Reviewerly/exHarmony huggingface dataset:
Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory

The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales. Such a feature can be introduced into a neural architecture by an appropriate modularization of the dynamic memory. In this paper we propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. First, we show how to extend the architecture of a simple RNN by separating its hidden state into different modules, each subsampling the network hidden activations at different frequencies. Then, we discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies. Each new module works at a slower frequency than the previous ones and it is initialized to encode the subsampled sequence of hidden activations. Experimental results on synthetic and real-world datasets on speech recognition and handwritten characters show that the modular architecture and the incremental training algorithm improve the ability of recurrent neural networks to capture long-term dependencies.
Artificial Intelligence Enabled User Experience Research

This paper describes an approach to a prototypic system that aims to automate user research activities. Building digital products that fulfil user needs requires an analysis of its users, their contexts and their tasks. These user research activities commonly require a large amount of human effort, resulting in collections of unlinked quantitative and qualitative data sets. Our system aims to analyse and combine “human-made” user research data and extract meaningful insights through machine learning methods. This paper highlights the challenges we faced while building the first prototypes, our need for labelled data and how we managed to overcome its scarcity. Finally, we describe the first prototype which consists of a keyword extractor, sentiment extractor and text annotator, providing a programmatic way to analyse user research data.
The appropriateness of developing the media: experts’ validation and students’ response of learning media based on augmented reality technology for natural science lesson

Abstract The development in science are so rapid in this era. Many experts exploited medias with new technologies in science learning based on the development of 21st century educatiom. Technology is used as innovative learning media, that can follow the times. Technology which is integrated in learning is strategies for achieving learning purposes. This research is conducted to develop and find out appropriatness of learning media based on augmented reality technology for natural science lesson. The media was validated by the experts and science teachers. The samples were 30 of 8th grade students. The method was descriptive quantitative and the instruments were validation sheets and student questionnaires. Based on the results, it shows validation of media is 83%, subject 82%, language 87%, and from teachers are 88%. While the results of evaluation stage are 91%. It can be concluded that is suitable to used as learning. Then, this media can be used for science learning process in classroom and can be used for teachers as a reference for developing other learning media.
A Robust Surf-Based Online Human Tracking Algorithm Using Adaptive Object Model

Vision-based tracking is a requisite to a large number of applications in computer vision. Tracking algorithms are expected to deal with problems like full occlusion/partial occlusion, luminance variation, scale, pose, and camera motion. Initially, color and contour-based human tracking was a trend but it had inherent shortcomings that were overcome by the interest (descriptor) point-based tracking. But these tracking methods had its own limitations like the number of matching points obtained vary axiomatically from one frame to another and may subside over time, and the high computational complexity in a running video. This paper comes up with an adaptive model to describe the object model which under non-stationary conditions will track a human. This algorithm uses a foreground model of target obviates the need of a classifier, also accounts for human motion and its pose change, and SURF as feature detector descriptor is used along with a simple auto-regressive (AR) predictor to pledge with the cases of partial or full occlusion. The efficacy of the algorithm is proven by the simulation results obtained.
Multi-linear Strategy Extraction for QBF Expansion Proofs via Local Soundness

In applications, QBF solvers are expected to not only decide whether a given formula is true or false but also return a solution in the form of a strategy. Determining whether strategies can be efficiently extracted from proof traces generated by QBF solvers is a fundamental research task. Most resolution-based proof systems are known to implicitly support polynomial-time strategy extraction through a simulation of the evaluation game associated with an input formula, but this approach introduces large constant factors and results in unwieldy circuit representations. In this work, we present an explicit polynomial-time strategy extraction algorithm for the $$\forall \hbox {-}\mathsf{Exp}\hbox {+}\mathsf{Res}$$ proof system. This system is used by expansion-based solvers that implement counterexample-guided abstraction refinement (CEGAR), currently one of the most effective QBF solving paradigms. Our argument relies on a Curry-Howard style correspondence between strategies and $$\forall \hbox {-}\mathsf{Exp}\hbox {+}\mathsf{Res}$$ derivations, where each strategy realizes an invariant obtained from an annotated clause derived in the proof system.
SADDLE: Secure Aerial Data Delivery with Lightweight Encryption

Low-cost devices in the Internet of Things (IoT) can be integrated into Unmanned Aerial Systems (UAS) as a means of collecting remote data and forwarding to central collection points. However, sensitive data is subject to compromise, and should be protected using cryptography. In order to minimize threats to authenticity, data should be encrypted using session keys known only to participating nodes. In general, however, incorporation of capabilities required to both generate secure session keys and encrypt or decrypt sensitive data is difficult in low-cost IoT installations, due to resource and performance constraints. In this research, we implement a combined public and secret key secure data delivery system in a low-cost aerial platform, which incorporates cryptographic accelerators and required peripherals, in the Zybo Z7-10 System-on-Chip. Components are integrated using a flexible and extensible Applications Programming Interface (API) in a hardware-software design approach, and flown on a low-cost F450 ARF quad-copter drone. Resulting components consume 60% of the slice resources of the Zybo FPGA, and achieve a takeoff weight of 1.2 kg. A flight demonstration is performed, where sensitive data, collected at a remote sensor, is securely delivered to a host.
Automatic Age Estimation: A Survey

Since, aging is non-reversible process. Human face and gait change with time which reflects major variations in appearance, the vast majority of people are able to easily recognize human traits like emotional states, where they can tell if the person is happy, sad or angry from the face, likewise, it is easy to determine the gender of the person. However, knowing person’s age is a very challenge task. Hence, significant interest in the computer vision and pattern recognition research community is given to automatic age estimation. This paper, presents a thorough analysis of recent research in aging and age estimation. Discuss popular algorithms used in age estimation and existing models. Underline age estimation challenges especially using RGB images. Finally insights for future research based on depth map and Kinect camera.
Improving the Professional Competence of the Teacher in the Organization of Research Activities of Students

The article justifies the need to organize research activities of students in conditions of creation of information and educational environment, introduction and implementation of federal state educational standards of general education, which require improvement of professional competence of the teacher. The features of conducting research activities, including using digital tools and digital laboratories, are considered.
Mastering the cyber security skills crisis

The cyber security skills crisis is a key policy issue in many countries, and governments look to universities to address it. The chapter outlines cyber security education as being multi-faceted and multidisciplinary and then identifies current gaps in university-based offerings. The chapter proposes a new multi-level matrix, the Cyberspace Education Framework. This framework allows a high-level comprehensive view of cyberspace education. Current generalist Master’s programmes in Australia are investigated and it is proposed that mission-specific and purpose-driven courses may better prepare students and address the skills crisis than generalist degrees. A survey of cyber security Master’s students at one university and discussions with other stakeholders revealed a contrast between expectations. The chapter then compares the current educational learning outcomes of Master’s programmes in Australia with the knowledge, skills, and abilities (KSAs) set out in the US Government’s work standards document as a proxy for what would be required for five cyber work roles of high national importance to Australia. The chapter concludes that the requirement for purpose-driven and mission-specific cyber security education is increasing and recommends that this become a focus of new initiatives in cyber security education.
Mobile Apps for Boarding House Recommendation

&lt;p class="0abstractCxSpFirst"&gt; &lt;/p&gt;&lt;p&gt;This research aims to build a boarding house recommender system based on the Fuzzy TOPSIS method. This application can be utilized by the student community to discover a suitable boarding house. This research uses the classic life cycle (waterfall) as a software development model. The waterfall model consists of 5 stages, namely, communication, planning, modeling, construction, and deployment. In this study, we focus on the construction and deployment stage.&lt;br /&gt;To test and assess the system, we use a questionnaire filled by the respondents. Two arrangements of surveys created, one for the students and another for mobile application experts. We use ISO 9126 as a base to assess the system in terms of functionality, usability, content, reliability, and performance. As a result, these mobile apps graded 3.54 and categorized as an excellent system. This system is well-honorable and acknowledged by the student community, which gives a superior, simple method for promoting and reliable tool for finding appropriate boarding houses. The users recommend adding some features such as boarding communities, ratings, reviews so users can communicate with each other and increase the effectiveness and functionality of the application.&lt;/p&gt;
Usability Evaluation of Car Cockpit Based on Multiple Objective Measures

With the development of science and technology, many new human-machine interaction methods have appeared in cars. Therefore, how to improve the interaction efficiency in human-machine interaction has become one of the important research topics. Our research focuses on the interaction between car cockpit and driver. To make a usability evaluation of car cockpit, we designed multiple sets of comparative experiments with different concurrent tasks. In the experiment, we collected front scene binocular image and car speed to calculate driving performance, driver’s heart rate and eye movement to represent driver’s physiological state. Specifically, for front scene analysis, we simplified the feature point matching method and obtained quite accurate object distance estimation. Experimental data showed that car speed was closer to the required speed in speed control task than speed + direction control task or speed + temperature control task; distance was adjusted better in distance control task than distance + temperature control task; driver’s heart rate was higher and has more fluctuation during the operation of secondary tasks; driver diverted their visual attention from the road to inside instruments more frequently during manual control than voice control. These results indicate when the task is more difficult or there is interference from secondary task, the driving performance would decrease and driver would be more stressed. And manual control task is more disruptive to driving performance than voice control task, but it takes more time. Finally, driving will be safer and more effective when using voice control instead of manual control.
Combined Fuzzy Controllers with Embedded Model for Automation of Complex Industrial Plants

This paper devoted to design of the combined fuzzy controllers (CFC) with built-in model for the automatic control systems (ACS) of the complex industrial plants (CIP). The proposed CFC is designed for the ACS of the reactor temperature of the specialized pyrolysis plant (SPP) and tested in comparison with other controllers to demonstrate its advantages. The analysis of the computer simulation results confirms the high efficiency of the CFC with the proposed structure.
Improving The Performance of Alumni Achievement Assessment by Integrating Website-Based Tracer Study Information Systems and Telegram API

It is the mission of Vocational High School (SMK) education in Indonesia to produce highly competitive and skilled graduates. One standard for evaluating the success of vocational education is assessing the achievements of alumni. The evaluation process can be carried out with programmed alumni tracking activities or commonly known as tracer studies. However, the implementations of tracer studies that have been carried out by vocational schools in Kediri are still using the manual method. These conditions make the school find many obstacles in the implementation of tracer studies. Some schools have used technology with e-mail applications but have financial constraints. This research aims to improve the performance of assessing the alumni achievement of Vocational Schools in Kediri by integrating the website-based tracer study information system with social media Telegram API. Usability testing that gave 4.83 of 5 scales has shown that the integration can improve the performance and benefits of an application compared to the application before integration
PENERAPAN METODE MULTI ATTRIBUTE UTILITY THEORY (MAUT) DALAM PEMETAAN TINGKAT DAMPAK BENCANA BANJIR DI KABUPATEN BANTUL

Flood disaster is a dangerous disaster, an event that occurs due to overflow of water resulting in submerged land is called a flood disaster. Almost every year Bantul Regency is affected by floods due to high rainfall. The flood disaster that struck in Bantul Regency made the Bantul District Disaster Management Agency (BPBD) difficult to handle so that it needed a mapping of the level of the impact of the flood disaster to minimize the occurrence of floods and provide information to the public.This study will create a system to map the level of impact of floods in Bantul Regency with a decision support method namely Multi Attribute Utility Theory (MAUT). The MAUT method stage in determining the level of impact of flood disasters through the process of normalization and matrix multiplication. The method helps in determining the areas affected by floods, by managing the Indonesian Disaster Information Data (DIBI). The data managed is data on criteria for the death toll, lost victims, damage to houses, damage to public facilities, and damage to roads. Each criteria data has a value that can be used to determine the level of impact of a flood disaster. The stages for determining the level of impact of a disaster require a weighting calculation process. The results of the weighting process display the scoring value which has a value of 1 = low, 2 = moderate, 3 = high. To assist in determining the affected areas using the matrix normalization and multiplication process the process is the application of the Multi Attribute Utility Theory (MAUT) method.This study resulted in a mapping of the level of impact displayed on google maps. The map view shows the affected area points and the level of impact of the flood disaster in Bantul Regency. The mapping produced from the DIBI data in 2017 produced the highest affected area in the Imogiri sub-district. The results of testing the data can be concluded that the results of this study have an accuracy rate of 95% when compared with the results of the mapping previously carried out by BPBD Bantul Regency. The difference in the level of accuracy is because the criteria data used are not the same as the criteria data used by BPBD in Bantul Regency so that the accuracy rate is 95%.
Coronavirus and the Surveillance State

The coronavirus pandemic is challenging our ideas about the fundamental responsibilities of government and the proper limits of government demands on the governed. These issues are particularly pressing when it comes to government surveillance. How far should government agencies and representatives go to monitor the well-being of individual Americans and, more importantly, to reach out with corrective action, when things go wrong? To answer these questions we have to go far beyond policies for health and illness, and grapple anew with tensions between government efforts to protect people (and administer justice) and the perils of oppressive overreach.
New Formulation for Predicting Daily Reference Evapotranspiration (ET0) in the Mediterranean Region of Algeria Country: Optimally Pruned Extreme Learning Machine (OPELM) Versus Online Sequential Extreme Learning Machine (OSELM)

This chapter aims to investigate the capabilities and usefulness of two new data-driven techniques: optimally pruned extreme learning machine (OPELM) and online sequential extreme learning machine (OSELM) newly applied and compared for predicting daily reference evapotranspiration (ET0) in the Mediterranean region of Algeria. Using large data sets from east to west regions of Algeria, the models were developed using several well-known climatic variables as inputs: daily maximum and minimum air temperatures, wind speed, and relative humidity. The proposed models were compared using several well-known statistical indexes: root mean square error (RMSE), mean absolute error (MAE), and coefficient of correlation (R). The obtained results have shown that all the proposed models present high prediction accuracy and the OPELM models provide better overall performances compared to the OSELM models
Technology Development with Older People: The Role of “Unfettered Design”

This article introduces a new method to design with older people: unfettered design. We discuss three points of concern with existing methods of involving older users: unspecific design outcomes, prejudiced views on older users, and underlying power asymmetries, which can diminish the innovative capacities of the older adults involved. To overcome these issues, we argue for the benefits of adopting unfettered design. Through unfettered design, the users involved are given the space to explore design ideas on their own terms, and unusual and unsolicited responses are explicitly desired and listened to. This, in turn, may help designers to elicit novel design ideas and identify potentials for innovative technologies. Thereby, designers may learn about the concerns and potentials for which older adults would find technologies particularly helpful - a crucial aspect considering rather low levels of technology uptake in this population segment. We illustrate our approach by means of examples from our own design experiences, and we formulate four core principles of unfettered design: ongoing reflection, retained impartiality, a distinct focus on the participants’ view and remaining flexible throughout the procedure. Having undergone several waves of both technological and social change, older adults possess unique experiences and tacit knowledge that can serve as valuable inspirations for designers. Unfettered design can be particularly helpful to benefit from these competences during design projects.
K-means Algorithm Description and the Application of Cluster Analysis in Heavy Truck Vehicle Fault

This paper describes the k-means clustering algorithm and proposes the key problems and optimization methods used by the algorithm. The algorithm is used to analyze the fault data of heavy-truck vehicles in different driving environments, which can realize the information integration, exploratory classification and rule analysis of vehicle faults, and provide strong data support for the overall scientific evaluation and prediction of vehicles.
Stochastic Complex-valued Neural Networks for Radar

Neural networks (NNs) prove to be performant in learning nonlinear models, but their mechanisms are yet to be fully understood. Since signal models in radar are inherently nonlinear with respect to unknown range, Doppler or angles, and moreover, radar processing is intrinsically stochastic, stochastic NNs which tie the numerical capability of NNs with the probabilistic inferences can enhance model-based radar processing. Indeed, radar data are complex-valued while most algorithms based on NNs are real-valued and furthermore, lack of uncertainty assessment. To address these issues, we elaborate, in the present paper, a stochastic complex-valued NNs framework for radar. We show that these networks can achieve parameter estimation with refined learned models from radar measurements and provide an indicator of the uncertainty on the estimation. We also build a stopping criterion based on the detection principles, so that the NNs training stops when there is noise only in data. Finally, the performances of the networks are illustrated in simulation.
Research on Innovation and Application of Multi-dimensional Interactive Teaching Mode in Smart Classroom: Taking Junior Physics Applied Research as an Example

Abstract There are many phenomena such as the interaction between teachers and students is not smooth and rich in the current physics teaching of junior high school. With the help of “Guangdong Education Cloud” platform and the Digital Textbook, this paper mainly discussed multi-dimensional interactive and collaborative learning strategies, and built a multi-dimensional interactive cloud classroom model and application model. The implementation process of cloud interactive classroom teaching of junior middle school in physics was introduced as an example, and the validity of the model was verified according to the contrastive analysis of the data before and after the implementation of the multi-dimensional interactive cloud classroom.
Парадигма Программированияon Systematization of Programming Paradigms by Decision-Making Priorities

The report is devoted to the analysis of the method of comparison of programming languages, convenient for assessing the expressive power of languages and the complexity of the programming systems. The method is adapted to substantiate practical, objective criteria of program decomposition, which can be considered as an approach to solving the problem of factorization of very complicated definitions of programming languages and their support systems. The article presents the results of the analysis of the most well-known programming paradigms and outlines an approach to navigation in the modern expanding space of programming languages, based on the classification of paradigms on the peculiarities of problem statements and semantic characteristics of programming languages and systems with an emphasis on the criteria for the quality of programs and priorities in decision-making in their implementation.