Reviewerly/exHarmony huggingface dataset:
Driving Points Prediction for Abdominal Probabilistic Registration

Inter-patient abdominal registration has various applications, from pharmakinematic studies to anatomy modeling. Yet, it remains a challenging application due to the morphological heterogeneity and variability of the human abdomen. Among the various registration methods proposed for this task, probabilistic displacement registration models estimate displacement distribution for a subset of points by comparing feature vectors of points from the two images. These probabilistic models are informative and robust while allowing large displacements by design. As the displacement distributions are typically estimated on a subset of points (which we refer to as driving points), due to computational requirements, we propose in this work to learn a driving points predictor. Compared to previously proposed methods, the driving points predictor is optimized in an end-to-end fashion to infer driving points tailored for a specific registration pipeline. We evaluate the impact of our contribution on two different datasets corresponding to different modalities. Specifically, we compared the performances of 6 different probabilistic displacement registration models when using a driving points predictor or one of 2 other standard driving points selection methods. The proposed method improved performances in 11 out of 12 experiments.
Collision avoidance control algorithm for USV based on dynamic constraints

Autonomous planning of navigation trajectory is a key research element of USV and a basic condition for safe and autonomous navigation. In this paper, an autonomous planning collision avoidance control algorithm for USV is introduced. TFMS is used to trajectory planning and dynamic window method for real-time collision avoidance, a hybrid planning algorithm of dynamic path planning algorithm and parallel planning of autonomous collision avoidance control algorithm is built to achieve autonomous navigation control of USV. The setting method of local target points has been improved to shorten the running time of the control system. The validation was carried out in different static and dynamic environments. A realistic collision avoidance environment model is established by extracting the environmental data from the port map of Dalian port. The result demonstrates that the method is effective in controlling the USV to sail safely and quickly in complex real scenarios with various dynamic impediments.
Analysing the Personality traits using machine learning techniques

The term "personality" refers to a person's unique set of traits that influence their habits, behaviours, attitudes, and mental patterns. As part of this research, XGBoost is used to predict four personality traits based on the Myers-Briggs Type Indicator (MBTI) model[1] , Feeling from input text using Machine Learning Techniques, namely XGBoost classifier XGBoost. The experiments make use of a publicly available benchmark dataset from Kaggle. Prior study has revealed that the dataset has a high degree of skewness. This can be reduced using the Re-sampling technique, which employs random oversampling. Also used for character analysis is text pre-processing techniques such as tokenization (word stemming), stop-word deletion (stop words), and feature selection. This research lays the groundwork for the creation of a system for identifying people's personalities, which might be used by organisations to better attract and select employees and to better serve their clients. Overall, the results of all classifiers across all personality qualities are acceptable, but the performance of the XGBoost classifier stands out by obtaining precision and accuracy of above 99 percent for various personality features.
Identifying Adaptation Changes in Collections of Self-Adaptive Systems

Self-adaptive and self-organised behaviour of technical systems should lead to higher robustness, performance and less administration effort. However, the resulting increased autonomy of the systems also leads to new kinds of challenges: Continuous self-monitoring and evaluation of their own behaviour becomes necessary. Part of such a self-assessment is the identification of causal events for abnormal or even disturbed system behaviour – as a basis for explanations to users and as a basis for learning processes. In this paper, we present an approach that uses metrics to self-assess the distributed system behaviour of autonomous and self-organised subsystems based on external monitoring to determine the timing and location of causal events. Using a swarm simulation, we show that the corresponding events can be identified with high accuracy.
Lightweight Deep Learning Model for Object Detection in Resource Constrained Environments

Since CNN began airing its programmes, the interdisciplinary subject of computer vision has experienced tremendous growth, and self-driving cars have assumed centre stage. Object detection is a crucial component of computer vision. Object detection helps with surveillance, vehicle detection, pose estimation, and other tasks. A simplistic solution to this issue would be to select distinct areas of interest from the image and use a CNN to determine whether the object is present in that area. The issue with this method is that the objects of interest could be located differently in the image's space and have various aspect ratios. Therefore, you would have to choose a big number of locations, which could cause a computational disaster. Recent studies on image recognition tasks have demonstrated the superior performance of Recurrent Convolutional Neural Networks (RCNNs). In this study, we take it a step further and discuss the challenge of object detection using DNNs, i.e., accurately localizing objects of different classes in addition to classifying them. We describe an approach to object detection as a regression problem to object bounding box masks that is both straightforward and effective. We outline a multi-scale inference method that a few application programs can use to quickly and affordably detect objects at high resolution. On several datasets, the technique performs at the cutting edge.
Categorizing Data for Sentimental Analysis by Auto-focusing mechanism using Natural Language Processing

Sentimental Analysis or Opinion Mining is one of the major areas of NLP. SA is discourse mining of text that identifies and extracts subjective data in supply material, and serves businesses to grasp the social sentiment of their complete product or service. However, sentiments of social media streams perform only normal intent classification setting limit to its extensive usage. Aim is to use this sentiment scrutiny at its best and also reducing the cost and time factors. Because SA is widely use by big scale companies but small-scale industries are still far away from it as either it is too time consuming or not affordable. So here with a little change in self-attention mechanism which reduces the time of processing. Apparently in last few years this field has grabbed a lot of attention resulting in advancement of technologies like machine learning, deep learning neural network algorithms, whose contributions are giving more worthier outputs. But neural networks are good at learning automatically through semantic representations of high dimensional data without a well framed engineering design. That means they have auto learning ability but not auto focusing ability to identify the important sentiments objects in the statement, this is where attention mechanism is applied. Now as stated motive is that small scale businesses can also afford on aspects of cost and time this self-attention mechanism is combined with POS tagging.
Augmenting Additional Quality of Services for Adaptability and Reliability of Cloud Infrastructure

Adoption of Cloud computing has been increased by multi fold in the recent past in order to cope up with business requirements. There are few Quality of Service (QoS) metrics which augment the availability of cloud hosted applications. However, still there are quite a few challenges to be addressed in order to improve degree of trust in cloud computing. One of the key challenges observed is, there were no quality of services (QoS) metrics augmented with the PaaS which checks certain parameters covering Disk space utilization and Memory utilization etc., which are the root cause for any priority incidents pertaining to cloud hosted service availability. This paper aims to study the current quality of services available and augment additional quality of services to scale up the services for need of the hour requirements covering site reliability engineering (SRE). Experimentation has been done with the help of major cloud services platforms such as AWS and results were analyzed and attached to this paper.
Evaluation of Statistical Fault Analysis Using Input Timing Violation of Sequential Circuit on Cryptographic Module Under IEMI

In encryption circuits, the threat of fault injection analysis remains a considerable problem. More specifically, clock glitches generated by intentional electromagnetic (EM) irradiation cause faulty operations and estimate internal secret keys. Generating clock glitches via intentional EM interference (IEMI) can be performed without opening the equipment, which makes it a real threat. Previous secret key analysis via IEMI has focused on setup time violations. It requires the clock glitch to occur near the critical path delay of the encryption circuit. This article examines the faults owing to timing violations of inputs to the sequential circuit and discusses the possibility of obtaining the secret key from the output of the faulty ciphertext. The input timing violation of the sequential circuit covers all times during the operation. The bias of the output value of the sequential circuit owing to input timing violations is evaluated using a measurement system in which the sequential circuit alone was extracted. Secret key analysis of encryption circuits using the bias of output values is performed for three different implementations of the advanced encryption standard to demonstrate its feasibility. The results indicate that secret key analysis is possible over a wide range of shortened clock period, regardless of the implementation method.
Deep Learning-Assisted Automatic Modulation Classification in Adaptive FBMC/OQAM Systems

This manuscript presents a novel blind modulation classification method applied to adaptive FBMC/OQAM systems which utilizes a neural network to combine different information types for distinguishing between different modulation formats. In the considered bit-loaded multicarrier transmission system, the receiver classifies the bit allocation table based on the received signal. Since the transmission channels from transmit and receive direction are correlated in time-division duplex systems, important side information about the used modulation schemes on each subcarrier is available, thus enabling a much more reliable classification. A deep learning-based algorithm is investigated numerically that extracts features from preprocessed data such as likelihood and receiver bandwidth efficiency values. Simulation results show a superior performance in typical wireless local area network scenarios compared to existing approaches.
An Adaptive Low Illumination Color Image Enhancement Method Using Dragonfly Algorithm

In order to improve the visual perception of color images under low illumination conditions, an adaptive enhancement method is proposed to enhance the contrast and brightness, enrich the color and avoid over-enhancement. Firstly, the original low illumination image is converted from RGB color space to L*a*b* color space, and a novel gray level transformation function namely piecewise sine function is proposed to improve the brightness of L* channel image. In addition, dragonfly algorithm is utilized to optimize the parameters in piecewise sine function to achieve the best brightness adjustment effect. Then a novel saturation enhancement method is proposed to enrich color information. Subsequently, a fitness function that takes into account both the degree of overall brightness enhancement and the suppression of information loss is applied as the objective function of dragonfly algorithm. Ultimately, the processed L*a*b* color space is transformed back to RGB color space to get the enhanced image. Experimental results verify the effectiveness of the proposed method.
An Efficient User Trust Computation Using Machine Learning Methods in Online Social Networks

Social networks are good platforms for likeminded people to exchange their views and thoughts. With the rapid growth of web applications, social networks became huge networks with million numbers of users. On the other hand, number of malicious activities by untrustworthy users also increased. Users must estimate the people trustworthiness before sharing their personal information with them. Since the social networks are huge and complex, the estimation of user trust value is not trivial task and could gain main researchers focus. Some of the mathematical methods are proposed to estimate the user trust value, but still they are lack of efficient methods to analyze user activities. In this paper “An Efficient Trust Computation Methods Using Machine Learning in Online Social Networks- TCML” is proposed. Here the twitter user activities are considered to estimate user direct trust value. The trust values of unknown users are computed through the recommendations of common friends. The available twitter data set is unlabeled data, hence unsupervised methods are used in categorization (clusters) of users and in computation of their trust value. In experiment results, silhouette score is used in assessing of cluster quality. The proposed method performance is compared with existing methods like mole and tidal where it could outperform them.
A Two-Stage Domain Adaptation Approach Based on Contrastive Learning and Unsupervised Semantic Segmentation

As a pixel-level prediction task in computer vision, semantic segmentation relies on sufficient and relatively fine image annotation for its excellent performance. This paper proposes a more flexible two-stage semantic segmentation framework for domain adaptation from virtual scenes to real street scenes. In the first stage, supervised pixel-level contrastive learning is performed using virtual scenes and their semantic labels, enabling the model to learn a highly structured pixel-level feature space. In the second stage, the pixel discriminant model obtained in the first stage is used for semantic prediction of the real scene to achieve the adaptation of the target domain. In this stage, the prediction results of the real scene are used as pseudo-labels to perform unsupervised segmentation of single-frame images in an iterative training manner. In particular, according to the sequence characteristics of street view images, this method iteratively trains and predicts consecutive frames of images, enabling the model to be fine-tuned and iterated as the environment changes. By exploring the above framework, we use semantic segmentation labels easily obtained in virtual scenes to carry out scene adaption. More importantly, knowledge mining in source domain and adaptive adjustment in target domain can be flexibly independent. We apply this method to environment perception for autonomous driving. In sequential street scenes, dynamic iterative prediction is performed on consecutive frames. This approach enhances the adaptability to various target domains. Finally we achieve more competitive results in the domain adaptation experiments of GTA->Cityscapes and GTA->ApolloScape.
Data Preprocessing with IRM for skill extraction using NMF

To provide examinees with appropriate question items, identifying and evaluating users' latent and acquired skills is important in e-learning systems. These latent skills are defined in terms of a Q-matrix. Constructing Q-matrices manually is relatively labor-intensive, because doing so requires expert insight as well as verbal examination to identify which skills students have mastered. Methods to extract a Q-matrix from examination results automatically using nonnegative matrix factorization (NMF) have been explored, and improved methods such as online NMF have been proposed, which extracts an immutable Q-matrix from time-series data. In this study, we propose a data preprocessing method to improve the accuracy of NMF using a co-clustering method called an infinite relational model (IRM). We also describe experiments conducted using synthetic data, in which learners' skills and the bias in the number of their responses were evaluated, and show that the results demonstrate the efficacy of the proposed approach.
BoVW-CAM: Visual Explanation from Bag of Visual Words

Classical computer vision solutions were used to extract image features designed by human experts for encoding visual scenes into vectors. Machine learning algorithms were then applied to model such vector space and assign labels to unseen vectors. Alternatively, such space could be composed of histograms generated using the Bag of Visual Words (BoVW) that compute the number of occurrences of clustered handcrafted features/descriptors in each image. Currently, Deep Learning methods automatically learn image features that maximize the accuracy of classification and object recognition. Still, Deep Learning fails in terms of interpretability. To tackle this issue, methods such as Grad-CAM allow the visualization of regions from input images that support the predictions generated by Convolutional Neural Networks (CNNs), i.e. visual explanations. However, there is a lack of similar visualization techniques for handcrafted features. This fact obscures the comparison between modern CNNs and classical methods for image classification. In this work, we present the BoVW-CAM that indicates the most important image regions for each prediction given by the BoVW technique. This way, we show a novel approach to compare the performance of learned and handcrafted features in the image domain.
Adapting teachers to education technologies and digital transformation

The purpose of this study is to compare the features of the work of teachers of two regional universities under challenging conditionsand to identify problems and further prospects for the transformation of information technologies in the university. The study was conducted among 145 Russian and 102 Polish teachers. The developed questionnaire consisted of 20 questions on the research topic. The anonymous study was conducted in July 2020 after the second academic semester. The analysis of the results showed 100% adaptation of the teachers of University of Economy in Bydgoszcz in the transition to the distance learning format and 75% adaptation of the teachers of Transbaikal State University in the transition to the distance learning format. The older teachers of the TSU, who were used to working offline, were not in the mood to fully use information technologies in the future after the end of the pandemic, and some respondents were sceptical about distance learning format. The WSG used a synchronous form of delivery of materials, in contrast to the TSU, which used a more asynchronous format. WSG employees plan to make the distance learning format one of the components of the training system in the future. The results indicate the need for universities to continue improving digital transformation, which is currently one of the most effective means for involving students in the educational process.
A Comprehensive Study of Shape Orientation Methods for Extraction of Phase-Preserving Fourier Descriptors

Abstract The magnitude-based Fourier descriptors (FD) are frequently used in shape-based image retrieval, due to their efficiency and effectiveness. Unlike the phase-preserving Fourier descriptors, the magnitude-based Fourier descriptors are inherently invariant under rotation and starting point change, but they discard all valuable information contained in the phase of the Fourier coefficients (FCs). In order to preserve the coefficients’ phase, the orientation and starting point of the shape must be determined. In this paper, we conducted a comprehensive evaluation of different state-of-the-art methods for determining nominal shape orientation, which can be used to extract phase-preserving Fourier descriptors: the point of maximal radius, the axis of least inertia (moments), the phase of the first harmonic, the cross-correlation, the Procrustes distance and the pseudomirror points. The methods were compared in terms of sensitivity to non-rigid transformations, retrieval performance, computational complexity and computational time. The experimental results give insight into the pros and cons of all analyzed methods.
Density Division Face Clustering Based on Graph Convolutional Networks

Supervised clustering methods cluster images using graph convolutional networks (GCN) via linkage prediction, and have shown significant improvements over the traditional clustering algorithms (e.g., K-means, DBScan, etc.) in terms of clustering effectiveness. However, existing supervised clustering approaches are always time-consuming, which may limit their usage. The high computation overhead is mainly resulted from generating and processing a large amount of subgraphs, each of which is generated for one image instance in order to infer the linkage between them. To tackle the high computation problem, we propose a new density division clustering approach based on GCN, and our experiments demonstrate that the new approach is both time-efficient and effective. The approach divides the data into high-density and low-density parts, and only performs GCN subgraph link inference on the low-density parts, which highly reduces redundant calculations. Meanwhile, to ensure sufficient contextual information extraction for low-density parts, it generates adaptive subgraphs instead of fixed-size subgraphs. Our experimental evaluations over multiple datasets show that our proposed approach is five-time faster than state-of-the-art algorithms with even higher accuracy.
A Differential Evolution Algorithm with Adaptive Population Size Reduction Strategy

Aiming at the problem that the differential evolution algorithm easily falls into a local optimum and results in premature convergence, a new differential evolution algorithm with an adaptive population size reduction strategy (APRDE) is proposed. Firstly, in the mutation and crossover operation, to balance the local exploitation and global exploration capabilities of the algorithm, a parameter adaptive tunning scheme based on the hyperbolic tangent function and Cauchy distribution is proposed to adaptively adjust the parameter factors. Secondly, an ordered mutation strategy is adopted to guide the direction of mutating and enrich the diversity of the population. Lastly, after each evolution iteration, adaptively reducing the population size according to the error between the fitness values of individuals and the current optimal. The proposed algorithm is compared with 5 other optimization algorithms on 8 typical benchmark functions. The results show that the algorithm has a great improvement in solution accuracy, stability and convergence speed.