Reviewerly/exHarmony huggingface dataset:
Results on the MFCC extraction for improving audio capabilities of TIAGo service robot

The purpose of this paper is to obtain through simulations high correct classification rates for isolated audio events detection. To obtain the audio signals, we have used a service robot named TIAGo that simulates scenarios from our everyday life. Mel Frequency Cepstral Coefficients features will be extracted for each audio signal. Then will be classified based on the k-Nearest Neighbors algorithm. To better analyze the performance, besides Mel Frequency Cepstral Coefficients coefficients, 6 more coefficients, non- Mel Frequency Cepstral Coefficients, will be extracted. The number of neighbors for the k-Nearest Neighbors algorithm will vary and also the percent value that represents the number of audio signals used for training or for testing. Simulations will be done also about the metrics and distance. For this, Euclidean and Manhattan metric-distance will be implemented. All these scenarios and combinations of them will be perform through this paper. The highest correct classification rate, 99.27%, is obtained for Mel Frequency Cepstral Coefficients using 70% of input data for training, 5 neighbors and the Euclidean metric.
Contextual Skill Proficiency via Multi-task Learning at LinkedIn

The ability to infer an individual's expertise for a given skill has proven to be crucial in creating economic opportunity for every talent of the global workforce. Applications ranging from recommending relevant job opportunities to talents to providing better candidate suggestions to recruiters, all benefit from deep understanding of the skill "proficiency" of the talent pool.
Automatic road traffic signs detection and recognition using ‘You Only Look Once’ version 4 (YOLOv4)

This paper presents an approach to detect traffic signs using You Only Look Once version 4 (YOLOv4) model. The traffic sign detection and recognition system (TSDR) play an essential role in the intelligent transportation system (ITS). TSDR can be utilized for driver assistance and, eventually, driverless cars to reduce accidents. When driving an automobile, the driver's attention is usually drawn to the road. On the other hand, most traffic signs are situated on the side of the road, which may have contributed to the collision. TSDR allows drivers to view traffic sign information without having to divert their attention. Due to the existence of a large background, clutter, fluctuating degrees of illumination, varying sizes of traffic signs, and changing weather conditions, TSDR is an important but difficult process in intelligent transport systems. Many efforts have been made to find answers to the major issues that they face. The objective of this study addresses road traffic sign detection and recognition using a technique that initially detects the bounding box of a traffic sign. Then the detected traffic sign will be recognized for usage in a speeded-up process. Since safe driving necessitates real-time traffic sign detection, the YOLOv4 network was employed in this research. YOLOv4 was evaluated on our dataset, which consisted of manual annotations to identify 43 distinctive traffic signs classes. It was able to achieve an average recognition accuracy of 84.7%. Overall, the work adds by presenting a basic yet effective model for real-time detection and recognition of traffic signs.
Hierarchical hashing-based multi-source image retrieval method for image denoising

Image denoising is a fundamental problem in image processing. One prominent image denoising approach is to exploit the non-local self-similarity (NSS) prior of natural images. How to find and utilize similar image patches is the key to the NSS-based method. However, most NSS-based methods only use noisy patches within the noisy image itself, whose number and quality are quite limited. To exploit the similar clean patches in external image datasets, in this paper, we propose a hierarchical hashing-based multi-source image retrieval method. First, based on deep hashing, a multi-source image-level retrieval method is proposed to search clean appearance-alike images from the external dataset. Second, on the basis of NSS prior and image coherence hypothesis, an improved continuous sensitive hashing method is designed from the image patch level to create a dense mapping between the external similar patches and the target patches. Finally, using the searched similar clean image patches, a simplified denoising method is developed based on the 3D transformation and thresholding technique. Experiments on two kinds of image datasets have demonstrated the effectiveness of the proposed method.
CareBlocks: A Blockchain-based Health Information Sharing Framework for Medical IoT

Blockchain is a continually growing immutable ledger used for bookkeeping all the data recorded over the network in chronological order. In the healthcare sector, the power of blockchain can be harnessed to eliminate the use of a centralized hospital database or cloud to store health data. Since data storage in the third-party systems leads to the loss of the user’s control over their health data and may lead to privacy leakage, we propose CareBlocks, a secure health information sharing framework for medical Internet of Things environments using blockchain. The framework constitutes five layers: data layer, network layer, incentive layer, consensus layer, and application layer, when ordered from the bottom to the top. In CareBlocks, the access to the users’ data is limited only to the user itself, and this is achieved by encrypting the health data, and the data is also protected with fine-grained access control. The users have the authority to decide which doctors can have access to their health-related data, and this preference can be modified anytime by the user. Since it is critical to ensure privacy and high availability to the patient’s records, the IPFS platform has been leveraged to store the patient data where these records are encrypted using secret-key cryptography. The proposed architecture is composed of four sub-blockchains that interact among themselves to provide the desired usability. PatientChain plays the role of storing all the medical records in one place, and DoctorChain similarly stores the diagnosis in an encrypted format. ChemistChain and InsuranceChain allow the patients to make the payment and claim of funds limited only to a few clicks and reduce paperwork. The use of blockchain to store health data also makes the system tamper-resistant, thereby avoiding medical disputes. With PlusCoin, a cryptocurrency unit, CareBlocks enables users to pay their fees to the doctor and the chemist. The proposed blockchain-based framework allows smooth interactions among healthcare entities without losing the integrity and privacy requirements.
Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images

Accurate water surface elevation estimation is essential for understanding nearshore processes, but it is still challenging due to limitations in measuring water level using in-situ acoustic sensors. This paper presents a vision-based water surface elevation estimation approach using multi-view datasets. Specifically, we propose a visual domain adaptation method to build a water level estimator in spite of a situation in which ocean wave height cannot be measured directly. We also implemented a semi-supervised approach to extract wave height information from long-term sequences of wave height observations with minimal supervision. We performed wave flume experiments in a hydraulic laboratory with two cameras with side and top viewpoints to validate the effectiveness of our approach. The performance of the proposed models were evaluated by comparing the estimated time series of water elevation with the ground-truth wave gauge data at three locations along the wave flume. The estimated time series were in good agreement within the averaged correlation coefficient of 0.98 and 0.90 on the measurement and 0.95 and 0.85 on the estimation for regular and irregular waves, respectively.
Good Things Come to Those Who Wait

Cloud-enabled schedulers execute jobs on either fixed resources or those acquired on demand from cloud platforms. Thus, these schedulers must define not only a scheduling policy, which selects which jobs run when fixed resources become available, but also a waiting policy, which selects which jobs wait for fixed resources when they are not available, rather than run on on-demand resources. As with scheduling policies, optimizing waiting policies requires a priori knowledge of job runtime. Unfortunately, prior work has shown that accurately predicting job runtime is challenging. In this paper, we show that optimizing job waiting in the cloud is possible without accurate job runtime predictions. To do so, we i) speculatively execute jobs on on-demand resources for a small time and cost to learn more about job runtime, and ii) develop a ML model to predict wait time from cluster state, which is more accurate and has less overhead than prior approaches that use job runtime predictions. We evaluate our approach on a year-long batch workload consisting of 14 million jobs, and show that it yields a cost and average wait time within 4% and 13%, respectively, of the optimal.
New Method of Automated Terminology Extraction: Case Study of Russian-Language Textbooks

In this article, we report the initial results of our research into the ways different terminological subsystems are organised and function in Russian secondary school textbooks. Traditionally, extraction of terms from specialised texts has been done manually, by reading the texts through and compiling lists of terms manually. While reliable and clear with regard to selection criteria, the procedure is ill-suited for large volumes of data and does not yield additional metadata, e.g. frequency of terms’ occurrence and co-occurrence, their syntagmatic connections and systematic relations. This project involves (1) creating a full-text corpus of Russian school textbooks for years of study from 5 to 11 that have been included in the Federal List of the Ministry of Education of the Russian Federation, (2) automatic term extraction and stratification using computational linguistics tools aimed to analyse distributional semantics, and (3) training a deep neural network capable of identifying the academic subject, year of study and teaching topic given a particular set of terms. Our research results may be of significant interest for terminology studies and can also be used as an assistance in creating various types of teaching literature for schools. The reported study was funded by RFBR, project number 19-29-14032 mk.
Innovative Research on Legal Talents Training Model in the Era of Artificial Intelligence

With the advent of the Internet Plus Era, the continuous development of modern technologies such as cloud computing, big data, blockchain and artificial intelligence have triggered all-round changes in all areas of society. In the era of artificial intelligence, legal education should consider taking the road of intelligence, in order to respond to the new requirements of the era of artificial intelligence for the cultivation of legal talents. In addition, it is necessary for us to face the difficult problem of “artificial intelligence plus law” interdisciplinary talent cultivation directly. Transforming the idea of cultivation and innovate the teaching means and methods through the innovation mode of “three in one”, which is beneficial to realize the combination of theory and practice. To summary, promoting the intellectualization of legal education is helpful to cultivate compound legal talents and explore the way forward of China’s intelligent legal education.
Research on The secure Transmission Method of Cloud Computing Data

The security of information transmission in cloud computing network is an important part of cloud system security, and it is also the most vulnerable part to malicious attacks. It is very necessary to protect the data in network transmission, and one of the most effective methods is to data encryption. This paper divides the data transmission in the cloud system into two parts to study, one part is the data communication inside the cloud system, and the other part is the interaction between the cloud system and the user, that is, the data communication between the cloud system and the outside. This paper proposes a Simple Secure Communication Model (SSCM) that is suitable for internal cloud communication. For data transmission outside the cloud system, a method of combining software and hardware with USB Key is proposed.
Forming the Basics of Foreign Language Teachers’ Methodological Creativity During Linguistic Training at a University

This article considers the issue of indirect methodological training of a foreign language teacher and the formation of the methodological creativity basis in foreign language classes at the university. The authors analyzed the existing research within the framework of the stated problem and found that the process of integrated students’ linguistic and methodological training at the university has psychological and pedagogical, socio-psychological, psycholinguistic and creative grounds. The experience of practical work allowed authors to identify some groups of difficulties that students have during their pedagogical practice. They include: difficulties of goal-setting, difficulties in achieving the goals of the lesson, difficulties in reflecting on their own activities. It is argued that their consideration and overcoming are possible due to the reliance on the didactic potential of foreign language classes. From this point of view, the formation of the students’ methodological competence takes place, on the one hand, and, the mastery of a foreign language as a means of communication, a means of teaching and a means of organizing pedagogical communication is realized on the other hand. Integrated linguistic and methodological training of future foreign language teachers is organized in two stages: reproductive and reproductive-creative. On the basis of the presented materials, the authors developed a four-staged technology for the formation of students’ methodological skills in foreign language classes.
A Lexical Semantic Network Approach to Facilitate Chinese History Vocabulary Learning

Both Chinese vocabulary and Chinese historical knowledge play an important role in the acquisition of Chinese as a second language. This paper combines the two by collecting historical stories of the Han Dynasty for foreign Chinese learners as a corpus, and then, based on Modern Chinese Dictionary, we calculate the similarity of words in the corpus, in order to generate a lexical semantic network. Based on the generated semantic network, we push content for Chinese learners that is similar to the words and articles they already know.
Revisiting Pierre Gy's formula (TOS) – A return to size-density classes for applications to contaminated soils, coated particular aggregates and mixed material systems

For some real-world material systems, estimations of the incompressible sampling variance based on Gy's classical s2(FSE) formula from the Theory of Sampling (TOS) show a significant discrepancy with empirical estimates of sampling variance. In instances concerning contaminated soils, coated particular aggregates and mixed material systems, theoretical estimates of sampling variance are larger than empirical estimates, a situation which does not have physical meaning in TOS. This has led us to revisit the development of estimates of s2(FSE) from this famous constitutional heterogeneity equation and explore the use of size-density classes for mixed material systems (mixtures of both analyte-enriched and coated particles), an approach which has been mostly unused since Gy's original derivation. This approach makes it possible to avoid taking into account the granulometric and liberation factors from Gy's classical treatment, and present grounds for criticising the use of 'standard' input values of critical parameters such as f: = 0.5, and g: = 0.25. But, as always, the "liberation factor" (l) issue still plays an important role, which is paid due attention. The constitutional heterogeneity formula based on size-density classes is presented in a form that allows for easy implementation in practice, within specified limitations. We present extensive experimental results from real-world systems. Using the "SDCD model" with published data reproduced the relative sampling variances calculated for the standard "mineral-like matrices", but more importantly corrected the relative sampling variance calculated for real contaminants by several orders of magnitudes. In all cases, the recalculated relative sampling variances were decreased to below their corresponding experimental measurements, now fully as expected from TOS, substantiating our development.
Research on personalized recommendation algorithm based on fusion product review evaluation index

The advent of the Internet era has brought about the rapid development of e-commerce. With the increasing number of online shopping users and online shopping products (services), the problem of information overload has also received more and more attention. Therefore, important tools such as recommendation systems and search engines have emerged, which can better help consumers make decisions among many products and improve purchase efficiency. This article proposes a targeted personalized recommendation algorithm for users who have a clear purchase goal. Combining search engines and keywords entered by users, the product candidate set is filtered out, and then sorted according to the comprehensive score of the product, and recommended to the user to rank top N products, increase the transaction rate.
Performance Prediction and Fine-Grained Resource Provision of Virtual Machines via LightGBM

It is significant to accurately predict the performance of virtual machines (VMs), and then provide the corresponding fine-grained resources according to users’ requirements for both users and cloud resource providers in IaaS cloud computing. In this paper, based on the idea of LightGBM, we first analyze the hardware/software, configuration and then runtime environmental features that may have impacts on the VM performance, and then propose a VM performance prediction model with Gradient-based One-side Sampling (GOSS) method, called VPGB. VPGB pays more attentions on the data instances that with the larger gradients so as to speed up the model training process and then predicts the VM performance accurately. In addition, based on the prediction results, we apply the genetic algorithm to find the optimal fine-grained resources configuration and then provide for users. Experimental results show that VPGB-based method can predict the VM performance accurately and provide the fine-grained VM resources for users effectively.
Customer’s awareness, trust, discomfort and acceptance of anti-money laundering practices in Malaysian Banks

Purpose This study aims to examine the associations between customers’ awareness of money laundering and terrorism financing, trust in banking secrecy measures and discomforts in fulfilling the bank’s anti-money laundering (AML) procedure and their acceptance of existing practices of banks regarding AML and counter-terrorism financing. Design/methodology/approach This study adapts a set of survey instruments developed and validated by prior studies to collect the required data. A convenient sample of 160 Malaysian bank customers aged 18 and above were surveyed to collect the data. Findings This study finds a significant relationship between the respondents’ awareness of money laundering and terrorism financing, trust in banking secrecy measures and their acceptance of the bank’s AML and counter-terrorism financing practices. However, no significant relationship is documented between the level of discomforts experienced by customers in satisfying the banks’ AML requirements and their acceptance of the banks’ AML practices. These results hold even after controlling for alternative explanations of the customers’ acceptance of banking practices examined in the extant literature: age, gender, location, literacy level and occupation. Research limitations/implications This study extends the literature on customers’ acceptance of banking practices more broadly by providing empirical evidence on the role of customers’ awareness on issues underlying the banking practices and their trust in the bank’s secrecy measures. Practical implications This study also provides some practical contributions by shedding some light on the factors that could help banks increase the acceptance of AML practices among their customers. Thus, the findings of this paper help banks focus their effort on these factors and hence increase acceptance rate more effectively. Originality/value Drawing on the elements of the theory of reasoned actions and technology acceptance model and the extant research on trust-privacy and comfortability in a banking setting, this study proposes an integrated approach that is theoretically and empirically grounded.
White-Box ECDSA: Challenges and Existing Solutions

White-box cryptography aims to protect secret keys when an algorithm is to be executed in an exposed environment, possibly fully controlled by an attacker. While this field enjoys a significant interest from researchers, a large majority of works focus on block ciphers, and asymmetric cryptography has been very little studied to date. This is in contrast with actual needs and usages by the industry. Indeed, most commercial white-box solutions offer asymmetric cryptography, and most notably the ECDSA signature. This paper provides a deeper comprehension on the challenges of such white-box ECDSA implementations. In particular, we highlight the existence of particularly devastating attacks, induced for instance by the lack of a reliable source of randomness in the white-box context. We also give an insight into the actual strategies for securing products in the field. To this end, we analyse the sole source of information – which is the patents filled by companies – and discuss how they respond to existing threats.
“Without Electricity/Gravity…” Generating Ideas About the Fate of Civilization

In the modern technological world, the concept of creativity is associated with a timeline and a focus on the future. The accelerating change of life under the influence of high-tech changes requires new approaches to the discourse about the future. Creativity as the most “lively” and unpredictable component of technological development is difficult to define, measure, and evaluate. The rapid development of technologies has brought the creation of ideas and their implementation much closer. The most popular issue is becoming the discourse about the future in the format of foresights, which represent the programming of the future. In this study, the authors propose a direction for generating ideas other than tasks for evaluating individual divergent thinking and the foresight format. The assumption of the absence of one of the basic foundations of the existence of modern humanity was used as an incentive to generate ideas about the fate of our civilization, its problems, and opportunities. Understanding the impact of technology development on society, the ability to assess the consequences of technological decisions is an important competence of a modern engineer. The article presents a general picture of the student's answers to the question about the consequences of the absence of gravity (N = 100) and electricity (N = 150). Along with disaster scenarios, students present the solutions for adaptation, positive consequences, and options for using new opportunities. The answers trace the influence of mass culture, popular media discourse, scientific facts related to the topic, and everyday experience, which in some cases are evaluated and revised in a new context.