Reviewerly/exHarmony huggingface dataset:
Depression Analysis of Bangla Social Media Data using Gated Recurrent Neural Network

Nowadays, micro-blogging sites like Twitter, Facebook, YouTube, etc., have become much popular for social interactions. People are expressing their depression over social media, which can be analyzed to identify causes behind their depression. Most of the researches on emotion and depression analysis are based on questionnaires and academic interviews in non-Bengali languages, especially English. These traditional methods are not always suitable for detecting human depression. In this paper, we introduced Gated Recurrent Neural Network based depression analysis approach on Bangla social media data. We collected Bangla data from Twitter, Facebook and other sources. We selected four hyper-parameters, namely, number of Gated Recurrent Unit (GRU) layers, layer size, batch size and number of epochs, and presented step by step tuning for these Hyper-parameters. The results show the effects of these tuning steps and how the steps can be beneficial in configuring GRU models for gaining high accuracy on a significantly smaller data set. This will help psychologists and concerned authorities of society detect depression among Bangla speaking social media users. It will also help researchers to implement Natural Language Processing tasks with Deep Learning methods.
Models in the Wild: On Corruption Robustness of Neural NLP Systems

Natural Language Processing models lack a unified approach to robustness testing. In this paper we introduce WildNLP - a framework for testing model stability in a natural setting where text corruptions such as keyboard errors or misspelling occur. We compare robustness of deep learning models from 4 popular NLP tasks: Q&A, NLI, NER and Sentiment Analysis by testing their performance on aspects introduced in the framework. In particular, we focus on a comparison between recent state-of-the-art text representations and non-contextualized word embeddings. In order to improve robustness, we perform adversarial training on selected aspects and check its transferability to the improvement of models with various corruption types. We find that the high performance of models does not ensure sufficient robustness, although modern embedding techniques help to improve it. We release the code of WildNLP framework for the community.
AB-LSTM

Detection of scene text in arbitrary shapes is a challenging task in the field of computer vision. Most existing scene text detection methods exploit the rectangle/quadrangular bounding box to denote the detected text, which fails to accurately fit text with arbitrary shapes, such as curved text. In addition, recent progress on scene text detection has benefited from Fully Convolutional Network. Text cues contained in multi-level convolutional features are complementary for detecting scene text objects. How to explore these multi-level features is still an open problem. To tackle the above issues, we propose an Attention-based Bidirectional Long Short-Term Memory (AB-LSTM) model for scene text detection. First, word stroke regions (WSRs) and text center blocks (TCBs) are extracted by two AB-LSTM models, respectively. Then, the union of WSRs and TCBs are used to represent text objects. To verify the effectiveness of the proposed method, we perform experiments on four public benchmarks: CTW1500, Total-text, ICDAR2013, and MSRA-TD500, and compare it with existing state-of-the-art methods. Experiment results demonstrate that the proposed method can achieve competitive results, and well handle scene text objects with arbitrary shapes (i.e., curved, oriented, and horizontal forms).
The information content of Sarbanes-Oxley in predicting security breaches

How effective is compliance with the Sarbanes-Oxley Act (SOX) in identifying and eliminating firm threats from information systems breaches? We investigated publicly reported security breaches of internal controls in corporate systems to determine whether SOX assessments are information bearing with respect to breaches which can lead to materially significant losses and misstatements. The results of our tests varied significantly between breach types. SOX Section 404 adverse decisions on effectiveness of controls occurred in 100% of credit card data breaches and around 33% of insider breaches. SOX 404 audits provided a contrarian "effective" control decision on 88% of situations where there was a control breach concerning a portable device. This suggests that employees are subverting particularly strict internal controls by using portable devices that can be carried outside the physical boundaries of the firm. We found that management and SOX 404 auditors do not general agree on the underlying internal control situation at any time. Instead the SOX 404 auditors were likely to discover material weaknesses and "educate" management and internal audit teams about the importance of these control weaknesses. SOX attestations were poor at identifying control weaknesses from unintended disclosures, physical losses, hacking and malware, stationary devices, and situations where the cause of the breach was unknown. Hazard and occupancy structural models were constructed to extrapolate to a larger population. Results showed that both SOX 302 and 404 audits provided information germane to the frequency of breaches, with SOX 404 being three times as informative as section 302 reports. The hazard model found an expected 2.88% reduction in breaches when SOX 302 controls were effective. Management's "material weakness' attestations provided no information in this structural model, whereas there would be around a 1% increase in breach occurrence when there exist significant deficiencies. SOX 404 attestations were the most informative; a negative SOX 404 attestation is projected to increase the frequency of breaches by around 8.5%. We concluded that the strength of internal controls attested in SOX reports is likely to be a significant factor in the occurrence of a security breach in a specific period.
Uncalibrated Visual Servoing for Underwater Vehicle Manipulator Systems with an Eye in Hand Configuration Camera

This paper presents an uncalibrated visual servoing scheme for underwater vehicle manipulator systems (UVMSs) with an eye-in-hand camera under uncertainties. These uncertainties contain vision sensor parameters, UVMS kinematics and feature position information. At first, a linear separation approach is addressed to collect these uncertainties into vectors, and this approach can also be utilized in other free-floating based manipulator systems. Secondly, a novel nonlinear adaptive controller is proposed to achieve image error convergence by estimating these vectors, the gradient projection method is utilized to optimize the restoring moments. Thirdly, a high order disturbance observer is addressed to deal with time-varying disturbances, and the convergence of the image errors is proved under the Lyapunov theory. Finally, in order to illustrate the effectiveness of the proposed method, numerical simulations based on a 9 degrees of freedom (DOFs) UVMS with an eye-in-hand camera are conducted. In simulations, the UVMS is expected to track a circle trajectory on the image plane, meanwhile, time-varying disturbances are exerted on the system. The proposed scheme can achieve accurate and smooth tracking results during simulations.
Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

While several research studies have focused on analyzing human behavior and, in particular, emotional signals from visual data, the problem of synthesizing face video sequences with specific attributes (e.g. age, facial expressions) received much less attention. This paper proposes a novel deep generative model able to produce face videos from a given image of a neutral face and a label indicating a specific facial expression, e.g. spontaneous smile. Our framework consists of two main building blocks: an image generator and a frame sequence generator. The image generator is implemented as a deep neural model which combines generative adversarial networks and variational auto-encoders, while the sequence generator is a label-conditioned recurrent neural network. In the proposed framework, given as input a neural face and a label, the sequence generator outputs a set of hidden representations with smooth transitions corresponding to video frames. Then, the image generator is used to decode the hidden representations into the actual face images. To impose that the net generates videos consistent with the given label, a novel identity adversarial loss is proposed. Our experimental results demonstrate the effectiveness of the framework and the advantage of introducing an adversarial component into recurrent models for face video generation.
Energy-Efficient Architecture for CNNs Inference on Heterogeneous FPGA

Due to the huge requirements in terms of both computational and memory capabilities, implementing energy-efficient and high-performance Convolutional Neural Networks (CNNs) by exploiting embedded systems still represents a major challenge for hardware designers. This paper presents the complete design of a heterogeneous embedded system realized by using a Field-Programmable Gate Array Systems-on-Chip (SoC) and suitable to accelerate the inference of Convolutional Neural Networks in power-constrained environments, such as those related to IoT applications. The proposed architecture is validated through its exploitation in large-scale CNNs on low-cost devices. The prototype realized on a Zynq XC7Z045 device achieves a power efficiency up to 135 Gops/W. When the VGG-16 model is inferred, a frame rate up to 11.8 fps is reached.
A comprehensive framework to rank cloud-based e-learning providers using best-worst method (BWM)

Purpose Today, the high cost of e-learning systems’ implementation and the difficulty of managing the infrastructures motivate educational institutions toward application of cloud-based e-learning systems. This new system should be aligned with the academics’ aims and pedagogical principles to be beneficial for learners and instructors. Therefore, the vendor selection of learning systems is one of the most important processes to migrate toward cloud-based e-learning. The purpose of this paper is to develop a new framework to facilitate the vendor selection of cloud-based e-learning systems in the cloud market. Design/methodology/approach To identify the initial criteria as to the vendor selection of cloud-based e-learning services, a literature review is done. To enrich the initial criteria, a focus group of experts is investigated, and the framework developed; then, a survey analysis is conducted to validate the proposed framework. The extracted criteria and sub-criteria are weighted and prioritized using best-worst method (BWM). Findings The results indicate that the main dimensions of vendor selection framework as regards cloud-based e-learning systems are managerial, technological and pedagogical factors. The rank orders and weights of the mentioned aspects and their sub-criteria are calculated using the BWM. Practical implications The proposed framework helps managers to get a big picture of requirements as to cloud-based e-learning and more effectively to select appropriate vendors in this initiative. In the vendor selection process, managers must pay attention to technological issues as well as managerial and pedagogical considerations. Originality/value Cloud-based e-learning systems are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, there are little studies on how educational institutions and organizations could be able to select appropriate cloud-based e-learning systems. This paper explores the ignored but critically important subject of cloud-based e-learning. The main contribution of this paper is to propose a novel and integrated framework containing the important aspects of vendor selection in cloud-based e-learning services. The proposed framework comprises managerial, technological and pedagogical aspects simultaneously as well as sub-criteria denoting each aspect.
A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution

The success of search-based optimisation algorithms depends on appropriately balancing exploration and exploitation mechanisms during the course of the search. We introduce a mechanism that can be used with Differential Evolution (de) algorithms to adaptively manage the balance between the diversification and intensification phases, depending on current progress. The method—Similarity-based Neighbourhood Search (sns)—uses information derived from measuring Euclidean distances among solutions in the decision space to adaptively influence the choice of neighbours to be used in creating a new solution. sns is integrated into explorative and exploitative variants of jade, one of the most frequently used adaptive de approaches. Furthermore, shade, which is another state-of-the-art adaptive de variant, is also considered to assess the performance of the novel sns. A thorough experimental evaluation is conducted using a well-known set of large-scale continuous problems, revealing that incorporating sns allows the performance of both explorative and exploitative variants of de to be significantly improved for a wide range of the test-cases considered. The method is also shown to outperform variants of de that are hybridised with a recently proposed global search procedure, designed to speed up the convergence of that algorithm.
Hyperbolic‐singular‐value‐decomposition‐based square‐root accurate continuous‐discrete extended‐unscented Kalman filters for estimating continuous‐time stochastic models with discrete measurements

Summary This paper presents novel square‐root accurate continuous‐discrete extended‐unscented Kalman filtering (ACD‐EUKF) algorithms for treating continuous‐time stochastic systems with discrete measurements. The time updates in such methods are fulfilled as those in the extended Kalman filter whereas their measurement updates are copied from the unscented Kalman filter. All this allows accurate predictions of the state mean and covariance to be combined with accurate measurement updates. The main weakness of this technique is the need for the Cholesky decomposition of predicted covariances derived in time‐update steps. Such a factorization is highly sensitive to numerical integration and round‐off errors committed, which may result in losing the covariance's positivity and, hence, failing the Cholesky decomposition. The latter problem is usually solved in the form of square‐root filtering implementations, which propagate not the covariance matrix but its square root instead. Here, we devise square‐root ACD‐EUKF methods grounded in the singular value decomposition (SVD). The SVD rooted in orthogonal transforms is applicable to any ACD‐EUKF with nonnegative weights, whereas the remaining ones, which can enjoy negative weights as well, are treated by means of the hyperbolic SVD based on J ‐orthogonal transforms. The filters constructed are presented in a concise algorithmic form, which is convenient for practical use. Their two particular versions grounded in the classical and cubature unscented Kalman filtering parameterizations are examined in severe conditions of tackling a radar tracking problem, where an aircraft executes a coordinated turn. These are also compared to their non‐square‐root predecessor and other methods within the target tracking scenario with ill‐conditioned measurements.
Block-oriented correlation power analysis with bitwise linear leakage: An artificial intelligence approach based on genetic algorithms

Correlation power analysis (CPA) is known as a powerful method used to launch side-channel attacks on cryptographic devices. In the classic approach, the key is recovered word by word, whose length is usually determined by S-box. For parallel hardware implementations, the power consumption of the target intermediate state except the analyzed word is regarded as noise, which not only reduces the efficiency of CPA but also is a wast of information. Improved methods combining CPA with genetic algorithms were introduced by Zhang et al. (2015), and extended by Ding et al. (2019), in which all key words were processed simultaneously and power consumptions of S-box operations are fully utilized. While, for most hardware implementations, the leakage of S-box operations is not significant enough to support power analysis, such as implementing S-box and mixColumn together or locating registers after addRoundkey instead of S-box in AES. In this paper, we focus on a class of block ciphers which involve keys with XOR operation, and have bitwise linear leakages in their implementations. As far as we know, most block ciphers especially light weight block ciphers belong to this kind. Taking full use of genetic algorithms, a method processing a candidate key as a whole block instead of a combination of key words is proposed. We customize the genetic algorithm for this block-oriented CPA (BCPA) by selecting operators and determining parameters experimentally with respect to a 128-bit block cipher. simulation experimental results show that to achieve success rate 90%, BCPA requires only 600 traces which is 78.13% less than classic CPA and the corresponding computation cost of correlation coefficient is 60% less than classic CPA. when compared with key enumeration algorithm, our method requires 33.33% less traces to achieve success rate 90%, and has exponentially lower time complexity. experiments performed on SAKURA-G board verify the efficiency of BCPA when applied on AES-128. the number of traces required by BCPA to recover the whole key almost reaches the theoretical minimal threshold of attacks based on correlation coefficients, and is nearly 47.14% of classic CPA.
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks

In recent years, plenty of metrics have been proposed to identify networks that are free of gradient explosion and vanishing. However, due to the diversity of network components and complex serial-parallel hybrid connections in modern DNNs, the evaluation of existing metrics usually requires strong assumptions, complex statistical analysis, or has limited application fields, which constraints their spread in the community. In this paper, inspired by the Gradient Norm Equality and dynamical isometry, we first propose a novel metric called Block Dynamical Isometry, which measures the change of gradient norm in individual block. Because our Block Dynamical Isometry is norm-based, its evaluation needs weaker assumptions compared with the original dynamical isometry. To mitigate the challenging derivation, we propose a highly modularized statistical framework based on free probability. Our framework includes several key theorems to handle complex serial-parallel hybrid connections and a library to cover the diversity of network components. Besides, several sufficient prerequisites are provided. Powered by our metric and framework, we analyze extensive initialization, normalization, and network structures. We find that Gradient Norm Equality is a universal philosophy behind them. Then, we improve some existing methods based on our analysis, including an activation function selection strategy for initialization techniques, a new configuration for weight normalization, and a depth-aware way to derive coefficients in SeLU. Moreover, we propose a novel normalization technique named second moment normalization, which is theoretically 30% faster than batch normalization without accuracy loss. Last but not least, our conclusions and methods are evidenced by extensive experiments on multiple models over CIFAR10 and ImageNet.
A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification

Computer vision systems are insensitive to the scale of objects in natural scenes, so it is important to study the multi-scale representation of features. Res2Net implements hierarchical multi-scale convolution in residual blocks, but its random grouping method affects the robustness and intuitive interpretability of the network. We propose a new multi-scale convolution model based on multiple attention. It introduces the attention mechanism into the structure of a Res2-block to better guide feature expression. First, we adopt channel attention to score channels and sort them in descending order of the feature’s importance (Channels-Sort). The sorted residual blocks are grouped and intra-block hierarchically convolved to form a single attention and multi-scale block (AMS-block). Then, we implement channel attention on the residual small blocks to constitute a dual attention and multi-scale block (DAMS-block). Introducing spatial attention before sorting the channels to form multi-attention multi-scale blocks(MAMS-block). A MAMS-convolutional neural network (CNN) is a series of multiple MAMS-blocks. It enables significant information to be expressed at more levels, and can also be easily grafted into different convolutional structures. Limited by hardware conditions, we only prove the validity of the proposed ideas through convolutional networks of the same magnitude. The experimental results show that the convolution model with an attention mechanism and multi-scale features is superior in image classification.
Eye Tracking in Visual Search Experiments

Over the last 30 years, eye tracking has grown in popularity as a method to understand attention during visual search, principally because it provides a means to characterize the spatiotemporal properties of selective operations across a trial. In the present chapter, we review the motivations, methods, and measures for using eye tracking in visual search experiments. This includes a discussion of the advantages (and some disadvantages) of eye tracking data as a measure spatial attention, compared with more traditional reaction time paradigms. In addition, we discuss stimulus and design considerations for implementing experiments of this type. Finally, we will discuss the major measures that can be extracted from an eye tracking record and discuss the inferences that each allow. In the course of this discussion, we address both experiments using abstract arrays and experiments using real-world scene stimuli.
Two‐factor authentication using biometric based quantum operations

Abstract Two‐way authentication methods are utilized in every online user authentication transactions. Use of one time passwords (OTP) have proved to be more secure method, than one factor authentication when the two authentication schemes are carried out on different media. However, current use of OTP restricts authentication to the device itself rather than the user. The fraud cases with OTP based transactions have also increased due to growth of technology. Hence, there is a requirement of enhancing security of OTP based transactions. The study in this paper anchors on mathematically proven properties of quantum cryptography and utilizes the quantum entanglement property to generate quantum OTP ( QOTP ) for authenticating the user based on its biometrics. The proposed method takes into consideration various quantum computing capabilities of user, thus paving path for gradual upgradation of infrastructure. The work in the paper gives the outline of infrastructure required for implementing the proposed model and concludes by comparing security analysis of existing and proposed model against man‐in‐the‐middle attack (MITM) and user end vulnerabilities to bring forth the importance of QOTP. The practical implementation of the proposed model is envisaged as future research work due to evolving nature of quantum experiments.