Reviewerly/exHarmony huggingface dataset:
Milestones and New Frontiers in Deep Learning

Only very recently, deep learning models have been globally used as the basis for most of the on-going research on several fundamental computing areas, such as computer vision, information extraction, data generation and data understanding. The research in this field is still a black box for many people, particularly, regarding its more mathematical aspects. Users of high level packages often struggle to understand the reasoning behind the many building blocks of deep learning, such as convolutional layers, batch normalization and activation functions. In this tutorial, we seek to introduce the field of deep learning to a broad audience of students and professionals, walking through two important milestones of deep learning understanding: (1) the intuition behind the neural networks and batch gradient descent algorithm with back propagation, and (2) the convolutional network and its use as a feature extraction technique. These milestones lay a solid conceptual and mathematical foundation in which the many other deep learning concepts can be explored and built on. In addition, we will present new frontiers in deep learning techniques which can be used by the audience to gain a deeper understanding and to provide the key pointers to the areas that are currently being mostly sought in the literature.
A Sociotechnical History of the Ultralightweight Wheelchair: A Vehicle of Social Change

The emergence of the ultralightweight wheelchair has transformed the lives of millions of disabled people. It has radically changed the principles and practices of wheelchair design, manufacture, and prescription and redefined wheelchair users and wheelchair use. Designed and built largely by wheelchair users themselves, it was driven initially by a desire to improve sport performance and later by a wish for improved access to the community and built environment. In this paper, we draw on oral histories and documentary sources to reconstruct its sociotechnical history. We employ the analytical concept of “boundary object” to illuminate how the wheelchair as a technological artifact is implicated in relations of social change and show the role of wheelchair users in the development and emergence of the ultralightweight wheelchair. We highlight the tensions and negotiations within this history and the push and pull between different social groups. The emergence of the ultralightweight wheelchair helped to reconfigure ideas about wheelchairs and their users and allowed wheelchairs to gain a foothold within broader social and technological infrastructures. What makes this account powerful is that this is a success story for a group who have historically been excluded from design processes.
Learning to Transform Vietnamese Natural Language Queries into SQL Commands

In the field of data management, users traditionally manipulates their data using structured query language (SQL). However, this method requires an understanding of relational database, data schema, and SQL syntax as well as the way it works. Database manipulation using natural language, therefore, is much more convenient since any normal user can interact with their data without a background of database and SQL. This is, however, really tough because transforming natural language commands into SQL queries is a challenging task in natural language processing and understanding. In this paper, we propose a novel two-phase approach to automatically analyzing and converting natural language queries into the corresponding SQL forms. In our approach, the first phase is component segmentation which identifies primary clauses in SQL such as SELECT, FROM, WHERE, ORDER BY, etc. The second phase is slot- filling that helps extract sub-components for each primary clause such as SELECT column(s), SELECT aggregation operation, etc. We carefully conducted an empirical evaluation for our method using conditional random fields (CRFs) on a medium-sized corpus of natural language queries in Vietnamese, and have achieved promising results with an average accuracy of more than 90%.
Implementation of a Telerobotic System Based on the Kinect Sensor for the Inclusion of People with Physical Disabilities in the Industrial Sector

This article describes the design and implementation of a telerobotic system through the Kinect sensor for the inclusion of people with physical disabilities in the industrial environment. For the development of the system, a master slave architecture was used and the communication was made wirelessly using the HC-05 module that uses a Bluetooth v2.0 protocol, for the movement of the servomotors of the 4 OFD manipulator and Arduino board was used. The validation of the system was carried out by performing a manual and an automatic process by a group of people who manipulated the robot by hand gestures.
The Influence of Sampling on Imbalanced Data Classification

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. These measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes.
Capsule-Net for Urdu Digits Recognition

A capsule is formed when a group of additional neurons is added to existing convolutional layer in a typical convolutional neural network (CNN). Capsules have activity vector that represents instantiation parameters of an object or part of an object. Capsule network has recently been introduced by Hinton to overcome the shortcomings of typical CNN model trained with back-propagation. In this work, we investigate the use of capsule networks for recognition of handwritten digits of Urdu. Our results show that a multi-layer capsule network achieves better results (98.5% accuracy) than deep auto-encoder (97.3% accuracy) and a convolutional neural network (96%), especially when we have digits that are highly overlapped.
A chimerical dataset combining physiological and behavioral biometric traits for reliable user authentication on smart devices and ecosystems

We present a chimerical dataset that combines both physiological and behavioral biometric traits, for reliable user authentication on smart devices and ecosystems [1]. The data are composed of statistical features computed from swipe-gesture, voice-prints, and face-images. The swipe and voice-prints data presented hereinafter are collected using a customized Android application - DriverAuth, however, the face data is obtained from the MOBIO Dataset [2]. We collected 10,320 swipe and voice-prints samples from 86 users worldwide by collaborating with a professional crowd-sourcing platform and formed a chimerical dataset adjunct to the publicly available MOBIO dataset with our collected dataset. The dataset consists of various statistical features computed from the raw data for all three traits, i.e., swipe, voice-print, and face.
Identifying Mass-based local anomalies using Binary Space Partitioning

The performance of WSNs relies on the quality of the collected data. One of the main challenges is how to efficiently deliver the sensed measurements to the destination with a maximum fidelity to the probed data. Data measured by sensors require an efficient ranking measure to differentiate between normal and anomalous values. In this paper, we propose a local anomaly detector algorithm that uses Half-space trees to train and test data instances. It is based on mass ranking with difference in the way we score data and isolate anomalies. Experimental results show that our algorithm has better performance with high accuracy, low time and space complexity comparing to Local Outlier Factor and Mass estimation.
Augmenting Guitars for Performance Preparation

A substantial number of Digital Musical Instruments (DMIs) are built upon existing musical instruments by digitally and physically intervening in their design and functionality to augment their sonic and expressive capabilities. These are commonly known as Augmented Musical Instruments (AMIs). In this paper we survey different degress of invasiveness and transformation within augmentations made to musical instruments across research and commercial settings. We also observe a common design rationale among various AMI projects, where augmentations are intended to support the performer's interaction and expression with the instrument. Consequently, we put forward a series of minimally-invasive supportive Guitar-based AMI designs that emerge from observational studies with a community of practicing musicians preparing to perform which reveal different types of physical encumbrances that arise from the introduction of additional resources beyond their instrument. We then reflect on such designs and discuss how both academic and commercially-developed DMI technologies may be employed to facilitate the design of supportive AMIs.
Analyzing the Effect of Stochastic Transitions in Policy Gradients in Deep Reinforcement Learning

Policy gradient methods in deep reinforcement learning have received increasing attention over the last few years, mainly because of their several successful applications to challenging sequential decision-making tasks, such as playing Atari games from pixels and solving simulated robotics tasks. Several publically available benchmarks have enabled the development and evaluation of novel policy gradient methods; however, few incorporate environment stochasticities as a feature. We hypothesize that environments with stochastic dynamics may hinder the performance of these methods and propose an empirical analysis to evaluate so. We implement stochastic variations of a classic cartpole balancing task and select and implement three algorithms: Vanilla Policy Gradient, Natural Policy Gradient, and TRPO. Our results show that convergence in these environments is slower for all three algorithms and that policies obtained in the deterministic setting aren't useful even though the expected effect of each action is the same. We suggest that both further analyses of policy gradient algorithms in stochastic environments and new benchmarks that incorporate these tasks are necessary for better evaluating existing and novel solutions.
A novel enhanced hybrid recursive algorithm: Image processing based augmented reality for gallbladder and uterus visualisation

Background: Current Augmented Reality systems in liver and bowel surgeries, are not accurate enough to classify the hidden parts such as gallbladder and uterus which are behind the liver and bowel. Therefore, we aimed to improve the visualization accuracy of bowel and liver augmented videos to avoid the unexpected cuttings on the hidden parts. Methodology: The proposed system consists of an Enhanced hybrid recursive matching and λ-parameterization techniques to improve the visualization. In addition, Mean Shift Filter is also added to improve the matching process while image registration. Results: Results proved that, the accuracy is improved in terms of liver and bowel surgeries Visualization errors about 0.53 mm and 0.22 mm respectively. Similarly, it can produce 2 more frames/sec compared to the current system. Conclusion: The proposed system worked towards the visualization of gallbladder and uterus while liver and bowel surgeries. So, this study solved the visualization issues, which are caused by neighbouring and hidden parts.
Mutual requirements evolution by combining different information systems

We propose a method of eliciting requirements for several different systems together. We focus on systems used by one user at the same time become such systems inherently give influences on with other. We expect such influences help a requirements analyst to be aware of unknown requirements of the user. Any modeling notations are used to explore the combination among systems causing such influences because the differences among the notations give diverse viewpoints to the analyst. To specify such mutual influences, we introduce semantic tags represented by stereo types. We also introduce other semantic tags so that the analyst can judge whether the combination brings advantages to the user. We apply our method to an example and we confirm the method works.
The Detection and Classification of Microcalcifications in the Visibility-Enhanced Mammograms Obtained by using the Pixel Assignment-Based Spatial Filter

In this paper, we proposed a computer aided diagnosis (CAD) system which has the pixel assignment-based a spatial filter to enhance the visibility of microcalcifications in mammograms. ...
The Effect of Contents and Available Viewpoints on QoE of Multi-view Video and Audio over WebRTC

This paper evaluates the effect of contents and the number of available viewpoints on Quality of Experience (QoE) of Multi-View Video and Audio (MVV-A) transmission utilizing Web Real-Time Communication (WebRTC) media channel, which employs UDP-based transmission into Web technologies. According to viewpoint change requests, this system switches audiovisual streams. We assess QoE through a subjective experiment with various network conditions. In the experiment, we employ two types of contents and two numbers of available viewpoints. QoE is evaluated multidimensionally. We then show that the content and the number of viewpoints affect QoE.
Improving the Adaptation Process for a New Smart Home User

Artificial Intelligence (AI) has been around for many years and plays a vital role in developing automatic systems that require decision using a data- or model-driven approach. Smart homes are one such system; in them, AI is used to recognize user activities, which is a fundamental task in smart home system design. There are many approaches to this challenge, but data-driven activity recognition approaches are currently perceived the most promising to address the sensor selection uncertainty problem. However, a smart home using a data-driven approach exclusively cannot immediately provide its new occupant with the expected functionality, which has reduced the popularity of the data-driven approach. This paper proposes an approach to develop an integrated personalized system using a user-centric approach comprising survey, simulation, activity recognition and transfer learning. This system will optimize the behaviour of the house using information from the user's experience and provide required services. The proposed approach has been implemented in a smart home and validated with actual users. The validation results indicate that users benefited from smart features as soon as they move into the new home.
An Automatic Remote Monitoring System for Large Networks

Monitoring the functionality of large networks is a difficult problem more and more encountered lately due to the expansion of existing networks and the development of applications that require large networks of sensors, detection systems and telecommunications networks. The main objective of this paper is to automatically monitor a network which comprises of a high number of sensors (e.g. 124) deployed on a large surface (more than 20 sq. km). The collected data is used to model physical phenomena in the atmosphere, thus the quality of data is important and is affected by the incorrect functionality of the equipment. The proposed solution is beyond the original scientific purpose of the network and can be successfully applied in other applications or other network configurations.
ABQ: Active Buffer Queueing in Datacenters

Congestion control is always an active research field for guaranteeing the Quality of Services (QoS) of datacenter networks (DCNs). However, the current end-to-end TCP design enables both senders and receivers not to directly and precisely obtain the congestion information, incurring inaccurate or non-real-time adjustments. Moreover, switches that act as fundamental primitives in DCN are usually used for reactive congestion feedbacks (e.g., Ack and ECN), leading to underused for proactive congestion control. In this article, we propose a novel active queue management (AQM) scheme called Active Buffer Queueing (ABQ) which leverages active buffer queueing of DCN switches to achieve both high-throughput and loss-free transmissions. When the traffic pattern in DCN is changed intensely in flow size, number and interval, the feedback information to TCP end hosts are imprecise. Unlike other AQMs, the key idea of ABQ is to adjust the flow rate (or packet pace) directly in the switch according to the real-time congestion state. We explain the design rationale behind ABQ and present simulation results of its performance. Finally, we discuss the implementation on the NetFPGA.
Latent Semantic Analysis and Winnowing Algorithm Based Automatic Japanese Short Essay Answer Grading System Comparative Performance

In this paper, advanced of research on e-learning application for short essay grading system had been conducted. This system was developed based on the needs of Japanese Language study program for short essay examination that required time and focus for finishing those tasks. Human abilities are limited by their energy, so that cognitive assessment objectivity could decrease in line with the elapsed time. Latent Semantic Analysis (LSA) and Winnowing Algorithm are two methods used in developing the automatic short essay answer grading system called SIMPLE-O by Department of Electrical Engineering, Universitas Indonesia. These two algorithms are chosen based on its ability to do semantic analytic without the needs of understanding about the characteristic of its languages. LSA used Singular Value Decomposition (SVD) as its main method, besides Winnowing algorithm is based on fingerprinting. These algorithms are applied into the automatic system to assess the Japanese language exam with close results between them with average accuracy of Winnowing algorithm is only 1.06% lower than LSA that could gain 87.78%. These two algorithms should be suitable for grading short essay answer in Japanese language.
On the Asymptotic Sample Complexity of HGR Maximal Correlation Functions in Semi-supervised Learning

The Hirschfeld-Gebelein-Rényi (HGR) maximal correlation has been shown useful in many machine learning applications, where the alternating conditional expectation (ACE) algorithm is widely adopted to estimate the HGR maximal correlation functions from data samples. In this paper, we consider the asymptotic sample complexity of estimating the HGR maximal correlation functions in semi-supervised learning, where both labeled and unlabeled data samples are used for the estimation. First, we propose a generalized ACE algorithm to deal with the unlabeled data samples. Then, we develop a mathematical framework to characterize the learning errors between the maximal correlation functions computed from the true distribution and the functions estimated from the generalized ACE algorithm. We establish the analytical expressions for the error exponents of the learning errors, which indicate the number of training samples required for estimating the HGR maximal correlation functions by the generalized ACE algorithm. Moreover, with our theoretical results, we investigate the sampling strategy for different types of samples in semisupervised learning with a total sampling budget constraint, and an optimal sampling strategy is developed to maximize the error exponent of the learning error. Finally, the numerical simulations are presented to support our theoretical results.
How Do Code Changes Evolve in Different Platforms? A Mining-Based Investigation

Code changes are performed differently in the mobile and non-mobile platforms. Prior work has investigated the differences in specific platforms. However, we still lack a deeper understanding of how code changes evolve across different software platforms. In this paper, we present a study aiming at investigating the frequency of changes and how source code, build and test changes co-evolve in mobile and non-mobile platforms. We developed regression models to explain which factors influence the frequency of changes and applied the Apriori algorithm to find types of changes that frequently co-occur. Our findings show that non-mobile repositories have a higher number of commits per month and our regression models suggest that being mobile significantly impacts on the number of commits in a negative direction when controlling for confound factors, such as code size. We also found that developers do not usually change source code files together with build or test files. We argue that our results can provide valuable information for developers on how changes are performed in different platforms so that practices adopted in successful software systems can be followed.
Analysis on Dropout Regularization

Dropout, including Bernoulli dropout (equivalently random node fault) and multiplicative Gaussian noise (MGN) dropout (equivalently multiplicative node noise), has been a technique in training a neural network (NN) to achieve better performance. While simulation results have demonstrated its success, not many work has been done to explain why it works (or why it does not work). In this paper, the objective functions $$\mathcal{L}({\mathbf w})$$ of the learning algorithms with Bernoulli dropout and MGN dropout are derived and thus their regularization effects are analyzed. It is found that learning with Bernoulli dropout cannot improve the generalization of a NN if its weights are not scaled down after training. If we further let $$\mathcal{J}({\mathbf w})$$ be the desired measure of a NN with such inherent dropout, we clarify a misconception that $$\mathcal{L}({\mathbf w}) = \mathcal{J}({\mathbf w})$$. The model attained by learning with dropout is not the desired model that can tolerate such inherent dropout.