Reviewerly/exHarmony huggingface dataset:
Designing a Chatbot for Helping Parenting Practice

In today’s society, many families do not have children due to various reasons. The reasons include the pressure brought by childcare, and that double-income families do not have time to raise children, especially novice parents who will retreat. For this reason, we made a chatbot to solve problems. Through the questionnaires collected by our study, we found that most novice parents use INFANBOT when their children cry, thus, we used a baby’s cry as an example here. When parents face a baby’s cry, they can tap the button “burst into tears!”. The chatbot will immediately tell parents how to solve the problem. The authors designed a chatbot system named INFANBOT that combined the concepts of infancy health education to alleviate parents’ troubles at work and parenting. It also reduced the anxiety of caregivers during the parenting process and gave them correct parenting knowledge. The INFANBOT is a real-time system that can provide real-time services to novice parents. Additionally, when the user is using INFANBOT, the system will record the problems encountered and invite the user to fill in a questionnaire at an appropriate time to improve the system. After a preliminary study, we found that INFANBOT can solve most of the problems encountered by users. Statistically, all respondents gave above 4.5 points in the Likert-type five-point scale. Therefore, most respondents felt that INFANBOT could solve their problems effectively and quickly. The INFANBOT system developed by this study is designed to meet the needs of users. The system design of INFANBOT established in this study met the needs of its users and can help users improve their parenting troubles. This study also has positive effects and contributions to society: 1. After using INFANBOT, users can effectively improve their knowledge of children’s health education. 2. After using INFANBOT, users feel recognition of the professionalism of the health care knowledge provided by the robot, which can effectively improve the user’s parenting problems. 3. Most of the users are satisfied with the positive results after using INFANBOT, so novice parents and parents who feel anxious about parenting can quickly search for common parenting problems on the LINE community software.
Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of blurred aerial remote sensing images. Moreover, the average PSNR of this paper’s algorithm tested on the publicly available dataset RealBlur-R reached 41.02 dB, surpassing the latest SOTA algorithm.
uGrapher: High-Performance Graph Operator Computation via Unified Abstraction for Graph Neural Networks

As graph neural networks (GNNs) have achieved great success in many graph learning problems, it is of paramount importance to support their efficient execution. Different graphs and different operators present different patterns during execution. However, there is still a gap in the existing GNN acceleration research to explore adaptive parallelism. We show that existing GNN frameworks rely on handwritten static kernels, which fail to achieve the best performance across different graph operators and input graph structures. In this work, we propose uGrapher, a unified interface that achieves general high performance for different graph operators and datasets. The existing GNN frameworks can easily integrate our design for its simple and unified API. We take a principled approach that decouples a graph operator's computation and schedule to achieve that. We first build a GNN-specific operator abstraction that incorporates the semantics of graph tensors and graph loops. We explore various schedule strategies based on the abstraction that can balance the well-established trade-off relationship between parallelism, locality, and efficiency. Our evaluation shows that uGrapher can bring up to 29.1× (3.5× on average) performance improvement over the state-of-the-art baselines on two studied NVIDIA GPUs.
DeepUM: Tensor Migration and Prefetching in Unified Memory

Deep neural networks (DNNs) are continuing to get wider and deeper. As a result, it requires a tremendous amount of GPU memory and computing power. In this paper, we propose a framework called DeepUM that exploits CUDA Unified Memory (UM) to allow GPU memory oversubscription for DNNs. While UM allows memory oversubscription using a page fault mechanism, page migration introduces enormous overhead. DeepUM uses a new correlation prefetching technique to hide the page migration overhead. It is fully automatic and transparent to users. We also propose two optimization techniques to minimize the GPU fault handling time. We evaluate the performance of DeepUM using nine large-scale DNNs from MLPerf, PyTorch examples, and Hugging Face and compare its performance with six state-of-the-art GPU memory swapping approaches. The evaluation result indicates that DeepUM is very effective for GPU memory oversubscription and can handle larger models that other approaches fail to handle.
FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks

Attention mechanisms, primarily designed to capture pairwise correlations between words, have become the backbone of machine learning, expanding beyond natural language processing into other domains. This growth in adaptation comes at the cost of prohibitively large memory requirements and computational complexity, especially at higher number of input elements. This limitation is due to inherently limited data reuse opportunities and quadratic growth in memory footprints, leading to severe memory-boundedness and limited scalability of input elements. This work addresses these challenges by devising a tailored dataflow optimization, called FLAT, for attention mechanisms without altering their functionality. This dataflow processes costly attention operations through a unique fusion mechanism, transforming the memory footprint quadratic growth to merely a linear one. To realize the full potential of this bespoke mechanism, we propose a tiling approach to enhance the data reuse across attention operations. Our method both mitigates the off-chip bandwidth bottleneck as well as reduces the on-chip memory requirement. FLAT delivers 1.94x (1.76x) speedup and 49% and (42%) of energy savings compared to the state-of-the-art Edge (Cloud) accelerators with no customized dataflow optimization. When on-chip resources are scarce (20 KB-200 KB), FLAT yields, on average, 1.5x end-to-end latency reduction across a diverse range of conventional attention-based models with input sequence lengths ranging from 512-token to 64K-token. Our evaluations demonstrate that state-of-the-art DNN dataflow applied to attention operations reach the efficiency limit for inputs above 512 elements. In contrast, FLAT unblocks transformer models for inputs with up to 64K elements.
BeeHive: Sub-second Elasticity for Web Services with Semi-FaaS Execution

Function-as-a-service (FaaS), an emerging cloud computing paradigm, is expected to provide strong elasticity due to its promise to auto-scale fine-grained functions rapidly. Although appealing for applications with good parallelism and dynamic workload, this paper shows that it is non-trivial to adapt existing monolithic applications (like web services) to FaaS due to their complexity. To bridge the gap between complicated web services and FaaS, this paper proposes a runtime-based Semi-FaaS execution model, which dynamically extracts time-consuming code snippets (closures) from applications and offloads them to FaaS platforms for execution. It further proposes BeeHive, an offloading framework for Semi-FaaS, which relies on the managed runtime to provide a fallback-based execution model and addresses the performance issues in traditional offloading mechanisms for FaaS. Meanwhile, the runtime system of BeeHive selects offloading candidates in a user-transparent way and supports efficient object sharing, memory management, and failure recovery in a distributed environment. The evaluation using various web applications suggests that the Semi-FaaS execution supported by BeeHive can reach sub-second resource provisioning on commercialized FaaS platforms like AWS Lambda, which is up to two orders of magnitude better than other alternative scaling approaches in cloud computing.
Real-Time Deep Learning Approach for Pedestrian Detection and Suspicious Activity Recognition

Pedestrian detection, tracking, and suspicious activity recognition have grown increasingly significant in computer vision applications in recent years as security threats have increased. Continuous monitoring of private and public areas in high-density areas is very difficult, so active video surveillance that can track pedestrian behavior in real time is required. This paper presents an innovative and robust deep learning system as well as a unique pedestrian data set that includes student behavior like as test cheating, laboratory equipment theft, student disputes, and danger situations in institutions. It is the first of its kind to provide pedestrians with a unified and stable ID annotation. Again, presented a comparative analysis of results achieved by the recent deep learning approach to pedestrian detection, tracking, and suspicious activity recognition methods on a recent benchmark dataset. Finally, paper concluded with investigation new research directions in vision-based surveillance for practitioners and research scholars.
BERT-CNN: Improving BERT for Requirements Classification using CNN

Requirements classification is considered a crucial task in requirements engineering. The analysis of functional and Non-functional requirements (NFRs) requires domain knowledge. NFRs are considered quality attributes, that hold critical information about the constraints upon which the success of software depends. Usually, requirements are expressed in natural language, and extraction and classification of such requirements become a challenging task. Various automatic techniques for requirements classification are exploited by existing studies. Recently, Transfer learning as a new deep learning model attracts the attention of researchers, which excelled in different Natural Language Processing tasks. Transfer learning-based BERT pre-trained model achieved more promising results than state-of-the-art approaches. This proposed research presents a Bidirectional Encoder-Decoder Transformer-Convolutional Neural Network (BERT-CNN) model for requirements classification. Then, the convolutional layer is stacked over the BERT layer for performance enhancement. This research work conducted an experiment on the PROMISE dataset of 625 requirements. The experiment results demonstrate that the proposed model performs better than the state-of-the-art baseline approach.
Practically motivated adaptive fusion method with tie analysis for multilabel dispersed data

The goal of this paper is to propose an adaptive fusion method that aggregates the results of predictions obtained for a set of classifiers. The problem of selecting the best fusion method for a given data set is an important but difficult task. For different data sets, different fusion methods work best, however, it is not possible to predict in advance which method will be appropriate for a given data set. In the proposed adaptive method, the weights assigned to the fusion methods are adjusted iteratively. The proposed method is based on empirically developed formulas. The results obtained using this method were compared with the results obtained from the online learning method — the Multiplicative Weights approach, which is asymptotically optimal (Arora, S., Hazan, E., & Kale, S., 2012). The multiplicative weights update method: a meta-algorithm and applications. Theory Comput., 8, 121–164). However, the results obtained with the use of the proposed method turns out to be better when applied to a finite set of test objects. In addition, a comparative analysis is provided when fusion methods are used simultaneously. Also, the results obtained when different fusion methods are used individually were compared. It was shown that the results obtained by the adaptive fusion method are much better.
A dual-model semi-supervised self-organizing fuzzy inference system for data stream classification

Semi-supervised learning from data streams is widely considered as a highly challenging task to be further researched. In this paper, a novel dual-model self-organizing fuzzy inference system composed of two recently introduced evolving fuzzy systems (EFSs) is proposed for semi-supervised learning from data streams in infinite delay environments. After being primed with a small amount of labelled data during the warm-up period, the proposed model is able to continuously self-learn and self-expand its knowledge base from unlabelled data on a chunk-by-chunk basis with minimal human expert involvement. Thanks to its dual-model structure, the proposed model combines the merits of the two EFS models such that it can continuously identify new prototypes from new pseudo-labelled data to self-improve its knowledge base whilst keeping the impact of pseudo-labelled errors on its decision-making minimized. Numerical examples based on various benchmark problems demonstrate the efficacy of the proposed method, showing its strong potential in real-world applications by offering higher classification accuracy over the state-of-the-art competitors whilst retaining high computational efficiency.
Detection and Classification of Rheumatoid Nodule using Deep Learning Models

In the past few years there have been many cases in which the patient may get some form of nodule on the body and it becomes very difficult to identify the type of nodule and its relative disease. Sometimes the nodule may be a simple skin lesion or it may be cancerous cyst or it may be due to a severe condition of rheumatoid arthritis. This research work concentrates on classifying the nodules from a given data set of images and detecting the Rheumatoid Nodule (RN) and its risks. Many studies have been conducted for detection of different nodules but specific to rheumatoid nodule identification and its risks on other organs are minimum as per my cognizance. In imaging study, Deep Convolutional Neural Networks (DCNN) have shown efficient accomplishment, hence the model proposed uses dual three-dimensional Deep Convolutional Networks design for the detection of RN and an improved Res Net for classification, respectively. Rheumatoid Nodule detection was performed through Pure Deep Convolutional Networks (PDCNN) and then feature selection was done using Particle Swarm Optimization (PSO) and later classification was done using the Residual Network (Res-Net) on proficiently learned features from Particle Swarm Optimization. The Deep Learning (DL) model for Rheumatoid Nodule detection and classification, blended with medical aspects, aids in minimizing the errors and False Positive (FP) rate and help in early detection of rheumatoid nodule. The recommended system was assessed on dataset collected from different orthopaedic hospitals in the city and improved results were obtained in comparison to the prevailing approaches.
Hindi to English Multimodal Machine Translation on News Dataset in Low Resource Setting

This work proposes a multimodal Hindi-to-English machine translation on a news corpus by integrating multiple input modalities. The experimental dataset comprises of an image from a news article and its caption in the English-Hindi language. The parallel English-Hindi dataset is constructed by translating the image caption from English-language news articles into Hindi. Unlike standard images used for NLP tasks, images in news articles frequently depict significant events, locations, or individuals. The caption provides a more extensive description of the image, resulting in the inclusion of multiple identified entities. Our methodology consists of encoders for each input modality, allowing for simultaneous consideration of both text and image. We undertake two tests to examine how effectively the image content improves MT systems when 1) the entire image is considered first and 2) the largest object recognized in the image is considered. We employ Byte Pair Encoding (BPE) to represent the text, and the visual attributes are extracted from the image with VGG-19, a pre-trained CNN model. In terms of BLEU and chrF, our Multimodal Machine Translation (MMT) systems are superior than the baseline unimodal NMT system. Our MMT system outperforms the NMT model by +1.8 BLEU and +0.03 chrF.
Crowd counting analysis using deep learning: a critical review

The term "crowd counting" refers to the practise of counting the number of people present in a certain area. Urban planning, medical services, emergency preparedness, public security, strategic planning, and defence all seem to be domains where this method may be used. Occlusion, size and perspective distortion, and non-uniform distribution are all problems that crowd counting approaches face. As the population density grows, so does the complexity of the calculations. Great advances in deep convolution neural networks (CNNs) and datasets are largely responsible for the tremendous development in crowd count approaches seen in the past few years. In this paper we assess recent efforts and provide a complete evaluation of modern deep learning-based crowd counting systems. This paper discusses some classic and deep learning-based crowd counting approaches. We examine detection-based, regression-based, and classic density estimation approaches briefly. For the purpose of estimating the crowd density and count for the provided crowd scene image, we have evaluated the recent 10 publications on crowd counting using deep learning. We also go through the most widely used datasets. In conclusion, these investigations demonstrated a high degree of precision and offered good illustrations of the potential of AI in crowd counting. From paper to paper, there were significant differences in the approaches and algorithms used to address the crowd counting and density mapping challenge. We also examine the possible uses of crowd counting as well as the difficulties associated with it. As a result, fresh study is being conducted in this area.
Text Sentiment Analysis based on Multichannel Convolutional Neural Networks and Syntactic Structure

Over the last few years, the recognition of social media has grown exponentially, and emotional evaluation in critiques, feedback and evaluations from social media has become extra effective inside the studies field. excessive satisfactory, emotional analysis expresses ideas about real-time gadgets, merchandise, films and tweet evaluations. With the sheer quantity of person-generated text on social media, Emotional evaluation (SA) has become an fundamental a part of NLP with many programs, including statistics retention and retrieval techniques, net design, and plenty extra. Convolutional Neural community (CNN) and Recurrent Neural community (RNN) were widely used in the area of textual emotional analysis and feature yielded wonderful results. during the last few years, the recognition of social media has grown exponentially, emotional evaluation in critiques, comments and opinions from social media has end up more effective within the studies subject. excessive high-quality, emotional analysis expresses ideas about real-time items, products, movies and tweet reviews. Bidirectional Recurrent Neural community can triumph over CNN's failure to extract semantic data for lengthy textual content, however it can't extract the area features of the textual content as CNN can do it. The model can pay attention to key phrases inside the mood the separation of polarity in a sentence by using way of attention and combined with the benefits of CNN extracting nearby textual content capabilities and CNN redirected to extract long-term semantic information textual content, which develops the potential to extract a textual content element using a model. The experimental outcomes on the IMDB film evaluation dataset which show that the proposed version can extract rich textual content features can attain higher results with the schooling accuracy appears to be 87% and schooling loss is 0.2852 while the validation accuracy is 86% and validation loss is 0.3263. The check accuracy effects as 86.132% and check loss is 0.330.
Offline HWR Accuracy Enhancement with Image Enhancement and Deep Learning Techniques

Handwriting recognition (HWR) is the ability of a machine to recognize handwritten text present in images, scanned or photographed. While much of the work reported in this field deals with scanned images, recognizing handwritten content from camera-captured images is still a challenge. While researchers are focusing their efforts to improve on the recognition process of such characters, the current work focuses efforts outside the recognition process. This work employs image enhancement techniques, noise removal, and binarization with adaptive thresholding & segmentation, to increase HWR accuracy. Additionally, the work of employs deep learning-based language models, such as BERT & BART, to further enhance the accuracy of recognized text. It is observed that HWR accuracy increases when smaller images such as word images are used as input to the HWR engine. On the post-processing front, BART is demonstrated to be superior in enhancing the accuracy of HWR-recognized text documents in a comparative study presented in this work. An accuracy improvement of 20+% is achieved with the BART model enhanced with transfer learning through a localized dataset to handle HWR-specific error patterns.