Reviewerly/exHarmony huggingface dataset:
Active Learning Strategies for Weakly-Supervised Object Detection

Object detectors trained with weak annotations are affordable alternatives to fully-supervised counterparts. However, there is still a significant performance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically selected from the training set using ``box-in-box'' (BiB), a novel active learning strategy designed specifically to address the well-documented failure modes of weakly-supervised detectors. Experiments on the VOC07 and COCO benchmarks show that BiB outperforms other active learning techniques and significantly improves the base weakly-supervised detector's performance with only a few fully-annotated images per class. BiB reaches 97% of the performance of fully-supervised Fast RCNN with only 10% of fully-annotated images on VOC07. On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency. Our code is publicly available at https://github.com/huyvvo/BiB.
Impact of the communicated information content on employee resistance to change

Communicating information to employees is identified as one of the most important and effective measures.However, there is a lack of research publications that specifically examine what specific information is relevant to employees, what specific content of information can encourage employees to resist change or reduce their negative reactions towards change.In this context, the problem of the study is what specific content of information can reduce or increase employee resistance to change.The subject is the impact of the content of information on employee resistance to change.The aim is to examine the impact of the content of information on employee resistance to change.Objectives: 1) To define the concept of employee resistance to change; 2. To analyse the impact of communicating information to employees on employee resistance to changes; 3) To determine the impact of the content of specific information on employee resistance to change in specific organizations.An empirical survey-an anonymous paper questionnaire of employees of public passenger transportation companies -was carried out.The results may be instrumental for devising efficient economic policies.
PETR: Rethinking the Capability of Transformer-Based Language Model in Scene Text Recognition

The exploration of linguistic information promotes the development of scene text recognition task. Benefiting from the significance in parallel reasoning and global relationship capture, transformer-based language model (TLM) has achieved dominant performance recently. As a decoupled structure from the recognition process, we argue that TLM's capability is limited by the input low-quality visual prediction. To be specific: 1) The visual prediction with low character-wise accuracy increases the correction burden of TLM. 2) The inconsistent word length between visual prediction and original image provides a wrong language modeling guidance in TLM. In this paper, we propose a Progressive scEne Text Recognizer (PETR) to improve the capability of transformer-based language model by handling above two problems. Firstly, a Destruction Learning Module (DLM) is proposed to consider the linguistic information in the visual context. DLM introduces the recognition of destructed images with disordered patches in the training stage. Through guiding the vision model to restore patch orders and make word-level prediction on the destructed images, visual prediction with high character-wise accuracy is obtained by exploring inner relationship between the local visual patches. Secondly, a new Language Rectification Module (LRM) is proposed to optimize the word length for language guidance rectification. Through progressively implementing LRM in different language modeling steps, a novel progressive rectification network is constructed to handle some extremely challenging cases (e.g. distortion, occlusion, etc.). By utilizing DLM and LRM, PETR enhances the capability of transformer-based language model from a more general aspect, that is, focusing on the reduction of correction burden and rectification of language modeling guidance. Compared with parallel transformer-based methods, PETR obtains 1.0% and 0.8% improvement on regular and irregular datasets respectively while introducing only 1.7M additional parameters. The extensive experiments on both English and Chinese benchmarks demonstrate that PETR achieves the state-of-the-art results.
Magnetic-Tunnel-Junction-Based True Random-Number Generator with Enhanced Generation Rate

True random-number generators (TRNGs) are crucial in modern information technology, especially in cryptographic systems. TRNGs can be realized via stochastic physical processes, such as the thermally activated magnetization switching in a magnetic tunnel junction (MTJ). The fluctuations in MTJ-based TRNGs are still too slow for many practical purposes, though. This study proposes that the simple application of a small magnetic field can increase the generation rate of a MTJ-TRNG by several orders of magnitude, up to gigahertz frequencies. This approach could turn commercialized MTJ-MRAM devices into fast TRNGs with minimal modification.
Factors Affecting Students’ Continued Usage Intention of E-Learning During COVID-19 Pandemic: Extending Delone &amp; Mclean IS Success Model

Due to COVID-19 pandemic, electronic learning (e-learning) has been considered as a main tool for delivering education all over the world. The study aims at identifying the factors affecting students’ continued usage intention of e-learning during COVID-19 pandemic and the mediating role of student satisfaction. To achieve that, a research model has been developed through combining Delone &amp; McLean model, diffusion of innovation theory, and COVID-19 perceived risk and self-efficacy as contextual factors. The data was collected through surveying students around the country, (542) questionnaires were collected, (525) responses are accepted for data analysis from the total questionnaires that were distributed electronically. Smart PLS 2 was used to assess the proposed model. The results showed that compatibility, Relative advantage, COVID-19 Perceived Risk, and satisfaction have a direct significant influence on students’ continued usage intention. However, all the factors have been found to have an indirect significant impact on students' continued usage intention when the factor “satisfaction” mediated the relationships.
The UAV Path Coverage Algorithm Based on the Greedy Strategy and Ant Colony Optimization

Today, the development of unmanned aerial vehicles (UAVs) has attracted significant attention in both civil and military fields due to their flight flexibility in complex and dangerous environments. However, due to energy constraints, UAVs can only finish a few tasks in a limited time. The problem of finding the best flight path while balancing the task completion time and the coverage rate needs to be resolved urgently. Therefore, this paper proposes a UAV path coverage algorithm base on the greedy strategy and ant colony optimization. Firstly, this paper introduces a secondary advantage judgment and optimizes it using an ant colony optimization algorithm to reach the goal of minimum time and maximum coverage. Simulations are performed for different numbers of mission points and UAVs, respectively. The results illustrate that the proposed algorithm achieves a 2.8% reduction in task completion time while achieving a 4.4% improvement in coverage rate compared to several previous works.
VNIR estimation of heavy metals concentrations in suburban soil with multi-scale geographically weighted regression

• Spatial relationships between soil chromophores and heavy metals were explored. • Spatial correlations with TFe 2 O 3 were main reason that Cr and Ni can be estimated. • MGWR outperformed traditional regression methods for VNIR estimations of Cr and Ni. • Varied scales and bandwidths contribute to improving model performance. Visible and near-infrared reflectance (VNIR) spectroscopy serves as a promising alternative to detect heavy metal contamination in suburban soils. However, the traditional regression method assumes a global rather than a non-stationary relationship between soil spectra and heavy metals. This study explored the spatially varying relationships between soil chromophores (e.g., SOM and TFe 2 O 3 ) and heavy metals, and further investigated its implication for heavy metal estimation with VNIR. Specifically, we (i) explore the spatial patterns and covariation of soil chromophore and heavy metals, (ii) verify the feasibility of using multi-scale geographically weighted regression (MGWR) in the VNIR estimation of heavy metals, and (iii) investigate the potential mechanism underlying the models. Specifically, continuum removal and derivation were used to preprocess the reflectance spectra, and then a random frog algorithm was performed to select candidate feature wavelengths, followed by the removal of wavelengths with a correlation higher than 0.80 to avoid collinearity. Standard errors were calculated to further eliminate the variables with a high variance of coefficients. The remaining wavelengths were retained as inputs of the estimation models. The results demonstrated that MGWR outperformed its competitors, resulting in an increase in the coefficient of determination and a decrease in the residual sum of squares. Our study revealed the spatial heterogeneities of the relationships between heavy metals concentrations and soil spectra, and found that the spatial correlation between heavy metals and TFe 2 O 3 (p < 0.01) was the main reason that Cr and Ni can be estimated by using VNIR. The MGWR method not only improves the capability of VNIR in the estimation of heavy metal concentrations, but promotes our understanding of the estimation mechanism.
Drone Navigation Using Region and Edge Exploitation-Based Deep CNN

Drones are unmanned aerial vehicles (UAV) utilized for a broad range of functions, including delivery, aerial surveillance, traffic monitoring, architecture monitoring, and even War-field. Drones confront significant obstacles while navigating independently in complex and highly dynamic environments. Moreover, the targeted objects within a dynamic environment have irregular morphology, occlusion, and minor contrast variation with the background. In this regard, a novel deep Convolutional Neural Network(CNN) based data-driven strategy is proposed for drone navigation in the complex and dynamic environment. The proposed Drone Split-Transform-and-Merge Region-and-Edge (Drone-STM-RENet) CNN is comprised of convolutional blocks where each block methodically implements region and edge operations to preserve a diverse set of targeted properties at multi-levels, especially in the congested environment. In each block, the systematic implementation of the average and max-pooling operations can deal with the region homogeneity and edge properties. Additionally, these convolutional blocks are merged at a multi-level to learn texture variation that efficiently discriminates the target from the background and helps obstacle avoidance. Finally, the Drone-STM-RENet generates steering angle and collision probability for each input image to control the drone moving while avoiding hindrances and allowing the UAV to spot risky situations and respond quickly, respectively. The proposed Drone-STM-RENet has been validated on two urban cars and bicycles datasets: udacity and collision-sequence, and achieved considerable performance in terms of explained variance (0.99), recall (95.47%), accuracy (96.26%), and F-score (91.95%). The promising performance of Drone-STM-RENet on urban road datasets suggests that the proposed model is generalizable and can be deployed for real-time autonomous drones navigation and real-world flights.
No evidence that mandatory open data policies increase error correction

Using a database of open data policies for 199 journals in ecology and evolution, we found no detectable link between data sharing requirements and article retractions or corrections. Despite the potential for open data to facilitate error detection, poorly archived datasets, the absence of open code and the stigma associated with correcting or retracting articles probably stymie error correction. Requiring code alongside data and destigmatizing error correction among authors and journal editors could increase the effectiveness of open data policies at helping science self-correct.
“I am going this way”: Gazing Eyes on Self-Driving Car Show Multiple Driving Directions

Modern cars express three moving directions (left, right, straight) using turn signals (i.e., blinkers), which is insufficient when multiple paths are toward the same side. As such, drivers give additional hints (e.g., gesture, eye contact) in the conventional car-to-pedestrian interaction. As more self-driving cars without drivers join the public roads, we need additional communication channels. In this work, we discussed the problem of self-driving cars expressing their fine-grained moving direction to pedestrians in addition to blinkers. We built anthropomorphic robotic eyes and mounted them on a real car. We applied the eye gazing technique with the common knowledge: I gaze at the direction I am heading to. We found that the eyes can convey fine-grained directions from our formal VR-based user study, where participants could distinguish five directions with a lower error rate and less time compared to the conventional turn signals.
Delving Globally into Texture and Structure for Image Inpainting

Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To this end, deep models exhibit powerful superiority to capture them, yet constrained on the local spatial regions. In this paper, we delve globally into texture and structure information to well capture the semantics for image inpainting. As opposed to the existing arts trapped on the independent local patches, the texture information of each patch is reconstructed from all other patches across the whole image, to match the coarsely filled information, specially the structure information over the masked regions. Unlike the current decoder-only transformer within the pixel level for image inpainting, our model adopts the transformer pipeline paired with both encoder and decoder. On one hand, the encoder captures the texture semantic correlations of all patches across image via self-attention module. On the other hand, an adaptive patch vocabulary is dynamically established in the decoder for the filled patches over the masked regions. Building on this, a structure-texture matching attention module anchored on the known regions comes up to marry the best of these two worlds for progressive inpainting via a probabilistic diffusion process. Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting. The extensive experiments over the benchmarks validate its superiority. Our code is available at https://github.com/htyjers/DGTS-Inpainting.
On-chip Optical Phase Monitoring in Multi-Transverse-Mode Integrated Silicon-based Optical Processors

We design a Multi-Transverse-Mode Optical Processor (MTMOP) on 220 nm thick Silicon Photonics exploiting the first two quasi-transverse electric modes (TE0 and TE1). The objective is to measure the optical phase, required for programming the optical processor, without use of conventional optical phase detection techniques ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , coherent detection). In the proposed design, we use a novel but simple building block that converts the optical phase to optical power. Mode TE0 carries the main optical signal while mode TE1 is for programming purposes. The MTMOP operation relies on the fact that the group velocity of TE0 and TE1 propagating through a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mode-sensitive</i> phase shifter are different. The mode-sensitive phase shifter is a waveguide with 0.96 µm width underneath a titanium-tungsten heater. Increasing the width of the phase shifter to 4 µm, the propagation becomes mode-insensitive. We use an unbalanced Mach-Zehnder interferometer (MZI) consists of a mode-sensitive and mode-insensitive phase shifters in the two arms. We set the bias of the phase shifters so that TE0 propagating in the two arms constructively interfere while this will not be the case for TE1. Hence, we detect the phase shift applied to TE0 by measuring the variation in the optical power of TE1. To the best of our knowledge, this design is the first attempt towards realizing a programmable optical processor with fully integrated programming unit exploiting multimode silicon photonics.
Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

In-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.A retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.The prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.
When move acceptance selection hyper-heuristics outperform Metropolis and elitist evolutionary algorithms and when not

Selection hyper-heuristics (HHs) are automated algorithm selection methodologies that choose between different heuristics during the optimisation process. Recently, selection HHs choosing between a collection of elitist randomised local search heuristics with different neighbourhood sizes have been shown to optimise standard unimodal benchmark functions from evolutionary computation in the optimal expected runtime achievable with the available low-level heuristics. In this paper, we extend our understanding of the performance of HHs to the domain of multimodal optimisation by considering a Move Acceptance HH (MAHH) from the literature that can switch between elitist and non-elitist heuristics during the run. In essence, MAHH is a non-elitist search heuristic that differs from other search heuristics in the source of non-elitism. We first identify the range of parameters that allow MAHH to hillclimb efficiently and prove that it can optimise the standard hillclimbing benchmark function OneMax in the best expected asymptotic time achievable by unbiased mutation-based randomised search heuristics. Afterwards, we use standard multimodal benchmark functions to highlight function characteristics where MAHH outperforms elitist evolutionary algorithms and the well-known Metropolis non-elitist algorithm by quickly escaping local optima, and ones where it does not. Since MAHH is essentially a non-elitist random local search heuristic, the paper is of independent interest to researchers in the fields of artificial intelligence and randomised search heuristics.
Bangla Font Recognition using Transfer Learning Method

Font detection and similar font suggestions are essential in computer vision, document analysis, pattern recognition, web development, and core work for graphics designers and UX-UI engineers. Even though Bangla is one of the most communicated languages globally and the growing popularity of Bangla in online publications and social media platforms, there has been a noticeable demand for Bangla fonts. However, there is no appreciable work for font detection in Bangla, unlike other high resource languages like English, Chinese, Hindi, Arabic, Spanish, and German. This work represents a model to recognize Bangla fonts from images using the transfer learning method. In Image Processing and Classification, resources are insufficient for such work in Bangla, so it is required to build as much raw data as possible to train the model. Therefore, as part of the work, 6500 raw images of five different fonts are used, and with augmentation 26000 image data are created to train and 2600 images to validate the model. Three transfer learning models, which are VGG-16, VGG-19, and Xception are applied. Among them, VGG-16 archives the highest accuracy of 96.23%. This paper is the first publicly available work on Bangla font recognition using the Transfer Learning approach to the best of our knowledge.
Load image inpainting: An improved U-Net based load missing data recovery method

Dealing with large percentage data missing is always a challenge for load data recovery. This paper, drawing on ideas from image inpainting, formulates load missing data recovery problem as load image inpainting and the improved U-Net is proposed to restore the load image. First, the 1-dimensional load data is constructed into 2-dimensional load image. Additionally, missing data presents as irregular black holes on the load image. Then, the improved U-Net which introduces residual network (ResNet) and convolutional block attention module (CBAM) is proposed to specifically restore the incomplete load image. Meanwhile, mean absolute error (MAE) and structural similarity (SSIM) are utilized to evaluate data recovery accuracy and load pattern recovery similarity respectively. Load missing data recovery results based on the actual industrial load dataset are presented to verify the effectiveness of the proposed method.