Reviewerly/exHarmony huggingface dataset:
MGCMA: Multi-scale Generator with Channel-wise Mask Attention to generate Synthetic Contrast-enhanced Chest Computed Tomography

Medical images, including computed tomography (CT) assist doctors and physicians in diagnosing anatomic structures and various internal pathologies. In CT, intravenous contrast media is often applied, which are chemicals developed to aid in the characterization of pathology by enhancing the capabilities of an imaging modality to differentiate between different biological tissues. Especially, with the use of contrast media, thorough examinations of the patients can be possible. However, contrast media can have severe adverse and side effects such as hypersensitive reaction to generalized seizures. Yet, without contrast media, it is difficult to diagnose patients that have disorders in the internal organs. With the help of DNN models, especially generative adversarial network (GAN), contrast-enhanced CT (CECT) images can be synthetically generated from non-contrast CT (NCCT) images. GANs or autoencoder-based models have been proposed to generate contrast-enhanced CT images; however, the synthesized image does not fully reflect and have crucial spots where contrast has not been synthesized. Thus, in order to enhance the quality of the CECT image, we propose MGCMA, a multi-scale generator with a channel-wise mask attention module for generating synthetic CECT images from NCCT images. Our extensive experiments demonstrate that our model outperforms other baseline models in various metrics such as SSIM and LPIPS. Also, generated images from our approach achieve plausible outcomes from the domain experts' (e.g., physicians and radiologists) evaluations.
Digital Forgetting Using Key Decay

During the recent development of information technology and the prevalent breakthroughs of its services, more digital data tend to be readily stored online. Although the massive advantages, there is a pivotal necessity for curating digital data forgetting. Online content can pose perilous threats in terms of privacy and security that may hinder the right to be forgotten, encompassed by the GDPR act, since the released data can be archived and accessed retrospectively. Prior approaches focused on various access heuristics and elastic expiration times to make the data unreachable to some extent. However, there are still many pending issues related to the proposed studies, such as securing ephemeral key storage and co-ownership data deletion. In this paper, we attempt to tackle the problem of storing ephemeral keys during the estimated validity period. Hence, we devise a novel concept called key decay over time, which can achieve the ephemeral existence of the key. The decay idea entails the gradual, irreversible corruption of the key with time passing. In the current work, we combine the concept of gradual time elapsing and corruption into a single notion of the decay rate. Meanwhile, the irreversibility merit formed by randomness and various obfuscation strategies impedes retrospective attacks. Over time, the decay rate will give an estimated range for the key to be destroyed entirely. Finally, we implement and thoroughly assess a proof-of-concept regarding the key decay, including computational complexity and security analysis.
Student Research Abstract: Unsupervised Key Term Extraction of Tornado Narratives from NOAA Storm Events Database

Disaster records are often composed of key metrics of the disaster event and its monetary cost. While the cost of the event is helpful for insurance adjustments, the monetary impact of a disaster is not conducive for disaster preparedness planners to build community resilience. Often, there are natural language narratives about the disaster event, like in the National Oceanic and Atmospheric Administration (NOAA) Storm Events Database, that contain essential information but are not easily retrievable because of their unstructured nature. These narratives need to be text mined in order to retrieve the impacts of the disasters in order to structure the data for further use. The method proposed in this abstract is a critical first step in the process. It is an unsupervised key term extraction method using sentence transformers to create embeddings that are then clustered, and assigned key terms by utilizing the highest term frequency-inverse document frequency (tf-idf) scores for the sentences in the narratives.
Gradient-based Counterfactual Generation for Sparse and Diverse Counterfactual Explanations

Counterfactual generation has attracted attention as a technique that generates samples, called counterfactual explanations, which provide a guidance to modify an input instance for changing its class label in real-world applications. Generation of multiple counterfactual explanations gives people various options to change their input instance according to their preferences or capabilities. To generate multiple counterfactual explanations, this paper proposes a gradient-based method which dynamically selects some subsets of attributes of the given instance to be tweaked for diverse counterfactual searches. It also proposes a loss-based update rule for one-hot encoded categorical attributes which is used to produce feasible and effective counterfactual explanations for instances with both categorical and continuous features. We conducted some comparative experiments on the six public datasets to evaluate the performance of the proposed method. The experiment results showed that the proposed method generates valid and diverse counterfactual explanations with a smaller number of attribute value modifications compared with the existing works.
ComplAI: Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models

In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model's behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised ML models from an overall responsibility perspective. It helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model and (c) compare different models from an overall perspective thereby facilitating actionable recourse for improvement of the models. ComplAI is model agnostic and works with different supervised machine learning scenarios and frameworks, seamlessly integrable with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems. The theory version of the paper and a demo version is available in this link (use the Zip Password: Welcome2022!). An detailed version of the paper is available in Arxiv.
Quasi-CliquePool: Hierarchical Graph Pooling for Graph Classification

Graph Neural Networks (GNNs) have revolutionized graph learning through efficiently learned node embeddings and achieved promising results in various graph-related tasks such as node and graph classification. Within GNNs, a pooling operation reduces the size of the input graph by grouping nodes that share commonalities intending to generate more robust and expressive latent representations. For this reason, pooling is a critical operation that significantly affects downstream tasks. Existing global pooling methods mostly use readout functions like max or sum to perform the pooling operations, but these methods neglect the hierarchical information of graphs. Clique-based hierarchical pooling methods have recently been developed to overcome global pooling issues. Such clique pooling methods perform a hard partition between nodes, which destroys the topological structural relationship of nodes, assuming that a node should belong to a single cluster. However, overlapping clusters widely exist in many real-world networks since a node can belong to more than one cluster. Here we introduce a new hierarchical graph pooling method to address this issue. Our pooling method, named Quasi-CliquePool, builds on the concept of a quasi-clique, which generalizes the notion of cliques to extract dense incomplete subgraphs of a graph. We also introduce a soft peel-off strategy to find the overlapping cluster nodes to keep the topological structural relationship of nodes. For a fair comparison, we follow the same procedure and training settings used by state-of-the-art pooling techniques. Our experiments demonstrate that combining the Quasi-Clique Pool with existing GNN architectures yields an average improvement of 2% accuracy on four out of six graph classification benchmarks compared to other existing pooling methods.
Robust DeepFake Detection Method based on Ensemble of ViT and CNN

With the development of convolutional neural networks (CNN) and generative adversarial networks (GAN) in recent years, classifying fake videos produced through DeepFake has become a very difficult task. Most previous studies on DeepFake Detection were focused on finding DeepFake artifacts through CNN. DeepFake detection using CNN has high accuracy, but is vulnerable to noisy inputs such as side faces, shadowed faces, and low-quality images. In addition, although it has the advantage of being able to learn quickly through inductive bias, it tends to be overfitted to specific datasets, showing low accuracy in manipulated videos created with a different type of DeepFake from training datasets.
A Multi-layered Collaborative Framework for Evidence-driven Data Requirements Engineering for Machine Learning-based Safety-critical Systems

In the days of AI, data-centric machine learning (ML) models are increasingly used in various complex systems. While many researchers are focusing on specifying ML-specific performance requirements, not enough guideline is provided to engineer the data requirements systematically involving diverse stakeholders. Lack of written agreement about the training data, collaboration bottlenecks, lack of data validation framework, etc. are posing new challenges to ensuring training data fitness for safety-critical ML components. To reduce these gaps, we propose a multi-layered framework that helps to perceive and elicit data requirements. We provide a template for verifiable data requirements specifications. Moreover, we show how such requirements can facilitate an evidence-driven assessment of the training data quality based on the experts' judgments about the satisfaction of the requirements. We use Dempster Shafer's theory to combine experts' subjective opinions in the process. A preliminary case study on the CityPersons dataset for the pedestrian detection feature of autonomous cars shows the usefulness of the proposed framework for data requirements understanding and the confidence assessment of the dataset.
A Secure Optical Encryption Using DWT and Bit Swapping

Because of its beneficial qualities, including quick processing and penalization, optical security solutions have acquired a lot of success. Numerous optical encryption solutions based on various encoding algorithms are offered by optics. In recent years, optical encryption has gained popularity as a study area. The creation of safe picture encryption methods has been made easier and more effective by the chaos-based cryptographic algorithms. In this research, a new encryption scheme created on Discrete Wavelet Transform (DWT) and bit swapping. The cover image is initially distributed into its bit plane. Extracting Least Significant Bit (LSB) from the bit plane, convert 3x3 blocks and given DWT with swapping bits for encryption. According to the experimental findings, the suggested algorithm achieves better performance and is highly secured.
Zero-Shot Taxonomy Mapping for Document Classification

Classification of documents according to a custom internal hierarchical taxonomy is a common problem for many organizations that deal with textual data. Approaches aimed to address this challenge are, for the vast majority, supervised methods, which have the advantage of producing good results on specific datasets, but the major drawbacks of requiring an entire corpus of annotated documents, and the resulting models are not directly applicable to a different taxonomy. In this paper, we aim to contribute to this important issue, by proposing a method to classify text according to a custom hierarchical taxonomy entirely without the need of labelled data. The idea is to first leverage the semantic information encoded into pre-trained Deep Language Models to assigned a prior relevance score for each label of the taxonomy using zero-shot, and secondly take advantage of the hierarchical structure to reinforce this prior belief. Experiments are conducted on three hierarchically annotated datasets: WebOfScience, DBpedia Extracts and Amazon Product Reviews, which are very diverse in the type of language adopted and have taxonomy depth of two and three levels. We first compare different zero-shot methods, and then we show that our hierarchy-aware approach substantially improves results across every dataset.
A Solid Waste Management System Using Smart Bins in a Decentralized Manner in Ethereum Blockchain Network for Incentivization

Waste generation in the world is raising in a huge way that the requirement to devise new solutions is crucial. The Solid Waste Management, SWM finds it difficult to handle manually these waste generated day by day, as a result of urbanization. With the advent of the technologies, it is possible to find some really good solutions for the waste segregation, waste collection, tracking, and documenting on the entire process. In this paper, we propose an automated solid waste management system by bringing the IoT and block chain technologies together. Here the data related to the bin is collected and is passed to the blockchain smart contracts that can automate incentives. The system enables waste segregation, which will ease the further procedures in SWM. Smart contract is deployed in the public blockchain Ethereum network platform and the automated reward based on the weight and type of the waste through an API is proposed in this paper.
Web Performance Engineering - Finding Best Data Structure For Automatically Collected HTML Locators

The research basically aims to identify finding the best Data Structure and automatic detection of locators. This in turn leads to tracking (navigating) for the URL and its content. The term “Locators” can be described as an address that identifies a web element uniquely that is no repeatable within the webpage, It's a command that informs Selenium Integrated Development Environment which Graphical User Interface elements like - Text Box, Buttons, Check Boxes etc, it needs to operate on. Detecting correct GUI elements is a prior work for generating an automation script but, precise identification of GUI elements is much more difficult. Sometimes, you might even end up working with incorrect GUI elements or no elements at all, therefore, using the right locator ensures that the tests are faster, more reliable, or has lower maintenance over releases.
A Literature Survey on Estimating Uncertainty in Deep Learning Models: Ensuring safety in Intelligent Systems

Popular Deep learning models suffer many drawbacks such as making wrong predictions with great confidence, lack of uncertainty estimation capability, and failure in real-time scenarios. The main reason for the uncertainty is due to the large gap between how neural networks are trained in practice and how they are evaluated in deployment. When it comes to safety-critical applications, it is very important to build confidence in the output that is obtained. A well-calibrated uncertainty quantification method can tell whether a model is confident in its predictions or not. This survey focuses on techniques used for uncertainty quantification in deep learning.
Synthetic Dataset Generation From Histopathology Images For Quantizing Necrosis In Post Neo-Adjuvant Chemotherapy Resection Specimen

The death of tumor tissues after chemotherapy is the most important decision factor in the treatment plan in the case of Osteosarcoma and Renal Cell Carcinoma. The percentage of necrosis accumulated due to such chemotherapy will help the doctors to assess the severity of cancer in the patient. Deep learning algorithms can automatically segment the region of necrosis and find the volume of the region using digital histopathology images. This can reduce the inter-observer disagreement that arises at the time of manual segmentation by the pathologists. But the undersupply of data, especially in pathology images is an ever-time obstacle in deep learning algorithms. In our study we have created a synthetic dataset from 47 images which are captured and manually annotated by experienced pathologists. Each image captured is of dimensions 2592×1932, which are split into patches of size 512×512. So we can have 15 patches from each image and thereby we created a dataset of 705 samples. Different augmentation techniques can be applied to this dataset that can again increment the number.
Secure Multimodal Biometric System Based on Robust LSB-DWT Digital Watermarking Algorithm

In contemporary year, multimedia data is transmitted and accessed through Internet. Despite of its advantages, unauthorized copying, distribution and hacking of data has created security issues. So secure transmission and access of the biometric data has to be suggested. A single biometric identification is not preferable in most highly secured areas due to their parody attacks and ageing. These limitations can be overcome by deploying this system that incorporate information from multiple sources for personnel identification. In this project multiple sources are incorporated using Digital watermarking algorithm comprising both the spatial and frequency domain approach. Even though the digital watermarking scheme is highly secured, network hackers may easily trap the image and it's key. So to increase the security, encryption and decryption of watermarked image using RSA algorithm is employed at the transmission and the reception side respectively. So the cipher text image is alone transmitted in the network increasing the security of the multimodal data. The Stillness of this proposed scheme is measured by Quality Index (QI), Similarity of the original and the recovered images for various attacks and Peak Signal to Noise Ratio (PSNR).
A Comprehensive and Systematic Review of Deep Learning Based Object Recognition Techniques for the Visually Impaired

Object detection is one of computer vision's most essential and challenging visual recognition tasks. The primary focus is to detect objects belonging to different classes and assign each one of them a predefined class label. The dearth of support at diagnosis centers, restricted access to actions and data, stigma created by society, and lack of employment frequently lead visually challenged individuals to seclusion. Existing technologies include A Path Force Feedback Belt (PF belt), Eye Substitution, and Radio Frequency Identification Walking Stick (RFIWS). They detect and localize objects to offer the visually impaired an understanding of the environment around them utilizing the operations of sensors. These systems do not provide real-time responses and are suitable only for outdoor coverage. Due to ultrasonic or radio frequency limitations, the detection range of these systems is between 1m to 5m. The primary intent of this work is to deliver a complete and extensive analysis of the research done in the field of object recognition for VI-challenged people.
Modeling a Conversational Agent using BDI Framework

Building conversational agents to help humans in domain-specific tasks is challenging since the agent needs to understand the natural language and act over it while accessing domain expert knowledge. Modern natural language processing techniques led to an expansion of conversational agents, with recent pretrained language models achieving increasingly accurate language recognition results using ever-larger open datasets. However, the black-box nature of such pretrained language models obscures the agent's reasoning and its motivations when responding, leading to unexplained dialogues. We develop a belief-desire-intention (BDI) agent as a task-oriented dialogue system to introduce mental attitudes similar to humans describing their behavior during a dialogue. We compare the resulting model with a pipeline dialogue model by leveraging existing components from dialogue systems and developing the agent's intention selection as a dialogue policy. We show that combining traditional agent modelling approaches, such as BDI, with more recent learning techniques can result in efficient and scrutable dialogue systems.
The Importance of Diverse User Goals When Designing an Automated COVID Risk Counselor

The COVID-19 Pandemic has required everyone to understand and respond to the ever-evolving threats during daily life. The overall goal of our work is to develop a system based on an interactive conversational agent that functions as a ‘risk counselor’ by providing personalized information to people who have concerns about the risk of COVID infection when participating in daily situations. Such a system could also be useful for people who have concerns about other kinds of infection (e.g., HIV), and more broadly, risk in other domains (e.g., radon exposure in the home). As an initial step, we conducted MTurk surveys to elicit people’s concerns about COVID risk in daily situations. A key takeaway from this study is that when designing an agent, there needs to be a better framing of risk-related recommendations about daily situations, which may be particularly effective for educating people about the risk and encouraging actions that address risk. This framing should also address diverse user goals, including emotional responses, uncertainty, planning, and opinions about these situations.
A Study on Aspect Based Sentiment Analysis in Malayalam Language

Expressing views and ideas have become simple and convenient with the Internet Era we are in now. Everyone has the freedom and privilege of using social media for the same. There arises a demand for computers to understand the user opinions and analyze the same for better understanding of user needs and sentiments. With the advent of the Internet age, we are now in persons expressing their feelings in their native tongue more often. There is a need to train systems to understand languages like Malayalam which is less explored. In this paper we have made a review on research works done in the field of sentiment analysis on Malayalam Language Script. We have given an extra focus to aspect-based Sentiment analysis and works which categorizes text by aspects and finds sentiment connecting to each of these aspects.
Text Mining and Analysis Based on XLNet-BiLSTM Model

In the light of the BERT model relies on masking part of the word to achieve bi-directional prediction of text, but does not take into account the possible association between masked and unmasked words, resulting in loss of information and long-term dependency and inadequate context-deep semantic mining in RNN, an emotion classification model based on XLNet BiLSTM is used. This paper first uses the XLNet pre-training language model to generate context-dependent word vectors to represent text information in a distributed way; Then the word vector is input into the BiLSTM network to analyze and calculate the deep semantics of the text; Finally, softmax function is used to output emotional polarity classification. Train and test the model by mining the text about the Lantern Festival on some social and media platforms. The experimental results show that the accuracy rate of the model is 0.958, and the loss rate is 0.182.