Reviewerly/exHarmony huggingface dataset:
A Robust Speaker Clustering Method Based on Discrete Tied Variational Autoencoder

Recently, the speaker clustering model based on aggregation hierarchy cluster (AHC) is a common method to solve two main problems: no preset category number clustering and fix category number clustering. In general, model takes features like i-vectors as input of probability and linear discriminant analysis model (PLDA) aims to form the distance matric in long voice application scenario, and then clustering results are obtained through the clustering model. However, traditional speaker clustering method based on AHC has the shortcomings of long-time running and remains sensitive to environment noise. In this paper, we propose a novel speaker clustering method based on Mutual Information (MI) and a non-linear model with discrete variable, which under the enlightenment of Tied Variational Autoencoder (TVAE), to enhance the robustness against noise. The proposed method named Discrete Tied Variational Autoencoder (DTVAE) which shortens the elapsed time substantially. With experience results, it outperforms the general model and yields a relative Accuracy (ACC) improvement and significant time reduction.
Design Patterns for Compensating Controls for Securing Financial Sessions

We formally extend the processes of design patterns from software engineering into the area of session management and transactional risk management, in an effort to improve the reliability, predictability, and security in identity. This work addresses identity management systems that sustain sessions across extended supply chains of multiple technologies and policies. The design patterns will help promote better practices in the design and development of these session-management and transaction-management systems, and the overview of compensating controls will record common practices in addressing the inconsistencies faced by real-world systems in working across technical and policy domains. An initial design pattern for compensating controls was developed. It addresses purchasing products or services from a web merchant with PCI DSS serving as the backdrop for existing controls and for which compensating controls might be needed. The approach used for this initial design pattern for compensating controls may be used in other areas so that a repository of these documents may be available to various stakeholders to provide more secure and robust systems. Likewise, the holistic approach employed here by incorporating multiple perspectives-business, legal, and technical-provides a useful framework for addressing the organizational realities of deploying and protecting information systems from various threats.
In Situ Techniques for Characterization of Strain Localizations and Time Sequence of Deformation Processes

This chapter gives a comprehensive overview on in situ characterization techniques, which are useful for the characterization of strain localizations and the temporal evolution of deformation processes. The chapter is divided into four parts regarding (i) general remarks, (ii) in situ imaging techniques, (iii) in situ acoustic emission measurements and (iv) in situ full-field measurement techniques. The part of in situ imaging techniques is divided into (i) optical microscopy and scanning electron microscopy, whereas the part of full-field measurement techniques is separated into digital image correlation (measurement of displacement and/or strain fields) and infrared thermography (measurement of thermal fields). For all in situ techniques, a short historical overview is given together with a summary of the basic knowledge and a review of state-of-the-art research (starting from the year 2000 provided in the appendix).
An Improved Deep Neural Network Model Based on Searching Space Limitation for Object Localization

Abstract Image object localization is the primary research direction in computer vision. A lots of algorithms had been proposed based on the ResNet structure and good results have been obtained on mainstream datasets. However, these methods still have the problem of too large ROI search space on weakly supervised data. This paper proposes a method that further limits the search space by directly extracting the high-level parameters of ResNet. The better search results and search efficiency are achieved and the candidate ROI search space is better reduced. The space information of the original images can be retained and training/testing time can be saved. The experimental results on the benchmark datasets show that this network structure has good object localization accuracy under weakly supervised data and the search efficiency is improved compared to other methods.
A Hybrid Collaborative Filtering Recommendation Algorithm Based on User Attributes and Matrix Completion

Abstract Collaborative filtering is a popular strategy in recommendation system. Traditional collaborative filtering relies on the user-item rating matrix that encodes the individual ratings of users for items to make recommendations. However, in the real-world, the rating matrix is highly sparse, and many new users do not have rating records, thus traditional collaborative filtering could not provide satisfactory recommendations. To alleviate this issue, we propose a hybrid algorithm that utilizes LMaFit to complete rating matrix, reducing the degree of sparsity, and provides a hybrid user-similarity to supply a good support for recommending to new users in the condition of cold start. Extensive experiment results on real-world datasets show the proposed algorithm has a better performance than other methods.
On EPP effects and the properties of Core Functional Categories

In this paper I argue that EPP effects are not special to T but reflect more general formal properties of the Core Functional Categories (CFCs) C, T, and v. I argue that CFCs require a specifier that matches their formal features and provide cross-linguistic evidence that this requirement can be satisfied in similar ways in all three CFCs. I also suggest that this requirement is motivated by the need for Agree operations to apply in a uniform way, with an unvalued feature always functioning as a probe and a feature that enters the derivation already valued always serving as a goal. Finally, I explore the consequences of this approach for our understanding of EPP effects in null subject languages.
an adventure in ambiguity with<i>one fish two fish</i>

Demonstrates how an 8-word title consisting of 4 modifier noun pairs, each containing the same noun, can be multiply ambiguous. One ambiguity concerns the possibility of interpreting the noun as either singular or plural; a second, whether the first modifier-noun pair is interpreted as distinct from the second pair; and a third, whether the third and fourth pairs refer to the first and second pairs or not. Investigates the connection between syntax and punctuation to determine to what extent these possible interpretations can be disambiguated with sentence internal punctuation.
Spatial prediction of landslide susceptibility based on the neighborhood rough set

Abstract This paper discusses the feasibility of reducing the landslide inducing factors by the neighborhood rough set algorithm in data processing section, which could improve the accuracy and timeliness of landslides susceptibility prediction models effectively. 15 predisposing factors for a continuous value that has not been graded were reduced by nighborhood rough set, a granularity calculation method, based on the importance degree of each factor. Then the combination of factors before and after optimization was put into random forest (RF) and support vector machine (SVM) for modelling. ROC curve and statistical indicators show that: the average performance of the reduced factors combination is superior to that before optimization. In addition, we used the RF which has a better performs in evaluation to map the landslides susceptibility in Jiuzhaigou area, discuss the timeliness of the assessment of landslides prediction and the weight of the predisposing factors.
Face Feature Recovery via Temporal Fusion for Person Search

Searching actors from videos by a single portrait image is a challenging task, due to large variations of video scenes and intra-person appearance. To tackle this problem, most recent works apply deep neural networks for detecting and extracting robust facial features for matching. However, when the face of an actor is not detected due to occlusion, such image-matching based strategies would not be applicable. To address the issue, we propose a unique framework of "Face Feature Recovery via Temporal Fusion" to synthesize virtual facial features by observing both temporal and contextual information. Once such face features are extracted, a simple extension to the k-nearest neighbors for re-ranking, "Iterative k-nearest Multi-fusion", is presented to utilize both face and body features for improved person search. We conduct extensive experiments to evaluate the performance of our framework on the challenging extended version of the Cast Search in Movies (ECSM) dataset [1]. Without utilizing tracklet information during training, the proposed approach still performs favorably against recent works in searching actors of interest from movie videos. Besides, we also show the proposed approach can be fused with them to further improve the performance.
Fast Single-View 3D Object Reconstruction with Fine Details Through Dilated Downsample and Multi-Path Upsample Deep Neural Network

Three-dimensional (3D) object reconstruction is among the most important research areas in the field of computer vision. Its purpose is to reconstruct the overall shape of an object from its twodimensional (2D) image. With the development of deep learning, many methods based on convolutional neural networks (CNNs) have been applied in related research.To achieve 3D shape reconstruction with low computation time, we focus on the commonly used method: single-image reconstruction. The main issue of using a single image as an input is that the reconstruction shape often lacks structural detail. To address this issue, we proposed two methods: the dilated downsample block and the multi-path upsample block. The dilated downsample block extracts more features and the multi-path upsample block uses the features in our architecture. Thereafter, we concatenate the encoder and decoder with corresponding layers to keep the image features in reconstruction process.Finally, we perform experiments on the dataset provided by Choy et al. Results show that our method achieves 67.7% intersection over-union (IoU) accuracy, 3.6% higher than state-of-the-art method, VTN. Compared to the PSVH method, our result achieves 71.4%, an increase of 3.4%. Our average reconstruction time is 13 ms, approximately 25 times faster than PSVH.
Management Process of Students’ Professional Patriotism Formation in Multicultural Educational Environment

The chapter is devoted to the study of management features of secondary vocational education system in conditions of innovative changes. The prospects for development of a new form of educational institutions management are considered. This form results in development of “professional patriotism” in more than 70% of students and contributes to reduction of the state’s need in regular labour force. Former students seldom try to find a job in the chosen profession after receiving a diploma of professional education, because the modern education is aimed at development of professional skills without any sense of importance and love for the chosen profession.
Detecting Mismatch Between Text Script and Voice-Over Using Utterance Verification Based on Phoneme Recognition Ranking

The purpose of this study is to detect the mismatch between text script and voice-over. For this, we present a novel utterance verification (UV) method, which calculates the degree of correspondence between a voice-over and the phoneme sequence of a script. We found that the phoneme recognition probabilities of exaggerated voice-overs decrease compared to ordinary utterances, but their rankings do not demonstrate any significant change. The proposed method, therefore, uses the recognition ranking of each phoneme segment corresponding to a phoneme sequence for measuring the confidence of a voice-over utterance for its corresponding script. The experimental results show that the proposed UV method outperforms a state-of-the-art approach using cross modal attention used for detecting mismatch between speech and transcription.
Position Constraint Loss For Fashion Landmark Estimation

Fashion landmark estimation aims at locating functional key points of clothes, which has wide potential applications in electronic commerce. However, due to the occlusion and weak outline information, landmark estimation occurs outliers and duplicate detection problems. To alleviate these issues, we propose Position Constraint Loss (PCLoss) to constrain error landmark locations by utilizing the position relationship of landmarks. Specifically, PCLoss adds a regularization term for each landmark to regularize their relative positions, and it can be easily applied to both regression and heatmap based methods without extra computation during inference. Unlike existing approaches that propagate landmark information between feature layers by specific network structures, PCLoss introduces position relations of landmarks in the label space without modifying the network structure. In addition, we leverage the skeleton-like relation of clothing to further strengthen position constraints between landmarks. Extensive experimental results on DeepFashion, FLD and FashionAI demonstrate that our methods can effectively increase the performance of mainstream frameworks by a large margin.
Multitask Learning for Darpa Lorelei’s Situation Frame Extraction Task

This paper describes a novel approach of multitask learning for an end-to-end optimization technique for document classification. The application motivation comes from the need to extract "Situation Frames (SF)" from a document within the context of DARPA’s LORELEI program targeting humanitarian assistance and disaster relief. We show the benefit of our approach for extracting SF: which includes extracting document types and then linking them to entities. We jointly train a hierarchical document classifier and an auto-encoder using a shared word-level bottleneck layers. Our architecture can exploit additional monolingual corpora in addition to labelled data for classification, thus helping it to generalize over a bigger vocabulary. We evaluate these approaches over standard datasets for this task. Our methods show improvements for both document type prediction and entity linking.
An Associative Optimizing Method on Reliability and Cost in Clouds

Cloud computing enables users to use shared system resources in an efficient pay-as-you-go manner. However, the reliability of cloud computing is a challenging proposition. Cloud users cannot improve the reliability of their cloud environment from the hardware and system level. On the other hand, due to the cost and complexity of the system, improving the reliability of specific users using practical methods is not straightforward for the operators of cloud systems. In this paper, we attempt to optimize the reliability of the cloud system by using the correlation between cost and reliability. We conduct a comprehensive and detailed analysis of the reliability of clouds, extract vital features that can be used to improve system reliability and consider cost constraints. We propose the reliability model and virtual machine (VM) provisioning/request model; based on this model, we propose an SLA-oriented reliability assurance scheme to continuously optimize the reliability of clouds. The experimental results not only indicate the validity of the model and schemes proposed in this paper but also provide a new perspective for the continuous optimization of cloud system reliability.
Design of Microwave Dual-band Monopole Antenna Based on Gaussian Process

Electromagnetic simulation has become an important tool in modern antenna structure design. However, accurate simulations of real antenna models are expensive, so designing automation by using EM solvers in optimized antennas may be prohibitive because of its high computational cost. In this article, a gaussian process model as the fitness function of the particle swarm algorithm instead of electromagnetic simulation software to optimize the microwave dual-band monopole antenna has been demonstrated.
Computation integrity assurance for emerging distributed computation outsourcing environments, the case of block withholding attack on Bitcoin pools

Recently emerged distributed computation outsourcing environments (e.g. volunteer computing, crowdsourcing, cloud computing, and pooled mining in cryptocurrencies) provide many new opportunities and challenges. One important new challenge in these environments is assuring the correctness and validity of the computation output when computation nodes have motivation for lazy, malicious, or hoarding behaviour. In this study, the authors provide a definition for this new challenge, which they refer to as ‘computation integrity assurance’, and classify different types of computations in which this new challenge is important. Then, they discuss the ‘Block Withholding’ attack in cryptocurrency mining pools as a case study. In this example, they demonstrate how the authors’ proposed classification can help in solving challenges of this context.
Performance Modeling and Verification of Load Balancing in Cloud Systems Using Formal Methods

Server virtualization technologies enable the cloud to perform load balancing by migrating virtual machines from heavily loaded physical machines to lightly loaded physical machines. A range of load balancing algorithms has been proposed to reach this goal. However, to evaluate the degree of reliability for such algorithms, quantitative guarantees are needed. In this paper, we build a virtual machine migration model based on a previous load balancing algorithm and construct an MDP-based performance model in PRISM, a probabilistic model checker. We then demonstrate that we can check a range of properties described in probabilistic temporal logic languages regarding the performance of the algorithm to answer questions that are otherwise hard for traditional methods to provide. Finally, we summarized the takeaways for cloud service providers/managers based on verification results.
Adaptive Resolution Change Using Uncoded Areas and Dictionary Learning-Based Super-Resolution in Versatile Video Coding

The concept of Adaptive Resolution Change (ARC) in video coding is already known from former international standards such as MPEG-4 [1]. However, in MPEG-4 linear filters are used for upsampling, which is crucial to coding video at varying resolution. With the rise of machine learning-based super-resolution methods in the last decade, powerful algorithms outperforming conventional upsampling methods were developed. This contribution introduces an ARC concept using un-coded areas within a frame of a video sequence and a Dictionary Learning (DL)-based Super-Resolution (SR) scheme. In this concept, a frame contains a picture at different resolution levels, which are spatially separated by the use of slices and tiles. Generally, a tile can be marked as coded or un-coded. Slices which do not hold any coded tiles are omitted from the bitstream. Thus, only one resolution level needs to be coded, while the other is generated at the decoder side. At the encoder a rate-distortion decision is made in order to decide, which resolution level should be coded. Simulation results show that gains with respect to the Versatile Video Coding (VVC) standard in development can be achieved at low bitrates.
A linguistically-informed way of introducing Japanese verbs to second language learners

Abstract This paper describes how the Japanese speakers’ knowledge is organized in regards to verbs, and proposes a linguistically-informed way of introducing it to second language learners. It is maintained by a number of researchers that each verb is stored with the information of its argument structure in the speaker’s mental lexicon. That is, a given verb is stored with the information of how many arguments it takes and what types of arguments they are. In this paper, capitalizing on this assumption, we will maintain that the knowledge of the native speakers of Japanese is organized in such a way that if a verb gives rise to n -number of different meanings, there are n -number of lexical entries, and each such entry is independently stored with the information concerning the meaning of the verb, the verb arguments and their accompanying particles. After the description of the organization of Japanese speakers’ knowledge in regards to verbs, as an effective way of introducing this to Japanese language learners, the paper proposes the format of an innovative approach to Japanese verbs reference book. This proposed format capitalizes on full sentence definitions in the sense of the Collins Cobuild Dictionary .
An Improved Frame-Unit-Selection Based Voice Conversion System Without Parallel Training Data

A frame-unit-selection based voice conversion system proposed earlier by us is revisited here to enhance its performance in both speech naturalness and speaker similarity. Speaker independent, bilingual (Mandarin Chinese and American English) deep neural net (DNN) acoustic model’s output, frame-level phone posterior probability (PPP), is used to represent the phonetic information. The corresponding frame-level F0 is used as the prosodic information. Kullback-Leibler divergence (KLD) between source and target PPPs (phonetic distortion) and the absolute difference between normalized source and target F0 (prosodic distortion) are used for selecting target frame candidates to construct a search lattice. The optimal target unit trajectory is obtained by Viterbi algorithm which tries to minimize the dynamic acoustic difference between the acoustic trajectory of the source speech and target candidates. The obtained spectral trajectory together with the enhanced pitch period and pitch correlation trajectory are sent to LPCNet vocoder to synthesize the converted waveforms. Compared with the top-rank system in Voice Conversion Challenge 2018, our new system can achieve on-par performance on studio to studio American English VC test, and better performance on non-studio to studio Mandarin Chinese VC test, in both speech naturalness MOS and speaker similarity DMOS.
A Design of Decision Engine for 5G Auto-driving Based on Ontology Slice

With the access to 5G network, auto-driving vehicles can upload their sensors' information to Mobile Edge Computing (MEC) and get driving decision results relaying on the powerful computing ability of MEC. This article presents an architecture of intelligent decision engine running on MEC for 5G auto-driving (IDEA) by adopting the concept of ontology slice. IDEA constructs an ontoligy slice for every vehicle including sensor information, vehicle status, rule bases, etc. and provides logical isolated resources to prevent interference between ontologies. This paper gives out the method of implementing and managing of the ontology slices and method of scheduling reasoner. Besides, to make better and safer decisions, this paper also proposes an algorithm, combining an analytic strategy algorithm (WADD) and a heuristic strategy algorithm (TTB), considering both vehicle's velocity and sensors' refresh rate, to deal with incomplete information before making decesions. With simulation, it is shown that IDEA can provide efficient and reliable auto-driving decision service with low processing time.