Reviewerly/exHarmony huggingface dataset:
Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks

Breast Cancer is one of the mostly encountered cancers among women which involve the age group of 60-80 years mostly. The traditional methodology involves use of mammogram scan followed by various other clinical tests for assuring cancer prevailing in the body manually, which involves mistakes and delay in detection. Many times, it is detected using the biopsy method where tissue removed from the breast is studied under a microscope. This entire process is done by the histopathologies, and if he is not well trained, it may lead to wrong diagnosis. In order to improve the diagnosis by proper detection, automatic analysis of histopathology images has helped the pathologists in efficient diagnosis. Recently the Convolutional neural networks (CNN) have become a preferred deep learning method for breast cancer classification. In this paper, we have proposed CNN architecture based on Local Binary Pattern (LBP) images as input and then compare their classification results by a standard CNN based on origin images as input. Here, classification approach is proposed for automatic classification into either moderate stage or mild stage of cancer. An image dataset of 100 images is used in this approach and 80% of the dataset is used for training and the rest 20% of the images used for testing. 100% classification accuracy is obtained with CNN architecture. The results are compared with various state-of-art machine learning models.
Novel Chipless RFID Concept with High Data Capacity

We propose a new concept for passive RFID tags and an associated reading and decoding strategy. The information is encoded in the orientation of small wires inside the tag. A circular polarization millimeter-wave imaging system is used as an interrogation unit. Decoding is achieved by reconstructing a radar image of the tag and using the Hough transform to determine the wires' orientations. We demonstrate the first-ever use of multilayer image-based RFID tags. According to our results, up to four layers can be used. An information content of 251 bit (7 bit/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) was obtainable, which is unprecedented in image-based RFID.
Preprocessing of the Physical Leakage Information to Combine Side-Channel Distinguishers

The security and privacy of modern computing devices have become an important design metric with the unprecedented increase in the amount of personal information stored in the digital domain. Side-channel attacks have been demonstrated to be one of the primary threats for the security and privacy of these devices. Understanding the working principles of side-channel attacks has, therefore, become an important research problem. An efficient preprocessing technique is proposed in this work for an attack scenario where the amount of time to collect physical leakage (PL) and access to the device is limited. The proposed preprocessing technique utilizes different side-channel distinguishers to decrease the required number of PL measurements for a given success rate by enhancing the quality of the leakage signal. Two commonly used distinguishers, Pearson correlation and mutual information, are combined in this work. For the first time, combined distinguishers are used to improve the performance of both the preprocessing and the attack steps. The success rate of the proposed attack framework outperforms the conventional single distinguisher side-channel attacks by 33% and 30% for unmasked advanced encryption standard (AES) and masked AES, respectively.
Online Meta-Learning for Scene-Diverse Waveform-Agile Radar Target Tracking

A fundamental problem for waveform-agile radar systems is that the true environment is unknown, and trans-mission policies which perform well for a particular tracking instance may be sub-optimal for another. Additionally, there is a limited time window for each target track, and the radar must learn an effective strategy from a sequence of measurements in a timely manner. This paper studies a Bayesian meta-learning model for radar waveform selection which seeks to learn an inductive bias to quickly optimize tracking performance across a class of radar scenes. We cast the waveform selection problem in the framework of sequential Bayesian inference, and introduce a contextual bandit variant of the recently proposed meta-Thompson Sampling algorithm, which learns an inductive bias in the form of a prior distribution. Each track is treated as an instance of a contextual bandit learning problem, coming from a task distribution. We show that the meta-learning process results in an appreciably faster learning, resulting in significantly fewer lost tracks than a conventional learning approach equipped with an uninformative prior.
Understanding Event Predictions via Contextualized Multilevel Feature Learning

Deep learning models have been studied to forecast human events using vast volumes of data, yet they still cannot be trusted in certain applications such as healthcare and disaster assistance due to the lack of interpretability. Providing explanations for event predictions not only helps practitioners understand the underlying mechanism of prediction behavior but also enhances the robustness of event analysis. Improving the transparency of event prediction models is challenging given the following factors: (i) multilevel features exist in event data which creates a challenge to cross-utilize different levels of data; (ii) features across different levels and time steps are heterogeneous and dependent; and (iii) static model-level interpretations cannot be easily adapted to event forecasting given the dynamic and temporal characteristics of the data. Recent interpretation methods have proven their capabilities in tasks that deal with graph-structured or relational data. In this paper, we present a Contextualized Multilevel Feature learning framework, CMF, for interpretable temporal event prediction. It consists of a predictor for forecasting events of interest and an explanation module for interpreting model predictions. We design a new context-based feature fusion method to integrate multiple levels of heterogeneous features. We also introduce a temporal explanation module to determine sequences of text and subgraphs that have crucial roles in a prediction. We conduct extensive experiments on several real-world datasets of political and epidemic events. We demonstrate that the proposed method is competitive compared with the state-of-the-art models while possessing favorable interpretation capabilities.
Empirical Evaluation of MakerDAO's Resilience

Stablecoins are cryptocurrencies with the aim to reduce the price volatility by design. This increases their acceptance as an instrument of payment. Centralized approaches facilitate stable organizational structures, at the cost of introducing dependencies. Decentralized projects, on the other hand, face several challenges, one of them at the core of stablecoins: stability. In this paper, we assess the stability of the MakerDAO protocol, one of the major decentralized stablecoins. We conduct a measurement study regarding MakerDAO's resilience during the first year of its full protocol, from November 2019 to 2020, including the cryptocurrency crisis in March 2020. Our analysis is based on the publicly available transaction data of Ethereum that documents the activities of MakerDAO's contracts executing the protocol. We state requirements for resilience and introduce empirical metrics, based on which we test the resilience hypotheses against the transaction data. While the events of March 2020 were unfortunate for several users, the resilience at protocol level proved to be good even under stress.
Investigating Deep Learning Approaches on the Security Analysis of Cryptographic Algorithms

This paper studies the use of deep learning (DL) models under a known-plaintext scenario. The goal of the models is to predict the secret key of a cipher using DL techniques. We investigate the DL techniques against different ciphers, namely, Simplified Data Encryption Standard (S-DES), Speck, Simeck and Katan. For S-DES, we examine the classification of the full key set, and the results are better than a random guess. However, we found that it is difficult to apply the same classification model beyond 2-round Speck. We also demonstrate that DL models trained under a known-plaintext scenario can successfully recover the random key of S-DES. However, the same method has been less successful when applied to modern ciphers Speck, Simeck, and Katan. The ciphers Simeck and Katan are further investigated using the DL models but with a text-based key. This application found the linear approximations between the plaintext–ciphertext pairs and the text-based key.
Analysis of average waiting time and server utilization factor using queueing theory in cloud computing environment

In industry-academy studies, the cloud computing model goes way above the ground. Cloud has emerged as a fantastic business model for service users and, depending on consumer requirements, can be used pay per usage base. Due to inadequate hardware or software resources, When the quantity of client requests for their high-demand service requirements is large, they prefer to wait in a server queue. As a result, in this study, Reduction in overall waiting time and server utilization factor has been focused on. Comparison has been made on average waiting time and analysis made on server utilization using the M/M/c queuing model.
A New Online Approach for Moving Cast Shadow Suppression in Traffic Videos

In applications of traffic video analysis, moving vehicles can induce cast shadows that have negative impacts on the system performance. Here, a new online cast shadow removal method is proposed which integrates pixel-based, region-based, and statistical modeling techniques to detect shadows. Specifically, the global foreground modeling(GFM) method is first applied in order to segment the moving objects along with their cast shadows from the stationary background. The potential shadow pixels are identified by considering the physics-based properties of reflection and comparing the changes in color values in the corresponding background and foreground locations in terms of brightness and chromaticity. A new region-based shadow detection method is proposed using an illumination invariant feature as the input to the k-means clustering method in order to partition each foreground component into separate segments. Each segment is classified into object and shadow based on its portion of potential shadows, the amount of gradient information introduced, and the number of extrinsic terminal points contained. Afterward, the background and foreground values in the RGB and HSV color-spaces are utilized to construct six-dimensional feature vectors which are modeled by a mixture of Gaussian distributions to classify the foreground pixels into shadows and objects. Lastly, the results of the previous steps are integrated for final shadow detection. Experiments using public video data ‘Highway-1’ and ‘Highway-3’, and real traffic video data provided by the New Jersey Department of Transportation (NJDOT) demonstrate the effectiveness of the proposed method.
Efficiency Analysis of Cryptographic Algorithms for Image Data Security in Cloud Environment

Cloud computing uses a network of remote servers hosted on the Internet to store, manage, and process data. As the cloud is explicitly unsafe, from the time the cloud platform was utilized for storage many researchers and analysts have been trying to develop many encryption algorithms to provide excellent security on the data. The algorithms' weaknesses, performance and strength should be known to apply a suitable cryptography algorithm to an application. This paper evaluates the efficiency of the encryption techniques AES, DES, 3DES, Blowfish, RC4 and RSA on the basis of image data security and performance in the cloud environment. The algorithms have been subjected to different analytic tests such as differential cryptanalysis, statistical analysis, key sensitivity analysis, and performance analysis to analyses the efficiency of each algorithm for security and performance.
Cooperative density-aware representation learning for few-shot visual recognition

Few-shot visual recognition has achieved remarkable advances along with the rise of deep learning. Its goal is to learn the model parameter from the base category for transferring it to the novel category with limited annotations. However, most of the existing few-shot visual recognition approaches mainly focus on extracting a global feature representation of the sample, which fails to encode the semantic information. To alleviate this issue, this paper presents a novel cooperative density-aware representation learning approach for few-shot visual recognition. Specifically, we first yield the high-level semantic features of the query set and the support set by leveraging a shared convolutional neural network. A cooperative density loss module is then designed to optimize the model to form the discriminative features by incorporating the density global classification loss and the density few-shot loss. The density few-shot loss conducts the semantic alignment with regional features by the mutual information finding manner while the density global classification loss supervises each regional feature lead to more precise classification. Comprehensive experiments in few-shot visual recognition benchmarks validate the effectiveness and superiority of our proposed approach, and elaborate ablations explain the utility of different modules.
Securing Financial Inclusiveness Adoption of Blockchain FinTech Compliance

Financial technology (FinTech) as part of financial inlcussion changes conventional business models to be information technology minded. The presence of FinTech in the wider community makes it easy for access to financial service products and transactions and payment systems more practically, efficiently, and economically. Unfortunately, as the security risk in transacting increases, cyber security in the financial services industry and FinTech service providers is considered a major target by cybercriminals. This study proposed a security management approach through hybrid blockchain method implemented through flask framework and encryption to protect transaction data. The results are promising. Referring to accuracy, this study successfully reduces data leakage and misuse of personal data and financial data in FinTechs.
Applying Transfer Learning to Sentiment Analysis in Social Media

Context: Sentiment analysis is an NLP technique that can be used to automatically obtain the sentiment of a crowd of end-users regarding a software application. However, applying sentiment analysis is a difficult task, especially considering the need of obtaining enough good quality data for training a Machine Learning (ML) model. To address this challenge, transfer learning can help us save time and get better performance results with a limited amount of data. Objective: In this paper, we aim at identifying to which degree transfer learning improves the results of sentiment analysis of messages shared by end-users in social media. Method: We propose a tool-supported framework able to monitor and analyze the sentiment of tweets with different ML models and settings. Using the proposed framework, we apply transfer learning and conduct a set of experiments with multiple datasets. Results: The performance of different ML models with transfer learning from different datasets are obtained and discussed, showing how different factors affect the results, and discussing how they have to be considered when applying transfer learning.
Multi‐source <scp>BERT</scp> stack ensemble for cross‐domain author profiling

Abstract Author profiling is the computational task of inferring an author's demographics (e.g., gender, age etc.) based on text samples written by them. As in other text classification tasks, optimal results are usually obtained by using training data taken from the same text genre as the target application, in so‐called in‐domain settings. On the other hand, when training data in the required text genre is unavailable, a possible alternative is to perform cross‐domain author profiling, that is, building a model from a source domain (e.g., Facebook posts), and then using it to classify text in a different target domain (e.g., e‐mails.) Methods of this kind may however suffer from cross‐domain vocabulary discrepancies and other difficulties. As a means to ameliorate these, the present work discusses a particular strategy for cross‐domain author profiling in which multiple source domains are combined in a stack ensemble architecture of pre‐trained language models. Results from this approach are shown to compare favourably against standard single‐source cross‐domain author profiling, and are found to reduce overall accuracy loss in comparison with optimal in‐domain gender and age classification.
Capturing Global Informativeness in Open Domain Keyphrase Extraction

Open-domain KeyPhrase Extraction (KPE) aims to extract keyphrases from documents without domain or quality restrictions, e.g., web pages with variant domains and qualities. Recently, neural methods have shown promising results in many KPE tasks due to their powerful capacity for modeling contextual semantics of the given documents. However, we empirically show that most neural KPE methods prefer to extract keyphrases with good phraseness, such as short and entity-style n-grams, instead of globally informative keyphrases from open-domain documents. This paper presents JointKPE, an open-domain KPE architecture built on pre-trained language models, which can capture both local phraseness and global informativeness when extracting keyphrases. JointKPE learns to rank keyphrases by estimating their informativeness in the entire document and is jointly trained on the keyphrase chunking task to guarantee the phraseness of keyphrase candidates. Experiments on two large KPE datasets with diverse domains, OpenKP and KP20k, demonstrate the effectiveness of JointKPE on different pre-trained variants in open-domain scenarios. Further analyses reveal the significant advantages of JointKPE in predicting long and non-entity keyphrases, which are challenging for previous neural KPE methods. Our code is publicly available at https://github.com/thunlp/BERT-KPE.
Adversarial 3D Human Pointcloud Completion From Limited Angle Depth Data

Most research in 3D objects and its occluded region reconstruction from a single perspective focuses on object completion from a synthetically generated dataset. This leaves a major knowledge gap when morphing 3D object reconstruction from an imperfect real-world frame. As a solution to this problem, we propose a three-stage deep auto-refining adversarial neural network capable of denoising and refining real-world depth data for a full human body posture shape completion. The proposed solution achieves results which are on par with other state-of-the-art approaches in both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EarthMover’s</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chamfer</i> distances, 0.059 and 0.079, respectively, while having the benefit of reconstructing from mask-less depth frames. Visual inspection of reconstructed pointcloud suggests great adaptation capabilities to the majority of real-world depth sensor noise deformities for both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiDAR</i> and structured light depth sensors.
Acute Lymphoblastic Leukemia Classification in Nucleus Microscopic Images using Convolutional Neural Networks and Transfer Learning

Leukemia is a disease caused by the abnormal production of abnormal blood cells. In Acute Lymphoblastic Leukemia (ALL), lymphoblast cells do not develop into lymphocytes. To diagnose the disease, we need to differentiate between lymphocytes and lymphoblasts. However, lymphocytes and lymphoblasts have similar morphologies. Several studies using computer vision have been developed to distinguish lymphocytes from lymphoblasts. This study aims to compare deep and wide deep learning architectures to classify segmented blood cell images. The “deep” architecture employed in this study was DenseNet201, whereas the “wide” architecture was Wide-ResNet-50-2. We also employed ResNet50 as a baseline. This study utilizes transfer learning to reduce the training steps needed. In addition, we measured the impact of dataset preprocessing using histogram equalization on the classifier performance. We found that DenseNet201 model has the best performance with an AUC score of 86.73% and that histogram equalization makes the performance worse.
Recommending Datasets for Scientific Problem Descriptions

The steadily rising number of datasets is making it increasingly difficult for researchers and practitioners to be aware of all datasets, particularly of the most relevant datasets for a given research problem. To this end, dataset search engines have been proposed. However, they are based on user's keywords and, thus, have difficulty determining precisely fitting datasets for complex research problems. In this paper, we propose a system that recommends suitable datasets based on a given research problem description. The recommendation task is designed as a domain-specific text classification task. As shown in a comprehensive offline evaluation using various state-of-the-art models, as well as 88,000 paper abstracts and 265,000 citation contexts as research problem descriptions, we obtain an F1-score of 0.75. In an additional user study, we show that users in real-world settings are 88% satisfied in all test cases. We therefore see promising future directions for dataset recommendation.
Implementation and evaluation of the DAOM framework and support tool for designing blockchain decentralized applications

Abstract Inter-organizational collaboration is an important aspect of organizational operations. Traditional systems that support organizations in executing these collaborations are inefficient, not inter-operable and insecure. Novel functions provided by blockchain technology yields the potential for addressing problems that affect organizational collaborations by enabling tamper-proof, transparent, and secure systems for the exchange of information between organizations. Still, a proper approach for building blockchain-decentralized applications (DApps) that support inter-organizational collaborations is missing. The DAOM framework addresses this gap by providing a model-driven design approach for building DApps. This paper shows the development of the semantics of the DAOM framework, implementation of the support tool, and the evaluation of the DAOM framework and support tool. We conducted an evaluation to understand the usefulness of the DAOM framework in developing blockchain DApps and the effectiveness of the support tool in producing DAOM diagram models. The evaluation result shows that the framework is useful and applicable for developing DApps for inter-organizational collaborations. Furthermore, evaluation of the tool support shows that DApps can be modelled efficiently and correctly with the implemented enterprise-modelling software.