Reviewerly/exHarmony huggingface dataset:
The Influence of Feature Selection on Job Clustering for an E-recruitment Recommender System

Recommender systems aim to effectively recommend items to the user based on their profile. An online recruitment system recommends jobs for a candidate according to his profile and can also act in reverse, recommending more qualified candidates for a particular job. Defining which variables will be used impacts directly the recommendation quality so that, when using the most important variables, we have a better assertiveness in the process. The goal of this work is to select the most important features of an online recruitment database using feature selection techniques. More specifically, we used the algorithms of Mitra, SUD and ACA to perform feature selection. The datasets used were derived from the original dataset assuming three distinct scenarios: the dataset containing the attributes related with the jobs’ features; the dataset containing the bag of words of the description feature of the jobs; and the dataset resulting from the union of the two previous ones. The features’ subsets selected in each of the above scenarios had their performance evaluated in a clustering task. The results obtained in each scenario show a performance gain of the clustering process when feature selection is made over the original data. Also, it was observed that the jobs’ features result in better performance than the other two cases.
Natural Language Processing Based Dialog System Generation and Management Platform

In this paper, we describe a dialog system generation platform that can help people without technical backgrounds to create chatbots in different domains such as banking, insurance, tourism etc. Using this system chatbots ranging from QA bots to conversational chatbots that can have more complex dialogues can be created. Using general Turkish datasets, a language model was trained with ELMo architecture to ensure domain independency. Intent detection models employed by chatbots created by this platform uses this language model for training. Besides, dialogue act classification is used by conversation policy manager to make conversations consistent. Lastly, platform includes an analytics tool that uses clustering algorithms to make analysis based on previous chatbot conversations.
Decentralised indoor smart camera mapping and hierarchical navigation for autonomous ground vehicles

In this work, the authors propose a novel decentralised coordination scheme for autonomous ground vehicles to enable map building and path planning with a network of smart overhead cameras. Decentralised indoor smart camera mapping and hierarchical navigation supports the automatic generation of waypoint graphs for each camera in an environment and allows path planning through the environment across multiple camera fields of view, or subviews. The proposed solution utilises the growing neural gas algorithm to learn the topology of unoccupied working space in each subview for maintaining a dynamic waypoint graph on each camera. The authors’ pathing solution leverages a modified version of the A* algorithm to compute paths in a decentralised and hierarchical fashion. Waypoint generation was simulated and analysed on a generated environment to ensure it is both effective and efficient, while path planning was simulated on various randomised hierarchical graphs to effectively compare the proposed Decentralised‐A* (D‐A*) algorithm against standard greedy search. The proposed method efficiently handles the cases where other robot navigation methods are otherwise weak and ineffective, while still providing avenues for further optimisation of resource overhead for both the smart camera network as well as the robots themselves.
Understanding of cultural-semiotic environment in the tutor’s concept of education

This article attempts to build a discourse of cultural-semiotic environment for the tutor's concept of education. In order to achieve the set task, analysis is conducted on the case from educational practice, including the author's, pertaining to discovery of a drawn image by a child. In the process of such discovery, the author differentiated three phases: formation of problematic situation, its resolution through drawing a scheme, and learning the reality with secondary objects. This material is interpreted within the framework of a new extended semiotic approach, which includes semiotics not only signs and sign systems, but also schemes, icons, symbols, and works of art. The article discusses the cultural-semiotic environment, which stimulates mental development of a young person, and in addition to semiotic means, features communication, resolution of problematic situation, and ascertainment of the structured explanation by results of the entire process. The author analyzes position of a tutor as a fundamentally different from educational position of the pedagogy of formation. The author also offers a semiotic version of the genesis of culture, in which he distinguishes three different formats &amp;#8210; actual semiotic, social, and anthropological, which impacts the formation of personality. In conclusion, the article presents a description of current situation in culture and education, as well as thoughts on the role and objectives of the tutor movement thereof.
A Novel Explainable Recommender for Investment Managers

This paper presents a novel recommendation system for investment managers using real data from asset management companies. The recommender can be viewed as a fuzzy expert system. As a matter of fact, this is an explainable recommender that works as a one-class classifier with an explanation. The inference rules, explanations, and visualizations of the recommender’s results are illustrated.
Improving Basketball Recognition Accuracy in Samsung Gear S3 Smartwatch using Three Combination Sensors

Development of Internet of Things (IoT) devices become popular to make it easier for people to recognize activity from wireless devices. Activity recognition has been widely used at various levels of computing. Smartwatch is one of IoT wearable devices used by researchers since its advantage for open source Human Activity Recognition (HAR) programming usage. Smartwatch in many published articles uses two sensors to accomplish HAR, which are accelerometer and gyroscope. However, the data obtained from the two sensors still too many restrictions in detecting sports activities such as basketball, football, and many more activities having an extreme movement. Moreover, previous experiments evaluate the impact caused by combining another sensor to get more precise of the activity recognition accuracy. Samsung Smartwatch Gear S3 has an audio sensor data that can be obtained from devices and have a promising result to improve recognition accuracy. This research proposed recognition accuracy by combining Accelerometer, Gyroscope, and Audio sensor to achieve improving accuracy from 69% become around 90% extreme movement recognition accuracy. The experiments show that Human Activity Recognition proposed is capable to detect Basketball activities on the Samsung Gear S3 smartwatch.
Biometric Class Attendance Register

The paper presents an on-going work to design an automatic class attendance register. The proposed design uses a student's index finger to register his/her attendance, with the use of radio frequency identification (RFID) card as a backup. The attendance register is uploaded onto a web-based database for recording and analysis. The hardware and software components of the design have been largely done. Preliminary test results show that the design successfully enrolls, registers, logs and stores attendance data.
Rolling Horizon NEAT for General Video Game Playing

This paper presents a new Statistical Forward Planning (SFP) method, Rolling Horizon NeuroEvolution of Augmenting Topologies (rhNEAT). Unlike traditional Rolling Horizon Evolution, where an evolutionary algorithm is in charge of evolving a sequence of actions, rhNEAT evolves weights and connections of a neural network in real-time, planning several steps ahead before returning an action to execute in the game. Different versions of the algorithm are explored in a collection of 20 GVGAI games, and compared with other SFP methods and state of the art results. Although results are overall not better than other SFP methods, the nature of rhNEAT to adapt to changing game features has allowed to establish new state of the art records in games that other methods have traditionally struggled with. The algorithm proposed here is general and introduces a new way of representing information within rolling horizon evolution techniques.
DPVGeo: Delay-based Public Verification of Cloud Data Geolocation

Knowing the geolocation of cloud data becomes an urgent problem, which relates to cloud user equity (e.g., service compliance), service performance (e.g., disaster tolerance) and government regulations (e.g., GDPR). Unfortunately, data owners lose physical control after outsourcing data to the cloud service providers, while cloud service providers have the motivation (reducing economic costs and maximizing profits) and ability to move the data to other data centers in different geolocations. As a consequence, verifying whether the cloud data are in a specific geolocation is worthy of concern.In this paper, we propose a novel cloud data public verification scheme, DPVGeo, which allows any entity to verify the actual geolocation of cloud data remotely. In DPVGeo, we first design an atomic proof method, which divides the proof into several minimum computation units (i.e., atomic proof), and subtly only considers the normal operations (i.e., addition and multiplication), ignoring the time-consuming exponentiation operations, to obtain accurate response delay. Second, we utilize a thresholdbased closest-shortest approach to verify the geolocation of cloud data based on the response delay with high accuracy. Besides, we select both blocks and sectors randomly during each challenge to defend against the potential attacks (e.g., outsourcing attack, generation attack and replay attack). Finally, we perform a series of prototype implementations in real network environment to validate the performance of our design. The experimental results and security analysis show that our scheme is efficient and secure against semi-honest cloud service providers.
Investigations of HMM-Based Speech Recognition Technology

We are in an era of informatization and big data. In this context, speech data are increasing and becoming more and more frequently used. When we choose and use a lot of speech information on the Internet, speech recognition technology shows its great importance. In this paper, by studying speech signal preprocessing, HMM model, MFCC feature parameter extraction and other technologies, a speech recognition algorithm based on HMM is derived to achieve a more accurate result of speech words and phrases.
Continuous Improvement of Medical Diagnostic Systems with Large Scale Patient Vignette Simulation

Differential diagnostic systems provide a ranked list of highly prob-able diseases given a patient's profile and symptoms. Evaluation of diagnostic algorithms in literature has been limited to a small set of hand-crafted patient vignettes. Testing with high coverage and gaining insights for improvements are challenging because of thesize and complexity of the knowledge base. Furthermore, scalable practical methodologies for evaluation and deployment of such systems are missing in the literature. Here, we address this challenge using a novel patient vignette simulation algorithm within an iterative clinician-in-the-loop methodology for semi-automatically evaluating and deploying medical diagnostic systems in production.We evaluate our algorithms and methodology through a case study of a real product and knowledge base curated by medical experts.We conduct multiple iterations of the methodology, report novel accuracy measures, and discuss insights from our experience in applying this method to production
Using Extremely Simplified Functional Size Measures for Effort Estimation

Background: Functional size measures of software are widely used for effort estimation, because they provide a fairly objective quantification of software size, which is one of the main factors affecting software development effort. Unfortunately, in some conditions, performing the standard Function Point Analysis process may be too long and expensive. Moreover, functional measures could be needed before functional requirements have been elicited completely and at the required detail level. Aim: Basing effort estimation on measures that are simpler than Function Points---hence, faster and cheaper to obtain---could be beneficial, if good estimation accuracy is possible. In this paper, the level of accuracy that can be obtained using simple measures instead of Function Points---as defined by IFPUG, the International Function Point User Group---is evaluated, based on an empirical study. Method: An analysis of the data provided in the ISBSG dataset was performed. Effort models based on standard IFPUG Function Points and on the number of transactions were derived. The accuracy of the effort estimates delivered by the two models were then compared. Results: Effort models based on the number of transactions appear marginally less accurate than models based on standard IFPUG Function Points for new development projects, and marginally more accurate for projects extending previously developed software. Conclusions: Based on the results of the empirical study, it seems that using the number of transactions instead of the standard IFPUG Function Points measures does not cause estimate accuracy to change to a practically appreciable extent. This conclusion applies to effort models using size measure as the only independent variable. Additional research is necessary to evaluate the performance of effort models based on the number of transactions in combination with other factors.
Vehicle detection for intelligent traffic surveillance system

Due to the dramatical grow of road transport, Advanced Driver Assistance Systems (ADAS) are being one of the most popular system. The main challenge of these systems is to improve driving safety and reduce accidents. Robust and effective vehicle detection is a critical step. However, vehicle detection meets many difficulties such as complex background, different size, model and orientations of vehicle. To solve this problem, this paper introduces an approach for traffic vehicle detection based on multi-scale covariance descriptor (MSCOV) for the image description and support vector machine classifier (SVM) for the data classification. This method is evaluated and compared to existing detection approach. The result of this approach outperforms existing vehicle detection system using the same dataset.
NaviKey: An Invisible Keyboard with Navigation Keys

Virtual keyboards are widely used for text input on touch devices currently. However, the traditional virtual keyboard often takes a large portion of precious screen space, which leads to the contradiction between the display output space and the text input space. In order to solve this problem, we have proposed a partial-hidden keyboard with several navigation keys called Navikey. By keeping only a few keys as navigation keys, we reduce the occlusion of the screen display content by the keyboard greatly, and the text input experience is similar to the traditional virtual keyboard. Experiments show that users prefer to choose a partial-hidden keyboard input method which provides more information, which causes the acceptance of NaviKey is much higher than that of a totally hidden keyboard. Moreover, the input speed and accuracy of Navikey are almost the same as the traditional virtual keyboard. This shows that NaviKey can still obtain higher input efficiency and user acceptance while greatly reducing the screen space occupation.
Face Recognition Based on PCA and Neural Network

This paper presented a face recognition method based on PCA and Back Propagation Neural Network which is robust and simple algorithm based on image captured by camera. The proposed algorithm is done on three stages, namely face detection, face feature extraction and face recognition. The face detection is performed using Haar-Like Feature. The Haar-Like feature analyzes the pixels in the image into squares by function. The method uses the Adaboost learning algorithm to select a small number of important feature from a large data set. The detected face is then extracted using PCA. PCA will select and reduce the face feature based on eigenvalues of correlation matrix data. We obtain 500 features face extracted using PCA. The face feature becomes the input to neural network for recognition process. The neural network is developed with two hidden layers with 15 nodes on each and three nodes of output layer. The experiment is performed in real time environment. Using 5 faces image data with each data is taken 100 times, the experimental result showed the satisfactory result with 87.5% recognition rate in average.
Path Planning Algorithm for Robotic Lawnmower using RTK-GPS Localization

Recently, automatic lawnmowers have been widely used in families and public areas and the usage is also increasing rapidly. In order to design an efficient automatic lawnmower, this paper proposes an efficient complete coverage path planning algorithm. In this paper, we propose a coverage path planning algorithm for RTK-based autonomous lawnmower with the ability to detect obstacles and to plan a collision-free path simultaneously by using various sensors. The key technology is the complete coverage path planner which plans an efficient path with low duplication rate. The duplication rate of planned paths is about 150%.
Adaptive segmentation of traditional cultural pattern based on superpixel Log-Euclidean Gaussian metric

In order to improve the accuracy of objects similarity measurement of traditional cultural pattern segmentation and adaptively determine the number of segmentations, we propose an adaptive segmentation algorithm based on a new superpixel Log-Euclidean Gaussian metric (SLEGM) in this paper. We first propose to use the SLEGM to effectively characterize superpixels for more accurate measurement of their similarity. Because the space of Gaussians sample covariance matrix distribution is not a linear space but a Riemannian manifold, we map this manifold via matrix logarithm into a linear space, which enables us to handle Gaussians with Euclidean operations. Under the SLEGM framework, we develop an improved spectral clustering algorithm that can adaptively determine the number of clusters to achieve the adaptive segmentation for the traditional culture pattern. Extensive evaluations on the Berkeley Segmentation Data Set (BSDS500) benchmark verify that our algorithm outperforms the state-of-the-art techniques of the same category under four evaluation metrics, achieving 74.3% F-measure, 13.52% under segmentation error, 83.4% boundary recall and 97.79% achievable segmentation accuracy. Further experiments on our challenging Traditional Cultural Pattern Database (TCPD) indicate the effectiveness of our algorithm for segmenting the complex patterns.
Introducing Canvas

Rapidly creating educational content that is effective, engaging, and low-cost is a challenge. We present Canvas, a tool for educators that addresses this challenge by enabling the generation of educational video, led by an intelligent virtual agent, that combines rapid nonverbal behavior generation techniques with end-user facing authoring tools. With Canvas, educators can easily produce compelling educational videos with a minimum of investment by leveraging existing content provided by the tool (e.g., characters and environments) while incorporating their own custom content (e.g., images and video clips). Canvas has been delivered to the Smithsonian Science Education Center and is currently being evaluated internally before wider release. We discuss the system, feature set, design process, and lessons learned.
Classification Of Human Facial Expressions For Emotion Recognition Using A Distributed Computer System

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image flies were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed are taken into account; and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.
CommunIT Building

Despite the promise of social media to bring the world closer together, large segments of local communities, globally, remain misunderstood by or invisible to mainstream society. This problem is attributed, in large part, to digital media’s ascendancy over physical, public space as the locus for civic discourse—the loss of informal and structured encounters between members of communities there. This chapter presents our development and early evaluation of a novel cyber-physical platform, communIT, for community building across diverse local community groups. Deployed in underused public spaces, communIT is an origami-like, folding, robotic surface of billboard scale, with embedded peripherals, that changes form in response to group needs for group co-creation and sharing of media. By collaboratively making and sharing media with communIT, local groups can tell stories, share experiences and aspirations, and advocate within the larger community. Such civic discourse promises the potential to transform personal identity and self-representation, community awareness and responsibility, and wider social relationships with policy-makers.
Automated extraction of domain knowledge in practice

Software product line supports structured reuse of software artifacts in order to realize the maintenance and evolution of the typically large number of variants, which promotes the industrialization of software development, especially for software-intensive products. However, for a legacy system, it is non-trivial to gain information about commonalities and differences of the variants. Meanwhile, software requirements specifications as the initial artifacts can be used to achieve this information to generate a domain model. Unfortunately, manually analyzing these requirements is time-consuming and inefficient. To address this problem, we explored the usage of feature extraction techniques to automatically extract domain knowledge from requirements to assist domain engineers. In detail, we applied Doc2Vec and a clustering algorithm to process the requirements for achieving the initial feature tree. Moreover, we utilized key words/phrases extraction techniques to provide key information to domain engineers for further analyzing the extraction results. In particular, we developed a GUI to support the extraction process. The empirical evaluation indicates that most of the extracted features and terms are beneficial to improve the process of feature extraction.