Reviewerly/exHarmony huggingface dataset:
Pneumatic Reservoir Computing for Sensing Soft Body: Computational Ability of Air in Tube and Its Application to Posture Estimation of Soft Exoskeleton

Sensing and computing the body states of soft-bodied robots require new methods.Recent studies on soft robotics have shown their feasibility to be applied for these purposes; however, they only addressed solid parts and not the behavior of inner fluids in soft robotics.In this study, we investigated the possibility of a framework that can be used to estimate the body state by exploiting air dynamics in tubes connecting chambers.The framework was designed on the basis of the concept of physical reservoir computing.We focused on a case with a single tube connection.A benchmark task emulated a nonlinear system and was evaluated by simulation.The results showed that the computational ability depended on the inner diameter and length of the tube and can be increased by selecting a suitable diameter and length.We physically implemented the framework for the posture estimation of a soft exoskeleton using pneumatic artificial rubber muscles (PARMs) as the connected chambers and evaluated the accuracy of estimation of a thigh angle.The estimation accuracy showed a similar trend as a function of the tube properties as that observed in the simulation.The framework can exploit the dynamics of air in a tube and may be useful for the state estimation of soft-bodied robots.
Multi-scale Xception based depthwise separable convolution for single image super-resolution

The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performance in image super-resolution tasks. However, existing approaches related with image super-resolution fail to use the features information of low-resolution images as well as do not recover the hierarchical features for the final reconstruction purpose. In this research work, we have proposed a new architecture inspired by ResNet and Xception networks, which enable a significant drop in the number of network parameters and improve the processing speed to obtain the SR results. We are compared our proposed algorithm with existing state-of-the-art algorithms and confirmed the great ability to construct HR images with fine, rich, and sharp texture details as well as edges. The experimental results validate that our proposed approach has robust performance compared to other popular techniques related to accuracy, speed, and visual quality.
Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area.
Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection

Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant features are removed, reducing the complexity of the data and reducing training time. The objective of this paper was to design an optimizer that combines the well-known metaheuristic population-based optimizer, the grey wolf algorithm, and the gradient descent algorithm and test it for applications in feature selection problems. The proposed algorithm was first compared against the original grey wolf algorithm in 23 continuous test functions. The proposed optimizer was altered for feature selection, and 3 binary implementations were developed with final implementation compared against the two implementations of the binary grey wolf optimizer and binary grey wolf particle swarm optimizer on 6 medical datasets from the UCI machine learning repository, on metrics such as accuracy, size of feature subsets, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> -measure, accuracy, precision, and sensitivity. The proposed optimizer outperformed the three other optimizers in 3 of the 6 datasets in average metrics. The proposed optimizer showed promise in its capability to balance the two objectives in feature selection and could be further enhanced.
A Deep Learning-Based Framework for Human Activity Recognition in Smart Homes

Human behavior modeling in smart environments is a growing research area treating several challenges related to ubiquitous computing, pattern recognition, and ambient assisted living. Thanks to recent progress in sensing devices, it is now possible to design computational models able of accurate detection of residents’ activities and daily routines. For this goal, we introduce in this paper a deep learning-based framework for activity recognition in smart homes. This framework proposes a detailed methodology for data preprocessing, feature mining, and deep learning techniques application. The novel framework was designed to ensure a deep exploration of the feature space since three main approaches are tested, namely, the all-features approach, the selection approach, and the reduction approach. Besides, the framework proposes the evaluation and the comparison of several well-chosen deep learning techniques such as autoencoder, recurrent neural networks (RNN), and some of their derivatives models. Concretely, the framework was applied on the “Orange4Home” dataset which represents a recent dataset specially designed for smart homes research. Our main findings show that the best approach for efficient classification is the selection approach. Furthermore, our overall results outperformed baseline models based on random forest classifiers and the principal component analysis technique, especially the results of our RNN-based model for the all-features approach and the results of our autoencoder-based model for the feature reduction approach.
Quantum computing and computational law

Quantum computing technology will greatly enhance the abilities of the emerging field of computational law to express, model, and operationalise law in algorithmic form. Foreshadowing the harnessing of the power of quantum computing technology by the legal sector, this essay targets, with reference to computational complexity theory, the categories of computational problems which quantum computers are better equipped to deal with than are classical computers (‘quantum supremacy’). Subsequently, the essay demarcates the possible contours of legal ‘quantum supremacy’ by showcasing three anticipated legal fields of quantum technology: optimisation problems, burdens of proof, and machine learning. Acknowledging that the exact manifestation of quantum computing technology in the legal sector is as yet difficult to predict, the essay posits that the meaningful utilisation of quantum computing technology at a later stage presupposes a creative imagination of possible use-cases at the present.
Coarse-to-fine-grained method for image splicing region detection

In this study, we aim to improve the accuracy of image splicing detection. We propose a progressive image splicing detection method that can detect the position and shape of spliced region. Because image splicing is likely to destroy or change the consistent correlation pattern introduced by color filter array (CFA) interpolation process, we first used a covariance matrix to reconstruct the R, G and B channels of image and utilized the inconsistencies of the CFA interpolation pattern to extract forensics feature. Then, these forensics features were used to perform coarse-grained detection, and texture strength features were used to perform fine-grained detection. Finally, an edge smoothing method was applied to realize precise localization. As compared to the state-of-the-art CFA-based image splicing detection methods, the proposed method has a high-level detection accuracy and strong robustness against content-preserving manipulations and JPEG compression.
Federated Learning for Audio Semantic Communication

In this paper, the problem of audio semantic communication over wireless networks is investigated. In the considered model, wireless edge devices transmit large-sized audio data to a server using semantic communication techniques. The techniques allow devices to only transmit audio semantic information that captures the contextual features of audio signals. To extract the semantic information from audio signals, a wave to vector (wav2vec) architecture based autoencoder is proposed, which consists of convolutional neural networks (CNNs). The proposed autoencoder enables high-accuracy audio transmission with small amounts of data. To further improve the accuracy of semantic information extraction, federated learning (FL) is implemented over multiple devices and a server. Simulation results show that the proposed algorithm can converge effectively and can reduce the mean squared error (MSE) of audio transmission by nearly 100 times, compared to a traditional coding scheme.
Anomaly Detection of Water Level Using Deep Autoencoder

Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to detect anomaly pattern(s) of the water levels and report the analysis and time/point(s) of abnormality. This research’s motivation is the level difficulty and time-consuming managing facilities responsible for controlling water levels due to the rare occurrence of abnormal patterns. Consequently, we employed deep autoencoder, one of the types of artificial neural network architectures, to learn different patterns from the given sequences of data points and reconstruct them. Then we use the reconstructed patterns from the deep autoencoder together with a threshold to report which patterns are abnormal from the normal ones. We used a stream of time-series data collected from sensors to train the model and then evaluate it, ready for deployment as the anomaly detection system framework. We run extensive experiments on sensor data from water tanks. Our analysis shows why we conclude vanilla deep autoencoder as the most effective solution in this scenario.
Skeleton-based relational reasoning for group activity analysis

Research on group activity recognition mostly leans on the standard two-stream approach (RGB and Optical Flow) as their input features. Few have explored explicit pose information, with none using it directly to reason about the persons interactions. In this paper, we leverage the skeleton information to learn the interactions between the individuals straight from it. With our proposed method GIRN, multiple relationship types are inferred from independent modules, that describe the relations between the body joints pair-by-pair. Additionally to the joints relations, we also experiment with the previously unexplored relationship between individuals and relevant objects (e.g. volleyball). The individuals distinct relations are then merged through an attention mechanism, that gives more importance to those individuals more relevant for distinguishing the group activity. We evaluate our method in the Volleyball dataset, obtaining competitive results to the state-of-the-art. Our experiments demonstrate the potential of skeleton-based approaches for modeling multi-person interactions.
Neural ranking models for document retrieval

Abstract Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.
Hybrid Wolf Pack Algorithm and Particle Swarm Optimization Algorithm for Breast Cancer Diagnosis

In women, breast cancer is deadly disease which is increased the death rate of women.By exploiting the mammogram images, a precise and early recognition of breast cancer is a complex task.Therefore, a new breast cancer recognition technique was proposed that considered five important stages: segmentation, preprocessing, feature extraction, feature selection as well as classification.Initially, by exploiting the median filtering as well as Contrast Limited Adaptive Histogram Equalization (CLAHE), input mammogram images are preprocessed.Subsequently, through the region growing method, the preprocessed images are fed to segmentation.Then, from the segmented image, texture, geometric and gradient features are extracted.The feature vector length is higher, it is important to choose optimal features.Moreover, the optimal features chosen are performed using the proposed optimization method.After completing the selection of the optimal features, they are fed to the classification procedure including the Neural Network (NN) classifier.As an innovation, to improve the precision of diagnosis (benign as well as malignant), the NN weight is chosen optimally.The NN weight optimization and the optimal feature selection are attained using the Hybrid Wolf Pack Algorithm (WPA) and Particle Swarm Optimization (PSO) Algorithm called the Hybrid WPA-PSO algorithm.At last, the performance analysis is performed between the proposed and conventional techniques.
Research on Teaching Quality Evaluation Model of Physical Education Based on Simulated Annealing Algorithm

One of the most significant components of the teaching department is the evaluation of teaching quality. The traditional teaching quality evaluation model has the problems of low weight calculation accuracy and long evaluation time. With the development of educational informatization, modern information processing technology can be used to effectively evaluate teachers’ teaching quality. In this article, a physical education teaching quality evaluation model based on the simulated annealing algorithm is proposed. An evaluation index system is established based on the construction principles of the evaluation index system followed by the construction of a judgment matrix to calculate the weight of the evaluation index. The simulated annealing algorithm is employed to effectively optimize the weight of the evaluation index and improve the evaluation accuracy. In addition, the analytic hierarchy process (AHP) is used to test the consistency of the judgment matrix, and the weight ranking results of the evaluation indexes are obtained to complete the teaching quality evaluation of physical education. The experimental results show that the performance of the proposed model in terms of evaluation weight calculation accuracy and evaluation calculation time is higher than that of the existing models. Therefore, the proposed model can better meet the requirements of physical education teaching quality evaluation.
Monocular Depth Estimation Through Virtual-World Supervision and Real-World SfM Self-Supervision

Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mono</i> cular <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${d}$ </tex-math></inline-formula> epth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${e}$ </tex-math></inline-formula> stimation by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${v}$ </tex-math></inline-formula> irtual-world <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula> upervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision, and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.
Dict-BERT: Enhancing Language Model Pre-training with Dictionary

Pre-trained language models (PLMs) aim to learn universal language representations by conducting self-supervised training tasks on largescale corpus.Since PLMs capture word semantics in different contexts, the quality of word representations highly depends on word frequency, which usually follows a heavy-tailed distribution in the pre-training corpus.Thus, the embeddings of rare words on the tail are usually poorly optimized.In this work, we focus on enhancing language model pre-training by leveraging definitions of the rare words in dictionary.To incorporate a rare word definition as a part of input, we fetch it from the dictionary and append it to the end of the input text sequence.In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word-level and sentence-level alignment between the input text and rare word definition to enhance language representations.We evaluate the proposed model named Dict-BERT on the GLUE benchmark and eight specialized domain datasets.Extensive experiments show that Dict-BERT significantly improves the understanding of rare words and boosts model performance on various NLP downstream tasks.
A Novel Efficient Quantum Random Access Memory

Owing to the significant progress in manufacturing desktop quantum computers, the quest to achieve efficient quantum random access memory (QRAM) became inevitable. In this paper, we propose a novel efficient random access memory for quantum computers. The proposed QRAM has a fixed structure and can be used efficiently to store both known and unknown classical/quantum data. The storage capacity of the proposed QRAM is more efficient than that of the classical RAMs and can be used to store both classical and quantum information. Furthermore, the proposed model can access an arbitrary location in O(1) compared with other state-of-the-art models.
Incorporating Relative Position Information in Transformer-Based Sign Language Recognition and Translation

Recent advancements in machine translation tasks, with the advent of attention mechanisms and Transformer networks, have accelerated the research in Sign Language Translation (SLT), a spatio-temporal vision translation task. Fundamentally, Transformers are unaware of the sequential orderings in input, and therefore position-information should be explicitly fed into them. The sequence learning capability of Transformers is heavily dependent on this ordering information. Compared to the existing Transformer models for SLT that use baseline version with sinusoidal position embedding, this work focuses on incorporating a new positioning scheme into the Transformer networks, in the context of SLT. This is the first work in SLT that exploits the positioning scheme of Transformers for optimizing translation scores. The study proposes Gated Recurrent Unit (GRU)-Relative Sign Transformer (RST) for jointly learning Continuous Sign Language Recognition (CSLR) and translation. This model significantly improves the video translation quality. In this approach, GRU acts as the relative position encoder and RST is the Transformer model with relative position incorporated in the Multi-Head Attention (MHA). The evaluation was done on the RWTH-PHOENIX-2014T benchmark dataset. This study reports state-of-the-art Bilingual Evaluation Understudy (BLEU-4) score of 22.4 and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score of 48.55 for SLT, with GRU-RST. The best Word Error Rate (WER) obtained with this approach is 23.5. A detailed study of the position encoding schemes of Transformers is presented. Further, we analyze the translation performance under various combinations of the positioning schemes.
Teachers’ Views on The Use of Chatbots to Support English Language Teaching in a Mobile Environment

The development in machine learning has allowed chatbots to be widely applied into educational settings. However, limited study has investigated teacher’s views on its usage for teaching and learning. This paper reports an exploratory study on English as a Second Language (ESL) teachers’ views with regards to the use of chatbots for their teaching and learning delivery in a mobile environment. Using survey research design, views from 142 ESL teachers were gathered using questionnaires, which consist of Likert-scale items and open-ended questions. The teachers were sampled using purposive sampling method. The items and questions were developed based on the principles of the Community of Inquiry (CoI) framework, which focuses on social, cognitive and teaching presence. Data from the Likert-scaled items were analyzed using descriptive statistics while open-ended questions were coded thematically. The findings showed that teachers perceived the use of chatbots in giving feedback to their students as very helpful though some of them needed extra training on how to use them. They also thought chatbots can simulate an interaction cycle for students to practice the tar-get language. In addition, the teachers believed chatbots augmented a greater level of social presence, which eventually creates an environment for their students to be active. All in all, the findings provided valuable insights on the proper integration of chatbots in teaching and learning while gauging essential affordances and constraints of its use from ESL teachers’ perspective.