Reviewerly/exHarmony huggingface dataset:
Influence model with opinions and trust score evaluations under the leader-follower environment

A group decision-making process is introduced by utilizing the influence model together with a matrix of interpersonal influences and an opinion matrix. The opinion matrix is constructed with the opinions/advice from one group of experts towards the other. Experts are divided into two groups, one which has more experienced, skilled and qualified persons is known as the group of opinion leaders and the other is known as the group of opinion followers. Sometimes, decision-makers are ordinary agents and their opinion formation is profoundly influenced by opinion leaders. The truthfulness of opinion leaders and the interpersonal influences of decision-makers is also taken into account. Also, a modified definition of trust score evaluation is presented with the understanding of the fact that the maximum trust which a decision-maker can do upon some opinion leader is his/her truthfulness. On the basis of this definition, a trust score matrix is constructed and the influence model is modified to take into account that matrix.
LDA-Enhanced Federated Learning for Image Classification with Missing Modality

In practical pattern recognition, e.g., image classification or recognition, the problem of missing modality, i.e., new patterns never trained by a learner pop up, can cause a dramatic decrease on the recognition accuracy. Existing algorithms as few-shot learning (FSL) and zero-shot learning (ZSL) have not sufficiently used information from other users or clients. If patterns or knowledge from other sources can be utilized as much as possible, the damage of missing modality is expected to be reduced. Privacy protection must be considered when trying to fetch information from other users. Motivated by these, an enhanced federated learning with linear discriminant analysis (LDA) is developed here. The data of each user is regarded as a client, and the features of each client are first extracted with neural network-based classification. These features are uploaded to the central server and then aggregated with LDA as a central classification to possibly achieve all patterns’ features. The trained LDA is then downloaded to the client to fulfill the pattern recognition. The proposed algorithm is applied to an image classification, and the experimental results demonstrate its efficiency in dealing with pattern recognition with missing modality.
Analysis of Global Research Proceedings in Artificial Intelligence

This study aims to discover the trends among the proceedings in the domain of research in Artificial Intelligence. To understand the history and predict the future direction of AI-focused research, publications in the Scopus scholarly database of Elsevier have been examined. The relevant data has been analyzed to understand the underlined trend. Scopus is one of the most reputed and renowned scientific publication group, which is why it has been chosen for this study. The data has been analyzed in a way so that findings may be categorized according to the frequency of keywords, funding sources, affiliation-organizations, country, language, etc. The findings have been ordered in separate points while visualizing and disseminating important outcomes via infographics and graphs.
Bias: Bijective Input And Surjectivity In Zero Shot Learning

Zero-shot learning suffers from the issue of generalisation due to domain shift across seen and unseen classes. In this paper, we propose a method that extends the usual approach of learning a mapping between semantic and visual embedding spaces by ensuring it to be surjective. This functional constraint along with triplet loss prevents the model from overfitting to seen classes. We also use a bijective feature extractor to complement our proposal. Experimental results on benchmark datasets depict that our method out-performs standard approaches in conventional and generalised scenarios.
Soundcool: A Business Model for Cultural Industries Born Out of a Research Project

Abstract Soundcool is a system for musical, sound and visual collaborative creation through mobile phones, tablets and other interfaces. This paper describes the creation of the app from the outset, illustrating not only how the Soundcool® system has been developed from the initial idea to the current reality, but also the evolution that the app has had during this period and how it has become ready to use. The research group in the Universitat Politecnica de Valencia (UPV) has played a key role in the development of the app. We also explain the opportunities for Soundcool in different markets and economic sectors. Last but not least, we describe how the idea has been financed to make it a reality.
Critical Problem for codes over finite chain rings

The Critical Problem in matroid theory is the problem for finding the maximum dimension of a subspace that contains no element of a fixed subset S of Fqk. This problem was posed by H. Crapo and G.-C. Rota and has been one of the significant problems in matroid theory. It can be interpreted in terms of a linear code over a finite field as to find the critical exponent of a linear code. This paper introduces the critical exponents of linear codes over finite chain rings, and extends Kung's upper bound on the critical exponent of a representable matroid over Fq to a linear code over a finite chain ring. Consequently, we present some codes whose critical exponents attain the bound.
Multi-Hop Reasoning for Question Answering with Knowledge Graph

Multi-hop Question Answering over Knowledge Graphs (KGQA) in previous studies has achieved remarkable results by exploiting the prediction property of Knowledge Graphs (KG) embedding. However, when facing Chinese sentences, its answer selection performs poorly. We improve the method for KGQA by combining the traditional method for KGQA with a lattice based CNN (LCN) model. We refine the granularity of questions and answers to make its coverage more extensive and generalizable, and expand the answer set to improve the performance in single results.
Exploring Source Separation as a Countermeasure for Voice Conversion Spoofing Attack

Voice conversion is a high-risk spoofing technique for automatic speaker verification systems where the identity of imposter speaker’s speech is transformed to that of genuine speaker’s without altering the linguistic content. In this work, we propose a countermeasure by combining an unsupervised co-channel source separation framework based on non-negative matrix factorization and convolutional neural network (CNN)-based binary classifier. We also propose to model voice conversion (VC) spoofed speech as an instantaneous mixture of estimate of target speech and the artifacts introduced during the process of VC. Further, the efficiency of proposed countermeasure is evaluated using CNN-based automatic speaker verification system. For evaluation, voice conversion challenge 2016 dataset which consist of 18 different voice conversion algorithm’s results is considered. The ASV system’s vulnerability for the above database was tested using false alarm rate (FAR) and was found to be ranging from 18.8 to 62.4%. The proposed countermeasure showed to give excellent performance for 3 known and 15 unknown voice conversion attacks by reducing false alarm rate of speaker verification system below from 2.8% for all 18 VC algorithms.
Christine P. Chai's Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong<i>et al.</i>

Journal of the Royal Statistical Society: Series B (Statistical Methodology)Volume 84, Issue 1 p. 43-44 DISCUSSION CONTRIBUTION Christine P. Chai's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al. Christine P. Chai, Corresponding Author Christine P. Chai cpchai21@gmail.com Microsoft Corporation, Redmond, Washington, USA Disclaimer: The opinions and views expressed here are those of the author and do not necessarily state or reflect those of Microsoft. Correspondence Christine P. Chai, Microsoft Corporation, Redmond, WA, USA. Email: cpchai21@gmail.comSearch for more papers by this author Christine P. Chai, Corresponding Author Christine P. Chai cpchai21@gmail.com Microsoft Corporation, Redmond, Washington, USA Disclaimer: The opinions and views expressed here are those of the author and do not necessarily state or reflect those of Microsoft. Correspondence Christine P. Chai, Microsoft Corporation, Redmond, WA, USA. Email: cpchai21@gmail.comSearch for more papers by this author First published: 12 August 2021 https://doi.org/10.1111/rssb.12458 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume84, Issue1February 2022Pages 43-44 RelatedInformation
Enhanced PMF Model to Predict User Interest for Web API Recommendation

Many methods focus solely on the relationship between the API and the user and fail to capture their contextual value. Because of this, they could not get better accuracy. The accuracy of the API recommendation can be improved by considering the effect of API contextual information on their latent attribute and the effect of the user time factor on the latent attribute of the user through the deep learning-based matrix factorization method (DL-PMF). In this chapter, a CNN (convolutional neural network) with an attention mechanism for the hidden features of web API elements and an LSTM (long-term and short-term memory) network is introduced to find the hidden features of service users. Finally, the authors combined PMF (probabilistic matrix factorization) to estimate the value of the recommended results. Experimental results obtained by the DL-PMF method show better than the experimental results obtained by the PMF and the ConvMF (convolutional matrix factorization) method in the recommended accuracy.
Layer-Wise Customized Weak Segmentation Block And Aiou Loss For Accurate Object Detection

The anchor-based detectors handle the problem of scale variation by building the feature pyramid and directly setting different scales of anchors on each cell in different layers. However, it is difficult for box-wise anchors to guide the adaptive learning of scale-specific features in each layer because there is no one-to-one correspondence between box-wise anchors and pixel-level features. In order to alleviate the problem, in this paper, we propose a scale-customized weak segmentation (SCWS) block at the pixel level for scale customized object feature learning in each layer. By integrating the SCWS blocks into the single-shot detector, a scale-aware object detector (SCOD) is constructed to detect objects of different sizes naturally and accurately. Furthermore, the standard location loss neglects the fact that the hard and easy samples may be seriously imbalanced. A forthcoming problem is that it is unable to get more accurate bounding boxes due to the imbalance. To address this problem, an adaptive IoU (AIoU) loss via a simple yet effective squeeze operation is specified in our SCOD. Extensive experiments on PASCAL VOC and MS COCO demonstrate the superiority of our SCOD.
Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning

Voice conversion (VC) transforms the speaking style of a source speaker to the speaking style of a target speaker by keeping linguistic information unchanged. Traditional VC techniques rely on parallel recordings of multiple speakers uttering the same sentences. Earlier approaches mainly find a mapping between the given source–target speakers, which contain pairs of similar utterances spoken by different speakers. However, parallel data are computationally expensive and difficult to collect. Non-parallel VC remains an interesting but challenging speech processing task. To address this limitation, we propose a method that allows a non-parallel many-to-many voice conversion by using a generative adversarial network. To the best of the authors’ knowledge, our study is the first one that employs a sinusoidal model with continuous parameters to generate converted speech signals. Our method involves only several minutes of training examples without parallel utterances or time alignment procedures, where the source–target speakers are entirely unseen by the training dataset. Moreover, empirical study is carried out on the publicly available CSTR VCTK corpus. Our conclusions indicate that the proposed method reached the state-of-the-art results in speaker similarity to the utterance produced by the target speaker, while suggesting important structural ones to be further analyzed by experts.
Temporal Extension for Encoder-Decoder-based Crowd Counting Approaches

Crowd counting is an important aspect to safety monitoring at mass events and can be used to initiate safety measures in time. State-of-the-art encoder-decoder architectures are able to estimate the number of people in a scene precisely. However, since most of the proposed methods are based to solely operate on single-image features, we observe that estimated counts for aerial video sequences are inherently noisy, which in turn reduces the significance of the overall estimates. In this paper, we propose a simple temporal extension to said encoder-decoder architectures that incorporates local context from multiple frames into the estimation process. By applying the temporal extension a state-of-the-art architectures and exploring multiple configuration settings, we find that the resulting estimates are more precise and smoother over time.
Oron: Towards a Dynamic Analysis Instrumentation Platform for AssemblyScript

The dynamic nature of JavaScript may lead to challenges and issues regarding efficiency and security. Analysis tools can help developers tackle some of these issues. In the context of web applications, dynamic analyses are best suited for handling those dynamic features but may affect the programs execution performance. In a first experiment, we attempted to improve the performance of the Aran dynamic analysis platform for JavaScript by utilizing WebAssembly. The extension caused extra performance hits due to context switches between JavaScript and WebAssembly. Because these context switches are inevitable, we decided to refit our work for the analysis of AssemblyScript, a variant of TypeScript which compiles to WebAssembly (and therefore excluding context switches). In this work, we explore this approach in the form of a new source code instrumentation platform named Oron, which allows for the instrumentation of AssemblyScript code. The presented platform is evaluated and shows promising improvements which provide a solid basis for efficient dynamic analysis of AssemblyScript applications.
Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data

The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.
Study on the Process Optimization of Interior Design Computer Renderings

Abstract this article in view of the interior design renderings produced by using teaching material in the production process is not reasonable, lead to figure out the problem such as slow speed, puts forward the solution to the optimization of rendering process including import CAD drawings, change the material and light set sequence and for lighting and material test, using the photon image to speed up the figure, will figure out the TGA format to higher quality of TIF files, such as material channel image is generated by the transformation plug-in, effectively improve the speed and quality of interior design renderings, enable students to master the method of making the practical higher, improve the teaching effect.
Inferiority complex of higher vocational students based on improved random sampling

Some factors affecting the physical and mental health of vocational college students, the sense of inferiority plays a very important role in cultivating students with physical and mental health. Inverse random under sampling algorithm is improved based on integrated learning, which can improve the performance of the classifier. Stacking integrated learning and flip random sampling reduction algorithm SIRUS is proposed. Select the individual subjective factors studied in this paper is important in self-attribution and social objective factors are important social support factors, and the only demographic variables is a significant difference.
Password guessers under a microscope: an in-depth analysis to inform deployments

Password guessers are instrumental for assessing the strength of passwords. Despite their diversity and abundance, comparisons between password guessers are limited to simple success rates. Thus, little is known on how password guessers can best be combined with or complement each other. To extend analyses beyond success rates, we devise an analytical framework to compare the types of passwords that guessers generate. Using our framework, we show that different guessers often produce dissimilar passwords, even when trained on the same data. We leverage this result to show that combinations of computationally cheap guessers are as effective in guessing passwords as computationally intensive guessers, but more efficient. Our framework can be used to identify combinations of guessers that will best complement each other. To improve the success rate of any guesser, we also show how an effective training dataset can be identified for a given target password dataset, even when the target dataset is hashed. Our insights allow us to provide a concrete set of practical recommendations for password checking to effectively and efficiently measure password strength.
An empirical analysis of the impact of financial development on the income gap between urban and rural residents in China - based on the PVAR model

Although the level of China's financial industry has developed rapidly, but the domestic problem of urban-rural development imbalance is becoming more and more serious, which has formed a serious obstacle to the development and progress of social economy. In order to explore the relationship between the level of China’s financial development capacity and the income gap between urban and rural residents, this paper surveys relevant data from 30 provinces, municipalities and regions in a total of 17 years from 2002 to 2018, and introduces the income gap between urban and rural residents, the scale of financial development, and efficiency. Established a PVAR model based on related variables such as financial development structure, and then conducted co-integration test, Granger causality test, GMM estimation, impulse response and variance decomposition. The empirical results show that there is a long-term equilibrium relationship between various variables, and the urban-rural income gap It is the Granger reason for the scale of financial development, but there is no reverse causality. Through dynamic analysis, it is found that the urban-rural income gap is mainly affected by its own reasons. The scale of financial development can narrow the urban-rural income gap to a certain extent, and the efficiency of financial development is to a certain extent. Will widen the income gap between urban and rural areas, and the explanatory effect of the financial development structure can be ignored.
A Spherical Reusable Virtual Writing Surface for Physics Teaching

The paper deals with the educational opportunities of a multifunctional 360 degree virtual sphere which can be used as a surface for records, a room for multimedia watching, an Internet browser, a spherical screen for demonstrating digital objects. Here is considered a widely available and strongly inexpensive mobile version of VR (Virtual Reality) technology. Physics teachers can easy draw and explain formulas, diagrams, figures, tables, very quickly erase and again draw or operate them in the virtual space around the students. It is applicable for individual and group learning, online and in-person courses. The paper focuses on not only the general information about the discussed problem, definition and description of a VR sphere and its characteristics, but also the methodology and practical aspects of teaching inside virtual reality. In addition it describes the controversial teachers’ opinion about activity in the VR sphere, and the students’ attitudes towards this instrument of physics learning.
Training is the foundation of security

No business, no matter what its size, is immune from the threat of a cyber attack. This is evidenced by the number of cases rising year on year, particularly during the height of the pandemic, with the UK's National Cyber Security Centre (NCSC) reporting a 20% increase in serious incidents compared to 2018-2019. Cyber attacks are also becoming more innovative and we're already seeing an increase in new and advanced tactics, such as fileless malware and crypto-miners.
CO-llaborative VI-rtual D-esign: A Collaborative Autoethnography on Conducting Exclusively Online, Data-Led Collaborations in the Creative Industries

This collaborative autoethnographic story of #DataCreativities articulates data traces found within the rapid move online in education and creative sectors in Melbourne, Australia. As a result of the lockdowns imposed to combat the initial spread of COVID-19, this collaboratory began within the anxieties of 2020. #DataCreativities takes a data-related approach to understanding the fast-paced shift to making, learning, teaching, and living in a crisis through research and art. Twelve months on, we figure (out) our own data and practice. We ask: What does CO-llaborative VI-rtual D-esign look like, how can it be established, and how can it be sustained?