Reviewerly/exHarmony huggingface dataset:
Design of Internet Product Interface Based on Dynamic Model

Human-computer interaction technology is an interdisciplinary subject based on ergonomics, design art, design psychology, computer science, sociology and other disciplines. It studies the interactive relationship between people and computers, and human-computer interaction interface is the core of this research. This paper launches the design of Internet product interface based on dynamic model. Mark the part of speech of the candidate feature words, find out the nouns or noun phrases, prune and filter out the conventional words to obtain the extended product feature words, and add them into the seed feature word bank and remove duplicates to obtain a new seed feature bank; Until no new words appear in the seed feature library, the final product feature library is obtained. The results show that the interactive design guided by dynamic semantics replaces or folds some buttons by gestures and hidden operations. By reducing the number of buttons, we can avoid the redundant interface and misoperation caused by too tight buttons. And after some buttons are converted into gestures, the number of buttons on the interface will be reduced, thus reducing the discrimination burden of the user in the operation process and making the user’s operation smoother.
Quantum Machine Learning Approach for Eigen Solving and Fourier Series Analysis

When parametrized quantum circuits are viewed as models that translate data inputs into predictions, quantum computers can be utilized for supervised learning. Many crucial theoretical aspects of these models remain unknown, even though considerable work has been done to understand the practical consequences of this method. Quantum models can access richer and richer frequency spectra by repeatedly repeating straightforward data-encoding gates. Since simulation time increases exponentially, simulation-based training cannot be used to big QML models. Variational quantum algorithms and quantum GAN are expected to give the best results if applied for different health care applications in microseconds. The present work explains how QML can be applied for variational quantum eigen solving. This paper also explains quantum Fourier transform analysis.
Bisinger: Bilingual Singing Voice Synthesis

Although Singing Voice Synthesis (SVS) has made great strides with Text-to-Speech (TTS) techniques, multilingual singing voice modeling remains relatively unexplored. This paper presents BiSinger, a bilingual pop SVS system for English and Chinese Mandarin. Current systems require separate models per language and cannot accurately represent both Chinese and English, hindering code-switch SVS. To address this gap, we design a shared representation between Chinese and English singing voices, achieved by using the CMU dictionary with mapping rules. We fuse monolingual singing datasets with open-source singing voice conversion techniques to generate bilingual singing voices while also exploring the potential use of bilingual speech data. Experiments affirm that our language-independent representation and incorporation of related datasets enable a single model with enhanced performance in English and code-switch SVS while maintaining Chinese song performance. Audio samples are available at https://bisinger-svs.github.io.
The FFCP Model for Predicting Click-Through Rate

In advertisement recommendations, click-through rate (CTR) prediction is a hugely sparse problem where embedding techniques and interaction models are needed to extract useful information from the data. Several CTR prediction and interaction models have been developed over the years that consider different parts of low-order and higher-order interactions. We develop the FFCP model, which combines multiple beneficial layers into a single model for better performance. An ablation study is performed to show the contribution of each model component. Our experiments demonstrate that FFCP achieves better AUC and loss than the 6 other models we compared on both of the two commonly used CTR prediction datasets and improves F1 results. The improvements reach 0.29% in AUC and 0.31% in loss.
Can Unpaired Textual Data Replace Synthetic Speech in ASR Model Adaptation?

To boost training and adaptation of end to end (E2E) automatic speech recognition (ASR) models, several approaches to use paired speech-text input together with unpaired text input have emerged. They aim at improving the model performance on rare words, personalisation, and long tail. In this work, we present a systematic study of the impact of such training/adaptation and compare it to training with synthetic utterances generated by text-to-speech engines. We experiment with in-house and CommonVoice datasets and conclude that using text data for adaptation is effective, but is outperformed by adapting with synthetic audio even when the TTS engine is sub-optimal. This challenges recent literature on the difficulties of using TTS data including catastrophic forgetting, feature misalignment, and pronunciation errors, which motivated the use of text-only adaptation.
Improving Severity Preservation of Healthy-to-Pathological Voice Conversion With Global Style Tokens

In healthy-to-pathological voice conversion (H2P-VC), healthy speech is converted into pathological while preserving the identity. The paper improves on previous two-stage approach to H2 P-VC where (1) speech is created first with the appropriate severity, (2) then the speaker identity of the voice is converted while preserving the severity of the voice. Specifically, we propose improvements to (2) by using phonetic posteriorgrams (PPG) and global style tokens (GST). Furthermore, we present a new dataset that contains parallel recordings of pathological and healthy speakers with the same identity which allows more precise evaluation. Listening tests by expert listeners show that the framework preserves severity of the source sample, while modelling target speaker’s voice. We also show that (a) pathology impacts x-vectors but not all speaker information is lost, (b) choosing source speakers based on severity labels alone is insufficient.
Maximum Correntropy Ensemble Kalman Filter

In this article, a robust ensemble Kalman filter (EnKF) called MC-EnKF is proposed for nonlinear state-space model to deal with filtering problems with non-Gaussian observation noises. Our MC-EnKF is derived based on maximum correntropy criterion (MCC) with some technical approximations. Moreover, we propose an effective adaptive strategy for kernel bandwidth selection. Besides, the relations between the common EnKF and MC-EnKF are given, i.e., MC-EnKF will converge to the common EnKF when the kernel bandwidth tends to infinity. This justification provides a complementary understanding of the kernel bandwidth selection for MC-EnKF. In experiments, non-Gaussian observation noises significantly reduce the performance of the common EnKF for both linear and nonlinear systems, whereas our proposed MC-EnKF with a suitable kernel bandwidth maintains its good performance at only a marginal increase in computing cost, demonstrating its robustness and efficiency to non-Gaussian observation noises.
Blockchain Technology-enabled Accounting and Auditing Practices: A Scoping Review

This manuscript is an attempt to review published literature on the different ways in which blockchain technology can influence the discipline of accounting in general, and more specifically the discipline of auditing. The rationale for this scoping review is to dissect blockchain technology and discover avenues that can advance transparency and conviction in accounting practices; and improved decision-making. This manuscript studies and consolidates four main themes of literature that blockchain technology has brought to the forefront. These dimensions of accounting include: event-approach to accounting; concurrent-approach to accounting; triple-verified entry accounting and uninterrupted or seamless auditing. The findings can encourage consolidated efforts of accounting and auditing practitioners, system architects, and policymakers, toward the architecture of blockchain ecosystems, in sync with digital transformation.
From a Filipino Morphological and Template-based Stemming:A Text Based Analyzer and Design

This study presents the design and implementation of the stochastic template stemming and morpho-tactical system for the diverse and complex Philippine language focusing on its national language. The method aims to utilize a stochastic template stemming that is based on the Filipino data sets attributes. Numerous Filipino word forms from various linguistic resources were collected and featured-analyzed to obtain its language structural features for the stemming methods and template generation. Stemming processes includes feature, and lexical evaluation, dictionary search morphological analysis, and stochastic template stemming. A stemming template was utilized, and the attributes were examined to execute stemming test. Index and stemming characteristics were gathered and utilized for the structural word categorization. For the entire stemming testing sets, a 90.65% stemming accuracy from mis-stemming measure, and a 23.2% under-stemming and a 0.000111% over stemming error index were produced.
KAQ: A Non-Intrusive Stacking Framework for Mean Opinion Score Prediction with Multi-Task Learning

Non-intrusive speech quality assessment aims to replace the time-consuming subjective evaluation metric, i.e., mean opinion score (MOS), by designing a trainable model to predict the MOS. In this work, we propose a non-intrusive stacking framework, named KAQ, to automatically achieve MOS prediction. KAQ benefits from the stacking algorithm to harness the capabilities of multiple well-performing machine learning models. And these models are trained to predict not only MOS but also intelligibility score via a multi-task learning. The experimental results show that KAQ achieves significantly better performance than the baselines in the 2023 VoiceMOS challenge and also wins second place in terms of mean square error at the utterance and system levels.
Cross-Modal Alignment With Optimal Transport For CTC-Based ASR

Temporal connectionist temporal classification (CTC)-based automatic speech recognition (ASR) is one of the most successful end to end (E2E) ASR frameworks. However, due to the token independence assumption in decoding, an external language model (LM) is required which destroys its fast parallel decoding property. Several studies have been proposed to transfer linguistic knowledge from a pretrained LM (PLM) to the CTC based ASR. Since the PLM is built from text while the acoustic model is trained with speech, a cross-modal alignment is required in order to transfer the context dependent linguistic knowledge from the PLM to acoustic encoding. In this study, we propose a novel cross-modal alignment algorithm based on optimal transport (OT). In the alignment process, a transport coupling matrix is obtained using OT, which is then utilized to transform a latent acoustic representation for matching the context-dependent linguistic features encoded by the PLM. Based on the alignment, the latent acoustic feature is forced to encode context dependent linguistic information. We integrate this latent acoustic feature to build conformer encoder-based CTC ASR system. On the AISHELL-1 data corpus, our system achieved 3.96 % and 4.27 % character error rate (CER) for dev and test sets, respectively, which corresponds to relative improvements of 28.39 % and 29.42% compared to the baseline conformer CTC ASR system without cross-modal knowledge transfer.
LAE-ST-MOE: Boosted Language-Aware Encoder Using Speech Translation Auxiliary Task for E2E Code-Switching ASR

Recently, to mitigate the confusion between different languages in code-switching (CS) automatic speech recognition (ASR), the conditionally factorized models, such as the language-aware encoder (LAE), explicitly disregard the contextual information between different languages. However, this information may be helpful for ASR modeling. To alleviate this issue, we propose the LAE-ST-MoE framework. It incorporates speech translation (ST) tasks into LAE and utilizes ST to learn the contextual information between different languages. It introduces a task-based mixture of expert modules, employing separate feed-forward networks for the ASR and ST tasks. Experimental results on the ASRU 2019 Mandarin-English CS challenge dataset demonstrate that, compared to the LAE-based CTC, the LAE-ST-MoE model achieves a 9.26 % mix error reduction on the CS test with the same decoding parameter. Moreover, the well-trained LAE-ST-MoE model can perform ST tasks from CS speech to Mandarin or English text.
Comparative Analysis using Machine Learning Techniques for Detecting and Mitigating Phishing

Phishing is a malicious technique employed by hackers to illicitly obtain confidential user information for their own misuse. In contemporary times, three notable approaches have emerged as effective countermeasures against such attacks: awareness-focused strategies, blacklist-based methods, and machine learning (ML) techniques. Hence, this systematic paper aims to provide researchers, readers, and users with an analysis of diverse proposals from other scholars on how to effectively combat phishing using ML algorithms. The research focuses on phishing detection utilizing ML techniques, establishing a taxonomy of machine learning models by examining their respective advantages and disadvantages. Furthermore, the performance analysis based on the available datasets of the ML classifiers such as NB, SVM, KNN, RF, and ANN is evaluated in terms of the performance metrics and the accuracy values of the classifiers on kfold at 10 is 82.40%, 83.00%, 83.60%, 84.39%, and 84.84%, and TP at 8 is 81.03%, 81.62%, 82.21%, 83.23%, and 83.77%, respectively.
End-To-End Training of a Neural HMM with Label and Transition Probabilities

We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modeled and learned explicitly. Most contemporary sequence-to-sequence models allow for from-scratch training by summing over all possible label segmentations in a given topology. In our approach there are explicit, learnable probabilities for transitions between segments as opposed to a blank label that implicitly encodes duration statistics.We implement a GPU-based forward-backward algorithm that enables the simultaneous training of label and transition probabilities.We investigate recognition results and additionally Viterbi alignments of our models. We find that while the transition model training does not improve recognition performance, it has a positive impact on the alignment quality. The generated alignments are shown to be viable targets in state-of-the-art Viterbi trainings.
Joint Energy-Based Model for Robust Speech Classification System Against Dirty-Label Backdoor Poisoning Attacks

Our novel technique utilizes a Joint Energy-based Model (JEM) that integrates both discriminative and generative approaches to increase resistance against dirty-label backdoor attacks. Our approach is especially effective when the trigger is short or hardly perceivable. We simulate the attack on the Speech Commands Dataset consisting of 1s audio clips. During training, we use JEM to model a view of the input implemented by a randomly selected 610ms window. During inference, we combine all (40) possible views utilizing a generative part of JEM. The resulting system has slightly decreased accuracy but significantly increased resistance shown in multiple scenarios. Interestingly, replacing JEM with a standard discriminative model (Disc) provides increased resistance with a lesser effect compared to JEM but maintains accuracy. We introduce an extension motivated by semi-supervised training that further improves JEM but not Disc. JEM can also benefit from Gaussian noise during evaluation.
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimers Speech Detection

The early-stage Alzheimer’s disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy risks because the data of specific patients are exclusive to each medical institution. A common practice is to use federated learning to protect the patients’ data privacy. However, its distributed learning process also causes performance reduction. To alleviate this problem while protecting user privacy, we propose a federated contrastive pre-training (FedCPC) performed before federated training for AD speech detection, which can learn a better representation from raw data and enables different clients to share data in the pre-training and training stages. Experimental results demonstrate that the proposed methods can achieve satisfactory performance while preserving data privacy.
Transformer Attractors for Robust and Efficient End-To-End Neural Diarization

End-to-end neural diarization with encoder-decoder based attractors (EEND-EDA) is a method to perform diarization in a single neural network. EDA handles the diarization of a flexible number of speakers by using an LSTM-based encoder-decoder that generates a set of speaker-wise attractors in an autoregressive manner. In this paper, we propose to replace EDA with a transformer-based attractor calculation (TA) module. TA is composed of a Combiner block and a Transformer decoder. The main function of the combiner block is to generate conversational dependent (CD) embeddings by incorporating learned conversational information into a global set of embeddings. These CD embeddings will then serve as the input for the transformer decoder. Results on public datasets show that EEND-TA achieves 2.68% absolute DER improvement over EEND-EDA. EEND-TA inference is 1.28 times faster than that of EEND-EDA.
Investigating The Effect of Language Models in Sequence Discriminative Training For Neural Transducers

In this work, we investigate the effect of language models (LMs) with different context lengths and label units (phoneme vs. word) used in sequence discriminative training for phoneme-based neural transducers. Both lattice-free and N-best-list approaches are examined. For lattice-free methods with phoneme-level LMs, we propose a method to approximate the context history to employ LMs with full-context dependency. This approximation can be extended to arbitrary context length and enables the usage of word-level LMs in lattice-free methods. Moreover, a systematic comparison is conducted across lattice-free and N-best-list-based methods. Experimental results on Librispeech show that using the word-level LM in training outperforms the phoneme-level LM. Besides, we find that the context size of the LM used for probability computation has a limited effect on performance. Moreover, our results reveal the pivotal importance of the hypothesis space quality in sequence discriminative training.
What more can LEGO Teach Agile Methods

Agile methods are considered relatively new and areas of development for them are emerging. In this study, we discuss how Agile methods can be improved with LEGO, which is a relatively more established and proven field used by a larger number of profiles of users. For this, we analyzed "A Lego Brickumentary" documentary for a thematic analysis resulting in 83 Level-1 distinct codes that were summarized under 39 categories (Level-2). In the final, these categories were grouped into 3 main- categories (Level-3); creativity, system thinking, and customer orientation. From the results, we realized a harmony of people and system thinking to create customer value, empowered by creativity, open development platforms, community development, diversity, idea development processes, and many more. When viewed chronologically, based on the results, Agile will likely face a dilemma like in the LEGO history that will determine its fate; imposing a centralized thinking or embracing endless possibilities of practices by users. Among these two, this study proposes to pave the way for a better understanding of democratized agility with more comprehensive perspectives of diverse and empowered people using their creativity.
Fault Detection Model Using Measurement Data in Fiber Optic Internet Lines

In this study, a model has been developed to predict potential faults in advance based on performance metrics of various fiber-optic internet lines, as well as alarm (fault data) and performance measurement values from the 5 hours prior to the occurrence of the alarm. Performance metrics that vary over time have been analyzed in a time-series format based on alarm numbers, and anomaly detection methods have been used to label the data for any potential patterns that may occur in the performance metrics specific to the alarm. The labeled data was then fed into a classification model to create a model that enables to detect possible patterns in the relevant performance values for the specific fault type. The best performing model was Random Forest Classifier with accuracy and F1 scores of 0.89 and 0.84 respectively.
Generative Pre-trained Transformer (GPT) Models for Irony Detection and Classification

The tasks of identifying and classifying ironic texts remain an ongoing challenge, necessitating continued exploration for enhanced solutions in NLP. This study delves into assessing the effectiveness of Generative Pre-trained Transformer (GPT) models, which have emerged in recent years, in handling irony detection and classification tasks through the implementation of zero-shot learning and few-shot learning methods in English texts. Additionally, we compare GPT text embedding models with GloVe, a proven text embedding model, utilizing various machine learning and deep learning approaches. Within this study, we employed the SemEval-2018 Task 3 dataset, curated as part of the Semantic Evaluation 2018 workshop. The most noteworthy achievement in binary classification, namely irony detection, is an F1 score of 68.9%, attained by the text-davinci-003 model through few-shot learning, with access to forty-two samples for training. In terms of multiclass classification, namely irony classification, the text-embedding-ada-002 text embedding model, in conjunction with the Gaussian Naive Bayes algorithm, attained the best result with an F1 score of 48.5%. The best results obtained in the study achieved comparable results with previous studies.
Visualizing the Path of a Photo Taker from Image Metadata

One of the most challenging investigations in forensic analysis is the work conducted on smartphones. Specifically using smartphone data to provide evidence for a case is becoming a norm nowadays. Many Android device applications use and work with location services that assist investigators in working with log files that store valuable information. Images captured on Android devices will save geolocation information, date, and time in the properties. In this work, a tool is developed to acquire the date and geolocation for each taken photo so that the path of the photo taker can be displayed on Google Maps. The work showed that the application can take about 4 and a half seconds to process four images and display the map to the investigator.