Reviewerly/exHarmony huggingface dataset:
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning

Current state-of-the-art approaches in the field of Handwritten Text Recognition are predominately single task with unigram, character level target units. In our work, we utilize a Multi-task Learning scheme, training the model to perform decompositions of the target sequence with target units of different granularity, from fine to coarse. We consider this method as a way to utilize n-gram information, implicitly, in the training process, while the final recognition is performed using only the unigram output. Unigram decoding of such a multi-task approach highlights the capability of the learned internal representations, imposed by the different n-grams at the training step. We select n-grams as our target units and we experiment from unigrams till fourgrams, namely subword level granularities. These multiple decompositions are learned from the network with task-specific CTC losses. Concerning network architectures, we propose two alternatives, namely the Hierarchical and the Block Multi-task. Overall, our proposed model, even though evaluated only on the unigram task, outperforms its counterpart single-task by absolute 2.52% WER and 1.02% CER, in the greedy decoding, without any computational overhead during inference, hinting towards successfully imposing an implicit language model.
High-Order Markov Random Field as Attention Network for High-Resolution Remote-Sensing Image Compression

Content-weighted compression scheme for high-resolution remote-sensing (RS) images can be well modeled by Markov random field (MRF)-oriented attention. This article addresses high-resolution RS image compression by incorporating MRF into attention mechanism. To this end, we reformulate the attention mechanism with MRF-based probabilistic graph modeling implicitly and combine the target of image compression and parameter learning of MRF in a unified framework, namely high-order MRF-oriented attention (HMA) network. Specifically, HMA extends key-value query (KVQ) pairwise terms of the vanilla attention to high-order terms, by which the prior information could be expressed effectively to boost performance of high-resolution RS image compression. It is noted that several superiorities of HMA are listed. First, unlike the vanilla attention network that apt to yield coarse features, HMA is capable of output more pleasing decoding results. Second, HMA can accelerate the convergence in the training of the deep neural networks (DNNs), thus facilitating deploying it on resource-limited IOT devices. Third, HMA demonstrates its potential of processing semantic joint task. Moreover, We thoroughly evaluate our approach on standard data sets of varying resolutions, the proposed framework performs favorably against most image coding standards and DNN-based codecs on the ISPRS Vaihingen data set and the USC-SIPI data set especially at low bit rates.
Breaking LPA-resistant cryptographic circuits with principal component analysis

In this paper, a novel hardware attack based on principal component analysis (PCA) is proposed to break a leakage power analysis (LPA)-resistant cryptographic circuit (CC) efficiently. Although the added false keys which are used for masking the secret key of the LPA-resistant CC are secure and effective against regular LPA attacks, they may be precisely modeled by eigenvalues and eigenvectors under PCA. After performing the proposed PCA on the LPA-resistant CC, all the added false keys can be removed to expose the corresponding secret key. As shown in the result, only 2000 number of plaintexts are sufficient to crack an LPA-resistant CC by utilizing the proposed PCA-assisted LPA attacks. • Leakage power analysis (LPA) attacks are able to leak the secret key of cryptographic circuits efficiently. • False key-controlled aggressive voltage scaling (FKCAVS) technique belongs to a lightweight LPA attack countermeasure. • Principle component analysis (PCA) can be utilized for removing the added false keys within an FKCAVS technique. • Eigenvalues and eigenvectors generated by PCA are the key components for breaking an FKCAVS technique.
Measure of the Content Creation Score on Social Network Using Sentiment Score and Passion Point

Social network is one of efficient tools for spreading information. The evaluation of the content creation of a user is a useful feature to improve the ability of information propagation on social network. In this paper, the measures for evaluating the user’s content creation are proposed. They include the passion point of a user with a brand and the quality of the user’s posts. The passion point is computed based on the sentiment score of posting and the activity of the user. The quality of the user’s posts is computed through the analyzing of the post’s content. Those measures are combined to analyze the interesting of posts. The proposed method has been tested and get the positive experimental results.
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog system.Most dialog systems are trained on a pool of annotated OOD data to achieve this goal.However, collecting the annotated OOD data for a given domain is an expensive process.To mitigate this issue, previous works have proposed generative adversarial networks (GAN) based models to generate OOD data for a given domain automatically.However, these proposed models do not work directly with the text.They work with the text's latent space instead, enforcing these models to include components responsible for encoding text into latent space and decoding it back, such as auto-encoder.These components increase the model complexity, making it difficult to train.We propose OodGAN, a sequential generative adversarial network (SeqGAN) based model for OOD data generation.Our proposed model works directly on the text and hence eliminates the need to include an auto-encoder.OOD data generated using OodGAN model outperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative improvement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR 0.95) (Zheng et al., 2020).
Benefits and Challenges of Adopting SAFe - An Empirical Survey

Abstract During the last two decades, turbulent business environments tempted firms to adopt agile methods to cope with the ever-changing customer demands. The success of agile methods in small and co-located teams inspired companies to apply them to large-scale endeavors. Agile scaling frameworks, such as the Scaled Agile Framework (SAFe), have been proposed by practitioners to scale agile practices to large projects and enterprises. Companies are increasingly taking these frameworks into use. However, the number of quantitative empirical studies assessing the benefits and challenges of adopting the agile scaling frameworks is still limited. This paper starts filling in this gap by presenting the results from a survey of 100 industry participants around the world on their perception of the benefits and challenges of adopting the SAFe framework. Our results show that the SAFe adoption improves transparency, as well as collaboration and dependency management between agile teams. The most commonly mentioned challenges of the SAFe adoption are organizational politics, difficulties in establishing an agile mindset, change resistance, and team formation challenges.
Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.
Deep motion blur removal using noisy/blurry image pairs

Removing spatially variant motion blur from a blurry image is a challenging problem as image blur can be complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel-free single image deblurring can be achieved, but questions about deblurring performance persist. To improve performance, we proposed a deep convolutional neural network to restore a sharp image from a noisy/blurry image pair captured in quick succession. Two neural network structures, Deblur Long Short-Term Memory (LSTM) and DeblurMerger, are presented to fuse the pair of images in either sequential or parallel manner. To boost the training, gradient loss, adversarial loss, and spectral normalization are leveraged. The training dataset that consists of pairs of noisy/blurry images and the corresponding ground truth sharp image is synthesized based on the benchmark dataset GOPRO. We evaluated the trained networks on a variety of synthetic datasets and real image pairs. The results demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively. DeblurLSTM achieves the best debluring performance, while DeblurMerger achieves nearly the same result but with significantly less computation time.
Formal Impact Metrics for Cyber-physical Attacks

Cyber-Physical systems (CPSs) are exposed to cyber- physical attacks, i.e., security breaches in cyberspace that adversely affect the physical processes of the systems.We define two probabilistic metrics to estimate the physical impact of attacks targeting cyber-physical systems formalised in terms of a probabilistic hybrid extension of Hennessy and Regan's Timed Process Language. Our impact metrics estimate the impact of cyber-physical attacks taking into account: (i) the severity of the inflicted damage in a given amount of time, and (ii) the probability that these attacks are actually accomplished, according to the dynamics of the system under attack. In doing so, we pay special attention to stealthy attacks, i. e., attacks that cannot be detected by intrusion detection systems. As further contribution, we show that, under precise conditions, our metrics allow us to estimate the impact of attacks targeting a complex CPS in a compositional way, i.e., in terms of the impact on its sub-systems.
Micro-CT image denoising with an asymmetric perceptual convolutional network

Micro-CT has important applications in biomedical research due to its ability to perform high-precision 3D imaging of micro-architecture in a non-invasive way. Because of the limited power of the radiation source, it is difficult to obtain a high signal-to-noise image under the requirement of temporal resolution. Therefore, low-dose CT image denoising has attracted considerable attention to improve the image quality of micro-CT while maintaining time resolution. In this paper, an end-to-end asymmetric perceptual convolutional network (APCNet) is proposed to enhance the network's ability to capture and retain image details by improving the convolutional layer and introducing an edge detection layer. Compared with the previously proposed denoising models such as DnCNN, CNN-VGG, and RED-CNN, experiments proved that our proposed method has achieved better results in both numerical indicators and visual perception.
Static Embeddings as Efficient Knowledge Bases?

Recent research investigates factual knowledge stored in large pretrained language models (PLMs).Instead of structural knowledge base (KB) queries, masked sentences such as "Paris is the capital of [MASK]" are used as probes.The good performance on this analysis task has been interpreted as PLMs becoming potential repositories of factual knowledge.In experiments across ten linguistically diverse languages, we study knowledge contained in static embeddings.We show that, when restricting the output space to a candidate set, simple nearest neighbor matching using static embeddings performs better than PLMs.E.g., static embeddings perform 1.6% points better than BERT while just using 0.3% of energy for training.One important factor in their good comparative performance is that static embeddings are standardly learned for a large vocabulary.In contrast, BERT exploits its more sophisticated, but expensive ability to compose meaningful representations from a much smaller subword vocabulary.
A New Marine Disaster Assessment Model Combining Bayesian Network with Information Diffusion

There are two challenges in the comprehensive marine hazard assessment. The influencing mechanism of marine disaster is uncertain and disaster data are sparse. Aiming at the uncertain knowledge and small sample in assessment modeling, we combine the information diffusion algorithm and Bayesian network to propose a novel assessment model. The information diffusion algorithm is adopted to expand associated samples between disaster losses and environmental conditions. Then the expanded data sets are used to build the BN-based assessment model through structural learning, parameter learning and probabilistic reasoning. The proposed model is applied to the hazard assessment of marine disasters in Shanghai. Experimental comparison results show that it is capable of dealing with uncertainty effectively and achieving more accuracy risk assessment under the small sample condition.
Mobile English language learning: a systematic review of group size, duration, and assessment methods

Technology-mediated learning has become a valuable tool, especially in the English language classroom. Nowadays, mobile learning is considered a significant educational tool for promoting education among learners. Several recent studies have attempted to examine different aspects of mobile learning. The size of the experimental group, and the duration and suitability of the assessment methods are important aspects in designing experimental studies in the context of mobile English language learning. Yet, very few studies have paid attention to these aspects. Therefore, this study aimed to investigate the utilisation of state-of-the-art mobile technology in English language learning, especially in terms of the size of the experimental groups, and the duration and suitability of the assessment methods. Following a systematic review protocol, 151 articles published between 2010 and 2017 were selected for analysis. Furthermore, this study considered, separately, three factors addressed in the reviewed studies: language acquisition problems, types of participants, and targeted English skills. The findings showed that the group size and study duration significantly differed among the studies based on the above factors. Questionnaires and tests were found to be the most common methods of assessment for each factor, whether used separately or in combination. The results of this review can assist in the formulation of experimental designs to mitigate the challenges and facilitate the effective use of mobile learning in English language learning.
Reducing Entropy Overestimation in Soft Actor Critic Using Dual Policy Network

In reinforcement learning (RL), an agent learns an environment through hit and trail. This behavior allows the agent to learn in complex and difficult environments. In RL, the agent normally learns the given environment by exploring or exploiting. Most of the algorithms suffer from under exploration in the latter stage of the episodes. Recently, an off-policy algorithm called soft actor critic (SAC) is proposed that overcomes this problem by maximizing entropy as it learns the environment. In it, the agent tries to maximize entropy along with the expected discounted rewards. In SAC, the agent tries to be as random as possible while moving towards the maximum reward. This randomness allows the agent to explore the environment and stops it from getting stuck into local optima. We believe that maximizing the entropy causes the overestimation of entropy term which results in slow policy learning. This is because of the drastic change in action distribution whenever agent revisits the similar states. To overcome this problem, we propose a dual policy optimization framework, in which two independent policies are trained. Both the policies try to maximize entropy by choosing actions against the minimum entropy to reduce the overestimation. The use of two policies result in better and faster convergence. We demonstrate our approach on different well known continuous control simulated environments. Results show that our proposed technique achieves better results against state of the art SAC algorithm and learns better policies.
Deep blur detection network with boundary-aware multi-scale features

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Pathwise Conditioning of Gaussian Processes

As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer. Monte Carlo methods act as a convenient bridge for connecting intractable mathematical expressions with actionable estimates via sampling. Conventional approaches for simulating Gaussian process posteriors view samples as draws from marginal distributions of process values at finite sets of input locations. This distribution-centric characterization leads to generative strategies that scale cubically in the size of the desired random vector. These methods are prohibitively expensive in cases where we would, ideally, like to draw high-dimensional vectors or even continuous sample paths. In this work, we investigate a different line of reasoning: rather than focusing on distributions, we articulate Gaussian conditionals at the level of random variables. We show how this pathwise interpretation of conditioning gives rise to a general family of approximations that lend themselves to efficiently sampling Gaussian process posteriors. Starting from first principles, we derive these methods and analyze the approximation errors they introduce. We, then, ground these results by exploring the practical implications of pathwise conditioning in various applied settings, such as global optimization and reinforcement learning.
Deep Unsupervised Hashing by Global and Local Consistency

Hashing is widely-used in approximate nearest neighbor search for its computational efficiency. Most of the existing unsupervised hashing methods are based on local consistency that the Hamming distance between two images should be small if their features are similar. However, many false similar pairs may be included for the insufficient representation of features. Here we proposed deep unsupervised hashing by Global and Local Consistency (GLC). Specifically, GLC has two components named semantic information generating and semantic consistency learning, and each component is conducted from both global and local views. From local view, GLC introduces reliable graph and penalty graph to capture local signals with high confidence to preserve the semantic structure. From global view, GLC includes a distribution loss to capture the global consistency with cluster signals. Extensive experimental results on three widely-used benchmark datasets show that GLC performs better than existing state- of-the-art methods.
RETRACTED: Psychological and pedagogical foundations of practice-oriented learning of future STEAM teachers

In the modern educational environment, it has become popular to use practice-oriented learning technologies in the preparation of future teachers. Scientists and educators are increasingly resorting to the practice of STEAM education (science, technology, engineering, arts, and mathematics), which is based on interdisciplinarity and the integration of five scientific fields into a single learning system for solving specific problems taken from real life. This study is aimed at studying the development of psychological and pedagogical skills of future teachers in the framework of practice-oriented learning. The research sample included 46 students of S. Yessenov Caspian University of Technologies and Engineering (Kazakhstan) aged 21 to 23 years. The participants were divided into experimental and control groups. In the experimental group, students additionally studied according to the course “STEAM education” developed by the authors. Formation of psychological and pedagogical skills of the experiment participants was assessed using pre- and post-tests. Experimental group students showed higher results in comparison with the control group. The difference between the mean values (M=3.4) of the general indicator of the experimental and control groups is statistically significant (p<0.05). Comparison of the indicators of the final and preparatory study stages showed that there were positive changes in the results of assessing psychological and pedagogical skills in the experimental group, the efficiency coefficient (Ce)>1. The results of this study can be useful for teachers, administrators of higher educational institutions, etc.
Automated white corpuscles nucleus segmentation using deep neural network from microscopic blood smear

The white corpuscles nucleus segmentation from microscopic blood images is major steps to diagnose blood-related diseases. The perfect and speedy segmentation system assists the hematologists to identify the diseases and take appropriate decision for better treatment. Therefore, fully automated white corpuscles nucleus segmentation model using deep convolution neural network, is proposed in the present study. The proposed model uses the combination of ‘binary_cross_entropy’ and ‘adam’ for maintaining learning rate in each network weight. To validate the potential and capability of the above proposed solution, ALL-IDB2 dataset is used. The complete set of images is partitioned into training and testing set and tedious experimentations have been performed. The best performing model is selected and the obtained training and testing accuracy of best performing model is reported as 98.69 % and 99.02 %, respectively. The staging analysis of proposed model is evaluated using sensitivity, specificity, Jaccard index, dice coefficient, accuracy and structure similarity index. The capability of proposed model is compared with performance of the region-based contour and fuzzy-based level-set method for same set of images and concluded that proposed model method is more accurate and effective for clinical purpose.