Reviewerly/exHarmony huggingface dataset:
Investigating global language networks using Google search queries

Over the centuries, languages have evolved and influenced each other. These changes might have been caused by long wars, economic superiority, religious imposition or scientific domination. These macro-level forces at micro-level deeply involve individuals who want to translate their messages into another language. Therefore, the frequency of translation requests from a language to another might be an acceptable measure to show the necessity of those languages to each other given speakers of those languages are unable to communicate due to linguistic barriers. In this paper, we first collect the average number of translation requests per month from the Google search engine for one hundred languages and then visualize them by using customized presentation software to identify features and patterns. Based on the extracted information, the rank and the position of each language in the global language network have been investigated with respect to four criteria: translation balance, centrality, the number of connections and native speaker population, and interconnections between language families. Our study sheds light on the individual’s consumption behavior of machine translation in different cultures. Our findings might be interesting especially for policymakers who design school curricula.
Extracting Customer Reviews from Online Shopping and Its Perspective on Product Design

This paper presents a study on how we can extract helpful review from customers and its effect on the early phase when designing a product. We present a framework for analyzing the reviews from online shopping sites by detecting helpful reviews, aspects, and top reviews. We conduct a through analysis on review comments of the Amazon site and form a novel framework which can automatically extract useful information from review documents and it can also collaboratively work between designers and opinion customers. Experimental results on helpful review identification and sentiment classification showed that the proposed model achieved promising results. We also conduct an interview with designers to assert whether or not the proposed framework is effective. The results showed that the proposed framework is helpful for designers.
Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation

In recent years, semantic segmentation methods based on fully convolutional networks (FCNs) have achieved great success.However, these methods tend to produce inconsistency and isolated class labels, mainly because the end-to-end mapping of FCN essentially treats each pixel independently.As a postprocessing approach, the conditional random field (CRF) has been widely used to alleviate this problem.However, the inference of CRF is usually very time-consuming in computation.To solve this problem, we present a new method, called locally shared features (LSFs), to model local dependence between pixels.The LSF encourages adjacent pixels to have similar features by making them share certain properties with each other.This is achieved by concatenating features around a pixel, including the pixel itself.Our experimental results indicate-the LSF approach delivers comparable or better performance than the CRF method with respect to the accuracy and local smoothness in segmentation output, while obtaining a significant gain in computational efficiency.
Evaluating the extension mechanisms of the knowledge discovery metamodel for aspect-oriented modernizations

Crosscutting concerns are an intrinsic problem of legacy systems, hindering their maintenance and evolution. A possible solution is to modernize these systems employing aspect-orientation, which provides suitable abstractions for modularizing these kind of concerns. Architecture-Driven Modernization is a more specific kind of software reengineering focused on employing standard metamodels along the whole process, promoting interoperability and reusability across different tools/vendors. Its main metamodel is the Knowledge Discovery Metamodel (KDM), which is able to represent a significant amount of system details. However, up to this moment, there is no extension of this metamodel for aspect-orientation, preventing software engineers from conducting Aspect-Oriented Modernizations. Therefore, in this paper we present our experience on creating a heavyweight and a lightweight extension of KDM for aspect-orientation. We conducted two evaluations. The first one showed all aspect-oriented concepts were represented in both extensions. The second one was a experiment, in which we have analyzed the productivity of software engineers using both extensions. The results showed that the heavyweight extension propitiate a more productive environment in terms of time and number of errors when compared to the lightweight one.
Modified tropical algebra based median filter for removing salt and pepper noise in digital image

Noise is the information damage that may occur in the image due to the changes in information during the transmission process. In order to overcome these problems, it is necessary to do filtering process on the image. Until now many filtering algorithms have been proposed to remove noise. Most existing methods only work for low level noise. In this study, the authors proposed an efficient and easy-to-understand filtering algorithm using the concept of tropical algebra and singular value decomposition (SVD). The SVD will be used to detect noise in 3 × 3 templates. Furthermore, if noise is detected then new pixels will be obtained by using the concept of tropical operations. The results of this study show that the proposed method provides better results from the existing methods in terms of quantitative and visual.
Measuring distances in Hilbert space by many-particle interference

The measures of distances between points in a Hilbert space are one of the basic theoretical concepts used to characterize properties of a quantum system with respect to some etalon state. These are not only used in studying fidelity of signal transmission and basic quantum phenomena but also applied in measuring quantum correlations, and also in quantum machine learning. The values of quantum distance measures are very difficult to determine without completely reconstructing the state. Here we demonstrate an interferometric approach to measuring distances between quantum states that in some cases can outperform quantum state tomography. We propose a direct experimental method to estimate such distance measures between two unknown two-qubit mixed states as Uhlmann-Jozsa fidelity (or the Bures distance), the Hilbert-Schmidt distance, and the trace distance. The fidelity is estimated via the measurement of the upper and lower bounds of the fidelity, which are referred to as the superfidelity and subfidelity, respectively. Our method is based on the multiparticle interactions (i.e., interference) between copies of the unknown pairs of qubits.
System-independent ASR error detection and classification using Recurrent Neural Network

This paper addresses errors in continuous Automatic Speech Recognition (ASR) in two stages: error detection and error type classification. Unlike the majority of research in this field, we propose to handle the recognition errors independently from the ASR decoder. We first establish an effective set of generic features derived exclusively from the recognizer output to compensate for the absence of ASR decoder information. Then, we apply a variant Recurrent Neural Network (V-RNN) based models for error detection and error type classification. Such model learn additional information to the recognized word classification using label dependency. As a result, experiments on Multi-Genre Broadcast Media corpus have shown that the proposed generic features setup leads to achieve competitive performances, compared to state of the art systems in both tasks. Furthermore, we have shown that V-RNN trained on the proposed feature set appear to be an effective classifier for the ASR error detection with an Accuracy of 85.43%.
A three-phase approach to differentially private crucial patterns mining over data streams

Frequent patterns mining over transactional data streams is an important task for a wide range of online data mining applications. Nevertheless, mining crucial patterns is even more appropriate than frequent patterns over transactional data streams, because crucial patterns are the subset of frequent patterns with the minimum storage cost and information lossless extraction. In this paper, we argue that the privacy of mining crucial patterns from data streams (i.e., aggregating information from individuals) is more likely to be leaked than static scenarios, due to successive releases. However, to the best of our knowledge, there is little work on differential privacy in continuously publishing crucial patterns from data streams. To this end, this paper proposes a real-time differentially private crucial pattern computation algorithm which designs a three-phase mechanism (i.e., the preprocessing phase, the deep-going calculation phase, and the noise-mining phase) at every timestamp. The algorithm is able to not only improve the utility of the crucial pattern statistics as much as possible which satisfy differential privacy, but also reduce the average mining time without incurring high maintenance cost according to the feature of crucial patterns. To reduce the number of calls to crucial pattern computation algorithm, we design two-dissimilarity formulas according to the relationship between frequent patterns and crucial patterns to decide to return either low noisy statistic or accurately approximated statistic in the first two phases. When the low noisy statistic needs to be turned, the algorithm goes into the noise-mining phase. To obtain private crucial patterns, we first filter crucial pattern candidate set by perturbing the scoring functions, and then add independent Laplace noise to their supports. Finally, we conduct extensive experiments on dense datasets and sparse datasets to show the effectiveness and efficiency of our algorithm.
Towards predictable performance via two-layer bandwidth allocation in cloud datacenter

In today’s production-grade cloud datacenters, cloud service providers do not offer any bandwidth guarantee between VMs, which results in unpredictable performance of tenants’ applications. The research community has recognized this problem; however, existing solutions to bandwidth allocation fail to take into consideration tenants’ request for bandwidth and the actual bandwidth usage of applications simultaneously, which leads to a waste of bandwidth resources or unpredictable performance. To address these issues, we present SpongeNet, a bandwidth allocation solution that consists of three components through two layers—static bandwidth guarantees at the tenant layer and a dynamic rate allocation at the application layer to realize predictable performance. The first component, named FGVC model, is a network abstraction model that provides a simple, accurate and flexible way for tenants to specify network requirements and achieve high utilization through bandwidth saving. The second component is a two-phase VM placement algorithm that provides optimal combinations of ordering policies and dispatching policies to meet multiple goals. The third component, named E–F runtime mechanism, can achieve the fairness between guaranteed and unguaranteed tenants in utilizing the unused bandwidth resources. Extensive simulations based on real application traces and 3-level tree topology show that SpongeNet enhances bandwidth saving when compared to the state-of-the-art solutions (e.g., the Oktopus system), and significantly improves the throughput rate by 18% and response time by 92%. With a small prototype implementation on a 7-server testbed, we demonstrate that SpongeNet provides fair work-conserving bandwidth guarantee among all tenants, even in extreme cases.
Deep Learning-Based Sustainable Data Center Energy Cost Minimization With Temporal MACRO/MICRO Scale Management

Recently, distributed sustainable data centers based on renewable power generators have been deployed in order to efficiently reduce both the energy cost and carbon emission. Dynamic right-sizing (DRS) and frequency scaling (FS) have been considered as promising solutions to tune the computing capacity corresponding to the dynamic renewable power capacity. However, in existing works, the inaccurate power prediction and the uncoordinated decision making of DRS/FS still lead to low service quality and high energy cost. In this paper, we propose a novel joint optimization method for energy efficient distributed sustainable data centers. The proposed method adopts long short-term memory approach to improve the prediction accuracy of renewable power capacity for a long period, and unsupervised deep learning (DL) solver to resolve the coordinated DRS/FS optimization. Furthermore, we present the MACRO/MICRO (MAMI) time scale-based data center management technique to achieve both high energy efficiency and low wake-up transition overhead of DRS. To evaluate the proposed DL-based MAMI optimizer, we use the real trace data of renewable power capacity from the U.S. Measurement and Instrumentation Data Center. The experimental results demonstrate that our method reduces the energy cost by 25% compared with conventional metaheuristics while guaranteeing the service response delay requirement.
Multiple Kernel Fuzzy Clustering With Unsupervised Random Forests Kernel and Matrix-Induced Regularization

Although kernel fuzzy clustering can handle non-spherical clusters by mapping data to a more separable feature space, its performance is highly determined by the setting of kernels. So, the multiple kernel fuzzy clustering (MKFC) is proposed to obtain the flexibility in designing an optimal kernel from a large set of candidates. In MKFC, many predefined general kernels like Gaussian and polynomial ones are linearly aggregated and the weights of kernels are adjusted automatically. However, the performance of MKFC is greatly hampered by two noticeable problems. First, MKFC only uses predefined general kernels and pays less attention to the inherent structure of specific data. This leads to the trouble of selecting proper base kernels for different data. The second problem is the ignorance of correlations between kernels in MKFC. It results in redundant kernels being used to define the feature space. This paper solves the two problems simultaneously by introducing a new MKFC model. Based on unsupervised random forests (RFs), some data-dependent kernels are generated and combined with others to build a more representative feature space. The correlations between kernels are also calculated and inserted into the objective function of fuzzy clustering as a matrix-induced regularization to encourage the diversity in kernels. We name the new model as MKFC with unsupervised RFs kernel and matrix-induced regularization. The optimization algorithm for the new model is derived, and the experiments on benchmark datasets demonstrate its superiority over other MKFC approaches.
Quizbot: Exploring Formative Feedback with Conversational Interfaces

Conversational interfaces (also called chatbots) have recently disrupted the Internet and opened up endless opportunities for assessment and learning. Formative feedback that provides learners with practical instructions for improvement is one of the challenging tasks in self-assessment settings and self-directed learning. This becomes even more challenging if a user’s personal information such as learning history and previous achievements cannot be exploited for data protection reasons or are simply not available. This study seeks to explore the opportunities of providing formative feedback in chatbot-based self-assessment. Two main challenges were faced: the limitations of the messenger as an interface that restricts visual representation of the quiz questions, and zero information about the user to generate adaptive feedback. Two types of feedback were investigated regarding their formative effect: immediate feedback, which was given after answering a question, and cumulative feedback detailing strengths and weaknesses of the user in each of the topics covered along with the directives for improvement. A chatbot called SQL Quizbot was deployed on Facebook Messenger for the purposes of this study (Try out the prototype at https://www.messenger.com/t/2076690849324267 ). A survey conducted to disclose users’ perception of the feedback reveals that more than 80% of the users find immediate feedback helpful. Overall this study shows that chatbots have a great potential as an aiding tool for e-learning systems to include an interactive component into feedback in order to increase user motivation and retention.
Efficiencies of logical Bell measurements on Calderbank-Shor-Steane codes with static linear optics

We show how the efficiency of a logical Bell measurement (BM) can be calculated for arbitrary CSS codes with the experimentally important constraint of using only transversal static linear-optical BMs on the physical single-photon qubit level. For this purpose, we utilize the codes' description in terms of stabilizers in order to calculate general efficiencies for the loss-free case, but also for specific cases including photon loss. These efficiencies can be, for instance, used for obtaining transmission rates of all-optical quantum repeaters. In the loss-free case, we demonstrate that the important class of CSS codes with identical physical-qubit support for the two logical Pauli ($Z$ and $X$) operators can only achieve a logical BM efficiency of $\frac{1}{2}$ if one always employs the same ancilla-free static linear optical BMs on the physical level. We apply our methods to various CSS codes including two-dimensional planar color and planar surface codes. We then find that in many cases, the fixed use of the standard linear optical BM for all physical BMs is suboptimal and performing linear optical transformations before doing the standard linear optical BM (still without any ancillary photons and without any feedforward) can increase the efficiency enormously. In fact, using this generalization in the no-loss (or sometimes also in the low-loss) case allows us, on the one hand, to improve the logical BM efficiency of quantum parity codes compared to previously known results and, on the other hand, it also enables us to enhance the efficiency of two-dimensional planar color codes, whose efficiency is otherwise subject to the above $\frac{1}{2}$ bound, to arbitrarily close to unity.
Image Denoising Based on HOSVD With Iterative-Based Adaptive Hard Threshold Coefficient Shrinkage

Natural images often have self-similarity, which can be used to remove noise. Therefore, many current denoising methods denoise by processing similar image block matrix. Aiming at the problem that these methods will destroy the two-dimensional structure of image blocks when they are expanded into one-dimensional column vectors, a new image denoising method based on high-order singular value decomposition is proposed. Several similar image blocks are stacked into three-dimensional arrays and treated as a third-order tensor; then, higher-order singular value decomposition can be performed. For the core tensor obtained by decomposition, an iterative algorithm with adaptive hard threshold coefficient shrinkage is proposed. The experimental results show that the proposed method outperforms the state-of-the-art methods in peak-signal-to-noise ratio, structural similarity, and visual effects.
Frequency Hopping and Parallel Driving With Random Delay Especially Suitable for the Charger Noise Problem in Mutual-Capacitive Touch Applications

Charger noise can cause inaccurate touch points and fake touch points to appear; this can cause a device to behave incorrectly. The intensity of charger noise could be much larger than the intensities of the original touch signals. Furthermore, the frequency of charger noise varies for each different charger. Therefore, industry experts have identified charger noise as the most difficult problem in capacitive touch applications. The demand for a solution to this problem has become crucial for the mobile market. In this paper, we prove that a particular combination of frequency hopping and repeated integration is an effective method to handle this problem. In addition, we propose an efficient discrete Fourier transform-based algorithm to select an effective sensing frequency. We propose parallel driving with random delay to enhance the signal-to-noise ratio (SNR). We show an efficient hardware–software co-design that facilitates the application of our methods in touch ICs. The experimental results show that our methods can increase SNR by over 45 dB and can find an effective sensing frequency fast and dynamically.
End-to-End Temporal Action Detection Using Bag of Discriminant Snippets

Detecting human actions in long untrimmed videos is a challenging problem. Existing temporal-action detection methods have difficulties in finding the precise starting and ending times of the actions in untrimmed videos. In this letter, we propose a temporal-action detection framework that can detect multiple actions in an end-to-end manner, based on a Bag of Discriminant Snippets (BoDS). BoDS is based on the observation that multiple actions and the background classes have similar snippets, which cause incorrect classification of action regions and imprecise boundaries. We solve this issue by finding the key-snippets from the training data of each class and compute their discriminative power, which is used in BoDS encoding. During testing of an untrimmed video, we find the BoDS representation for multiple candidate proposals and find their class label based on a majority voting scheme. We test BoDS on the Thumos14 and ActivityNet datasets and obtain state-of-the-art results. For the sports subset of ActivityNet dataset, we obtain a mean Average Precision (mAP) value of 29% at 0.7 temporal Intersection over Union (tIoU) threshold. For the Thumos14 dataset, we obtain a significant gain in terms of mAP, i.e., improving from 20.8% to 31.6% at tIoU = 0.7.
Learning distance to subspace for the nearest subspace methods in high-dimensional data classification

The nearest subspace methods (NSM) are a category of classification methods widely applied to classify high-dimensional data. In this paper, we propose to improve the classification performance of NSM through learning tailored distance metrics from samples to class subspaces. The learned distance metric is termed as ‘learned distance to subspace’ (LD2S). Using LD2S in the classification rule of NSM can make the samples closer to their correct class subspaces while farther away from their wrong class subspaces. In this way, the classification task becomes easier and the classification performance of NSM can be improved. The superior classification performance of using LD2S for NSM is demonstrated on three real-world high-dimensional spectral datasets.
About Digital Avatars for Control Systems Using Big Data and Knowledge Sharing in Virtual Industries

The authors in this chapter show the essence, dignity, current state, and development prospects of avatar-based management using blockchain technology for improving implementation of economic solutions in the digital economy of Russia. The purpose of this chapter is not to review the existing published work on avatar-based models for policy advice but to try an assessment of the merits and problems of avatar-based models as a solid basis for economic policy advice that is mainly based on the work and experience within the recently finished projects Triple H Avatar, an avatar-based software platform for HHH University, Sydney, Australia. The agenda of this project was to develop an avatar-based closed model with strong empirical grounding and micro-foundations that provides a uniform platform to address issues in different areas of digital economic and creating new tools to improve blockchain technology using the intelligent visualization techniques for big data analytics.