Reviewerly/exHarmony huggingface dataset:
Dynamic Path Based DNN Synergistic Inference Acceleration in Edge Computing Environment

Deep Neural Networks (DNNs) have achieved excellent performance in intelligent applications. Nevertheless, it is elusive for devices with limited resources to support computationally intensive DNNs, while employing the cloud may lead to prohibitive latency. Better solutions are exploiting edge computing and reducing unnecessary computation. Multi-exit DNN based on the early exit mechanism has an impressive effect in the latter, and in edge computing paradigm, model partition on multi-exit chain DNNs is proved to accelerate inference effectively. However, despite reducing computations to some extent, multiple exits may lead to instability of performance due to variable sample quality, performance inferior to the original model especially in the worst case. Furthermore, nowadays DNNs are universally characterized by a directed acyclic graph (DAG), complicating the partition of multi-exit DNN exceedingly. To solve the issues, in this paper, considering online exit prediction and model execution optimization for multi-exit DNN, we propose a Dynamic Path based DNN Synergistic inference acceleration framework (DPDS), where exit designators are designed to avoid iterative entry for exits; to further promote computational synergy in the edge, the multi-exit DNN is dynamically partitioned according to network environment to achieve fine-grained computing offloading. Experimental results show that DPDS can significantly accelerate DNN inference by 1.87× to 6.78×.
Lightweight Security Framework for IoT Enabled Tracking of COVID-19 and its Variants

COVID-19 became the headlines of every nation because of its unrivalled transmission speed and outbreaks in humans, but its new variants are raising new challenges after every few months iteratively. Keeping track of travel history and maintaining a record of people coming in contact with any COVID affected individual has become a prime concern to control the spread of this pandemic disease. The existing tracking system lags in fetching records of people who came in contact with the affected one and maintaining travel history across the local boundaries of the cities. In this position paper, we are proposing a framework for an IoT driven blockchain (BC) based secured tracking system that gathers users&#x0027; travel and meeting history, and it may help with remote health monitoring. The data gathered for the same is treated as immutable and achieves interoperability with the help of Smart Contracts (SC). It could be proved as a useful framework for post COVID-19 economic revival with the help of an IoT driven blockchain based secured model for remote health monitoring &#x0026; chain tracking.
Indonesian News Extractive Text Summarization Using Latent Semantic Analysis

News text is a text that contains important information that is happening to be disseminated to the public. In the news, the more information, the more text is displayed. Of course, it takes a lot of time to read the entire text of the news. Automatic text summarization is needed to help readers understand the content of the news text quickly. In this study, the application of the latent semantic analysis method with the GongLiu, Steinberger Jezek, and Cross techniques will be applied to automatic text summarization. The test data will be tested by using local news about politics. By comparing the rate the three methods previously mentioned, Gongliu is considered the best amongst the three methods since it has the highest Rogue value and the fastest processing time.
Improving the Performance of Naïve Bayes Algorithm by Reducing the Attributes of Dataset Using Gain Ratio and Adaboost

Naive Bayes is one of the most well-known data mining algorithms for classification. Naive Bayes is a simple and effective learning theory that does not need various parameters. However, Naive Bayes also has its drawbacks. The obstacle faced by naive bayes is its performance and accuracy decreases when the data to be classified contains a large number of features and dimensions. In this study, the author will improve the accuracy and performance of naive bayes by using the gain ratio in reducing attributes and boosting using Adaboost. The gain ratio is used to select and reduce features in the dataset. And adaboost approach for ensemble learning to improve the performance of Naive Bayes in classifying the data set of clinical heart failure records. Naive Bayes will be used as a base learner. Validation is carried out using the 10-fold Cross Validation approach. While the confusion matrix is used to test the accuracy. The naive bayes performance before combined with the gain ratio and adaboost is the TPR value of 0.29, the TNR value of 0.96, the NPV value of 0.94, the FNR value of 0.71, the FOR value of 0.06 and the naive bayes accuracy. 0.91. After combining naive bayes with gain ratio and Adaboost, naive bayes performance improves. Namely, the accuracy of Naive Bayes performance increased to 0.94 and the TPR, TNR and NPV values also increased to 0.571, 0.975 and 0.9642.
A Comparative Study of Thermal Face Recognition Based on Haar Wavelet Transform (HWT) and Histogram of Gradient (HoG)

A comprehensive evaluation of the visual features applied to the proposed face recognition method on thermal images is presented. Comparative performance analysis is carried out on two different features which includes Histogram of Gradients (HoG), and Haar Wavelet Transform (HWT) plus Principle Component Analysis (PCA), resulting two different classifiers which include Support Vector Machine (SVM) and Multi-Layer Perception (MLP) is conducted. The comparisons are made for the following cases. Case-1 feature extraction (HoG) and classify (SVM), Case-2 feature extraction (HoG) and classify (MLP), Case-3 feature extraction (HWT & PCA) and classify (SVM), and Case-4 feature extraction (HWT & PCA) and classify (MLP). The face recognition experiments were carried out and compared by using python programming. The image dataset has 4400 images obtained from 15 subjects of various ages and genders recorded by the IR-BF503 thermal camera with several head poses, occlusions and illuminations. The performance of the face recognition method is measured based on the MSE, PNSR, bit rate, ratio, and time compression, accuracy, precision, recall, and F1 score. The purpose of this method comparison is to find a suitable method for facial recognition which is used to measure body temperature and heart rate. Finally, the algorithm implemented to case-4 showed a good performance due to its highest accuracy, F1 score, and recall.
Graph neural network for merger and acquisition prediction

This paper investigates the application of graph neural networks (GNN) in Mergers and Acquisitions (M&A) prediction, which aims to quantify the relationship between companies, their founders, and investors. M&A is a critical management strategy to decide if the company is to grow or downsize, and M&A prediction has been a challenging research topic in the past few decades. However, the traditional methods of predicting M&A probability are only based on the company's fundamentals, such as revenue, profit, or news. Instead, GNN takes full advantage of those relationship data to expand feature dimension and improve the prediction result. Our M&A prediction solution integrates with the topic model for text analysis, advanced feature engineering, and several tricks to boost GNN. The approach achieves a high Area-Under-Curve score (AUC) 0.952, which is better than the previous record 0.888. The true positive rate is 83% with a low false positive rate 7.8%, which performance is better than the previous benchmark record 70.9%/10.6%.
Monte carlo tree search for trading and hedging

Monte Carlo Tree Search (MCTS) has had very exciting results in the field of two-player games. In this paper, we analyze the behavior of these algorithms in the financial field, in trading where, to the best of our knowledge, it has never been applied before and in option hedging. In particular, using MCTS algorithms capable of handling stochastic states and continuous actions, we setup a practical framework testing it on real data both in the trading and hedging case.
Detection of weapons using Efficient Net and Yolo v3

With only 9% of the world's population, Latin America has one of the highest rates of violence in the world, generating insecurity, crime, robberies, weapons and homicides. In this project we worked with object detection to detect various types of weapons in public spaces such as stores, ATMs, streets, among others. Several trainings with different data sets and different neural network models were evaluated on the plataform Google colaboraty. Two models were used for training, Yolo v3 and Efficient D0, the models were trained with four categories of firearms; pistol, submachine gun, shotgun and rifle. The results of the experiments show that Yolo v3 is the best network for detecting firearms with an accuracy of 0.80 out of 1.
Modelling the Social Interactions in Grey Wolf Optimizer

Swarm Intelligence has been successfully used for solving high-dimensional and multimodal optimization problems. However, the wide range of swarm-based techniques, operators, and parameters requires prior knowledge before applying them to real-world problems. Because of this, we have been studying the meso-level characteristics that emerge from the social interactions within the swarm to understand each swarm-based technique’s unique characteristics. In this paper, we model and study the interaction network of the Grey Wolf Optimizer (GWO) to capture its social behaviour. We used Portrait divergence to compare the similarity between network structures over experiments, simulations and iterations of the GWO. We also used Kullback divergence to compare the probability distributions of the network flows varying over experiments, simulations and iterations of the GWO. Furthermore, we discovered we could identify the GWO convergence using the interaction network approach. Comparing different simulations, we found that the wolves communicate using a stable network structure but not necessarily a stable network flow indicating variance in the number of highly influential wolves. We also point out patterns found in GWO that appears to be similar to other swarm-based algorithms (GPSO and FSS).
More than Extracting "Important" Sentences: the Application of PEGASUS

Pre-trained language models may reduce the amount of training data required. Among the models, PEGASUS, a recently proposed self-supervised approach, is trained to generate the pseudo-summary given the partially masked document. PEGASUS uses gap sentence generation for summarization. The most important sentences are masked, and then PEGASUS predicts the masked sentences as the output summary. In this study, however, we apply PEGASUS in a novel downstream task. We reformulate the task to generate the masked question part in a primary math word problem. In the past research, PEGASUS has shown good potentials on the few-shot datasets, so we try a smaller set of primary math text problems as well. The fine-tuning dataset sizes used in this study are 1000, 500, 50, 10 samples. Their performance are measured by a non-weighted average of the ROUGE-1, ROUGE-2, and ROUGE-L scores. The results show the outstanding performance of PEGASUS applied in our novel downstream task.
Improved Game Units Balancing In Game Design Through Combinatorial Optimization

Game balancing is an essential part of game design, and it plays a vital role as the balancing results directly affect the players' experiences. At present, there is no well-admitted definition of game balance. Generally, the game designers hold their own envisions of “balancing” in specific contextualized environments. Accordingly, it is a great challenge for game designers/developers to understand and establish. Since game balance is a multifaceted concept, there is no prescriptive guideline either. In this work, numerical balancing is selected to investigate the possibility of applying engineering optimization techniques to the game unit balancing in a quantitative manner. Currently, although the definite rules of game mechanics could be expressed mathematically, the game developers tune different attributes manually to evaluate the changes in an iterative way, which is tedious and time-consuming. Moreover, the balancing quality and efficiency strongly rely on the game developers' personal skills. To address this issue, we modeled the balancing process as a combinatorial optimization problem, in which the assessment metrics of game units are optimization objectives, and the attributes of game units are variables. The “balancing” of game units is interpreted as minimizing the standard deviations of the assessment metrics, and the evolutionary algorithm such as NSGA-II is used to find the optimization solutions. A typical case study is employed to demonstrate the proposed idea, and the optimization results show that the proposed method can simultaneously improve the numerical balancing in terms of quality and efficiency. At last, we summarize the contributions of the work and discuss how the proposed method can be further improved for game balancing in future work.
A Multi-Stage Vision Transformer for Fine-grained Image Classification

Different from general image classification, Fine-grained visual classification(FGVC) aims at recognizing the subclass of an object. This task is challenging due to the differences between subcategories that might be very small. At present, Vision Transformer(ViT) achieved huge success in traditional classification tasks benefited from its innate attention mechanism. Recent works extend the vision trans-former into the FGVC task and achieve state-of-the-art performance. However, those transformer-based structures inevitably harm the discriminative regions while also requiring a high computational cost. To address these problems, we propose a pooling-based vision transformer model with overlapping patches(OP-ViT), which inserts the pooling layer into a transformer encoder to form a multi-stage hierarchical structure. In particular, the overlapping patches scheme can preserve the informational region effectively. We evaluate the model on two fine-grained mushroom datasets. The experimental results show that the proposed model achieves competitive performance with fewer parameters and computational overhead.
Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation

Studies show that developers&#x2019; answers to the mobile app users&#x2019; feedbacks on app stores can increase the apps&#x2019; star rating. To help app developers generate answers that are related to the users&#x2019; issues, recent studies develop models to generate the answers automatically. Aims: The app response generation models use deep neural networks and require training data. Pre-Trained neural language Models (PTM) used in Natural Language Processing (NLP) take advantage of the information they learned from a large corpora in an unsupervised manner, and can reduce the amount of required training data. In this paper, we evaluate PTMs to generate replies to the mobile app user feedbacks. Method: We train a Transformer model from scratch and fine tune two PTMs to evaluate the generated responses, which are compared to RRGEN, a current app response model. We also evaluate the models with different portions of the training data. Results: The results on a large dataset evaluated by automatic metrics show that PTMs obtain lower scores than the baselines. However, our human evaluation confirm that PTMs can generate more relevant and meaningful responses to the posted feedbacks. Moreover, the performance of PTMs has less drop compared to other model when the amount of training data is reduced to 1/3. Conclusion: PTMs are useful in generating responses to app reviews and are more robust models to the amount of training data provided. However, the prediction time is 19X than RRGEN. This study can provide new avenues for research in adapting the PTMs for analyzing mobile app user feedbacks.
Perceptions Towards MySejahtera, e-Penjana: Identifying Key Factors

Hybrid systems are increasingly popular to reduce complexity as well as to integrate the best of diverse options. This paper explores Malaysians' perceptions towards the MySejahtera contact tracing app and e-Penjana, its accompanying e-wallet service provider. Findings highlight that mapping to the Capability Maturity Model and design thinking in MySejahtera-e-Penjana, has led to a needs-based government-citizen communication means and an open/scalable/extensible supply chain strategy. Analyses using logistic regression and clustering indicate that usefulness, ease of use and social norms are the most important in accepting MySejahtera, and for e-Penjana, convenience, variety and the types of incentives. Consequently, we gather requirements on a healthcare chatbot, to aid/educate the seniors, who may be less tech-savvy. We surmise, for the above apps, data mining can lead to context-aware help, and useful related product recommendations. With additional integration and smart partnerships, it is a testbed for interconnected extensions.
Attention Residual Network for White Blood Cell Classification with WGAN Data Augmentation

In medicine, white blood cell (WBC) classification plays an important role in clinical diagnosis and treatment. Due to the similarity between classes and lack of training data, the precise classification of WBC is still challenging. To alleviate this problem, we propose an attention residual network for WBC image classification on the basis of data augmentation. Specifically, the attention residual network is composed of multiple attention residual blocks, an adaptive average pooling layer, and a full connection layer. The channel attention mechanism is introduced in each residual block to use the feature maps of WBC learned by a high layer to generate the attention map for a low layer. Each attention residual block also introduces depth separable convolution to extract the feature of WBC and decrease the training costs. The Wasserstein Generative adversarial network (WGAN) is used to create synthetic instances to enhance the size of training data. Experiments on two image datasets show the superiority of the proposed method over several state-of-the-art methods.
Mobile robot path planning based on improved genetic algorithm

In view of the disadvantage that the traditional genetic algorithm cannot be planned when it is applied to robot path planning in a special environment, the population initialization method is improved first, and the path planned by the two-way RRT algorithm replaces a part of the initialization population to form an elite population. Then this paper improve the fitness function, add path smoothing coefficients and obstacle coefficients to ensure path quality, and at the same time add elite strategies to speed up the convergence speed; finally, the traditional genetic algorithm, two-way RRT algorithm, and the algorithm of this paper is carried out in the same map environment. The results show that the elite population genetic algorithm proposed in this paper overcomes the shortcomings of traditional genetic algorithms that are difficult to plan paths in special environments, has higher adaptability to the environment, and improves the success rate of path planning.
Human Segmentation for Classroom Video: dealing with the small size overlapped and distorted human

Segmenting humans and recognizing different areas of a body from a complex scenario is a fundamental and critical step for developing technology-enhanced classroom teaching video reviewing systems. Such systems could elevate laborious video reviewing processes and assist educators to improve their teaching quality. The current state-of-the-art instance segmentation techniques do not meet the requirements to solve problems found in classrooms, including human overlapping and occluded, and high object size variation. Thus, this paper presents an integrated method that combines a general-purpose instance segmentation with a robust Face Detection algorithm. The proposed method can detect and segment humans in the classroom environment. Human faces are also detected and matched to each human instance to enrich the required data for classroom environment analysis. The system was trained and tested on a custom annotated dataset consist of 1,000 images of students in classrooms and situations with different sizes and numbers of human. Our combined method can segment 86.46% of the instances, with 69.50% of the mean Intersection over Union (mIoU) and perform better than end-to-end Fine-Tuned Mask RCNN.
Online Florist System Based on JavaWeb

The sudden COVID-19 pandemic made us feel the limitations of offline sales. In order to let consumers fully realize the convenience of the Internet era and highlight the advantages of online sales, a personalized online florist system (OFS) is developed. The website takes JSP (JavaServer Pages) &#x002B; SSM (Spring &#x002B; SpringMVC &#x002B; MyBatis) framework as the development technology and based on B/S (Browser/Server) structure, an online florist system based on JavaWeb is designed and implemented.
TIAS: Two-level Information-Agnostic Job Scheduling in GPU Clusters

In recent years, deep learning algorithms have shown a trend towards larger models and larger datasets. Centralized training is unable keep up with the training requirements due to limited storage and computing resources, thus distributed learning is becoming an important area of research for improving learning efficiency. There are many studies on using the features of deep learning workload to design a central scheduler for production clusters.While existing work has been focusing on overall completion time and resource efficiency, little attention has been paid to the execution deadlines. To achieve a balance between the goals of deadline and non-deadline jobs, we design a Two-level Information-Agnostic Scheduling strategy(TIAS), which can schedule the two kinds of jobs together without knowing jobs&#x2019; training duration. In the first level, we use different priority calculation methods for the two kinds of jobs; in the second level, we design a new indicator "queue urgency" based on three observations to sort deadline jobs within the same queue. Experiments on a trace-driven simulator prove that TIAS can achieve the best trade-off between deadline miss rate and non-deadline jobs&#x2019; average job completion time(JCT) compared to existing solutions.
An Improved Faster R-CNN Algorithm for Pedestrian Detection

Pedestrian detection is an important branch of computer vision and has been the focus of research due to its wide range of applications. Although commonly used object detection model Faster R-CNN has achieved good results. However, there are still some shortcomings in the specific task of detecting pedestrians. This paper made three improvements to the Faster R-CNN to better adapt it to the pedestrian detection task. First, we did a lot of experiments and finally chose MobileNetv2 as our backbone network. Second, we designed a multi-branch feature pyramid network (M-FPN), which is used to better integrate the model's shallow feature information with the deep feature information improved the model's ability to detect pedestrians. Finally, an attention region proposal network SE-RPN is used to improve the model's ability to focus on pedestrian features and suppress attention to background interference features. The experimental results show that the improvement strategy proposed in this paper has achieved better results. These strategies improve the average accuracy of Faster R-CNN on our self-built dataset by 6.14% and the detection speed by 27fps. The AP on Caltech dataset reaches 87.01%, and the detection speed can achieve 39.4fps.