Reviewerly/exHarmony huggingface dataset:
An algorithm for extracting the center of linear structured light fringe based on directional template

The center line of the line structured light stripe extraction is the key step in the process of laser measurement line structure. In the laser extraction process, a new method based on adaptive orientation template is proposed to extract subpixel of the center of optical stripe for the influence of the quality of light source and the inconsistent width of the image stripe on the object surface. This algorithm firstly determines the optical fringe region and calculates the width of each section of the fringe by contour detection. Then use the skeleton thinning method to obtain the coarse center of gravity of the laser stripes. According to the width of each cross section of the light stripe, generate a direction template of the corresponding size and find the normal vector direction of each point on the skeleton. Finally, the subpixel position of the fringe center is determined by gray gravity center method on the normal vector. The experimental results show that this method can eliminate the influence of non-uniform noise and has a better extraction effect on light fringe with non-uniform width.
Illumination Planning for Measuring Per-Pixel Surface Roughness

Measuring per-pixel surface roughness is useful for machine vision applications such as visual inspection. The surface roughness can be recovered from specular reflection components, but a large number of images taken under different lighting and/or viewing directions is required in general so that sufficient specular reflection components are observed at each pixel. In this paper, we propose a robust and efficient method for per-pixel estimation of surface roughness. Specifically, we propose an illumination planning based on noise propagation analysis; it achieves the surface roughness estimation from a small number of images taken under the optimal set of light sources. Through the experiments using both synthetic and real images, we experimentally show the effectiveness of our proposed method and our setup with a programmable illumination and a polarization camera.
Augmented Reality to facilitate the process of Teaching - Learning in school textbooks

The printed school book is a didactic work produced to facilitate the teaching-learning process, evidencing the task of the graphic designer as a generator of visual structures that involve diagramming, printing, and distribution processes. The objective of this research is focused on understanding at what levels of Basic Elementary (Sub-level 2), learning problems occur and if the editorial graphics in conjunction with the application of new information and communication technologies with the use of Augmented Reality improve these learnings, In-depth surveys were conducted with teachers and professionals from 4 Fiscal educational institutions that currently participate in interdisciplinary teams that support school integration environments. The analysis of the surveys made it possible to determine at what levels there are greater learning deficiencies and what are the limitations in their textbooks. As a result of the research, the first chapter of the second-grade Language and Literature book was graphically redesigned, incorporating an augmented reality application for solving exercises and tasks, achieving greater interest in verification with the traditional method and the virtual method. and student performance
Improving whatsapp Video Statuses using FFMPEG and Software based encoding

Modern devices have larger screens and higher resolutions thereby showing the loss in video quality even at smaller video display sizes in WhatsApp status. This paper tests and evaluates a way in which the video quality for WhatsApp statuses can be improved using software encoding and different encoding parameters. This paper also evaluates the availability of software encoding in modern mobile devices which help in better encoding of videos at the cost of using CPU. The audio codec is kept the same as the one WhatsApp uses and for the video codec as well, after some study we have decided to stick with H.264 [1], [2]. We used FFMPEG on mobile to first make a demo application and change parameters to allow us to remain under the size constraint of whatsapp [3]. With all this, we tried to improve the quality of videos and ensure that the file size constraint put by WhatsApp is never passed even under the worst conditions. For video quality assessment, we have used 3 methods, Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR) and Video Multi-Method Assessment Fusion (VMAF) [4], [5], [6].
A Comprehensive Review of Deep Reinforcement Learning for Object Detection

Object detection is one of the most fundamental and significant problems in computer vision, and has received great attention in recent years. At the same time, it is applied to various fields, such as Large image processing and Abstract-Object Detection. The aim of Object detection is detecting an instance of the visual certain class (such as humans, bikes) in digital images. Deep Reinforcement Learning (DRL), as a modern machine learning technology, has been greatly inspired by deep learning and reinforcement learning. DRL has both their advantages, excellent perception, and decision-making ability. In this work, we aim to provide a comparative review of deep reinforcement learning for object detection tasks to place different approaches in context.
GCExp: Goal-Conditioned Exploration for Object Goal Navigation

In this paper, we address the highly challenging problem of object goal navigation. The agent, in an unseen environment, has to perceive its surroundings to identify and navigate towards potential regions where the specified goal category can occur. Rather than developing goal driven exploration policies, we aim to adapt the existing exploration policies that maximize scene coverage to be goal-conditioned. Thus, we propose a standalone scene understanding module to identify potential regions where the goal occurs. We also propose Goal-Conditioned Exploration (GCExp), an algorithm that entails the integration of our novel scene understanding module with any existing exploration policy. We test our solution in photo-realistic simulation environments using state-of-the-art exploration policy, Active Neural Slam [1], and show improved performance over the same on every evaluation metric.
Chaos Engineering For Understanding Consensus Algorithms Performance in Permissioned Blockchains

A critical component of any blockchain or distributed ledger technology (DLT) platform is the consensus algorithm. Blockchain consensus algorithms are the primary vehicle for the nodes within a blockchain network to reach an agreement. In recent years, many blockchain consensus algorithms have been proposed mainly for private and permissioned blockchain networks. However, the performance of these algorithms and their reliability in hostile environments or the presence of byzantine and other network failures are not well understood. In addition, the testing and validation of blockchain applications come with many technical challenges. In this paper, we apply chaos engineering and testing to understand the performance of consensus algorithms in the presence of different loads, byzantine failure and other communication failure scenarios. We apply chaos engineering to evaluate the performance of three different consensus algorithms (PBFT, Clique, Raft) and their respective blockchain platforms. We measure the blockchain network's throughput, latency, and success rate while executing chaos and load tests. We develop lightweight blockchain applications to execute our test in a semi-production environment. Our results show that using chaos engineering helps understand how different consensus algorithms perform in a hostile or unreliable environment and the limitations of blockchain platforms. Our work demonstrates the benefits of using chaos engineering in testing complex distributed systems such as blockchain networks.
SAT-Based Cryptanalysis of Salsa20 Cipher

Modeling through Boolean encoding is often used for the investigation of various kinds of algorithms or other computer systems. Such an approach can be used successfully to cryptanalysis of symmetric ciphers. In this case, cipher testing begins with encoding the cipher algorithm into a Boolean propositional formula. Then the randomly selected bits representing plaintext and cryptographic key are encoded as formulas too. Specially dedicated programs, called SAT solvers, which can solve an SAT problem, can then compute ciphertext values for such inputs. The final cryptanalysis is an attempt at computing a key value from plaintext and ciphertext. In this way, we perform cryptanalysis with plaintext and ciphertext. In this paper, we show how SAT techniques behave for the Salsa20 cipher which is one of a stream cipher widely used in many security systems of computer networks. In our work, we compared a number of selected SAT solvers. Some are relatively old but still very efficient and some are modern and popular.
HEIFU - Hexa Exterior Intelligent Flying Unit

The number of applications for which UAVs can be used is growing rapidly, either because they can perform more efficiently than traditional methods or because they can be a good alternative when there are risks involved. Indeed, as a result of some incidents that could have resulted in disastrous accidents, the European Union is tightening regulations regarding the use of drones and requiring formal training as well as logged missions from those who want to use UAVs above a certain MTOW for whatever reason, whether domestic or professional. If the application requires BVLOS flights, the limitations become much more stringent. In this article HEIFU is presented, a class 3 hexacopter UAV that can carry up to an 8 kg payload (having a MTOW of 15 kg) and a wingspan of 1.5 m, targeting applications that could profit much from having fully automated missions. Inside, an AI engine was installed so that the UAV could be trained to fly, following a pre-determined mission, but also detect obstacles in real-time so that it can accomplish its task without incidents. A sample use case of HEIFU is also presented, facilitating the temporal replication of an autonomous mission for an agricultural application.
Application of Data Mining Technology in the Early Warning of Safety Risks

With the advent of the Internet era, Internet, cloud computing, and big data technologies are developing rapidly and their applications are becoming more mature. In this context, it is essential to pay attention to the construction of network system and information safety. At present, there are increasing number of risk events caused by insufficient network defense and early warning. Although many enterprises, administrative departments and individuals pay attention to the hidden dangers of data safety and apply innovative data mining technology to risk prevention, the limitation of the application of data mining technology is also very obvious, most of which guides the rectification through the analysis of hidden danger data, but ignores the mining and utilization of the potential value behind the data. Based on this, this work first analyzed the implementation method and framework of data mining technology in the early warning of safety risk, and then summarized system requirements and safety risk prediction model taking food safety risk as an example.
Chaotic Isomorphic Elliptic Curve Cryptography for Secure Satellite Image Encryption

This paper provides a new Cascade Modular Tent Logistic Map (CMTLM) with a simple structure, more variable parameters, more complex bifurcation diagram, and large Lyabanove exponent values to improve the chaotic properties of the suggested cryptosystem. Also, a Modified Hill Cipher (MHC) is introduced in cooperating with the Isomorphic Elliptic Curve Cryptography (IECC) to increase the key-space. Thus, the system can resist differential and statistical attacks. The proposed system is used as a Secure Satellite Image Encryption (SSIE) system, which serves as a protection scheme for satellite images based on isomorphic elliptic curve and cascade modular chaotic Tent Logistic map. Simulation results and performance analyses are presented to illustrate the effectiveness and efficiency of the proposed cryptosystem.
Two Stream Pose Guided Network for Vehicle Re-identification

chapter Two Stream Pose Guided Network for Vehicle Re-identification Share on Authors: Saifullah Tumrani University of Electronic Science and Technology of China, China University of Electronic Science and Technology of China, ChinaView Profile , Parivish Parivish Shaheed Benazir Bhutto University Saghar Campus, Pakistan Shaheed Benazir Bhutto University Saghar Campus, PakistanView Profile , Abdullah Aman Khan University of Electronic Science and Technology of China, China University of Electronic Science and Technology of China, ChinaView Profile , Wazir Ali University of Electronic Science and Technology of China, China University of Electronic Science and Technology of China, ChinaView Profile Authors Info & Claims IPMV 2021: 2021 3rd International Conference on Image Processing and Machine Vision (IPMV)May 2021 Pages 11–16https://doi.org/10.1145/3469951.3469954Online:21 August 2021Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Etna: Harvesting Action Graphs from Websites

Knowledge bases, such as Google knowledge graph, contain millions of entities (people, places, etc.) and billions of facts about them. While much is known about entities, little is known about the actions these entities relate to. On the other hand, the Web has lots of information about human tasks. A website for restaurant reservations, for example, implicitly knows about various restaurant-related actions (making reservations, delivering food, etc.), the inputs these actions require and their expected output; it can also be automated to execute those actions. To harvest action knowledge from websites, we propose Etna. Users demonstrate how to accomplish various tasks in a website, and Etna constructs an action-state model of the website visualized as an action graph. An action graph includes definitions of tasks and actions, knowledge about their start/end states, and execution scripts for their automation. We report on our experience in building action-state models of many commercial websites and use cases that leveraged them.
Contextual semantics using hierarchical attention network for sentiment classification in social internet-of-things

To steer impactful data-driven possibilities, Social Internet of Things (SIoT) comes with the collaboration of IoT and social networking and finds deeper insights into consumer behavior for better user experiences. Social networks can permeate intelligence to aid autonomous decision making by enhancing the service needs and communication among object peers in a SIoT. But the user-generated data has multiple layers of meaning which necessitate AI-driven solutions such as sentiment analysis to handle the dynamics. As people express opinions in complex ways and use rhetorical devices like sarcasm, irony, and implication etc., considering only the lexical content can be misleading. Moreover, intra-textual and sub-sentential reversals, topic drift, negation can further misrepresent sentiment, fostering the need to recognize and incorporate contextual semantics for increasing the sentiment classification accuracy. This research evaluates the use of hierarchical attention network (HAN) to classify sentiments in real-time Twitter data, including the multiple sentence tweets and multi-tweet threads. HAN allows differential contribution of various parts of tweet (tweet-sentence-word) to its essential meaning as it introduces two attentive mechanisms and the context-dependent importance of the parts of tweet are considered when constructing the representation of the document. The model is evaluated on two benchmark datasets and compares favorably to state-of-the-art approaches giving an effective solution to tweet-level analysis of sentiments in SIoT.
Demonstration of Weblinks: A Rich Linking Layer Over the Web

Modern browsers, as we know them from the Web, are used to query and present a variety of different resources. This usually happens by traversing links (i.e., URIs) in hypertext documents. The creation of new links however, is impossible to ordinary users, because they usually are recipients, but not owners of the received resource. In this paper, we demonstrate a browser plugin called "Weblinks", which offers its users an additional and rich linking layer over the existing Web. This enhances the notion of links as strings (i.e., URIs) in today's Web context to links as rich objects (n-ary, unidirectional, or bidirectional), which can be created, traversed or shared by anyone using the Weblinks browser plugin.
Effect of Redesigning Public Shared Space Amid the COVID-19 Pandemic on Physical Distancing and Traffic Safety

The concept of redesigning public spaces to encourage physical distancing amid the COVID-19 pandemic is being tested around the world. In Canada, municipalities are reallocating underutilized road lanes for active modes of transportation, such as walking and cycling. We evaluated the usage and benefit of these shared spaces to ensure redesign efforts are optimally allocated. We analyzed two sets of closed-circuit television (CCTV) footage before and after the change, covering April 7–13, 2020, at two locations using automated computer vision techniques. We detected and recorded physical distancing violations, traffic safety risks such as midblock crossing, speeds, and traffic conflicts, and generated trajectory maps of all road users. It was found that the redesign was utilized effectively by road users and improved physical distancing compliance without compromising traffic safety. The proposed framework also provides an innovative tool to automatically gather, extract, share, and analyze real-world data to improve response to the COVID-19 pandemic as well as future outbreaks of contagious diseases.
Framework for Machine Learning Based Task Allocation in DASD Environment

Agile software development (ASD)[1], in today’s time has turn out to be one of the main torrent methodologies. In order to hunt for bigger markets and cheaper labor, software development companies are switching towards Distributed development environments. In Agile terms this approach is well understood as distributed Agile software development (DASD). In DASD environment allocation of tasks has been an vicinity of concern because ad – hoc based approaches for task allocation may lead to customer dissatisfactions leading to project failures. Many research fellows have operated on different techniques for task allocation. In the paper, a novel approach for quantitative task allocation in DASD has been wished-for The approach seems to be promising for allocation of tasks in DASD environment.
MTLV

Multi-Task Learning (MTL) for text classification takes advantage of the data to train a single shared model with multiple task-specific layers on multiple related classification tasks to improve its generalization performance. We choose pre-trained language models (BERT-family) as the shared part of this architecture. Although they have achieved noticeable performance in different downstream NLP tasks, their performance in an MTL setting for the biomedical domain is not thoroughly investigated. In this work, we investigate the performance of BERT-family models in different MTL settings with Open-I (radiology reports) and OHSUMED (PubMed abstracts) datasets. We introduce the MTLV (Multi-Task Learning Visualizer) library for building Multi-task learning-related architectures which use existing infrastructure (e.g., Hugging Face Transformers and MLflow Tracking). Following previous work in computer vision, we clustered tasks and trained a separate model on each cluster (Grouped Multi-Task Learning (GMTL)). Contextual representation of the class labels (Tasks) and their descriptions was used by the library as features to cluster the tasks. We observed that grouping tasks for training with few models (GMTL) outperforms the MTL also GMTL is computationally more efficient than the STL setting (a separate model is trained for each task).
Video Multimethod Assessment Fusion Based Rate-Distortion Optimization for Versatile Video Coding

The emerging visual quality assessment metric VMAF that fuses several elementary metrics by SVM regression has shown a higher correlation with human perception. In this paper, we introduce VMAF into the traditional video coding task as the distortion metric, which needs to be optimized to explore the potential of perceptual quality improvement. Specifically, we propose a multi-granularity VMAF based rate-distortion optimization framework. A frame level visual quality adaption is first conducted by taking the quantization characteristics into account at the coarse-grained adjustment step. Within each frame, a CTU level Lagrangian multiplier and corresponding quantization parameter adaption are carried out based on the content of each CTU at the fine-grained adjustment step. The proposed method has been incorporated into the latest video coding standard – VVC. Experimental results show compared with the conventional rate-distortion optimization for SSE, the proposed method achieves an average 3.30% BD-rate reduction in VMAF.
Constrained Center Loss for Convolutional Neural Networks

From the feature representation's point of view, the feature learning module of a convolutional neural network (CNN) is to transform an input pattern into a feature vector. This feature vector is then multiplied with a number of output weight vectors to produce softmax scores. The common training objective in CNNs is based on the softmax loss, which ignores the intra-class compactness. This brief proposes a constrained center loss (CCL)-based algorithm to extract robust features. The training objective of a CNN consists of two terms, softmax loss and CCL. The aim of the softmax loss is to push the feature vectors from different classes apart. Meanwhile, the CCL aims at clustering the feature vectors such that the feature vectors from the same classes are close together. Instead of using stochastic gradient descent (SGD) algorithms to learn all the connection weights and the cluster centers at the same time. Our CCL-based algorithm is based on the alternative learning strategy. We first fix the connection weights of the CNN and update the cluster centers based on an analytical formula, which can be implemented based on the minibatch concept. We then fix the cluster centers and update the connection weights for a number of SGD minibatch iterations. We also propose a simplified CCL (SCCL) algorithm. Experiments are performed on six commonly used benchmark datasets. The results demonstrate that the two proposed algorithms outperform several state-of-the-art approaches.