Reviewerly/exHarmony huggingface dataset:
OS-Learn: mobile learning application for operating system with augmented reality / Alif Faisal Ibrahim and Muhammad Hazman Yussri

Theoretical subjects or courses are often disliked among students due to its massive load of information which often lead to misconception or confusion. As a result, most students would resolve to rote learning instead. Therefore, the purpose of this research is to design and develop OS-Learn, a mobile application created to help students in learning theoretical subjects such as operating systems concepts. The core element of OS-Learn would be active engagement, such as the use of visualization and improving memory retention. OS-Learn use AR elements as part of its visualization technique. ADDIE model was selected as the framework to design and develop the application. User acceptance testing was performed after the development phase to obtain feedback from potential users which are university students. Thirty students were selected to evaluate the mobile app based on its design, navigation, and content. Each respondent was given set of tasks and were required to complete a questionnaire based on their experience using the application. The outcome was mostly positive where most respondents were satisfied with the overall design of the mobile application. Feedback and suggestions were also collected then reviewed to make necessary adjustments to the application before being deployed.
One Size Does Not Fit All

Edge sensing with micro-power pulse-Doppler radars is an emergent domain in monitoring and surveillance with several smart city applications. Existing solutions for the clutter versus multi-source radar classification task are limited in terms of either accuracy or efficiency, and in some cases, struggle with a trade-off between false alarms and recall of sources. We find that this problem can be resolved by learning the classifier across multiple time-scales. We propose a multi-scale, cascaded recurrent neural network architecture, MSC-RNN, comprised of an efficient multi-instance learning (MIL) Recurrent Neural Network (RNN) for clutter discrimination at a lower tier, and a more complex RNN classifier for source classification at the upper tier. By controlling the invocation of the upper RNN with the help of the lower tier conditionally, MSC-RNN achieves an overall accuracy of 0.972. Our approach holistically improves the accuracy and per-class recalls over machine learning models suitable for radar inferencing. Notably, we outperform cross-domain handcrafted feature engineering with purely time-domain deep feature learning, while also being up to ~3X more efficient than a competitive solution.
Try-on with AR: Impact on Sensory Brand Experience and Intention to Use the Mobile App

To build user interfaces, modern immersive platforms are rapidly being used to create customer experiences. The present research aims to examine the efficacy of augmented reality in the production of sensory brand experience and intention to use the mobile app with an integrated AR feature. Following the Technology Acceptance Model and Uses and Gratifications Theory, this research uses utilitarian benefits (perceived ease of use and perceived usefulness) and hedonic benefits (perceived enjoyment and flow experience), in defining the essential factors that need to be considered while integrating AR in a mobile app. Primary data were collected from 168 respondents who were made to use the AR feature on the Lenskart mobile app (e-commerce portal for eyewear in India) where after scanning their face, respondents could try the different frames of spectacles and sunglasses to see which one best suits them. To assess the research model for reliability and validity, confirmatory factor analysis was used. Structural equation modelling was used to test the said impact. Results demonstrate that the effective use of augmented reality has a significant impact on the creation of sensory brand experiences and the intent to use the mobile app using the AR feature. Thus, the paper helps in establishing how a mobile app with an integrated AR app becomes an important touchpoint with the consumer in the digital era.
Augmented Reality Point Of Interest (POI) Pariwisata Kota Padang Berbasis Android

The city of Padang has many tourist attractions so that the city of Padang is one of the tourist destinations. Tourists who come not only from within the country but also from abroad. For that we need a technology that can promote tourist objects to visiting tourists. The technology that is currently becoming popular is augmented reality. Augmented reality can provide more innovative information so that tourists are interested in visiting the objects they are visiting. This augmented reality technology will be applied to Android-based smartphones and use 3D objects. This augmented reality application uses the point of interest method, where tourists use a smartphone camera to recognize a map image of the city of Padang and send the results of capturing these images to be processed and display visually according to predetermined points.
Narrative Strategies and Sound Space in Nau dos Insensatos by Conrado Silva at the XXth São Paulo Art Biennial

Nau dos Insensatos is an electroacoustic composition by Conrado Silva commissioned for the XXth São Paulo Art Biennial of 1989. The analysis of the piece reveals that an intrinsic narrative content can be deduced from the sequence of materials, topics, and formal organization used in the composition. The theoretical framework used to reach these conclusions considered the music narrative theory of Byron Almén, besides the classical semiotic interpretation of C. S. Peirce. Brief accounts of the principles of these theories were provided to subsidy the interpretation. As the creation of the piece had a direct relation with the space where it should be performed, the paper also reports the problems generated by the building’s acoustics, besides some contextual information about the exhibition. Theoretical information about the relation between acoustic space and music, applied to music of three other Brazilian composers enhances the approach to Conrado Silva’s music.
Deep Visual Anomaly Detection with Negative Learning

With the increase in the learning capability of deep convolution-based architectures, various applications of such models have been proposed over time. In the field of anomaly detection, improvements in deep learning opened new prospects of exploration for the researchers whom tried to automate the labor-intensive features of data collection. First, in terms of data collection, it is impossible to anticipate all the anomalies that might exist in a given environment. Second, assuming we limit the possibilities of anomalies, it will still be hard to record all these scenarios for the sake of training a model Third, even if we manage to record a significant amount of abnormal data, it’s laborious to annotate this data on pixel or even frame level. Various approaches address the problem by proposing one-class classification using generative models trained on only normal data. In such methods, only the normal data is used, which is abundantly available and doesn’t require significant human input. However, such approaches have two drawbacks. First, these are trained with only normal data and at the test time, given abnormal data as input, still generate normal-looking output. This happens due to the hallucination characteristic of generative models, which is not desirable in anomaly detection systems because of their need to be accurate and reliable. Next, these systems are not capable of utilizing abnormal examples, however small in number, during the training. In this paper, we propose anomaly detection with negative learning (ADNL), which employs the negative learning concept for the enhancement of anomaly detection by utilizing a very small number of labeled anomaly data as compared with the normal data during training. The idea, which is fairly simple yet effective, is to limit the reconstruction capability of a generative model using the given anomaly examples. During the training, normal data is learned as would have been in a conventional method, but the abnormal data is utilized to maximize loss of the network on abnormality distribution. With this simple tweaking, the network not only learns to reconstruct normal data but also encloses the normal distribution far from the possible distribution of anomalies. In order to evaluate the efficiency of our proposed method, we defined the baseline using Adversarial Auto-Encoder (AAE). Our experiments show significant improvement in area under the curve (AUC) over conventional AAE. An extensive evaluation, which has been carried out on the MNIST dataset as well as a locally recorded pedestrian dataset, is reported in this paper.
Machine Learning Aided Demodulator in MISO Beamforming Systems

In this study, a machine learning (ML) aided demodulator was designed for equalization and demodulation of the signal at the receiver in a multiple-input single-output (MISO) beamforming system. To observe the accuracy of MLaided demodulator in the presence of Rayleigh channel; XGBoost, LightGBM, and Linear Support Vector Machine (SVM) algorithms are used. Several scenarios were discussed where the number of antennas, signal-to-noise ratio (SNR), channel perfection, and channel information availability changed. As a result of 72 different measurements, it was observed that Linear SVM operates at slightly lower bit error rate (BER) levels among 3 algorithms. However, the SVM has higher time complexity and could not provide a significant advantage over other algorithms. Therefore, the use of the LightGBM algorithm, with a very low amount of BER sacrifice, greatly reduces time complexity.
Новое структурирование психики с позиций аттенцио-мнемической концепции: научно-практические аспекты

Here is the methodological basis, new ideas about the composition and properties of at-tention, identified with the working memory and the mechanism of converter psychogenesis. The historical forerunners of the ideas under consideration are shown. A systemic, integrative-differentiated structuring of mental phenomena is proposed. On the basis of the new approach, the probable connection of the morphophysiological features of the brain with the proposed sys-tem of structuring the psyche is considered. The concept has a specific scientific and practical addressing to co-adaptive educational technology and co-adaptive medical and psychological assistance to the population.
Study on Path Planning Method of Lunar Rover

During the process of lunar detection tele-operation,scientific exploration target is determined by sensing data and analyzing stage by stage in order to achieve planning of different level. According to planning requirements of different level and considering the complex terrain constrains in exploration on the far-side of the moon,an integrated environment cubic model of the lunar surface is established and a search algorithm of multiple constrained navigation points in the exploration period planning is proposed, through which the navigation points determination is realized considering detection point attainability, illumination condition,communication attainability and other factors comprehensively. For the navigation unit planning, a smooth curve path search algorithm is designed considering terrain attainability cost, mobile mileage cost and operation cost. The result shows the effectiveness and real-timeby the verification in simulation experiment environment.
Faster Lifting for Two-Variable Logic Using Cell Graphs

We consider the weighted first-order model counting (WFOMC) task, a problem with important applications to inference and learning in structured graphical models. Bringing together earlier work [Van den Broeck et al., 2011, 2014], a formal proof was given by Beame et al. [2015] showing that the two-variable fragment of first-order logic, FO^2, is domain-liftable, meaning it admits an algorithm for WFOMC whose runtime is polynomial in the given domain size. However, applying this theoretical upper bound is often impractical for real-world problem instances. We show how to adapt their proof into a fast algorithm for lifted inference in FO^2, using only off-the-shelf tools for knowledge compilation, and several careful optimizations involving the cell graph of the input sentence, a novel construct we define that encodes the interactions between the cells of the sentence. Experimental results show that, despite our approach being largely orthogonal to that of Forclift [Van den Broeck et al., 2011], our algorithm often outperforms it, scaling to larger domain sizes on more complex input sentences.
Proposing an algorithm for selecting and evaluating the level of questions for Gifted Schools

Generating tests from question banks using the elements extracted manually and randomly consumes a great deal of time and effort. The quality of the resulting tests is often inaccurate. The tests that have been created may not fully correspond to the requirements that were previously formulated, so this study focused on innovative ways to enhance this. The process by optimizing execution time and generating results closely meeting the requirements for extraction, this paper proposes a Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in configuring the Hybrid PSP-GA algorithm to generate multiple-choice tests based on the difficulty levels of the questions and the extracted evaluations, shown Experimental results that PSO speeds up the extraction process, improves the quality of tests, and is more efficient in most criteria such as execution time, search area and contrast, and that the experiments and analyses with the proposed mutation operator have proven the success of the GA method with a very high rate, the research is distinguished by a hybrid PSO-GA technique to solve optimization problems, Further improving the balance between exploration and exploitation capabilities through the integration of genetic factors, Designed an electronic system for selecting and evaluating For questions and their level of difficulty, based on the hybrid algorithm.
THERADIA: Digital Therapies Augmented by Artificial Intelligence

Digital plays a key role in the transformation of medicine. Beyond the simple computerisation of healthcare systems, many non-drug treatments are now possible thanks to digital technology. Thus, interactive stimulation exercises can be offered to people suffering from cognitive disorders, such as developmental disorders, neurodegenerative diseases, stroke or traumas. The efficiency of these new treatments, which are still primarily offered face-to-face by therapists, can be greatly improved if patients can pursue them at home. However, patients are left to their own devices which can be problematic. We introduce THERADIA, a 5-year project that aims to develop an empathic virtual agent that accompanies patients while receiving digital therapies at home, and that provides feedback to therapists and caregivers. We detail the architecture of our agent as well as the framework of our Wizard-of-Oz protocol, designed to collect a large corpus of interactions between people and our virtual assistant in order to train our models and improve our dialogues.
It's Complicated: Improving Decisions on Causally Complex Topics

Author(s): Kleinberg, Samantha; Marsh, Jessecae | Abstract: We make frequent decisions about how to manage our health, yet do so with information that is highly complex or received piecemeal. Causal models can provide guidance about how components of a complex system interact, yet models that provide a complete causal story may be more complex than people can reason about. Prior work has provided mixed insights into our ability to make decisions with causal models, showing that people can use them in novel domains but that they may impede decisions in familiar ones. We examine how tailoring causal information to the question at hand may aid decision making, using simple diagrams with only the relevant causal paths (Experiment 1) or those paths highlighted within a complex causal model (Experiment 2). We find that diagrams tailored to a choice improve decision accuracy over complex diagrams or prior knowledge, providing new evidence for how causal models can aid decisions.
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders

Detecting out-of-distribution (OOD) samples is of paramount importance in all machine learning applications. Deep generative modeling has emerged as a dominant paradigm to model complex data distributions without labels. However, prior work has shown that generative models tend to assign higher likelihoods to OOD samples compared to the data distribution on which they were trained. First, we propose Adversarial Mirrored Autoencoder~(AMA), a variant of Adversarial Auto-Encoder, which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We also propose a latent space regularization to learn a compact manifold for in-distribution samples. The use of AMA produces better feature representations that improve anomaly detection performance. Second, we put forward an alternative measure of anomaly score to replace the reconstruction-based metric which has been traditionally used in generative model-based anomaly detection methods. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
PREDICTION OF REUSABILITY OF COMPONENT-BASED SOFTWARE (CBS)

Software reusability development methodology like Component-Based Software (CBS) has a progressive impact on various software parameters such as reliability, quality as compared to the traditional way of software development. Previous studies show that for an integrated system, there is a requirement of a scientific model for evaluation and up-gradation of various parameters of the component. In the integrated environment, the adaptability of every component is not assessed by component metrics lonely. Hence, there is a need for new software reusability metric. In this paper, a reusable metric designed for CBS has been evaluated to validate the scope of reusability. Using Chidamber and Kemerer (CK) metrics, new Commercial-off-the-shelf (COTS) metrics have been introduced. This is an investigation report to measure the COTS reusability. The reusability of Object-oriented components and inter components have been calculated using multilevel inheritance and student management system. The parameters used to calculate reusability of COTS based system are Coupling Between Objects (CBO), Coupling Within Component and Coupling Between Component. The calculated result of three objects are X=2, Y=4, Z=4 and three components outputs are Response Of Component (RFC) of Component 1=10, RFC of Component 2=14, RFC of Component 3=12. Finally, Response Of Component (RFC) of Component-Based System is 36. Another program Student Management System consist four objects such as Student = 2, Conti.edu = 2, Graduation = 2, Undergraduate = 2. Two component results are RFC of Component 1 = 8, RFC of Component 2 = 9, RFC of Component-Based System (CBS) = 17. The purpose of the study is to give direction to software developers about component reusability by measuring cost and effort factors.
The seven myths of encrypted traffic scanning

The belief that encrypted traffic is secure in a world where businesses rely increasingly on the Internet is a widespread misunderstanding that is as persistent as it is misguided. While TLS encryption was developed to protect traffic from prying eyes, adversaries have unfortunately begun to leverage this traffic to hide their attacks, turning the use of encryption into a potential threat.
Twitter Sentiment Analysis using Deep Learning

The whole world is changing rapidly with current innovations, using the Internet, has become a fundamental requirement in people's lives. Nowadays, a massive amount of data made by social networks based on daily user activities. Gathering and analyzing people's opinions are crucial for business applications when they are extracted and analyzed accurately. This data helps the corporations to improve product quality and provide better customer service. But manually analyzing opinions is an impossible task because the content is unorganized. For this reason, we applied sentiment analysis that is the process of extracting and analyzing the unorganized data automatically. The primary steps to perform sentiment analysis include data collection, pre-processing, word embedding, sentiment detection, and classification using deep learning techniques. This work focused on the binary classification of sentiments for three product reviews of fast-food restaurants. Twitter is chosen as the source of data to perform analysis. All tweets were collected automatically by using Tweepy. The experimented dataset divided into half of the positive and half of the negative tweets. In this paper, three deep learning techniques implemented, which are Convolutional Neural Network (CNN), Bi-Directional Long Short-Term Memory (Bi-LSTM), and CNN-Bi-LSTM, The performance of each of them measured and compared in terms of accuracy, precision, recall, and F1 score Finally, Bi-LSTM scored the highest performance in all metrics compared to the two other techniques.
Sistem Penempahan Fotografi Estar Art Production Berasaskan Android

Android-Based Estar Art Production Booking System is a photography service booking system for Estar Art Production companies that offer photography services for various events and events. This booking system was developed to make it easier for customers to make reservations and to assist administrators in the management of bookings that have been made by customers. The development of this system is based on a prototyping model. Among the activities in this prototype model are identifying the problem statement and system objectives, identifying system requirements, designing the system interface and database. The system was developed using Flutter for application development for client and photographeruse while Bracket is used for web development for administrator use and uses PhpMyAdmin as database. This booking system was developed in order to help from the aspect of booking the Estar Art Production photography service and to be able to help from the aspect of photographer work schedule management and bookings that have been made by customers from the beginning of the process to completion to be more organized and efficient.
Feasibility Study of a Blockchain Use in Nutraceuticals Supply Chain

The application of blockchain technology (BCT) is still in its nascent, and most of them are about ﬁnance; newer attention of BCT is its role in the supply chain. This work provides a feasibility study and adaptability with expectations on the use of blockchain in the nutraceuticals supply chain (NSC), objectives gauged were the feasibility, adaptability and opportunities of BCT in the NSC. The current work constitutes a theoretical framework based on two sets of questionnaires posed to prospective consumers and other possible stakeholders; the challenges and opportunities were determined through analysis of the interviews; do the stakeholders get the transparency and the trackability as they desire, is it possible to deploy supplementary information via BCT, could this be used to track nutraceuticals ﬂow with enhanced information sharing. Further, a private blockchain as the Hyperledger Fabric framework could enhance the attributes desired to bring in the traceability and avoid counterfeit and fake sale of Nutraceuticals.