Reviewerly/exHarmony huggingface dataset:
Study of application of data mining market basket analysis for knowing sales pattern (association of items) at the O! Fish restaurant using apriori algorithm

The development of the food and beverage culinary industry is growing very rapidly. Making food and beverage business owners, especially restaurants, have to make the right decision to stay in a very strong competition, restaurant owners must be ready to always innovate and remain to be able to meet consumer needs through products that can attract customers and determine strategies promotions that can boost sales. Stored transaction data has information that can be extracted by data mining techniques, for example knowing the pattern of sales in purchases by consumers. Information about sales patterns can be used by O! Fish restaurants to create more potential promotional strategies to boost sales by referring to items (menus) that are often purchased together. . To be able to find out the purchase patterns by consumers simultaneously, knowing what products are often purchased simultaneously can be used data mining techniques using a priori algorithms. A priori algorithm is used to generate association rules. Information about the association's rules in purchasing items (menus) by consumers can be used by O! Fish restaurants to create more potential promotional strategies to boost sales by referring to a combination of items that are often purchased simultaneously. Later the results of this study are in the form of a website-based application to analyze purchasing patterns (item association rules) by consumers where the purchase pattern can be used as recommendations in determining the promotion development strategy for O! Fish restaurants.
Intelligent Conversational Agents in Healthcare: Hype or Hope?

New developments in healthcare require an increased disease self-management of patients. Intelligent digital assistants equipped with a conversational user interface are intended to support patients in this challenging task by providing reminders, answering questions, or supporting in self-monitoring tasks. In this paper, we study the potentials of intelligent conversational agents in healthcare. We realized three systems for three different use cases (patient education, disease management, self anamnesis). Based on these implementations and experiences with usability tests, we performed an analysis of strengths, weaknesses, opportunities and threats (SWOT) using a questionnaire. The results show that conversational agents used in healthcare applications can be helpful. However, they have to be integrated into the healthcare process, supporting also the interaction between the healthcare team and a patient. In order to be attractive for a long-term usage, the scope of operation should autonomously adapt to the current health situation of a patient to provide relevant functionality as needed.
A Traversable Wormhole Teleportation Protocol in the SYK Model

In this paper, we propose a concrete teleportation protocol in the SYK model based on a particle traversing a wormhole. The required operations for the communication, and insertion and extraction of the qubit, are all simple operators in terms of the basic qubits. We determine the effectiveness of this protocol, and find a version achieves almost perfect fidelity. Many features of semiclassical traversable wormholes are manifested in this setup.
Entanglement in three coupled harmonic oscillators

We develop an approach in solving exactly the problem of three-body oscillators including general quadratic interactions in the coordinates for arbitrary masses and couplings. We introduce a unitary transformation of three independent angles to end up with a diagonalized Hamiltonian. Using the representation theory of the group $SU(3)$, we explicitly determine the solutions of the energy spectrum. Considering the ground state together with reduced density matrix, we derive the corresponding purity function that is giving rise to minimal and maximal entanglement under suitable conditions. The cases of realizing one variable among three is discussed and know results in literature are recovered.
Dense-CaptionNet: a Sentence Generation Architecture for Fine-grained Description of Image Semantics

Automatic image captioning, a highly challenging research problem, aims to understand and describe the contents of the complex scene in human understandable natural language. The majority of the recent solutions are based on holistic approaches where the scene is described as a whole, potentially losing the important semantic relationship of objects in the scene. We propose Dense-CaptionNet, a region-based deep architecture for fine-grained description of image semantics, which localizes and describes each object/region in the image separately and generates a more detailed description of the scene. The proposed network contains three components which work together to generate a fine-grained description of image semantics. Region descriptions and object relationships are generated by the first module, whereas the second one generates the attributes of objects present in the scene. The textual descriptions obtained as an output of the two modules are concatenated to feed as an input to the sentence generation module, which works on encoder-decoder formulation to generate a grammatically correct but single line, fine-grained description of the whole scene. The proposed Dense-CaptionNet is trained and tested using Visual Genome, MSCOCO, and IAPR TC-12 datasets. The results establish a new state-of-the-art when compared with the existing top performing methodologies, e.g., Up-Down-Captioner, Show, Attend and Tell, Semstyle, and Neural Talk, especially on complex scenes. The implementation has been shared on GitHub for other researchers: http://bit.ly/2VIhfrf
Becoming familiar: how infrastructure engineers begin to use collaborative virtual reality in their interdisciplinary practice

The design community has recently shown increased interest in using virtual reality (VR) in project review contexts. While single-user headsets currently attract most VR-related attention, room-like immersive VR environments can help facilitate design team engagement and shared exploration of projects. However, to date relatively little research concerns how large-scale VR environments are used in and adapted for professional practice. To address this gap, we set up a bespoke portable VR display system called 3D-MOVE in a major UK construction office to investigate how project team members used and evaluated collaborative VR processes. Over a three-month period, we conducted ten video-recorded VR sessions to observe how engineering professionals familiarize themselves with VR in order to help inform its deployment in practice. The study results show that emergent discussions about design models and questioning of design-related assumptions dominated all observed sessions, even though they were staged as technology demonstrations; which supports the social aspects of large-scale collaborative VR processes. However, before participants could focus on design review, they had to familiarize themselves with the VR technology and time required to do so varied depending on the complexity of the VR configuration. As the participants engaged with the VR environment, they reflected on their processes, requirements and expectations and provided feedback for improving the VR experience. Articulating this familiarization with collaborative VR can inform its deployment with respect to minimizing the learning curve and any distractions or discomfort associated with its use while maximizing the aspects of value-added collaborative engagement. Additional considerations concerning content, interactivity and logistics emerged as necessary to address before VR technologies can become standard practice.
Learning With Feature Evolvable Streams

Learning with streaming data has attracted much attention during the past few years. Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited-lifespan sensors will change when these sensors are substituted by new ones. In this article, we propose a novel learning paradigm: Feature Evolvable Streaming Learning where old features would vanish and new features would occur. Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance. Specifically, we learn a mapping from the overlapping period to recover old features and then we learn two models from the recovered features and the current features, respectively. To benefit from the recovered features, we develop two ensemble methods. In the first method, we combine the predictions from two models and theoretically show that with the assistance of old features, the performance on new features can be improved and we provide a tighter bound when the loss function is exponentially concave. In the second approach, we dynamically select the best single prediction and establish a better performance guarantee when the best model switches. Experiments on both synthetic and real data validate the effectiveness of our proposal.
Deep person re-identification in UAV images

Abstract The person re-identification is one of the most significant problems in computer vision and surveillance systems. The recent success of deep convolutional neural networks in image classification has inspired researchers to investigate the application of deep learning to the person re-identification. However, the huge amount of research on this problem considers classical settings, where pedestrians are captured by static surveillance cameras, although there is a growing demand for analyzing images and videos taken by drones. In this paper, we aim at filling this gap and provide insights on the person re-identification from drones. To our knowledge, it is the first attempt to tackle this problem under such constraints. We present the person re-identification dataset, named DR one HIT (DRHIT01), which is collected by using a drone. It contains 101 unique pedestrians, which are annotated with their identities. Each pedestrian has about 500 images. We propose to use a combination of triplet and large-margin Gaussian mixture (L-GM) loss to tackle the drone-based person re-identification problem. The proposed network equipped with multi-branch design, channel group learning, and combination of loss functions is evaluated on the DRHIT01 dataset. Besides, transfer learning from the most popular person re-identification datasets is evaluated. Experiment results demonstrate the importance of transfer learning and show that the proposed model outperforms the classic deep learning approach.
TEACHERS’ OBSTACLES IN UTILIZING INFORMATION AND COMMUNICATION TECHNOLOGY

This study aims to study teacher barriers to utilizing information and communication media (ICT) for learning media. This research was carried out at Public Elementary School 24 Pulau Rimau located in Karang Manunggal village, Pulau Rimau sub-district, Banyuasin district. The subjects of this study were teachers at Public Elementary School 24 Pulau Rimau who tested 16 people. In conducting this research, data from certain sources are needed that are as expected in this study. The technique of collecting data is by observation and interview. Data were analyzed using percentages to see the percentage of respondents' answers. Furthermore, all data is processed by analyzing qualitative data, namely data reduction, data display, and data verification. The formulation of the problem in this study is what forms are questioned by the teacher in utilizing technology-based media in Public Elementary School 24 Pulau Rimau ? From the results of research on teacher barriers to using Information Technology (ICT) in Public Elementary School 24 Pulau Rimau, two aspects related to the ability of teachers and Information and Communication Technology (ICT) were inadequate. The obstacle of the teacher's ability to master ICT is that the teacher is not proficient in using computers, judging from the percentage of teacher answers, 75% say they are not proficient in using technology-based media, whereas using computers is one of the activities that support computer-based media. In addition, teachers do not have an advanced educational background in the use of ICT media. Another obstacle in terms of facilities, in Public Elementary School 24 Pulau Rimau there is no electricity network (PLN). In addition to not having a PLN, there is also the unavailability of computer facilities and laptops at the Public Elementary School 24 Pulau Rimau.
A Novel Approach to Discover Ontology Alignment

Background: The rise of knowledge-rich applications has made ontologies as a common reference point to link the legacy IT systems. The interoperability and integration of two disparate systems in the same domain demand for the resolution of the heterogeneity problem. The major source of heterogeneity lies in the classical representation scheme of ontologies. Objective: Our objective is to present a novel approach to discover ontology alignment by exploiting the comprehensive knowledge structure, where every entity is represented and stored as a knowledge unit. Methods: We have created the dataset ourselves by using protégé tool because no dataset is available based on the idea of comprehensive knowledge structure. Results: The proposed approach always detects correct alignments and achieves optimal or near to optimal performance (in term of precision) in case of equivalence relationship. Conclusion: The aim of this paper is not to make a full-fledged matching/alignment tool, but to emphasize the importance of distinctive features of an entity while performing entity matching. The matchers are therefore used as black boxes and may be filled based on user’s choice.
On Matrix Momentum Stochastic Approximation and Applications to Q-learning

Acceleration is an increasingly common theme in the stochastic optimization literature. The two most common examples are Nesterov's method, and Polyak's momentum technique. In this paper two new algorithms are introduced for root finding problems: 1) PolSA is a root finding algorithm with specially designed matrix momentum, and 2) NeSA can be regarded as a variant of Nesterov's algorithm, or a simplification of PolSA. The PolSA algorithm is new even in the context of optimization (when cast as a root finding problem). The research surveyed in this paper is motivated by applications to reinforcement learning. It is well known that most variants of TD- and Q-learning may be cast as SA (stochastic approximation) algorithms, and the tools from general SA theory can be used to investigate convergence and bounds on convergence rate. In particular, the asymptotic variance is a common metric of performance for SA algorithms, and is also one among many metrics used in assessing the performance of stochastic optimization algorithms. There are two well known SA techniques that are known to have optimal asymptotic variance: the Ruppert-Polyak averaging technique, and stochastic Newton-Raphson (SNR). The former algorithm can have extremely bad transient performance, and the latter can be computationally expensive. It is demonstrated here that parameter estimates from the new PolSA algorithm couple with those of the ideal (but more complex) SNR algorithm. The new algorithm is thus a third approach to obtain optimal asymptotic covariance. These strong results require assumptions on the model. A linearized model is considered, and the noise is assumed to be a martingale difference sequence. Numerical results are obtained in a non-linear setting that is the motivation for this work: In PolSA implementations of Q-learning it is observed that coupling occurs with SNR in this non-ideal setting.
Disentanglement through Nonlinear ICA with General Incompressible-flow Networks (GIN)

A central question of representation learning asks under which conditions it is possible to reconstruct the true latent variables of an arbitrarily complex generative process. Recent breakthrough work by Khemakhem et al. (2019) on nonlinear ICA has answered this question for a broad class of conditional generative processes. We extend this important result in a direction relevant for application to real-world data. First, we generalize the theory to the case of unknown intrinsic problem dimension and prove that in some special (but not very restrictive) cases, informative latent variables will be automatically separated from noise by an estimating model. Furthermore, the recovered informative latent variables will be in one-to-one correspondence with the true latent variables of the generating process, up to a trivial component-wise transformation. Second, we introduce a modification of the RealNVP invertible neural network architecture (Dinh et al. (2016)) which is particularly suitable for this type of problem: the General Incompressible-flow Network (GIN). Experiments on artificial data and EMNIST demonstrate that theoretical predictions are indeed verified in practice. In particular, we provide a detailed set of exactly 22 informative latent variables extracted from EMNIST.
Evaluating global and local sequence alignment methods for comparing patient medical records

Sequence alignment is a way of arranging sequences (e.g., DNA, RNA, protein, natural language, financial data, or medical events) to identify the relatedness between two or more sequences and regions of similarity. For Electronic Health Records (EHR) data, sequence alignment helps to identify patients of similar disease trajectory for more relevant and precise prognosis, diagnosis and treatment of patients.We tested two cutting-edge global sequence alignment methods, namely dynamic time warping (DTW) and Needleman-Wunsch algorithm (NWA), together with their local modifications, DTW for Local alignment (DTWL) and Smith-Waterman algorithm (SWA), for aligning patient medical records. We also used 4 sets of synthetic patient medical records generated from a large real-world EHR database as gold standard data, to objectively evaluate these sequence alignment algorithms.For global sequence alignments, 47 out of 80 DTW alignments and 11 out of 80 NWA alignments had superior similarity scores than reference alignments while the rest 33 DTW alignments and 69 NWA alignments had the same similarity scores as reference alignments. Forty-six out of 80 DTW alignments had better similarity scores than NWA alignments with the rest 34 cases having the equal similarity scores from both algorithms. For local sequence alignments, 70 out of 80 DTWL alignments and 68 out of 80 SWA alignments had larger coverage and higher similarity scores than reference alignments while the rest DTWL alignments and SWA alignments received the same coverage and similarity scores as reference alignments. Six out of 80 DTWL alignments showed larger coverage and higher similarity scores than SWA alignments. Thirty DTWL alignments had the equal coverage but better similarity scores than SWA. DTWL and SWA received the equal coverage and similarity scores for the rest 44 cases.DTW, NWA, DTWL and SWA outperformed the reference alignments. DTW (or DTWL) seems to align better than NWA (or SWA) by inserting new daily events and identifying more similarities between patient medical records. The evaluation results could provide valuable information on the strengths and weakness of these sequence alignment methods for future development of sequence alignment methods and patient similarity-based studies.
Missing values imputation based on fuzzy C-Means algorithm for classification of chronic obstructive pulmonary disease (COPD)

Chronic Obstructive Pulmonary Disease (COPD) is one of the most causes of death in the world. World Health Organization (WHO) reported that in 2016 COPD was the third leading cause of death worldwide with around 3 million deaths, equivalent to 5.2% of deaths worldwide. For this reason, further research needs to be done on CPOD. Unfortunately, the data collected in the study does not contain all the desired data, is called as a missing value. Missing value is a problem for all types of data analysis. Several ways that can be applied to handle missing value, by filtering data (ignore or remove data) and imputing data. Ignoring or removing data can reduce the amount of information contained in the data and can cause low accuracy to generate from the data analysis process. To overcome this problem, imputation data will be carried out at the preprocessing stage to obtain complete data which is expected to increase the accuracy of the data analysis performed. Many imputations method can be used, such as mean imputation and Fuzzy C-Means (FCM). Fuzzy C-Means is a clustering method that allows one part of the data to belong to two or more groups based on their membership function. The complete dataset was trained with Decision Tree classifier to observe the performance in terms of accuracy for mean and FCM method. The analysis of proposed imputation on classification shows that FCM slightly accurate compare to mean imputation method.
Working Memory-Driven Neural Networks with a Novel Knowledge Enhancement Paradigm for Implicit Discourse Relation Recognition

Recognizing implicit discourse relation is a challenging task in discourse analysis, which aims to understand and infer the latent relations between two discourse arguments, such as temporal, comparison. Most of the present models largely focus on learning-based methods that utilize only intra-sentence textual information to identify discourse relations, ignoring the wider contexts beyond the discourse. Moreover, people comprehend the meanings and the relations of discourses, heavily relying on their interconnected working memories (e.g., instant memory, long-term memory). Inspired by this, we propose a Knowledge-Enhanced Attentive Neural Network (KANN) framework to address these issues. Specifically, it establishes a mutual attention matrix to capture the reciprocal information between two arguments, as instant memory. While implicitly stated knowledge in the arguments is retrieved from external knowledge source and encoded as inter-words semantic connection embeddings to further construct knowledge matrix, as long-term memory. We devise a novel paradigm with two ways by the collaboration of the memories to enrich the argument representation: 1) integrating the knowledge matrix into the mutual attention matrix, which implicitly maps knowledge into the process of capturing asymmetric interactions between two discourse arguments; 2) directly concatenating the argument representations and the semantic connection embeddings, which explicitly supplements knowledge to help discourse understanding. The experimental results on the PDTB also show that our KANN model is effective.