Reviewerly/exHarmony huggingface dataset:
Channel Balancing for Accurate Quantization of Winograd Convolutions

It is well known that Winograd convolution algorithms speed up the widely used small-size convolutions. However, the problem of quantization of Winograd convolutions is challenging - while quantization of slower Winograd algorithms does not cause problems, quantization of faster Winograd algorithms often leads to a significant drop in the quality of models. We introduce a novel class of Winograd algorithms that balances the filter and input channels in the Winograd domain. Unlike traditional Winograd convolutions, the proposed convolution balances the ranges of input channels on the forward pass by scaling the input tensor using special balancing coefficients (the filter channels are balanced offline). As a result of balancing, the inputs and filters of the Winograd convolution are much easier to quantize. Thus, the proposed technique allows us to obtain models with quantized Winograd convolutions, the quality of which is significantly higher than the quality of models with traditional quantized Winograd convolutions. Moreover, we propose a special direct algorithm for calculating the balancing coefficients, which does not require additional model training. This algorithm makes it easy to obtain the post-training quantized balanced Winograd convolutions - one should just feed a few data samples to the model without training to calibrate special parameters. In addition, it is possible to initialize the balancing coefficients using this algorithm and further train them as trainable variables during Winograd quantization-aware training for greater quality improvement.
COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots

Disparities in COVID-19 information and vaccine access have emerged during the pandemic. Individuals from historically excluded communities (eg, Black and Latin American) experience disproportionately negative health outcomes related to COVID-19. Community gaps in COVID-19 education, social, and health care services (including vaccines) should be prioritized as a critical effort to end the pandemic. Misinformation created by the politicization of COVID-19 and related public health measures has magnified the pandemic's challenges, including access to health care, vaccination and testing efforts, as well as personal protective equipment. Information and Communication Technology (ICT) has been demonstrated to reduce the gaps of marginalization in education and access among communities. Chatbots are an increasingly present example of ICTs, particularly in health care and in relation to the COVID-19 pandemic.This project aimed to (1) follow an inclusive and theoretically driven design process to develop and test a COVID-19 information ICT bilingual (English and Spanish) chatbot tool named "Ana" and (2) characterize and evaluate user experiences of these innovative technologies.Ana was developed following a multitheoretical framework, and the project team was comprised of public health experts, behavioral scientists, community members, and medical team. A total of 7 iterations of ß chatbots were tested, and a total of 22 ß testers participated in this process. Content was curated primarily to provide users with factual answers to common questions about COVID-19. To ensure relevance of the content, topics were driven by community concerns and questions, as ascertained through research. Ana's repository of educational content was based on national and international organizations as well as interdisciplinary experts. In the context of this development and pilot project, we identified an evaluation framework to explore reach, engagement, and satisfaction.A total of 626 community members used Ana from August 2021 to March 2022. Among those participants, 346 used the English version, with an average of 43 users per month; and 280 participants used the Spanish version, with an average of 40 users monthly. Across all users, 63.87% (n=221) of English users and 22.14% (n=62) of Spanish users returned to use Ana at least once; 18.49% (n=64) among the English version users and 18.57% (n=52) among the Spanish version users reported their ranking. Positive ranking comprised the "smiley" and "loved" emojis, and negative ranking comprised the "neutral," "sad," and "mad" emojis. When comparing negative and positive experiences, the latter was higher across Ana's platforms (English: n=41, 64.06%; Spanish: n=41, 77.35%) versus the former (English: n=23, 35.93%; Spanish: n=12, 22.64%).This pilot project demonstrated the feasibility and capacity of an innovative ICT to share COVID-19 information within diverse communities. Creating a chatbot like Ana with bilingual content contributed to an equitable approach to address the lack of accessible COVID-19-related information.
Neural Face Identification in a 2D Wireframe Projection of a Manifold Object

In computer-aided design (CAD) systems, 2D line drawings are commonly used to illustrate 3D object designs. To reconstruct the 3D models depicted by a single 2D line drawing, an important key is finding the edge loops in the line drawing which correspond to the actual faces of the 3D object. In this paper, we approach the classical problem of face identification from a novel data-driven point of view. We cast it as a sequence generation problem: starting from an arbitrary edge, we adopt a variant of the popular Transformer model to predict the edges associated with the same face in a natural order. This allows us to avoid searching the space of all possible edge loops with various handcrafted rules and heuristics as most existing methods do, deal with challenging cases such as curved surfaces and nested edge loops, and leverage additional cues such as face types. We further discuss how possibly imperfect predictions can be used for 3D object reconstruction. The project page is at https://manycore-research.github.io/faceformer.
MimicME: A Large Scale Diverse 4D Database for Facial Expression Analysis

Recently, Deep Neural Networks (DNNs) have been shown to outperform traditional methods in many disciplines such as computer vision, speech recognition and natural language processing. A prerequisite for the successful application of DNNs is the big number of data. Even though various facial datasets exist for the case of 2D images, there is a remarkable absence of datasets when we have to deal with 3D faces. The available facial datasets are limited either in terms of expressions or in the number of subjects. This lack of large datasets hinders the exploitation of the great advances that DNNs can provide. In this paper, we overcome these limitations by introducing MimicMe, a novel large-scale database of dynamic high-resolution 3D faces. MimicMe contains recordings of 4, 700 subjects with a great diversity on age, gender and ethnicity. The recordings are in the form of 4D videos of subjects displaying a multitude of facial behaviours, resulting to over 280, 000 3D meshes in total. We have also manually annotated a big portion of these meshes with 3D facial landmarks and they have been categorized in the corresponding expressions. We have also built very powerful blendshapes for parameterising facial behaviour. MimicMe will be made publicly available upon publication and we envision that it will be extremely valuable to researchers working in many problems of face modelling and analysis, including 3D/4D face and facial expression recognition $$^{\dagger }$$ . We conduct several experiments and demonstrate the usefulness of the database for various applications. ( $$^{\dagger }$$ https://github.com/apapaion/mimicme )
A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

Sign language is the predominant mode of communication for the Deaf community.For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated.In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the Deaf and people with hearing impairment by increasing accessibility.This research aims to review and analyze articles related to sign language recognition based on the sensor-based glove system, in order to identify academic motivations, challenges, and recommendations related to this field.The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus.The articles were chosen based on our inclusion and exclusion criteria.The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition.Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures.Moreover, recognition accuracy typically achieved results higher than 90%.However, most experiments were carried out with a limited number of gestures.Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.
Asking Follow-Up Clarifications to Resolve Ambiguities in Human-Robot Conversation

When a robot aims to comprehend its human partner's request by identifying the referenced objects in Human-Robot Conversation, ambiguities can occur because the environment might contain many similar objects or the objects described in the request might be unknown to the robot. In the case of ambiguities, most of the systems ask users to repeat their request, which assumes that the robot is familiar with all of the objects in the environment. This assumption might lead to task failure, especially in complex real-world environments. In this paper, we address this challenge by presenting an interactive system that asks for follow-up clarifications to disambiguate the described objects using the pieces of information that the robot could understand from the request and the objects in the environment that are known to the robot. To evaluate our system while disambiguating the referenced objects, we conducted a user study with 63 participants. We analyzed the interactions when the robot asked for clarifications and when it asked users to redescribe the same object. Our results show that generating followup clarification questions helped the robot correctly identify the described objects with fewer attempts (i.e., conversational turns). Also, when people were asked clarification questions, they perceived the task as easier, and they evaluated the task understanding and competence of the robot as higher. Our code and anonymized dataset are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/IrmakDogan/Resolving-Ambiguities.
Reconfigurable and High-Efficiency Polynomial Multiplication Accelerator for CRYSTALS-Kyber

Recently, the National Institute of Standards and Technology (NIST) has identified the first four quantum-resistant algorithms for post-quantum cryptography (PQC) standardization. CRYSTALS-Kyber (Kyber) is the only public-key encryption and key-establishment algorithm among them. In this article, we propose a reconfigurable, high-speed, and area-efficient polynomial multiplication accelerator for Kyber to facilitate its practical applications. The cornerstone of polynomial multiplication is the butterfly unit (BU) structure, composed of modular addition, subtraction, and multiplication. For the modular multiplication, we adopt the Barrett reduction method and reduce the size of operands leveraging the form of modulus with a novel formula transformation, which significantly decreases the computational complexity and increases the maximum clock frequency. On the hardware side, we make four BU modules constitute a binomial arithmetic core (Bi-Core) as the basic reconfigurable unit. The memory access scheme tailored for parallel processing is explored with data-reusing and memory-grouping methods, and a compact control logic is devised. The complete polynomial multiplication architecture is coded with Verilog and implemented on a Xilinx Artix-7 xc7a100t-3 device. Experiment results demonstrate that our implementations with different configurations all outperform the state-of-the-art works in area efficiency by up to 39% improvement in terms of area-time product (ATP). Moreover, the proposed design with four Bi-Cores achieves the fastest speed among existing designs.
RealFlow: EM-Based Realistic Optical Flow Dataset Generation from Videos

Obtaining the ground truth labels from a video is challenging since the manual annotation of pixel-wise flow labels is prohibitively expensive and laborious. Besides, existing approaches try to adapt the trained model on synthetic datasets to authentic videos, which inevitably suffers from domain discrepancy and hinders the performance for real-world applications. To solve these problems, we propose RealFlow, an Expectation-Maximization based framework that can create large-scale optical flow datasets directly from any unlabeled realistic videos. Specifically, we first estimate optical flow between a pair of video frames, and then synthesize a new image from this pair based on the predicted flow. Thus the new image pairs and their corresponding flows can be regarded as a new training set. Besides, we design a Realistic Image Pair Rendering (RIPR) module that adopts softmax splatting and bi-directional hole filling techniques to alleviate the artifacts of the image synthesis. In the E-step, RIPR renders new images to create a large quantity of training data. In the M-step, we utilize the generated training data to train an optical flow network, which can be used to estimate optical flows in the next E-step. During the iterative learning steps, the capability of the flow network is gradually improved, so is the accuracy of the flow, as well as the quality of the synthesized dataset. Experimental results show that RealFlow outperforms previous dataset generation methods by a considerably large margin. Moreover, based on the generated dataset, our approach achieves state-of-the-art performance on two standard benchmarks compared with both supervised and unsupervised optical flow methods. Our code and dataset are available at https://github.com/megvii-research/RealFlow .
Using discord conversations as program comprehension aid

Modern communication platforms used in software development host daily conversations among developers and users about a wide range of topics pertaining to software systems, such as language features, APIs, code artifacts like classes and methods, design patterns, usage examples, code reviews, bug reporting and fixing. Discord servers are one of these virtual community hubs that have seen a steep rise in popularity, as coordination and aggregation means for communities of developers. Although Discord supports filter-based search functionalities, the sheer volume, velocity, and small granularity of single messages make it hard to find useful results, let alone complete discussions revolving around particular themes. One reason is that the concept of a discussion, which we call a conversation, does not exist as an explicit concept. We argue that extracting and analyzing such conversations can be used fruitfully to aid program comprehension.
Approximate Quantum Circuit Reconstruction

Current and imminent quantum hardware lacks reliability and applicability due to noise and limited qubit counts. Quantum circuit cutting — a technique dividing large quantum circuits into smaller subcircuits with sizes appropriate for the limited quantum resource at hand — is used to mitigate these problems. However, classical postprocessing involved in circuit cutting generally grows exponentially with the number of cuts and quantum counts. This article introduces the notion of approximate circuit reconstruction. Using a sampling-based method like Markov Chain Monte Carlo (MCMC), we probabilistically select bit strings of high probability upon reconstruction. This avoids excessive calculations when reconstructing the full probability distribution. Our results show that such a sampling-based postprocessing method holds great potential for fast and reliable circuit reconstruction in the NISQ era and beyond.
A Survey on Cross-Chain Technology: Challenges, Development, and Prospect

With the continuous development of blockchain technology, many blockchain applications, such as digital currencies in the form of tokens, are deployed.However, due to the lack of data and value inter-blockchain transmission methods, these chain applications form many data islands.Therefore, crosschain technology is proposed and applied in distributed transaction platforms, digital finance, e-government and many other different fields.Although cross-chain technology just started, the cross-chain technology and its applications are gradually improving to meet the development needs of blockchain system.At the same time, they have already realized asset transfers across heterogeneous blockchains and platforms, and some projects have realized cross-chain information exchange.At present, cross-chain technology includes the following typical methods: Hash-locking, Notary scheme, Distributed private key control, Sidechains/Relays and Tight coupling.This paper analyzes the above methods from the aspect of techniques, security requirements, and technical limitation, and compares their applicability.With public blockchains and consortium blockchains, this paper analyzes their problems and challenges in cross-chain technology and introduces the main approaches of existing solutions.By analyzing the existing cross-chain technologies and problems, this paper further introduces eight popular cross-chain projects and their application prospects.Finally, this paper summarizes and proposes the future research topics and development trends of cross-chain technology.
Procedural Generation of Narrative Worlds

A narrative world typically consists of several interrelated locations that, all together, fully support enacting a given story. For this, each location in a narrative world features all the objects as required there by the narrative, as well as a variety of other objects that plausibly describe or decorate the location. Procedural generation of narrative worlds poses many challenges, including that, First, it cannot lean only on domain knowledge (e.g., patterns of objects commonly found in typical locations), and, second, it involves a temporal dimension, which introduces dynamic fluctuations of objects between locations. In this article, we present a novel approach for the procedural generation of narrative worlds, following two stages: first, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">narrative world mold</i> is generated (only once) for a given story; second, the narrative world mold is used to create one (or more) possible narrative worlds for that story. For each story, its narrative world mold integrates spatio-temporal descriptions of its locations with the object semantics and the domain knowledge previously acquired on typical locations. We describe how a narrative world mold can be generated, as well as how it can be fed to existing procedural generation methods, to create a variety of narrative worlds that fit that narrative. We evaluate our own implementation of this approach, performing a number of experiments that illustrate both the expressive power of narrative world molds and their ability to steer the generation of narrative worlds.
SlimBox: Lightweight Packet Inspection over Encrypted Traffic

Due to the explosive increase of enterprise network traffic, middleboxes that inspect packets through customized rules have been widely outsourced for cost-saving. Despite promising, redirecting enterprise traffic to remote middleboxes raises privacy concerns about the exposure of corporate secrets. To address this, existing solutions mainly apply searchable encryption (SE) to encrypt traffic and rules, enabling middlebox to perform pattern matching over ciphertexts without learning any sensitive information. However, SE is designed for searching pre-chosen keywords, and may cause extensive costs when applied directly to inspecting traffic in which the keywords cannot be determined in advance. The inefficiency of existing SE-based approaches motivates us to investigate a privacy-preserving and lightweight middlebox. To this end, this paper designs <inline-formula><tex-math notation="LaTeX">$\mathsf{SlimBox}$</tex-math></inline-formula> , which rapidly screens out potentially malicious packets in constant time while incurring only moderate communication overhead. Our main idea is to fragment a traffic/rule string into sub-patterns to achieve conjunctive sub-pattern matching over ciphertexts, while incorporating the position information into the secure matching process to avoid false positives. Experiment results on real datasets show that <inline-formula><tex-math notation="LaTeX">$\mathsf{SlimBox}$</tex-math></inline-formula> can achieve a good tradeoff between matching latency and communication cost compared to prior work.
Multi-Scale Context-Aware R-Cnn for Few-Shot Object Detection in Remote Sensing Images

In the field of remote sensing image object detection, the popular CNN-based methods need a large-scale and diverse dataset that is costly, and have limited generalization abili-ties for new categories. The few-shot object detection can be driven using only a few annotated samples. Existing few-shot detection methods are mainly designed for natural images, which ignore multi-scale objects and complex environments in remote sensing images. To tackle these challenges, we pro-pose a two-stage multi-scale method based on context mech-anism. Guided by the context-aware module, the multi-scale contextual information around the object is effectively extract and adaptively is combined into the ROI features to enhance the classification ability of the detector, which can reduce the classification confusion. Comparative experiments on public remote sensing image dataset RSOD show the effectiveness of our method.
Path Planning for Multiple Unmanned Surface Vehicles Using Glasius Bio-Inspired Neural Network With Hungarian Algorithm

In this article, we focus on the cooperative path planning for unmanned surface vehicles (USVs) composed of single-USV path planning and multi-USVs task assignment. First, the Glasius bioinspired neural network (GBNN) is used to calculate the neural activity for the discretized working space of USV, and the ocean current is considered in the definition of neuron connection weight especially. The standard path of single USV for obstacle avoidance between start point and destination can hence be planned. Then, based on the result of neural activity values from GBNN, the cost matrix of the Hungarian algorithm is built and modified for the task assignment among multi-USVs, and the unbalanced problem is well resolved. Consequently, the task points are allocated to USVs and prioritized effectively, and each USV just needs to visit the corresponding task points respectively along the standard paths by GBNN to accomplish the task. Finally, the simulation results demonstrate the feasibility and efficiency of the proposed algorithm.