Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Networks for Vehicle Transformation in Overhead and Satellite Imagery

The ability to create and detect synthetic video is becoming critically important to scene understanding. Techniques for synthetic manipulation and augmentation of data increases diversity within available datasets, while not requiring laborious labeling efforts. That is, the ability to create synthetic video can enable augmentation of small realistic datasets on which to further train Artificial Intelligence and Machine Learning (AI/ML) algorithms. Thus, it may be desirable to convert images of vehicles in satellite and overhead imagery to other varieties of vehicles. In this work, we leverage generative adversarial networks to transform cars into trucks (and vice versa). We leverage an attention-based masking approach that assists the network in transformation of the object and not background. In addition, we demonstrate the benefits of numerous data augmentation procedures, including presenting a new artificial dataset of vehicles from an aerial perspective and introducing novel augmentation techniques appropriate for our network architectures. Experiments are conducted for this unique application on both real and artificial datasets with state-of-the-art results.
A Timeslot-Aware Shared Path Protection Scheme in QKD-based Optical Networks

A timeslot-aware shared path protection scheme is proposed to improve the timeslot utilization in the quantum key distribution (QKD) optical network. Simulation results show that the proposed scheme performs well in terms of success probability, timeslot utilization of secret-key requests, and the number of secret-key consumption.
Is the Next Winter Coming for AI? Elements of Making Secure and Robust AI

While the recent boom in Artificial Intelligence (AI) has given rise to the technology's use and popularity across many domains, the same boom has exposed vulnerabilities of the technology to many threats that could cause the next "AI winter". AI is no stranger to "winters", or drops in funding and interest in the technology and its applications. Many in the field consider the early 1970's as the first AI winter with another proceeding in the late 1990's and early 2000's. There is some consensus that another AI winter is all but inevitable in some shape or form, however, current thoughts on the next winter do not consider secure and robust AI and the implications of the success or failure of these areas. The emergence of AI as an operational technology introduces potential vulnerabilities to AI's longevity. The National Security Commission on AI (NSCAI) report outlines recommendations for building secure and robust AI, particularly in government and Department of Defense (DoD) applications. However, are they enough to help us fully secure AI systems and prevent the next "AI winter"? An approaching "AI Winter" would have a tremendous impact in DoD systems as well as those of our adversaries. Understanding and analyzing the potential of this event would better prepare us for such an outcome as well as help us understand the tools needed to counter and prevent this "winter" by securing and robustifying our AI systems. In this paper, we introduce the following four pillars of AI assurance, that if implemented, will help us to avoid the next AI winter: security, fairness, trust, and resilience.
Few-Shot Object Detection in Unseen Domains

Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data by transferring knowledge gained on abundant base classes. FSOD approaches commonly assume that both the scarcely provided examples of novel classes and test-time data belong to the same domain. However, this assumption does not hold in various industrial and robotics applications, where a model can learn novel classes from a source domain while inferring on classes from a target domain. In this work, we address the task of zero-shot domain adaptation, also known as domain generalization, for FSOD. Specifically, we assume that neither images nor labels of the novel classes in the target domain are available during training. Our approach for solving the domain gap is twofold. First, we leverage a meta-training paradigm, where we learn the domain shift on the base classes, then transfer the domain knowledge to the novel classes. Second, we propose various data augmentations techniques on the few shots of novel classes to account for all possible domain-specific information. To constraint the network into encoding domain-agnostic class-specific representations only, a contrastive loss is proposed to maximize the mutual information between foreground proposals and class embeddings and reduce the network’s bias to the background information from target domain. Our experiments on the T-LESS, PASCAL-VOC, and ExDark datasets show that the proposed approach succeeds in alleviating the domain gap considerably without utilizing labels or images of novel categories from the target domain.
Improving gesture recognition with object tracking and image stabilization to communicate with industrial Cobots

Collaborative robots (Cobots) coexist and interact with human workers in industrial environments to accomplish certain tasks. In this project, a set of static and dynamic gestures have been defined to allow humans to communicate with the cobots, simplifying the operation of the cobot from the distance. For the cobot not to confuse these gestures with random movement of the workers, and to distinguish different gestures from each other, each gesture consist of a specific sequence of movements. If either the person or the cobot move during the performance of a gesture sequence, misdetections can occur. In this work, we propose a methodology that combines a traditional image stabilization algorithm with region extraction using information from object tracking algorithms to reduce gesture misdetections by compensating for the movement of a subject within the image while performing a gesture. Our results show that while we cannot see much improvement (measured in terms of the distance of pose points to the reference point of the gesture detector) with simply using information from the tracking algorithm, the improvement more than triples when combined with a traditional Kalman-based image stabilization algorithm.
Fully Convolutional Fractional Scaling

Fully convolutional networks can be applied to any size input but till now do not support non-integer scaling. We introduce a fully convolutional fractional scaling component, FCFS. Our architecture is simple with an efficient single layer implementation. Examples and code implementations of three common scaling methods are published.
The Study of Fashion Style Classification: Harajuku-type Kawaii and Street Fashion

This work provides a comparison among four fashion image classification models, which are a random forest classification method, a convolutional neural network (CNN), a CNN model trained with preprocessed images that a human head and face are removed, and a CNN model trained with images that a head, face, and background are removed. We are interested in three fashion categories, including Harajuku kawaii style, Thai street style, and European street fashion styles. We retrieved images from Google Images, and adopted various preprocessing techniques, which were human detection, clothing segmentation, landmark detection, and colorfulness matrix to locate humans and clothes, and extract fashion features. We grouped the images into 2 classes, which were Harajuku kawaii fashion and other street fashion styles. Results showed that the accuracy score from the CNN model without cropping and background removing process was greater than the others. However, when we validated the models using the fashion dataset that was evaluated by fashion experts, the result indicated that the performance of the CNN model trained with the head-cropped image dataset was greater than the others. We concluded that the deep learning model trained with the head-cropped dataset achieved better results because it learned only clothing features, which are the most significant information in the fashion domain.
3D point cloud quality assessment method using Mahalanobis distance

In this work, we suggest a Reduced-reference (RR) method assess the visual quality of a deformed point cloud with a reference point cloud of ideal quality. To do so, we extract geometrical and perceptual attributes of both reference and distorted PC. Estimates of their statistical properties (Entropy, Mean, Standard Deviation, Median, Kurtosis, and Skewness) form a features vector for each PC. The perceptual metric between two point clouds is computed using the Mahalanobis distance between their feature vectors. Finally, the random forest regressor is employed to estimate the quality score prediction. To validate our method, a set of experiments are conducted on an open subjective colored point cloud dataset (SJTU-PCQA). The results show that the suggested quality assessment method surpasses some contending methods in regards to correlation with average opinion scores.
Eldercare Smart Home Sensor Based System: Approach, Deployment and Insights

The population is aging rapidly and the number of elderly people will continue increasing. With the recent advances in technology, emergent research has been conducted in order to tackle the possibility of using various types of sensors deployed in the homes of elderly people to gather data from their environment and physiology. One objective is directed toward correlating this data to activities of daily living (ADL’s), find patterns in these activities or data and examine whether there are deviations from normal.In this paper, we present the knowledge and experience gained from deploying a smart home system using affordable, accessible and non-intrusive sensors into four homes. The sensor types are specifically chosen to keep the privacy and limit the resentment of the individual in mind. We have been collecting data since November 2021 and will continue gathering data, recruiting elders and improving the system. We present the system components and examples of sensor data received on the server side of the system. Insights on potential placements of the sensors and the possible alternatives are given to guide future research that aims to deploy quickly and efficiently a consumer grade smart home system for health monitoring. In the end, we report the challenges and lessons learned from deploying in a real-world environment.
Utilization of Artificial Intelligence in Automated Image Analysis

The advancement of machine learning and artificial intelligence has created potential for the automation of the time-consuming process of analyzing, evaluating, and interpreting large quantities of imagery. In order for the large-scale deployment of artificial intelligence in image analysis to be made possible, a new image rating system akin to the more traditional National Interpretability Rating Scale (NIIRS) must be created, so as to aid in the defining of system requirements in the development of new sensors. Through prototyping models using the open-source object detection library Detectron2 alongside proxy imagery from the Overhead Imagery Research Data Set (OIRDS), current limitations faced by machine learning algorithms in the context of target identification in aerial imagery are brought to light, including how they may affect a new image-rating scale, and insights into how such limitations could be overcome.
User Intent Acquisition and Translation in Cloud-Network Integration Environment

Cloud-network integration is a profound change in infrastructure brought about by the deep integration of communication and information technology. In this new environment, increasing experts begun to pay attention to the diversity and high dynamics of user intents and the variety of services. To enable users’ intent in different scenarios to be accurately captured and transferred to the lower-level module for Quality of Service (QoS) control, this paper proposes a method combining Bi-directional Long Short-Term Memory-Conditional Random Field based entity recognition technique (BiLSTM-CRF) and QoS accurate mapping technique, which can capture and translate users’ intent in cloud-network integration environments. This method takes into account the complexity of the intent in the new environment and can more accurately map the QoS parameters that users actually need (such as throughput, response time, etc.). Especially for ambiguous intents, parameters that match user’s expectations will be obtained.
Adversarial Barrel! An Evaluation of 3D Physical Adversarial Attacks

Computer vision models based on Deep Neural Networks (DNNs) are vulnerable to adversarial attacks. It has also been demonstrated that physical adversarial attacks can affect computer vision models though printed medium or physical 3D objects. However, the efficacy of physical adversarial attacks, is highly variable under real-world conditions. In this research, we leverage a synthetic validation environment to evaluate 2D and 3D physical adversarial attacks on state-of the-art object detection models (Faster-RCNN, RetinaNet, YOLOv3, YOLOv4). Using the Unreal Engine, we create synthetic environments to evaluate the limitations of physical adversarial attacks. We evaluate 2D adversarial patches under varying lighting conditions and poses. We optimize the same adversarial attacks for 3D shapes including a pyramid, a cube, and a barrel (cylinder), and evaluate the robustness of the 3D physical attacks against the 2D attack baseline. We test our attacks against object-detection models trained on MSCOCO, VIRAT, VISDRONE, and synthetic datasets. By advancing physical adversarial attacks and validation-methodology, we improve our ability to red-team computer vision models with a goal toward defending and assuring AI systems used in fields like Transportation, and Security.
What Does Learning About Time Tell About Outdoor Scenes?

In this paper, we explore the potential of utilizing time-stamps as labels for Deep Learning from webcams, surveillance cameras, and other fixed viewpoint image situations. Specifically, we explore if learning to classify images by the time they were taken uncovers interesting patterns and behaviors in the scenes captured by these cameras. We describe approaches to building datasets with large quantities of images and their accompanying labels, making them suitable for large-scale deep learning approaches. We share our results from the initial deep learning experiments.
Elliptic Curve Scalar Multiplication over Prime Field for both Affine and Jacobian Coordinates

Cryptography is commonly used in everyday life for secure web browsing, secure bank transactions, Emails, IoT devices etc. for preventing any malicious party from entering into any network and stealing important data. Conventional Rivert Shamir Adleman Algorithm is used almost everywhere, with the advent of Industry 4.0 there is a dire need for a cryptosystem which can work in very low power devices, this can be achieved through Elliptic Curve (EC) based Cryptosystem. This paper focuses on implementation of EC Group arithmetic operations such as Point Addition and Point Doubling over finite prime fields (E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> ) in Affine Coordinate (AC) and Jacobian Coordinates (JC). Here, Group operations are implemented by using VIVADO software. It proves that ECC based Group operations achieves better processing time and clock cycle.
Cerebellum-Inspired Artificial Neural Networks Architecture

This article presents a new neural network architecture based on the histology of the cerebellum. The developed architecture can be used in combination with existing machine-learning methods. The novelty of our approach relies on the natural representation of the cerebellum as an analog computer designed to resolve systems of differential equations. A sample differential equation in a computational simulation is given.
Audio Watermark Insertion and Enrichment of Speech via Kalman Filter and LSB

In today's era, it has become extremely important to protect your data, of any form. Digital media today can be easily hacked and used wrongly with misuse of copyright. In this paper, we have understood the importance and the need for watermarking, which enables verification as well as copyright protection. There are several methods for watermarking of audio signals, but in this paper, we have gone with the Least Significant Bit method (LSB). We have discussed the algorithm for watermark insertion as well as extraction using the method mentioned. At the same time, presence of noise corrupts a speech and degrades its quality. Here comes the need for speech enhancement. We have gone with Kalman filter for this task which is an efficient recursive filter that is capable of calculating the internal state of a linear dynamic system from a number of noisy quantifications.
Wheelchair and PC Volume Control Using Hand Gesture

Wheelchairs are mechanically controlled devices that allow the user to move about independently using hand gestures and angle of rotation via nrf signal transmission and receiving. Driving the wheelchair requires less human effort and force. The wheelchair also has an obstacle detection system, which decreases the risk of colliding with any object that is detected along the way. These devices are particularly beneficial for moving from one location to another. The gadgets can also be utilized in healthcare facilities for elderly people who have trouble moving around. For people who have lost their mobility, the devices are a lifesaver. Hand Gesture Recognition and feature extraction via a webcam are used to control the system in real time. The image is recorded using a webcam connected to the system, which is then processed and smoothed before being detected to control the volume and mouse virtually. This paper also presents the experimental results based on hand movements.
Deep Learning Based Application in Detecting Wrinkle and Predicting Age

One of the natural process that happens in human body is aging, estimating age is one of the challenging task as it includes variety of factors like gender, lifestyle, workplace and mental health. The basic objective of this work is to recognize the human face using suitable face recognition technique, abstract the features of recognized face using appropriate feature extraction method. Although there are numerous applications for predicting age based on face photos and other skin-related elements, we propose a suitable and accurate method which detects wrinkles and predicts age by comparing and analyzing the various advanced technologies present.
Towards Smart Data Mining Support Based on Annotation Beneficial Grouping Method

A data mining (DM) system requires many steps, such as pre-processing the data, execution of the data mining algorithm, and extracting mining results. They can be a straightforward but traditional procedure. Each step has multiple choices; only a few variations provide promising results. Both beginners and specialists in data mining require assistance with the vast reach and non-trivial experiences throughout the composition and collection of DM methods. The intelligent exploration assistant prototype enables customers who have access to a systemic list of valid DM processes to raise the ranks of these valid processes in accordance with a variety of criteria. This helps customer's select DM processes that are appropriate and may be more efficient than other options, and it also ensures that appropriate alternatives are not overlooked. The prototypes demonstrate that a single IDA is still capable of having relevant lists and performing successful classification even in basic classification procedures. The potential for an IDA to play a significant role as a medium for the exchange of information within a data mining community is investigated. In conclusion, it has been shown that the claims have been proved by demonstrating that cost-sensitive classes have used a more nuanced procedure utilizing information gained from the KDDCUP competition in 1998.
An Approach to Disambiguate the Innate Sense of a Message with the Help of Emoji

This project aims to understand the tonality of a text using emoticons as the predictor. The emoticons have transpired to be an increasingly significant and standardized part of modern textual inputs. The combination of visual and textual content in the same message builds a compact form of communication, sometimes which becomes tough to portray by the words only. Emoticon prediction and word-emoticon embedding could be used for any kind of Sentiment Analysis based works like Opinion Mining, User Profiling etc. This work focuses on identifying the tone of emotion behind a body of text which is used to analyze the sentiment of a user according to h(is)er comments or messages in online media. This experiment is carried out on the comments collected from some Twitter handles, Facebook walls and Whatsapp conversations. During the experiment, it is observed that in a few cases the senses of different comments are not possible to retrieve only from the words, but the texts which are combined with some emoticons are evaluated correctly. Challenges faced in this work are explained at the end of this report.
Histopathological Cancer Detection with Deep Neural Networks

Cancer is one of the most dreaded diseases faced by mankind today, largely because of the huge medical cost & high mortality rate. It is a serious and potentially life-threatening medical condition that can affect different parts of the body. The treatment and severity of any sort of cancer is determined by its accurate diagnosis for an affected person. There were approx. 10 million deaths recorded in the year 2020 due to cancer. Also, the study of histopathological cancer using modern technologies such as machine learning and deep neural networks became an area of interest. Research on histopathological cancer is quite popular among the researchers using different machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Self-supervised Learning (SSL), Feed-forward Neural Network (FNN), etc. These techniques are very prominent in detecting cancer at its early stages. Hence the survival rate of patients can be increased as the spreading of malignant cells can be reduced, upon detection. The research conducted on Convolutional Neural Network (CNN) shows the highest accuracy in classifying the cancer cells & non-cancer cells. The accuracy recorded was between 93%-96%. The paper aims to explore the use of Convolutional Neural Network (CNN) as a potential model in detection of histopathological cancer with its accuracy and its ability to detect at the early stage for an affected person.