Reviewerly/exHarmony huggingface dataset:
Incorporating Spatio-Temporal Information in Frustum-ConvNet for Improved 3D Object Detection in Instrumented Vehicles

Environmental perception is a key task for autonomous vehicles to ensure intelligent planning and safe decision-making. Most current state-of-the-art perceptual methods in vehicles, and in particular for 3D object detection, are based on a single-frame reference. However, these methods do not effectively utilise temporal information associated with the objects or the scene from the input data sequences. The work presented in this paper corroborates the use of spatial and temporal information through multi-frame, lidar, point cloud data to leverage spatio-temporal contextual information and improve the accuracy of 3D object detection. The study also gathers more insights into the effect of inducing temporal information into a network and the overall performance of the deep learning model. We consider the Frustum-ConvNet architecture as the baseline model and propose methods to incorporate spatio-temporal information using convolutional-LSTMs to detect the 3D object detection using lidar data. We also propose to employ an attention mechanism with temporal encoding to stimulate the model to focus on salient feature points within the region proposals. The results from this study shows the inclusion of temporal information considerably improves the true positive metric specifically the orientation error of the 3D bounding box from 0.819 to 0.784 and 0.294 to 0.111 for cars and pedestrian classes respectively on the customized subset of nuScenes training dataset. The overall nuScenes detection score (NDS) is improved from 0.822 to 0.837 compared to the baseline.
Servo System Control Using Gestures

This paper is devoted to designing a Convolutional Neural Network (CNN) to control a servo system by recognizing gestures. The design will be in Matlab and a Programmable logic controller (PLC). In the first step, the CNN is designed and verified in Matlab. Then the structure of the CNN is exported into PLC, where a general processing algorithm using the imported structure takes place. The trained CNN recognizes gestures and controls a one axis servo system using Motion Function Blocks (MFB). The paper will demonstrate the results with experiments.
Autoencoder Model Exploration for Multi-Layer Video Compression

The use of autoencoder models for image and video compression have been explored by a number of works published in recent years. While those works perform the original data compression in a single layer, in this work we propose the use of autoencoder models in two-layered video coding. The adoption of multi-layer encoder provides scalability and allows us for decoupling the traditional video coding implementation from the NN solutions. By restricting the use of the Neural Network (NN) solution in the enhancement layer, it becomes possible to decode the base layer bitstream without the necessity of running the decoding process with the NN. We implemented and evaluated two autoencoder models: one using a symmetric encoder/decoder architecture, and an asymmetric alternative that employs more layers on the decoder side. The models were trained to compress residues for a scenario using All Intra encoding with spatial scalability. The Asymmetric model outperformed the Symmetric one by providing better compression rates and quality results, which is confirmed by the respective BD-Rate and BD-PSNR average results of -17.06% and 0.7dB, respectively.
The Problem of Discovery in Version Control Systems

Version Control Systems, used by developers to keep track of the evolution of their code, model repositories as Merkle graphs of revisions. In order to synchronize efficiently between different instances of a repository, they need to determine the common knowledge that they share. This process is called discovery. In this paper, we provide theoretical definitions for the problem of discovery, establish some universal upper and lower bounds on the amount of data that needs to be exchanged, as well as NP-hardness for a restricted variant (with only 2 round-trips). We also present and analyze some algorithms that are used in extant VCSs, such as Mercurial and Git, and propose an algorithm based on chain-decomposition.
Crime Scene Documentation

Crime scene documentation is a critical role of the crime scene investigator. Once items are moved or removed from a scene, their original context is lost. Documentation methods are used to preserve scene and evidence context and document crime scene investigator activities. Documentation includes notes, photography, videography, sketching, mapping, and the use of technology such as UAVs and laser scanners.
Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-Driven Image Coding

Today, visual data is often analyzed by a neural network without any human being involved, which demands for specialized codecs. For standard-compliant codec adaptations towards certain information sinks, HEVC or VVC provide the possibility of frequency-specific quantization with scaling lists. This is a well-known method for the human visual system, where scaling lists are derived from psycho-visual models. In this work, we employ scaling lists when performing VVC intra coding for neural networks as information sink. To this end, we propose a novel data-driven method to obtain optimal scaling lists for arbitrary neural networks. Experiments with Mask R-CNN as information sink reveal that coding the Cityscapes dataset with the proposed scaling lists result in peak bitrate savings of 8.9 % over VVC with constant quantization. By that, our approach also outperforms scaling lists optimized for the human visual system. The generated scaling lists can be found under https://github.com/FAU-LMS/VCM_scaling_lists.
Perception-Distortion Trade-Off in the SR Space Spanned by Flow Models

Flow-based generative super-resolution (SR) models learn to produce a diverse set of feasible SR solutions, called the SR space. Diversity of SR solutions increases with the temperature (τ) of latent variables, which introduces random variations of texture among sample solutions, resulting in visual artifacts and low fidelity. In this paper, we present a simple but effective image ensembling/fusion approach to obtain a single SR image eliminating random artifacts and improving fidelity without significantly compromising perceptual quality. We achieve this by benefiting from a diverse set of feasible photorealistic solutions in the SR space spanned by flow models. We propose different image ensembling and fusion strategies which offer multiple paths to move sample solutions in the SR space to more desired destinations in the perception-distortion plane in a controllable manner depending on the fidelity vs. perceptual quality requirements of the task at hand. Experimental results demonstrate that our image ensembling/fusion strategy achieves more promising perception-distortion trade-off compared to sample SR images produced by flow models and adversarially trained models in terms of both quantitative metrics and visual quality.
Evaluation of Automatically Generated Video Captions Using Vision and Language Models

Vision and language models are easily transferred to other tasks. In particular, they have been demonstrated to work well in the evaluation of automatic image captioning. This has made it possible to evaluate systems without the need for references or additional information apart from the image and the caption. However, these models do not provide a straightforward way of evaluating videos. In this paper, we propose using these models for video captioning evaluation. We explore the use of both single image-based evaluation and different methods to include data from multiple frames. Experiments demonstrate that using clustering methods to select a few frames to compute the final score gives an excellent correlation with human judgment. The bias in the human annotations can also influence the metric, so we propose filtering the human assessments to discard outliers and improve the evaluation process.
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting from Class-Specific Image Priors

Assuming a known degradation model, the performance of a learned image super-resolution (SR) model depends on how well the variety of image characteristics within the training set matches those in the test set. As a result, the performance of an SR model varies noticeably from image to image over a test set depending on whether characteristics of specific images are similar to those in the training set or not. Hence, in general, a single SR model cannot generalize well enough for all types of image content. In this work, we show that training multiple SR models for different classes of images (e.g., for text, texture, etc.) to exploit class-specific image priors and employing a post-processing network that learns how to best fuse the outputs produced by these multiple SR models surpasses the performance of state-of-the-art generic SR models. Experimental results clearly demonstrate that the proposed multiple-model SR (MMSR) approach significantly outperforms a single pre-trained state-of-the-art SR model both quantitatively and visually. It even exceeds the performance of the best single class-specific SR model trained on similar text or texture images.
DIFAI: Diverse Facial Inpainting using StyleGAN Inversion

Image inpainting is an old problem in computer vision that restores occluded regions and completes damaged images. In the case of facial image inpainting, most of the methods generate only one result for each masked image, even though there are other reasonable possibilities. To prevent any potential biases and unnatural constraints stemming from generating only one image, we propose a novel framework for diverse facial inpainting exploiting the embedding space of StyleGAN. Our framework employs pSp encoder and SeFa algorithm to identify semantic components of the StyleGAN embeddings and feed them into our proposed SPARN decoder that adopts region normalization for plausible inpainting. We demonstrate that our proposed method outperforms several state-of-the-art methods.
Retina-Inspired Spatio-Temporal Filtering for Dynamic Video Coding

The goal of this work is to propose a simple yet efficient way to dynamically transform a sequence of images according to the functional properties of the visual system. To achieve this goal, we extend to video sequences the Retina-Inspired Filter (RIF), which we have recently proposed for still images. Under the assumption that the input signal remains constant for a given time, the RIF decomposition was proven to be invertible, meaning that the image could be perfectly recovered. In this paper, we relax this assumption into a piece-wise constant input and we prove that RIF can be applied to a Group Of Pictures (GOP). Under the condition that a GOP consists of frames without strong pixel motion, we mathematically prove and experimentally show that when RIF is applied to GOP, whatever the size of the GOP is, we are still able to perfectly recover the video frames and at the same time simplify the complexity of the whole process. In addition, we show that while the GOP size increases, the memory cost required to store this amount of frames is sufficiently reduced.
Spatial Moment Pooling Improves Neural Image Assessment

In recent years, there has been widespread attention drawn to convolutional neural network (CNN) based blind image quality assessment (IQA). A large number of works start by extracting deep features from CNN. Then, those features are processed through spatial average pooling (SAP) and fully connected layers to predict quality. Inspired by full reference IQA and texture features, in this paper, we extend SAP (1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> moment) into spatial moment pooling (SMP) by incorporating higher order moments (such as variance, skewness). Moreover, we provide learning friendly normalization to circumvent numerical issue when computing gradients of higher moments. Experimental results suggest that simply upgrading SAP to SMP significantly enhances CNN-based blind IQA methods and achieves state of the art performance.
Lighter and Faster Two-Pathway CMRNet for Video Saliency Prediction

Existing dynamic saliency prediction models face challenges like inefficient spatio-temporal feature integration, ineffective multi-scale feature extraction, and lacking domain adaptation because of huge pre-trained backbone networks. In this paper, we propose a two pathway architecture with effective feature integration of spatial and temporal domains at multiple scales for video saliency prediction. Frame and optical flow pathways extract features from video frame and optical flow maps, respectively using a series of cross-concatenated multi-scale residual (CMR) blocks. We name this network as two-pathway CMRNet (TP-CMRNet). Every CMR block follows a feature fusion and attention module for merging features from two pathways and guiding the network to weigh salient regions, respectively. A bi-directional LSTM module is used for learning the task by looking at previous and next video frames. We build a simple decoder for feature reconstruction into the final attention map. TP-CMRNet is comprehensively evaluated using three benchmark datasets: DHF1K, Hollywood-2, and UCF sports. We observe that our model performs at par with other deep dynamic models. In particular, we outperform all the other models with a lesser number of model parameters and lower inference time.
Real Time Smart Object Detection using Machine Learning

Abstract: Efficient and accurate object detection has been an important topic in the advancement of computer vision systems. With the advent of deep learning techniques, the accuracy for object detection has increased drastically. The project aims to incorporate state-of-the-art technique for object detection with the goal of achieving high accuracy with a real-time performance. A major challenge in many of the object detection systems is the dependency on other computer vision techniques for helping the deep learning based approach, which leads to slow and non-optimal performance. In this project, we use a completely deep learning based approach to solve the problem of object detection in an end-to-end fashion. The network is trained on the most challenging publicly available data-set, on which a object detection challenge is conducted annually. The resulting system is fast and accurate, thus aiding those applications which require object detection
Formation Control of Wheeled Mobile Robots in Chained Form Using Potential Functions

In recent years, using autonomous mobile robots in a multi agent and intelligent environment, designing the formation control is of particular importance. In this paper, after a preliminary introduction of chained systems and potential functions that have been applied in the design of the formation control. Using the goal and collision avoidance potential functions, the formation control of a group of systems whose equations of motion are in the form of chained systems is designed. Considering the conditions of the problem, the multiple agents whose dynamical systems are in the equation of a chained systems, has a limited control design in such a way that different agents are at the start of movement in different status and do not collide along the path of motion and ultimately reach the intended destination by using goal and collision avoidance potential functions. In the following, with the help of the proposed model, the control path is designed for a group of wheeled robots and its control and linear velocity and angular velocities are obtained. Then, simulation of eight wheeled mobile robots with chained dynamics, each of them at the initial are on a unit circle environment, is carried out to the desired goals where the octahedron is a regular octagon. The results obtained from the simulation that the robots reach their desired goals without collision with the other robots indicate the accuracy and efficiency of the suggested method in the design of the formation control for a group of robots in chained form, including wheeled mobile robots.
A Dynamic Heterogeneous Information Network Embedding Method Based on Meta-Path and Improved Rotate Model

Aiming at the current situation of network embedding research focusing on dynamic homogeneous network embedding and static heterogeneous information network embedding but lack of dynamic information utilization, this paper proposes a dynamic heterogeneous information network embedding method based on the meta-path and improved Rotate model; this method first uses meta-paths to model the semantic relationships involved in the heterogeneous information network, then uses GCNs to get local node embedding, and finally uses meta-path-level aggression mechanisms to aggregate local representations of nodes, which can solve the heterogeneous information utilization issues. In addition, a temporal processing component based on a time decay function is designed, which can effectively handle temporal information. The experimental results on two real datasets show that the method has good performance in networks with different characteristics. Compared to current mainstream methods, the accuracy of downstream clustering and node classification tasks can be improved by 0.5~41.8%, which significantly improves the quality of embedding, and it also has a shorter running time than most comparison algorithms.
Implementation of intrusion prevention system (IPS) to analysis triad cia on network security attacks on web server

Computer networks are built to achieve the main goal of communicating with each other . During the transmission process, it is expected that information can be conveyed quickly, efficiently and safely. Network security serves to avoid damage or even data loss caused by attacker activities during the communication process. Security aspects that need to be maintained in data information are Confidentiality, Integrity and Availability. Intrusion Prevention System is a solution that can maintain network security from various attacks. The Intrusion Prevention System will act as a protector on the network by detecting and preventing suspicious traffic on nodes in a network. The Intrusion Prevention System in its implementation has several tools which are used in this study, namely Snort and IPTables. Testing is done by performing attacks on the Web Server. The attacks carried out are Port Scanning, DDoS attacks and Brute Force. The results of this study are based on the CIA Triad with the three attacks having different characteristics in terms of cause and effect. On the defense side, Port Scanning and Brute Force can be easily prevented by IPS, but in DDoS attacks there are differences in results between drop and reject rule. In a DDoS attack with an action drop rule, it can recover the web server in 160 seconds while the action reject rule can be restored at 145 seconds which normally can be recovered in a DDoS attack in 165 seconds. The IPS server can also reduce resources when there is a DDoS attack by 9.2% .
THE INTERACTION BETWEEN SELF-REGULATED LEARNING STRATEGIES AND EFL TEENAGER LEARNERS’ POSTCARD WRITING AT ENGLISH CENTER IN MEKONG DELTA, VIETNAM

The current study aims at investigating the possible interaction of EFL teenage learners’ postcard writing performance (according to A2 level) and their self-regulated learning strategies at an English center. The research also helps to determine the level of interaction between EFL teenagers’ self-regulated learning (SRL) strategies and their postcard writing performance. Thus, it also examined the frequency of use of SRL strategies in writing among those learners. A total of 74 learners completed 32 items in the self-regulated learning strategies questionnaire including six dimensions of three categories namely environmental processes, behavioral processes, and personal processes. Then, three successful writers and three less successful writers were invited into the semi-structured interview. The findings indicated that SRL strategies had a positive impact on EFL teenage learners' postcard writing. The more SRL strategies used in writing, the higher the learners' score. Among these strategies, environmental factors may have a stronger influence than behavioral or personal factors. Specifically, environmental structuring and help-seeking strategies are most frequently used. The findings also showed that EFL teenage learners use SRL strategies to a moderate degree when given writing tasks. Besides, the results of the interview reveal that successful learners self-regulated better than less successful ones. They also self-evaluated their writing more frequently than those who are less successful. Based on the findings of this study, pedagogical implications and recommendations for further study are presented.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0559/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Validation Information System Munaqasyah Exam Registration Based Android

The information systems study program (SI Study Program) Faculty of science and technology is one of the study programs at the State Islamic University of North Sumatra, Medan. Based on the Decree of the Chancellor of the State Islamic University of North Sumatra Medan Number 417L of 2016 concerning the Enforcement of SOPs for the Implementation of the Munaqasyah Exam. The Munaqasyah exam is the last test that must be passed by State Islamic University of North Sumatra, Medan students before obtaining a bachelor's degree (S1) about being responsible for scientific work which is the result of research that has been done. The registration process for the Munaqasyah exam in the information systems study program uses google forms so that students have difficulty getting information about the registration status and the information systems study program must first collect data and student registration documents and carry out validation. By doing this, the registration and validation process for the Munaqasyah exam will be no more effective and efficient. The purpose of this study is to build an android-based information system that can be used to register for the Munaqasyah exam and validate the registration. The system development method used is Extreme Programming (XP) while in designing the system the author uses UML (Unified Modeling Language). In the development of information systems, the author uses Kodular and google spreadsheet databases. With the information system, it is hoped that the registration process and registration validation will be more effective and efficient
Web Based Studio Management System for Studios and Color Labs

Simple interfaces are provided by studio management systems for maintaining studio data. It is possible to run a studio effectively with this studio management system. Also, there is the ability to securely store the data of the customers who come to the institution. Also, this data can be updated and deleted at any time. Additionally, it includes the customer's name, phone number, order type, dates, instructions for the required pictures and videos, as well as details on the pertinent occasions and dates. The information about the amounts paid and the relevant dates from the day the customer gave the advance is stored in this system. Orders are separated into pieces and processed gradually in accordance with the orders placed by the respective consumers. Also, this system has been set up in such a way that customers can get a bill with the amount spent after providing the required data. The main aim of introducing a computerized system for studio management is to increase the effectiveness and overcome the drawbacks of a manual system.