Reviewerly/exHarmony huggingface dataset:
A Cloud-Based System for In-Home Fall Detection and Activity Assessment

Falls are a major global health problem that may result in long-term health issues, disabilities, and even death (~650,000 fatalities each year). Each year approximately 37.3 million elderly worldwide experience a fall event that is severe enough to require medical attention. This research proposes a system for activity assessment and fall detection intended for in-home applications using the Internet of Things (IoT) for real-time detection of falls events and potentially fall prevention using a low-cost, wearable sensor system. While the proposed system uses an integrated MEMS sensor (i.e. accelerometer, gyroscope) for biomechanical monitoring and event detection, the developed algorithms can also be deployed onto other smart devices. The system communicates periodically with a cloud server system for uploading, archiving, and analyzing sensor data. Data is transferred to the cloud. Once data is received, the cloud server can provide alerts (i.e. automated calls, SMS, EMAIL) to formal and informal caregivers as well as emergency services when falls or other emergencies occur. Additionally, individuals and their caregivers can access and review personalized activity information to assess health, wellness and independence. Preliminary tests using healthy subjects performing Activity Daily Living (ADL) is quite promising, with specificity to detect fall of 100% in about 200 h normal activities in real-world setting. This research will also present practical challenges to deployment of the proposed system in real-world settings including usability, performance, and feedback from end-users.
Simultaneous Amplification: The Case of Non-interactive Zero-Knowledge

In this work, we explore the question of simultaneous privacy and soundness amplification for non-interactive zero-knowledge argument systems (NIZK). We show that any $$\delta _s-$$ sound and $$\delta _z-$$ zero-knowledge NIZK candidate satisfying $$\delta _s+\delta _z=1-\epsilon $$ , for any constant $$\epsilon >0$$ , can be turned into a computationally sound and zero-knowledge candidate with the only extra assumption of a subexponentially secure public-key encryption. We develop novel techniques to leverage the use of leakage simulation lemma (Jetchev-Peitzrak TCC 2014) to argue amplification. A crucial component of our result is a new notion for secret sharing $$\mathsf {NP}$$ instances. We believe that this may be of independent interest. To achieve this result we analyze following two transformations: Then we show that using these transformations in a zig-zag fashion we can obtain our result. Finally, we also present a simple transformation which directly turns any $$\mathsf {NIZK}$$ candidate satisfying $$\delta _s,\delta _z<1/3 -1/\mathsf {poly}(\lambda )$$ to a secure one.
Bayesian nonparametric priors for hidden Markov random fields

One of the central issues in statistics and machine learning is how to select an adequate model that can automatically adapt its complexity to the observed data. In the present paper, we focus on the issue of determining the structure of clustered data, both in terms of finding the appropriate number of clusters and of modeling the right dependence structure between the observations. Bayesian nonparametric (BNP) models, which do not impose an upper limit on the number of clusters, are appropriate to avoid the required guess on the number of clusters but have been mainly developed for independent data. In contrast, Markov random fields (MRF) have been extensively used to model dependencies in a tractable manner but usually reduce to finite cluster numbers when clustering tasks are addressed. Our main contribution is to propose a general scheme to design tractable BNP–MRF priors that combine both features: no commitment to an arbitrary number of clusters and a dependence modeling. A key ingredient in this construction is the availability of a stick-breaking representation which has the threefold advantage to allowing us to extend standard discrete MRFs to infinite state space, to design a tractable estimation algorithm using variational approximation and to derive theoretical properties on the predictive distribution and the number of clusters of the proposed model. This approach is illustrated on a challenging natural image segmentation task for which it shows good performance with respect to the literature.
ArchCaMO - A Maturity Model for Software Architecture Description Based on ISO/IEC/IEEE 42010:2011

Academia and Industry have acknowledged that having a fully described software architecture is a crucial asset for software development and maintenance. The description of a software architecture is read by many stakeholders when developing and maintaining complex software systems which are composed by multiple elements, including software, systems, hardware and processes. The software architecture as a development product is useful for technical activities, such as describing the views and concerns of the future software products, as well as for management activities, including allocating tasks to each team and as an input for project management activities. One main issue when describing the software architecture is knowing what elements must be included in the architecture, and at what level of detail. Therefore, it is not unusual that software architects have to deal with difficulties in terms of how to describe the architecture. This paper brings two main purposes: first is the idea of establishing levels of a maturity model for software architectures, which can help in organizing, describing and communicating the software architecture for multiple stakeholders, and, second, a way to evaluate how mature is the software architecture.
An Improved Radar and Infrared Sensor Tracking Fusion Algorithm Based on IMM-UKF

In a radar and infrared sensor synergistic tracking system, an improved radar and infrared sensor cooperative tracking algorithm is proposed based on interacting multiple model and unscented Kalman filter (IMMUKF), which can reduce the radar radiation time. Firstly, the tracking model for the radar and infrared sensor is built and IMMUKF is employed as a filter. Secondly, a novel tracking quality factor is designed for control the radar’s radiation. The residual of the new information obtained by comparing the filtering result with the estimated measurement is selected as a criterion. Finally, the working time of radar and infrared sensor is adaptively controlled. And the simulation results show that the proposed method can reduce the radiation time with excellent tracking accuracy.
Coupling the design of benchmark with algorithm in landscape-aware solver design

Following a general trend in artificial intelligence, Evolutionary Computation has, in recent years, witnessed substantial performance gains from landscape-aware selection and parameter tuning of algorithmic modules. Such approaches, however, are critically relying on suitable benchmarks, or training sets, that provide the appropriate blend of performance and generality. With this position paper we argue that, on a landscape analysis basis, the benchmark design problem will form a substantial part of the next-generation of automated, on-demand algorithm design principles.
PERBEDAAN HASIL BELAJAR SISWA KELAS X SMA MENGGUNAKAN MODEL PEMBELAJARAN KOOPERATIF TIPE STUDENT FACILITATOR AND EXPLAINING (SFE) DAN MODEL PEMBELAJARAN KONVENSIONAL PADA MATERI TRIGONOMETRI

This study aims to determine whether there are differences in learning outcomes of class X students of SMA 12 Ambon using cooperative learning model Student Facilitator and Explaining (SFE) and conventional learning models on trigonometric material. The type of research is experimental research (Experimental Research) with the Post-Test Only Control Group Design. The analysis technique is descriptive statistical analysis and inferential test statistics consisting of normality test data, homogeneity test and hypothesis test. The data are processed using SPSS version 20.0. The results of this study are (1) there are differences in learning outcomes using the cooperative learning model Student Facilitator and Explaining and conventional learning models, which shows that the value of t_hitung 3.1128 is greater than the value of t_tabel 2.0003 and the value of Sig. (2-tailed) smaller than the value of alpha 0.05 which is 0.002; (2) learning outcomes of students who use the cooperative learning model Student Facilitator and Explaining type are better when compared to those using conventional learning models
Parameter-less, population-sizing DSMGA-II

Limiting the number of required settings is an important part of any evolutionary method development. The final objective of this process is a method version that is parameter-less. Based on the research results presented that far, the leading methods in the combinatorial optimization are Linkage Tree Genetic Algorithm (LTGA), Parameter-less Population Pyramid (P3) and Dependency Structure Matrix Genetic Algorithm II (DSMGA-II). P3 was originally proposed as a parameter-less method, while LTGA and DSMGA-II in their original propositions both require one parameter that is the population size. Recently a population-sizing technique was used to propose a parameter-less version of LTGA (psLTGA). However, the population-sizing was not introduced in DSMGA-II that misses its effective parameter-less version. Therefore, to fill this gap, in this paper we propose a Population-sizing DSMGA-II (psDSMGA-II) that is parameter-less. We also show that psDSMGA-II is more effective than its predecessor and that it may successfully compete with psLTGA and P3.
A Survey on Mixed-Attribute Outlier Detection Methods

In the data era, outlier detection methods play an important role. The existence of outliers can provide clues to the discovery of new things, irregularities in a system, or illegal intruders. Based on the data, outlier detection methods can be classified into numerical, categorical, or mixed-attribute data. However, the study of the outlier detection methods is generally conducted for numerical data. Meanwhile, many real-life facts are presented in mixed-attribute data. In this paper, the researcher presents a survey of outlier detection methods for mixed-attribute data. The methods are classified into four types, namely, categorized, enumerated, combined, and mixed outlier detection methods for mixed-attribute data. Through this classification, the methods can be easily analyzed and improved by applying appropriate functions.
A New Traversal Method for Virtual Reality:

One of the biggest issues facing VR as a platform is the limitation of the user’s physical space. Not everyone has a lab, empty warehouse, or open space in their home or office, and even if they do, the hardware also limits the physical space the user can take advantage of.
Automatic Discovery Mechanism of Blockchain Nodes Based on the Kademlia Algorithm

The emergence of the Ethereum platform has led to the unprecedented development of blockchain technology. Ethereum ensures that blockchain technology becomes the core technology of Bitcoin, and users can expand and develop other applications on the Ethereum platform. P2P networks are applied in Ethereum to achieve peer-to-peer communication in transactions, with nodes being equal to each other and without trust. Ethereum is a peer-to-peer distributed network. Inter-node communication does not pass through a trustworthy intermediary, and each node can get the latest data by sending requests to its neighboring nodes. Each node acts as both a server and a service provider. This mode guarantees the data synchronization of all the nodes in Ethereum, so it is very important to study the automatic discovery of nodes. This paper mainly analyzes the automatic discovery mechanism of nodes based on the Kademlia algorithm, including principle of the protocol, the process of communication handshake and the specific algorithm mechanism. At last, we used the Mist wallet to observe the results of automatic discovery of nodes in the Ethereum client’s, and used Python to do a simple experiment for automatic discovery of Ethereum nodes.
Research on Path Planning Method of Humanoid Robot Based on Improved Genetic Algorithm

Abstract Mobile robot is a comprehensive subject developed rapidly in recent years. In the near future, robots may appear in many aspects of your life. Robots can replace humans to complete many tasks, among which it is very critical to accurately reach the destination, so the path planning of mobile robots has become a research hotspot in related fields.In this paper, the path planning technology of mobile robot based on genetic algorithm is studied. Based on the traditional method, some improved algorithms and new solutions are proposed to improve the computational efficiency of the algorithm and search an optimal path between the starting point and the target point.
Recent Trends in Text to Speech Synthesis of Indian Languages

A Text To Speech (TTS) synthesizer is a computer application capable of converting arbitrary input text into speech.This conversion broadly involves two steps, namely, text processing and speech synthesis.Text processing converts the entered text to a sequence of synthesis units, while speech synthesis is the generation of an acoustic wave form corresponding to each of these units.Naturalness and intelligibility are the most important qualities expected from a TTS system.In this paper we aim to provide an overview of various techniques for text to speech synthesis, discuss their characteristics, summarize and compares advantages and drawbacks.We have listed various Text-to-Speech synthesis frameworks developed and implemented at different Indian institutes.
Achievable Rate Bounds on Poisson Channel with a Sample-Based Practical Photon-Counting Receiver

We investigate the achievable rate for a practical receiver under on-off keying modulation based on Kullback-Leibler (KL) divergence and Chernoff &#x03B1;-divergence. The asymptotic tightness of the derived bounds is analyzed for large symbol duration and large or low peak power. We prove the tightness of the derived bounds for large symbol duration, large peak power with zero background radiation with exponential convergence rate, and for low peak power of order two. Moreover, we propose an approximation on the achievable rate in the low background radiation and medium symbol duration regime, which is more accurate compared with the derived upper and lower bounds in the medium signal to noise ratio (SNR) regime. The proposed bounds and the approximations are evaluated by the numerical results.
Community Regularization of Visually-Grounded Dialog

The task of conducting visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers in natural language. We posit that requiring artificial agents to adhere to the rules of human language, while also requiring them to maximize information exchange through dialog is an ill-posed problem. We observe that humans do not stray from a common language because they are social creatures who live in communities, and have to communicate with many people everyday, so it is far easier to stick to a common language even at the cost of some efficiency loss. Using this as inspiration, we propose and evaluate a multi-agent community-based dialog framework where each agent interacts with, and learns from, multiple agents, and show that this community-enforced regularization results in more relevant and coherent dialog (as judged by human evaluators) without sacrificing task performance (as judged by quantitative metrics).
DIMENSIONS OF INTERPERSONAL TEACHERS’ SKILLS IN SCHOOL ENVIRONMENT

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 1126-1136ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0351Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis

This paper describes Tübingen-Oslo team’s participation in the cross-lingual morphological analysis task in the VarDial 2019 evaluation campaign. We participated in the shared task with a standard neural network model. Our model achieved analysis F1-scores of 31.48 and 23.67 on test languages Karachay-Balkar (Turkic) and Sardinian (Romance) respectively. The scores are comparable to the scores obtained by the other participants in both language families, and the analysis score on the Romance data set was also the best result obtained in the shared task. Besides describing the system used in our shared task participation, we describe another, simpler, model based on linear classifiers, and present further analyses using both models. Our analyses, besides revealing some of the difficult cases, also confirm that the usefulness of a source language in this task is highly correlated with the similarity of source and target languages.
An Efficient Knowledge-Based Text Pre-processing Approach for Twitter and Google+

People nowadays prefer sharing their opinions towards various products and services frequently on social networking sites (SNSs). These online reviews are huge in size and act as a goldmine for organizations to understand and monitor public reviews of their products and services. But these online reviews are highly unstructured in nature due to the presence of various linguistic features like hashtags, URLs, misspelled words, emoticons and many more. This highly unstructured data makes sentiment classification a challenging task. Hence, data pre-processing is an underlying and fundamental step in sentiment analysis. In the present work, authors have rigorously explored a series of pre-processing steps and observed that the sequence order of pre-processing steps affects the overall results. Hence, a sequence order of pre-processing steps has been proposed and implemented on two different social networks - Twitter and Google+. Twitter has been selected because of its tremendous popularity among netizens and Google+ has been selected because the domain of data for the proposed approach closely matches with the users’ interests on Google+. As the existing approach for handling data on Twitter cannot be implemented directly to handle Google+ data, a modified approach for Google+ has been suggested and implemented by the authors. In addition, some new dictionaries for handling linguistic features have been compiled and existing dictionaries have also been modified to improve pre-processing results. The proposed approach is implemented to evaluate the overall results.
Quantum Behaved Fruit Fly Optimization Algorithm for Continuous Function Optimization Problems

In this paper, we study the fruit fly in the fruit fly optimization algorithm (FOA) system moving in a quantum multi-dimensional space and propose a quantum behaved fruit fly optimization algorithm (QFOA) for the continuous function optimization problem. Computational experiments and comparisons are carried out based on a set of benchmark functions. The computational results show the advantage of QFOA to the original FOA.
A Survey on Extreme Learning Machine and Evolution of Its Variants

Extreme Learning Machine (ELM) is most popular emerging learning algorithm that modify classical ‘Generalized’ single hidden layer feed forward network. Though some traditional gradient based learning algorithm like variant Levenberg-Marguardt (LM) and Back propagation (BP) are widely utilized for training in multi layer FFNN but some drawbacks of this mechanism are the most prime issue to promote ELM. It imparts efficient learning solutions for different practices of classification and regression under supervise learning. ELM sharply deals with the messes arise from the gradient based learning algorithm like stopping criteria, learning rate, learning epoch, local minimum etc. But due to some fallibility the different concepts of variants of ELM are presented. This paper clarifies about Extreme Learning Machine along with different types of variants.
Quantum Advantage for the LOCAL Model in Distributed Computing

There are two central models considered in (fault-free synchronous) distributed computing: the CONGEST model, in which communication channels have limited bandwidth, and the LOCAL model, in which communication channels have unlimited bandwidth. Very recently, Le Gall and Magniez (PODC 2018) showed the superiority of quantum distributed computing over classical distributed computing in the CONGEST model. In this work we show the superiority of quantum distributed computing in the LOCAL model: we exhibit two computational tasks that can be solved in a constant number of rounds in the quantum setting but require Omega(n) rounds in the classical (randomized) setting, where n denotes the size of the network.
Streaming Recommendation Algorithm with User Interest Drift Analysis

Recommender system is an effective way to solve the problem of information overload, and remarkable progress has been achieved along with the research and applications in both academic and industrial communities. However, the scalability of the conventional recommendation algorithms has been challenged by the exponential growth of the resource data size, and the increasing time span of the data also raises new requirements on the time-awareness of the algorithm. Therefore, a dynamic recommendation model monitoring the user interest drift has become an important task for streaming recommender system. In this paper, an incremental matrix factorization model named streamGBMF is proposed which utilizes the genre information as the resource feature. The proposed model can be updated in real-time according to the streaming data. To achieve the online updating, two kinds of forgetting mechanism are embedded to analyze the users’ current interest and preference accurately and timely. To evaluate the performance of our proposed model, the experiments are designed on the popular dataset MovieLens, and different algorithms are compared in streaming environment. The results show that our approach can effectively accelerate the model training process, and the recommendation performance can be improved by real-time user interest drift detection with proposed forgetting mechanisms.