Reviewerly/exHarmony huggingface dataset:
Improving Gaussian Embedding for Extracting Local Semantic Connectivity in Networks

Gaussian embedding in unsupervised graph representation learning aims to embed vertices into Gaussian distributions. Downstream tasks such as link prediction and node classification can be efficiently computed on Gaussian distributions. Existing Gaussian embedding methods depending on adjacent neighbors of vertexes leave out of consideration of indirect connectivity information carried by remote vertice, which is still a part of local semantic connectivity in a network. In this paper, we propose an unsupervised graph representation model GLP2Gauss to improve Gaussian embedding by coarsening graph as indirect local connectivity. First, we decompose the original graph into paths and subgraphs. Secondly, we present a path-based embedding strategy combined with the Gaussian embedding method to maintain better local relevance of embedded nodes. Experiments evaluated on benchmark datasets show that GLP2Gauss has competitive performance on node classification and link prediction for networks compared with off-the-shelf network representation models.
Handwriting Styles Clustering: Feature Selection and Feature Space Analysis based on Online Input

In this paper, we perform a detailed analysis of feature extraction approaches and existing solutions for the problem of handwriting styles clustering. We consider a number of handcrafted features for handwritten text, perform a feature space analysis for finding the best set of extracted features and build an expert system for identifying and clustering handwriting styles using unsupervised methods based on this set. We observed an improvement in clustering results by analyzing the described in this paper metrics for clustering evaluation (such as the Dunn index, silhouette, within- and between-cluster sums of squared errors), therefore we can conclude that the clustering based on such a subset is much more efficient. The results outlined below can be used for handwriting style determination, which is an important step in designing systems for text recognition and localization, authentication and verification tasks, and also can be applied to the problem of the detection of the mental or nervous disorders.
Research on Ground Fault Monitoring Method for High Voltage Line Based on Phase Difference Method

When a ground fault occurs in the distribution network, it will cause a large-scale and long-term power outage on the entire line, which will have a significant impact on the safe and stable operation of the power system. In order to improve the real-time and accuracy of online monitoring of distribution network ground faults, a suitable ground fault monitoring method based on the phase difference detection on transmission lines is proposed. The way we detect the occurrence of ground faults is as follows: we first measure the three-phase voltage, current, and difference of their phase on the transmission line, and then we use the sudden changes of the phase difference between voltage and current as our criterion to determine whether ground faults occur or not. In order to search ground faults rapidly on the entire transmission line, it is necessary to install measurement devices at important nodes. According to the comparison of simulation tests, we find that the ground fault location judgment method based on the phase difference can detect the ground faults accurately, which is of great significance to guarantee the safety of the power supply.
Identification for Property of Luochong Kiln Based on Frequency-tree

Kiln is the structure and site to fire and produce ceramic. The property of kiln is mainly divided into civil kiln and official kiln. The property of Luochong Kiln, which is located in the western suburb of Fanchang County, has so far been uncertain. We put forward a method of FP-growth mining to identify the property of Luochong Kiln. We discriminate by comparing Luochong Kiln and Kechong Kiln, both belong to the important parts of Fanchang Kiln, to determine whether there is a difference between the frequent items. Through launching Top-k expert questionnaire and analyzing 100 experts' discriminant results of the frequent items, we conclude that Luochong Kiln is official kiln.
A Two-Fitness Resource Scheduling Strategy Based on Improved Particle Swarm Optimization

The performance optimization of cloud platform for big data processing is a research hotspot, among which resource scheduling is the most important. Through the analysis of the internal resource scheduling mechanism of CloudStack, the two-level scheduling of resources plays an important role in task optimal span, load balance and other aspects. In this paper, aiming at optimizing IaaS service performance and taking CloudStack platform as the research object, a dual fitness resource scheduling strategy based on improved particle swarm optimization is proposed. First of all, PSO algorithm with high precision and fast convergence speed is used to optimize the two-level resource scheduling, which can shorten the scheduling time when the scheduling requirements are met. Secondly, aiming at the problem of “prematurity” of particle swarm optimization (PSO), this paper USES simulated annealing algorithm to optimize the traditional PSO. Finally, aiming at the two pole resource scheduling, this paper proposes the virtual machine deployment algorithm based on improved particle swarm and the dual fitness task scheduling algorithm based on Improved Particle Swarm respectively, and carries out simulation in CloudSim simulation tool. The simulation results show that the algorithm proposed in this paper can effectively improve the optimal span and optimize the load balance.
Communicating Next-Generation 911 with local 911 professionals: Preliminary recommendations

Next-Generation 911 (NG911) infrastructure will replace analog systems designed to support voice services for landline 911 callers with digital, IP-based systems that will allow smartphone users to “call” 911 via voice, text, image, and streaming video. This brief paper reports findings from a workshop conducted at the 2019 911 Early Adopters' Summit, during which local 911 professionals from across the United States reflected on strengths, weaknesses, opportunities, and threats associated with the adoption of NG911 infrastructure and, in particular, multimedia 911 services. Workshop participants pointed to long-standing issues NG911 stands to exacerbate, including high non-emergency call volumes, poor staff retention, and inadequate psychological support which the influx of multimedia 911 calls, including those with graphic imagery, may worsen. At the same time, participants looked to local, existing resources to mitigate these issues and exploit new opportunities afforded through NG911 infrastructure. Using these findings, preliminary recommendations are offered to improve information resources available to local 911 professionals adopting NG911 systems for effective and efficient multimedia 911 services.
Do Channels Matter? Illuminating Interpersonal Influence on Music Recommendations

Researchers and service providers have focused on leveraging social information acquired from interactions between users to improve the accuracy of system recommendations. However, few have explained the characteristics of music recommendations through interpersonal relationships. To investigate how interpersonal relationships affect users’ evaluation of music recommendation, we conducted a survey-based study that compared two types of recommendation channels—interpersonal (i.e., from friends) and non-interpersonal (i.e., from systems). We found that relevance was evaluated higher in music recommended from non-interpersonal channels on average, while diversity, novelty, and serendipity were higher in interpersonal channels. Non-interpersonal channels surpassed interpersonal channels in terms of convenience, frequency, and adoption rate. These results illustrate that interpersonal and non-interpersonal channels have different strengths and that digital streaming platforms, which have mainly provided system recommendations thus far, need to better support interpersonal channels for richer user experience.
Co-design of Color Identification Applications Using Scenario-Based Personas for People with Impaired Color Vision

Designers and system analysts have long struggled to extract and repurpose data from user research by laboriously presenting content in the form of storyboards, behavioral-type personas, or journey maps. An alternative is to convey insights from user research through scenario-based personas that represent user research data through storytelling. This provides a more streamlined way to convey data rather than character-based personas; however, scenarios are effortful for developers to articulate and envision. In this work, we empower assistive technology development teams to access authentic user experiences with scenario-based personas through tangible and digital artifacts. Scenario-based personas were used for conveying the results of a user analysis study for color identification mobile applications for people with visual impairments. We developed scenario-based personas for persons with impaired color vision based on the main contexts identified in user research studies. The method combines personas depicted in silhouettes of people with impaired color vision and scenario contexts that capture the place and activities. Silhouettes were used in the artifacts to reduce the bias that a face often generates in persona-based scenarios. Preliminary findings suggest that scenario-based persona tools were effective in describing the stories and context behind why a person with a visual disability would consider a color identification application. Through this method, scenario-based personas were able to foster understanding of the application’s target user population by showing their main contexts of using these mobile applications and create playful and tangible artifacts to capture and convey such user information to designers and developers in the Humanistic Co-Design community. Methodological considerations for using scenario-based personas in user research will be discussed.
Micro-Economic Benefits of Peer-Producing Containerized Network Functions

Discrete, non-virtualized network elements are characterized by large costs, limited functions, vendor lock-in, and limited orchestration.Virtualization technologies like virtual machines (VM) and containers have expanded the scope of virtual resource utilization through consolidation of workloads that were previously running on multiple servers by running them on a single server.With the advent of Network Functions Virtualization (NFV), industries are able to reduce the micro-economic factors associated with vendor proprietary model such as transaction costs and (physical and human) asset specificity to deal with vendor vulnerabilities in contractual relationships because Virtual Network Functions (VNFs) can virtualize dedicated networking functions that were traditionally performed by vendor appliances such as routers, switches, firewalls, and load balancers.Even though virtualization technologies (VMs and containers) and NFV have demonstrated their benefits in the market, little attention has been devoted to the development and adoption of containers to build VNFs.This research paper identifies micro-economic factors, such as transaction costs, associated with searching, buying, provisioning, and maintenance of vendor proprietary appliances and compares them with the coordination costs associated with the adoption of containerized VNFs.This comparative analysis could be used to identify the type of network operators that could serve as key organizers (the network operator who can benefit largely by adopting containerized VNFs) of an open source peer production model as well as other firms that could serve as individual contributors.Furthermore, to identify various rewards and incentives that a managerial firm can leverage to motivate its employees to participate in such an effort, a quantitative survey was conducted (with Tier 1, Tier 2 and Tier 3 Service Providers) to identify the managerial incentives such as bonuses, rewards, peer recognition, and promotion targeting varied network operator firms to accurately capture and analyze employee interests/motivation.
Iterated constructions of completely normal polynomials

The Rσ,t-transform introduced by Bassa and Menares can be used to construct families of irreducible polynomials in Fq[x]. This iterative construction is a generalization of Cohen's R-transform. For this transform, Chapman proved that under some conditions, the polynomials in the resulting family are completely normal. In this paper we establish conditions ensuring that the polynomials obtained by using the Rσ,t-transform are completely normal polynomials and we give a simple proof of Chapman's result.
Utilization of Human-Robot Interaction for the Enhancement of Performer and Audience Engagement in Performing Art

Recently, computer-supported interactive technologies have played a significant role as complementary tools in creation of extraordinary artworks. These technologies have been used in art to explore their utility to compose new creative concepts, and to enrich the dimensions of artistic performances to strengthen the engagement between the performer and the audience. Since integrating the audience into the artistic performance has a significant role in enhancing an individual pleasure, a robotic medium holds great potential in bringing about new opportunities for artistic performances. In this preliminary study, we observed the effects of eye gazing behaviours of a minimal robot on audience engagement and connectedness in regard to an artistic performance. With this paper, the results from the data of a limited number of participants show that the audience tended to be distracted by the robot’s existence, however, the gazing behavior of the robot maintain a feeling of connectivity between the robot and the audience.
Analysing Mobile VR Games for Learning a Sport: A Pistol Target Shooting VR Game Use Case

In recent years, Virtual Reality (VR) technology has gained popularity and has seen a rise in the amount of different VR headsets for personal use. In this regard, research in the use of VR have started to explore the benefits of this technology both for entertainment purposes and for non-entertainment purposes. In this paper, we analyze the effectiveness of mobile VR games for learning a sport. We present our prototype VR game for evaluating whether one can learn or improve their skill at pistol target shooting. We conduct several experiments, including on-site pistol target shooting at a shooting range to assess and compare the performance of those who have played the VR game and those who haven’t. The results from these experiments are presented in this paper.
How Magic Is Zero?

Distributions of open source software packages dedicated to specific programming languages facilitate software development by allowing software projects to depend on the functionality provided by such reusable packages. The health of a software project can be affected by the maturity of the packages on which it depends. The version numbers of the used package releases provide an indication of their maturity. Packages with a 0.y.z version number are commonly assumed to be under initial development, implying that they are likely to be less stable, and depending on them may be less healthy.
The Use of Agent-Based Models As Non-Player Characters in Serious Games

One of the shortcomings of many modern serious games and medical simulations lies in their inability to model even some modicum of unpredictability of real life situations. Interactions with a standardized patient may teach healthcare professional students how to diagnose a clinical condition, better manage a patient, or help them improve their bedside manners, but such simulated interactions will not prepare the learners to deal with unpredictability of clinical situations, interruption, and task switching. Distractions occur from colleagues, clinical decision support alerts, pagers, smartphones, or audible alarms. All these interruptions can potentially alter the course of patient care and the outcome of a patient's treatment. A simulated virtual patient (VP) may teach critical thinking skills, but once a student has successfully diagnosed a VP, the simulation stops providing educational value. In this paper we propose a generalizable method for integrating agent-based models into serious games and simulations. In the proposed paradigm, a human player (learner) takes on the role of a single agent in the model (e.g, a healthcare professional), while the output of the model controls the environment, the rules of agent interactions, and all the other agents that the human player interacts with (non-player characters). Moreover, we will present two use cases demonstrating that the use of agent-based models as behavior controllers for non-player characters introduces a degree of unpredictability in a virtual patient simulation and in a serious game designed to teach middle and high-school students about the spread of infectious diseases.
A Study on the Deep Learning based Prediction of Production Demand by using LSTM under the State of Data Sparsity

Abstract Rapid market condition changes and growing uncertainties require accurate product demand forecasting to reduce the risks of business operations. So far, recurrent neural networks (RNNs) have been introduced as the representative models for forecasting time series data. However, it showed limited performance when learning time pattern occurring over a long period of time. This study performs deep learning-based production forecasting analysis with Long-term short-term memory (LSTM) and dropout algorithm as the alternative for the conventional time series analysis methods and the exponential smoothing method. Our result provides the reference for sales target setting, facility investment and production planning, inventory control, supply chain management, and marketing strategy establishment.
Optimized K-Means clustering algorithm using an intelligent stable-plastic variational autoencoder with self-intrinsic cluster validation mechanism

Clustering is one of the most important tasks in exploratory data analysis [1, 55, 59]. K-means are the most popular clustering algorithms [51, 61]. This is because of their ability to adapt to new examples and to scale up to large datasets. They are also easily understandable and computationally faster [57, 60, 3, 62]. However, the number of clusters, K, has to be specified by the user [50]. Random process is the norm of searching for appropriate number of clusters, until convergence [53, 5]. Several variants of the k-means algorithm have been proposed, geared towards optimal selection of the K [8, 48]. The objective of this paper is to analyze the scaling up problems associated with these variants for optimizing K in the k-means clustering algorithms. Finally, a more enhanced "hybrid autoencoder-based" k-means will be developed and evaluated against the existing variants.
An Effective and Efficient Re-ranking Framework for Social Image Search

With the rapidly increasing popularity of social media websites, large numbers of images with user-annotated tags are uploaded by web users. Developing automatic techniques to retrieval such massive social images attracts much attention of researchers. The method of social image search returns top-k images according to several keywords input by users. However, the returned results by existing methods are usually irrelevant or lack of diversity, which cannot satisfy user's veritable intention. In this paper, we propose an effective and efficient re-ranking framework for social image search, which can quickly and accurately return ranking results. We not only consider the consistency of visual content of images and semantic interpretations of tags, but also maximize the coverage of the user's query demand. Specifically, we first build a social relationship graph by exploring the heterogeneous attribute information of social networks. For a given query, to ensure the effectiveness, we execute an efficient keyword search algorithm over the social relationship graph, and obtain top-k relevant candidate results. Moreover, we propose a novel re-ranking optimization strategy to refine the candidate results. Meanwhile, we develop an index to accelerate the optimization process, which ensures the efficiency of our framework. Extensive experimental conducts on real-world datasets demonstrate the effectiveness and efficiency of proposed re-ranking framework.
A Novel Gesture Detection Technique to Increase Security in NFC Contactless Smartcards

Using a testing platform, composed by off-the-shell and commercial products, this paper describes and implements a Near Field Communication (NFC) authentication system based on encrypted and biometric features. With Radio Frequency Identification (RFID) tags and readers, operating in the HF band, a novel gesture recognition pattern is designed and tested. In addition to the biometric signature, an encryption mechanism is implemented following the design of previous work by other authors. Finally, it is evaluated the security of the system, summarizing the results obtained.
Fuzzy wavelet neural networks applied as inferential sensors of neonatal incubator dynamics

Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wavelet neural network (FWNN) can be regarded as a prominent technique, consisting of the combination of wavelet neural network (WNN) and adaptive network-based fuzzy inference system (ANFIS). This work proposes the use of FWNN to infer the temperature and humidity values inside the incubator in order to certify the equipment operation. Results obtained with the analyzed neural system have shown the generalization and inference capacities of FWNNs, thus allowing their application to practical tasks aiming to increase the efficiency of incubators.
Noisy Multiple-Control Fredkin Gate in Nuclear Spin Based Qubits Chain

The Fredkin gate is a reversible self-inverse logic gate that maintains parity, so it is widely used in DNA computing, low-power CMOS, quantum computing, and nanotechnology. The paper proposes a design of the multiple-control Fredkin gate based on the model of a chain of atoms with nuclear spins one-half in a spinless semiconductor matrix. The allowed transitions realizing the gate operation for two π-pulses are analyzed. The effect of frequency noise and decoherence on the fidelity of the multiple-control Fredkin gate are discussed.
A Two-Step Password Authentication System for Alzheimer Patients

Alzheimer's disease, the most common type of dementia, is ranked sixth amongst the leading causes of death in the United States. As the disease progresses, individuals affected will experience challenges with memory loss, vision impairment, word-finding, and reasoning. When riddled with such symptoms, password memorization can pose a problem. Even though there are several authentication systems in play, none considers all signs and symptoms Alzheimer's disease can cause. We propose a two-step password authentication system that would utilize geolocation and fingerprint biometric screening to assist this specific population by providing a more secure way to access their information.