Reviewerly/exHarmony huggingface dataset:
Outlier Detection for Streaming Task Assignment in Crowdsourcing

Crowdsourcing aims to enable the assignment of available resources to the completion of tasks at scale. The continued digitization of societal processes translates into increased opportunities for crowdsourcing. For example, crowdsourcing enables the assignment of computational resources of humans, called workers, to tasks that are notoriously hard for computers. In settings faced with malicious actors, detection of such actors holds the potential to increase the robustness of crowdsourcing platform. We propose a framework called Outlier Detection for Streaming Task Assignment that aims to improve robustness by detecting malicious actors. In particular, we model the arrival of workers and the submission of tasks as evolving time series and provide means of detecting malicious actors by means of outlier detection. We propose a novel socially aware Generative Adversarial Network (GAN) based architecture that is capable of contending with the complex distributions found in time series. The architecture includes two GANs that are designed to adversarially train an autoencoder to learn the patterns of distributions in worker and task time series, thus enabling outlier detection based on reconstruction errors. A GAN structure encompasses a game between a generator and a discriminator, where it is desirable that the two can learn to coordinate towards socially optimal outcomes, while avoiding being exploited by selfish opponents. To this end, we propose a novel training approach that incorporates social awareness into the loss functions of the two GANs. Additionally, to improve task assignment efficiency, we propose an efficient greedy algorithm based on degree reduction that transforms task assignment into a bipartite graph matching. Extensive experiments offer insight into the effectiveness and efficiency of the proposed framework.
Computer Assisted Diagnosis of Breast Cancer Using Histopathology Images and Convolutional Neural Networks

In recent years, breast cancer has become one of the most prevalent kinds of cancer. Breast Ultrasound, Diagnostic Mammogram, Magnetic Resonance Imaging (MRI), and other imaging modalities are routinely used to diagnose breast cancer. Doctors make final judgments about treatments, drugs, and other matters based on biopsy results, which are regarded the standard diagnostic approach for cancer. However, this is a time-consuming process that also necessitates extensive pathologist training and expertise. Each pathology lab receives around 300-500 slides per day. This overburdens the pathologists and increases the misdiagnosis rate in the biopsy results. In order to provide timely error free results to the patients, the research community focuses more on the development of Computer Aided Diagnosis (CAD) System to assist pathologists to diagnose cancer. Recent developments in Deep Learning techniques made the CAD systems more effective in detecting breast cancer at an early stage with a great accuracy. In this paper, we present a CAD system that recognises histopathology images to diagnose breast cancer using a Convolutional Neural Network (CNN). DenseNet201, ResNet50 and MobileNetV2 are used in this work. These are trained and tested using the openly available BreakHis and BACH datasets. The datasets are subjected to binary and multi-class classifications. Accuracy, Precision, Recall, F1 Score, and AUC are all performance measures that are used to evaluate the model’s performance. For Binary classification, the model built using MobileNetV2 with Sigmoid as activation function displayed a higher accuracy of 97% - 98% and in the case of multi-class classification, again the model built using MobileNetV2 with Softmax as activation function displayed a higher accuracy of 91% - 92% for both Magnifican Independant (MI) and Magnification Dependant (MD) cases.
The Consequences of Integrity Attacks on E-Governance

With the development of new advanced technology, people's expectations and grievances of improving services and values in all aspects of life are growing. Changes in technology are offering better solutions to problems, hence improving existing systems. At the same time, new technologies are presenting new security and privacy violations. As information resources become more digitized, infrastructure and digital data are also facing increasing challenges. For advanced nations, the security parameters and optimization techniques have been thoroughly tested and are in good working order. However, these issues have yet to be adequately addressed in developing countries. An unauthorized person applies several different methods and techniques in the modern age for getting self-profit. The major goal of the research is to discover and assess the implications of integrity attacks and threats that have been used in e-governance research during the last 15 years. This research will be supported in assessing the security of various organizations that are working under e-governance.
The Impact of Blockchain Technology on Advanced Security Measures for E-Government

Implementation of blockchain with e-government has raised several complexities. When an area has satisfied the requirements for e-government implementation, new challenges will appear. As a result of the information technology revolution, governments and industries are being forced to deliver more effective and secure internet services. Every government in the world attempts to provide the public with electronic services that are fast, quick, and beneficial for the users. Blockchain is considered to have significant potential benefits for the government since it is a combination of technologies such as distributed ledgers, privacy, authentication, and consensus mechanisms. However, this advanced technology is still in its development, and e-government faces a number of difficulties and challenges. The goal of this chapter is to evaluate this advanced technology in the context of high-level e-government security and privacy implementation measures and other technical issues during the adaption of blockchain technology.
Accent Classification of Native and Non-Native Children using Harmonic Pitch

To combat the Covid-19 outbreak, the education system shifted away from the classroom to distinct e-learning on digital platforms, which made effective use of voice-based recognition systems, especially for preliterate children. Children’s speech recognition systems face multiple challenges owing to their immature vocal tracts, and they demand more intelligence due to the fact that children with diverse accents utter words differently. Accent refers to a unique style of pronouncing a language, particularly one associated with a specific nation, place, or socio-economic background. This paper aims to extract reliable acoustic and prosodic speech cues of accent for classification of native and non-native preschool children using harmonic pitch estimation along with Mel Frequency Cepstral Coefficients (MFCCs) to train the k-Nearest Neighbour (k-NN) classifier. The experimental results reveal that the proposed robust model outperforms various feature extractors in accent classification of native and non-native children in terms of accuracy & F-Measure and more discriminate against noisy environments.
Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling

In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labeled data is selected uniformly at random from the population of interest. Stratified sampling, while posing additional analytical challenges, is highly applicable to many real world problems. Moreover, no SSL methods currently exist for estimating the prediction performance of a fitted model when the labeled data is not selected uniformly at random. In this paper, we propose a two-step SSL procedure for evaluating a prediction rule derived from a working binary regression model based on the Brier score and overall misclassification rate under stratified sampling. In step I, we impute the missing labels via weighted regression with nonlinear basis functions to account for stratified sampling and to improve efficiency. In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model. The final estimator is then obtained with the augmented imputations. We provide asymptotic theory and numerical studies illustrating that our proposals outperform their supervised counterparts in terms of efficiency gain. Our methods are motivated by electronic health record (EHR) research and validated with a real data analysis of an EHR-based study of diabetic neuropathy.
E-Government Privacy and Security Challenges in the Context of Internet of Things

The internet of things (IoT) is becoming more significant in everyday life as a mechanism for making major decisions in different fields as smart devices and data in real time are connected and updated. IoT is being used in a variety of ways to provide digital services to the public. Online payment, property purchase, and sailing are just a few examples. On the other hand, users' complaints about the safety and privacy of their personal information are growing. The internet of things (IoT) is becoming more popular and significantly enhances e-government. This chapter primarily focuses on how potential users can obtain information to use the internet of things and its related services within the e-government sectors. There are several technological, administrative, and political challenges to IoT adoption problems in e-government and legal problems that must be solved to develop effective and required applications. It's crucial to explore these problems and potential solutions.
Speed limits for two-qubit gates with weakly anharmonic qubits

We consider the implementation of two-qubit gates when the physical systems used to realize the qubits possess additional quantum states in the accessible energy range. We use optimal control theory to determine the maximum achievable gate speed for two-qubit gates in the qubit subspace of the many-level Hilbert space, and we analyze the effect of the additional quantum states on the gate speed. We identify two competing mechanisms. On one hand, higher energy levels are generally more strongly coupled to each other. Under suitable conditions, this stronger coupling can be utilized to make two-qubit gates significantly faster than the reference value based on simple qubits. On the other hand, a weak anharmonicity constrains the speed at which the system can be adequately controlled: according to the intuitive picture, faster operations require stronger control fields, which are more likely to excite higher levels in a weakly anharmonic system, which in turn leads to faster decoherence and uncontrolled leakage outside the qubit space. To account for this constraint, we modify the pulse optimization algorithm to avoid pulses that lead to appreciable population of the higher levels. In this case, we find that the presence of the higher levels can lead to a significant reduction in the maximum achievable gate speed. We also compare the optimal-control gate speeds with those obtained using the cross-resonance or selective-darkening gate protocol. We find that this protocol, with some parameter optimization, can be used to achieve a relatively fast implementation of the controlled-NOT gate. These results can help the search for optimized gate implementations in realistic quantum computing architectures, such as those based on superconducting circuits. They also provide guidelines for desirable conditions on anharmonicity that would allow optimal utilization of the higher levels to achieve fast quantum gates.
Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network

Image quality assessment (IQA) algorithm aims to quantify the human perception of image quality. Unfortunately, there is a performance drop when assessing the distortion images generated by generative adversarial network (GAN) with seemingly realistic textures. In this work, we conjecture that this maladaptation lies in the backbone of IQA models, where patch-level prediction methods use independent image patches as input to calculate their scores separately, but lack spatial relationship modeling among image patches. Therefore, we propose an Attention-based Hybrid Image Quality Assessment Network (AHIQ) to deal with the challenge and get better performance on the GAN-based IQA task. Firstly, we adopt a two-branch architecture, including a vision transformer (ViT) branch and a convolutional neural network (CNN) branch for feature extraction. The hybrid architecture combines interaction information among image patches captured by ViT and local texture details from CNN. To make the features from the shallow CNN more focused on the visually salient region, a deformable convolution is applied with the help of semantic information from the ViT branch. Finally, we use a patch-wise score prediction module to obtain the final score. The experiments show that our model outperforms the state-of-the-art methods on four standard IQA datasets and AHIQ ranked first on the Full Reference (FR) track of the NTIRE 2022 Perceptual Image Quality Assessment Challenge. Code and pretrained models are publicly available at https://github.com/IIGROUP/AHIQ
Effect of data resampling on feature importance in imbalanced blockchain data: comparison studies of resampling techniques

Cryptocurrency blockchain data encounter a class-imbalance problem due to only a few known labels of illicit or fraudulent activities in the blockchain network. For this purpose, we seek to compare various resampling methods applied to two highly imbalanced datasets derived from the blockchain of Bitcoin and Ethereum after further dimensionality reductions, which is different from previous studies on these datasets. Firstly, we study the performance of various classical supervised learning methods to classify illicit transactions or accounts on Bitcoin or Ethereum datasets, respectively. Consequently, we apply various resampling techniques to these datasets using the best performing learning algorithm on each of these datasets. Subsequently, we study the feature importance of the given models, wherein the resampled datasets directly influenced on the explainability of the model. Our main finding is that undersampling using the edited nearest-neighbour technique has attained an accuracy of more than 99% on the given datasets by removing the noisy data points from the whole dataset. Moreover, the best-performing learning algorithms have shown superior performance after feature reduction on these datasets in comparison to their original studies. The matchless contribution lies in discussing the effect of the data resampling on feature importance which is interconnected with explainable artificial intelligence (XAI) techniques.
Classification of EEG signals: An interpretable approach using functional data analysis

Electroencephalography (EEG) is a noninvasive method to record electrical activity of the brain. The EEG data is continuous flow of voltages, in this paper, we consider them as functional data, and propose a three-stage algorithm based on functional data analysis, with the advantage of interpretability. Specifically, the time and frequency information are extracted by wavelet transform in the first stage. Then, functional testing is utilized to select EEG channels and frequencies that show significant differences for different human behaviors. In the third stage, we propose to use penalized multiple functional logistic regression to interpretably classify human behaviors. With simulation and a scalp EEG data as validation set, we show that the proposed three-stage algorithm provides an interpretable classification of the scalp EEG signals.
LETR: A Lightweight and Efficient Transformer for Keyword Spotting

Transformer recently has achieved impressive success in a number of domains, including machine translation, image recognition, and speech recognition. Most of the previous work on Keyword Spotting (KWS) is built upon convolutional or recurrent neural networks. In this paper, we explore a family of Transformer architectures for keyword spotting, optimizing the trade-off between accuracy and efficiency in a high-speed regime. We also studied the effectiveness and summarized the principles of applying key components in vision Transformers to KWS, including patch embedding, position encoding, attention mechanism, and class token. On top of the findings, we propose the LeTR: a lightweight and highly efficient Transformer for KWS. We consider different efficiency measures on different edge devices so as to reflect a wide range of application scenarios best. Experimental results on two common benchmarks demonstrate that LeTR has achieved state-of-the-art results over competing methods with respect to the speed/accuracy trade-off.
Knowledge Transfer from Large-Scale Pretrained Language Models to End-To-End Speech Recognizers

End-to-end speech recognition is a promising technology for enabling compact automatic speech recognition (ASR) systems since it can unify the acoustic and language model into a single neural network. However, as a drawback, training of end-to-end speech recognizers always requires transcribed utterances. Since end-to-end models are also known to be severely data hungry, this constraint is crucial especially because obtaining transcribed utterances is costly and can possibly be impractical or impossible. This paper proposes a method for alleviating this issue by transferring knowledge from a language model neural network that can be pretrained with text-only data. Specifically, this paper attempts to transfer semantic knowledge acquired in embedding vectors of large-scale language models. Since embedding vectors can be assumed as implicit representations of linguistic information such as part-of-speech, intent, and so on, those are also expected to be useful modeling cues for ASR decoders. This paper extends two types of ASR decoders, attention-based decoders and neural transducers, by modifying training loss functions to include embedding prediction terms. The proposed systems were shown to be effective for error rate reduction without incurring extra computational costs in the decoding phase.
RawNeXt: Speaker Verification System For Variable-Duration Utterances With Deep Layer Aggregation And Extended Dynamic Scaling Policies

Despite achieving satisfactory performance in speaker verification using deep neural networks, variable-duration utterances remain a challenge that threatens the robustness of systems. To deal with this issue, we propose a speaker verification system called RawNeXt that can handle input raw waveforms of arbitrary length by employing the following two components: (1) A deep layer aggregation strategy enhances speaker information by iteratively and hierarchically aggregating features of various time scales and spectral channels output from blocks. (2) An extended dynamic scaling policy flexibly processes features according to the length of the utterance by selectively merging the activations of different resolution branches in each block. Owing to these two components, our proposed model can extract speaker embeddings rich in time-spectral information and operate dynamically on length variations. Experimental results on the VoxCeleb1 test set consisting of various duration utterances demonstrate that RawNeXt achieves state-of-the-art performance compared to the recently proposed systems. Our code and trained model weights are available at https://github.com/wngh1187/RawNeXt.
Self-Knowledge Distillation via Feature Enhancement for Speaker Verification

As the most widely used technique, deep speaker embedding learning has become predominant in speaker verification task recently. Very large neural networks such as ECAPA-TDNN and ResNet can achieve the state-of-the-art performance. However, large models are computationally unfriendly in general, which require massive storage and computation resources. Model compression has been a hot research topic. Parameter quantization usually results in significant performance degradation. Knowledge distillation demands a pretrained complex teacher model. In this paper, we introduce a novel self-knowledge distillation method, namely Self-Knowledge Distillation via Feature Enhancement (SKDFE). It utilizes an auxiliary self-teacher network to distill its own refined knowledge without the need of a pretrained teacher network. Additionally, we apply the self-knowledge distillation at two different levels: label level and feature level. Experiments on Voxceleb dataset show that our proposed self-knowledge distillation method can make small models have comparable or even better performance than large ones. Large models can also be further improved when applying our method.
Single-Shot Balanced Detector for Geospatial Object Detection

Geospatial object detection is an essential task in remote sensing community. One-stage methods based on deep learning have faster running speed but cannot reach higher detection accuracy than two-stage methods. In this paper, to achieve excellent speed/accuracy trade-off for geospatial object detection, a single-shot balanced detector is presented. First, a balanced feature pyramid network (BFPN) is designed, which can balance semantic information and spatial information between high-level and shallow-level features adaptively. Second, we propose a task-interactive head (TIH). It can reduce the task misalignment between classification and regression. Extensive experiments show that the improved detector obtains significant detection accuracy with considerable speed on two benchmark datasets.
A Comprehensive Study on Passwordless Authentication

In the technology today, user-based authentication and password are now widely used in all information systems and services. Most of the university also uses this type of authentication method for many services, but the password is in danger. By providing a password-protected verification system for the most usable and secure organization. In the old days, the password was used as the best authentication system to prevent unauthorized access. Now the technology in the authentication system is growing day by day so that the password is changed to be more secure. However, the vulnerability of this traditional system has prompted the industry and researchers to find a new alternative where there is no threat such as theft, hacking and cracking passwords. This study discusses in more detail the key strategies for verifying the authenticity of a password in detail and sets out an attempt to explain details and process of each technology. The paper consists of an extensive review of the research conducted in past several years and this research study has presented a review of recent research works which are mainly conducted for improvising the security with the end to end encryption process.