Reviewerly/exHarmony huggingface dataset:
Developing Persuasive Mobile Games for African Rural Audiences

With the advent of cheap Android phones in the African Tech market, most people living in the rural areas of many African countries now have access to smartphones. These phones give them the opportunity to have an almost identical mobile phone experience as people living in urban areas or the Western world. This development opens a window of opportunity to leverage this high penetration of mobile devices to design application such as persuasive game interventions to assist individuals living in these communities to modify, change or shape their behaviours and attitudes in a desirable way. This paper explores the challenges and issues encountered in the design and use of persuasive mobile games as a tool to promote behaviour change among people living in the Rural African communities. It also highlights how these challenges affect the implementation of persuasive strategies, suggests design solutions for overcoming these challenges, and how persuasive games can be optimized to be appropriate for the target rural African populations. Some of these challenges are technically oriented (internet connectivity issues) while others are non-technically oriented (language diversity).
RAPIDO: a rejuvenating adaptive PID‐type optimiser for deep neural networks

The authors present a novel gradient descent algorithm called RAPIDO for deep learning. It adapts over time and performs optimisation using current, past and future information similar to the PID controller. The proposed method is suited for optimising deep neural networks that consist of activation functions such as sigmoid, hyperbolic tangent and ReLU functions because it can adapt appropriately to sudden changes in gradients. They experimentally study the authors' method and show the performance results by comparing with other methods on the quadratic objective function and the MNIST classification task. The proposed method shows better performance than the other methods.
How Many Times Should a Pedagogical Agent Simulation Model Be Run?

When using simulation modeling to explore pedagogical phenomena, there are several issues a designer/practitioner should consider. One of the most important decisions has to do with determining how many runs of a simulation to perform in order to be confident in the results produced by the simulation [1]. With a deterministic model, a single simulation run is adequate. This issue becomes more challenging when part of the simulation model is based on stochastic elements. One of the solutions that has been used to address this challenge in other research communities is the use of Monte Carlo simulation [2]. Within the AIED research community, however, this question of how many times should a pedagogical simulation model be run to produce predictions in which the designer can have confidence has received surprisingly little attention. The aim of this paper is to explore this issue using a pedagogical simulation model, SimDoc, designed to explore longer term mentoring issues [3]. In particular, we demonstrate how to run this simulation model over many iterations until the accumulated results of the iteration runs reach a statistically stable level that matches real world performance but also has appropriate variability among the runs. We believe this approach generalizes beyond our simulation environment and could be applied to other pedagogical simulations and would be especially useful for medium and high fidelity simulations where each run may take a long time.
Unsupervised Deep Clustering for Fashion Images

In many visual domains like fashion, building an effective unsupervised clustering model depends on visual feature representation instead of structured and semi-structured data. In this paper, we propose a fashion image deep clustering (FiDC) model which includes two parts, feature representation and clustering. The fashion images are used as the input and are processed by a deep stacked autoencoder to produce latent feature representation, and the output of this autoencoder will be used as the input of the clustering task. Since the output of the former has a great influence on the later, the strategy adopted in the model is to integrate the learning process of the autoencoder and the clustering together. The autoencoder is trained with the optimal number of neurons per hidden layers to avoid overfitting and we optimize the cluster centroid by using stochastic gradient descent and backpropagation algorithm. We evaluate FiDC model on a real-world fashion dataset downloaded from Amazon where images have been extracted into 4096-dimensional visual feature vectors by convolutional neural networks. The experimental results show that our model achieves state-of-the-art performance.
Multiscope Contextual Information for Saliency Prediction

Features of different pattern complexity and extracted from multiple context scopes all provide important information for saliency prediction. Our model considers saliency can essentially be described by a combination of relatively simple features, and the scale of features varies. For this reason, improvements for saliency prediction model in three aspects are proposed to capture multiple contexts, and in final stage saliency prediction is made in a comprehensive way. The flexibility of proposed approach makes it possible to convert many existing convolutional neural networks into saliency prediction models. Experiments on two benchmark datasets show that the proposed approach is capable of converting a VGG base model into a competitive saliency prediction model.
A Utility-Optimized Framework for Personalized Private Histogram Estimation (Extended Abstract)

Local differential privacy (LDP), as a strong and practical notion, has been applied to deal with privacy issues in data collection. However, existing LDP-based strategies mainly focus on utility optimization at a single privacy level while ignoring various privacy preferences of data providers and multilevel privacy demands for statistics. In this poster, we for the first time propose a framework to optimize the utility of histogram estimation with these two privacy requirements. To clarify the goal of privacy protection, we personalize the traditional definition of LDP. We design two independent approaches to minimize the utility loss: Advanced Combination, which composes multilevel results for utility optimization, and Data Recycle with Personalized Privacy, which enlarges sample size for an estimation. We demonstrate their effectiveness on privacy and utility. Moreover, we embed these approaches within a Recycle and Combination Framework and prove that the framework stably achieves the optimal utility by quantifying its error bounds. On real-world datasets, our approaches are experimentally validated and remarkably outperform baseline methods.
New applications of electronic speckle pattern interferometry in novel materials and structures

Electronic Speckle Pattern Interferometry (ESPI) is a full-field technique based on interferometry that has found applications in a wide range of domains mostly related in the field of non-destructive testing. The continuous progress of electronics, image analysis and processing has allowed during the last years this technique to expand its application horizons. In this contribution we review the use of ESPI as optical metrology tool for new industrial applications, biomaterials and non-destructive evaluation technique for novel materials. For the first topic different examples found in literature have been shown and discussed. In fact, ESPI has been successfully employed for assessing the integrity of stainless steel kegs or for measuring the deformation of the surface of a heated mirror, and also for evaluating residual stress fields in pipes. Further interesting studies were about thermomechanical behavior of a Printed Wired Board. Biomedical field can benefit of ESPI technology since ESPI has been employed for measuring surface displacements during inflation testing of ocular tissues or to assessing strain in bone-implant interfaces. Finally, one more example of application regarding non-destructive testing technique describes some interesting results for characterizing novel “green” and thus sustainable composite materials.
Data Mining Techniques Applied in Tax Administrations: A Literature Review

Because the tax administrations have the control of evasion and the risk of non-payment among their main activities, they have used historical information and analysis techniques that allow to identify the source of the evasion problem, discover information, select high-risk taxpayers, create models, offer new services, design methods to control taxpayers, and evaluate decisions. However, the tax administrations does not have unlimited resources for control process. Consequently, the management model used in tax administrations must seek the voluntary compliance of taxpayers through intelligent controls with efficient use of resources. Additionally, those controls must explore a large amount of structured, semi-structured and unstructured information from different sources of information that has been accumulated over the years. This study aims to present a literature review about the use of data mining techniques in tax administrations. Therefore, we analyze works in scientific data bases and documents of organizations that support tax administrations. Our study focuses on identifying problems that could be resolved with data mining, limits that have been risen, results that have been obtained by applying data mining in tax administrations and techniques that have been used. Our study could be useful to devise how data mining could support the activities of tax administrations and encourage the application of new data mining techniques in the analysis of information.
An Extensive Study On Automated Aspect And Aspect Category Summarization Technique To Influence On Sentimental Analysis Of Co-Occurrence Data

The classification of the aspect and its category from the product and service reviews has become a primary concern for the consumer in decision making. Nowadays reviews becoming more valuable in making wise decisions. Many advanced approaches based on supervised method and unsupervised method models has helped to provide this objective in terms of summarization. The key challenges were propagating on sentence summarization and orientation. Due to unsatisfactory results, there exists an exploration for unsupervised learning model through utilization of the sentiment analysis for developing and idea in an effective way. In this paper, the detailed analysis is carried out on existing literature to identify aspects and aspect categories using unsupervised model. The aspect categories play a major role in providing useful information about the particular assumption of a certain idea. It essentially attains a useful representation of the reviews automatically and it identify the typical sentiment assigning of sentences. The aspect category is determined on basis of context co-occurrence frequency. In addition, lexical representation is carried out for each category. For analysis of each technique, the breakdowns and obtained performance are included.
A No-Reference Image Quality Model for Object Detection on Embedded Cameras

Automatic video analysis tools are an indispensable component in imaging applications. Object detection, the first and the most important step for automatic video analysis, is implemented in many embedded cameras. The accuracy of object detection relies on the quality of images that are processed. This paper proposes a new image quality model for predicting the performance of object detection on embedded cameras. A video data set is constructed that considers different factors for quality degradation in the imaging process, such as reduced resolution, noise, and blur. The performances of commonly used low-complexity object detection algorithms are obtained for the data set. A no-reference regression model based on a bagging ensemble of regression trees is built to predict the accuracy of object detection using observable features in an image. Experimental results show that the proposed model provides more accurate predictions of image quality for object detection than commonly known image quality measures.
A Comparative Study on Image Registration Techniques for SAR Images

High resolution Synthetic Aperture Radar (SAR) images are extensively employed in many applications like object tracking, object detection, image fusion, image mosaicing. Image registration is mandatory process for all these applications. To register SAR images, feature based registration methods have been successfully deployed in recent years. State of art detectors like Harris Corner, SIFT, SURF, BRIEF, ORB, etc. have been applied to align SAR images. Among all feature detectors, SIFT and SURF algorithm proved to give better solutions for the SAR image registration problem due to its invariance and robustness. So in this paper, the attempt is made to give a detailed survey of SIFT and SURF based SAR image alignment.
Total contribution score and fuzzy entropy based two‐stage selection of FC, ReLU and inverseReLU features of multiple convolution neural networks for erythrocytes detection

The proposed system aims at automatic erythrocytes detection using ensemble of selected features of multiple convolution neural networks (CNNs) to overcome the shortcomings of existing works arising due to the highly overlapping characteristics of handcrafted features. The main merit of this work lies in the proposed two‐stage feature selection algorithm, which completely eliminates the chances of information loss inherent in traditional CNNs, occurring due to the suppression of negative values of features by rectified linear unit (ReLU) and also largely reduces the feature dimensionality. Moreover, it is the first algorithm proposed, which is capable of selectively suppressing the positive or negative values or none of each feature depending upon its respective significance in classification, in contrary to the previously proposed variants of ReLU. Firstly, it constructs a feature space for each CNN by performing inter‐selection among its Fully connected, ReLU and InverseReLU features and selecting features possessing minimum Fuzzy Entropy and maximum newly formulated Total Contribution Score values simultaneously. Secondly, it performs intra‐selection within each selected feature space, eliminating less significant features which simultaneously satisfy the redundancy and non‐relevancy criteria stated here. Finally, by performing detection using the feature‐ensemble, this method registers 98.6% mAP, proving its excellence over existing works.
Object-based Activity Recognition Using Egocentric Video Based on Web Knowledge

This study attempts to recognize daily activities based on a wearable camera without using training data prepared by a user in her environment. Recently, deep learning frameworks have been publicly available, and we can now easily use deep convolutional neural networks (CNNs) pre-trained on a large image data set. In our method, we first detect objects used in the user's activity from her first-person images using a pre-trained CNN for object recognition. We then estimate an activity of the user using the object detection result because objects used in an activity strongly relate to the activity. To estimate the activity without using training data, we utilize knowledge on the Web because the Web is a repository of knowledge that reflects real-world events and common sense. Specifically, we compute semantic similarity between a list of the detected object names and a name of each activity class based on the Web knowledge. The activity class with the largest similarity value is the estimated activity of the user.
Automatic Evaluation of Students’ Discussion Skill Based on their Heart Rate

Discussion which consists of several Q&A segments (question and answer pairs) is often considered as one of the most familiar types of intellectual and creative activities at meetings. Evaluating students’ answer-quality of Q&A segments in discussion and giving them feedback can effectively help them improve their discussion skills. Considering that the discussion process is a type of cognitive activity, which could result in changes in certain physiological data, such as heart rate (HR) variability (HRV). In this study, we argue that students’ HR data can be used to effectively evaluate the answer-quality of their Q&A segments and as a method of automatic evaluation of students’ discussion-skill compared with using traditional NLP such as semantic analysis. In order to confirm this, we used a non-invasive device, i.e., Apple Watch, to collect real-time updated HR data of students during their discussions in our lab-seminar environment, their HR data were analyzed based on Q&A segments, and three machine-learning models were generated for evaluation: logistic regression, support vector machine, and random forest. The significant HR and HRV features (metrics) were also discussed using a feature selection method. Comparative experiments were conducted involving semantic data of Q&A statements alone and a combination of HR and semantic data. We also gave an experimental investigation on HR and HRV features robustness on the new data set we collected additionally.
Neural Networks and Deep Learning

Deep learning is a group of optimisation methods for artificial neural networks. The field consists of three major branches.
Design Challenges for Mobile and Wearable Systems to Support Learning on-the-move at Outdoor Cultural Heritage Sites

This paper presents a novel set of design challenges for the development of mobile and wearable applications to be used at cultural heritage sites. These challenges were drawn out based on a user study that was carried out to evaluate a mobile application prototype, SmartC. SmartC was designed for supporting people in taking learning opportunities at sites whenever they need informally while they are on the move. Augmented reality and wearable computing, i.e. smart eye glasses, were used in this research with the aim of bringing the past to life, as well as enhancing visitors’ engagement. SmartC was evaluated by 26 participants, potential end-users, in the field. The evaluation study mainly focused on the interaction and usability aspects, which contribute to the field of HCI. The paper outlines several issues and challenges that were identified based on the evaluation study, summarised as: (1) interaction design related; (2) wearable computing related; (3) surroundings and environment related; (4) learner related; (5) context of use; and (6) technical issues. This paper also identifies aspects that relate to methods to be used and applied to such cases for evaluation studies.
Brainstorming versus a Scenario-based Approach

Although important advances have been made to improve the requirements elicitation process, this problem is still a challenging research topic for the Requirements Engineering community. Motivated by this fact, we propose the CREAS method (Creative Requirements Elicitation Assisted by Scenarios), an approach for requirements elicitation that leverages and brings together scenario-based and creativity techniques. In this paper, we seek to answer the following research question: Does CREAS have a better impact on the elicitation of requirements with respect to a Brainstorming process? To answer this question, we have designed and run a quasi-experiment to compare CREAS and Brainstorming under similar conditions. The participants were required to elicit the requirements for two different problems in two different sessions using both approaches. Results evidence the existence of significant differences between the methods. The statistical outcomes show that the requirements obtained with CREAS are more complete, precise and present less over-specification.
Signal Processing as Practice

This paper describes the evolution of a "hybrid violin" developed and played by the author and reflects on the process of developing its sensorimotor dynamics as a salient affordance of real-time digital signal processing. The hybrid violin consists of a technical ensemble of hardware and software elements, including an acoustic violin, microphone, custom sensor glove (alto.glove), and customized ergonomic shoulder rest embedded with voice coils for haptic feedback coupled to digital audio output. Through trial-and-error revision by the designer, sensory feedforward and feedback paths are effectively symmetrized, thereby engaging the claim of the "enactive" approach to cognition as a creative practice: perception drives action and action drives perception. The hybrid violin catalyzes improvised performance insofar as it is responsive to all gesture and embeds no assumptions about expressive intention. The system allows for experimentation with violin technique by continuously and intensively tracking gestural and auditory inputs.
Shape-based Image Classification and Identification System for Digital Holograms of Marine Particles and Plankton

shape-based image analysis system has been developed for classifying and identifying holographic images of plankton and other microscopic marine organisms. The system consists of two parts: imCLASS and imIDENT. imCLASS enables the user to rapidly prepare training data, and then the prepared data can be used to train imIDENT such that it has the ability to automatically classify and identify new images. In our tests, imCLASS took 65.6 s to process 298 raw images, and then 20.1 s to extract and process the features of the images. The processing time of classification of the training data was roughly 17 min. Afterwards the classified training data were used to train imIDENT. This procedure took 49.0 s. After it is trained, imIDENT was able to identify 95 test images with 85.3% accuracy in 41.9 s.
Improving data classification accuracy in sensor networks using hybrid outlier detection in HAR

Managing and Mining mobile sensor data has become a topic of advanced research in several fields of computer science, such as the distributed systems, the database systems, and data mining. The main objective of the sensor based applications is to make the real-time decision which has been proved t o be very challenging due to the high resource-constrained computing and the enormous volume of sensor data generated by Wireless Sensor Networks (WSNs). This challenge motivates the sensor research community to explore new data mining techniques to extract information from large continuous raw data streams obtained from WSNs. Existing traditional data mining methods are not directly suited to WSNs due to the aggressive nature of sensor data and the presence of anomalies or outliers in WSNs. This work provides an overview of how traditional outlier detection method algorithms are revised and implemented in the application of Human Activity Recognition (HAR). Based on the limitations of the existing technique, a hybrid outlier detection method is proposed.
Contextualized Word Embeddings in a Neural Open Information Extraction Model

Open Information Extraction (OIE) is a challenging task of extracting relation tuples from an unstructured corpus. While several OIE algorithms have been developed in the past decade, only few employ deep learning techniques. In this paper, a novel OIE neural model that leverages Recurrent Neural Networks (RNN) using Gated Recurrent Units (GRUs) is presented. Moreover, we integrate the innovative contextual word embeddings into our OIE model, which further enhances the performance. The results demonstrate that our proposed neural OIE model outperforms the existing state-of-art on two datasets.