Reviewerly/exHarmony huggingface dataset:
Decoupling Zero-Shot Semantic Segmentation

Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not been seen in the training. Existing works formulate ZS3 as a pixel-level zeroshot classification problem, and transfer semantic knowledge from seen classes to unseen ones with the help of language models pre-trained only with texts. While simple, the pixel-level ZS3 formulation shows the limited capability to integrate vision-language models that are often pre-trained with image-text pairs and currently demonstrate great potential for vision tasks. Inspired by the observation that humans often perform segment-level semantic labeling, we propose to decouple the ZS3 into two sub-tasks: 1) a classagnostic grouping task to group the pixels into segments. 2) a zero-shot classification task on segments. The former task does not involve category information and can be directly transferred to group pixels for unseen classes. The latter task performs at segment-level and provides a natural way to leverage large-scale vision-language models pre-trained with image-text pairs (e.g. CLIP) for ZS3. Based on the decoupling formulation, we propose a simple and effective zero-shot semantic segmentation model, called ZegFormer, which outperforms the previous methods on ZS3 standard benchmarks by large margins, e.g., 22 points on the PAS-CAL VOC and 3 points on the COCO-Stuff in terms of mIoU for unseen classes. Code will be released at https://github.com/dingjiansw101/ZegFormer.
An Empirical Study of End-to-End Temporal Action Detection

Temporal action detection (TAD) is an important yet challenging task in video understanding. It aims to simultaneously predict the semantic label and the temporal interval of every action instance in an untrimmed video. Rather than end-to-end learning, most existing methods adopt a head-only learning paradigm, where the video encoder is pre-trained for action classification, and only the detection head upon the encoder is optimized for TAD. The effect of end-to-end learning is not systematically evaluated. Besides, there lacks an in-depth study on the efficiency-accuracy trade-off in end-to-end TAD. In this paper, we present an empirical study of end-to-end temporal action detection. We validate the advantage of end-to-end learning over head-only learning and observe up to 11% performance improvement. Besides, we study the effects of multiple design choices that affect the TAD performance and speed, including detection head, video encoder, and resolution of input videos. Based on the findings, we build a mid-resolution baseline detector, which achieves the state-of-the-art performance of end-to-end methods while running more than 4×faster. We hope that this paper can serve as a guide for end-to-end learning and inspire future research in this field. Code and models are available at https://github.com/xlliu7/E2E-TAD.
SimCC: A Simple Coordinate Classification Perspective for Human Pose Estimation

The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE) for years due to high performance. However, the long-standing quantization error problem in the 2D heatmap-based methods leads to several well-known drawbacks: 1) The performance for the low-resolution inputs is limited; 2) To improve the feature map resolution for higher localization precision, multiple costly upsampling layers are required; 3) Extra post-processing is adopted to reduce the quantization error. To address these issues, we aim to explore a brand new scheme, called SimCC, which reformulates HPE as two classification tasks for horizontal and vertical coordinates. The proposed SimCC uniformly divides each pixel into several bins, thus achieving sub-pixel localization precision and low quantization error. Benefiting from that, SimCC can omit additional refinement post-processing and exclude upsampling layers under certain settings, resulting in a more simple and effective pipeline for HPE. Extensive experiments conducted over COCO, CrowdPose, and MPII datasets show that SimCC outperforms heatmap-based counterparts, especially in low-resolution settings by a large margin. Code is now publicly available at https://github.com/leeyegy/SimCC .
Blockchain-Augmented Digital Supply Chain Management: A Way to Sustainable Business

The objective of this article is to assist the reader in understanding the journey from traditional Supply Chain Management to Digital Supply Chain Management. It aims to augment the concept of Digital Supply Chain Management with blockchain technology and create an extensive literature review to assist in formulating the gaps and discovering the variables that contribute towards the efficiency of a Blockchain-Based Digital Supply Chain. Moreover, this article aims to validate the impact of specified parameters resulting in customer retention and market leadership for an organization. Digital technologies such as the Internet of Things, blockchain, etc., are disrupting the traditional ways of doing business and creating value propositions for customers. Supply Chain Management is a key business process for an organization that helps them compete in the market. Organizations have seized competition not as individual brands but as supply chains. Digital Supply Chain Management is the implementation of digital technologies to capture customer data at every interaction to create customer engagement strategies. This article provides an empirical analysis of parameters influencing a Blockchain-Augmented Digital Supply Chain resulting in customer retention and market leadership and shows how, through a Blockchain-Based Digital Supply Chain, the business objective of being a customer-centric organization is assisted with the customer data generated at each interaction that is enabled.
Machine-Learning Methods on Noisy and Sparse Data

Experimental and computational data and field data obtained from measurements are often sparse and noisy. Consequently, interpolating unknown functions under these restrictions to provide accurate predictions is very challenging. This study compares machine-learning methods and cubic splines on the sparsity of training data they can handle, especially when training samples are noisy. We compare deviation from a true function f using the mean square error, signal-to-noise ratio and the Pearson R2 coefficient. We show that, given very sparse data, cubic splines constitute a more precise interpolation method than deep neural networks and multivariate adaptive regression splines. In contrast, machine-learning models are robust to noise and can outperform splines after a training data threshold is met. Our study aims to provide a general framework for interpolating one-dimensional signals, often the result of complex scientific simulations or laboratory experiments.
Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail

Artificial Intelligence enabled Virtual Assistants (VAs) are evolving rapidly, offering promising opportunities to provide personalized shopping assistance and 24/7 frontline service. They can reduce the workload of human employees while delivering a seamless customer experience. Despite these benefits, the adoption of AI VAs as a retail marketing channel is patchy. The objective of this study is to reveal the multidisciplinary factors impeding the adoption of AI VAs in retail using the Fuzzy AHP method. Seven main barriers and thirty-seven sub-barriers are identified, classified, and ranked. Findings reveal that customer-related barriers are the most significant. These are followed by technical and financial barriers that retailers face in implementing AI chatbots.
Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People

Current artificial intelligence systems for determining a person's emotions rely heavily on lip and mouth movement and other facial features such as eyebrows, eyes, and the forehead. Furthermore, low-light images are typically classified incorrectly because of the dark region around the eyes and eyebrows. In this work, we propose a facial emotion recognition method for masked facial images using low-light image enhancement and feature analysis of the upper features of the face with a convolutional neural network. The proposed approach employs the AffectNet image dataset, which includes eight types of facial expressions and 420,299 images. Initially, the facial input image's lower parts are covered behind a synthetic mask. Boundary and regional representation methods are used to indicate the head and upper features of the face. Secondly, we effectively adopt a facial landmark detection method-based feature extraction strategy using the partially covered masked face's features. Finally, the features, the coordinates of the landmarks that have been identified, and the histograms of the oriented gradients are then incorporated into the classification procedure using a convolutional neural network. An experimental evaluation shows that the proposed method surpasses others by achieving an accuracy of 69.3% on the AffectNet dataset.
CNN Based Moving Object Detection from Surveillance Video in Comparison with GMM

Automatic moving object classification has become more important in modern intelligent detection and visual monitoring systems. Moving object recognition from pictures and vehicle type categorization is essential for an effective visual surveillance system. Moving object movement must be limited as much as feasible. Most of the research projects that have been done so far have simply looked at how to increase the percentage of forecasts, which has poor real-time performance and uses more processing power. A network categorization system used by CNN can swiftly recognize moving objects. Convolution neural networks (CNN) based on deep learning neural networks are compared with Gaussian mixture model (GMM) based object detection in this paper to increase the accuracy and F-score of the object detection. The mean accuracy for CNN is 95.69% and the mean accuracy for GMM is 93.22%. The mean F-score for CNN is 87.02 and the mean F-score for GMM is 84.77.
Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention

Monocular Depth Estimation (MDE) aims to predict pixel-wise depth given a single RGB image. For both, the convolutional as well as the recent attention-based models, encoder-decoder-based architectures have been found to be useful due to the simultaneous requirement of global context and pixel-level resolution. Typically, a skip connection module is used to fuse the encoder and decoder features, which comprises of feature map concatenation followed by a convolution operation. Inspired by the demonstrated benefits of attention in a multitude of computer vision problems, we propose an attention-based fusion of encoder and decoder features. We pose MDE as a pixel query refinement problem, where coarsest-level encoder features are used to initialize pixel-level queries, which are then refined to higher resolutions by the proposed Skip Attention Module (SAM). We formulate the prediction problem as ordinal regression over the bin centers that discretize the continuous depth range and introduce a Bin Center Predictor (BCP) module that predicts bins at the coarsest level using pixel queries. Apart from the benefit of image adaptive depth binning, the proposed design helps learn improved depth embedding in initial pixel queries via direct supervision from the ground truth. Extensive experiments on the two canonical datasets, NYUV2 and KITTI, show that our architecture outperforms the state-of-the-art by 5.3% and 3.9%, respectively, along with an improved generalization performance by 9.4% on the SUNRGBD dataset. Code is available at https://github.com/ashutosh1807/PixelFormer.git.
The metaverse as a breakthrough for operations and supply chain management: implications and call for action

Purpose The metaverse development is in the early stages in most organizations and supply chains. There has been exponential growth in metaverse investments by leading tech and other types of companies and governments worldwide. This article aims to shed light on the topic by providing detailed insights for the operations and supply chain management (O&amp;SCM) community concerning the potential, opportunities and challenges associated with the metaverse. Design/methodology/approach The authors mapped 15 benefits and 15 challenges regarding metaverse in O&amp;SCM-related fields from the literature, which in turn were empirically tested by a panel with 150 experts from more than 12 countries, from operations and supply chains and with experience in metaverse technologies. Findings The authors found notable similarities and differences between metaverse adopters and non-adopters in the O&amp;SCM. Accordingly, some benefits and challenges are expected before and after the implementation, but it's still relevant. In contrast, there are ones that change their importance after the implementation. Research limitations/implications First, this paper points out the need for an urgent call for action to develop high-quality research on the interplay between metaverse and O&amp;SCM. Second, the metaverse will reshape several established business models by offering new products and services, consequently resulting in the remodeling of O&amp;SCM. Third, our paper provides a call for action to engage the community of scholars and practitioners to consider the metaverse as one of the last frontiers of O&amp;SCM in the digital age. Originality/value This paper is one of the first that investigates the metaverse benefits, challenges and expectations in the O&amp;SCM. Also, it provides robust directions by an empirical approach to the metaverse as a new and important research stream for O&amp;SCM and related fields. The authors provide a prospective research agenda that scholars and practitioners could use as a roadmap to capture metaverse opportunities in O&amp;SCM.
An Effective and Adaptable K-means Algorithm for Big Data Cluster Analysis

Tradition K-means clustering algorithm is easy to fall into local optimum, poor clustering effect on large capacity data and uneven distribution of clustering centroids. To solve these problems, a novel k-means clustering algorithm based on Lévy flight trajectory (Lk-means) is proposed in the paper. In the iterative process of LK-means algorithm, Lévy flight is used to search new positions to avoid premature convergence in clustering. It is also applied to increase the diversity of the cluster, strengthen the global search ability of K-means algorithm, and avoid falling into the local optimal value too early. Nevertheless, the complexity of hybrid algorithm is not increased in the process of Lévy flight optimization. To verify the data clustering effect of LK-means algorithm, experiments are conducted to compare it with the k-means algorithm, XK-means algorithm, DDKmeans algorithm and Canopyk-means algorithm on 10 open source data sets. The results show that LK-means algorithm has better search results and more evenly distributed cluster centroids, which greatly improves the global search ability, big data processing ability and uneven distribution centroids of cluster of K-means algorithm.
Improved bald eagle search algorithm for global optimization and feature selection

The use of metaheuristics is one of the most encouraging methodologies for taking care of real-life problems. Bald eagle search (BES) algorithm is the latest swarm-intelligence metaheuristic algorithm inspired by the intelligent hunting behavior of bald eagles. In recent research works, BES algorithm has performed reasonably well over a wide range of application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency and has a tendency to stuck in local optima which affects the final outcome. This paper introduces a modified BES (mBES) algorithm that removes the shortcomings of the original BES algorithm by incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), and Transition & Pharsor operators. OBL is embedded in different phases of the standard BES viz. initial population, selecting, searching in space, and swooping phases to update the positions of individual solutions to strengthen exploration, CLS is used to enhance the position of the best agent which will lead to enhancing the positions of all individuals, and Transition & Pharsor operators help to provide sufficient exploration–exploitation trade-off. The efficiency of the mBES algorithm is initially evaluated with 29 CEC2017 and 10 CEC2020 global optimization benchmark functions. In addition, the practicality of the mBES is tested with a real-world feature selection problem and five engineering design problems. Results of the mBES algorithm are compared against a number of classical metaheuristic algorithms using statistical metrics, convergence analysis, box plots, and the Wilcoxon rank sum test. In the case of composite CEC2017 test functions F21-F30, mBES wins against compared algorithms in 70% test cases, whereas for the rest of the test functions, it generates good results in 65% cases. The proposed mBES produces best performance in 55% of the CEC2020 test functions, whereas for the rest of the 45% test cases, it generated competitive results. On the other hand, for five engineering design problems, the mBES is the best among all compared algorithms. In the case of the feature selection problem, the mBES also showed competitiveness with the compared algorithms. Results and observations for all tested optimization problems show the superiority and robustness of the proposed mBES over the baseline metaheuristics. It can be safely concluded that the improvements suggested in the mBES are proved to be effective making it competitive enough to solve a variety of optimization problems.
Novel image cryptosystem based on new 2D hyperchaotic map and dynamical chaotic S-box

Chaotic systems are widely used in image encryption due to their sensitivity to initial values, ergodicity, and other properties; many image encryption algorithms based on chaotic systems have been studied in the past few years. To obtain a more secure encryption algorithm, this work firstly proposes a new two-dimensional discrete hyperchaotic map, which has a wider continuous chaotic interval, larger Lyapunov exponents and passed all NIST and part of TestU01 tests. Then, we apply the proposed map to generate S-boxes and combine them in pairs; finally, twelve S-boxes are obtained, and the elements of the plaintext image are grouped, each group of pixels is summed, and modular operations are used to specify specific S-boxes. Next, each set of elements is bitwise XOR with the corresponding S-box. Finally, the cipher image is obtained by scrambling using chaotic signal. Experiments show that compared with some other encryption algorithms, the proposed S-box-based encryption method has higher security, and it resists to common attacks.
ChatGPT and Its Possible Impact on Library Reference Services

AbstractAbstractThis column reports a simple test of ChatGPT with some library reference prompts and article writing prompts in February 2023, and compares the answers from traditional library chatbots with ChatGPT’s. It also tries to call attention to possible lessons the library community may want to learn from previous transformative technology changes such as Google and Web 2.0.Keywords: AIartificial intelligencechatbotChatGPTlibrary servicesreference services
DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization

Automatic modulation classification (AMC) is an essential and indispensable topic in the development of cognitive radios. It is the cornerstone of adaptive modulation and demodulation capabilities to perceive and understand surrounding environments and make corresponding decisions. In this paper, we propose a priori regularization method in deep learning (DL-PR) for guiding loss optimization during model training process. The regularization factor designed by the combination of inter-class confrontation factor, global and dimensional divergence can help increase the inter-class distance and reduce the intra-class distance of samples. While preserving the original information of received signals as much as possible, it makes full use of the prior knowledge in the signal transmission process and ultimately helps deep learning models to be well generalized on signals with various signal-to-noise ratios (SNRs). As far as we know, this is the first attempt to regularize deep learning models based on SNR distribution of samples to improve AMC accuracy. Moreover, it can be proved that priori regularization can be interpreted as implicit data augmentation and model ensemble methods. By comparing with a series of state-of-the-art AMC methods and different regularization techniques on the public dataset RadioML 2016.10a, experimental results of multiple deep learning models illustrate the superiority of DL-PR, including CNN with accuracy of 62.6% and inference time of 0.82 ms per signal, LSTM with 61.8% and 0.87 ms, and hybrid CNN–LSTM with 64.2% and 0.94 ms. In practical applications, DL-PR can be also easily applied to complex environments due to its robustness to hyper-parameters and SNR estimation.
Enhanced Security for Electronic Health Care Information Using Obfuscation and RSA Algorithm in Cloud Computing

Recently, the electronic heath record (EHR) has become the chosen method to maintain a patient's health information. The advancement of cloud computing enables users to access their data with flexibility, providing large storage capability at low costs, which encourages EHR maintainers to consider shifting from their own storage to the cloud. In cloud computing, it is no doubt that securing EHRs poses a gigantic challenge. Various security properties like access control, data privacy, and scalable access between different clouds needs to be satisfied. This article presents a secure and efficient blueprint for securing data confidentiality on cloud computing storage. The proposed framework is carried out for EHR confidential data on cloud storage. Moreover, the proposed approach combines the obfuscation and RSA encryption together to enforce confidentiality and authentication. Through this framework, the data confidentiality and authentication scheme on EHR information can be enforced on clouds storage.
A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends

Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and solve common real-world problems. Machine learning and deep learning are Artificial intelligence technologies that use algorithms to predict outcomes more accurately without relying on human intervention. However, the opaque black box model and cumulative model complexity can be used to achieve. Explainable Artificial Intelligence (XAI) is a term that refers to Artificial Intelligence (AI) that can provide explanations for their decision or predictions to human users. XAI aims to increase the transparency, trustworthiness and accountability of AI system, especially when they are used for high-stakes application such as healthcare, finance or security. This paper offers systematic literature review of XAI approaches with different application and observes 91 recently published articles describing XAI development and applications in healthcare, manufacturing, transportation, and finance. We investigated the Scopus, Web of Science, IEEE Xplore and PubMed databases, to find the pertinent publications published between January 2018 to October 2022. It contains the published research on XAI modelling that were retrieved from scholarly databases using pertinent keyword searches. We think that our systematic review extends to the literature on XAI by working as a roadmap for further research in the field.