Miracle-dz/UniFAR-FTUs huggingface dataset:
Aggregation Modeling and Equivalence of Large-Scale Photovoltaic and Energy Storage Combined Power Generation System

In order to study the large-scale photovoltaic (PV) and energy storage (ES) combined power generation system (CPGS) and shorten the time of simulation, the equivalent aggregation model is established by the way of parameter equivalence on the foundation of the PV unit and ES unit models. The detail model (DM) and equivalent model (EM) are respectively built in PSCAD/EMTDC to compare with each other. Moreover, the electromechanical transient model of the large-scale PV-ES CPGS is formed. Based on this model, the energy complementary strategy of the PV-ES CPGS is proposed. The simulation results represent that the EM established can precisely reflect the overall operation characteristics of large-scale PV-ES CPGS.
Encrypted Distributed Dictionaries

End-to-end encrypted databases have been heavily studied in the last two decades. A crucial aspect that previous work has neglected, however, is that real-world databases are distributed in the sense that data is partitioned among a cluster of nodes—as opposed to being stored on a single node. In this work, we initiate the study of encrypted distributed data structures which are end-to-end encrypted variants of distributed data structures; themselves fundamental to the design of distributed databases. In particular, we design and analyze encrypted variants of distributed dictionaries (EDDX), which are an important building block in distributed sys-tem design and have applications ranging from content delivery networks to off-chain storage networks for blockchains and smart contracts. We formalize the notion of an encrypted DDX and provide simulation-based security definitions that capture the security properties one would desire from such an object. We propose an EDDX construction that uses a distributed hash table (DHT) as a black box. Interestingly, we show that our construction leaks information probabilistically, where the probability is a function of how well the underlying DHT load balances its data. We also show that in order to be securely used with our construction, a plaintext DHT needs to satisfy a form of “pro-grammability”, a property that usually only emerges in context of cryptographic primitives. To show that these properties are indeed achievable in practice, we study the balancing properties of the Chord DHT—arguably one of the most influential DHT—and show that it is also programmable. Finally, we consider the problem of encrypted DDXs in the context of transient networks, where nodes can be arbitrarily added or removed from the network.
An Intrusion Detection System Dataset for a Multi-Agent Cyber-Physical Conveyor System

Industry 4.0 is built upon the foundation of connecting devices and systems via Internet of Things (IoT) technologies, with Cyber- Physical Systems (CPS) serving as the backbone infrastructure. Although this approach brings numerous benefits like improved performance, responsiveness and reconfigurability, it also introduces security concerns, making devices and systems vulnerable to cyber attacks. There is a need for effective techniques to protect these systems, and the availability of datasets becomes essential to support the development of such techniques. This paper presents a dataset based on the collection of traffic information exchanged in a self-organizing conveyor system using the multi-agent systems (MAS) architecture and containing various intelligent conveyor modules. The dataset comprises data collected at the network and agent levels under normal system operation, denial of service (DoS) attacks, and malicious agent attacks. An intrusion detection system that integrates Fast Fourier Transform (FFT) and Machine Learning (ML) analysis is developed to demonstrate the utility of this dataset.
Energy Consumption in Smart Buildings using Machine Learning

Energy is the indispensable factor of modern societies. From last few decades, there is rapid increase in world’s energy consumption due to raise of population and their comfort demands. Buildings are accountable for 40% of global energy hence Building Energy Management has become crucial topic nowadays for reducing energy wastage The ability to forecast the amount of energy used in SB and plan for that use is essential for the implementation of energy-efficient management systems. The proposed method for estimating the amount of energy that is used in buildings, as well as offering suggestions for cutting down on energy use and lowering expenses. The systems use the information like temperature humidity air quality etc. This information is analyzed by machine learning forecast the amount of energy that is used by smart buildings.
Comparing Apples and Oranges: Comparability problems caused by intransparent Data Cleansing for AI Applications

No AI applications without data. Data is like oil for combustion engines - the most important “fuel” for AI applications. That’s why great attention must be paid to data provision and data quality assurance. Real-world data usually contains fuzziness, errors, outliers, and often many unusable records. There are myriad reasons why data in its original form is often unsuitable for model development (e.g., technical errors, unexpected user input). Data cleaning is therefore an extremely critical and tedious task that forms the basis for high-quality results with AI algorithms. Although this fact is widely known, the importance of this step is usually not considered in depth when comparing different AI algorithms and applications, leading to misleading comparisons. In this paper, we therefore provide an overview of the most common challenges in data cleaning to make AI comparable. To help address these challenges, we propose a novel systematic and machine-readable data cleaning protocol to improve comparability and reproducibility in the field of AI.
Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges

Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31–134 x smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward.
Enhancing the Detection of Fake News in Social Media based on Machine Learning Models

Inaccurate or misleading reporting, sometimes known as “fake news,” has become a significant problem in modern society. Fake news is a severe problem in the digital landscape, where anyone can publish their thoughts as though they were facts. However, research into what social media users take in is still in its infancy, and ongoing efforts aim to learn more about how social media users can identify false information. Discovering a reliable method for identifying fake news has been the biggest obstacle. Using supervised learning techniques that rely on labelled data to determine whether or not a piece of text is genuine helps us become aware of such stories. This study uses multiple methods, such as logistic regression, the XGBoost model, and the Random Forest model, to examine the characteristics of news articles and determine whether they are authentic. These models are typically trained on labelled data, with additional features derived from CNN-URL and Reddit data. The goal is to create more reliable sources of information and reduce the spread of misinformation by creating models that can accurately distinguish between real and fake news.
Neural Network-based Approach to Predict Protein Secondary Structure

Protein Secondary structure prediction is an emerging topic in bioinformatics to understand briefly the functions of protein and their role in drug invention, medicine and biology. In our research we have applied two recurrent neural network based approach Bi-LSTM (Bidirectional Long Short-Term Memory) and LSTM (Long Short-Term Memory). Our research was focused on primary structure up to 134 in length of amino acids. Initially our proposed model produced a ‘Indexed Lexicon of corpus’ using tri-gram conversion for primary structure strings. Each primary structure tri-gram transformed snippets is substituted with its associated index mentioned in ‘Indexed corpus’. The indexed parameter vector inputted into our proposed Bi-LSTM and LSTM model. We got best accuracy when we have used two Bi-LSTM and three LSTM layers respectively in Bi-LSTM and LSTM models. To prevent biasness and minimize overfitting problem we have utilized two dropout layers for each of Bi-LSTM and LSTM model. We have operated our model on ccPDB 2.0 benchmark dataset. There is total eight states protein secondary structure in this dataset. For this sst8 secondary structure we have achieved 83.24% accuracy for our proposed LSTM model and 89.10% accuracy for our Bi-LSTM model. We have configured our model to run for 50 epochs with batch size 64. For compilation of our models we have utilized ‘adam’ optimizer and the ‘categorical crossentropy’ loss function. To make dataset balanced to our model we have also employed 5-fold cross validation.
Evaluation of Different State of Charge Estimation Techniques for Electric Vehicles

Electric Vehicle (EV) has recently been gaining a great market capital and therefore Battery Management System (BMS) for batteries have become a very crucial aspect for various parameter estimation and proper handling of battery in various environmental conditions. One of the main parameters estimations for BMS is State of Charge (SoC) estimation. The SoC helps to determine the capacity that is remaining inside of the battery at every instance of time and helps to predict proper remaining range and determine the performance of the electric vehicle. Therefore, proper SoC estimation becomes very crucial when it comes to EV. However due to nonlinear and time varying behavior of the battery it becomes quite complex to determine the proper capacity remaining or available in the battery. The modeling of battery, polarization effect on battery is discussed. A comparative study of SoC estimation is done using Open Circuit Voltage method, Columbs Counting method, Kalman Filter sing Matlab and Simulink.
Multiple Disease Prediction by Applying Machine Learning and Deep Learning Algorithms

A large number of machine-learning projects for healthcare inspection concentrate on just one disease at one time. One analysis is performed for each of the following conditions: diabetes, cancer, skin disease, and heart disease. A single analysis cannot forecast more than one disease using a same system. Numerous machine learning approaches can perform predictive analytics on colossal data sets. Although using predictive modeling in healthcare is challenging, it will eventually help practitioners make quick choices about the health and treatment of patients based on vast amounts of data. In this post, a system that uses the Flask API to forecast numerous diseases is proposed. Tensor flow, Flask API, and machine learning techniques were used to implement multiple illness analyses. The model behavior is saved using Python pickling, and the pickle file is loaded when needed using Python unpickling. The goal in this effort is to create a web application that can forecast numerous diseases, such as breast cancer, diabetes, heart disease, malaria, and pneumonia, using the principles of machine learning and deep learning. The finished model’s behavior will be captured in a Python pickle file. The Flask API is created. The parameters of the disease must be sent together with the disease name when using this API. The relevant model will be called by the Flask API, which then returns the patient’s state. In order to track patient condition and provide early warnings to patients in order to reduce death rates, it is important to identify the most common diseases.
Prediction of RNA Binding Proteins from Amino Acid Sequences Using Machine Learning Techniques

In recent years, various researches have been conducted in bioinformatics for predicting RNA-Binding proteins(RBPs), and it is considered one of the most demanding tasks. In this present covid era, this may have significance in medical research, prevention of diseases and many other aspects of health. Several experimental designs have been used for identifying RBPs. Moreover, identifying RBPs by computational prediction may have a guiding principle over the experimental designs. This paper aims to present two approaches in machine learning and deep learning area that can be used for the prediction of RNA Binding proteins with better performance and accurate results. It uses physicochemical factors of amino acids, which have an impact on the RNA binding mechanism, and these features are effectively encoded in order to be fed into an Extreme Gradient Boosting ensemble model and also a convolutional neural network for learning. The performance of developed models is compared with an existing model with the same dataset. While evaluating the results, proposed models provide more accurate results.
Primitive Exponents of a Class of Two-colored Digraphs

A special two-colored digraph with two cycles was considered,whose uncolored digraph consists of onecycle and one cycle.Using the method of combinatorial matrix theory and graph theory,the primitivity conditions and the upper bound on the exponent wer given.Further,the extremal digraphs that meet the upper bound were characterized.
Identification & Detection of Industrial Faults using Arduino Bot

The main purpose of this research study is to provide solutions to faults/hazards/threats that occur in Industry 4.0 with the rise of new technologies, and devices so these problems can be identified and preventive action can be taken before any crisis. These incidents are caused due to leakage of gas, fire, boiler/cylinder explosions, overheating of operating machines, and other manmade disasters or improper handling of chemical, biological and radioactive material, etc. Therefore, it becomes important to identify such incidents at an early stage to prevent major accidents and save the lives of employees in Industry 4.0 and surrounding livelihood. Considering the above scenario, the authors present a solution called Industrial Fault Monitoring Bot. This robot is deployed in the manufacturing plant to monitor safety conditions in different units. The robot swiftly navigates through various units, checks the safety status, and reports this to the safety unit. In this paper, the authors will elaborate on the design, working, and technologies used for it.
Mobile Health Applications towards Sustainable Healthcare: A Healthcare Professionals’Perspective

The objective of the study is to explore and examine the contextual factors towards the long-term sustainability of mobile health applications, as well as determine the elements influencing the effective implementation of mHealth technology from the perspective of healthcare providers. The basic idea of this paper is to include multi-disciplinary commentaries of healthcare professionals to evaluate the sustainability aspects of mHealth applications. The study is based on qualitative semi-structured interviews with healthcare experts and employs a Grounded Theory approach to explore and assess the efficacy and long-term sustainability of mHealth applications. A total of 12 healthcare practitioners were interviewed for the study. They were healthcare professionals, including medical practitioners and clinical consultants, as well as health researchers, academicians, specialists, and suppliers of healthcare apps. The findings depict that mHealth technology is widely beneficial in practices for healthcare management in the public health domain. To achieve sustainability of mHealth and realise its potential in public health governance, this assessment points out an integrated approach covering healthcare practitioners’ concerns and contextual considerations for effective implementation, sustainability, and scale-up of mHealth interventions.
Fire and Smoke Detection of Power System Transmission and Distribution Equipment

Fire in power system is easy to cause heavy casualties and a large number of property losses. Because of the complex and changeable environment, it is important to accurate early fire detection in current research hotspot issues. Aiming at the fire safety detection of power system transmission, transformation and distribution equipment, a smoke and fire target detection method based on YOLOV5 model is proposed according to the questions of small target size, which is easy to be confused with the actual situation. According to the characteristics of smoke and fire movement changes, the network is improved, and a detection layer is added to the original detection layer, so that the feature map can continue to be up-sampled and the detection accuracy can be improved.
A multi-scale sentiment recognition network based on deep learning

In recent years, mental health has gradually become a hot topic of concern due to the increase in life stress and the epidemic. Mental health problems may affect the individual's living condition, learning and working efficiency in light cases, or produce mental diseases that prevent normal learning or working. In order to detect psychological problems in a more timely manner and achieve the diagnosis of psychological diseases, people actively research intelligent methods. However, the existing models lead to inaccurate recognition and unsatisfactory accuracy in practical applications due to the angle and occlusion of the face. Therefore, in this paper, we propose a multi-scale deep learning network model for faces, which reduces the effects produced by angles and occlusions by fusing feature information from different scales; and a loss function is proposed to increase the importance of difficult samples in the training process, which makes the model training better. Experimental results on the challenging RAF_DB dataset show that the proposed model exhibits better facial expression recognition accuracy than the existing techniques.
A New Type of Asymmetric Four-Level Inverter

Aiming at the problem of diode-clamped inverter with a large number of switches and the lack of output vector of asymmetric topology, a new asymmetric four-level topology is proposed. Compared with the traditional diode-clamped four-level inverter, this structure reduces the use of switching tubes and reduces the cost; And the DC side voltage divider capacitance is reduced to two, the voltage division ratio is 1:2, and a floating capacitor is added, which makes up for the missing output vector caused by the asymmetric inverter. Because it compensates for the missing output vector, the ideal output waveform can be obtained by using the traditional SVPWM modulation algorithm, avoiding the development of new algorithms. Finally, simulations were built, which proved the feasibility and effectiveness of the topology.
News Analytics to Predict Malaysian Telco Stock Price Performance: Data Visualization

The stock market refers to the collection of markets and exchanges where regular activities of buying, selling and issuance of shares of publicly held companies take place. Stock Market Index is the reading used in comparing the price of the stock market each day and it is very dynamic and susceptible to quick changes therefore a large dataset is needed to create a proper prediction model. Many believe that it follows a random walk pattern. In this project, machine learning is used so that a prediction can be made by monitoring past index patterns and predicting how they would behave in the future. Natural Language Processing is a scoring process of a word in a sentence that translates the word meaning into machine-readable value. With this, we can identify a certain topic of a stock market to be either positive or negative. Knowing these values, we can train the machine learning model to predict the price index trend and see whether news and posts related to stock price affect the market. The result of the prediction is visualized and analyzed to identify the accuracy of the data and view the disparity between the predictions and the actual value.
Data driven approach to identify a flow-based Botnet Host using Deep Learning

The internet’s technological advancements exposed the globe to its weaknesses as well. The risk of exploitation has also increased as a result of larger network cores cooperating to combat cyber threats, which continue to be a severe problem for the entire world. Recurrent Neural Networks (RNN)-based deep learning techniques have recently advanced to new levels in a variety of fields and applications. The risk of forged accounts is greater than ever thanks to increased network use and traffic. The challenge to identify a malicious host on the internet has always been a challenge from the development perspective. The job of binary classification to label a host as a botnet has not made any significant progress and thus still, the internet faces the issue of botnets taking over many active and important connections exploiting the network, controlling compromised hosts to spam other hosts on the network, launch DDoS attacks and more. This paper attempts to provide a novel approach for evolving the comprehensive framework for controlling botnet host prediction and uses them to handle real time cases. To attain greater recognition accuracy, we use Gated Recurrent Unit (GRU) as a hybrid Recurrent Neural Network (RNN) model. We take an evolving time series input from a network station for several days which depicts data flow i.e., count of connections from different devices recognized by their IPs, and these features are used from the IP flow to provide capability to recognize the host on a network as a potential threat. Threat detection of such botnets is important not only from the perspective of stopping them but also to find significant insights about the targeted attack to understand future trends and make the networks persistent against them.