Reviewerly/exHarmony huggingface dataset:
Memory-Efficient Class-Incremental Learning for Image Classification

With the memory-resource-limited constraints, class-incremental learning (CIL) usually suffers from the "catastrophic forgetting" problem when updating the joint classification model on the arrival of newly added classes. To cope with the forgetting problem, many CIL methods transfer the knowledge of old classes by preserving some exemplar samples into the size-constrained memory buffer. To utilize the memory buffer more efficiently, we propose to keep more auxiliary low-fidelity exemplar samples, rather than the original real-high-fidelity exemplar samples. Such a memory-efficient exemplar preserving scheme makes the old-class knowledge transfer more effective. However, the low-fidelity exemplar samples are often distributed in a different domain away from that of the original exemplar samples, that is, a domain shift. To alleviate this problem, we propose a duplet learning scheme that seeks to construct domain-compatible feature extractors and classifiers, which greatly narrows down the above domain gap. As a result, these low-fidelity auxiliary exemplar samples have the ability to moderately replace the original exemplar samples with a lower memory cost. In addition, we present a robust classifier adaptation scheme, which further refines the biased classifier (learned with the samples containing distillation label knowledge about old classes) with the help of the samples of pure true class labels. Experimental results demonstrate the effectiveness of this work against the state-of-the-art approaches. We will release the code, baselines, and training statistics for all models to facilitate future research.
Seastar

Graph neural networks (GNNs) have achieved breakthrough performance in graph analytics such as node classification, link prediction and graph clustering. Many GNN training frameworks have been developed, but they are usually designed as a set of manually written, GNN-specific operators plugged into existing deep learning systems, which incurs high memory consumption, poor data locality, and large semantic gap between algorithm design and implementation. This paper proposes the Seastar system, which presents a vertex-centric programming model for GNN training on GPU and provides idiomatic python constructs to enable easy development of novel homogeneous and heterogeneous GNN models. We also propose novel optimizations to produce highly efficient fused GPU kernels for forward and backward passes in GNN training. Compared with the state-of-the art GNN systems, DGL and PyG, Seastar achieves better usability, up to 2 and 8 times less memory consumption, and 14 and 3 times faster execution, respectively.
Entangling Quantum Generative Adversarial Networks

Generative adversarial networks (GANs) are one of the most widely adopted machine learning methods for data generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (an entangling quantum GAN, EQ-GAN) that overcomes limitations of previously proposed quantum GANs. Leveraging the entangling power of quantum circuits, the EQ-GAN converges to the Nash equilibrium by performing entangling operations between both the generator output and true quantum data. In the first multiqubit experimental demonstration of a fully quantum GAN with a provably optimal Nash equilibrium, we use the EQ-GAN on a Google Sycamore superconducting quantum processor to mitigate uncharacterized errors, and we numerically confirm successful error mitigation with simulations up to 18 qubits. Finally, we present an application of the EQ-GAN to prepare an approximate quantum random access memory and for the training of quantum neural networks via variational datasets.
A Two-stage Text Feature Selection Algorithm for Improving Text Classification

As the number of digital text documents increases on a daily basis, the classification of text is becoming a challenging task. Each text document consists of a large number of words (or features) that drive down the efficiency of a classification algorithm. This article presents an optimized feature selection algorithm designed to reduce a large number of features to improve the accuracy of the text classification algorithm. The proposed algorithm uses noun-based filtering, a word ranking that enhances the performance of the text classification algorithm. Experiments are carried out on three benchmark datasets, and the results show that the proposed classification algorithm has achieved the maximum accuracy when compared to the existing algorithms. The proposed algorithm is compared to Term Frequency-Inverse Document Frequency, Balanced Accuracy Measure, GINI Index, Information Gain, and Chi-Square. The experimental results clearly show the strength of the proposed algorithm.
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

• An adaptive and transfer guided metaheuristic is proposed for Evolutionary Multitasking. • Synergies between tasks are analyzed along the search in a dynamic way. • 4 different combinatorial optimization problems have been considered. • 11 multitasking scenarios are solved comprised by 5 to 20 instances. • Proposed AT-MFCGA is compared with MFEA, MFEA-II and MFCGA. Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned Evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process.
Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

The eye movement analysis with hidden Markov models (EMHMM) method provides quantitative measures of individual differences in eye-movement pattern. However, it is limited to tasks where stimuli have the same feature layout (e.g., faces). Here we proposed to combine EMHMM with the data mining technique co-clustering to discover participant groups with consistent eye-movement patterns across stimuli for tasks involving stimuli with different feature layouts. Through applying this method to eye movements in scene perception, we discovered explorative (switching between the foreground and background information or different regions of interest) and focused (mainly looking at the foreground with less switching) eye-movement patterns among Asian participants. Higher similarity to the explorative pattern predicted better foreground object recognition performance, whereas higher similarity to the focused pattern was associated with better feature integration in the flanker task. These results have important implications for using eye tracking as a window into individual differences in cognitive abilities and styles. Thus, EMHMM with co-clustering provides quantitative assessments on eye-movement patterns across stimuli and tasks. It can be applied to many other real-life visual tasks, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.
Fault tolerance in LWT-SVD based image watermarking systems using three module redundancy technique

Increasing the robustness, reliability and security of the digital image watermarking methods against image processing and geometrics attacks are a key issues especially for image copyright protection. Most of existing methods concentrate on increasing the robustness and/or security aspects. In this paper, a robust and reliable digital image watermarking method is proposed by combination of lifting wavelet transform and Singular Value Decomposition (SVD) along with Three Module Redundancy (TMR) technique. Also to increase the security of the proposed method, at first, the watermark image is encrypted using improved Arnold transform. Then, SVD is applied to the encrypted watermark image. Using the TMR technique, the singular values of the encrypted watermark image are embedded in three wavelet subbands of the host image using appropriate scaling factors. To obtain the balance between robustness and imperceptibility, scaling factor of each subband is determined by Artificial Bee Colony (ABC) algorithm. According to the evaluations, compared to existing works, the proposed method is robust, and also has a high imperceptibility. Also by employing TMR, as a fault tolerance mechanism, the proposed system is more reliable and can work properly in more situations compared to the state-of-the-art methods.
Exploring the relationship between cryptocurrencies and hedge funds during COVID-19 crisis

In this paper, we investigated the relationship between cryptocurrency market and hedge funds in two different ways. First, we focus on the dependence between Cryptocurrency hedge funds and conventional hedge funds strategies using VAR and VECM models, while analyzing the impact of COVID-19 on the hedge funds' values. Secondly, we choose between ARDL and ARDL-ECM models to study the effects of cryptocurrency price changes on Crypto- Currency hedge funds' values during COVID-19 crisis. Our empirical findings demonstrate that there is substantial interactions between Crypto-Currency and conventional hedge funds. The COVID-19 pandemic has significant negative impact on the performance of the following hedge funds: Event Driven, Relative Value and Distressed Debt fund strategies, this has reflected in a significant drop in their values during this critical period. However, we demonstrate that COVID-19 pandemic did not affect the relationship between crypto-currency hedge funds and both bitcoin and Ethereum. These findings hold profound implications for hedge funds managers, cryptocurrency market main players and policy makers. Our study is crucial in forecasting the performance of these markets especially during global pandemics.
A Global-Local Coupling Two-Stage Path Planning Method for Mobile Robots

The path planning of mobile robots is an optimization problem that is difficult to solve directly owing to its nonlinear characteristics. This letter proposes the “global-local” Coupling Two-Stage Path Planning (CTSP) method. First, the globally optimal solution in the configuration space is given by the global planner. Then, in the local planning stage, the optimal solution of the local environment is constantly searched, guided by the prior information of the globally optimal solution. The strategy used in the global planning stage is the iterative optimization method based on an initial solution. The local planning stage adopts the sampling-evaluation strategy, that is, sampling the candidate paths and then using the evaluation function to perform path selection. The proposed method has two innovations: 1) a novel global iterative optimization method is proposed and 2) a new cost function for evaluating the sampled paths is constructed, which improves the coupling of the global and local paths. We implement and test this method in a simulation environment, where the experimental results verify the effectiveness of the proposed method.
Global Sensing and Its Impact for Quantum Many-Body Probes with Criticality

Quantum sensing is one of the key areas which exemplifies the superiority of quantum technologies. Nonetheless, most quantum sensing protocols operate efficiently only when the unknown parameters vary within a very narrow region, i.e., local sensing. Here, we provide a systematic formulation for quantifying the precision of a probe for multi-parameter global sensing when there is no prior information about the parameters. In many-body probes, in which extra tunable parameters exist, our protocol can tune the performance for harnessing the quantum criticality over arbitrarily large sensing intervals. For the single-parameter sensing, our protocol optimizes a control field such that an Ising probe is tuned to always operate around its criticality. This significantly enhances the performance of the probe even when the interval of interest is so large that the precision is bounded by the standard limit. For the multi-parameter case, our protocol optimizes the control fields such that the probe operates at the most efficient point along its critical line. Interestingly, for an Ising probe, it is predominantly determined by the longitudinal field. Finally, we show that even a simple magnetization measurement significantly benefits from our optimization and moderately delivers the theoretical precision.
Deep transfer learning mechanism for fine-grained cross-domain sentiment classification

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Reinforced SVM method and memorization mechanisms

The paper is devoted to two problems: (1) reinforcement of SVM algorithms, and (2) justification of memorization mechanisms for generalization. (1) Current SVM algorithm was designed for the case when the risk for the set of nonnegative slack variables is defined by l1 norm. In this paper, along with that classical l1 norm, we consider risks defined by l2 norm and l∞ norm. Using these norms, we formulate several modifications of the existing SVM algorithm and show that the resulting modified SVM algorithms can improve (sometimes significantly) the classification performance. (2) Generalization ability of existing learning algorithms is usually explained by arguments involving uniform convergence of empirical losses to the corresponding expected losses over a given set of functions. However, along with bounds for uniform convergence of empirical losses to the expected losses, the VC theory also provides bounds for relative uniform convergence. These bounds lead to a more accurate estimate of the expected loss. Advanced methods of estimating of expected risk of error have to leverage these bounds, which also support mechanisms of training data memorization, which, as the paper demonstrates, can improve classification performance.
Thai Sign Language Recognition: an Application of Deep Neural Network

Thai Sign Language (TSL) is the national sign language for Thai deaf people or hearing impaired in Thailand. These people with disabilities can use sign language to communicate with people with disabilities but face obstacles in communicating daily with ordinary people. Technology should play a key role in helping disadvantaged people achieve a better quality of life. This research aims to find ways to create Thai sign language recognition applications and be developed for real-time sign language translation in the next step. We purpose a simple approach with a MediaPipe framework that helps to extract the hand landmark from video on the preprocessing step and use that landmark to build the model for recognition hand gestures with various Recurrent neural networks (RNN). The result showed that the model builds with LSTM, BLSTM and GRU has an accuracy greater than 90 percent. This approach can produce an accurate close to the traditional approach.
Optimizing Semantic Deep Forest for tweet topic classification

Nowadays, topic detection from Twitter attracts the attention of several researchers around the world. Different topic classification approaches have been proposed as a result of these research efforts. However, four of the major challenges faced in this context are the use of handcrafted features, the use of Deep Learning algorithms with so many parameters, the fact that their performance is still limited and the lack of sufficient labeled datasets. We propose, Semantic Deep Forest (SDF), a topic classification approach that incorporates contextual Word2vec, WordNet and Deep Forest to detect topic from Twitter accurately. Moreover, extensive parameter sensitivity analysis were conducted to fine-tune the parameters of SDF for our Tweet topic classification task to achieve the best performance. We conducted experiments on three benchmark datasets with standard evaluation scenarios. Experimental results show that: (1) the proposed contextual word2vec models can be successfully used for tweet topic classification and outperform existing state-of-the-art embedding model; (2) The proposed SDF improve the accuracy of tweet topic classification and outperform existing state-of-the-art classification approaches; (3) the proposed SDF does not require huge amount of labeled data in order to achieve good performance, which is the lack in the majority of the state-of-the-art approaches.
Lessons on mobile apps for COVID-19 from China

The COVID-19 pandemic has impacted the global society and human life profoundly. Many countries have launched COVID-19 mobile apps with a wide range in how these apps work. While it is hoped that these apps can assist in the fight against COVID-19, many are worried about user privacy. China implemented "health code" systems, which assigned neighborhoods and citizens a specific health code, meant to indicate their risk of having been exposed to COVID-19. The most widely used health code systems were hosted on the popular apps WeChat and Alipay, each with billions of users. Some experts argued that China's use of mobile applications was essential to its successful combat against COVID-19. Included in this study are a summary of mobile technology usage in China, a review of previous studies of mobile technology in healthcare, and a brief survey of some existing mobile applications for COVID-19 that were implemented. Also included are outcomes of interviews with healthcare and public safety experts and a public survey to understand how mobile applications were used in China's response to COVID-19. The interviews revealed four important themes: personal privacy, community involvement, government involvement, and situational specificity. It was found that a key concern was maintaining a balance between collecting and utilizing personal information, as well as protecting this information. In addition, close collaboration between local communities and the national government was essential. Mobile applications assisted in communication and coordination but did not replace the work of people such as delivery drivers and contact tracers. Our results also showed that there was room for improvement, especially accessibility for the elderly or those unfamiliar with technology. Similar results were obtained from our survey. It was interesting to find that the apps were mostly used for "accessing information on COVID-19." In addition, respondents overwhelmingly identified "information" as the most valuable feature of COVID-19 apps. Both interview and survey results revealed the importance of providing information as a primary function of COVID-19 apps. Based on our findings we distilled four main lessons: mobile applications should assist in existing COVID-19 responses, inform users, protect users' personal information, and adapt to users' environments. We recommend that public health officials and app developers take these lessons into consideration when developing COVID-19 related mobile applications. In addition, we encourage researchers to utilize this report as a jumping off point for further research.
Adaptive thresholding technique based classification of red blood cell and sickle cell using Naïve Bayes Classifier and K-nearest neighbor classifier

Detection of anomalous cells by analyzing the microscopic blood smear plays a key role in identification of various blood diseases in medical field. It becomes easier with robust segmentation steps followed by a classifier for automatic recognition of normal as well as anomalous blood cells. Sickle cell anemia is a disease that changes shape of the red blood cell (RBC) into sickle cell which can be detected by use of automatic classifier. In this reported work, a novel robust hybrid segmentation method is introduced for segmenting normal RBCs and sickle cells. The hybrid segmentation process proposed here is a combination of two segmentation methods namely fuzzy C-means and adaptive (local) thresholding. Four different adaptive thresholding methods are experimented here in combination with fuzzy C-means. The features extracted for carrying out this analysis are major axis, minor axis, aspect ratio, area, perimeter, form factor (metric value), eccentricity and solidity of each of the cells present in the sample blood smear. These eight features are used for training and testing the classifiers. For classification, two supervised classifiers, i.e. Naïve Bayes classifier and K-nearest neighbor classifier have been demonstrated on a dataset of 10 image data samples and the evaluated results are compared for each of the hybrid combinations. The comparison shows that the proposed hybrid combination of fuzzy C-means and NICK's thresholding alongwith K-nearest neighbor classifier gives the best performance in terms of the considered performance parameters. This combination gives a significant classification accuracy of 98.87% with lowest computational time.
StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization

In recent years, neural vocoders have surpassed classical speech generation approaches in naturalness and perceptual quality of the synthesized speech. Computationally heavy models like WaveNet and WaveGlow achieve best results, while lightweight GAN models, e.g. MelGAN and Parallel WaveGAN, remain inferior in terms of perceptual quality. We therefore propose StyleMelGAN, a lightweight neural vocoder allowing synthesis of high-fidelity speech with low computational complexity. StyleMelGAN employs temporal adaptive normalization to style a low-dimensional noise vector with the acoustic features of the target speech. For efficient training, multiple random-window discriminators adversarially evaluate the speech signal analyzed by a filter bank, with regularization provided by a multi-scale spectral reconstruction loss. The highly parallelizable speech generation is several times faster than real-time on CPUs and GPUs. MUSHRA and P.800 listening tests show that StyleMelGAN outperforms prior neural vocoders in copy-synthesis and Text-to-Speech scenarios.