Reviewerly/exHarmony huggingface dataset:
Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain

Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.
An Attribute-Based Access Control for IoT Using Blockchain and Smart Contracts

With opportunities brought by the Internet of Things (IoT), it is quite a challenge to maintain concurrency and privacy when a huge number of resource-constrained distributed devices are involved. Blockchain have become popular for its benefits, including decentralization, persistence, immutability, auditability, and consensus. Great attention has been received by the IoT based on the construction of distributed file systems worldwide. A new generation of IoT-based distributed file systems has been proposed with the integration of Blockchain technology, such as the Swarm and Interplanetary File System. By using IoT, new technical challenges, such as Credibility, Harmonization, large-volume data, heterogeneity, and constrained resources are arising. To ensure data security in IoT, centralized access control technologies do not provide credibility. In this work, we propose an attribute-based access control model for the IoT. The access control lists are not required for each device by the system. It enhances access management in terms of effectiveness. Moreover, we use blockchain technology for recording the attribute, avoiding data tempering, and eliminating a single point of failure at edge computing devices. IoT devices control the user’s environment as well as his or her private data collection; therefore, the exposure of the user’s personal data to non-trusted private and public servers may result in privacy leakage. To automate the system, smart contracts are used for data accessing, whereas Proof of Authority is used for enhancing the system’s performance and optimizing gas consumption. Through smart contracts, ciphertext can be stored on a blockchain by the data owner. Data can only be decrypted in a valid access period, whereas in blockchains, the trace function is achieved by the storage of invocation and the creation of smart contracts. Scalability issues can also be resolved by using the multichain blockchain. Eventually, it is concluded from the simulation results that the proposed system is efficient for IoT.
Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

Nowadays, museums are developing chatbots to assist their visitors and to provide an enhanced visiting experience. Most of these chatbots do not provide a human-like conversation and fail to deliver the complete requested knowledge by the visitors. There are plenty of stand-alone museum chatbots, developed using a chatbot platform, that provide predefined dialog routes. However, as chatbot platforms are evolving and AI technologies mature, new architectural approaches arise. Museums are already designing chatbots that are trained using machine learning techniques or chatbots connected to knowledge graphs, delivering more intelligent chatbots. This paper is surveying a representative set of developed museum chatbots and platforms for implementing them. More importantly, this paper presents the result of a systematic evaluation approach for evaluating both chatbots and platforms. Furthermore, the paper is introducing a novel approach in developing intelligent chatbots for museums. This approach emphasizes graph-based, distributed, and collaborative multi-chatbot conversational AI systems for museums. The paper accentuates the use of knowledge graphs as the key technology for potentially providing unlimited knowledge to chatbot users, satisfying conversational AI’s need for rich machine-understandable content. In addition, the proposed architecture is designed to deliver an efficient deployment solution where knowledge can be distributed (distributed knowledge graphs) and shared among different chatbots that collaborate when is needed.
Decoupled Contrastive Learning

Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented “views” of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart. However, behind the impressive success of CL-based techniques, their formulation often relies on heavy-computation settings, including large sample batches, extensive training epochs, etc. We are thus motivated to tackle these issues and establish a simple, efficient, yet competitive baseline of contrastive learning. Specifically, we identify, from theoretical and empirical studies, a noticeable negative-positive-coupling (NPC) effect in the widely used InfoNCE loss, leading to unsuitable learning efficiency concerning the batch size. By removing the NPC effect, we propose decoupled contrastive learning (DCL) loss, which removes the positive term from the denominator and significantly improves the learning efficiency. DCL achieves competitive performance with less sensitivity to sub-optimal hyperparameters, requiring neither large batches in SimCLR, momentum encoding in MoCo, or large epochs. We demonstrate with various benchmarks while manifesting robustness as much less sensitive to suboptimal hyperparameters. Notably, SimCLR with DCL achieves $$68.2\%$$ ImageNet-1K top-1 accuracy using batch size 256 within 200 epochs pre-training, outperforming its SimCLR baseline by $$6.4\%$$ . Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve $$72.3\%$$ ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning. We believe DCL provides a valuable baseline for future contrastive SSL studies.
Minimal AR: visual asset optimization for the authoring of augmented reality work instructions in manufacturing

This work investigates the possibility of using a novel “minimal AR” authoring approach to optimize the visual assets used in augmented reality (AR) interfaces to convey work instructions in manufacturing. In the literature, there are no widely supported guidelines for the optimal choice of visual assets (e.g., CAD models, drawings, and videos). Therefore, to avoid the risk of having AR technical documentation based only on the author’s preference, our work proposes a novel authoring approach that enforces the minimal amount of information to accomplish a task. Minimal AR was tested through a simulated AR LEGO-based assembly task. The performance (completion time, mental workload, errors) of 40 users was evaluated with 4 combinations of visual assets in 4 tasks with an increasing amount of information needed. The main result is that visual assets with an excess of information do not significantly increase performance. Therefore, the location of a specified object should be “minimally” authored by an auxiliary model (e.g., a circle and an arrow). For identifying an object within a couple, color coding is preferred to using additional visual assets. If more than two objects must be identified, a drawing visual asset is also needed. Only when the orientation of a selected object must be conveyed, animated product models are required. These insights could be helpful for an optimal design of AR work instructions in a wide range of industrial fields.
Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.
Exposing deepfakes using a deep multilayer perceptron – convolutional neural network model

Creating deepfakes has rapidly become easier and more accessible due to advancements in hardware and computing. The harmful nature of deepfakes urges immediate action to improve detection of such doctored videos. In this work, we build a deep hybrid neural network model to detect deepfake videos. Using facial landmarks detection, we extract data pertaining to various facial attributes from the videos. This data is passed to a multilayer perceptron to learn differences in real and deepfake videos. Simultaneously, we use a convolutional neural network to extract features and train on the videos. We combine these two models to build a multi-input deepfake detector. A subset of the Deepfake Detection Challenge Dataset along with the Dessa Dataset is used to train the model. The proposed model provides good classification results with an accuracy of 84% and an AuC score of 0.87.
Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation

Salient object detection in optical remote sensing images (ORSI-SOD) has been widely explored for understanding ORSIs. However, previous methods focus mainly on improving the detection accuracy while neglecting the cost in memory and computation, which may hinder their real-world applications. In this paper, we propose a novel lightweight ORSI-SOD solution, named CorrNet, to address these issues. In CorrNet, we first lighten the backbone (VGG-16) and build a lightweight subnet for feature extraction. Then, following the coarse-to-fine strategy, we generate an initial coarse saliency map from high-level semantic features in a Correlation Module (CorrM). The coarse saliency map serves as the location guidance for low-level features. In CorrM, we mine the object location information between high-level semantic features through the cross-layer correlation operation. Finally, based on low-level detailed features, we refine the coarse saliency map in the refinement subnet equipped with Dense Lightweight Refinement Blocks, and produce the final fine saliency map. By reducing the parameters and computations of each component, CorrNet ends up having only 4.09M parameters and running with 21.09G FLOPs. Experimental results on two public datasets demonstrate that our lightweight CorrNet achieves competitive or even better performance compared with 26 state-of-the-art methods (including 16 large CNN-based methods and 2 lightweight methods), and meanwhile enjoys the clear memory and run time efficiency. The code and results of our method are available at https://github.com/MathLee/CorrNet.
A Compact and Scalable Hardware/Software Co-design of SIKE

We present efficient and compact hardware/software co-design implementations of the Supersingular Isogeny Key Encapsulation (SIKE) protocol on field-programmable gate arrays (FPGAs). In order to be better equipped for different post-quantum scenarios, our architectures were designed to feature high-flexibility by covering all the currently available parameter sets and with support for primes up to 1016 bits. In particular, any of the current SIKE parameters equivalent to the post-quantum security of AES-128/192/256 and SHA3-256 can be selected and run on-the-fly. This security scalability property, together with the small footprint and efficiency of our architectures, makes them ideal for embedded applications in a post-quantum world. In addition, the proposed implementations exhibit regular, constant-time execution, which provides protection against timing and simple sidechannel attacks. Our results demonstrate that supersingular isogeny-based primitives such as SIDH and SIKE can indeed be deployed for embedded applications featuring competitive performance. For example, our smallest architecture based on a 128-bit MAC unit takes only 3415 slices, 21 BRAMs and 57 DSPs on a Virtex 7 690T and can perform key generation, encapsulation and decapsulation in 14.4, 24.4 and 26.0 milliseconds for SIKEp434 and in 52.3, 86.4 and 93.2 milliseconds for SIKEp751, respectively.
Robots can’t take my job: antecedents and outcomes of Gen Z employees’ service robot risk awareness

Purpose There has been a dramatic increase in the adoption of service robots in hotels, potentially replacing the human workforce. Drawing on Social Amplification of Risk Framework, this study aims to examine the moderating effect of transformational leadership on the indirect relationships between Gen Z employees’ tech-savviness and social skills on industry turnover intention via service robot risk awareness (SRRA). Design/methodology/approach This study collected two-wave time-lagged multilevel data of 281 frontline Gen Z hotel employees from 54 departments in China. Participants were asked to rate their tech-savviness, social skills and SRRA in the first survey. They rated their supervisor’s transformational leadership and industry turnover intention one week later. Findings Multilevel path analysis results showed SRRA mediates the negative indirect relationship of Gen Z employee’s tech-savviness and social skills on industry turnover intention. Transformational leadership weakened the positive effect of SRRA on industry turnover intention. Originality/value This study contributes to the growing literature on service robots by investigating the antecedents and outcomes of employees’ SRRA. To the best of the authors’ knowledge, it is one of the first empirical studies investigating the role of leadership to mitigate the negative consequences of employee’s SRRA. Managers can use the results of this study to implement training programs and ensure that employees and service robots successfully coexist in the workplace.
Empirical Analysis of EIP-1559

A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs.
Online Trajectory Replanning for Sudden Environmental Changes During Automated Parking: A Parallel Stitching Method

Trajectory planning for automated parking has been widely known as more challenging than that for on-road driving due to the nonconvex kinematics, high-dimensional collision-avoidance constraints, and difficulty to determine the global optimum among many local optima. Changes in a dynamic environment easily make a previously planned parking trajectory invalid. Compared with the on-road planners which evade newly emerged obstacles via a nudge or side pass, a parking planner has to replan a completely different trajectory for evasion. A qualified online trajectory replanner should run fast, ensure trajectory continuity, and avoid extra halfway stops. This paper proposes a parallel stitching strategy to fulfill these demands. When the ego vehicle tracks an originally planned parking trajectory online, an evasive trajectory begins to be replanned once a new obstacle is found to block the way. Thereafter, a connective trajectory is planned, the two ends of which are future poses along the original trajectory and the evasive trajectory, respectively. The two ends of the connective trajectory are chosen by greedily evaluating various candidates via parallel computation, which ensures that our replanner runs fast with high solution quality. According to our real-world experiments and simulations, the proposed replanner outperforms the existing ones w.r.t. solution speed and quality. As an interesting feature, our proposed replanner also supports switching to a better homotopy class online.
From Description to Correspondence: Anthropology in Real Time

Anthropology’s critical examination of its own founding conception of the human—as a being that, by its own nature, transcends nature—inevitably entailed a parallel critique of the cognitivist underpinnings of design science, according to which the very mechanisms of thought render thought capable of the intelligent design of mechanisms. In this spirit, Lucy Suchman urges us not to reinvent anthropology as design but rather to adopt a critical anthropology of design as part of a wider anthropology of the contemporary. Such an anthropology, she argues, would require “ethnographic projects that articulate the cultural imaginaries and micropolitics that delineate design’s promises and practices”. Anthropology, in our view, is a generous, open-ended, holistic, comparative, and yet critical inquiry into the conditions and potentials for human life in the one world we all inhabit. Anthropologists have long been alert to the implications of their presence in the situations in which they carry out fieldwork.
On the Informativeness of Descriptive Statistics for Structural Estimates

We propose a way to formalize the relationship between descriptive analysis and structural estimation. A researcher reports an estimate ĉ of a structural quantity of interest c that is exactly or asymptotically unbiased under some base model. The researcher also reports descriptive statistics<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mover accent="true"><mi>γ</mi><mo>ˆ</mo></mover></math>that estimate features γ of the distribution of the data that are related to c under the base model. A reader entertains a less restrictive model that is local to the base model, under which the estimate ĉ may be biased. We study the reduction in worst‐case bias from a restriction that requires the reader's model to respect the relationship between c and γ specified by the base model. Our main result shows that the proportional reduction in worst‐case bias depends only on a quantity we call the informativeness of<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mover accent="true"><mi>γ</mi><mo>ˆ</mo></mover></math>for ĉ . Informativeness can be easily estimated even for complex models. We recommend that researchers report estimated informativeness alongside their descriptive analyses, and we illustrate with applications to three recent papers.
Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization

Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre- and/or post-processing step for better disease diagnosis. The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for denoising of CT images by effectively utilizing the global spatial correlation and local smoothness properties. The tensor nuclear norm is used to describe the global properties and the tensor total variation is used to characterize the local smoothness as well as to improve global smoothness. The resulting optimization problem is solved by the Alternative Direction Method of Multipliers (ADMM) technique. Experimental results on simulated and real CT data prove that the proposed methods outperform the state-of-art works.
An overview and perspectives on bidirectional intelligence: Lmser duality, double IA harmony, and causal computation

Advances on bidirectional intelligence are overviewed along three threads, with extensions and new perspectives. The first thread is about bidirectional learning architecture, exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed. Interestingly, either or two of these dualities actually takes an important role in recent models such as U-net, ResNet, and DenseNet. The second thread is about bidirectional learning principles unified by best yIng-yAng (IA) harmony in BYY system. After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions, maximum likelihood, variational principle, and several other learning principles are summarised as exemplars of the BYY learning, with new perspectives on advanced topics. The third thread further proceeds to deep bidirectional intelligence, driven by long term dynamics (LTD) for parameter learning and short term dynamics (STD) for image thinking and rational thinking in harmony. Image thinking deals with information flow of continuously valued arrays and especially image sequence, as if thinking was displayed in the real world, exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning. In contrast, rational thinking handles symbolic strings or discretely valued vectors, performing uncertainty reasoning and problem solving. In particular, a general thesis is proposed for bidirectional intelligence, featured by BYY intelligence potential theory (BYY-IPT) and nine essential dualities in architecture, fundamentals, and implementation, respectively. Then, problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective. First, variants and extensions are suggested for AlphaGoZero like searching tasks, such as traveling salesman problem (TSP) and attributed graph matching (AGM) that are turned into Go like problems with help of a feature enrichment technique. Second, reasoning activities are summarized under guidance of BYY IPT from the aspects of constraint satisfaction, uncertainty propagation, and path or tree searching. Particularly, causal potential theory is proposed for discovering causal direction, with two roads developed for its implementation.