Reviewerly/exHarmony huggingface dataset:
Toward Effective Domain Adaptive Retrieval

This paper studies the problem of unsupervised domain adaptive hashing, which is less-explored but emerging for efficient image retrieval, particularly for cross-domain retrieval. This problem is typically tackled by learning hashing networks with pseudo-labeling and domain alignment techniques. Nevertheless, these approaches usually suffer from overconfident and biased pseudo-labels and inefficient domain alignment without sufficiently exploring semantics, thus failing to achieve satisfactory retrieval performance. To tackle this issue, we present PEACE, a principled framework which holistically explores semantic information in both source and target data and extensively incorporates it for effective domain alignment. For comprehensive semantic learning, PEACE leverages label embeddings to guide the optimization of hash codes for source data. More importantly, to mitigate the effects of noisy pseudo-labels, we propose a novel method to holistically measure the uncertainty of pseudo-labels for unlabeled target data and progressively minimize them through alternative optimization under the guidance of the domain discrepancy. Additionally, PEACE effectively removes domain discrepancy in the Hamming space from two views. In particular, it not only introduces composite adversarial learning to implicitly explore semantic information embedded in hash codes, but also aligns cluster semantic centroids across domains to explicitly exploit label information. Experimental results on several popular domain adaptive retrieval benchmarks demonstrate the superiority of our proposed PEACE compared with various state-of-the-art methods on both single-domain and cross-domain retrieval tasks. Our source codes are available at https://github.com/WillDreamer/PEACE.
A Natural Language Processing System using CWS Pipeline for Extraction of Linguistic Features

Understanding the rules of grammar and linguistic features is essential to understanding the context of a language, which helps to understand that language. Similarly, for Natural Language processing, the linguistic feature allows understanding of the language. This paper introduced how Coreference, Word-sense, and Semantic knowledge (CWS) of linguistic features work. It would improve the Natural Language Understanding (NLU) and Natural Language Processing (NLP) tasks of any NLP model and NLP applications (either existing or new). This paper proposed a CWS pipeline method to enhance the efficiency and performance of NLP applications like text summarization, information retrieval, question-answer, machine reading comprehension, etc. The proposed CWS pipeline model used a pre-trained CoNLL-2012 coreference dataset extracted from the famous Ontonotes-5.0 dataset for the English language. The model implementation is done in Python language. The performance evaluation is done using the standard CoNLL-2012 coreference dataset for the English language. The coreference marked output is evaluated against the manually tagged gold standard dataset. The proposed CWS pipeline model gives 78.98% of the average F1 score on the MUC metric, 1.78% higher than the previous models' top result. CWS pipeline model performs better than existing models.
Novelty Detection for Risk-based User Authentication on Mobile Devices

User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. For several decades, user authentication was based on the “something the user knows”, known also as knowledge-based user authentication. Recent studies state that although knowledge-based user authentication has been the most popular for authenticating an individual, nowadays it is no more considered secure and convenient for the mobile user as it is imposing several limitations. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Toward this direction, user authentication based on the “something the user is” has caught the attention. This category includes authentication methods which make use of human physical characteristics (also referred to as physiological biometrics), or involuntary actions (also referred to as behavioral biometrics). In particular, risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. In this context, we, firstly, present an overview of user authentication on mobile devices and discuss risk-based user authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Afterwards, a set of novelty detection algorithms for risk estimation is tested and evaluated to identify the most appropriate ones for risk-based user authentication on mobile devices.
An innovative pick-up and transport robot system for casualty evacuation

This paper addresses the search and rescue scenario Casualty Evacuation (CasEvac) at the European Land Robot Trial (ELROB). A disaster response robot can be sent into areas to rescue victims where it is too dangerous for human rescue due to environmental issues like the danger of collapse or radioactivity. If injured persons are no longer able to move, the robot must be able to rescue them from the danger zone. This paper addresses this scenario and describes our system design, the manipulator tool and the innovative control mechanism for transporting victims. The experiment was tested at the competition and compared with other solutions from the participating teams and currently implemented developments.
Exploring Augmented Reality for Teaching Design for Additive Manufacturing

Abstract Additive Manufacturing (AM) has a great potential of disrupting product design and supply chains in many industries by means of its unique capabilities when compared to traditional manufacturing. A wide range of designers would like to take advantage of AM to improve their designs, but they need assistance in learning and breaking out of their conventional manufacturing mindset in the early phases of the design process. Therefore, this study explores the use of Augmented Reality (AR) to enhance the learning experience of the existing Design Heuristics for Additive Manufacturing using Design for Additive Manufacturing (DfAM) cards. In this study, we propose a modification of DfAM cards to include AR markers into the existing card design and hence provide a comprehensive visualization along with the information about heuristics and examples on the DfAM cards. This helps the user to understand the real-world structure of the final printed product before it is printed. The cross-platform game engine Unity is used for developing the AR models for this research. We also investigate the advantages that AR can provide as a visual interface. An expert review is conducted to obtain development feedback and a trial training session with students is carried out. The student evaluated positively the use of the AR app in their DfAM lecture and exercise.
Automatic Detection of Various Types of Lung Cancer Based on Histopathological Images Using a Lightweight End-to-End CNN Approach

Lung cancer is one of the main causes of death and illness, and malignant lung tumours are the leading cause of both. According to reports, lung cancer incidence is on the rise. Lung cancer histopathology is an important element of patient care. Using artificial intelligence methods for the identification of lung cancer can become a highly valuable approach. In this article, we offer a modified lightweight end-to-end deep learning strategy based on convolutional neural networks (CNN) to accurately identify lung cancer. In this method, the input histopathology pictures are normalized before being fed into the CNN model, which is then used to detect lung cancer. The effectiveness of our approach is assessed using a publicly accessible database of histopathological pictures and compared to the most advanced cancer detection methods already in use. The examination of the results indicates that the suggested deep model for lung cancer diagnosis yields results of 0.995 percent, which is a better accuracy than other approaches. Due to this excellent outcome, our method is computationally effective.
Handwritten digits recognition using transfer learning

Researchers often focus on building models that maximize overall predictive accuracy. In practice, however, it can be important for a model to yield good accuracy with each class value. Toward this end, a new recognition with a one-step verification methodology is proposed. It emphasizes the accuracy of each class value. The proposed discriminative system constructs an ensemble using several deep Convolutional Neural Networks (CNNs) with the help of statistical information. To the best of our knowledge, this is the first ensemble model that combines many deep CNNs with a focus on maximizing the accuracy for each class, rather than just overall accuracy. Experimental results show that the demonstration models achieved accuracy in the range of 97.82% to 99.72% within only a few epochs, rivaling the state-of-the-art. These results indicate that the performance of the proposed approach substantially improves the intra-class correlation, leading to improved classification accuracy for each class.
A visually meaningful double-image encryption scheme using 2D compressive sensing and multi-rule DNA encoding

Abstract A visually meaningful double-image encryption scheme using 2D compressive sensing and multi-rule DNA encoding is presented. First, scrambling, diffusing and 2D compressive sensing are performed on the two plain images, and two privacy images are obtained, respectively. Then, the two privacy images are re-encrypted using DNA encoding theory to obtain two secret images. Finally, integer wavelet transform (IWT) is performed on the carrier image to obtain the wavelet coefficients, then the two secret images are embedded into the wavelet coefficients and 2k correction is performed, and the obtained result is processed by inverse IWT to obtain a visually meaningful encrypted image. DNA encoding rules selected for the pixel values of different positions in the two privacy images, and DNA operations performed between the two privacy images and the key streams at different positions are controlled by the chaotic system. The application of 2D compressive sensing reduces the amount of data, thus increasing the encryption capacity of the system. The introduction of DNA encoding theory and the double-image embedding process increases the security of the system. The simulation results demonstrate the feasibility of the scheme, and it has high data security and visual security.
Zero-Shot Image Enhancement with Renovated Laplacian Pyramid

In this research, we tackle image enhancement task both in the traditional and Zero-Shot learning scheme with renovated Laplacian pyramid. Recent image enhancement fields experience power of Zero-Shot learning, estimating output from information of an input image itself without additional ground truth data, aiming for avoiding collection of training dataset and domain shift. As requiring ”zero” training data, introducing effective visual prior is particularly important in Zero-Shot image enhancement. Previous studies mainly focus on designing task specific loss function to capture its internal physical process. On the other, though incorporating signal processing methods into enhancement model is efficaciously performed in supervised learning, is less common in Zero-Shot learning. Aiming for further improvement and adding promising leaps to Zero-Shot learning, this research proposes to incorporate Laplacian pyramid to network process. First, Multiscale Laplacian Enhancement (MLE) is formulated, simply enhancing an input image in the hierarchical Laplacian pyramid representation, resulting in detail enhancement, image sharpening, and contrast improvement depending on its hyper parameters. By combining MLE and introducing visual prior specific to underwater images, Zero-Shot underwater image enhancement model with only seven convolutional layers is proposed. Without prior training and any training data, proposed model attains comparative performance compared with previous state-of-the-art models.
Systematic Literature Review on Educational Effectiveness of Project-Based Learning for Software Development

In light of the social demand for the training of software engineers and the increasing attention to ProjectBased Learning (PBL), many studies have conducted software development as the subject matter of PBL. However, the educational effectiveness of PBL for software development and its measurement methods are not uniform, as many methods have been reported. This study conducted a systematic literature review of the educational effectiveness of PBL for software development.
Crowd Density Estimation using Imperfect Labels

Density estimation is one of the most widely used method for crowd counting in which a deep learning model learns from head annotated crowd images to estimate crowd density in unseen images. Typically, the learning performance of the model is highly impacted by the accuracy of the annotations and inaccurate annotations may lead to localization and counting errors during prediction. A significant amount of works exist on crowd counting using perfectly labelled datasets but none of these explore the impact of annotation errors on the model accuracy. In this paper, we investigate the impact of imperfect labels (both noisy and missing labels) on crowd counting accuracy. We propose a system that automatically generate imperfect labels using a deep learning model (called annotator) which are then used to train a new crowd counting model (target model). Our analysis on two crowd counting models and two benchmark datasets shows that the proposed scheme achieves accuracy closer to that of the model trained with perfect labels showing robustness of crowd models to annotation errors.
Adversarial Attacks and Mitigations on Scene Segmentation of Autonomous Vehicles

In this study, we focus on the effectiveness of adversarial attacks on the scene segmentation function of autonomous driving systems (ADS). We explore both offensive as well as defensive aspects of the attacks in order to gain a comprehensive understanding of the effectiveness of adversarial attacks with respect to semantic segmentation. More specifically, in the offensive aspect, we improved the existing adversarial attack methodology with the idea of momentum. The adversarial examples generated by the improved method show higher transferability in both targeted as well as untargeted attacks. In the defensive aspect, we implemented and analyzed five different mitigation techniques proven to be effective in defending against adversarial attacks in image classification tasks. The image transformation methods such as JPEG compression and low pass filtering showed good performance when used against adversarial attacks in a white box setting.
An acute leukemia subtype classification procedure based on blood sample images

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Lee Weng Hui, Mohd Yusoff Mashor, Nurul Hazwani Abd Halim, Rosline Hassan; An acute leukemia subtype classification procedure based on blood sample images. AIP Conf. Proc. 21 February 2023; 2562 (1): 060003. https://doi.org/10.1063/5.0133929 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Sentiment Analysis and Sarcasm Detection in Indian General Election Tweets

Social media usage has increased to an all-time high level in today's digital world. The majority of the population uses social media tools (like Twitter, Facebook, and YouTube) to share their thoughts and experiences with the community. Analysing the sentiments and opinions of the common public is very important for both the government and the business people. This is the reason behind the activeness of many media agencies during the election time for performing various kinds of opinion polls. In this chapter, we have worked towards analysing the sentiments of the people of India during the Lok Sabha election of 2019 using the Twitter data of that duration. We have built an automatic tweet analyser using the transfer learning technique to handle the unsupervised nature of this problem. We have used the linear support vector classifiers method in our machine learning model, also the term frequency-inverse document frequency (TF-IDF) methodology for handling the textual data of tweets. Further, we have increased the capability of the model to address the sarcastic tweets posted by some of the users, which has not been yet considered by the researchers in this domain.
Face Recognition-Based Smart Glass for Alzheimer’s Patients

Alzheimer’s Disease (AD) is a neurodegenerative disorder that damages the human brain, leading to dementia. The most difficult challenge that an AD patient faces is forgetting people’s names. The study aims to assist AD patients with a moderate stage by designing a smart glass to identify a person. Basically, it depends on face recognition technology. The glass allows you to track the patient in case of aimless wandering, in addition to talking with the patient. The design was implemented by dividing the system into hardware and software. The hardware consists of the Raspberry Pi 4, Pi camera, ultrasonic sensor, and GPS module. Python coded the software to run a Viola-Jones-based face recognition algorithm. A Local Binary Pattern was used to extract features based on machine learning principles. The Support vector machine (SVM) and the K-Nearest Neighbors (K-NN) were employed as classifiers. A mobile application named “Forget Me Not” was developed to support the patient and the caregiver as well. Results demonstrated satisfaction with the design through capturing one thousand images of 250 people and training the system. Classification accuracy of 84.45% and 93.36% for the SVM and K-NN, respectively, was achieved. Furthermore, the mobile application facilitates the usage of glass. Thus, the study presents an assistance tool for AD patients to coexist with society.
Towards Efficient Multi-Codec Streaming

One of the biggest challenges in modern-era streaming is the fragmentation of codec support across receiving devices. For example, modern Apple devices can decode and seamlessly switch between H.264/AVC and HEVC streams. Most new TVs and set-top boxes can also decode HEVC, but they cannot switch between HEVC and H.264/AVC streams. And while most older devices/streaming clients can only receive and decode H.264/AVC streams. With the arrival of next-generation codecs - such as AV1 and VVC, the fragmentation of codec support across devices becomes even more complex. This situation brings a question - how can we serve such a population of devices most efficiently by using codecs delivering the best performance in all cases yet producing the minimum possible number of streams and such that the overall cost of media delivery is minimal? In this paper, we explain how this problem can be formalized and solved at the stage of dynamic generation of encoding profiles for ABR streaming. The proposed solution is a generalization of the context-aware encoding (CAE) class-of techniques, considering multiple sets of renditions generated using each codec and codec usage distributions by the population of the receiving devices. We also discuss several streaming system-level tools needed to make the proposed solution practically deployable.
Efficient reinforcement learning with least-squares soft Bellman residual for robotic grasping

Grasping control of intelligent robots has to deal with the difficulties of model uncertainties and nonlinearities. In this paper, we propose the Kernel-based Least-Squares Soft Bellman residual Actor–Critic (KLSAC) algorithm for robotic grasping. In the proposed approach, a novel linear temporal-difference learning algorithm using the least-squares soft Bellman residual (LS2BR) method is designed for policy evaluation. In addition, KLSAC adopts a sparse-kernel feature representation method based on approximate linear dependency (ALD) analysis to construct features for continuous state–action space. Compared with typical deep reinforcement learning algorithms, KLSAC has two main advantages: firstly, the critic module has the capacity for rapid convergence by computing the fixed point of the linear soft Bellman equation via the least-squares optimization method. Secondly, the kernel-based features construction approach only requires predefining the basic kernel function and can improve the generalization ability of KLSAC. The simulation studies on robotic grasping control were conducted in the V-REP simulator. The results demonstrate that compared with other typical RL algorithms (e.g., SAC and BMPO), the proposed KLSAC algorithm can achieve better performance in terms of sample efficiency and asymptotic convergence property. Furthermore, experimental results on a real UR5 robot validated that KLSAC performed well in the real world.
Credit Card Fraud Detection using Machine Learning Techniques

The COVID-19 pandemic has caused a huge decline in money usage, with everything turning online these days. It has contributed to an increase in contactless payments that was unimaginable before. A credit card is the most extensively used method of payment, and it is becoming increasingly digital as the number of daily electronic transactions increases, making it more vulnerable to fraud. Credit card firms have suffered losses because of widespread card fraud. The most common worry is the recognition of credit card fraud. As a result, organizations are looking toward advanced device understanding technologies since they can handle a lot of data and spot irregularities that humans would miss. The development of effective To stop these losses, fraud detection algorithms are essential. An increasing number of these algorithms rely on cutting-edge computer methods that can assist fraud investigators. However, the appearance of the full-proof Fraud Detection System demands the use of high performing algorithms that are both exact and sturdy enough to handle massive amounts of data. The algorithm is run using open-source software using R statistical programming. This project tries to provide options by studying several fraud detection systems and highlighting their strengths and limitations.
SeisBlue: a deep-learning data processing platform for seismology

Deep learning has greatly improved the efficiency of earthquake detection and phase picking tasks, as demonstrated by neural network models such as PhaseNet and EQTransformer. However, the code released by these authors is not production-ready software that can be easily integrated into our lab's workflow. To solve this problem, we developed "SeisBlue," a platform that brings all the necessary steps together in one place. It includes these major components: database client, data inspector, data converter, model trainer, model evaluator, and pick associator, and is designed to be modular and interchangeable to allow for easy experimentation with different combinations.SeisBlue has been used in several major earthquake events in Taiwan, including the 918 Taitung earthquake (magnitude 6.9 Mw). In this event, we were able to capture over 1,200 events near real-time in just two days - a task that would have taken over a month to complete manually. The quickly-released earthquake catalog provided insight into the complex behavior of the blind fault.