Reviewerly/exHarmony huggingface dataset:
OptBertDCNN: A framework based on BERT and optimized Deep Convolutional Neural Network for MQA

Multilingual Question Answering (MQA) generates accurate answers to the user’s query despite the context language. MQA has gained popularity as individuals increasingly pose questions in both English and their native languages on social media. However, conventional Question Answering (QA) systems encounter difficulties in handling multiple languages. The development of MQA models is hindered by the lack of large-scale benchmark datasets, impeding the achievement of high performance compared to monolingual systems. An effective MQA system called OptBertDCNN is proposed to address this, leveraging multilingual cased Bidirectional Encoder Representations from Transformers (mBERT) and Optimized Deep Convolutional Neural Networks (CNN). OptBertDCNN employs a BERT tokenizer to segment sentences into tokens, enabling the extraction of features such as word embeddings from the pre-trained model, TF-IDF scores, SentiWordNet scores, and statistical features from both context and question tokens. These informative features are then inputted into OptBertDCNN, which is trained using the EHMQuAD dataset. Notably, OptBertDCNN achieves outstanding performance metrics, including an exact match of 0.755, precision of 0.765, recall of 0.773, and F1-score of 0.769. These results unequivocally demonstrate the effectiveness of OptBertDCNN in addressing the challenges of multilingual question answering.
Automatic detection of DBSCAN parameters using BAT algorithm

In contrast to previous clustering approaches; a density-based method called DBSCAN is such a clustering algorithm which creates non-geometrically structured clusters. DBSCAN recognizes the low-density points as outliers and groups the densely packed points together. The settings of DBSCAN's input variables, notably the Minimum Points and Epsilon supplied by the user, heavily influence the clusters that are produced. In this research, we present a modified DBSCAN based scheme where density-based clusters are formed using the DBSCAN parameters that are detected by the BAT algorithm automatically. Unlike conventional initialization using zero, the suggested method intelligently initializes the BAT population (using the bent point acquired from the dataset's KNN graph). The accuracy of the proposed method, which was calculated using the purity function, ranges from 70% to 100% when applied to ten datasets.
An Effective Approach for Image-Based Forgery Detection

The prevalence of picture tampering and alteration has grown significantly in recent years across several industries. The objective of this research work is to identify and draw attention to violations committed when manipulating digital photographs nowadays. The system identifies areas of the image that are likely to be altered using a combination of feature extraction, image segmentation, and machine learning approaches. Its effectiveness is then tested using a publicly accessible dataset of altered photos.
Semantic Analysis and Classification of Emails through Informative Selection of Features and Ensemble AI Model

The emergence of novel types of communication, such as email, has been brought on by the development of the internet, which radically concentrated the way in that individuals communicate socially and with one another. It is now establishing itself as a crucial aspect of the communication network which has been adopted by a variety of commercial enterprises such as retail outlets. So in this research paper, we have built a unique spam-detection methodology based on email-body sentiment analysis. The proposed hybrid model is put into practice and preprocessing the data, extracting the properties, and categorizing data are all steps in the process. To examine the emotive and sequential aspects of texts, we use word embedding and a bi-directional LSTM network. this model frequently shortens the training period, then utilizes the Convolution Layer to extract text features at a higher level for the BiLSTM network. Our model performs better than previous versions, with an accuracy rate of 97–98%. In addition, we show that our model beats not just some well-known machine learning classifiers but also cutting-edge methods for identifying spam communications, demonstrating its superiority on its own. Suggested Ensemble model’s results are examined in terms of recall, accuracy, and precision
Credit card fraud detection using XGBoost for imbalanced data set

Frauds using credit card are easily done by the fraudsters. Due to increase in fraud rates all over the world, various machine learning algorithms are being used by analysts and researchers to detect and analyze frauds in online transactions. However, training data set may have a few instances of one and more instances of another class in case of binary classification particular class which makes result biased. Hence, this paper sets an objective to give methodology which is able to detect fraud accurately in case for skewness of data. The proposed method compares different techniques to handle imbalance problem and chooses best approach out of these and uses XGBoost as classifier to predict whether transaction is fraudulent or not. The developed method is evaluated using European credit card fraud dataset and obtained better F1 score, recall and accuracy as 82.78%, 78.9% and 99.3% respectively as compared to other algorithms taken under study.
Comparative survey analysis of the CNN and LBPH Face Recognition Learning Algorithms

Face recognition technology has made it possible for computers to recognize human faces. It is an application that uses algorithms to extract distinctive traits from facial images and compares them with a database of recognized faces. Facial recognition has numerous real-world uses in social networking, healthcare, access control, security and surveillance, and more. Due to the acquisition and processing of sensitive personal data, it has also sparked questions about ethics and privacy. The Deep Learning Convolutional Neural Network and the Local Binary Pattern Histogram (LBPH) are two face recognition algorithms that are compared in this paper. The study aims to evaluate the performance of these algorithms under different conditions, including variations in pose, expression and illumination. The relevant theories of LBPH and CNN are first briefly discussed, and the performance comparison method is then described. The choice between these two algorithms will depend on the particular needs of the application and the resources that are available. Overall, this work offers insightful information regarding the advantages and disadvantages of LBPH and CNN for face recognition, assisting in the selection of an algorithm for practical applications.
Dynamic Background Modeling and Foreground Detection using Orthogonal Projection onto the Subspace of Moving Objects

Moving object detection and recognition is an important and very relevant topic for any video surveillance application. Object detection in video sequences is a challenging task due to several hindrances such as illumination variations, shadows, dynamic background, and clutter background. Existing methods are inadequate in addressing these challenges. Therefore, the author proposes a novel system for moving object detection in video based on Orthogonal Preserving Projection (OLPP). OLPP generates orthogonal basis functions which preserve locality better than Locality Preserving Projection (LPP), as it is non-orthogonal which makes it hard in reconstructing the data. Therefore, OLPP is deemed to pertain higher power of discrimination than LPP. The proposed method was tested on standard and the author's own dataset. The results obtained with the proposed system were compared with existing methods and the results are satisfactory. The proposed system is efficient and robust for detecting and recognizing moving objects in video surveillance applications.
Mitigating Adversarial Attacks using Pruning

The advent of deep learning has revolutionized the technology industry and has made Deep Neural Networks (DNNs) the powerhouse of many modern day software applications. Well-trained DNNs are able to perform complex tasks such as speech recognition, object detection and image classification with high precision and accuracy. However, the task of training such complex networks at times requires enormous amount of computational resources for which the task is often outsourced to third parties. Recent work suggests that outsourcing the training task can act as a favourable gateway for a malicious trainer to induce a backdoor in the model, which when triggered can force the model into performing in a predefined way which has been set up by the malicious trainer. This paper starts by giving an overview on how such attacks are induced and consequently discusses and provides experimental proof on the various strategies which can be used to neutralise such attacks. We use the l1 and l2 norm to identify weights which are susceptible to be poisoned and prune them away by setting their value to zero. We further inspect the efficiency of layer wise and global pruning. We infer from our experiments that fine-tuning the model for a few epochs after the fine-pruning stage has been completed helps the model to regain its lost accuracy and provides better test time accuracy. During this study, we understand that performing fine-pruning in the later layers is more effective. By pruning the last layer along with fine-tuning the model after fine-pruning has been completed, we achieve 99.96% and 86.05% accuracy for the clean validation and test dataset respectively and consequently witness a drop in attach success rate from 99 to 0 and near 0 in some cases.
Energy-Aware Dynamic Virtual Machine Scheduling in Cloud Computing: A Survey

Dynamic virtual machine scheduling is a crucial research area in cloud computing, to optimize resource utilization, reduce energy consumption, and ensuring performance and availability by dynamically managing and scheduling virtual machines (VMs) across servers. Effective dynamic VM scheduling can significantly impact the efficiency and sustainability of data centers by efficiently allocating resources based on workload demand. However, the dynamic nature of workload demand presents one of the primary challenges in dynamic VM scheduling. Sudden spikes or drops in server resource usage can lead to severe SLA violation concerns and energy inefficiency. To mitigate these challenges, scheduling algorithms need to balance the workload demand across servers while ensuring that servers are not overloaded or underloaded. In this work, we present a comprehensive comparison of state-of-the-art techniques for dynamic VM scheduling, evaluating their performance based on several key factors, including objective function, approach, the technique of VM scheduling, VM placement approach, VM selection approach, and server underload and overload detection techniques. Our comparison highlights the strengths and weaknesses of different approaches and provides insights into the design of efficient dynamic VM scheduling algorithms. The findings of this work can guide the development of novel techniques that address the challenges of dynamic VM scheduling and improve the efficiency and sustainability of data centers.
Nonlinear-by-Linear: Guaranteeing Error Bounds in Compressive Autoencoders

Neural network-based autoencoders have been successfully used in many image and video compression applications. Unfortunately, these approaches do not provide error guarantees on an instance by instance basis, making their use limited in scientific and other applications. In this paper, we propose a new Guaranteed Autoencoder (GAE) that provides error guarantees on all instances. Our approach is based on a key observation that neural networks with piecewise linear activation units (PLUs) can be represented as instance-specific linear operators. This implies that a global model can be converted into a set of local models. The generated linear operators then enable us to find the direct and linear relationship between the compressed data and reconstructed data. We are then able to represent the residual of the reconstruction of each instance in the SVD basis of the corresponding linear operator and then augment the reconstruction until an error bound is met. Additionally, we show that the above approach based on the use of linear operators can be applied in the presence of both fully connected and convolutional layers. Thus, our approach is reasonably general and should find a large number of applications. We demonstrate our approach using both large-scale scientific data and public-domain image datasets. Experimental results show that the GAE can successfully satisfy user-provided error bounds at an instance level while achieving high compression. Additionally, the computational overhead of our approach is small.
Towards a Modern Quality Framework

Quality frameworks have been used in requirements engineering (RE) for a long time to help elicit and document quality requirements. However, existing quality frameworks have major issues that hamper their applicability, particularly in RE, but also in other fields such as the design of digital systems. In this paper, we discuss the issues of existing quality frameworks and propose a new quality model, which has been designed for application as a quality framework in RE as well as in the design of digital systems. We present the rationale and requirements for our new model, introduce the model and sketch its application. Our work contributes to the improvement of quality frameworks used in RE and Digital Design.
Incorporating Presence Conditions into Goal Models that Evolve Over Time

Goal modeling helps analysts understand the needs and motivations of stakeholders. Recent work has investigated considering how these intentions change over time. In this paper, we investigate the problem of specifying and reasoning about goal models when some stakeholders and their intentions are not present for the entirety of a timeline under consideration. With inspiration from software product lines, we implement presence conditions to specify periods of time when an actor or intention is included/excluded. By incorporating presence conditions, goal models can more accurately represent and visualize real-world conditions and correctly propagate model information. We implement our approach in BloomingLeaf and evaluate it using a concrete example.
Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) have become a relevant technique for modeling and forecasting time series due to their advantages in dealing with uncertainty and simulating the dynamics of complex systems. Although numerous univariate and multivariate FCM-based forecasting models have been presented in the literature, one of the still open questions is how to enable FCMs to forecast multivariate time series for multiple-input, multiple-output (MIMO) systems with an efficient learning mechanism. from a computational point of view. This paper suggests a randomized MIMO FCM-based forecasting approach called MO-RHFCM to predict low-dimensional multivariate time series. More specifically, MO-RHFCM is a hybrid model merging the concepts of multivariate fuzzy time series, high order FCM (HFCM), and Echo State Networks (ESN). The structure of MO-RHFCM consists of three layers: input layer, reservoir (internal) layer, and output layer. Only the output layer is trainable using the Least Squares minimization algorithm; hence training the proposed MO-RHFCM method is fast and simple. The weights inside each sub-reservoir are selected randomly and remain fixed during the training process. The obtained results indicate the efficacy and validity of the proposed MO-RHFCM technique compared with some machine learning and deep learning baseline models.
Robust Automatic Modulation Classification Using Domain-Adversarial Neural Network with Data Inconsistency

Automatic modulation classification (AMC) is an important wireless communications technology. With the rapid development of deep learning (DL), DL based AMC has been widely used because of its powerful classification ability. Previous research on DL based AMC usually assumes that the data distribution in the training and inference phases is consistent. However, in practical applications, the uncertainty of the communications environment often leads to inconsistent data distributions between training and inference, and resulting in the decrease of classification accuracy. To combat the problem, this paper proposes a domain-adversarial neural network based modulation recognition algorithm. The proposed algorithm uses classifiers and domain classifiers for adversarial training to enable the feature extractor to extract features that have both class-specificity and domain-inconsistency. Experimental results show that the algorithm can effectively reduce the side effects of data inconsistency caused by channel variations, improving the model's generalization and robustness.
EFC-CenterNet: A Lightweight Network for Prohibited Object Detection

Security inspection is an indispensable aspect of contemporary life and it plays a crucial role in ensuring personal safety at all times. In this regard, the accurate and prompt detection of prohibited objects is imperative. To further improve the detection accuracy of CenterNet on prohibited objects images while maintaining its high-speed detection capabilities, we make the following improvements. Firstly, to tackle the challenges related to complex and overlapping x-ray images, the Efficient Channel Attention module (ECA-Net) has been introduced to augment the feature extraction ability of the CenterNet for prohibited objects. Secondly, the Feature Pyramid Network (FPN) has been employed to amplify the feature acquisition capability of small prohibited objects. Finally, the Complete-IoU (CIoU) loss has been implemented to achieve faster convergence by minimizing the distance between the predicted bounding box and the corre-sponding ground truth while ensuring scale invariance of the two bounding boxes. The experimental results demonstrate that the EFC-CenterNet can effectively balance both accuracy and speed in real-time detection of contraband items, achieving an impressive 84.27% mAP and 52.48 FPS.
ACR360: A Dataset on Subjective 360° Video Quality Assessment Using ACR Methods

The recent advances in immersive technologies have been essential in the development of a wide range of novel standalone and networked immersive media applications.The concepts of virtual reality, augmented reality, and mixed reality relate to different compositions of real and computer-generated virtual objects.In this context, 360 • video streaming has become increasingly popular offering improved immersive experiences when viewed on a head-mounted display (HMD).An important component in the development of novel immersive media systems are subjective tests in which participants assess the quality of experience of representative test stimuli.In this paper, the annotated ACR360 dataset is presented which is publicly available on GitHub.The ACR360 dataset contains a wide range of psychophysical and psychophysiological data that was collected in subjective tests on 360 • video quality.The test stimuli were shown on an HMD and rated according to the absolute category rating (ACR) and modifed ACR (MACR) methods.To support an easy exploration and utilization of the ACR360 dataset by the research community, its structure on GitHub is described and a comprehensive illustration of analysis options are provided for each data category.The ACR360 dataset may be used for conducting meta-analysis in combination with other datasets to improve precision and to pursue research questions that cannot be answered by an individual study.
A Bi-LSTM Autoencoder Framework for Anomaly Detection - A Case Study of a Wind Power Dataset

Anomalies refer to data points or events that deviate from normal and homogeneous events, which can include fraudulent activities, network infiltrations, equipment malfunctions, process changes, or other significant but infrequent events. Prompt detection of such events can prevent potential losses in terms of finances, information, and human resources. With the advancement of computational capabilities and the availability of large datasets, anomaly detection has become a major area of research. Among these, anomaly detection in time series has gained more attention recently due to the added complexity imposed by the time dimension. This study presents a novel framework for time series anomaly detection using a combination of Bidirectional Long Short Term Memory (Bi-LSTM) architecture and Autoencoder. The Bi-LSTM network, which comprises two unidirectional LSTM networks, can analyze the time series data from both directions and thus effectively discover the long-term dependencies hidden in the sequential data. Meanwhile, the Autoencoder mechanism helps to establish the optimal threshold beyond which an event can be classified as an anomaly. To demonstrate the effectiveness of the proposed framework, it is applied to a real-world multivariate time series dataset collected from a wind farm. The Bi-LSTM Autoencoder model achieved a classification accuracy of 96.79% and outperformed more commonly used LSTM Autoencoder models.
Applications of Uncalibrated Image Based Visual Servoing in Micro- and Macroscale Robotics

We present a robust markerless image based visual servoing method that enables precision robot control without hand-eye and camera calibrations in 1, 3, and 5 degrees-of-freedom. The system uses two cameras for observing the workspace and a combination of classical image processing algorithms and deep learning based methods to detect features on camera images. The only restriction on the placement of the two cameras is that relevant image features must be visible in both views. The system enables precise robot-tool to workspace interactions even when the physical setup is disturbed, for example if cameras are moved or the workspace shifts during manipulation. The usefulness of the visual servoing method is demonstrated and evaluated in two applications: in the calibration of a micro-robotic system that dissects mosquitoes for the automated production of a malaria vaccine, and a macro-scale manipulation system for fastening screws using a UR10 robot. Evaluation results indicate that our image based visual servoing method achieves human-like manipulation accuracy in challenging setups even without camera calibration.
Hierarchical Democratized Learning Towards Computation and Communication Efficient Distributed Systems

Distributed modeling and monitoring are commonly used in modern industries. Because of the ever-growing demands for energy conservation and carbon dioxide emission reduction, attempts have been made to improve the energy efficiency of distributed systems. Here, we propose a hierarchical democratized learning framework to optimize distributed computation and communication consumptions. First, we split edge devices into logical learning groups which cooperate with the regional task-related server. Then, each learning group performs hierarchical learning through the generalization and specialization procedures. Through hierarchical clustering, the computation tasks cooperate in a more efficient way with reduced communication requirements at the same time. We conducted experiments on the widely used datasets, FMNIST and CIFAR-10, demonstrating lower computation and communication costs of hierarchical democratic modeling compared with existing federated analytics models.