Reviewerly/exHarmony huggingface dataset:
Breast Cancer Cell Segmentation Using Attention-Based Deep Neural Network

Early diagnosis is crucial for the treatment of breast cancer, the most prevalent disease in women globally. Segmenting cancer cells from photographs of breast tissue is an important first step in the assessment of breast cancer. Here, we propose a deep neural network that uses attention to aid in the separation of breast cancer cells. The proposed technique employs an encoder-decoder architecture with an attention mechanism to zero on the most relevant parts of a picture. We tested the proposed approach on a collection of breast tissue pictures and compared the results with those obtained using the current best practices. Our experiments show that the proposed technique achieves outstanding performance, with an accuracy of 97.26 percent, sensitivity of 95.26 percent, specificity of 98.98 percent, and Jaccard index of 0.89.
Multivariate Travel Time Forecasting in a Traffic Network Using Fuzzy Cognitive Mapping

Traffic forecasting is a crucial component in the implementation of efficient traffic management and control. While various works have been made in this area, most of the current works focus on traffic flow forecasting. Although traffic flow is an important feature in traffic modeling, other variables such as travel time, are also useful in developing robust traffic models. Travel time forecasting is a relatively underexplored area in the literature, especially when additional features are considered in generating the forecasts. This paper uses the fuzzy cognitive mapping approach to develop a multivariate travel time forecasting model. The construction of the FCM uses the pseudoinverse learning algorithm. A case study was performed in a road network in Cebu, Philippines with 20 selected routes. The results of the study showed that the FCM model outperforms the benchmark models in terms of directionality and magnitude. The paper significantly contributes to the literature in three ways. Firstly, it is one of the few works that investigate multivariate travel time forecasting. Secondly, it pioneers the application of FCM to multivariate traffic forecasting. Finally, it is one of the few works offering lenses in the Philippines. The findings in this study would be beneficial for stakeholders, such as traffic practitioners, policymakers, and urban planners in the development of relevant initiatives.
Smart Bottle to Revolutionise Healthcare Industry

When the patients are hospitalised, they put their faith in medical professionals to take good care of them while they are vulnerable and in pain. Receiving IV through fluids is one of the features for being hospitalised. In the large setups like hospital, a doctor may have to attend several patients and is a huge task for a single person to keep updated about each patient's IV bottle level. Despite of being careful, as the nurse has a tight schedule, he/she may tend to forget to change the IV bottle timely which may cause air bubbles to center the patient's blood system or reverse blood flow to the IV/electrolyte bottle may be caused which leads to complications. To elude this, an IOT based device is proposed which uses an advanced technology where the weight data of the IV bottle has been fed to the cloud through NodeMCU microcontroller. This project aims to inform the nurse about the IV bottle level through wireless. Using android studio and fire base which belong to development environment, an app is created. The app displays the level of IV bottle while providing the details of the patient. It raises an alarm if the weight of the bottle is below a specified limit and also communicates to the respective doctor or the caretaker by means of an SMS, indicating the room number of the patient.
Study of ECAPA-TDNN Models for Spoken Language Identification Task

Spoken Language Identification (LID) is the task to identify the language spoken by the speaker regardless of gender, accent and age. In this paper, we study a state-of-the-art deep learning (DL) architecture, named Emphasized Channel Attention, Propagation, and Aggregation in Time Delay Neural Networks (ECAPA-TDNN) in context of LID task. This study focuses on the effects of different input and system parameters on performance of a LID system namely (i) channel size in ECAPA-TDNN model (ii) training data duration and language diversity, and (iii) parameter initialization on ECAPA-TDNN training. This study is carried on VoxLingua107 speech corpora having 107 languages. It is observed that the performance of ECAPA-TDNN model is influenced by all of the above factors. The study brings out that optimal parameter settings can help in achieving a trade off between reasonable accuracy and resource requirements such as amount of training data and time required for training. These are important practical considerations in building of models for language identification task.
Research Challenges for Legal Document Summarization

Legal judgment documents are detailed and contains legal terms and codes. These characteristics of legal documents makes it complex to read and analyze, which makes processing legal documents a challenging task. This raises a need for generating automatic summaries. Several techniques have been used by researchers to summarize legal documents such as traditional methods, legal specific approaches and transformer models based approaches. This research focuses on role of summarization in legal domain and various methods for summary generation. We summarize Indian judgment documents with various state of art methods for comparative study. The analysis opened various research challenges.
Label Confidence Based Ensemble Partial Label Learning

Partial Label Learning (PLL) aims to train a multi-class classifier to deal with the problem that each training instance is associated with multiple candidate labels, but only one of them is true. For PLL, the averaging-based disambiguation strategy treats all candidate labels equally, however, this strategy may lead to the model prediction of candidate labels being stronger than the model prediction of valid labels. In this paper, decision trees are retrofitted by considering the inherent properties of PLL, and a method named PL-RF (label confidence based ensemble partial label learning) is proposed that can overcome the defects of averaging-based disambiguation strategies and obtain better classification performance by ensembleing multiple partial label decision trees, then determining the final predicted label by voting on each tree. Extensive experiments over both real-world and synthetic partial label data sets empirically justify the effectiveness of PL-RF, which achieves more competitive performance than other state-of-the-art PLL methods.
Government Tender Allocation Using Blockchain Technology

The objective of this paper is to present a blockchain-based tender allocation project that aims to use blockchain technology's decentralized and secure nature to create a transparent and efficient system for distributing government contracts and other tenders. The problem in earlier models of tender-based allocation systems was that they were centralized and vulnerable to cyber and phishing attacks. Hence, it was needed to decentralize the whole process. In traditional tender allocation systems, ensuring the process is transparent and fair can be difficult. This can lead to suspicions of favoritism and corruption. This is a problem for both government and private organizations that are looking to secure contracts. A blockchain-based solution can address these issues by providing a decentralized, secure, and transparent platform for the allocation of tenders, enabling trustless and anonymous interactions, and providing an open, accessible platform for all participants. In this work, we present a blockchain-based architecture for public procurements that are both secure and efficient. Our approach is to increase the efficiency of the system by changing the decision-making parameters. Our paper will provide a tamper-proof record of the tender process, increase transparency, and reduce potential fraud.
Intelligent Smishing Detection System for Massive Users

Smishing has always been an important means of black-gray drainage. Currently, interception measures are primarily deployed at the network communication layer of SMS transmission. In response, black-gray industry organizations continuously improve their evasion techniques to bypass the interception strategies along the communication chain. This article presents a novel phishing text message interception method that involves collaboration between the terminal and the cloud. Its advantage lies in the combination of deep learning models for text classification and the identification of reserved information. Firstly, the optimization process involves leveraging a massive corpus in the vertical domain, consisting of hundreds of millions of samples, to optimize the Bert model and TextCNN network. This optimization includes incorporating proprietary features such as statistics, pinyin, and glyph. As a result, classification models are specifically designed for both the cloud and terminals, enabling efficient recognition of ordinary phishing text messages. Secondly, to address spear-phishing attacks and various types of spoofing in phishing text messages, an enhanced named entity recognition technology is employed. This technology allows for the extraction of URLs, QQ numbers, WeChat IDs, mobile phone numbers, and other suspected fraudulent reserved information from the text messages. Subsequently, dedicated models for feature fusion of URLs and heuristic discrimination strategies for network accounts are established based on this extracted information. Combining the two aforementioned detection methods, the paper has implemented an intelligent phishing text message detection system, which has been deployed and applied in a real environment with of hundred millions users. The system achieved a recall rate of 98.32% and an accuracy rate of 99.44%.
Path Planning Method Based on Extended Random Artificial Potential Field

The artificial potential field method is a commonly used algorithm for robot local path planning. However, it is prone to the issues of falling into local minima and generating paths that do not adhere to the robot’s kinematics. In light of this, we have proposed an enhanced approach called the stochastic artificial potential field method. By filtering the obstacle locations and introducing a randomly directed repulsive force within a specific angular range, we address the problem of unbalanced forces on the robot, thereby mitigating the issue of falling into local minima. Furthermore, we eliminate oscillations and sharp bends in the robot’s trajectory through Bessel optimization. Experimental results demonstrate that the stochastic artificial potential field method effectively resolves the challenges of local minima and path conformity with robot kinematics.
Sentiment Classification Based on RoBERTa and Data Augmentation

Sentiment analysis is one of the hot research topics in natural language processing, aiming to analyze the subjective sentiment expressed in text through inductive reasoning. Common deep learning-based sentiment classification models often require the integration of word embedding techniques. However, conventional approaches to generating word embeddings struggle to effectively capture the bidirectional semantic features of text. RoBERTa, with its unique attention mechanism and masked technique, enables the capture of more comprehensive features in the text. In this paper, we introduce the RoBERTa model to the task of Chinese comments sentiment classification and address the limited data issue by employing data augmentation methods to expand the dataset. Through a series of experiments conducted on three different datasets, we demonstrate the effectiveness of the sentiment classification model that combines RoBERTa with data augmentation methods.
Manipulating Multi-Agent Navigation Task Via Emergent Communications

This paper presents a new approach for multi-agent communication via emergent language in a navigation task. The task involves a tourist and a guide who communicate through an emerged language grounded in the environment to help the tourist reach a target place. The paper proposes a collaborative multi-agent reinforcement learning framework that enables the agents to generate and understand emergent language, with the goal of solving the task. We evaluate the results on a simulated navigation game with 3,000 scenarios and multi-turn dialogues. Results show that the proposed approach achieves competitive performance in both language understanding and task success rate. The paper also provides explanations of the emerged language by comparing the language patterns with the environment structures.
Survey of Multimodal Sentiment Analysis Based on Deep Learning

This paper provides a comprehensive review of deep learning applied to multimodal sentiment analysis. Firstly, it briefly outlines the concept, background, and significance of multimodal sentiment analysis. Subsequently, it delves into the application of deep learning techniques in the domain of multimodal sentiment analysis. Furthermore, the paper summarizes advancements in multimodal fusion and interaction technologies. It concludes by discussing future trends and prospects of multimodal sentiment analysis.
Comparative Study of Hashing and Analysis of Collision Resolution Techniques

Most data structures use hash tables. A key method in computer science for effectively storing, accessing, and searching for data is hashing. It involves converting a key into a hash value and using this value to access the corresponding data in a table. Hash table utilization involves trade-offs, just like many other aspects of computer science. The implementation of hash-based containers has two key problems: the collision resolution mechanism and the hash function. However, collisions may occur when two keys are mapped to the same hash value. In this paper, the study is focused on the concept of collision in hashing along with how collision is resolved by comparing the performance of these collision resolution techniques in terms of their time and space complexities. Our findings indicate that each technique has its advantages and disadvantages, and that the best technique to use, relies on the particular application's needs. Therefore, this paper can serve as a guide for developers and researchers to choose the most suitable collision resolution technique for their specific use case.
URF4CCT: A Text Understanding Framework for Chinese Telecom Fraud Cases

With the increasing prevalence of telecom fraud cases in recent years, there is a growing need for efficient and accurate text understanding techniques to aid police officers in investigating and combating these criminal activities. However, the the complexity of Chinese case texts, the absence of a unified framework for the domain, and the reliance on specialized data annotations pose significant challenges in extracting and representing key information from such texts. In this interdisciplinary study, we propose a unified representation framework for Chinese telecom fraud case texts named URF4CCT using pretrained language models. By employing prompt technology with prior vocabulary enhancement module, URF4CCT improves the model’s ability to capture the unique characteristics of telecom fraud case texts. Experimental results demonstrate the superior performance of our proposed method in low-resource and few-shot scenarios. This research contributes to the development of effective text understanding techniques for telecom fraud cases and facilitates police officers’ investigation efforts in online criminal activities.
Unsupervised Optical Mark Localization for Answer Sheet Based on Energy Optimization

Optical Mark Recognition (OMR) is a crucial automated data entry technique in industries such as education, finance, and consulting. However, the localization of optical marks in diverse scenarios remains challenging. Aiming at the problems of diverse types of optical marks, irregular printing quality, and irregular array layout in various examinations, questionnaires, and homework scenarios, this paper presents the first proposal of the energy optimization algorithm for accurate optical mark localization. The algorithm introduces a Single Mark Localization Model (SMLM) within an unsupervised framework. By considering pixel distribution, regional pixel proportion, and aspect ratio, the SMLM achieves precise localization of optical marks. In the experimental evaluation of over 18,000 answer sheets across 82 different marking symbols and layouts, a precision rate of 99.82% and a recall rate of 99.03% were achieved. The results show that the algorithm has strong robustness and versatility. Thus, our study significantly advances the field of optical mark recognition by providing a practical solution to the optical mark localization problem.
Game confrontation model of USV based on multi-agent reinforcement learning

Based on the excellent performance of deep reinforcement learning in solving multi-agent game problems, this paper proposes a multi-unmanned surface vehicle (multi-USV) confrontation game model. The model aims to solve the game problem among multi-USV, learn the strategies and behaviors of USV through deep reinforcement learning, and complete the confrontation game. We carry out experiments in the simulation environment, and apply the model to the cooperative game confrontation task of multiple USV. The results show that the model can learn the strategy of joint attack on enemy vehicles, which has a good practical function great and research value.
A Data Safety Approach Based on Image Steganography

Presently, the threshold for laptop-based interactions is making it easy for us internationally, to convey data, and also to check financial institution balances, offset payments. Recording securely is an important component that should be the way to ensure communication. Steganography is a method that prevents unauthorized users from accessing important data. With the help of steganography, facts can be hidden in service items including images, text and audio recordings, movies, while performing the function of transmitting data. As a result, this study builds on the many studies found within steganography. It is also used to analyze images that have been done on steganography, and the strengths and weaknesses of each work are explained individually. In this paper, we propose a technique of hiding a data in the image steganography by applying the open CV algorithm along with the Tkinter tool and is implemented in python. We have applied the algorithm for 8-bit pixel of an image which makes the images to display more clearly.
A Undersampling-DoppelGANger based Data Generation Method for Unbalanced BGP Data

As one of the main border gateway protocol (BGP) security schemes at present, the research on machine learning-based BGP anomaly detection technology faces severe data imbalance due to the lack of BGP anomaly data. To solve this problem, this paper proposes an unbalanced BGP data generation method based on undersampling technology and DoppelGANger, called US-DPGAN. Firstly, random undersampling is performed on majority-class samples to reduce the imbalanced ratio of the dataset and reduce the bias impact of majority-class samples on the data generated by DoppelGANger. Then, we use the trained DoppelGANger model to generate anomaly samples that match the original data distribution and add these samples to the original dataset to create a balanced BGP dataset. Through qualitative and quantitative experiments, we demonstrate that the data generated by the proposed US-DPGAN method better fits the distribution of the original data compared with the original DoppelGANger method. In addition, the anomaly detection model constructed using the US-DPGAN generated data outperforms some other methods in terms of detection accuracy and F1-Score.
CMCLNet Cross-Modality Attention Fusion and Cross-Level Feature Interaction for RGBD salient object detection

So as to address the issue of edge defects in RGB-D based salient object detection, we propose a novel structure called Cross Modality Attention Fusion and Cross-Level Feature Interaction (CMCL), with the aim of improving edge quality and effectively extracting salient object from complex backgrounds. Our network is an encoder-decoder structure, specifically, (1) a cross-modality attention fusion (CMAF) module is proposed, which enhance and fuse relevant information of RGB images and depth map, then output enhanced features; (2) the cross-level feature interaction (CLFI) module is proposed to improve edge quality in the encoder stage, combining low-level edge features with high-level semantic information; (3) In the decoder stage, we put forward a gate fusion unit (GU) to reduce background noise, enhancing the significant features of the images. We carry on experiments quantitatively and qualitatively on six publicly available RGB-D datasets and compared them using four evaluation indicators. The results show that our network outperforms mainstream salient detection methods to a certain extent, which is of great significance for solving the key problem of salient object detection in computer vision.
3M: Multi-Task Multi-Prompt Learning Model for Aspect Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is an NLP task that classify fine-grained sentiment towards one specific aspect from the same text. While Pretrained language models (PLMs) like BERT have been widely researched in ABSA, attaching aspects to corresponding sentiment remains challenging. In this paper, we propose Multi-task Multi-prompt learning Model (3M) to introduce more proper semantic information for different task aims. In 3M, the ABSA task is divided into two subtasks, a classification task for aspect categorization and a generation task for sentiment analysis. For specific task, we design respective prompt learning approach to help model understand ABSA more accurately. Note that the overfitting using BERT on ABSA, we utilize the method of float loss to restrict the training not too small. The experiment result shows that 3M achieves results comparable to SOTA.
Mapping the Scientific Landscape of Internet of Things and Artificial Intelligence Using VOSViewer

The Internet of Things and Artificial Intelligence are two quickly expanding disciplines that are coming together to provide everyone with exciting new prospects and help automate every industry. The Internet of Things (IoT) refers to a network of interconnected devices, sensors, and software that can communicate with each other and exchange data over the internet. Artificial Intelligence (AI) and Machine Learning (ML) algorithms are being used to analyze and predict the output. The IoT has gained significant attention in recent years due to its potential to transform various industries, including healthcare, transportation, and agriculture. The convergence of IoT and AI is opening up new possibilities in a wide range of industries and applications. Some common bibliometric measures used in IoT research include citation counts, co-citation analysis, and bibliographic coupling. Citation counts measure the number of times an article has been cited by other articles, while co-citation analysis examines the frequency of the articles. Bibliometric analysis is carried out using the most powerful tool, VOSviewer, which is widely used in the academic community for bibliometric analysis and visualization.