Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Semantic Graph Construction and Pooling Approach for Cross-language Code Retrieval

In today's diverse programming landscape, developers often face the challenge of implementing identical functionalities across multiple programming languages for various versions of a software system. This necessitates automated solutions to facilitate cross-language code-to-code retrieval, thereby reducing costs and enhancing developer productivity. Existing methods for this task tend to overlook the intricate semantics and inherent hierarchies present in source code. In this paper, we introduce HERONPO, a novel approach that employs a hierarchical graph construction and pooling mechanism tailored for the cross-language code-to-code retrieval task. Our methodology commences with the utilization of compilers to derive the Intermediate Representation (IR) from source codes. Subsequently, we architect our hierarchical variable-centric flow graphs, which adeptly transform the IR into a detailed hierarchical semantic graph. This transformation ensures a robust and learnable representation of code semantics. To ensure seamless end-to-end training of our hierarchical graph, we meticulously devise a pooling mechanism that aligns with the unique attributes of our proposed hierarchical graph. We assess the efficacy of HERONPO through experiments on a publicly available dataset comprising 78K solutions from programming contests, spanning Java, Python, and C languages. The experimental results are promising: HERONPO demonstrates a significant improvement, achieving up to 10% Mean Reciprocal Rank (MRR) enhancement across all Java-C, Java-Python, C-Python, C-Java, Python-J Java, and Python-C retrieval tasks.
Application of Mixed Reality in Industrial and Collaborative Robot Control

With the advent of the fourth industrial revolution, the creation of robots and their support software for organized industrial settings to the autonomous and collaborative creation of hybrid environments are the top priorities. The proliferation of collaborative robots has led to various issues, including the inability of hybrid teams to utilize them effectively and the programming requires high technical skill. Easier, more effective, and more user-friendly human-robot interaction may be achieved by combining several new technologies, including augmented and mixed reality, digital twins, the Internet of Things, and 3D simulation. In this paper, we propose a solution to demonstrate the new approach for human-robot interaction and easy programming with MR technology on both industrial and collaborative robots.
Facial Emotion Depth Index

Conventional methods for emotion detection typically entail classifying emotions into predefined categories, potentially yielding restricted and imprecise depictions of an individual’s emotional disposition. In this research endeavor, we embark on a fresh approach aimed at discerning emotions and gauging their intensity through the utilization of an innovative emotion depth index. By forecasting continuous values that mirror the magnitude of emotion, this methodology affords a heightened level of precision and a more intricate comprehension of an individual’s emotional state. Furthermore, this study delves into the potential real-world applications of this technology, spanning from psychological counseling and customer service to market research. Our findings strongly indicate that the emotion depth index bears the promise to revolutionize the landscape of emotion detection systems, presenting a more precise and finely tuned portrayal of an individual’s emotional condition.
Unveiling a New Vulnerability in Modern Power Systems: Leveraging Publicly-Available LMPs for Crafting Cyber-Attacks

Modern power systems are increasingly vulnerable to stealthy false data injection attacks (FDIAs) targeting state estimation. Executed without the operator’s knowledge, these attacks can lead to severe consequences, including economic losses, power outages, and full system blackouts. Previously, it was believed that executing destructive FDIAs requires full understanding of the operator’s response to such attacks, which demands in-depth knowledge of power system details, including generation prices and system constraints. Nevertheless, this paper reveals a noteworthy vulnerability wherein an attacker can employ publicly accessible historical locational marginal price (LMP) data and either all or a limited subset of phasor measurement unit (PMU) data to construct a predictive model. This model, utilizing a neural network, takes the loading profile of a section or the entire system as its input to forecast the LMPs. Such a model can be applied in designing high-damage FDIAs. Rigorous testing on the IEEE 30-, IEEE 57-, and IEEE 118- bus test systems demonstrates the model’s ability to predict the LMPs of the system with impressive accuracy and precision. This finding not only validates the model’s efficacy but also underscores the pressing need to address the newfound vulnerability of modern power systems against attackers utilizing public data.
Smart Entry System for College Using IoT and RFID

Smart technologies are being used more and more to make our lives better in every aspect. One of the most difficult and pervasive issues in organizations has a solution in Smart Entry Management. Checking is important in an organization. Attendance is essential for many institutions, including colleges and schools. We plan to solve this problem at college using some technology. Technologies have been used to address this problem. However, the best alternative to RFID (Radio Frequency Identification) to tackle this problem. Here, an object or person is identified and tracked via radio waves. RFID communication is conducted wirelessly by electromagnetic and electrostatic interaction, with the radio frequency spectrum serving as the medium of exchange. Our solution is an admission system based on Radio Frequency Identification (RFID), which mandates that all staff members and students wear ID cards and not exceed the allotted time. The design of an RFID-based entry monitoring system that uses an RFID tag attached to an ID card to uniquely identify each student and staff member is described in this project. If a student or staff fails to come to the entrance on exact time that has been fixed, then their absence will be intimated to their parent’s mobile number and respective staff’s mobile number through ESP 32 Wi-fi module.
Vulnerability and adaptive capacity in livelihood responses to oil spill in Bodo, Niger Delta

The environmental impacts of oil pollution in the Niger Delta have been well-documented, with research centered on transnational environmental justice issues, institutional failings at government level, and the political strategies of high profile representatives of the Ogoni community. However, the responses of affected communities has elicited relatively limited attention from academic researchers, and in particular, little is understood about the complex factors that shape how households have sought to tackle the impact of environmental degradation. This thesis addresses this gap by analysing the ways in which rural households in this region have sought to adapt and rebuild their livelihoods. The thesis combines insights from vulnerability science with a sustainable livelihoods approach to develop a conceptual framework that draws attention to the social dynamics of capacity and resilience as expressed in households’ everyday livelihood practices, and the factors that support or impede these. The study deploys a mixed method case study approach in the Ogoni community of Bodo, where people continue to struggle with the aftermath of two incidents of spillage from a Shell Petroleum Development Company of Nigeria (SPDC) pipeline in 2008. Questionnaires, semi-structured interviews, focus group discussions and a desk-based survey of regional natural resource management policies are used to elicit data on land use, livelihood strategies and the impacts of oil spill, and to explore factors shaping livelihood responses to the oil spill and interventions that have followed in its wake. Findings show that social differences are critical in shaping vulnerability and capacity, and that patterns of disadvantage have become more entrenched not only as a result of oil spill, but through the unintended consequences of institutional and household responses. Gender emerges as a particularly salient factor that has contributed to variations in household resilience in the face of oil spill and the socio-political environment that followed. Environmental degradation had a particularly heavy impact on women’s farming and shell fish collecting activities, damaging household economies, but also dismantling many of the social networks that formed around women’s collective labour. Moreover, corporate social responsibility interventions had unintended gender consequences, as emphasis was given to reinforcing ‘traditions’ that limited women’s empowerment. The research findings advance an understanding of the complex gender dynamics that result in socially differentiated pathways towards greater vulnerability or resilience in the face of anthropogenic environmental hazards, in this case relating to oil pollution and its clean-up in Ogoniland.
A Comprehensive Review of Machine Learning Techniques for Lung Cancer Detection

Despite being the most common cancer worldwide, lung cancer often stays undiagnosed until advanced stages. In 2022, lung cancer will be responsible for 22% of all cancer-related fatalities globally, as reported by the World Health Organization. The lungs may develop nodules, which are benign tumours. Multiple lung nodules might be seen in certain patients. The lungs and the heart are both capable of developing nodules. It is unusual for lung nodules to not be malignant. Nodules that aren't cancerous and won't metastasize are called noncancerous benign nodules. Malignant nodules may form and spread rapidly, and they are also carcinogenic. To detect cancerous nodules early, an automated approach is needed that uses X-ray and computed tomography images of the lungs. When it comes to deep learning algorithms, there are a lot of options available. Automated detection systems are expected to be resilient, efficient, and secure in the future. Professional researchers and radiologists may benefit from this study by learning more about the most recent developments in computer-aided detection systems. This paper's technique and validation findings are meticulously described in each section for easier comparison.
Empirical evaluation of automated code generation for mobile applications by AI tools

The rapid advancement of AI technologies has opened up new possibilities for automating various aspects of software development. Mobile app development, in particular, can benefit from AI-powered tools that assist developers in writing code more efficiently, providing suggestions, and reducing the time required for implementation. This document aims to explore how well a modern artificial intelligence tool can assist a mobile application development process. For this work, ChatGPT 3.5 was used to generate a mobile application from scratch using the Flutter framework, while the complete process was evaluated at each step. The evaluation criteria for the experience considered four indicators: code quality, solution quality, response time, and comparison with human-generated code. Results show that, up to a certain level of complexity and by using an interactive process of increasingly detailed prompts, the AI tool is capable of generating functional code, that can be the base for the inclusion of a more complex logic or structure.
AcadChain: A Blockchain Integrated Secure and Privacy Preserved Student Feedback System

Student feedback data is typically stored on centralized servers and can potentially be linked to individual identities, leading to concerns about repercussions or bias in evaluations. This lack of anonymity can inhibit students from providing honest and candid feedback. Because of this reason, a secure anonymous platform is needed as it provides the students with an uninhibited channel to offer their feedback. To overcome this, an architecture incorporating Blockchain into a Student Feedback System is proposed to address traditional systems’ anonymity and transparency issues. This paper proposes an implementation of a robust student feedback system that fosters a dynamic exchange of feedback and enhances interpersonal relationships and collaboration between students and teachers. This constructive feedback loop facilitates the improvement of faculty performance and facilitates a greater understanding of students’ educational requirements, ultimately leading to enhanced academic outcomes.
Visualization of the outliers detected by the SLAMICP library

During the navigation of a mobile robot in an unconstrained environment, unexpected objects may appear that block the planned path of the robot. Detecting and identifying these obstacles is key to avoiding possible crashes. The SLAMICP library provides efficient computation of the location of the robot while simultaneously identifying outlier points in a LiDAR scan, which enables higher data throughput. This paper evaluates the obstacle detection capabilities offered by the outlier detection feature of the SLAMICP library. Various test obstacles have been used to assess the ability of the library to differentiate elements that are not part of the original mapped area. These test objects include different boxes representing static obstacles and a walking person as a dynamic obstacle. The results show that all test objects were successfully detected and located within the map. The shape of the static obstacles was reconstructed by adding together the location of the outliers across multiple scans. Using the same method with the dynamic obstacle revealed the trajectory of the walking person.
Harmonics and Reliability Analysis of Distribution Network in Presence of Multi-DG using PSO and DigSILENT

This paper presents an approach to the PSO for the optimal allocation of DGs. To improve the voltage profile, reduce harmonics, and upgrade the network’s overall system reliability, a multi-objective function $({\mathbf {F_{m-obj}}})$ has been implemented. This ${\mathbf {F_{m-obj}}}$ also decreases reactive and active power losses. The optimization process involves load flow calculations for the DN and computer simulations performed using MATLAB-DigSILENT to analyze the impact of DG. The IEEE 33 bus DN is executed in this work to incorporate DG. DG effects are investigated for reliability and harmonics under with-DG and without-DG conditions. The system performance outcomes have been improved, such as reactive power, active power, voltage profile, THD, and reliability. Thus, the suggested approach produces superior effects using the presented method.
Adversarial Training with Domain Generalization

Adversarial training (AT) with a self-adversarial pattern is one of the most effective means to enhance model’s robustness. Domain generalization (DG) is capable of addressing the issue that models trained in the source domain appear to underperform when applied to the target domain by reducing the domain distribution variance. This means that the model trained with DG can generalize well to the out-of-distribution (OOD) datasets, which coincides with the goal of AT. Therefore, we view natural images and their adversarial images as two domains (i.e., natural domain and adversarial domain), and hope that using the DG to bridge the probability distributions gap between the two domains. Respecting this, we propose a DG-guided AT method (AT-DG). Specifically, in the process of learning the domain labels of the images, we leverage contrastive learning to achieve feature alignment between the two domains to learn the cross-domain invariant features of the data, and to enhance the adversarial robustness. Experimental results demonstrate that our approach effectively improves the robustness and generalization against various adversarial attacks.
Considerations of Extremum-Seeking Control based on the Sinusoidal Perturbation Applied to the MPPT Converter with PWM Modulation

This article analyzes the extremum-seeking control based on sinusoidal perturbation applied to a maximum power point tracking (MPPT) converter with pulse width modulation (PWM). With the proposed algorithm it is possible for the MPPT converter to track the optimal set point, with a fast transient response, as evaluated through simulation results. Further details involving the proposed control algorithm and the MPPT converter performance were discussed throughout this paper as well.
Wind energy forecasting using Decomposition Technique and Machine Learning and Designing Multi-Task EMS

Mitigating climate change is one of the main benefits of using renewable energy sources. Due to its affordability and environmental friendliness, wind energy has so received more attention. Wind, however, is highly sporadic in nature because it depends on environmental factors. Because of this dependence, applying and planning for wind energy is a difficult task. Therefore, it is necessary to develop an adaptable, reliable model that takes those aspects into account and aids in lowering forecasting mistakes. In this work, wind energy as a renewable source for the production of power has been chosen, but as we know that wind is intermittent in nature and due to this intermittency, it becomes a challenging task to perform high precision forecasting of wind power. The primary goal is to anticipate the amount of wind power in the next few hours or days, regardless of the wind direction. Different models have different errors and efficiencies but we have chosen the best accurate model which performs the forecasting with less error and high accuracy. The accurate wind energy forecasting is necessary so as to meet the load demand at any time otherwise there will be imbalance in the frequency and the voltage.
Advanced SMS Spam Detection using Deep Complex Models and Sine-Cosine Algorithm

With the increasing use of mobile phones and messaging services, SMS spam has become a significant issue for users. In this paper, we propose a novel approach1 to tackle this problem by using Sine-Cosine Algorithm (SCA) and Complex Multi-Layer Perceptron (C-MLP). Specifically, we apply the SCA method to reduce the dimensionality of the feature space and CMLP to improve the performance of spam detection. Also, in this paper, we investigate the effectiveness of different classification algorithms, including Support Vector Machines, Random Forests, K-nearest neighbors, Naive Bayes, bagging, and voting approaches. Our experimental results show that the proposed approach achieves high accuracy and outperforms existing methods in terms of both accuracy and F-measure. The proposed approach can be helpful in designing effective SMS spam filters and improving the overall user experience.1All the code used in this research is publicly available on the first author’s GitHub repository: https://github.com/seper-sw/SMS-Spam-Detection.git
Music Recommendation System Using Face Emotion Recognition

This paper proposed a new way to play music on the go. Most current methods involve manual music processing, using wearables, audio classification. Instead, it is recommended switching to manual connection and working traditionally. There convolutional neural network is used for emotion recognition. Using Pygame and Tkinter for better visualization. Our proposed method will minimize the time required to achieve the results and the total cost of installing the system. System evaluation is experimented on the FER2013 dataset, using the built-in camera to capture face expressions. The Music playlist is updated by analyzing current user preferences. It outperforms the computation time performance compared to the algorithm in the current literature.
Efficiency of IDRONE Technology in Finding Pest-Free Solutions for Tea Farming-Looper Caterpillar and Tea Leaf Hopper

Tea manufacturing sectors have started focusing towards the prospects of IDrone (Internet of Drones) Technology in the aspects of farming, agriculture and crop monitoring. IDrones have turned out to be the most significant tool for highly exact data acquisition. IDrones may contribute to quick observation of tea plantations for insect and pest attacks. Early detection of infection may be considered to reduce the number of insecticides and pesticides and also to decrease the impact. With the help of multi-spectral sensors, IDrones may also capture images by demonstrating the tea plants’ conditions in the context of maturity level and their productivity. Maps in IDrones are helps to make and plan decisions for planting using high-resolution cameras. This paper focuses on finding pest-free solutions for tea farming, especially concentrated on Looper Caterpillar and Tea Leaf Hopper by using the image dataset that captured using IDrones. This analysis also provides beneficial findings on the impact of IDrones technology towards finding pest-free solutions in the context of tea farming.
Immersive Computing: Vision, Infrastructure, and Use Cases

Immersive computing signifies a paradigm shift in how people interact with digital content and their surrounding 3D environments. In this vision paper, we first outline a comprehensive roadmap for advancing immersive computing, encompassing virtual, augmented, and mixed reality, which are often collectively referred to as extended reality, and immersive content delivery, including 360-degree panoramic video streaming and volumetric video streaming, by identifying key challenges and opportunities. We then describe CoMIC, a research infrastructure that offers a collaborative, visual-first, and hologram-based computing space and a quality-driven, multi-site, and immersive communication framework for collaborative mobile immersive computing. Given the interdisciplinary nature of immersive computing, we present several key use cases that could be enabled by CoMIC. We conclude this paper by envisioning a future where our digital experiences will transcend the boundaries of screens and devices, immersing us in a world where the virtual and physical seamlessly merge.
A New Modular Structure for High Voltage Pulse Generation

In recent years, high-voltage pulsed power supplies are attracting substantial attention because of their demands in various industrial applications. Reliability, modularity, and high-power density are some of the desirable characteristics that should be present in such power supplies. In this paper, a new modular pulse generator (MPG) topology is introduced that inherently meet such qualifications. Reducing the semiconductor switches counts and improving the gain of the pulse generators are the main goals behind the design of this converter. MPG requires a low voltage dc source and sufficiently generates high voltage pulsed output. The pulsed output can be suitably varied in terms of pulse-amplitude, size, and frequency. A detailed discussion on MPGs operational principle is made and steady-state results are analyzed. To confirm the validity of the concept, a 2-stage model for MPG is designed on powersim (PSIM) platform and the results, thus obtained are discussed.