Reviewerly/exHarmony huggingface dataset:
“RESUME SELECTOR” Using Pyspark and Hadoop

Resumes are commonly used for recruitment of employees by companies, but the selection process is not automated and the technology used to store and evaluate resumes is outdated. Hence, there is a need to develop a system that can accommodate huge amount of resumes received by the company and process them in real time. So, our proposed system uses the capabilities of Hadoop Framework to store Terabytes of data in a cluster to improve the efficiency of selection process. Further, PySpark is used to process the data parallelly in a distributed environment which generates the result in an efficient manner. The proposed algorithm works on keyword-based search (KBS) to filter out all the required skills from resumes. Further, the aggregate weightage for each resume is computed and checked against a confidence level to select the resumes. Due to distributed and parallel computation, our system performs in a more efficient and accurate manner than the traditional systems.
ExpReal: a Writing Language and System for Authoring Texts in Interactive Narrative

As interactive narratives, by definition, change according to the user’s choices (dynamic story), so do the dialogue utterances by the characters. Writing all possible utterances manually faces scaling problems. This motivates the use of natural language generation techniques. We present ExpReal, a surface realizer and templating language that allows authors of interactive narratives to write flexible and enriched templates while maintaining control over their use. Templates are automatically selected based on author-specified conditions relating to the world state (e.g. characters’ emotions) or the current task at hand. ExpReal has been developed to support at least three languages (English, French and Dutch).
5‐3: <i>Invited Paper:</i> Towards Cost‐Effective ARIMR Display Systems: The Emergence of an Industrial Hardware Ecosystem allowing low cost mass production.

Augmented, mixed and virtual Reality (AR/MR) headsets as well as smart glasses have the potential to revolutionize how we work, communicate, travel, learn, teach, shop and get entertained. An MR headset places virtual content into the user's view of the real world, either via an optical see through mode (AR/MR) or video pass‐through‐mode (VR/MR). Today, the return on investment for MR use has been demonstrated widely for the enterprise and defense sectors, but only partially for consumer. In order to meet the high market expectations especially for the upcoming consumer field, several challenges must be addressed, most of them related to display and optical technologies addressing size, form factor, weight, costs, power consumption and, of course, performance (such as brightness, resolution, and contrast).
Testing of car speed approximation equation from yaw mark using radio control car simulator

Abstract In forensic science, yaw mark is one of the interesting evidences from the traffic accident. It caused by a vehicle wheel slides on the surface of the road when the speed before braking of the vehicle is high enough. Therefore, prediction of the vehicle speed is important to compare with the legal maximum speed. This research focused on the simulation of a car’s braking on a curve using the radio control car. High-speed video and Tracker Video Analysis (Tracker) software were used to investigate the positions and speeds of the radio control car. The braking path from Tracker software was assumed as the yaw mark in the real accident scene. The equation of the braking path was investigated using the total least-squares spiral curve fitting. The speeds of the car for each position of the braking path were calculated by the method from Daily’s research and the new method modified from Daily’s research. The predicted speed was compared with the one measured from Tracker. The results were similar at the beginning of the braking path, but increasingly different when the speed of the radio control car decreased. Moreover, the modified Daily’s method can investigate the speed better than the Daily’s method for the speed based on a clothoid spiral fitting and Archimedean spiral fitt ing. Findings from this study can be used to develop instructional materials for intermediate mechanics to demonstrate a real application.
Frequentie en luie-stoeltaalkunde

Frequency and arm-chair linguistics. A Frisian linguist looks back on Dutch Linguistics and on himself In this contribution I focus on a paper written by Maarten Lemmens on progressive constructions, which appeared in Dutch Linguistics (2015). This paper illustrates the usefulness of frequency in linguistic research convincingly. Additionally I discuss the tension within generative grammar between vision on the one hand and a methodologically adequate treatment of data on the other hand. Finally, I look back on my own activities as a linguist, presenting some personal thoughts about the question what the usefulness is of mediocrity.
Human Behavior Traits Detection and Avoidance Using Secure Shell Based Environment

Now a day it is not possible to have mobile phone with network connection all time. That time anyone use user's username and password to login user account without user permission sever sent the security notification to the user mail ID but due to poor network coverage or zero data connect the security notification cannot reach the user. In case user use two step authentication to secure the account but two step authentication having token vulnerability so we purpose to secure the login page vulnerability by adding new authentication. In this authentication is extra layer for security it automatically find unauthorized user and block the device and keep users personal information's safe.
A workable framework

The immediacy of patients concerns requires decisions to be made. Justice demands that those decisions should be of acceptable ethics. Yet, the previous chapters have demonstrated the limitations because of relatively unexplored notions of the ethics of everyday medicine and the problematic nature of developing ethical systems. Adjudicating justice has been approached as a deduction from ethical principles yet the quasideductive approach actually is the logical Fallacy of Confirming the Consequence. Fallacies are so named because of the risk of invalid conclusions. Yet, there are “work arounds.” Further, while a fully developed logical structure is yet to be developed, it is important to have good habits of thought—intellectual virtues.
Two notions of sub-behaviour for session-based client/server systems: 10 Years Later

invited-talk Two notions of sub-behaviour for session-based client/server systems: 10 Years Later Share on Authors: Franco Barbanera Universita di Catania, Italy Universita di Catania, ItalyView Profile , Ugo de'Liguoro Universita di Torino, Italy Universita di Torino, ItalyView Profile Authors Info & Claims PPDP '20: Proceedings of the 22nd International Symposium on Principles and Practice of Declarative ProgrammingSeptember 2020 Article No.: 2Pages 1–3https://doi.org/10.1145/3414080.3414082Published:08 September 2020 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
PSO and Genetic modeling of Deep Features for Road Passibility Analysis during Floods

In recent years, social media outlets have been widely exploited for disaster analysis and retrieving relevant information. Social media information can help in several ways, such as finding the mostly affected areas and information on casualties and scope of the damage etc. In this paper, we tackle a specific facet of social media in natural disasters, namely the identification of passable routs in a flooded region. In detail, we propose several solutions for two relevant tasks, namely (i) identification of flooded and non-flooded images in a collection of images retrieved from social media, and (ii) identification of passable roads in a flooded region. To this aim, we mainly rely on existing deep models pre-trained on ImageNet and Places dataset, where the models pre-trained on ImageNet extract object specific and the ones pre-trained on places dataset extract scene-level features. In order to properly utilize the object and scene-level features, we rely on different fusion methods including Particle Swarm Optimization (PSO) and Genetic Modeling of the deep features in a late fusion manner. The evaluation of the proposed methods are carried out on the large-scale datasets provided for MediaEval- 2018 benchmarking competition on Multimedia and Satellites. The results demonstrate significant improvement in the performance over the baselines.
Effects of membership functions for fuzzy logic controlled autonomous mobile robot

There is a growing trend in autonomous robotics research. A consistent collision avoidance and path following method is needed for an intelligent and operative mobile robot navigation. Usually robots are fitted with sensors for detecting the surrounding. Nevertheless, they still are unreliable due to ambiguity in the surroundings. Fuzzy logic has been long-established as an suitable tool for handling ambiguity that arises from vague data. Many Studies have presented Fuzzy logic models for obstacle avoidance wheeled robots frequently leading to a dead zone and inability to avoid obstacles. We presented a model with 8 inputs, 2 outputs and 27 rules for the robot movement. The research investigates the possibility of upholding uncertainty by changing controller membership functions to achieve optimum results. The study was implemented and tested through simulation by V-REP and MATLAB software. The outcomes reveal that tuning of membership functions enhance controller performance.
A Framework to Evaluate Webpage Segment Recognizers

Many webpages are inaccessible because they cannot be read by non-sighted human users, nor can they be appropriately indexed by search engines. Using tools that can identify and classify the functional elements of pages, it is possible to create accessible versions. A way to achieve this goal is to extract segments and assign the right semantic role to them.
DIALOGIC: A Toolkit for Generative Interactive Dialog

Open-ended narrative games present vast surfaces that require large amounts of compelling writing. Automated solutions have made little progress in this domain and talented human writers who code are few and far between. Thus the question of how to augment writers with digital tools—without requiring them to become programmers, or to need continual assistance from programmers—is a crucial one for the field. To address this question, we present Dialogic1 , a scripting language, execution environment, and set of online tools designed to support skilled human authors in creating engaging interactive writing for games, leveraging generative strategies atop a simple syntax familiar to writers. We describe the system's goals and motivations, architecture, and technical details, and evaluate its use in two production-quality titles.
Exploratory Methods for Evaluating Recommender Systems

A common and recently widely accepted problem in the field of machine learning is the black box nature of many algorithms. In practice, many machine learning algorithms can only be viewed and evaluated in terms of their inputs and outputs, without taking their internal workings into account. Perhaps the most notorious examples in this context are artificial neural networks and deep learning techniques, but they are certainly not the only techniques that suffer from this problem. Matrix factorisation models for recommendation systems, for example, suffer from the same lack of interpretability. Our research focuses on applying and adapting pattern mining techniques to gain meaningful insights in recommendation algorithms by analysing them in terms of both their input and output, also allowing us to compare different algorithms and discover the hidden biases that lead to those differences.
Effect of IFAD-VCDP on Input-Output Commercialization of Rice Farmers in Niger State of Nigeria

The present research empirically determined the effect of IFAD programme on output and input commercialization among rice farmers in Niger State of Nigeria using field survey data elicited from 111 participants and 185 non-participants (90 and 95 non-participants from exposed and control groups respectively) drawn via multi-stage sampling design. The collected data were analyzed using Heckman’s model. The empirical finding showed that the programme has no effect on output commercialization due to the poor market outlet as a result of no provision of established off-takers. However, the input support aspect of the programme mandate exerts an effect on the input commercialization among the farmers in the studied area. Furthermore, the poor incentive for extension agents, non-productive large household dominated by weaker people and one-way income traffic affected output commercialization among the rice farmers in the study area. Therefore, based on these, the programme should link the farmers with off-takers so as to sustain the viability of the programme even after its life span. In addition, to tackle poor extension contact, the farmers should effectively and efficiently use their social capital to introduce farmer to farmer extension approach by using private extension service as the technical support unit.
Developing Work in Confidence, Similarity Structure, and Modeling User Event Time

Broadly, the goal of my research is to develop modeling techniques for recommender systems data in the streaming context. Streaming models in recommender systems have received attention in demanding applications such as social media streams and news delivery as model requirements are complex. To date, my work in recommender systems has focused on collaborative filtering with work in exploiting structure in similarity evaluation, modeling event time of user activity, and confidence measures for individual predictions. Exploiting underlying structure in similarity evaluation is shown to positively impact prediction accuracy. Modeling event times of user activity presents many applications in soliciting user involvement in ecommerce settings. Early work is in progress in developing confidence measures for individual rating predictions. While these efforts have not been focused on the streaming environment to date, they do form a basis for future work in which computational demands are higher. Extensions to the streaming domain are discussed.
The Role of Distance Learning in the Transformation Processes of National Higher Education Systems

The emergence and active spread of distance learning has fundamentally changed national higher education systems around the world, making them more open and convenient, which is especially important now during the COVID-19 pandemic. At the same time, national higher education systems face certain challenges associated with the spread of distance and mobile learning technologies. This paper shows the role of distance learning and mobile learning technologies in the transformational changes of higher education systems, analyzes the possibilities and prospects for the development of these technologies, taking into account the growing trends of digitalization of modern society. The empirical material for writing this work was the data of the Google search engine, which made it possible to analyze the frequency of search queries for the phrase “distance learning” and its equivalent in Russian, which made it possible to draw conclusions about the dynamics of user interest in the problem under consideration.
Saliency Detection in Textured Images

Recently, salient object detection has achieved significant development. Unfortunately, existing methods mainly depend on color differences, not effective for textured images. This is because the visual patterns of textures cannot be well measured with existing methods. In this paper, we address this challenge by using windowed inherent variation to capture texture information and meanwhile performing edge-ware superpixel segmentation. Thus, superpixels can be well utilized to distinguish contents from textures for improving saliency detection. We further employ background and foreground priors via graph-based manifold ranking to improve saliency estimation. For evaluating our method, we collected 200 textured images from literature to build a dataset. With both qualitative and quantitative evaluations on our dataset and other two benchmarks, the results show that our approach can significantly promote saliency detection in textured images, compared with the other state-of-the-art methods.
A Service Composition optimization Model Based on Petri Nets and Service Contracts

With the increase of Web services on the Internet, the services available to users have also become diversified. How to quickly and accurately provide the appropriate services for users has become a research hotspot. Therefore, this paper proposes a service composition optimization method based on the extended Petri net. This method realizes the automatic mapping from the user functional requirements to the service process of the abstract layer firstly. Secondly, the user’s non-functional requirements constraints are labeled on the service process by means of the service contract semantics of the extended Petri net, and then the service composition optimization model is fully and accurately defined. At last, in order to efficiently solving the optimization model, the SCoriented fuzzy matching algorithm and the SC-oriented service composition optimization genetic algorithm are designed. The proposed method can quickly and accurately provide the highquality service solutions for users.
Design Strategy of Wearable Equipment Based on Smart Home Pension System

Wearable device is a new type of IoT product that has the function of directly contacting the human body and collecting physiological physical data and can provide critical intelligent services for the home care system. This paper analyzes the inevitable trend of the development of the smart home pension model, expounds the entry point and function of the wearable equipment applied to the smart home pension system, and puts forward the hardware and software design strategies and challenges for the future development of wearable equipment. The practical application of wearable equipment in the smart home pension system can provide specialized old-age pension services for the elderly, contribute to solving the problems of social livelihood, and provide a new idea for the construction of smart home pension service system.
Measurements drive progress in directed evolution for precise engineering of biological systems

Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function. At the same time, the increasingly routine availability of large, high-quality data sets supports the integration of machine learning with directed evolution. Together, these advances herald striking capabilities for engineering biology.
Balancing Relevance and Discovery to Inspire Customers in the IKEA App

IKEA stores are designed to engage and inspire customers as they make their way from the entrance to check-out. To enable a great experience for every individual, our home-furnishing experts strive to provide the best possible input and advice, catering to individual needs and helping customers realise their vision for life at home.
Improved Metric-Learning in Artificial Intelligence

As artificial intelligence develops, it has become more and more concerned to measure the distance and similarity between different samples, especially in classification. It is known that AI meets the difficulty to properly describe the distance between different samples. Metric-learning is one field of machine learning performed on training samples in order to better describe the distribution of training samples, thereby improving the performance of classification. This paper proposes a new triplet constraint and uses the topology information combined with the unlabeled samples to design the regularizer of the model to ensure that the model meets the three basic assumptions of semi-supervised learning: smoothness, low density and manifold. This paper provides a new semi-supervised metric-learning model by using the new triplet constraint and regular terms. Then, using a gradient descent algorithm to solve the model, the optimal Mahalanobis matrix is obtained, which could ensure the Mahalanobis matrix is positive definite and symmetry during the iterative process. The Mahalanobis matrix solved by the model can be applied in clustering, classification and many other artificial intelligence fields. In this paper, the model is used for classification problem as an application. The experimental results are consistent with the idea of the model, reflecting the advantages of this model compared to other models, providing a new method to measure the sample distance in artificial intelligence and machine learning.
Rhythmic Thought and Practice in the Indian Subcontinent

The remarkable facility in rhythmic play demonstrated by musicians and dancers throughout the Indian subcontinent is as impressive as it can be bewildering for the listener. From local and regional practices, through devotional and popular genres, to the heavily theorized concert traditions of the North (Hindustani music) and South (Karnatak music), rhythmic complexity abounds. A performance may begin without even a pulse, where melodies seem to float unpredictably in musical space. Yet increasing rhythmic regularity leads to the establishment of repetitive sequences of beats, both evenly and unevenly distributed, which provide the frameworks for elaborate melodic and rhythmic compositions, variations, and improvisations. The entrance of drums – also essentially melodic in their subtle manipulations of pitch, timbre, stress, and resonance – is invariably a moment of great visceral as well as intellectual excitement. Together, singers, dancers, instrumentalists, and drummers build their performances around the anchors provided by the beats; they subdivide these beats in myriad ways, playing with different rhythmic densities and syncopations. The thrilling, rapidly articulated sequences with their offbeat stresses can temporarily disorient the listener until all seems to resolve in a triumphant convergence of surface rhythm and target beat. The rhythmic system as a whole and the individual frameworks of beats that serve to organize rhythmic expression are known as tala.