Reviewerly/exHarmony huggingface dataset:
PVR-AFM: A Pathological Voice Repair System based on Non-linear Structure

Objective Speech signal processing has become an important technique to ensure that the voice interaction system communicates accurately with the user by improving the clarity or intelligibility of speech signals. However, most existing works only focus on whether to process the voice of average human but ignore the communication needs of individuals suffering from voice disorder, including voice-related professionals, older people, and smokers. To solve this demand, it is essential to design a non-invasive repair system that processes pathological voices. Methods In this paper, we propose a repair system for multiple polyp vowels, such as /a/, /i/ and /u/. We utilize a non-linear model based on amplitude-modulation (AM) and a frequency-modulation (FM) structure to extract the pitch and formant of pathological voice. To solve the fracture and instability of pitch, we provide a pitch extraction algorithm, which ensures that pitch's stability and avoids the errors of double pitch caused by the instability of low-frequency signal. Furthermore, we design a formant reconstruction mechanism, which can effectively determine the frequency and bandwidth to accomplish formant repair. Results Finally, spectrum observation and objective indicators show that the system has better performance in improving the intelligibility of pathological speech.
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing

This paper presents the Source-Free Domain Adaptation shared task held within SemEval-2021.The aim of the task was to explore adaptation of machine-learning models in the face of data sharing constraints.Specifically, we consider the scenario where annotations exist for a domain but cannot be shared.Instead, participants are provided with models trained on that (source) data.Participants also receive some labeled data from a new (development) domain on which to explore domain adaptation algorithms.Participants are then tested on data representing a new (target) domain.We explored this scenario with two different semantic tasks: negation detection (a text classification task) and time expression recognition (a sequence tagging task).
Embracing Less Common Evaluation Strategies for Studying User Experience in NIME

Assessment of user experience (UX) is increasingly important in music interaction evaluation, as witnessed in previous NIME reviews describing varied and idiosyncratic evaluation strategies.This paper focuses on evaluations conducted in the last four years of NIME (2017 to 2020), compares results to previous research, and classifies evaluation types to describe how researchers approach and study UX in NIME.While results of this review confirm patterns such as the prominence of short-term, performer perspective evaluations, and the variety of evaluation strategies used, they also show that UX-focused evaluations are typically exploratory and limited to novice performers.Overall, these patterns indicate that current UX evaluation strategies do not address dynamic factors such as skill development, the evolution of the performerinstrument relationship, and hedonic and cognitive aspects of UX.To address such limitations, we discuss a number of less common tools developed within and outside of NIME that focus on dynamic aspects of UX, potentially leading to more informative and meaningful evaluation insights.
Auto-tagging of Short Conversational Sentences using Natural Language Processing Methods

In this study, we aim to find a method to autotag sentences specific to a domain. Our training data comprises short conversational sentences extracted from chat conversations between company's customer representatives and web site visitors. We manually tagged approximately 14 thousand visitor inputs into ten basic categories, which will later be used in a transformer-based language model with attention mechanisms for the ultimate goal of developing a chatbot application that can produce meaningful dialogue.We considered three different stateof- the-art models and reported their auto-tagging capabilities. We achieved the best performance with the bidirectional encoder representation from transformers (BERT) model. Implementation of the models used in these experiments can be cloned from our GitHub repository and tested for similar auto-tagging problems without much effort.
Survey on Applications of Blockchain in E-Governance

Block chain is a distributed ledger that offers secure and immutable storage. E–governance allows exchange of information between government and citizens or organizations through latest information and communication technologies. The E governance services facilitate efficient and fast access of services; on the other hand it raises potential risks of breaching into privacy and Security. Blockchain based e-governance system offers secure and transparent services without intermediary. This paper investigates the scope and challenges of adopting blockchain technology as a platform for E-Governance.
Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach

During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories.
Call-By-Value, Again!

The quest for a fully abstract model of the call-by-value λ-calculus remains crucial in programming language theory, and constitutes an ongoing line of research. While a model enjoying this property has not been found yet, this interesting problem acts as a powerful motivation for investigating classes of models, studying the associated theories and capturing operational properties semantically.We study a relational model presented as a relevant intersection type system, where intersection is in general non-idempotent, except for an idempotent element that is injected in the system. This model is adequate, equates many λ-terms that are indeed equivalent in the maximal observational theory, and satisfies an Approximation Theorem w.r.t. a system of approximants representing finite pieces of call-by-value Bohm trees. We show that these tools can be used for characterizing the most significant properties of the calculus - namely valuability, potential valuability and solvability - both semantically, through the notion of approximants, and logically, by means of the type assignment system. We mainly focus on the characterizations of solvability, as they constitute an original result. Finally, we prove the decidability of the inhabitation problem for our type system by exhibiting a non-deterministic algorithm, which is proven sound, correct and terminating.
Research on the Reciprocal Mechanism of Hybrid Governance in Blockchain

This paper aims to discuss the reciprocal mechanism of governance-by-blockchain and governance-of-blockchain within the context of hybrid governance, a model that combines both on- chain and off-chain methods to create reciprocal structure. Assuming the contingency theory, the effectiveness of a hybrid model depends on the collective interactions between technology, management, and public sphere. We found that technical innovations in governing methods promote the formation of effective technology management and the performance of blockchain as a governing tool is symbiotically linked to the consistent regulation of blockchain. After considering the different possible implementations of blockchain for governance, we concluded that the probability of success is highest in consortium blockchain, followed by public blockchain and private blockchain. By studying the Dream Valley Blockchain Project, a site currently attempting to realize the hybrid model on the scale of a small city, we confirmed some of the concerns regarding blockchain governance. In conclusion, the interactions between blockchain and governance are mutually beneficial. A significant amount of research and development still remains before hybrid governance is achieved. However, the difficulty surrounding the task of managing rapidly developing technology can be alleviated best by incorporating technological advancements into the governance structure
Utilizing Statistical Tests for Comparing Machine Learning Algorithms

The mean result of machine learning models is determined by utilizing k-fold cross-validation. The algorithm with the best average performance should surpass those with the poorest. But what if the difference in average outcomes is the consequence of a statistical anomaly? To conduct whether or not the mean result differences between two algorithms is genuine then statistical hypothesis test is utilized. Using statistical hypothesis testing, this study will demonstrate how to compare machine learning algorithms. The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate.
Recognition of Indian Sign Language Alphabets for Hearing and Speech Impaired People using Deep Learning

A large portion of the hearing and speech impaired are deprived of any education and just around 1-2% of the hearing and speech impaired get schooling in Indian sign language. The large portion of them is incapable of using theoretical languages, having difficult issues to express themselves or understanding printed texts. So, there is need to build an auto- mated translation system with innovations for helping hearing and speech impaired people in communicating among them. Sign language recognition is an evolving research area in computer vision and machine learning. In this paper, recognition of Indian sign language alphabets will be carried out using deep learning and machine learning. A large database of images of sign language alphabets will be captured through web camera. The network is train by providing input and expected outputs. The project will explore the various deep learning algorithms such as AlexNet and GoogLeNet will be used for training the images. The trained model using various architectures will be used for testing the other Indian sign language alphabets. The comparative study of accuracy of various architecture will be carried out.
Balancing risk with virtual private networking during a pandemic

When the pandemic struck and teaching went online worldwide, universities had to make pressing decisions that balanced cybersecurity against other factors, including health and safety, usability, and cost. One such challenge Indiana University (IU) faced was how to accommodate the secure telecommunications needs of 130,000 faculty, staff, and students who would now be teaching, learning, doing research, and working from home. Some universities reflexively promoted virtual private network (VPN) use for all activities. Such an approach would have been unsustainable at IU, however, owing both to the licenses and resources needed for the sheer number of users and to the high-throughput applications on which they rely. Perhaps even worse, it would have increased the chances that the VPN would be unavailable during a critical incident or other situation in which secure communications must be guaranteed. Instead, IU launched an awareness campaign demonstrating exactly when VPN use is and isn’t needed. In addition, network staff employed a VPN feature called split tunneling to reduce the load. This article discusses the advantages and disadvantages of this approach and how IU made the decision to balance both sides of the risk equation to ensure the continued advancement of its mission throughout the pandemic.
Two-stage Training for Learning from Label Proportions

Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utilize end-to-end pipelines to obtain the proportional loss with Kullback-Leibler divergence between the bag-level prior and posterior class distributions. However, the unconstrained optimization on this objective can hardly reach a solution in accordance with the given proportions. Besides, concerning the probabilistic classifier, this strategy unavoidably results in high-entropy conditional class distributions at the instance level. These issues further degrade the performance of the instance-level classification. In this paper, we regard these problems as noisy pseudo labeling, and instead impose the strict proportion consistency on the classifier with a constrained optimization as a continuous training stage for existing LLP classifiers. In addition, we introduce the mixup strategy and symmetric cross-entropy to further reduce the label noise. Our framework is model-agnostic, and demonstrates compelling performance improvement in extensive experiments, when incorporated into other deep LLP models as a post-hoc phase.
Adaptive Batch Scheduling for Open-Domain Question Answering

Open-domain question answering aims to get answers for given questions from a set of documents. Recently, dual encoder architecture is widely adopted to dense passage retrieval for question answering. In-batch negative sampling is typically used to gather extra negative samples during training. In this paper, we propose adaptive batch scheduling to enhance the performance of in-batch negative sampling. The proposed algorithm schedules training batches to increase the difficulty of the sampled negatives by in-batch negative sampling during training. We evaluated the proposed approach on the two well-known document retrieval benchmark datasets MSMARCO and Natural Questions. The evaluation result shows that the proposed adaptive batch scheduling could significantly improve the document retrieval performances of dual encoder architecture document retrieval systems.
RETRACTED ARTICLE: Edge computing and financial service industry financing risk innovation based on data mining technology

Since information systems are widely used in the management of financial institutions, financial institutions generate a large amount of data in their daily use. However, a large amount of data has not been effectively used. In daily management, financial managers can receive surface information through potential techniques such as simple statistics, sorting, and inspection. With the rapid development of mobile communications and the popularization of smart mobile devices, the number of mobile services has increased sharply, and mobile terminals have become resource-constrained devices. Traditional cloud computing can no longer meet the growing business needs. In order to improve the quality of service for users, mobile edge computing technology has become one of the key technologies of the fifth-generation mobile communication system. It reduces the burden on the core network and provides effective solutions to improve user experience. The financial service industry is an industry that produces services and provides intangible monetary and financial services. The financial service industry is an important part of economic growth. Its development trend and development status are directly related to the process of national economic development. Effective financing and investment work can bring economic benefits to state-owned enterprises, but there are certain financial risks in the process of financing and investment activities. In order to ensure the steady development of enterprises, the financial management department should correctly understand the types of financial risks in the investment and financing links and take targeted prevention and control measures.
Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19

One way to prevent the spread of the COVID-19 virus is to check body temperature regularly. However, checking body temperature manually by directing the thermogun at someone's face is still often found. This study implements the use of the AMG8833 thermal camera to detect a person's body temperature without making any contact. The AMG8833 is a general-purpose temperature detection camera so to be used as a temperature meter, its accuracy needs to be improved by regression. The purpose of this research is to improve the performance of AMG833 as a thermal camera with AdaBoost regression. AdaBoost is a type of ensemble learning that uses several decision tree models. For face detection, the system uses the Haar Cascade method. The test results show that the decision tree model produces an R-Squared value of 0.93 and an RMSE of 0.21. Meanwhile, AdaBoost succeeded in improving the performance of the regression model with a higher R-Squared value and a lower RMSE value with values of 0.95 and 0.18, respectively.
Online Learning of Action Models for PDDL Planning

The automated learning of action models is widely recognised as a key and compelling challenge to address the difficulties of the manual specification of planning domains. Most state-of-the-art methods perform this learning offline from an input set of plan traces generated by the execution of (successful) plans. However, how to generate informative plan traces for learning action models is still an open issue. Moreover, plan traces might not be available for a new environment. In this paper, we propose an algorithm for learning action models online, incrementally during the execution of plans. Such plans are generated to achieve goals that the algorithm decides online in order to obtain informative plan traces and reach states from which useful information can be learned. We show some fundamental theoretical properties of the algorithm, and we experimentally evaluate the online learning of the action models over a large set of IPC domains.
RETRACTED ARTICLE: A user preference tree based personalized route recommendation system for constraint tourism and travel

Personalized recommendation systems recommend the target destination based on user-generated data from social media and geo-tagged photos that are currently available as a most pertinent source. This paper proposes a tourism destination recommendation system which uses heterogeneous data sources that interprets both texts posted on social media and images of tourist places visited and shared by tourists. For this purpose, we propose an enhanced user profile that uses user-location vector with LDA and Jaccard coefficients. Moreover, a new tourist destination tree is constructed using the posts extracted from trip advisor where each node of the destination tree consists of tourist destination data. Finally, we build a personalized recommendation system based on user preferences, A* algorithm and heuristic shortest path algorithm with cost optimization based on the backtracking based traveling salesman problem solution, tourist destination tree and tree-based hybrid recommendations. Here, the 0/1 knapsack algorithm is used for recommending the best tourist destination travel route plans according to the travel time and cost constraints of the tourists. The experimental results obtained from this work depict that the proposed user centric personalized destination and travel route recommendation system is providing better recommendation of tourist places than the existing systems by handling multiple heterogeneous data sources efficiently for recommending optimal tour plans with minimum cost and time.
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images

Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class. One way to alleviate this problem is to acquire a strong inductive bias via meta-learning on similar tasks. In this paper, we show that such inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. Specifically, we propose a novel part-based self-supervised representation learning scheme to learn transferable representations by maximizing the similarity of an image to its discriminative part. To mitigate the overfitting in few-shot classification caused by data scarcity, we further propose a part augmentation strategy by retrieving extra images from a base dataset. We conduct systematic studies on miniImageNet and tieredImageNet benchmarks. Remarkably, our method yields impressive results, outperforming the previous best unsupervised methods by 7.74% and 9.24% under 5-way 1-shot and 5-way 5-shot settings, which are comparable with state-of-the-art supervised methods.
Retracted: An enhance the data security performance using an optimal cloud network security for big data cloud framework

Venkatesan B, Chitra S. An enhance the data security performance using an optimal cloud network security for big data cloud framework, International Journal of Communication Systems 2021, (https://doi.org/10.1002/dac.4854). The above article published online on 9 August 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor and John Wiley and Sons Ltd. Following publication, it has come to our attention that the people named as the Guest Editors of the Special Issue were being impersonated by a fraudulent entity and the articles were not reviewed in line with the journal's peer review standards. The Editor has determined that the articles do not meet the required scholarly standards of the journal, and therefore has taken the decision to retract the article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.
Neural Machine Translation Approach for Singlish to English Translation

Comprehension of “Singlish” (an alternative writing system for Sinhala language) texts by a machine had been a requirement for a long period. It has been a choice of many Sri Lankan’s writing style in casual conversations such as small talks, chats and social media comments. Finding a method to translate Singlish to Sinhala or English has been tried for a couple of years by the research community in Sri Lanka and many of the attempts were tried based on statistical language translation approaches due to the challenge of finding a large dataset to use Deep Learning approaches. This research addresses the challenge of preparing a data set to evaluate deep learning approach’s performance for the machine translation activity for Singlish to English language translation and to evaluate Seq2Seq Neural Machine Translation (NMT) model. The proposed seq2seq model is purely based on the attention mechanism, as it has been used to improve NMT by selectively focusing on parts of the source sentence during translation. The proposed approach can achieve 24.13 BLEU score on Singlish-English by seeing ~0.26 M parallel sentence pairs with 50 K+ word vocabulary.