Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Software Effort Estimation using Optimized C&amp;K Metrics

Research has focused on the technique of measuring the genuine software maintenance effort in case of object-oriented system using C&K metrics. Previous studies have demonstrated that assessing maintenance effort is a time-consuming and costly Endeavour. Thus, various soft computing methodologies must be employed to prioritize and assess effort. The purpose of this research is to look at several maintenance effort estimation methodologies in software testing in order to come up with an algorithm that leverages object-oriented complexity inferred from C&K metrics to estimate maintenance work Finally, the proposed study will investigate and compare the recommended approach to other algorithms. In a literature analysis, the research paper offered an overview of existing approaches for estimating maintenance labor. For object-oriented applications, research has created an effort estimate technique based on enhancing C&K measures. The recommended technique prioritizes modules employing the attributes WMC, NOC, CBO, LCOM, DIT, and RFC during effort estimation using C&K metrics in object-oriented programs. This technique was intended to measure the difficulty of object-oriented programming sustained programming. Particle swarm optimization was utilized to find the ideal weight value. For various modules, object-oriented metrics are utilized to generate rankings. A solution for calculating effort has been offered, as well as a unique way for ranking utilizing a weight mechanism.
Comparative Analysis of Pattern Matching Algorithms Using DNA Sequences

DNA sequences are the most crucial in a cell as they code for the generation of proteins that affect the metabolism of an organism. Some viral pathogens can affect the host cells by integrating their DNA into the host cell’s DNA. After integration, the replication of the host cell occurs in favor of the virus. During integration, DNA sequences that encode viral proteins are integrated with the DNA of host cells. Finding the position of viral DNA becomes highly tedious as DNA sequences are extremely long. This job can be made easier with the use of pattern-matching algorithms. In this paper, a comparative analysis of different pattern matching algorithms has been presented. The algorithms taken up are Brute-Force, Knuth Morris Pratt, Rabin Karp, Boyer Moore, Shift-Or, and Z-Algorithm. DNA sequences have been used as a medium to compare these algorithms. The algorithms have been analyzed using different parameters such as run time complexity, pre-processing time complexity, and search time complexity. The best algorithm can be used to detect diseases caused by DNA integrating pathogens at early stages to reduce complications at later times.
Thai Sentiment Analysis for Social Media Monitoring using Machine Learning Approach

Sentiment analysis has become essential in marketing research to understand customer attitudes or opinions. This research proposes a Thai sentiment analysis framework for social media. Our framework is a four-step process that is very simple and easy to implement. To create an effective framework, the proposed method was tested on two different datasets for further support in any domain. The experiments were conducted on ten well-known classifiers from the Scikit-learn library. The comparative performance of machine learning algorithms for sentiment analysis shows that Logistic Regression and SVM yield the highest accuracy.
Student-sponsored projects in a capstone course

Undergraduate students in all disciplines sometimes struggle to see the connections between what they learn in the classroom and how it will be used in practice. Capstone courses, internships, and co-op experiences can help students bridge this gap by allowing students to put theory into practice on real-world projects and by connecting students to industry practices and partners. These experiences can better prepare students for their careers, but they may also shield students from real-world risks, such as lack of product feasibility or viability, and from the non-technical tasks of business planning and startup tasks, in order to make the academic aspects of the course manageable and scalable. In this work, we present our experience offering undergraduate Software Engineering students the opportunity to self-sponsor a year-long capstone project. Students began the course with four software product ideas, none of which had been assessed for feasibility or viability. During the academic year, students were responsible for product discovery, technology selection, and technical development following good Software Engineering practices. They also created business and marketing plans, and formed an LLC. This paper1 describes the processes, practices and timeline we followed for a student-sponsored capstone team. We also share our lessons learned and offer suggestions for student-sponsored projects in computing capstone courses.
A Multimodal Approach to CBIR Using Dimensionality Reduction Techniques

In the realm of computer vision, image processing, and image retrieval is one of the most rapidly growing disciplines. The image can be retrieved using the annotation and the content - based. CBIR (Content-based image retrieval) is becoming more popular in a range of areas, as well as data mining, education, diagnostic imaging, preventing crime, weather prediction and remote sensing. We can browse, search and retrieve images using an image retrieval system. This paper presents the CBIR using an orthogonal combination (OC) the Local Binary pattern (LBP), center-symmetric local binary pattern (CS-LBP) and PCA (Principal Component Analysis) of LBP. By computing the Gray-level difference, this approach connects the referenced pixel to its surrounding neighbors. Choosing the most appropriate images that correspond to the search image from the stored database is the paper’s primary objective. The proposed model has a combination of OC-LBP, CS-LBP, and PCA-LBP.
Developing a Methodology for Co-creation using Extended Reality Technologies

In the past decades there is a lot of heed towards Human Centered Design (HCD) Approach in which users are involved for needs collection or validation sessions of the prototypes for problem identification. A successful design relies also on how well different stakeholders are proactively involved since the beginning or even in the creative phase of product development. Therefore, in the recent times, innovative organizations are shifting their ideation strategies towards the concept of “co-creation” in order to improve their creative businesses. Co-creation is known to draw in final users or customers in the design process to collaborate and interact in jointly generating solutions and creating value. Thus, the platform where co-creators engage plays an influential role in improving the creative participation and thus user experience. Traditional co-creation platforms limit a user in various forms. The immense growth of innovative technologies such as Extended Reality (XR) tools have expanded to interactive and collaborative environment developments regulating new ways of working. My PhD research proposes XR tools as potential digital co-creation platforms and focuses on establishing the key factors that influence “co-creation” process in XR by laying out a methodology and developing number of applications fulfilling these factors during product development process promoting the idea. This paper describes the principles of co-creation, the importance of cocreation platforms, elements of co-creation and outlines few application scenarios developed in the context of product development process striving to demonstrate XR environment as a co-creation space. The research will also provide interesting insights into emerging design practices and expects to study the factors that affect a user in this innovative co-creative XR world and collect practical recommendations. Through ISMAR'22 Doctoral Consortium, I would like to gain expert inputs on validation methods for the applications developed that can strengthen the concept proposal.
Investigating Effects of Centralized Learning Decentralized Execution on Team Coordination in the Level Based Foraging Environment as a Sequential Social Dilemma

In this work, we investigate the effects of centralized learning decentralized execution algorithms on agent coordination in a modified version of the Level Based Foraging environment that behaves as a sequential social dilemma. We show that with individual agent rewards, Level Based Foraging becomes a sequential social dilemma. When compared with previously reported results on the Level based Foraging environment using joint rewards [13], we observe significant convergence rate improvements for algorithms that perform state action value estimation: IQL, VDN and QMIX.
Evaluation of Different ML and Text Processing Techniques for Hate Speech Detection

Social media has become a domain that involves a lot of cyberbullying and hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims of cyberbullying. In this paper, we investigate the applicability and performance of a variety of machine learning techniques with the help of text processing techniques in the pursuit of a reliable system for identifying hate speech. We evaluate Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors on three different datasets. We used Term Frequency-Inverse Document Frequency (TF-IDF), unigram, bigram, trigram, combination of unigram and bigram, and combination of unigram, bigram, and trigram for the text corpus read by the machine learning techniques to see which approach gives better results. Additionally, since the datasets are unbalanced, we under-sampled and over-sampled the data and investigated the results. Our results show that on all three datasets over-sampling the minority class and using a combination of unigram, bigram, and trigram gave the best results with Random Forests achieving the highest F1-score of at least 0.96 on the three datasets. This was followed by SVM and Logistic Regression, which achieved F1-scores of at least 0.939.
A multi-stage restoration method for degraded images with light scattering and absorption

Degraded images with light scattering and absorption such as haze, underwater and sandstorm images always suffer from low contrast, detail loss, and color distortion. To solve these problems, herein, we propose a multi-stage image restoration method which includes three steps: dehazing, detail preserving and color correction. Dehazing includes estimations of ambient light and transmission map. Ambient light is estimated by fusing scene depth map and high illumination area, and transmission map is modified by adaptive linear transformation. Then, an objective function with Relative Total Variation (RTV) and edge preservation component is proposed to preserve details and suppress noise. Finally, an effective color correction method is introduced to correct color distortion and avoid over-correction. Experiments on haze, sandstorm and underwater images demonstrate that our method can obtain high quality results with high contrast, clear visibility, and natural color. Additional experiments suggest that our method can also enhance low-light images.
A Lightweight Denoising Method Based on Noise2Void for X-ray Pseudo-color Images in X-ray Security Inspection

For public security and crime prevention, dualenergy X-ray detection technology has been widely used in the security inspection. Due to the special working environment of the security checker, the generated X-ray pseudo-color images contain a lot of noise. While the image denoising methods based on deep learning have made tremendous progress in the field of image denoising, most existing methods require huge training datasets and clean target images, which limits the application of these methods. In this paper, considering that clean target images are impossible to be obtained for X-ray pseudo-color images, we design a lightweight denoising method based on Noise2Void, which does not require clean target images. Moreover, a lightweight denoising network based on the depthwise separable convolution and U-net is designed to reduce computational consumption while guaranteeing denoising performance, thus ensuring wider applicability and adaptability. The method is evaluated on our real X-ray pseudo-color image dataset with the existing methods and the experimental results demonstrate that our method has better denoising performance and wider applicability.
Improving Content-Aware Video Streaming in Congested Networks with In-Network Computing

Network congestion and packet loss pose an ever-increasing challenge to video streaming. Despite the research efforts toward making video encoding schemes resilient to lossy network conditions, forwarding devices have not considered monitoring packet content to prioritize packets and minimize the impact of packet loss on video transmission. In this work, we advocate in favor of in-network computing employing a packet drop algorithm and an in-network hardware module to devise a solution for improving content-aware video streaming in congested network. Results show that our approach can reduce intra-predicted packet loss by over 80% at negligible resource usage and performance costs.
Toward Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline

Recently, 3D vision-and-language tasks have attracted increasing research interest. Compared to other vision-and-language tasks, the 3D visual question answering (VQA) task is less exploited and is more susceptible to language priors and co-reference ambiguity. Meanwhile, a couple of recently proposed 3D VQA datasets do not well support 3D VQA task due to their limited scale and annotation methods. In this work, we formally define and address a 3D grounded question answering (GQA) task by collecting a new 3D VQA dataset, referred to as flexible and explainable 3D GQA (FE-3DGQA), with diverse and relatively free-form question-answer pairs, as well as dense and completely grounded bounding box annotations. To achieve more explainable answers, we label the objects appeared in the complex QA pairs with different semantic types, including answer-grounded objects (both appeared and not appeared in the questions), and contextual objects for answer-grounded objects. We also propose a new 3D VQA framework to effectively predict the completely visually grounded and explainable answer. Extensive experiments verify that our newly collected benchmark datasets can be effectively used to evaluate various 3D VQA methods from different aspects and our newly proposed framework also achieves the state-of-the-art performance on the new benchmark dataset. The datasets and the source code are available via <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zlccccc/3DVL_Codebase</uri> .
Cooperative-competitive Game-based Method for Local Path Planning with Obstacle Avoidance in Racing Scenarios

In this paper, a cooperative-competitive game based planning method (CCGP) is proposed to address the local path planning with obstacle avoidance in racing scenario. During the racing, the robots in the same group cooperate with each other to achieve the goal, while competing with another group of robots. The goal of each group of robots is to reach the same target point ahead of the other group of robots, and there is no collision with other robots and static obstacles. This cooperation competition relationship is described as a cooperation competition game in theory. The objective function of each robot is formulated as an optimization problem, and the collision avoidance in robot-robot and robot-obstacle is regarded as constraints. The rationality of the proposed objective function in the local path planning problem is theoretically analyzed. Then MPC is used iteratively to compute the approximate optimal local trajectory of each robot under Nash equilibrium by considering the best response of other robots, without collision with other robots and obstacles. We compare our method with the MPC method by conducting racing simulations in a scene with static obstacles. The results show that our method has better performance, and the robots in our group can obstruct and overtake robots of the MPC group, while they avoiding collision with teammates and static obstacles.
A Chronic Disease Medication Data Sharing Model Based on Blockchain

Chronic Disease Medication Data (CDMD) includes a great number of valuable patients’ information collected from smart pillboxes. CDMD sharing in the Chronic Disease Medication Compliance Management System is helpful to improve the medical level and service. Blockchain provides a new way to solve the problems, e.g., privacy security, user control rights, and single point of failure, in data sharing. In this paper, we propose a secure CDMD sharing method in a decentralized way based on blockchain and cryptography technology to realize secure data sharing among entities by a model of ’on-chain deposition to confirm rights, off-chain storage of transmission data’. This method can enable secure sharing of medication administration data between institutions, avoiding the trustless problem caused by data centralization.
Deep Attention Factorization Machine Network for Distributed Recommendation System

With the proposal of Microsoft's Deep Crossing, Google's Wide & Deep, FNN, DeepFM and other deep learning models, deep learning recommendation model has become the mainstream of recommendation system. These models can realize interactive learning of different features, but do not consider the impact of attention mechanisms on the prediction results. Therefore, we introduce the multi-head attention mechanism into DeepFM, and propose a deep attention factorization machine network (DAFMN). We add an attention layer between the embedding layer of user behavior features and the FM and DNN layers in this model. So that the accuracy of the recommendation results can be improved according to the user's attention to different intersection features. In addition, we use HDFS and Spark to realize distributed storage and offline computing of features to improve parallel processing capabilities. We conducted comparative tests on two public datasets, and show that the GAUC of the DAFMN model achieves 5.24% and 3.53% improvement in RelaImpr compared with the base model.
DE with A new population initialization method for the dynamic multidimensional knapsack problems

Differential Evolution (DE) is a well-known continuous optimization algorithm that performs very well on many continuous optimization problems, and some recent studies show that the DE algorithm is experimentally proven to be equally good at solving discrete problems like the Multidimensional knapsack Problem (MKP). But due to the limitation of solving speed, it is difficult for the DE algorithm to perform well in the speed- hungry dynamic multidimensional knapsack problem(DMKP). To this end, this paper proposes a new population initialization method (NPIM), which enables the initial population quality to be improved by leaps and bounds through information reorganization. NPIM can even be used as a separate algorithm to solve MKP when the problem scale is small. Experimentally, it was found that NPIM doubled the speed of the DE algorithm to solve MKP
3D Human Skeleton Estimation Based on RGB Image Sequence and Graph Convolution Network

We propose a technique of 3D human skeleton estimation from RGB image sequence. Our method uses two-stages of deep learning networks. The first stage is to estimate enhanced 2D skeletons (2D image coordinates and relative-depths for all joints). The sequence of enhanced 2D skeletons is represented as a spatial-temporal graph (STG) which is then input to the second stage composed of Graph Convolutional Network (GCN) as the backbone. Techniques of high-order feature representations for joints, multi-stream feature adjustments, and denoising, were developed to further promote the accuracy performance for the estimated 3D skeletons. The Human3.6M dataset was used for training and testing. Experimental results show that our multi-stream GCN-based network can extract useful information from the input sequence efficiently. From experiments, the mean per joint position error (MPJPE) of the 3D skeletal joints is 47.27 mm when a sequence of 31 RGB frames are considered.
Knowledge Selection and Local Updating Optimization for Federated Knowledge Distillation With Heterogeneous Models

Federated learning (FL) is a promising distributed learning paradigm in which multiple edge devices (EDs) collaborate to train a shared model without exchanging privacy-sensitive raw data. With the heterogeneous architectures of local models in FL, knowledge distillation (KD) offers a new way to deal with model heterogeneity by aggregating knowledge instead of model parameters. Usually, there is no pre-trained teacher model in the FL setup, to apply KD in FL, it is critical to design efficient knowledge aggregation mechanism. To this end, in this paper, we first analyze the relationship between each local model convergence rate and the knowledge generated by selecting predicted logits. Then, an optimization problem based on this relationship is formulated to schedule the predicted logits for efficient knowledge aggregation. An iterative algorithm called predicted logits selection (PLS) is designed to solve this problem. After that, we propose a threshold-based technique to optimize the local model updating options with/without KD for each ED in order to limit the performance degradation of local models caused by misleading knowledge. Meanwhile, we optimize both the threshold selection and the reasonable distillation intensity during the FL process. Extensive experiments based on convolutional neural network (CNN) models and real-world datasets demonstrate the superior performance gain of the proposed approach over existing benchmark methods.
Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes

Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models. State-of-art approaches for semi-supervised learning rely on student-teacher models trained using a multi-stage process, and considerable data augmentation. Custom networks have been developed for the weakly-supervised setting, making it difficult to adapt to different detectors. In this paper, a weakly semi-supervised training method is introduced that reduces these training challenges, yet achieves state-of-the-art performance by leveraging only a small fraction of fully-labeled images with information in weakly-labeled images. In particular, our generic sampling-based learning strategy produces pseudo ground-truth (GT) bounding box annotations in an online fashion, eliminating the need for multi-stage training, and student-teacher network configurations. These pseudo GT boxes are sampled from weakly-labeled images based on the categorical score of object proposals accumulated via a score propagation process. Empirical results <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code is available at: https://github.com/akhilpm/SemiWSOD on the Pascal VOC dataset, indicates that the proposed approach improves performance by 5.0% when using VOC 2007 as fully-labeled, and VOC 2012 as weak-labeled data. Also, with 5-10% fully annotated images, we observed an improvement of more than 10% in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> AP, showing that a modest investment in image-level annotation, can substantially improve detection performance.