Reviewerly/exHarmony huggingface dataset:
Dynamic Network Quantization for Efficient Video Inference

Deep convolutional networks have recently achieved great success in video recognition, yet their practical realization remains a challenge due to the large amount of computational resources required to achieve robust recognition. Motivated by the effectiveness of quantization for boosting efficiency, in this paper, we propose a dynamic network quantization framework, that selects optimal precision for each frame conditioned on the input for efficient video recognition. Specifically, given a video clip, we train a very lightweight network in parallel with the recognition network, to produce a dynamic policy indicating which numerical precision to be used per frame in recognizing videos. We train both networks effectively using standard backpropagation with a loss to achieve both competitive performance and resource efficiency required for video recognition. Extensive experiments on four challenging diverse benchmark datasets demonstrate that our proposed approach provides significant savings in computation and memory usage while outperforming the existing state-of-the-art methods. Project page: https://cs-people.bu.edu/sunxm/VideoIQ/project.html.
An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

The world has witnessed recently a global outbreak of coronavirus disease (COVID-19). This pandemic has affected many countries and has resulted in worldwide health concerns, thus governments are attempting to reduce its spread and impact on different aspects of life such as health, economics, education, and politics by making emergent decisions and policies (e.g., lockdown and social distancing). These new regulations influenced people’s daily life and cast significant burdens, concerns, and disparities on various population groups. Taking the wrong actions and enforcing bad decisions by some countries result in increasing the contagion rate and more catastrophic results. People start to post their opinions and feelings about their government’s decisions on different social media networks, and the data received through these platforms present a very useful source of information that affects how governments perceive and cope with the current the pandemic. Jordan was one of the top affected countries. In this paper, we proposed a decision support system based on the sentiment analysis mechanism by combining support vector machines with a whale optimization algorithm for automatically tuning the hyperparameters and performing feature weighting. The work is based on a hybrid evolutionary approach that aims to perform sentiment analysis combined with a decision support system to study people’s posts on Facebook to investigate their attitudes and feelings toward the government’s decisions during the pandemic. The government regulations were divided into two periods: the first and latter regulations. Studying public sentiments during these periods allows decision-makers in the government to sense people’s feelings, alert them in case of possible threats, and help in making proactive actions if needed to better handle the current pandemic situation. Five different versions were generated for each of the two collected datasets. The results demonstrate the superiority of the proposed Whale Optimization Algorithm &amp; Support Vector Machines (WOA-SVM) against other metaheuristic algorithms and standard classification models as WOA-SVM has achieved 78.78% in terms of accuracy and 84.64% in term of f-measure, while other standard classification models such as NB, k-NN, J84, and SVM achieved an accuracy of 69.25%, 69.78%, 70.17%, and 69.29%, respectively, with 64.15%, 62.90%, 60.51%, and 59.09% F-measure. Moreover, when comparing our proposed WOA-SVM approach with other metaheuristic algorithms, which are GA-SVM, PSO-SVM, and MVO-SVM, WOA-SVM proved to outperform the other approaches with results of 78.78% in terms of accuracy and 84.64% in terms of F-measure. Further, we investigate and analyze the most relevant features and their effect to improve the decision support system of government decisions.
A Fractional Fourier Based Medical Image Authentication Approach

Patient medical information in all forms is crucial to keep private and secure, particularly when medical data communication occurs through insecure channels.Therefore, there is a bad need for protecting and securing the color medical images against impostors and invaders.In this paper, an optical medical image security approach is introduced.It is based on the optical bit-plane Jigsaw Transform (JT) and Fractional Fourier Transform (FFT).Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach.A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and security.To confirm the efficiency of the suggested security approach for secure color medical image communication, various assessments on different color medical images are examined based on different statistical security metrics.Furthermore, a comparative analysis is introduced between the suggested security approach and other conventional cryptography protocols.The simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly secure.It has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches with achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images.
Video Autoencoder: self-supervised disentanglement of static 3D structure and motion

A video autoencoder is proposed for learning disentangled representations of 3D structure and camera pose from videos in a self-supervised manner. Relying on temporal continuity in videos, our work assumes that the 3D scene structure in nearby video frames remains static. Given a sequence of video frames as input, the video autoencoder extracts a disentangled representation of the scene including: (i) a temporally-consistent deep voxel feature to represent the 3D structure and (ii) a 3D trajectory of camera pose for each frame. These two representations will then be re-entangled for rendering the input video frames. This video autoencoder can be trained directly using a pixel re-construction loss, without any ground truth 3D or camera pose annotations. The disentangled representation can be applied to a range of tasks, including novel view synthesis, camera pose estimation, and video generation by motion following. We evaluate our method on several large-scale natural video datasets, and show generalization results on out-of-domain images. Project page with code: https://zlai0.github.io/VideoAutoencoder.
Computing Ground State Properties with Early Fault-Tolerant Quantum Computers

Significant effort in applied quantum computing has been devoted to the problem of ground state energy estimation for molecules and materials. Yet, for many applications of practical value, additional properties of the ground state must be estimated. These include Green's functions used to compute electron transport in materials and the one-particle reduced density matrices used to compute electric dipoles of molecules. In this paper, we propose a quantum-classical hybrid algorithm to efficiently estimate such ground state properties with high accuracy using low-depth quantum circuits. We provide an analysis of various costs (circuit repetitions, maximal evolution time, and expected total runtime) as a function of target accuracy, spectral gap, and initial ground state overlap. This algorithm suggests a concrete approach to using early fault tolerant quantum computers for carrying out industry-relevant molecular and materials calculations.
Self-Supervised 3D Face Reconstruction via Conditional Estimation

We present a conditional estimation (CEST) framework to learn 3D facial parameters from 2D single-view images by self-supervised training from videos. CEST is based on the process of analysis by synthesis, where the 3D facial parameters (shape, reflectance, viewpoint, and illumination) are estimated from the face image, and then recombined to reconstruct the 2D face image. In order to learn semantically meaningful 3D facial parameters without explicit access to their labels, CEST couples the estimation of different 3D facial parameters by taking their statistical dependency into account. Specifically, the estimation of any 3D facial parameter is not only conditioned on the given image, but also on the facial parameters that have already been derived. Moreover, the reflectance symmetry and consistency among the video frames are adopted to improve the disentanglement of facial parameters. Together with a novel strategy for incorporating the reflectance symmetry and consistency, CEST can be efficiently trained with in-the-wild video clips. Both qualitative and quantitative experiments demonstrate the effectiveness of CEST.
Subjective Image Quality Assessment With Boosted Triplet Comparisons

In subjective full-reference image quality assessment, a reference image is distorted at increasing distortion levels. The differences between perceptual image qualities of the reference image and its distorted versions are evaluated, often using degradation category ratings (DCR). However, the DCR has been criticized since differences between rating categories on this ordinal scale might not be perceptually equidistant, and observers may have different understandings of the categories. Pair comparisons (PC) of distorted images, followed by Thurstonian reconstruction of scale values, overcomes these problems. In addition, PC is more sensitive than DCR, and it can provide scale values in fractional, just noticeable difference (JND) units that express a precise perceptional interpretation. Still, the comparison of images of nearly the same quality can be difficult. We introduce boosting techniques embedded in more general triplet comparisons (TC) that increase the sensitivity even more. Boosting amplifies the artefacts of distorted images, enlarges their visual representation by zooming, increases the visibility of the distortions by a flickering effect, or combines some of the above. Experimental results show the effectiveness of boosted TC for seven types of distortion (color diffusion, jitter, high sharpen, JPEG 2000 compression, lens blur, motion blur, multiplicative noise). For our study, we crowdsourced over 1.7 million responses to triplet questions. We give a detailed analysis of the data in terms of scale reconstructions, accuracy, detection rates, and sensitivity gain. Generally, boosting increases the discriminatory power and allows to reduce the number of subjective ratings without sacrificing the accuracy of the resulting relative image quality values. Our technique paves the way to fine-grained image quality datasets, allowing for more distortion levels, yet with high-quality subjective annotations. We also provide the details for Thurstonian scale reconstruction from TC and our annotated dataset, KonFiG-IQA, containing 10 source images, processed using 7 distortion types at 12 or even 30 levels, uniformly spaced over a span of 3 JND units.
A blockchain-based Shamir's threshold cryptography for data protection in industrial internet of things of smart city

The Industrial Internet of Things (IIoT) is a typical infrastructure of a smart city. Due to the limited storage and computing capabilities of industrial IoT devices, the data sensed and collected by these devices is usually stored in the cloud, and encryption is usually used to ensure the privacy and confidentiality of sensitive data in the cloud. However, since the keys used for data encryption and decryption are usually difficult to manage, it is easy to cause security and privacy issues. To this end, this article proposes a blockchain-based IIoT data protection scheme. In this scheme, in order to prevent the loss of the private key and the disclosure of privacy, we use the Shamir secret sharing algorithm to split and encrypt the private key, and publish it on the blockchain. Experimental results show that this scheme can not only effectively prevent data from being stolen, but also effectively protect the encryption key.
CEREBRUM‐7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes

Ultra-high-field magnetic resonance imaging (MRI) enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks in accurate and fast methods for segmenting 7-tesla (7T) brain MRI. We here present CEREBRUM-7T, an optimised end-to-end convolutional neural network, which allows fully automatic segmentation of a whole 7T T1w MRI brain volume at once, without partitioning the volume, pre-processing, nor aligning it to an atlas. The trained model is able to produce accurate multi-structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on and is suitable for neuroimaging studies, such as layer functional MRI studies. Taking advantage of a fine-tuning operation on a reduced set of volumes, we also show how it is possible to effectively apply CEREBRUM-7T to different sites data. Furthermore, we release the code, 7T data, and other materials, including the training labels and the Turing test.
An information behavior theory of transitions

Abstract This paper proposes a theory of life transitions focused on information behavior. Through a process of meta‐ethnography, the paper transforms a series of influential theories and models into a theory of transitions for use in Information Science. This paper characterizes the psychological processes involved in transitions as consisting of three main stages, Understanding, Negotiating, and Resolving, each of which have qualitatively different information behaviors and which require different types of information support. The paper discusses the theoretical implications of this theory and proposes ways in which the theory can be used to provide practical support for those undergoing transitions.
A Design Space for Applying the Freytag's Pyramid Structure to Data Stories

Data stories integrate compelling visual content to communicate data insights in the form of narratives. The narrative structure of a data story serves as the backbone that determines its expressiveness, and it can largely influence how audiences perceive the insights. Freytag's Pyramid is a classic narrative structure that has been widely used in film and literature. While there are continuous recommendations and discussions about applying Freytag's Pyramid to data stories, little systematic and practical guidance is available on how to use Freytag's Pyramid for creating structured data stories. To bridge this gap, we examined how existing practices apply Freytag's Pyramid by analyzing stories extracted from 103 data videos. Based on our findings, we proposed a design space of narrative patterns, data flows, and visual communications to provide practical guidance on achieving narrative intents, organizing data facts, and selecting visual design techniques through story creation. We evaluated the proposed design space through a workshop with 25 participants. Results show that our design space provides a clear framework for rapid storyboarding of data stories with Freytag's Pyramid.
Visual question answering by pattern matching and reasoning

Traditional techniques for visual question answering (VQA) are mostly end-to-end neural network based, which often perform poorly (e.g., low accuracy) due to lack of understanding and reasoning. To overcome the weaknesses, we propose a comprehensive approach with following key features. (1) It represents inputs, i.e., an image I and a natural language question Qnl as an entity-attribute graph and a query graph, respectively, and employs pattern matching to find answers; (2) it leverages reinforcement learning based model to identify a set of policies that are used to guide visual tasks and construct an entity-attribute graph, based on Qnl; (3) it employs a novel method to parse a question Qnl and generate corresponding query graph Q(uo) for pattern matching; and (4) it integrates inference scheme to further improve result accuracy, in particular, it learns a graph-structured classifier for missing value inference and a co-occurrence matrix for candidate selection. With these features, our approach can not only process visual tasks efficiently, but also answer questions with high accuracy. To evaluate the performance of our approach, we conduct empirical studies on Soccer, Visual-Genome and GQA, and show that our approach outperforms the state-of-the-art methods in result accuracy and system efficiency.
Floating photovoltaic system for Indian artificial reservoirs—an effective approach to reduce evaporation and carbon emission

Abstract Floating photovoltaic system for reservoirs is a recent innovative technology that is highly advantageous in reducing evaporation while generating solar power. In addition, the integration of floating photovoltaic systems with the existing hydroelectric power plants will increase renewable power production. The present study aims to assess the electrical performance of floating photovoltaic systems in major reservoirs with existing hydroelectric power plants in India. The reservoirs with large water surface area were selected for the study, and a model floating photovoltaic system with a 5-MW capacity was designed for the selected reservoirs. The numerical analysis showed that installing floating photovoltaic systems will result in an annual energy yield of 160 GWh. Further, the systems also save 1.40 million cubic meters of water per day and also help in generating additional energy of 514.80 MWh/day from the saved water through its integration with hydroelectric power plants. A single-axis tracking mechanism to the floating photovoltaic systems will increase the annual energy generation by 11%. The detailed cost analysis and carbon emission analysis were also carried out. The results indicate that the tracking mechanisms increase the total installation cost of the systems. The annual carbon emission reduction from the floating photovoltaic systems accounts for about 3.30 million tons of CO 2 . The obtained results highlight the suitability of this innovative technology for installation in Indian reservoirs and its effectiveness in reducing evaporation and carbon emission. Graphic abstract
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features through phase-weighted summation or channel-attention based concatenation. However, these methods ignored the spatial (pixel-wise) relationships between different phases, hence leading to insufficient feature integration. In addition, the performance of existing methods remains subject to the uncertainty in segmentation, which is particularly acute in tumor boundary regions. In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation. To this end, we introduce a spatial aggregation module (SAM), which encourages per-pixel interactions between different phases, to make full use of cross-phase information. Moreover, we devise an uncertain region inpainting module (URIM) to refine uncertain pixels using neighboring discriminative features. Experiments on an in-house multi-phase CT dataset of focal liver lesions (MPCT-FLLs) demonstrate that our method achieves promising liver tumor segmentation and outperforms state-of-the-arts.
Orthogonal sampling moiré method and its application in microscale deformation field measurement

Sampling moiré (SM) method is one of the modern deformation measurement methods in photomechanics. This method realizes the visualization measurement of deformation field by a sparse sampling of grating images, showing the advantages of high efficiency and strong robustness. However, in the traditional sampling moiré method, the unidirectional sampling mode is used with the result that the two-dimensional deformation cannot be acquired at the same time. In this paper, by combining the orthogonal sampling and Fourier phase analysis techniques, an orthogonal sampling moiré (OSM) method is proposed with which the two-directional moiré phase of specimen grating can be separated synchronously. The accuracy of the OSM method for the deformation measurement is verified by numerical experiments and PMMA uniaxial tensile tests. As an application, in a scanning electron microscope (SEM), the deformation field near the crack tip of small fatigue cracks is measured, based on which the crack opening displacement (COD) and the stress intensity factors (K) are obtained. Furthermore, the crack closure effects at different temperatures are characterized. The experimental results show that the crack closure significantly affects the process of small crack growth at both room temperature and high temperature, and the crack opening load at high temperature is greater than that at room temperature, which indicates that small cracks are easier to close at high temperature.
AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.