Reviewerly/exHarmony huggingface dataset:
Diagnosis of Breast Cancer on Imbalanced Dataset Using Various Sampling Techniques and Machine Learning Models

Breast Cancer is the second most leading cause of death among women. The early detection of the disease increases the chances of survival of the patient. Therefore, there is always a need for techniques that can accurately predict the presence of cancer. Data Mining is one such powerful technique that can assist clinicians to effectively use the data for timely prediction of the disease. In the medical domain, data is usually imbalanced with unequal distribution of the positive and negative classes. Imbalanced datasets introduce a bias in the model and can thus reduce the accuracy of the minority class predictions. In the case of cancer detection, the mammographic data is highly imbalanced, and predicting the positive (minority) class is of the utmost importance. To achieve this, different models using various class balancing techniques are built and evaluated. The experiments show that the performance of the weighted approach and the undersampling technique is better than oversampling and hybrid techniques. The best performing classifiers are the weighted XGBoost model and Stacking ensemble with the average AUC of 0.78 and 0.76 respectively.
Competing Miners: A Synergetic Solution for Combining Blockchain and Edge Computing in Unmanned Aerial Vehicle Networks

Edge computing (EC) is very useful and particularly promising for many practical unmanned aerial vehicle (UAV) applications. Integrating the blockchain to this technology strengthens privacy protection and data integrity and also prevents data from being easily leaked. However, the required operations in the blockchain are computationally heavy because a blockchain requires devices to solve a complicated proof-of-work (PoW) puzzle to add new data (i.e., a block) to the blockchain. Solving a PoW requires substantial amounts of time and energy, which are big concerns for UAVs. In this article, we suggest a synergetic solution to address this issue based on multiple competing miners in a blockchain. Specifically, we present two novel frameworks for combining the blockchain and EC to effectively overcome several critical limitations when applying the blockchain to UAV and EC tasks, respectively. The goal of both of these proposed frameworks is to reduce both the time spent on mining and the energy consumption for the EC. We first look at the fundamentals of the blockchain with competing miners. Then, our proposed frameworks are described with experimental results, through which important insights are drawn. We finally discuss application scenarios for our proposed frameworks, the related technical challenges, and future research directions.
Canonical Face Embeddings

We present evidence that many common convolutional neural networks (CNNs) trained for face verification learn functions that are nearly equivalent under rotation. More specifically, we demonstrate that one face verification model’s embeddings (i.e., last–layer activations) can be compared directly to another model’s embeddings after only a rotation or linear transformation, with little performance penalty. This finding is demonstrated using IJB-C 1:1 verification across the combinations of ten modern off-the-shelf CNN-based face verification models which vary in training dataset, CNN architecture, method of angular loss calculation, or some combination of the 3. These networks achieve a mean true accept rate of 0.96 at a false accept rate of 0.01. When instead evaluating embeddings generated from two CNNs, where one CNN’s embeddings are mapped with a linear transformation, the mean true accept rate drops to 0.95 using the same verification paradigm. Restricting these linear maps to only perform rotation produces a mean true accept rate of 0.91. These mappings’ existence suggests that a common representation is learned by models despite variation in training or structure. We discuss the broad implications a result like this has, including an example regarding face template security.
Learning end-to-end phase retrieval using only one interferogram with mixed-context network

Deep learning techniques are always bound with big data and large, sophisticated models. In this paper, we show that this is not necessarily true for the task of end-to-end phase retrieval in off-axis interferometric quantitative phase imaging. For this task, we first introduce a new loss function, called bucket error rate (BER), for addressing the problem of imbalanced data distribution by balancing loss-bias of target and background area adaptively. With BER, we demonstrate that a U-Net model can learn the underneath logic for converting a raw interferogram to a phase map from only one training sample. At last, we present a novel mixed-context network (MCN) which can simultaneously aggregate local- and global-contextual information. Experimental results show that compared to U-Net, the proposed MCN is more accurate, more compact, and can be trained faster.
Two-Exposure Image Fusion Based on Cross Attention Fusion

High Dynamic Range (HDR) imaging requires the fusion of images captured with multiple exposure ratios in the same scene to cover the entire dynamic range. With only a few low dynamic range (LDR) images, it remains a challenging task. The paper presents a novel two-exposure image fusion model that features the proposed Cross Attention Fusion Module (CAFM) to use one image's highlight to compensate for the other's content loss caused by under-exposure or over-exposure. The CAFM consists of Cross Attention Fusion and Channel Attention Fusion to achieve a dual-branch fusion for producing superior fusion results. The extensive experimental results on benchmark HDR public datasets demonstrate that the proposed model performs favorably against the state-of-the-art image fusion methods.
Robust Bayesian fusion of continuous segmentation maps

The fusion of probability maps is required when trying to analyse a collection of image labels or probability maps produced by several segmentation algorithms or human raters. The challenge is to weight the combination of maps correctly, in order to reflect the agreement among raters, the presence of outliers and the spatial uncertainty in the consensus. In this paper, we address several shortcomings of prior work in continuous label fusion. We introduce a novel approach to jointly estimate a reliable consensus map and to assess the presence of outliers and the confidence in each rater. Our robust approach is based on heavy-tailed distributions allowing local estimates of raters performances. In particular, we investigate the Laplace, the Student's t and the generalized double Pareto distributions, and compare them with respect to the classical Gaussian likelihood used in prior works. We unify these distributions into a common tractable inference scheme based on variational calculus and scale mixture representations. Moreover, the introduction of bias and spatial priors leads to proper rater bias estimates and control over the smoothness of the consensus map. Finally, we propose an approach that clusters raters based on variational boosting, and thus may produce several alternative consensus maps. Our approach was successfully tested on MR prostate delineations and on lung nodule segmentations from the LIDC-IDRI dataset.
Facilitating Physics Learning Using Ubiquitous-Physics App With Learning Guided Map (gMap) in Authentic Contexts

This article integrated inquiry behaviors and a guided learning map (gMap) into a mobile app called Ubiquitous-Physics (U-Physics), which helps students to explore inclined plane phenomena in authentic contexts. The article investigated inquiry behaviors such as interpreting graphs, applying formulas, drawing conclusions, and peer collaboration, and how these influenced learning achievement. The app&#x0027;s and the learning activities&#x2019; effects on learning perceptions and motivation were also analyzed. A total of 45 first-grade vocational high school students majoring in housekeeping management were volunteered and divided into an experimental with gMap (<i>n</i> &#x003D; 20) and a control group without gMap (<i>n</i> &#x003D; 25). The results show that the inquiry behaviors of the experimental group who used U-Physics with gMap significantly outperformed the control group. Analysis of the correlations among inquiry behaviors and learning achievement revealed that the quantity of learning records on gMap had the greatest influence on learning achievement. Additionally, most students reported positive perceptions of the U-Physics app and were highly motivated to use it for future learning. These findings confirm that exploring and conducting physics phenomena outside of the classroom enhances students&#x2019; understanding of the physics phenomena being studied. It also enriches their authentic experiences because an outdoor activity can provide useful experience to connect students&#x2019; knowledge with daily life environments, enrich their scientific understanding, and stimulate their motivation to learn physics anytime at anywhere.
An Overview of Text Steganalysis

With the rapid development of the Internet, network information security is progressively under menace. Text steganography is one of the key reasons to affect information content security. It aims to hide confidential information in text carriers in a concealment system. In case that text steganography is used by criminals, malicious information can easily be transmitted through the Internet without being discovered by a third party. In contrast, text steganalysis is an effective technique to solve this problem by detecting whether a text carrier contains secret information. This paper presents an overview of current text steganalysis methods starting from 2006. We discuss the basic text steganalysis model and compare the pros and cons of these algorithms, hoping to offer some perceptions and motivations for future research directions.
Detection of Malicious URLs through an Ensemble of Machine Learning Techniques

This paper aims to classify URLs and web pages into legitimate and malicious sites to alert users and allow safer browsing through the internet. Through this process we have found various points of interest and attributes that bring to light the characteristics of these malicious sources, allowing us to be aware of and prevent any damage it might cause. These attributes relate to the domain registration of the URLs, the URL text, the structure of the web page and its contents. The application of models such as BERT, LSTM, Decision Trees and their amalgamation as an ensemble result in a pragmatic solution to the problem in the form of an ensemble giving an accuracy of 95.3&#x0025;. It also uses concepts such as web page reputation, Internal Links and External Links of a web page. The method of classification used in this paper where both Natural Language Processing techniques and Machine Learning models with such a vast variety of features have been combined has not been implemented earlier. We conclude the paper by suggesting methods to improve to solve the problem.
IP Geolocation through Geographic Clicks

IP geolocation databases map IP addresses to their physical locations. They are used to determine the location of online users when their precise location is unavailable. These databases are vital for a number of online services, including search engine personalization, content delivery, local ads, and fraud detection. However, IP geolocation databases are often inaccurate. In this work we present two novel approaches to improving IP geolocation by mining search engine click logs. First, we show that we can derive which URLs have local affinity by clustering clicks from IPs with known locations. We demonstrate that we can further propagate these URL locations to IP addresses with unknown locations. Our approach significantly outperforms two state-of-the-art commercial IP geolocation databases by 25 and 36 percentage points at a distance error of 10 kilometers, respectively. Second, we present an alternative method of assigning locations to URLs when IP location training data is not available, by instead extracting locations from the body of web documents. This second approach also outperforms the baselines by 7 and 17 percentage points, respectively, and has higher coverage than the first method. Finally, we also demonstrate that our two approaches outperform the academic state of the art based on mining query logs.
Integral Analysis of Saturnin Using Bit-Based Division Property

Saturnin is a suite of lightweight symmetric algorithms which is proposed in ToSC2020 and also is a second-round NIST Lightweight competition candidate. Its aim is to provide security both in classical and post-quantum setting. In this paper, we provide a security analysis of Saturnin block cipher against integral distinguisher using bit-based division property and propose a 9-round integral characteristic, which is the first integral cryptanalysis results externally published on Saturnin. We also provide an evaluation of the tightness of the data complexity bounds for integral distinguishers given by the designers for a different number of rounds of Saturnin.
To Transfer or Not To Transfer (TNT): : Action Recognition in Still Image Using Transfer Learning

Lack of training data and small datasets is one of the main challenges in human action recognition in still images. For this reason, training a network from scratch does not lead to good results just by using this little training data. Hence, many existing methods use the transfer learning techniques such as fine-tuning a pre-trained network with initial weights from ImageNet. It is important to note that some of the weights obtained from ImageNet are not suitable for human action recognition tasks. On the other hand, in fine-tuning the network, suitable initial weights for human action recognition may be changed. This paper proposes a method called To Transfer or Not To Transfer (TNT) based on knowledge distillation. In this method, a none trainable teacher with ImageNet weights is employed to train a light student network for action recognition tasks. In order to transfer relevant knowledge and not to transfer insufficient knowledge from teacher to student, a To Transfer or Not To Transfer Loss (TNTL) function is introduced in this paper. The proposed method is evaluated on Stanford 40 and Pascal VOC datasets, and the results show the superiority of this method over existing methods that exploit more parameters.
On the Use of Local Fixations and Quality Measures for Deep Face Recognition

Automatic face recognition systems have achieved impressive performance in the last decade, thanks to the advances of deep learning techniques. However, these methods are highly dependent on the quality of the acquired face data, which is influenced by several factors. Changes in the face appearance due to variations in illumination, head pose, and visual occlusions, do not affect all regions of the face in the same way. Therefore, a novel approach to extract the quality information from facial regions for building a local deep representation is proposed. In order to determine the local image quality, face images are divided into regions and different quality measures are computed. The good quality regions are used to obtain the final deep face representation. The same strategy is applied for representing face videos by processing every frame of a video sequence as an individual image. Four deep face models are used to evaluate the proposal on five challenging databases, containing both still face images and videos. The performance obtained from the experiments carried out, demonstrates that the proposed approach outperforms the original models on all the tested datasets. The results from the performed longitudinal experimental testing also demonstrates the capability of the proposed deep model to retain robust and highly discriminant textural information from the face image while discarding the data segments corrupted by the considered noise sources.
Design of Optimal Active Low-Pass Filter by Hybrid Intensified Current Search

This paper proposes the optimal fourth-order Butterworth active low-pass filter design by the hybrid intensified current search (HICuS) algorithm. With AI-based modern optimization, the HICuS algorithm is one of the most efficient metaheuristic optimizers. The sum of squared error between the ideal characteristic curve and the actual characteristic curve of the fourth-order Butterworth active low-pass filter is set as the objective function to be minimized by the HICuS algorithm. Results obtained by the HICuS are compared with those obtained by the genetic algorithm (GA), artificial bee colony (ABC), particle swarm optimization (PSO) and ant colony optimization (ACO). As results, the fourth-order Butterworth active low-pass filter designed by the HICuS provides more optimal characteristic than other existing algorithms.
Lowering the T-depth of Quantum Circuits via Logic Network Optimization

The multiplicative depth of a logic network over the gate basis {∧ , ⊕ , ¬} is the largest number of ∧ gates on any path from a primary input to a primary output in the network. We describe a dynamic programming based logic synthesis algorithm to reduce the multiplicative depth of logic networks. It makes use of cut enumeration, tree balancing, and exclusive sum-of-products (ESOP) representations. Our algorithm has applications to cryptography and quantum computing, as a reduction in the multiplicative depth directly translates to a lower T -depth of the corresponding quantum circuit. Our experimental results show improvements in T -depth over state-of-the-art methods and over several hand-optimized quantum circuits, for instance, of AES, SHA, and floating-point arithmetic.
Robust regularization for single image dehazing

Generally, conventional image dehazing methods attempt to improve the initial transmission map by using guidance information from the input image as a structural prior. However, these methods do not consider the structural differences between the guidance and the initial transmission map. Furthermore, these methods provide limited robustness against outliers. This paper tackles these problems by optimizing a nonconvex energy function that jointly leverages structural information from the transmission map and guidance. Specifically, a nonconvex regularizer, and correspondingly, an efficient energy function are proposed. The energy function is solved using a majorize-minimize algorithm. Consequently, an improved transmission map is obtained in which the edges are well-preserved. Ultimately, this improved transmission map provides a high-quality haze-free image that has vivid colors and fine details. Experiments were performed on various synthetic and real hazy image datasets. The performance of the proposed method is compared with the state-of-the-art methods by using the root mean square error (RMSE), structural similarity index (SSIM), fog aware density evaluator (FADE), and visibility level descriptor (VLD). Both the qualitative and quantitative results demonstrate the effectiveness of the proposed robust regularization.
EFFICIENCY INCREASING OF INFORMATION SEARCH ON A GIVEN TOPIC ON THE INTERNET

The article discusses the ways to overcome the difficulties associated with surfing the Internet for certain information. Many users are not able to explicitly make up a search request according to their information needs, and their search is often limited to haphazard use of several most popular search engines. Most search engine research is technical. At the same time, in recent years more and more researchers have paid the attention to the study of information search issues in the global network. These studies are at the junction of computer science and psychology. It focuses on information behavior and user needs, cognitive factors and information practices of users. The article examines the previously applied conceptual models of information retrieval, formulates a conceptual model of information retrieval on a given topic. The main properties of user interaction with search engines and factors that should be taken into account are considered. The procedure for information search and analysis in universal and scientific search systems is described. The description of experimental testing of the proposed conceptual search model is provided. The article proposes a new conceptual model of search in universal and scientific search engines. This model contributes to the formation of a metacognitive search strategy. In addition, a generalized indicator of the efficiency of information retrieval using such a model is proposed. The proposed conceptual search model can be applied in the educational process. In particular, the article provides an example of students’ use of the elements of the formed metacognitive strategy when doing Bachelor degree paper research.
An Efficient Algorithm for Ranking Candidates in E-Recruitment System

Over the last decade, the growth of e-recruitment has resulted in the expansion of web channels dedicated to candidate recruitment, making it easy to find and apply for jobs. However, as a result, today&#x2019;s human resource managers are inundated with applications for each job opening. This leads to the production of significant number of documents, referred to as resumes or curriculum vitae (CV). Optimal processing of this data is necessary from a Human Resource strategic and economic perspective, where cost and time effectiveness is paramount. We propose an efficient ranking algorithm to overcome the high time and cost complexity associated with the pairwise comparison of candidates in the state-of-the-art Multi-Criteria Decision Making (MCDM) based ranking algorithm. This algorithm is integrated with matrix sorting and pruning based solution to enhance its scalability. Our proposed algorithm was tested on three different datasets: real-world recruitment, simulated DBLP, and synthetic datasets. Our algorithm shows promising results, which makes it effective and efficient on real-world resume ranking processes.
Performance Analysis of Distributed Deep Learning Frameworks in a Multi-GPU Environment

Deep Learning frameworks, such as TensorFlow, MXNet, Chainer, provide many basic building blocks for designing effective neural network models for various applications (e.g. computer vision, speech recognition, natural language processing). However, run-time performance of these deep learning frameworks varies significantly even when training identical deep network models on the same GPUs. This study presents an experimental analysis and performance model for assessing deep learning models (Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLP), Autoencoder) on three frameworks: TensorFlow, MXNet, and Chainer, in a multi-GPU environment. We analyse factors that influence these frameworks' performance by computing the running time of each framework in our proposed model, taking load imbalance factor into account. The evaluation results highlight significiant differences in the scalability of the frameworks, and the importance of load balance in parallel distributed deep learning.