Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliable Keyword Query Interpretation on Summary Graphs

The semantic gap between keyword queries and search intents behind them motivates intensive studies on keyword query interpretation, which aims to interpret a keyword query to structured queries (a.k.a. patterns) representing most possibly relevant search intents. However, there still lacks of study on an important issue: how to guarantee the patterns are “reliable”, which means the structured queries can be evaluated as really existing results. In this paper, we regard the reliability as a new metric for ranking patterns, and present a keyword query interpretation approach to find both reliable and relevant pattern trees on an arbitrary summary graph of underlying data. Specifically, we first propose a reliability estimation model to measure how possibly a pattern tree can be evaluated as a nonempty result set by statistics under reasonable assumptions. Second, we develop constrained top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="zhong-ieq1-3144001.gif"/></alternatives></inline-formula> search algorithms that guarantee to return the optimal pattern trees for a specific keyword query. Moreover, to improve the efficiency of online search, we also design elaborate indexes, search heuristics and pruning strategies. Lastly, we perform comprehensive experiments on two real-world datasets, DBpedia and Yago, with both QALD-9 queries and random queries. The observations indicate our approach improves the accuracy and overall quality of top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="zhong-ieq2-3144001.gif"/></alternatives></inline-formula> results significantly.
Design and Fabrication of Multi-functioned Seed Sowing Machine

The most important occupation and the main backbone of our Indian economy is Agriculture. About 58% of the population is involved directly in this occupation and the remaining population is indirectly involved either as consumers or retailers. Automation in the agriculture field will yield good returns and ease of work. This proposed work provides the automation of sowing seeds depending on the seeds, land, and other environmental parameters. Seed-Sowing is the initial and important phase in the growth of plants and cultivation. Seed-sowing is a dependent process and its growth is determined by the soil type it sowed, its external environmental conditions, and the place it sowed. The planting depth of the seed and the diameter of the hole determines the seed germination and the sapling stages. Before fabricating an automatic seed-sowing machine, more factors have to be taken into consideration. The main idea behind this automatic machine is to reduce the duration and effort taken by the farmers. In our country, the same land is utilized for growing different crops in a year. Fabricating a seed-sowing machine that is suitable for intercrop agriculture usage is the main task of the proposed work. Proper sowing, the right amount of sowing, correct interspacing between the holes, and accurate diameter of the holes are the objectives of the proposed work. This proposed system has an additional feature of plowing the land before sowing and closing the holes after the seed is dispersed. The entire process is designed in two-dimensional and three-dimensional software and the individual components like wheels, seed feeder, motors, and the battery are selected and designed accordingly.
Heart Rate and Pupil Dilation As Reliable Measures of Neuro-Cognitive Load Classification

Due to its limited capacity, working memory can become overloaded with extra activities that do not directly contribute to learning. According to cognitive load theory, working memory overload reduces task performance. Thus, monitoring the individual's current mental workload is essential to avoid dealing with the effects of cognitive overload. Heart rate and pupil dilation are two important metrics that can appropriately be measured at a low cost. These two signals have been generated to classify the participants' cognitive load levels in this study. Ninety-eight (98) participants volunteered in the studies, and we assessed their cognitive workloads using psychophysiological measurements generated during the experiment and performance characteristics obtained from the virtual driving system. The driving system continuously monitored the subjects' driving performance parameters, including heart rate and pupil dilation. The experiment involved driving tasks in a virtual environment, and some popular machine learning algorithms have been applied for user classification. Data analysis of the signals reveals that the heart rate and pupil dilation could appropriately be used to determine the cognitive workload of the individuals. Also, using multimodal data fusion, the accuracy of the cognitive load classification can be improved.
Opportunities of Machine Learning Techniques in the Catering Industry- A Survey

The human labor force is essential in manufacturing and packing food items. In some cases, human workers cause the food and catering industries to fail to maintain a demand-driven supply chain, food quality and ingredient planning. Industrial automation with mobile access is considered as the best solution to overcome these problems. The main intention of this study is to review various Artificial Intelligence (AI) techniques and find opportunities for incorporating Machine Learning (ML) in the catering industry, with a particular focus on the ingredient planner and its move toward automation. Mobile apps have become the most promising solution for several analysis, particularly for food delivery applications, but the need for food preparation in the field of catering services related to traditional techniques is still not well recognized. In this research, a qualitative analysis has been conducted for the 500 food items and their requirements through the survey, which will be implemented in the Ingredient Planner (IP) mobile application to help customers and caterers using AI-based techniques. Furthermore, a multi-functional model is implemented to fully utilize big data for ingredient analyses in food items, reducing computational time, manual calculation time, and food waste.
Interactive Machine Learning for Multimodal Affective Computing

Affective computing [1] is an expanding field that is taking on new forms with the development of more powerful computing devices and better modality fusion techniques. However, most datasets - and their resulting models - are limited to basic modalities (speech, text, and video) [2]. Additionally, the lack of labeled emotion data inclusive of comprehensive modalities has been a hindrance to further development in affective computing [3]. Furthermore, emotional expressions can be inherently ambiguous, thus resulting in multiple equally valid representations in expression and perceptions [4]-[6]. Such ambiguity creates challenges for the modalities required to capture the affective expression and the perceived affective states (annotated labels).
Estimating the Un-sampled pH Value via Neighbouring Points Using Multi-Layer Neural Network - Genetic Algorithm

This study shows a new method to estimate unsampled pH value by utilizing neighboring pH, which according to recent literature, has not been done yet. In investigating this method, three algorithms are used: Neural Network-Genetic Algorithm (MLNN-GA), MLNN with backpropagation (MLNN-BP), and averaging method. MLNNGA and MLNN-BP are inputted with four pH values from distant adjacent locations on a similar basin. MLNN-GA and MLNN-BP utilize GA and backpropagation respectively to update the weight. GA optimizer is used in MLNN-GA where the result of each learning weight will be the initial weight of the next learning process. All three methods are compared based on RMSE, MSE and MAPE. MLNN-GA yielded the lowest average RMSE =0.026265, average MSE =0.000886 and average MAPE =0.003985 compared to MLNN-BP (average RMSE =0.042644, average MSE =0.002648, average MAPE =0.006862) and averaging method (average RMSE =0.136629, average MSE = 0.026128, average MAPE =0.150400). Noticeably, estimating unsampled pH value utilizing neighboring pH by using MLNNGA shows a better performance than MLNN-BP and averaging method.
Virtual Potential Field-Based Motion Planning for Human-Robot Collaboration via Kinesthetically Guided Teleoperation

Driven by the evolving role of modern robots in collaboration with human operators, this paper proposes a virtual potential field-based motion planning and control strategy to realize effective human-robot collaboration through kinesthetic guidance via robot teleoperation. The motion of the teleoperated robot is determined by the human operator using a haptic device programmed to replicate movement under the influence of the virtual potential field constructed based on the obstacles and objectives in the environment. This approach facilitates seamless cooperation between the human and collaborative robot. It can also be readily generalized to include moving obstacles and goals in a dynamic environment. The results suggest that the virtual potential field can effectively enhance the safety and accuracy of human-robot cooperation. The developed method has promising applications in human-centric robotics fields such as medical robotics and robot-assisted surgeries.
Time-Inconsistent Stochastic Linear-quadratic Differential Game

We consider a general time-inconsistent stochastic linear-quadratic differential game. The time-inconsistency arises from the presence of quadratic terms of the expected state as well as state-dependent term in the objective functionals. We define an equilibrium strategy, which is different from the classical one, and derived a sufficient conditions for equilibrium strategies via a system of forward-backward stochastic differential equations. When the state is one-dimensional and the coefficients are all deterministic, we find an explicit equilibrium strategy. The uniqueness of such equilibrium strategy is also given.
An Investigation on Selecting of Resonant Inverter Topologies for Efficient Wireless Power Transmission

To reduce switching loss in a DC-to-DC resonant converter of a Wireless Power Transmission (WPT) system, soft-switching scheme in a multi-megahertz frequency is desirable. To improve the WPT system’s overall performance and power efficiency, choosing proper architecture of key building blocks of the DC-to-DC converter such as an inverter, a rectifier and impedance matching networks is an important issue. For giving better sight to the designer to achieve an optimal performance and improve the efficiency of the desired WPT system, in this paper most recently important classes of inverters such as Class D, E and DE are investigated by simulation and their performance is presented for different loads. As impedance matching networks have significant impact on the system efficiency and robustness, especially when the output load changes, different impedance matching networks are deployed and the performance of the converters are compared. The results of the simulation show that by selecting a suitable matching network the circuit efficiency increases and when the output load changes, the range of efficiency variations is reduced.
Automated Monitoring and Control System for Greenhouses Using Modern Speech Processing Through Mathematical-Based Methods

This paper aims to provide an automation system for remote monitoring and control of strategically located actuators in a prototype hydroponic greenhouse using voice command recognition. The concept combines the use of the Rosenberg Model and the Fourier Transform for linear signal processing in discrete time with applied mathematical methods for voice signal filtering. The mathematical model required for the analysis and capture of the speech spectrum was developed in MATLAB. In addition, a system with temperature, humidity, heat index, illumination and level sensors is incorporated to send information to the user through an Arduino Mega with a Bluetooth HC-06 module as a communication protocol for the control and monitoring of the environmental parameters that indicate in the greenhouse. The article concludes by proposing a development of a mobile application for communication between the user and the prototype with the possibility of generating records for the evolution of environmental parameters over time.
Towards multimodal expression of information reliability in HRI

In this paper, we discuss preliminary studies on expressive presentation in the context of human-robot interaction. The focus is on the robot conveying its attitude towards the information it is presenting to a human partner using non-verbal means, in this case facial expressions. The goal of the research is to understand better how natural social behaviour and emotional stance of the speaker can manifest itself in practical information providing settings. We present a small prototype of the Furhat robot application where the robot interacts with human partners and provides information which it judges reliable or not, and conveys its attitude with facial expressions. The assumption is that the users are more likely to consider the information that the robot presents as reliable and trustworthy if it is accompanied by the robot's positive and supporting facial expressions (e.g. smile) and consequently, accept and adopt the information as part of their own knowledge, whereas if the robot accompanies its presentation with a frowning or disgusted facial expression, the user is likely to associate the content with negative connotations and decrease the reliability and trustworthiness of the information.
Reliability Assessment of Distribution System With Distributed Generation: A Case Study

The modern electrical network aims to deliver secure and reliable electricity to customers. With the upgradation in the technology and penetration of distributed generation (DG), the existing distribution system is getting more complex and decentralized system. With increasing global warming and carbon emissions, the deployment of renewable energy sources with the electric grid is getting pace. Reliability is one of the most important aspects of the distribution system, as the distribution system is directly connected to the customers The existing distribution system is facing a lot of problems due to malfunction in the system The world needs a reliable and secure power system without any interruption and maloperation. DGs and their deployment with the grid can play a major role in enhancing the reliability of the existing distribution system. This paper investigated a case study of the distribution system reliability assessment by deploying DG in the system. Different cases are discussed and we analyzed after deploying DG the system reliability improves. For case study implementation IEEE 9 bus system is analyzed. Electrical Transient Analyzer Program (ETAP) software is used for modeling and reliability analysis
Methods for Explaining CNN-Based BCI: A Survey of Recent Applications

Convolutional neural networks (CNN) have achieved state-of-the-art results in many Brain-Computer Interface (BCI) tasks, yet their applications in real-world scenarios and attempts at further optimizing them may be hindered by their non-transparent, black box-like nature. While there has been ex-tensive research on the intersection of the fields of explainable artificial intelligence (AI) and computer vision on explaining CNN for image classification, it is an open question how commonly the methods for explaining CNNs are used when CNNs are a part of a BCI setup. In the present study, we survey BCI studies from 2020 to 2022 that deploy CNNs to find out how many of them use explainable AI methods for better understanding of CNNs and which such methods are used in particular. Our findings are that explainable AI methods were used in 13.7 percent of the surveyed publications, and the majority of the studies in which these methods were used employed the t-distributed stochastic neighbour embedding (t-SNE) method.
Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features; they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN.
Road Damage Detection using Deep Learning

Road damage occurs when the function and structure of road are unable to service the traffic above it optimally. In general, the damage is caused by flaws in planning and implementation, uneven maintenance, poor drainage, and poor road user behaviour. It has a negative impact on driving comfort, road safety, and vehicle condition, and it may cause a number of accidents. To address this issue, this study presents a Region-based Convolutional Neural Network (R-CNN) for locating the dangerous path. This type of neural network can find essential information in both time series and picture data is the RCNN. As a result, it is extremely useful for image-associate tasks including image identification, object categorization, and design recognition. A RCNN uses linear algebra methods such as matrix multiplication to discover patterns inside an image. Find the photographs first and pre-process them, then extract the features and choose them from the feature set of previously damaged images. Finally, categorise the captured photos to obtain the optimum result. When compared to other current approaches, the suggested method is more accurate.
Learning Specific Features for KOA Diagnosis: Autoencoder Network vs. Feature Selection

Knee osteoarthritis (KOA) is a degenerative joint disease that seriously affects patients’ quality of life. Due to the global aging population and obesity, the number of people living with KOA is expected to increase. Although KOA is the dominant disease in Traditional Chinese Medicine (TCM), the clinical diagnosis is currently made by Western medicine based on the patient's medical history, signs and symptoms, and image examinations. This study aims to find specific features for KOA using deep autoencoder and traditional feature selection, thus providing a diagnosis reference and exploring the advantages and disadvantages of both approaches and their interpretability.
A Novel Common Grounded Type $1-\varphi$ Five-Level Boost PV Inverter with Reduced Device Count

Common-grounded multi-level inverters with the input voltage boost ability are more common for grid-tied PV system applications. In this paper, a new five-level inverter topology is introduced that can generate a five-level voltage at the output stage, statically increase the input voltage, fully transfer the reactive power, eliminate the leakage current due to common-grounded capability, reduce the number of the power electronic components, self-balancing capability of switched capacitors voltage based on operation modes, and equality of switched capacitors voltage. The operation of the proposed inverter along with its comparison with other similar inverter topologies and the simulation results of the proposed inverter under different loads are demonstrates in this paper. The results indicate that the performance of the proposed inverter in the grid-connected PV system is best than other compared topologies. Also, experimental results with a prototype have been presented, which validates the simulation results and proves the applicability of the proposed inverter in grid-tied PV systems.
An Improved H6-Type Single-Phase PV Inverter with Suppressed Leakage Current

In low-power grid-connected photovoltaic (PV) systems, as the dominant area of them is regularly outreaching all around the world in the last decade, transformerless inverters are competently accepted because of their higher efficiency, lighter weight, smaller volume, and lower cost contrasted to PV inverters that possess the transformer. However, the challenge of hazardous capacitive leakage current in transformerless topologies should be carefully addressed. Further, according to grid standards, reactive power feeding capability must be applied by the smart grid-connected PV inverter to support the grid. In this paper, In order to meet these requirements, an improved H6-type topology, namely IH6-BDC inverter, is proposed for single-phase grid-connected operation, in which a bi-directional diode clamping cell is used to improve common-mode (CM) characteristic. The suggested inverter is simulated utilizing MATLAB/Simulink environment to verify the professed approach's performance. Further, loss analysis using the PSIM software is adopted to accredit the efficiency of the suggested inverter structure. The simulation consequences show 97.91% efficiency at 3.1-kW rated power with a 20 kHz switching frequency.
An enhanced interpretable deep learning approach for diabetic retinopathy detection

Diabetic Retinopathy (DR) is a consequence of type1 or type-2 diabetes. It is critical to identify complications early since they may result in visual issues such as retinal detachment, vitreous hemorrhage, and glaucoma. The interpretability of automated classifiers for medical diagnoses such as diabetic retinopathy is critical. The primary issue is the difficulties inherent in inferring reasonable conclusions from them. In recent years, numerous efforts have been made to transform deep learning classifiers from statistical black box machines with high confidence to self-explanatory models. The concern of effective data preprocessing before classification remains unsolved. Although the application of machine Learning schemes has proven to be effective when trained in a supervised way, it still has limitations with data redundancy, feature selection, and human expert interference. Hence, a combinatorial deep learning approach is proposed to interpret diabetic retinopathy (DR) detection. The proposed method combines the Shapley Additive Explainability (SHAP) and Local 127:1679 Model-Agnostic Explanations (LIME) to analyze the deep learning output effectively. Results from our experiment show that our proposed approach outperformed the existing schemes in detecting DR.