Reviewerly/exHarmony huggingface dataset:
Computer Communication Security Evaluation System Based on Artificial Intelligence

The computer communication security evaluation system is one of the current research hotspots. Through artificial intelligence technologies such as machine learning and deep learning, communication data can be analyzed and processed, thereby improving the guarantee ability of communication security. This study conducted in- depth research on computer communication security evaluation systems based on artificial intelligence, mainly using a combination of literature review and experimental analysis methods to summarize key technologies in feature extraction and selection, neural network models, dataset quality and quantity, etc. This study found that feature extraction and selection are one of the key technologies in computer communication security evaluation systems based on artificial intelligence. The quality and quantity of datasets also had a significant impact on the performance and accuracy of the system. The accuracy, recall, and Fl values of this experiment were 89.75%, 90.75%, and 88.25%, respectively. The computer communication security evaluation system based on artificial intelligence can analyze and process different security events, effectively improving the guarantee ability of communication security.
A Fine-Grained Analysis of Public Opinion toward Chinese Technology Companies on Reddit

In the face of the growing global influence and prevalence of Chinese technology companies, governments world-wide have expressed concern and mistrust toward these companies. There is a scarcity of research that specifically examines the widespread public response to this phenomenon on a large scale. This study aims to fill in the gap in understanding online public opinion toward Chinese technology companies using Reddit data, a popular news-oriented social media platform. We employ the state-of-the-art transformer model to build a reliable sentiment classifier. We then use LDA to extract the topics associated with positive and negative comments. We also conduct content analysis by studying the changes in the semantic meaning of the companies’ names over time. Our main findings include the following: 1) Notable difference exists in the proportions of positive comments (8.42%) and negative comments (14.12%); 2) Positive comments are mostly associated with the companies’ consumer products, such as smartphones, laptops, and wearable electronics. Negative comments have a more diverse topic distribution (notable topics include criticism toward the platform, dissatisfaction with the companies’ smartphone products, companies’ ties to the Chinese government, data security concerns, 5G construction, and general political discussions); and 3) Characterization of each technology company is usually centered around a particular predominant theme related to the company, while real-world political events may trigger drastic changes in users’ characterization.
Usage of Machine Learning and Monte Carlo Method as Probabilistic Hybrid Tool to Predict Next Pandemic

This paper explores the scenarios where the combination of algorithms based at Machine Learning with Monte Carlo method can be advantageous to assess the next pandemic. Thus, taking into account the experience of Covid-19, and the design of strategies that have served to alleviate the crisis cause by the spreading of Corona virus, in this paper a general algorithm is proposed. Essentially the proposal takes into account the gathered statistics along the years 2020-2021-2022 so that it constitutes a pattern that might be perceived again in next pandemics. In concrete, this work aims to present simulations taking as input the data of Covid-19. Special attention is paid on the initial period. It is because it was observed similar patterns in most countries that have reported a fast propagation of virus. Thus the MItchell's criteria are employed to identify the beginning of pandemic as well as the Monte Carlo method is also used in order to accept potential realistic events of pandemic, whereas the ones that turned out to be inconsistent to Covid-19 data and patterns of propagation are rejected. From the results, a discussion about the validity of this study is finally given.
Image Captioner: Captioning for Visual Impact

Image captioning is the process of generating a textual description that accurately represents the features of an image. In the realm of deep learning, this task is of utmost significance and has a wide range of uses. Image captioning involves converting an image, represented as a sequence of pixels, into a sequence of words that are relevant to the image. It can be seen as an end-to-end and sequence-to-sequence challenge, as both the language and visual aspects need to be processed. In this regard, recurrent neural networks are employed for the language processing, while convolutional neural networks are used to extract feature vectors from the images. The findings of this study highlight the effectiveness of this method and show how diverse applications in image processing and description generation are possible. This work promotes the incorporation of these methods into actual image captioning systems in order to provide more precise and contextually appropriate image descriptions.
Proposed a Solution Based on Functional Dependency Graphs for Transactional Fact Table Error Correction

Process mining refers to the set of techniques used to mine the event logs of process-oriented information systems that manage and execute processes to gain hidden knowledge about an organization's actual business processes. Data warehouses are a form of database that stores data from different sources. Data warehouses contain three types of tables: transactional fact table, periodic fact table, and cumulative fact table. The transactional fact table, contains a frequent error in the mapping between the primary key, cumulative fact table measure and dimension level granularity. This paper proposed a solution based on functional dependency graphs to correct the frequent errors in the transactional fact table.
Fuzzy Mathematical Reliability Modeling Based on Robustness Analysis

In reliability modeling, there are uncertainties and fuzziness issues that require special methods and techniques for processing and analysis. In this paper, the fuzzy mathematical method based on robustness analysis is used to model reliability. The specific steps include problem description, determination of fuzzy variables and fuzzy sets, establishment of fuzzy relations and fuzzy rules, and fuzzy reasoning and analysis. Through experimental testing, the four test response times of the model in this article are 0.1s, 0.13s, 0.08s, and 0.11s, respectively, which are superior to the other two method models. This proves that the method proposed in this paper can effectively handle uncertainty and fuzziness problems, and improve the robustness and reliability of the system. Meanwhile, this method has high flexibility and scalability, and can adapt to the needs of different fields and application scenarios.
Determining the Major Contributing Features to Predict Breast Cancer Imposing ML Algorithms with LIME and SHAP

Significant advancements have been achieved recently in the use of artificial intelligence (AI) to solve healthcare-related problems. A potential method for identifying and treating many illnesses is machine learning (ML). The “Breast Cancer Dataset” was used in this work to perform classification tasks using ML techniques including Support Vector Machine (SVM), Random Forest (RF) and MultiLayer Perceptron (MLP) for identifying the forms of breast cancer, either Benign or Malignant. The SVM, RF, and MLP achieved impressive PR scores exceeding 0.98. However, MLP exhibited superior performance among these algorithms, achieving an exceptional AUC value of 99.71. Conversely, SVM slightly outperformed the other algorithms in terms of training accuracy, with a value of 97.90%. This work used shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) to shed light on how these categorization algorithms made judgments. These interpretability methodologies showed that the majority of characteristics consistently predicted the presence of malignant or benign cancer types. This study demonstrates how ML algorithms' decision-making procedures may be understood, possibly promoting the use of ML in clinical settings for medical diagnosis.
A TDOA Method for Underwater Target Location Using Distributed Multi-Agent Systems with Experiment Verification

The passive location methods for underwater target is particularly critical to both military and civilian domains, with Time Difference of Arrival (TDOA) gaining widespread utilization across diverse applications. In this paper, the underwater target location method based on the TDOA with distributed multi-agent systems (UTL-DMS) is proposed to apply in the shallow water environment. The location of target is solved by constructing a set of pseudo-linear equations, requiring three observers of the distributed multi-agent system. The simulation results show that average location error is 1.48 m. The distributed multi-agent systems are constructed. The experiment is held to verify the method in the Qian Dao Lake in Hangzhou. All location errors are less than 20 m, which demonstrate the performance of the method by the experiment verification on a lake.
Web-Based Principal Training Platform Validation: Alpha and Beta Test

Aims of this study is to conduct a validation test on a training website for school leaders skills in the success of the freedom to learn policy that has been developed by the research team. This study uses a research and development approach, in the context of developing a website-based principal training platform. The validation test is carried out in two stages, namely the expert test and the user test. Based on validation tests by experts and users of the training website developed in the very good category, this shows the training website is feasible to be used in training activities to improve school principals' skills in facilitating learning independence in schools. The website developed is also useful both practically and theoretically.
Shared Learning Platform for English Based on Feature Extraction Algorithm

Feature extraction algorithms are of great significance in the fields of machine learning and computer vision, but currently English learning resources are limited and there is a lack of a shared learning platform to promote knowledge dissemination and communication. 20 experts were invited to participate, and the results indicated that participants using the platform achieved significant improvements in algorithm accuracy, with an average improvement range of 0.06 to 0.07. This showed that the English shared learning platform for feature extraction algorithms plays an important role in improving algorithm performance. This platform not only provides learning resources and communication opportunities, but also promotes algorithm improvement and knowledge dissemination, thus providing valuable tools for the learning and development of feature extraction algorithms. The recall rate of the Bag-of-words model model is 0.80; that of the TF-IDF is 0.84; that of the Word2Vec is 0.88. Future research should further optimize the user friendliness of the platform and strengthen cooperation and communication to promote further development in this field.
Complementary Attention-Based Deep Learning Detection of Fake Faces

The access to large-scale public databases along with the fast progress of deep learning techniques have led to the generation of very realistic fake content. This has raised significant concerns because of their use in social media and the generation of fake news. Thus, the detection of such manipulations has become an increasingly important research area, especially the detection of fake faces has become very important in the field of digital forensics. This paper presents a complementary attention-based deep learning system to detect fake faces. This system effectively incorporates our proposed simple Layer-Integrated Channel Attention (LICA) and Scaled Spatial Attention (SSA) mechanisms in VGG network architecture to capture the importance along each channel and at each spatial location to distinguish between real and manipulated faces and improve detection performance. Our extensive experimental results demonstrate that the proposed system outperforms the state-of-the-art system in detecting fake faces generated by each of the four commonly used manipulations including entire face synthesis, identity swap, attribute manipulation, and expression swap in terms of both accuracy and Area Under Curve (AUC) metrics. It also achieves better performance than the state-of-the-art system to detect fake faces generated by any of the four aforementioned manipulations.
Transferring Lottery Tickets in Computer Vision Models: a Dynamic Pruning Approach

Deep neural networks can achieve state-of-the-art results on small size datasets by transferring the backbone from a network pre-trained on large datasets. Recent work has shown that pruned networks can also be used as pre-trained models in transfer learning. In this paper, we proposed a novel framework, Transferring Lottery Ticket (TLT), to adapt both masks and weights of a pre-trained and pruned network dynamically during the knowledge transfer to downstream tasks. We show that the lottery tickets of downstream tasks are dramatically different from each other and from the one obtained from the pre-trained network. Thus, both masks and weights need to be learned to better adapt a pre-trained model to the target domain. Our extensive experiments on multiple computer vision tasks, such as image classification and segmentation, show that the transferred networks with adapted masks outperform the ones with original masks at various pruning ratios.
A Simplified Design of Fast Error Correction Module Using Reed-Solomon Code

A Forward Error Correction (FEC) module realized in hardware on Field Programmable Gate Array (FPGA) is having more significance in the practical control applications. This is due to its ability to process the data (encoding and decoding) much faster through FPGA and able to overcome the limitations of data transfer. But the real challenge in such a design is the run time reconfiguration. Lot of researches happening on this issue and there is no standard reference available. Also, there is no flexibility in the existing software solutions. Considering this, in this work, the authors were tried to perform a basic software implementation of FEC module using Reed Solomon (RS) code on a custom made system-on-chip with a soft core processor followed by the RTL level implementation of RS code which can be dynamically controlled. To achieve faithful data transfer between two terminals, two NIOS II SOCs (System on Chip) were built on FPGAs. For the implementation, Altera Cyclone III FPGA starter board was used. Quartus II 10.1 Design Software with NIOS II Embedded Design Suite adopted for configuring the FPGA. The performance of the developed model is verified by monitoring the content message bits before and after corruption of data at the receiver terminal. A comparative study was carried out between hardware and software implementations.
A Comparison of Interfaces for Learning How to Play a Mixed Reality Handpan

In the realm of music therapy, Virtual Reality (VR) has a long-standing history of enriching human experiences through immersive applications, spanning entertainment games, serious games, and professional training in various fields. However, the untapped potential lies in using VR games to support mindfulness through music. We present a new approach utilizing a virtual environment to facilitate learning how to play the handpan – an instrument in the shape of a spherical dish with harmonically tuned notes used commonly in the sound healing practice of mindfulness. In a preliminary study we compared six interfaces, where the highlighted path interface performed best. However, participants expressed preference for the standard interface inspired by rhythm games like Guitar Hero.
Introducing the Partial Clustering Problem

In this paper we introduce the partial clustering problem. Given o objects, select $p \le o$ of them such that the selected objects form an optimal clustering with respect to a given cost function f. The problem can find various applications where due to capacity constraints, only part of the data objects can be selected, and the selection criteria are based on how good a clustering the partial data can form. Partial clustering generalizes clustering, and is significantly harder involving nested combinatorial optimization. For the introduced problem, we also propose generic algorithms without specifications of the cost function, and perform preliminary experiments for initial verification.
The Elusive Enigma: Unraveling Rogue Wi-Fi's Chessboard of Deception with Man-in-the-Middle Mastery and Rogue Access Point Intrigue

Rogue Wi-Fi networks pose a significant threat to individuals and businesses in the Philippines, as they expose personal information to malicious actors. This meta-analysis explores selected methods used in Man-in-the-Middle (MitM) attacks via rogue access points. The study includes a Rogue Wi-Fi simulation and emphasizes personal data protection awareness. The research references 5 sources from databases like IEEE Xplore, ScienceDirect, and Google Scholar. The researchers provide insights into Wireless Access Points (WAPs) and recommend solutions for network immunity, outlining strategies used by cybercriminals and preventive measures taken by their victims.
Distributed Concept Drift Detection for Efficient Model Adaptation with Big Data Streams

Predictive models are essential in big data platforms to tackle the needs of several real-world applications. However, static models are known to be prone to obsolescence in dynamic environments. While concept drift detection represents a viable way to deal with this problem, it is scarcely explored in the context of big data streams. In this paper, we propose a distributed drift detection workflow based on the DDM algorithm paired with a predictive model. Our workflow updates the predictive model as soon as drifts are detected, adjusting to the most recent data characteristics. To enable the analysis of large-scale datasets, we leverage Pandas UDFs and Apache Spark, efficiently distributing this workload across multiple worker node instances. Our experiments on two real-world drift detection datasets show the positive results obtained in terms of Speedup, Scaleup, and a limited impact in detection delay in comparison to a single worker node instance.
Driving into Uncertainty: An Adversarial Generative Approach for Multivariate Scenario Generation

Many decisions in transportation management must be made before the uncertainty of possible traffic conditions is revealed. When making decisions with lasting implications over a medium to long timeframe, it is essential to consider not only the most probable scenario, possibly obtained through a forecasting model but also a range of potential outcomes. This approach allows for effective risk mitigation across a spectrum of scenarios, including less probable ones, and enhances the resilience of planning strategies. In this paper, we demonstrate the development of a generative model capable of learning the multivariate joint probability distribution of link speeds on a road network, using real data collected from sensors. The proposed model has shown its ability to generate scenarios that preserve correlations among variables, while producing samples that faithfully represent the empirical marginal distributions. To further enhance the performance of our Generative Adversarial Network (GAN) model, we employed a Variational AutoEncoder (VAE) for pre-training the generator network. Experimental results, conducted on three distinct benchmark datasets, highlight the potential of the proposed model in generating new scenario samples of multivariate variables. The Wasserstein distance between the generated distribution and the real data, confirms the good performance of our model with respect to state of the art models, based on copulae.
Embodied Epistemology: A Meta-Cognitive Exploration of Chatbot-Enabled Document Analysis

In this paper, novel research is presented that constructs a PDF chatbot using ChatGPT 3.5 Turbo and the LLM Model. The framework, chatGPT 3.5, streamlines chatbot creation, enabling scalable AI/LLM applications. The potent LLM Model facilitates text generation, language translation, and original content creation, along with insightful responses to user queries. The approach combines chatGPT 3.5 Turbo and the LLM Model to develop a chatbot proficient in addressing PDF-related inquiries. Utilizing data from uploaded PDFs and the LLM Model, the chatbot generates informative text responses to customer questions. The research relies on the LangChain framework, while a Streamlit based homepage enhances user interactions. This work exemplifies the LangChain and LLM Model’s potential to craft engaging chatbots, adaptable across domains like customer service, education, and research. The chat bot, a valuable resource for addressing product queries, offering educational aid, and providing tutoring, effectively responds to user inquiries about PDF files.
Path Study of Robot Obstacle Avoidance Based on Heuristic Algorithm

Aiming at the robot obstacle avoidance problem, this paper establishes the nonlinear 0-1 integer planning models of the shortest path and the shortest time path for the robot to avoid obstacles from one point in the region to another point, respectively. Meanwhile, this paper constructs an effective heuristic algorithm for the nonlinear 0-1 integer planning model with NP attribute, and uses MATLAB software programming to find the shortest paths of O→A, O→B, O→C, O→A→B→A→C, and also obtains the shortest time path of O→A, and the shortest paths of all types of paths obtained are all globally optimal.
Detection of Fake Twitter Accounts Using Ensemble Learning Model

The prevalence of fake accounts on social media platforms poses significant threats to user safety and data integrity. The ability to detect such accounts before users interact with them is crucial in safeguarding the network against advanced attacks and cyber threats. With an increasing number of Internet users relying on social media for daily tasks, including cloud sharing, news reading, messaging, product reviews, and event discussions, the risk of encountering cybercriminals using fake accounts has amplified. In this research paper, we present an approach for detecting false identities in the Twitter platform. Our proposed system utilizes a range of categorization algorithms, including Naive Bayes and Decision Tree algorithms with ensemble learning models, to effectively identify fake social media profiles. By integrating these powerful algorithms, we achieve an impressive accuracy rate of 98.93% in distinguishing fake accounts from genuine ones. The contribution of this research lies in providing a robust and reliable solution to combat the proliferation of fake accounts on Twitter. By enabling the timely detection and elimination of fraudulent profiles, our approach strengthens the network's security and protects users from malicious entities, including trolls, internet fraudsters, deceptive advertising campaigns, and sexual predators.