Reviewerly/exHarmony huggingface dataset:
Cascaded unequal modulus decomposition in Fresnel domain based cryptosystem to enhance the image security

In this paper, we proposed an iterative attacking algorithm to the pure phase asymmetric optical cryptosystem based on unequal modulus decomposition. Furthermore, we have proposed a secure asymmetric cryptosystem based on cascaded unequal modulus decomposition in the Fresnel domain. Fresnel transform and unequal modulus decomposition are highly sensitive to their parameters. An input grayscale image is first convoluted with a random phase mask, then Fresnel transformed, and followed by unequal modulus decomposition. This process is cascaded twice to get an encrypted image, which is quite random and looks like a noisy white image. The scheme is validated on various types of grayscale and binary images, but simulations are demonstrated for Binary dots, Boat, Jetplane, Medical, and Vegetable images. The results show that the proposed cryptosystem eliminates the silhouette problem and endures statistical and special attacks. The statistical attack based on the histogram, entropy, and correlation distribution analysis on neighbouring pixels is performed on the proposed cryptosystem. Results are shown in terms of statistical metrics such as correlation coefficient and mean squared error between plaintext and ciphertext. The proposed scheme also resists noise attack. The results show that the proposed scheme is highly secure and resist the cryptographic attacks.
Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search

Automatic surgical gesture recognition is fundamental for improving intelligence in robot-assisted surgery, such as conducting complicated tasks of surgery surveillance and skill evaluation. However, current methods treat each frame individually and produce the outcomes without effective consideration on future information. In this paper, we propose a framework based on reinforcement learning and tree search for joint surgical gesture segmentation and classification. An agent is trained to segment and classify the surgical video in a human-like manner whose direct decisions are re-considered by tree search appropriately. Our proposed tree search algorithm unites the outputs from two designed neural networks, i.e., policy and value network. With the integration of complementary information from distinct models, our framework is able to achieve the better performance than baseline methods using either of the neural networks. For an overall evaluation, our developed approach consistently outperforms the existing methods on the suturing task of JIGSAWS dataset in terms of accuracy, edit score and F1 score. Our study highlights the utilization of tree search to refine actions in reinforcement learning framework for surgical robotic applications.
HeTOnto: A Novel Approach for Conceptualization, Modeling, Visualization, and Formalization of Domain Centric Ontologies for Heat Transfer

Ontology is a means of capturing knowledge and displaying it in a highly comprehensible way to make it both machine-understandable and human-understandable. In this paper, the methodology of modeling an ontology is described using the processes of domain analysis, conceptualization, implementation, and evaluation. Semantic Descriptions for most of the concepts are provided, and the transfiguration from OWL to RDF is proposed. The relationship between the various concepts is established by defining many relationship attributes like "isAPartOf", "hasAPartOf", "consistsOf", "isAnExhaustiveSubclassOf" and various other attributes which deepen the domain of the ontology. In this paper, an ontology for Heat Transfer as a domain is proposed modeling all the intricacies of Heat Transfer into a dynamic Ontology. The domain is thoroughly studied, and the modeled ontology is evaluated by the domain experts, and the required changes are incorporated. Finally, the ontology is Quantitatively and Qualitatively evaluated using the Semiotics Approach for ontology evaluation and a spectacular reusability ratio of 92.63%.
What Makes a Data-GIF Understandable?

GIFs are enjoying increasing popularity on social media as a format for data-driven storytelling with visualization; simple visual messages are embedded in short animations that usually last less than 15 seconds and are played in automatic repetition. In this paper, we ask the question, “What makes a data-GIF understandable?” While other storytelling formats such as data videos, infographics, or data comics are relatively well studied, we have little knowledge about the design factors and principles for “data-GIFs”. To close this gap, we provide results from semi-structured interviews and an online study with a total of 118 participants investigating the impact of design decisions on the understandability of data-GIFs. The study and our consequent analysis are informed by a systematic review and structured design space of 108 data-GIFs that we found online. Our results show the impact of design dimensions from our design space such as animation encoding, context preservation, or repetition on viewers understanding of the GIF's core message. The paper concludes with a list of suggestions for creating more effective Data-GIFs.
Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions

Convolutional neural networks (CNN) is a popular architecture in machine learning for its predictive power, notably in computer vision and medical image analysis. Its great predictive power requires extensive computation, which encourages model owners to host the prediction service in a cloud platform. This article proposes a CNN prediction scheme that preserves privacy in the outsourced setting, i.e., the model-hosting server cannot learn the query, (intermediate) results, and the model. Similar to SecureML (S&P’17), a representative work that provides model privacy, we employ two non-colluding servers with secret sharing and triplet generation to minimize the usage of heavyweight cryptography. We made the following optimizations for both overall latency and accuracy. 1) We adopt asynchronous computation and SIMD for offline triplet generation and parallelizable online computation. 2) As MiniONN (CCS’17) and its improvement by the generic EzPC compiler (EuroS&P’19), we use a garbled circuit for the non-polynomial ReLU activation to keep the same accuracy as the underlying network (instead of approximating it in SecureML prediction). 3) For the pooling in CNN, we employ (linear) average-pooling, which achieves almost the same accuracy as the (non-linear, and hence less efficient) max-pooling exhibited by MiniONN and EzPC. Considering both offline and online costs, our experiments on the MNIST dataset show a latency reduction of <inline-formula><tex-math notation="LaTeX">$122\times$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$14.63\times$</tex-math></inline-formula> , and <inline-formula><tex-math notation="LaTeX">$36.69\times$</tex-math></inline-formula> compared to SecureML, MiniONN, and EzPC; and a reduction of communication costs by <inline-formula><tex-math notation="LaTeX">$1.09\times$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$36.69\times$</tex-math></inline-formula> , and <inline-formula><tex-math notation="LaTeX">$31.32\times$</tex-math></inline-formula> , respectively. On the CIFAR dataset, our scheme achieves a lower latency by <inline-formula><tex-math notation="LaTeX">$7.14\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$3.48\times$</tex-math></inline-formula> and lower communication costs by <inline-formula><tex-math notation="LaTeX">$13.88\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$77.46\times$</tex-math></inline-formula> when compared with MiniONN and EzPC, respectively.
Practical Modeling and Analysis of Blockchain Radio Access Network

The continually rising demand for wireless services and applications in the era of Internet of things (IoT) and artificial intelligence (AI) presents a significant number of unprecedented challenges to existing network structures. To meet the rapid growth need of mobile data services, blockchain radio access network (B-RAN) has emerged as a decentralized, trustworthy radio access paradigm spurred by blockchain technologies. However, many characteristics of B-RAN remain unclear and hard to characterize. In this study, we develop an analytical framework to model B-RAN and provide some basic fundamental analysis. Starting from block generation, we establish a queuing model based on a time-homogeneous Markov chain. From the queuing model, we evaluate the performance of B-RAN with respect to latency and security considerations. We uncover a more comprehensive picture of the achievable performance of B-RAN by connecting latency and security. At last, we present experimental results via an innovative prototype and validate the proposed model.
Optimal Management of Water Resources in Large Main Canals with Cascade Pumping Stations

A mathematical model of the unsteady flow of water on sections of main canals by limited hydraulic structures has been developed. And also the statement of problems of optimal control of water distribution in the main canals is developed. A fundamental solution is obtained for a one-dimensional differential equation describing the unsteady movement of water in sections of main canals.
VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations

Cloud-based visualization services have made visual analytics accessible to a much wider audience than ever before. Systems such as Tableau have started to amass increasingly large repositories of analytical knowledge in the form of interactive visualization workbooks. When shared, these collections can form a visual analytic knowledge base. However, as the size of a collection increases, so does the difficulty in finding relevant information. Content-based recommendation (CBR) systems could help analysts in finding and managing workbooks relevant to their interests. Toward this goal, we focus on text-based content that is representative of the subject matter of visualizations rather than the visual encodings and style. We discuss the challenges associated with creating a CBR based on visualization specifications and explore more concretely how to implement the relevance measures required using Tableau workbook specifications as the source of content data. We also demonstrate what information can be extracted from these visualization specifications and how various natural language processing techniques can be used to compute similarity between workbooks as one way to measure relevance. We report on a crowd-sourced user study to determine if our similarity measure mimics human judgement. Finally, we choose latent Dirichl et al.ocation (LDA) as a specific model and instantiate it in a proof-of-concept recommender tool to demonstrate the basic function of our similarity measure.
Empirical analysis of transaction malleability within blockchain-based e-Voting

Blockchain is a disruptive technology that has been used to address a wide range of challenges in diverse domains including voting, logistics, healthcare and finance. Transaction malleability is one of the critical threats for blockchain, which can facilitate double-spending attacks by tampering with the state of a blockchain. This paper investigates the potential of transaction malleability attack within a blockchain-based application (e-voting) with the aim to identify settings which can lead to a successful transaction malleability attack. Therein, we aim to highlight conditions which can cause transaction malleability attack so as to help develop appropriate protection mechanisms. In particular, a successful execution of transaction malleability attack is presented which was conducted on a blockchain testbed hosting an e-voting application. The experiments identified significance of parameters such as network delay and block generation rate to successfully execute transaction malleability attack and have highlighted directions for future research.
<i>PipelineProfiler:</i> A Visual Analytics Tool for the Exploration of AutoML Pipelines

In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to generate end-to-end ML pipelines. While these techniques facilitate the creation of models, given their black-box nature, the complexity of the underlying algorithms, and the large number of pipelines they derive, they are difficult for developers to debug. It is also challenging for machine learning experts to select an AutoML system that is well suited for a given problem. In this paper, we present the Pipeline Profiler, an interactive visualization tool that allows the exploration and comparison of the solution space of machine learning (ML) pipelines produced by AutoML systems. PipelineProfiler is integrated with Jupyter Notebook and can be combined with common data science tools to enable a rich set of analyses of the ML pipelines, providing users a better understanding of the algorithms that generated them as well as insights into how they can be improved. We demonstrate the utility of our tool through use cases where PipelineProfiler is used to better understand and improve a real-world AutoML system. Furthermore, we validate our approach by presenting a detailed analysis of a think-aloud experiment with six data scientists who develop and evaluate AutoML tools.
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Devices

The explosive growth in video streaming requires video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive. In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high efficiency and high performance. The key idea of TSM is to shift part of the channels along the temporal dimension, thus facilitate information exchanged among neighboring frames. It can be inserted into 2D CNNs to achieve temporal modeling at zero computation and zero parameters. TSM offers several unique advantages. First, TSM has high performance; it ranks the first on the Something-Something leaderboard upon submission. Second, TSM has high efficiency; it achieves a high frame rate of 74fps and 29fps for online video recognition on Jetson Nano and Galaxy Note8. Third, TSM has higher scalability compared to 3D networks, enabling large-scale Kinetics training on 1,536 GPUs in 15 minutes. Lastly, TSM enables action concepts learning, which 2D networks cannot model; we visualize the category attention map and find that spatial-temporal action detector emerges during the training of classification tasks. The code is publicly available at https://github.com/mit-han-lab/temporal-shift-module.
Self-Mimic Learning for Small-scale Pedestrian Detection

Detecting small-scale pedestrians is one of the most challenging problems in pedestrian detection. Due to the lack of visual details, the representations of small-scale pedestrians tend to be weak to be distinguished from background clutters. In this paper, we conduct an in-depth analysis of the small-scale pedestrian detection problem, which reveals that weak representations of small-scale pedestrians are the main cause for a classifier to miss them. To address this issue, we propose a novel Self-Mimic Learning (SML) method to improve the detection performance on small-scale pedestrians. We enhance the representations of small-scale pedestrians by mimicking the rich representations from large-scale pedestrians. Specifically, we design a mimic loss to force the feature representations of small-scale pedestrians to approach those of large-scale pedestrians. The proposed SML is a general component that can be readily incorporated into both one-stage and two-stage detectors, with no additional network layers and incurring no extra computational cost during inference. Extensive experiments on both the CityPersons and Caltech datasets show that the detector trained with the mimic loss is significantly effective for small-scale pedestrian detection and achieves state-of-the-art results on CityPersons and Caltech, respectively.
Histologic grading of breast carcinoma: a multi-institution study of interobserver variation using virtual microscopy

<h2>Abstract</h2> Breast carcinoma grading is an important prognostic feature recently incorporated into the AJCC Cancer Staging Manual. There is increased interest in applying virtual microscopy (VM) using digital whole slide imaging (WSI) more broadly. Little is known regarding concordance in grading using VM and how such variability might affect AJCC prognostic staging (PS). We evaluated interobserver variability amongst a multi-institutional group of breast pathologists using digital WSI and how discrepancies in grading would affect PS. A digitally scanned slide from 143 invasive carcinomas was independently reviewed by 6 pathologists and assigned grades based on established criteria for tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). Statistical analysis was performed. Interobserver agreement for grade was moderate (<i>κ</i> = 0.497). Agreement was fair (<i>κ</i> = 0.375), moderate (<i>κ</i> = 0.491), and good (<i>κ</i> = 0.705) for grades 2, 3, and 1, respectively. Observer pair concordance ranged from fair to good (<i>κ</i> = 0.354–0.684) Perfect agreement was observed in 43 cases (30%). Interobserver agreement for the individual components was best for TF (<i>κ</i> = 0.503) and worst for MC (<i>κ</i> = 0.281). Seventeen of 86 (19.8%) discrepant cases would have resulted in changes in PS and discrepancies most frequently resulted in a PS change from IA to IB (<i>n</i> = 9). For two of these nine cases, Oncotype DX results would have led to a PS of 1A regardless of grade. Using VM, a multi-institutional cohort of pathologists showed moderate concordance for breast cancer grading, similar to studies using light microscopy. Agreement was the best at the extremes of grade and for evaluation of TF. Whether the higher variability noted for MC is a consequence of VM grading warrants further investigation. Discordance in grading infrequently leads to clinically meaningful changes in the prognostic stage.
Towards Clustering-friendly Representations

Finding a suitable data representation for a specific task has been shown to be crucial in many applications. The success of subspace clustering depends on the assumption that the data can be separated into different subspaces. However, this simple assumption does not always hold since the raw data might not be separable into subspaces. To recover the "clustering-friendly" representation and facilitate the subsequent clustering, we propose a graph filtering approach by which a smooth representation is achieved. Specifically, it injects graph similarity into data features by applying a low-pass filter to extract useful data representations for clustering. Extensive experiments on image and document clustering datasets demonstrate that our method improves upon state-of-the-art subspace clustering techniques. Especially, its comparable performance with deep learning methods emphasizes the effectiveness of the simple graph filtering scheme for many real-world applications. An ablation study shows that graph filtering can remove noise, preserve structure in the image, and increase the separability of classes.
Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance

Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues. We propose a self-supervised approach to incorporate semantic guidance into a GAN based stain normalization framework and preserve detailed structural information. Our method does not require manual segmentation maps which is a significant advantage over existing methods. We integrate semantic information at different layers between a pre-trained semantic network and the stain color normalization network. The proposed scheme outperforms other color normalization methods leading to better classification and segmentation performance.
Modeling, simulation and experimental realization of a new nonlinear fuzzy PID controller using Center of Gravity defuzzification

Though Center of Gravity (CoG) defuzzification is a well-known and long-standing method in the history of fuzzy systems, because of its computational complexity, its use in the field of modeling of fuzzy controllers is almost nil. From literature, it appears that modeling of fuzzy Proportional Integral Derivative (FPID) controllers is rarely attempted using CoG defuzzification. In fact, none of the FPID controller models are obtained using both two-dimensional input space and CoG defuzzification. The available mathematical models of fuzzy Proportional Integral (FPI) and fuzzy Proportional Derivative (FPD) controllers using two-dimensional input space and CoG defuzzification were due to Arun and Mohan (2017). In this paper, the authors make an attempt to model and design an FPID controller using two-dimensional input space and CoG defuzzification. The incremental control effort produced by the newly developed FPID controller is found by combining the individual control efforts produced by incremental FPI and incremental FPD controllers. The incremental FPI and incremental FPD controller structures are unveiled using two-dimensional input space, CoG defuzzification, Min t-norm, Max t-conorm, and Larsen Product (LP) inference. The applicability and usefulness of the newly obtained FPID controller are depicted with simulation and real-time experimentation.
A cognitive brain model for multimodal sentiment analysis based on attention neural networks

Multimodal sentiment analysis is one of the most attractive interdisciplinary research topics in artificial intelligence (AI). Different from other classification issues, multimodal sentiment analysis of human is a much finer classification problem. However, most current work accept all multimodalities as the input together and then output final results at one time after fusion and decision processes. Rare models try to divide their models into more than one fusion modules with different fusion strategies for better adaption of different tasks. Additionally, most recent multimodal sentiment analysis methods pay great focuses on binary classification, but the accuracy of multi-classification still remains difficult to improve. Inspired by the emotional processing procedure in cognitive science, both binary and multi-classification abilities are improved in our method by dividing the complicated problem into smaller issues which are easier to be handled. In this paper, we propose a Hierarchal Attention-BiLSTM (Bidirectional Long-Short Term Memory) model based on Cognitive Brain limbic system (HALCB). HALCB splits the multimodal sentiment analysis into two modules responsible for two tasks, the binary classification and the multi-classification. The former module divides the input items into two categories by recognizing their polarity and then sends them to the latter module separately. In this module, Hash algorithm is utilized to improve the retrieve accuracy and speed. Correspondingly, the latter module contains a positive sub-net dedicated for positive inputs and a negative sub-nets dedicated for negative inputs. Each of these binary module and two sub-nets in multi-classification module possesses different fusion strategy and decision layer for matching its respective function. We also add a random forest at the final link to collect outputs from all modules and fuse them at the decision-level at last. Experiments are conducted on three datasets and compare the results with baselines on both binary classification and multi-classification tasks. Our experimental results surpass the state-of-the-art multimodal sentiment analysis methods on both binary and multi-classification by a big margin.