Reviewerly/exHarmony huggingface dataset:
From Data Pre-processing to Hate Speech Detection: An Interdisciplinary Study on Women-targeted Online Abuse

The term “misogyny” conveys hatred and disrespect for women. It is a type of sexism that stems from the idea that women are less valuable than men and can take many forms, such as verbal abuse, sexual harassment, and physical violence. One area where misogyny is particularly prevalent is in online spaces. Women who speak out and share their experiences online often face vicious attacks, including violence, rape, and death threats. This phenomenon, often referred to as “online harassment,” can have devastating effects on women’s mental health and well-being. Despite the growing recognition of this issue, there is still a lack of understanding about the nature and extent of hate speech toward women on social media. In this paper, the authors aim to address this gap by analyzing a dataset of tweets on Twitter containing hate speech toward women. The authors introduced the Measuring Hate Speech corpus, a dataset used while studying hate speech towards women. From this dataset, the authors extract some of them to build the model. And authors proposed Machine Learning algorithms like Logistic Regression with 0.92% accuracy. And to look further to fill the gap, the authors developed Support Vector Machine (SVM) algorithm, Topic Modeling technique to extract the topics from the corpus. This study contributes to a better understanding of hate speech on social media platforms as Twitter informs strategies for resisting this pervasive problem.
A Recognizer and Parser for Basic Sentences in Telugu using CYK Algorithm

The scientific and technical field of computational linguistics seeks to comprehend spoken and written language from a computational standpoint. The way of describing rules and semantics in linguistics paved the beginning of natural language processing research for various languages spoken in the world. Over 700 languages are spoken in India alone, out of an estimated 7,000 spoken worldwide. Telugu is one of the most predominantly spoken languages in the states of Andhra Pradesh and Telangana. This proposed work presents a syntactical parsing technique on some basic sentences in Telugu. The Cocke-Younger-Kasami algorithm has been implemented to parse these basic sentences and also infer their grammatical structure. At present, there are very few language processing tools for Indian languages. Hence, an effort has been made to efficiently parse a few simple sentences in Telugu. The syntactical parser that has been developed acts as a recognizer and parser which can not only recognize and parse the grammatically correct sentences but can also recognize the grammatically incorrect sentences. This recognizer cum parser is then evaluated using the performance metrics like accuracy, precision and recall.
Improved Breast Cancer Detection using Wavelet Based Image Enhancement

Mammogram and ultrasound being the most effective imaging modality for breast cancer detection . The quality of image is to be enhanced for efficient detection of cancer. Mammogram being a low contrast image and ultrasound being indifferentiable requires image enhancement before being processed for detection of cancer. Thus, an image enhancement workflow is essential for mammogram and ultrasound images for improving the detection of breast cancer. Therefore, this work enhances breast images using bottom hat feature extraction, CLAHE, wavelet transformation and sharpening of image. The detection framework uses Deep learning networks for detection of cancer from enhanced image. The proposed methodology results in improved metrics in enhanced breast mammogram and ultrasound scan image . Further, the enhanced image shows an increased accuracy in detection of breast cancer as benign or malignant using Convolutional Neural Network (CNN) architecture.
Assessing Wearable Human Activity Recognition Systems Against Data Poisoning Attacks in Differentially-Private Federated Learning

Differentially-Private Federated Learning (DPFL) is an emerging privacy-preserving distributed machine learning paradigm that allows for the automatic recognition of human activities using wearable sensors without compromising users’ sensitive data. However, this decentralized approach makes the system vulnerable to poisoning attacks, where malicious agents can inject contaminated data during local model training. This paper presents the results of our research on designing, developing, and evaluating a holistic model for data poisoning attacks in DPFL-based human activity recognition (HAR) systems. Specifically, we focus on label-flipping poisoning attacks, where the label of a sensor reading is maliciously changed during data collection. To investigate the impact of such attacks, we develop a simulator that explores key design issues, such as the correlation between the level of differential privacy, the level of poisoning, the number of communication rounds, and the number of agents in the system. Our findings shed light on the effectiveness of label contamination attacks in DPFL-based HAR systems and can inform the development of more robust and secure models.
Automatic segmentation and classification of the liver tumor using deep learning algorithms

Liver tumors are one of the life-threatening cancers with the fastest-growth rates worldwide. Early detection of tumors may therefore reduce morbidity and increase the survival rate. The development of automated techniques for the precise segmentation of hepatic tumors is essential for assisting doctors in tumor diagnosis and preoperative planning for surgical treatment of the liver which reduces the risk of surgical resection. The classification and segmentation of hepatic tumors in Computerized Tomography (CT) scan pose a great challenge due to noise, unclear boundaries, heterogeneity, and variability in tumor tissue appearance, shape, size, and location. In this study, we describe a novel method for automatic segmentation and classification of hepatic tumors in CT scan images using Deep Convolutional Neural Networks. For tumor segmentation, we created a modified Dense U-net model. The classification framework is based on a novel deep CNN with a pre-trained VGG-16 network to distinguish between normal and malignant liver tumors. The proposed system was evaluated based on MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge dataset and obtained the best result with a Dice Score of 95.40%, Jaccard Index of 92%, and accuracy of 92.60% for segmentation and the classification model has achieved an accuracy of 96%, Sensitivity of 95.80%, Specificity of 96.20% and Precision of 95.80%.
Change Detection Method for Wavelength- Resolution SAR Images Based on Bayes’ Theorem: An Iterative Approach

This paper presents an iterative change detection (CD) method based on Bayes' theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plusnoise statistical models.The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction.The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest.Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed.Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions.The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system.Results show that the proposed iterative CD algorithm performs better than the other methods.
Facial Expression Recognition Using Hierarchical Features With Three-Channel Convolutional Neural Network

Aiming at the problem of insufficient feature extraction and low recognition rate of traditional convolutional neural network in facial expression recognition, a multi-layer feature recognition algorithm based on three-channel convolutional neural network (HFT-CNN) was proposed. In the three-channel convolutional neural network, one channel trained the local image of the eyes and eyebrows extracted from the facial expression image, another channel trained the local image of the mouth extracted from the facial expression image, and the last channel tained the whole facial expression image. In order to better extract the facial features of the three channel, the higher-order tensor singular value decomposition was used to decompose the convolution kernel to remove the redundancy.The detail convolution kernel was constructed for the eyes and eyebrow and mouth image channeles, while the contour convolution kernel was constructed for the whole facial expression image channel. Finally, the features extracted from the three network channels were fused to obtain the expression classification results. Facial expression recognition experiments were carried out on CK+ and JAFFE facial expression datasets respectively, and HFT-CNN was compared to traditional deep neural networks and the state of the art deep neural networks, the network structure in this paper achieved higher recognition rates with fewer network layers.
Trust-Based Distributed Set-Membership Filtering for Target Tracking Under Network Attacks

For target tracking problems in wireless sensor networks subject to malicious network attacks, this paper proposes a distributed set-membership filtering algorithm based on trust dynamic combination strategy.The algorithm has a prediction-correction recursive updating structure similar to Kalman filtering, by introducing the clustering fusion step of received data from other nodes between the prediction step and the measurement correction update step, the clustering fusion step uses K-means to cluster and classify the data of trusted and untrusted nodes, the target state is updated by the fusion of trusted received data set, to improve the resistance to various wicked network attacks.Simulation results show that compared with the traditional distributed set-membership filtering method, the proposed method has better target tracking performance in the face of wicked network attacks such as random attacks, false data injection, replay attacks, and hybrid attacks.
Aerial Insights: Deep Learning-Based Human Action Recognition in Drone Imagery

Human action recognition is critical because it allows machines to comprehend and interpret human behavior, which has several real-world applications such as video surveillance, robot-human collaboration, sports analysis, and entertainment.The enormous variety in human motion and appearance is one of the most challenging problems in human action recognition.Additionally, when drones are employed for video capture, the complexity of recognition gets enhanced manyfold.The challenges including, the dynamic background, motion blur, occlusions, video capture angle, and exposure issues gets introduced that need to be taken care of.In this article, we proposed a system that deal with the mentioned challenges in drone recorded red-green-blue (RGB) videos.System first splits the video into its constituent frames and then performs a focused smoothing operation on the frames utilizing a bilateral filter.As a result, the foreground objects in the image gets enhanced while the background gets blur.After that a segmentation operation is performed using a quick shift segmentation algorithm that separates out human silhouette from the original video frame.The human skeleton was extracted from the silhouette, and key-points on the skeleton were identified.Thirteen skeleton key-points were extracted, including the head, left wrist, right wrist, left elbow, right elbow, torso, abdomen, right thigh, left thigh, right knee, left knee, right ankle, and left ankle.Using these key-points, we extracted normalized positions, their angular and distance relationship with each other, and 3D point clouds.By implementing an expectation maximization algorithm based on Gaussian mixture model, we drew elliptical clusters over the pixels using the key-points as the central positions to represent the human silhouette.Landmarks were located on the boundaries of these ellipses and were tracked from the beginning until the end of activity.After optimizing the feature matrix using a naïve Bayes feature optimizer, the classification is performed using a deep convolutional neural network.For our experimentation and the validation of our system, three benchmark datasets were utilized i.e., the UAVGesture, the DroneAction, and the UAVHuman dataset.Our model achieved a respective action recognition accuracy of 0.95, 0.90, and 0.44 on the mentioned datasets.
Seq2Dense U-Net: Analyzing Sequential Inertial Sensor Data for Human Activity Recognition Using Dense Segmentation Model

In this article, we have demonstrated a novel, generalized Seq2Dense U-Net model for classifying different activities (time-series data obtained from an accelerometer and gyroscope) and sampling them by considering individual sampling points. For training and classifying activities, the triaxial data from the smart devices are mapped as a linear set of sampling points via the sliding window technique and fed to the model. Our proposed model overcame the challenge of a multiclass window that incorrectly labels different classes of sampling points within a window as a class, as reported by previous studies. The comparative study shows that our method can recognize pixel-level function from corresponding time-series data of human activities. To validate and consolidate the effectiveness of the model, an ablation study has been performed on the components of the classification block. Furthermore, the network depth analysis provides the optimum depth with respect to the number of parameters. We have performed extensive studies on the Sanitation, UCI, and UCI HAPT datasets and calculated various performance metrices to assess the performance of our proposed model. Our proposed model performed significantly better on every individual dataset compared to other models as illustrated in the comparative study and provided the best results on the UCI dataset. For the UCI HAR dataset, our model gives an accuracy score of 0.954781, precision of 0.941473, recall of 0.940618, F1 score of 0.940699 followed by Matthew’s correlation coefficient (MCC), and kappa score of 0.948767 and 0.948630, on test data, respectively.
A Real-Time and Efficient Optical Flow Tracking Accelerator on FPGA Platform

Optical flow is a highly efficient visual tracking algorithm, which is commonly used to estimate pixel movement between two consecutive images in a video sequence. However, its high computational complexity and large number of computations become a bottleneck that hinders the performance of embedded vision systems. When applied to simultaneous localization and mapping (SLAM), it is necessary to consider not only time consumption, but also the overall accuracy of the system, causing even greater difficulties. In this paper, a real-time multi-scale Lucas Kanade (LK) optical flow hardware accelerator with parallel pipeline architecture is proposed. The designed circuit meets the high precision and real-time performance required by SLAM while fully considering the limitations of hardware resources. It is deployed on Xilinx Zynq SoC and achieves a frame rate of 93 fps for feature tracking of continuous frame images at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$752\times 480$ </tex-math></inline-formula> resolution. Compared with the implementation on ARM CPU, the average speed is increased by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.5\times $ </tex-math></inline-formula> . Finally, the feasibility and applicability of the hardware accelerator system designed in this paper are verified on the SLAM system. Experimental results on a public dataset show that the average Root Mean Square Error (RMSE) of this work is 0.189 m, indicating that the hardware accelerator has comparable precision with existing state-of-the-art software algorithms, achieving a great balance of performance and precision.
A pathway model to five lines of accountability in cybersecurity governance

In an in-depth field study, we investigate cyber security governance configurations vis-à-vis the five lines of accountability (5 LoA) – that is, the Three Lines Model extended by the accountability of executive management and the board of directors (IIA, 2020). The aim is to explore the configurations adopted by organizations in governing cybersecurity, and why it would matter for cyber security whether the five lines of accountability are adopted. We define the type of the 5 LoA adoption by: (i) the segregation of the lines that spans from blended to segregated and (ii) the level of engagement of those in line roles that ranges from low to high. In this way, we identify four types of adoption of the 5 LoA: 'no adoption, 'ostensible', 'implicit', and 'explicit' adoption. We theorize how the type of adoption of the 5 LoA is affected by the interplay of institutional forces and organizations' need for efficiency and effectiveness, and develop a pathway model for organizations' adoption of the 5 LoA. We find that organizations that adopt the 5 LoA with clear segregation between these lines ('ostensible' and 'explicit' adoption) are those subject to prudential regulation (coercive forces), whereas efficiency motives and mimetic forces drive organizations to seek fluidity and flexibility by 'blending' the segregated lines ('implicit' adoption) to ensure fast reactions to changing environment. Regardless of the segregation between lines and whether they are blended or not, we found that all organizations see scope to improve the level of engagement in the 5 LoA to improve the effectiveness of cyber security governance.
Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey

Abstract In this survey, we present a systematic review of 3D hand pose estimation from the perspective of efficient annotation and learning. 3D hand pose estimation has been an important research area owing to its potential to enable various applications, such as video understanding, AR/VR, and robotics. However, the performance of models is tied to the quality and quantity of annotated 3D hand poses. Under the status quo, acquiring such annotated 3D hand poses is challenging, e.g., due to the difficulty of 3D annotation and the presence of occlusion. To reveal this problem, we review the pros and cons of existing annotation methods classified as manual, synthetic-model-based, hand-sensor-based, and computational approaches. Additionally, we examine methods for learning 3D hand poses when annotated data are scarce, including self-supervised pretraining, semi-supervised learning, and domain adaptation. Based on the study of efficient annotation and learning, we further discuss limitations and possible future directions in this field.
Institutional quality and renewable energy capital flows in Africa

Abstract This paper investigates the linear and nonlinear relationship between institutional quality and renewable energy capital flows across 20 African countries between 2002 and 2017. The empirical evidence is based on the generalised method of moments estimation technique. The findings suggest that institutional quality has a significant and propelling effect on renewable energy capital flows, implying that strong institutions induce investments in renewable energy in Africa. From an extended analytical exercise, the study further shows a nonlinear inverted U-shaped relationship between institutional quality and energy capital flows. This indicates that the enhancing effect of institutional quality on energy capital flows is achieved at a certain threshold level. In the extreme case, the impact of institutional quality on energy capital flows diminishes. The study recommends that African countries initiate reforms to tame any adverse effect of institutional quality on renewable energy investments in its extreme case. Thus, African countries should develop strong institutions and reinforce their regulatory frameworks because strong institutions can improve societal welfare by reducing political, social and economic unpredictability while boosting trust and investment in renewable energy.
Applications of Data Science and Artificial Intelligence

A series of waves have marked the history of artificial intelligence (AI) [...]
Path distance of a quantum unitary evolution

The Hamiltonian and evolution time of a quantum unitary system completely determine a unitary operator, which turns an initial state ${\ensuremath{\rho}}_{0}$ into its time-evolved state ${\ensuremath{\rho}}_{t}$ and may also turn another state ${\ensuremath{\sigma}}_{0}$ into its time-evolved state ${\ensuremath{\sigma}}_{t}$. Although the two pairs of states $({\ensuremath{\rho}}_{0},{\ensuremath{\rho}}_{t})$ and $({\ensuremath{\sigma}}_{0},{\ensuremath{\sigma}}_{t})$ are governed by the same Hamiltonian and experience equal evolution periods, their state distances are not necessarily equal to each other. Specifically, the evolution operator contains information about the evolution other than that reflected by two specific states connected through it. As a matter of fact, two pairs of states with different state distances may have equal path distances. Here we propose a geometric measure for the path distance contained in a quantum unitary evolution, which depends only on the unitary operator itself and is independent of any pair of states connected through it. This path distance meets the basic requirements for a good measure of distance and is valid during the whole evolution process, no matter how long the evolution time is. This path distance is bounded by the path length of the evolution from above and by the Bures angle between the two states connected through it from below. It has potential applications in quantum information processing, such as providing another way to study the optimal control between two quantum states. We finally present the form of Hamiltonian to realize a unitary operator on demand.
Mask-guided network for image captioning

Attention mechanisms have been widely adopted for image captioning because of their powerful performance. In this paper, we propose a Mask Captioning Network (MaC) consisting of an object layer and a background layer to capture the objects and scenes of an image to generate a sentence. To this end, we leverage the Mask RCNN to detect salient regions at the pixel level instead of a bounding box in the object layer. Meanwhile, in the background layer, a CNN model is used to encode the scene features. In addition, MaC is implemented in both LSTM-based and Transformer-based image captioning architectures. We introduce a mask-guided transformer encoder with additional features to enhance the model. Experimental results show that our model significantly outperforms (with a much richer sentence) baseline models and achieves comparable results with state-of-the-art methods on MSCOCO and Flickr30k datasets.