Reviewerly/exHarmony huggingface dataset:
Modality-Agnostic Debiasing for Single Domain Generalization

Deep neural networks (DNNs) usually fail to generalize well to outside of distribution (OOD) data, especially in the extreme case of single domain generalization (single-DG) that transfers DNNs from single domain to multiple unseen domains. Existing single-DG techniques commonly devise various data-augmentation algorithms, and remould the multi-source domain generalization methodology to learn domain-generalized (semantic) features. Nevertheless, these methods are typically modality-specific, thereby being only applicable to one single modality (e.g., image). In contrast, we target a versatile Modality-Agnostic Debiasing (MAD) framework for single-DG, that enables generalization for different modalities. Technically, MAD introduces a novel two-branch classifier: a biased-branch encourages the classifier to identify the domain-specific (superficial) features, and a general-branch captures domain-generalized features based on the knowledge from biased-branch. Our MAD is appealing in view that it is pluggable to most single-DG models. We validate the superiority of our MAD in a variety of single-DG scenarios with different modalities, including recognition on 1D texts, 2D images, 3D point clouds, and semantic segmentation on 2D images. More remarkably, for recognition on 3D point clouds and semantic segmentation on 2D images, MAD improves DSU by 2.82% and 1.5% in accuracy and mIOU.
IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients

Integrated Gradients (IG) as well as its variants are well-known techniques for interpreting the decisions of deep neural networks. While IG-based approaches attain state-of-the-art performance, they often integrate noise into their explanation saliency maps, which reduce their interpretability. To minimize the noise, we examine the source of the noise analytically and propose a new approach to reduce the explanation noise based on our analytical findings. We propose the Important Direction Gradient Integration (IDGI) framework, which can be easily incorporated into any IG-based method that uses the Reimann Integration for integrated gradient computation. Extensive experiments with three IG-based methods show that IDGI improves them drastically on numerous interpretability metrics. The source code for IDGI is available at https://github.com/yangruo1226/IDGI.
Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers

Autoregressive transformers have shown remarkable success in video generation. However, the transformers are prohibited from directly learning the longterm dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregressive process. In this paper, we propose Memory-efficient Bidirectional Transformer (MeBT) for end-to-end learning of longterm dependency in videos and fast inference. Based on recent advances in bidirectional transformers, our method learns to decode the entire spatio-temporal volume of a video in parallel from partially observed patches. The proposed transformer achieves a linear time complexity in both encoding and decoding, by projecting observable context tokens into a fixed number of latent tokens and conditioning them to decode the masked tokens through the cross-attention. Empowered by linear complexity and bidirectional modeling, our method demonstrates significant improvement over the autoregressive transformers for generating moderately long videos in both quality and speed. Videos and code are available at https://sites.google.com/view/mebt-cvpr2023.
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers

Position Embeddings (PEs), an arguably indispensable component in Vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks.However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed.This caveat naturally raises a series of interesting questions about the impact of PEs on accuracy, privacy, prediction consistency, etc.To tackle these issues, we propose a Masked Jigsaw Puzzle (MJP) position embedding method.In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded.Meanwhile, for the nonoccluded patches, the PEs remain the original ones but their spatial relation is strengthened via our dense absolute localization regressor.The experimental results reveal that 1) PEs explicitly encode the 2D spatial relationship and lead to severe privacy leakage problems under gradient inversion attack; 2) Training ViTs with the naively shuffled patches can alleviate the problem, but it harms the accuracy; 3) Under a certain shuffle ratio, the proposed MJP not only boosts the performance and robustness on large-scale datasets (i.e., ImageNet-1K and ImageNet-C, -A/O) but also improves the privacy preservation ability under typical gradient attacks by a large margin.
Tangentially Elongated Gaussian Belief Propagation for Event-Based Incremental Optical Flow Estimation

Optical flow estimation is a fundamental functionality in computer vision. An event-based camera, which asynchronously detects sparse intensity changes, is an ideal device for realizing low-latency estimation of the optical flow owing to its low-latency sensing mechanism. An existing method using local plane fitting of events could utilize the sparsity to realize incremental updates for low-latency estimation; however, its output is merely a normal component of the full optical flow. An alternative approach using a frame-based deep neural network could estimate the full flow; however, its intensive non-incremental dense operation prohibits the low-latency estimation. We propose tangentially elongated Gaussian (TEG) belief propagation (BP) that realizes incremental full-flow estimation. We model the probability of full flow as the joint distribution of TEGs from the normal flow measurements, such that the marginal of this distribution with correct prior equals the full flow. We formulate the marginalization using a message-passing based on the BP to realize efficient incremental updates using sparse measurements. In addition to the theoretical justification, we evaluate the effectiveness of the TEGBP in real-world datasets; it outperforms SOTA incremental quasi-full flow method by a large margin. (The code is available at https://github.com/DensoITLab/tegbp/).
TransDocAnalyser: A Framework for Semi-structured Offline Handwritten Documents Analysis with an Application to Legal Domain

State-of-the-art offline Optical Character Recognition (OCR) frameworks perform poorly on semi-structured handwritten domain-specific documents due to their inability to localize and label form fields with domain-specific semantics. Existing techniques for semi-structured document analysis have primarily used datasets comprising invoices, purchase orders, receipts, and identity-card documents for benchmarking. In this work, we build the first semi-structured document analysis dataset in the legal domain by collecting a large number of First Information Report (FIR) documents from several police stations in India. This dataset, which we call the FIR dataset, is more challenging than most existing document analysis datasets, since it combines a wide variety of handwritten text with printed text. We also propose an end-to-end framework for offline processing of handwritten semi-structured documents, and benchmark it on our novel FIR dataset. Our framework used Encoder-Decoder architecture for localizing and labelling the form fields and for recognizing the handwritten content. The encoder consists of Faster-RCNN and Vision Transformers. Further the Transformer-based decoder architecture is trained with a domain-specific tokenizer. We also propose a post-correction method to handle recognition errors pertaining to the domain-specific terms. Our proposed framework achieves state-of-the-art results on the FIR dataset outperforming several existing models.
The Application of Stratified Teaching Method in Junior Middle School English Teaching

In junior middle school English teaching, teachers should improve students' comprehensive English ability based on the training requirements of core literacy. For English teaching in junior middle school, listening is an important content. Teachers should design a scientific listening teaching mode based on the characteristics of junior high school students. At present, there are still some problems in the middle school English listening teaching in middle schools. Teachers should analyze the problems in listening teaching, and at the same time take the form of stratified teaching to meet students' differentiated and personalized learning needs, which plays an important role in promoting students to complete English listening learning more efficiently.
Starting from a Cottage, One Will Succeed in Ten Thousand Bones, Pleading Guilty to the Thorn, Huigen has Already Repaired the Golden Body

In recent years, financial fraud incidents in the capital market have occurred frequently. Among them, not only the "Shuangkang incident" that caused a sensation in the Chinese capital market, but also the counterfeiting incident of Ruixing Coffee, a Chinese concept stock that caused a sensation in the US capital market. While the fraudulent amount of 2.2 billion yuan exploded, it also sounded the alarm for all listed companies. In the face of major risks and setbacks, and facing a market that is also full of uncertainties, Ruixing quickly took the blame, adjusted its strategy and recharged, and bottomed out after going through the trough. For this reason, this article conducts an in-depth analysis and research on the case of Ruixing’s financial fraud after bottoming out, and analyzes Ruixing’s defects in handling financial issues from the aspects of financial compliance, financial management, strategic accounting, and corporate governance. Provide corresponding reference and reference for other listed companies in my country in dealing with financial aspects. This article first analyzes the status of Ruixing Company and the case study of financial fraud to provide a background basis for the following analysis, and then analyzes the internal reasons of the company and the external environmental factors of the overseas market that caused Ruixing’s financial fraud from the perspective of financial compliance, and finally combines the above reasons Carry out corresponding suggestions and countermeasures for financial compliance in the process of overseas listing of Chinese companies; then from the perspective of financial management, combine financial management theory with Ruixing's solution to bottoming out and rebound, and finally come up with relevant responses from regulatory agencies, creditor's rights, and shareholders Measures; From the perspective of strategic management accounting, first compare with Starbucks’ five-force model, and draw the corresponding measures taken by Ruixing in terms of market analysis and departmental cooperation; Deal with the demands of internal contention, and then provide corresponding policy recommendations from four aspects.
Diagnostic proficiency test using digital cytopathology and comparative assessment of whole slide images of cytologic samples for quality assurance program in Korea

The Korean Society for Cytopathology introduced a digital proficiency test (PT) in 2021. However, many doubtful opinions remain on whether digitally scanned images can satisfactorily present subtle differences in the nuclear features and chromatin patterns of cytological samples.We prepared 30 whole-slide images (WSIs) from the conventional PT archive by a selection process for digital PT. Digital and conventional PT were performed in parallel for volunteer institutes, and the results were compared using feedback. To assess the quality of cytological assessment WSIs, 12 slides were collected and scanned using five different scanners, with four cytopathologists evaluating image quality through a questionnaire.Among the 215 institutes, 108 and 107 participated in glass and digital PT, respectively. No significant difference was noted in category C (major discordance), although the number of discordant cases was slightly higher in the digital PT group. Leica, 3DHistech Pannoramic 250 Flash, and Hamamatsu NanoZoomer 360 systems showed comparable results in terms of image quality, feature presentation, and error rates for most cytological samples. Overall satisfaction was observed with the general convenience and image quality of digital PT.As three-dimensional clusters are common and nuclear/chromatin features are critical for cytological interpretation, careful selection of scanners and optimal conditions are mandatory for the successful establishment of digital quality assurance programs in cytology.
Extracting chain lines and laid lines from digital images of medieval paper using spectral total variation decomposition

Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale.
Quantification of the interannual variability of the nationwide electric power supply from photovoltaic systems in Japan

Abstract Information on the variation in photovoltaic (PV) power generation is essential for resource assessment. This work investigated the interannual variability of the nationwide electric power supply from PV systems in Japan. Objectives of this study were twofold: one was the quantification of the annual variability of the nationwide PV power supply. The other was identifying the causes of the variability. However, the time span of available observation data on the PV power supply is inadequate to evaluate its variability, as PV systems have been rapidly installed in recent years. We used simulation to bypass this limitation. Due to the lack of available information for modeling, a hybrid modeling approach, combining a parametric model, and estimating parameters by fitting the model to observations, was employed. Nationwide PV power supply simulations were performed using historical weather data for 30 years, from 1991–2020. The long-term simulation data enabled us to quantify the interannual variability of the nationwide PV power generation. The annual variability measured with the range from the minimum to the maximum was approximately 9% of the mean. The variability for each month was less than 30% of the monthly mean for every month except for July when it was approximately 40%. An increasing trend in the annual mean PV power supply was observed over the 30 years, with an increase of 0.16% per year of the mean over the whole period. We found that the variations in sea surface temperature (SST) in the Tropics are factors contributing to the variability of nationwide PV power supply. Specifically, the variation in SST in the tropical Indian Ocean is one of the possible driving factors of the annual variability. The framework proposed in this study can provide valuable information for assessing solar energy resources on an interannual scale.
Word Syllabification for Indonesian Language using Transformer

Syllabification is a process from word to a series of syllable. Syllabification can be used in Natural Language Processing (NLP) such as speech recognition, text-to-speech, rhyme detection, and many more. Syllabification of the Indonesian language will refers to the General Guidelines for Indonesian Language Spelling (PUEBI). The corpus for this research containing the main word and the syllable. The corpus for this research is using ”50k KBBI 5-k fold” and combined with ”103k Named Entity 5-k fold”. The evaluation for this model is using Word Error Rate (WER). WER of previous deep learning model for syllabification is still high with 3.75% WER. The objective of this research is to lower the WER for deep learning using Transformer and Syllable Tagging because it can accept long contextual dependency. The evaluation result of this model is 3.68% WER and can be used universally for Indonesian words because the margin between Formal Words and Named Entity Words is close with the average result. Thus, this model currently the better model for Indonesian syllabification deep learning model according from the average WER is lower than the other deep learning model.
Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning

Traffic accidents are events that are not wanted by everyone when traveling. Unfortunately, based on the facts released by WHO in 2020 [1], traffic accidents are still the top 10 causes of death in low-income countries, as well as data from the NSHTA [2] mentions 38,824 people died on U.S. roads. The Indonesian Ministry of Transportation also released data that in the last 5 years accident cases have always reached more than 100,000 cases [3]. Of course, the facts that have been mentioned have shown that accident tragedies still often occur. One of the causes of accidents is Driver Distraction. Driver Distraction can be divided into several distractions [4], one of the distractions is visual distraction [5]. In this research, visual distraction activities will be detected by entering the key points of eye position and time domain of the video. The key points will be taken from the face mesh using the mediapipe. Then the detection of visual distraction activities will be tried using deep learning that can remember information from previous times such as LSTM and GRU. This research is expected to help develop a system of visual distraction activities so as to reduce the risk of accidents.
Towards an All-Silicon DV-QKD Transmitter Sourced by a Ge-on-Si Light Emitter

We investigate the behavior of a Ge-on-Si light source and demonstrate its feasibility for polarization-encoded discrete-variable quantum key distribution following the BB84 protocol, enabling a potential “all-silicon” QKD scheme which can operate well below the necessary QBER limit and successfully generate secret keys.
High-Resolution Art Painting Completion using Multi-Region Laplacian Fusion

Image completion has made impressive advancements based on deep learning approaches. However, even with advanced deep learning such as Generative Adversarial Networks (GAN), the restored area is not always optimal due to small-scale texture synthesis in high resolution and inferring missing information about image content from distant contexts, resulting in distorted lines and unnatural colors, especially in art painting completion with complicated structures and textures. Although several precious art paintings have been well-preserved by curators in museums, some frequent damages such as scratches, torn-out areas, and holes are still visible and require challenging physical repairs. Therefore, for practical refinement, some researchers convert them into high-resolution digital paintings to generate crisp brush strokes, textures, shapes, and tones by assuming similarities with the original physical ones. Based on these observations, we propose proceeding with a high-resolution art painting completion by applying a superior traditional method, named Multi-Region Laplacian Fusion. We attempt to recover irregular missing regions expected as the damages of ordinary paintings that often occur. To address high-resolution inpainting, we integrate two completions using the Laplacian pyramid and patch-based propagation. We then apply Alpha blending among both results to yield the fused reaction completion. Our experiments firmly validate the effectiveness of our proposed method to complete art paintings with random irregular missing regions.
Heterogeneously Integrated III–V on Silicon Photonics for Neuromorphic Computing

At Hewlett Packard Labs, an energy-efficient, heterogeneous III–V on silicon photonics platform has been developed as the underlying device-level foundation for innovative neuromorphic computing architectures. The latest research on our III–V on silicon photonic neuromorphic device platform will be presented and discussed.
IoT Based Assistive Robot System Design

Though robotic arms have provided increased independence for individuals with upper extremity disabilities, intuitive control the arms remain an issue. In this research, an IoT-based assistive robotic system has been developed using a Kinova JACO2 arm, Kinect camera, and IoT smart sensors. The proposed system was tested and compared with smart sensors attached. The results show the proposed system design can reduce the motion time with enhanced reliability and accuracy with IoT sensors applied. This work could have great impact for existing users of JACO as it will make picking, dropping and other daily life activities a much simpler and more efficient for people with limited upper limb mobility.
Design and Realization of Intelligent Image Recognition System Based on Computer

The update of modern intelligent technology has driven the progress of computer related technology. The tide of technological revolution is impacting the economic system all over the world. The emergence of information technology has helped the intelligent information industry break through the bottleneck period of traditional development. Computer image intelligent recognition technology is gradually mature. This form puts forward higher requirements for China's economic construction. On this basis, the design of image recognition system has gradually become the main research object of experts. This paper explains the relevant theories of image recognition technology. Finally, the design and implementation of image recognition system are proposed.