Reviewerly/exHarmony huggingface dataset:
FacialPen: Using Facial Detection to Augment Pen-Based Interaction

Pen-based interactions have been ubiquitously adopted on mobile and stationary devices, but the usability can be further augmented through the use of advanced techniques. In this work, we propose FacialPen, a prototype that uses facial gestures to trigger commands for pen-based manipulation. In our prototype, a fisheye camera is mounted to the end of a stylus that provides a broad view from which to capture the human face. We facilitated an elicitation study to identify natural and user-defined gestures for interactions with facial expressions. Different gestures can be further discerned via face detection and a classification pipeline. We designed a sketching demonstration application to explore usage scenarios and evaluated the effectiveness of FacialPen through a qualitative study. The user study posits that FacialPen supports efficiency by reducing screen widgets, enabling the continuity of creation work and liberating the user’s stylus holding postures when switching sketch functions.
Fast Three-dimensional Reconstruction Using Composite Three-frequency Heterodyne Method

It is a challenge to accelerate the acquisition speed of the three-frequency heterodyne due to the property of multi- frame. In this paper, we propose a composite three-frequency heterodyne technique. Specifically, the frequency division multiplexing (FDM) technique is applied to combine three phase- shifting patterns of each frequency into a composite pattern by modulating each phase-shifting pattern onto the carrier pattern and summing them. This multiplexing technique reduces 9 projection images in the three-frequency heterodyne method to 3 images. Experimental results show that the average plane fitting error of the reconstructed white plate is approximately 0.58mm, which is comparable to other commonly used methods while accelerating the acquisition speed.
An Attentive Listening System for Autonomous Android ERICA: Comparative Evaluation with Human Attentive Listeners

An attentive listening system for autonomous android ERICA is presented. Our goal is to realize a humanlike natural attentive listener for elderly people. The proposed system generates listener responses: backchannels, repeats, elaborating questions, assessments, and generic responses. The system incorporates speech processing using a microphone array and real-time dialogue processing including continuous backchannel prediction and turn-taking prediction. In this study, we conducted a dialogue experiment with elderly people. The system was compared with a WOZ system where a human operator played the listener role behind the robot. As a result, the system showed comparable scores in basic skills of attentive listening, such as easy to talk, seriously listening, focused on the talk, and actively listening. It was also found that there is still a gap between the system and the human (WOZ) for high-level attentive listening skills such as dialogue understanding, showing interest, and empathy towards the user.
Social semiotics: communicative and socio-cultural practices. The Russian-speaking contribution to the development of social semiotics in 1970–2000s

The intensive transformation of literally all spheres of life in modern society stimulates the search for an adequate conceptual apparatus in order to understand these processes. The purpose of this work is to show the possibilities of applying semiotic analysis to social-cultural phenomena and processes, which involves going beyond the traditional limitation of semiotics with linguistic phenomena and the reduction of social semiotics to the pragmatic contexts of language use. At the same time, particular attention is given to approaches and developments obtained in the last quarter of the previous century by Russian-speaking authors such as Yuri Lotman, Georgy Bogin, Aron Brudnyj, Tamara Dridze, Aleksandr Portnov, Vladimir Sulimov, as well as their colleagues and students. The priority function and significance of the developments of the aforementioned authors and schools is specifically associated with the discovery of non-trivial possibilities of the semiotic approach, which goes beyond considering the peculiarities of using language in various social practices for the semiotic analysis of such practices themselves.
IGSO for QoS Awareness Web Service Composition Optimization

In order to overcome the problems of low efficiency and poor service quality in web service composition optimization methods, an improved Glowworm Swarm Optimization (IGSO) algorithm is proposed. Considering the subjective feeling of users, flexible intervals are introduced to describe the user's QoS requests in this paper. In IGSO, elite guidance strategy is adopted, with the global information introduced during the location-update phase to accelerate the searching speed, which can effectively overcome the shortcomings appeared in traditional Glowworm Swarm Optimization (GSO). Simulation results show that IGSO has better convergence and stability than traditional GSO, and a better optimistic effect is achieved.
Brightness Intensity-Based Transient Motion Prediction

Brightness intensity of patterns generated from moving object frames is considered in motion estimation. The object is given excitation from 10 to 300 Hz frequencies to cover both steady and transient motion. The movement is captured using high-speed and high-resolution cameras. Patterns from a base image are developed to correlate the motion captured. Correlation parameter is obtained at every frequency, as a measure of motion estimation effectiveness. The technique is applicable in estimating both 2D and 3D motions through noncontact measurement method. The speckling is done on the object to form templates. The distortion in the lens is undertaken for perfect displacement calculation. The technique is applicable to estimate the motion in the specified region also. The templates play an important role in mathematical calculations for displacement. An optical flow method based on intensity calculations is applied in the formation of templates. The lens distortion effects are also considered for image point alteration. The technique is valid for both fine and coarse patterns.
EXTRA: An Experience-driven Control Framework for Distributed Stream Data Processing with a Variable Number of Threads

In this paper, we present design, implementation and evaluation of a control framework, EXTRA (Experience-driven conTRol frAmework), for scheduling in general-purpose Distributed Stream Data Processing Systems (DSDPSs). Our design is novel due to the following reasons. First, EXTRA enables a DSDPS to dynamically change the number of threads on the fly according to system states and demands. Most existing methods, however, use a fixed number of threads to carry workload (for each processing unit of an application), which is specified by a user in advance and does not change during runtime. So our design introduces a whole new dimension for control in DSDPSs, which has a great potential to significantly improve system flexibility and efficiency, but makes the scheduling problem much harder. Second, EXTRA leverages an experience/data driven model-free approach for dynamic control using the emerging Deep Reinforcement Learning (DRL), which enables a DSDPS to learn the best way to control itself from its own experience just as a human learns a skill (such as driving and swimming) without any accurate and mathematically solvable model. We implemented it based on a widely-used DSDPS, Apache Storm, and evaluated its performance with three representative Stream Data Processing (SDP) applications: continuous queries, word count (stream version) and log stream processing. Particularly, we performed experiments under realistic settings (where multiple application instances are mixed up together), rather than a simplified setting (where experiments are conducted only on a single application instance) used in most related works. Extensive experimental results show: 1) Compared to Storm’s default scheduler and the state-of-the-art model-based method, EXTRA substantially reduces average end-to-end tuple processing time by 39.6% and 21.6% respectively on average. 2) EXTRA does lead to more flexible and efficient stream data processing by enabling the use of a variable number of threads. 3) EXTRA is robust in a highly dynamic environment with significant workload change.
Kernel density estimation in mixture models with known mixture proportions

In this article, we consider the density estimation for data with a mixture structure, where the component densities are assumed unknown, but for each observation, the probabilities of its membership to the subpopulations are known or estimable from other resources. Data of this kind arise from practice and have wide applications. Motivated from the classical kernel density estimation method for a single population, we propose a weighted kernel density estimation method to estimate the component density functions nonparametrically. Within the framework of the EM algorithm, we derive an algorithm that computes our proposed estimates effectively. Via extensive simulation studies, we demonstrate that our methods outperform the existing methods in most occasions. We further compare our methods with existing methods by real data examples.
The correlation polarization method: A novel automatic technique for seismic event detection and first-break picking

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsThe correlation polarization method: A novel automatic technique for seismic event detection and first-break pickingAuthors: Maher NasrBernard GirouxJ. Christian DupuisMaher NasrNRS-ETESearch for more papers by this author, Bernard GirouxNRS-ETESearch for more papers by this author, and J. Christian DupuisUniversité LavalSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3568668.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe present a new single-level method for microseismic event detection and first-break picking. The method is added to the arsenal of window-based techniques that are available to deal with multicomponent data. It was conceived to be less time consuming and easy to parameterize. The proposed method estimates the linear polarization level through a new rectilinearity function that analyzes the signal polarization using the correlation matrix. This innovative approach allows users to establish a meaningful set of metrics for the interpretation of rectilinearity. These metrics facilitate the choice of thresholds used to distinguish seismic events from ambient noise. The performance of this novel time-domain approach is presented for synthetic and real microseismic data. Results show that the proposed method can be an efficient alternative to more traditional schemes.Keywords: correlation, polarization, 3C, microseismic, seismic attributesPermalink: https://doi.org/10.1190/segam2021-3568668.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Maher Nasr, Bernard Giroux, and J. Christian Dupuis, (2021), "The correlation polarization method: A novel automatic technique for seismic event detection and first-break picking," SEG Technical Program Expanded Abstracts : 2061-2065. https://doi.org/10.1190/segam2021-3568668.1 Plain-Language Summary Keywordscorrelationpolarization3Cmicroseismicseismic attributesPDF DownloadLoading ...
Modified Binary Ant Colony Optimization for Drift Compensation

In data analytics and pattern recognition, feature selection is a critical task to provide a subset of features with minimum redundancy. This reduces the computation time as well as cost. In this manuscript, a correlation based feature selection approach based on a modified binary ant colony optimization algorithm (MBACO) is proposed. Combined with random forest regression, the proposed MBACO algorithm is customized for a drift compensation application. In this application, the ant road map is initialized to avoid the local optimum. The proposed method is compared with that of binary particle swarm optimization on a well-known UCI dataset. Experimental results show that the proposed method exhibits better performance over the binary particle swarm optimization based approach.
The Impact of Entity Cards on Learning-Oriented Search Tasks

Entity cards are a common occurrence in today's web Search Engine Results Pages (SERPs). SERPs provide information on a complex information object in a structured manner. Typically, they combine data from several search verticals. They have been shown to: (i) increase users' engagement with the SERP; and (ii) improve decision making for certain types of searches (such as health searches). In this paper, we investigate whether the benefits of showing entity cards also extend to the Search as Learning (SAL) domain. Do learners learn more when entity cards are present on the SERP? To answer this question, we designed a series of learning-oriented search tasks (with a minimum search time of 15 minutes), and conducted a crowdsourced Interactive Information Retrieval (IIR) user study (N=144) with four interface conditions: (i) a control with no entity cards; (ii) displaying relevant entity cards; (iii) displaying somewhat relevant entity cards; and (iv) displaying non-relevant entity cards. Our results show that (i) entity cards do not have an effect on participants' learning, but (ii) they do significantly impact participants' search behaviours across a range of dimensions (such as the dwell time and search session duration).
NOESIS: Surfing the technological wave from Portugal

Noesis, founded in 1995, was a Portuguese business consulting firm that provided services and solutions aimed at supporting its customers to increase competitiveness, facilitate management, reduce costs and optimize processes, having as core competencies the technological background of its founding partners. Because of the company’s tenacity and strategic focus, 11 years later, in the beginning of 2016, Noesis was a leader in the Portuguese Quality Management market for the independent provision of software testing services, according to a study from International Data Corporation. In January 2016, Noesis had more than 500 employees in its different locations: Lisbon, Porto, Coimbra, Brussels, São Paulo, Dublin and Rotterdam, ensuring the adequate response to customer challenges in Portugal and international markets. The company’s outlook was positive, but the competition was increasing, and interest rates were expected to rise in the nearby future. Having a substantial amount of capital leveraging and two failed international ventures (Angola and Brazil), should Noesis continue to invest and grow at a fast pace or take some time to consolidate its operations?
Challenges faced by Business Analysts eliciting User Requirements in the software industry: A survey-based study

Requirement gathering is considered as one of the most important activity is software development lifecycle (SDLC). If requirements are not gathered properly then there is huge expectation gap between what the customer desire and what the developer built. Currently, there are various techniques used to elicit software requirements such as interview, questionaries, and prototyping etc. However, identify the common risks arise and adopting the best methodology in requirement gathering is still a topic of debate. In this paper, we performed a survey-based study featuring 21 participants aims to identify various aspects in requirement gathering process such as best approaches, techniques, problems occurred, risks involved, and benefits. The participants involved in this study are from diverse industrial roles (i.e., business analyst, software developer and technical lead etc.) and are working in requirement gathering process of real software projects. The main findings of this survey study shows that many participants (16/21, 76.2%) use interview approach followed by prototyping approach (13/21, 61.9%) to elicit the requirements. Moreover, related to potential risks, (9/21, 42.9%) participants responds insufficient user involvement and ambiguous requirements as most common risks experienced by business analyst, whereas (1/21, 4.8%) participant select gold plating as less frequent risk arise during requirement gathering.
Distilling the Requirements of Gödel’s Incompleteness Theorems with a Proof Assistant

Abstract We present an abstract development of Gödel’s incompleteness theorems, performed with the help of the Isabelle/HOL proof assistant. We analyze sufficient conditions for the applicability of our theorems to a partially specified logic. In addition to the usual benefits of generality, our abstract perspective enables a comparison between alternative approaches from the literature. These include Rosser’s variation of the first theorem, Jeroslow’s variation of the second theorem, and the Świerczkowski–Paulson semantics-based approach. As part of the validation of our framework, we upgrade Paulson’s Isabelle proof to produce a mechanization of the second theorem that does not assume soundness in the standard model, and in fact does not rely on any notion of model or semantic interpretation.
Finding Web Application Vulnerabilities with an Ensemble Fuzzing

Cyberattacks have been a constant on the Internet and their impact and cost have risen to billions of dollars. They are usually associated with the exploitation of vulnerabilities in web applications, the most common form of accessing services and organizations’ data. These applications support a myriad of services for handling multiples daily needs of our lives (e.g., social media, e-commerce, bank account access). There are currently almost 2 billion of websites active [1] and the number of vulnerabilities disclosed and associated with applications they handle continues growing [2] . Cross Site Scripting (XSS) and SQL injection (SQLi) are the two most prevalent web vulnerabilities and they pose the first place in OWASP Top 10 [3] . Although these vulnerability classes are really well known, they continue appearing in recent applications used by millions of consumers [4] .
Development of a Python Program Simulating Motions of Celestial Bodies

Abstract While the research of the celestial body is essential to understand the universe, it requires considerable costs in time and equipment. However, the computation methods can be used to analyze the astrophysics process in a simple approach. In this paper, a model of computing the motion of solar system based on Euler’s method is established. The model divided the process into infinite small time steps, and iteration is used to represent integration and update the position of planets over the process. The NumPy and matplotlib are used to visualize the motion. By the analysis of momentum and energy through the process and the application in the 2-body situation, the model is proved to be valid and accurate.
Dependency Parsing Representation Learning for Open Information Extraction

Open information extraction (OIE) aims to extract structured information from text. Specifically, it extracts arguments and their logical relationships from a sentence. However, as a syntactic feature commonly used in OIE, the tree structure of dependency parsing (DP) is often overlooked. In this paper, we propose a dependency parsing representation learning model for OIE. This model can fully represent the tree structure and edge information of DP. Then we fuse the learned DP embedding in a Graph Convolutional Network-based OIE model. The experiments show that the proposed OIE model outperforms four baselines on three open-sourced data sets.
Usability Evaluation of Novel Text CAPTCHA Schemes Based on Colors and Shapes

Multiple web platforms are an imminent need of web security mechanisms to prevent vulnerable attacks, automated spammers and breach of data. One such security mechanism is to ensure that only authenticated users access them and one such cybersecurity algorithm is CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). In this paper, we have designed novel and efficient text CAPTCHA schemes named MACS-TCHA (Manipulation of Alphanumeric characters, Colors and Shapes—Turing test to tell Computers and Human Apart). These CAPTCHAs are developed by manipulating alphanumeric characters, shapes and colors for better usability. Various user survey studies with different factors have evaluated our proposed CAPTCHA with significant improvement in terms of its usability.
Monarch Butterfly Optimization for Reliable Scheduling in Cloud

Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources. The scheduling of the cloud tasks is a well-recognized NP-hard problem. The Task scheduling problem is convoluted while convincing different objectives, which are dispute in nature. In this paper, Multi-Objective Improved Monarch Butterfly Optimization (MOIMBO) algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal solutions. Three different dispute objectives, such as makespan, reliability, and resource utilization, are deliberated for task scheduling problems.The Epsilon-fuzzy dominance sort method is utilized in the multi-objective domain to elect the foremost solutions from the Pareto optimal solution set. MOIMBO, together with the Self Adaptive and Greedy Strategies, have been incorporated to enrich the performance of the proposed algorithm. The capability and effectiveness of the proposed algorithm are measured with NSGA-II and MOPSO algorithms. The simulation results prompt that the proposed MOIMBO algorithm extensively diminishes the makespan, maximize the reliability, and guarantees the appropriate resource utilization when associating it with identified existing algorithms.
Research on Intelligent Tutor System of Spoken English based on Phonetic Evaluation

In order to improve the accuracy of oral English intelligent tutor system and to improve students' oral English proficiency, this paper proposes a novel oral English intelligent tutor system based on phonetic evaluation technology. The system is based on speech evaluation technology and combines artificial intelligence technology and speech recognition technology. The results show that the oral English intelligent tutor system based on phonetic evaluation has a significant application effect, which can not only improve the accuracy of the oral English intelligent tutor system, but also enhance students' interest in oral English learning.