Reviewerly/exHarmony huggingface dataset:
CNO-LSTM: A Chaotic Neural Oscillatory Long Short-Term Memory Model for Text Classification

Long Short-Term Memory (LSTM) networks are unique to exercise data in its memory cell with long-term memory as Natural Language Processing (NLP) tasks have inklings of intensive time and computational power due to their complex structures like magnitude language model Transformer required to pre-train and learn billions of data performing different NLP tasks. In this paper, a dynamic chaotic model is proposed for the objective of transforming neurons states in network with neural dynamic characteristics by restructuring LSTM as Chaotic Neural Oscillatory-Long-Short Term Memory (CNO-LSTM), where neurons in LSTM memory cells are weighed in substitutes by oscillatory neurons to speed up computational training of language model and improve text classification accuracy for real-world applications. From the implementation perspective, five popular datasets of general text classification including binary, multi classification and multi-label classification are used to compare with mainstream baseline models on NLP tasks. Results showed that the performance of CNO-LSTM, a simplified model structure and oscillatory neurons state in exercising different types of text classification tasks are above baseline models in terms of evaluation index such as Accuracy, Precision, Recall and F1. The main contributions are time reduction and improved accuracy. It achieved approximately 46.76% of the highest reduction training time and 2.55% accuracy compared with vanilla LSTM model. Further, it achieved approximately 35.86% in time reduction compared with attention model without oscillatory indicating that the model restructure has reduced GPU dependency to improve training accuracy.
Autoencoder Based Feature Selection for Phishing URL Attack Detection in IoT Using Stacked Autoencoder (AFS-SAE)

With the rapid rise of the economy and technology, phishing assaults have increased in recent years. Phishing can also be done with publicly available sources by attackers with less technical knowledge. Businesses and customers both suffer financial losses, lowering their trust in e-commerce. As a result, countermeasures must be implemented to prevent phishing attempts on the website. Phishing is a type of cybercrime when an attacker targets a specific person or an organisation by elevating or promoting them as an official person of the organisation via an organization e-mail, fake websites, social media, or other communication means. In a phishing assault, the attacker sends phishing e-mails with bogus links or attachments that can perform a variety of tasks, such as capturing the victim’s login credentials or stealing any other crucial data [28]. These e-mails cause victims to lose money through the internet. Phishing is a harmful rising threat on the internet in which attackers attempt to steal the user’s login credentials such as usernames, passwords, or even bank details by exploiting pirated websites. There are some current security mechanisms, such as firewalls, but they are ineffective in blocking many types of attacks since they depend on a set of established criteria that only identify and block predetermined attacks. We need a defence system that can learn attack possibilities so that it can identify and block not just current attacks but also future attacks. Phishing website detection and prevention at the network layer, the proposed work uses a enhanced Dragonfly optimization technique for feature selection to train an Recurrent Neural Network (RNN) model. The dataset, which comprises of a collection of legal and phishing websites, is used to train the model and compared the results using the accuracy, recall, precision, and F1-score criteria.
A Good Data Augmentation Policy is not All You Need: A Multi-Task Learning Perspective

Data augmentation, which improves the diversity of datasets by applying image transformations, has become one of the most effective techniques in visual representation learning. Usually, the design of augmentation policies faces a diversity-difficulty trade-off. On the one hand, a simple augmentation leads to a low training set diversity, which can not improve model performance significantly. On the other hand, an excessively hard augmentation has an overlarge regularization effect which harms model performance. Recently, automatic augmentation methods have been proposed to address this issue by searching the optimal data augmentation policy from a predefined searching space. However, these methods still suffer from heavy searching overhead or complex optimization objectives. In this paper, instead of searching the optimal augmentation policy, we propose to break the diversity-difficulty trade-off from a multi-task learning perspective. By formulating model learning on the augmented images and the original images as the auxiliary task and the primary task in multi-task learning respectively, the hard augmentation does not directly influence the training of the primary branch and thus its negative influence can be alleviated. Hence, neural networks can learn valuable semantic information even with a totally random augmentation policy. Experimental results on ten datasets for four tasks demonstrate the superiority of our method over the other twelve methods. Codes have been released in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ArchipLab-LinfengZhang/data-augmentation-multi-task</uri> .
A Perceptual Quality Metric for Video Frame Interpolation

Research on video frame interpolation has made significant progress in recent years. However, existing methods mostly use off-the-shelf metrics to measure the quality of interpolation results with the exception of a few methods that employ user studies, which is time-consuming. As video frame interpolation results often exhibit unique artifacts, existing quality metrics sometimes are not consistent with human perception when measuring the interpolation results. Some recent deep learning-based perceptual quality metrics are shown more consistent with human judgments, but their performance on videos is compromised since they do not consider temporal information. In this paper, we present a dedicated perceptual quality metric for measuring video frame interpolation results. Our method learns perceptual features directly from videos instead of individual frames. It compares pyramid features extracted from video frames and employs Swin Transformer blocks-based spatio-temporal modules to extract spatio-temporal information. To train our metric, we collected a new video frame interpolation quality assessment dataset. Our experiments show that our dedicated quality metric outperforms state-of-the-art methods when measuring video frame interpolation results.
Computation-in-Memory with Hybrid Integration of Non-Volatile Memory &amp; SRAM for Reservoir Computing

This paper proposes Non-Volatile Memory (NVM) & SRAM Hybrid Computation-in-Memory <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\text{CiM})$</tex> for Reservoir Computing. This paper is the first proposal to recognize event data only by Reservoir Computing. Proposed Reservoir Computing achieves 87.9% accuracy for Event-based Vision Sensor (EVS) gesture dataset with prompt learning. With a detailed memory error analysis, proposed Reservoir Computing can tolerate 0.1% bit-error rate (BER) for both EVS gesture and MNIST. NVM & SRAM Hybrid CiM is proposed, which optimizes the memory type based on the error tolerance of each layer's weights. The memory cell array area of CiM decreases by 89.2% and 0.1% BER of NVM is acceptable.
Application of Extreme Learning Machine (ELM) Classification in Detecting Phishing Sites

Phishing site is a website created by internet criminals as closely as possible to resemble a real site to trick internet users by making it look like accessing a site from an official website. In overcoming the many phishing sites that exist in this study, the Extreme Learning Machine (ELM) classification method is used because ELM is one of the algorithms that is often used in classification and regression in machine learning. In this study, the accuracy value obtained from the test which was repeated 10 times was between 82-84% and the time between 5–11 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> with the best accuracy of 84.02% with a time of 7.98 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> , the accuracy results generated from the ELM algorithm are indeed not very good. This large amount occurs because of the overfitting experienced by the formed classification model so that the false positives obtained are quite large. Referring to the dataset itself, the most influential feature or attribute in the labeling of phishing sites is the time domain expires, if the time domain expires has reached 200 days then the site has a phishing site label. In this study, ELM was compared with several other machine learning algorithms such as Support Vector Machine (SVM), Naive Bayes and Decision Tree.
Performant coherent control: bridging the gap between high- and low-level operations on hardware

Scalable coherent control hardware for quantum information platforms is rapidly growing in priority as their number of available qubits continues to increase. As these systems scale, more calibration steps are needed, leading to challenges with system instability as calibrated parameters drift. Moreover, the sheer amount of data required to run circuits with large depth tends to balloon, especially when implementing state-of-the-art dynamical-decoupling gates which require advanced modulation techniques. We present a control system that addresses these challenges for trapped-ion systems, through a combination of novel features that eliminate the need for manual bookkeeping, reduction in data transfer bandwidth requirements via gate compression schemes, and other automated error handling techniques. Moreover, we describe an embedded pulse compiler that applies staged optimization, including compressed intermediate representations of parsed output products, performs in-situ mutation of compressed gate data to support high-level algorithmic feedback to account for drift, and can be run entirely on chip.
Rethinking Logic Minimization for Tabular Machine Learning

Tabular datasets can be viewed as logic functions that can be simplified using two-level logic minimization to produce minimal logic formulas in disjunctive normal form (DNF), which in turn can be readily viewed as an explainable decision rule set for binary classification. However, there are two problems with using logic minimization for tabular machine learning. First, tabular datasets often contain overlapping examples that have different class labels, which have to be resolved before logic minimization can be applied since logic minimization assumes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistent</i> logic functions. Second, even without inconsistencies, logic minimization alone generally produces complex models with poor generalization because it exactly fits all data points, which leads to detrimental <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overfitting</i> . How best to remove training instances to eliminate inconsistencies and overfitting is highly non-trivial. In this paper, we propose a novel statistical framework for removing these training samples so that logic minimization can become an effective approach to tabular machine learning. Using the proposed approach, we are able to obtain comparable performance as gradient boosted and ensemble decision trees, which have been the winning hypothesis classes in tabular learning competitions, but with human-understandable explanations in the form of decision rules. To our knowledge, neither logic minimization nor explainable decision rule methods have been able to achieve state-of-the-art performance before in tabular learning problems.
Blended Synchronous and Asynchronous Learning: Its Effectiveness for Teaching Grammar

The online EFL teaching and learning in this COVID-19 pandemic era has been conducted synchronously and asynchronously. However, the implementation of blended synchronous and asynchronous learning in the online grammar class was still scarce. Therefore, this study was conducted with the purpose of knowing whether or not the use of blended synchronous and asynchronous learning was effective for teaching grammar. This study was a pre-experimental research using one-group pretest-posttest design. The population of this study was the students of the second semester in the English department of a private university located in East Java. Because there was only one class in this second semester so that the sample was the same as this population. There were fifteen students in this sample. In collecting the data, the researchers used the pretest and posttest of grammar. After the data of pretest and posttest were in the normal distribution, the testing of hyphothesis using t-test especially paired samples test was conducted. The result of this t-test showed that the Sig. (2-tailed) 0.000 was lower than 0.05. It revealed that the difference of the mean scores of pretest and posttest was significant. Thus, the use of blended synchronous and asynchronous learning was effective for teaching grammar.
Cornerformer: Purifying Instances for Corner-Based Detectors

Corner-based object detectors enjoy the potential of detecting arbitrarily-sized instances, yet the performance is mainly harmed by the accuracy of instance construction. Specifically, there are three factors, namely, 1) the corner keypoints are prone to false-positives; 2) incorrect matches emerge upon corner keypoint pull-push embeddings; and 3) the heuristic NMS cannot adjust the corners pull-push mechanism. Accordingly, this paper presents an elegant framework named Cornerformer that is composed of two factors. First, we build a Corner Transformer Encoder (CTE, a self-attention module) in a 2D-form to enhance the information aggregated by corner keypoints, offering stronger features for the pull-push loss to distinguish instances from each other. Second, we design an Attenuation-Auto-Adjusted NMS (A3-NMS) to maximally leverage the semantic outputs and avoid true objects from being removed. Experiments on object detection and human pose estimation show the superior performance of Cornerformer in terms of accuracy and inference speed.
Speaker Verification in Multi-Speaker Environments Using Temporal Feature Fusion

Verifying the identity of a speaker is crucial in modern human-machine interfaces, e.g., to ensure privacy protection or to enable biometric authentication. Classical speaker verification (SV) approaches estimate a fixed-dimensional embedding from a speech utterance that encodes the speaker's voice characteristics. A speaker is verified if his/her voice embedding is sufficiently similar to the embedding of the claimed speaker. However, such approaches assume that only a single speaker exists in the input. The presence of concurrent speakers is likely to have detrimental effects on the performance. To address SV in a multi-speaker environment, we propose an end-to-end deep learning-based SV system that detects whether the target speaker exists within an input or not. First, an embedding is estimated from a reference utterance to represent the target's characteristics. Second, frame-level features are estimated from the input mixture. The reference embedding is then fused frame-wise with the mixture's features to allow distinguishing the target from other speakers on a frame basis. Finally, the fused features are used to predict whether the target speaker is active in the speech segment or not. Experimental evaluation shows that the proposed method outperforms the x-vector in multi-speaker conditions.
Arbitrary-Oriented Ship Detection based on Deep Learning

This paper explores a ship detector from an aerial view, where we focus on predicting ship orientation and improving recall rate for dense objects. First, since ships appear as small objects in remote sensing images, we enhance the backbone network in feature extraction, we reconstruct a classification network as a feature extractor, and then cascade a feature pyramid network for feature fusion. Second, to reduce the adverse effect of post-processing on dense predictions, we use rotated rectangular bounding boxes to represent ships, while adding angle predictions to the dense head. Finally, in order to increase the difference of features at different angles, we propose a target encoding. For the same kind of ships with different orientations, under this encoding rule, other predictions of the network, such as width and height, will have the same regression target. Considering both speed and accuracy, our method performs better in ship detection than other general detectors.
PondusHand: Estimation Method of Fingertips Force by User's Forearm Muscle Deformation based on Calibration with Mobile Phone's Touch Screen

If it is possible to estimate fingertip force, you will know the proper fingertip force, such as a cooking, massage, or pet handling expert. Our goal is to establish a method for estimating the force applied to fingertips from muscle deformation sensors attached with a forearm. In previous studies, machine learning-based methods were developed with muscle deformation information for estimating fingertip force. However, the condition of the muscles is changed in several hours, and thus the laborious calibration was required to be performed many times. In addition, since the system was calibrated with only one pressure sensor, it could not handle various pressure applications with multiple fingers, resulting in low accuracy. In this study, we present an estimation method: PondusHand, that automatically calibrates the force of multiple fingertips when pushing the mobile phone panel in various styles each time. In the experiment, it was confirmed that our method could estimate the fingertip force with twice the accuracy of the existing method (error 0. 7N, n=6). In future, our system will be applied to various fingertip force quantifiers, such as pottery, musical performances, cooking, and palpation. Fingertip force estimation, Muscle Deformation, Touch Screen
An Optimized Selection of Statistical Disclosure Control Methods—A Case Study Involving Microdata from the Polish Survey of Accidents at Work

The growing demand for increasingly detailed statistical data and the need to protect statistical confidentiality requires the use of a variety of advanced methods of Statistical Disclosure Control (SDC). The following article presents the principles and effects of an optimal selection of SDC methods for microdata from the 2017 survey of accidents at work, which are to be made available to interested users for scientific purposes. The variables from the survey were divided into groups depending on the extent to which they can be used to form unique combinations of values and the measurement scale on which relevant data are expressed. Based on this classification, an optimal selection of SDC tools was made. Two paths of the SDC process were proposed. In both approaches, targeted record swapping was used for hierarchical key variables, post-randomization (PRAM) for relevant categorical quasi-identifiers and noise addition for continuous variables. However, in the first approach, remaining key categorical quasi-identifiers were subject to local suppression, while in the second one—micro-aggregation based on Gower’s distance was applied. Several other possibilities of the SDC process (defined mainly by relevant parameterization) were also considered. Advantages and disadvantages of particular options considered in the study were discussed.
An Uncertain Graph Privacy Preserving Scheme Based on Node Similarity in Social Networks

Since the rise of social networking sites, social networking has become an integral part of daily life, containing not only a large amount of worthwhile data for research, but also personal information about individuals. In the analysis of social network data, users' private information may be revealed, so ensuring user privacy before data publication is important. Focusing on this problem, we propose an Uncertain Graph scheme based on Node Similarity (UG-NS), which can not only preserve user privacy in social networks but also maintain high data utility. In our scheme, we obtain the uncertain graph in two steps. Firstly, we assign edge probabilities to the edges of the original graph based on the node similarity. Secondly, we add some noisy edges and add Differential Privacy noise to these edges. In contrast to other existing uncertain graph schemes, our scheme considers the structural characteristics of the original network when assigning edge probabilities, using the Node2Vec model to calculate the similarity between node pairs. In addition, our scheme adds noise to the graph, which can provide better privacy preserving. A number of experiments have shown that our scheme provides a well-balanced between user privacy and data availability when transforming certain graphs into uncertain graphs.
Quantum Convolutional Circuits for Earth Observation Image Classification

The amount of study on Quantum Machine Learning (QML) is increasing extensively due to its potential advantages in terms of representational power and computational resources. These advances suggest a possibility to extend its usage into the context of Earth Observations, where Machine Learning (ML) plays an important role due to its extensive amount of data to be manipulated. This paper presents our preliminary results of binary quantum classifiers, which consist of Quantum Convolutional Neural Networks (QCNNs), applied on Earth Observation datasets, EuroSAT and SAT4, with classically-reduced features. Especially, we compare the performance of different data embedding techniques and quantum circuits for binary classification tasks.
Accelerating the Training of Single Layer Binary Neural Networks using the HHL Quantum Algorithm

Binary Neural Networks are a promising technique for implementing efficient deep models with reduced storage and computational requirements. The training of these is however, still a compute-intensive problem that grows drastically with the layer size and data input. At the core of this calculation is the linear regression problem. The Harrow-Hassidim-Lloyd (HHL) quantum algorithm has gained relevance thanks to its promise of providing a quantum state containing the solution of a linear system of equations. The solution is encoded in superposition at the output of a quantum circuit. Although this seems to provide the answer to the linear regression problem for the training neural networks, it also comes with multiple, difficult-to-avoid hurdles. This paper shows, however, that useful information can be extracted from the quantum-mechanical implementation of HHL, and used to reduce the complexity of finding the solution on the classical side.