Reviewerly/exHarmony huggingface dataset:
A Steganography Embedding Method Based on CDF-DWT Technique for Reversible Data Hiding Application Using Elgamal Algorithm

Steganography is a data hiding method mainly used in the security purposes. While hiding more data in the embedding process the data may be lost and also cause some security problems. To avoid this problem a Steganography Embedding method is used. In this manuscript, Steganography Embedding method based on Cohen–Daubechies–Feauveau Discrete Wavelet Transform (CDF-DWT) technique to data hiding application using Elgamal algorithm is proposed. In this the cover image is taken for hiding the secret data. Then the cover image edges are detected and filtered with Speeded-Up Robust Features (SURF) method. Then the input secret data is encrypted with Elgamal algorithm. Then the secret data is hided under cover image for obtaining the stego image by process of Embedding using CDF DWT technique. In this data in the stego images are unreadable. To get readable secured data is extracted from the stego image and the data’s are decrypted to get secured secret data. The objective of this method is to safe guard the secret using Steganography method and to increase embedding efficiency, Embedding capacity and carrier capacity and to reduce the execution time. The MATLAB simulation results of the proposed CDF DWT technique with Elgamal algorithm portrays better outcomes such as Peak to Signal Noise Ratio (PSNR), Mean Square Error (MSE) (lower), Bit Error Rate (BER) was Lower, Execution time provides is lower, Carrier Capacity and Embedding Capacity are much higher and the values are compared with the existing method such as Elliptic Galois in cryptography and in the Steganography method with Adaptive Firefly Algorithm (EGC-AFA), Reversible Data hiding within Encrypted images Via Adaptive Embedding Strategy with block selection (RDHEI-AES), Double Linear Regression Prediction based Reversible Data Hiding in Encrypted Image (DLRP-RDHEI) respectively.
A systematic review approach to the understanding of intercreativity as an educational resource

Intercreativity is a phenomenon with significant social, cultural and educational implications in the postdigital era. Its meaning refers to the fact of solving problems and making a collective production. However –in a historical and philosophical context that has led to the rise and importance of knowledge production– intercreativity is a phenomenon insufficiently analysed. Searching a variety of interdisciplinary databases, this paper summarises a systematic review conducted among 49 scientific publications that mentioned the term intercreativity and associated it to other theoretical concepts. The period of time covered was 2002–2021 and peer-review papers in Spanish and English languages were collected following the PRISMA checklist and flow diagram. The results suggest that applying intercreative strategies in education is crucial in a social context where participation and communication are essential and in which education moves to digital spaces that are by nature open and cooperative. Not restricted only to digital environments, the nature of these spaces tends to support the intercreative practices as well as the values derived from it. Intercreativity in education entails an intersubjective production of knowledge, collaborative strategies and the development of critical pedagogies that position digital education as a vehicle for social transformation towards solidarity and community.
Localizing weak microseismic events using transfer learning with a deep neural network

ABSTRACT Retrieving accurate microseismic source locations induced by hydraulic‐fracturing operations is an important step to gain insights into the hydraulically stimulated reservoir volume. Recently, deep neural networks have been proposed that directly recover source locations from the seismic waveforms. The optimal performance of the proposed deep neural networks usually requires large training sets. The need for a large training set can be circumvented if a previously trained deep neural network can be used to start the training process with its weights instead of randomly initialized weights. These weights can then be fine‐tuned using a smaller training set, which is also known as transfer learning . In this work, we implement a transfer learning workflow to update the weights of a deep neural network that was initially trained on a large synthetic dataset to localize microseismic events. We present two methods of processing, namely one post‐monitoring mode and one continuous mode where the processing takes place during the monitoring period. We apply the methods to field data from a hydraulic fracturing site in Texas, USA. In the first scenario, a subset of the field data from the entire monitoring period is used to update the weights of the deep neural network, which is then applied to the remaining data resulting in mean and median distances of 227 and 182 m, respectively, compared to the results of a good localization method. In the second scenario, the deep neural network is updated daily with previously detected and located events and applied to the events detected the following day. Since the observed data used for training generally do not cover a wide range of source locations, we enrich the training set with synthetic data. The addition of synthetics for transfer learning ensures that the updated deep neural network provides accurate source locations for events with locations far from locations used during transfer learning. Transfer learning combining synthetic and real data performs significantly better (more consistent) locations than transfer learning without synthetics.
Relative entropies of coherence of X states in three-dimensional mutually unbiased bases

Abstract In this paper, we calculate the density matrices of one class of X states in the three-dimensional mutually unbiased bases and find that the density matrices are equal to their corresponding conjugate transpose matrices. After obtaining the relations of these density matrices, we calculate the relative entropies of coherence for the density matrices of X states in these nontrivial mutually unbiased bases and obtain that their relative entropies of coherence are equal. At last, the density matrices of other two classes of X states in these mutually unbiased bases are discussed. We also investigate their relative entropies of coherence and clarify their relationships.
Within the scope of prediction: Shaping intrinsic rewards via evaluating uncertainty

The agent of reinforcement learning based approaches needs to explore to learn more about the environment to seek optimal policy. However, simply increasing the frequency of stochastic exploration sometimes fails to work or even causes the agent to fall into traps. To solve the problem, it is essential to improve the quality of exploration. An approach, referred to as the scope of prediction based on uncertainty exploration (SPE), is proposed, taking advantage of the uncertainty mechanism and considering the stochasticity of prospecting. As by uncertainty mechanism, the unexpected states make more curiosity, the model derives higher uncertainty by projecting future scenarios to compare with the actual future to explore the world. The SPE method utilizes a prediction network to predict subsequent observations and calculates the mean squared difference value of the real observations and the following observations to measure uncertainty, encouraging the agent to explore unknown regions more effectively. Moreover, to reduce the noise interference caused by uncertainty, a reward-penalty model is developed to discriminate the noise by current observations and action prediction for future rewards to improve the interference ability against noise so that the agent can escape from the noisy region. Experiment results showed that deep reinforcement learning approaches equipped with SPE demonstrated significant improvements in simulated environments.
Design and Field Trial of Lumino in Homes: Supporting Reflective Life by Archiving and Showing Daily Moods with Light Colors

Although archiving daily moods in a diary is a common behavior, reflection is difficult because of the pressure brought about by continuous self-tracking of personal moods. In this paper, we developed Lumino, a standalone device that enables users to log their daily moods with colored lights and switch mode to show the log by physically sliding a circular lighting plate. The results of our three-week in-field study with six participants revealed that Lumino helped users encounter emotional archives as it separated reflective experiences from daily life activities. Moreover, Lumino helped prevent deep reflection on negative emotions and maintained privacy through abstract color expression. We also found user's various trials to control emotion reflections in their own usage patterns. We propose considerations for augmenting daily mood recording experiences combined with existing diary practices and further implications for designing how to support positively the negative reflections in everyday spaces.
One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees

Sample-based estimation, which uses a sample to estimate population parameters (e.g., SUM, COUNT, and AVG), has various applications in database systems. A sampler defines how samples are drawn from a population. Various samplers have been proposed (e.g., uniform sampler, stratified sampler, and measure-biased sampler), since there is no single sampler that works well in all cases. To overcome the "one size does not fit all" challenge, we study how to combine multiple samplers to estimate population parameters, and propose SamComb, a novel bandit-based sampler combination framework. Given a set of samplers, a budget, and a population parameter, SamComb can automatically decide how much budget should be allocated to each sampler so that the combined estimation achieves the highest accuracy. We model this sampler combination problem as a multi-armed bandit (MAB) problem and propose effective approaches to balance the exploration and exploitation trade-off in a principled way. We provide theoretical guarantees for our approaches and conduct extensive experiments on both synthetic and real datasets. The results show that there is a strong need to combine multiple samplers, in order to obtain accurate estimations without the knowledge about population predicates and distributions, and SamComb is an effective framework to achieve this goal.
Multilevel Monte Carlo for solving POMDPs on-line

Planning under partial observability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for systems with complex dynamics remains challenging. Most on-line solvers rely on a large number of forward simulations and standard Monte Carlo methods to compute the expected outcomes of actions the robot can perform. For systems with complex dynamics, for example, those with non-linear dynamics that admit no closed-form solution, even a single forward simulation can be prohibitively expensive. Of course, this issue exacerbates for problems with long planning horizons. This paper aims to alleviate the above difficulty. To this end, we propose a new on-line POMDP solver, called Multilevel POMDP Planner (MLPP), that combines the commonly known Monte-Carlo-Tree-Search with the concept of Multilevel Monte Carlo to speed up our capability in generating approximately optimal solutions for POMDPs with complex dynamics. Experiments on four different problems involving torque control, navigation and grasping indicate that MLPP substantially outperforms state-of-the-art POMDP solvers.
Resolution Reconstruction Using Patch-Match And Wavelet Decomposition

This paper proposes a new method to preserve edges and enhance image details while reconstructing image resolution based on Discrete Wavelet Transform (DWT) image decomposition combined with a patch-matching technique using a single low-resolution image. Discrete wavelet transformation on the low-resolution input provides LL, LH, HL, and HH subbands considered for further process. Later, bicubic interpolation approximates the three subbands LH, HL, and HL of the inputted low-resolution image. Next, the patch-match approach is employed to estimate the HF details of the input image that replaces the LL subband. Then, the inverse DWT is applied on the four subbands to obtain a sharper reconstructed image. The findings obtained on various images indicate that the proposed approach preserves edges and enhances the fine details with minimal distortion and smoothing. The quantitative and visual results of the presented method demonstrate its efficacy compared to existing techniques.
Elements of the methodology for teaching the theme "Creating and processing of sound information" using the free sound editor Audacity

The article presents the framework of the methodology for teaching the theme "Creating and processing of sound information" of the informatics course at the level of basic general education using the free cross-platform offline sound editor Audacity. The concept of "teaching methodology" refers to the process of transferring from the teacher to the student a certain necessary and sufficient set of algorithms for creating certain information objects that correspond to the information technology being studied. As a result of the analysis of possible software options to support the teaching of the theme under consideration in the informatics course, it is concluded that offline free cross-platform sound editors with low minimum system requirements is the best option. One of the most successful software options is the Audacity audio editor, which has a number of advantages over its counterparts. The article questions the principle of invariance of the method of teaching information technology, in particular technology for creating and processing sound information, with respect to the software and hardware used in the educational process, and substantiates the conclusion about the partial failure of this principle. A possible variant of the framework of the methodology for teaching the theme of the informatics course under consideration is given, which includes the division of the learning content into seven stages. Each of the stages includes a description of the presentation of the relevant educational material, including the technology for performing practical work aimed at mastering the basic elements of the functionality of the Audacity sound editor by students. The description of the author’s teaching methodology also contains the lesson planning corresponding to it on the theme " Creating and processing of sound information" of the informatics course for the basic school. The advantages of the proposed teaching methodology include the possibility of its use for both full-time and distance learning, as well as the low cost of its use in the educational process of a general education school. In addition, the methodology under consideration ensures a high resistance of the educational process to the destructive impact on the information structure of Russian educational organizations, which includes, among other things, an unmotivated refusal of licenses to use foreign-made proprietary software.
An Improved Order-Preserving Pattern Matching Algorithm Using Fingerprints

Two strings of the same length are order isomorphic if their relative orders are the same. The order-preserving pattern matching problem is to find all substrings of text T that are order isomorphic to pattern P when T(|T|=n) and P(|P|=m) are given. An O(mn+nqlogq+q!)-time algorithm using the O(m+q!) space for the order-preserving pattern matching problem has been proposed utilizing fingerprints of q-grams based on the factorial number system and the bad character heuristic. In this paper, we propose an O(mn+2q)-time algorithm using the O(m+2q) space for the order-preserving pattern matching problem, but utilizing fingerprints of q-grams converted to binary numbers. A comparative experiment using three types of time series data demonstrates that the proposed algorithm is faster than existing algorithms because it reduces the number of order isomorphism tests.
Preservice Teachers’ Perceptions of Linguistic Abilities and Privacy Policies in the Use of Visual Materials During Their Own and Their Tutors’ Lessons

This article analyzes the capacity for improvement of digital observation in initial teacher training to investigate whether the implementation of visual materials is effective for the reinforcement of previously taught content, the assessment of one’s own linguistic abilities and whether it motivates learners. To do so, the subjects analyzed classroom behavior and learning typology through the reflection of their own videos recorded with children and tutors. Data was collected through 12 closed-ended questions that follow the Likert scale and one open-ended question for general reflection. The questions focused on the motivation and interest of the students with respect to the videos viewed, comprehension, learning, perceived usefulness, and the problem arising with the privacy of the Students’ personal data. The responses were classified and their frequencies and percentages were calculated. The results show that implementing video tools in the classroom and reflecting on their content afterward can be an effective means of assessing one’s learning and language skills. In addition, the study highlights the inherent complexities posed by EU GDPR regulations and the significant barriers to integrating video technology into the classroom for schoolchildren in these contexts.
Adaptive-Attentive Geolocalization From Few Queries: A Hybrid Approach

We tackle the task of cross-domain visual geo-localization, where the goal is to geo-localize a given query image against a database of geo-tagged images, in the case where the query and the database belong to different visual domains. In particular, at training time, we consider having access to only few unlabeled queries from the target domain. To adapt our deep neural network to the database distribution, we rely on a 2-fold domain adaptation technique, based on a hybrid generative-discriminative approach. To further enhance the architecture, and to ensure robustness across domains, we employ a novel attention layer that can easily be plugged into existing architectures. Through a large number of experiments, we show that this adaptive-attentive approach makes the model robust to large domain shifts, such as unseen cities or weather conditions. Finally, we propose a new large-scale dataset for cross-domain visual geo-localization, called SVOX.
Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models

The analysis of kin relations from visual data represents a challenging research problem with important real-world applications. However, research in this area has mostly been limited to the analysis of facial images, despite the potential of other physical (human) characteristics for this task. In this paper, we therefore study the problem of kinship verification from ear images and investigate whether salient appearance characteristics, useful for this task, can be extracted from ear data. To facilitate the study, we introduce a novel dataset, called KinEar, that contains data from 19 families with each family member having from 15 to 31 ear images. Using the KinEar data, we conduct experiments using a Siamese training setup and 5 recent deep learning backbones. The results of our experiments suggests that ear images represent a viable alternative to other modalities for kinship verification, as 4 out of 5 considered models reach a performance of over 60% in terms of the Area Under the Receiver Operating Characteristics (ROC-AUC).
Knowledge graphs for Smart Cities

The paper deals with the research of Smart City ontologies and Knowledge graphs and proposes a new organizational structure on how to use them in practical Smart city management. An illustrative example uses a design of a knowledge graph for parking problems in Prague. The results confirmed the usefulness of Knowledge graphs connected to the city’s data platform. The presented methodology can be used as a practical tool for monitoring the city’s sustainability and resiliency.
Use of an augmented reality sand table for satellite remote sensing education

This experiment utilized advanced visualization technology for the delivery of an introductory remote sensing lesson in an undergraduate geography course. Given the numerous immersive visualization solutions available, it is now possible to leverage the capabilities of augmented, mixed, and virtual reality (AR, MR, and VR) technology in a classroom learning environment. For instructors, it is important to understand how advanced visualization technologies can enhance the learning experience and enable greater knowledge retention. To validate the use of an AR sand table/sandbox in a group academic setting, approximately 400 participants were evaluated to assess the impact on students’ retention of both foundational and practical remote sensing concepts. This work represents a between-subjects design with two AR sand table experimental groups (instructor sand table interaction, and student sand table interaction) and one control group with no sand table exposure. The use of the AR sand table in a classroom environment was found to improve retention of foundational remote sensing knowledge and elevate the assessment performance for subjects identified as lower performers.
Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network

The most crucial event in a mining project is the selection of an appropriate mining method (MMS). Consequently, determining the optimal choice is critical because it impacts most of the other key decisions. This study provides a concise overview of the development of multiple selection methods using a cascade-forward backpropagation neural network (CFBPNN). Numerous methods of multicriteria decision-making (MCDM) are discussed and compared herein. The comparison includes several factors, such as applicability, subjectivity, qualitative and quantitative data, sensitivity, and validity. The application of artificial intelligence is presented and discussed using CFBPNN. The Chengchao iron mine was selected for this investigation to pick the optimum mining method. The results revealed that cut and fill stoping is the most appropriate mining method, followed by sublevel and shrinkage stoping methods. The least appropriate method is open-pit mining, followed by room and pillar and longwall mining methods.
Bitcoin Price Prediction- an Analysis of Various Regression Methods

Cryptocurrency is a fascinating area of research developed due to the rapid development of financial technologies. One of the well-received cryptocurrencies is Bitcoin. The motivation behind this paper is to predict bitcoin prices with high accuracy using various regression-based models. Bitcoin is the fastest growing cryptocurrency. We have seen drastic changes in bitcoin prices over time. To make predictions for such changes, we use machine learning techniques over real-time data recorded for every 24-hour time interval since the presence of bitcoin beginning in the year 2009. We select eleven different regression models, analyze these models and obtain the best regression-based model for bitcoin price prediction. The results obtained from the study depict that the Bayesian ridge regressor outperforms all the other regression-based models followed by the Linear regressor.