Reviewerly/exHarmony huggingface dataset:
Designing and Evaluating the Usability of a Machine Learning API for Rapid Prototyping Music Technology

To better support creative software developers and music technologists’ needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. This paper presents a usability evaluation study of an application programming interface for interactive machine learning with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. Based on the findings obtained through the cognitive dimensions framework, we present an analysis and characterisation of an ML API and discuss its design trade-offs and usability issues. Additionally, we discuss the merits and challenges of the application of the cognitive dimensions framework to the evaluation of machine learning APIs.
An Application of Zipf's Law for Prose and Verse Corpora Neutrality for Hindi and Marathi Languages

Availability of the text in different languages has become possible, as almost all websites have offered multilingual option.Hindi is considered as official language in one of the states of India.Hindi text analysis is dominated by the corpus of stories and poems.Before performing any text analysis token extraction is an important step and supports many applications like text summarization, categorizing text and so on.Token extraction is a part of Natural language processing (NLP).NLP includes many steps such as preprocessing the corpus, lemmatization and so on.In this paper the tokens are extracted by two methods and on two corpora.BaSa, a context-based term extraction technique having different NLP activities, e.g.Term Frequency Inverse Document Frequency (TF-IDF) and Zipf's law are used to count and compare extracted tokens.Further token comparison between both of the methods is achieved.The corpus contains proses and verses of Hindi as well as the Marathi language.Common tokens from corpora of verses and proses of Marathi as well as Hindi are identified to prove that both of them behave same as per as NLP activities are concerned.The betterment of BaSa over Zipf's law is proved.Hindi Corpus includes 820 stories and 710 poems and Marathi corpus includes 610 stories and 505 poems.
A True Random Number Generator Based on Gait Data for the Internet of You

The Internet of Things (IoT) is more and more a reality, and every day the number of connected objects increases.The growth is practically exponential -there are currently about 8 billion and expected to reach 21 billion in 2025.The applications of these devices are very diverse and range from home automation, through traffic monitoring or pollution, to sensors to monitor our health or improve our performance.While the potential of their applications seems to be unlimited, the cyber-security of these devices and their communications is critical for a flourishing deployment.Random Number Generators (RNGs) are essential to many security tasks such as seeds for key-generation or nonces used in authentication protocols.Till now, True Random Number Generators (TRNGs) are mainly based on physical phenomena, but there is a new trend that uses signals from our body (e.g., electrocardiograms) as an entropy source.Inspired by the last wave, we propose a new TRNG based on gait data (six 3-axis gyroscopes and accelerometers sensors over the subjects).We test both the quality of the entropic source (NIST SP800-90B) and the quality of the random bits generated (ENT,.From this in-depth analysis, we can conclude that: 1) the gait data is a good source of entropy for random bit generation; 2) our proposed TRNG outputs bits that behave like a random variable.All this confirms the feasibility and the excellent properties of the proposed generator.
Blended learning in the epoch of digital transformation

Basic tendency of today – digital transformation – renders substantial influence on education. Introduction of mass open online courses (MOOCs) in an educational process became the natural response of institutions of higher education to modern trends. The article suggests a short analysis of introduction of MOOCs driven in Russia and in the world. In Saint Petersburg polytechnic university active development of mass open on-line of courses is conducted that can be used in the educational process of any institution of higher education, and is actively used in the university itself. In Institute of industrial management, economy and trade of Saint Petersburg polytechnic university online-courses have been introduced into the educational process while realizing the disciplines from 2017. Today the experience achieved allowed to inculcate online-courses in all curricula of POLYTECH. The article also provides the results of change of progress, forms of communications and problems aroused due to MOOCs introduction. The ways to solve the given problems are suggested. The next step in development of education is passing to the construction of adaptive educational trajectories allowing not only operatively to react to the calls of labor-market, but also to prepare the “specialists of the future”.
Knowledge base enrichment by relation learning from social tagging data

There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation. Research in this line mostly exploits data co-occurrence and often overlooks the complex and ambiguous meanings of tags. Furthermore, there have been few comprehensive evaluation studies regarding the quality of the discovered knowledge. We propose a supervised learning method to discover subsumption relations from tags. The key to this method is quantifying the probabilistic association among tags to better characterise their relations. We further develop an algorithm to organise tags into hierarchies based on the learned relations. Experiments were conducted using a large, publicly available dataset, Bibsonomy, and three popular, human-engineered or data-driven knowledge bases: DBpedia, Microsoft Concept Graph, and ACM Computing Classification System. We performed a comprehensive evaluation using different strategies: relation-level, ontology-level, and knowledge base enrichment based evaluation. The results clearly show that the proposed method can extract knowledge of better quality than the existing methods against the gold standard knowledge bases. The proposed approach can also enrich knowledge bases with new subsumption relations, having the potential to significantly reduce time and human effort for knowledge base maintenance and ontology evolution.
A BI-Model Approach for Handling Unknown Slot Values in Dialogue State Tracking

In this paper, we present an end-to-end bi-model structure for dialogue state tracking, which can handle the scenarios when the spoken language understanding model with a predefined slot candidate list is absent. Furthermore, the model structure described in this paper can effectively extract unknown slot values and still maintain the state-of-the-art performance on DSTC2 benchmark. We also compare our model in detail with an existing end-to-end dialogue state tracking model using pointer network which can also handle the unknown slot values, and demonstrates that how the bi-model structure can benefit the task and hence gives better performance.
Unsupervised Neural Machine Translation with Indirect Supervision.

Neural machine translation~(NMT) is ineffective for zero-resource languages. Recent works exploring the possibility of unsupervised neural machine translation (UNMT) with only monolingual data can achieve promising results. However, there are still big gaps between UNMT and NMT with parallel supervision. In this work, we introduce a multilingual unsupervised NMT (\method) framework to leverage weakly supervised signals from high-resource language pairs to zero-resource translation directions. More specifically, for unsupervised language pairs \texttt{En-De}, we can make full use of the information from parallel dataset \texttt{En-Fr} to jointly train the unsupervised translation directions all in one model. \method is based on multilingual models which require no changes to the standard unsupervised NMT. Empirical results demonstrate that \method significantly improves the translation quality by more than 3 BLEU score on six benchmark unsupervised translation directions.
Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant’s operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types.
Local theorems for arithmetic compound renewal processes when Cramer's condition holds

We continue the study of the compound reneal processes (c.r.p.), where the moment Cramer's condition holds (see [1]-[10], where the study of c.r.p. was started).In the paper arithmetic c.r.p.Z(n) are studied.In such processes random vector ξ = (τ, ζ) has the arithmetic distribution, where τ > 0 defines the distance between jumps, ζ defines the values of jumps.For this processes the fine asymptotics in the local limit theorem for probabilities P(Z(n) = x) has been obtained in Cramer's deviation region of x ∈ Z In [6]-[10] the similar problem has benn solved for non-lattice c.r.p., when the vector ξ = (τ, ζ) has the non-lattice distribution.
Cross-Stained Segmentation from Renal Biopsy Images Using Multi-Level Adversarial Learning

Segmentation from renal pathological images is a key step in automatic analyzing the renal histological characteristics. However, the performance of models varies significantly in different types of stained datasets due to the appearance variations. In this paper, we design a robust and flexible model for cross-stained segmentation. It is a novel multi-level deep adversarial network architecture that consists of three sub-networks: (i) a segmentation network; (ii) a pair of multi-level mirrored discriminators for guiding the segmentation network to extract domain-invariant features; (iii) a shape discriminator that is utilized to further identify the output of the segmentation network and the ground truth. Experimental results on glomeruli segmentation from renal biopsy images indicate that our network is able to improve segmentation performance on target type of stained images and use unlabeled data to achieve similar accuracy to labeled data. In addition, this method can be easily applied to other tasks.
An Evaluation Model of the Learning Effect of Physical Education Major Courses in Colleges

There are several problems with the evaluation of the learning effect of physical education (PE) major courses in colleges, namely, the diversity of constraints and the lack of multiple perspectives. To solve the problems, this paper puts forward a novel model to evaluate the said learning effect. Firstly, the authors identified the problems and principles of learning effect evaluation of PE major courses in colleges, and established an evaluation index system from the perspectives of teachers and students. On this basis, an effective evaluation algorithm was developed to quantify the learning effect through gray clustering analysis. The proposed evaluation model can accurately assess the learning effect of PE major courses. The research findings enjoy great significance in theoretical innovation and engineering application.
Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar

Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.
Time Reversal Based Robust Gesture Recognition Using Wifi

Gesture recognition using wireless sensing opened a plethora of applications in the field of human-computer interaction. However, most existing works are not robust without requiring wearables or tedious training/calibration. In this work, we propose WiGRep, a time reversal based gesture recognition approach using Wi-Fi, which can recognize different gestures by counting the number of repeating gesture segments. Built upon the time reversal phenomenon in RF transmission, the Time Reversal Resonating Strength (TRRS) is used to detect repeating patterns in a gesture. A robust low-complexity algorithm is proposed to accommodate possible variations of gestures and indoor environments. The main advantages of WiGRep are that it is calibration-free and location and environment independent. Experiments performed in both line of sight and non-line-of-sight scenarios demonstrate a detection rate of 99.6% and 99.4%, respectively, for a fixed false alarm rate of 5%.
The rate function and the fundamental function for multidimensional compound renewal process

We consider two multidimensional compound renewal processes Z(t) and Y(t).Assuming that the increments satisfy the Cramer's condition, we define and investigate the rate functions and the fundamental functions for the processes Z(t) and Y(t).
Nash Equilibrium and Social Optimization of Transactions in Blockchain System Based on Discrete-Time Queue

Blockchain technology has been used in many fields such as data management, cloud computing and Internet of Things with the features of decentralization, transparency and immutability.In order to study the performance of a blockchain system with a light-load traffic, we establish a discrete-time non-exhaustive vacation queue with batch service and gated service.In this model, we regard transaction initiation, mining processing and block verification as arrival, vacation and service period, respectively.By using an embedded Markov chain method and a regeneration cycle approach, we derive the average response time of transactions.Experiment results with analysis and simulation show that the average response time of transactions is impacted by the arrival rate of transactions.Finally, we study the Nash equilibrium behavior and the socially optimal behavior of transactions, and present a pricing policy for transactions to maximize the social profit.
Continualization of Probabilistic Programs With Correction

Abstract Probabilistic Programming offers a concise way to represent stochastic models and perform automated statistical inference. However, many real-world models have discrete or hybrid discrete-continuous distributions, for which existing tools may suffer non-trivial limitations. Inference and parameter estimation can be exceedingly slow for these models because many inference algorithms compute results faster (or exclusively) when the distributions being inferred are continuous. To address this discrepancy, this paper presents Leios. Leios is the first approach for systematically approximating arbitrary probabilistic programs that have discrete, or hybrid discrete-continuous random variables. The approximate programs have all their variables fully continualized. We show that once we have the fully continuous approximate program, we can perform inference and parameter estimation faster by exploiting the existing support that many languages offer for continuous distributions. Furthermore, we show that the estimates obtained when performing inference and parameter estimation on the continuous approximation are still comparably close to both the true parameter values and the estimates obtained when performing inference on the original model.
Generating Random Floating-Point Numbers by Dividing Integers: A Case Study

A method widely used to obtain IEEE 754 binary floating-point numbers with a standard uniform distribution involves drawing an integer uniformly at random and dividing it by another larger integer. We survey the various instances of the algorithm that are used in actual software and point out their properties and drawbacks, particularly from the standpoint of numerical software testing and data anonymization.
Semantic Search Enhanced with Rating Scores

This paper presents SemSime, a method based on semantic similarity for searching over a set of digital resources previously annotated by means of concepts from a weighted reference ontology. SemSime is an enhancement of SemSim and, with respect to the latter, it uses a frequency approach for weighting the ontology, and refines both the user request and the digital resources with the addition of rating scores. Such scores are High, Medium, and Low, and in the user request indicate the preferences assigned by the user to each of the concepts representing the searching criteria, whereas in the annotation of the digital resources they represent the levels of quality associated with each concept in describing the resources. The SemSime has been evaluated and the results of the experiment show that it performs better than SemSim and an evolution of it, referred to as S e m S i m R V .
Experimental demonstration of hidden nonlocality with local filters

The violation of the Bell inequalities implies that quantum mechanics cannot be interpreted using any local hidden variable theory. However, particular quantum states that can originally be described using a local hidden variable model surprisingly exhibit nonlocality by employing local filters before a standard Bell test is performed. This is referred to as hidden nonlocality. In this study, we provide the experimental demonstration of hidden nonlocality through linear optics towards the local states which are put forword by Hirsch et al ., PRL 111 , 160402 (2013). A class of local states is generated through a spontaneous parametric down-conversion process, and the violation of the Clauser–Horne–Shimony–Holt (CHSH)–Bell inequality is observed by applying local filters. Our experimental results confirm the superiority of local filters, and throw light on deep understanding the intriguing phenomena of hidden nonlocality and local states.
A FRAMEWORK FOR INCREMENTAL PARALLEL MINING OF INTERESTING ASSOCIATION PATTERNS FOR BIG DATA

Association rule mining plays a very important role in the distributed environment for Big Data analysis. The massive volume of data creates imminent needs to design novel, parallel and incremental algorithms for the association rule mining in order to handle Big Data. In this paper, a framework is proposed for incremental parallel interesting association rule mining algorithm for Big Data. The proposed framework incorporates interestingness measures during the process of mining. The proposed framework works to process the incremental data, which usually comes at different times, the user's important knowledge is explored by processing of new data only, without having to return from scratch. One of the main features of this framework is to consider the user domain knowledge, which is monotonically increased. The model that incorporates the users’ belief during the extraction of patterns is attractive, effective and efficient. The proposed framework is implemented on public datasets as well as it is evaluated based on the interesting results that are found.
Improvement of the Deep Forest Classifier by a Set of Neural Networks

A Neural Random Forest (NeuRF) and a Neural Deep Forest (NeuDF) as classification algorithms, which combine an ensemble of decision trees and neural networks, are proposed in the paper. The main idea underlying NeuRF is to combine the class probability distributions produced by decision trees by means of a set of neural networks with shared parameters. The networks are trained in accordance with a loss function which measures the classification error. Every neural network can be viewed as a non-linear function of probabilities of a class. NeuDF is a modification of the Deep Forest or gcForest proposed by Zhou and Feng, using NeuRFs. The numerical experiments illustrate the outperformance of NeuDF and show that the NeuRF is comparable with the random forest
Mechanical and Electronic Video Stabilization Strategy of Mortars with Trajectory Correction Fuze Based on Infrared Image Sensor

For a higher attack accuracy of projectiles, a novel mechanical and electronic video stabilization strategy is proposed for trajectory correction fuze. In this design, the complexity of sensors and actuators were reduced. To cope with complex combat environments, an infrared image sensor was used to provide video output. Following the introduction of the fuze's workflow, the limitation of sensors for mechanical video stabilization on fuze was proposed. Particularly, the parameters of the infrared image sensor that strapdown with fuze were calculated. Then, the transformation relation between the projectile's motion and the shaky video was investigated so that the electronic video stabilization method could be determined. Correspondingly, a novel method of dividing sub-blocks by adaptive global gray threshold was proposed for the image pre-processing. In addition, the gray projection algorithm was used to estimate the global motion vector by calculating the correlation between the curves of the adjacent frames. An example simulation and experiment were implemented to verify the effectiveness of this strategy. The results illustrated that the proposed algorithm significantly reduced the computational cost without affecting the accuracy of the motion estimation. This research provides theoretical and experimental basis for the intelligent application of sensor systems on fuze.
Immersive Technology for Medical Education: Technology Enhance Immersive Learning Experiences

Immersive Technology was a trend of the interesting technology in presentation, demonstration, imitation. These techniques could be used in medical education. This article would present immersive technology knowledge which consisted of Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR). Immersive technology could also be applied to medical education in 5 parts such as Treatment, Education, Rehabilitation, Training, Surgery, and equipment for medical education media development.