Reviewerly/exHarmony huggingface dataset:
Smart Virtual Care Centers in the Context of Performance and Privacy

US and Europe have been experiencing an unprecedented increase in the number of older adults. Increased life expectancy and increasingly aging population present challenges in terms of costs associated with elderly healthcare and wellbeing, as well as decreasing availability of health personnel. Similar trends are occurring in the rest of the world. There is a strong push to promote remote telehealth monitoring centers or virtual care centers (VCCs) as a way to address health care needs, especially for the aging population. Technology advances, such as Internet of Things and Smart Built Environments, support proliferation of smart and secure telehealth care that can be implemented at all levels. Remote patient monitoring, combined with personalized interventions and coaching, serves as the foundation for VCCs. However, there are trade-offs with regard to privacy and changes in the living environment and the overall scalability. We need to explore how technology can be utilized to provide health services while protecting and preserving privacy of all stakeholders. A distributed VCC (DVCC), created from individual VCCs and crossing regional and national borders, can provide effective, continuous service with ability to adjust to unforeseen events and emergencies. We discuss a meta-model-based approach to protect patients’ data while improving the efficiency of the care center personnel, which will allow remote care centers to more easily scale as need to provide round-the-clock monitoring.
A Dataset and Baselines for e-Commerce Product Categorization

We make available a document collection of a million product titles from 3,008 anonymized categories of the rakuten.com product catalog. The anonymization has been done due to intellectual property rights on the underlying data organization taxonomy. Our analysis of the characteristics of the 800,000 training and 20,000 validation titles show that they match the test set of 180,000 titles. Twenty six independent teams participated in an automatic product categorization challenge on this dataset. We present results and analysis and suggest strong baselines for this collection and task.
Enabling Spoken Dialogue Systems for Low-Resourced Languages—End-to-End Dialect Recognition for North Sami

In this paper, we tackle the challenge of identifying dialects using deep learning for under-resourced languages. Recent advances in spoken dialogue technology have been strongly influenced by the availability of big corpora, while our goal is to work on the spoken interactive application for the North Sami language, which is classified as one of the less-resourced languages spoken in Northern Europe. North Sami has various variations and dialects which are influenced by the majority languages of the areas in which it is spoken: Finnish and Norwegian. To provide reliable and accurate speech components for an interactive system, it is important to recognize the speakers with their Finnish or Norwegian accent. Conventional approaches compute universal statistical models which require a large amount of data to form reliable statistics, and thus they are vulnerable to small data where there is only a limited number of utterances and speakers available. In this paper we will discuss dialect and accent recognition in under-resourced context, and focus on training an attentive network for leveraging unlabeled data in a semi-supervised scenario for robust feature learning. Validation of our approach is done via two DigiSami datasets: conversational and read corpus.
Improvement of Data Throughput in Data-Intensive Cloud Computing Applications

In recent years, Hadoop framework is popularly known for providing cost-effective solutions to process large-scale data intensive applications in a distributed manner. One of the key issues that can significantly affect the performance of data-intensive cloud computing is computation load balancing among cluster nodes. Replica placement in HDFS plays a significant role in data availability and balanced utilization of clusters. In the current replica placement policy of Hadoop Distributed File System (HDFS), the replicas of data blocks cannot be evenly distributed across cluster's nodes, so the current HDFS must rely on load balancing utility for balancing replicas' distributions, which results in more time and resources consuming. In this paper, we address the load balancing problem and present an innovative replica placement policy for HDFS for data-intensive computing on massive datasets on the cloud system. It can perfectly balance the computing load among cluster's nodes in both homogeneous and heterogeneous cluster environments. Experimental results of the proposed solution confirm that, the proposed replica placement scheme gives better data nodes utilization than the default replica placement policy of Hadoop.
Living on the Edge: Serverless Computing and the Cost of Failure Resiliency

Serverless computing platforms have gained popularity because they allow easy deployment of services in a highly scalable and cost-effective manner. By enabling just-in-time startup of container-based services, these platforms can achieve good multiplexing and automatically respond to traffic growth, making them particularly desirable for edge cloud data centers where resources are scarce. Edge cloud data centers are also gaining attention because of their promise to provide responsive, low-latency shared computing and storage resources. Bringing serverless capabilities to edge cloud data centers must continue to achieve the goals of low latency and reliability. The reliability guarantees provided by serverless computing however are weak, with node failures causing requests to be dropped or executed multiple times. Thus serverless computing only provides a best effort infrastructure, leaving application developers responsible for implementing stronger reliability guarantees at a higher level. Current approaches for providing stronger semantics such as “exactly once” guarantees could be integrated into serverless platforms, but they come at high cost in terms of both latency and resource consumption. As edge cloud services move towards applications such as autonomous vehicle control that require strong guarantees for both reliability and performance, these approaches may no longer be sufficient. In this paper we evaluate the latency, throughput, and resource costs of providing different reliability guarantees, with a focus on these emerging edge cloud platforms and applications.
Dynamic Difficulty Adjustment with a simplification ability using neuroevolution

This paper describes a concept of a dynamic difficulty adjustment system that learns and adapts online to the style of the player's in-game behavior based on the neuroevolution using additionally the base of catalogues. A neural network trained through evolutionary algorithms is used to achieve better adaptation. In this case, genetic algorithms alter the weights of the neural network. In addition to adaptation, the proposed method provides an opportunity to downgrade difficulty when it is necessary or respond to rapid changes in the player skill level. The test game in the genre of real-time first-person fighting is designed to validate the efficiency of the proposed model. Multilayer perceptron (MLP) architecture was selected as a neural network's topology to achieve the maximum correlation between an approximation of the loss function and speed of the neural network. The article describes the advantages and limitations of the proposed concept in comparison with other approaches.
Blockchain Meets Cybersecurity: Security, Privacy, Challenges, and Opportunity

The interest in blockchain technology has grown over the years as a result of its prospect to transform business processes. The number of companies rushing to explore blockchain applications has grown rapidly due to its inherent robustness to cyberattack. This book chapter explores the use of blockchain technology in the Internet of Things (IoT) and the relationship between blockchain and cybersecurity technologies in the IoT ecosystem. We give an insight to security and privacy using blockchain and the substantial positive changes this technology will bring alongside its challenges.
GSEL: A Genetic Stacking-Based Ensemble Learning Approach for Incident Classification

A large amount of incident narratives is collected across all industries around the world. These narratives are usually written manually, which might include a significant amount of errors. Due to the size and complexity of such narrative data, advanced text mining and natural language processing (NLP) techniques are essentially required to extract useful information. Automatic document classification is one of such important tasks in NLP. Therefore, to reduce the dimensionality problem in data, the study proposes a genetic stacking-based ensemble learning (GSEL) method using Skip-gram model and Doc2vec framework. The classifiers, namely logistic regression, random forest, k-nearest neighbor, multi-layer perceptron (MLP), and support vector machine are used and their outputs are ensembled to produce better accuracy in prediction. A real-coded genetic algorithm (GA) is used to tune the parameters of ensemble method. Results reveal that the proposed approach is capable of handling a huge amount of text data in analysis and predict with enhanced accuracy as compared to other state-of-the-art algorithms.
Toward Low-Cost Automated Evaluation Metrics for Internet of Things Dialogues

We analyze a corpus of system-user dialogues in the Internet of Things domain. Georgila, Kallirroi Our corpus is automatically, semi-automatically, and manually annotated with a variety of features both on the utterance level and the full dialogue level. Gordon, Carla The corpus also includes human ratings of dialogue quality collected via crowdsourcing. Choi, Hyungtak We calculate correlations between features and human ratings to identify which features are highly associated with human perceptions about dialogue quality in this domain. Boberg, Jill We also perform linear regression and derive a variety of dialogue quality evaluation functions. Jeon, Heesik These evaluation functions are then applied to a held-out portion of our corpus, and are shown to be highly predictive of human ratings and outperform standard reward-based evaluation functions. Traum, David
Realtime Adaptive Virtual Reality for Pain Reduction

Recent years have seen digital game mediums taking conventional amusement, entertainment and leisure industries by storm. They have revolutionized the system to the extent that the industry cannot now even dream to do without this overwhelming reality. The same game mediums that have capitalized on intrinsic leisure aspects have simultaneously focused with equal vigor on other equally, if not more, important collateral objectives. This paper builds on this concept and discusses a work in progress currently being carried out at the University of Malta. It proposes the use of games as a means of distraction therapy for individuals undergoing painful clinical treatment procedures. The creation of an adaptive Virtual Reality (VR) game within an Artificial Intelligence framework will without doubt be of a significantly greater benefit to the community than mere entertainment applications.
Overview of the NLPCC 2019 Shared Task: Open Domain Semantic Parsing

Semantic Parsing is a key problem for many artificial intelligence tasks, such as information retrieval, question answering and dialogue system. In this paper, we give the overview of the open domain semantic parsing shared task in NLPCC 2019. We first review existing semantic parsing datasets. Then, we describe open domain semantic parsing shared task in this year’s NLPCC, especially focusing on the dataset construction. The evaluation results of submissions from participating teams are presented in the experimental part.
Sequential Training of Neural Networks With Gradient Boosting

This paper presents a novel technique based on gradient boosting to train the final layers of a neural network (NN). Gradient boosting is an additive expansion algorithm in which a series of models are trained sequentially to approximate a given function. A neural network can also be seen as an additive expansion where the scalar product of the responses of the last hidden layer and its weights provide the final output of the network. Instead of training the network as a whole, the proposed algorithm trains the network sequentially in $T$ steps. First, the bias term of the network is initialized with a constant approximation that minimizes the average loss of the data. Then, at each step, a portion of the network, composed of $J$ neurons, is trained to approximate the pseudo-residuals on the training data computed from the previous iterations. Finally, the $T$ partial models and bias are integrated as a single NN with $T \times J$ neurons in the hidden layer. Extensive experiments in classification and regression tasks, as well as in combination with deep neural networks, are carried out showing a competitive generalization performance with respect to neural networks trained with different standard solvers, such as Adam, L-BFGS, SGD and deep models. Furthermore, we show that the proposed method design permits to switch off a number of hidden units during test (the units that were last trained) without a significant reduction of its generalization ability. This permits the adaptation of the model to different classification speed requirements on the fly.
Fast Construction of Almost Optimal Symbol Distributions for Asymmetric Numeral Systems

A crucial task in the design of an efficient ANS encoder consists in choosing a favourable symbol distribution. This task seems to be hard, due to its combinatorial nature, in particular for the tANS variant of ANS, which is the focus of this work. We present a fast technique that builds almost optimal symbol distributions for the stream variant of tANS.
Docker-based Cloud System for Computer Programming Labs

Recently, the importance of software education has been emphasized all over the world. In Korea, software education has been introduced for elementary schools that have applied for software education since 2015, and software education has been adopted as a regular subject in all elementary schools since 2017. As the importance of the software industry grows, interest in coding education is increasing. In Korea, students must complete 16 hours of instruction in elementary school and 34 hours in middle school from 2019. In Korea, however, there are not enough professional teachers who majored in software, and many schools have poor laboratory environments. For successful software coding education, a basic hands-on environment should be supported. It is also difficult for the teacher to analyze and score all students' program sources during class. In this paper, we propose a computer labs management system that can be executed within seconds by using images already generated by Docker for software education at school. And we implemented a programming practice management system using Docker. Our system provides the ability for teachers to automatically identify and score students' source code as they conduct coding training.
UX methods in the data lab

Based on a case study at a national science lab, this paper outlines strategies for demonstrating the value of technical communication research when working with subject matter experts in both data and domain sciences. This paper argues that technical communicators can find common goals with experts in highly technical fields using UX methods as the medium of identification, particularly in the realm of scientific computing. This paper outlines a qualitative usability method created for data scientists at the lab to validate highly specialized scientific visualization applications.
Downsampling Blockchain Algorithm

In blockchain, every full node has to store all history transactions in block bodies. However, with the rapid growth of transactions, the storage bloating problem has emerged, and made it challenging for a mobile system to afford the storage and synchronization overhead. This paper proposes to downsample block bodies to reduce the nodes' storage. By downsampling block bodies by M times, only about 1/M of the block bodies with high information entropy are downloaded and stored. Performance analysis demonstrates that the proposed downsampling nodes and full nodes have similar broadcast accuracy with appropriate M. The simulation results show that the proposed algorithm can provide a better cost-effective choice for nodes between broadcast accuracy and storage.
Correlation-coupling entropy as a measure of strong electron correlation and fragment-conditional density spin polarization as a measure of electron entanglement

Quantum entanglement has been one of the hottest topics in current day physics, since it is the driving force behind quantum cryptography, quantum teleportation, and quantum computing. Several measures of quantification of entanglement have been proposed, each of which can often only be applied to a few specific systems. In this paper we derive a kinematic measure of entanglement that is capable of giving a full description of Einstein-Podolsky-Rosen entanglement in molecular systems. The associated ``coupled entropy'' energy contribution generates the correct amount of strong correlation energy for the ${\mathrm{H}}_{2}$ and ${\mathrm{N}}_{2}$ prototype systems, and is shown to be able to perform the same feat if it is explicitly used as the correlation component in the density-matrix functional context. And, finally, we propose a nonkinematic way to measure the entanglement of spins by using the conditional density of the system.
Revealing Subgroup Structure in Ranked Data Using a Bayesian WAND

Abstract Ranked data arise in many areas of application ranging from the ranking of up-regulated genes for cancer to the ranking of academic statistics journals. Complications can arise when rankers do not report a full ranking of all entities; for example, they might only report their top-M ranked entities after seeing some or all entities. It can also be useful to know whether rankers are equally informative, and whether some entities are effectively judged to be exchangeable. Revealing subgroup structure in the data may also be helpful in understanding the distribution of ranker views. In this paper, we propose a flexible Bayesian nonparametric model for identifying heterogeneous structure and ranker reliability in ranked data. The model is a weighted adapted nested Dirichlet (WAND) process mixture of Plackett–Luce models and inference proceeds through a simple and efficient Gibbs sampling scheme for posterior sampling. The richness of information in the posterior distribution allows us to infer many details of the structure both between ranker groups and between entity groups (within-ranker groups). Our modeling framework also facilitates a flexible representation of the posterior predictive distribution. This flexibility is important as we propose to use the posterior predictive distribution as the basis for addressing the rank aggregation problem, and also for identifying lack of model fit. The methodology is illustrated using several simulation studies and real data examples. Supplementary materials for this article are available online.
Constrained de Bruijn Codes and their Applications

A sequence s = (s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,⋯,s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) is called a (b, h)-constrained de Bruijn sequence if all substrings of length h starting within b consecutive positions are distinct. A set of (b, h)-constrained de Bruijn sequences is called a (b, h)-constrained de Bruijn code. A (b, h)-constrained de Bruijn sequence was constructed and used as a component of a code correcting multiple limited-shift-errors in racetrack memories. In this work, we show that a (b, h)-constrained de Bruijn code can correct deletions and sticky-insertions and also can determine the locations of these errors in an ℓ-symbol read channel. We also show that it is possible to use sequences from a (b, h)-constrained de Bruijn code to construct a code correcting shift-errors in racetrack memories. As a consequence, we improve the rates on previous known codes.It is shown in this work that a (b, h)-constrained de Bruijn code is a constrained code avoiding a set of specific patterns. Finally, we present some techniques to compute the maximum asymptotic rate and find some efficient encoding/decoding algorithms for (b, h)-constrained de Bruijn codes.
A New Approach to the Implementation of Histogram Equalization in Image Processing

Histogram equalization is one of the most well-known techniques of image processing in the spatial domain and is widely applied to enhance images in various applications owing to its high efficiency and simplicity. However, the traditional definition of histogram equalization procedure does not satisfy a number of basic requirements to image intensity transformations. This paper proposes a new approach to defining a histogram equalization procedure based on the analysis of the basic requirements to the image intensity transformation function. Based on this approach, we propose a new implementation for the histogram equalization procedure that satisfies the basic requirements to image intensity transformations and meets the criterion of self-duality.
E-Content and Tool Selection in the Cloud-based Online-CADCOM Platform for Computer-Aided Design in Communications

The paper deals with the Cloud-based version of the Online-CADCOM platform for assisting computer-aided design of communication circuits and systems. It illustrates the e-content included in the platform. The category of analog filters is selected for illustration of automated tool selection for a given specification. Multicriteria Decision Analysis is used for automated tool selection and it consists in Decision Matrix and MAUT model. Mandatory and desirable criteria for analog filter design are defined and weight coefficients are associated to them. A real-life filter design example is solved using this method.
Simulating CLIR Translation Resource Scarcity using High-resource Languages

We study the impact of translation resource scarcity on the performance of cross-language information retrieval (CLIR) systems. To do that, we develop a contrastive analysis framework that uses high-resource languages to simulate low-resource languages. In the framework, we focus on parallel translation corpora and aim to better understand the factors that impact CLIR performance. We argue that both low- and high-resource corpora are needed to develop that understanding. Hence, we take the approach of starting with a true low-resource language and systematically down-sampling a high-resource language to become an artificial low-resource language-the reverse perspective of existing research. We formalize the problem as the Resource Scarcity Simulation (RSS) problem. We model the problem with a family of set covering problems, formulate with integer linear programming, and prove that the problem is actually NP-hard. To this end, we provide two greedy algorithms with polynomial complexities. We compare and analyze our approach with alternate techniques using four high-resource languages (French, Italian, German, and Finnish) down-sampled to simulate two low-resource languages (Somali and Swahili). Our experimental results suggest that language families are important for the RSS problem. We simulate Somali with German, and Swahili with Finnish, achieving 98% and 97% on the similarity percentage in terms of CLIR performance, respectively.