Reviewerly/exHarmony huggingface dataset:
Man is What He Eats: A Research on Hinglish Sentiments of YouTube Cookery Channels Using Deep learning

Our study focuses on the sentiment analysis of Hinglish comments by multi-label text classification on cookery channels of YouTube using Deep learning. Multi-layer perceptron (MLP) with different parameters was implemented in our study to investigate the various sentiments in the comments. We have modelled and evaluated MLP by varying the number of neurons, layers, optimizers, activation functions with the various feature engineering methods such as tf-idf, count vectorizer, pre-trained embeddings and customizedembeddings. These experiments were conducted on two datasets they are Kabita’s Kitchen and NishaMadhulika’s dataset. From the investigation, we concludedKabita’s Kitchen dataset has the highest accuracy 98.53% and NishaMadulika’shas 98.48% accuracy in MLP.This outcome of the experiment was evaluated based on careful analysis on tests conducted during our study
A Technology-driven Overview on Blockchain-based Academic Certificate Handling

Academic certificates have a significant influence on the job market, proving a particular competence or skill of a recipient. However, the ability to verify the authenticity of certificates does not follow its relevance in the labor market, causing several companies to exploit this inefficiency to falsify information or even to make fake certificates. In this context, several proposals based on blockchain appear as a technological alternative to increase the transparency and the ease of verification of these certificates. This chapter discusses the main proposals toward the handling of academic certificates from a technological point of view, discussing the technical aspects that may influence the relationship between confidentiality and transparency as well as application requirements such as performance and reliability in contrast to the blockchain characteristics. Finally, this chapter summarizes the key challenges and opportunities based on this discussion outlining future directions for academic certificate management.
Examining the Impact of Dubai Smart Government Characteristics on User Satisfaction

Information and telecommunication technology (ICT) are today practiced in various public sectors and are considered as a cost-effective and convenient means to encourage openness, transparency, and to reduce corruption. It has also put innovation and ICT more than ever at the heart of smart development. Presently, this phenomenon has also been adopted by governments so as to cope with various problems created by increasing urban populations in their countries. The main objective of this study is to examine the influence of Dubai smart government characteristics on the user satisfaction. Online survey was used to collect data for this study, the sample size was determined as 250 users of Dubai smart government services, who are users who got the services from five major strategic or government partners of smart government establishment: Dubai Police, RTA, DEWA, DHA, and Dubai Municipality. PLS (Partial Least Squares) SEM-VB (Structural Equation Modelling-Variance Based) was employed to assess the research model by utilising the software SmartPLS 3.0. This paper adds to the existing literature of smart government characteristics (Information System Quality, Relationship with Public Agencies, Leadership, Accountability and Transparency, and Productivity) and user satisfaction (Usefulness, Awareness, Service Quality, Trust, and Social Influence). The results of this study have the potential to give further insights into Dubai government to improve their users’ satisfaction
Strategies to Prevent and Control of Cybercrime against Women and Girls

The emergence of ICT has provided an unrivalled opportunity for women to exploit their capabilities to improve their quality of life as well as to contribute to the welfare of the society. Internet has also become a social and communication tool with e-mails being an efficient communication tool. Internet offers new possibilities for networking and participative democracy but feminists are failing to challenge the use of the Internet for furthering more dangerous and discriminatory agendas, including Internet crime committed against women and the proliferation of pornography. Many youth are engaging socially through common social network like WhatsApp, Facebook, twitter, msn, my space and are actually addicted to these Internet social activities, making them vulnerable to Internet crimes. The increased dependency of individual / organisations on cyberspace has led to the increase in the cybercrimes. In India we find there is minimal proper training and education regarding such developments and the ignorance towards these advancements has paved way for cybercrimes. Even the authorities and officers who handle such cases have no proper training and the essential expertise for undertaking cybercrimes against women and girls in particular. India should consider adopting a coactive approach wherein technology and proper legislative framework combine to fight the cybercrime in the society. The threatening atmosphere created by such heinous cybercrimes against women and girls need radical change in technology, behaviour and legal affairs; facilitated by proper education and training. This study examines the problems and issues of cybercrime against women and girls in order to exhibit preventive strategies and means to fight back with the support of cohesive forces.
QULATIS: A Quantum Error Correction Methodology toward Lattice Surgery

Due to the high error rate of a qubit, detecting and correcting errors on it is essential for fault-tolerant quantum computing (FTQC). Surface code (SC) associated with its decoding algorithm is one of the most promising quantum error correction (QEC) methods because it has high fidelity and requires only nearest neighbor qubits connectivity. To realize FTQC, we need a decoder circuit capable of not only QEC in a 3-D lattice to deal with errors in measurement on ancillary qubits but also quantum operations on logically constructed qubits. Whereas several methods to perform logical operations on SC, such as lattice surgery (LS), are known, no practical decoders supporting them have been proposed yet.One of the most promising QC implementations today is made up of superconducting qubits that are located in a cryogenic environment. To reduce the hardware complexity of QC and latency of QEC, we are supposed to perform QEC in a cryogenic environment. Hence a power-efficient decoder is required due to the limited power budget inside a dilution refrigerator.In this paper, we propose an online-QEC algorithm that supports LS with a practical decoder circuit, as well as a new FTQC architecture. We design a key building block of the proposed architecture with a hybrid of SFQ- and Cryo-CMOS-based digital circuits and evaluate it with a SPICE-level simulation. Each logic element includes about 2400 Josephson junctions, and power consumption is estimated to be 2.07 &#x03BC;W when operating with a 2 GHz clock frequency. We evaluate the decoder performance by a quantum error simulator for an essential operation of LS with code distances up to 11, and it achieves a 0.6% accuracy threshold. In an LS-based architecture further supporting a magic-state distillation protocol, which is expected to run for near-term universal quantum computing, we evaluate the QEC performance and power consumption of the architecture and show that it is practical to be operated in 4-K temperature region of a dilution refrigerator.
Integrated Sensing Devices for Disease Prevention and Health Alerts in Smart Homes

The rapid development of elderly population is changing demographics in Europe and North America and imposes barriers to healthcare systems that may reduce the quality of service. Telemedicine is a potential solution supporting the real-time and remote monitoring of subjects as well as bidirectional communication with medical personnel for care delivery at the point of perception. Smart homes are private spaces where young or elderly, healthy or diseased-suffering, or disabled individuals spend the majority of their time. Hence, turning smart homes into diagnostic spaces for continuous, real-time, and unobtrusive health monitoring allows disease prediction and prevention before the subject perceives any symptoms. According to the World Health Organization, health, well-being, and quality of life assessment require the monitoring of interwoven domains such as environmental, behavioral, physiological, and psychological. In this work, we give an overview on sensing devices and technologies utilized in smart homes, which can turn the home into a diagnostic space. We consider the integration of sensing devices from all four WHO domains with respect to raw and processed data, transmission, and synchronization. We apply the bus-based scalable intelligent system to construct a hybrid topology for hierarchical multi-layer data fusion. This enables event detection and alerting for short-time as well as prediction and prevention for long-time monitoring.
Estimation of Linear Regression with the Dimensional Analysis Method

Dimensional Analysis (DA) is a mathematical method that manipulates the data to be analyzed in a homogenized manner. Likewise, linear regression is a potent method for analyzing data in diverse fields. At the same time, data visualization has gained attention in tendency study. In addition, linear regression is an important topic to address predictive models and patterns in data study. However, it is still pending to attack the manipulation of uncertainty related to the data transformation. In this sense, this work presents a new contribution with linear regression, combining the Dimensional Analysis (DA) to address instability and error issues. In addition, our method provides a second contribution related to including the decision maker’s attitude involved in the study. Therefore, the experimentation shows that DA manipulates the regression problem under a complex situation that the outcome may have in the investigation. A real-life case study is used to demonstrate our proposal.
A survey of elliptic curves for proof systems

Elliptic curves have become key ingredients for instantiating zero-knowledge proofs and more generally proof systems. Recently, there have been many tailored constructions of these curves that aim at efficiently implementing different kinds of proof systems. In this survey we provide the reader with a comprehensive overview on existing work and revisit the contributions in terms of efficiency and security. We present an overview at three stages of the process: curves to instantiate a SNARK, curves to instantiate a recursive SNARK, and also curves to express an elliptic-curve related statement. We provide new constructions of curves for SNARKs and generalize the state-of-the-art constructions for recursive SNARKs. We also exhaustively document the existing work and open-source implementations.
FedSeC: a Robust Differential Private Federated Learning Framework in Heterogeneous Networks

Federated learning (FL) is considered to be a promising paradigm to solve data privacy disclosure in large-scale machine learning. To further enhance the privacy protection of federated learning, prior works incorporate the differentially private data perturbation into the federated system. But it is not feasible given the impairment of the model from noise, as adding Gaussian noise to achieve differential privacy (DP) deteriorates the accuracy of the model. In particular, the assumption that the sophisticated system is homogeneous is not realistic for real scenarios. Heterogeneous networks exacerbate noise disruptions. In this paper, we present FedSeC, a novel differential private federated learning (DP-FL) framework which operates with robust convergence and high-accuracy while achieving adequate privacy protection. FedSeC improves upon naive combinations of federated learning and differential privacy approaches with an updates-based optimization of relative-staleness and semi-synchronous approach for fast convergence in heterogeneous networks. Moreover, we propose a valid client selection scheme to trade-off fair resource allocation and discriminatory incentives. Through extensive experimental validation of our method in three different heterogeneities, we show that FedSeC outperforms the previous state-of-the-art method.
A deep deterministic policy gradient algorithm based on averaged state-action estimation

Deep Reinforcement Learning (DRL), one of the most popular research topics in artificial intelligence, has achieved a breakthrough in continuous control tasks. Nonetheless, the DRL algorithm's instability and local optimality have a bad influence impact on its performance. The Deep Deterministic Policy Gradients (DDPG) algorithm uses a "soft" update to slow down the target value rate of change to alleviate this problem. However, there is still a specific target approximate error variance. The variance will aggravate the degree of the data dispersion and reduce the stability of the model. This paper proposed the DDPG with averaged state-action estimation (Averaged-DDPG) algorithm. It aims to minimize the adverse effects of conflict, which calculates the action reward by averaging the estimated values of previously learned Q values, thus reducing the training process's fluctuation and improving the algorithm's performance. The evaluation results in continuous control tasks show that Averaged-DDPG can enhance the agent's learning efficiency and training balance more effectively than the original DDPG algorithm.
Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective

Abstract Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical aspects of its use, is sparse. This study uses Activity Theory (AT) as a theoretical lens to examine AI-powered B2B marketing as a collective activity system, and to illuminate the contradictions that emerge when adopting and implementing AI into traditional B2B marketing practices. AT is appropriate in the context of this study, as it shows how contradictions act as a motor for change and lead to transformational changes, rather than viewing tensions as a threat to prematurely abandon the adoption and implementation of AI in B2B marketing. Based on eighteen interviews with industry and academic experts, the study identifies contradictions with which marketing researchers and practitioners must contend. We show that these contradictions can be culturally or politically challenging to confront, and even when resolved, can have both intended and unintended consequences.
Systematic Review of Intelligent Tutoring Systems for Hard Skills Training in Virtual Reality Environments

Advances in immersive virtual reality (I-VR) technology have allowed for the development of I-VR learning environments (I-VRLEs) with increasing fidelity. When coupled with a sufficiently advanced computer tutor agent, such environments can facilitate asynchronous and self-regulated approaches to learning procedural skills in industrial settings. In this study, we performed a systematic review of published solutions involving the use of an intelligent tutoring system (ITS) to support hard skills training in an I-VRLE. For the seven solutions that qualified for the final analysis, we identified the learning context, the implemented system, as well as the perceptual, cognitive, and guidance features of the utilized tutoring agent. Generally, the I-VRLEs emulated realistic work environments or equipment. The solutions featured either embodied or embedded tutor agents. The agents’ perception was primarily based on either learner actions or learner progress. The agents’ guidance actions varied among the solutions, ranging from simple procedural hints to event interjections. Several agents were capable of answering certain specific questions. The cognition of the majority of agents represented variations on branched programming. A central limitation of all the solutions was that none of the reports detailed empirical studies conducted to compare the effectiveness of the developed training and tutoring solutions.
A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications

The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population’s diversity and accelerates the algorithm’s convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance.
Effective Parametrization of Low Order Bézier Motion Primitives for Continuous-Curvature Path-Planning Applications

We propose a new parametrization of motion primitives based on Bézier curves that suits perfectly path-planning applications (and environment exploration) of wheeled mobile robots. The individual motion primitives can simply be calculated taking into account the requirements of path planning and the constraints of a vehicle, given in the form of the starting and ending orientations, velocities, turning rates, and curvatures. The proposed parametrization provides a natural geometric interpretation of the curve. The solution of the problem does not require optimization and is obtained by solving a system of simple polynomial equations. The resulting planar path composed of the primitives is guaranteed to be C2 continuous (the curvature is therefore continuous). The proposed primitives feature low order Bézier (third order polynomial) curves. This not only provides the final path with minimal required turns or unwanted oscillations that typically appear when using higher-order polynomial primitives due to Runge’s phenomenon but also makes the approach extremely computationally efficient. When used in path planning optimizers, the proposed primitives enable better convergence and conditionality of the optimization problem due to a low number of required parameters and a low order of the polynomials. The main contribution of the paper therefore lies in the analytic solution for the third-order Bézier motion primitive under given boundary conditions that guarantee continuous curvature of the composed spline path. The proposed approach is illustrated on some typical scenarios of path planning for wheeled mobile robots.
Social Interpretable Tree for Pedestrian Trajectory Prediction

Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories. Specifically, a path in the tree from the root to leaf represents an individual possible future trajectory. SIT employs a coarse-to-fine optimization strategy, in which the tree is first built by high-order velocity to balance the complexity and coverage of the tree and then optimized greedily to encourage multimodality. Finally, a teacher-forcing refining operation is used to predict the final fine trajectory. Compared with prior methods which leverage implicit latent variables to represent possible future trajectories, the path in the tree can explicitly explain the rough moving behaviors (e.g., go straight and then turn right), and thus provides better interpretability. Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford Drone datasets demonstrate that our method is capable of matching or exceeding the performance of state-of-the-art methods. Interestingly, the experiments show that the raw built tree without training outperforms many prior deep neural network based approaches. Meanwhile, our method presents sufficient flexibility in long-term prediction and different best-of-K predictions.
Robust Twin Bounded Support Vector Classifier With Manifold Regularization

Support vector machine (SVM), as a supervised learning method, has different kinds of varieties with significant performance. In recent years, more research focused on nonparallel SVM, where twin SVM (TWSVM) is the typical one. In order to reduce the influence of outliers, more robust distance measurements are considered in these methods, but the discriminability of the models is neglected. In this article, we propose robust manifold twin bounded SVM (RMTBSVM), which considers both robustness and discriminability. Specifically, a novel norm, that is, capped <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm, is used as the distance metric for robustness, and a robust manifold regularization is added to further improve the robustness and classification performance. In addition, we also use the kernel method to extend the proposed RMTBSVM for nonlinear classification. We introduce the optimization problems of the proposed model. Subsequently, effective algorithms for both linear and nonlinear cases are proposed and proved to be convergent. Moreover, the experiments are conducted to verify the effectiveness of our model. Compared with other methods under the SVM framework, the proposed RMTBSVM shows better classification accuracy and robustness.
A Robust Approach for Image Super-Resolution using Modified Very Deep Convolution Networks

This research presents a Modified Very Deep Convolutional Network (MVDCN) for Single Image Super Resolution (SISR). The proposed method is based on modified CNN, in which different image features for training also apply up-sampling as well as residual images, which is a fundamental step of SISR, and the depth of the network is 20. For the improvement of the presented method, it applies the fusion of the bi-cubic method with the proposed modified residual image attributes with Very Deep Convolutional Networks (VDCN). The presented method shows better results in terms of the two base parameters of the proposed method. These are PSNR and SSIM. These two parameters are major parameters for the result analysis of image super resolution (ISR). There are different data sets available for the training and testing of the presented method, such as test datasets “Set5’ [15] and ‘Set14’ [26]. Both are primarily used as benchmarks by various researchers; however, in other works, the data set “Urban100” is very interesting because it contains many challenging images that fail by many of the existing methods. Final data set ‘B100’, natural images from Berkeley University. The proposed MVDCN shows better results as compared to other previous methods.