Reviewerly/exHarmony huggingface dataset:
A Correlation-Based Feature Weighting Filter for Naive Bayes

Due to its simplicity, efficiency, and efficacy, naive Bayes (NB) has continued to be one of the top 10 algorithms in the data mining and machine learning community. Of numerous approaches to alleviating its conditional independence assumption, feature weighting has placed more emphasis on highly predictive features than those that are less predictive. In this paper, we argue that for NB highly predictive features should be highly correlated with the class (maximum mutual relevance), yet uncorrelated with other features (minimum mutual redundancy). Based on this premise, we propose a correlation-based feature weighting (CFW) filter for NB. In CFW, the weight for a feature is a sigmoid transformation of the difference between the feature-class correlation (mutual relevance) and the average feature-feature intercorrelation (average mutual redundancy). Experimental results show that NB with CFW significantly outperforms NB and all the other existing state-of-the-art feature weighting filters used to compare. Compared to feature weighting wrappers for improving NB, the main advantages of CFW are its low computational complexity (no search involved) and the fact that it maintains the simplicity of the final model. Besides, we apply CFW to text classification and have achieved remarkable improvements.
Chatbot learning partners: Connecting learning experiences, interest and competence

Conversation practice, while paramount for all language learners, can be difficult to get enough of and very expensive. In this mobile age, chatbots are an obvious means of filling this gap, but have yet to realize their potential as practice partners. The current study was undertaken to examine why chatbots are not yet a substantial instrument for language learning engagement/practice, and to provide direction for future practice and chatbot development. To this end, building on a recent experimental study examining chatbot novelty effects, students undertook a pair of conversation activities: human and human-chatbot (via speech-to-text software). Immediately following the practice conversations, students' interest in the two partners was surveyed and open-ended textual feedback was collected. With these data sources and prior standardised test results, regression and content analysis of the data was undertaken. Findings indicated: 1) prior interest in human conversation partners was the best single predictor of future interest in chatbot conversations; 2) prior language competency was more strongly linked to interest in chatbot than human conversations; 3) that the qualitative experience of having “learned more” with the chatbot was strongly connected to task interest, even when reporting communication difficulties. Implications for practicing languages with currently available chatbots, for chatbots and related educational technology as sources of student interest and directions for chatbots future development are discussed.
An image compression and encryption algorithm based on chaotic system and compressive sensing

For a linear image encryption system, it is vulnerable to the chosen-plaintext attack. To overcome the weakness and reduce the correlation among pixels of the encryption image, an effective image compression and encryption algorithm based on chaotic system and compressive sensing is proposed. The original image is first permuted by the Arnold transform to reduce the block effect in the compression process, and then the resulting image is compressed and re-encrypted by compressive sensing, simultaneously. Moreover, the bitwise XOR operation based on chaotic system is performed on the measurements to change the pixel values and a pixel scrambling method is employed to disturb the positions of pixels. Besides, the keys used in chaotic systems are related to the plaintext image. Simulation results verify the effectiveness and reliability of the proposed image compression and encryption algorithm with considerable compression and security performance.
Delegated Proof of Stake With Downgrade: A Secure and Efficient Blockchain Consensus Algorithm With Downgrade Mechanism

Blockchain technology has a wide range of applications in the fields of finance, credit reporting and intellectual property, etc. As the core of blockchain, consensus algorithm affects the security and performance of blockchain system directly. In the past 10 years, there have been about 30 consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Ripple Protocol Consensus Algorithm (RPCA) and AlgoRand. But their security, stability and operating efficiency still lag far behind our actual needs. This paper introduces the computing power competition of PoW into DPoS to design an improved consensus algorithm named Delegated Proof of Stake with Downgrade (DDPoS). Through the further modification, the impact of both computing resources and stakes on generating blocks is reduced to achieve higher efficiency, fairness, and decentralization in consensus process. Then a downgrade mechanism is proposed to quickly replace the malicious nodes to improve the security. The simulation experiments in blockchain system show that the proposed consensus algorithm is significantly more efficient than PoW and PoS, but slightly lower than DPoS. However, its degree of centralization remains far below that of DPoS. And through the downgrade mechanism, the proposed consensus algorithm can detect and downgrade the malicious nodes timely to ensure the security and good operation of system.
A comprehensive study on deep learning bug characteristics

Deep learning has gained substantial popularity in recent years. Developers mainly rely on libraries and tools to add deep learning capabilities to their software. What kinds of bugs are frequently found in such software? What are the root causes of such bugs? What impacts do such bugs have? Which stages of deep learning pipeline are more bug prone? Are there any antipatterns? Understanding such characteristics of bugs in deep learning software has the potential to foster the development of better deep learning platforms, debugging mechanisms, development practices, and encourage the development of analysis and verification frameworks. Therefore, we study 2716 high-quality posts from Stack Overflow and 500 bug fix commits from Github about five popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to understand the types of bugs, root causes of bugs, impacts of bugs, bug-prone stage of deep learning pipeline as well as whether there are some common antipatterns found in this buggy software. The key findings of our study include: data bug and logic bug are the most severe bug types in deep learning software appearing more than 48% of the times, major root causes of these bugs are Incorrect Model Parameter (IPS) and Structural Inefficiency (SI) showing up more than 43% of the times.We have also found that the bugs in the usage of deep learning libraries have some common antipatterns.
A survey of industrial augmented reality

This article aims to evaluate the impact of Augmented Reality (AR) applicability and usefulness on real industrial processes by employing a systematic literature review (SLR). The SLR was performed in five digital libraries to identify articles and reviews concerning the AR applicability from 2012 to 2018. A patent search in Google’s patents database was also conducted, for the same period. This paper describes how AR has been applied, which industries are most interested in the technology, how the technology has been developed to meet industry needs, as well as the benefits and challenges of AR. This survey concludes by providing a starting point for companies interested in integrating AR into their processes and proposing future directions for AR developers and researchers.
MADNet: A Fast and Lightweight Network for Single-Image Super Resolution

Recently, deep convolutional neural networks (CNNs) have been successfully applied to the single-image super-resolution (SISR) task with great improvement in terms of both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). However, most of the existing CNN-based SR models require high computing power, which considerably limits their real-world applications. In addition, most CNN-based methods rarely explore the intermediate features that are helpful for final image recovery. To address these issues, in this article, we propose a dense lightweight network, called MADNet, for stronger multiscale feature expression and feature correlation learning. Specifically, a residual multiscale module with an attention mechanism (RMAM) is developed to enhance the informative multiscale feature representation ability. Furthermore, we present a dual residual-path block (DRPB) that utilizes the hierarchical features from original low-resolution images. To take advantage of the multilevel features, dense connections are employed among blocks. The comparative results demonstrate the superior performance of our MADNet model while employing considerably fewer multiadds and parameters.
MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care

Abstract The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases, and the ability to generalize these emerging technologies. We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias.
BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network

Multi-level feature fusion is a fundamental topic in computer vision for detecting, segmenting and classifying objects at various scales. When multi-level features meet multi-modal cues, the optimal fusion problem becomes a hot potato. In this paper, we make the first attempt to leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to develop a novel cascaded refinement network. In particular, we 1) propose a bifurcated backbone strategy (BBS) to split the multi-level features into teacher and student features, and 2) utilize a depth-enhanced module (DEM) to excavate informative parts of depth cues from the channel and spatial views. This fuses RGB and depth modalities in a complementary way. Our simple yet efficient architecture, dubbed Bifurcated Backbone Strategy Network (BBS-Net), is backbone independent and outperforms 18 SOTAs on seven challenging datasets using four metrics.
A Metric Learning Reality Check

Deep metric learning papers from the past four years have consistently claimed great advances in accuracy, often more than doubling the performance of decade-old methods. In this paper, we take a closer look at the field to see if this is actually true. We find flaws in the experimental methodology of numerous metric learning papers, and show that the actual improvements over time have been marginal at best. Code is available at github.com/KevinMusgrave/powerful-benchmarker .
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting. However, for the actual driving task, the global context of the 3D scene is key, e.g. a change in traffic light state can affect the behavior of a vehicle geometrically distant from that traffic light. Geometry alone may therefore be insufficient for effectively fusing representations in end-to-end driving models. In this work, we demonstrate that imitation learning policies based on existing sensor fusion methods under-perform in the presence of a high density of dynamic agents and complex scenarios, which require global contextual reasoning, such as handling traffic oncoming from multiple directions at uncontrolled intersections. Therefore, we propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. We experimentally validate the efficacy of our approach in urban settings involving complex scenarios using the CARLA urban driving simulator. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
Voxel Transformer for 3D Object Detection

We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds. Conventional 3D convolutional backbones in voxel-based 3D detectors cannot efficiently capture large context information, which is crucial for object recognition and localization, owing to the limited receptive fields. In this paper, we resolve the problem by introducing a Transformer-based architecture that enables long-range relationships between voxels by self-attention. Given the fact that non-empty voxels are naturally sparse but numerous, directly applying standard Transformer on voxels is non-trivial. To this end, we propose the sparse voxel module and the submanifold voxel module, which can operate on the empty and non-empty voxel positions effectively. To further enlarge the attention range while maintaining comparable computational overhead to the convolutional counterparts, we propose two attention mechanisms for multi-head attention in those two modules: Local Attention and Dilated Attention, and we further propose Fast Voxel Query to accelerate the querying process in multi-head attention. VoTr contains a series of sparse and submanifold voxel modules, and can be applied in most voxel-based detectors. Our proposed VoTr shows consistent improvement over the convolutional baselines while maintaining computational efficiency on the KITTI dataset and the Waymo Open dataset.
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

While several datasets for autonomous navigation have become available in recent years, they have tended to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in object or background appearance and strong adherence to traffic rules. We propose DS, a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes. It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity. Consistent with real driving behaviors, it also identifies new classes such as drivable areas besides the road. We propose a new four-level label hierarchy, which allows varying degrees of complexity and opens up possibilities for new training methods. Our empirical study provides an in-depth analysis of the label characteristics. State-of-the-art methods for semantic segmentation achieve much lower accuracies on our dataset, demonstrating its distinction compared to Cityscapes. Finally, we propose that our dataset is an ideal opportunity for new problems such as domain adaptation, few-shot learning and behavior prediction in road scenes.
A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform

Design of hardware accelerators for neural network (NN) applications involves walking a tight rope amidst the constraints of low-power, high accuracy and throughput. NVIDIA’s Jetson is a promising platform for embedded machine learning which seeks to achieve a balance between the above objectives. In this paper, we provide a survey of works that evaluate and optimize neural network applications on Jetson platform. We review both hardware and algorithmic optimizations performed for running NN algorithms on Jetson and show the real-life applications where these algorithms have been applied. We also review the works that compare Jetson with similar platforms. While the survey focuses on Jetson as an exemplar embedded system, many of the ideas and optimizations will apply just as well to existing and future embedded systems. It is widely believed that the ability to run AI algorithms on low-cost, low-power platforms will be crucial for achieving the “AI for all” vision. This survey seeks to provide a glimpse of the recent progress towards that goal.
Security, Performance, and Applications of Smart Contracts: A Systematic Survey

Blockchain is the promising technology of recent years, which has attracted remarkable attention in both academic studies and practical industrial applications.The smart contract is a programmable transaction that can perform a sophisticated task, execute automatically, and store on the blockchain.The smart contract is the key component of the blockchain, which has made blockchain a technology beyond the scope of the cryptocurrencies and applicable for a variety of applications such as healthcare, IoT, supply chain, digital identity, business process management, and more.Although in recent years the progress toward improving blockchain technology with the focus on the smart contract has been impressive, there is a lack of reviewing the smart contract topic.This paper systematically reviews the key concepts and proposes the direction of recent studies and developments regarding the smart contract.The research studies are presented in three main categories: 1) security methods and tools; 2) performance improvement approaches; and 3) decentralized applications based on smart contracts.
An evolutionary gravitational search-based feature selection

With recent advancements in data collection tools and the widespread use of intelligent information systems, a huge amount of data streams with lots of redundant, irrelevant, and noisy features are collected and a large number of features (attributes) should be processed. Therefore, there is a growing demand for developing efficient Feature Selection (FS) techniques. Gravitational Search algorithm (GSA) is a successful population-based metaheuristic inspired by Newton’s law of gravity. In this research, a novel GSA-based algorithm with evolutionary crossover and mutation operators is proposed to deal with feature selection (FS) tasks. As an NP-hard problem, FS finds an optimal subset of features from a given set. For the proposed wrapper FS method, both K-Nearest Neighbors (KNN) and Decision Tree (DT) classifiers are used as evaluators. Eighteen well-known UCI datasets are utilized to assess the performance of the proposed approaches. In order to verify the efficiency of proposed algorithms, the results are compared with some popular nature-inspired algorithms (i.e. Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), and Grey Wolf Optimizer (GWO)). The extensive results and comparisons demonstrate the superiority of the proposed algorithm in solving FS problems.
Learning Linear Transformations for Fast Image and Video Style Transfer

Given a random pair of images, a universal style transfer method extracts the feel from a reference image to synthesize an output based on the look of a content image. Recent algorithms based on second-order statistics, however, are either computationally expensive or prone to generate artifacts due to the trade-off between image quality and runtime performance. In this work, we present an approach for universal style transfer that learns the transformation matrix in a data-driven fashion. Our algorithm is efficient yet flexible to transfer different levels of styles with the same auto-encoder network. It also produces stable video style transfer results due to the preservation of the content affinity. In addition, we propose a linear propagation module to enable a feed-forward network for photo-realistic style transfer. We demonstrate the effectiveness of our approach on three tasks: artistic style, photo-realistic and video style transfer, with comparisons to state-of-the-art methods.
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. The results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.
Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g., security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to boost the performance of pedestrian detection significantly. A novel illumination-aware weighting mechanism is present to depict illumination condition of a scene accurately. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which is used to supervise the training of pedestrian detector. Putting all of the pieces together, we present an effective framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset.