Reviewerly/exHarmony huggingface dataset:
Application of blood donor routine detector using K-Nearest neighbors

Abstract The application of blood donor routine detection using the K-nearest Neighbors method at UTD PMI (Unit Transfusi Darah, Palang Merah Indonesia , Indonesian Red Cross, Hereafter PMI) is a system that aims to provide the class status of the donors aimed at facilitating the employee in selecting the right donor to be recontacted. The K-Nearest Neighbors method is used to calculate the distance of the test data with the training data. The K-nearest Neighbors method is chosen because it has a simple algorithm, works based on the shortest distance from the test data sample to the training data sample to determine the distance. The creation of this system is built by adapting the waterfall model. The objective is that the system is able to implement the K-nearest Neighbors method to help determine the class status of the donors and that the system can send SMS gateway to the donors that donor information can be publicly published. The test calculation uses full train full set testing on all training data and the result of K5=84 %, K7=88 %, K9=87 %, K11=82 %, K13=77 %, K15=75 %. The highest result is on the K7.
Cognitive Solutions in the Enterprise: A Case Study of UX Benefits and Challenges

The consumer market has witnessed a proliferation of cognitive solutions. This increase in consumer expectations for AI technology has led enterprise IT leaders to develop cognitive solutions to improve employee productivity, enhance marketing and sales insights, and make better data-driven decisions. As UX designers supporting the enterprise, we have been gaining experience working with cognitive solutions in multiple contexts, from sentiment analysis of people and news for sellers, cognitively-enhanced conflict resolution of conference calls, capability analysis of team performance, to various chatbots. We will discuss several different cognitive solutions that have been created for the enterprise and provide some recommendations and best practices.
Facial gender recognition using Gabor-DCT feature extraction

Facial Gender Identification has vast application in human computer interaction, determines customer profile in shopping centers, and restricted permission to enter in prohibited zone, criminal profile analysis. This paper presents a robust process for illumination invariant compact feature extraction using Gabor filter for the automatic recognition for facial gender identification system. Face has uniqueness in edges and texture pattern for different gender category. Gabor filter can extract edge and textural patterns of faces but generate problem of huge dimensions and redundancy feature coefficients. In order to enhance the efficiency and accuracy of the system, this problem of enormous redundancy as well as dimension can be solved by proposing a new feature namely average-DCT feature reduction technique. Proposed Gabor-DCT has precise, accurate and compact feature pattern as well as early throughput for facial gender identification compared to other state of art method of Gabor filter.
An Expert-Model &amp; Machine Learning Hybrid Approach to Predicting Human-Agent Negotiation Outcomes

We present the results of a machine-learning approach to the analysis of several human-agent negotiation studies. By combining expert knowledge of negotiating behavior compiled over a series of empirical studies with neural networks, we show that a hybrid approach to parameter selection yields promise for designing -more effective and socially intelligent agents. Specifically, we show that a deep feedforward neural network using a theory-driven three-parameter model can be effective in predicting negotiation outcomes. Furthermore, it outperforms other expert-designed models that use more parameters, as well as those using other, more limited techniques (such as linear regression models or boosted decision trees). We anticipate these results will have impact for those seeking to combine extensive domain knowledge with more automated approaches in human-computer negotiation.
Empirical evaluation of five algorithms for the initialization phase of the k-Means algorithm

A recurring problem in a wide variety of research areas such as pattern recognition, machine learning, data mining and statistics, among others, is characterized as a clustering problem. Such a problem can be described in a simplistic way as: given a set of data (observations, objects, points, etc. ), group similar data into clusters (groups). A clustering of a given data set is then characterized as a set of clusters, in which elements belonging to a cluster are similar to each other and elements belonging to distinct clusters are not similar. Clustering algorithms are non-supervised algorithms and, among the many available in the literature, the k-Means, that uses a random initizalization process, can be considered one of the most popular and successful. The performance of the k-Means, however, is highly dependent on a ‘good’ initialization of the k cluster centers (centroids), as well as on the value assigned to the number (k) of clusters the final clustering should have. This paper addresses experiments using five initialization algorithms available in the literature namely, the Method1, the k-Means++, the CCIA, the Maedeh and Suresh and the SPSS algorithms, to empirically evaluate their contribution for improving the k-Means performance.
Personalized Recommendation Leveraging Social Relationship

The goal of recommender system is recommending products users may be interested in. Recommending content is an important task in many platforms. But many platforms only have implicit feedback information from users. Each single user generates behavior on only a few products lead to sparse datasets from users. And traditional recommending algorithms show poor performance when face the two problems. In this paper, we present a social bayesian personalized ranking based on similar friends (SFSBPR). We alleviate the sparsity problem when recommend by users’ implicit feedback. Experiment on real-world sparse datasets show that our method outperforms current state-of-the-art recommendation method.
Dynamic Graph CNN with Attention Module for 3D Hand Pose Estimation

Recently, 3D hand pose estimation methods taking point cloud as input show the most advanced performance. We present a new 3D deep learning hand pose estimation network for an unordered point cloud. Our approach utilizes EdgeConv layer as the basic element, where an attention embedding version EdgeConv layer is proposed for feature extraction in hand pose estimation task. To improve the result, we design a hand pose improvement network that inputs points whose are in the neighbor of the estimated fingers and outputs a rectify hand pose. We evaluate our method on several famous datasets to prove that our method can get excellent result compared to some most advanced methods.
Compositional Data Analysis of Glucose Profiles of Type 1 Diabetes Patients

Time spent in different glucose ranges indicate the occurrence of adverse events and measure the quality of glucose control in type one diabetes (T1D) patients. This work proposes a Compositional Data (CoDa) approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles limited to 6-h duration were analyzed at four different times of the day These glucose profiles were distributed into time spent in five glucose ranges, which determine the composition. The log-ratio coordinates of the compositions were categorized through a clustering algorithm, which later made possible the obtainment of a linear model that should be used to predict the category of a 6-h period in different times of day. Leave-one-out cross-validation was performed, achieving an average above 90% of correct classification. A probabilistic model of transition between the category of the past 6-h of glucose to the category of the future 6-h period was obtained. Results show that the CoDa approach not only works as new analysis tool and is suitable for the categorization of glucose profiles, but also is a complementary tool for the prediction of different categories of glucose control. This prediction could assist patients to take correction measures in advance to adverse situations.
An Improved Fuzzy Cognitive Mapping Method

Fuzzy Cognitive Mapping (FCM) models the evolution of a system over time. However, FCM tends to produce the same steady state values under different initial conditions. To address this issue, this study suggests an improved FCM method that integrates the reachability matrix of the classical Cognitive Mapping method into FCM. The proposed method produces more distinctive and representative steady state values by appropriately incorporating the initial conditions. The applicability and effectiveness of the improved method are shown by an example.
Holodeck as a Medium for Future Enacted, Immersive, Narrative Experiences

This paper describes how the fictional concept of Holodeck can be seen as a new medium for future enacted immersive, narrative experiences. In essence the Holodeck can be understood as highly visual multisensory experience environment that can be employed for multiple purposes. The paper tackles the medium through the holonovel writing activity and pursues its design process by defining elements for science-fiction prototypes, i.e. short fictional stories. After introductory literature review, the paper presents important terminology for the holonovel design process, including the setup, important stakeholders (Holodeck characters), and those critical units and entities that are needed for defining the settings.
Time-Limits and Summaries for Faster Relevance Assessing

Relevance assessing is a critical part of test collection construction as well as applications such as high-recall retrieval that require large amounts of relevance feedback. In these applications, tens of thousands of relevance assessments are required and assessing costs are directly related to the speed at which assessments are made. We conducted a user study with 60 participants where we investigated the impact of time limits (15, 30, and 60 seconds) and document size (full length vs. short summaries) on relevance assessing. Participants were shown either full documents or document summaries that they had to judge within a 15, 30, or 60 seconds time constraint per document. We found that using a time limit as short as 15 seconds or judging document summaries in place of full documents could significantly speed judging without significantly affecting judging quality. Participants found judging document summaries with a 60 second time limit to be the easiest and best experience of the six conditions. While time limits may speed judging, the same speed benefits can be had with high quality document summaries while providing an improved judging experience for assessors.
EML: A Scalable, Transparent Meta-Learning Paradigm for Big Data Applications

The work presentedKamath, Uday in this chapter is motivated by two important challenges that arise when applying ML techniques to big data applications: the scalability of an ML technique as the training dataDomeniconi, Carlotta increases significantly in size, and the transparency (understandability) of the induced models. To address these issues we describe andShehu, Amarda analyze a meta-learning paradigm, EML, that combines techniques from evolutionary computation and supervised learning to produceDe Jong, Kenneth a powerful approach for inducing transparent models for big data ML applications.
Personal access to documents using different literacy levels

Providing information in ‘easy-to-read’ aims at improving accessibility through easier understandable text which is more appropriate for different target groups. Particularly, legal notifications are often hard to understand. This paper presents an app and its’ underlying online system that enables users to read such information, adapted to their individual reading skills. The online system allows creating and managing translations of complex information of notifications. It maps a three-step process of translation including stakeholders located at different institutions on a content management system, which provides information of scalable complexity in terms of language level and level of detail. Users use their personal mobile devices to access this information. The app is used to identify documents with the built-in QR-code reader utilizing the back-facing camera of the smart device. After identification of this specific instance of the document, the app fetches the information, including personal data, and presents it at the user-preferred language level. Privacy and security issues were considered as well as requirements necessary to ensure accessibly.
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings

We develop and investigate several crosslingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models.We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment.Our results support representation transfer as a scalable approach for modular cross-lingual alignment of neural sentence embeddings, where we observe better performance compared to joint models in intrinsic and extrinsic evaluations, particularly with smaller sets of parallel data.
SWAP at SemEval-2019 Task 3: Emotion detection in conversations through Tweets, CNN and LSTM deep neural networks

Emotion detection from user-generated contents is growing in importance in the area of natural language processing.The approach we proposed for the EmoContext task is based on the combination of a CNN and an LSTM using a concatenation of word embeddings.A stack of convolutional neural networks (CNN) is used for capturing the hierarchical hidden relations among embedding features.Meanwhile, a long short-term memory network (LSTM) is used for capturing information shared among words of the sentence.Each conversation has been formalized as a list of word embeddings, in particular during experimental runs pre-trained Glove and Google word embeddings have been evaluated.Surface lexical features have been also considered, but they have been demonstrated to be not usefully for the classification in this specific task.The final system configuration achieved a micro F1 score of 0.
3D Flow Entropy Contour Fitting Segmentation Algorithm Based on Multi-Scale Transform Contour Constraint

Image segmentation is a crucial topic in image analysis and understanding, and the foundation of target detection and recognition. Image segmentation, essentially, can be considered as classifying the image according to the consistency of the region and the inconsistency between regions, it is widely used in medical and criminal investigation, cultural relic identification, monitoring and so forth. There are two outstanding common problems in the existing segmentation algorithm, one is the lack of accuracy, and the other is that it is not widely applicable. The main contribution of this paper is to present a novel segmentation method based on the information entropy theory and multi-scale transform contour constraint. Firstly, the target contour is initially obtained by means of a multi-scale sample top-hat and bottom-hat transform and an improved watershed method. Subsequently, in terms of this initial contour, the interesting areas can be finely segmented out with an innovative 3D flow entropy method. Finally, the sufficient synthetic and real experiments proved that the proposed algorithm can greatly improve the segmentation effect. In addition, it is widely applicable.
A Cross-Sectional Study Using Wireless Electrocardiogram to Investigate Physical Workload of Wheelchair Control in Real World Environments

The wheelchair is a key invention that provides individuals with limitations in mobility increased independence and participation in society. However, wheelchair control is a complicated motor task that increases physical and mental workload. New wheelchair interfaces, including power-assisted devices can further enable users by reducing the required effort especially in more demanding environments. The protocol engaged novice wheelchair users to push a wheelchair with and without power assist in a simple and complex environment using wireless Electrocardiogram (ECG) to approximate heart rate (HR). Results indicated that HR determined from ECG data, decreased with use of the power-assist. The use of power-assist however did reduce behavioral performance, particularly within obstacles that required more control.
A Panel Data Set of Cryptocurrency Development Activity on GitHub

Cryptocurrencies are a significant development in recent years, featuring in global news, the financial sector, and academic research. They also hold a significant presence in open source development, comprising some of the most popular repositories on GitHub. Their openly developed software artifacts thus present a unique and exclusive avenue to quantitatively observe human activity, effort, and software growth for cryptocurrencies. Our data set marks the first concentrated effort toward high-fidelity panel data of cryptocurrency development for a wide range of metrics. The data set is foremost a quantitative measure of developer activity for budding open source cryptocurrency development. We collect metrics like daily commits, contributors, lines of code changes, stars, forks, and subscribers. We also include financial data for each cryptocurrency: the daily price and market capitalization. The data set includes data for 236 cryptocurrencies for 380 days (roughly January 2018 to January 2019). We discuss particularly interesting research opportunities for this combination of data, and release new tooling to enable continuing data collection for future research opportunities as development and application of cryptocurrencies mature.
Electronic and microscopic analysis of offset imprints of barcodes on cardboards

The results of analysis of offset imprints of barcodes on cardboards have been offered in this article. The influence of the imprints surface structure on the ink adhesion and the influence of additional varnishing on the barcodes stability to abrasion have been researched. Electronic and microscopic analysis was carried out and their quality parameters were determined - roughness, contrast, optical density.
Highly spectrally undersampled vowels can be classified by machines without supervision

An unsupervised automatic clustering algorithm (k-means) classified 1282 Mel frequency cepstral coefficient (MFCC) representations of isolated steady-state vowel utterances from eight standard German vowel categories with fo between 196 and 698 Hz. Experiment I obtained the number of MFCCs (1-20) in connection with the spectral bandwidth (2-20 kHz) at which performance peaked (five MFCCs at 4 kHz). In experiment II, classification performance with different ranges of fo revealed that ranges with fo > 500 Hz reduced classification performance but it remained well above chance. This shows that isolated steady state vowels with strongly undersampled spectra contain sufficient acoustic information to be classified automatically.
An Optimized AES Masking Method for Resisting Side Channel Analysis

In order to against the side channel analysis attack such as power, electromagnetic waves, and time attack, an optimized masking method is proposed for Advanced Encryption Standard (AES) algorithm in this paper. This scheme adopts random hamming distance mask and offset randomization strategy in the first two rounds, the last round and two rounds randomly selected in the middle of the AES algorithm to ensure the security of each intermediate value. The scheme then adopts fixed-value mask strategy in the five rounds in the middle, which can reduce the time and space consumption to recalculate S box, improve the efficiency of the algorithm and reduce the production cost. To compare with other defence methods, we build a power analysis platform and conduct extensive experiments. The experimental results show that our optimized AES masking method is more secure than the compared methods, and it is able to resist second-order correlation power analysis (CPA) attacks.
Using Augmented Reality for Learning Naval Operations

Augmented reality (AR) is a technology that extends the perception of human reality by allowing, for example, inserting virtual objects in real environments. With augmented reality, learning can be more effective with graphics and interactivity in the manipulation of content. With this work, we intend to apply the AR technology in a process of developing a solution in learning context. For this purpose, different types of devices were analyzed in order to select the type of equipment with better characteristics for use in teaching/learning solutions. An agile development process was tailored to be used in the development of AR applications. The process was then instantiated in the development of an AR solution for learning purposes of ships’ maneuvers in formations in the context of naval operations. The effectiveness of the AR solution was then validated through statistical tests of data collected from a survey to a sample of users.
Prototyping in Human Computer Interaction

For a successful project, the usability and ergonomics of UI are the first priority factors for achieving acceptance. From client perspective, accomplishment of a task dependably relies upon quality of interaction amongst client and framework. In this manner, special apparatuses are required for the trading of information. The need to utilize this sort of apparatuses utilized as a part of life cycle show, known as prototyping. Among existing models, there is no model, which is capable of modeling the interfaces and the algorithms for implementing those models to create the interfaces with a special purpose procedural implementation that we are using today. Developers of software with the perspective of usability use number of techniques to engage users and to see the interaction with functionality of the application. This paper includes how and when developer use prototyping techniques and the review of several approaches of prototyping.
Improving Distribued Subgraph Matching Algorithm on Timely Dataflow

The subgraph matching problem is defined to find all subgraphs of a data graph that are isomorphic to a given query graph. Subgraph matching plays a vital role in the fields of e-commerce, social media and biological science. CliqueJoin is a distributed subgraph matching algorithm that is designed to be efficient and scalable. However, CliqueJoin is originally developed on MapReduce, thus the performance of the algorithm can be affected by the notorious I/O issue of MapReduce while processing multi-round join tasks. Meanwhile, CliqueJoin does not propose a cost evaluation strategy for labelled graphs, which limits its application in practice where most real-world graphs are labelled. Targeting the limitations of CliqueJoin, we propose CliqueJoin++ to improve CliqueJoin in two aspects. Firstly, we implement CliqueJoin++ on the Timely dataflow system instead of MapReduce to avoid considerable I/O cost. Secondly, we extend the cost evaluation function in CliqueJoin to compute optimal join plans for labelled graphs in the distributed context. Extensive experiments have been conducted to show that the proposed method is up to 10 times faster than the MapReduce version for unlabelled matching, and it achieves good performance and scalability for labelled matching.