Reviewerly/exHarmony huggingface dataset:
UltraSonic Watch

Two-factor authentication (2FA) provides an extra layer of security as it requires two pieces of evidence to be provided to an authentication mechanism before granting access to a user. However, people do not prefer 2FA; a reason for this is that 2FA requires performing extra actions. In this late-breaking work, we present UltraSonic Watch which provides a seamless 2FA through ultrasound. We report a small-scale within-subjects study (N=30) which investigates the performance of UltraSonic Watch and the participants' experience (in terms of perception, preference, and willingness to adopt). The results are promising as they revealed that ultrasound can be used to provide an efficient 2FA mechanism, transparent to the users, who are positive in adopting such an approach to increase the security of the authentication process.
The methods of using interactive technologies during teaching foundations of scientific research at higher educational establishments

Unpreparedness and the absence of motivational interest to science from students of educational level «Bachelor» causes the problem of low scientific activeness at the following educational levels and leads to violating the standards of academic etiquette, unconscious using the ways of plagiarism in scientific texts. The aim of the scientific activities of students mastering economic specialties; specifying the lesson model of specialized course, which envisages studying the fundamentals and methodology of scientific research. The research was made by using the methods of testing, questioning, observing, and pedagogical experiment, the participants of which were the students of higher educational levels «Bachelor» (fourth-year students – 43 persons) and «Master» (first-year students – 53 persons, second-year students – 38 persons) of specialty 071 «Accounting and taxation» at State Agrarian and Engineering University in Podilya and Poltava State Agrarian Academy.The scientific novelty of using teaching methods is in adapting the traditional course «Foundations of scientific research» to the corresponding educational program (in this case the field of knowledge 07 «Economics and entrepreneurship», specialty - 071 «Accounting and Taxation», the course «Methodology of scientific research in accounting»).The author’s approach is based on uniting students’ academic work with individual research and envisages the using of interactive training methods, such as discussion; interactive dialogue ‘human-computer»; situation analysis; the method of «incident»; business play. The presented lesson model on the topic «Linking and reviewing the text of the scientific-research task» with using the methods of situation analysis and business play enables to solve the following study aims: developing analytical thinking and skills of self-analysis; consolidating practical skills of work with information; broadening communicative competence; raising the motivation to work and scientific-research activities. The effectiveness of the described pedagogical experiment is confirmed by positive dynamics of structural changes as to disclosing the scientific potential and corresponding growth of the number of actual approbations of the research results obtained by the students at State Agrarian and Engineering University in Podilya and Poltava State Agrarian Academy.
Performance Analysis of Particle Swarm Optimization for Feature Selection

One of the key task in data mining is the selection of relevant features from datasets with high dimensionality. This is expected to reduce the time and space complexity, and consequently improve the performance of data mining algorithms for tasks such as classification. This study presents an empirical study of the effect of particle swarm optimization as a feature selection technique on the performance of classification algorithms. Two dataset from different domains were used: SMS spam detection and sentiment analysis datasets. Particle swarm optimization is applied on the datasets for feature selection. Both the reduced and raw dataset are separately classified using C4.5 decision tree, k-nearest neighbour and support vector machine. The result of the analysis showed that the improvement of classifier performance is case-dependent; some significant improvements are noticed in the sentiment analysis datasets and not in the SMS spam dataset. Although some marginal effect are observed on performance, it implies that with particle swarm optimization features selection the space complexity is reduced while maintaining the accuracy of the classifiers. Keywords—classification, feature selection, machine learning, particle swarm optimization, text mining
Predicting Popularity of Open Source Projects Using Recurrent Neural Networks

GitHub is the largest open source software development platform with millions of repositories on variety of topics. The number of stars received by a repository is often considered as a measure of its popularity. Predicting the number of stars of a repository has been associated with the number of forks, commits, followers, documentation size, and programming language in the literature. We extend prior studies in terms of input features and algorithm: We define six features from GitHub events corresponding to the development activities, and additional six features incorporating the influence of users (followers and contributors) on the popularity of projects into their development activities. We propose a time-series based forecast model using Recurrent Neural Networks to predict the number of stars received in consecutive k days. We assess the performance of our proposed model with varying k (1, 7, 14, 30 days) and with varying input features. Our analysis on five topmost starred repositories in data visualization area shows that the error rate ranges between 19.76 and 70.57 among the projects. The best performing models use either features from development activities only, or all metrics including all the features.
Quantum-capacity bounds in spin-network communication channels

Using the Lieb-Robinson inequality and the continuity property of the quantum capacities in terms of the diamond norm, we derive an upper bound on the values that these capacities can attain in spin-network communication i.i.d. models of arbitrary topology. Differently from previous results we make no assumptions on the encoding mechanisms that the sender of the messages adopts in loading information on the network.
IEEE Access Special Section Editorial: Multimedia Analysis for Internet-of-Things

Big data processing includes both data management and data analytics. The data management step requires efficient cleaning, knowledge extraction, and integration and aggregation methods, whereas Internet-of-Multimedia-Things (IoMT) analysis is based on knowledge modeling and interpretation, which is more often performed by exploiting deep learning architectures. In the past couple of years, merging conventional and deep learning methodologies has exhibited great promise in ingesting multimedia big data, exploring the paradigm of transfer learning, association rule mining, and predictive analytics etc.
Asymptotic properties of neural network sieve estimators

Neural networks are one of the most popularly used methods in machine learning and artificial intelligence nowadays. Due to the universal approximation theorem (Hornik et al. (1989)), a neural network with one hidden layer can approximate any continuous function on a compact support as long as the number of hidden units is sufficiently large. Statistically, a neural network can be classified into a nonlinear regression framework. However, if we consider it parametrically, due to the unidentifiability of the parameters, it is difficult to derive its asymptotic properties. Instead, we considered the estimation problem in a nonparametric regression framework and use the results from sieve estimation to establish the consistency, the rates of convergence and the asymptotic normality of the neural network estimators. We also illustrate the validity of the theories via simulations.
A Heterogeneous AdaBoost Ensemble Based Extreme Learning Machines for Imbalanced Data

Extreme learning machine (ELM) is an effective learning algorithm for the single hidden layer feed-forward neural network (SLFN). It is diversified in the form of kernels or feature mapping functions, while achieving a good learning performance. It is agile in learning and often has good performance, including kernel ELM and Regularized ELM. Dealing with imbalanced data has been a long-term focus for the learning algorithms to achieve satisfactory analytical results. It is obvious that the unbalanced class distribution imposes very challenging obstacles to implement learning tasks in real-world applications, including online visual tracking and image quality assessment. This article addresses this issue through advanced diverse AdaBoost based ELM ensemble (AELME) for imbalanced binary and multiclass data classification. This article aims to improve classification accuracy of the imbalanced data. In the proposed method, the ensemble is developed while splitting the trained data into corresponding subsets. And different algorithms of enhanced ELM, including regularized ELM and kernel ELM, are used as base learners, so that an active learner is constructed from a group of relatively weak base learners. Furthermore, AELME is implemented by training a randomly selected ELM classifier on a subset, chosen by random re-sampling. Then, the labels of unseen data could be predicted using the weighting approach. AELME is validated through classification on real-world benchmark datasets.
A Sensor Platform for Non-invasive Remote Monitoring of Older Adults in Real Time

The population of older adults is increasing across the globe; this growth is predicted to continue into the future. Most older adults prefer to live in their own home, but many live alone without immediate support. Living longer is often coupled with health and social problems and difficulty managing daily activities. Therefore, some level of care is required, but this is costly. Technological solutions may help to mitigate these problems by recognising subtle changes early and intervening before problems become unmanageable. Understanding a person’s usual behaviour when carrying out Activities of Daily Living (ADL) makes it possible to detect and respond to anomalies. However, current commercial and research monitoring systems do not offer an analysis of ADL and are unable to detect subtle changes. To address this gap, we propose the STRETCH (Socio-Technical Resilience for Enhancing Targeted Community Healthcare) sensor platform that is comprised of non-invasive sensors and machine learning techniques to recognise changes and allow early interventions. The paper discusses design principles, modalities, system architecture, and sensor network architecture.
AuthGPS

While GPS (Global Positioning System) navigation and GPS based autonomous car driving techniques are mature, the security of GPS signal has not been a primary system concern. In fact, it has been shown that an ordinary GPS can be easily spoofed by a low-cost, open-source based software defined radio (SDR) system such as bladeRF and HackRF [3, 4] which can cause serious complications to the navigation system of the car especially for self-driving cars which are driven based on the information from sensors, cameras, and GPS. In this work, we design a new GPS authentication system called lightweight authentication GPS (AuthGPS) to authenticate GPS signal against GPS spoofing and LTE base station broadcast message spoofing. In the past, there have been many successful attempts on GPS spoofing which resulted in shifting the destination of the car to the spoofer's desired location. In a case where the car is connected to the Internet via LTE network, the navigation system can find out if the GPS is spoofed by acquiring the correct GPS satellite information from LTE base stations' location information. However, a skillful attacker can spoof the LTE base station signals [7] as well using another SDR which will produce spoofed LTE base station information. Hence giving wrong information about GPSinformation according to the will of the attacker. Currently, there are defense methods against the GPS spoofing [6]. One of them is signal-processing-based methods. By monitoring unusual or unreasonable signal changes at GPS receivers, GPS spoofing attack can be detected. Received Power Monitoring (RPM) looks at all the received amplitude and automatic gain control (AGC) setpoint [1]. The receiver will sense drastic power jump if a spoofing attack occurs. However, an overly powerful spoofing attack with noise is not detectable in the case of this mechanism. Another way to detect spoofing is the symmetric-key encryption mechanism of GPS signals. According to previous research [2], encrypted precision code for anti-spoofing referred to as P(Y) code, might not be able to be spoofed. A spoofing attack can be detected by calculating cross-correlation between spoofed coarse/acquisition code, referred to as C/A code, and P(Y) code. Unfortunately, this P(Y) code is only for military purpose while the C/A code can be publicly accessible. This method requires knowledge of specific key which is not revealed to the public to decrypt P(Y) code. Monitoring the direction of arrival of the signals can be one of the methods [6]. A GPS receiver measures the direction-of-arrival vector with more than 3 antennas. Even though this signal-geometrybased system with multiple antennas makes GPS robust, an attacker can spoof signals from multiple directions, which is more difficult to detect the spoofing. The idea here is to develop a system that can discriminate the spoofed signal without complex computation or heavy message exchange. An important component of AuthGPS is a verification of valid GPS satellite information via LTE base stations' location information. We propose 6-digit one-time password-based authentication system in this paper.
Boosting Discrimination Information Based Document Clustering Using Consensus and Classification

Adequate choice of term discrimination information measure (DIM) stipulates guaranteed document clustering. Exercise for the right choice is empirical in nature, and characteristics of data in the documents help experts to speculate a viable solution. Thus, a consistent DIM for the clustering is a mere conjecture and demands intelligent selection of the information measure. In this work, we propose an automated consensus building measure based on a text classifier. Two distinct DIMs construct basic partitions of documents and form base clusters. The consensus building measure method uses the clusters information to find concordant documents and constitute a dataset to train the text classifier. The classifier predicts labels for discordant documents from earlier clustering stage and forms new clusters. The experimentation is performed with eight standard data sets to test efficacy of the proposed technique. The improvement observed by applying the proposed consensus clustering demonstrates its superiority over individual results. Relative Risk (RR) and Measurement of Discrimination Information (MDI) are the two discrimination information measures used for obtaining the base clustering solutions in our experiments.
Unsupervised online multitask learning of behavioral sentence embeddings

Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora. Recent research, however, has shown that sentence embeddings trained using in-domain data or supervised techniques, often through multitask learning, perform better than unsupervised ones. Representations have also been shown to be applicable in multiple tasks, especially when training incorporates multiple information sources. In this work we aspire to combine the simplicity of using abundant unsupervised data with transfer learning by introducing an online multitask objective. We present a multitask paradigm for unsupervised learning of sentence embeddings which simultaneously addresses domain adaption. We show that embeddings generated through this process increase performance in subsequent domain-relevant tasks. We evaluate on the affective tasks of emotion recognition and behavior analysis and compare our results with state-of-the-art general-purpose supervised sentence embeddings. Our unsupervised sentence embeddings outperform the alternative universal embeddings in both identifying behaviors within couples therapy and in emotion recognition.
BricklAyeR: A Platform for Building Rules for AmI Environments in AR

With the proliferation of Intelligent Environments, the need for configuring their behaviors to address their users’ needs emerges. In combination with the current advances in Augmented and Virtual Reality and Conversational Agents, new opportunities arise for systems which allow people to program their environment. Whereas today this requires programming skills, soon, when most spaces will include smart objects, tools which allow their collaborative management by non-technical users will become a necessity. To that end, we present BricklAyeR, a novel collaborative platform for non-programmers, that allows to define the behavior of Intelligent Environments, through an intuitive, 3D building-block User Interface, following the Trigger-Action programming principle, in Augmented Reality, with the help of a Conversational Agent.
Feature Engineering for Search Advertising Recognition

Feature engineering is one of the key technologies in the research of search advertisigng recognition. Most of the existing search advertising methods are selected according to the prior knowledge, which is too subjective to be popularized. Taking the advertisement of Ali search advertising as the research object, a feature processing method based on the pre-analysis of store and user data is put forward, and then the conversion rate is predicted with XGBoost (eXtreme Gradient Boosting). Experiments show that compared with other priori Feature Engineering, the proposed method can significantly improve the prediction results.
On Dimensional Linguistic Properties of the Word Embedding Space

Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties. In this work, we analyze word embeddings in terms of their principal components and arrive at a number of novel and counterintuitive observations. In particular, we characterize the utility of variance explained by the principal components as a proxy for downstream performance. Furthermore, through syntactic probing of the principal embedding space, we show that the syntactic information captured by a principal component does not correlate with the amount of variance it explains. Consequently, we investigate the limitations of variance based embedding post-processing algorithms and demonstrate that such post-processing is counter-productive in sentence classification and machine translation tasks. Finally, we offer a few precautionary guidelines on applying variance based embedding post-processing and explain why non-isotropic geometry might be integral to word embedding performance.
pyro: a framework for hydrodynamics explorations and prototyping

pyro is a Python-based simulation framework designed for ease of implementation and exploration of hydrodynamics methods.It is built in a object-oriented fashion, allowing for the reuse of the core components and fast prototyping of new methods.
PNemu: An Extensible Modeling Library for Adaptable Distributed Systems

PNemu is an extensible Python library primarily tailored for modeling adaptable distributed discrete-event systems by means of standard (Low- and High-level) Petri nets. The core of PNemu is composed of a number of modules for the editing and interactive simulation of models. In particular, it supplies a number of off-the-shelf building blocks to easily formalize self-adaptation having a decentralized control through multiple interacting feedback loops. PNemu can be used in conjunction with other software tools to efficiently compute the state space and perform formal verification activities. This paper describes the PNemu structure, features, and usage.
Clustering-Based Article Identification in Historical Newspapers

This article focuses on the problem of identifying articles and recovering their text from within and across newspaper pages when OCR just delivers one text file per page.We frame the task as a segmentation plus clustering step.Our results on a sample of 1912 New York Tribune magazine shows that performing the clustering based on similarities computed with word embeddings outperforms a similarity measure based on character n-grams and words.Furthermore, the automatic segmentation based on the text results in low scores, due to the low quality of some OCRed documents.
Pulse-enhanced two-photon interference with solid state quantum emitters

The ability to entangle distant quantum nodes is essential for the construction of quantum networks and for quantum information processing. For solid-state quantum emitters used as qubits, it can be achieved by photon interference. When the emitter is subject to spectral diffusion, this process can become highly inefficient, impeding the achievement of scalable quantum technologies. We study two-photon interference in the context of a Hong-Ou-Mandel (HOM)-type experiment for two separate quantum emitters, with different detunings with respect to a specific target frequency. We evaluate the second order coherences that characterize photon indistinguishability between the two emitters. We find that the two-photon interference operation that is inefficient in the absence of a control protocol, when the two detunings are different and spectral overlap is lessened, can be highly improved by a periodic sequence of $\ensuremath{\pi}$ pulses at a set target frequency. Photon indistinguishability in solid state and other quantum emitters subject to spectral diffusion can thus be enhanced by the proposed pulse sequence and similar external control protocols despite the fluctuations in the environment.
Buzz groups facilitate collaborative learning and improve histopathological competencies of students

Traditional teacher-centered histopathology training is based on theoretical lectures and practical tutorials. We hypothesize that learning outcomes improve if students are activated by demonstrating cardinal features of slides to each other and discussing their pathogenesis. Buzz groups (BGs) could facilitate this. The aim of this study was to investigate the effectiveness of student-centered BGs, i.e., peer-teaching, versus traditional teacher-centered histopathology teaching. Furthermore, we compared digital with analog microscopy.In addition to traditional guided instruction and explanation of slides, neighboring students demonstrated to each other histopathological features and discussed associated pathogenesis.After only 4 course lessons, the BG students did much better than the control group (ANCOVA p = 0.002; F = 9.7). Then the control group also applied the BG technique. After another 4 lessons, the control group was able to catch up almost completely (ANCOVA p = 0.36; F = 0.9). Overall, there was no difference in time on task.Collaborative BGs improve the learning of histopathological competencies. They motivate and activate students to learn. The course also increased the appreciation of students for histopathology. For BGs, digital microscopy was better suited than traditional analog microscopy. The application of BGs in the context of analyzing microscopic images should be disseminated and studied on larger cohorts.