Reviewerly/exHarmony huggingface dataset:
Cybersecurity Policy and Strategy Management in FinTech

The problem of cybersecurity is not limited to a particular department or section, but it is an enterprise-wide problem. It requires an interdisciplinary approach to address various cybersecurity issues arising from different sources in the organization. A comprehensive cybersecurity policy and strategy ensures healthy cybersecurity practices in the organization. This chapter provides an overwhelming introduction to the cybersecurity policies and strategies used to protect FinTech institutions from deadly cyberattacks. It prevents several cyberattacks by following fundamental cyber practices and educating users, employees, and people in the organization. These policies provide an extensive overview of a wide range of cyber practices, from selecting a password to predicting a cyberattack.
Inferring DNN layer-types through a Hardware Performance Counters based Side Channel Attack

Recent trends of the use of deep neural networks (DNNs) in mission-critical applications have increased the threats of microarchitectural attacks on DNN models. Recently, researchers have proposed techniques for inferring the DNN model based on microarchitecture-level clues. However, existing techniques require prior knowledge of victim models, lack generality, or provide incomplete information of the victim model architecture. This paper proposes an attack that leaks the layer-type of DNNs using hardware performance monitoring counters (PMCs).
FastTacotron: A Fast, Robust and Controllable Method for Speech Synthesis

Recent state-of-the-art neural text-to-speech synthesis models have significantly improved the quality of synthesized speech. However, the previous methods have remained several problems. While autoregressive models suffer from slow inference speed, non-autoregressive models usually have a complicated, time and memory-consuming training pipeline. This paper proposes a novel model called FastTacotron, which is an improved text-to-speech method based on ForwardTacotron. The proposed model uses the recurrent Tacotron architecture but replacing its autoregressive attentive part with a single forward pass to accelerate the inference speed. The model also replaces the attention mechanism in Tacotron with a length regulator like the one in FastSpeech for parallel mel-spectrogram generation. Moreover, we introduce more prosodic information of speech (e.g., pitch, energy, and more accurate duration) as conditional inputs to make the duration predictor more accurate. Experiments show that our model matches state-of-the-art models in terms of speech quality and inference speed, nearly eliminates the problem of word skipping and repeating in particularly hard cases, and possible to control the speed and pitch of the generated utterance. More importantly, our model can converge just in few hours of training, which is up to 11.2x times faster than existing methods. Furthermore, the memory requirement of our model grows linearly with sequence length, which makes it possible to predict complete articles at one time with the model. Audio samples can be found in https://bit.ly/3xguaCW.
Data Sensitive Algorithm Integrated with Compression Technique for Secured and Efficient Utilization of Cloud Storage

The success and popularity of cloud computing is obvious from technological advancement. It is gaining confidence of clients by offering cost effective architecture supporting computation transmission and data storage. These trustworthy data services face lots of challenges just because of loss of control over data as well as cloud abstract nature. For handling security related issues, lots of encryption – decryption algorithms have already been designed and implemented. All of pre-existing algorithms require key from the user and then start encryption accordingly. The main objective of this research paper is to design and implement new algorithms that can produce key on sensing data automatically and consequently encrypt data on the basis of key produced. Algorithm1 has been designed into phases, in first phase it senses the data and generates key, in second phase it encrypts the data and in third phase it compresses the data. Algorithm2 has also been design into phases, in first phase it decompresses, in second phase it decrypts the data. Algorithm3 has been designed only. Its implementation is under progress and may be seen in future scope of the paper. For securing data, we have used the concept of encryption-decryption and for efficient utilization of cloud storage we have compressed the data by using GZIP java inbuilt library with ZIP package. Collectively Algorithm1 and Algorithm2 have been entitled as Data Sensitive Algorithm (DSA).
Visual Recognition of Local Kashmiri Objects with Limited Image Data using Transfer Learning

Learning to recognize object categories is a challenging task for computers and the task becomes more difficult if the image data is small in size. Traditional machine learning methods require extensive training data to generalize and produce accurate results. Seeking inspiration from human perception, it has been found using prior knowledge about related tasks helps in learning new tasks. Transfer Learning is based on this natural learning process and can help to reproduce the remarkable human capability of recognizing objects from just one single view. In this manuscript we explored transfer learning techniques along with state-of-the-art object recognition models to discover improved ways of performing Visual Object Recognition for Object categories with limited image data. A small data set was built from scratch having images for four local object categories and then a model was developed using pre trained Inception-v3 model for classifying them. The results were compared with stock 3 Layer Convolutional model. The proposed model obtained a respectable accuracy result of around 90% while the stock model had an accuracy of 70%. Considering the fact that the dataset used is very tiny (training portion of the data set had only 320 images for all categories, 80 per category) the results obtained are encouraging. Thus, this work further strengthens the fact that transfer-based techniques can be utilized for computer vision tasks with limited data.
Research on English Education Auxiliary Teaching System Based on MOOC

In the traditional English education auxiliary teaching system, the response time is long and the student satisfaction rate is low. Keeping these issues in mind, we propose a MOOC-based English education auxiliary teaching system that consists of hardware and functional modules of the teaching system and the database. The hardware of the English education auxiliary teaching system consists of the database server, memory, and remote controller. The functional modules include the basic teaching module based on MOOC, the assessment module, and the system management module. We conducted a number of experiments, and the simulations showed that the proposed system has a considerably shorter response time than its competitors. We conducted a survey of the proposed system and the existing systems in ten different colleges, and the results showed that the proposed system has a higher students’ satisfaction rate.
Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

Many payment platforms hold large-scale marketing campaigns, which allocate incentives to encourage users to pay through their applications. To maximize the return on investment, incentive allocations are commonly solved in a two-stage procedure. After training a response estimation model to estimate the users' mobile payment probabilities (MPP), a linear programming process is applied to obtain the optimal incentive allocation. However, the large amount of biased data in the training set, generated by the previous biased allocation policy, causes a biased estimation. This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy. To overcome this obstacle, we propose a bias correction adversarial network. Our method leverages the small set of unbiased data obtained under a full-randomized allocation policy to train an unbiased model and then uses it to reduce the bias with adversarial learning. Offline and online experimental results demonstrate that our method outperforms state-of-the-art approaches and significantly improves the performance of the resulting allocation policy in a real-world marketing campaign.
A Smart Modeling Tool for Model-Driven Engineering of Ubiquitous Applications : Covid-19 Contact-Tracer

The present study propose a smart tooled process based on an MDA approach for the design of ubiquitous applications taking into account context information in order to deliver high-added value services. We will present our platform-independent model (PIM) associated with a graphical modeling workshop (editor) which will allow us to instantiate in a graphical way in order to obtain PIM models, which will be transformed into PSM. We illustrate its implementation on a prototype application in a pervasive environment that tracks the movements of persons who have tested positive to anticipate COVID-19 contaminations.
Application of The Nearest Neighbor Algorithm for Classification of Online Taxibike Sentiments In Indonesia In The Google Playstore Application

Abstract Google playstore is an official platform that provides applications to services, and not only that, user can perform assessments and reviews after using the Google playstore. One of the applications and services provided by Google playstore is online transportation. There's a couple of online transports available but at this time's research just scanned the official transport account of gojek and grab online. The data used in this study were application review data and service review data from the two online transportations which are processed using the K-Nearest Neighbor and Improved K-Nearest Neighbor algorithms by comparing the accuracy values. An experiment was conducted to divide the data with 5 K on the K-Fold Cross-Validation. The results of the comparison of the KNN application data were that Grab had a higher accuracy of 77.77% compared to Gojek which had an accuracy value of 66.24%. In terms of the Data Services using the IKNN algorithm, Grab had a higher accuracy value of 78.23% while Gojek had an accuracy value of 48.19%. Meanwhile, from the time complexity of the process, the IK-NN algorithm is better than the K-NN.
Immersive Traditional Chinese Portrait Painting: Research on Style Transfer and Face Replacement

Traditional Chinese portrait is popular all over the world because of its unique oriental charm. However, how to use neural network to express the aesthetic and feelings in instantiated Chinese portrait effectively is still a challenging problem. This paper proposes a Photo to Chinese Portrait method (P-CP) providing immersive traditional Chinese portrait painting experience. Our method can produce two groups intriguing pictures. One is Chinese Portrait Style Picture, the other is Immersive Chinese Portrait Picture. We pay attention to neural style transfer for traditional Chinese portrait for the first time, and have trained a fast feedforward generative network to extract the corresponding style. The generative network principle is explored to guide the style transfer adjustment in detail. Face replacement is added to form a more appealing stylized effect. We also solve the problems of color and light violation, and unnatural seam. We hope this work offers a deeper and immersive conversation between modern society and antiques, and provides a useful step towards related interdisciplinary areas.
Development of Behavior based Robot manipulation using Actor-Critic architecture

Developing behavior based robotic manipulation is a very challenging but necessary task to be solved, especially for humanoid and social robots. Fundamental robotic tasks such as grasping, pick and place, trajectory following are at present solved using conventional forward and inverse kinematics (IK), dynamics and trajectory planning, whereas we learn these complex tasks using past experiences. In this paper, we explore developing behavior based robotic manipulation using reinforcement learning, more specifically learning directly from experiences through interactions with the real world and without knowing the transition model of the environment. Here, we propose a multi agent paradigm to gather experiences from multiple environments in parallel along with a model for populating new generation of agents using Evolutionary Actor-Critic Algorithm (EACA). The agents are of actor-critic architecture and both of them comprises of general purpose neural networks. The actor-critic architecture enables the model to perform well both in high dimensional state space and high dimensional action space which is very crucial for all robotic applications. The proposed algorithm is benchmarked with respect to different multi agent paradigm but keeping the agent’s architecture same. Reinforcement learning, being highly data intensive, requires the use of the CPU and GPU cores to be done judiciously for sampling the environment as well as for training, the details of which have been described here. We have run rigorous experiments for learning joint trajectories on the open gym based KUKA arm manipulator, where our proposed method achieves learning stability within 300 episodes, as compared to the state-of-the-art actor-critic and Advanced Asynchronous Actor-Critic (A3C) algorithms both of which take more than 1000 episodes for learning the same task, showing the effectiveness of our proposed model.
Peirce and Archaeology: Recent Approaches

The philosophy of Charles S. Peirce, and in particular his theory of signs (semiotic), has seen increasing interest within archaeological theory over the past 20 years. This article reviews Peirce's most influential ideas within archaeology, directs readers to where in Peirce's voluminous works they can find these ideas, and discusses how each of them has been applied by archaeologists to a variety of different research topics. In addition to the semiotic, these ideas include Peirce's metaphysical doctrine of synechism; his methodological pragmatism; abductive logic; and the phenomenological concepts of firstness, secondness, and thirdness. Finally, I discuss two research areas—materiality and paleolithic archaeology—in which a combination of Peirce's ideas has led to important new insights.
The relationship between different types of leadership, client's identity, and self-confidence and auditors' impartiality

Purpose This paper aims to assess the effect of different leadership types, the client's identity and auditors' self-confidence on auditors' impartiality. Design/methodology/approach This paper is a descriptive-survey type, and the collected data are based on a predesigned questionnaire distributed in January 2020. The PLS software is used for data analysis, and the statistical population of this paper includes employed auditors in enlisted audit firms on the Official Association of Auditors. When the parameters were insignificant, the obtained probability from the model fitting was used for hypothesis testing, and the appropriateness of the model was assessed via the structural equations. Findings The results show a significant relationship between charismatic, transformational, participatory, delegating and bureaucratic leadership and auditors' impartiality. There is also an association between the client's identity and the auditor's impartiality. The client's identity mediates the relationship between transformational leadership and the auditor's impartiality. Moreover, there is a significant relationship between self-confidence and auditor's impartiality. Originality/value This paper enjoys an innovative method in the field of behavioural auditing. The effect of transformational leadership on auditor's impartiality with the mediatory role of the client's identity shows the in-depth client–auditor relationship has been taken for granted and not examined previously, so the results of this paper can lend a helping hand to audit firms to enhance the organisational performance.
Designing and Building communIT

Many subgroups in the US remain marginalized from, misunderstood by, or invisible to the larger communities they reside in. Technologies supporting community building, more generally, have focused on apps, but these apps can fall short of making visible and heard subgroups such as the LGTBQ+, immigrant, and black populations. In response to this shortcoming, we report on the design iterations and an early evaluation of communIT—an interactive artifact for making visible and heard subgroups towards building community. To inform the design of communIT, we conducted in our lab a design studio study (N=57), a co-design activity with a to-scale prototype (N= 12), and a co-design activity with a full-scale prototype (N=28). This paper offers a design exemplar of a large-scale, cyber-physical artifact that might support groups in shaping their identities, practices, and roles in the larger community.
Knock and talk

Modern webpages are amalgamations of resources requested from various public Internet services. In principle though, webpages can also request resources from localhost and devices in the LAN, providing a degree of internal network access to external entities. Prior work has demonstrated how this access can be used for supporting web attacks, particularly for profiling and fingerprinting users.
Robust Watermarking Approach for 3D Multiresolution Meshes Based on Multi-wavelet Transform, SHA512 and Turbocodes

Since the 3D mesh security 3D mesh security has become intellectual property, 3D watermarking algorithms have continued to appear to secure 3D meshes shared by remote users. The originality of this approach is to insert copyright data in multiresolution domain using multi-wavelet transform which allowed us to significantly increase the size of the watermark. Our algorithm includes then two rounds of insertion where each one applies a different type of wavelet transform. The first one undergoes coefficients generated by Haar wavelet transform. As for the second iteration, it inserts data into coefficients resulting from Schauder wavelet transform. The watermark, which is a combination of the mesh description and source mesh signature (generated using SHA512) are encoded using a Turbo-code before being inserted into wavelet coefficients. Embedding data includes a transformation of these later to spherical coordinate system and a modulation steps. Finally watermarked mesh is reconstructed using inverse wavelet transform. The experimentation of our approach has shown a very high insertion rate due to the use of the hybrid insertion domain, while maintaining the mesh quality. Watermarked mesh and extracted data are obtained in real time. Our approach is also robust against the most popular attacks. Our results showed that the present approach improves the existing works.
A community-based hybrid blockchain architecture for the organic food supply chain

This paper presents a novel blockchain architecture to incorporate community-level trust into the organic food supply chain by hybridizing Proof of Authority (PoA) and Federated Byzantine Agreement (FBA) consensus protocols. Community-level trust is an important aspect in the organic agriculture industry. Organic farming, in most parts of the world, happens in small scale farms where the farmers represent rural and less-privileged communities. Even though third-party certification systems exist for quality assurance in organic farming, due to many socio-economic reasons, participatory guarantee systems (PGS) have become a popular alternative among organic farmers and consumers. However, such participatory guarantee systems are still prone to frauds and have limitations in scalability as well. With the recent rise of blockchain technology, there is an emerging trend to adopt blockchain technology to enhance the credibility of organic food supply chains and mitigate the risk of fraudulent transactions. However, despite the popularity of participatory guarantee systems among organic farmer communities, the blockchain researchers have paid little attention to develop blockchain architectures by adopting the community-level trust into their consensus protocols. The hybrid consensus mechanism presented in this paper addresses that gap in existing blockchain research. Apart from discussing the details of the proposed blockchain architecture and the underlying consensus protocol, this paper also presents a qualitative analysis on the proposed architecture based on expert opinions.
Proximal Policy Optimization with Continuous Bounded Action Space via the Beta Distribution

Reinforcement learning methods for continuous control tasks have evolved in recent years generating a family of policy gradient methods that rely primarily on a Gaussian distribution for modeling a stochastic policy. However, the Gaussian distribution has an infinite support, whereas real world applications usually have a bounded action space. This dissonance causes an estimation bias that can be eliminated if the Beta distribution is used for the policy instead, as it presents a finite support. In this work, we investigate how this Beta policy performs when it is trained by the Proximal Policy Optimization (PPO) algorithm on two continuous control tasks from OpenAI gym. For both tasks, the Beta policy is superior to the Gaussian policy in terms of agent's final expected reward, also showing more stability and faster convergence of the training process. For the CarRacing environment with high-dimensional image input, the agent's success rate was improved by 63% over the Gaussian policy.
BlockXR: A Novel Tangible Block-Based Programming Platform

Block-based programming is typically based solely on graphical user interfaces (GUIs). Some approaches, however, provide tangible interaction. They usually allow users to create block programs with GUIs to control physical robots or use physical blocks to create programs displayed on a vertical display. With these systems, users split their attention between the GUI and the physical objects. Split-attention effects have been shown to increase task completion time and hinder learning. BlockXR applies projected augmented reality and computer vision to provide users with tangible interaction for programming and feedback in the same plane. A comparative study with a GUI-based system determined that BlockXR scored significantly higher in the user experience scores of Attractiveness, Stimulation, and Novelty. We found no significant differences between usability scores. In this work, we describe the system and the quantitative and qualitative feedback from an user study.
Efficient Parabolic Optimisation Algorithm for Adaptive VQE Implementations

Computational chemistry is one of the most promising applications of quantum computing, mostly thanks to the development of the Variational Quantum Eigensolver (VQE) algorithm. VQE is being studied extensively and numerous optimisations of VQE’s sub-processes have been suggested, including the encoding methods and the choice of excitations. Here we focus on a usually overlooked component of VQE, which is the choice of the classical optimisation algorithm. We introduce the parabolic optimiser that we designed specifically for the needs of VQE. We then continue to benchmark the parabolic optimiser against COBYLA for various implementations of VQE. We found that the parabolic optimizer consistently outperforms traditional optimization algorithms in multiple VQE implementations. Specifically, we show that the parabolic optimizer requires fewer quantum experiments compared to COBYLA while producing circuits with a similar amount of CNOTs.