Reviewerly/exHarmony huggingface dataset:
A Portable Multiscopic Camera for Novel View and Time Synthesis in Dynamic Scenes

We present a portable multiscopic camera system with a dedicated model for novel view and time synthesis in dynamic scenes. Our goal is to render high-quality images for a dynamic scene from any viewpoint at any time using our portable multiscopic camera. To achieve such novel view and time synthesis, we develop a physical multiscopic camera equipped with five cameras to train a neural radiance field (NeRF) in both time and spatial domains for dynamic scenes. Our model maps a 6D coordinate (3D spatial position, 1D temporal coordinate, and 2D viewing direction) to view-dependent and time-varying emitted radiance and volume density. Volume rendering is applied to render a photo-realistic image at a specified camera pose and time. To improve the robustness of our physical camera, we propose a camera parameter optimization module and a temporal frame interpolation module to promote information propagation across time. We conduct experiments on both real-world and synthetic datasets to evaluate our system, and the results show that our approach outperforms alternative solutions qualitatively and quantitatively. Our code and dataset are available at https://yuenfuilau.github.io/.
Multi-strategy Enhanced Neural Machine Translation for Chinese Minority Languages

This paper presents HW-TSC’s submissions to CCMT 2022 Chinese Minority Language Translation task. We participate in three language directions: Mongolian $$\rightarrow $$ Chinese Daily Conversation Translation, Tibetan $$\rightarrow $$ Chinese Government Document Translation, and Uighur $$\rightarrow $$ Chinese News Translation. We train our models using the Deep Transformer architecture, and adopt enhancement strategies such as Regularized Dropout, Tagged Back-Translation, Alternated Training, and Ensemble. Our enhancement experiments have proved the effectiveness of above-mentioned strategies. We submit enhanced systems as primary systems for the three tracks. In addition, we train contrast models using additional bilingual data and submit results generated by these contrast models.
CodePanorama

Software projects change hands frequently. Oftentimes, developers are interested in the quality of the code before taking over responsibility on a project. This quality is commonly assessed using various code metrics, reducing the code into a handful of numbers. While useful, these numerical reductions quickly become detached from the real code. CodePanorama uses an alternative approach to summarize code not into numbers, but into images. By generating zoomed-out images of the code-base, the human eye can quickly spot anomalies without the need to rely on numerical metrics and statistics. This paper describes the tool CodePanorama, the images it generates, and the insights that can be gained from these images. We finally invite the software engineering community to start using it.
Ontology-based Recommender System with Descriptive Analytics in e-Commerce

The amount of information and users has been increasing at a remarkable rate in recent years. This is where the recommender system comes in, recommender system is a system that generates a list of recommended products for the user. Recommender system has outshined as one of the important features in an e-Commerce portal. Several recommender techniques have been proposed, yet, problems such as cold-start item problem, cold-start user problem and data sparsity problem still existed. The expected outcomes of this paper are an ontology-based recommender system, combining the collaborative and content-based filtering approaches. In addition, the recommender system also generates recommendations by considering user preferences based on social network association. The experimental evaluation indicated that the proposed approach works more efficiently and effectively improves the recommendation.
Sentiment Analysis of Public on Social Media about Covid-19 origin theory using Machine Learning

Covid-19 has been found in Wuhan, China, for approximately a year and a half ago, and the virus's origin remains a mystery. However, it has been in the news in recent weeks, with reports suggesting that an infectious disease was spilled in a Chinese laboratory, which was previously refuted by a hoax in the area. In this research paper, we have presented a model where there will be a sentimental analysis based on users’ comments on social media about the origin of corona virus. Nowadays most people express their feelings and the truth around them and many lies on social media. And we are taking this opportunity to do a sentimental analysis of the true, false, and confusing feelings that people have expressed on social media about the origin of corona virus. We used 20000 data (comments) taken from corona virus-related popular Facebook news posts. In order to achieve the maximum results, we used five distinct machine learning classifiers, and our support vector machine and logistic regression model outscored them all. The support vector model had a testing accuracy rate of 83.73 %, whereas logistic regression had an accuracy rate of 81.39 %. The important thing about our research is that at the end of the whole work, thousands of people's personal feelings, truths, hesitations, and confusion come together to know a strong possibility about the origin of the corona virus.
Role of Blockchain Technology in Agriculture Supply Chain: A Systematic Literature Review

Agriculture is a science related to cultivation and production of plants and livestock. The agriculture supply chain is an integrated process for planning and control of the flow of agricultural goods, information and money across the entire value. The agriculture supply chain system can provide all involved stakeholders with a single source of timely information. Traditional agriculture supply chain methods compromises the stakeholders trust, traceability, transparency and auditability of food items. The usage of blockchain technology ensures these compromises with the help of peer to peer network, distributed ledger, mining and consensus protocols. In this review, we have conducted a systematic literature review in order to investigate and synthesize all literature regarding state-of-the-art blockchain technology in the agriculture food supply chain. This SLR has been developed by reviewing carefully selected literature work published between 2015 and 2021 from reputed paper repositories. In this article, we have proposed a taxonomy based on key areas of agriculture supply chain using blockchain technology. Additionally, we have proposed a framework for agriculture supply chain based on blockchain technology. Finally, research gaps, challenges and limitations have been discussed to give a clear direction for future to the developers and researchers in the field of agriculture supply chain.
Blockchain, Innovation to the Value Chain and Improvement in the Management of Peruvian Family Farming

Blockchain technology has been defined as an ensure and transparency tool for all interested parties. This study aims to identify key points within agro-industrial value chain using blockchain, to add trust and eliminate economic development barriers supported by the literature review.
Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Human Motion Prediction

Previous works on human motion prediction follow the pattern of building a mapping relation between the sequence observed and the one to be predicted. However, due to the inherent complexity of multivariate time series data, it still remains a challenge to find the extrapolation relation between motion sequences. In this paper, we present a new prediction pattern, which introduces previously overlooked human poses, to implement the prediction task from the view of interpolation. These poses exist after the predicted sequence, and form the privileged sequence. To be specific, we first propose an InTerPolation learning Network (ITP-Network) that encodes both the observed sequence and the privileged sequence to interpolate the in-between predicted sequence, wherein the embedded Privileged-sequence-Encoder (Priv-Encoder) learns the privileged knowledge (PK) simultaneously. Then, we propose a Final Prediction Network (FP-Network) for which the privileged sequence is not observable, but is equipped with a novel PK-Simulator that distills PK learned from the previous network. This simulator takes as input the observed sequence, but approximates the behavior of Priv-Encoder, enabling FP-Network to imitate the interpolation process. Extensive experimental results demonstrate that our prediction pattern achieves state-of-the-art performance on benchmarked H3.6M, CMU-Mocap and 3DPW datasets in both short-term and long-term predictions.
Efficient Scalable Migrations in the Cloud

Cloud based migration centers around themes such as scalability, reduced expenditure, maintenance and security. Cloud computing services proffer the ability to spin up and spin down clusters based on the load, provide various version support, cloud native tools, hardware and software capabilities. However, cloud based migration pose securitychallenges due to reliance on multiple, potentially third partyclouds.Migration is of six types known as the 6Rs viz. a) Lift and shift, b) Refactoring, c) Re-platforming, d) Re-purchasing, e) Retiring and f) Retaining. In Lift and shift, one simply “liff”s or copies applications, workloads, or databases from on premises to the cloud with no changes. Refactoring is also known as rearchitecting, which usually deals with modifying applications. Based on resource consumption and business needs, refactoring is usually favoured along with the first approach, but this is time consuming and expensive compared to the former. In replatforming though, optimizations along with few infrastructure changes are done, to avail all the benefits of the respective target cloud offerings.In this paper, we discuss few migration use cases at Walmart, all centred around the second and the third migration strategies and discuss the solution approaches, which resulted in remarkable gains.
When Less May Be More: Exploring Similarity to Improve Experience Replay

We propose the COMPact Experience Replay (COMPER) as a reinforcement learning method that seeks to reduce the required number of experiences to agent training regarding the total accumulated rewards in the long run. COMPER uses temporal difference learning with predicted target values for sets of similar transitions and a new experience replay approach based on two memories of transitions. We present an assessment of two possible neural network architectures for the target network with a complete analysis of the memories’ behavior, along with detailed results for 100,000 frames and about 25,000 iterations with a small experience memory on eight challenging 2600 Atari games on the Arcade Learning Environment (ALE). We also present results for a Deep Q-Network (DQN) agent with the same experimental protocol on the same set of games as a baseline. We demonstrate that COMPER can approximate a good policy from a small number of frame observations using a compact memory and learning the similar transitions’ sets dynamics using a recurrent neural network.
Feedforward Neural Network Reconstructed from High-order Quantum Systems

Neural Networks (NNs) are widely used because of their superior feature extraction capabilities, among which Feedforward Neural Network (FNN) is used as the basic model for theoretical research. Recently, Quantum Neural Networks (QNNs) based on quantum mechanics have received extensive attention due to their ability to mine quantum correlations and parallel computing. Since two classical bits are required to simulate one qubit (i.e., quantum bit) on a classical computer, it brings challenges for simulating complex quantum operations or building large-scale QNNs on a classical computer. Hardy et al. extended the classical and quantum probability theories to the Generalized Probability Theory (GPT), so it is possible to construct high-order quantum systems. This paper regards the entire feature extraction and integration process of FNN as the evolution process of the high-order quantum system, and then leverages quantum coherence to describe the complex relationship between the features extracted by each layer of the network model. Intuitively, we reconstruct FNN to change the general vector processed by each layer into the state vector of the high-order quantum system. The experimental results on four mainstream datasets show that FNN reconstructed from the high-order quantum system is significantly better than the classical counterpart.
Learning to Weight Samples for Dynamic Early-Exiting Networks

Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks allow easy samples to be output at early exits, removing the need for executing deeper layers. While existing works mainly focus on the architectural design of multi-exit networks, the training strategies for such models are largely left unexplored. The current state-of-the-art models treat all samples the same during training. However, the early-exiting behavior during testing has been ignored, leading to a gap between training and testing. In this paper, we propose to bridge this gap by sample weighting. Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers. The training of hard samples (mostly exit from deeper layers), however, should be emphasized by the late classifiers. Our work proposes to adopt a weight prediction network to weight the loss of different training samples at each exit. This weight prediction network and the backbone model are jointly optimized under a meta-learning framework with a novel optimization objective. By bringing the adaptive behavior during inference into the training phase, we show that the proposed weighting mechanism consistently improves the trade-off between classification accuracy and inference efficiency. Code is available at https://github.com/LeapLabTHU/L2W-DEN .
CADET: A Collaborative Agile Data Exploration Tool for Mixed Reality

The need to understand and communicate the nuances of complex situational information is an ever-present requirement in Command and Control (C2). It is often difficult for remote users of a system to clearly understand what a user is trying to relay. Mixed Reality (MR) technology presents a significant opportunity for exploring and communicating C2 data. In this paper, we present our system, CADET, as step towards enriching the collaborative C2 user ex-perience by allowing users to remotely and locally perform adhoc analysis through information displays created by hand interactions and speech in MR.
Complex Scenario-Oriented Fine-Grained Visual Classification Platform

In recent years, fine-grained visual classification (FGVC) algorithms have achieved excellent performance across a variety of datasets. However, it is still rare to see these algorithms applied in daily life. The main reasons for this are i) the algorithms are developed based on different design guidelines and cannot be deployed in the same environment; ii) there is not a simple and efficient platform to present the algorithm's results to the user - the accuracy is meaningless to the users. To address the above problem, we built a complex scenario-oriented fine-grained visual classification platform. The platform consists of a PyTorch-based fine-grained visual recognition algorithm library (FGL) and a WeChat applet-based user interaction module (WEM). We can quickly develop new algorithms or readily apply existing algorithms in the same environment through FGL. Driven by FGL, the WEM enables users to achieve fine-grained recognition of complex scenes interactively. In addition to showing the user the fine-grained labels of objects, we will also show how the model makes decisions to help the user master the ability to recognise the fine-grained object so that everyone can become a domain expert. A video demo shows an example of the proposed platform in a real-world scenario: https://reurl.cc/rRZE7O.
Formation of Text from Indian Sign Language using Convolutional Neural Networks

Approximately 5% of people around the world suffer from a "disabling" hearing loss that will require rehabilitation. Around 2.42 million Deaf and Mute (D&M) individuals live in India, the world's second most populous country. In modern-day society, people do not think of these impairments as disabilities; it is merely perceived as a lifestyle choice. In reality, life is a lot harder for them as D&M individuals cannot communicate verbally, having to resort to mediums such as using visual media and finger spelling to communicate. However, the vast majority of non-D&M people are unaware of sign language, resulting in an extensive communication gap. This paper addresses this very issue by proposing a ML model that recognizes the Indian Sign Language (ISL) using Convolutional Neural Network (CNN) for sign language recognition and a Graphical User Interface (GUI) that captures these hand gestures and translates them into text. An ISL dataset with 31,945 images was used, and using the proposed approach an accuracy of 98.82% was achieved with the CNN model. This serves as the backend for a GUI which converts ISL to text.
Improving Video Model Transfer with Dynamic Representation Learning

Temporal modeling is an essential element in video understanding. While deep convolution-based architectures have been successful at solving large-scale video recognition datasets, recent work has pointed out that they are biased towards modeling short-range relations, often failing to capture long-term temporal structures in the videos, leading to poor transfer and generalization to new datasets. In this work, the problem of dynamic representation learning (DRL) is studied. We propose dynamic score, a measure of video dynamic modeling that describes the additional amount of information learned by a video network that cannot be captured by pure spatial student through knowledge distillation. DRL is then formulated as an adversarial learning problem between the video and spatial models, with the objective of maximizing the dynamic score of learned spatiotemporal classifier. The quality of learned video representations are evaluated on a diverse set of transfer learning problems concerning many-shot and few-shot action classification. Experimental results show that models learned with DRL outperform baselines in dynamic modeling, demonstrating higher transferability and generalization capacity to novel domains and tasks.
AlignQ: Alignment Quantization with ADMM-based Correlation Preservation

Quantization is an efficient network compression approach to reduce the inference time. However, existing approaches ignored the distribution difference between training and testing data, thereby inducing a large quantization error in inference. To address this issue, we propose a new quantization scheme, Alignment Quantization with ADMM-based Correlation Preservation (AlignQ), which exploits the cumulative distribution function (CDF) to align the data to be i.i.d. (independently and identically distributed) for quantization error minimization. Afterward, our theoretical analysis indicates that the significant changes in data correlations after the quantization induce a large quantization error. Accordingly, we aim to preserve the relationship of data from the original space to the aligned quantization space for retaining the prediction information. We design an optimization process by leveraging the Alternating Direction Method of Multipliers (ADMM) optimization to minimize the differences in data correlations before and after the alignment and quantization. In experiments, we visualize non-i.i.d. in training and testing data in the benchmark. We further adopt domain shift data to compare AlignQ with the state-of-the-art. Experimental results show that AlignQ achieves significant performance improvements especially in low-bit models. Code is available at https://github.com/tinganchen/AlignQ.git.
Large Parallax Image Stitching via Structure Preservation and Multi-matching

In image stitching tasks, parallax ghosts and composite ghosts widely exist in stitching results whose input images usually have complex moving objects (especially multiple pedestrians) and large parallax scenes, which bring greater challenges to image stitching. In order to efficiently remove these ghosts and maintain visually natural stitching results, we propose a novel image stitching method via structure preservation and multi-matching (SPMM). Specifically, the method is composed of the structure preservation module based on grid constraints that eliminates parallax ghosts by ensuring the alignment of overlapping regions and keeping object structures in non-overlapping regions; and the composite ghost removal module based on Multi-Matching, which effectively eliminates composite ghosts caused by multiple pedestrians and obtains natural stitching results. The extensive experiments on five different image stitching frameworks and real datasets demonstrate the effectiveness and advantages of the proposed method.
Understanding Cybersecurity Standards

This chapter discusses cybersecurity standards and frameworks to assess and improve cybersecurity strategy adoption in organizations.
Urdu Named Entity Recognition with Attention Bi-LSTM-CRF Model

The named entity recognition (NER) task is a challenging problem in natural language processing (NLP), especially for languages with very few annotated corpora such as Urdu. In this paper we proposed an Attention-Bi-LSTM-CRF method and applied it to the MK-PUCIT Corpus which is the latest NER dataset available for the Urdu language. In addition to word-level embedding, we used an embedding-level focus mechanism. The output of the embedding layer was fed into a bidirectional-LSTM encoder unit, accompanied by another self-attention layer to boost the system's accuracy. Our Attention-Bi-LSTM-CRF model demonstrated an F1-score of 92%. The cumulative findings of the experiments show that our approach outperforms existing methods, thus yielding a new UNER (Urdu Named Entity Recognition) state-of-the-art performance.
Online Illumination Learning for Interactive Global Illumination in Augmented Reality

Augmenting virtual objects into a real scene requires estimating the scene illumination so that the augmented objects can become visually coherent with real objects. We propose an online technique that learns the illumination from image sequences captured by a hand-held device. We approximate the illumination with multiple linear models, and the coefficients and bandwidth parameters of the models are updated progressively in a data-driven way. Our online learning enables us to seamlessly integrate virtual objects into a real scene by rendering the objects with the estimated lights. We demonstrate that our framework can provide a high-quality global illumination result in augmented reality at interactive rates.
A Transformer-Based Longer Entity Attention Model for Chinese Named Entity Recognition in Aerospace

Chinese aerospace knowledge includes many long entities, such as professional terms, equipment names, and cabinets. However, current Named Entity Recognition (NER) algorithms typically address these longer and shorter entities uniformly. In this paper, a Longer Entity Attention (LEA) model based on the transformer is proposed. After the transformer encoding layer, LEA integrates sentence tags, sets thresholds according to the length of entities, and processes the hidden layer features of entities larger than the defined threshold to enhance the ability of the model to recognize longer entities. In addition, we construct an Aerospace Chinese NER dataset (ACNE) containing rich entity categories and domain knowledge. Experimental results demonstrate that LEA outperforms previous state-of-the-art models on ACNE, and shows a significant improvement on longer entities in each threshold range on OntoNotes 5.0 and ACNE datasets.