Reviewerly/exHarmony huggingface dataset:
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models

We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses. Previous studies have reported the effectiveness of combining a small number of NLMs. In contrast, in this study, we combine up to eight NLMs, i.e., forward/backward long short-term memory/Transformer-LMs that are trained with two different random initialization seeds. We combine these NLMs through iterative lattice generation. Since these NLMs work complementarily with each other, by combining them one by one at each rescoring iteration, language scores attached to given lattice arcs can be gradually refined. Consequently, errors of the ASR hypotheses can be gradually reduced. We also investigate the effectiveness of carrying over contextual information (previous rescoring results) across a lattice sequence of a long speech such as a lecture speech. In experiments using a lecture speech corpus, by combining the eight NLMs and using context carry-over, we obtained a 24.4% relative word error rate reduction from the ASR 1-best baseline. For further comparison, we performed simultaneous (i.e., non-iterative) NLM combination and 100-best rescoring using the large ensemble of NLMs, which confirmed the advantage of lattice rescoring with iterative NLM combination.
Exact Sparse Super-Resolution Via Model Aggregation

This paper studies the problem of discrete super-resolution. Existing stability guarantees rely on the fact that certain separation conditions are satisfied by the true support. However, such structural conditions have not been exploited in the corresponding algorithmic designs. This paper proposes a novel Bayesian approach based on the model aggregation idea that can generate an exact sparse estimate, and maintain the required structures of the support. The proposed method is implemented within the MCMC framework and empirically provides better support recovery than available algorithms.
A Neural Prosody Encoder for End-to-End Dialogue Act Classification

Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems. Prosodic features such as energy and pitch have been shown to be useful for DAC. Despite their importance, little research has explored neural approaches to integrate prosodic features into end-to-end (E2E) DAC models which infer dialogue acts directly from audio signals. In this work, we propose an E2E neural architecture that takes into account the need for characterizing prosodic phenomena co-occurring at different levels inside an utterance. A novel part of this architecture is a learnable gating mechanism that assesses the importance of prosodic features and selectively retains core information necessary for E2E DAC. Our proposed model improves DAC accuracy by 1.07% absolute across three publicly available benchmark datasets.
Deep Markov Clustering for Panoptic Segmentation

Panoptic segmentation is a challenging scene understanding task that unifies semantic segmentation and instance segmentation. Namely, each pixel of an image is assigned a semantic label and an instance id. Existing works have elaborated end-to-end panoptic segmentation networks and made great progress in non-proposal-based methods. In this work, we adopt a box-free strategy and incorporate a graph-based clustering method to merge repetitive kernel weights for object instances. An alternative graph-based clustering algorithm like Markov clustering performs effective random walks for unsupervised clustering without pre-defined cluster numbers. Our proposed deep Markov clustering scheme provides an efficient alternative to guarantee instance-aware label prediction in both training and inference stages. On the COCO dataset, our method achieves promising accuracy (PQ=42.1), which is comparable with state-of-the-art methods.
On the Acquisition of Stationary Signals Using Uniform ADCS

In this work, we consider the acquisition of stationary signals using uniform analog-to-digital converters (ADCs), i.e., employing uniform sampling and scalar uniform quantization. We jointly optimize the pre-sampling and reconstruction filters to minimize the time-averaged mean-squared error (TMSE) in recovering the continuous-time input signal for a fixed sampling rate and quantizer resolution and obtain closed-form expressions for the minimal achievable TMSE. We show that the TMSE-minimizing pre-sampling filter omits aliasing and discards weak frequency components to resolve the remaining ones with higher resolution when the rate budget is small. In our numerical study, we validate our results and show that sub-Nyquist sampling often minimizes the TMSE under tight rate budgets at the output of the ADC.
Using Machine Learning Algorithm for Proverb Retrieval and Expansion for Indian Languages

Many philosophical thinkers, research scholars and poets have expatiate their knowledge and experiences in the form of a proverb since ancient days. A proverb is short and known sentence that expresses the perceived truth based on the experience, moral values, wisdom and traditional aspects, symbolic and noteworthy form in different languages as they inherit from their culture and languages. The research paper focuses on developing a digital archive for Indian proverbs in twenty two official languages. The online Cross Lingual Proverb Retrieval and Expansion (CLPRE) digital archive is built by using Machine Learning Algorithm, currently digital archive focuses on four Indian languages such as English, Hindi, Kannada and Marathi. The supervised digital archive has currently corpus size of 250 proverbs for each language. The online digital proverb can be mainly used for education.
Efficient Identification of Melanocytic Nuclei in Pathology Images for Melanoma Diagnosis Using A Weakly-Supervised Deep Learning Framework

In this paper, we introduce a weakly-supervised deep learning framework that detects atypical melanocytic nuclei in histopathology images for the diagnosis of Melanoma with a nuclei detection accuracy of 92% surpassing the-state-of-the-art fully automated techniques. The proposed method will support pathologists in providing an objective, accurate and reliable diagnosis of Melanoma and minimizes the missed detection rate of the tumor. This will accordingly enhance the disease prognosis and improve the treatment efficacy.
Fast Task-Specific Adaptation in Spoken Language Assessment with Meta-Learning

Automatic spoken language assessment plays an important role in assessing English proficiency of non-native learners, which involves tasks ranging from restricted tasks such as Repeat Sentences to more open-ended tasks such as unconstrained spontaneous speech. Traditional methods typically focus on specific task types and rely on a significant amount of human-labelled data. In this paper, we propose a fast adaptation framework with meta-learning for various task types in spoken language assessment under low-resource settings. To better adapt to tasks with different grading criteria, we incorporate a memory network acting as an external memory for these criteria. Experimental results based on data from different spoken language tests demonstrate the superiority of the proposed method to the baselines in Pearson correlation coefficient and accuracy when adapted to various task types, especially in low-resource settings.
Impact of Abstract Factory and Decorator Design Patterns on Software Maintainability: Empirical Evaluation using CK Metrics

Gang of Four (GoF) design patterns are the famous set of twenty-three reusable and robust solutions to certain established problems in object-oriented programming that still guides software developers and designers despite having been published in mid-90&#x2019;s. However, it is relatively difficult to explicitly determine the overall appropriateness and suitability of a design pattern in a software system. Thus, this work attempts to select one attribute from the McCall&#x2019;s software quality model &#x2013; maintainability, and empirically evaluate it before and after applying selected design patterns (Abstract Factory and Decorator) within a relatively simple problem scenario to keep the focus on and prove the positive effect of design patterns in object-oriented software solutions in terms of their maintainability.
SOA-based Information Integration Platform for Educational Management Decision Support System

The resource management platform based on the resource library has different structures, which brings a lot of inconvenience to the sharing of educational resources. However, the scale of the school is constantly expanding, the number of campuses and departments within the school increases, and the number of teachers and students increases. These new circumstances need the development of an open, secure, and effective school administration system that can operate online at any time and from any location. The “plan norm model” of educational management decision-making goals controls the decision-making dynamic structure. Whether the decision-making object is an “economic man” or a “social man,” its dynamic structure is built on a top-down control sequence, with the only variation being the control mechanisms used. In colleges and universities, educational administration management is an essential aspect of educational management. It is a work with strong timeliness, heavy workload, and high accuracy requirements, affecting the overall situation of teaching. This paper proposes an information integration platform based on SOA, which integrates various enterprise application systems into a unified platform by using a loose coupling structure, realizes information sharing among application systems, and meets the needs of cross-departmental enterprise business. As a new architecture idea, service-oriented architecture can package existing assets and reuse existing assets. It can also minimize the impact of demand changes because its implementation is loosely coupled. When users need to add or modify a function, they only need to make a few modifications in the presentation layer or business process layer.
A Low-Cost Embedded Security System for UAV-Based Face Mask Detector Using IoT and Deep Learning to Reduce COVID-19

Nowadays, the most effective method against the virus is wearing a mask. Hence, it is fundamental to wear a mask appropriately at open places like general stores and shopping malls. This paper proposes a novel human face mask detection method from UAV-captured frame sequences to solve the aforementioned problem. The proposed approach involves an offline stage and an inference stage. The offline stage generates the mask or no-mask by utilizing a convolutional neural network. We trained our model on a face mask dataset, and this enhancement allows the suggested system to obtain high accuracy in detecting unmasked people. The inference stage uses the already generated model to detect no mask humans and sends the alert to the smartphone-based Internet of Things. At this stage, Jetson nano was used to implement an embedded powerful real-time application for UAV-based face mask detection that runs at high frames per second. The proposed system monitors and detects people who have not worn a mask. Also, we used IoT techniques to send the pictures and notifications to the nearest police station to apply forfeit when it detects unmasked people. The main contributions in this paper lie in adjusting the deep learning, embedded platforms, IoT techniques, and Tello drone, generally dedicated to detecting unmasked people at a low cost. On average, detection accuracy is 99% based on the experimental evaluation of the proposed deep learning model for UAV-based face mask detection on the provided dataset. Overall, the proposed method can help decrease the spread of COVID-19 and other transmissible diseases.
A Bibliometric Analysis of Blockchain and its applications in IOT and ML for Improved Decision Making

The fundamental goal of this research is to examine the evolution of Blockchain technology, particularly in the areas of IoT and machine learning. Decision-making has become more complex in the modern era as there are more options to pick from. This research explains how blockchain is used in the decision-making process. Blockchain stores the data where it is highly impossible to hack or edit this data. The research methodology used is Bibliometric Analysis. Bibliometric analysis is the analysis of books, articles, and other publications using statistical methods. There were a total of 18,978 publications available in Scopus from 2012 to 2021 on Blockchain. There were a total of 18,994 publications on Blockchain overall. Country-wise, year-wise, topic-wise, journal-wise, institution-wise and research field-wise analysis is done in this study. By the obtained data, the performance of Blockchain will be analysed. According to the study, Blockchain has a wide range of applications in IoT and ML, all of which improve decision-making. This analysis provides valuable insights and adds benefit for researchers to understand the overall development of Blockchain.
Blockchain Adoption in Indian Manufacturing Supply Chain using T-O-E Framework

In the domain of supply chain, there exists an immense potential for Blockchain technology. When implemented in the right way, Blockchain can improve the efficiency and competitiveness of manufacturing supply chain networks and thereby increase customer satisfaction. In spite of its undisputed capability in improving the efficiency of supply chain networks, the degree of acceptance of Blockchain, since its inception, remains very low. This paper studies the major hindrances experienced by manufacturing firms in adopting Blockchain for supply chain. A theoretical model is developed based on Technology-Organization-Environment (TOE) framework and analysed using the SmartPLS tool for partial least squares technique. The findings of this study suggests that there is a significant impact of all three latent variables of TOE Framework i.e., technological barriers, organizational barriers and environmental barriers on Blockchain adoption by manufacturing supply chains. The parameters in these barriers are lack of block-chain standardization, lack of government regulation and lack of financial resources. The results are implied for policy makers and related agencies to take suitable actions to overcome these barriers and encourage adoption of block chain in Indian manufacturing supply chain. It will also help researchers to further study the way to reduce the impact of these barriers.
MOS Predictor for Synthetic Speech with I-Vector Inputs

Based on deep learning technology, non-intrusive methods have received increasing attention for synthetic speech quality assessment since it does not need reference signals. Meanwhile, i-vector has been widely used in paralinguistic speech attribute recognition such as speaker and emotion recognition, but few studies have used it to estimate speech quality. In this paper, we propose a neural-network-based model that splices the deep features extracted by convolutional neural network (CNN) and i-vector on the time axis and uses Transformer encoder as time sequence model. To evaluate the proposed method, we improve the previous prediction models and conduct experiments on Voice Conversion Challenge (VCC) 2018 and 2016 dataset. Results show that i-vector contains information very related to the quality of synthetic speech and the proposed models that utilize i-vector and Transformer encoder highly increase the accuracy of MOSNet and MBNet on both utterance-level and system-level results.
Semantic Association Network for Video Corpus Moment Retrieval

This paper considers Semantic Association Network (SAN) for Video Corpus Moment Retrieval (VCMR) which localizes temporal moment that best corresponds to the given text query in a corpus of videos. Collaborations among common semantics from multi-modal inputs are essential for effectively understanding video together with subtitle and text query. For this collaboration, SAN associates common semantics within the same modality (by Intra Semantic Association) and across different modalities (by Inter Semantic Association) with dedicated module referred to as Modality Semantic Association (MSA). SAN surpasses existing state-of-the-art performance on the TVR and DiDeMo benchmark datasets. Extensive ablation studies and qualitative analyses show the effectiveness of the proposed model.
A Nonlinear Steerable Complex Wavelet Decomposition of Images

Signal and image representations that are steerable are essential to capture efficiently directional features. However, those that are successful at achieving directional selectivity usually use too many subbands, resulting in low computational efficiency. In this paper, we propose a two-dimensional nonlinear transform that uses only two subbands to achieve rotation invariance property, and enjoys a mirror reconstruction making it similar to a "tight frame". The two-subband structure is merged into a unique, concise, complex-valued subband that approximates a Wirtinger gradient which is naturally steerable. Complete steerability, though, is achieved by utilizing the Fourier-Argand representation, which provides a steerable filter able to estimate the amplitude and direction of image features, even in the presence of very high noise. We demonstrate the efficiency of the representation by comparing how it performs in wavelet-based denoising algorithms.
An object detection method for heavy fog scenes based on image defogging and sample enhancement

Deep convolutional neural network has achieved superior recognition performance on many public object detection datasets. However, under the weather conditions of rain or fog, the scarcity of samples has always been the problems restricting the accuracy of detection and identification. To solve this problem, this paper proposed an object detection method for heavy fog scenes based on image defogging and sample enhancement. Firstly, generative adversarial network (GAN) is adopted to remove the fog from images, and then achieve sample enhancement by a style transfer network, which keeps the image content basically unchanged and transform the style of image texture. Fog-free dataset after sample enhancement can reduce the influence of the texture information on the network model and make it pay more attention to the contour information of the object shape. The experimental results on I-HAZE and REISDE dataset show that our proposed method can effectively improve the object detection precision and the mAP (mean average precision) can be improved by up to 15%.
Predicting Phishing Vulnerabilities Using Machine Learning

This paper examines the ability to use machine learning to predict an undergraduate student&#x2019;s actions upon receiving a phishing email. The machine learning models used in this work were trained with actual phishing results augmented with student&#x2019;s background and administrative data. The ultimate goal of this project is to better identify members of an organization that are at risk from phishing, to provide targeted cyber security training. This targeted training will increase the security posture of an organization and minimize unnecessary training and productivity loss.The results of multiple machine learning techniques demonstrate that this approach is viable with validation accuracy ranging from 49 to 86%. Other metrics are used to evaluate the viability of the approaches, recall is determined to be the most important. The model with the best performance in validation using these two metrics was a Support Vector Machine (SVM). The SVM approach was able to predict whether a cadet would be compromised upon receipt of a phishing attack with a 55% accuracy while maintaining a recall score of 71%.When using the trained model on new data after training and validation the Logistic Regression model had the highest performance, accurately predicting whether a cadet would be compromised upon receipt of a phishing attack with a 86% accuracy while maintaining a recall score of 16%.
Extreme learning machine computational method of modeling energy gap of doped zinc selenide nano-material semiconductor

Zinc selenide (ZnSe) semiconductor belongs to a class of wide energy gap material with low visible region optical absorption, high optical coefficient (nonlinear) and fascinating optoelectronic as well as photocatalytic features which foster and strengthen its applications in resisting thermal shock, photoelectric fields (which include dielectric mirrors, shot wavelength lasers and light emitting diode), buffer layer of solar cell and many photocatalytic environmental cleanliness applications. Nanostructured ZnSe semiconductors occupy a special place in application domain due to the characteristic features attached to the crystallite size of the material which include high fluorescence quantum yields, solvent dispersibility and adjustable emission color among others. Effective utilization of nanostructured ZnSe semiconductor involves tuning of its energy gap through doping mechanisms which is accompanied with distortion in the parent lattice structure. In this contribution, extreme learning machine (ELM) computational intelligent method is proposed for predicting the band gap energy of doped ZnSe nanostructured semiconductor using the material crystallite size and lattice parameter as descriptors to the models. The developed ELM based model using sine (Sine), sigmoid (Sig) and transig (Trang) activation functions were compared with the existing SVR-GA and SPR models in the literature using various performance metrics. The developed Sine-ELM model shows superior performance over Sig-ELM, Trang-ELM, SVR-GA (2021) and SPR (2021) models with performance improvement of 12.74%, 64.89%, 68.87% and 75.62% using mean squared error (MSE) metric for testing data samples. The superiority of the model developed was further justified through validation with external set of data. The precision and superiority of the ELM based models developed would eventually ease quick characterization and tuning of energy gap of ZnSe nanostructured semiconductors for various applications.
Multimodal Graph Signal Denoising Via Twofold Graph Smoothness Regularization with Deep Algorithm Unrolling

We propose a denoising method of multimodal graph signals with twofold smoothness regularization. Graph signal processing assumes that a signal has an underlying structure that is represented by a graph. In each node of the graph, we often have multimodal data or features that are correlated across modalities. Since these multimodal data are measured by various sensors, the observed data will be noisy. In this paper, we assume that a multimodal signal is smooth on two underlying graphs: One is a spatial graph (i.e., relationship among nodes) and the other is a modality graph (i.e., relationship among modalities). We formulate a regularized minimization problem based on smoothness on the twofold graphs. The problem is solved with an alternating minimization scheme. To avoid a hand-crafted parameter tuning that is usually costly and converges to local minima, we utilize deep algorithm unrolling (DAU) to train the parameters in the algorithm. To validate the proposed method, we conduct experiments on synthetic data and demonstrate that our method outperforms various existing graph signal denoising methods.