Reviewerly/exHarmony huggingface dataset:
Anatomía Radiológica y realidad aumentada: metodología basada en la ludificación para su aprendizaje

In recent years, gamification in the classroom has gone from being used primarily in the school environments to also being used at universities. The present work shows and compares a gamification experience carried out in students belonging to both higher education and non-university higher education. It is a questionnaire-type game based on real clinical cases of Radiology and Radiological Anatomy, in which the student advances as the answer shown through augmented reality is correct. After playing, the theoretical-practical knowledge acquired will be analyzed and statistically significant improvements will be observed. Finally, satisfaction surveys indicate that both student profiles prefer to use gamification tools in their learning and the development of their skills.
USING MOBILE AUGMENTED REALITY TECHNOLOGIES IN ARCHITECTURAL EDUCATION

Effective applied research is based on close collaboration between research and industry.The teaching and learning methods used for decades in Architectural learning process should be reviewed taking into account habits of learning and the existing challenges provided by contemporary information technologies for a new generation of students.This paper introduces a pilot study based on using Augmented Reality (AR) as a new tool in architectural learning process.AR is an emerging technology which enables participants to interact with digital information embedded within the physical environment.Egyptian educational architectural institutions today are largely unaware of new concepts such as Augmented Reality opportunities for architectural practice, and the prototypes that are being developed by researchers worldwide.The paper goal is to present how it is very helpful to use such new advanced technology in architectural learning process.Testing its possibilities for graphical and spatial capabilities and recognition improvements for the first year architectural students in the Building Construction course, at the Department of Architecture, Menoufia University, Egypt.The case study was applied at the first semester of the academic year of 2015-2016.
Early Warning System Technology Innovation Study in Flood Disaster Mitigation in Aren Village Area Bontang Disability

&lt;em&gt;AWLR with the application of I Am Aren is been CSR program of PT Pupuk Kaltim that was developed as a flood disaster mitigation in order to be able to minimize the loss of society cause of flooding. Obtaining data used black-box testing techniques, surveys, and interviews which are then analyzed inductively. Respondents involved as many as 40 people for the survey and 20 people for the interview. The research approach used qualitative research. The results showed that the black-box testing for the recording feature was following the procedure and could provide accurate information. Respondents did not undergo problems in the using and the results of the implementation of the tool were able to reduce losses to 74.4% from the previous loss.&lt;/em&gt;
PENGEMBANGAN APLIKASI SISTEM INFORMASI PENJUALAN AKSESORIS KENDARAAN BERBASIS WEB E-COMMERCE MENGGUNAKAN METODE WATERFALL

Spare parts are goods that consist of several components that form a single unit and have a specific function. Motorbike spare parts are needed by many workshops, both official and private workshops which are spread in shops, but in the process there are still many stores using conventional information systems, so sometimes when there are frequent errors in financial calculations, stock items, and sales notes. With the existence of such problems there should be something innovation in the sale of motorcycle spare parts, Many companies or business entities that use information technology to improve productivity and work efficiency in business, the author tries to innovate motorcycle spare parts marketing media by building a sales site online or more often called e-commerce using internet facilities using the development model used in developing this software is the waterfall model. aims to build an application that can be used by all parties both consumers and sellers to provide convenience to consumers in online purchases without having to come to the store, and make it easier for sellers to process customer order data.
Designing User Interaction with Linked Data in Historical Archives

In this article, we present solutions to visualize and interact with linked data in historical archives considering three different scenarios: search, individual record view, and creation of relationships. The created solutions were designed using non-functional mockups and were based on the CIDOC-CRM model, a model created and applied in the museums community liable to be extended to other cultural heritage institutions, being our solutions an application of this model to archives. A sample of 20 archival professionals was selected to evaluate the elements included in the proposed solutions. The evaluation sessions consisted in structured interviews supported by an introductory video and a survey. The think-aloud protocol was applied during the sessions. We conducted both a quantitative and qualitative analysis to the collected answers. From this analysis, we conclude that the majority of the participants showed great receptivity to the solutions presented and recognized many benefits in the application of linked data. Our contributions also include an exploratory study of some existing linked data systems, giving particular attention to their visualization and interaction modes.
Bayesian Quantile Regression in Differential Equation Models

In many situations, nonlinear regression models are specified implicitly by a set of ordinary differential equations. Often, mean regression may not adequately represent the relationship between the predictors and the response variable. Quantile regression can give a more complete picture of the relationship, can avoid distributional assumptions and can naturally handle heteroscedasticity. However, quantile regression driven by differential equations has not been addressed in the literature. In this article, we consider the problem and adopt a Bayesian approach. To construct a likelihood without distributional assumptions, we consider all quantile levels simultaneously. Because of the lack of an explicit form of the regression function and the indeterminate nature of the conditional distribution, evaluating the likelihood and sampling from the posterior distribution are very challenging. We avoid the computational bottleneck by adopting a “projection posterior” method. In this approach, the implicit parametric family of regression function of interest is embedded in the space of smooth functions, where it is modeled nonparametrically using a B-spline basis expansion. The posterior is computed in the larger space based on a prior without constraint, and a “projection” on the parametric family using a suitable distance induces a posterior distribution on the parameter. We illustrate the method using both simulated and real datasets.
MODEL PEMBELAJARAN BAHASA BERBASIS TUGAS (PBBT) MELALUI PEMBELAJARAN DARING

PBBT is a language learning method that focuses on learners. In practice, PBBT is a language learning system that is focused on the task. Assignments given to learners are central to language teaching activities. In general the assignment of students can be said as a basic concept of PBBT. However, in this case the assignment of students at the beginning of learning becomes the hallmark of this method. With this method the teacher's role as a mediator, facilitator, and supervisor is more optimal. PBBT enables students to produce target languages through communicative task completion. The way PBBT works contains the following principles: (1) assignments must be designed to foster student motivation; (2) tasks must be accessed with information technology or the internet; and (3) assignments can foster student independence, tenacity, and honesty. In addition, the current learning process is not just a transfer of knowledge from teachers to learners, but has shifted through electronic media, be it computers, devices, and cellular phones that can be accessed anytime and anywhere according to the wishes of the learner. That as an effort to the presence of information technology and the internet must be utilized as well as possible to develop learning.
AN ANALYSIS OF THE CORRELATION OF CONSUMER PURCHASE PATTERNS USING APRIOPRI METHOD (A CASE STUDY OF CV. VIFOR CIPTA SOLUSI)

CV. Vifor Cipta Solusi is a company that provides electronic equipment. The competition in business, especially among companies providing electronic equipment, is growing. To increase sales of the products, business people in this field need to set a strategy. This research applied the data mining with association technique to find out strong combinations between items. Apriori algorithm is part of the association method in data mining which aims to find out frequent item sets from a certain set of data. The apriori algorithm process is carried out by determining frequent itemsets that meet the requirements of the predetermined minimum support and minimum confidence. From these two measurements, the final association rule can be obtained by which the value of the accuracy (validity) of the association is calculated using the lift ratio. This research obtained 2 association rules with item attributes totaling 294 types of products from 20 main attributes with 1813 records processed from 5282 raw transaction data from January to December 2019. The strong rules obtained are MagnetToner HP Laserjet 85 A, Drum Toner HP Laserjet 85 A =&gt; HP Laserjet 85 A toner with a support value of 3.42%, and a confidence value of 81.58% with a lift ratio of 5.00.If a consumer buys an HP Laserjet 85 A MagnetToner and a HP Laserjet 85 A Toner Drum, the consumer will also buy an HP Laserjet 85 A Toner.
Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance

Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. With the help of auxiliary gradient-space guidance, the network can concentrate more on the spatial relationship of the organ boundary. Experimental results on two clinically acquired CT-MRI datasets demonstrate the effectiveness of our proposed approach.
NUMERICAL DIFFERENTIATION VIA HERMITE WAVELETS

In this research, numerical technique based on Hermite wavelets is established to find the numerical differentiation.Proposed technique is based on the expansion of unknown function into a series of basis of Hermite wavelets.Some numerical experiments have been performed to illustrate the accuracy of the proposed technique.
Witnessing nonclassicality through large deviations in quantum optics

Non-classical correlations in quantum optics as resources for quantum computation are important in the quest for highly-specialized quantum devices. The standard way to investigate such effects relies on either the characterization of the inherent features of sources and circuits or the study of the output radiation of a given optical setup. The latter approach demands an extensive description of the output fields, but often overlooks the dynamics of the sources. Conversely, the former discards most of the information about the single trajectories, which are observed in experimental measurements. In this work we provide a natural link between the two frameworks by exploiting the thermodynamics of quantum trajectories. This procedure enables investigation of the quantum properties of the photon fields from a generic source via the analysis of the fluctuations and correlations of time-integrated quantities associated with the photon counting of the emitted light.
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection

The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. Before the implementation of Machine Learning, the detection of abnormalities were manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. Nowadays, it is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving. In this paper, different scenarios have been set in order to train and test a switching DBN for Perimeter Monitoring Task using a semantic segmentation for the DBN model and Hellinger Distance metric for abnormality measurements.
Fourier 1-norm and quantum speed-up

Understanding quantum speed-up over classical computing is fundamental for the development of efficient quantum algorithms. In this paper, we study such problem within the framework of the quantum query model, which represents the probability of output $$x \in \{0,1\}^n$$ as a function $$\pi (x)$$ . We present a classical simulation for output probabilities $$\pi $$ , whose error depends on the Fourier 1-norm of $$\pi $$ . Such dependence implies upper-bounds for the quotient between the number of queries applied by an optimal classical algorithm and our quantum algorithm, respectively. These upper-bounds show a strong relation between Fourier 1-norm and quantum parallelism. We show applications to query complexity.
Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems

Solving multiagent problems can be an uphill task due to uncertainty in the environment, partial observability, and scalability of the problem at hand. Especially in an urban setting, there are more challenges since we also need to maintain safety for all users while minimizing congestion of the agents as well as their travel times. To this end, we tackle the problem of multiagent pathfinding under uncertainty and partial observability where the agents are tasked to move from their starting points to ending points while also satisfying some constraints, e.g., low congestion, and model it as a multiagent reinforcement learning problem. We compile the domain constraints using propositional logic and integrate them with the RL algorithms to enable fast simulation for RL.
Towards learning visual semantics

We envision visual semantics learning (VSL), a novel methodology that derives high-level functional description of given software from its visual (graphical) outputs. By visual semantics, we mean the semantic description about the software’s behaviors that are exhibited in its visual outputs. VSL works by composing this description based on visual element labels extracted from these outputs through image/video understanding and natural language generation. The result of VSL can then support tasks that may benefit from the high-level functional description. Just like a developer relies on program understanding to conduct many of such tasks, automatically understanding software (i.e., by machine rather than by human developers) is necessary to eventually enable fully automated software engineering. Apparently, VSL only works with software that does produce visual outputs that meaningfully demonstrate the software’s behaviors. Nevertheless, learning visual semantics would be a useful first step towards automated software understanding. We outline the design of our approach to VSL and present early results demonstrating its merits.
Design and Development of Tajweed Science Learning System Model Based on Android

This research was conducted to design a modeling that is used to build an Android-based Tajweed science learning system. The method used in the development of the Android-based Tajweed science learning system is the waterfall method. The learning system that was built adopted several components of LTSA. The system that was built focuses on the achievement of learning materials Tajweed science as part of the overall learning process. To design the system modeling in this study, UML was used. After the system design is complete, the design is implemented into Java coding which will later be exported into an apk that can be installed on an Android device.  the results show that the system can run well on Android version 5.0, all features in the system can run well and in accordance with the design of the system designed with UML. Learning of Tajweed knowledge based on Android makes it easier to learn because it can overcome the learning of Tajweed science which is physically not possible and overcome the limitations of time, place and access. UML modeling is very suitable to be used in design to design a system model used in the application of the study of recitation.
NLP Service APIs and Models for Efficient Registration of New Clients

State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP.This has led to one-sizefits-all public API-based NLP service models by major AI companies, serving large numbers of clients.Neither (hardware deficient) clients nor (heavily subscribed) servers can afford traditional fine tuning.Many clients own little or no labeled data.We initiate a study of adaptation of centralized NLP services to clients, and present one practical and lightweight approach.Each client uses an unsupervised, corpus-based sketch to register to the service.The server uses an auxiliary network to map the sketch to an abstract vector representation, which then informs the main labeling network.When a new client registers with its sketch, it gets immediate accuracy benefits.We demonstrate the success of the proposed architecture using sentiment labeling, NER, and predictive language modeling.
Ensemble Knowledge Distillation for CTR Prediction

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. However, current research focuses primarily on building complex network architectures to better capture sophisticated feature interactions and dynamic user behaviors. The increased model complexity may slow down online inference and hinder its adoption in real-time applications. Instead, our work targets at a new model training strategy based on knowledge distillation (KD). KD is a teacher-student learning framework to transfer knowledge learned from a teacher model to a student model. The KD strategy not only allows us to simplify the student model as a vanilla DNN model but also achieves significant accuracy improvements over the state-of-the-art teacher models. The benefits thus motivate us to further explore the use of a powerful ensemble of teachers for more accurate student model training. We also propose some novel techniques to facilitate ensembled CTR prediction, including teacher gating and early stopping by distillation loss. We conduct comprehensive experiments against 12 existing models and across three industrial datasets. Both offline and online A/B testing results show the effectiveness of our KD-based training strategy.
Spoken Medical Prescription Acquisition Through a Dialogue System on Smartphone: Perspective of a Healthcare Software Company

Industrial Medical Practice Management Software (PMS) have appeared in health institutions to reduce medication errors which affect several million people worldwide each year. However, practitioners must enter information manually into PMS which decreases the time devoted to care. In this paper, we describe the approach and some experiments of the implementation of an initial spoken dialogue system in this low-resourced domain in an industry-oriented setting. The main objective is to provide a natural language interface as an alternative to typing prescriptions in PMS. We highlight some of the difficulties of using deep-learning systems in an industrial context and discuss how these systems could be used while enabling full traceability. To overcome the lack of annotated speech data, we present a way to generate aligned data for machine-learning systems and discuss the limitations of using artificial data generation. We report on the findings of human evaluation conducted on the initial prototype with 2 medical experts and 2 naive users and discuss the results of each module of the dialogue system from an industrial perspective.
Facial Expression Recognition using CNN

People have consistently been able to perceive and recognize faces and their feelings. Presently PCs can do likewise. We propose a model which recognizes human faces and classifies the emotion on the face as happy, angry, sad, neutral, surprise, disgust or fear. It is developed utilizing a convolutional neural network(CNN) and involves various stages. All these are carried out using a dataset available on the Kaggle repository named fer2013. Precision and execution of the neural system can be assessed utilizing a confusion matrix. We applied cross-approval to decide the ideal hyper-parameters and assessed the presentation of the created models by looking at their training histories.
Reinforcement Learning with Time-dependent Goals for Robotic Musicians.

Reinforcement learning is a promising method to accomplish robotic control tasks. The task of playing musical instruments is, however, largely unexplored because it involves the challenge of achieving sequential goals - melodies - that have a temporal dimension. In this paper, we address robotic musicianship by introducing a temporal extension to goal-conditioned reinforcement learning: Time-dependent goals. We demonstrate that these can be used to train a robotic musician to play the theremin instrument. We train the robotic agent in simulation and transfer the acquired policy to a real-world robotic thereminist. Supplemental video: this https URL
(Co)inductive Proof Systems for Compositional Proofs in Reachability Logic

Reachability Logic is a formalism that can be used, among others, for expressing partial-correctness properties of transition systems. In this paper we present three proof systems for this formalism, all of which are sound and complete and inherit the coinductive nature of the logic. The proof systems differ, however, in several aspects. First, they use induction and coinduction in different proportions. The second aspect regards compositionality, broadly meaning their ability to prove simpler formulas on smaller systems, and to reuse those formulas as lemmas for more complex formulas on larger systems. The third aspect is the difficulty of their soundness proofs. We show that the more induction a proof system uses, and the more specialised is its use of coinduction (with respect to our problem domain), the more compositional the proof system is, but the more difficult its soundness proof becomes. We also briefly present mechanisations of these results in the Isabelle/HOL and Coq proof assistants.