Reviewerly/exHarmony huggingface dataset:
Unsupervised clustering-based analysis of the measured eye-tracking data

Visual attention and its modeling are getting more and more focus during the past decades. It has been used for several years in various fields, such as the automotive industry, robotics, or even in diagnostic medicine. So far, the research has focused mainly on the generalization of the collected data, although on the contrary, the identification of unique features of the visual attention of the individuals remains an open research topic. The aim of this paper is to propose a methodology which is able to cluster people into groups based on individualities in their visual attention patterns. Unlike the former research approaches focused on the classification problem where the class of the subjects is required to be known, we focus our work on the open research problem of unsupervised machine learning based on the measured data about subjects’ visual attention, solely. Our methodology is based on the clustering method which utilizes individual feature vectors created from measured visual attention data. Proposed feature vectors forming up the fingerprint of the attention of an individual are based on the direction of saccades of individuals. Our proposed methodology is designed to work with a limited set of the measured eye-tracking data without any additional information.
Fitness Landscape Analysis: From Problem Understanding to Design of Evolutionary Algorithms

As an effective kind of optimization technique, evolutionary algorithms (EAs) have been widely used in many fields. In the context of EAs, to determine which algorithm would be the most appropriate for the specific problem, the techniques of fitness landscape analysis (FLA) are often used to identify the landscape features of the optimization problem, and select the best algorithm for the problem according to the features. In fact, the landscape features can also be considered as useful feedback for guiding the search procedure of EAs. In recent years, there are some seminal works which use FLA techniques to design EAs, the key concept of them is to adaptively configure algorithms according to the landscape features. In this work, we provide a briefly survey on this emerging research topic, including two aspects: 1) Some representative FLA techniques and the applications to the design of EAs are introduced, with the aim of highlighting the possible ways of extending the FLA techniques from problem understanding to the design of EAs. 2) The constraint of the existing FLA techniques is discussed, and the possible solution, the online FLA techniques, is also presented. So, in this survey, the related FLA techniques and corresponding EAs variants are reviewed according to the answers to the two questions.
The deep-feedback deraining network based on the self-attention mechanism

In recent years, image deraining has received extensive attention in image restoration and computer vision, and scholars mainly use it in fields such as video surveillance and autonomous driving. On rainy days, the rain marks will block the background of the scene, and the visibility of many scenes is low. In rainy images, the appearing alternately rain marks make it hard to differentiate between the background details and rain marks of the image, and the contrast and color of the target in the image will be attenuated to varying degrees, which leads to unclear expression of background information and makes some video or image systems unable to work properly. Therefore, it is very important to eliminate the influence of the rainy day on the scene of the image. This paper proposes the deraining network via the deep feedback based on the self-attention mechanism, the network uses feedback connections to feedback high-order information to low-order information and corrects the low-order information. Then inputs the image representation tensor fully processed by multiple feedback blocks into the self-attention mechanism, and the important features are fully extracted. The results prove that the network has strong reconstruction ability, and can generate clear and high-quality deraining images.
QRANE: lifting QASM programs to an affine IR

This paper introduces QRANE, a tool that produces the affine intermediate representation (IR) from a quantum program expressed in Quantum Assembly language such as OpenQASM. QRANE finds subsets of quantum gates prescribed by the same operation type and linear relationships, and constructs a structured program representation expressed with polyhedral iteration domains and access relations, all while preserving the original semantics of the quantum program. We explore various policies for deciding amongst different delinearization strategies and discuss their effect on the quality of the reconstruction. Our evaluation demonstrates the high coverage and efficiency obtained with QRANE while enabling research on the benefits of affine transformations for large quantum circuits. Specifically, QRANE reconstructs affine iteration domains of up to 6 dimensions and up to 184 points per domain.
Asymptotic properties of words in semi-Markov sequences

It is well known that some enzymes, proteins, amino-acids between other biological molecules have more than one way to be coded in the DNA. That means, there are some biological molecules that can be identified by a set of sequences. For instance, the enzyme SmaI can be recognized by the words [Formula: see text] and [Formula: see text]. In this paper, we count the number of times that a biological sequence occurs through the DNA by any of its configurations, i.e. we provide the strong law of large numbers for a word sequence. To achieve our goal, we consider that DNA is modeled by an ergodic semi-Markov chain. We also present the Central Limit Theorem. Additionally, we compute the first hitting position of a set of words.
“We All Know It’s Wrong, But…”: Moral Judgment of Cyberbullying in U.S. Newspaper Opinion Pieces

This study uses the theory of dyadic morality to analyze construction of cyberbullying as a contested social issue in U. S. newspaper opinion pieces. The theory of dyadic morality posits that when we claim harm, we are motivated to identify a cause of harm and a suffering victim. This moral triangulation indicts determinants of harm and suggests preferred solutions. Analysis of U.S. opinion writing identified a tension between perception of cyberbullying as epidemic and the belief that some aggression was normative, that harm from speech was suspect, and that concern about cyberbullying was overblown. Cyberbullying served as a politically charged example of how technology is shaping adolescent social life and mental health; or how claims to victimhood are threats to free speech. Attention to moral dyad constructs in editorials and opinion pieces can help identify how competitive frames assert claims of moral validity in constructing arguments.
Improved Context Aware PSO Task Scheduling in Cloud Computing

One of the major advantages of switching to the clouds is the scalability ability of the applications. Contrary to the grids, the ability to scale the cloud resources allows their real-time provisioning so as to meet the application constraints Generation of optimal schedule for given set of tasks and machines. Different experiments show that although having an optimum solution is almost impossible but having a sub-optimal solution using heuristic algorithms seems possible. In this paper, we propose a Context Aware PSO Task Scheduling scheme to analyze various scenarios with different parameters in cloud computing system corresponding to APSO In each scenario, we change one parameter and keep other parameters constant.
Oversampling by genetic algorithm and k-nearest neighbors for network intrusion problem

A class imbalance problem is a problem in which the number of majority class and minority class varies greatly. In this article, we propose an oversampling method using GA and k-Nearest Neighbors (kNN) to deal with a network intrusion, a class imbalance problem. We use GA as the main algorithm and use a kNN as its fitness function. We compare the proposed method with a very popular oversampling technique which is a SMOTE family. The experimental results show that the proposed method provides better Accuracy, Precision, and F-measure values than a SMOTE family in almost all datasets with almost all classifiers. Moreover, in some datasets with some classifiers, the proposed method also gives a better Recall value than a SMOTE family as well. This is because the proposed method can generate new intruders in a more independent area than a SMOTE family.
Implementation and Comparative Analysis of Variants of LSB Steganographic Method

In this 21st century, the inability to securely transmit information over a digital medium has been a major problem because the information stands a chance of being intercepted by hackers before it reaches its destination. Therefore Stenography was introduced to prevent sensitive information from been accessed by unauthorized persons. Stenography is a method of encrypting data as related to cryptography but hides a message in plain sight thereby making it a more secure method of sending across secret information. In this study variants of the least significant bit (LSB) method was implemented and performance comparative analysis was carried out across three image format. The result obtained from that the mean square error (MSE) and peak signal to noise ratio PSNR analysis, indicate that PNG and Bitmap image performed better than GIF images
Object Detection Experiment in CBIR Works using Gradation Contour Line

Image retrieval continues to be developed using an alternative approach that is not only based on the information contained in the image. Retrieval techniques to achieve good quality also provide low computational value even though they have to process large-dimensional image data. Usually, CBIR searches for images using a query that begins with the feature extraction process (image content). Image feature parameters that can be used to shoot images can be histograms, colors, textures, and shapes, certain types of objects, certain types of events, individual names, locations, and emotions. However, indexing of images with the contour line feature is still quite attractive, CBIR with more distinctive features on an object is still rarely used. The results of this study using contour lines as a feature in object detection work, the results obtained to match the image with an accuracy of 83% - 84%. The test data used 550 object images taken randomly from the Caltech 101 dataset with several contour error experiments. The highest value of image matching results in the object detection task with contour line characteristics was 84.381%. This is obtained with the condition error size = 10, Contour Error = 10, and error X = 10. This means that the best contour line feature is obtained with a contour error of 10.
Analysis of Graphical Password Systems-Recognition Based GPS and Recall Based GPS

User Authentication is a vital initial step inside any organization security framework since it sets up the identity of the client. User Authentication is a significant part of data security. Alphanumeric passwords are the most well-known and broadly took on method for user validation. In any case, there are a few disservices appended to the alphanumeric types of verification for instance, client pick passwords that are not difficult to figure (dates of births, their names, vehicle plate number) in other to recollect them, on the grounds that troublesome passwords are not effectively recollected; this achieved the option of graphical passwords, research have been completed to confirmation that people think that it is more straightforward to review pictures. The paper talks about different methodologies of utilizing graphical passwords. In this paper, the two categories of the Graphical User Authentication Algorithm: Recognition-based and Recall-based has been discussed. The fundamental calculations of graphical passwords are being looked at dependent on security and ease of use boundaries.
Development of an Intention Estimation Model Based on Gaze and Face Information for Electric Wheelchair Operation Support

In recent years, various user interfaces have been developed to meet the diverse needs of physically disabled persons. In this paper, we developed a method to identify gazing and facial movements based on gaze time and eye/face information and developed an electric wheelchair that can be operated with the user’s "natural gazing and facial movements. This intention estimation model is composed of 1DCNN and LSTM layers. First, 1DCNN is used to extract features from gaze and face information, and then the extracted features are input to LSTM to estimate the user’s intentions regarding movement. The evaluation experiments suggest that the combination of gaze and face information improves the estimation accuracy and contributes to the classification. Furthermore, it was confirmed that adding a convolutional filter layer to the LSTM layer improved the accuracy.
WEB PAGE CLASSIFICATION WITH DEEP LEARNING METHODS

Today, millions of websites on the Internet are widely used to access information.For effective use of web pages with increasing numbers every day, they need to be well classified.In this study, binary and multi-class classification models have been created which can classify web pages with high accuracy.In our experiments, URLs and categories of English web pages in the Open Directory Project (ODP) were used.Training dataset was created by pulling web page texts from URL information.To our knowledge, this is the first comprehensive web page classification dataset for Turkish.In this study, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning methods which are effective in text classification are used.Word embedding was used instead of n-gram approaches commonly used for feature extraction in text classification studies.In this study, hyper-parameter optimization was performed for deep learning models.Binary and multi-class classification models were created with the best parameters.Binary classification models were compared with the results of another study, and multi-class classification models were compared with each other.The performances of all models were examined by considering their training time and f1 scores.
On equivalent classes of minimal Abelian codes

Let Fq be a finite field and G a finite abelian group. An abelian code is an ideal of Fq[G]. Two abelian codes c1 and c2 of Fq[G] are equivalent if there exists an automorphism of G whose linear extension to Fq[G] maps c1 onto c2. MacWilliams determined the number of equivalent classes of minimal abelian codes (minimal ideals) in F2[G] for cyclic group G of odd cardinality. Miller claimed that MacWilliams' result remains true in general, which is however not correct as pointed out by Ferraz, Guerreiro and Polcino Milies. In this paper we completely determine the number of equivalent classes of minimal abelian codes for any Fq[G].
Research on Cross-domain Representation Learning Based on Multi-network Space Fusion

In recent years, graph representation learning has played a huge role in the fields and research of node clustering, node classification, link prediction, etc., among which many excellent models and methods have emerged.These methods can achieve better results for model training and verification of data in a single space domain.However, in real scenarios, the solution of cross-domain problems of multiple information networks is very practical and important, and the existing methods cannot be applied to cross-domain scenarios, so we research on crossdomain representation is based on multi-network space integration.This paper conducts representation learning research for cross-domain scenarios.First, we use different network representation learning methods to perform representation learning in a single network space.Second, we use the attention mechanism to fuse representations in different spaces to obtain a fusion representation of multiple network spaces; Finally, the model is verified through cross-domain experiments.The experimental results show that the fusion model proposed in this paper can improve the performance of cross-domain scenarios.
Deep-learning-based cryptanalysis of two types of nonlinear optical cryptosystems

The two types of nonlinear optical cryptosystems (NOCs) that are respectively based on amplitude-phase retrieval algorithm (APRA) and phase retrieval algorithm (PRA) have attracted a lot of attention due to their unique mechanism of encryption process and remarkable ability to resist common attacks. In this paper, the securities of the two types of NOCs are evaluated by using a deep-learning (DL) method, where an end-to-end densely connected convolutional network (DenseNet) model for cryptanalysis is developed. The proposed DL-based method is able to retrieve unknown plaintexts from the given ciphertexts by using the trained DenseNet model without prior knowledge of any public or private key. The results of numerical experiments with the DenseNet model clearly demonstrate the validity and good performance of the proposed the DL-based attack on NOCs.
Observing effects in various contexts won't give us general psychological theories

Generalization does not come from repeatedly observing phenomena in numerous settings, but from theories explaining what is general in those phenomena. Expecting future behavior to look like past observations is especially problematic in psychology, where behaviors change when people's knowledge changes. Psychology should thus focus on theories of people's capacity to create and apply new representations of their environments.
Intrinsic subtype classification of breast lesions on mammograms by contrastive learning

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. Once cancer is found, the best treatment is selected based on the characteristic of cancer. In this study, we investigated a method to classify breast cancer lesions into four molecular subtypes to assist diagnosis and treatment planning. Because of a limited number of samples and imbalanced types, the lesions were classified based on the similarities of samples using a contrastive learning. The convolutional neural network (CNN) was trained by self-supervised method using paired views of the same lesions with contrastive loss. The subtype was determined by k-nearest neighbor classifier using deep features obtained by the trained network. The proposed model was tested using 385 cases by a 4-fold cross validation. The results are compared with CNN models without and with pretraining. The result indicates the potential usefulness of the proposed method. The computerized subtype classification may support a prompt treatment planning and proper patient care.
A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendations

Abstract Most studies on recommender systems focus on collaborative algorithm approaches over content‐based recommendation due to their better accuracy results. However, the advantage of the latter is that it is more effective and more transparent with user applications. This article proposes WordRecommender, an explainable content‐based algorithm that calculates similarity by semantic proximity. Its preprocessing step involves analyses of movie reviews to obtain aspects, defined as relevant words of high sentimental value. Recommendations are generated by a neighbourhood algorithm that calculates the similarity of films based on the semantic proximity of the aspects ordered by their emotional score. It can also consider a semantic comparison of metadata using the most related aspects from the recommended movie and one enjoyed by the user for the production of textual explanations. The accuracy of the algorithm was competitive with those of other baseline neighbourhood methods, and the semantic data of items can be the source of both representative information and reasoning in recommender systems.
New approach in assembly, disassembly and maintenance processes I4.0 by displaying in augmented reality environment

Abstract In the current ever-accelerating time of deployment of new production systems associated by various manipulating, technological and maintenance procedures, there is a demand from the point of view of the industrial sphere for shortening these operations and efficiency of the selected processes. It is the ability to visualize these processes using modern augmented reality (AR) technology that is the way to achieve the desired aspects. Visualization via smartphones, tablets, headsets in the AR environment allows users to provide comprehensive information with the exact procedure and information about the required tasks (assembly, disassembly, service operations, maintenance, wiring diagrams, etc.). The use of AR technology, also in view of the ongoing pandemic situation Covid 19, makes possible to involve less qualified operators in the industrial sphere. Deployment of the less skilled operators in the production sphere, using AR technology, can make up for the shortage of skilled labour that is currently lacking in every industry. In this way, it is also possible to keep the industrial sector in good condition without undesirable factors such as production shutdowns, long service operations, and poorly trained service personnel.
Performance Evaluation of Convolutional Neural Network Based Models On Signature Verification System

Even though people moving to digital documents with digital signature for authentication, most of the areas such as land records, agreement between parties, legal certificates, identification cards etc., uses only handwritten signature. Verifying signatures is an important one because a fraudulent signature would affect the real owner too much. Hence, recognizing genuine signatures becomes essential to avoid such frauds. To recognize the signature, deep learning technique is used in this work since it produces highest accuracy and it does not require too much preprocessing. A convolutional neural network (CNN) based deep learning model is mostly used for image processing, classification, and segmentation. As CNN algorithm learns more than KNN, SVM etc., CNN is used in this work for better classification. The CNN based models such as VGG16, Inception v3, CNN - with three and four convolution layers are trained for this classification. The dataset is created by collecting signatures from 10 different users with 50 signatures each. Out of 500 signatures, 400 is taken for training and 100 is used for testing. Among these four models Inception V3 produced highest accuracy of 95&#x0025; with preprocessed images whereas the same model produced only 88&#x0025; when unprocessed images are given as in input.
Automated Cattle Classification and Counting Using Hybridized Mask R-CNN and YOLOv3 Algorithms

Computer vision is an interdisciplinary research topic that investigates how computers can understand image data or videos at a high level. One of the most promising but difficult problems in intelligent livestock management is accurate and reliable animal counts in camera-acquired imagery. To detect and count cattle, most contemporary systems rely on hardware such as sensors, drones, and algorithms such as CNN (Convolutional Neural Networks), R-CNN (Regional-Convolutional Neural Networks), and YOLO (You Look Only Once). Mask R-CNN has a greater prediction accuracy, but YOLOv3 efficiently classifies at a high speed, and these algorithms are often regarded as more accurate and quick methods. This work proposes a novel technique called Mask-YOLOv3 for classifying and counting mixed livestock, including cow, sheep, and horse, that combines the advantageous aspects of both of these algorithms in order to increase detection and counting accuracy. The proposed algorithm can be used effectively for early detection of undesired activities like cattle loss, cattle theft etc.