Reviewerly/exHarmony huggingface dataset:
Writer independent handwritten signature verification on multi-scripted signatures using hybrid CNN-BiLSTM: A novel approach

Authenticating important documents by identifying individuals using handwritten signatures make signature verification a critical task. Interpersonal similarity and intrapersonal variation of individuals along with high skilled imitation of signature structure make automatic signature verification a challenging task. In such scenarios, a signature verification system should detect the small differences between genuine and forged signatures with high efficacy. This paper proposed a novel approach towards offline signature verification where a hybrid deep learning network, consisting of a Convolutional Neural Network and a Bidirectional Long Short Term Memory network is used. Signature written with freehand and an imitation of it are almost identical structurally. Deep Neural Network is used to recognize skilled forgery from genuine signatures because of its capability to learn critical details and subtle patterns from the image pixels. A Convolutional Neural Network is trained, and the trained network is then used to extract diverse features of the signature images. The generated feature vectors are then used for classification using Bidirectional Long Short Term Memory. The hybrid deep learning network classifies the input signature as skilled forgery or genuine with high accuracy. For this work, state-of-the-art datasets, such as, GPDS-300, GPDS-Bengali, GPDS-Devanagari, CEDAR, BHSig260-Bengali, BHSig260-Hindi, and a local dataset, Meitei Mayek signature are used. In order to verify the robustness of the system, different multi-scripted offline signatures belonging to multi-lingual Indian society, are used for evaluation. The experimental results determined from multi-scripted signatures, exhibit that the proposed system is found comparable to many state-of-the-art systems and in some specific cases, it out performs some of the existing systems.
Stain-Independent Deep Learning–Based Analysis of Digital Kidney Histopathology

Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain augmentation is proposed to develop stain-independent CNNs requiring only one annotated stain. In this benchmark study on stain independence in digital pathology, this approach is comprehensively compared with state-of-the-art techniques including image registration and stain translation, and several modifications thereof. A previously developed CNN for segmentation of periodic acid-Schiff-stained kidney histology was used and applied to various immunohistochemical stainings. Stain augmentation showed very high performance in all evaluated stains and outperformed all other techniques in all structures and stains. Without the need for additional annotations, it enabled segmentation on immunohistochemical stainings with performance nearly comparable to that of the annotated periodic acid-Schiff stain and could further uphold performance on several held-out stains not seen during training. Herein, examples of how this framework can be applied for compartment-specific quantification of immunohistochemical stains for inflammation and fibrosis in animal models and patient biopsy specimens are presented. The results show that stain augmentation is a highly effective approach to enable stain-independent applications of deep-learning segmentation algorithms. This opens new possibilities for broad implementation in digital pathology.
Digital Competences of Pre-service Teachers in Italy and Poland

Abstract The aim of this research was to compare the level of digital competence of future pedagogical staff (students of pedagogical faculties) in Italy and Poland. The research was conducted using original measurement tools and knowledge tests. The triangulation of techniques and research tools made possible the determination of the level of knowledge of the positive and negative features of the development of the information society, as well as the proficiency and frequency of use of the most popular websites and software. The research was conducted in the first half of 2022 using stratified sampling in both countries (N = 1209, IT = 604, PL = 605). Based on the data collected, it was noted that: (1) Pre-service teachers most often use software such as word processors and presentation creation tools; (2) This group very rarely uses software to create web pages, create visual material, or edit video; (3) The least problematic software that students use are word processors and multimedia presentations; (4) Among the typical ICT mediated activities that cause problems are: searching for and installing freeware (PL), installing and configuring parental control software (PL, IT), creating websites (PL, IT), searching for freely licensed images, and identifying plagiarism (PL); (5) Polish students have more theoretical knowledge about e-risks and the possibilities of the digital world than their Italian counterparts; (6) In most domains, the Italian future teachers rate their competences higher; (7) 53.81% of the respondents in IT and 38.68% in PL received lower results in competence tests, and handling ICT in selected areas causes problems for these students; (8) Both in PL and IT the frequency of ICT use and its seamless integration are predictors for assessing the effectiveness of ICT use in education.
An approach for day-ahead interval forecasting of photovoltaic power: A novel DCGAN and LSTM based quantile regression modeling method

In order to effectively quantify the uncertainty of PV power and improve the forecasting accuracy, a day-ahead interval forecasting method of PV power based on multi-correlation parameter scenarios generation is proposed. The historical PV power data is divided into a limited set of scenarios representing different output and fluctuation characteristics through the K-means clustering algorithm; Combined with the strong correlation feature parameters determined by the correlation coefficient, the characteristic curves of PV power and multi-correlation parameter in different scenarios are generated through the deep convolutional generative adversarial networks (DCGAN) model; The average value of each characteristic curve and other explanatory variables are input together into a quantile regression long short-term memory (QRLSTM) model to achieve day-ahead PV power forecasting, and obtain the forecasting interval under different confidence levels. The results of the experiment of a PV power station in Yangzhou, Jiangsu Province, China show that the forecasting performance of the proposed method under different evaluation indicators is 17.9755% and 18.1571% higher than that of the Gaussian process regression (GPR) model and the single QRLSTM model, respectively, which has obvious advantages.
Large-Scale Unsupervised Semantic Segmentation

Empowered by large datasets, e.g., ImageNet and MS COCO, unsupervised learning on large-scale data has enabled significant advances for classification tasks. However, whether the large-scale unsupervised semantic segmentation can be achieved remains unknown. There are two major challenges: i) we need a large-scale benchmark for assessing algorithms; ii) we need to develop methods to simultaneously learn category and shape representation in an unsupervised manner. In this work, we propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to help the research progress. Building on the ImageNet dataset, we propose the ImageNet-S dataset with 1.2 million training images and 50k high-quality semantic segmentation annotations for evaluation. Our benchmark has a high data diversity and a clear task objective. We also present a simple yet effective method that works surprisingly well for LUSS. In addition, we benchmark related un/weakly/fully supervised methods accordingly, identifying the challenges and possible directions of LUSS. The benchmark and source code is publicly available at https://github.com/LUSSeg.
Discriminative and Geometry-Preserving Adaptive Graph Embedding for dimensionality reduction

Learning graph embeddings for high-dimensional data is an important technology for dimensionality reduction. The learning process is expected to preserve the discriminative and geometric information of high-dimensional data in a new low-dimensional subspace via either manual or automatic graph construction. Although both manual and automatic graph constructions can capture the geometry and discrimination of data to a certain degree, they working alone cannot fully explore the underlying data structure. To learn and preserve more discriminative and geometric information of the high-dimensional data in the low-dimensional subspace as much as possible, we develop a novel Discriminative and Geometry-Preserving Adaptive Graph Embedding (DGPAGE). It systematically integrates manual and adaptive graph constructions in one unified graph embedding framework, which is able to effectively inject the essential information of data involved in predefined graphs into the learning of an adaptive graph, in order to achieve both adaptability and specificity of data. Learning the adaptive graph jointly with the optimized projections, DGPAGE can generate an embedded subspace that has better pattern discrimination for image classification. Results derived from extensive experiments on image data sets have shown that DGPAGE outperforms the state-of-the-art graph-based dimensionality reduction methods. The ablation studies show that it is beneficial to have an integrated framework, like DGPAGE, that brings together the advantages of manual/adaptive graph construction.
Multitasking optimization via an adaptive solver multitasking evolutionary framework

Multitasking optimization (MTO) aims to solve multiple tasks simultaneously in a single run. Many multitasking evolutionary algorithms (MTEAs) have been developed in recent years for solving MTO problems. Existing MTEAs typically use a single solver to handle multiple optimization tasks. However, different tasks have distinct characteristics, such as convex, nonconvex, and multimodal. If one could automatically find a best-fitting solver for each task, it would be more efficient to solve different tasks. To this end, we propose a multitasking evolutionary framework based on adaptive solver selection, namely MTEA-SaO, where a suitable knowledge transfer strategy is embedded. The proposed method can be featured as 1) It explicitly assigns several solver subpopulations to each task and adaptively picks a best-fitting solver for each task, and 2) It enables knowledge transfer using the implicit similarities between tasks. The experimental results have demonstrated the effectiveness of solver adaptation and knowledge transfer strategies and the overall superior performance of MTEA-SaO.
DRL-S: Toward safe real-world learning of dynamic thermal management in data center

Deep Reinforcement Learning has been researched for Dynamic Thermal Management in Data Centers. An objective of Dynamic Thermal Management is to minimize power consumption while satisfying a set of hard and soft constraints. However, there is a lack of research on the safety issues of applying Deep Reinforcement Learning in Data Centers during real-world learning, limiting its deployment. To this end, this paper proposes a new method named DRL-S, which can solve both hard and soft constraints simultaneously during the learning stage. In addition to optimizing the main objective, Lagrangian-based Constrained Deep Reinforcement Learning and Reward Shaping enforce policies to satisfy soft constraints through extensive online learning. However, the policy typically fails to satisfy the hard constraints due to the random sampling of actions to encourage exploration and the imperfect policy at the initial online learning stage. To ensure hard constraints are satisfied, we further propose to utilize parameterized Shielding, integrating the approximation of the system dynamics and the projection of the action space to predict the safety of candidate actions and provide backup actions when necessary. Results show that the Lagrangian-based method and Reward Shaping can gradually learn policies to reduce soft constraint violations. The former can better balance the relationship between the main objective and violations by updating Lagrangian multipliers. DRL-S can also effectively avoid extreme temperatures without affecting the normal learning process of vanilla algorithms. The asymptotic power consumption is more than 12% lower than the baseline controller.
The rise of chatbots: The effect of using chatbot agents on consumers' responses to request rejection

Abstract This research investigates consumers’ perceptions and evaluations of robot service agents compared with human service agents when service requests are rejected. Six studies were conducted. The results show that when consumers receive a rejection of their service request, they evaluate the service less negatively if the service is handled by a chatbot agent versus a human agent. The reason is that consumers have lower expectations that robots will be able to provide flexible services to them. Consequently, their dissatisfaction with the request rejection is lower when the service is handled by robots. However, the aforementioned effect is not observed (1) when consumers have not experienced the service yet, (2) when their service request has been accepted, or (3) when the service agent conveys emotions to apologize for request rejection.
3D Mineral Prospectivity Mapping of Zaozigou Gold Deposit, West Qinling, China: Deep Learning-Based Mineral Prediction

This paper focuses on the scientific problem of quantitative mineralization prediction at large depth in the Zaozigou gold deposit, west Qinling, China. Five geological and geochemical indicators are used to establish geological and geochemical quantitative prediction model. Machine learning and Deep learning algorithms are employed for 3D Mineral Prospectivity Mapping (MPM). Especially, the Student Teacher Ore-induced Anomaly Detection (STOAD) model is proposed based on the knowledge distillation (KD) idea combined with Deep Auto-encoder (DAE) network model. Compared to DAE, STOAD uses three outputs for anomaly detection and can make full use of information from multiple levels of data for greater overall robustness. The results show that the quantitative mineral resources prediction by applying the STOAD model has a good performance, where the value of Area Under Curve (AUC) is 0.97. Finally, three main mineral exploration targets are delineated for further investigation.
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design

Attention-based neural networks have become pervasive in many AI tasks. Despite their excellent algorithmic performance, the use of the attention mechanism and feedforward network (FFN) demands excessive computational and memory resources, which often compromises their hardware performance. Although various sparse variants have been introduced, most approaches only focus on mitigating the quadratic scaling of attention on the algorithm level, without explicitly considering the efficiency of mapping their methods on real hardware designs. Furthermore, most efforts only focus on either the attention mechanism or the FFNs but without jointly optimizing both parts, causing most of the current designs to lack scalability when dealing with different input lengths. This paper systematically considers the sparsity patterns in different variants from a hardware perspective. On the algorithmic level, we propose FABNet, a hardware-friendly variant that adopts a unified butterfly sparsity pattern to approximate both the attention mechanism and the FFNs. On the hardware level, a novel adaptable butterfly accelerator is proposed that can be configured at runtime via dedicated hardware control to accelerate different butterfly layers using a single unified hardware engine. On the Long-Range-Arena dataset, FABNet achieves the same accuracy as the vanilla Transformer while reducing the amount of computation by 10$\sim66\times$ and the number of parameters 2$\sim22\times$. By jointly optimizing the algorithm and hardware, our FPGA-based butterfly accelerator achieves 14.2$\sim23.2\times$ speedup over state-of-the-art accelerators normalized to the same computational budget. Compared with optimized CPU and GPU designs on Raspberry Pi 4 and Jetson Nano, our system is up to $273.8\times$ and $15.1\times$ faster under the same power budget
The Effect of Apparent and Intellectual Sustainability Independence on the Credibility Gap of the Accounting Information

This study aims to recognize the sustainability independence of the Jordanian Association of Certified Public Accountants (JACPA/JCPA) and its impact on the credibility gap of the accounting information of companies operating in Jordan. This study demonstrates the effects of the apparent and intellectual sustainability independence on the credibility gap of accounting information. A total of 93 online questionnaires were analyzed using multiple regressions. The results revealed an impact of the apparent independence of the JCPA on the quality of the information credibility gap related to service fees, and no statistically significant impact for both consulting and accounting service fees was found. This study also concludes research regarding the impact of intellectual independence of the JCPA on the information credibility gap regarding the code of professional ethics and the commitment of auditing offices to their customers.
Deep MinCut: Learning Node Embeddings by Detecting Communities

• Our node embeddings are both interpretable and competent for classification tasks. • Our graph representation learning process is scalable. • Our interpretable node embeddings outperform baselines. • Our technique is robust to different experimental settings. • Our embedding reveals graph’s community structure, especially a hierarchy one. We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph-structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide insights into the graph structure, so that a separate clustering step is no longer needed. DMC learns both, node embeddings and communities, simultaneously by minimizing the mincut loss , which captures the number of connections between communities. Striving for high scalability, we also propose a training process for DMC based on minibatches. We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.
Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition

The writing style of the same writer varies from instance to instance in Arabic and English handwritten digit recognition, making handwritten digit recognition challenging. Currently, deep learning approaches are applied in many applications, including convolutional neural networks (CNNs) modified to produce other models, such as local binary convolutional neural networks (LBCNNs). An LBCNN is created by fusing a local binary pattern (LBP) with a CNN by reformulating the LBP as a convolution layer called a local binary convolution (LBC). However, LBCNNs suffer from the random assignment of 1, 0, or -1 to LBC weights, making LBCNNs less robust. Nevertheless, using another LBP-based technique, such as center-symmetric local binary patterns (CS-LBPs), can address such issues. In this paper, a new model based on CS-LBPs is proposed called center-symmetric local binary convolutional neural networks (CS-LBCNN), which addresses the issues of LBCNNs. Furthermore, an enhanced version of CS-LBCNNs called threshold center-symmetric local binary convolutional neural networks (TCS-LBCNNs) is proposed, which addresses another issue related to the zero-thresholding function. Finally, the proposed models are compared to state-of-the-art models, proving their ability by producing a more accurate and significant classification rate than the existing LBCNN models. For the bilingual dataset, the TCS-LBCNN enhances the accuracy of the LBCNN and CS-LBCNN by 0.15% and 0.03%, respectively. In addition, the comparison shows that the accuracy acquired by the TCS-LBCNN is the second-highest using the MNIST and MADBase datasets.
Depth Map Recovery Based on a Unified Depth Boundary Distortion Model

Depth maps acquired by either physical sensors or learning methods are often seriously distorted due to boundary distortion problems, including missing, fake, and misaligned boundaries (compared with RGB images). An RGB-guided depth map recovery method is proposed in this paper to recover true boundaries in seriously distorted depth maps. Therefore, a unified model is first developed to observe all these kinds of distorted boundaries in depth maps. Observing distorted boundaries is equivalent to identifying erroneous regions in distorted depth maps, because depth boundaries are essentially formed by contiguous regions with different intensities. Then, erroneous regions are identified by separately extracting local structures of RGB image and depth map with Gaussian kernels and comparing their similarity on the basis of the SSIM index. A depth map recovery method is then proposed on the basis of the unified model. This method recovers true depth boundaries by iteratively identifying and correcting erroneous regions in recovered depth map based on the unified model and a weighted median filter. Because RGB image generally includes additional textural contents compared with depth maps, texture-copy artifacts problem is further addressed in the proposed method by restricting the model works around depth boundaries in each iteration. Extensive experiments are conducted on five RGB-depth datasets including depth map recovery, depth super-resolution, depth estimation enhancement, and depth completion enhancement. The results demonstrate that the proposed method considerably improves both the quantitative and visual qualities of recovered depth maps in comparison with fifteen competitive methods. Most object boundaries in recovered depth maps are corrected accurately, and kept sharply and well aligned with the ones in RGB images.
Fusing Global and Local Features for Generalized AI-Synthesized Image Detection

With the development of the Generative Adversarial Networks (GANs) and DeepFakes, AI-synthesized images are now of such high quality that humans can hardly distinguish them from real images. It is imperative for media forensics to develop detectors to expose them accurately. Existing detection methods have shown high performance in generated images detection, but they tend to generalize poorly in the real-world scenarios, where the synthetic images are usually generated with unseen models using unknown source data. In this work, we emphasize the importance of combining information from the whole image and informative patches in improving the generalization ability of AI-synthesized image detection. Specifically, we design a two-branch model to combine global spatial information from the whole image and local informative features from multiple patches selected by a novel patch selection module. Multi-head attention mechanism is further utilized to fuse the global and local features. We collect a highly diverse dataset synthesized by 19 models with various objects and resolutions to evaluate our model. Experimental results demonstrate the high accuracy and good generalization ability of our method in detecting generated images. Our code is available at https://github.com/littlejuyan/FusingGlobalandLocal.