Reviewerly/exHarmony huggingface dataset:
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.
White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems

This paper presents a novel meta-heuristic algorithm so-called White Shark Optimizer (WSO) to solve optimization problems over a continuous search space. The core ideas and underpinnings of WSO are inspired by the behaviors of great white sharks, including their exceptional senses of hearing and smell while navigating and foraging. These aspects of behavior are mathematically modeled to accommodate a sufficiently adequate balance between exploration and exploitation of WSO and to assist search agents to explore and exploit each potential area of the search space in order to achieve optimization. The search agents of WSO randomly update their position in connection with best-so-far solutions, to eventually arrive at the optimal outcome. The performance of WSO was comprehensively benchmarked on a set of 29 test functions from the CEC-2017 test suite for several dimensions. WSO was further applied to solve the benchmark problems of the CEC-2011 evolutionary algorithm competition to prove its reliability and applicability to real-world problems. A thorough analysis of computational and convergence results was presented to shed light on the efficacy and stability levels of WSO. The performance score of WSO in terms of several statistical methods was compared with 9 well-established meta-heuristics based on the solutions generated. Friedman’s and Holm’s tests of the results showed that WSO revealed reasonable solutions, in terms of global optimality, avoidance of local minima and solution quality, compared to other existing meta-heuristics.
Is Blockchain a Silver Bullet for Supply Chain Management? Technical Challenges and Research Opportunities

ABSTRACT Blockchain technology is based on the idea of a distributed, replicated, and immutable digital ledger that enables parties to conduct business in a trustful and transparent way without the need for a central authority or intermediary. Its most popular application thus far is in payment system applications, e.g., bitcoin. This disruptive technology is expected to contribute significant business value to multiple industry sectors, including supply chain management (SCM), where it can provide greater visibility, accountability and trust in interorganizational business collaboration. In this article, we review some fundamental concepts of Hyperledger Fabric, one of the most mature permissioned blockchain implementations. Further, we use the context of a food supply chain to highlight key design and implementation challenges for blockchain, and provide a strategic assessment of its prospects. Our aim is to dispel misguided notions and myths about blockchain as a silver bullet for all businesses. We believe it is important to penetrate the hype to allow a more realistic understanding of this technology. Blockchain is a high‐cost, high‐overhead storage medium. It is viable only when its higher cost is counterbalanced by the set of benefits that are identified by a careful and thorough analysis of the business. Thus, it will be used mainly for storing important data related to interorganizational transactions among partners where trust is lacking and provenance and visibility are critical. Our paper offers enterprises a systematic way to understand the real costs and risks of blockchain adoption. The insights gained in the SCM context also apply to other areas such as financial services and healthcare that could leverage the full potential of blockchain technology.
Sea-Thru: A Method for Removing Water From Underwater Images

Robust recovery of lost colors in underwater images remains a challenging problem. We recently showed that this was partly due to the prevalent use of an atmospheric image formation model for underwater images. We proposed a physically accurate model that explicitly showed: 1)~the attenuation coefficient of the signal is not uniform across the scene but depends on object range and reflectance, 2)~the coefficient governing the increase in backscatter with distance differs from the signal attenuation coefficient. Here, we present a method that recovers color with the revised model using RGBD images. The \emph{Sea-thru} method first calculates backscatter using the darkest pixels in the image and their known range information. Then, it uses an estimate of the spatially varying illuminant to obtain the range-dependent attenuation coefficient. Using more than 1,100 images from two optically different water bodies, which we make available, we show that our method outperforms those using the atmospheric model. Consistent removal of water will open up large underwater datasets to powerful computer vision and machine learning algorithms, creating exciting opportunities for the future of underwater exploration and conservation.
Integrating augmented reality into problem based learning: The effects on learning achievement and attitude in physics education

This study investigates the effects of Problem Based Learning (PBL) assisted with Augmented Reality (AR) on learning achievement and attitude towards physics subjects as a part of science education. The sample of the study included 91 seventh graders from a province in the north of Turkey. A quasi-experimental design with two experimental groups and a control group was utilized. Based upon marker-based AR technologies, FenAR software was developed to support with PBL activities in the classroom. The experimental results indicated that integrating AR into PBL activities both increased students' learning achievement and promoted their positive attitudes towards physics subjects. This technology contributed to students' long-term retention of the concepts in the field of physics. In semi-structured interviews, the students emphasized that AR applications were more useful, realistic, and interesting for their learning; helped them to understand and analyse the problem scenarios. Apart from educational advantages, AR applications may lead to physical disorders among some of the students. It has been suggested that AR technology can be a potential and effective tool for activating students' positive emotions in PBL process. Moreover, implications on use of AR for physics education and recommendations for further studies are also discussed in the study.
Graph Convolutional Networks with EigenPooling

Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have been proven to improve the performance of many graph related tasks such as node classification and link prediction. To apply graph neural networks for the graph classification task, approaches to generate thegraph representation from node representations are demanded. A common way is to globally combine the node representations. However, rich structural information is overlooked. Thus a hierarchical pooling procedure is desired to preserve the graph structure during the graph representation learning. There are some recent works on hierarchically learning graph representation analogous to the pooling step in conventional convolutional neural (CNN) networks. However, the local structural information is still largely neglected during the pooling process. In this paper, we introduce a pooling operator $\pooling$ based on graph Fourier transform, which can utilize the node features and local structures during the pooling process. We then design pooling layers based on the pooling operator, which are further combined with traditional GCN convolutional layers to form a graph neural network framework $\m$ for graph classification. Theoretical analysis is provided to understand $\pooling$ from both local and global perspectives. Experimental results of the graph classification task on $6$ commonly used benchmarks demonstrate the effectiveness of the proposed framework.
CM-GANs

It is known that the inconsistent distributions and representations of different modalities, such as image and text, cause the heterogeneity gap, which makes it very challenging to correlate heterogeneous data and measure their similarities. Recently, generative adversarial networks (GANs) have been proposed and have shown their strong ability to model data distribution and learn discriminative representation. It has also been shown that adversarial learning can be fully exploited to learn discriminative common representations for bridging the heterogeneity gap. Inspired by this, we aim to effectively correlate large-scale heterogeneous data of different modalities with the power of GANs to model cross-modal joint distribution. In this article, we propose Cross-modal Generative Adversarial Networks (CM-GANs) with the following contributions. First, a cross-modal GAN architecture is proposed to model joint distribution over the data of different modalities. The inter-modality and intra-modality correlation can be explored simultaneously in generative and discriminative models. Both compete with each other to promote cross-modal correlation learning. Second, the cross-modal convolutional autoencoders with weight-sharing constraint are proposed to form the generative model. They not only exploit the cross-modal correlation for learning the common representations but also preserve reconstruction information for capturing the semantic consistency within each modality. Third, a cross-modal adversarial training mechanism is proposed, which uses two kinds of discriminative models to simultaneously conduct intra-modality and inter-modality discrimination. They can mutually boost to make the generated common representations more discriminative by the adversarial training process. In summary, our proposed CM-GAN approach can use GANs to perform cross-modal common representation learning by which the heterogeneous data can be effectively correlated. Extensive experiments are conducted to verify the performance of CM-GANs on cross-modal retrieval compared with 13 state-of-the-art methods on 4 cross-modal datasets.
FastDepth: Fast Monocular Depth Estimation on Embedded Systems

Depth sensing is a critical function for robotic tasks such as localization, mapping and obstacle detection. There has been a significant and growing interest in depth estimation from a single RGB image, due to the relatively low cost and size of monocular cameras. However, state-of-the-art single-view depth estimation algorithms are based on fairly complex deep neural networks that are too slow for real-time inference on an embedded platform, for instance, mounted on a micro aerial vehicle. In this paper, we address the problem of fast depth estimation on embedded systems. We propose an efficient and lightweight encoder-decoder network architecture and apply network pruning to further reduce computational complexity and latency. In particular, we focus on the design of a low-latency decoder. Our methodology demonstrates that it is possible to achieve similar accuracy as prior work on depth estimation, but at inference speeds that are an order of magnitude faster. Our proposed network, FastDepth, runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using only the TX2 CPU, with active power consumption under 10 W. FastDepth achieves close to state-of-the-art accuracy on the NYU Depth v2 dataset. To the best of the authors' knowledge, this paper demonstrates real-time monocular depth estimation using a deep neural network with the lowest latency and highest throughput on an embedded platform that can be carried by a micro aerial vehicle.
Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pathological field yet as currently well-addressed tasks are only the tip of the iceberg. Whole slide image (WSI) classification is a quite challenging problem. First, the scarcity of annotations heavily impedes the pace of developing effective approaches. Pixelwise delineated annotations on WSIs are time consuming and tedious, which poses difficulties in building a large-scale training dataset. In addition, a variety of heterogeneous patterns of tumor existing in high magnification field are actually the major obstacle. Furthermore, a gigapixel scale WSI cannot be directly analyzed due to the immeasurable computational cost. How to design the weakly supervised learning methods to maximize the use of available WSI-level labels that can be readily obtained in clinical practice is quite appealing. To overcome these challenges, we present a weakly supervised approach in this article for fast and effective classification on the whole slide lung cancer images. Our method first takes advantage of a patch-based fully convolutional network (FCN) to retrieve discriminative blocks and provides representative deep features with high efficiency. Then, different context-aware block selection and feature aggregation strategies are explored to generate globally holistic WSI descriptor which is ultimately fed into a random forest (RF) classifier for the image-level prediction. To the best of our knowledge, this is the first study to exploit the potential of image-level labels along with some coarse annotations for weakly supervised learning. A large-scale lung cancer WSI dataset is constructed in this article for evaluation, which validates the effectiveness and feasibility of the proposed method. Extensive experiments demonstrate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin with an accuracy of 97.3%. In addition, our method also achieves the best performance on the public lung cancer WSIs dataset from The Cancer Genome Atlas (TCGA). We highlight that a small number of coarse annotations can contribute to further accuracy improvement. We believe that weakly supervised learning methods have great potential to assist pathologists in histology image diagnosis in the near future.
Behavior sequence transformer for e-commerce recommendation in Alibaba

Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendations. However, most of these works just concatenate different features, ignoring the sequential nature of users' behaviors. In this paper, we propose to use the powerful Transformer model to capture the sequential signals underlying users' behavior sequences for recommendation in Alibaba. Experimental results demonstrate the superiority of the proposed model, which is then deployed online at Taobao and obtain significant improvements in online Click-Through-Rate (CTR) comparing to two baselines.
Rise of Machine Agency: A Framework for Studying the Psychology of Human–AI Interaction (HAII)

Abstract Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.
$\mathsf{LightChain}$: A Lightweight Blockchain System for Industrial Internet of Things

While the intersection of blockchain and Industrial Internet of Things (IIoT) has received considerable research interest lately, the conflict between the high resource requirements of blockchain and the generally inadequate performance of IIoT devices has not been well tackled. On one hand, due to the introductions of mathematical concepts, including Public Key Infrastructure, Merkle Hash Tree, and Proof of Work (PoW), deploying blockchain demands huge computing power. On the other hand, full nodes should synchronize massive block data and deal with numerous transactions in peer-to-peer network, whose occupation of storage capacity and bandwidth makes IIoT devices difficult to afford. In this paper, we propose a lightweight blockchain system called LightChain, which is resource-efficient and suitable for power-constrained IIoT scenarios. Specifically, we present a green consensus mechanism named Synergistic Multiple Proof for stimulating the cooperation of IIoT devices, and a lightweight data structure called LightBlock to streamline broadcast content. Furthermore, we design a novel Unrelated Block Offloading Filter to avoid the unlimited growth of ledger without affecting blockchain's traceability. The extensive experiments demonstrate that LightChain can reduce the individual computational cost to 39.32% and speed up the block generation by up to 74.06%. In terms of storage and network usage, the reductions are 43.35% and 90.55%, respectively.
Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization

Reverse engineering is a manually intensive but necessary technique for understanding the inner workings of new malware, finding vulnerabilities in existing systems, and detecting patent infringements in released software. An assembly clone search engine facilitates the work of reverse engineers by identifying those duplicated or known parts. However, it is challenging to design a robust clone search engine, since there exist various compiler optimization options and code obfuscation techniques that make logically similar assembly functions appear to be very different. A practical clone search engine relies on a robust vector representation of assembly code. However, the existing clone search approaches, which rely on a manual feature engineering process to form a feature vector for an assembly function, fail to consider the relationships between features and identify those unique patterns that can statistically distinguish assembly functions. To address this problem, we propose to jointly learn the lexical semantic relationships and the vector representation of assembly functions based on assembly code. We have developed an assembly code representation learning model \emph{Asm2Vec}. It only needs assembly code as input and does not require any prior knowledge such as the correct mapping between assembly functions. It can find and incorporate rich semantic relationships among tokens appearing in assembly code. We conduct extensive experiments and benchmark the learning model with state-of-the-art static and dynamic clone search approaches. We show that the learned representation is more robust and significantly outperforms existing methods against changes introduced by obfuscation and optimizations.
Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure

With the continued development of artificial intelligence (AI) technology, research on interaction technology has become more popular.Facial expression recognition (FER) is an important type of visual information that can be used to understand a human's emotional situation.In particular, the importance of AI systems has recently increased due to advancements in research on AI systems applied to AI robots.In this paper, we propose a new scheme for FER system based on hierarchical deep learning.The feature extracted from the appearance feature-based network is fused with the geometric feature in a hierarchical structure.The appearance feature-based network extracts holistic features of the face using the preprocessed LBP image, whereas the geometric feature-based network learns the coordinate change of action units (AUs) landmark, which is a muscle that moves mainly when making facial expressions.The proposed method combines the result of the softmax function of two features by considering the error associated with the second highest emotion (Top-2) prediction result.In addition, we propose a technique to generate facial images with neutral emotion using the autoencoder technique.By this technique, we can extract the dynamic facial features between the neutral and emotional images without sequence data.We compare the proposed algorithm with the other recent algorithms for CK+ and JAFFE dataset, which are typically considered to be verified datasets in the facial expression recognition.The ten-fold cross validation results show 96.46% of accuracy in the CK+ dataset and 91.27% of accuracy in the JAFFE dataset.When comparing with other methods, the result of the proposed hierarchical deep network structure shows up to about 3% of the accuracy improvement and 1.3% of average improvement in CK+ dataset, respectively.In JAFFE datasets, up to about 7% of the accuracy is enhanced, and the average improvement is verified by about 1.5%.
Auto-sklearn: Efficient and Robust Automated Machine Learning

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on the Python machine learning package scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub Auto-sklearn, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won six out of ten phases of the first ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of Auto-sklearn.