Reviewerly/exHarmony huggingface dataset:
Research and Implementation of Code Similarity Detection Technology Based on Deep Learning

To enhance code security and stability, code similarity detection techniques are crucial for identifying known software code vulnerabilities, particularly in open-source code. Traditional code clone detection methods based on Abstract Syntax Tree (AST) have shown limitations in achieving satisfactory results. To address this, researchers have turned to deep neural network models for extracting latent semantic features from code. This paper presents a novel methodology that leverages an enhanced AST to extract semantic information from the source code. A Tree-Long Short-Term Memory (Tree-LSTM) neural network is then utilized to encode the AST into function feature vectors. The similarity between code snippets is determined through cosine similarity calculations on these feature vectors. The experimental results demonstrate the significant advantages of this approach in code similarity detection, enabling effective identification and prevention of known code vulnerabilities. Thus, ensuring software security and stability.
Algorithm Used in Video Event Recognition &amp; Classification with Hierarchical Modeling

Video media data is easier to transmit, produce, and distribute over the world. There has been a lot of information lost since advancements in video analysis and processing have lagged behind the data's exponential increase. The necessity for effective recovery of video data content is, thus, pressing. Because there are so many potential classifications, identifying and labeling occurrences may be difficult. In order to help human operators keep an eye on everything, researchers have built algorithms to better understand the semantic content of videos so they can better spot potential threats. The Yolo method for object detection and the hierarchical context model for event recognition are both explored in this work using a common surveillance video dataset. We hope that by employing these methods, we can enhance the efficiency with which video data material is retrieved by better identifying and classifying occurrences.
Design and Fabrication of an Electric Tricycle

When it comes to movement, physically challenged persons confront several difficulties. These folks have been seen utilizing mobility aids including wheelchairs, crutches, and prosthetic limbs to get around regularly. However, not all types of rural terrain are ideal for this kind of equipment. The design and construction of an electric tricycle that will enable persons with disabilities to travel about on any surface will be the main emphasis of this project. The hub motor of the electric tricycle, which is made to move in a to-and-fro motion, controls the tricycle's movement. The motor is powered by electrical energy that is stored in the batteries. This tricycle has a top speed of 30 kilometers per hour. The center of gravity calculations shows that the tricycle remains stable at a speed below its critical speed.
Photonic Reservoir Computing for Spectrum Awareness Applications

Photonic reservoir computing can perform efficient and ultra-fast information processing in both fiber and radiofrequency (RF) networks. In this communication, we provide an overview of this topic with an emphasis on applications related to RF fingerprinting, cognitive radio, and spectrum awareness.
PreCog: Near-Storage Accelerator for Heterogeneous CNN Inference

Computational Storage Devices (CSD) with near-storage acceleration is gaining popularity for data-intensive applications, by moving power-efficient hardware acceleration closer to data. However, because the power-constrained near-storage accelerator is often not powerful enough by itself to handle all computation requirements of an application, it must intelligently cooperate with other computation and acceleration units in the system. In this work, we explore how a near-storage accelerator can best fit into a larger computer system in the context of CNN inference. We demonstrate that an attractive configuration is using the near-storage accelerator to offload only the first convolution and pooling layers, where the accelerator can achieve almost an order of magnitude better performance compared to a general convolution accelerator. Targeting only the first layer allows some FPGA-specific floating-point computation optimizations such as pre-determine the range of output exponents, performing a costly floating point normalization task only once, as well as pack more input into the datapath to mitigate the performance impact of wide strides of the convolution filter. We package these optimizations into a flexible library we call Static Range Float (SRFloat), and construct a prototype system called PreCog. We evaluate PreCog implemented on the Samsung SmartSSD platform, and demonstrate over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3}\times$</tex> performance efficiency compared to conventional convolution accelerators on prominent CNN models without introducing a communications bottleneck.
Framework to Create Sentiment Lexicons For Extremism Detection in Text Documents

Hate speech or extremism detection among the text posts of social media is a major problem nowadays. Hate speech or extremism in text documents can be detected using machine learning methods or using sentiment lexicons. Machine learning methods require already classified data for training. In this paper we have proposed a framework to create sentiment lexicons for any language. In these lexicons terms will be added manually. The sentiment lexicons created by this framework may contain n-grams also where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{n} &gt; =1$</tex> . As the proposed framework is used for creating sentiment lexicons for extremism or hate speech detection, it is better to include only negative terms in sentiment lexicons.
ERNIE-BC: An ERNIE-BiLSTM-CRF Fusion Model for Segmentation-based Event Extraction

Event extraction constitutes a pivotal technique aimed at extracting pertinent information from unstructured text. Conventional approaches to event extraction have exhibited limitations in capturing the complex meaning nuances inherent in textual sequences. In this paper, we had derived the inspiration from sequence labeling techniques and harnessed the potential of a large-scale Chinese pre-trained model to propose a novel segmentation-based event extraction method based on a fusion of ERNIE-BiLSTM-CRF (referred to ERNIE-BC). By combining the BiLSTM and CRF algorithms, ERNIEBC enabled the thorough capturing of sequential textual features. Furthermore, we separated the processes of event detection classification and event element extraction, leveraging event category information as prior knowledge to augment the effectiveness of event element extraction. Additionally, we developed an event segmentation approach that successfully resolved the challenge of overlapping elements within multi-event texts. Experimental evidence supported the remarkable performance of ERNIE-BC in event detection classification, event trigger word extraction, and role extraction subtasks.
An Explainable Deep Learning Model for Prediction of Severity of Alzheimer’s Disease

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer’s disease (AD). AD diagnosis and prognosis heavily rely on neuroimaging information, particularly magnetic resonance imaging (MRI). We present a deep learning model framework that integrates a local data-driven interpretation method that explains the relationship between the predicted AD severity from the CNN and the input MR brain image. The deep explainer uses SHapley Additive exPlanation values to quantity the contribution of different brain regions utilized by the CNN to predict outcomes. We conduct a comparative analysis of three high-performing CNN models: DenseNet121, DenseNet169, and Inception-ResNet-v2. The framework shows high sensitivity and specificity in the test sample of subjects with varying levels of AD severity. We also correlated five key AD neurocognitive assessment outcome measures and the APOE genotype biomarker with model misclassifications to facilitate a better understanding of model performance.
Enhancing Phishing Website Detection Using Particle Swarm Optimization and Feature Selection Techniques

In recent years, Web phishing attacks have evolved, eroding customer trust in online services. Traditional blacklist-based approaches struggle to detect sophisticated phishing websites, including newly deployed ones. Researchers are turning to machine learning techniques for early detection but facing limitations in feature selection and parameter tuning. This paper presents an innovative approach that combines Particle Swarm Optimization (PSO) with feature selection techniques, including correlation and mutual information, and tree-based feature selection. The goal is to accurately differentiate between legitimate and phishing websites by identifying relevant features. A comprehensive dataset of diverse website features is used, and correlation and mutual information methods are used to assess feature importance. A tree-based feature selection algorithm refines the feature set, and PSO optimizes the parameters by exploring the feature space. Thorough experimentation is needed to fine-tune features and parameters for accurate detection. Experimental results show the superiority of the proposed approach, eliminating irrelevant features and improving efficiency and classification performance through PSO optimization. Integrating PSO with feature selection provides a robust framework, addressing tuning challenges and improving accuracy.
Estimation of Fuzzy Models from Mixed Data Sets with pyFUME

pyFUME is a python package for the automatic estimation of fuzzy inference systems. Fuzzy models are considered among the most interpretable, understandable, and transparent methods that are currently available, making them ideal for the development of Interpretable AI systems. Such models are suitable for the creation of decision support systems in extremely sensitive domains where the right to an explanation is particularly important, like medicine and healthcare. pyFUME can automatically estimate the antecedent sets and the consequent parameters of a Takagi-Sugeno fuzzy model directly from data, and deliver an executable fuzzy model implemented with the Simpful python library. The main limitation of pyFUME was that it was not well-equipped to deal with purely categorical, non-ordinal variables since it used distance metrics suitable for continuous variables to cluster the data for determining the fuzzy model’s structure. In this paper, we introduce a new version of pyFUME that supports mixed (i.e., continuous and categorical) data sets, relying on a novel version of fuzzy Cprototypes clustering. Our results show that our new approach is effective, leading to better fitting with respect to models based only on continuous features. We also present alternative plotting methods tailored for categorical variables, which improves the overall interpretability of the estimated discrete fuzzy sets.
Technological Intervention: Prevention of Crime Using AI and IoT

This paper study the use of artificial intelligence (AI) and Internet of Things (IoT) technologies for the prevention of offences against states. The purpose of this paper is to examine the use cases of AI and IoT technologies in the prevention of offences against states, including surveillance, cyber security, and data analysis. The paper also examines the benefits of using AI and IoT technologies in combination, as well as the challenges and ethical considerations in their implementation. The implications for future research and the use of AI and IoT technologies in the prevention of offences against states are also discussed. Overall, this paper highlights the potential of AI and IoT technologies in enhancing security and presenting offences against states, while also emphasizing the importance of addressing the challenges and ethical considerations associated with their implementation.
Comparative Study of Recursive and Non-Recursive Approach in TREE Traversal

Tree traversal is a technique which involves visiting, verifying, and updating each node in a tree just once. This paper gives insight on the various approaches for the same. The tree can be visited in two ways recursively and non-recursively. This paper initiates an exploratory analysis between recursive and non-recursive algorithms for inorder, preorder, and postorder traversals. The execution time and the space-time complexity are analysed. Although the recursive approach performs efficiently in accordance with the time complexity. The non-recursive approach is given more weightage for specific occasions. In this frame of reference, this paper offers to answer the pertinent questions - which approach performs better? and when to use which approach? This paper can not only serve as a guide for researchers but also for programmers who aim on storing information and representing hierarchical data but also throws light on providing efficient algorithms for insertion, deletion, and searching.
A Deep Unsupervised Representation Learning Architecture using Coefficient of Variation in Hidden Layers

The aim of this paper is to present a novel cov-RBM model on the basis of Restricted Boltzmann Machine (RBM) and the coefficient of variation (CoV), where the learning process is instructed by the CoV eigenvalues with respect to the hidden layer. Further, a deep unsupervised representation learning architecture (DURLA) is proposed to explore the capacity for representation learning with continuous excitation of CoV eigenvalues. We subject the proposed DURLA to extensive experimentation on nine real-valued datasets. The final experimental results show that our methods have a better effect than traditional and state-of-the-art ways.
FieldHAR: A Fully Integrated End-to-End RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors

In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activ-ity recognition (HAR) from heterogeneous sensors using artificial neural networks (ANN) optimized for FPGA or ASIC integration. FieldHAR aims to address the lack of apparatus to transform complex HAR methodologies often limited to offline evaluation to efficient runtime edge applications. The framework uses parallel sensor interfaces and integer-based multi-branch convolutional neural networks (CNNs) to support flexible modality extensions with synchronous sampling at the maximum rate of each sensor. To validate the framework, we used a sensor-rich kitchen scenario HAR application which was demonstrated in a previous offline study. Through resource-aware optimizations, with FieldHAR the entire RTL solution was created from data acquisition to ANN inference taking as low as 25% logic elements and 2% memory bits of a low-end Cyclone IV FPGA and less than 1% accuracy loss from the original FP32 precision offline study. The RTL implementation also shows advantages over MCU-based solutions, including superior data acquisition performance and virtually eliminating ANN inference bottleneck.
Solar Power Prediction in North India Using Different Regression Models

Prediction or forecasting the power output of a solar photovoltaic (PV) system is very important for the efficient planning of the energy distribution and utilization. This study uses machine learning algorithms such as linear regression, lasso regression, ridge regression, elastic net regression, support vector regression, and XGBoost regression to forecast the power production of a PV system installed at The NorthCap University, Gurugram (Delhi/NCR). The recorded data of irradiance and ambient temperature was collected from solar PV system over a three-month period which recorded every day at three-hour intervals. The data set includes 733 datapoints in three distinct dimensions: irradiance, ambient temperature, and power output from August to October 2022. The findings demonstrate that XGBoost Regression is the most accurate of the regression models examined, as per the R-squared value achieved between the predicted and actual power output values. All other models also behaved comparable for this data of August to October, as during these months the climate variations and hence irradiance variations are not unusual.
A Study of Recent Advancements in Deep Learning for Natural Language Processing

Natural Language Processing has gained interests and progress over the last decade. Its success can be attributed to the recent advances made in the field of Deep Learning. This review provides a detailed overview of recent advancements in deep learning techniques specially in the last decade (2013–2023) used for Natural Language Processing, including the architectural changes and capability enhancements. In this review we discussed the advancements in application areas like language modeling, machine translation and dependency parsing, while also comparing the models presented in each application areas. We compared the language models against their size, parameters, deep learning technique, features and drawbacks; dependency parsing models against their unlabeled and labeled accuracy scores, and machine translation models against their BLEU scores for WMT'14 English-French and English-German tasks. We also explored the potential challenges and limitations in these areas. Finally, we reviewed the current state-of-the-art multimodal model like GPT-4 and discussed how these models are evolving towards a general form of artificial intelligence.
Improvements in Filters of Strapdown Inertial Navigation System

In this paper, a strapdown inertial navigation system (SINS) with improved filters has been presented. The performance of the SINS relies on the position and velocity data which is further affected by the type of filter. The classic Kalman Filter (KF) performs much worse in solving the state estimation issue for Gaussian white noise. The Maximum Versoria Criterion (MVC) is used to apply an adaptive filter to impulse noise. It is useful for non-Gaussian noise with a low exponential calculation. The Maximum Versoria Criterion Extended Kalman Filter (MVC-EKF) based estimation model has been proposed to reduce the impact of anomalous data on state estimation with reasonable computation cost. The root mean square error (RMSE) is evaluated under the EKF, MCC-EKF and the proposed algorithms. The MVC-EKF outperforms as compared to others. The mean calculation time improved by 34.9%. It is very beneficial for future navigation systems in the military.
Low-Complexity Classification of Unencrypted IoT Traffic Based on Skewness and Protocol Information

In this paper, we focus on statistical traffic classification of Internet of Things (IoT) traffic for network security purposes. In particular, the goal is to design an algorithm aimed at distinguishing between normal (benign) network traffic and a few types of network attacks, namely, scanning and Distributed Denial of Service (DDoS). To this end, we use a single feature associated with the proto field in the IP header, which is shown to be the best for classifying these three specific traffic classes. In particular, we propose a simple threshold classifier in which a third-order flow statistics, i.e., the skewness of such a protocol feature, is analyzed and compared with proper thresholds, which are offline computed. The performance of our system is investigated using the reference ToN-IoT dataset, which contains heterogeneous data coming from different IoT nodes and scenarios. Our results show that, even if it should be extended to other datasets, the considered threshold classifier can achieve good classification accuracy for some of the traffic classes when input data is shuffled to better fit the statistical properties of the considered flows.
Oikonomos: An Opportunistic, Deep-Learning, Resource-Recommendation System for Cloud HPC

The cloud has become a powerful environment for deploying High-Performance Computing (HPC) applications. However, the size and heterogeneity of cloud-hardware offerings poses a challenge in selecting the optimal cloud instance type. Users often lack the knowledge or time necessary to make an optimal choice. In this work, we propose Oikonomos, a data-driven, opportunistic, resource-recommendation system for HPC applications in the cloud. Oikonomos trains a Multi-layer Perceptron (MLP) to predict the performance of a given HPC application, for different input parameters and instance types. It, then, calculates the cost of executing the application on different instance types and proposes the one best-fitting the user's needs. We deployed Oikonomos on a diverse mix of HPC workloads, and found that for all applications, it approached an optimal policy. The optimal instance type was chosen in 90% of the cases for seven out of eight applications, scoring a Mean Absolute Percentage Error (MAPE) consistently below 20%. This demonstrated that Oikonomos can provide a practical, general-purpose, resource-recommendation system for cloud HPC.
Performance Evaluation of Blockchain Platforms

Blockchain (BC) is a cryptographic technology that enables the decentralized storage of an immutable record of transactions. As a result, many industries are interested in integrating BC into their IT systems. Although BC-based platforms are used in enterprise solutions, concerns regarding accessibility, privacy, performance, and scalability are still legitimate. Permissioned Blockchain (PBC) frameworks provide a solution to securely store private records. The main objective of this paper is to focuses on the performance and scalability of popular private BC platforms. To evaluate each platform, we varied the workloads and assessed performance metrics. Our comparative analysis provides valuable insights into the strengths and weaknesses of each platform, enabling enterprises to make informed decisions when choosing a private BC solution.
Cloud Instance Resources Prediction Based on Hidden Markov Model

Accurate prediction of the type of cloud resource instances is increasingly important for public cloud users and cloud service providers as it relates to reasonable reservation of resources to minimize costs for both parties. However, existing methods cannot predict the instance type of cloud resources based on the workload requested by the user’s application, and rarely consider the characteristics of workload data changes for the real-time prediction. To address these challenges, this paper proposes a cloud resource instance prediction model to predict appropriate cloud instance resource usage in a timely manner. First, we use trend degree (TD) to classify all requested workload into three types of waveform trend patterns. Next, a method based on Hidden Markov model (HMM) is established. Finally, a reasonable cloud instance type can be predicted according to the requested workload. The simulation results show that this method can predict cloud resource instance types, and has higher prediction accuracy than the existing related methods.