security and sustainable digitalization. The paper argues that cyber security is critical to the advancement of sustainable digitalization and that both fields must be integrated to achieve their objectives. The paper examines the various risks associated with cyber security, such as privacy violations and data breaches, and highlights the need for flexible cyber security policies and regulations that can adapt to the rapidly changing digital landscape. Moreover, the paper recommends a multi-stakeholder approach to cyber security that takes into account the interests of all parties involved, including governments, businesses, and individuals. The paper provides a valuable resource for policymakers, researchers, and practitioners in the field of cyber security and sustainable development, helping them to navigate the complex challenges and opportunities of integrating cyber security into sustainable digitalization.
Complexity and Analytical-creative Approaches at Scale: Iconicity, Monstrosity, and #GraphPoem

By revisiting concepts such as monstrosity and iconicity, this chapter reconsiders digital writing from a complex system perspective involving multiscale architecture, performativity and intermedia. The case in point is the Graph Poem project-applying graph theory and natural language processing in poetry- particularly with its analytical-creative approaches informing certain computationally assembled anthologies coming out of the project. The algorithms deployed in those anthologies operate at different scales, expanding networked text corpora by adding poem-nodes based on both small-scale, poetry diction related, and largescale, network-topology-relevant criteria. Such methodology illustrates a politically updated data-focused relevance of monstrosity beyond the recent post-disciplinary theory, as well as a concept of iconicity drawing mainly on writing theory and performance studies. A complexity-informed notion of digital writing thus emerges foregrounding scale, monstrosity and iconicity as conjoined features of digital space and the digital in general.
Aspect-Oriented Unsupervised Domain Adaptation for Polarity Classification

This paper proposes a novel method of unsupervised domain adaptation for Aspect Based Sentiment Analysis. Unlike previous studies, the domain is not defined as a genre of texts but as a target aspect for which the polarity should be identified. The labeled training data of the target domain is constructed by two methods. One is automatic labeling, where polarity labels of sentences in the target domain are determined by a classifier trained from labeled data in the source domain. The other is Cross-Aspect Review Generation. After extracting domain-specific sentiment words and keywords for each source and target domain, new labeled sentences in the target domain are generated by substituting sentiment words and keywords in sentences in the source domain. Then, the generated sentence is filtered out when its fluency measured by the pseudo-log-likelihood score is too low. Finally, a polarity classifier in the target domain is obtained by fine-tuning Bidirectional Encoder Representations from Transformers (BERT) using the constructed labeled data. Experimental results show that the average accuracy of our proposed method is 65.8% and 80.0% on the restaurant and laptop datasets respectively, which significantly outperforms the baselines.
On the Modeling of Quantum-Limited Amplifiers for Quantum Computing and Quantum Sensing

This paper presents an analytic modeling approach for quantum-limited parametric amplifiers with applications in quantum computing and quantum sensing. We describe a quantum model for calculating the gain and the added input fluctuations in terms of energy quanta (photons) of traveling-wave parametric amplifiers, including losses and dispersion.
Trends and Challenges of Text-to-Image Generation: Sustainability Perspective

Abstract Text-to-image generation is a rapidly growing field that aims to generate images from textual descriptions. This paper provides a comprehensive overview of the latest trends and developments, highlighting their importance and relevance in various domains, such as art, photography, marketing, and learning. The paper describes and compares various text-to-image models and discusses the challenges and limitations of this field. The findings of this paper demonstrate that recent advancements in deep learning and computer vision have led to significant progress in text-to-image models, enabling them to generate high-quality images from textual descriptions. However, challenges such as ensuring the legality and ethical implications of the final products generated by these models need to be addressed. This paper provides insights into these challenges and suggests future directions for this field. In addition, this study emphasises the need for a sustainability-oriented approach in the text-to-image domain. As text-to-image models advance, it is crucial to conscientiously assess their impact on ecological, cultural, and societal dimensions. Prioritising ethical model use while being mindful of their carbon footprint and potential effects on human creativity becomes crucial for sustainable progress.
A Robust Coverless Video Steganography Based on the Similarity of Inter-Frames

With a deeper understanding of the security issues in steganography, coverless steganography has become a hotspot due to no modification to the carriers. However, the existing coverless video steganographic algorithms have considered a few types of video attacks. In this paper, a robust coverless video steganography based on the similarity of inter-frames is proposed. First, a public video database is selected and preprocessed to construct a Secret Communication Video Database (SCVD). The similarity score between the first and last frames is calculated for video sorting to utilize the temporal characteristics of videos. After that, the mapping table between the secret information and the SCVD is designed for both senders and receivers. Finally, each secret information segment can be represented by one video sequence in the SCVD according to the mapping table to accomplish the data hiding and extraction. Experimental results show that the proposed method performs much better in capacity, robustness, and security than the state-of-the-art methods. It is worth mentioning that the proposed method overcomes the security issue of transmitting a large amount of auxiliary information in coverless video steganographic algorithms.