Reviewerly/exHarmony huggingface dataset:
Research Trend Analysis for EO-IR Image Registration

EO sensor and IR sensor have complementary characteristics. Therefore, registering two different sensors (especially EO-IR) to obtain accurate multi-images is an important part of many image-based applications such as surveillance and automatic target recognition (ATR). However, since images obtained from different sensors have significant differences in image content, an additional method is required to the existing registration technique. In general, there are classical techniques and deep learning techniques for multiple image registration techniques, and this paper introduces the techniques and development trends for them.
Quality of Information Matters: Recommending Web Services for Performance and Utility

Widely used in modern software systems, web services have become a standard means of provisioning remote resources. As the number of available web services increases, multiple services that satisfy the same functional requirement can be used interchangeably. Given a set of interchangeable services, a software developer needs to find a web service that would provide the best performance and utility. However, web services are recommended based only on their system-related performance characteristics (so called QoS, whose properties include latency, reliability, availability, etc.), while their data-related performance characteristics (e.g., data freshness, correctness, coverage, etc.) are often overlooked. As a consequence, a recommended service may end up delivering information that is inaccurate or outdated, but with high performance. To address this problem, this paper introduces Quality of Information (QoI), a quality metric complementary to QoS, that measures to which degree a web service satisfies data-related non-functional requirements. To minimize the manual effort required to evaluate the results of invoking individual services, we introduce a comparative testing methodology based on the new concept of Objects of Interest (OI). By using OI, developers can normalize the relevant information obtained from dissimilar services, so it can be automatically compared. To concretely realize our ideas, we create QiSR, a system that recommends web services based on their QoI metrics. QiSR helps developers in determining how to match services’ input and output with application data requirements and how to measure the information quality of services. To evaluate the effectiveness of QiSR, we test it on representative manually selected web services. Our evaluation shows that services recommended based on both QoI and QoS exhibit better combined performance and utility than services recommend on QoS alone.
Indoor Multipedestrian Multicamera Tracking Based on Fine Spatiotemporal Constraints

As indoor space is the primary place for pedestrian activities, obtaining intelligent monitoring of indoor pedestrians is crucial for intelligent video surveillance. Previous studies have verified the effectiveness of spatiotemporal constraints in multitarget multicamera tracking (MTMCT). Pedestrians are generally subjected to fine spatiotemporal constraints within buildings, based on which the indoor geographic information system (GIS) technology can obtain automatic spatiotemporal modeling. Combined with artificial intelligence (AI) technology, we established a research framework of “GIS+AI+IMPMCT. ” Specifically, we proposed indoor multipedestrian multicamera tracking (IMPMCT) based on fine spatiotemporal constraints. First, we used GIS to map the indoor monitoring images of buildings and automatically model the fine spatiotemporal relationship among the semantics of the entrance of the surveillance zone. Subsequently, we used the machine learning model of pedestrian localization and tracking to obtain local trajectories of pedestrians and combined them with map information to extract entrance semantics of trajectories. Finally, we used the local trajectory semantics and surveillance entrance semantic constraints to obtain a fine spatiotemporal constraint weight matrix between trajectories and fused pedestrian’s apparent features to obtain trajectory matching results. To verify our method, we established an IMPMCT data set containing fine indoor spatiotemporal information. Our method obtained an IDF1 of 0.805, which is better than those of other methods. Furthermore, the tracking results obtained by the proposed method contained both image space and geospatial trajectories.
Mitigating Label Noise in GANs via Enhanced Spectral Normalization

Label noise is a ubiquitous issue in GANs, which degrades the generalization ability of the discriminator and usually leads to instability when training GANs. This issue stems from both real data and generated data. Previous works either only consider one of these two sources, or are not robust enough to noisy labels. In this paper, we revisit spectral normalization in robust learning with noisy labels. Based on its pros and cons, we propose to combine spectral normalization and weight decay to regularize the discriminator, which enjoys a more robust training process. To extend to conditional GANs, we propose to balance the relative importance of marginal matching and conditional matching in the projection discriminator. The proposed Enhanced Spectral Normalization for Generative Adversarial Networks (ESNGAN) can be easily integrated into various existing GANs frameworks without excessive additional cost. The effectiveness of the proposed method is validated on the CIFAR10, LSUN Church, CelebA, and ImageNet datasets, including the unconditional image generation task and the class-conditional image generation task. We also show that the proposed method can further improve the performance of the high-resolution image generation task.
Imaging model of the off-axis non-central spherical mirrored stereo vision sensor

In order to expand the field-of-view of mirrored stereo vision sensor, replacing planar mirrors with spherical mirrors has become an effective solution. However, using the convex spherical mirror for reflection imaging will lead to image curl deformation. In this paper, to address the limitation of the structure, we proposed a novel imaging model of the off-axis non-central spherical mirrored vision sensor. This model can realize the points mapping from three-dimensional space to the two-dimensional image plane through the spherical mirror reflection. We derived the imaging model based on the optical path tracing principle, and established the corresponding relationship between spherical and planar mirrored virtual points to realize spherical distortion rectification. The imaging effect of the model was verified, and noises were added for error analysis. The research quantitatively describes the mapping relationship and expands the field-of-view, providing a new idea for establishing the measurement model of mirrored stereo vision sensor.
BNET: Batch Normalization With Enhanced Linear Transformation

Batch normalization (BN) is a fundamental unit in modern deep neural networks. However, BN and its variants focus on normalization statistics but neglect the recovery step that uses linear transformation to improve the capacity of fitting complex data distributions. In this paper, we demonstrate that the recovery step can be improved by aggregating the neighborhood of each neuron rather than just considering a single neuron. Specifically, we propose a simple yet effective method named batch normalization with enhanced linear transformation (BNET) to embed spatial contextual information and improve representation ability. BNET can be easily implemented using the depth-wise convolution and seamlessly transplanted into existing architectures with BN. To our best knowledge, BNET is the first attempt to enhance the recovery step for BN. Furthermore, BN is interpreted as a special case of BNET from both spatial and spectral views. Experimental results demonstrate that BNET achieves consistent performance gains based on various backbones in a wide range of visual tasks. Moreover, BNET can accelerate the convergence of network training and enhance spatial information by assigning important neurons with large weights accordingly.
Learning fair representations for accuracy parity

With the application of artificial intelligence in various fields of society, algorithm fairness has become a concern in social decision-making. Researchers have proposed various fair algorithms to achieve statistical parity and equal opportunity of outputs. However, the accuracy parity of algorithms has not been fully discussed. Existing fair algorithms either ignore the accuracy parity or sacrifice the joint accuracy of the model while achieving accuracy parity. A novel fairness algorithm referred to as balanced fair representation for accuracy parity (BFA) is proposed to reduce the bias on the accuracy among sensitive attribute groups while maintaining their joint accuracy. BFA uses representation learning methods to learn similar representations from different sensitive attribute groups. The learned representations are used as training data to learn a fair classification model. The classification error of the model is constrained to be independent of the sensitive attributes. To obtain similar representations, BFA calculates the characteristic scores of different sensitive attribute groups, respectively. BFA takes the difference between scores of different sensitive attribute groups from the same label as the constraint objective of learning the representation. To achieve accuracy parity, BFA calculates the cross entropy loss of neural networks from the learned similar representations and reduces the correlation between the loss and sensitive information. Empirical results show that the proposed representations provide a better trade-off between accuracy parity and joint accuracy than state-of-the-art works.
cpgQA: A Benchmark Dataset for Machine Reading Comprehension Tasks on Clinical Practice Guidelines and a Case Study Using Transfer Learning

Biomedical machine reading comprehension (bio-MRC), a crucial task in natural language processing, is a vital application of a computer-assisted clinical decision support system. It can help clinicians extract critical information effortlessly for clinical decision-making by comprehending and answering questions from biomedical text data. While recent advances in bio-MRC consider text data from resources such as clinical notes and scholarly articles, the clinical practice guidelines (CPGs) are still unexplored in this regard. CPGs are a pivotal component of clinical decision-making at the point of care as they provide recommendations for patient care based on the most up-to-date information available. Although CPGs are inherently terse compared to a multitude of articles, often, clinicians find them lengthy and complicated to use. In this paper, we define a new problem domain – bio-MRC on CPGs – where the ultimate goal is to assist clinicians in efficiently interpreting the clinical practice guidelines using MRC systems. To that end, we develop a manually annotated and subject-matter expert-validated benchmark dataset for the bio-MRC task on CPGs – cpgQA. This dataset aims to evaluate intelligent systems performing MRC tasks on CPGs. Hence, we employ the state-of-the-art MRC models to present a case study illustrating an extensive evaluation of the proposed dataset. We address the problem of lack of training data in this newly defined domain by applying transfer learning. The results show that while the current state-of-the-art models perform well with 78% exact match scores on the dataset, there is still room for improvement, warranting further research on this problem domain. We release the dataset at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mmahbub/cpgQA</uri> .
Nexus between financial integration, capital market development and economic performance: Does institutional structure matters?

This study investigates the role of institutional structure on asymmetries dynamic impact of financial integration, capital market development on economic performance in Sub-Saharan Africa (SSA). The study classified economic performance into RGDPC, nominal gdp and human capital development, and employed (PNARDL) modeling framework, and a panel of 16 nations of SSA over the period 1996-2019. The finding of this research output can be summarize as thus: i.) In the long run, a rise in positive shock to the financial integration index leads to a rise in RGDPC, while a negative shock to FI leads to a fall in RGDPC. ii.) Both shocks (positive and negative) to MCAP reduce RGDPC. Institutional quality index (INSQI) is revealed to have a positive and significant impact on RGDPC in the long run and indeed intensify their asymmetries. iii.) Both shocks to FI exert inverse influence on nominal GDPC, while positive and negative shocks to MCAP exert a positive and negative influence on GDPC, respectively. INSQI also affects GDPC negatively and significantly and indeed reduces their asymmetries. iv.) Positive and negative shocks to FI reduce HCD as well as shocks to MCAP. INSQI, on the other hand, adds to HCD and intensifies their asymmetries. However, the lack of consistency in the results across the models suggest that the interplay between these variables are still undeveloped relative to other continents of the world, and the benefits are yet to be adequately harnessed.
More learning with less labeling for face recognition

In this paper, we propose an improved face recognition framework where the training is started with a small set of human annotated face images and then new images are incorporated into the training set with minimum human annotation effort. In order to minimize the human annotation effort for new images, the proposed framework combines three different strategies, namely self-paced learning (SPL), active learning (AL), and minimum sparse reconstruction (MSR). As in the recently proposed ASPL framework [1], SPL is used for automatic annotation of easy images, for which the classifiers are highly confident and AL is used to request the help of an expert for annotating difficult or low-confidence images. In this work, we propose to use MSR to subsample the low-confidence images based on diversity using minimum sparse reconstruction in order to further reduce the number of images that require human annotation. Thus, the proposed framework provides an improvement over the recently proposed ASPL framework [1] by employing MSR for eliminating “similar” images from the set selected by AL for human annotation. Experimental results on two large-scale datasets, namely CASIA-WebFace-Sub and CACD show that the proposed method called ASPL-MSR can achieve similar face recognition performance by using significantly less expert-annotated data as compared to the state-of-the-art. In particular, ASPL-MSR requires manual annotation of only 36.10% and 54.10% of the data in CACD and CASIA-WebFace-Sub datasets, respectively, to achieve the same face recognition performance as the case when the whole training data is used with ground truth labels. The experimental results indicate that the number of manually annotated samples have been reduced by nearly 4% and 2% on the two datasets as compared to ASPL [1].
A Parallel Implementation of the Differential Evolution Method

Global optimization is a widely used technique that finds application in many sciences such as physics, economics, medicine, etc., and with many extensions, for example, in the area of machine learning. However, in many cases, global minimization techniques require a high computational time and, for this reason, parallel computational approaches should be used. In this paper, a new parallel global optimization technique based on the differential evolutionary method is proposed. This new technique uses a series of independent parallel computing units that periodically exchange the best solutions they have found. Additionally, a new termination rule is proposed here that exploits parallelism to accelerate process termination in a timely and valid manner. The new method is applied to a number of problems in the established literature and the results are quite promising.
An Integrated Application of Motion Sensing and Eye Movement Tracking Techniques in Perceiving User Behaviors in a Large Display Interaction

In public use of a large display, it is a usual phenomenon that multiple users individually participate in respective tasks on a common device. Previous studies have categorized such activity as independent interaction that involves little group engagement. However, by investigating how users approach, participate in, and interact with large displays, we found that parallel use is affected by group factors such as group size and between-user relationship. To gain a thorough understanding of individual and group behaviors, as well as parallel interaction task performance, one 70-inch display-based information searching task and experiment was conducted, in which a mobile eye movement tracking headset and a motion sensing RGB-depth sensor were simultaneously applied. The results showed that (1) a larger group size had a negative influence on the group users’ concentration on the task, perceived usability, and user experience; (2) a close relationship between users contributed to occasional collaborations, which was found to improve the users’ task completion time efficiency and their satisfaction on the large display user experience. This study proves that an integrated application of eye movement tracking and motion sensing is capable of understanding individual and group users’ behaviors simultaneously, and thus is a valid and reliable scheme in monitoring public activities that can be widely used in public large display systems.
Design of Fuzzy PID Controller Based on Sparse Fuzzy Rule Base for CNC Machine Tools

The robustness of the control algorithm plays a crucial role in the precision manufacturing and measurement of the CNC machine tool. This paper proposes a fuzzy PID controller based on a sparse fuzzy rule base (S-FPID), which can effectively control the position of a nonlinear CNC machine tool servo system consisting of a rotating motor and ball screw. In order to deal with the influences of both the internal and external uncertainties in the servo system, fuzzy logic is used to adjust the proportion, and integral and differential parameters in real-time to improve the robustness of the system. In the fuzzy inference engine of FPID, a sparse fuzzy rule base is used instead of a full-order fuzzy rule base, which significantly improves the computational efficiency of FPID and saves a lot of RAM storage space. The sensitivity analysis of S-FPID verifies the self-tuning ability of its parameters. Furthermore, the proposed S-FPID has been compared with the PID and FPID via simulation and experiment. The results show that compared with the classical PID controller, the overshoot of the S-FPID controller is reduced by 74.29%, and the anti-interference ability is increased by 62.43%; compared with FPID algorithm, the efficiency of the SPID is improved by 87.25% on the premise of a slight loss in robustness.
Characterization of tunable coupler without a dedicated readout resonator in superconducting circuits

Tunable couplers have recently emerged as an essential component in superconducting quantum processors. These couplers usually do not have readout resonators to save control or hardware overhead; this makes it challenging to characterize and calibrate the coupler, which will undoubtedly limit the performance of a fast two-qubit gate. Here, we theoretically demonstrate that by mapping the coupler's state onto that of its neighboring qubit, the high-contrast readout of the coupler can be realized. We experimentally implement such a readout scheme with a single-shot readout fidelity of 86%, which is mainly limited by the thermal population of our sample. We further demonstrate how to calibrate the flux crosstalk related to the coupler and its flux distortion that are ignored in the previous works. These calibration steps are imperative for realizing robust and accurate quantum operations in frequency-tunable superconducting circuits.
Video Sequence Segmentation Based on K-Means in Air-Gap Data Transmission for a Cluttered Environment

An air gap is a technique that increases the security of information systems. The use of unconventional communication channels allows for obtaining communication that is of interest to the attacker as well as to cybersecurity engineers. One of the very dangerous forms of attack is the use of computer screen brightness modulation, which is not visible to the user but can be observed from a distance by the attacker. Once infected, the computer can transmit data over long distances. Even in the absence of direct screen visibility, transmission can be realized by analyzing the modulated reflection of the monitor's afterglow. The paper presents a new method for the automatic segmentation of video sequences to retrieve the transmitted data that does not have the drawbacks of the heretofore known method of growth (filling) based on an analysis of adjacent pixels. A fast camera operating at 380 fps was used for image acquisition. The method uses the characteristics of the amplitude spectrum for individual pixels, which is specific to the light sources in the room, and clustering with the k-means algorithm to group pixels into larger areas. Then, using the averaging of values for individual areas, it is possible to recover the 2-PAM (pulse-amplitude modulation) signal even at a 1000 times greater level of interference in the area to the transmitted signal, as shown in the experiments. The method does not require high-quality lenses.
NARX Deep Convolutional Fuzzy System for Modelling Nonlinear Dynamic Processes

This paper presents a new approach for modelling nonlinear dynamic processes (NDP). It is based on a nonlinear autoregressive with exogenous (NARX) inputs model structure and a deep convolutional fuzzy system (DCFS). The DCFS is a hierarchical fuzzy structure, which can overcome the deficiency of general fuzzy systems when facing high dimensional data. For relieving the curse of dimensionality, as well as improving approximation performance of fuzzy models, we propose combining the NARX with the DCFS to provide a good approximation of the complex nonlinear dynamic behavior and a fast-training algorithm with ensured convergence. There are three NARX DCFS structures proposed, and the appropriate training algorithm is adapted. Evaluations were performed on a popular benchmark—Box and Jenkin’s gas furnace data set and the four nonlinear dynamic test systems. The experiments show that the proposed NARX DCFS method can be successfully used to identify nonlinear dynamic systems based on external dynamics structures and nonlinear static approximators.