Reviewerly/exHarmony huggingface dataset:
Memetic Spider Monkey Optimization for Spam Review Detection Problem

Spider monkey optimization (SMO) algorithm imitates the spider monkey's fission–fusion social behavior. It is evident through literature that the SMO is a competitive swarm-based algorithm that is used to solve difficult real-life problems. The SMO's search process is a little bit biased by the random component that drives it with high explorative searching steps. A hybridized SMO with a memetic search to improve the local search ability of SMO is proposed here. The newly developed strategy is titled Memetic SMO (MeSMO). Further, the proposed MeSMO-based clustering approach is applied to solve a big data problem, namely, the spam review detection problem. A customer usually makes decisions to purchase something or make an image of someone based on online reviews. Therefore, there is a good chance that the individuals or companies may write spam reviews to upgrade or degrade the stature or value of a trader/product/company. Therefore, an efficient spam detection algorithm, MeSMO, is proposed and tested over four complex spam datasets. The reported results of MeSMO are compared with the outcomes obtained from the six state-of-art strategies. A comparative analysis of the results proved that MeSMO is a good technique to solve the spam review detection problem and improved precision by 3.68%.
Connections between scattered linear sets and MRD-codes.

The aim of this paper is to survey on the known results on maximum scattered linear sets and MRD-codes. In particular, we investigate the link between these two areas. In A new family of linear maximum rank distance codes (2016) Sheekey showed how maximum scattered linear sets of $\mathrm{PG}(1,q^n)$ define square MRD-codes. Later in Maximum scattered linear sets and MRD-codes (2017) maximum scattered linear sets in $\mathrm{PG}(r-1,q^n)$, $r>2$, were used to construct non square MRD-codes. Here, we point out a new relation regarding the other direction. We also provide an alternative proof of the well-known Blokhuis-Lavrauw's bound for the rank of maximum scattered linear sets shown in Scattered spaces with respect to a spread in $\mathrm{PG}(n,q)$ (2000).
Towards Quantifiable Dialogue Coherence Evaluation

Automatic dialogue coherence evaluation has attracted increasing attention and is crucial for developing promising dialogue systems.However, existing metrics have two major limitations: (a) they are mostly trained in a simplified two-level setting (coherent vs. incoherent), while humans give Likert-type multi-level coherence scores, dubbed as "quantifiable"; (b) their predicted coherence scores cannot align with the actual human rating standards due to the absence of human guidance during training.To address these limitations, we propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel framework aiming to train a quantifiable dialogue coherence metric that can reflect the actual human rating standards.Specifically, QuantiDCE includes two training stages, Multi-Level Ranking (MLR) pre-training and Knowledge Distillation (KD) fine-tuning.During MLR pre-training, a new MLR loss is proposed for enabling the model to learn the coarse judgement of coherence degrees.Then, during KD fine-tuning, the pretrained model is further finetuned to learn the actual human rating standards with only very few human-annotated data.To advocate the generalizability even with limited finetuning data, a novel KD regularization is introduced to retain the knowledge learned at the pre-training stage.Experimental results show that the model trained by QuantiDCE presents stronger correlations with human judgements than the other state-of-the-art metrics. 1
Voice-Based Speaker Identification and Verification

In recent years, the advancement in voice-based authentication leads in the field of numerous forensic voice authentication technology. For verification, the speech reference model is collected from various open-source clusters. In this chapter, the primary focus is on automatic speech recognition (ASR) technique which stores and retrieves the data and processes them in a scalable manner. There are the various conventional techniques for speech recognition such as BWT, SVD, and MFCC, but for automatic speech recognition, the efficiency of these conventional recognition techniques degrade. So, to overcome this problem, the authors propose a speech recognition system using E-SVD, D3-MFCC, and dynamic time wrapping (DTW). The speech signal captures its important qualities while discarding the unimportant and distracting features using D3-MFCC.
An optimization of color halftone visual cryptography scheme based on Bat algorithm

Abstract Visual cryptography is a cryptographic technique that allows visual information to be encrypted so that the human optical system can perform the decryption without any cryptographic computation. The halftone visual cryptography scheme (HVCS) is a type of visual cryptography (VC) that encodes the secret image into halftone images to produce secure and meaningful shares. However, the HVC scheme has many unsolved problems, such as pixel expansion, low contrast, cross-interference problem, and difficulty in managing share images. This article aims to enhance the visual quality and avoid the problems of cross-interference and pixel expansion of the share images. It introduces a novel optimization of color halftone visual cryptography (OCHVC) scheme by using two proposed techniques: hash codebook and construction techniques. The new techniques distribute the information pixels of a secret image into a halftone cover image randomly based on a bat optimization algorithm. The results show that these techniques have enhanced security levels and make the proposed OCHVC scheme more robust against different attacks. The OCHVC scheme achieves mean squared error (MSE) of 95.0%, peak signal-to-noise ratio (PSNR) of 28.3%, normalized cross correlation (NCC) of 99.4%, and universal quality index (UQI) of 99.3% on average for the six shares. Subsequently, the experiment results based on image quality metrics show improvement in size, visual quality, and security for retrieved secret images and meaningful share images of the OCHVC scheme. Comparing the proposed OCHVC with some related works shows that the OCHVC scheme is more effective and secure.
Constructive and Toxic Speech Detection for Open-Domain Social Media Comments in Vietnamese

The rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.
Binary pseudo-random array test standard optimized for characterization of interferometric microscopes

We describe a technique for measuring the instrument transfer function (ITF) of an interferometric microscope, allowing both characterization and data processing to increase the fidelity and effective resolution of the tool. The technique, based on test samples structured as two-dimensional (2D) binary pseudo-random arrays (BPRAs), employs the unique properties of the BPRA patterns in the spatial frequency domain. The inherent 2D power spectral density of the pattern has a deterministic white-noise-like character that allows direct determination of the ITF with uniform sensitivity over the entire spatial frequency range and field-of-view of an instrument. As such, the BPRA samples satisfy the characteristics of a test standard: functionality, ease of specification and fabrication, reproducibility, and low sensitivity to manufacturing error. We discuss the results of the development and application of highly randomized (HR) BPRA test samples with elementary feature sizes in the range from 80 nm and up to 2.5 μm, optimized for the ITF characterization of interferometric microscopes broadly used for 2D optical surface profiling. The data acquisition and analysis procedures for different applications of the ITF calibration technique developed are also discussed.
Kapur's entropy for multilevel thresholding image segmentation based on moth-flame optimization

Multilevel thresholding is a reliable and efficacious method for image segmentation that has recently received widespread recognition. However, the computational complexity of the multilevel thresholding method increases as the threshold level increases, which causes the low segmentation accuracy of this method. To overcome this shortcoming, this paper presents a moth-flame optimization (MFO) established on Kapur's entropy to clarify the multilevel thresholding image segmentation. The MFO adjusts exploration and exploitation to achieve the best fitness value. To validate the overall performance, MFO is compared with other algorithms to realize the global optimal solution to maximize the target value of Kapur's entropy. Some critical evaluation indicators are used to determine the segmentation effect and optimization performance of each algorithm. The experimental results indicate that MFO has a faster convergence speed, higher calculation accuracy, better segmentation effect and better stability.
A survey on Deep Learning Based Eye Gaze Estimation Methods

In recent years, deep-learning systems have made great progress, particularly in the disciplines of computer vision and pattern recognition. Deep-learning technology can be used to enable inference models to do real-time object detection and recognition. Using deep-learning-based designs, eye tracking systems could determine the position of eyes or pupils, regardless of whether visible-light or near-infrared image sensors were utilized. For growing electronic vehicle systems, such as driver monitoring systems and new touch screens, accurate and successful eye gaze estimates are critical. In demanding, unregulated, low-power situations, such systems must operate efficiently and at a reasonable cost. A thorough examination of the different deep learning approaches is required to take into consideration all of the limitations and opportunities of eye gaze tracking. The goal of this research is to learn more about the history of eye gaze tracking, as well as how deep learning contributed to computer vision-based tracking. Finally, this research presents a generalized system model for deep learning-driven eye gaze direction diagnostics, as well as a comparison of several approaches.
Blockchain as an enabling technology in the COVID-19 pandemic: a systematic review

The impacts caused by the unprecedented transmission of COVID-19 have given rise to new challenges that are shaking the structures of humanity. Several enabling technologies are currently being used as key strategies in creating improvements and responses to the difficulties created by the pandemic and blockchain is one of these solution proposals. Within this scenario, this work aims to study and analyze how the blockchain technology can help in the struggle against the COVID-19 pandemic through a systematic review of the literature. Although the study is limited by the moment when the crisis is still in progress, the results show that it is clear that the adoption of the blockchain can effectively help in the fight against the coronavirus, considering that the main features of the blockchain can support the successful implementation of many use cases. This paper has the role of assisting academics and professionals in identifying the application focus of the blockchain, as well as showing the main opportunities and challenges and the relevance of the subject to the current context of the pandemic.
State of the Art in Cybersecurity and Smart Grid Education

This paper was developed within the draft "Cybersecurity Curricula Recommendations for Smart Grids" framework. The paper deals with modern education in the field of cybersecurity of intellectual networks, using a systematic reading and analysis of literature from universities and private schools. Based on the research, basic conclusions were carried out, and essential requirements and recommendations were put forward for cybersecurity research programs on smart grids.
Dynamic Encoder Transducer: A Flexible Solution for Trading Off Accuracy for Latency

We propose a dynamic encoder transducer (DET) for on-device speech recognition. One DET model scales to multiple devices with different computation capacities without retraining or finetuning. To trading off accuracy and latency, DET assigns different encoders to decode different parts of an utterance. We apply and compare the layer dropout and the collaborative learning for DET training. The layer dropout method that randomly drops out encoder layers in the training phase, can do on-demand layer dropout in decoding. Collaborative learning jointly trains multiple encoders with different depths in one single model. Experiment results on Librispeech and in-house data show that DET provides a flexible accuracy and latency trade-off. Results on Librispeech show that the full-size encoder in DET relatively reduces the word error rate of the same size baseline by over 8%. The lightweight encoder in DET trained with collaborative learning reduces the model size by 25% but still gets similar WER as the full-size baseline. DET gets similar accuracy as a baseline model with better latency on a large in-house data set by assigning a lightweight encoder for the beginning part of one utterance and a full-size encoder for the rest.
Construction of a qudit using Schrödinger cat states and generation of hybrid entanglement between a discrete-variable qudit and a continuous-variable qudit

We show that a continuous-variable (CV) qudit can be constructed using quasiorthogonal cat states of a bosonic mode, when the phase encoded in each cat state is chosen appropriately. With the constructed CV qudit and the discrete-variable (DV) qudit encoded with Fock states, we propose an approach to generate the hybrid maximally entangled state of a CV qudit and a DV qudit by using two microwave cavities coupled to a superconducting flux qutrit. This proposal relies on the initial preparation of a superposition of Fock states of one cavity and the initial preparation of a cat state of the other cavity. After the initial state of each cavity is prepared, this proposal requires only two basic operations, i.e., the first operation employs the dispersive coupling of both cavities with the qutrit while the second operation uses the dispersive coupling of only one cavity with the qutrit. The entangled state production is deterministic and the operation time decreases as the dimensional size of each qudit increases. In addition, during the entire operation, the coupler qutrit remains in the ground state and thus decoherence from the qutrit is significantly reduced. As an example, we further discuss the experimental feasibility for generating the hybrid maximally entangled state of a DV qutrit and a CV qutrit based on circuit QED. This proposal is universal and can be extended to accomplish the same task, by using two microwave or optical cavities coupled to a natural or artificial three-level atom.
Membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization

A fuzzy theoretic analytical approach was recently introduced that leads to efficient and robust models while addressing automatically the typical issues associated to parametric deep models. However, a formal conceptualization of the fuzzy theoretic analytical deep models is still not available. This paper introduces using measure theoretic basis the notion of membership-mapping for representing data points through attribute values (motivated by fuzzy theory). A property of the membership-mapping, that can be exploited for data representation learning, is of providing an interpolation on the given data points in the data space. An analytical approach to the variational learning of a membership-mappings based data representation model is considered.
Federated Learning

Now we are in an era of technology transformation in our everyday life, where data play a key role in the decision making and bringing the action into reality. These data are collected from many distributed sources. Another important concept in this process is machine learning (ML) and data analytics. Federated learning (FL) is the term coined by Google. It facilitated the distributed learning process and shared the results to the outcomes to the central entity instead of conducting the complete learning process at the centre. In the traditional machine learning approach, data are brought to the model. Whereas FL brings ML techniques to the data used at end devices. In a nutshell, FL, which is also known as distributed learning deals with both centralized and distributed devices. The central entity(server) selects the statistical model which is to be trained. Then, it sends the trained model to multiple decentralized devices or servers. These distributed nodes train the model locally with their own data. Finally, the central entity pools the results from the distributed nodes and prepares a common model without accessing the data from the other nodes.1,2
"Jol" or "Pani"?: How Does Governance Shape a Platform's Identity?

In this paper, we explore how sociotechnical systems support and impede the identity performances and identity expression of communities that have experienced a long history of colonialism, where colonization is the practice through which a foreign power reshapes the social structures and systems of other societies. We conducted a trace ethnography among members of a specific digital platform-Bengali Quora (BnQuora). BnQuora is part of the question and answer (Q&amp;A) platform Quora, where people with this particular ethnolinguistic identity come together to engage in conversations about their identities; identities which were shaped through a long history of colonization in the Global South. In drawing on a conceptual framework that brings together identity performativity, governance, content moderation, and surveillance, we find that the sociotechnical mechanisms of governance that mediate people's performances on the BnQuora platform give rise to a kind of platform identity-certain identities are privileged while others are pushed to the margins based on linguistic practices, nationalities, and religious affiliations. We illustrate this through the themes of moderators as prison guards, collective surveillance as enforcing a majority identity, algorithmic coloniality, and staging as self-imprisonment. Finally, we discuss the ways in which governance shapes a platform's identity and can create, strengthen, and reinforce coloniality.
Training Fuzzy Neural Network via Multiobjective Optimization for Nonlinear Systems Identification

The design of a fuzzy neural network (FNN) has long been a challenging problem since most methods rely on approximation error to train an FNN, which may easily result in overfitting phenomenon to degrade the generalization performance. To improve the generalization performance, an FNN with a multiobjective optimization algorithm (MOO-FNN) is proposed in this article. First, the multilevel learning objectives are designed around the generalization performance to guide the training process of an FNN. Then, the method utilizes the approximation error, the structure complexity, and the output smoothness indicators instead of a single indicator to improve the evaluation accuracy of generalization performance. Second, an MOO algorithm with continuous–discrete variables is developed to optimize the FNN. Then, MOO is able to use a novel particle update method to adjust both the structure and parameters rather than adjusting them separately, thereby achieving suitable generalization performance of the FNN. Third, the convergence of MOO-FNN is analyzed in detail to guarantee its successful applications. Finally, the experimental studies of MOO-FNN have been performed on model identification of nonlinear systems to verify the effectiveness. The results illustrate that MOO-FNN has a significant improvement over some state-of-the-art algorithms.
Automatic migration from synchronous to asynchronous JavaScript APIs

The JavaScript ecosystem provides equivalent synchronous and asynchronous Application Programming Interfaces (APIs) for many commonly used I/O operations. Synchronous APIs involve straightforward sequential control flow that makes them easy to use and understand, but their "blocking" behavior may result in poor responsiveness or performance. Asynchronous APIs impose a higher syntactic burden that relies on callbacks, promises, and higher-order functions. On the other hand, their nonblocking behavior enables applications to scale better and remain responsive while I/O requests are being processed. While it is generally understood that asynchronous APIs have better performance characteristics, many applications still rely on synchronous APIs. In this paper, we present a refactoring technique for assisting programmers with the migration from synchronous to asynchronous APIs. The technique relies on static analysis to determine where calls to synchronous API functions can be replaced with their asynchronous counterparts, relying on JavaScript's async/await feature to minimize disruption to the source code. Since the static analysis is potentially unsound, the proposed refactorings are presented as suggestions that must be reviewed and confirmed by the programmer. The technique was implemented in a tool named Desynchronizer. In an empirical evaluation on 12 subject applications containing 316 synchronous API calls, Desynchronizer identified 256 of these as candidates for refactoring. Of these candidates, 244 were transformed successfully, and only 12 resulted in behavioral changes. Further inspection of these cases revealed that the majority of these issues can be attributed to unsoundness in the call graph.
Systems approach to cloud computing adoption in an emerging economy

Purpose Cloud computing (CC) services have given a tremendous boost to the creation of efficient and effective solutions. With developing countries at a slow pace of adoption, this research aims to identify factors and their interrelationships influencing the adoption of CC in a developing country context. The developing countries are enjoying numerous benefits from CC services; however, its low adoption is still a question in developing economies; hence, the authors have selected the context of information and communication technology (ICT) firms in India. Design/methodology/approach The qualitative research method is used where experts from thirteen ICT firms in India are interviewed. Findings Sixteen factors, twenty-eight subfactors, and 25 interrelationships are revealed through content analysis. Further, causal loop diagrams are proposed to display the behavior of cause and effect of these factors from a system's perspective. This will help to understand the relationships among the factors in order to enhance the speed of CC adoption. Possible financial loss and resistance to change are found as the key barriers to adoption. The proposed interrelationships can guide both policymakers and service providers for designing effective CC policies. Originality/value This is the first scholarly work that identifies interrelationships among factors and subfactors, thereby providing a holistic picture to decision-makers while making a choice on whether to adopt cloud services or continue with on premise data centers and servers.