A Scalable System Architecture for Activity Detection with Simple Heuristics

Refine BING using effective cascade ranking

This study designed and evaluated a reproducible, cloud-accessible virtual laboratory architecture for teaching network automation in higher education. The goal was to address the lack of cost-effective and scalable infrastructures that enable students to develop automation skills aligned with current industry demands. The proposed design integrated Cisco Packet Tracer’s REST API, Docker containerization, Cloudflare Tunnel, and Google Colab to facilitate real-time programmatic interaction with simulated networks through Python. A design-based research (DBR) methodology was adopted, and a pilot was conducted with 35 undergraduate students enrolled in the “Network Management” course at the Technical University of Machala (Ecuador). The intervention consisted of three structured labs, a pre-test/post-test assessment, and a perception survey. Findings revealed statistically significant learning gains, with post-test scores showing marked improvement over pre-test results. Students also reported high satisfaction and engagement, highlighting the accessibility and novelty of combining cloud platforms with network simulators. The study concludes that this architecture provides a feasible and pedagogically sound model for integrating network automation into curricula. Practical implications include lowering barriers to access, fostering active learning, and offering institutions a scalable, low-cost alternative to physical labs.

Detection of objects precisely in video surveillance system is vital for many applications such as abnormal activity detection, congestion analysis, person identification, military fields. For detection of objects, background subtraction and temporal detection methods are used. In background subtraction method moving and static objects which enters into a camera viewing region has been detected. In this method reference frame will be captured during camera initialization. It uses a reference frame as background, detects present frame and takes the difference between two frames. In this paper objects are detected in multiple regions separately from single camera instead of using multiple cameras to monitor different objects in different regions. And also user will have control to select required single or multiple regions to detect the object. This will reduces the overall cost of the system and increases the processing speed of the system if we choose a particular region. Accuracy of the object detection in different regions increases since it allows using different parameters like thresholding separately. So that it monitors different activities at the same time. Also detection of objects based on comparison, object of interest from stored data base is presented using single static camera view with good real time performance.

The precise location of the tropical cyclone (TC) center is critical for intensity estimation and trajectory prediction. Due to the variability of TC morphology and structure, there are still some challenges in locating its center automatically. The ability of the deep convolutional network to capture multilevel structural features of the images is exploited. Furthermore, a two-step scheme for locating the TC center is proposed, which contains the object detection for TCs with deep learning and the comprehensive decision for TC centers. In the object detection, considering the statistical scale distribution of TCs, the global and local features extracted by the network are combined to form the fusion feature maps through the upsampling and concatenation. The changes in the TC scale are accommodated by two different scale outputs. A high detection rate and a low false alarm rate are obtained with the object detection, which provides an initial position for the TC center. Within the scope of the TCs, the final position of the center is obtained through segmentation, edge detection, circle fitting, and comprehensive decision. The experimental results show that the average latitude and longitude error of the proposed method is about 0.237$^\circ$. For the TC in the initial phase or dissipation stage, the location results are usually superior to the results of the comparison algorithms.

Object detection and recognition is an integral part of Computer Vision. It has a multitude of applications ranging from character recognition to video analysis. Object detection now play a crucial role in industries like security, video, medical, sports, and many more. With the latest research, rapid development in deep learning, and computational image understanding object detection will be more prevalent. In the future, we will be able to implement object detection in much more mundane workings of our life. Our review paper will focus on the existing techniques that are present in the community and how each technique is different from the other techniques. We will start with an introduction about object detection as a tool and technique. This will be followed by a brief background discussing the most fundamental steps involved and the basic architecture of object detection. Then, we focus on several existing techniques and perform comparative analyses of these techniques to draw meaningful conclusions. These techniques include two-stage models such as R-CNN, Fast R-CNN, and Faster R-CNN and single-stage models YOLOv1, YOLOv2, YOLOv3, YOLOv4, and SSD. Finally, we will conclude by presenting some directions that provide a scope for further analyses along that direction and hope to suggest different techniques that can be implemented in different sectors for optimal results.

Recently, network security has become an extremely vital issue that beckons the development of accurate and efficient solutions capable of effectively defending our network systems and the valuable information journeying through them. In this article, a distributed multiagent intrusion detection system (IDS) architecture is proposed, which attempts to provide an accurate and lightweight solution to network intrusion detection by tackling issues associated with the design of a distributed multiagent system, such as poor system scalability and the requirements of excessive processing power and memory storage. The proposed IDS architecture consists of (i) the Host layer with lightweight host agents that perform anomaly detection in network connections to their respective hosts, and (ii) the Classification layer whose main functions are to perform misuse detection for the host agents, detect distributed attacks, and disseminate network security status information to the whole network. The intrusion detection task is achieved through the employment of the lightweight Adaptive Sub-Eigenspace Modeling (ASEM)-based anomaly and misuse detection schemes. Promising experimental results indicate that ASEM-based schemes outperform the KNN and LOF algorithms, with high detection rates and low false alarm rates in the anomaly detection task, and outperform several well-known supervised classification methods such as C4.5 Decision Tree, SVM, NN, KNN, Logistic, and Decision Table (DT) in the misuse detection task. To assess the performance in a real-world scenario, the Relative Assumption Model, feature extraction techniques, and common network attack generation tools are employed to generate normal and anomalous traffic in a private LAN testbed. Furthermore, the scalability performance of the proposed IDS architecture is investigated through the simulation of the proposed agent communication scheme, and satisfactory linear relationships for both degradation of system response time and agent communication generated network traffic overhead are achieved.

Automatic dress code verification (ADCV) uses advanced technology to quickly and fairly check if people are following the dress code. It is more accurate than humans and does not have personal biases. This can be helpful in places like schools, universities, and offices where dress codes are important for safety. One of the great things about it is that it saves time and money. It does not require people to manually check the dress codes, which can be slow and not always consistent. This technology can be adjusted to fit different dress code rules, whether it's a formal office dress or a school uniform. The technology can be customized for different dress code rules, from formal office attire to school uniforms. One notable advantage of ADCV is its capacity to save both time and financial resources. The system gets rid of the requirement for people to manually check dress codes, which can be slow and inconsistent. The technology's flexibility extends to customization for different dress code rules, addressing the unique needs of diverse settings. It is important to highlight the evolution of ADCV from previous models, where limitations existed in terms of singular application focus. In earlier iterations, functionalities such as face recognition or object detection could only be implemented individually, creating a gap in the comprehensive identification of individuals that encompassed both facial features and attire. The proposed system aims to rectify these shortcomings by integrating capabilities, ensuring a more holistic approach to dress code verification. This enhancement signifies a significant stride toward a more versatile and effective solution for monitoring and enforcing dress codes in various environments. In the previous models the only one application could be designed. For instance, the face recognition or the object detection is only possible individually. It cannot do both identification of person’s face and attire of the person. Considering these disadvantages, the rectification of these problems will be achieved through the implementation of the proposed system. ADCV's impact extends beyond its immediate application in dress code enforcement. As a pioneering example of the intersection between computer vision and artificial intelligence.

Vehicle-mounted laser scanners are able to observe the vehicles environment in order to detect, track and classify the surrounding objects and thus providing data for active safety systems. The latest development of IBEO combines several innovations. The receiver diodes are arranged in an array, which enables simultaneous measurements in 4 horizontal planes, e.g. to compensate pitching of the vehicle. In addition a multi target capability is integrated. This technique enables the detection of two distances with a single measurement, thus enhancing the robustness against rain. This paper introduces improved high speed object detection and high performance object tracking algorithms for real-time data processing. Additionally a classification of the road users is possible. A system architecture for detection and modelling of dynamic traffic scenes is introduced in order to provide a general idea of the different tasks necessary to reach the aim of a complete environmental model using a sensor for a wide range of applications.

Accurate detection of white matter (WM) lesions is essential for diagnosing and monitoring Multiple Sclerosis (MS), but manual lesion identification is challenging and time-consuming. This study employs the “no new U-Net” (nnU-Net) version 2 architecture to enhance the lesion segmentation process. We trained our model with a fine-tuned version of the default nnU-Net configuration incorporating extreme oversampling and a smaller learning rate to improve new or evolving lesion detection. Results showed that our nnU-Net v2 achieved a F1 score of 0.73 for baseline lesions and 0.75 for new or evolving lesions, demonstrating notable performance in identifying both types of lesions, and that the model generalized well to the MSSEG-2 dataset. This study highlights the capabilities of the nnU-Net v2 architecture for robust WM lesion detection in longitudinal cohorts. The final phase involved packaging our top-performing ensemble of models into a Docker container for easy usage, enabling the automatic distinction between baseline and new or evolving lesions.

We propose a novel learning algorithm, called Bagging-Adaboost ensemble algorithm with genetic algorithm post optimization, for object detection that uses local shape-based feature. The feature is motivated by the scheme that use the chamfer distance as a shape comparison measure. It can be calculated very quickly using a look-up table. Random sampling boosting algorithm is used to select a discriminative edge shape features set from a over-complete dictionary of features and form an object detector. Genetic Algorithm post optimization procedure is used to remove based classifiers which cause higher error rates. The resulting classifier consists of fewer base classifiers yet achieves better generalization performance. To demonstrate our method we trained a system to detect pedestrians in complex natural scenes. Experimental results show that our system can extremely rapidly detect objects with high detection rate. The result is very competitive with other published object detection schemes. The learning techniques can be extended to detect other objects.

Machine learning has been used in many areas, such as object detection, pattern recognition, and data dining. Most machine learning algorithms require a high-quality training set. The performance of machine learning would be improved when the informative samples are selected for training. This paper proposes a straightforward definition of boundary samples and provides an effective method to estimate the threshold. The proposed procedure evaluates the weighted average of distance to estimated thresholds for each sample and then selects the boundary samples from initial data. The experimental results show that detectors trained by the selected data have both a higher detection rate and a lower false positive rate compared to the one using training samples which are selected randomly.

The article explores modern neural network architectures for the automatic detection and recognition of marine surface objects and obstacles of given classes throughout the full image area, applicable for execution in real or near real time on an optoelectronic vision system to au-tomate and improve the safety of civil marine navigation. A formal statement of the problem of automatic detection of objects on images is given. The state-of-the-art algorithms for detecting objects in images based on use of artificial convolutional neural networks were reviewed, their comparison was made and a reasonable choice was made in favor of the most efficient neuralnetwork architecture in terms of computational complexity to recognition accuracy. The subject area is studied, as well as publicly available databases of surface objects suitable for use in the training of algorithms using artificial neural networks. The article concluded that there is insuffi-cient labeled data for training neural network algorithms, as a result of which the authors inde-pendently collected research images and video sequences, prepared and labeled the collected data containing surface marine objects and other obstacles that represent a navigation hazard for ships. Based on the selected neural network architecture, a new neural network algorithm for automatic full-frame detection and recognition of surface objects was developed, and an artificial neural network was trained using the prepared database of images of typical objects. The resulting algorithm was tested by the authors on a validation data set, the quality of its work was estimated using various metrics, and the algorithm’s performance was measured. Conclusions are made about the necessity to expand the collected database of images of typical marine objects, further steps are proposed to improve the accuracy of the developed software and algorithmic complex and its implementation to be used in a marine optoelectronic machine vision system for automa-tion and improving the safety of civil navigation.

This paper proposes a self-created multi-layer cascaded architecture for multi-view face detection. Instead of using predefined a priori about face views, the system automatically divides the face sample space using the kernel-based branching competitive learning (KBCL) network at different discriminative resolutions. To improve the detection efficiency, a coarse-to-fine search mechanism is involved in the procedure, where the boosted mirror pair of points (MPP) classifiers is employed to classify image blocks at different discriminatory levels. The boosted MPP classifiers can approximate the performance of the standard support vector machines in a hierarchical way, which allows background blocks to be excluded quickly by simple classifiers and the ‘face like' parts remained to be judged by more complicate classifiers. Experimental results show that our system provides a high detection rate with a particularly low level of false positives.

The digitalisation of finance influenced the emergence of new technological concepts for existing user needs. Financial technology, or fintech, provides improved services for customers and new economic value for businesses. As such, fintech services require on-demand availability on a 24/7 basis. For this reason, they are often deployed in cloud environments that allow connectivity with ubiquitous devices. This allows customers to perform online transactions, which are overseen by the respective financial institutions. However, such cloud-based systems introduce new challenges for information security. On one hand, they represent attractive targets for cyberattacks. On the other, financial frauds can still go unnoticed by the financial institutions in charge. This paper contributes to both challenges by introducing the concept for a cloud-based system architecture for fraud detection and client profiling in the banking domain. Therefore, a systematic risk assessment was conducted in this context, and exploitation probabilities were inferred for multiple attack scenarios. In addition, formal verification was accomplished in order to determine the effects of successful vulnerability exploits. The consequences of such security violations are discussed, and considerations are given for improving the resilience of fintech systems.

The combined radar system architecture for joint radar target detection and communication is considered. In this system we use complex quasi-continuous waveform of the big time-bandwidth product and duration with the combined modulation. This waveform is used in interrupted continuous wave radar technique. With reference to mariner radar [1] for a radar-location and communication it is offered complex coherent radar waveform with phase and linear frequency modulation. Problems of waveform synthesis, performance characteristic of the combined system are discussed.