Reviewerly/exHarmony huggingface dataset:
Linguistic Materials and Metrics for the Creation of Well-Controlled Swedish Speech Perception Tests

Purpose As factors influencing human word perception are important in the construction of speech perception tests used within the speech and hearing sciences, the purposes of this study were as follows: first, to develop algorithms that can be used to calculate different types of word metrics that influence the speed and accuracy of word perception and, second, to create a database in which those word metrics were calculated for a large set of Swedish words. Method Based on a revision of a large Swedish phonetic dictionary, data and algorithms were developed by which various frequency metrics, word length metrics, semantic metrics, neighborhood metrics, phonotactic metrics, and orthographic transparency metrics were calculated for each word in the dictionary. Of the various word metric algorithms used, some were Swedish language reimplementations of previously published algorithms, and some were developed in this study. Results The results of this study have been gathered in a Swedish word metric database called the AFC-list . The AFC-list consists of 816,404 phonetically transcribed Swedish words, all supplied with the word metric data calculated. The full AFC-list has been made publicly available under the Creative Commons Attribution 4.0 International license. Conclusion The results of this study constitute an extensive linguistic resource for the process of selecting test items in new well-controlled speech perception tests in the Swedish language. Supplemental Material https://doi.org/10.23641/asha.8330009
Modeling Instantaneous Quality of Experience Using Machine Learning of Model Trees

For service providers and operators, successful root cause analysis is essential for satisfactory service provisioning. However, reasons for sudden trend changes of the instantaneous Quality of Experience (QoE) may not always be immediately visible from underlying service- or network-level monitoring data. Thus, there is the challenge to pinpoint such moments of change in provisioning, and model the impact on instantaneous QoE, as a lead in root cause analysis. This work investigates the potential of Machine Learning (ML) of deriving time-interval-based models for instantaneous QoE ratings, obtained from a set of publicly available rating traces. In particular, the paper demonstrates the capability of the ML algorithm M5P to model trends of instantaneous QoE through model trees, consisting of piecewise linear functions over time. It is shown how and to which extent these functions can be used to estimate moments of change. Furthermore, the model trees support earlier assumptions about exponential shapes of instantaneous QoE over time as reactions to sudden changes of provisioning, such as video freezes.
The Topography of a Third Order IIR Digital Filter Zeros and Poles in the z-Plane Discretized due to the Quantization of the Direct Form Coefficients

Earlier it was found that the zeros and poles of IIR digital filters with finite word length are elements of the set of algebraic numbers. Therefore, not every point of the unit circle of the z-plane can be a zero and / or a pole of such digital filters. The position of admissible positions for zeros and poles depends on the degree of algebraic numbers and the length of the fractional part of the coefficients of the equivalent canonical structure of the corresponding order. The formation of the corresponding configurations is considered as a $\boldsymbol{z}$ -plane discretization due to the quantization of the filter coefficients. The z-plane discretization for second-order filters has been well studied. The geometric locus of the corresponding algebraic numbers is a system of concentric circles, that is, plane algebraic curves of the second degree. This paper presents the results of studying the geometrical place of the third order IIR filter zeros and poles.
Similar Finger Gesture Recognition using Triplet-loss Networks

We propose an efficient gesture recognition method for continuous finger gestures in untrimmed videos. We aim to discriminate similar finger gestures such as flicking. This type of gestures which are conducted only by the orientation and movement of the fingers tends to be similar, making them difficult for a correct classification since a clear temporal boundary of each target gesture is ambiguous. Thus, the recognition should focus on the accuracy to find the temporal boundaries of the target gestures. We proposed a framework based on a triplet-loss network which learns to decrease the distance of true positive boundaries while increasing that of false positive ones. Finally, we adopt a temporal representation of the segmented gesture using a stack of feature maps for gesture classification. Real-time processing and high performance are achieved with relatively compact deep learning models, which are evaluated on a new dataset of vehicle driver finger gestures. Our approach outperforms the results of previous works for online temporal segmentation and gesture classification, and it can run in real-time at 53 fps.
Recommendation System of Big Data Based on PageRank Clustering Algorithm

Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. We use clustering algorithms to discover communities (clusters) in the data. The algorithms finding graph clusters using the personalized PageRank vectors to determine a set of clusters and optimizing the jumping parameter α subject to several cluster variance measures in order to capture the graph structure according to PageRank clustering algorithms and then use the clusters for building a recommendation system that can recommend products to customers based on their buying behavior. Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper generated in recommendation system by PageRank clustering algorithm.
INTEGRATED APPROACH TO PRIMARY SCHOOLCHILDREN’S GEOMETRIC CONCEPTIONS FORMATION

The article reveals the possibility of primary schoolchildren’s geometric conceptions forming on the basis of the integrated approach that helps pupils to see and realize that geometric phenomena take place not only in mathematics but also in nature and visual arts. It is shown that modern school focuses on the study of autonomous subjects, which without interdisciplinary connections do not allow teaching and educating primary schoolchildren in a versatile way. That is why the ability to transfer knowledge from one subject area to another is an indicator of a pupil’s intellectual development, it forms a holistic view of nature and society. The integrated approach to teaching primary schoolchildren is presented by the example of a fragment of a Fine Art lesson at primary school (grade 2).
ENLACE: A Combination of Layer-Based Architecture and Wireless Communication for Emotion Monitoring in Healthcare

Owing to the increase in the number of people with disabilities, as a result of either accidents or old age, there has been an increase in research studies in the area of ubiquitous computing and the Internet of Things. They are aimed at monitoring health, in an efficient and easily accessible way, as a means of managing and improving the quality of life of this section of the public. It also involves adopting a Health Homes policy based on the Internet of Things and applied in smart home environments. This is aimed at providing connectivity between the patients and their surroundings and includes mechanisms for helping the diagnosis and prevention of accidents and/or diseases. Monitoring gives rise to an opportunity to exploit the way computational systems can help to determine the real-time emotional state of patients. This is necessary because there are some limitations to traditional methods of health monitoring, for example, establishing the behavior of the user’s routine and issuing alerts and warnings to family members and/or medical staff about any abnormal event or signs of the onset of depression. This article discusses how a layer-based architecture can be used to detect emotional factors to assist in healthcare and the prevention of accidents within the context of Smart Home Health. The results show that this process-based architecture allows a load distribution with a better service that takes into account the complexity of each algorithm and the processing power of each layer of the architecture to provide a prompt response when there is a need for some intervention in the emotional state of the user.
Know-Center at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter using CNNs

This paper presents the Know-Center system submitted for task 5 of the SemEval-2019 workshop. Given a Twitter message in either English or Spanish, the task is to first detect whether it contains hateful speech and second, to determine the target and level of aggression used. For this purpose our system utilizes word embeddings and a neural network architecture, consisting of both dilated and traditional convolution layers. We achieved average F1-scores of 0.57 and 0.74 for English and Spanish respectively.
DIGITAL SKILLS FROM SILENT TO ALPHA GENERATION: AN OVERVIEW

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 5134-5143ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1271Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Research on Fuzzy Enhanced Learning Model of Multienhanced Signal Learning Automata

With the continuous iteration of information technology, information presents an explosive development trend, so how to effectively classify, retrieve, and summarize information is very important. Data classification and mining technology as the key technology to solve the aforementioned problems is of great significance. At present, in the fields of data processing, information classification and machine learning reinforcement learning, it is usually based on the learning automata, but the traditional learning automata algorithm cannot meet the development of information technology in terms of convergence and learning rate. In order to improve the convergence stability and learning efficiency of the learning automata algorithm in random environment and enhance its learning ability in dealing with large-scale space and action, this paper innovatively proposes an adaptive fuzzy reinforcement learning model of the learning automata, which makes full use of the randomness of gradient information to solve its stability problem, and abandons the original reinforcement learning method. For the linear function, an adaptive fuzzy numerical superposition algorithm is used to further enhance its learning rate and improve its stability. The experimental results show that the proposed learning automata fuzzy reinforcement learning model has practical application value, and can solve the problems of unstable convergence and poor learning efficiency of the current learning automata.
Modeling Population Structure Under Hierarchical Dirichlet Processes

We propose a Bayesian nonparametric model to infer population admixture, extending the hierarchical Dirichlet process to allow for correlation between loci due to linkage disequilibrium. Given multilocus genotype data from a sample of individuals, the proposed model allows inferring and classifying individuals as unadmixed or admixed, inferring the number of subpopulations ancestral to an admixed population and the population of origin of chromosomal regions. Our model does not assume any specific mutation process, and can be applied to most of the commonly used genetic markers. We present a Markov chain Monte Carlo (MCMC) algorithm to perform posterior inference from the model and we discuss some methods to summarize the MCMC output for the analysis of population admixture. Finally, we demonstrate the performance of the proposed model in a real application, using genetic data from the ectodysplasin-A receptor (EDAR) gene, which is considered to be ancestry-informative due to well-known variations in allele frequency as well as phenotypic effects across ancestry. The structure analysis of this dataset leads to the identification of a rare haplotype in Europeans. We also conduct a simulated experiment and show that our algorithm outperforms parametric methods.
Analyzing the Impact of {GDPR} on Storage Systems

The recently introduced General Data Protection Regulation (GDPR) is forcing several companies to make significant changes to their systems to achieve compliance. Motivated by the finding that more than 30% of GDPR articles are related to storage, we investigate the impact of GDPR compliance on storage systems. We illustrate the challenges of retrofitting existing systems into compliance by modifying Redis to be GDPR-compliant. We show that despite needing to introduce a small set of new features, a strict real-time compliance (e.g., logging every user request synchronously) lowers Redis' throughput by 20x. Our work reveals how GDPR allows compliance to be a spectrum, and what its implications are for system designers. We discuss the technical challenges that need to be solved before strict compliance can be efficiently achieved.
Experiments in Cuneiform Language Identification

This paper presents methods to discriminate between languages and dialects written in Cuneiform script, one of the first writing systems in the world. We report the results obtained by the PZ team in the Cuneiform Language Identification (CLI) shared task organized within the scope of the VarDial Evaluation Campaign 2019. The task included two languages, Sumerian and Akkadian. The latter is divided into six dialects: Old Babylonian, Middle Babylonian peripheral, Standard Babylonian, Neo Babylonian, Late Babylonian, and Neo Assyrian. We approach the task using a meta-classifier trained on various SVM models and we show the effectiveness of the system for this task. Our submission achieved 0.738 F1 score in discriminating between the seven languages and dialects and it was ranked fourth in the competition among eight teams.
EyeHacker

In this study, we introduce EyeHacker, which is an immersive virtual reality (VR) system that spatiotemporally mixes the live and recorded/edited scenes based on the measurement of the users' gaze. This system updates the transition risk in real time by utilizing the gaze information of the users (i.e., the locus of attention) and the optical flow of scenes. Scene transitions are allowed when the risk is less than the threshold, which is modulated by the head movement data of the users (i.e., the faster their head movement, the higher will be the threshold). Using this algorithm and experience scenario prepared in advance, visual reality can be manipulated without being noticed by users (i.e., eye hacking). For example, consider a situation in which the objects around the users perpetually disappear and appear. The users would often have a strange feeling that something was wrong and, sometimes, would even find what happened but only later; they cannot visually perceive the changes in real time. Further, with the other variant of risk algorithms, the system can implement a variety of experience scenarios, resulting in reality confusion.
Heuristic Voting as Ordinal Dominance Strategies

Decision making under uncertainty is a key component of many AI settings, and in particular of voting scenarios where strategic agents are trying to reach a joint decision. The common approach to handle uncertainty is by maximizing expected utility, which requires a cardinal utility function as well as detailed probabilistic information. However, often such probabilities are not easy to estimate or apply.To this end, we present a framework that allows for “shades of gray” of likelihood without probabilities. Specifically, we create a hierarchy of sets of world states based on a prospective poll, with inner sets contain more likely outcomes. This hierarchy of likelihoods allows us to define what we term ordinally-dominated strategies. We use this approach to justify various known voting heuristics as bounded-rational strategies.
Retrieving objects from clutter using a mobile robotic manipulator

This paper presents a task and motion planning method for retrieving a target object from clutter using a mobile robotic manipulator. We consider a configuration of objects where the density of the space is high. Thus, the robot may have to relocate some obstacles to retrieve the target without collisions. Our goal is to reduce the time for accomplishing the retrieval task by computing a relocation plan, which is a sequence of obstacles to be relocated, and determining the pose of the mobile base where the plan can be executed successfully. We propose a planning method that aims to minimize the number of obstacles to be relocated. Depending on the poses of the obstacles to be manipulated, the base pose of the robot is determined such that the travel distance of the end-effector manipulating the obstacles is minimized. We test the proposed method in a high-fidelity simulated experiment. Our method is compared to a method finding a relocation plan within a limited range of search space.
Tarimliq: A new internal metric for software clustering analysis

Clustering techniques are utilized in the maintenance process of a software system to understand it. Different clustering algorithms have been proposed for this purpose in the literature. In the field of software clustering, a number of external criteria are presented to evaluate and validate the obtained clusters. External criteria use a reference clustering to evaluate an achieved clustering. Because of the comparison with reference clustering, the validity and accuracy of these methods are reliable in the assessment. When there is no reference clustering, internal criteria are used to validate clustering algorithms. Since there is no internal criterion for evaluating software clustering algorithms, the internal criteria available in data clustering are employed. In this paper, we propose an internal metric for evaluating software clustering algorithms. The results on Mozilla Firefox, as a large-scale software, demonstrate that the proposed internal metric is more accurate than the tested internal criteria and can also be a suitable alternative for external criteria.
String-to-String Interpretations with Polynomial-Size Output

String-to-string MSO interpretations are like Courcelle's MSO transductions, except that a single output position can be represented using a tuple of input positions instead of just a single input position. In particular, the output length is polynomial in the input length, as opposed to MSO transductions, which have output of linear length. We show that string-to-string MSO interpretations are exactly the polyregular functions. The latter class has various characterisations, one of which is that it consists of the string-to-string functions recognised by pebble transducers. Our main result implies the surprising fact that string-to-string MSO interpretations are closed under composition.
Robust Driver-Automation Shared Control For A Lane Keeping System Using Interval Type 2 Fuzzy Method

In this paper, we investigate a type 2 fuzzy method for the lane keeping assistant system considering the interaction with the human driver. Concerning the vehicle longitudinal velocity is time-variant, a conventional type 1 fuzzy model is valid as there is no uncertainty in the control system. However, the vehicle speed is not only time-varying but also imprecise in the presence of measurement error. Thus, we design a new type 2 fuzzy controller to remove uncertainty resulted from imprecise measurement. Meanwhile, a continuous variable is introduced to modify the designed assistant torque according to the driver’s current activity, which can realize the interaction between the driver and the automation system. In addition, we introduce an energy-to-peak performance index to attenuate effectiveness of the external disturbance on the control system. Considering the transient performance of the closed-loop control system, the $\mathcal{D}$-stability method is applied. Finally, a simulation is tested to demonstrate the effects of the proposed method compared with an conventional type 1 fuzzy controller.
High Monetary Rewards and High Academic Article Outputs: Are China’s Research Publications Policy Driven?

This study compares China’s research article outputs rankings with its innovation rankings in the world, in the perspective of China’s high monetary rewards for research publications. It finds that there is a mismatch between China’s publication outputs and “research quality and broad influence on inventions,” and that the high monetary rewards for publications may have also led to some academic misconduct. This study’s findings also correlate with the findings of studies on China’s patent applications, which were found to be policy driven and to be results of policies which incentivize patenting but decrease patent quality. Some awareness of the issues related to policy-driven publications would help librarians as well as readers in selecting journal articles.
Implicit Intention and Activity Recognition of a Human Using Neural Networks for a Service Robot Eye

Introduction of the assistive robot concept has created numerous ways to restore vital degrees of independence for the elderly and disabled people on their Activities of Daily Living (ADL). The most important aspect of an assistive robot is to understand the user's intentions with minimum number of interactions. Based on these facts, in this study we suggest a novel method to recognize the implicit intention of a human user, by using verbal communication, behavior recognition and motion recognition from the combination of machine learning, computer vision and voice recognition technologies. After recognizing the implicit intension of the user, the system will be able to identify the necessary objects from the domestic area that is going to help the human user and point them out to fulfil his/her intention. By far, this study is expected to simplify the human robot interaction (HRI) while consequently enhancing the adoption of assistive technologies and improving the user's independence in ADL. These findings will certainly help to guide future designs on implicit intention recognition and activity recognition to an accurate intention inference algorithm and intuitive HRI.
Optimized Coherent State Based Quantum Cryptography With High Robust for Networks Deployment

Quantum cryptography enables unconditional security, which would be one of most promising techniques for the internet of things networks deployment. While backwards existing in the realistic systems may be explored by the eavesdroppers, thus threatening the information security. We present a coherent state based quantum cryptography protocol, which has capability of high robust. Concretely, real local oscillator and discrete modulation strategies are adopted for such purpose. The former circumvents the side-channel attack effectively, and the latter providers long transmission distance. Numerical simulations are performed, during which the optimized modulation variance is taken into account to achieve long transmission distance and high secrete key rate. The results reveal that phase mismatch and weak reference pulse resulting from the real local oscillator will degrade the performance, as well as voltage fluctuation from the modulator. Also, discrete modulation with more states may outperform over those less states in secret key rate for short transmission range.