Reviewerly/exHarmony huggingface dataset:
Perspectives for Using Smart Augmented Reality for the Future in Social Computing and Collaborative Assistance

Overall, the paper explores perspectives for future developments, identifies challenges, and potential advantages for using smart augmented reality for the future in social computing and collaborative assistance. The paper identifies seven major challenges when using computer-mediated communication today. It provides perspectives and visions on how future computer-mediated communication might be improved when Smart AR is used, especially when smart assistants are employed. Moreover, a computer system architecture has been conceived that provides direction on how the approaches suggested in the paper might be realized.
Vision Transformer Based Photo Capturing System

Portrait photo is one of the most crucial documents that many people need for official transactions in many public and private organizations. Despite the developing technologies and high resolution imaging devices, people need such photographer offices to fulfil their needs to take photos. In this study, a Photo Capturing System has been developed to provide infrastructure for web and mobile applications. After the system detects the person's face, facial orientation and facial expression, it automatically takes a photo and sends it to a graphical user interface developed for this purpose. Then, with the help of the user interface of the photo taken by the system, it is automatically printed out. The proposed study is a unique study that uses imaging technologies, deep learning and vision transformer algorithms, which are very popular image processing techniques in several years. Within the scope of the study, face detection and facial expression recognition are performed with a success rate of close to 100\% and 95.52\%, respectively. In the study, the performances of Vision Transformer algorithm is also compared with the state of art algorithms in facial expression recognition.
Classifier Probability Calibration Through Uncertain Information Revision

There has been a lot of interest in explainable and trustworthy machine learning over the past few years. In some problems, it is not enough to predict correctly the true class label, but also to provide the probability that the prediction is true. This probability makes it possible to have confidence or not in the prediction. In this paper, we propose a new approach to calibrate the probabilities of a machine learning model through a post-processing step. The objective is to exploit some positive results such as that calibration is rather better on a small number of categories or subsets of classes than on a large number of classes. Based on this observation, our calibration approach, based on probabilistic belief revision, calibrates the predicted probabilities on the classes with the probabilities of subsets of classes. Preliminary experimental studies show very promising results, especially on certain datasets such as those of hierarchical classification.
Defining Key Concepts in Information Science Research: The Adoption of the Definition of Feature

This paper analyzes the definitions of the concept feature in the information science literature. The concept of feature has been defined in various ways over the last three decades. To be able to obtain a common understanding of a feature in information science, it is important to conduct a thorough analysis of the definitions that can be used in research and in practice. The main contribution of this paper is a categorization of the existing definitions, which highlights similarities and differences. By means of a Concept Definition Review process, we gather a total of 23 definitions from Google Scholar using five search queries complemented by backward snowballing. Our analysis organizes the definitions according to their level of abstraction and the taken viewpoint. Within the range of analyzed definitions, we do not wish to argue that one is better or worse than another. We provide, however, guidelines for the selection of a definition for a given goal. These guidelines include: popularity based on the citations count, the research field, the abstraction level, and the viewpoint.
Pre-requisite Concepts for Security and Privacy

Nowadays, technology has reached a level that it is dissolved in our lives. Expanding rapidly in the last several decades, information security has become a major concern for everyone connected to the Internet. Information security ensures that our information is only available to the authorized user (receiver) and prevents any changes or modifications of information. In order to get this high level security various methods, techniques and algorithms have been developed. Cryptography can be defined as a method that encrypts the data, depending on the algorithm use. This changes the data in different form which is unreadable to the person unless it is changed to original form by the authorized person (receiver) using same encryption algorithm.
Pose Estimation for Gesture Recovery in Occluded Television Videos

Gestures play an important role in human communication and are important for AI systems that interact with humans to understand. One way to train such systems is to use videos of naturalistic human interactions. However, in such videos, occlusion is often a problem that can prevent accurate gesture recovery. In this thesis, we propose an approach to recover gestures under occlusion by transforming frames and joint key point positions using rotations and translation operations. We use a pose estimation algorithm from prior work to generate multiple options for each frame corresponding to the transformation. We then use dynamic programming with a learned cost function that trades off accuracy and smoothness to recover the pose sequence with the lowest cost. The features for the cost function use known human motion characteristics, such as the minimum jerk principle. We evaluate our system with videos of TV programs collected by the Red Hen Lab and show that our approach is competitive with or outperforms the state of the art.
Towards an Architectural Concept for a Wearable Recommendation System to Support Workplace Productivity and Well-Being

Modern information technology has a high potential to assist people in their daily life via assistance systems. However, such systems surprisingly still lack appropriate solutions tackling the challenges of modern work-life. By now, work aspects such as productivity have been considered mainly separately from other aspects such as one's health and fitness level. Regarding the latter, wearable technologies (like smartwatches or fitness trackers) are commonly used to gather sensor data (such as GPS, heart rate, etc.) and provide supportive recommendations. To support employees in promoting balance between different aspects of their work-life such as productivity and well-being, these features should be more integrated than this is the case in existing systems. To this end, we present an architectural concept for a wearable recommendation system designed to provide personal recommendations with the ultimate goal of supporting workplace productivity and well-being. In addition, our architectural concept also covers the aspect of user feedback to allow for improvements regarding the relevance of recommendations. We derived our conceptual architecture from related work, by considering the characteristics of the technology to be integrated and motivational scenarios describing the intended use of the system. With our architecture, we hope to inspire future efforts towards wearable recommender systems that integrate productivity and well-being.
Designing Robust Blind Color Image Watermarking-Based Authentication Scheme for Copyright Protection

Digital watermarking is a process of embedding information within digital content mostly for authentication and digital right management purposes. In this article, a new robust blind color image watermarking scheme based on two-level Discrete Wavelet Transform (DWT)-Discrete Fourier Transform (DFT) has been proposed for copyright protection and authentication. Additionally, a new geometric attack detection and correction procedure has been introduced in this scheme. The proposed scheme can thwart several signal processing, geometric and hybrid attacks. The cover image is transformed using DWT and DFT before embedding the encrypted watermark. Here, encryption of watermark is done using AES in CBC (Cipher Block Chaining) mode. Finally, the encrypted watermark is embedded in transform domain of cover image by using the logistic map based random position generator. Experimental results show that the proposed scheme is better than the relevant existing schemes in terms of imperceptibility and robustness.
Optimal Sampling Rate from Shannon-Nyquist Theorem for Improved Fuel Production of Solar Thermal Pyrolysis Systems

Solar radiation is a continuous signal that when inappropriately sampled can lead to inaccurate signal reconstruction and aliasing. This is a problem for technologies that require accurate solar signal reconstruction such as control systems for pyrolysis reactors. This work sort to solve this issue by utilizing Shannon-Nyquist theorem to find the optimal forecast solar radiation sampling rate for a solar thermal polyethylene pyrolysis reactor. Utilizing a long short term memory neural network, it was found that solar radiation data from the Australian Bureau of Meteorology could be forecast a day ahead of time in 1 minute increments with an RMSE 102.03 W/m2, and the subsequent Nyquist sample rate pyrolysis fuel output simulation could achieve double the accuracy with an RMSE of 100.03 kg compares to a 10 minute sampling rate simulations of 221.69 kg.
Political Polarity Classification Using NLP

Twitter is a popular platform for sharing our perspective with the world and perceiving the expert opinions of the popular figures on day-to-day affairs. Politicians also find an outlet here for their campaigns, thereby reaching out to a vast audience online. In this research, we focus on identifying and classifying the subtleties of such political tweets that aim to influence the crowd. However, the noise in the dataset concerning linguistic anomalies makes it challenging to apply the direct classification methods. We begin by preprocessing the raw tweets to tackle grammatical and semantic issues. Further, natural language processing (NLP) tools such as Word2Vec that help in preserving semantic and syntactical relationships are incorporated. The classification accuracy is affected by this technique because grammatical structures distort with Word2Vec. Bigram count of special tokens is added to the resulting set of features to solve this problem. A Receiver Operating Characteristic (ROC) curve is used to measure the accuracy by selecting a different set of features, once using a Naïve Bayes classifier and once using random forest.
Sliding Window-Based High Utility Item-Sets Mining Over Data Stream Using Extended Global Utility Item-Sets Tree

High utility item-sets mining (HUIM) is a special topic in frequent item-sets mining (FIM). It gives better insights for business growth by focusing on the utility of items in a transaction. HUIM is evolving as a powerful research area due to its vast applications in many fields. Data stream processing, meanwhile, is an interesting and challenging problem since, processing very fast generating a huge amount of data with limited resources strongly demands high-performance algorithms. This paper presents an innovative idea to extract the high utility item-sets (HUIs) from the dynamic data stream by applying sliding window control. Even though certain algorithms exist to solve the same problem, they allow redundant processing or reprocessing of data. To overcome this, the proposed algorithm used a tree like structure called extended global utility item-sets tree (EGUI-tree), which is flexible to store and retrieve the mined information instead of reprocessing. An experimental study on real-world datasets proved that EGUI-tree algorithm is faster than the state-of-the-art algorithms.
A Set of Tools for Extraction and Manipulation of Speech Fundamental Frequency

The present paper describes a set of tools created and used for fundamental frequency extraction and manipulation, prosody and speech perception analysis and speech synthesis. The tools were implemented as Praat and Python scripts and were created for different purposes in projects over the last few years. The paper presents the functionality and possible usage of the tools in phonetic research.
Construction and Quantitative Analysis of Jiangsu Dialect Function Word Knowledgebase

The traditional studies of Chinese dialects focus on phonetics, phonology and vocabulary, while the studies of grammar such as function words still need to be well conducted. Function words are important means of expressing grammatical meaning in a language, and they are the key features for distinguishing different dialects. In addition, the current application of Chinese information processing, such as dialect conversion, dialect analysis, also needs the information of function words. Therefore, this paper uses the framework of HowNet to describe the features of function words in Jiangsu Dialects, including Yangzhou, Yancheng, Nanjing, Suzhou and Xuzhou. Thus, we build a knowledge base of Jiangsu dialects’ function words. Then we make statistics on types of function words and their formal expressions. After the statistics, we find that in some cases, a function word has more than one formal expressions, a formal expression can represent multiple meanings. With the hierarchy of HowNet, we also calculate the similarity of five dialects, finding that the similarity is about 80%.
A Stigmergy-Based Island Model for Dynamic Evaluation of Constraint-Handling Techniques and Differential Evolution Algorithms

A nontrivial task is to choose an evolutionary algorithm (EA) and a constraint-handling technique, among countless possibilities, leading to a robust and efficient mechanism to solve constrained optimization problems (COPs). The main motivation of this article is to automate the decision on the EA coupled with a constraint-handling technique able to find the best performance in solving COPs. From this issue, this work proposes a methodology based on the stigmergy island model (IM), where EAs and constraint-handling techniques are distributed throughout the islands, and their performances are dynamically evaluated along the evolutionary process to guide the migration of solutions between them. The stigmergy IM is a strategy for implementing the hybrid IMs dynamically and adaptively, inspired by the natural phenomenon of stigmergy. According to the EAs applied on the islands, the weighted connections and distribution of solutions between islands are adjusted over migrations. The proposed model, which is the main contribution of this work, identifies and explores the most suitable algorithm for COPs among those applied in the initial topology. In this sense, different hybrid alternatives based on different differential evolution algorithms and constraint-handling techniques are presented to evaluate the performance of the proposed IM.
The Effect of Imbalanced Classes on Students' Academic Performance Prediction

Imbalanced classes in data mining have more challenges in the educational data mining field. This is because most of the datasets collected from educational records are imbalanced by nature. Some classes dominate others and cause bias predictions. This paper studies the effects of the imbalanced classes on the performance of seven different classifiers, which are J48, Random Forest, k-Nearest Neighbors, Naïve Bayes, Random Tree, SVM, and Linear Regression. Moreover, the effectiveness of the SMOTE technique for handling imbalanced data is evaluated against these classifiers. This will be done through the proposal of an early predictive model that predicts student’s academic performance and recommends their appropriate department in a multi-disciplinary institute. According to our results, the Random Forest technique is the best and has the highest level of accuracy is 94.585%.
When Choice is (not) an Option: Nudging and Techno-Regulation Approaches to Behavioural Cybersecurity

The field of behavioural cybersecurity, with a focus on the behaviour of end-users when working to improve the overall cybersecurity of a system or organisation, is gaining ground. As the field is still relatively small, most of the focus lies on awareness campaigns, or the occasional behavioural change intervention. Another way of looking at improving cybersecurity behaviour is by taking a closer look at the way systems present options and choices to end-users. The selection, design, and presentation of these options can strongly influence end-users’ behaviour, thereby hindering or supporting the security of systems. These options can be categorised under two approaches: nudging and techno-regulation. The former aims to gently push end-users towards a preferred (safer) course of action, while the latter forcefully removes any unwanted (riskier) options, thereby improving cybersecurity at the cost of freedom of choice. The current paper outlines and compares these two approaches in terms of mechanisms, effectiveness, and potential unwanted side effects. Furthermore, the applicability of these methods to improve cybersecurity behaviour and the ethical dilemmas associated with applying these methods are discussed.
A Bilevel Decomposition Approach for Many Homogeneous Computing Tasks Scheduling in Software-Defined Industrial Networks

To confront the great challenge of industrial big data, the software-defined industrial networks (SDINs) are introduced to dynamically coordinate these data flows among the heterogeneous and distributed computing resources. Deciding how to more efficiently schedule many homogeneous computing tasks, which extensively appear in SDIN, becomes of critical importance. To this end, this article first illustrates some related notations and assumptions of the homogeneous computing tasks and computing networks, from which a new targeted optimization model is formulated. Then, the model is significantly enhanced by reformulating all the nonlinear constraints and inventively establishing the symmetry-breaking constraints and computation time cuts. Furthermore, considering the computational complexity, the scheduling process with many homogeneous computing tasks is further viewed as two associated phases: computing nodes assignment and tasks sequencing. As a result, a novel bilevel decomposition algorithm is proposed using Lagrangian decomposition and a new form of Lagrangian relaxation. Finally, a real industrial scenario is chosen, and three comparison algorithms are used to demonstrate the preponderant performance of the proposed algorithm.
Image Novelty Detection Based on Mean-Shift and Typical Set Size

The detection of anomalous or novel images given a training dataset of only clean reference data (inliers) is an important task in computer vision. We propose a new shallow approach that represents both inlier and outlier images as ensembles of patches, which allows us to effectively detect novelties as mean shifts between reference data and outliers with the Hotelling $$T^2$$ test. Since mean-shift can only be detected when the outlier ensemble is sufficiently separate from the typical set of the inlier distribution, this typical set acts as a blind spot for novelty detection. We therefore minimize its estimated size as our selection rule for critical hyperparameters, such as, e.g., the size of the patches is crucial. To showcase the capabilities of our approach, we compare results with classical and deep learning methods on the popular datasets MNIST and CIFAR-10, and demonstrate its real-world applicability in a large-scale industrial inspection scenario.
An Algorithm Of Deformation Image Correction Based on Spatial Mapping

The original image has geometric deformation in geometric position, shape, size and orientation due to shooting angle or capturing in the process of image shooting. It brings a lot of inconveniences and great challenges in image processing field such as image fusion, de-noising, recognition and segmentation. An adaptive image deformity correction algorithm for the quadrilateral and the triangular is proposed, in order to improve the processing ability and recognition accuracy of deformation images. Firstly, the deformation image is pre-processed and the contour of image edge is extracted. And then, discrete points of image edge are found to locate image edge accurately. Finally, the deformation of the quadrilateral or the triangle is processed into standard rectangle or equilateral triangle image by using the proposed algorithm of three-dimensional homography transformation, so as to complete the transformation from irregular image to regular image adaptively. A lot of experiments show that the proposed algorithm is superior to the traditional methods such as Hough transform and Radon transform, which improves the effect of deformation images correction, solves the problem of geometric deformation images well, and provides a new technical method for image processing of deformation images.
Weak Bounding Box Supervision for Image Registration Networks

Image registration is a fundamental task in medical image analysis. Many deep learning based methods use multi-label image segmentations during training to reach the performance of conventional algorithms. But the creation of detailed annotations is very time-consuming and expert knowledge is essential. To avoid this, we propose a weakly supervised learning scheme for deformable image registration that uses bounding boxes during training. By calculating the loss function based on these bounding box labels, we are able to perform an image registration with large deformations without using densely labeled annotations. The performance of the registration of inter-patient 3D Abdominal CT images can be enhanced by approximately 10% only with little annotation effort in comparison to unsupervised learning methods. Taken into account this annotation effort, the performance also exceeds the performance of the label supervised training.
Single and Ensemble CNN Models with Out-Category Penalty for Image Classification

In recent years, the technology of machine learning has been developing rapidly. Among them, neural network technology has had a great impact on various fields such as image recog- nition and natural language processing. Among them, CNN or Convolutional Neural Network have been effective in the field of image recognition. However, most of these CNNs learn only the features of the image, and do not learn the meta-information of the image. In this study, we proposed a CNN and its ensemble method that can learn meta-information by using out- category penalties. Experiments were conducted on the CIFAR-10 and CIFAR-100 datasets, and the results show that the proposed method has high accuracy and small out-category error in both single and ensemble models.
A Hyperparameters Tuning and Gaussian Smoothing Based FC- Siamese Network for Object Tracking

Fully-Convolutional Siamese Neural Networks (FCSNN) usually hardly adapt to complex scenes such as appearance change, scale change, similar objects interference and so on. Our first proposed technique surpasses the original Fully-Convolutional Siamese Networks (SiamFC) on VOT 2016 dataset with an increase in precision, precision AUC, and IOU compared to the original SiamFC. Our second proposed technique also reveals notable improvements in the all above mentioned performance metrics over the original Siam-FC with precision increment, and respectively, for precision AUC, IOU and speed. Our paper includes the tracking and all experiments are conducted offline. Although Gaussian Blur implementation shows significant improvements, the more consistent results are yielded by the implementation of the Leaky ReLU function in the activation layer with zero initialization for biases and constant initialization for weights in addition with brightness adjustment compared to the Gaussian smoothing implementation.