Reviewerly/exHarmony huggingface dataset:
Modeling Cognitive Load in Mobile Human Computer Interaction Using Eye Tracking Metrics

Modeling cognitive load of user interaction based on ocular parameters have become a dominant method for exploring usability evaluation of interfaces for systems and applications. Growing importance of Artificial Intelligence in Human Computer Interaction (HCI) has proposed many approaches to understand users’ need and enhance human centric method for interface design. In particular, machine learning-based cognitive modeling, using eye tracking parameters have received more attention in the context of smart devices and applications. In this context, this paper aims to model the estimated cognitive load values for each user into different levels of cognition like very high, high, moderate, low, very low etc., while performing different tasks on a smart phone. The study focuses on the use behavioural measures, ocular parameters along with eight traditional machine learning classification algorithms like Decision Tree, Linear Discriminant Analysis, Random Forest, Support Vector Machine, Naïve Bayes, Neural Network, Fuzzy Rules with Weight Factor and K-Nearest Neighbor to model different levels of estimated cognitive load for each participant. The data set for modeling consisted of 250 records, 11 ocular parameters as prediction variables including age and type of task; and three types of classes (2-class, 3-class, 5-class) for classifying the estimated cognitive load for each participant. We noted that, Age, Fixation Count, Saccade Count, Saccade Rate, Average Pupil Dilation are the most important parameters contributing to modeling the estimated cognitive load levels. Further, we observed that, the Decision Tree algorithm achieved highest accuracy for classifying estimated cognitive load values into 2-class (86.8%), 3-class (74%) and 5-class (62.8%) respectively. Finally, from our study, it may be noted that, machine learning is an effective method for predicting 2-class-based (Low and High) cognitive load levels using ocular parameters. The outcome of the study also provides the fact that ageing affects users’ cognitive workload while performing tasks on smartphone.
All Play Together: Design Concepts of a Sensory Play Equipment Aimed to an Inclusive Play Experience

Active, meaningful, independent play is crucial for the development of health, emotional and social well-being of all children, at any age and with different abilities. Nowadays, in the field of outdoor play equipment there is a growing attention to the needs of an increasingly wide range of users with various abilities. However, the current solutions do not meet fully inclusive requirements due to physical and social barriers limiting access, participation and play experience. Thus, interaction and socialization for children with disability remain goals still far from being achieved. This article presents a line of outdoor sensory play equipment to investigate how sensorial and emotional aspects of the play, which present a fertile ground for inclusion, can be translated in design solutions by adopting Human-Centred Design approaches, such as, field observations, semi-structured interviews and focus groups together with a deep analysis concerning market worldwide.
Real-time Object Detection of Retail Products for Eye Tracking

Object detection is one important task in automatically analyzing eye tracking video data. This paper presents a real-time object detection method of retail products based on deep learning for eye tracking system. In the proposed approach, an eye tracking based Convolutional Neural Networks is constructed to obtain the feature of original images. Then, a weighted bounding box selection strategy based on gaze location is used for object detection. Besides, parameters are adjusted according to gaze location of eye tracking. Experimental results show that our method can achieve better accuracy for eye tracking than other existing methods in the detection of retail products.
Monitoring particle trajectories for wave function parameter aquisition in quantum edge computation

Artificial intelligence(AI) technology expected as a robot brain is advancing day by day. However, computers are insufficient to drive AI without the dramatic development of their processing power. On the other hand, the development of quantum computers is progressing to break the barrier of classical computers. Moreover, there has been an argument that quantum mechanics is one of the leading ideas to explain human mental power and such an argument has become active recently. But unfortunately we cannot mount the quantum processor on the robot brain as its edge computer. This is because large-scale equipments that guarantee ultra-low temperature are required to maintain superposition, which is a key point of quantum computers. In this paper, we show a method of performing quantum computation with a classical mechanical device. We found that wave function parameters can be determined by monitoring particles trajectories under quantum fluctuations. We take up Deutsch's coin authenticity decision problem. We emphasize that it can be developed as hardware, not just an algorithm.
Joint knowledge-powered topic level attention for a convolutional text summarization model

Abstractive text summarization (ATS) often fails to capture salient information and preserve the original meaning of the content in the generated summaries due to a lack of background knowledge. We present a method to provide the topic information based on the background knowledge of documents to a deep learning-based summarization model. This method comprises a topic knowledge base (TKB) and convolutional sequence network-based text summarization model with knowledge-powered topic level attention (KTOPAS). TKB employs conceptualization to retrieve the semantic salient knowledge of documents and the knowledge-powered topic model (KPTopicM) to generate coherent and meaningful topic information by utilizing the knowledge that represents the documents well. KTOPAS obtains knowledge-powered topic information (also called topic knowledge) from TKB and incorporates the topic knowledge into the convolutional sequence network through a high-level topic level attention to resolve the existing issues in ATS. KTOPAS introduces a tri-attention channel to jointly learn the attention of the source elements over the summary elements, the source elements over topic knowledge, and topic knowledge over the summary elements to present the contextual alignment information from three aspects and combine them using the softmax function to generate the final probability distribution which enables the model to produce coherent, concise, and human-like summaries with word diversity. By conducting experiments on datasets, namely CNN/Daily Mail and Gigaword, the results show that our proposed method consistently outperforms the competing baselines. Moreover, TKB improves the effectiveness of the resulting summaries by providing topic knowledge to KTOPAS and demonstrates the quality of the proposed method.
Optimizing Data Placement in Multi-cloud Environments Considering Data Temperature

Most data in the real world have spatial and temporal attributes, and some other essential data attributes also have temporal and spatial variability. In the research field of cloud and edge computing, these features always have great impact on data placement and task scheduling. However, these critical spatiotemporal features have been largely ignored by existing studies. To this end, this work firstly synthesizes data popularity, geographical location distribution and other spatiotemporal features to abstract the definition of data temperature, and a temperature calculation model is proposed to reflect the spatiotemporal correlation and variation trend. Then, we put forward a multi-cloud dynamic storage strategy considering data temperature to improve service quality and reflect the value of data temperature. Experiments are performed to evaluate the proposed strategy, which can effectively reduce the total cost and improve data availability.
From Elementary User Wishes and Domain Models to SQL-Specifications

In the development of (software) systems, new user wishes must usually be implemented very quickly. This poses a real challenge for system development. This challenge led from waterfall to incremental, agile, and even continuous development. In this paper we treat the research question how to come from elementary user wishes and simple domain models all the way to concrete SQL-specifications in a quick, straightforward, and traceable way. We will follow the classical distinction between the static part (i.e., the data structures) and the dynamic part (i.e., the processes) of the system under development. We also explain how these different aspects are coordinated. Moreover, we will distinguish between the Problem Analysis part and the Software Design part of system development. We introduce the notions of elementary User Wish and textual System Sequence Description, which help us to start in an early phase of development, to align our subsequent development steps, and to consider and treat a sequence of SQL-executions as one whole.
Using Eye Tracking for Research on Learning and Computational Thinking

This paper presents a conceptual discussion of the theoretical constructs and perspectives in relation to using eye tracking as an assessment and research tool of computational thinking. It also provides a historical review of major mechanisms underlying the current eye-tracking technologies, and a technical evaluation of the set-up, the data capture and visualization interface, the data mining mechanisms, and the functionality of freeware eye trackers of different genres. During the technical evaluation of current eye trackers, we focus on gauging the versatility and accuracy of each tool in capturing the targeted cognitive measures in diverse task and environmental settings—static versus dynamic stimuli, in-person or remote data collection, and individualistic or collaborative learning space. Both theoretical frameworks and empirical review studies on the implementation of eye-tracking suggests that eye-tracking is a solid tool or approach for studying computational thinking. However, due to the current constraints of eye-tracking technologies, eye-tracking is limited in acting as an accessible and versatile tool for tracking diverse learners’ naturalistic interactions with dynamic stimuli in an open-ended, complex learning environment.
Multiaccurate Proxies for Downstream Fairness

We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time — in other words, how can we train a model to be fair by race when we don’t have data about race? We adopt a fairness pipeline perspective, in which an “upstream” learner that does have access to the sensitive features will learn a proxy model for these features from the other attributes. The goal of the proxy is to allow a general “downstream” learner — with minimal assumptions on their prediction task — to be able to use the proxy to train a model that is fair with respect to the true sensitive features. We show that obeying multiaccuracy constraints with respect to the downstream model class suffices for this purpose, provide sample- and oracle efficient-algorithms and generalization bounds for learning such proxies, and conduct an experimental evaluation. In general, multiaccuracy is much easier to satisfy than classification accuracy, and can be satisfied even when the sensitive features are hard to predict.
Senegalese Road Crash Data Analysis Based On Clustering And Association Rule Mining

This paper presents a study on road accident data in Senegal by identifying associations within accident cases. We use unsupervised classification techniques such as clustering and association rule mining. Pre-processing of our data set revealed subgroup structures within the data. To reduce the association rule mining algorithm's search space, we used the k-modes clustering method as the main segmentation task on road accident data. Then, association rule mining helps identify the different circumstances associated with an accident in each group obtained by the k-modes algorithm. The results of the study show that to improve road safety, the authorities in charge of road transportation could orient their prevention policies towards three main aspects: vehicle fleet renewal, respect for the Highway Code, and prohibition of large vehicles circulation, such as trucks used for goods transportation at rush hours.
Personalized Video Recommendation Integrating User Portrait and Collaborative Filtering

In order to improve the quality of recommendation and user-perceived service, this paper constructs a personalized recommendation model that integrates user portraits and collaborative filtering. First, build a portrait label system based on user characteristics, use time decay function and TF-IDF to obtain label weights, calculate user similarity through user feature labels, and merge it with user similarity obtained by user-based collaborative filtering algorithm to reconcile the weights. Obtain the comprehensive similarity of users, then take Top-N in descending order to form the final personalized recommendation. This paper conducts experimental verification through Douban website, and uses offline experiments to prove that compared with a single algorithm, a video personalized recommendation model that combines user portraits and collaborative filtering algorithms can improve the quality of personalized recommendations to a certain extent.
Multi-Pole Road Sign Detection Based on Faster Region-based Convolutional Neural Network (Faster R-CNN)

Building an approach system that is able to serve various types of traffic signs is a challenge. The important stages in handling an object are finding objects, dividing them into several categories, and marking objects with bounding boxes. However, in reality, monitoring traffic signs objects is quite difficult because it is based on various factors such as; other closed objects, driving times, or traffic sign conditions. This study aims to measure the level of precision in monitoring traffic signs (detection speed of 4-6 frames per second) from video recording (single camera) using the Faster Region based Convolutional Neural Network (Faster R-CNN) algorithm. The traffic sign detection system uses the Faster R-CNN algorithm with Inception v2 model which is implemented in the TensorFlow API framework. The Faster R-CNN consists of 2 different modules. The first module is a deep convolutional neural network which functions to build the area to be detected, which is called the Regional Proposal Network (RPN), and the second module is the Fast R-CNN detector which functions to use the previously proposed area. This system is one unit, a detection network based on the results of the manufacture and testing of a traffic sign detection system based on the Faster R-CNN method, so it can be shown that there is no difference in the results of detection of traffic signs in day and night conditions. Where the precision testing for detection of traffic signs during the day and at night is 100%.
A coevolutionary approach to deep multi-agent reinforcement learning

Traditionally, Deep Artificial Neural Networks (DNN's) are trained through gradient descent. Recent research shows that Deep Neuroevolution (DNE) is also capable of evolving multi-million-parameter DNN's, which proved to be particularly useful in the field of Reinforcement Learning (RL). This is mainly due to its excellent scalability and simplicity compared to the traditional MDP-based RL methods. So far, DNE has only been applied to complex single-agent problems. As evolutionary methods are a natural choice for multi-agent problems, the question arises whether DNE can also be applied in a complex multi-agent setting. In this paper, we describe and validate a new approach based on coevolution. To validate our approach, we benchmark two Deep coevolutionary Algorithms on a range of multi-agent Atari games and compare our results against the results of Ape-X DQN. Our results show that these Deep coevolutionary algorithms (1) can be successfully trained to play various games, (2) outperform Ape-X DQN in some of them, and therefore (3) show that coevolution can be a viable approach to solving complex multi-agent decision-making problems.
Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs

The veterinary field lacks automation in contrast to the tremendous technological advances made in the human medical field. Implementation of machine learning technology can shorten any step of the automation process. This paper explores these core concepts and starts with automation in sorting radiographs for canines by view and anatomy. There is a wide range of deep learning approaches explored in the literature; while these models can obtain good accuracy, they require a considerable amount of memory, which is an undesirable trait. As a result, in this paper, a new lightweight algorithm is proposed, inspired by AlexNet, Inception, and SqueezeNet. The proposed model has only 0.1 million parameters which is about 100 times less than the number of parameters in the state-of-the-art models such as AlexNet and ResNet. Moreover, it achieves higher accuracy than AlexNet, ResNet, DenseNet, and SqueezeNet, while having much less running time, and requiring less memory.
On Context Frames and Their Implementations

When an actor is selecting an action in order to fulfill its intents, in a given context, the actor’s knowledge and beliefs about the specific context will impact the possibility to achieve a desired outcome. The context is often affected by unobserved, or unmeasured, factors, which can impact the result of the desired outcome. The context specific knowledge and beliefs an actor has about a domain, together with the possibilities to evaluate and learn which actions shall be taken, are packaged into a context frame. Our intention with this study is to evaluate an implementation of such a context frame. The context frame concept is meant to support actors to fulfill their intents in a given knowledge domain, by enforcing the needed, and available, actions which cause effects on the outcomes. We have built our implementation of the context frame on the OODA-loop, Pask’s conversation theory, and structural causal models, by using a Bayesian approach, and probabilistic programming. The research approach is based on evaluation research. We evaluated our implementation with the help of a proof of concept. During the proof of concept we used data sets containing decisions about treatment and survival analysis regarding cancer patients, information obtained during focus group interviews, and questionnaire data. The proof of concept used to evaluate our implementation of a context frame was regarded as successful and the concept of context frames deemed as useful. Our division of a context frame in three parts, supported by four different types of analysis functions, made it easier to create a solution which supports evaluation and learning.
A State-of-the-Art Review of Deep Reinforcement Learning Techniques for Real-Time Strategy Games

Deep reinforcement learning is an active research point in the academic community now more than ever. The computational capabilities and the deep neural networks revolution can automatically find compact low-dimensional features of high-dimensional data. As the convolutional neural network gets better at dealing with the visual world, the more outstanding results show reinforcement-learning agents that can concur with various problems. Intending to develop stronger agents and go beyond the benchmark, we need environments that provide more challenge than the problems that already have solved. Real-time strategy games considered as perfect challenging environment for reinforcement learning agents because of their vast state-action spaces that mimic the state and action spaces in real-world problems. Besides, real-time strategy games are simulations with complicated interactions, so they have no cost when applying trial and error learning techniques. Deep reinforcement learning agents considered a promising step towards fully autonomous agents that learn from trial and error with little or no prior knowledge about the environment they were dealing with. The goal of this survey is to state the current state of the art of deep reinforcement learning (DRL) research in the real-time strategy game “StarCraft” introducing the creative ways in which neural networks can be used to bring us steps closer to create AI agents that can deal with the real world. Our survey will cover a theoretical background of reinforcement learning and discuss the families of reinforcement learning algorithms. We will also explain the deep reinforcement learning framework and its contributions to StarCraft. To conclude, we ask the following research question: How can deep reinforcement learning algorithms play StarCraft's full game?
Human Facial Expression Recognition Using Fusion of DRLDP and DCT Features

Recognition of facial expressions is a major challenge in the field of computer vision. Using single-function models, the level of acknowledgment even in controlled capture conditions is considerably small. This paper proposed a method for facial emotion classification with the fusion of dimensionality reduced local directional pattern and discrete cosine transform features using SVM classifier. Local characteristics are extracted utilizing DRLDP, and global characteristics are extracted from facial expression images using DCT. SVM is used to classify the face images into six emotions (surprise, smile, sad, anger, fear and disgust). This method is experimented on JAFFE database and compared with existing approaches shows higher classification rate.
Phishing Prevention Using Defense in Depth

Phishing attacks are very damaging, costing even moderately sized companies an average of over $1 million per year. It is also a fast growing and quickly evolving threat with nearly 1.5 million phishing websites created each month while each phishing site having an average duration of 54 h. The combination of the potential damage done by phishing attacks and the complex task of “staying ahead of the phishers” requires a layered approach composed of multiple complimentary components. In this paper, we investigate phishing prevention from three different perspectives including web security gateway, email security gateway, and in-depth security awareness training.
Research of Dynamic Mathematical Models of Adaptation of Members of Teams of Developers of Infocommunication Systems

A systematic review and a number of original results are presented, which are not exhaustive in the study of dynamic mathematical models of formation and functioning of team members of developers of infocommunication software systems. However, they reflect the general concept in the methodology of construction and study of applied mathematical models of dynamic functioning of companies - developers of infocommunication systems, and can be effectively used to solve urgent problems of a wide class of management of socio-economic systems. Also in the expanded research the complex of mechanisms of organizational management of innovative processes of development of firm of developers of infocommunication software systems, in particular, mechanisms of financing, institutional management, project management, motivation of the personnel and management of development of the personnel is considered. For the first time, many classes of dynamic models of team members' interaction based on group dynamics methods are considered. In our opinion, the further development of a mathematical description of the interaction of team members in the development of information and communication software systems is a promising task for future research.