Reviewerly/exHarmony huggingface dataset:
Deep Learning Visualization for Underspecification Analysis in Product Design Matching Model Development

In machine learning model development, the best model for deployment is usually chosen among the models trained during hyperparameter tuning based on the performance on the test data. However, with the underspecification problem, this practice can be shortsighted as the solution may not perform well in the production environment. Hence, developers need a better tool to comprehend model results in both development and production environments to guide the next iteration of development. In this work, we present the novel visualization system for tracking model results with underspecification analysis in mind. In our evaluation, we show both qualitative and quantitative analysis of model development for product design matching problem and the results suggest that our method helps developers gain a better understanding of the models being developed, resulting in better performance in production. Finally, this visualization has been used at Wazzadu.com to help developers make decisions more effectively in model development.
EFL Learners' Lexico-Grammatical Competence in Paper-Based Vs. Computer-Based in Genre Writing

With new technology, writing became a skill that is being developed year after year. The present study questions whether there is a difference between paper-based and computer-based writing in terms of errors and lexico-grammar. It aims at exploring sentence-level errors and lexico-grammatical competence in two writing genres in a collaborative writing environment within paper-based and computer-based writing. A sample of 73 female intermediate level learners participated in the study at the University of Business and Technology (UBT), in Saudi Arabia. This mixed-methods research is significant in the literature of second language writing since it highlights genre awareness, lexico-grammatical competence, analyzing errors, and collaboration in two styles of writing. The reading-based writing tasks acted as a reflection of the learners' lexico-grammatical competence on paper and via Web 2.0 tool (Padlet). Statistically, the Mann-Whitney U-tests showed that there was no significant difference between paper-based and computer-based groups in the sentence-level errors in narrative genre, whereas there was a significant difference between the two different tools of writing groups in the sentence-level errors in opinion genre. However, there was no significant difference between paper-based and computer-based groups in the clauses (lexico-grammar) of the two groups. Immediate semi-structured interviews were conducted and analyzed through NVIVO to get more insights from the learners to explain the comparison between the paper-based and the computer-based writing. In light of the significant findings, implications are sought to create an equillibrium between paper-based and computer-based writing, along with enhancing collaboration in second language writing.
Person Re-IDentification Based on Mutual Learning With Embedded Noise Block

Some person re-identification(Re-ID) algorithms based on deep learning utilizes a baseline as basis to modify, and add some strategies to achieve better performance. Different from the conventional methods, this work combines distillation with mutual learning to construct a person Re-ID model of mutual learning. In training, in view of the characteristics of metric learning, we introduce a mutual loss <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{M2}$ </tex-math></inline-formula> in the mutual learning network, so as to better promote the student networks to mine complementary information. In order to overcome the coupling problem in mutual learning, we designed a lightweight noise block and embedded it into mutual learning, which greatly improves the complementarity between networks. It should be added that the improvement achieved on the poor baseline can’t strictly prove the effectiveness of the research, so this paper constructs a person Re-ID baseline with relatively good performance, which is used as the student networks in mutual learning. Experiments demonstrate that the proposed person Re-ID algorithm based on mutual learning with embedded noise block achieves competitive performance on the Market1501, DukeMTMC-ReID, and CUHK-03 datasets.
GASP: Graph-Based Approximate Sequential Pattern Mining for Electronic Health Records

Sequential pattern mining can be used to extract meaningful sequences from electronic health records. However, conventional sequential pattern mining algorithms that discover all frequent sequential patterns can incur a high computational and be susceptible to noise in the observations . Approximate sequential pattern mining techniques have been introduced to address these shortcomings yet, existing approximate methods fail to reflect the true frequent sequential patterns or only target single-item event sequences. Multi-item event sequences are prominent in healthcare as a patient can have multiple interventions for a single visit. To alleviate these issues, we propose GASP, a graph-based approximate sequential pattern mining, that discovers frequent patterns for multi-item event sequences. Our approach compresses the sequential information into a concise graph structure which has computational benefits. The empirical results on two healthcare datasets suggest that GASP outperforms existing approximate models by improving recoverability and extracts better predictive patterns.
Smart drone with real time face recognition

This paper develops a solution to help the task force using a face recognition based Unmanned Aerial Vehicle (UAV) to identify the criminals, missing people, civilians and for surveillance. A facial recognition system is a technology which involves the understanding of how the faces are detected and recognized, usually employed to authenticate users through Identity verification services and computing facial features from a given image. This work elucidates an unmanned aerial vehicle with a camera attached that is linked to Face Recognition software and used to control a robot through a wireless remote. When the drone's camera angle is within 37 degrees, the accuracy is 98.6%, indicating that the suggested method can handle changing drone placements. Some applications are to assist the police officials with their investigations like search and rescue, investigations of incidents, crime analysis and crowd monitoring.
Web Interoperability for Ontology Development and Support with crowd 2.0

In this work, we treat web interoperability in terms of interchanging ontologies (as knowledge models) within user-centred ontology engineering environments, involving visual and serialised representations of ontologies. To do this, we deal with the tool interoperability problem by re-using an enough expressive ontology-driven metamodel, named KF, proposed as a bridge for interchanging both knowledge models. We provide an extensible web framework, named crowd 2.0, unifying the standard conceptual data modelling languages for generating OWL 2 ontologies from semantic visualisations. Visual models are designed as UML, ER or ORM 2 diagrams, represented as KF instances, and finally, formalised as DL-based models. Reasoning results may be newly incorporated into the shared KF instance to be visualised in any of the provided languages.
Graph Neural Network and its applications

Abstract In recent years, people have become more interested in expanding deep learning methods on graphs, and a lot of progress has been made in the field. Although traditional deep learning methods have been applied to extract the features of Euclidean spatial data with great success, the data in many practical application scenarios are generated from non-Euclidean spaces. The performance of traditional deep learning methods in processing non-Euclidean spatial data is still not optimistic. Driven by the success of many factors, the researchers used the ideas of convolutional networks, recurrent networks, and deep autoencoders to define and design the neural network structure for processing graph data. This is a new research hotspot— Graph Neural Networks (GNNs). It is necessary to summarize the latest GNNs-related studies and propose improved algorithms to further promote breakthroughs in more applications of GNNs. This paper mainly provides an overview of the current research status of graph neural networks and proposes future work from three aspects.
Comprehensive, multidimensional and correlative particle characterization of a saxolite and talcum compound to support the understanding of complex separation processes

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Demographic Effects on Mid-Air Gesture Preference for Control of Devices: Implications for Design

After eliciting 129 potential task-gesture combinations for 23 Smart TV tasks with a Canadian sample (N = 22), we then conducted studies that collected participant preference scores on mid-air bare-hand gestures for TV control in both Canada (N = 747) and China (N = 300), and we analyzed the effect of characteristics of individual participants on gesture preference scores. The results showed that age and cultural differences are important in determining task-gesture preferences. While exploratory, the present results indicate a need for more research in this area and suggest that one of two possible strategies may need to be adopted in designing future gesture interactions: 1) develop customized task-gesture combinations for different cultures and different age groups; 2) develop a core set of task-gesture combination possibilities and let users choose which gesture they want to use for each task.
Stable and efficient differential estimators on oriented point clouds

Abstract Point clouds are now ubiquitous in computer graphics and computer vision. Differential properties of the point‐sampled surface, such as principal curvatures, are important to estimate in order to locally characterize the scanned shape. To approximate the surface from unstructured points equipped with normal vectors, we rely on the Algebraic Point Set Surfaces (APSS) [GG07] for which we provide convergence and stability proofs for the mean curvature estimator. Using an integral invariant viewpoint, this first contribution links the algebraic sphere regression involved in the APSS algorithm to several surface derivatives of different orders. As a second contribution, we propose an analytic method to compute the shape operator and its principal curvatures from the fitted algebraic sphere. We compare our method to the state‐of‐the‐art with several convergence and robustness tests performed on a synthetic sampled surface. Experiments show that our curvature estimations are more accurate and stable while being faster to compute compared to previous methods. Our differential estimators are easy to implement with little memory footprint and only require a unique range neighbors query per estimation. Its highly parallelizable nature makes it appropriate for processing large acquired data, as we show in several real‐world experiments.
Learning Systems versus Future Everyday Domestic Life: A Designer’s Interpretation of Social Practice Imaginaries

Smart home technologies with the ability to learn over time promise to adjust their actions to inhabitants’ unique preferences and circumstances. For example, by learning to anticipate their routines. However, these promises show frictions with the reality of everyday life, which is characterized by its complexity and unpredictability. These systems and their design can thus benefit from meaningful ways of eliciting reflections on potential challenges for integrating learning systems into everyday domestic contexts, both for the inhabitants of the home as for the technologies and their designers. For example, is there a risk that inhabitants’ everyday lives will reshape to accommodate the learning system’s preference for predictability and measurability? To this end, in this paper we build a designer’s interpretation on the Social Practice Imaginaries method as developed by Strengers et al. to create a set of diverse, plausible imaginaries for the year 2030. As a basis for these imaginaries, we have selected three social practices in a domestic context: waking up, doing groceries, and heating/cooling the home. For each practice, we create one imaginary in which the inhabitants’ routine is flawlessly supported by the learning system and one that features everyday crises of that routine. The resulting social practice imaginaries are then viewed through the perspective of the inhabitant, the learning system, and the designer. In doing so, we aim to enable designers and design researchers to uncover a diverse and dynamic set of implications the integration of these systems in everyday life pose.
Deep CNN for Classification of Image Contents

In recent years the classification of images has made great progress and has been used in many fields. However, it may not be possible to classify images perfectly through the CNN because of overfitting and gradient vanishing. Most existing CNNs have too many parameters, as a result, it will take a long time to train the CNN and then to classify images. In this paper, an improved CNN, with fewer parameters, can perfectly solve the problems such as overfitting, gradient vanishing was developed. The number of designed CNN's parameters is 13M, less than that of other CNNs. In order to check the performance of the designed CNN, the database such as MNIST and CIFAR-10 were used to test the CNNs. The test result was 99.467% and 91.167% respectively. These results are similar to test accuracy of other existing CNNs. Therefore, it was confirmed that the designed CNN not only has fewer parameters than the other CNNs but also shows high test accuracy.
Evading Detection Systems by Generating Adversarial Malware Examples

Machine Learning is an immensely valuable tool nto ensure advanced identification and safety mechanisms for protecting our personal information when it comes to cyber security. The Generative Adversarial Network (GAN), which is the main focus of this article, is a particularly strong machine learning principle. The GAN has numerous information defense uses, including the validation of current threats outside the traditional monitoring system. As GANs become more popular, the need to defend against and recognize GAN attacks is also becoming increasingly urgent. This paper proposes a Genetic Alteration Network (GAN) model called Mal-wareGAN to generate malware examples of adversaries that can bypass model detection based on black box learning. Malware-GAN uses a replacement detector to match the black-box malwset device. A generative network model was trained to minimize the malicious probabilities of the replacement detector in adverse instances. Malware-GAN’s advantage over conventional gradient-dependent adversarial examples is that Malware-GAN can reduce the detection rate to almost zero and make it difficult to work defensively against adverse examples.
Modified Restoration Technique for Improved Visual Perception of Shallow Underwater Imagery

Images captured underwater often suffer from quality degradation such as low contrast, non-uniform illumination, etc. due to the attenuation and backscattering of light by suspended underwater particles.To overcome this, restoration-cum-enhancement techniques are necessary.Here we present the modified underwater light attenuation prior (MULAP) model using supervised linear regression model to restore the degraded image.The image formation model (IFM)based restoration depends on dual factors: background light and transmission map.Initially, datasets are collected on the close-range point-of-interest.Then, experimental analyses are carried out for those images using the IFM-based methods.For the above techniques, both subjective analysis and objective analysis are done by considering dual metrics such as universal quality index and visual information fidelity factor.Finally, the proposed MULAP shows overwhelming qualitative and quantitative results among other state-of-the-art techniques.
Deterministic latency networks: the enabler of edge data center synchronous operation [Invited]

High bandwidth demands combined with low latency applications shifted data processing from the centralized cloud to the edge data center (DC). We propose to leverage the deterministic data delivery in optical deterministic dynamic networks (DDNs) to operate the edge DC in a synchronous manner. We jointly optimize compute and network resources allocation to deliver data just in time to be processed at the compute resources. Numerical results show that our DDN-based approach can (1) accelerate the application total execution time by up to 70% from baseline approaches; (2) reduce performance variance for competing applications, therefore satisfying the service level objective for all; and (3) increase the efficiency of the edge DC in a heterogeneous application environment.
Autonomous Docking of Mobile Robots by Reinforcement Learning Tackling the Sparse Reward Problem

Most mobile robots are powered by batteries, which must be charged before their level become too low to continue providing services. This paper contributes a novel method based on Reinforcement Learning (RL) for the autonomous docking of mobile robots at their charging stations. Our proposal considers a RL network that is fed with images to visually sense the environment and with distance measurements to safely avoid obstacles, and produces motion commands to be executed by the robot. Additionally, since the autonomous docking is in essence a sparse reward task (the only state that returns a positive reward is when the robot docks at the charging station), we propose the usage of reward shaping to successfully learn to dock. For that we have designed extrinsic rewards that are built on the results of a Convolutional Neural Network in charge of detecting the pattern typically used to visually identify charging stations. The experiments carried out support our design decisions and validate the method implementation, reporting a $$\sim $$ 100% of success in the docking task with obstacle-free paths, and $$\sim $$ 93% when obstacles are considered, along with short execution times (10 s and 14 s on average, respectively).
Public-Key Reinforced Blockchain Platform for Fog-IoT Network System Administration

The number of embedded devices that connect to a wireless network has been growing for the past decade. This interaction creates a network of Internet-of-Things (IoT) devices where data travel continuously. With the increase of devices and the need for the network to extend via fog computing, we have fog-based IoT networks. However, with more endpoints introduced to it, the network becomes open to malicious attackers. This work attempts to protect fog-based IoT networks by creating a platform that secures the endpoints through public-key encryption. The servers are allowed to mask the data packets shared within the network. To be able to track all of the encryption processes, we incorporated the use of permissioned blockchains. This technology completes the security layer by providing an immutable and automated data structure to function as a hyper ledger for the network. Each data transaction incorporates a handshake mechanism with the use of a public-key pair. This design guarantees that only devices that have proper access through the keys can use the network. Hence, management is made convenient and secure. The implementation of this platform is through a wireless server–client architecture to simulate the data transactions between devices. The conducted qualitative tests provide an in-depth feasibility investigation on the network’s levels of security. The results show the validity of the design as a means of fortifying the network against endpoint attacks.
The Hubs: Design Insights for Walking Meeting Technology

As an active form of meeting, walking meetings can be beneficial for office workers who often have a sedentary work routine. Despite their substantial benefits in terms of health, social interactions, and creativity, walking meetings are not yet widely adopted. Some key barriers limiting their social acceptance and wider adoption, for instance, the difficulty to present files or take notes, might be addressed by technology. Using the Hubs - a network of stand-up meeting stations - as a design exemplar, we conducted a scenario-based survey (N = 186) to provide insights into how technological solutions can support the practice of walking meetings. Focusing on the size of the group and type of meetings, we identify scenarios of use and discuss design implications for the development of future technologies and service design components to support walking meetings.
Your Robot Is Watching 2: Using Emotion Features to Predict the Intent to Deceive

The capabilities and acceptance of social robots would be greatly improved by developing their ability to determine human intent. Trust, which is an important consideration in collaborations, is affected by the intent of the agents involved. Prediction of a human’s intent to deceive a robot is understudied and an important factor in calculating trust. We present a method that predicts the intent to deceive in a card game scenario that relies on facial emotion recognition. Video data collected through a human study where subjects played against three opponent types: Human, Robot, and Computer simulation, was used to validate model performance in the wild. We show that our method which uses Mini-batched K-Means not only facilitates online learning, but exceeds human performance in 17 out of 30 trials. In addition, this end-to-end pipeline removes the dependency on human annotations used in a prior method allowing for deployment on a robot.
The Use of Immersive Virtual Reality Technology to Deepen Learning in Singapore Schools

The implementation of immersive technologies such as virtual reality is contextualised as a strategy of exploration and experimentation. This brings about changes in the instructional design of virtual environments and pedagogies of lessons that achieve the outcomes of the student-centric values-driven paradigm of Singapore’s education system. Riverside Secondary School’s VR hub is a futuristic virtual reality classroom that provides immersive learning through sensorial experiences. Students’ feedback and results from a preliminary study validates the benefit that VR is a powerful means of visual learning to help students learn molecular and cellular representations that enhance interest and joy in learning. Students develop scientific empathy when VR give them the opportunity to actualise and be a cell or molecule as this generates a first-person perspective that can give rise to more accurate reasoning to explain scientific phenomena.
An Automatic Method for Understanding Political Polarization Through Social Media

Understanding political polarization is an important problem when one studies the culture of a democratic country. As a platform for discussing social issues, social media such as Twitter contains rich information about political polarization. In this paper, we propose an automatic method for discovering information from social media that can help people understand political polarization of the country. Previous researches have answered the “who” question, as they proposed methods for identifying ideal points of social media users. In our work, we make a step forward by answering the “what” question. Our method consists of two main techniques, namely, ideal point estimation and discriminative natural language processing. The inputs of our method are raw social media data, and the outputs are representative phrases for different political sides. Using real-world data from Twitter, we also verify that the representative phrases our method generates are consistent with our general knowledge of political polarization in Japan.