Reviewerly/exHarmony huggingface dataset:
SUTO-Solar Through-Turbulence Open Image Dataset

Imaging through turbulence has been the subject of many research papers in a variety of fields, including defence, astronomy, earth observations, and medicine. The main goal of such research is usually to recover the original, undisturbed image, in which the impact of spatially dependent blurring induced by the phase modulation of the light wavefront is removed. The number of turbulence-disturbed image databases available online is small, and the datasets usually contain repeating types of ground objects (cars, buildings, ships, chessboard patterns). In this article, we present a database of solar images in widely varying turbulence conditions obtained from the SUTO-Solar patrol station recorded over a period of more than a year. The dataset contains image sequences of distinctive yet randomly selected fragments of the solar chromosphere and photosphere. Reference images have been provided with the data using computationally intensive image recovery with the latest multiframe blind deconvolution technique, which is widely accepted in solar imaging. The presented dataset will be extended in the next few years as new image sequences are routinely acquired each sunny day at the SUTO-Solar station.
Elderly Fall Detection: A Lightweight Kinect Based Deep Learning Approach

Fall detection is one of the main issues for the elder's health care systems because of its economic and social impact. Whereas the primary metric of such a system remains its accuracy in terms of good detection of falls and avoiding either false detection or missing detection, its deployment raises many issues in terms of the number of devices, their nature (scalar, multimedia, Lidar, etc.) and the technique used. Generally, techniques based on multimedia processing provide better results but at the expense of a high CPU processing and consequently need appropriate devices. This paper explores an approach that uses less-powerful affordable devices (i.e., Raspberry Pi like) with multimedia sensors (i.e., Kinect) and a Deep Learning-based processing mechanism. More precisely, we applied LSTM (Long Short-Term Memory) on features extracted from the time series data acquired from the Kinect. Experimental results we obtained from our lightweight LSTM model on the Raspberry pi show that geometric features are more relevant for fall event detection. Our model achieves advanced performance with metrics that are usually considered (accuracy, precision, sensitivity, and specificity). Furthermore, our lightweight model is very promising for deployment on machines considered "low-cost."
Evaluation Measures and Applications for Explainable AI

Machine learning advances, particularly deep learning, have enabled us to design models that excel at increasingly complicated tasks. Because of the growing size and complexity of these models, it’s becoming more difficult to grasp how they arrive at their forecasts and when they go incorrect or even worse. Now, think of a situation in which we humans could open these black-box learning models and translate the content into a human-understandable format. This is known as Explainable Artificial Intelligence and there has been a lot of research in this field over the last few years mainly focusing on how to explain different types of models. The advancement of this research, raised a very important query: “Why does a model need to be explained?” So, the most accurate answer to this question is “TRUST”. TRUST that the models are making the correct decisions over the correct assumptions. TRUST that we can tell what happened when a model fails. TRUST that we can do on a model implemented on a large scale that the predictions are made in line with expectations. It’s hard to trust a system that’s not transparent about its internal processes. This paper discusses the evaluation measures and application areas of XAI. Some XAI-related concepts were also mentioned.
Human-AI Interfaces are a Central Component of Trustworthy AI

This chapter demonstrates the crucial role that human-AI interfaces play in conveying the trustworthiness of AI solutions to their users. Explainability is a central component of such interfaces, particularly in high-stake domains where human oversight is essential: justice, finance, security, and medicine. To successfully build and communicate trustworthiness, a user-centered approach to the design and development of AI solutions and their human interfaces is essential. In this chapter, we explain how proven methods for stakeholder analysis and user testing from human-computer interaction (HCI) research can be adapted to human-AI interaction (HAII) in support of this goal. The practical implementation of a user-centric approach is described within the context of AI applications in computational pathology.
Fundamental Fallacies in Definitions of Explainable AI: Explainable to Whom and Why?

There are many articles that show a discrepancy between the various motives for the construction of XAI (Explanatory Artificial Intelligence), which is not surprising, since this area began to be actively centralized and actively developed only 6 years ago. But the strange thing is that the motives not only do not converge but may contradict each other. This indicates that there are fundamental errors in the very construction of different XAI concepts. These errors create not only contradictions between different visions of XAI, but also common to many concepts of error. The main one is the absence or incorrect answer to the question “For whom exactly should AI be explained?”. Turning to human psychology and social processes that are accompanied by the exchange of explanations, we will try to consider what benefits the explanation brings to people and groups. Correcting fundamental errors in the construction of XAI concepts, we show that neural networks are no less explanatory AI than linear models and decision trees. Moreover, we will show what the neural network approach can do so that the explanation will not need to be exchanged for the quality of AI algorithms, and that they can even improve them.
The Effect of Automatic Text Simplification on L2 Readers’ Text Comprehension

Abstract Texts used in L2 classrooms have traditionally been simplified manually, but recent technological advances allow us to investigate whether automatic text simplification (ATS) software can help L2 learners comprehend texts in second and foreign languages. Participants were divided into low and high L2 reading proficiency groups and assigned to read either the authentic or automatically simplified version of a text and completed a free recall task and MC comprehension test. The results did not show any significant correlations among the variables of topic knowledge, topic interest, and MC comprehension, but there were correlations among L2 reading comprehension, MC comprehension, and free recall results. Results also showed that the automatically simplified text facilitated the comprehension of the more proficient readers but not the less proficient readers according to their performance on the free recall assessment. Implications are that L2 teachers cannot blindly use whatever text they want with ATS, and ATS software designers may need to reconsider the current conservative approach to simplification that many ATS tools use.
A real‐time privacy‐preserving scheme based on grouping queries for continuous location‐based services

Summary With the advancement of global positioning systems and communication technologies, location‐based services (LBS) have become widely used. However, user location privacy is vulnerable during service exchange, and location‐related queries may result in serious privacy disclosure issues. In this article, considering that attackers may make use of auxiliary information to track a mobile user, we propose a real‐time privacy‐preserving scheme based on the grouping of queries in continuous LBS. In this scheme, we first design the probability density functions to select virtual locations. Then, we group these virtual locations and the query location of the user into several queries. Simultaneously, we send these grouping queries to the LBS server. Following the steps outlined above, grouping queries can be used to conceal the true query. Moreover, the results of security analyses and performance evaluations show that the proposed scheme is effective and secure.
New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity

The main challenge in steganography algorithms is balancing between the size of the secret message (SM) that is embedded in the cover image (CI) and the quality of the stego-image (SI). This manuscript proposes a new steganography algorithm to hide a large amount of secret messages in cover images with a high degree of non-perception in the resulting images. The proposed algorithm applied a multi-channel deep data hiding and extraction algorithm (MCDHEA) based on a modified multi-level steganography (MLS) approach. This approach used a new modification of the least significant bits (NMLSB) to make it hard to extract a secret message from attackers. The secret message was distributed among n-blocks; each block was hidden into a sub-channel that included multi-level hiding and flows into the main channel. Different grayscale images were used through the levels of each sub-channel and using the color image in the last level of the main channel. The image size of the multi-level was expanded from one level to the next level, and at each level, lossless image compression using the Huffman coding algorithm was applied to enable the size of the data hiding at the next level. In addition, the encryption of secret messages and intermediate cover images based on the XOR encryption algorithm is applied before the hiding process. Finally, the number of bits to be replaced at each level for both sub and main channels was four bits per byte except at the last level of the main channel based on a new approach using a non-uniform number of bits replacements. This algorithm's performance was evaluated using various measures. The results show that the proposed technique is effective and better than the previous works concerning imperceptibility and robustness. Furthermore, the results show that the maximum peak signal-to-noise ratio (PSNR) of 61.2 dB for the payload of 18,750 bytes, the maximum video information fidelity (VIF) of 0.95 for the payload of 19,660 bytes, and the maximum structural similarity index measure (SSIM) of 0.999 for the payload of 294,912 bytes.
Digital Cytology

Microscopic evaluation of cytologic specimens can provide rapid diagnostic information and aid in formulating diagnostic and treatment plans. The primary benefit of cytologic evaluation is the rapid collection, processing, and evaluation of samples. However, physical transport of glass slides and body fluids to a diagnostic laboratory takes time and can negatively affect patient management. Digital cytology allows specimens to be processed in the clinic and immediately sent to pathologists. With technology becoming more affordable, digital cytology is revolutionizing the field of clinical pathology and patient care.
Remote sensing of Listvenite rock for Kaymaz Gold Deposit, Eskişehir-TÜRKİYE

The Kaymaz Gold Deposit (KGD) is an economically important gold deposit in Eskişehir, Central Anatolia, Türkiye. The deposit occurred in very rare non carbonate-containing and highly silicified listvenite rock, an alteration product of serpentine in the Tavşanlı Zone, located south of the İzmir–Ankara–Erzincan Suture Zone. We studied the alteration in the KGD through petrographic examination and XRD analyses. We used Fourier-transform infrared (FTIR) spectroscopy to study the spectral absorption properties of field samples. The absorbance and reflectance patterns of the samples were used to derive mineral maps using the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) and Sentinel-2 images. The argillized zone was mapped using ASTER band ratio images 4/6, 4/9, 7/6, and RGB (red-green-blue) composite images. In addition, the 11/2 and 10/11 band ratios of Sentinel-2 were used to map Fe-oxyhydroxide containing listvenite and serpentinite, respectively. The images of band ratios and derived RGBs discriminated the altered rocks as well as the metamorphic and ultramafic rocks occurring around the open-pit mine and can be used to explore new deposits in similar geological settings. The results were verified and confirmed in the field, which showed that the ore zone is highly silicified with significant Fe-oxyhydroxide content.
Knowledge Management

Multi-material-design is a promising approach for the automotive industry to operate economical lightweight design. Combining metallic materials with composites and short-fibre reinforced plastics enables to develop lightweight components with superior mechanical properties. One of the central challenges is that most car body developers in the automotive industry have gathered little experience with plastics, composites and multi-material designs. One approach of product development to meet this challenge is the provision of knowledge, for example through design rules and design principles. The scope of this chapter is to provide knowledge for developing multi-material designs. First, relevant knowledge is identified, and an access concept for the design rules is developed. Furthermore, what will be developed is a text similarity algorithm for identifying similar rules. Afterwards, the knowledge management system will be prototypically implemented and applied to two case studies.
Novel traveling quantum anonymous voting scheme via GHZ states

Based on traveling ballot mode, we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger (GHZ) states. In this scheme, each legal voter performs unitary operation on corresponding position of particle sequence to encode his/her voting content. The voters have multiple ballot items to choose rather than just binary options “yes” or “no”. After counting votes phase, any participant who is interested in voting results can obtain the voting results. To improve the efficiency of the traveling quantum anonymous voting scheme, an optimization method based on grouping strategy is also presented. Compared with the most existing traveling quantum voting schemes, the proposed scheme is more practical because of its privacy, verifiability and non-repeatability. Furthermore, the security analysis shows that the proposed traveling quantum anonymous voting scheme can prevent various attacks and ensure high security.
Affine-mapping based variational ensemble Kalman filter

Abstract We propose an affine-mapping based variational ensemble Kalman filter for sequential Bayesian filtering problems with generic observation models. Specifically, the proposed method is formulated as to construct an affine mapping from the prior ensemble to the posterior one, and the affine mapping is computed via a variational Bayesian formulation, i.e., by minimizing the Kullback–Leibler divergence between the transformed distribution through the affine mapping and the actual posterior. Some theoretical properties of resulting optimization problem are studied and a gradient descent scheme is proposed to solve the resulting optimization problem. With numerical examples we demonstrate that the method has competitive performance against existing methods.
Indoor Wireless Multipaths Outlier Detection and Clustering

Wireless communication systems have grown and developed significantly in recent years to fulfill the growing demand for high data rates across a wireless medium. Channel models have been used to develop various sturdy wireless systems for indoor and outdoor applications, and these are simulated in the form of datasets. The presence of outliers in clusters has been a concern in datasets, as it affects the standard deviation and mean of the dataset which reduces the data accuracy. In this study, the outliers in the Cooperation in Science and Technology (COST) 2100 MIMO channel model dataset were shifted to the means of the clusters using the Mean Shift Outlier Detection method. Afterward, the data is clustered using simultaneous clustering and model selection matrix affinity (SCAMSMA). The Mean Shift Outlier Detection method identified 52 and 46 multipaths as outliers and improved the clustering accuracy of the indoor scenarios by 3.5% and 0.93%, respectively. It also increased the precision of the clustering based on the decrease in standard deviation of the Jaccard indices from 0.2435 to 0.1807 and 0.3038 to 0.2075.
Study of the impact of recultivated granite stone quarry dumps on the environment and agricultural land in the Leningrad region

Abstract The environmental condition of the granite stone quarry dumps and the adjacent areas of Kuznechnoye village in the Leningrad Region of the Russian Federation was evaluated based on the results of field measurements of dust concentrations in the air (fraction size 1-10 microns) and analytical determination of chemical element contents in soil samples by spectral emission analysis and X-ray spectrometry methods. It has been established that dusting of unreclaimed dumps under conditions of humid climate, which is observed most of the year, and high pH values of soils leads to the formation of polyelement technogenic geochemical anomalies of Pb, Zn, Cu, Ba and Sr. The data obtained can be used in the selection of the most effective method of reclamation of dumps.
Machine Learning-based Time-slot Time-varying Filtering for Mandarin Tone Processing

Tone is one of the biggest differences between Mandarin as a tonal language and English, meaning that changes in frequency will appear in every character and determine their meaning. Therefore, the accurate identification of intonation plays an important role in Chinese language processing and learning. In the learning and training process of Mandarin Chinese, graphical feedback can provide valuable assistance to linguists or learners, providing them with more insight than simple accuracy ratings as they can determined what to change to improve their intonation based on this feedback. In this paper, we refer to the process of generating Mel-frequency cepstrum coefficients and employ a machine learning-aided time-slot filtering approach to obtain an intuitive transformation of pitch from Mel spectrum to geometric graphs as feedback to Chinese language workers and learners, and to help them improve their mastery of Mandarin tones. Our preliminary results show that these illustrations can accurately represent frequency changes in relation to time. At the same time, the lower computational cost of the optimization allows it to be used as learning software on various mobile platforms.
A new method of constructing adversarial examples for quantum variational circuits

A quantum variational circuit is a quantum machine learning model similar to a neural network. A crafted adversarial example can lead to incorrect results for the model. Using adversarial examples to train the model will greatly improve its robustness. The existing method is to use automatic differentials or finite difference to obtain a gradient and use it to construct adversarial examples. This paper proposes an innovative method for constructing adversarial examples of quantum variational circuits. In this method, the gradient can be obtained by measuring the expected value of a quantum bit respectively in a series quantum circuit. This method can be used to construct the adversarial examples for a quantum variational circuit classifier. The implementation results prove the effectiveness of the proposed method. Compared with the existing method, our method requires fewer resources and is more efficient.
Multiple High-Speed Vision for Identical Objects Tracking

In multi-object tracking of identical objects, it is difficult to return to tracking after occlusions occur due to three-dimensional intersections between objects because the objects cannot be distinguished by their appearances. In this paper, we propose a method for multi-object tracking of identical objects using multiple high-speed vision systems. By using high-speed vision, we take advantage of the fact that tracking information, such as the position of each object in each camera and the presence or absence of occlusion, can be obtained with high time density. Furthermore, we perform multi-object tracking of identical objects by efficiently performing occlusion handling using geometric constraints satisfied by multiple high-speed vision systems; these can be used by appropriately positioning them with respect to the moving region of the object. Through experiments using table-tennis balls as identical objects, this study shows that stable multi-object tracking can be performed in real time, even when frequent occlusions occur.
Children's participation in the design of digital artifacts in rural schools

The definition of Participatory Design (PD) includes a valid preoccupation with power balance between designers and target users in decision-making, which researchers and practitioners still struggle to attain. The situation is more critical when design involve vulnerable groups, like children. Children's contributions in design processes are often regarded as less valid, or unreasonable. Studies that apply PD with children often do it superficially, not taking into account the particularities of children's behaviour and the dynamics of their interaction with adults. In this sense, models and frameworks of child participation can give a more solid theoretical framing for PD with children. In this paper, we propose a theoretical model which draws from theories of child participation, for supporting PD with children. Then, we discuss the application of this model for qualitative analysis of a design process of digital educational artifacts with children from rural schools in Brazil. We perceived a focus on the co-creation of artifacts, but also a tendency of adults to assume a professorial attitude, undermining children's autonomy on project decisions. We also observed that cultural context and school rules have a strong impact on children's self-efficacy, limiting them to roles with less decision power. The results of applying the model to data analysis feed into planning of PD activities with children, helping researchers and designers to employ methods that promote their autonomy and participation.
Assessment of visual aesthetics through human judgments

Visual aesthetics is an essential factor of the perceived usability, credibility, and general quality of graphical user interfaces (GUIs). Its assessment as part of the software process is critical but highly subjective, making it difficult to obtain reliable and valid results. This article presents a systematic mapping analyzing measures and data collection instruments to understand how humans can assess GUI visual aesthetics with reliability and validity. We encountered 148 studies assessing visual aesthetics through human judgments, either as an unidimensional or a multidimensional construct. Most studies that assess visual aesthetics as a composition of multiple dimensions apply the classical/expressive or the VisAWI questionnaire. Yet, we observed large variability regarding research design, including the number of assessed GUIs, ratings, and raters. Although most studies report demographic data, very few analyze their influence on the results. Less than half of the studies evaluate reliability or validity. We expect the results to contribute to subjective assessments of GUI visual aesthetics, help practitioners select and use appropriate methods, and point out research opportunities in this area.