Reviewerly/exHarmony huggingface dataset:
Pixel-Wise Attention Residual Network for Super-Resolution of Optical Remote Sensing Images

The deep-learning-based image super-resolution opens a new direction for the remote sensing field to reconstruct further information and details from captured images. However, most current SR works try to improve the performance by increasing the complexity of the model, which results in significant computational costs and memory consumption. In this paper, we propose a lightweight model named pixel-wise attention residual network for optical remote sensor images, which can effectively solve the super-resolution task of multi-satellite images. The proposed method consists of three modules: the feature extraction module, feature fusion module, and feature mapping module. First, the feature extraction module is responsible for extracting the deep features from the input spatial bands with different spatial resolutions. Second, the feature fusion module with the pixel-wise attention mechanism generates weight coefficients for each pixel on the feature map and fully fuses the deep feature information. Third, the feature mapping module is aimed to maintain the fidelity of the spectrum by adding the fused residual feature map directly to the up-sampled low-resolution images. Compared with existing deep-learning-based methods, the major advantage of our method is that for the first time, the pixel-wise attention mechanism is incorporated in the task of super-resolution fusion of remote sensing images, which effectively improved the performance of the fusion network. The accuracy assessment results show that our method achieved superior performance of the root mean square error, signal-to–reconstruction ratio error, universal image quality index, and peak signal noise ratio compared to competing approaches. The improvements in the signal-to-reconstruction ratio error and peak signal noise ratio are significant, with a respective increase of 0.15 and 0.629 dB for Sentinel-2 data, and 0.196 and 1 dB for Landsat data.
The smallest constant of physics

This study suggests that the smallest physical constant exists for the product of space interval, time interval, and energy. The integer times of which correspond to the product of the energy, space interval, and time interval of any particle. Combined with fractal geometry, many parameters that cannot be calculated using current physical theory can be calculated through our new method. As a new theoretical prediction, these new results will be tested by the experiment.
Fuzzy style flat-based clustering

The recently developed fuzzy style k-plane clustering (S-KPC) algorithm displays promising clustering quality by leveraging both similarities and distinguishable styles between samples on stylistic data. However, S-KPC becomes vulnerable to similar styles that are not easily distinguishable. In this study, a novel fuzzy style flat-based clustering (FSFC) algorithm is proposed to overcome this vulnerability. In FSFC, a style flat matrix (SFM) is designed to project samples onto appropriate flats while maintaining the styles of different clusters in a reasonable manner. Based on SFM, the core of FSFC is to learn the potentially intersecting manifold structures of clusters in the projected flat space to make samples with the same style close to the cluster center and simultaneously far away from the other cluster centers. Furthermore, the objective function of FSFC can provide scale flexibility for each flat in the projected flat space. In particular, the optimization problem of FSFC can be decomposed into a series of sub-problems about the flat parameters, which can be locally optimized using the concave-convex procedure (CCCP). Extensive experiments on both synthetic and real-world datasets demonstrate the competitive clustering performance of FSFC. Moreover, FSFC outperforms some state-of-the-art manifold clustering algorithms on six case studies about stylistic data.
TOPSIS-based comprehensive measure of variable importance in predictive modelling

The real-world complex systems of transportation and insurance constantly produce massive data, and the number of variables used to capture these systems can sometimes be overwhelming. Predictive modelling of such high-dimensional data often comes with the increased complexity of models, leading to low model interpretability and performance. Therefore, balancing the model’s interpretability and prediction accuracy is crucial. Keeping a balance between these two aspects significantly improves prediction reliability. This will ensure that complex systems’ overall control and management can be statistically optimal. However, measuring variable importance to reduce the dimensionality of given problems may be challenging due to different data sources, methods employed, or both. This paper proposes a novel approach to variable selection based on a comprehensive variable importance measure. The proposed method formulates variable selection as a multi-criteria decision analysis (MCDA) problem and uses TOPSIS to solve it. It offers a systematic approach to selecting variables to construct more interpretable predictive models. Our contribution is a thorough investigation using simulations with known model characteristics and a rigorous test of the method’s robustness by analysing different datasets with different noise scenarios. To demonstrate the effectiveness of our approach, we apply it to the variable selection problem for the national collision database. We identify the most significant variables that may significantly influence the prediction of fatal accident rates.
Prolog: Past, Present, and Future

We argue that various extensions proposed for Prolog—tabling, constraints, parallelism, coroutining, etc.—must be integrated seamlessly in a single system. We also discuss how goal-directed predicate answer set programming can be incorporated in Prolog, and how it facilitates development of advanced applications in AI and automated commonsense reasoning.
Ergo: A Quest for Declarativity in Logic Programming

$$\mathcal{E}$$ rgo is a higher-level logic programming system developed by Coherent Knowledge Systems as a successor to Flora-2 [39]. From the start, Flora-2 and $$\mathcal{E}$$ rgo were designed with the explicit requirement of declarativity and usability using novel technologies developed over the years by the authors and their colleagues. Although $$\mathcal{E}$$ rgo programs are compiled into XSB [29] and they adopt many Prolog features, $$\mathcal{E}$$ rgo is altogether a different language. For instance, $$\mathcal{E}$$ rgo ’s core execution strategy is not the SLDNF of Prolog, but is instead based on the Well-Founded Semantics [31] and its core syntax is a combination of HiLog [6] and F-logic [20]. $$\mathcal{E}$$ rgo supports object-oriented modeling, logical meta-reasoning, defeasible reasoning, fully semantic update operators as in Transaction Logic [2, 3], explanations, and a variety of other features not found in Prologs. In this paper, we describe some of these novel features of $$\mathcal{E}$$ rgo with special emphasis on their relation to Prolog and how they contribute to the high degree of declarativeness of $$\mathcal{E}$$ rgo.
Prolog as a Knowledge Representation Language the Nature and Importance of Prolog

In the Computer Science literature, Prolog is usually characterized as a language for programming computers. That makes sense. Its inventors described Prolog as a programming language [14]; and its very name is an abbreviation for PROgrammation en LOGique (PROgramming in LOGic). Unfortunately, characterizing Prolog as primarily a programming language may be doing it a disservice. As argued in [5, 6] and elsewhere, it is also an excellent knowledge representation language. In fact, an argument can be made that Prolog’s main value lies not so much in programming as in knowledge representation.
Analysis of ENF Signal Extraction From Videos Acquired by Rolling Shutters

Electric network frequency (ENF) analysis is a promising forensic technique for authenticating multimedia recordings and detecting tampering. The validity of the ENF analysis heavily relies on the capability of extracting high-quality ENF signals from multimedia recordings. This paper analyzes and compares two representative methods for extracting ENF signals from visual signals acquired by cameras using the rolling-shutter mechanism. The first method proposed in prior work, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">direct concatenation</i> , ignores the idle period of each frame. The second method proposed in this paper, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">periodic zeroing-out</i> , inserts zeros to missing sample points instead of ignoring the idle period. Our theoretical analyses of using multirate signal processing reveal and experiments confirm that while the first method can extract ENF signals without knowing the exact value of camera read-out time, there exists some mild distortion to extracted ENF signals. In contrast, the second method taking the read-out time as the additional input is capable of extracting distortion-free ENF signals, and its frequency component of the highest strength is always located at the nominal frequency. Additionally, we examine aliased DC and negative ENF components caused by the two methods and show that their impact on the accuracy of frequency estimation is minimum. This paper facilitates the fundamental understanding of extracting ENF signals from videos. The research findings imply that the periodic zeroing-out method offers more accurate frequency estimates, but the performance improvement is not significant.
LCCF-Net: Lightweight contextual and channel fusion network for medical image segmentation

Accurate and fast recognition of medical images is a key step for clinical diagnosis. With the rapid development of convolutional neural networks in image processing, the convolutional neural network (CNN) approaches based on the U-shape structure have been used in various medical image recognition projects. However, the context information extraction capability of U-shape structure is insufficient and boundaries of segmentation results are blurred. In this paper, we propose a lightweight contextual and channel fusion network (LCCF-Net) for medical segmentation, which fuses multi-scale context information, preserves channel information and minimizes the amount of computation in the network. LCCF-Net is mainly composed of codec block, Shifted Windows Self-attention Connection (SWSC) block, Xception Group (XG) block and Multiple Pooling (MP) block. We design to input the features of each level of U-net encoder into their own SWSC blocks for self-attention mechanism operation, and then fuse with decoding features to participate in feature reconstruction. The proposed Multiple Pooling (MP) block combines multi-scale context information in high-level features, which can fuse rich context information. In order to reduce the number of parameters in the network, we design XG block instead of conventional convolution. Comprehensive results show that the proposed method outperforms other state-of-the-art methods for kidney tumor recognition, retinal vessel detection and COVID 19 segmentation.
High-dimensional time series analysis and anomaly detection: A case study of vehicle behavior modeling and unhealthy state detection

With the growing complexity of modern vehicles in the digital era, ensuring the reliability of vehicle system becomes a challenge that needs a responsive framework to monitor and alert the unhealthy states. An unhealthy state refers to a timestamp in which vehicle operates off the expected range of performance. Being able to detect an unhealthy state early not only helps to identify imminent performance failures but also indicates the necessity of deploying preventive maintenance. It is almost impossible to be aware of an imminent failure without modeling the behavior of various subsystems of vehicle. This study proposes a behavior modeling and anomaly detection approach to accurately analyze the operation of various vehicle subsystems and trigger early alerts of unhealthy states. We propose a Multi-Layer Long Short Term Memory (LSTM) network that is integrated with an Autoencoder architecture (ML-LSTMAE) to monitor and predict the operation of different subsystems of vehicle. Modern vehicles involve various subsystems with respective sensors that record various operating signals. The proposed model leverages multivariate time series data sequences that are recorded by different sensors to train an encoding–decoding scheme. We use One-Class Support Vector Machine (OCSVM) to analyze the reconstruction errors and build a support boundary. Then, the trained support boundary evaluates the prediction errors in test data to distinguish the healthy and unhealthy states of vehicle. We validate the efficiency of the proposed behavior modeling and anomaly detection approaches via evaluating two case study problems. First, a case study of an operating vehicle data that consists of 15 months time series records of 101 data channels. Second, a NASA bearing case study data that includes 20,110,378 observational records of four bearings time series sequences. The results of both case studies confirm the high accuracy of the proposed ML-LSTMAE network for learning the latent behavior of different operating subsystems. Moreover, the proposed OCSVM algorithm classifies the healthy and unhealthy states in both case studies, precisely.
RoFin: 3D Hand Pose Reconstructing via 2D Rolling Fingertips

Smart homes, medical devices, and education systems, among other emerging cyber-physical systems, hold immense promise for sensing-based user interfaces, especially for using fingers and hand gestures as system input. However, vision approaches compatible with time-consuming image processing adopt low 60 Hz location sampling rate (frame rate) for real-time hand gesture recognition. Furthermore, they are not suitable for low-light environment and long detection range. In this paper, we propose RoFin, which first exploits 6 temporal-spatial 2D rolling fingertips for real-time 3D reconstructing of 20-joint hand pose. RoFin designs active optical labeling for finger identification and enhances inside-frame 3D location tracking via high rolling shutter rate (5--8 KHz). These features enable great potentials for enhanced multi-user HCI, virtual writing for Parkinson suffers, etc. We implement RoFin prototypes with wearable gloves attached with low-power single-colored LED nodes and commercial cameras. The experiment results show that (1) In flexible sensing distances up to 2.5 m, RoFin achieves an average labeling parsing accuracy of 85%. (2) In comparison to vision-based techniques, RoFin improves the tracking grain with 4× more sampled points each frame. (3) RoFin reconstructs a hand pose in real time with 16 mm mean deviation error compared with Leap Motion under flexible distance.
Academic library marketing in the post-COVID world

The COVID pandemic forced academic libraries to quickly and dramatically adjust their operations. This article explores how marketing in academic libraries was represented in the research and practitioner literature during the pandemic through an examination of articles published between March 2020 and March 2023 as compared to LIS marketing articles published in the three previous years (between March 2017 and February 2020). Marketing was selected as the lens through which to examine library and information science (LIS) articles because marketing strategies and techniques offer powerful insights into how libraries perceive themselves and the communities they serve. The 2017–2020A set included 165 articles, while the 2020B–2023 set contained 93 articles. These articles were inductively coded to identify marketing-related concepts and themes. The most common marketing themes from the per-pandemic article set were marketing strategies and planning, marketing methods, social media, outreach, and engagement. The most common marketing themes from the pandemic article set were social media, marketing strategies and planning, library instruction and orientations, marketing methods, and perceptions of libraries. The categories that experienced the largest levels of change between the two periods were engagement (a reduction of 12.67 % during the pandemic), marketing methods (a reduction of 11.75 % during the pandemic), marketing strategies and planning (a reduction of 10.75 % during the pandemic), outreach (a reduction of 9.11 % during the pandemic), and library instruction and orientations (an increase of 8.44 % during the pandemic). Recommendations for library marketing practice are provided based on this literature analysis including the use of multiple physical and digital communication channels when developing marketing strategies; the importance of continuing to market core operational services, especially during challenging times; the need for libraries to regularly review their engagement methods as the needs and behaviours of users shift; and that there are many ways in which the voice of the user and the effectiveness of library marketing activities may be captured.
White blood cell automatic classification using deep learning and optimized quaternion hybrid moments

The structural characteristics of white blood cells (WBCs) can provide important information about human health status. For this reason, over the past four decades, researchers have sought ways to automate the classification and morphological analysis of white blood cells. In the present study, we propose a new method to classify white blood cells into four types: neutrophils, lymphocytes, monocytes, and eosinophils by combining the hybrid discrete moment quaternion approach with deep learning. The proposed classification method is generally divided into two main phases: the first one is called the preprocessing phase, which is devoted to the computation of the image moments to be classified using a new quaternion Meixner-Charlier hybrid moment that is characterized by the following parameters α,β,andφ. In addition, the grey wolf optimization algorithm is used to guarantee high classification accuracy by optimizing the local parameters of the new quaternion Meixner-Charlier hybrid moments. The classification phase, which is the second phase of our proposed convolutional neural network model, is dedicated to introducing image moments. We present various graphical measurements, including receiver operating characteristics, precision-recall curves, and a confusion matrix. To demonstrate the effectiveness of our classification method, we also employ numerical measures such as the F1 score, loss, and accuracy. Our results indicate that the cell types of neutrophils were determined with a 98.53% accuracy rate, lymphocytes with a 98.16% accuracy rate, monocytes with a 97.43% accuracy rate, and eosinophils with a 97.43% accuracy rate.
TinyPuff: Automated design of Tiny Smoking Puff Classifiers for Body Worn Devices

Smoking is a significant cause of death and deterioration of health worldwide, affecting active and passive smokers. Cessation of smoking contributes to an essential health and wellness application owing to the broad range of health problems such as cancer, hypertension, and several cardiopulmonary diseases. Personalized smoking-cessation applications can be very effective in helping users to stop smoking if there are detections and interventions done at the right time. This requires real-time detection of smoking puffs. Such applications are made feasible by day-long monitoring and smoking puff detection from unobtrusive devices such as wearables. This paper proposes a deep inference technique for the real-time detection of smoking puffs on a wearable device. We show that a simple, sequential Convolutional Neural Network (CNN) using only 6-axis Inertial signals can be utilized in place of complex and resource-consuming Deep Learning models. The accuracy achieved is comparable to State-of-the-Art techniques with an F1 score of 0.81, although the model size is tiny - 114 kB. Such small models can be deployed on the lowest configuration hardware platforms, achieving accurate but high-speed, low-power inference on conventional smartwatches. We ensure that the auto-designed models are directly compatible with resource-constrained platforms such as TensorFlow Lite and TensorFlow Lite for Microcontrollers (TFLM) without requiring further use of model reduction and optimization techniques. Our proposed approach will allow affordable wearable device manufacturers to run smoking detection on their devices, as it is tiny enough to fit TinyML platforms and is only dependent on IMU sensors that are universally available.
BystandAR: Protecting Bystander Visual Data in Augmented Reality Systems

Augmented Reality (AR) devices are set apart from other mobile devices by the immersive experience they offer. While the powerful suite of sensors on modern AR devices is necessary for enabling such an immersive experience, they can create unease in bystanders (i.e., those surrounding the device during its use) due to potential bystander data leaks, which is called the bystander privacy problem. In this paper, we propose BystandAR, the first practical system that can effectively protect bystander visual (camera and depth) data in real-time with only on-device processing. BystandAR builds on a key insight that the device user's eye gaze and voice are highly effective indicators for subject/bystander detection in interpersonal interaction, and leverages novel AR capabilities such as eye gaze tracking, wearer-focused microphone, and spatial awareness to achieve a usable frame rate without offloading sensitive information. Through a 16-participant user study,we show that BystandAR correctly identifies and protects 98.14% of bystanders while allowing access to 96.27% of subjects. We accomplish this with average frame rates of 52.6 frames per second without the need to offload unprotected bystander data to another device.
mm3DFace: Nonintrusive 3D Facial Reconstruction Leveraging mmWave Signals

Recent years have witnessed the emerging market of 3D facial reconstruction that supports numerous face-driven scenarios including modeling in virtual reality (VR), human-computer interaction, and affective computing applications. Current mainstream approaches rely on vision for 3D facial reconstruction, which may encounter privacy concerns and suffer from obstruction scenes and bad lighting conditions. In this paper, we present a nonintrusive 3D facial reconstruction system, mm3DFace, which leverages a millimeter wave (mmWave) radar to reconstruct 3D human faces that continuously express facial expressions in a privacy-preserving and passive manner. Based on the pre-processed mmWave signals, mm3DFace first extracts facial geometric features that capture subtle changes in facial expressions through a ConvNeXt model with triple loss embedding. Then, mm3DFace derives distance and orientation-robust facial shapes with 68 facial landmarks using region-divided affine transformation. mm3DFace next reconstructs facial expressions through a designed regional amplification method and finally generates 3D facial avatars that continuously express facial expressions. Extensive experiments involving 15 participants in real-world environments show that mm3DFace can accurately track 68 facial landmarks with 3.94% normalized mean error, 2.30mm mean absolute error, and 4.10mm 3D-mean absolute error, which is effective and practical in real-world 3D facial reconstruction.