Reviewerly/exHarmony huggingface dataset:
Effect of Covid-19 on India's Industry with Digitalization, IoT, and AI Techniques

In brief, the SARS-CoV-2 family of viruses is responsible for causing COVID-19 sickness. Six of the seven continents (Antarctica excepted) have seen this illness spread at an exponential pace. When it originally emerged, it wreaked havoc on numerous industries, including the consumer products market. The pandemic has created a new chapter in the age of technology. The COVID-19 has had a devastating impact on India's manufacturing sector. Changing the typical business structure is necessary to adapt to modern business needs.Culture, organisational structure, and productivity are only few of the areas that have undergone changes because of digitalization. Smarter integration of cutting-edge tools, procedures, and expertise has also contributed to this sea change. To create value for many different types of stakeholders and to learn how to swiftly adjust to new circumstances, technology is utilised in tandem with technological transformation.As a result of digitalization, there is no longer a need for intermediaries in the marketing and promotion of goods and services to consumers. Because of the global reach of COVID-19 and the resulting social distancing it produces, many businesses are feeling its effects. Many industries and companies will feel the effects of increased social distance. That isA well-established strategy for technological evolution is provided. The effects of social isolation on shopping habits and standards cannot be overstated. Research is being conducted to learn how the COVID-19 tragedy will affect consumers' propensity to make purchases. Consumers are shifting away from the conventional methods of making purchases by using online resources and strategies. According to studies, the internet advertising paradigm has entered our graphical depiction of the best ways to comprehend advertising strategies from successful companies.
Handwritten Character Recognition Using Machine Learning

Within the domain of pattern recognition, the automated identification of handwritten characters or symbols presents a complex handwriting recognition challenges. In this paper, a novel methodology is presented, which employs machine learning techniques to achieve accurate and efficient handwritten character recognition. The proposed method utilizes artificial neural networks (ANN), specifically convolutional neural networks (CNNs), to train a model capable of accurately recognizing and classifying handwritten characters. Experimental results demonstrate that the proposed approach achieves an impressive accuracy of 98.6% on a standard dataset. The high accuracy achieved demonstrates the effectiveness and feasibility of machine learning algorithms for handwriting recognition tasks and opens up possibilities for various applications in areas such as document analysis, optical character recognition and handwriting-based interfaces. increase.
Contrast Enhancement of Medical Images Using Otsu Thresholding

Low variation between medical pictures does not produce accurate interpretations during analysis or by medical professionals or by machines in an automated diagnostic system. Contrast enhancement technique outcomes in high contrast images to increase the visibility and interpretability. We proposed a hybrid method for contrast enhancement that uses a thresholding by Otsu as the fundamental procedure for binarization and one of the sub-image histograms is histogram equalized while using CLAHE to equalize the other portion. The results are compared with state-of-the-art contrast enhancement techniques such as MMBEBHE and BBHE and evaluation based on various quantitative image quality assessment metrics such as structural similarity index measurement (SSIM), average mean brightness error (AMBE), and mean absolute error (MAE), FSIM and entropy.
Enhancing Chinese Address Parsing in Low-Resource Scenarios through In-Context Learning

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and Generative Pre-trained Transformer (GPT) modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, which mitigates interference from unnecessary variations. We introduce the REB–KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model’s effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.
Facial Attributes Recognised From Partial Faces Using Segment-Based Techniques

Modern methods for identifying attributes from faces nearly invariably presuppose the existence of a whole, unobstructed face. As a result, their performance deteriorates for obscured and partially viewable faces. We propose SPLITFACE, the deep convolutional p2p technique in this article that is specifically created to carry out attribute recognition in partial occlusion faces. The suggested method uses a statistics methodology to identify which qualities are localised in which facial segments, using a number of facesections& the entire facial as input. Due to the network's distinctive architecture, which enables several segments to forecast each attribute, committee machine approaches can be used to combine local and global judgements to improve efficiency. By needing to rely just on existence of the entire face, SPLITFACE can accurately forecast those traits that are localised in the visible sections of the face. For benchmark analyses, we employ the LFWA and CelebA facial attribute databases. In order to assess how well attribute detection techniques perform on partial faces, we also change both datasets by masking the faces. Our analysis demonstrates that SPLITFACE performs noticeably better than other previous approaches, particularly for partial faces.
Q-learning for Waiting Time Control in CDN/V2V Live streaming

HTTP-based streaming has become the dominant technology for streaming due to the widespread adoption of the HTTP protocol. Many streaming providers use a combination of Content Delivery Network (CDN) and Viewer-to-Viewer (V2V) technology, known as Hybrid CDN/V2V live streaming, for both efficiency and cost-effectiveness. V2V technology allows for of-floading streaming traffic from the CDN and reducing operational costs, and WebRTC technology facilitates direct V2V transfer, as it is natively supported by all browsers. In a WebRTC-based V2V network, some viewers cache the video chunks on their devices, while others wait and fetch chunks from their neighbors. A common strategy used to determine when a viewer should stop waiting for chunk delivery and revert to the CDN is called Random Waiting Time Control (RWC). However, due to the complex dynamics in the V2V system, RWC is far from optimal. In this work, we have formulated the Waiting Time Control determination problem as a reinforcement learning problem and proposed a Q-learning-based Waiting Time Control (QWC) solution. We conducted offline experiments in the Grid5000 [1] testbed and validated our results through a 14-day A/B testing in the wild. Our findings showed that QWC improves overall streaming Quality-of-Experience (QoE) in rebuffering (-29% fewer events), video quality (+17 % higher), and buffer length (+5 % longer), with a slightly improved V2V ratio (+5 % more).
Benchmarking CMA-ES with Basic Integer Handling on a Mixed-Integer Test Problem Suite

We compare the performances of one implementation of CMA-ES (pycma version 3.3.0) for optimizing functions with both continuous and integer variables. The implementation incorporates a lower bound on the variance along the integer coordinates to keep the optimization from stalling. This benchmark will serve as a baseline for further works on pycma. Results show substantial improvement since the last benchmarked version of pycma. Also this implementation is competitive with other mixed integer algorithms.
A Multi-Modal Modulation Recognition Method with SNR Segmentation Based on Time Domain Signals and Constellation Diagrams

Deep-learning-based automatic modulation recognition (AMR) has recently attracted significant interest due to its high recognition accuracy and the lack of a need to manually set classification standards. However, it is extremely challenging to achieve a high recognition accuracy in increasingly complex channel environments and balance the complexity. To address this issue, we propose a multi-modal AMR neural network model with SNR segmentation called M-LSCANet, which integrates an SNR segmentation strategy, lightweight residual stacks, skip connections, and an attention mechanism. In the proposed model, we use time domain I/Q data and constellation diagram data only in medium and high signal-to-noise (SNR) regions to jointly extract the signal features. But for the low SNR region, only I/Q signals are used. This is because constellation diagrams are very recognizable in the medium and high SNRs, which is conducive to distinguishing high-order modulation. However, in the low SNR region, excessive similarity and the blurring of constellations caused by heavy noise will seriously interfere with modulation recognition, resulting in performance loss. Remarkably, the proposed method uses lightweight residuals stacks and rich ski connections, so that more initial information is retained to learn the constellation diagram feature information and extract the time domain features from shallow to deep, but with a moderate complexity. Additionally, after feature fusion, we adopt the convolution block attention module (CBAM) to reweigh both the channel and spatial domains, further improving the model’s ability to mine signal characteristics. As a result, the proposed approach significantly improves the overall recognition accuracy. The experimental results on the RadioML 2016.10B public dataset, with SNR ranging from −20 dB to 18 dB, show that the proposed M-LSCANet outperforms existing methods in terms of classification accuracy, achieving 93.4% and 95.8% at 0 dB and 12 dB, respectively, which are improvements of 2.7% and 2.0% compared to TMRN-GLU. Moreover, the proposed model exhibits a moderate parameter number compared to state-of-the-art methods.
Incorporating Multi-Hypotheses as Soft-Templates in Neural Headline Generation

Neural models are widely applied to headline generation. Template-based methods are a promising direction to overcome the shortcomings of the neural headline generation (NHG) model in generating duplicate or extra words. Previous works often retrieve relevant headlines from the training data and adopt them as the soft template to guide the NHG model. However, these works had two drawbacks: reliance on additional retrieval tools, and uncertainty regarding semantic consistency between the retrieved headline and the source article. The NHG model uncertainty can be utilized to generate hypotheses. The hypotheses generated based on a well-trained NHG model not only contain salient information but also exhibit diversity, making them suitable as soft templates. In this study, we use a basic NHG model to generate multiple diverse hypotheses as candidate templates. Then, we propose a novel Multiple-Hypotheses-based NHG (MH-NHG) model. Experiments on English headline generation tasks demonstrate that it outperforms several baseline systems and achieves a comparable performance with the state-of-the-art system. This indicates that MH-NHG can generate more accurate headlines guided by multiple hypotheses.
Multiscale Underwater Image Enhancement in RGB and HSV Color Spaces

Clear underwater images serve to explore and measure ocean resources. However, underwater images suffer from color deviations and hazy effects due to wavelength-dependent light attenuation and scattering. To address this problem, we propose a multiscale dual-color space underwater image enhancement network (MSDC-Net) comprising a color correction block and a deep learning-based network. Concretely, the color correction block compensates for the most absorbed color and limits the least one. First, the color histogram distributions of all channels are shifted to a similar range, which smooths and generalizes color deviations to a certain extent. Second, the corrected multiscale images are fed into a deep learning-based asymmetric multiscale encoder-decoder architecture, which works in RGB and HSV color spaces to extract rich and varied features. Then, the extracted features are integrated through the selective kernel concatenation (SKC) module. Finally, the decoder produces competitive outputs from the integrated features. Extensive experiments on real-world and synthetic underwater images demonstrate that the proposed MSDC-Net achieves outstanding results in subjective visual comparisons and objective metrics.
Secure framework for IoT applications using Deep Learning in fog Computing

An efficient offloading strategy provides a promising solution to latency-sensitive applications in a cloud fog environment, but the fog computing environment is not fully protected and fascinates the attackers due to its dynamic and hazardous environments. Several security-based frameworks have been proposed by the authors in an integrated cloud fog environment for IoT applications to mitigate, prevent and defend against vulnerabilities, but failed to address the mentioned issues. We have proposed a lightweight secure framework using Deep Learning (DL) techniques to detect security attacks and monitor the network traffic for IoT applications in this article. The proposed Artificial Neural Network (ANN) based model is trained and deployed over a cloud platform while the detection mechanism is implemented on the fog Nodes to reduce the possibilities of any vulnerabilities and delayed execution. The proposed lightweight framework performance is analyzed and tested over NSL-KDD datasets and compared with the respective baseline techniques Support Vector Machine (SVM), Logistic Regression and Decision Trees to validate the results. We observed from the experimental results that the proposed framework provides the guarantee to defend against vulnerabilities and surpass the baseline algorithms in terms of QoS parameters. Our framework achieved 99.43% accuracy, 99.26% precision and least (0.7396%) False Alarm rate.
<i>ArchRepair</i> : Block-Level Architecture-Oriented Repairing for Deep Neural Networks

Over the past few years, deep neural networks (DNNs) have achieved tremendous success and have been continuously applied in many application domains. However, during the practical deployment in industrial tasks, DNNs are found to be erroneous-prone due to various reasons such as overfitting and lacking of robustness to real-world corruptions during practical usage. To address these challenges, many recent attempts have been made to repair DNNs for version updates under practical operational contexts by updating weights (i.e., network parameters) through retraining, fine-tuning, or direct weight fixing at a neural level. Nevertheless, existing solutions often neglect the effects of neural network architecture and weight relationships across neurons and layers. In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i.e., block level). We first perform empirical studies to investigate the limitation of whole network-level and layer-level repairing, which motivates us to explore a novel repairing direction for DNN repair at the block level. To this end, we need to further consider techniques to address two key technical challenges, i.e., block localization , where we should localize the targeted block that we need to fix; and how to perform joint architecture and weight repairing . Specifically, we first propose adversarial-aware spectrum analysis for vulnerable block localization that considers the neurons’ status and weights’ gradients in blocks during the forward and backward processes, which enables more accurate candidate block localization for repairing even under a few examples. Then, we further propose the architecture-oriented search-based repairing that relaxes the targeted block to a continuous repairing search space at higher deep feature levels. By jointly optimizing the architecture and weights in that space, we can identify a much better block architecture. We implement our proposed repairing techniques as a tool, named ArchRepair , and conduct extensive experiments to validate the proposed method. The results show that our method can not only repair but also enhance accuracy and robustness, outperforming the state-of-the-art DNN repair techniques.
Linguistic Steganalysis in Few-Shot Scenario

Due to the widespread use of text in cyberspace, linguistic steganography, which hides secret information into normal texts, develops quickly in these years. While linguistic steganography protects users’ privacy, it also has the risk of being abused to endanger network security. Therefore, its corresponding detection technology, namely linguistic steganalysis, has attracted more and more researchers’ attention in the past several years. However, most of the current linguistic steganalysis methods rely heavily on a large number of labeled samples, which presents a significant gap from real-world scenarios where labeled steganographic samples are difficult to obtain. In this paper, we proposed the Pre-trained Language model with Self-training for Few-shot Linguistic Steganalysis (LSFLS) method which effectively copes with few-shot linguistic steganalysis through a small number of labeled samples and some auxiliary unlabeled samples. Numerous experiments have proved that the proposed method can achieve high detection accuracy of linguistic steganalysis when only a few labeled samples are provided (even less than 10), significantly improving the detection ability of existing methods in few-shot scenario. Furthermore, the experimental results demonstrate that the proposed method can maintain good detection capability in the case of data source mismatch and label unbalance. We believe that our work will greatly advance the practical application of linguistic steganalysis techniques.
Solar Photovoltaic Forecasting Using ANN Network for Central and Southern Regions of Jordan

The use of renewable energy has increased during the last several decades. The most popular renewable energy source is photovoltaic (PV) technology, which uses solar radiation to create electricity. However, a number of variables, such as position, weather, etc., have an impact on the production of PV electricity. It is crucial to control the inherent changeability of PV plants as they expand and contribute significantly to the production of grid power. Predicting solar PV is therefore essential for ensuring efficient and dependable grid functioning. The forecasting model's inputs were historic PV power output data from two solar power installations in Jordan's central and southern regions. The prediction of PV power production in this research takes into account a stacked long short-term memory network (LSTM), a crucial part of the deep recurrent neural network. This model and Nonlinear Autoregressive NARX have been contrasted (Dynamic Neural Network). The outcomes demonstrated equivalent, admirable performance for both the dynamic NARX ANN and the LSTM, with NARX being better. The dynamic ANN can be claimed to be superior to the deep neural network (DNN) for time-based performance modeling of PV systems with varying data.
Renal ultrasound image segmentation method based on channel attention and GL-UNet11

Kidney tumor is one of the common malignant tumors of urinary system. However, rapid and accurate segmentation of kidney tissue is still a challenging problem in the medical image processing field on account of its obscure onset and uncertain and discontinuous boundary of its anatomical structure. We proposed a deep learning method globa-local UNet11 (GL-UNet11) for ultrasonic image segmentation of kidney, renal parenchyma and renal sinus, aiming at the inaccuracy of internal and marginal segmentation in ultrasonic image segmentation. We deepen the UNet network to make it more expressive, and propose a new channel attention network, Global Local network (GL-UNet), which takes into account the influence of global channel and local channel on predicting the importance of each channel, so that the network can better focus on important information. At the same time, the proposed global-local network is added to the convolutional block of the deepened UNet subsampling part, which effectively enhances the features of the important channels. The Dice coefficient and the intersection over union (IOU) of our method reached 96.25% and 92.78% in the task of kidney segmentation. In the task of segmentating renal parenchyma, Dice coefficient reached 92.90% and IOU reached 86.75%. In the task of segmentating renal sinus, Dice coefficient reached 90.18% and IOU reached 82.12%, both of which were superior to other deep learning methods. The GL-UNet11 network proposed in this study compared with the previously proposed UNet and Segnet methods and SE-UNet, ECA-UNet and GL-UNet methods proposed in this paper, Great improvement has been made in both quantitative and qualitative parts. It has achieved good segmentation effect on the kidney segmentation data set, which is of great significance for the early detection and prognosis of kidney patients in the future.
Towards Classifying File Segments in Memory Using Machine-Learning

Digital media triage is a main challenge that faces a digital investigator. Knowing what might be useful during crime investigation could greatly save the investigator’s time and enhance outcomes. Memory investigation can get potential benefit from the triage process because of its scattered and diverse contents. The Operating System’s paging scheme might map the contents of a file into non-consecutive page frames in the physical memory, making the file-type classification and identification process harder. This paper tackles the memory content triage problem at the page level using a machine-learning methodology.