Reviewerly/exHarmony huggingface dataset:
Key Factor Analysis of Influencer Popularity based on Adjective and Emotion in Twitter User Opinions

In our society, it is crucial to comprehend popularity factors, particularly among influencers such as YouTubers and politicians. While popularity analysis, including prediction, is common, there's a lack of research on influencer popularity and no established approach. In this paper, we present a method to extract key influencer popularity factors using rank correlation analysis and fine-grained public opinions, including adjectives and emotions from Twitter data (now known as X). We analyze two distinct groups: YouTubers and politicians, demonstrating the effectiveness of our approach. Moreover, we pinpoint specific adjectives and emotions that exhibit correlations with their popularity, thereby highlighting variations in popularity factors among the designated target categories.
Deep Deterministic Policy Gradient for Nested Parallel Negotiation

In automated negotiation research, achieving effective negotiation strategies using reinforcement learning automatically is an essential topic recently. However, the existing research focuses on naive bilateral negotiations even though negotiations in real life have been done in parallel. Nested parallel negotiation is one of the critical categories of parallel negotiation, such as supply chain management, where an agent is required to simultaneously negotiate in two directions: the supply and demand sides. Especially the existing work of getting an effective negotiation strategy using reinforcement learning has not been achieved for nested parallel negotiation. This paper proposes a new reinforcement learning architecture based on Deep Deterministic Policy Gradient (DDPG) to learn an effective negotiation strategy for nested parallel negotiation. We formulate the environment for reinforcement learning for the nested parallel negotiation and propose a neural network architecture to learn a negotiation strategy efficiently. We evaluate the negotiation strategy based on our proposed architecture in the simulation environment for supply chain management. The results demonstrate that our proposed agent can achieve validation utilities that is comparable to or higher than the existing rule-based and learning-based agent strategies.
Measuring Vagueness and Subjectivity in Texts: From Symbolic to Neural VAGO

We present a hybrid approach to the automated measurement of vagueness and subjectivity in texts. We first introduce the expert system VAGO, we illustrate it on a small benchmark of fact vs. opinion sentences, and then test it on the larger French press corpus FreSaDa to confirm the higher prevalence of subjective markers in satirical vs. regular texts. We then build a neural clone of VAGO, based on a BERT-like architecture, trained on the symbolic VAGO scores obtained on FreSaDa. Using explainability tools (LIME), we show the interest of this neural version for the enrichment of the lexicons of the symbolic version, and for the production of versions in other languages.
PPPG-DialoGPT: A Prompt-based and Personality-aware Framework For Conversational Recommendation Systems

Conversational Recommendation Systems (CRSs) are multi-turn dialogue softwares that support recommendation goals. Having a CRS that generates personalized recommen-dations for its users is one of the challenging topics in the recommendation research field. This is due to the diversity of preferences that a user might have within a recommendation session. Recent CRS frameworks are still not really able to effectively provide items of interest to their users, as they do not consider the individuals' personality traits during the recommendation process. These traits are the enduring character-istics and behaviors that comprise a person's unique adjustment and distinguish him from other persons. Thus, considering them during the recommendation process could significantly boost the model performance. In this paper, we propose a personality-aware and prompt-based CRS framework named PPPG-DialoGPT “A Personality and Preference-aware Prompt-based Goal-oriented DialoGPT model”. Our proposed approach aims to benefit from the significant impact that personality traits and prompt-based learning frameworks have on improving the performance of different Natural Language Processing (NLP) and one-shot recommendation tasks. To the best of our knowledge, this paper is the first to employ both individual personality traits and prompts templates during a dialogue-based recommendation session in the field of conversational recommendation system. Experiments and results show that the use of the users' person-ality traits leads to an improvement in the performance of both recommendation and response generation tasks on the movie-based TG- Redial dataset.
Web Content Filtering Through Knowledge Distillation of Large Language Models

We introduce a state-of-the-art approach for URL categorization that leverages the power of Large Language Models (LLMs) to address the primary objectives of web content filtering: safeguarding organizations from legal and ethical risks, limiting access to high-risk or suspicious websites, and fostering a secure and professional work environment. Our method utilizes LLMs to generate accurate classifications and then employs established knowledge distillation techniques to create smaller, more specialized student models tailored for web content filtering. Distillation results in a student model with a 9 % accuracy rate improvement in classifying websites, sourced from customer telemetry data collected by a large security vendor, into 30 distinct content categories based on their URLs, surpassing the current state-of-the-art approach. Our student model matches the performance of the teacher LLM with 175 times less parameters, allowing the model to be used for in-line scanning of large volumes of URLs, and requires 3 orders of magnitude less manually labeled training data than the current state-of-the-art approach. Depending on the specific use case, the output generated by our approach can either be directly returned or employed as a pre-filter for more resource-intensive operations involving website images or HTML.
Toward Website Classification

The rapid growth of the internet has resulted in a vast virtual collection of diverse data on the Web, presenting a range of security challenges. To address these concerns, the implementation of automatic website classification systems has become crucial for filtering web content. This paper examines and compares various classification algorithms to identify the best approach for website classification. The existing literature primarily focuses on webpage classification, which serves as the foundation for our study. We establish three types of datasets containing 10 categories and evaluate the impact of websites metadata on the classification when combined or not with the websites content. We compare the results over two scenarios: the first one considers only the index webpages, while the second one includes the surrounding webpages. In the second scenario, we study and benchmark three aggregation methods: Majority vote, Borda count, and a Meta-Classifier based method. We show that for aggregating the results, the majority voting is the most suited to our problem. We consider Naive Bayes and SVM classifiers with a Doc2Vec model trained on our custom datasets, as well as a fine-tuned BERT. The results show that BERT outperforms the other models when considering a dataset that includes metadata combined with the content of surrounding webpages. However, SVM outperforms the two other classifiers when used on small texts extracted from the website's metadata.
Multi-Context Incremental Reasoning Over Data Streams

Data streams are generally generated on-the-fly and contain valuable knowledge to support the decision process in many use cases, such as smart cities or traffic monitoring. To this end, reasoning over streaming data suggests a reconsideration of the concept of a solution to a problem, and how to obtain it. As data in streams change continuously, all the conclusions based on expired data must be retracted, and additional or new information might also arrive and output additional derivations. Multiple works have investigated the use of Answer Set Programming (ASP) for streaming data. However, they generally do not provide window mechanisms, which are key factors in stream processing. Ticker is one of the recent solutions provided to tackle this issue. It is based on LARS, a Logic-based Framework for Analytic Reasoning over Streams, and has two reasoning strategies. One relies on Clingo for repeated solving, and the other uses a Truth Maintenance System to perform model updates. Despite the significant achievements that have been made, there is still room for improvement, especially when considering real-life applications. In this paper, we propose a new stream reasoning engine based on Ticker that overcomes its limitations. The proposed framework has been implemented and evaluated with a real-world benchmark, and a use-case scenario implementation shows promising results.
MKGS: Maintains the Original Structure of Knowledge Graph

In the field of graph-generated text, one of the core issues commonly explored is how to maintain the structural information of the graph to the maximum extent and reduce the problem of knowledge loss during training. Current research has mainly focused on exploring the ability of models to learn graph structures by increasing model size and refining the graph representation. In contrast, our work emphasizes the importance of perceiving and exploring edges in the graph itself. Edges provide a wide variety of structures to the graph structure, offering it freedom and diversity. Therefore, improving the model's ability to perceive edges could potentially enhance the task metrics of graph generation for text. To address this, we propose a graph-generated text model MKGS that maintains the original structure of the knowledge graph, effectively reducing knowledge loss during the learning process. Our approach achieves this at three levels: reorganizing the knowledge sequence as input to the model, enhancing edge perception during processing, and incorporating a graph rational activation function at the output. We validate our method using the Kg-to-text benchmark dataset WebNLG, where MKGS achieves a score of 66.22%. Additionally, the model exhibits fewer syntactic errors and produces smoother expressions in the generated text.
Implementing BDI Continual Temporal Planning for Robotic Agents

Making autonomous agents effective in real-life applications requires the ability to decide at run-time and a high degree of adaptability to unpredictable and uncontrollable events. Reacting to events is still a fundamental ability for an agent, but it has to be boosted up with proactive behaviors that allow the agent to explore alternatives and decide at run-time for optimal solutions. This calls for a continuous planning as part of the deliberation process that makes an agent able to reconsider plans on the base of temporal constraints and changes of the environment. Online planning literature offers several approaches used to select the next action on the base of a partial exploration of the solution space. In this paper, we propose a BDI continuous temporal planning framework, where interleave planning and execution loop is used to integrate online planning with the BDI control-loop. The framework has been implemented with the ROS2 robotic framework and planning algorithms offered by JavaFF.
In-Training Explainability Frameworks: A Method to Make Black-Box Machine Learning Models More Explainable

Despite ongoing efforts to make black-box machine learning models more explainable, transparent, and trustworthy, there is a growing advocacy for using only inherently inter-pretable models for high-stake decision making. For instance, post-hoc explanations have recently been criticized because they learn surrogate white-box (explainer) models that, while optimized to approximate the original predictive model, remain different from the latter. Moreover, the post-hoc models necessitate a post-hoc training phase at prediction time, that adds to the computational burden. In this paper, we propose two novel explainability approaches that make black-box models more explainable, which we call pre-hoc explainability and co-hoc explainability. Our goal is to maintain the black-box model's prediction accuracy while benefiting from the explanations that come with an inherently interpretable white-box model, and without the need for a post-hoc training phase at prediction time. In contrast to post-hoc methods, the black-box model training phase is guided by explanations that are used as a regularizer. Our experiments demonstrate the advantages of our proposed technique on three real-life datasets, in terms of fidelity, without compromising accuracy.
Dialogue Agents with Literary Character Personality Traits

To enhance the engagement of chatbots, we need to imbue them with unique personalities and speech patterns. However, creating a high-quality conversational training dataset for this task can be time-consuming and labor-intensive. To address this, we propose an automated system that uses character dialogues from literary works. We used the Chinese classic, (Dream of the Red Chamber), as our data source. Our system efficiently extracts dialogues and personality traits from the book, creates a personality map for each character, and generates responses that reflect these traits. It allows us to utilize the distinct linguistic styles that authors assign to characters with different personalities in classic books, making chatbots more human-like. Our evaluations demonstrate the efficacy of our system, with chatbots possessing personality traits proving more engaging than those lacking them. For transparency, we have made all our datasets and models available <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> Project Repository: https://github.com/SuperEDG/Hongloumeng_Project.
Multi-Channel Graph Neural Networks with Contrastive Learning for Social Recommendation

Social recommender systems based on graph neural networks can successfully model the social influence among users and effectively mitigate the issue of data sparsity. However, existing methods mostly focus on individual influence while ignoring group influence. The social influence exerted by different groups of users can vary, and the intensity can also vary. A user is often more susceptible to the influence of groups with preferences similar to his/her own. This paper proposes a social recommendation model called Multi-channel Graph neural network with Contrastive Learning (MGCL). MGCL classifies users into different groups according to their preferences, establishes separate social propagation channels for each group, and extracts the influence of different user groups to better represent a user. In addition, contrastive learning is utilized to improve the modeling of user preferences and social influence during model training. The superiority of the proposed MGCL over state-of-the-art baselines has been demonstrated through extensive experiments conducted on three benchmark datasets.
A Hybrid Model for Identifying Social Media Addiction Patterns Among Young People

Social media addiction is a growing concern among young people, with numerous negative impacts on mental health and well-being. In this study, our goal was to develop a hybrid machine learning model for spotting young people's patterns of social media addiction. To evaluate the hybrid model's accuracy in relation to the other two models, recurrent neural network-RNN and long short-term memory -LSTM models were used. Our findings show that the hybrid model has a precision of 67.55%, which is more than the accuracy of the RNN model-59.77 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and the LSTM model-65.42 %. These results suggest that the hybrid model can be more effective in identifying social media addiction patterns in young people.
A Novel Approach to Prevent Hijacking of Accounts in the Cloud

According to studies, majority of enterprises globally are now adopting cloud services, and the number of migrants to cloud environments in the industrialized nations is expanding ever since. Moving business data from a centrally hosted server to cloud storage has a lot of merits. However, it increases the chance of data security breach. An aaccount hijacking is the most frequent type of attack that happens in cloud system, it is a type of attack where hackers will get the account holder's credentials and control over the account. Considering the fact that cloud system is vulnerable to attacks, it is crucial to have a remediation mechanism like a secured authentication system. Authentication system which uses textual password can be used in cloud system, but it is vulnerable to regular attacks like brute-force attack, spyware attack, and so many more. The present research mainly focuses on creating a unique authentication system that prevents account hijacking in cloud by mitigating common attacks. To achieve the key objective a graphical password authentication system has been developed with OTP and colouring scheme in order to specifically minimize brute force and shoulder surfing attacks. It is a simple and easy to remember authentication system for customers while minimizing account hijacking in cloud at the same time. It is proved that the present approach is successful authentication system that is less vulnerable to shoulder surfing and brute force attack while involving easy way for customers to remember their passwords without hassle.
DAWSON: Data Augmentation using Weak Supervision On Natural Language

We propose a novel data augmentation model for text using all available data through weak supervision. To improve generalization, recent work in the field uses BERT and masked language modeling to conditionally augment data. These models involve a small, high-quality labeled dataset, but omit the abundance of unlabeled data which is likely to be present if one considers a model in the first place. Weak supervision methods make use of the vastness of unlabeled data, but largely omit the available ground truth labels. We combine data augmentation and weak supervision techniques into a holistic method, consisting of 4 training phases and 2 inference phases, to efficiently train an end-to-end model when only a small amount of annotated data is available. We outperform a conditional augmentation benchmark for the SST-2 task by 1.5, QQP task by 4.4, and QNLI task by 3.0 absolute accuracy percentage points, and show that data augmentation is also effective for natural language understanding tasks, such as QQP and QNLI.
A Brief Comparative Study of Metaheuristic Approaches for Hyperparameter Optimization of Machine Learning Model

Machine learning models have been successfully applied in numerous fields. Training a model is the most important aspect of machine learning for its successful application for the problem. To improve the training of a machine learning model thereby improve the performance, the selection of features and setting optimal parameters is crucial. Mainly two kinds of parameters are required to deal with, namely internal and external parameters. Internal parameters are model parameters and configurable such as weights of neural networks and their estimation can be done using data set. The hyperparameters such as learning rate, size of layers, number of layers, loss function etc, are external parameters and its values cannot be determined using the data set and it is not the part of the model. Its estimation can be done by the domain expert or using some trial-and-error techniques until it achieves some acceptable values. However, these techniques are highly time-consuming and cannot ensure the optimal values for these hyperparameters. In recent years different metaheuristic techniques have been applied to determine the optimal values of hyper parameters for machine learning models. In this paper we have conducted a brief comparative study of a few popular metaheuristic approaches applied for the hyperparameter optimization for various machine learning models. In this paper various evaluation measures have been considered for comparative analysis of metaheuristic approaches for hyperparameter optimization for deep learning model.
A Research Overview of Cloud Computing Data Transmission Security

The IT revolution known as cloud computing has enabled users to collectively utilize hardware and software, delivery of services, and data storage spaces through shared networks. Organizations and individuals alike benefit from the services offered by cloud storage providers because they can affordably store, transmit, and back up their ever-growing amounts of data on the cloud. Many sectors, including finance, healthcare, and education, are migrating to the cloud because of the cost-effectiveness of the services offered by the pay-as-you-go model, which is determined by the number of resources used. Data are susceptible to assault since all users on the network have equal access to transferring them. Data security in modern systems is almost entirely focused on protecting information when it is being stored in the cloud using internet security. Since data theft is a major issue, a system based on encrypted data transfers has been proposed. The authors suggest a method to increase system security during data transmission, which should make it more difficult for malicious actors to steal sensitive information. Adopting cloud computing is complicated by a number of research obstacles, including the need for a well-managed privacy, interoperability, and dependability. This study defines cloud computing, describes the many cloud models, and identifies the most pressing security concerns facing the cloud computing business today. In addition to analyzing the current state of cloud computing and its future prospects, this paper provides recommendations for service providers and businesses who want to increase their profitability via the use of cloud services despite the current economic downturn.
A Video Analysis System Using K-Nearest Neighbour Algorithm for Error Detection in Squat Form

In this paper, a video analysis system is developed using K-Nearest Neighbour (KNN) algorithm to analyze the accuracy of physiotherapy exercises. This system captures videos of patients undergoing physiotherapy treatments. The K-Nearest Neighbour (KNN) algorithm is used to assess the video and identify any deviations from proper during the physiotherapy treatments. To develop this configuration, a dataset of physiotherapy exercises performed by patients was collected by trained physiotherapists. This dataset is used to train the KNN algorithm, enabling it to optimize its performance by selecting the optimal number of neighbours and distance metric. The performance was evaluated using a separate dataset of physiotherapy exercises. the results show that the KNN algorithm accurately detected errors in the patients' exercise techniques. This study achieved a notable accuracy rate of 92% in detecting errors, thus highlighting the effectiveness of the proposed approach. Particularly, the video analysis system is designed to detect errors in the squat form, such as incorrect knee and hip alignment or improper movement. To accomplish this, a dataset of patients performing squats was collected and the KNN algorithm was trained on this dataset to detect deviations from the correct form. This present study provides a practical demonstration of the proposed video analysis system application in the context of physiotherapy treatment using the KNN algorithm. The main objective of this study is to the analysis of squat exercises and assess their proper execution.