Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition Based on Contextual Generative Adversarial Network

In recent years, deep neural networks have been widely concerned by researchers in facial expression recognition. However, insufficient facial training data of the public available database is a major challenge in deep learning, which will lead to an obvious decrease in the effectiveness of learning result; many data augmentation techniques have thus been widely used to enrich the training dataset. In this paper, we introduce a contextual loss function to construct a Contextual Generation Adversarial Network with one generator and one discriminator. The proposed method can map the neutral expression to six basic expressions to expand the database. The experimental results on CK + and KDEF databases show that the proposed method can effectively improve the ability to extract facial features and the ability to generate higher quality images. The data augmentation used the proposed method improves the recognition rate of facial expressions on KDEF and CK + datasets.
Activating accessible pedestrian signals by voice using keyword spotting systems

We describe the problem of partially sighted pedestrians who identify crossing the street as a difficult task. Pedestrian call buttons remain difficult to activate for partially sighted pedestrians. We believe that human-technology interfaces such as speech-related technologies can help to make this task easier. We propose a deep learning model for keyword spotting system where pedestrians can activate the pedestrian call button using their voice and thus enable them to cross the streets safely.
Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.
Challenges and Solutions of Project Management in Distributed Software Development

Challenge of developing software has increased because of several factors, e.g. complexity of these systems and lack of skilled available professional next of development companies. Thus, they should seek alternatives; one of them is global software development. Therefore, software project management should adapt to this reality and resolve challenges not previously found in the “traditional” management. In this paper, we present an initial list of challenges in project management in the context of global software development and solutions proposed by researchers and project managers to try to solve these challenges. We used Literature Systematic Mapping for finding challenges and solutions for software project management. We found 29 papers and used 20 initial papers, totalizing 49 papers. The findings showed 18 challenges which were listed with its solutions.
Considerations on privacy in the era of digitally logged lives

Purpose The continuous advancements in wearable sensing technologies enable the easy collection and publishing of visual lifelog data. The widespread adaptation of visual lifelog technologies would have the potential to pose challenges for ensuring the personal privacy of subjects and bystanders in lifelog data. This paper presents preliminary findings from a study of lifeloggers with the aim of better understanding their concerns regarding privacy in lifelog data. Design/methodology/approach In this study, we have collected a visual dataset of 64,837 images from 25 lifelogging participants over a period of two days each, and we conducted an interactive session (face to face conversation) with each participant in order to capture their concerns when sharing the lifelog data across three specified categories (i.e. Private (Only for Me) , Semi-Private (Family/Friends) and Public ). Findings In general, we found that participants tend to err on the side of conservative privacy settings and that there is a noticeable difference in what different participants are willing to share. In summary, we found that the categories of images that the participants wished to be kept private included personally identifiable information and professional information; categories of images that could be shared with family/friends include family moments or content related to daily routine lifestyle, and other visual lifelog data could potentially be made public ). Originality/value We analysed the potential differences in the willingness of 25 participants to share data. In addition, reasons for being a volunteer to collect lifelog data and how the lifelogging device affected the lifestyle of the lifelogger are analysed. Based on the findings of this study, we propose a set of challenges for the anonymisation of lifelog data that should be solved when supporting lifelog data sharing.
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects.

The development of advanced 3D sensors has enabled many objects to be captured in the wild at a large scale, and a 3D object recognition system may therefore encounter many objects for which the system has received no training. Zero-Shot Learning (ZSL) approaches can assist such systems in recognizing previously unseen objects. Applying ZSL to 3D point cloud objects is an emerging topic in the area of 3D vision, however, a significant problem that ZSL often suffers from is the so-called hubness problem, which is when a model is biased to predict only a few particular labels for most of the test instances. We observe that this hubness problem is even more severe for 3D recognition than for 2D recognition. One reason for this is that in 2D one can use pre-trained networks trained on large datasets like ImageNet, which produces high-quality features. However, in the 3D case there are no such large-scale, labelled datasets available for pre-training which means that the extracted 3D features are of poorer quality which, in turn, exacerbates the hubness problem. In this paper, we therefore propose a loss to specifically address the hubness problem. Our proposed method is effective for both Zero-Shot and Generalized Zero-Shot Learning, and we perform extensive evaluations on the challenging datasets ModelNet40, ModelNet10, McGill and SHREC2015. A new state-of-the-art result for both zero-shot tasks in the 3D case is established.
Construct dynamic graphs for hand gesture recognition via spatial-temporal attention

We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition. The key idea is to first construct a fully-connected graph from a hand skeleton, where the node features and edges are then automatically learned via a self-attention mechanism that performs in both spatial and temporal domains. We further propose to leverage the spatial-temporal cues of joint positions to guarantee robust recognition in challenging conditions. In addition, a novel spatial-temporal mask is applied to significantly cut down the computational cost by 99%. We carry out extensive experiments on benchmarks (DHG-14/28 and SHREC'17) and prove the superior performance of our method compared with the state-of-the-art methods. The source code can be found at this https URL.
Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation

Distributed photovoltaic (PV) generation often occurs “behind the meter”: a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.
Fashion Recommendation with Multi-relational Representation Learning

Driven by increasing demands of assisting users to dress and match clothing properly, fashion recommendation has attracted wide attention. Its core idea is to model the compatibility among fashion items by jointly projecting embedding into a unified space. However, modeling the item compatibility in such a category-agnostic manner could barely preserve intra-class variance, thus resulting in sub-optimal performance. In this paper, we propose a novel category-aware metric learning framework, which not only learns the cross-category compatibility notions but also preserves the intra-category diversity among items. Specifically, we define a category complementary relation representing a pair of category labels, e.g., tops-bottoms. Given a pair of item embeddings, we first project them to their corresponding relation space, then model the mutual relation of a pair of categories as a relation transition vector to capture compatibility amongst fashion items. We further derive a negative sampling strategy with non-trivial instances to enable the generation of expressive and discriminative item representations. Comprehensive experimental results conducted on two public datasets demonstrate the superiority and feasibility of our proposed approach.
TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce

A key solution to the Information Retrieval (IR)-based question answering (QA) models is to retrieve the most similar knowledge entries of a given query from a QA knowledge base, and then rerank those knowledge entries with semantic matching models. In this paper, we aim to improve an IR based e-commerce QA system-AliMe with proposed text matching models, including a basic Triple Convolutional Neural Network (TCNN) model and two Attention-based TCNN (ATCNN) models.
Towards a decentralised common data environment using linked building data and the solid ecosystem

With the emergence of Building Information Modelling (BIM), the construction industry is rapidly catching up with the digital revolution that has boosted productivity in virtually all economic sectors. In current practice, the focus of BIM lies on exchange of documents, often through proprietary formats exchanged using the Industry Foundation Classes (IFC). However, with web technologies such as RDF, OWL and SPARQL, a data- and web-based BIM paradigm becomes within reach. The decentralisation of data and decoupling of information and applications will enhance a more general adoption of Big Open BIM, and is expected to lower the BIM threshold for smaller companies that are active in different phases of the building life cycle. Since one of the promises of the Semantic Web and Linked Data is a highly improved interoperability between different disciplines, it is not necessary to reinvent the wheel for the setup of an infrastructure that supports such a network of decentralised tools and data. In this paper, we evaluate the specifications provided by the Solid project (Inrupt Inc.), a Linked Data-based ecosystem for Social Linked Data. Although the exemplary use case of the Solid ecosystem is decentralisation of data and applications for social network purposes, we notice a considerable overlap with recent ambitions and challenges for a web-based AECO industry (Architecture, Engineering, Construction and Operation). This includes standardised data representations, role- or actor-based authorisation and authentication and the need for modular and extensible applications, dedicated to a specific use case. After a brief introduction to Linked Data and its applications in the building industry, we discuss present solutions for building data management (Common Data Environments, multimodels, etc.). In order to translate these approaches towards a Linked Data context with minimal effort and maximal effect, we then review the Solid specifications for use in a construction-oriented web ecosystem. As a proof of concept, we discuss the setup of a web-service for creation and management of Linked Building Data, generated with the Solid-React generator. This application is envisaged as a bridge between the multiple data stores of different project stakeholders and the end user. It acts as an interface to a distributed Common Data Environment that also allows the generation of multi-models.
MotionGlyphs: Visual Abstraction of Spatio‐Temporal Networks in Collective Animal Behavior

Abstract Domain experts for collective animal behavior analyze relationships between single animal movers and groups of animals over time and space to detect emergent group properties. A common way to interpret this type of data is to visualize it as a spatio‐temporal network. Collective behavior data sets are often large, and may hence result in dense and highly connected node‐link diagrams, resulting in issues of node‐overlap and edge clutter. In this design study, in an iterative design process, we developed glyphs as a design for seamlessly encoding relationships and movement characteristics of a single mover or clusters of movers. Based on these glyph designs, we developed a visual exploration prototype, MotionGlyphs, that supports domain experts in interactively filtering, clustering, and animating spatio‐temporal networks for collective animal behavior analysis. By means of an expert evaluation, we show how MotionGlyphs supports important tasks and analysis goals of our domain experts, and we give evidence of the usefulness for analyzing spatio‐temporal networks of collective animal behavior.
Anonymous Collocation Discovery:Taming the Coronavirus While Preserving Privacy

Successful containment of the Coronavirus pandemic rests on the ability to quickly and reliably identify those who have been in close proximity to a contagious individual. Existing tools for doing so rely on the collection of exact location information of individuals over lengthy time periods, and combining this information with other personal information. This unprecedented encroachment on individual privacy at national scales has created an outcry and risks rejection of these tools. We propose an alternative: an extremely simple scheme for providing fine-grained and timely alerts to users who have been in the close vicinity of an infected individual. Crucially, this is done while preserving the anonymity of all individuals, and without collecting or storing any personal information or location history. Our approach is based on using short-range communication mechanisms, like Bluetooth, that are available in all modern cell phones. It can be deployed with very little infrastructure, and incurs a relatively low false-positive rate compared to other collocation methods. We also describe a number of extensions and tradeoffs. We believe that the privacy guarantees provided by the scheme will encourage quick and broad voluntary adoption. When combined with sufficient testing capacity and existing best practices from healthcare professionals, we hope that this may significantly reduce the infection rate.
Motion State Estimation of Target Vehicle under Unknown Time-Varying Noises Based on Improved Square-Root Cubature Kalman Filter

In the advanced driver assistance system (ADAS), millimeter-wave radar is an important sensor to estimate the motion state of the target-vehicle. In this paper, the estimation of target-vehicle motion state includes two parts: the tracking of the target-vehicle and the identification of the target-vehicle motion state. In the unknown time-varying noise, non-linear target-vehicle tracking faces the problem of low precision. Based on the square-root cubature Kalman filter (SRCKF), the Sage–Husa noise statistic estimator and the fading memory exponential weighting method are combined to derive a time-varying noise statistic estimator for non-linear systems. A method of classifying the motion state of the target vehicle based on the time window is proposed by analyzing the transfer mechanism of the motion state of the target vehicle. The results of the vehicle test show that: (1) Compared with the Sage–Husa extended Kalman filtering (SH-EKF) and SRCKF algorithms, the maximum increase in filtering accuracy of longitudinal distance using the improved square-root cubature Kalman filter (ISRCKF) algorithm is 45.53% and 59.15%, respectively, and the maximum increase in filtering the accuracy of longitudinal speed using the ISRCKF algorithm is 23.53% and 29.09%, respectively. (2) The classification and recognition results of the target-vehicle motion state are consistent with the target-vehicle motion state.
Academic Environment for the Development of Creative Fulfillment of Innovative Students

This article gives a critical overview of the current state of a problem of the development of students’ creative potential brought about by broad-scale processes of modernization of the domestic education system in the light of the new priorities, focused on development of a creative personality geared towards innovative activities, capable of self-improvement and self-development. The article substantiates and highlights essential and substantive characteristics of students’ innovative activity in terms of the competency-based approach. The academic environment promoting the development of innovative students’ abilities for creative self-fulfillment, experimentally tested in the process of their professional training is revealed. Based on the methods of theoretical and empirical research, it is shown that the engineered academic environment providing for innovative student’s creative potential development contributes to intensification of their professional training process. This academic environment is designed on the basis of interactive and heuristic teaching technologies. Such environment facilitates formation of the ability to independently detect and lay down a problem, search for its solution and verify its adequacy; to employ or look for the technologies feeding into solution of the problem; to analyze the efficiency of technology tools by comparing their features and functions. The results of the study can be used in working with innovative students during development of their creative potential.
Using supervised learning to classify metadata of research data by field of study

Many interesting use cases of research data classifiers presuppose that a research data item can be mapped to more than one field of study, but for such classification mechanisms, reproducible evaluations are lacking. This paper closes this gap: It describes the creation of a training and evaluation set comprised of labeled metadata, evaluates several supervised classification approaches, and comments on their application in scientometric research. The metadata were retrieved from the DataCite index of research data, pre processed, and compiled into a set of 613,585 records. According to our experiments with 20 general fields of study, multi layer perceptron models perform best, followed by long short-term memory models. The models can be used in scientometric research, for example to analyze interdisciplinary trends of digital scholarly output or to characterize growth patterns of research data, stratified by field of study. Our findings allow us to estimate errors in applying the models. The best performing models and the data used for their training are available for re use.
POS tagger model for Kannada text with CRF++ and deep learning approaches

Computational Linguistics is one of the interesting topics in the research field of Computer Science. This paper presents training for Part of Speech (POS) tagging on Kannada words using two techniques. First approach is supervised machine learning technique CRF++0.50 (Conditional Random Field). The second approach is a combination of word embedding and deep learning techniques. The total dataset used for this implementation is 1200 tagged Kannada sentences downloaded from Technology Development for Indian Languages (TDIL). We divided the dataset into 1100 sentences (13,600 words) as training data and 100 sentences (1053 words) as test data. The BIS (Bureau of Indian Standards) tagset is used in this work in which 27 major POS tags have been considered. An accuracy obtained through CRF++0.50 tool is 76% and that with deep learning technique is 71%. The precision, recall and f-score of each tag using both the techniques are considerable.
Toward anticipatory ubimus

This paper explores the emerging initiatives in ubiquitous music research that employ anticipatory systems.We provide a short introduction to the ubimus field, highlighting the differences with other technologically based approaches to music making.One of the objectives of ubimus research is to expand the range of the stakeholders that participate in creative music making.This is achieved through the development of metaphors for creative action by means of sociotechnical systems that target creativity, including ecologically based creative practices, interaction aesthetics, computational thinking and dialogics applied to music.Another objective entails a push for new forms of music making through the reappropriation of extant technologies or through the design and deployment of new behavioral, material or social resources tailored for ubiquitous music ecologies.Nevertheless, so far, few projects have considered the future creative actions as an object of research.This is the aim of anticipatory ubiquitous music.