Miracle-dz/UniFAR-FTUs huggingface dataset:
A Housing Price Prediction Method Based on Stacking Ensemble Learning Optimization Method

The growth of real estate sector has been significantly influenced in recent years by ongoing regulation of real estate acquisition policy and the effects of the COVID-19 epidemic on economy. The fluctuation in housing prices is one of the most concerning factors for prospective homeowners. Whether property prices can sustain a comparatively constant level for an extended period of time is a crucial factor for prospective homeowners. Numerous modeling and application techniques for prediction algorithms, together with the promotion of machine learning algorithms, offer fresh approaches to forecast residential real estate values. This work proposes a novel stacking ensemble learning method (DStacking) which is based on the diversity of learners including XGBoost and BP neural network. Through the application of ensemble learning algorithm, D-Stacking method can successfully predict the possible promotion of housing price. Housing price datasets from China and the USA were used in the experiments to guarantee the generalizability of findings. Experimental findings indicate that the diversity of base learners significantly affects the predictive power of D-Stacking method. Furthermore, the more diverse the models are, the more precise the predictions can be with proposed D-Stacking method. Compared with classical Stacking ensemble learning models, the proposed D-Stacking method demonstrates an excellent feasibility in reducing the RMSE to 0.869 and 1.029 across various datasets.
Dual-Arm Nanorobotic System for Measuring Embryo with Minimum Disturbances

The understanding of the embryo behavior, especially the early-stage development was hampered by the lack of proper diagnostic tools for clinical assessment before embryo implantation. Mechanical properties are label free biomarkers for the cells/embryo differentiation, as the atomic force microscopy (AFM) based experiment could simultaneously derive the surface morphological and mechanical information without damage to the cells. However, for the measurement of floating embryo, the lack of the constraint from the substrate will induce fluctuation and movement in lateral direction, causing failure in accurate measurement. Traditionally, researchers rely on the physical or chemical bounds to constrict the floating objects which may alter the objects’ properties and decrease the measurable region. Thus, it’s significant to implement the micromanipulator-based robot with AFM system to accomplish multiple tasks in the manipulation and measurement of single embryo. The system established in the current study could precisely manipulate and sense the living floating embryo without any extra treatment.
IoT based Wind Energy System Monitoring for Maximum Power Generation

In order to address the growing energy crisis, wind energy is employed as a substitute source of energy. Highly exposed locations are used to build wind farms. Therefore, people must travel to that specific spot to fix small problems as it occurs. It needs a lot of effort and time and has significant financial challenges. The Internet of Things (IoT) idea can be used to get around these constraints. This study proposes a wind energy conversion system (WECS) employing a maximum power point tracking (MPPT) controller with incremental conductance (IC) for maximum power generation. IoT application is used for the system's real-time monitoring, such as turbine speed, wind speed, direct current (DC) bus voltage, amount of generated power and temperature. Additionally, IoT has integrated a wide range of different sensors for measuring wind characteristics. The PIC microcontroller is used in this system and the WECS model is simulated in MATLAB. The proposed IoT based monitoring system proves that it improves monitoring precision, quality assurance, and the reliability of measuring method.
Energy simulation scenario to social housing building: combining heat pump and renewable energy system

Control and improvement of both the energy and environmental quality of buildings is responsible for almost 40% of the emissions caused by energy processes. 84% of Italian buildings are residential, making redevelopment approaches necessary for reducing emissions and consumption. RESHeat (Renewable Energy System for Residential Building Heating and Electricity Production) has been realized to heat and cool residential buildings with renewable energy. The RESHeat system consists primarily of a heat pump, photovoltaic/thermal modules, and two thermal energy storage units. One of the novelties of the RESHeat system is the use of waste thermal energy from the PV cooling system to ensure constant energy levels in the cold sink of the heat pump (COP over 5). Furthermore, PV-T is coupled with the system to maximize the amount of renewable energy produced. The study focuses on a social house building from the 1980s that is used as a demonstration site for the RESHeat system. It consists of a three-story building with 13 different-sized flats. The dynamic simulation was performed using TRNSYS software to compare the RESHeat system to the current situation, having two scenarios: A: Actual Heating, B: RESHeat System. By retrofitting existing systems, an efficient heating system and air conditioning system can be provided; these implementations can also significantly reduce overall consumption and CO2 emissions. The results show that the heat pump's yearly average COP is XX due to the energy storage unit. RESHeat system covers building heating needs through a combination of renewable energy sources and energy storage.
Artificial Intelligence in Data Analysis for Open-Source Investigations

The developed OSINT platform organizes data hierarchically and integrates a GPT model for faster, easier large data processing. Users can interact with the GPT model through natural language communication with a virtual agent for data processing commands. Open source investigations face challenges like vast data volumes and incorrect information. To address these issues, a real-time data processing tool is needed. Our OSINT platform integrates a GPT model trained to learn from data, enhancing the efficiency of open source investigations. We assessed various natural language processing models, focusing on the benefits of pretraining, fine-tuning. , and generative models in open source investigations. GPT models excel due to pretraining on extensive text data, allowing fine-tuning for specific tasks and domains. , giving investigators a robust tool for text analysis. The generative nature of GPT models benefits OSINT investigations by producing human-like text for extracting insights and identifying patterns. Fine-tuning enables customization to specific domains or topics. , increasing accuracy and reliability while reducing time and effort in data analysis. In conclusion. , our OSINT platform presents an innovative solution for open source investigations by incorporating a GPT model for efficient information processing. The Davinci model by OpenAI outperforms other evaluated models. , enhancing investigation efficiency and maintaining grammatical accuracy. This work highlights the significance of natural language processing models in open source investigations and paves the way for future research.
Local attribute-similarity weighting regression algorithm for interpolating soil property values

: Existing spatial interpolation methods estimate the property values of an unmeasured point with observations of its closest points based on spatial distance (SD). However, considering that properties of the neighbors spatially close to the unmeasured point may not be similar, the estimation of properties at the unmeasured one may not be accurate. The present study proposed a local attribute-similarity weighted regression (LASWR) algorithm, which characterized the similarity among spatial points based on non-spatial attributes (NSA) better than on SD. The real soil datasets were used in the validation. Mean absolute error (MAE) and root mean square error (RMSE) were used to compare the performance of LASWR with inverse distance weighting (IDW), ordinary kriging (OK) and geographically weighted regression (GWR). Cross-validation showed that LASWR generally resulted in more accurate predictions than IDW and OK and produced a finer-grained characterization of the spatial relationships between SOC and environmental variables relative to GWR. The present research results suggest that LASWR can play a vital role in improving prediction accuracy and characterizing the influence patterns of environmental variables on response variable. Local attribute-similarity weighting regression algorithm for interpolating soil property values. Int Agric Eng,
The naturalness criterion for supersymmetric scenarios

Supersymmetry (SUSY) has been considered since long ago the leading paradigm of beyond the Standard Model (SM) physics as a framework that tackles the SM hierarchy problem, provides gauge coupling unification and a well-behaved cold dark matter (DM) candidate. Nevertheless, current experimental searches for new physics seem to have cornered the minimal versions of these models in unnatural regions of their parameter space, according to the standard Natural SUSY scenario. With the aim of formulating a general naturalness criterion for minimal supersymmetric scenarios, we have carefully re-examined the standard fine-tuning measure, which only deals with the cancellations needed to obtain the electroweak symmetry breaking (EWSB) scale, introducing several improvements such as the mixing of the fine-tuning conditions and the dependence on the low and high-energy (HE) scales. Furthermore, we have outlined a method that allow to straightforwardly derive naturalness bounds on the initial parameters and mass spectrum of any minimal supersymmetric standard model (MSSM) defined at any HE scale. We have applied this method to specific scenarios to compute a complete set of naturalness bounds. and also employed it to find the most natural gauge-mediated SUSY breaking model. Contrary to what was expected, we show that Natural SUSY, in general, does not demand light stops. The most stringent upper bound from naturalness is that of the gluino mass, which typically sets the level of fine-tuning, but strongly depends on the HE scale. The most robust result of Natural SUSY is by far that Higgsinos should be rather light. Besides, we have investigated other potential sources of fine cancellations in the MSSM, that if present, must be combined with that of the EWSB scale. The most important being the tuning to obtain the experimental Higgs mass and that required to reproduce the correct DM relic abundance. We have quantified them with p-value like measures that allow us to multiplicatively combine them with the electroweak (EW) fine-tuning. Regarding DM, we have considered the lightest neutralino as the DM particle and explored the various possibilities for its mass, composition and interactions that could give rise to accurate arrangements of the initial parameters to achieve the observed DM relic density. Finally, to illustrate the utility of the above stated criteria to estimate the global degree of naturalness, we have applied all of them to a specific model that features low-mass neutralinos and sleptons at low-energy. We find that these scenarios are rather unnatural when taking into account the aforementioned sources of tuning, which would have gone unnoticed, if we have only considered the EW fine-tuning.
Automated Hydroponic System using IoT for Indoor Farming

Hydroponics is a farming method in which the crops are grown without the use of soil. The plants are placed in a water solution enriched with macro and micronutrients essential for plant growth. Hydroponics is also called as Controlled Environment Agriculture (CEA). In traditional farming, soil erosion and infertility of soil plays a major role by decreasing the crop yields. The use of pesticides is also a major concern. This paper proposes a fully automated Internet of Things (IoT) based hydroponic system which senses, monitors and controls important factors like pH, Total Dissolved Solids (TDS), environmental temperature, humidity and lighting inside the green house. Assortment of 7 sensors along with 6 actuators helped in developing the proposed system. With the usage of IoT a mobile application is developed for real time viewing of environmental and nutritional variables. A user notification is given in the mobile application if there are anomalous conditions detected in the green house. User would be able to view the green house with the mobile application, with the help of camera that is interfaced for live viewing. MQTT protocol is responsible for exchanging the data within remote devices and controllers. The crop selected for experimenting the proposed hydroponic model is coriander, which was monitored for 18 days and good results were seen. This proposed system demonstrates successful hydroponic system that is completely automated which reduces the manual labor work and grows a completely chemical and pesticide free crops.
IoT-Enabled Optimised Sensor-based System for Monitoring Grain Storage

In the recent past, most of the countries started developing the technologies to improve safety of food storage, as almost one-third of the food grains are wasted due to storage losses worldwide. On the other side, in India nearly 20% of food grains are lost every year and is majorly due to environmental conditions, like floods, heavy rains and lack of well-equipped infrastructure for packing, transportation, storing of grains. So, food is getting wasted from its production to consumption. To overcome this, an IoT (Internet of Things) -enabled optimized sensor-based system for monitoring grain storage is proposed. Here, the proposed system is focused to reduce food wastage during storage phase. The proposed model includes smart sensing devices to sense various parameters like humidity, temperature, CO2 (Carbon Dioxide), and fire/smoke. All the values of these sensors are collected from Arduino controller and stored in a cloud server through GSM (Global System for Mobile Communication) module. The parameters are then compared with threshold values for any abnormality in the storage place. If there is a change in any of the parameter then a controlled action will be initiated and an alert message will be sent to linked mobile numbers. In addition, proposed model consists of a fan to control humidity and temperature before necessary action being taken.
Automated Behavior Identification of Home Security Camera Traffic

With the widespread use of IoT devices in the market, home security cameras (HSCs) have become one of the most commonly used devices. However, the security risks of these devices, such as cyber attacks and privacy breaches, still remain a concern. To analyze the behavior of HSC devices, researchers and intruders often rely on traffic data. However, identifying the behavioral traffic of HSC devices is a challenging task because it is difficult to distinguish it from dynamic video traffic during transmission. We propose a new approach to solve this problem by analyzing the features of unidirectional packets present in non-TLS network traffic. Additionally, we found that manually labeling device behavior traffic data is time-consuming and often inaccurate. We introduce a two-factor automatic behavior labeling approach based on reverse traffic and operation logs to address this concern. Finally, we test the performance of multiple classifiers on two datasets: a real-world dataset and the IMC 2019 payload public dataset. Our experiments show that the proposed framework is able to extract and identify behavioral traffic automatically. The CNN-based classifier can accurately identify fine-grained behavioral traffic of HSC devices based on unidirectional upload traffic features. This study has implications for improving security threat awareness of IoT products and anomalous behavior detection of HSC devices.
An AI Framework for Modelling and Evaluating Attribution Methods in Enhanced Machine Learning Interpretability

In this paper, we propose a general methodology for estimating the degree of the attribution methods precision and generality in machine learning interpretability. Additionally, we propose a technique to measure the attribution consistency between two attribution methods. In our experiments, we focus on the two well-known model agnostic attribution methods, SHAP and LIME, then we evaluate them on two real applications in the attack detection field. Our proposed methodology highlights the fact that both LIME and SHAP are lacking precision, generality, and consistency. Therefore, more inspection is needed in the attribution research field.
Theoretical Bounds of the Self-Interference Cancellation-Bandwidth Integral in Full Duplex RF Front Ends

This paper studies the behavior of lumped self-interference cancellation (SIC) filters in simultaneous transmit-receive (STR) RF front ends. The crux that enables a different approach in this work for finding an optimal network solution is converting the SIC circuit design flow into an equivalent impedance-matching problem. As a result, we analyze a reflective SIC filter that complies with electric-balance duplexers utilizing known techniques. Bode, Fano, Matthaei, and others studied lumped network theory considerations and devised methodical steps for maximizing the impedance-matching bandwidth for reactive loads. Accordingly, a systematic approach that considers the analysis of the SIC-Bandwidth mutual relationship, and the optimal lumped networks, is detailed in this work. We indicate that the Chebyshev band-pass filter can provide optimal and lossless SIC filter performance, which is crucial for the overall STR performance. The realistic RF front-end in this work, which includes a simulated quadrature hybrid, and a measured Wi-Fi antenna with VSWR of 3:1, achieves > 40 dB of TX-RX isolation over 200 MHz around 5.5 GHz, utilizing a second order filter that employs only two realizable inductors.
Circulating Current Suppression Scheme for Interleaved Active Neutral Point Clamped Nine-Level Inverter

The interleaved active neutral point clamped nine-level (ANPC-9L) inverter can be used in 1500-V photovoltaic (PV) power generation because of its advantages of low voltage stress, high equivalent switching frequency, and small filter size. Circulating current between submodules is a key problem for ANPC-9L. A simple scheme to realize circulating current suppression for ANPC-9L inverter is presented in this article. The operation of the ANPC-9L is briefly discussed, and the model of coupled inductor is given. Analysis shows that the redundant working states of the inverter with the same output voltage can be used to suppress the circulating current. Another objective is to analyze circulating current imbalance at the band transition when using phase disposition sinusoidal pulsewidth modulation (PD SPWM) strategy. A solution is proposed to cancel current imbalance. Simulation and experiment verify the effectiveness of the proposed strategy.
Research on Plant Image Identification Based on Deep Learning

In recent years, increasing attention was paid to the methods from researchers that about the intelligent identification of plants and their diseases based on deep learning algorithms. In this paper, plant images were as the study object. Firstly we listed the Research results on traditional methods and deep learning methods of machine learning, and summarized the classification features of plant images and the general procedure of plant identification, Simultaneously we introduced the general algorithms for deep learning, and studied the structural features of convolutional neural networks, and described the classical model of convolutional neural networks, At the end, we compared experimentally the identification efficiency of VGG16+SVM classifier and VGG16+Softmax classifier on plant images. Experiments have shown that under the same conditions, the SVM classifier has a higher identification rate for plant images with single backgrounds, but the identification rate for plant images with complicate backgrounds is close to that of the softmax classifier, and the VGG16 algorithm needs improvement further in the identification rate on fine-grained plant images with too similar leaf shapes. This also proved that the identification and classification of plant images with complicated background and fine-grained is a major constraint in achieving intelligent identification on plant.
GLCNet: Global-Local Complementary Network for 3D Shape Recognition

Both point cloud-based and multi-view-based methods have achieved remarkable results in 3D shape recognition, yet there are few methods that combine the two types of data. In this paper, a novel Global-Local Complementary Network (GLCNet) based on multimodal data is proposed. The network obtains more powerful shape descriptors by stacking multiple layers of Global-Local Complementary Module (GLC Module). More specifically, the Global-Local Relation Score Module is first used to obtain the relationship between view features and global feature. The relationship is then utilized to facilitate the aggregation of view features and to filter out the more important ones. Finally, the aggregated view features are fused with the global features to form a stronger global feature. GLCNet enables the characteristics of various data to be fully utilized and achieves a true sense of complementarity of strengths and weaknesses. Extensive experiments on the benchmark dataset ModelNet show that GLCNet achieves state-of-the-art results in 3D shape classification and retrieval.
Latent Neural Phase Model for Synchronization Analysis

This study proposes the latent neural phase model (LNPM), a deep generative model that can estimate the phase of coupled oscillators and parameters of the phase equation simultaneously from irregularly sampled noisy observation data. LNPM can handle a wide range of phase equations e.g., equations with time-dependent parameters. Inheriting the property of the classical phase model, we can use LNPM to quantify the degree of collective synchronization of rhythms. We develop a parameter estimation algorithm that minimizes the evidence lower bound (ELBO) by using the reparametrization trick. The effectiveness of LNPM is confirmed by challenges that include Huygens's clock problem.
Area Efficient Implementation of Software-Defined Radio using FPGA

Due to the fact that the Software-defined radio (SDR) will be used in the field, it is essential that it has a low energy consumption and effective noise suppression. The proposed system is software define radio-Transceiver of OFDM implementation in FPGA which is used to reduce memory size and reduce power consumption. In order to get started monitoring the amount of energy used, several approaches were examined for each component. The amount of bits that were used to characterize the signal was decreased so that the influence it had on power consumption could be better understood. As a result of this modification, the amount of power used decreased from 3.01mW to 2.15mW. After making some little modifications to the algorithm selection, the power consumption was brought down to 2.15 mW. After that, the clock rate was slowed down for many parts of the system, which resulted in a reduction in the amount of power used.