Miracle-dz/UniFAR-FTUs huggingface dataset:
A SEPIC Converter with Adaptive Sliding Mode Control for Grid-Connected Solar PV Systems

In this proposed work, the new adaptive SMC controlled SEPIC converter is presented for grid connected Solar PV systems. Toxin free power is utilized in this work for the micro-grid for the coupling of AC loads with AC sources as well as DC loads with DC sources. The exploration presents a sun powered PV-based framework for fostering the capacity to supply the two weights while further developing DC transport bar voltage. The bidirectional converter is utilized to store energy in the energy storage system (ESS) through the bidirectional power stream. To meet the power interest by utilizing the power resources’ most outrageous usage of endless systems, battery span extension, and power converter gain improvement. The 3 Phase Inverter is utilized to change the rotating current voltage over to the exchanging current network structure and substitute current burdens. The new power converter is intended to increase the PV source voltage while keeping a steady DC associated voltage. To deal with the converter voltage, the MPPT strategy with a PID controller is utilized. In order to balance grid voltage and current the adaptive sliding mode controller is used for applying switching sequence for inverter. The outcomes are then acquired and approved utilizing MATLAB and Simulink programming.
Wi-CaL: WiFi Sensing and Machine Learning based Device-Free Crowd Counting and Localization

Wireless sensing represented by WiFi channel state information (CSI) is now enabling various fields of applications such as person identification, human activity recognition, occupancy detection, localization, and crowd estimation these days. So far, those fields are mostly considered as separate topics in WiFi CSI-based methods, on the contrary, some camera and vision-based crowd estimation systems intuitively estimate both crowd size and location at the same time. Our work is inspired by the idea that WiFi CSI also may be able to perform the same as the camera does. In this paper, we construct Wi-CaL, a simultaneous crowd counting and localization system by using ESP32 modules for WiFi links. We extract several features that contribute to dynamic state (moving crowd) and static state (location of the crowd) from the CSI bundles, then assess our system by both conventional machine learning (ML) and deep learning (DL). As a result of ML-based evaluation, we achieved 0.35 median absolute error (MAE) of counting and 91.4% of localization accuracy with five people in a small-sized room, and 0.41 MAE of counting and 98.1% of localization accuracy with 10 people in a medium-sized room, by leave-one-session-out cross-validation. We compared our result with percentage of non-zero elements metric (PEM), which is a state-of-the-art metric for crowd counting, and confirmed that our system shows higher performance (0.41 MAE, 81.8% of within-1-person error) than PEM (0.62 MAE, 66.5% of within-1-person error).
Lossy Data Compression using K-Means Clustering on Retinal Images using RStudio

The massive volume of data limits the use of data on portable devices like mobile phones, as well as memory-limited devices like video game consoles. As a result, the demand for data compression methods in the field of signal and image processing is skyrocketing. Data compression required advanced ways to serve better, with a greater compression ratio, due to the rising demands, which expected more sophisticated algorithms. This article primarily focuses on image compression (retinal images) utilizing the k-means clustering technique, which is a popular unsupervised classification strategy in machine learning. The compression challenge in this study uses two retinal pictures. The most useful open-source statistical calculation tool is R programming, which is employed here with RStudio, an Integrated Development Environment (IDE). The use of R (RStudio) has had an impact across all domains of research and engineering. The test retinal images had a compression ratio of 33.3 percent, according to simulation data.
Trust based Mechanism for Detection and Isolation of DDOS Attack in Software Defined Networks

SDN networks are different from the classic networks in terms of features. Different from SDN, in classic networks, networking tool stake decision regarding the handling of an arriving packet just on the address of its IP destination. The attacking nodes after making entry in the network may activate a DDoS attack. This research work is an attempt of detecting these compromised nodes by implementing a trust-based approach. The trust-assisted approach involves computing the trust level of the allnetwork nodes. The node with minimal trust is labelled as the malevolent node. The trust is calculated on the basis of the number of packets as transmitted node during the time period. The node which has transmitted maximum packets and consumes least resources will be marked as malicious node from the network. The proposed model is implemented in NS2 and the results are analysed in terms of certain parameters such as throughput, delay and overhead. To compute trust, the number of packets forwarded by the nodes in a certain period are considered.
Leaves Classification Through Image Segmentation and Convolutional Neural Network

The importance and motivation for the proposed research work are to achieve two main characteristics: the speed and the accuracy. For plant leaf detection through leaf image applying computer vision and deep learning techniques. Leaf owing to their sharp characteristic feature and volume commonness is a strong source of identification of plant species. Plant leaves are perceptibly most important part of its form, utilizing which detailed study of the given plant life cycle and its well-being is possible. It is a facet of the plant.
4x4 Butler Matrix Design for Multibeam Operation for Radar Application

The 5G is the current requirement of the spectrum shortage and the coverage of the intended user/direction. The high data rate and the large number of users can be accommodated using 5G frequency bands. A beamforming 4x4 Butler matrix with multibeam for radar applications is designed for the 5G applications. The proposed 4x4 Butler matrix is designed with 2:1 VSWR on Rogers RO3003 substrate, having a dimension of 19.35 ×16 × 0.254 mm 3. The Butler matrix design covers 28.12-35.24 GHz (7.12 GHz bandwidth) and 28.55-36.35 GHz (7.77 GHz bandwidth) frequency bands with more than 7 dB isolation among different ports. The gain of antenna is moe than 7.5 dBi at the ports and its radiation efficiency is more than 70% obtained at different ports.
Implementation of an Energy Efficient Framework for Air Quality Monitoring in the Cremation Center based on Improved chacha20 Stream Cipher for Secure Data Transmission

The Internet of Things (IoT) is recognized as a predominant era of future technology and is perceived as a network of devices that can communicate and acquire data over the internet. IoT has emerged as a prominent area of impact, potential, and innovation, attracting considerable interest from a multitude of sectors. The IoT devices are resource-constrained since they have low processing capabilities, limited power, and memory. Additionally, security is also a concern because these devices are more susceptible to attacks. In this paper, the author has proposed the Portable Air Quality and Emission Monitoring System (PAQEMS) that is used to determine the air quality and detect the emission of five harmful pollutants during the cremation process namely, particulate matter 2.5, particulate matter 10, carbon monoxide (CO), ammonia (NH3), and nitrogen dioxide (NO2). As IoT devices are power-hungry, so to minimize the power consumption, we proposed a combination of hybrid models wherein the system operates in two modes, the active mode, and hibernation mode, and an adaptive sampling approach making it an ultra-low energy-efficient system. We also implemented a security system consisting of the Message Queue Telemetry Transport (MQTT) protocol for message passing and payload encryption using the proposed new stream cipher called improved ChaCha20 which is an improved version of traditional ChaCha20. The experimental results that the average emission concentration of PM2.5, PM10, NO2, NH3, and CO, increased by 35.16 %, 16.77 %, 30.24 %, 69.83 %, and 16.47 % respectively, and that the power consumption by PAQEMS per day before applying the hybrid model is 665.64 A, while the power consumption drops to 345.6 A after applying the hybrid model. The results show that the designed system is reliable and effective for alerting people living nearby cremation centers in case of high levels of air pollution.
Coldness Presentation Depending on Motion to Enhance the Sense of Presence in a Virtual Underwater Experience

In virtual reality (VR), there is consumer demand for underwater world experiences. To address this demand, previous studies have presented coldness to enhance the sense of presence in virtual underwater experiences. We considered that we could further improve the sense of presence and the experiential quality of underwater VR simulations by making the coldness presentation more realistic. We also considered that methods of presenting coldness continuously would have a wider application range. In this research, with reference to actual water behavior, we propose a method that presents coldness depending on whether it is moving. We conducted an experiment comparing the sense of presence between the steady-state temperatures of $24.9 \pm 1.9 \mathrm { ^{circ}C} / 27.8 \pm 2.7 \mathrm { ^{circ}C}$ and the proposed method, the temperature of which moves between the aforementioned temperatures as the user moves and stops. The results show that the proposed method yielded higher scores than the higher steady-state temperature in “closeness of overall sensation” and “closeness of thermal sensation”. For the lower steady-state temperature, the proposed method achieved the same sense of presence improvement while removing less heat. This extends the experiential duration. Meanwhile, for those who noticed the temperature change, the proposed method yielded higher scores than the lower steady-state temperature in “realness” and “closeness of thermal sensation”. To further improve effectiveness of the proposed method, it is important to not only change the temperature but also to notice the temperature change. This method is suitable for several virtual underwater applications, such as education and training.
The Problem of Behaviour and Preference Manipulation in AI Systems

Statistical AI or Machine learning can be applied to user data in order to understand user preferences in an effort to improve various services. This involves making assumptions about either stated or revealed preferences. Human preferences are susceptible to manipulation and change over time. When iterative AI/ML is applied, it becomes difﬁcult to ascertain whether the system has learned something about its users, whether its users have changed/learned something or whether it has taught its users to behave in a certain way in order to maximise its objective function. This article discusses the relationship between behaviour and preferences in AI/ML, existing mechanisms that manipulate human preferences and behaviour and relates them to the topic of value alignment.
UPreG: An Unsupervised approach for building the Concept Prerequisite Graph

Today, there is a vast amount of online material for learners. However, due to the lack of prerequisite information needed to master them, a lot of time is spent in identifying the right learning content for mastering these concepts. A system that captures underlying prerequisites needed for learning diﬀer-ent concepts can help improve the quality of learning and can save time for the learners as well. In this work, we propose an unsupervised approach, UPreG, for automatically inferring prerequisite relationships between diﬀerent concepts using NLP techniques. Our approach involves extracting the concepts from unstructured texts in MOOC (Massively Open Online Courses) course descriptions, measuring semantic re-latedness between the concepts and statistically inferring the prerequisite relationships between related concepts. We conducted both qualitative and quantitative studies to validate the eﬀectiveness of our proposed approach. As there are no ground truth labels for these prerequisite relations, we conducted a user study for the evaluation of the prerequisite relations. We build the concept graph using prerequisite relations. We demonstrate few examples of the learning maps generated from the graph. The learning maps provide pre-requisite information and learning paths for diﬀerent concepts.
Grouping Source Code by Solution Approaches - Improving Feedback in Programming Courses

Various similarity measures for source code have been proposed, many rely on editor tree-distance. To support a lecturer in quickly assessing live or online exercises with respect to approaches taken by the students , we compare source code on a more abstract, semantic level. Even if novice student’s solutions follow the same idea, their code length may vary considerably – which greatly misleads edit and tree distance approaches. We propose an alternative similarity measure based on variable usage paths (VUP), that is, we use the way how variables are used in the code to elaborate code similarity. The ﬁnal stage of the measure involves a matching of variables in functions based on how the variable is used by the instructions. A preliminary evaluation on real data is presented.
Tracing Knowledge for Tracing Dropouts: Multi-Task Training for Study Session Dropout Prediction

Study session dropout prediction allows for educational systems to identify when a student would stop a study session which gives vital information to prolong learning activity. Student session dropout can depend on many factors that are involved with the engagement when using the system. The student’s knowledge level and their track records within the system are closely related to the student’s willingness to continue with their study. Knowledge tracing as a task models the user’s knowledge level given study history. The information from knowledge tracing can have signiﬁcant impact on predicting the student’s willingness to continue, which is why it is natural to train two tasks jointly for better generalization in dropout prediction task. While extensive research has been conducted individually on dropout prediction and knowledge tracing, the effect of jointly modeling two tasks has not been thoroughly investigated. Hence, we show that multi-task training of the study session dropout prediction model along with knowledge tracing boosts the performance of study session dropout prediction, especially on more challenging tasks and datasets. Speciﬁ-cally, with Transformer-based models, multi-task training signiﬁ-cantly improves Area Under Receiving Operator Curve (AUROC) by 3.62% in further N -step dropout prediction task, which is a study session dropout prediction task under a more practical setting. Moreover, under label-scarce and class-imbalance settings, our method shows improvements of AUROC up to 12.41% and 11.22%, respectively. Our results imply that knowledge tracing is closely related to study session dropout prediction and can transfer positive knowledge in multi-task training, which provides a new way to better predict dropouts especially in difﬁcult
Safety Aware Reinforcement Learning by Identifying Comprehensible Constraints in Expert Demonstrations

When used in real-world environments, agents must meet high safety requirements as errors have direct consequences. Besides the safety aspect, the explainability of the systems is of particular importance. Therefore, not only should errors be avoided during the learning process, but also the decision process should be made transparent. Existing approaches are limited to solving a single problem. For real-world use, however, several criteria must be fulfilled at the same time. In this paper we derive comprehensible rules from expert demonstrations which can be used to monitor the agent. The developed approach uses state of the art classification and regression trees for deriving safety rules combined with concepts in the field of association rule mining. The result is a compact and comprehensible rule set that explains the expert’s behavior and ensures safety. We evaluate our framework in common OpenAI environments. Results show that the elaborated approach is able to identify safety-relevant rules and imitate expert behavior especially in edge cases. Evaluations on higher dimensional observation spaces and continuous action spaces highlight the transferability of the approach to new tasks while maintaining compactness and comprehensibility of the rule set. 1
Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems

Access to high-quality education at scale is limited by the diﬃculty of providing student feedback on open-ended assignments in structured domains like computer programming, graphics, and short response questions. This problem has proven to be exceptionally diﬃcult: for humans, it requires large amounts of manual work, and for computers, until recently, achieving anything near human-level accuracy has been unattainable. In this paper, we present generative grading: a novel computational approach for providing feed-back at scale that is capable of accurately grading student work and providing nuanced, interpretable feedback. Our approach uses generative descriptions of student cognition, written as probabilistic programs, to synthesise millions of labelled example solutions to a problem; we then learn to infer feedback for real student solutions based on this cognitive model. We apply our methods to three settings. In block-based coding, we achieve a 50% improvement upon the previous best results for feedback, achieving super-human accuracy. In two other widely diﬀerent domains—graphical tasks and short text answers—we achieve major improvement over the previous state of the art by about 4x and 1.5x respectively, approaching human accuracy. In a real classroom, we ran an experiment where we used our system to augment human graders, yielding doubled grading accuracy while halving grading time.
Student Strategy Prediction using a Neuro-Symbolic Approach

Predicting student problem-solving strategies is a complex problem but one that can signiﬁcantly impact automated instruction systems since they can adapt or personalize the system to suit the learner. While for small datasets, learning experts may be able to manually analyze data to infer student strategies, for large datasets, this approach is infeasible. We develop a Machine Learning model to predict strategies from student data. While Deep Neural Network (DNN) based methods such as LSTMs can be applied for this task, they often have long convergence times for large datasets and like several other DNN-based methods have the inherent problem of overﬁtting the data. To address these issues, we develop a Neuro-symbolic approach for strategy prediction, namely a model that combines strengths of symbolic AI (that can encode domain knowledge) with DNNs. Speciﬁ-cally, we encode relationships in the data using Markov Logic and use symmetries among these relationships to train an LSTM more eﬃciently. In particular, we use an importance sampling approach where we sample the training data such that for clusters/groups of symmetrical instances (instances where the strategies are likely to be symmetric), we only pick representative samples for training the model instead of using the whole group. Further, since some groups may contain more diverse strategies than the others, we adapt the importance weights based on previously observed samples. Through empirical evaluation on the KDD EDM challenge datasets, we show the scalability of our approach.
Defining Optimal Cut-Points for Cardiorespiratory Fitness Associated With Overweight/Obesity in Children: A School-Based Study

The main purpose of the study was to define optimal criterion-referenced cut-points for cardiorespiratory fitness (CRF) associated with overweight/obesity. In this cross-sectional study, participants were 1,612 children aged 7–14 years (mean age ± SD = 9.7 ± 2.4 years; 52.5% girls). CRF was assessed by the Maximal multistage 20-m shuttle run test, from which maximal oxygen uptake (VO2max) was estimated. Anthropometric indices included body-mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR). Receiver operating characteristic (ROC) curves were performed to determine cut-off points. In boys, the optimal cut-off points of CRF in defining overweight/obesity for BMI, WC, and WHtR were 44.6, 46.4, and 46.9 mlO2/kg/min. The areas under the curves (AUC) were 0.83 (95% CI 0.78–0.88, p < 0.001), 0.77 (95% CI 0.71–0.83, p < 0.001), and 0.90 (95% CI 0.86–0.93, p < 0.001). In girls, the optimal cut-off points were 41.0, 40.8, and 40.7 mlO2/kg/min for BMI, WC, and WHtR, with the AUCs of 0.86 (95% CI 0.82–0.90, p < 0.001), 0.83 (95% CI 0.79–0.88), and 0.88 (95% CI 0.84–0.93, p < 0.001). In conclusion, our newly developed cut-off points for CRF assessed by the Maximal multistage 20-m shuttle run test may adequately detect primary school-aged boys and girls with general and abdominal obesity.
Volatile Signals From Guava Plants Prime Defense Signaling and Increase Jasmonate-Dependent Herbivore Resistance in Neighboring Citrus Plants

Intercropping can reduce agricultural pest incidence and represents an important sustainable alternative to conventional pest control methods. Citrus intercropped with guava (Psidium guajava L.) has a lower incidence of Asian citrus psyllid (ACP, Diaphorina citri Kuwayama) and huanglongbing disease (HLB), but the mechanisms are still unknown. In this study, we tested whether volatile organic compounds (VOCs) emitted by guava plants play a role in plant–plant communications and trigger defense responses in sweet orange (Citrus sinensis L. Osbeck) in the laboratory. The results showed that the behavioral preference and developmental performance of ACP on citrus plants that were exposed to guava VOCs were suppressed. The expression of defense-related pathways involved in early signaling, jasmonate (JA) biosynthesis, protease inhibitor (PI), terpenoid, phenylpropanoid, and flavonoid biosynthesis was induced in guava VOC-exposed citrus plants. Headspace analysis revealed that guava plants constitutively emit high levels of (E)-β-caryophyllene and (E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), which can induce the accumulation of JA and promote stronger defense responses of citrus to ACP feeding. In addition, exposure to guava VOCs also increased the indirect defense of citrus by attracting the parasitic wasp Tamarixia radiata. Together, our findings indicate that citrus plants can eavesdrop on the VOC cues emitted by neighboring intact guava plants to boost their JA-dependent anti-herbivore activities. The knowledge gained from this study will provide mechanisms underlying citrus-guava intercropping for the ecological management of insect pests.