Reviewerly/exHarmony huggingface dataset:
On the transparency order relationships between one Boolean function and its decomposition functions

The concept of transparency order (denoted by TO) is an important criterion of (n,m)-functions to resist against differential power analysis (DPA). In this paper, we give several transparency order relationships of some Boolean functions. We give the lower bound on the transparency order for Boolean function, and obtain the transparency order relationship between one Boolean function and its decomposition Boolean functions. Furthermore, we deduce one relationship among TO(f⊕g), TO(f), TO(g) and TO(fg) for any n-variable Boolean functions f,g. Finally, we study the transparency order for the sum function and for the product function between two variable-disjoint Boolean functions, and calculate the transparency order distributions of 4-variable and 5-variable balanced Boolean functions, respectively.
QuantNet: Learning to Quantize by Learning Within Fully Differentiable Framework

Despite the achievements of recent binarization methods on reducing the performance degradation of Binary Neural Networks (BNNs), gradient mismatching caused by the Straight-Through-Estimator (STE) still dominates quantized networks. This paper proposes a meta-based quantizer named QuantNet, which utilizes a differentiable sub-network to directly binarize the full-precision weights without resorting to STE and any learnable gradient estimators. Our method not only solves the problem of gradient mismatching, but also reduces the impact of discretization errors, caused by the binarizing operation in the deployment, on performance. Generally, the proposed algorithm is implemented within a fully differentiable framework, and is easily extended to the general network quantization with any bits. The quantitative experiments on CIFAR-100 and ImageNet demonstrate that QuantNet achieves the significant improvements comparing with previous binarization methods, and even bridges gaps of accuracies between binarized models and full-precision models.
Design of a Secure Energy Trading Model Based on a Blockchain

This study proposes a Secure Energy Trading Model design based on a Blockchain is an attempt to overcome the weak security and instability of the current energy trading system. The focal point of the design lies in the user-security features of the model, such as user authentication and identification, and the blockchain that every transaction goes through. The user-security feature provides a safer system for peer-to-peer energy trade, and the blockchain technology ensures the reliability of the trading system. Furthermore, the Secure Energy Trading Model supports a decentralized data control mechanism as a future measure for handling vast amounts of data created by IoT.
‘The Same Trade as Mozart’: Convincing the sceptics of electronic music’s value

In August 1969, the music documentary series Workshop (1964–78) focused on electronic music in a 55-minute-long film titled ‘The Same Trade as Mozart’. Produced and directed by David Buckton, the film included interviews with composers Karlheinz Stockhausen, Tristram Cary and Justin Connolly; BBC Radiophonic Workshop staff Desmond Briscoe, David Cain and John Baker, and the Workshop’s founder, Daphne Oram; and Peter Zinovieff, director of EMS (Electronic Music Studios). It presented electronic music in a number of contexts, such as education, pop production and live performance. Technological change in music has often provoked hostility among the public and critics, and the rapid advancement of electronic music post-Second World War was no exception. Adopting a mode of analysis more commonly encountered in studies of the public communication of science, this article considers ‘The Same Trade as Mozart’ as an attempt by electronic music’s advocates, such as those listed above, to convince sceptics of its value. While sceptical responses to the presence of new technologies in music have been widely noted and theorized by scholars in science and technology studies, we call attention to the strategies employed by the advocates of such technologies to defend themselves against such criticisms, including humour, heuristic explanations and a focus on electronic music’s educational and thus social value. The use of computers in electronic music was a new and contentious development in the field, requiring a greater degree of advocacy from its proponents. We examine how the computer’s role in composition is presented in ‘The Same Trade as Mozart’, compared with other media portrayals of computing in the 1960s. Drawing on theories of filmed musical performance, we discuss how visual tropes of ‘classical’ music are used in ‘The Same Trade as Mozart’ to challenge preconceptions about the relationships between composers, musicians and new technologies.
A Method for Improving Unsupervised Intent Detection using Bi-LSTM CNN Cross Attention Mechanism

Spoken Language Understanding (SLU) can be considered the most important sub-system in a goal-oriented dialogue system. SLU consists of User Intent Detection (UID) and Slot Filling (SF) modules. The accuracy of these modules is highly dependent on the collected data. On the other hand, labeling operation is a tedious task due to the large number of labels required. In this paper, intent labeling for two datasets is performed using an unsupervised learning method. In traditional methods of extracting features from text, the feature space that is obtained is very large, therefore we implemented a novel architecture of auto-encoder neural networks that is based on the attention mechanism to extract small and efficient feature space. This architecture which is called Bi-LSTM CNN Cross Attention Mechanism (BCCAM), crosswise applies the attention mechanism from Convolutional Neural Network (CNN) layer to Bi-LSTM layer and vice versa. Then, after finding a bottleneck on this auto-encoder network, the desired features are extracted from it. Once the features are extracted, then we cluster each sentence corresponding to its feature space using different clustering algorithms, including K-means, DEC, Agglomerative, OPTICS and Gaussian mixture model. In order to evaluate the performance of the model, two datasets are used, including ATIS and SNIPS. After executing various algorithms over the extracted feature space, the best obtained accuracy and NMI for ATIS dataset are 86.5 and 91.6, respectively, and for SNIPS dataset are 49.9 and 43.0, respectively.
Learning from Multi-class Positive and Unlabeled Data

Positive-unlabeled (PU) learning handles the problem of learning a predictive model from PU data. Past few years have witnessed the boom of PU learning, while the existing learning algorithms are limited to binary classification and cannot be directly applied to multi-class PU data. In this paper, we present an unbiased estimator of the original classification risk for multi-class PU learning, and show that the direct empirical risk minimization suffers from the severe overfitting problem because the risk is unbounded below. To address this problem, we propose an alternative risk estimator, and theoretically establish an estimation error bound. We show that the estimation error of its empirical risk minimizer achieves the optimal parametric convergence rate. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approach for multiclass PU learning.
Enclaves in the clouds

Trusted Execution Environments ('TEEs') or 'secure enclaves' aim at enabling more secure computation and data management. There is much enthusiasm for this technology, not least as we see increasing legal and regulatory attention on issues of security, privacy, and data management and use. With cloud providing the infrastructure for underpinning applications, data processing and analytics/ML, access to enclaves and enclave-backed technologies are increasingly being offered by service (cloud) providers - with the technology described as enabling confidential computing . This paper provides a high-level overview of the common security properties provided by enclaves, and considers how such technology, in being offered by cloud providers, relates to organizational legal and regulatory concerns. Focusing on data protection regulations, we explore the aspects of TEEs that might assist compliance, and who stands to benefit from the deployment of such technology in a service provision context.
Implementing Augmented Reality Into Immersive Virtual Learning Environments

Technology is ever-changing and ever-growing. One of the newest developing technologies is augmented reality (AR), which can be applied to many different existing technologies, such as computers, tablets, and smartphones. This chapter discusses the immersive learning process and the usage of AR into a simulated or in an artificial environment. Discussed is the background information on how the AR use in educational industries and the design process of AR immersive learning environment. The chapter also evaluates the benefits if immersive learning and AR.
Adaptive Quick Reduct for Feature Drift Detection

Data streams are ubiquitous and related to the proliferation of low-cost mobile devices, sensors, wireless networks and the Internet of Things. While it is well known that complex phenomena are not stationary and exhibit a concept drift when observed for a sufficiently long time, relatively few studies have addressed the related problem of feature drift. In this paper, a variation of the QuickReduct algorithm suitable to process data streams is proposed and tested: it builds an evolving reduct that dynamically selects the relevant features in the stream, removing the redundant ones and adding the newly relevant ones as soon as they become such. Tests on five publicly available datasets with an artificially injected drift have confirmed the effectiveness of the proposed method.
Vietnamese Context-Sensitive Malicious Spelling Error Correction

Spelling errors targeting specific keywords that users intentionally generate has seriously degraded the performance of social media control systems. In this paper, we show the severe effect of those misspellings and propose using a spelling correction approach for those targeted words based on context called word embedding. The data that we use within the limits of our work are Vietnamese spam email and hate speech. Also, we introduce a new and effective way to extract real misspellings to create reasonably synthetic data provided for our experiments. Our correction system results in a favorable performance on both synthetic and real data compared to Google.
The Application of Machine Learning in Activity Recognition with Healthy Older People Using a Batteryless Wearable Sensor

The development of human activity monitoring has allowed for simulation and prediction in a variety of application scenarios. The battery-free wearable sensor is performed on sternum level clothing, mainly by script execution. It is essential to monitor human health, sleep, etc. With the increasing population in the world, the aging population is also becoming larger and larger. Hospitals and nursing homes are in great need to prevent injuries such as older people falling indoors. Existing methods construct the wireless wearable human body multi-physiological parameter monitoring system with wireless sensor network and central monitoring module unit. However, this system has a high false alarm rate and low accuracy in predicting human activities.To solve this problem, we constructed an activity detection method based on several machine learning algorithms, including Decision Tree,Gradient Boosting Decision Tree (GBDT), Adaboosting, Bagging and Random Forest. Although the decision tree is quite intuitionistic, ensemble learning models can improve the accuracy of prediction significantly by combining a lot of decision trees. By comparing the errors of each model, the most suitable monitoring model for predicting the physiological events of the elderly was selected. It is found that Random Forest outperforms the other methods, and achieves 99.97% on the training data and 98.8% accuracy on testing data, respectively.
Surveillance System for Monitoring Social Distance

In the light of recent events, an epidemic - COVID-19 which took the world by surprise and continues to grow day by day. This paper describes an idea to control the spread of disease by monitoring Social Distancing. As of now from where we stand, the only way to avoid further spreading is to maintain proper social distance. Combining the advanced detection algorithms such as SSD, YOLO v4, and Faster-RCNN along with pedestrian datasets we reached the desired conclusion of calculating the distance between two detected persons in a video and identifying whether the social distancing norm is followed or not. This method can be implemented in CCTV’s, UAV’s, and on any other surveillance system. The rapid advancements in technologies led to more precise and accurate values.
Time series (re)sampling using Generative Adversarial Networks

We propose a novel bootstrap procedure for time series data based on Generative Adversarial networks (GANs). We show that the dynamics of common stationary time series processes can be learned by GANs and demonstrate that GANs trained on a single sample path can be used to generate additional samples from the process. We find that temporal convolutional neural networks provide a suitable design for the generator and discriminator, and that convincing samples can be generated on the basis of a vector drawn from a normal distribution with zero mean and an identity variance-covariance matrix. We demonstrate the finite sample properties of GAN sampling and the suggested bootstrap using simulations where we compare the performance to circular block bootstrapping in the case of resampling an AR(1) time series processes. We find that resampling using the GAN can outperform circular block bootstrapping in terms of empirical coverage. Finally, we provide an empirical application to the Sharpe ratio.
Research perspectives on fully homomorphic encryption models for cloud sector1

As there is a continuous delivery of big data, the researchers are showing interest in the applications of cloud computing concerning privacy, and security. On the other hand, many researchers and experts of cybersecurity have commenced on a quest for improving the data encryption to the models of big data and applications of cloud computing. Since many users of the cloud become public cloud services, confidentiality turns out to be a more compound problem. To solve the confidentiality problem, cloud clients maintain the data on the public cloud. Under this circumstance, Homomorphic Encryption (HE) appears as a probable solution, in which the information of the client is encrypted on the cloud in such a process that it permits few manipulation operations without decryption. The main intent of this paper is to present the systematic review of research papers published in the field of Fully Homomorphic Encryption (FHE) over the past 10 years. The encryption scheme is considered full when it consists of plaintext, a ciphertext, a keyspace, an encryption algorithm, and a decryption algorithm. Hence, the review mostly concentrates on reviewing more powerful and recent FHE. The contributions using different algorithms in FHE like Lattice-based, integer-based, Learning With Errors (LWE), Ring Learning With Errors (RLWE), and Nth degree Truncated polynomial Ring Units (NTRU) are also discussed. Finally, it highlights the challenges and gaps to be addressed in modeling and learning about competent, effectual, and vigorous FHE for the cloud sector and pays attention to directions for better future research.
Robust Video Watermarking Resilient To Inadvertent Attacks

The security of information contents exists as a problem for researchers about various techniques discussed in the reviews for protecting digital rights and copyright authentication. The method for providing secureness to right-owners is digital watermarking; i. e, the method of embedding secret text message in the file. The availability of online contents of audio clip, images and videos are rapidly growing. The efficiency of watermarking algorithm mainly follows on the properties of robust and also imperceptibility.Modern watermarking methods are capable of successfully attacking older watermarking methods. That arises the importance of designing recent and robust watermarking methods for resisting the attacks of watermarked videos. We developed in this project, relating new video watermarking technique based on combination of Discrete Wavelet Transform(DWT) and Singular Value Decomposition (SVD). To understand the robustness of watermarking algorithm, the watermarked video is subjected to inadvertent attacks. Performance analysis were done using evaluation metrics like NCC, PSNR and BER. The lower entropy frames are used for text watermark embedding. Compared the performance with existing methods in presence and absence of unintentional attacks like cropping, blurring, and adding noises like salt and pepper noise, speckle noise.
PhotoniQLAB: a framework for simulating photonic quantum information processing experiments

Abstract Simulators for photonic quantum information processing (PQIP) experiments are essentially different with currently available quantum-circuit simulators. In PQIP experiments, photons are usually encoded by multiple degrees of freedom, some of which are multi-level or even infinite-level. Moreover, the evolution of indistinguishable photons cannot be described elegantly by the model used in quantum-circuit simulators. A simulator focusing on PQIP experiments is urgently needed, as it plays an important role in PQIP experiments designing and verification. We developed PhotoniQLAB, an object-oriented framework designed for simulating PQIP experiments, which provides a virtual-lab user experience. The core simulation unit is a computer algebraic system based on the second quantization method. PhotoniQLAB only requires users to enter the structure information of a target PQIP experiment to conduct a simulation, as it can understand the topological structure by itself. The mathematical foundation and technical details of PhotoniQLAB are discussed in the paper. The performance of PhotoniQLAB, which is analyzed and used to simulate several experimental schemes in this paper, has been shown to be efficient enough for near-term PQIP experiments. PhotoniQLAB shows its flexibility and universality, through simulating more than 60 existing PQIP experiments in published papers. We believe that PhotoniQLAB will become a fundamental PQIP software infrastructure facilitating the analyses and designs of PQIP experiments.
Automatic Speech Recognition in Malayalam Using DNN-based Acoustic Modelling

In the paper, we describe Malayalam automatic speech recognition using DNN-based acoustic modelling. Mel-frequency cepstral coefficient features (MFCC) are extracted, and acoustic modelling is done using GMM-HMM, SGMM and DNN. The dependency of the performance on different factors like the type of modelling used and alignment algorithms employed are studied. Training and testing of the system were performed using the open-source Kaldi toolkit. We investigated the performance by varying number of hidden layers and hidden units. Testing of the models was carried out on very large vocabulary continuous Malayalam speech recognition task. We obtained word rate (WER) of 3.01% and the sentence error rate (SER) of 14.59% for the proposed system. DNN modelling shows significant improvement over GMM-HMM modelling framework in the design.
Some factors affecting granitic pluton topography: Pluton topography in the southern Lachlan Foldbelt

Where neotectonics are inactive, granite batholith topography is determined largely by the dissolution rates of the minerals of the component plutons. In southern New South Wales, Australia, plutons in the Kosciusko, Corryong, Berridale, and Murrumbidgee Batholiths allow an assessment of the extent to which faults, joints, grainsize and foliation may control a pluton's topography. Changes in mean elevation of zoned plutons closely follow the changes in mineralogy; more felsic zones invariably occur at higher elevations than more mafic zones. Faults clearly dictate major landscape features, such as linear valleys. Neither grainsize nor foliation show any appreciable effect on these landscapes. Joint spacing in these plutons has a positive correlation with the rock's grainsize, with an intrinsic value varying linearly as the grain size varies. Not all joints at the intrinsic spacing may be evident. In perennially wet valleys most joints become weathered and so visible, leading to joints mostly spaced at that intrinsic value. In quickly drying crests and ridges more incipient joints remain closed, leading to larger boulders and a wider average joint spacing. Thus the evident joint spacing in granites is determined by grainsize and landscape position.
Investigations and Compression of Genomic Data

Because of the considerable amount of human genome sequence data files (from 30 GB to 200 GB subjected to exposure) Genomic data compression has received huge momentum and one of the major problems faced by genomics laboratories is storage costs. This situation calls for a new data compression technique, which not only reduces the storage but makes the process efficient. Few attempts have been made in this regard to solve this problem from both hardware and software domains independently. In this review we advocate the need of a tailor-made hardware and software ecosystem which will exploit the current stand-alone solutions to the fullest. It is only when the sophisticated software runs on a state-of-the-art hardware, the indispensable problem of huge storage can be solved. The three major steps of genomic data compression are extraction of data, storage of data, and retrieval of the data. Hence, we propose a novel scheme based on computational optimization techniques which will be efficient in all the three stages of data compression.
Ontology-Based Approach to Construction of Intelligent Interface for Inductive Modeling Tools

This paper is devoted to the analysis of sources in the field of development and building intelligent user interfaces. Particular attention is paid to presenting an ontology-based approach to constructing the architecture of the interface, the tasks arising during the development, and ways for solving them. An example of construction of the intelligent user interface is given for software tools of inductive modeling based on the detailed analysis of knowledge structures in this domain.
Subjective and no-reference quality metric of domain independent images and videos

There has been an unprecedented explosion in creation and consumption of high definition videos and images. This has led to the development and adoption of several systems which manipulate graphic content. Most of these systems require a robust, no-reference, domain independent, and subjective quality metric. The state-of-the-art algorithms aimed at no-reference quality assessment of images and videos employ neural networks that are content domain dependent and require application specific training. Further, present systems are trained to estimate quality metric in accordance to objective metrics such as Peak Signal to Noise Ratio or Mean Squared Error. We propose a convolutional neural network architecture that leverages a variable length long short term memory. The model will also make use of the Laplacian as a fourth layer in the input to ensure content domain independence in quality estimation. The system has been trained on the images of KonIQ-10K dataset and tested on KonViD-1k video dataset and LIVE video dataset. It has also been tested on 1000 frames and videos from the VIRAT dataset and images from BSD300 and Set5 datasets with artificial blur and distortion applied. The quality estimations were compared to the mean opinion square given by five human viewers and resulted in a 11% root mean squared error. Thus the proposed model provides a subjective quality estimation of graphic content without any dependence on the domain of the content.