Reviewerly/exHarmony huggingface dataset:
Mono Lay out: Amodal scene layout from a single image

In this paper, we address the novel, highly challenging problem of estimating the layout of a complex urban driving scenario. Given a single color image captured from a driving platform, we aim to predict the bird's eye view layout of the road and other traffic participants. The estimated layout should reason beyond what is visible in the image, and compensate for the loss of 3D information due to projection. We dub this problem amodal scene layout estimation, which involves hallucinating scene layout for even parts of the world that are occluded in the image. To this end, we present MonoLayout, a deep neural network for realtime amodal scene layout estimation from a single image. We represent scene layout as a multi-channel semantic occupancy grid, and leverage adversarial feature learning to hallucinate " plausible completions for occluded image parts. We extend several state-of-the-art approaches for road-layout estimation and vehicle occupancy estimation in bird's eye view to the amodal setup and thoroughly evaluate against them. By leveraging temporal sensor fusion to generate training labels, we significantly outperform current art over a number of datasets.
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight

We propose a joint simulation and real-world learning framework for mapping navigation instructions and raw first-person observations to continuous control. Our model estimates the need for environment exploration, predicts the likelihood of visiting environment positions during execution, and controls the agent to both explore and visit high-likelihood positions. We introduce Supervised Reinforcement Asynchronous Learning (SuReAL). Learning uses both simulation and real environments without requiring autonomous flight in the physical environment during training, and combines supervised learning for predicting positions to visit and reinforcement learning for continuous control. We evaluate our approach on a natural language instruction-following task with a physical quadcopter, and demonstrate effective execution and exploration behavior.
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Learning a good representation is an essential component for deep reinforcement learning (RL). Representation learning is especially important in multitask and partially observable settings where building a representation of the unknown environment is crucial to solve the tasks. Here we introduce Prediction of Bootstrap Latents (PBL), a simple and flexible self-supervised representation learning algorithm for multitask deep RL. PBL builds on multistep predictive representations of future observations, and focuses on capturing structured information about environment dynamics. Specifically, PBL trains its representation by predicting latent embeddings of future observations. These latent embeddings are themselves trained to be predictive of the aforementioned representations. These predictions form a bootstrapping effect, allowing the agent to learn more about the key aspects of the environment dynamics. In addition, by defining prediction tasks completely in latent space, PBL provides the flexibility of using multimodal observations involving pixel images, language instructions, rewards and more. We show in our experiments that PBL delivers across-the-board improved performance over state of the art deep RL agents in the DMLab-30 and Atari-57 multitask setting.
Learning to Classify Intents and Slot Labels Given a Handful of Examples

Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems.Current IC/SF models perform poorly when the number of training examples per class is small.We propose a new few-shot learning task, few-shot IC/SF, to study and improve the performance of IC and SF models on classes not seen at training time in ultra low resource scenarios.We establish a fewshot IC/SF benchmark by defining few-shot splits for three public IC/SF datasets, ATIS, TOP, and SNIPS.We show that two popular few-shot learning algorithms, model agnostic meta learning (MAML) and prototypical networks, outperform a fine-tuning baseline on this benchmark.Prototypical networks achieves substantial gains in IC performance on the ATIS and TOP datasets, while both prototypical networks and MAML outperform the baseline with respect to SF on all three datasets.In addition, we demonstrate that joint training as well as the use of pre-trained language models, ELMo and BERT in our case, are complementary to these few-shot learning methods and yield further gains.
Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning

Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the diagnoses often challenge pathologists. Here we propose a rule-based system to differentiate thyroid nodules from intraoperative frozen sections using deep learning techniques. The proposed system consists of three components: (1) automatically locating tissue regions in the whole slide images (WSIs), (2) splitting located tissue regions into patches and classifying each patch into predefined categories using convolutional neural networks (CNN), and (3) integrating predictions of all patches to form the final diagnosis with a rule-based system. To be specific, we fine-tune the InceptionV3 model for thyroid patch classification by replacing the last fully connected layer with three outputs representing the patch's probabilities of being benign, uncertain, or malignant. Moreover, we design a rule-based protocol to integrate patches’ predictions to form the final diagnosis, which provides interpretability for the proposed system. On 259 testing slides, the system correctly predicts 95.3% (61/64) of benign nodules and 96.7% (148/153) of malignant nodules, and classify 16.2% (42/259) slides as uncertain, including 19 benign and 16 malignant slides, which are a sufficiently small number to be manually examined by pathologists or fully processed through permanent sections. Besides, the system allows the localization of suspicious regions along with the diagnosis. A typical whole slide image, with 80, 000 × 60, 000 pixels, can be diagnosed within 1 min, thus satisfying the time requirement for intraoperative diagnosis. To the best of our knowledge, this is the first study to apply deep learning to diagnose thyroid nodules from intraoperative frozen sections. The code is released at https://github.com/PingjunChen/ThyroidRule.
Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance

Ensuring effective public understanding of algorithmic decisions that are powered by machine learning techniques has become an urgent task with the increasing deployment of AI systems into our society. In this work, we present a concrete step toward this goal by redesigning confusion matrices for binary classification to support non-experts in understanding the performance of machine learning models. Through interviews (n=7) and a survey (n=102), we mapped out two major sets of challenges lay people have in understanding standard confusion matrices: the general terminologies and the matrix design. We further identified three sub-challenges regarding the matrix design, namely, confusion about the direction of reading the data, layered relations and quantities involved. We then conducted an online experiment with 483 participants to evaluate how effective a series of alternative representations target each of those challenges in the context of an algorithm for making recidivism predictions. We developed three levels of questions to evaluate users' objective understanding. We assessed the effectiveness of our alternatives for accuracy in answering those questions, completion time, and subjective understanding. Our results suggest that (1) only by contextualizing terminologies can we significantly improve users' understanding and (2) flow charts, which help point out the direction of reading the data, were most useful in improving objective understanding. Our findings set the stage for developing more intuitive and generally understandable representations of the performance of machine learning models.
On the generation, structure, and semantics of grammar patterns in source code identifiers

Identifiers make up a majority of the text in code. They are one of the most basic mediums through which developers describe the code they create and understand the code that others create. Therefore, understanding the patterns latent in identifier naming practices and how accurately we are able to automatically model these patterns is vital if researchers are to support developers and automated analysis approaches in comprehending and creating identifiers correctly and optimally. This paper investigates identifiers by studying sequences of part-of-speech annotations, referred to as grammar patterns. This work advances our understanding of these patterns and our ability to model them by (1) establishing common naming patterns in different types of identifiers, such as class and attribute names; (2) analyzing how different patterns influence comprehension; and (3) studying the accuracy of state-of-the-art techniques for part-of-speech annotations, which are vital in automatically modeling identifier naming patterns, in order to establish their limits and paths toward improvement. To do this, we manually annotate a dataset of 1,335 identifiers from 20 open-source systems and use this dataset to study naming patterns, semantics, and tagger accuracy.
NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image

We propose NormalGAN, a fast adversarial learning-based method to reconstruct the complete and detailed 3D human from a single RGB-D image. Given a single front-view RGB-D image, NormalGAN performs two steps: front-view RGB-D rectification and back-view RGB-D inference. The final model was then generated by simply combining the front-view and back-view RGB-D information. However, inferring back-view RGB-D image with high-quality geometric details and plausible texture is not trivial. Our key observation is: Normal maps generally encode much more information of 3D surface details than RGB and depth images. Therefore, learning geometric details from normal maps is superior than other representations. In NormalGAN, an adversarial learning framework conditioned by normal maps is introduced, which is used to not only improve the front-view depth denoising performance, but also infer the back-view depth image with surprisingly geometric details. Moreover, for texture recovery, we remove shading information from the front-view RGB image based on the refined normal map, which further improves the quality of the back-view color inference. Results and experiments on both testing data set and real captured data demonstrate the superior performance of our approach. Given a consumer RGB-D sensor, NormalGAN can generate the complete and detailed 3D human reconstruction results in 20 fps, which further enables convenient interactive experiences in telepresence, AR/VR and gaming scenarios.
The effect of knowledge management on competitive advantage and business performance: A study of silver craft SMEs

Objective: The objective of this article is to analyse the effect of knowledge management on the performance of silver craft SMEs through competitive advantage as a mediator.This study also explores the dimensions and indicators of each latent variable: knowledge management, competitive advantage, and business performance. Research Design & Methods:This research uses quantitative methods with SEM-PLS analysis techniques.The population in this study is silver craft SMEs in Indonesia, with 146 silver craft SMEs as a sample.Data was collected by questionnaires and FGD.Findings: This study found that knowledge management has no direct significant effect on business performance but -through competitive advantage -the effect of knowledge management on business performance of silver craft SMEs is positive and significant. Implications & Recommendations:The outcomes show that knowledge management ought to be connected to form items with competitive points of interest so that it can help to execute silver craft SMEs trade.The expected knowledge management is to incorporate knowledge acquisition, knowledge sharing, and knowledge application.Contribution & Value Added: This research surveys commerce execution not from a budgetary perspective but from a non-financial perspective.
Arbitrary Scale Super-Resolution for Medical Images

Single image super-resolution (SISR) aims to obtain a high-resolution output from one low-resolution image. Currently, deep learning-based SISR approaches have been widely discussed in medical image processing, because of their potential to achieve high-quality, high spatial resolution images without the cost of additional scans. However, most existing methods are designed for scale-specific SR tasks and are unable to generalize over magnification scales. In this paper, we propose an approach for medical image arbitrary-scale super-resolution (MIASSR), in which we couple meta-learning with generative adversarial networks (GANs) to super-resolve medical images at any scale of magnification in [Formula: see text]. Compared to state-of-the-art SISR algorithms on single-modal magnetic resonance (MR) brain images (OASIS-brains) and multi-modal MR brain images (BraTS), MIASSR achieves comparable fidelity performance and the best perceptual quality with the smallest model size. We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT). The source code of our work is also public. Thus, MIASSR has the potential to become a new foundational pre-/post-processing step in clinical image analysis tasks such as reconstruction, image quality enhancement, and segmentation.
A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Traffic safety is an important part of the roadway in sustainable development. Freeway traffic crashes typically cause serious casualties and property losses, being a serious threat to public safety. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are of effective ways to explore and identity freeway crash causes. However, the existing association rule mining algorithms still have some limitations in both efficiency and accuracy. Based on this consideration, using the freeway traffic crash data obtained from WDOT (Washington Department of Transportation), this research constructed a multi-dimensional multilevel system for traffic crash analysis. Considering the load balancing, the FP-Growth (Frequent Pattern- Growth) algorithm was optimized parallelly based on Hadoop platform, to achieve an efficient and accurate association rule mining calculation for massive amounts of traffic crash data; then, according to the results of the coupling mechanism among the crash precursors, the causes of freeway traffic crashes were identified and revealed. The results show that the parallel FPgrowth algorithm with load balancing constraints has a better operating speed than both the conventional FP-growth algorithm and parallel FP-growth algorithm towards processing big data. This improved algorithm makes full use of Hadoop cluster resources and is more suitable for large traffic crash data sets mining while retaining the original advantages of conventional association rule mining algorithm. In addition, the mining association rules model with the improvement of multi-dimensional interaction proposed in this research can catch the occurrence mechanism of freeway traffic crash with serious consequences (lower support degree probably) accurately and efficiently.
System for Identifying Fake Product using Blockchain Technology

Counterfeit items have become more prevalent in the industrial industry in recent years. This has an effect on the company’s name, sales, and profit. Blockchain technology is used to authenticate and identify counterfeit items.Using blockchain technology, transactions are recorded in the form of blocks in a distributed, decentralized, and digital ledger. A block cannot be changed or hacked since blockchain technology is secure. Blockchain technology eliminates the need for customers or users to rely on third parties to verify the product’s safety. In light of current advancements in mobile and wireless technologies, quick response (QR) codes provide a promising technique for addressing the practice of counterfeiting in this project. This is done by scanning a QR code, which is linked to a Blockchain, to identify counterfeit goods. As a consequence, this method may be used to record product information and generate a unique identifier for each product in a database. It looks up the user’s unique code in the Blockchain database and sees if there are any matches. When a code matches, the client will be notified; otherwise, the consumer will be notified that the product is counterfeit.
Strong coupling between a photon and a hole spin in silicon

Spins in semiconductor quantum dots constitute a promising platform for scalable quantum information processing. Coupling them strongly to the photonic modes of superconducting microwave resonators would enable fast non-demolition readout and long-range, on-chip connectivity, well beyond nearest-neighbor quantum interactions. Here we demonstrate strong coupling between a microwave photon in a superconducting resonator and a hole spin in a silicon-based double quantum dot issued from a foundry-compatible MOS fabrication process. By leveraging the strong spin-orbit interaction intrinsically present in the valence band of silicon, we achieve a spin-photon coupling rate as high as 330~MHz largely exceeding the combined spin-photon decoherence rate. This result, together with the recently demonstrated long coherence of hole spins in silicon, opens a new realistic pathway to the development of circuit quantum electrodynamics with spins in semiconductor quantum dots.
Grounded Copilot: How Programmers Interact with Code-Generating Models

Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But what is this new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants—with a range of prior experience using the assistant—as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants are bimodal : in acceleration mode , the programmer knows what to do next and uses Copilot to get there faster; in exploration mode , the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants.
Security analysis and enhancements of an image cryptosystem based on hyperchaotic system

The security system engaged to transfer securely the sensitive information over wireless channels must posses some innate features such as high resistivity to various cryptographic attacks, high sensitivity to secret key and plaintext, and strong statistical characteristics of encrypted content. Recently, an image cryptosystem is investigated to secure color images while transmission. The cryptosystem is based on permutation only cipher and employed synchronized 4D hyperchaotic systems. The cryptosystem has credibility of providing ample statistical encryption quality of ciphertext. This paper aims to investigate and analyze the security of this image cryptosystem in terms of its defects and attack resistivity. The security analysis uncovers that cryptosystem holds certain serious security defects and incapable to secure encrypted content. To support the claim, the inherent defects are highlighted; thereby an attack procedure is proposed to show that the cryptosystem is breakable under the proposed cryptanalysis. The attacker can recover complete plaintext image from ciphertext image without knowing secret key. Computer simulations are performed to justify the success of cryptanalysis. The image cryptosystem is all insecure for use in practical applications of image-based secure wireless communication. As a remedy, this paper also suggests security enhancements and an enhanced cryptosystem is proposed to make it completely resistive against above and other types of cryptanalytic attacks and increase on its plain image sensitivity and statistical encryption strength.
Lightweight Cipher for H.264 Videos in the Internet of Multimedia Things with Encryption Space Ratio Diagnostics

Within an Internet of Multimedia Things, the risk of disclosing streamed video content, such as that arising from video surveillance, is of heightened concern. This leads to the encryption of that content. To reduce the overhead and the lack of flexibility arising from full encryption of the content, a good number of selective-encryption algorithms have been proposed in the last decade. Some of them have limitations, in terms of: significant delay due to computational cost, or excess memory utilization, or, despite being energy efficient, not providing a satisfactory level of confidentiality, due to their simplicity. To address such limitations, this paper presents a lightweight selective encryption scheme, in which encoder syntax elements are encrypted with the innovative EXPer (extended permutation with exclusive OR). The selected syntax elements are taken from the final stage of video encoding that is during the entropy coding stage. As a diagnostic tool, the Encryption Space Ratio measures encoding complexity of the video relative to the level of encryption so as to judge the success of the encryption process, according to entropy coder. A detailed comparative analysis of EXPer with other state-of-the-art encryption algorithms confirms that EXPer provides significant confidentiality with a small computational cost and a negligible encryption bitrate overhead. Thus, the results demonstrate that the proposed security scheme is a suitable choice for constrained devices in an Internet of Multimedia Things environment.
Path integration maintains spatial periodicity of grid cell firing in a 1D circular track

Entorhinal grid cells are thought to provide a 2D spatial metric of the environment. In this study we demonstrate that in a familiar 1D circular track (i.e., a continuous space) grid cells display a novel 1D equidistant firing pattern based on integrated distance rather than travelled distance or time. In addition, field spacing is increased compared to a 2D open field, probably due to a reduced access to the visual cue in the track. This metrical modification is accompanied by a change in LFP theta oscillations, but no change in intrinsic grid cell rhythmicity, or firing activity of entorhinal speed and head-direction cells. These results suggest that in a 1D circular space grid cell spatial selectivity is shaped by path integration processes, while grid scale relies on external information.
Two-frame fringe pattern phase demodulation using Gram-Schmidt orthonormalization with least squares method

Gram-Schmidt (GS) orthogonal normalization is a fast and efficient two-frame fringe phase demodulation method. However, the precision of the GS method is limited due to the residual background terms and noise, as well as several approximation operations in the GS method. To obtain a phase map with higher accuracy, we propose an algorithm combining GS orthogonal normalization and least squares iterative (LSI) phase shift algorithm (GS&LSI). In our method, the phase was first obtained using GS method, and then a refinement operation using LSI was adopted to get the final wrapped phase map. Because of the LSI process, the demodulation result is greatly improved in many cases. Simulation and experimental result are presented to validate the potential of the proposed method.