Reviewerly/exHarmony huggingface dataset:
Effective Strategies for Detection of Abnormal Activities in Surveillance Video Streams

Computer vision plays a major role in the computing multimedia industries. One of the major applications that utilize computer vision is surveillance monitoring systems (SMS). It is used in real-time applications, such as remote locations, traffic, and human activity monitoring. These SMS are increasing day by day, and there is a growing need for an automatic efficient surveillance monitoring system to detect and project the abnormality to take immediate actions. The research focuses on developing a system with proposed autoencoder and long-term short-term memory (LSTM) to detect abnormal events from surveillance systems. The work is about detection of objects (persons) in video sequences. Each extracted frame has detected objects with objectless score. This score indicates accuracy and class of the objects present in the video sequences. multiple object tracking (MOT) is used to detect the objects using LSTM-based pre-trained model.
A DSR Study on Iterative Tool Development to Measure IT Process Maturity in an Agile Context

This paper presents the design of a tool for recurring quantitative self-assessment of IT Service Management (ITSM) process maturity in a financial institution implementing agile software development as a new way of working. This change brought on an increased need to monitor ITSM process performance, and a Design Science Research (DSR) project was launched to create an ITSM maturity assessment tool. Continual improvement of ITSM processes can be measured by performing a process maturity assessment, comparing the organization’s process performance against a best-practice reference set of processes. This paper reports a development project for a quantitative measuring survey-based tool. Due to the increasing use of agile methods, there is an increase in research attention to the coexistence of agile and ITSM maturity assessment tools. The results show that a company-wide ITSM process maturity assessment can be established as a survey-based self-assessment in an agile software development context. The aggregate scores from this self-assessment present a good indicator of the organization’s process performance, especially when complemented by a reference score. A key learning is that the iterative DSR methodology made it possible to create a tool that in good way measure ITSM process maturity in an agile context.
Building the Classifiers

Classification is a decision-making problem, in which we aim at the assignment of a correct class label to an observation. In the typical scenario, the set of available class labels is fixed beforehand and remains unchanged. Advanced data processing streams allow for a more flexible definition of this task. Typically, they admit the existence of a “novelty” class, to which some observations may be assigned.
A Framework for Identifying Theft Detection Using Multiple-Instance Learning

Closed-circuit televisions are widely used devices to prevent theft and dangerous events. Video surveillance for unusual activity detection is done by human supervision. This work involves constructing a convolutional neural network to identify normal and abnormal activities in an input video. In this model, the data is preprocessed and fed into a fully connected three-dimensional convolutional neural network model for feature extraction. The extracted features are sent into a three-layered, fully connected network for processing. The ranking loss function is calculated using multiple-instance learning to predict the anomaly score. The anomaly score obtained for a given segment is used to map the instance to the anomaly class. This model outputs a result that provides insights into the accuracy rate concerning the number of epochs run by the model. Based on this observation, the model is trained with normal and abnormal videos to identify the anomaly.
Security Monitoring Using Converse Lyapunov Function

This chapter introduces a novel framework to construct the region of attraction (ROA) of a power system centered around a stable equilibrium by using stable state trajectories of system dynamics. Most existing works on estimating ROA rely on analytical Lyapunov functions, which are subject to two limitations: the analytic Lyapunov functions may not be always readily available, and the resulting ROA may be overly conservative. This work overcomes these two limitations by leveraging the converse Lyapunov theorem in control theory to eliminate the need for an analytic Lyapunov function and learning the unknown Lyapunov function with the Gaussian Process (GP) approach. In addition, a Gaussian Process Upper Confidence Bound (GP-UCB) based sampling algorithm is designed to reconcile the trade-off between the exploitation for enlarging the ROA and the exploration for reducing the uncertainty of sampling region. Within the constructed ROA, it is guaranteed in the probability that the system state will converge to the stable equilibrium with a confidence level. Numerical simulations are also conducted to validate the assessment approach for the ROA of the single-machine infinite bus system and the New England 39-bus system. Numerical results demonstrate that our approach can significantly enlarge the estimated ROA compared to that of the analytic Lyapunov counterpart.
A New Method for Imbalanced Data Reduction Using Data Based Under Sampling

The machine learning classification techniques becomes inaccurate due to presence of imbalance. There are many balancing methods available among them data-based under sampling methods are mostly used. We have also considered it and proposed a new algorithm for imbalanced data reduction using under sampling (IDRUS) which address few problems faced by previous data under sampling methods. The IDRUS method calculates mean of majority dataset and from that obtained center sample to all remaining samples of majority dataset distances were calculated and arranged in descending order. The required imbalance data are deleted from top as there are farthest distances. The software defect prediction from PRedict or Models in Software Engineering (PROMISE) repository dataset is considered and performance measures (Geometric Mean, Accuracy, Precision, Recall, Specificity and F-Measure) were calculated. The comparing results of IDRUS are shown in tabular format of form “Mean ± Standard Deviation” which are better performance than the other.
Dynamic Resource Allocation Framework in Cloud Computing

In Cloud Computing environment, resource provisioning, resource allocation, and resource scheduling are the most complex issues in these days. Cloud user expects best resource utilization and Cloud provider expects revenue maximization by considering budget and time constraints. In this research paper, Dynamic Resource Allocation algorithm has been proposed to allocate the resources to Cloud users and Cloud providers in dynamic Cloud environment. The proposed algorithm has been simulated and tested using CloudSim simulator tool.
Conceptualizing Node.js Projects: A Graph-Oriented Technology-Specific Modeling Method

This paper advances our prior work in developing a modeling method for managing Node.js projects dependencies by adding a semantic analysis layer to the created models, benefitting from prior artifact-building research that makes it possible for diagrams of arbitrary semantics to be processed as RDF graphs. The purpose of models thus extends towards a knowledge-driven project management approach that handles a graph-based conceptualization of project management processes, development tasks and Node.js development resources (packages, APIs and their dependencies). With the continuous growth of the Node.js environment, managing complex projects that use this technology can be chaotic, especially when it comes to planning dependencies and module integration. The deprecation of a module can lead to serious crisis regarding various projects where that module was used; consequently, traceability of deprecation propagation becomes a key requirements in Node.js project management. The modeling method introduced in this paper provides a diagrammatic solution to managing module and API dependencies in a Node.js project. It is deployed as a modeling tool that can also generate REST API documentation and Node.js project configuration files that can be executed to install the graphically designed dependencies.
Self-Distilled Depth From Single-Shot Structured Light With Intensity Reconstruction

Depth from structured light (SL) is a mainstream approach for 3D acquisition. In this paper, we propose a unique depth reconstruction method for single-shot SL systems, which reconstructs the intensity image of the scene simultaneously. The merits of our method are twofold. First, the intensity image can be used to extract scene textures under ambient light from the captured image, parsing out the projected SL pattern and thus improving depth reconstruction performance. Second, the intensity information of the scene can be useful in many applications when additional RGB cameras are not available along with the SL system. The proposed method is realized by a dual-branch deep neural network for recovering depth and intensity, respectively, where the intermediate output of the intensity branch is fed into the depth branch. Specifically, we introduce a self-distillation strategy to facilitate training the network in an unsupervised manner.
Detection of Phishing Websites Using Machine Learning

In the cyber-world, phishing attack is increasing rapidly and is a major matter of concern, which is resulting in losing large amount of money of Internet users every year. Our study on this field develops and creates a model which will predict whether a URL is phishing or not, i.e., it can predict whether a URL is phishing or legitimate. The goal of this study is to develop a web application software that detects phishing URLs from the collection of over 5000 phishing URLs and 5000 legitimate URLs, which are collected from open source and are fragmented into 80% training samples and 20% testing samples. The study provided a model for URL classification into phishing and legitimate URLs.
Online Gaussian Process Learning for Security Assessment

The online small-signal stability assessment of complex power systems is typically a challenging problem due to uncertainties and parameter variations of system dynamics as well as the incurred high computational complexity. This chapter proposes a novel theoretical framework for dynamic small-signal stability assessment of power systems by estimating the region of attraction for operating states in real time. By analyzing the latest sampling data of power grids in a fixed time window, an up-to-date training set is constructed with the aid of converse Lyapunov function, which enables us to develop an online learning approach based on Gaussian Process (GP) to assess the stability level of power grids. As a result, an iteration algorithm is designed to update the assessment parameters by learning the input-output pairs in the training set. Theoretical analysis is conducted to ensure the existence of converse Lyapunov function for differential-algebraic system that serves to describe power system dynamics, as well as to estimate the region of attraction for operating states with a given confidence level. In particular, a practical method is proposed to leverage phasor measurement unit (PMU) data of real power grids for validating the online GP approach. Moreover, experimental validations are taken to substantiate the proposed online assessment approach by using PMU data of smart-grid infrastructure on EPFL campus. The proposed assessment approach contributes to situational awareness of human operators in the control station, thereby taking proactive remedial actions prior to emergencies.
Review on the Image Encryption with Hyper-Chaotic Systems

Image encryption using a chaotic system is a widely used technique. But, cryptanalysis of most chaotic encryption algorithms pointed out the disadvantages or drawbacks of the chaotic systems. As a remedy, a new and more efficient chaotic system with higher dimension is used recently and named as a hyper-chaotic system. The conditions of an efficient encryption algorithm can be fulfilled effectively by a hyper-chaotic system. Recently, several hyper-chaotic systems are proposed by different researchers, and some of them are used in image encryption techniques. But, some recent cryptanalysis on hyper-chaotic image encryption shows the drawback of these system. This work will discuss different hyper-chaotic systems on the basis of their efficiency to use in image encryption. This discussion will also include the parameters based on which one can choose an efficient hyper-chaotic system.
Topic Classification for Short Texts

In the context of TV and social media surveillance, constructing models to automate topic identification of short texts is a key task. This paper constructs worth-to-consider models for practical usage, employing Top-K multinomial classification methodology. We describe the full data processing pipeline, discussing about dataset selection, text preprocessing, feature extraction, model selection and learning, including hyperparameter optimization. We will test and compare popular methods including: standard machine learning, deep learning, and a fine-tuned BERT for topic classification.
Online Optimization Algorithm of the Quantum State Filter With Disturbance and Noise and Its Convergence Study

An online quantum state filter (OQSF) optimization algorithm in the existence of the sparse disturbance and Gaussian noise is proposed in this paper. The optimization algorithm is based on the continuous weak measurements (CWM) and the online measurement record sequence constructed. The OQSF problem is decomposed into three subproblems, and three variables are estimated in real-time at the same time. The convergence OQSF algorithm proposed is strictly proved by means of two lemmas proof of the lower bound function of the distance between the optimal solution and original one having sufficient decreasing space. By comparing the performances with existing algorithms, the developed OQSF algorithm proposed demonstrates advantages in the numerical performance comparisons.
Uncertainty-Driven Knowledge Distillation for Language Model Compression

Despite the remarkable performance on various Natural Language Processing (NLP) tasks, the parametric complexity of pretrained language models has remained a major obstacle due to limited computational resources in many practical applications. Techniques such as knowledge distillation, network pruning, and quantization have been developed for language model compression. However, it has remained challenging to achieve an optimal tradeoff between model size and inference accuracy. To address this issue, we propose a novel and efficient uncertainty-driven knowledge distillation compression method for transformer-based pretrained language models. Specifically, we design a method of parameter retention and feedforward network parameter distillation to compress N-stacked transformer modules into one module in the fine-tuning stage. A key innovation of our approach is to add the uncertainty estimation module (UEM) into the student network such that it can guide the student network's feature reconstruction in the latent space (similar to the teacher's). Across multiple datasets in the natural language inference tasks of GLUE, we have achieved more than 95% accuracy of the original BERT, while only using about 50% of the parameters.
RASK: Range Spatial Keyword Queries on Massive Encrypted Geo-Textual Data

Spatial keyword queries have attracted much attention over the past decade due to the popularity of location-based services and social networks, which brings great economic benefits. Geo-textual data are encrypted-and-delegated to public clouds for efficient management and utilization while preventing potential data leakage. However, it is still challenging to solve secure <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ra</u> nge <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> patial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</u> eyword queries on encrypted data since existing works are either vulnerable or inefficient. In this paper, a secure hybrid index is built to implement efficient filtering, by embedding nodes’ paths in a novel symmetrical kd-tree into inverted indexes and employing only lightweight cryptographic techniques. A concrete scheme RASK is constructed on the secure index by utilizing only a little storage and computing resources of clients. Furthermore, RASK+ is proposed based on secure virtual technology by migrating all storage burdens from clients to public clouds. Both schemes are theoretically proved to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">indistinguishable under adaptive chosen keyword attacks</i> (IND-CKA2). Through experimental evaluations on three real datasets within consistent environments, both schemes reduce the response time by about 50%-80% compared to state-of-the-art solutions (i.e., SKSE, LSKQ, etc.). The storage overheads for the cloud are also reduced by about 0.5-2 orders of magnitude.
Understanding Physiological and Behavioral Characteristics Separately for High-Performance Video-Based Hand Gesture Authentication

Video-based hand gesture authentication is a challenging fine-grained spatiotemporal analysis task, which requires models with the ability to extract stable physiological and behavioral features from dynamic gestures. Inspired by biological studies, we present a physiological-behavioral characteristic understanding network (PB-Net) for hand gesture authentication. According to the properties of physiological and behavioral characteristics, we design two branches, physiological branch (P-Branch) and behavioral branch (B-Branch), as well as corresponding data tailoring strategies for the PB-Net. The data tailoring strategies can produce two customized videos for the two branches, which can facilitate the analyses of physiological and behavioral characteristics. Besides, the data tailoring strategies can remove significant redundant information and thus can improve running efficiency. The P-Branch and B-Branch do not interfere with each other and focus on the distillation of physiological and behavioral features separately. Considering that the importance degree of the physiological and behavioral features could be different and changeable, we devise an adaptive physiological-behavioral feature fusion (APBF) module to automatically assign appropriate weights for the two features and merge them together to obtain a more satisfactory identity feature. Finally, the rationality, validity, and superiority of the PB-Net are fully demonstrated by extensive ablation experiments and sufficient comparisons with 23 excellent video understanding networks on the SCUT-DHGA dataset. The code is available at https://github.com/SCUT-BIP-Lab/PB-Net.
Cross-Modality Channel Mixup and Modality Decorrelation for RGB-Infrared Person Re-Identification

This paper focuses on RGB-infrared person re-identification, which is challenged by a large modality gap between RGB and infrared images. Most existing methods attempt to learn discriminative modality-invariant features. These methods make use of identity annotations while they do not sufficiently exploit intra-modality and cross-modality sample relations using modality annotations. In this paper, we propose a Cross-modality channel Mixup and Modality Decorrelation method (CMMD) that explores sample relations at both image and feature levels. This method is designed to reduce redundant modality-specific information of the representations and highlight modality-shared information. Specifically, we first design a cross-modality channel mixup (CCM) augmentation at the image level, which combines a random RGB channel and an infrared image to generate a new one by mixup, while keeping identity information unchanged. This augmentation can be integrated into other methods easily without introducing extra parameters or models. In addition, modality decorrelation quintuplet loss (MDQL) is further presented to mine hard samples in a batch, that is, positive/negative intra/cross-modality samples, to learn modality-invariant representations in the shared latent space at the feature level. This loss suggests that the closest negative sample and the farthest positive sample should have an equal probability of appearing in both modalities. Comprehensive experimental results on two challenging datasets, i.e., SYSY-MM01 and RegDB, demonstrate competitive performance of our method with state-of-the-art ones.
Mask-and-Edge Co-Guided Separable Network for Camouflaged Object Detection

Camouflaged object detection (COD) involves segmenting objects that share similar patterns, such as color and texture, with their surroundings. Current methods typically employ multiple well-designed modules or rely on edge cues to learn object feature representations for COD. However, these methods still struggle to capture the discriminative semantics between camouflaged objects (foreground) and background, possibly generating blurry prediction maps. To address these limitations, we propose a novel mask-and-edge co-guided separable network (MECS-Net) for COD that leverages both edge and mask cues to learn more discriminative representations and improve detection performance. Specifically, we design a mask-and-edge co-guided separable attention (MECSA) module, which consists of three flows for separately capturing edge, foreground, and background semantics. In addition, we propose a multi-scale enhancement fusion (MEF) module to aggregate multi-scale features of objects. The predictions are decoded in a top-down manner. Extensive experiments and visualizations demonstrate that our CNN-based and Transformer-based MECS-Net outperform 13 state-of-the-art methods on four popular COD datasets. Codes and results are available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/TomorrowJW/MECS-Net-COD</uri><inline-formula><tex-math notation="LaTeX">$\ast$</tex-math></inline-formula></sup> .
Histopathology Cancer Detection

Histopathology deals with studying of tissues of patients suffering from tumor and involves examining tissues or cells under a microscope to detect diseases. For this paper, we are focused on detecting cancer. Generally, this process of examining and detecting cancer is done by a trained doctor but now it can be detected by computer vision achieved by deep learning through Python by examining medical images of the person. Detection of cancer cells in tissues through lymph nodes is discussed in this paper.
Why Moving Fast and Breaking Things Makes Sense?

In the traditional approach to engineering system design, engineers usually come up with several possible designs, each improving on the previous ones. In coming up with these designs, they try their best to make sure that their designs stay within the safety and other constraints, to avoid potential catastrophic crashes. The need for these safety constraints makes this design process reasonably slow. Software engineering at first followed the same pattern, but then realized that since in most cases, failure of a software test does not lead to a catastrophe, it is much faster to first ignore constraints and then adjust the resulting non-compliant designs so that the constrains will be satisfied. Lately, a similar “move fast and break things” approach was applied to engineering design as well, especially when designing autonomous systems whose failure-when-testing is not catastrophic. In this paper, we provide a simple mathematical model explaining, in quantitative terms, why moving fast and breaking things makes sense.