Reviewerly/exHarmony huggingface dataset:
A Novel Construction of Efficient Substitution-Boxes Using Cubic Fractional Transformation

A symmetric block cipher employing a substitution-permutation duo is an effective technique for the provision of information security. For substitution, modern block ciphers use one or more substitution boxes (S-Boxes). Certain criteria and design principles are fulfilled and followed for the construction of a good S-Box. In this paper, an innovative technique to construct substitution-boxes using our cubic fractional transformation (CFT) is presented. The cryptographic strength of the proposed S-box is critically evaluated against the state of the art performance criteria of strong S-boxes, including bijection, nonlinearity, bit independence criterion, strict avalanche effect, and linear and differential approximation probabilities. The performance results of the proposed S-Box are compared with recently investigated S-Boxes to prove its cryptographic strength. The simulation and comparison analyses validate that the proposed S-Box construction method has adequate efficacy to generate efficient candidate S-Boxes for usage in block ciphers.
Robust registration for remote sensing images by combining and localizing feature- and area-based methods

Highly accurate registration is one of the essential requirements for numerous applications of remote sensing images. Toward this end, we have developed a robust algorithm by combining and localizing feature- and area-based methods. A block-weighted projective (BWP) transformation model is first employed to map the local geometric relationship with weighted feature points in the feature-based stage, for which the weight is determined by an inverse distance weighted (IDW) function. Subsequently, the outlier-insensitive (OIS) model aims to further optimize the registration in the area-based stage. Considering the inevitable outliers (e.g., cloud, noise, land-cover change), OIS integrates Huber estimation with the structure tensor (ST), which is an approach that is robust to residual errors and outliers while preserving edges. Four pairs of remote sensing images with varied terrain features were tested in the experiments. Compared with the-state-of-art algorithms, the proposed algorithm is more effective, in terms of both visual quality and quantitative evaluation.
Emoji and rapport management in Spanish WhatsApp chats

Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-mediated communication, but they may also perform pragmatic functions, such as signaling the illocutionary force of the utterance (Dresner and Herring, 2010), mitigating threatening formulations (Wilson, 1993), or strengthening expressive speech acts (Skovholt et al., 2014). Despite their growing popularity, little pragmatic research to date specifically addresses emoji. The present paper bridges this gap by exploring the functions of emoji in a corpus of WhatsApp chats written in Spanish. Drawing on Spencer-Oatey's (2000, 2005) rapport management framework, the analysis shows that emoji are used across different domains in the corpus: they not only upgrade or downgrade different speech acts (illocutionary domain), as pointed out by previous research, but they also contribute to achieving a successful interaction by signaling closing sections or by helping to negotiate openings (discourse domain), as well as serving as a way to frame playful interactions (stylistic domain). This study also shows that some practices related to the use of emoji may be influenced by Spanish culture.
PrivKV: Key-Value Data Collection with Local Differential Privacy

Local differential privacy (LDP), where each user perturbs her data locally before sending to an untrusted data collector, is a new and promising technique for privacy-preserving distributed data collection.The advantage of LDP is to enable the collector to obtain accurate statistical estimation on sensitive user data (e.g., location and app usage) without accessing them.However, existing work on LDP is limited to simple data types, such as categorical, numerical, and set-valued data.To the best of our knowledge, there is no existing LDP work on key-value data, which is an extremely popular NoSQL data model and the generalized form of set-valued and numerical data.In this paper, we study this problem of frequency and mean estimation on key-value data by first designing a baseline approach P rivKV within the same "perturbation-calibration" paradigm as existing LDP techniques.To address the poor estimation accuracy due to the clueless perturbation of users, we then propose two iterative solutions P rivKV M and P rivKV M + that can gradually improve the estimation results through a series of iterations.An optimization strategy is also presented to reduce network latency and increase estimation accuracy by introducing virtual iterations in the collector side without user involvement.We verify the correctness and effectiveness of these solutions through theoretical analysis and extensive experimental results.
Contracting in the Smart Era: The Implications of Blockchain and Decentralized Autonomous Organizations for Contracting and Corporate Governance

This paper explores blockchain technology’s potential to alter contracting both in the market and within organizations. We identify and discuss how blockchain reduces certain types of transaction c...
Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking.
From Gender Biases to Gender-Inclusive Design

In recent years, research has revealed gender biases in numerous software products. But although some researchers have found ways to improve gender participation in specific software projects, general methods focus mainly on detecting gender biases -- not fixing them. To help fill this gap, we investigated whether the GenderMag bias detection method can lead directly to designs with fewer gender biases. In our 3-step investigation, two HCI researchers analyzed an industrial software product using GenderMag; we derived design changes to the product using the biases they found; and ran an empirical study of participants using the original product versus the new version. The results showed that using the method in this way did improve the software's inclusiveness: women succeeded more often in the new version than in the original; men's success rates improved too; and the gender gap entirely disappeared.
On the Shoulder of the Giant

We propose a multi-scale Mixed Reality (MR) collaboration between the Giant, a local Augmented Reality user, and the Miniature, a remote Virtual Reality user, in Giant-Miniature Collaboration (GMC). The Miniature is immersed in a 360-video shared by the Giant who can physically manipulate the Miniature through a tangible interface, a combined 360-camera with a 6 DOF tracker. We implemented a prototype system as a proof of concept and conducted a user study (n=24) comprising of four parts comparing: A) two types of virtual representations, B) three levels of Miniature control, C) three levels of 360-video view dependencies, and D) four 360-camera placement positions on the Giant. The results show users prefer a shoulder mounted camera view, while a view frustum with a complimentary avatar is a good visualization for the Miniature virtual representation. From the results, we give design recommendations and demonstrate an example Giant-Miniature Interaction.
Drag:on

Standard controllers for virtual reality (VR) lack sophisticated means to convey a realistic, kinesthetic impression of size, resistance or inertia. We present the concept and implementation of Drag:on, an ungrounded shape-changing VR controller that provides dynamic passive haptic feedback based on drag, i.e. air resistance, and weight shift. Drag:on leverages the airflow occurring at the controller during interaction. By dynamically adjusting its surface area, the controller changes the drag and rotational inertia felt by the user. In a user study, we found that Drag:on can provide distinguishable levels of haptic feedback. Our prototype increases the haptic realism in VR compared to standard controllers and when rotated or swung improves the perception of virtual resistance. By this, Drag:on provides haptic feedback suitable for rendering different virtual mechanical resistances, virtual gas streams, and virtual objects differing in scale, material and fill state.
Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks

This paper presents a viewpoint from computer vision to the radar echo extrapolation task in the precipitation nowcasting domain. Inspired by the success of some convolutional recurrent neural network models in this domain, including convolutional LSTM, convolutional GRU and trajectory GRU, we designed a new sequence-to-sequence neural network structure to leverage these models in a realistic data context. In this design, we decreased the numbers of channels in high abstract recurrent layers rather than increasing them. We formulated the task as a problem of encoding five radar images and predicting 10 steps ahead at the pixel level, and found that using only the common mean squared error can misguide the training and mislead the testing. Especially, the image quality of last predictions usually degraded rapidly. As a solution, we employed some visual image quality assessment techniques including Structural Similarity (SSIM) and multi-scale SSIM to train our models. Experimental results show that our structure was more tolerant to increasing uncertainty in the data, and the use of image quality metrics can significantly reduce the blurry image issue. Moreover, we found that using SSIM was very effective and a combination of SSIM with mean squared error and mean absolute error yielded the best prediction quality.
Breast cancer outcome prediction with tumour tissue images and machine learning

Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N = 1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients.In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p = 0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p = 0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples.Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.
Time-Conditioned Action Anticipation in One Shot

The goal of human action anticipation is to predict future actions. Ideally, in real-world applications such as video surveillance and self-driving systems, future actions should not only be predicted with high accuracy but also at arbitrary and variable time-horizons ranging from short- to long-term predictions. Current work mostly focuses on predicting the next action and thus long-term prediction is achieved by recursive prediction of each next action, which is both inefficient and accumulates errors. In this paper, we propose a novel time-conditioned method for efficient and effective long-term action anticipation. There are two key ingredients to our approach. First, by explicitly conditioning our anticipation network on time allows to efficiently anticipate also long-term actions. And second, we propose an attended temporal feature and a time-conditioned skip connection to extract relevant and useful information from observations for effective anticipation. We conduct extensive experiments on the large-scale Epic-Kitchen and the 50Salads Datasets. Experimental results show that the proposed method is capable of anticipating future actions at both short-term and long-term, and achieves state-of-the-art performance.
New Noise-Tolerant Neural Algorithms for Future Dynamic Nonlinear Optimization With Estimation on Hessian Matrix Inversion

Nonlinear optimization problems with dynamical parameters are widely arising in many practical scientific and engineering applications, and various computational models are presented for solving them under the hypothesis of short-time invariance. To eliminate the large lagging error in the solution of the inherently dynamic nonlinear optimization problem, the only way is to estimate the future unknown information by using the present and previous data during the solving process, which is termed the future dynamic nonlinear optimization (FDNO) problem. In this paper, to suppress noises and improve the accuracy in solving FDNO problems, a novel noise-tolerant neural (NTN) algorithm based on zeroing neural dynamics is proposed and investigated. In addition, for reducing algorithm complexity, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is employed to eliminate the intensively computational burden for matrix inversion, termed NTN-BFGS algorithm. Moreover, theoretical analyses are conducted, which show that the proposed algorithms are able to globally converge to a tiny error bound with or without the pollution of noises. Finally, numerical experiments are conducted to validate the superiority of the proposed NTN and NTN-BFGS algorithms for the online solution of FDNO problems.
Application of AHP, TOPSIS, and TFNs to plant selection for phytoremediation of petroleum-contaminated soils in shale gas and oil fields

In order to reduce the cost of phytoremediators selection in phytoremediation on petroleum-contaminated soils, long-term experiments and in-situ trials included, an evaluation system for plant selection in phytroremediaiton was conducted in accordance with two case study, Changning shale gas field and Liaohe oil field. The evaluation system was constructed with two multi-objective decision-making methods, analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS), combined with triangular fuzzy numbers (TFNs). The comprehensive evaluation system was involved with three criteria and nine indexes. The calculated weights indicated that moisture was the most influential factor in the plants selection with the highest weight in both Changning shale gas field and Liaohe oil field. A total of 23 plants from these two study areas were used as alternative plants for phytoremediation and it suggested that Testuca arundinacea might be a suitable plant for the phytoremediation process after determination. Results of sensitivity analysis confirmed that the combination of the fuzzy AHP and TOPSIS in these process was reasonable and practicable. The successful application of fuzzy AHP and TOPSIS, to plant selection for the phytoremediation of petroleum-contaminated soils was also highlighted. The usefulness of the combined application of these two methodologies for decision-makers is anticipated. Such a combination is a convenient and effective approach to the selection of suitable plants for the phytoremediation of petroleum-contaminated soils and it can provide scientific and technological support for decision-makers during the supervision of petroleum-contaminated sites.
Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach

This paper proposes a quaternion-valued one-layer recurrent neural network approach to resolve constrained convex function optimization problems with quaternion variables. Leveraging the novel generalized Hamilton-real (GHR) calculus, the quaternion gradient-based optimization techniques are proposed to derive the optimization algorithms in the quaternion field directly rather than the methods of decomposing the optimization problems into the complex domain or the real domain. Via chain rules and Lyapunov theorem, the rigorous analysis shows that the deliberately designed quaternion-valued one-layer recurrent neural network stabilizes the system dynamics while the states reach the feasible region in finite time and converges to the optimal solution of the considered constrained convex optimization problems finally. Numerical simulations verify the theoretical results.
Using Augmented Reality Flashcards to Learn Vocabulary in Early Childhood Education

Augmented Reality (AR) flashcards have emerged as an important educational resource for language learning. However, there is limited research on its application in early childhood education. This article aims to explore the technology’s value in early childhood education by comparing it with traditional paper flashcards. To that end, a study was conducted in which 98 children, aged between 5 and 6 years, and four teachers, all from a Macau kindergarten, took part. One group of children had animal-related vocabulary using AR flashcards, while another group used traditional flashcards. To measure the effectiveness of the two approaches, the children underwent pre- and posttests on their vocabulary and the teachers were interviewed. For the data analysis, paired-sample t tests and independent-sample t tests were utilized, and the level of effect was measured. The results showed that both AR and traditional flashcards could significantly improve children’s vocabulary learning and that there was no significant difference in effectiveness between the AR and traditional flashcard methods. The teachers indicated that the children enjoyed the AR learning activities, but that there were certain challenges associated with using AR flashcards in a kindergarten setting.
Combining hierarchical clustering approaches using the PCA method

In expert systems, data mining methods are algorithms that simulate humans' problem-solving capabilities. Clustering methods as unsupervised machine learning methods are crucial approaches to categorize similar samples in the same categories. The use of different clustering algorithms to a given dataset produces clusters with different qualities. Hence, many researchers have applied clustering combination methods to reduce the risk of choosing an inappropriate clustering algorithm. In these methods, the outputs of several clustering algorithms are combined. In these research works, the input hierarchical clusterings are transformed to descriptor matrices and their combination is achieved by aggregating their descriptor matrices. In previous works, only element-wise aggregation operators have been used and the relation between the elements of each descriptor matrix has been ignored. However, the value of each element of the descriptor matrix is meaningful in comparison with its other elements. The current study proposes a novel method of combining hierarchical clustering approaches based on principle component analysis (PCA). PCA as an aggregator allows considering all elements of the descriptor matrices. In the proposed approach, basic clusters are made and transformed to descriptor matrices. Then, a final matrix is extracted from the descriptor matrices using PCA. Next, a final dendrogram is constructed from the matrix that is used to summarize the results of the diverse clustering. The experimental results on popular available datasets show the superiority of the clustering accuracy of the proposed method over basic clustering methods such as single, average and centroid linkage and previously combined hierarchical clustering methods. In addition, statistical tests show that the proposed method significantly outperformed hierarchical clustering combination methods with element-wise averaging operators in almost all tested datasets. Several experiments have also been conducted which confirm the robustness of the proposed method for its parameter setting.
An Image Encryption Scheme Based on Block Scrambling, Modified Zigzag Transformation and Key Generation Using Enhanced Logistic—Tent Map

Nowadays, the images are transferred through open channels that are subject to potential attacks, so the exchange of image data requires additional security in many fields, such as medical, military, banking, etc. The security factors are essential in preventing the system from brute force and differential attacks. We propose an Enhanced Logistic Map (ELM) while using chaotic maps and simple encryption techniques, such as block scrambling, modified zigzag transformation for encryption phases, including permutation, diffusion, and key stream generation to withstand the attacks. The results of encryption are evaluated while using the histogram, correlation analysis, Number of Pixel Change Rate (NPCR), Unified Average Change Intensity (UACI), Peak-Signal-to-Noise Ratio (PSNR), and entropy. Our results demonstrate the security, reliability, efficiency, and flexibility of the proposed method.