Reviewerly/exHarmony huggingface dataset:
Query reformulation system based on WordNet and word vectors clusters

Authors:
raw_author_name: Ravindra Keskar
raw_affiliation_string: Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India

Abstract: With tremendous evolution in the internet world, the internet has become a household thing. Internet users use search engines or personal assistants to request information from the internet. Search results are greatly dependent on the entered keywords. Casual users may enter a vague query due to lack of knowledge of the domain-specific words. We propose a query reformulation system that determines the context of the query, decides on keywords to be replaced and outputs a better-modified query. We propose strategies for keyword replacements and metrics for query betterment checks. We have found that if we project keywords into the vector space of word projection using word embedding techniques and if the keyword replacement is correct, clusters of a new set of keywords become more cohesive. This assumption forms the basis of our proposed work. To prove the effectiveness of the proposed system, we applied it to the ad-hoc retrieval tasks over two benchmark corpora viz TREC-CDS 2014 and OHSUMED corpus. We indexed Whoosh search engine on these corpora and evaluated based on the given queries provided along with the corpus. Experimental results show that the proposed techniques achieved 9 to 11% improvement in precision and recall scores. Using Google’s popularity index, we also prove that the reformulated queries are not only more accurate but also more popular. The proposed system also applies to Conversational AI chatbots like ChatGPT, where users must rephrase their queries to obtain better results.
Hybrid image encryption using advanced least significant bit algorithm, chaotic maps and DNA encoding for digital healthcare

Authors:
raw_author_name: P. Abinaya
raw_affiliation_string: Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India
raw_author_name: B. Lakshmanan
raw_affiliation_string: Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India

Abstract: Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted.
Explainer on GNN-based segmentation networks

Authors:
raw_author_name: Shuaimin Wu
raw_affiliation_string: University of Manchester, Oxford Rd, Manchester M13 9PL United Kingdom

Abstract: Abstract Graph Neural Networks (GNN) are powerful tools for deep learning. Similar to other neural networks, GNNs are complex models, in which humans can’t understand the decision-making procedures of the models. Therefore, it brings the need to explainability of GNNs. Explainability is critical for deep learning to support its predictions. In this paper, we will investigate the Grad-Cam and Integrated-Gradients explaining methods. The Grad-Cam applies a global average pooling over the feature activation mapping, and then which was followed by a ReLU activation to obtain an attribution. The Integrated-Gradients explains models by taking a line integral between the baseline image (a black image) and the source image. We demonstrate how Grad-Cam and the Integrated-Gradients methods explain the graph-deep model in semantic segmentation tasks over the Cityscapes dataset. FCN and LRASSP-MobileNet are used as a comparison to the DualGCN in the experiment to show the explaining effect.
Using nonclassical states to explore the superradiant phase transition in the quantum Rabi model

Authors:
raw_author_name: Junpeng Liu
raw_affiliation_string: School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
raw_author_name: Miaomiao Zhao
raw_affiliation_string: School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
raw_author_name: Yun-Tong Yang
raw_affiliation_string: School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
raw_author_name: Hong-Gang Luo
raw_affiliation_string: Beijing Computational Science Research Center, Beijing 100084, China; School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China

Abstract: The ground state of the quantum Rabi model (QRM) exhibits rich nonclassical states including the squeezed state, cat state, and entangled state in different parameter regimes. In this paper, we first use the polaron picture with high precision and fidelity to figure out the process of the superradiant phase transition (SPT) in view of the nonclassical states. The squeezing effect is considered to be a result of the higher-order excitations and important in order to understand the fundamental quantum physics of the SPT in the QRM. According to the polaron wave functions, the squeezed state extends across the entire range of parameters. Based on the squeezing and superradiance, a classification of the coupling strength for the nonclassical states containing the ground state is presented; the system sequentially goes through the vacuum states, the squeezed vacuum states, the squeezed cat states, and the cat states as the coupling strength increases. The process of the SPT is also demonstrated by the photon number distribution in Fock space, and then the novel behavior of parity chains for the SPT is revealed. Additionally, the nonclassical states can be intuitively characterized by the Wigner functions with analytical expressions in the polaron picture, and the Wigner negativity is evaluated to measure the nonclassicality. We find that a small but nonzero weighted antipolaron is necessary to form novel semicat states. Our paper provides guidance for preparing nonclassical states in experiments based on the light-matter interaction.
Risk management in the beverage production industry using FMEA and fuzzy cognitive map in an uncertain environment

Authors:
raw_author_name: Reza Rezaei
raw_affiliation_string: Department of Food Science and Technology, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

Abstract: Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach.
An improved DDPG algorithm based on evolution-guided transfer in reinforcement learning

Authors:
raw_author_name: Xueqian Bai
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China

Abstract: Abstract Deep Reinforcement Learning (DRL) algorithms help agents take actions automatically in sophisticated control tasks. However, it is challenged by sparse reward and long training time for exploration in the application of Deep Neural Network (DNN). Evolutionary Algorithms (EAs), a set of black box optimization techniques, are well applied to single agent real-world problems, not troubled by temporal credit assignment. However, both suffer from large sets of sampled data. To facilitate the research on DRL for a pursuit-evasion game, this paper contributes an innovative policy optimization algorithm, which is named as Evolutionary Algorithm Transfer - Deep Deterministic Policy Gradient (EAT-DDPG). The proposed EAT-DDPG takes parameters transfer into consideration, initializing the DNN of DDPG with the parameters driven by EA. Meanwhile, a diverse set of experiences produced by EA are stored into the replay buffer of DDPG before the EA process is ceased. EAT-DDPG is an improved version of DDPG, aiming at maximizing the reward value of the agent trained by DDPG as much as possible within finite episodes. The experimental environment includes a pursuit-evasion scenario where the evader moves with the fixed policy, and the results show that the agent can explore policy more efficiently with the proposed EAT-DDPG during the learning process.
Sketching of interactive VoIP traffic with multivariate statistical learning-based classification

Authors:
raw_author_name: R. Sangeetha
raw_affiliation_string: Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, India
raw_author_name: Bessy M. Kuriakose
raw_affiliation_string: Department of Computer Science and Engineering, Malla Reddy Engineering College for Women, India
raw_author_name: V. Edward Naveen
raw_affiliation_string: Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Tech, India
raw_author_name: A. Jenefa
raw_affiliation_string: Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, India
raw_author_name: A. Lincy
raw_affiliation_string: Department of Computer Science and Engineering, National Engineering College, India

Abstract: Classifying VoIP (Voice over Internet Protocol) traffic is vital for optimizing network performance and Quality of Service (QoS). This study introduces the Multivariate Statistical-Based Classification (MVSC) system, designed to classify network traffic with high accuracy and efficiency. As traditional methods struggle in the diverse and complex landscape of today’s network traffic, which includes voice, video, gaming, and data, the MVSC algorithm rises to the challenge. It employs Statistical Dissemination and leverages various statistical features such as Packet Size, Inter-Arrival Statistics, Packet and Data rates, Flow Length, and Five-tuple information to create nuanced profiles of network traffic packets. These packets are then grouped into distinct clusters based on their statistical attributes through Application Flow Cluster Grouping. A unique aspect of the MVSC system is its approach to representing each application flow as points in a two-dimensional space, where distances to predefined application profiles are calculated. The nearest profile then determines the type of VoIP traffic. Experimental results using university traffic data (KU-IDS) underscore the system’s high accuracy, consistently around 98-99% . These findings affirm the system’s suitability for real-time deployment. In summary, the MVSC system offers a robust and efficient solution for VoIP traffic classification, significantly boosting network performance and QoS, and proving to be an invaluable asset in contemporary network management.
Multi-CNN based logical reasoning system for facial expression recognition on small-sample datasets

Authors:
raw_author_name: Ning Zhou
raw_affiliation_string: School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China
raw_author_name: Bin Liu
raw_affiliation_string: School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China
raw_author_name: Jiawei Cao
raw_affiliation_string: School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China

Abstract: Facial expression recognition has long been an area of great interest across a wide range of fields. Deep learning is commonly employed in facial expression recognition and demonstrates excellent performance in large-sample classification tasks. However, deep learning models often encounter challenges when confronted with small-sample expression classification problems, as they struggle to extract sufficient relevant features from limited data, resulting in subpar performance. This paper presents a novel approach called the Multi-CNN Logical Reasoning System, which is based on local area recognition and logical reasoning. It initiates the process by partitioning facial expression images into two distinct components: eye action and mouth action. Subsequently, it utilizes logical reasoning based on the inherent relationship between local actions and global expressions to facilitate facial expression recognition. Throughout the reasoning process, it not only incorporates manually curated knowledge but also acquires hidden knowledge from the raw data. Experimental results conducted on two small-sample datasets derived from the KDEF and RaFD datasets demonstrate that the proposed approach exhibits faster convergence and higher prediction accuracy when compared to classical deep learning-based algorithms.
Improving polygon image segmentation by enhancing U-Net architecture

Authors:
raw_author_name: Da Li
raw_affiliation_string: Software college, Jiangxi Agricultural University, Nanchang, 330045, China

Abstract: Abstract The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges of polyps, which subsequently impacts the overall performance of segmentation. To address this issue, the current study introduces a novel approach by proposing a modified framework of U-Net, equipped with an enhanced edge loss function. This function is designed to ameliorate the accuracy of segmentation within polyp images. The aim is to elevate the model’s capacity to capture intricate details, specifically the edges, which is an area where standard U-Net structures often falter. Experimental outcomes of this study serve to underscore the effectiveness of the proposed approach in accomplishing superior segmentation of edges and improved overall performance in polyp recognition. By successfully tackling the challenges inherent to polyp edge segmentation, the modified U-Net model contributes significantly towards more precise diagnostic systems in the field of medical imaging. Consequently, this research is poised to make a valuable contribution to advancements in the prevention and early detection of colorectal cancer.
Impact of oxymoron features and deep learning techniques in the detection of sarcastic contents

Authors:
raw_author_name: K. Premalatha
raw_affiliation_string: Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, India

Abstract: Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to present a system that can automatically classify sarcastic phrases in multi-domain data. This multi-domain dataset consisting of 67850 sarcastic and non-sarcastic data is collected from various websites to identify sarcastic or non-sarcastic utterances. Multiple approaches are examined in this work to improve sarcasm identification: 1. A Combination of fasttext embedding, syntactic, semantic, lexical n-gram, and oxymoron features 2. TF-IDF feature weighting scheme 3. Three machine learning algorithms (SVM, Multinomial Naïve Bayes, and Random Forest), three deep learning algorithms (CNN, LSTM, MLP), and one ensemble model (CNN + LSTM) The CNN + LSTM model achieves a Precision of 91.32%, Recall of 92.85%, F-Score of 92.08%, accuracy of 92.01%, and Kappa of 0.84 by combining the fasttext embedding, bigram, syntactic, semantic, and oxymoron features with TF-IDF method. These experimental results show CNN + LSTM with a combination of all features outperforms the other algorithms in classifying the sarcasm in both datasets. The sarcasm classification performance of our dataset and another sarcasm news dataset was compared while applying the above model.
Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices

Authors:
raw_author_name: Bolun Zhang
raw_affiliation_string: Beijing University of Post and Telecommunication, Beijing, 100876, China

Abstract: Abstract With the development of Internet of Things technology, more and more devices are connected to the Internet, including not only traditional computers, mobile phones and other smart terminal devices, but also various sensor devices. These sensor devices can collect a variety of environmental information and physical quantities, such as temperature, humidity, air pressure, light intensity, vibration, etc. These data have the characteristics of real-time, scale and diversity, and need to be processed and analyzed by appropriate algorithms. On the basis of previous studies, this project summarized the application of various machine learning algorithms in device state detection, compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis, calculated the evaluation parameters of MSE, RMSE, MAE, MAPE, R² and other aspects of the machine learning regression model. Compare the effects of various regression models for better monitoring and prediction of equipment status. Through the analysis of a large number of historical data, different equipment state models can be established, and these models can be used to monitor and predict the current equipment state. This can effectively avoid production line downtime or other losses caused by equipment failures or abnormalities. At the same time, through the in-depth analysis of historical data, we can find some potential problems and take corresponding measures to prevent them. This project aims to summarize the application of various machine learning algorithms in device status detection, compare and contrast the differences of various machine learning algorithms in sensor device detection, realize efficient processing and analysis of sensor data, calculate MSE, RMSE, MAE, MAPE, R² and other evaluation parameters, and evaluate and compare each model. To provide more accurate, reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.
Empowering Micro-Credentials Using Blockchain and Artificial Intelligence

Authors:
raw_author_name: Rory McGreal
raw_affiliation_string: Athabasca University, Canada

Abstract: The recognition and transfer of credits is becoming essential for students, as an increasing number of them are studying at different institutions, often at the same time online, in both traditional and unconventional settings. Micro-credentials can aid in this process by providing easily accessible and transparent evidence of skills or knowledge, certified by an authority, based on small units of learning. The development of blockchain technology holds promise of becoming a useful enabler for supporting the storage and dissemination of micro-credentials on a global scale. Because of its immutability, blockchain can be used to attest to students' accomplishments securely and privately. Artificial intelligence (AI) can facilitate the maintenance and dissemination of micro-credentials, while ensuring that access is readily available for students under their control. So, AI can play a role in supporting blockchain-enabled micro-credentials. For educators, a basic understanding of the development of all three technologies is becoming essential.