Reviewerly/exHarmony huggingface dataset:
DNN-Based Cross-Lingual Voice Conversion Using Bottleneck Features

Cross-lingual voice conversion (CLVC) is a quite challenging task since the source and target speakers speak different languages. This paper proposes a CLVC framework based on bottleneck features and deep neural network (DNN). In the proposed method, the bottleneck features extracted from a deep auto-encoder (DAE) are used to represent speaker-independent features of speech signals from different languages. A DNN model is trained to learn the mapping between bottleneck features and the corresponding spectral features of the target speaker. The proposed method can capture speaker-specific characteristics of a target speaker, and hence requires no speech data from source speaker during training. The performance of the proposed method is evaluated using data from three Indian languages: Telugu, Tamil and Malayalam. The experimental results show that the proposed method outperforms the baseline Gaussian mixture model (GMM)-based CLVC approach.
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian

Over the last decade, a single algorithm has changed many facets of our lives - Stochastic Gradient Descent (SGD). In the era of ever decreasing loss functions, SGD and its various offspring have become the go-to optimization tool in machine learning and are a key component of the success of deep neural networks (DNNs). While SGD is guaranteed to converge to a local optimum (under loose assumptions), in some cases it may matter which local optimum is found, and this is often context-dependent. Examples frequently arise in machine learning, from shape-versus-texture-features to ensemble methods and zero-shot coordination. In these settings, there are desired solutions which SGD on 'standard' loss functions will not find, since it instead converges to the 'easy' solutions. In this paper, we present a different approach. Rather than following the gradient, which corresponds to a locally greedy direction, we instead follow the eigenvectors of the Hessian, which we call ridges. By iteratively following and branching amongst the ridges, we effectively span the loss surface to find qualitatively different solutions. We show both theoretically and experimentally that our method, called Ridge Rider (RR), offers a promising direction for a variety of challenging problems.
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation

Score function-based natural language generation (NLG) approaches such as REINFORCE, in general, suffer from low sample efficiency and training instability problems. This is mainly due to the non-differentiable nature of the discrete space sampling and thus these methods have to treat the discriminator as a black box and ignore the gradient information. To improve the sample efficiency and reduce the variance of REINFORCE, we propose a novel approach, TaylorGAN, which augments the gradient estimation by off-policy update and the first-order Taylor expansion. This approach enables us to train NLG models from scratch with smaller batch size -- without maximum likelihood pre-training, and outperforms existing GAN-based methods on multiple metrics of quality and diversity. The source code and data are available at this https URL
Approximation properties of hybrid shearlet-wavelet frames for Sobolev spaces

In this paper, we study a newly developed shearlet system on bounded domains which yields frames for $H^s(\Omega)$ for some $s\in \mathbb{N}$, $\Omega \subset \mathbb{R}^2$. We will derive approximation rates with respect to $H^s(\Omega)$ norms for functions whose derivatives admit smooth jumps along curves and demonstrate superior rates to those provided by pure wavelet systems. These improved approximation rates demonstrate the potential of the novel shearlet system for the discretization of partial differential equations. Therefore, we implement an adaptive shearlet-based algorithm for the solution of an elliptic PDE and analyze its computational complexity and convergence properties.
Research on data load balancing technology of massive storage systems for wearable devices

Because of the limited memory of the increasing amount of information in current wearable devices, the processing capacity of the servers in the storage system can not keep up with the speed of information growth, resulting in low load balancing, long load balancing time and data processing delay. Therefore, a data load balancing technology is applied to the massive storage systems of wearable devices in this paper. We first analyze the object-oriented load balancing method, and formally describe the dynamic load balancing issues, taking the load balancing as a mapping problem. Then, the task of assigning each data node and the request of the corresponding data node’s actual processing capacity are completed. Different data is allocated to the corresponding data storage node to complete the calculation of the comprehensive weight of the data storage node. According to the load information of each data storage node collected by the scheduler in the storage system, the load weight of the current data storage node is calculated and distributed. The data load balancing of the massive storage system for wearable devices is realized. The experimental results show that the average time of load balancing using this method is 1.75 ​h, which is much lower than the traditional methods. The results show the data load balancing technology of the massive storage system of wearable devices has the advantages of short data load balancing time, high load balancing, strong data processing capability, short processing time and obvious application.
The Effect of Blockchain in Transportation Malaysia

Purpose- This study aims to benefit both the manufacturing and academic society. Producing and spreading an industry-specific configuration (sales chain) to a more general configuration can be useful for companies that want to use the cluster network in the future. In addition, it shows better understand the technology of the blockchain in transportation in Malaysia. Significant of the Study- In other areas besides computer science and finance, there is a little scientific study in blockchain technology. Expanding the field of study is essential to understand this new technological trend and the management of the supply chain is an essential technological development in the coming years. Methodology-This study is base on a strategic alignment survey of Malaysian transport management chains. Findings-In addition, the main results of the specific strategic adaptation are finding indicators on the type of supply chains with blocking technology that can have positive strategic implications. Although the blocked pipeline revolutionises business and redefines logistics, the current study is limited to classify the categories of applicability of the framework and its consequences. Limitation / Implication- Based on academic and literary knowledge, we classify possible accreditation requests and provide a framework to identify the potential for blockchain the logistics sector. It helps managers regularly evaluate and initiate the development and successful deployment of organisational capabilities to adapt to technology.
Increasing Safety at Smart Elderly Homes by Human Fall Detection from Video using Transfer Learning Approaches

In this study, we investigate the problem of detecting humans fall from video images. Many of the existing methods try to solve the problem by manually defining a set of hand-crafted features for d ...
A Proposal for Nonlinear - Optics Based Quantum Gates in Integrated Photonic Circuits

A proposal for the implementation of single-qubit quantum gates based on a third order nonlinear process in integrated quantum circuits is presented.
Quasi-Deterministic Secure Quantum Communication Using Non-maximally Entangled States

Quantum communication in general helps deter potential eavesdropping in the course of transmission of bits to enable secure communication between two or more parties. In this paper, we propose a novel quasi-deterministic secure quantum communication scheme using non-maximally entangled states. The proposed scheme follows a simple procedure, and cases where the entanglement required can be significantly reduced to carry out the protocol successfully are discussed. Long sequences or the whole sequence of data can be sent after error checking for a potential eavesdropper. The maximum qubit efficiency of the proposed protocol is found to be 33.333%.
A hybrid approach of homomorphic encryption and differential privacy for privacy preserving classification

Privacy preserving data mining is a substantial research area that aims at protecting the privacy of individuals while enabling to perform data mining techniques. In this study, we propose a secure protocol that fulfils the privacy restriction by combining homomorphic encryption with differential privacy and integrate this protocol into Holte’s One Rule which is a simple, but accurate and efficient classification algorithm. The proposed method allows a researcher to get the answers of his/her queries to build One Rule classifier by processing the encrypted training dataset under Paillier’s cryptosystem and also applies differential privacy to minimize the privacy leakage of individuals as much as possible in this training dataset. Therefore, both of security and privacy of the individuals in the training dataset for classification are provided thanks to our proposed method; since neither the parties, nor the researcher attain any information about the individuals in the database. Besides the One Rule classifier, we apply our proposed privacy preservation model to Naïve Bayes classification algorithm for the performance comparison, and show the efficiency of the proposed method through experiments on real data from UCI repository.
A New Generalisation of the Goldwasser-Micali Cryptosystem Based on the Gap $$2^k$$-Residuosity Assumption

We present a novel public key encryption scheme that enables users to exchange many bits messages by means of at least two large prime numbers in a Goldwasser-Micali manner. Our cryptosystem is in fact a generalization of the Joye-Libert scheme (being itself an abstraction of the first probabilistic encryption scheme). We prove the security of the proposed cryptosystem in the standard model (based on the gap $$2^k$$ -residuosity assumption) and report complexity related facts. We also describe an application of our scheme to biometric authentication and discuss the security of our suggested protocol. Last but not least, we indicate several promising research directions.
Path Planning for Mobile Robots based on Semantic Mapping

In this paper, we present a path planning approach for mobile robots using semantic maps. Semantic mapping is qualitative description of the robot’s surroundings, that aims to augment the navigation capabilities and task planning for robots operating in human-robot scenarios. It presents a knowledge layer over an existing map representation such that the knowledge information can be used by robots to learn tasks in a human friendly way. We present a planning scheme using such semantic maps, in order to improve the goal reaching capabilities of the robot in a task-planning scenario. Our method can integrates existing path planners into the topological graph planner that produces faster goal searches.
IMPROVING STUDENTS’ ABILITY IN WRITING NARRATIVE TEXT BY USING STORY MAP TECHNIQUE OF THE EIGHT GRADE STUDENTS AT MTsDDI KEL.BARU TOLITOLI

The objective of the research is to identify whether the use of story map technique can improve students’ ability in writing narrative text. This pre-experimental study uses 23 students as a sample. They are grade eight at MTs DDI Kel. BaruTolitoli. The procedure of this research consists of pretest, treatment, and posttest. The researcher gives pretest and posttest to know the use of story map technique can improve the students’ ability in writing narrative text or not. The result of the analysis shows that story map technique contributes greatly to the students in comprehending narrative text. It can be seen by the difference of the mean score between pre-test and post-test. The mean score of pre-test is 62.30 while in the post-test improve up to 82.09. The data of this research are analyzed by SPSS Program 16. Moreover, the Minimum Mastery Criterion of English lesson is 75 (seventy-five). Then the data of both test are analyze by using T-test, and the result show that the T-test value 7.572 is greater than T-table value 2.074. It can be inferred that story map technique is very effective to improve students’ ability in writing narrative text. Keywords: Improving, reading comprehension, narrative text, scanning technique. Bal, Mieke 2009. Narratology (introduction to the theory of narrative) .Canada.Univerisity of Toronto press incorporated. Bushel Sharon. 2011. Writing a Narrative Paragraph. Available from: http://www.berniejones.com/lessonplans/36writingNarativeParagraph.htm Crystal David. 2008. A Dictionary of Linguistic and Phonetic. USA. Blackwell Publishing Dodd, Ellen Nancy. 2011. The Writer’s Compass. UK. F&W Media International LTD. Haris, David P. 1969. Testing English as second language . New York. McGraw Hill inc. Harmer, Jeremy. 2004. How to Teach Writing . Edinburgh Gate: Pearson Education Hudak.C Heater. 2008. Narrative Paragraph. USA. Weigel Publisher Inc. Melly. 2006. All kinds of Writing (Writing description or Deskriptive Writing). USA. Blackwell Publishing. McDonough, J., and Shaw, C. (2003) Materials and Methods in ELT . 2 nd ed. Maine : Blackwell Publishing. Saskatoon. 2011. Instructional Strategy Online . Available from: http://olc.spsd.sk.ca/de/pd/instr/strats/storymapping/index.html
Fritz John’s equation in mechanism design

We show the role that an important equation first studied by Fritz John plays in mechanism design.
INFLUENCE OF SOLAR ACTIVITIES ON CLIMATE CHANGE

The solar energy received by the atmosphere of the Earth is universally distributed, which constitutes the atmospheric dynamics.The changes observed in the climate of the Earth are due to solar output variations.Some detailed reviews have accounted for the influence of solar activities on the climate using sunspot number derived from peak Sun hours, but has gaps in analyzing critical statistical background information, seasonal, latitudinal and solar activity year validation.A number of statistical concepts are explained in order to enhance our scientific knowledge of climate response to solar activity variation.The influence of geomagnetism to climate response is also examined.This review will serve as analysis and research methodology discourse which could be an application of scientific method.Additionally, this review discusses the influence of the solar activity on climate change based on the existing literature, by focusing on solar activities other than sunspot number alone.Here, we also validate methods using forward and inverse modeling, mean percentage difference, and relative variability.Results indicated that the total solar irradiance (TSI) has remained the main focus of Sun-climate interaction with somewhat negligent on energetic particle precipitation resulting from geomagnetic disturbance reaching the atmosphere.However, statistical concept remains asset to investigate the solar activity influence on climate in both local and global latitudes.We therefore recommend provision of solar quantities data to researchers for further study.
BULGARIAN LIBRARIES FACED THE CHALLENGES OF GLOBAL INFORMATION ENVIRONMENT AND SUSTAINABLE DEVELOPMENT GOALS

E. Popova, Т. Todorova, E. TsvetkovaUniversity of Library Studies and Information Technologies (BULGARIA)
Modeling earthquake numbers by Negative Binomial Hidden Markov models

&amp;lt;p&amp;gt;Modeling seismicity data is challenging and it remains a subject of ongoing research. Assumptions about the distribution of earthquake numbers play an important role in seismic hazard and risk analysis. The most common distribution that has been widely used in modeling earthquake numbers is the Poisson distribution because of its simplicity and easy to use. However, the heterogeneity in earthquake data and temporal dependencies that are often present in many real earthquake sequences make the use of the Poisson distribution inadequate. So, we propose the use of a Hidden Markov model (HMM) with state-specific Negative Binomial distributions in which some states are allowed to approach the Poisson distribution. A HMM is a generalization of a mixture model where the different unobservable (hidden) states are related through a Markov process rather than being independent of each other. We parameterize the Negative Binomial distribution in terms of the mean and dispersion (clustering) parameter. Maximum likelihood estimates of the models&amp;amp;#8217; parameters are obtained through an Expectation-Maximization algorithm (EM-algorithm).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We apply the model to real earthquake data. We have selected the area of Killini Western Greece to test the proposed hypothesis. The area of Killini has been selected based on the fact that in a time window of 17 years three clusters of seismicity associated with strong mainshocks are included in the catalog. Application of the model to the data resulted in three states, representing different levels of seismicity (low, medium, high). The state that corresponds to the low seismicity level approaches the Poisson distribution while the other two states (medium and high) are following the Negative Binomial distribution. This result complies with the nature of the data. The variation within each state that is introduced to the model by the Negative Binomial distribution is greater in the states of medium and high seismicity.&amp;amp;#160;&amp;lt;/p&amp;gt;
Key Competencies of Physics Teachers

In China, the main goal of basic education curriculum reform is to develop students&amp;#39; key competencies. The physics teaching directed by key competencies would not only aim at the memory and reappearance of physics knowledge but also the application of physics knowledge and methods. As teachers who guide students&amp;#39; learning and implement teaching activities, the improvement and development of their key competencies plays an important role in the development of students. The key competencies of physics teachers consist of physics literacy, education and teaching literacy, scientific literacy, humanistic literacy, information literacy, and life-long learning ability.
Descriptor-Free Multi-view Region Matching for Instance-Wise 3D Reconstruction

This paper proposes a multi-view extension of instance segmentation without relying on texture or shape descriptor matching. Multi-view instance segmentation becomes challenging for scenes with repetitive textures and shapes, e.g., plant leaves, due to the difficulty of multi-view matching using texture or shape descriptors. To this end, we propose a multi-view region matching method based on epipolar geometry, which does not rely on any feature descriptors. We further show that the epipolar region matching can be easily integrated into instance segmentation and effective for instance-wise 3D reconstruction. Experiments demonstrate the improved accuracy of multi-view instance matching and the 3D reconstruction compared to the baseline methods.
Impact of social distancing on persons with sensory loss during COVID-19 pandemic

Recent mandates for social distancing due to the COVID-19 pandemic have had significant effects on the lives of older persons. Social distancing might have unequal effects on the lives of people with hearing impairment and vision impairment, possibly affecting their emotional well-being. We remotely surveyed 55 persons between the ages of 57–80 years (13 who were legally blind, 24 with significant hearing loss, and 18 healthy controls.) Initial surveys took place in April as shutdowns were mandated; these included current and retrospective responses concerning pre-pandemic conditions. Subsequent surveys of current experiences were conducted approximately monthly. Survey items included: number of weekly in-person and electronic interactions, satisfaction with access to communication, worry/concern levels, and effects of physical distancing and mask wearing on communication and stress. We noted an initial substantial reduction of in-person interactions for all groups, remaining low throughout. While remote electronic communications increased initially, electronic interactions for persons with sensory loss remained steady over time and for some these even decreased. Persons with hearing loss reported significant communication challenges due to mask wearing; persons with vision loss reported significant loss of connection due to physical distancing. Results show both widespread effects and specific effects on those with sensory loss.
A new type of quantum walks based on decomposing quantum states

In this paper, the 2-state decomposed-type quantum walk (DQW) on a line is introduced as an extension of the 2-state quantum walk (QW). The time evolution of the DQW is defined with two different matrices, one is assigned to a real component, and the other is assigned to an imaginary component of the quantum state. Unlike the ordinary 2-state QWs, localization and the spreading phenomenon can coincide in DQWs. Additionally, a DQW can always be converted to the corresponding 4-state QW with identical probability measures. In other words, a class of 4-state QWs can be realized by DQWs with 2 states. In this work, we reveal that there is a 2-state DQW corresponding to the 4-state Grover walk. Then, we derive the weak limit theorem of the class of DQWs corresponding to 4-state QWs which can be regarded as the generalized Grover walks.
Evolution of VRET to Assist in the Treatment of Phobias: a systematic review

People affected by some kind of phobia have a feeling of panic, which prevents them from leading a normal life, due to an illogical fear. Activities described as routine for some people bring up sensations that are uncontrollable to those suffering from phobia. This review was developed to systematically examine the evidence on virtual reality exposure therapy for phobias and to analyze the evolution of virtual reality environments, critically describing the main challenges in the field. Evidence has shown that virtual reality is an effective treatment for phobias and the environments provided have shown a significant evolution in the sense of presence, making the environments more realistic. We conclude that the virtual reality exposure therapy is an important tool for the treatment of phobias and a low cost option.
Design of temperature control system for infant radiant warmer based on Kalman filter-fuzzy PID

Abstract In order to improve the dynamic and static performance of the temperature control system of the baby radiant warmer, a Kalman filter-fuzzy PID (KFPID)algorithm is proposed on the basis of the ordinary incremental PID control algorithm. The system uses STM32 as the main controller. The temperature data is collected by the TLV2548 analog-to-digital conversion chip, and the feedback data is filtered by Kalman filter, and the bed surface of the warming table is controlled by the fuzzy PID control algorithm. After the simulation of Matlab, it can be seen that compared with the traditional PID temperature control algorithm, the KFPID algorithm has the characteristics of short adjustment time and almost zero overshoot, and the steady-state error is smaller when dealing with the same interference noise.