Reviewerly/exHarmony huggingface dataset:
DenForest: Enabling Fast Deletion in Incremental Density-Based Clustering over Sliding Windows

The density-based clustering is utilized for various applications such as hot spot detection or segmentation. To serve those applications in real time, it is desired to update clusters incrementally by capturing only the recent data. The previous incremental density-based clustering algorithms often represent clusters as a graph and suffer serious performance degradation. This is because a costly graph traversal is required to check whether a cluster is still connected whenever a point is removed. In order to address the problem of slow deletion, this paper proposes a novel incremental density-based clustering algorithm called DenForest. By maintaining clusters as a group of spanning trees instead of a graph, DenForest can determine efficiently and accurately whether a cluster is to be split by a point removed from the window in logarithmic time. With extensive evaluations, it is demonstrated that DenForest outperforms the state-of-the-art density-based clustering algorithms significantly and achieves the clustering quality comparable with that of DBSCAN.
Network Representation Learning With Community Awareness and Its Applications in Brain Networks

Previously network representation learning methods mainly focus on exploring the microscopic structure, i.e., the pairwise relationship or similarity between nodes. However, the mesoscopic structure, i.e., community structure, an essential property in real networks, has not been thoroughly studied in the network representation learning. We here propose a deep attributed network representation learning with community awareness (DANRL-CA) framework. Specifically, we design a neighborhood enhancement autoencoder module to capture the 2-step relations between node pairs. To explore the multi-step relations, we construct a community-aware skip-gram module based on the encoder. We introduce two variants of DANRL-CA, namely, DANRL-CA-AM and DANRL-CA-CSM, which incorporate the community information and attribute semantics into node neighbors with different methods. We compare two variant models with the state-of-the-art methods on four datasets for node classification and link prediction. Especially, we apply our models on a brain network. The superiority indicates the scalability and effectiveness of our method on various networks. Compared with DANRL-CA-AM, DANRL-CA-CSM can more flexibly coordinate the role of node attributes and community information in the process of network representation learning, and shows superiority in the networks with sparse topological structure and node attributes.
Towards Fast and Accurate Intimate Contact Recognition through Video Analysis

Intimate contact recognition has gained more attention in academia field in recent years due to the outbreak of Covid-19. However, state of the art solutions suffer from either inefficient accuracy or high cost. In this paper, we propose a novel method for COVID-19 intimate contact recognition in public spaces through video camera networks (CCTV). This method leverages distance detection and re-Identification algorithms, so pedestrians in close contact are re-identified, their identity information is obtained and stored in a database to realize contact tracing. We compare different social distance detection algorithms and the Faster-RCNN model outperforms other al-ternatives in terms of running speed. We also evaluate our Re-Identification model on two types of indicators in the PETS2009 dataset: mAP reaches 85.1%; rank-1, rank-5, and rank-10 reach 97.8%, 98.9%, and 98.9%, respectively. Experimental results demonstrate that our solution can be effectively applied in public places to realize fast and accurate automatic contact tracing.
Object Detection and Ranging System Based on Binocular Vision

Abstract For the Census transform stereo matching algorithm is susceptible to noise leading to mis-matching, a stereo matching algorithm based on the improved Census transform is proposed, which improves the Census transform reference value selection and incorporates the image gradient information to form the initial cost. Train the lightweight network model NanoDet to quickly detect and obtain the object position, and combine the object disparity and camera parameters to obtain the distance of the object, and transplant the system to an embedded device for practicality verification. After analyzing and verifying the experimental data, it shows that the anti-noise capability of the improved algorithm is significantly improved, and the proposed method can quickly complete the detection and ranging tasks on embedded devices with high reliability and practicality.
Text- und Rezeptionsanalyse verbinden – das LitLab an der Technischen Universität Darmstadt

No AccessText- und Rezeptionsanalyse verbinden – das LitLab an der Technischen Universität DarmstadtProf. Dr. Thomas WeitinProf. Dr. Thomas Weitin Technische Universität Darmstadt Deutschland Search for more papers by this authorPublished Online:Jun 2022https://doi.org/10.14220/mdge.2022.69.2.160SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous article Next article FiguresReferencesRelatedDetails Download book coverVolume 69Issue 2June 2022 ISSN: 0418-9426eISSN: 2196-8756HistoryPublished online:June 2022 PDF download
Generative Multi-Label Correlation Learning

In real-world applications, a single instance could have more than one label. To solve this task, multi-label learning methods emerged in recent years. It is a more challenging problem for many reasons, such as complex label correlation, long-tail label distribution, and data shortage. In general, overcoming these challenges and bettering learning performance could be achieved by utilizing more training samples and including label correlations. However, these solutions are expensive and inflexible. Large-scale, well-labeled datasets are difficult to obtain, and building label correlation maps requires task-specific semantic information as prior knowledge. To address these limitations, we propose a general and compact Multi-Label Correlation Learning (MUCO) framework. MUCO explicitly and effectively learns the latent label correlations by updating a label correlation tensor, which provides highly accurate and interpretable prediction results. In addition, a multi-label generative strategy is deployed to handle the long-tail label distribution challenge. It borrows the visual clues from limited samples and synthesizes more diverse samples. All networks in our model are optimized simultaneously. Extensive experiments illustrate the effectiveness and efficiency of MUCO. Ablation studies further prove the effectiveness of all the modules.
Trade-off on Sim2Real Learning: Real-world Learning Faster than Simulations

Deep Reinforcement Learning (DRL) experiments are commonly performed in simulated environments due to the tremendous training sample demands from deep neural networks. In contrast, model-based Bayesian Learning allows a robot to learn good policies within a few trials in the real world. Although it takes fewer iterations, Bayesian methods pay a relatively higher computational cost per trial, and the advantage of such methods is strongly tied to dimensionality and noise. In here, we compare a Deep Bayesian Learning algorithm with a model-free DRL algorithm while analyzing our results collected from both simulations and real-world experiments. While considering Sim and Real learning, our experiments show that the sample-efficient Deep Bayesian RL performance is better than DRL even when computation time (as opposed to number of iterations) is taken in consideration. Additionally, the difference in computation time between Deep Bayesian RL performed in simulation and in experiments point to a viable path to traverse the reality gap. We also show that a mix between Sim and Real does not outperform a purely Real approach, pointing to the possibility that reality can provide the best prior knowledge to a Bayesian Learning. Roboticists design and build robots every day, and our results show that a higher learning efficiency in the real-world will shorten the time between design and deployment by skipping simulations.
Coregistration of projections and extended unsharp masking filtering for improving the reconstruction of targets hidden in turbid media

We experimentally demonstrate an integrated approach for improving reconstruction quality of targets embedded within a turbid media by integrating multiple viewpoints (projections) together with an extended unsharp masking (eUSM) algorithm recently demonstrated in atmospheric solar images. In the setup, objects with differing geometrical shapes and sizes were embedded within media with a range of turbidity and varying attenuation and viscosity values. The medium was illuminated with a laser beam after which multiview images, obtained by lens array, were captured with a camera. In offline processing, each sub-image (viewpoint) was digitally cropped, extracted from the array, eUSM filtered, and qualitatively evaluated by contrast metrics. After sub-images were sorted and ranked based on metric values, the selected ‘best’ sub-images were shifted to a common center and co-added with others using a shift-and-add (SAA) method to form a single average image revealing the shape of the object. Linear polarization was also introduced into the setup to give higher contrast image quality. Moreover, by optimizing eUSM parameters, further improvement was achieved in the reconstruction. Experimental results illustrate the improvement of the proposed approach as compared to the previously described SAA method alone.
Normative judgment of power facilities based on machine learning and edge detection

Edge detection is widely used in target recognition, distance detection, angle measurement and equipment normative detection, etc. It is affected by factors such as image background complexity, shooting light, shooting angle, etc. The effect of single edge detection technology for target recognition is often unsatisfactory, especially when there are no obvious features in the shape and structure of the target to be recognized in the image, the image background will seriously affect the accuracy of object detection. This paper combines the target detection technology of machine learning with edge detection technology, locates the object to be recognized in the image through target detection, and crops the target image after removing the image background. Then the edge detection algorithm is used to extract the contour features of the cropped image, and the relevant data of the target object is calculated, which improves the target recognition accuracy and parameter accuracy in the abnormality recognition of the electric meter.
Dynamic Sign Language Translator

Sign language is a visual-based communication that utilizes hand movements and variations of hand shape to express speech and is the chief communication instrument for people with listening and speech impairments. The proposed Automated sign language translation work can aid such people who require sign language to communicate, have intention of enhanced usage of sign language. The work also can help people them to learn sign language, and present a convenient means of education, profession and life for people. Hand locating and sign language recognition methods can generally be divided into traditional methods and deep learning methods. With the glorious accomplishments of deep learning in the domain of computer vision, demonstrated that deep learning has several benefits, such as feature extraction and reliable modeling capability. Consequently, this paper studies various prepossessing and training techniques for automated sign language recognition, including Mediapipe for coordinates extraction, labeling each frame to replicate Ensemble techniques, and vertically stacking frames to test One-Shot techniques. The models were tested with a data-set that was prepared of 15 phrases with about 20 videos for each phrase to achieve 90% + accuracy.
A Comprehensive Study on Learning Strategies of Optimization Algorithms and its Applications

Optimization (or) Heuristic problems are termed as NP-hard problem. The algorithms used to estimate the heuristic value like Hill Climbing and Random Search have more impact on finding out the solution close to optimal solution. The problem identified in such algorithm is over estimation (Global optima) and under estimation (Local optima) of heuristic value. In both the cases, the search operation will not produce optimal solution. In the present work, the research carried out on optimization algorithms from 2017 to 2020 is presented along with applications and the limitations. The objective of the research work is to make the readers to understand the impact of Time and Space complexity of such optimization algorithms and their performance in implementing different learning strategies towards finding out the optimal solution. The research carried out in the present work, will help in providing the direction to the researcher doing research in the area of optimization algorithm and its applications.
Development of Wireless Transmission System for Microseismicity in Complex Mountainous Area

A large amount of data would be generated during the process of microseismic monitoring, and data transmission with the cable has some shortcomings such as poor anti-interference, time-consuming, laborious, high cost, and low construction efficiency. On the other hand, the influence of signal diffraction and the multipath effect will greatly cut down the energy of the signal during the wireless transmission in the complex mountainous area. The wireless transmission system based on Wi-Fi is designed in this paper, which includes the base station and data transceiver. The maximum distance and transmission rate at point-to-point communication of the base station are up to 1000 m and 150 Mbps, respectively, and it provides the communication backbone line for the network. The data transceiver adopts the network protocol based on Ad Hoc, and the transmission distance is up to 200 m at the condition of complex topography and lush mountains. We have applied the system to in-site microseismic monitoring with 28 geophones of the coalbed methane fracturing in South Chongqing. It achieved a good performance with a transmission rate of 1.2 Mbps, a time delay of 1.95 ms, and a signal strength of up to −52.3 dBm for real-time data transmission in the field. The results show that the system has the advantages of low BER, fast transmission speed, long communication distance, and stable and safety hardware and has a great value for more applications of microseismicity in complex topography.
Assessment of Usefulness of Block Chain Technology for Agriculture Application

Abstract: Agriculture is one of the most important sectors in any country. Increase in population all around the world has led to the need to increase the agricultural produce in the farms. To keep up with the growing demands, the agricultural sector is using IT based solutions like softwares, IOT etc. This technology shift has led to many problems as the farmers and the consumers are not aware of the technologies used and there is a possibility of mishandling of data by major stakeholders. Does there is lack of transparency and reliability on the systems used. This has led to financial Exploitation and adulteration in the agricultural industry. New technologies like blockchain can be used efficiently to prohibit such financial exploitation and adulteration. This project uses blockchain Technology the two track all the transactions that take place in the food chain from farmer to the end consumer. The system is made reliable due to the fact that underlying blockchain technology is very secure and hack proof. The data is stored very reliably on each participant's computers so that the data is available with transparency to each user. The underline blockchain Technology makes it possible to add new data into the blockchain and update the participants with the latest data as soon as possible. The project intends to raise alerts whenever any participant is found exploiting the system with higher margins than the threshold. The end consumer can trace back the food chain and hence any possibilities of adulteration or frauds can be traced back into the entire system. Keywords: Blockchain, Foodchain, Agriculture, Application, Tracking, Transparency,
Implementasi Aplikasi Dude Sebagai Network Management SMKN1 Gunung Guruh dengan Notifikasi Telegram

Abstract&#x0D; The development of information technology and especially networks is very rapid. Therefore, it is demanded to have a network system that can monitor the network quickly. Where an admin can quickly find out the network problem is on or off. This research was conducted to monitor the internet network at SMKN 1 Gunungguruh by means of display forms and information in the form of telegram notification messages about network conditions being monitored and sending data real-time to the administrator. With the hope that administrators can quickly handle network problems at SMKN 1 Gunungguruh. The results of the study show that network monitoring using the dude application with telegram notifications can be done in real time and an administrator can quickly find out network problems that occur.
Potential cyber-threats against Canada’s critical infrastructure: an investigation of online discussion forums

Critical infrastructures (CI) are connecting their systems to the internet at an increasing rate, providing the opportunity for malicious actors to attack these systems using cyber-weapons. Compromised systems then pose issues for the affected company, and may disrupt the broader population. The purpose of the current study was to analyze IP addresses collected from discussion forums, with a specific interest in which Canadian CIs could potentially be at risk of a cyber-attack. Overall, 897,524 IP addresses were extracted from 47,134,503 posts across 20 discussion forums, 39,164 of which were associated with Canadian CI. Of all Canadian CI sectors, the majority of IP addresses were found to belong to the information and technology sector. A thematic analysis of posts containing IP addresses was conducted, revealing that the majority of posters were sharing large lists of IP addresses with no context given. Lastly, a keyword search was employed on the forums in an attempt to understand discussions surrounding CI. A thematic analysis was employed on a random sample of posts containing keywords, revealing two major themes: Potential threat and threat information. Findings from this study reveal that information useful for conducting cyber-attacks against CI is being shared within these online forums.
Nearly most influential location selection with differentially private user locations in a road network

Abstract During the past decades, maximum influential location selection (Max-inf) problems have been of intense interest to the spatial database community. The Max-inf problem searches for a location that attracts as many clients as possible, so it is essential to collect the location information of each client for such a query. However, the client location is considered sensitive information, and location privacy has become an emerging issue. To resolve the privacy issue, we present a novel Max-inf problem in a differentially private manner, which is called DP-Max-inf in a road network. Differential privacy is a de-facto standard privacy protection technique that injects controlled noise into statistical query results. In addition, we present the influence region overlapping problem while applying differential privacy to the Max-inf problem using the conventional approach. To remedy this problem, we propose a network Voronoi region-based technique to guarantee query accuracy and a network Voronoi envelope-based pruning heuristic to improve query performance.
Correction of Secret Images Reconstructed from Noised Shared Images

Protecting sensitive images is nowadays a key issue in information security domain. As such, numerous techniques have emerged to securely transmit or store such multimedia data, as encryption, steganography or secret sharing. Most of today's secret image sharing methods relie on the polynomial-based scheme proposed by Shamir. However, some of the shared images distributed to the participants may be noised between their creation and their use to retrieve the secret image. Noise can be added to a shared image during transmission, storage or JPEG compression for example. However, to our knowledge, to date no analysis has been made on the impact of using a noised shared image in the reconstruction process of a secret image. In this paper, we propose a method to correct the errors during the reconstruction of a secret image using noised shared images.
Work in Progress: Virtual training of international university quality during the pandemic in 2020 at University of Sciences and Humanities Lima-Perú

The globalized Covid-19 pandemic arrived in Peru at the beginning of 2020, 15 days after starting the 2020-1 academic semester at the University of Sciences and Humanities. Most of the students were already enrolled, the schedules were already established, the teaching staff was already designated. However, the government took measures of social isolation total, prioritizing health over the economy. Education went virtual to 100%. Most of the students felt the economic impact on their families, however they decided to continue their university studies with hope, faith and illusion of an early normalcy. Virtual platforms won prominence at all educational levels, virtual learning teaching processes were a saving table for education. Virtual training is a necessity; however, its cost is a more visible brake than ever. The challenge is then, how to give quality virtual training to our university community, and at zero cost. Coursera International gave us that great opportunity.
Understanding Long Programming Languages with Structure-Aware Sparse Attention

Programming-based Pre-trained Language Models (PPLMs) such as CodeBERT have achieved great success in many downstream code-related tasks. Since the memory and computational complexity of self-attention in the Transformer grow quadratically with the sequence length, PPLMs typically limit the code length to 512. However, codes in real-world applications are generally long, such as code searches, which cannot be processed efficiently by existing PPLMs. To solve this problem, in this paper, we present SASA, a Structure-Aware Sparse Attention mechanism, which reduces the complexity and improves performance for long code understanding tasks. The key components in SASA are top-k sparse attention and Abstract Syntax Tree (AST)-based structure-aware attention. With top-k sparse attention, the most crucial attention relation can be obtained with a lower computational cost. As the code structure represents the logic of the code statements, which is a complement to the code sequence characteristics, we further introduce AST structures into attention. Extensive experiments on CodeXGLUE tasks show that SASA achieves better performance than the competing baselines.
Fast Image Super-Resolution Based on Multi-Feature Extraction Network

The research on image super-resolution plays a significant role in enhancing image quality and improving the utilization of massive existing image resources. The application of convolutional neural networks to super-resolution models has greatly improved the quality of super-resolution image, and the introduction of residual networks has made it possible to train more complex models. However, speeding up the processing while ensuring the quality is still a problem that needs to be solved. Based on convolutional neural network and residual structure, we propose a super-resolution model with multi-feature extraction module, considering both image quality and algorithm speed. Simultaneously extracting structural features and detail features of images, we increased processing speed more than 40% while ensuring the quality.