Reviewerly/exHarmony huggingface dataset:
Disruptive smart mobile pedagogies for engineering education

Mobile technologies are becoming more ubiquitous and multifaceted in today’s society. The rise of smart mobile devices like mobile phones, tablets, etc. has sparked a revolution in mobile learning paradigm. Educational researchers have started exploiting the potential and scope of smart mobile technologies in education. The mobile technology embraced by new smart pedagogies, can disrupt the notions of engineering education. We have examined the nature of several smart pedagogies in engineering education and explored the way they intersect with the mobile pedagogies. We have unpacked philosophies of modernization and disruption. We have then discussed smart mobile learning activities for engineering students, together with the pedagogical principles supporting them. We have also outlined the ways to implement ‘feasible disruptions’ through the smart mobile pedagogies.
COMMONING DESIGN AND DESIGNING COMMONS

This workshop explores the relevance of the notion of commons as an objective, and commoning as a way of doing and being for design. We invite the PD community to reflect on ways in which these concepts help us critically protect and support sustainable futures for communities of humans and non-humans. How can participatory design remain open to multiple ways of sharing and different worldviews? What would it mean for the participatory design community in terms of challenging established notions such as participation, facilitation, empowerment, to name but a few? How can participatory design contribute further to theoretical elaboration and activist practices?
DIA

Social media messaging applications(i.e. WhatsApp, Facebook) have reached 2.3 billion users in 2019, with the majority of users emerging from developing countries. The high usage among emergent users opens the possibility of designing text-based interventions for social change but such interventions rely on experts (i.e. doctors, educators, and moderators) knowledge which is scarce in developing contexts. Expert knowledge can be scaled up using chatbots but more research is needed to support emergent users who need context-specific support such as local language interventions or may not have regular internet connectivity. Therefore to support the design of chatbot based interventions in low resource contexts, we built DIA a chatbot architecture for low resource contexts to scale expert knowledge and support localization. DIA is a human-chatbot (humbot) hybrid system that organically learns topic-specific knowledge and local language from user interactions. We built a preliminary version of DIA on WhatsApp and deployed it to mentor 38 teachers in a rural context of Côte d'Ivoire. Through our preliminary deployment, we show that DIA can help (1) build a data-set of a topic and language-specific dialogues (2) understand users' online smartphone usage through chat logs and (3) collect survey data for through conversational interaction.
Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network

The interactions between humans and their environment, comprising living and non-living entities, can be studied via Social Network Analysis (SNA). Node classification, as well as community detection tasks, are still open research problems in SNA. Hence, SNA has become an interesting and appealing domain in Artificial Intelligence (AI) research. Immanent facts about social network structures can be effectively harnessed for training AI models in a bid to solve node classification and community detection problems in SNA. Hence, crucial aspects such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration in the course of analyzing the social network. These factors determine the nature and dynamics of a given social network. In this paper, we have proposed a unique framework, Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN), for studying and extracting meaningful facts from social network structures to aid in node classification as well as community detection tasks. Our proposition utilizes an edge sampling approach for exploiting features of the social graph, via learning the context of each actor with respect to neighboring actors/nodes, with the goal of generating vector-space embedding per actor. Successively, these relatively low-dimensional vector embeddings are fed as input features to a downstream classifier for classification tasks about the social graph/network. Herein RLVECN has been trained, tested, and evaluated on real-world social networks.
Anisotropic gradient-based filtering for object segmentation in medical images

A four-step approach for image filtering and object segmentation is explored. The key steps are i) a low-pass digital differentiator is used to compute the image gradient (vector) field; ii) the regularised anisotropic diffusion method is used to filter this vector field; iii) the modified image is reconstructed from the filtered gradient field as a least-squares best fit; iv) object segmentation is performed on the reconstructed image. The advantages of this approach is the easier identification of noise in the gradient field, and consequently the image filtering can be more effective. The least-squares fit allows for non-local adjustment to the image to improve overall smoothness while enhancing object contrast. Importantly one can also ensure that relevant feature boundary locations are preserved while appearing smoother due to the filtering. The proposed filtering methodology is compared to image filtering applied directly to the image intensity. A set of challenging medical images of mammography exams and confocal microscopy experiments are used as numerical tests.
Emergent Control of MPSoC Operation by a Hierarchical Supervisor / Reinforcement Learning Approach

MPSoCs increasingly depend on adaptive resource management strategies at runtime for efficient utilization of resources when executing complex application workloads. In particular, conflicting demands for adequate computation performance and power-/energy-efficiency constraints make desired application goals hard to achieve. We present a hierarchical, cross-layer hardware/software resource manager capable of adapting to changing workloads and system dynamics with zero initial knowledge. The manager uses rule-based reinforcement learning classifier tables (LCTs) with an archive-based backup policy as leaf controllers. The LCTs directly manipulate and enforce MPSoC building block operation parameters in order to explore and optimize potentially conflicting system requirements (e.g., meeting a performance target while staying within the power constraint). A supervisor translates system requirements and application goals into per-LCT objective functions (e.g., core instructions-per-second (IPS). Thus, the supervisor manages the possibly emergent behavior of the low-level LCT controllers in response to 1) switching between operation strategies (e.g., maximize performance vs. minimize power; and 2) changing application requirements. This hierarchical manager leverages the dual benefits of a software supervisor (enabling flexibility), together with hardware learners (allowing quick and efficient optimization). Experiments on an FPGA prototype confirmed the ability of our approach to identify optimized MPSoC operation parameters at runtime while strictly obeying given power constraints.
Performance Improvement of Gaussian Filter using SIMD Technology

Denoising is an important process before applying other post-processing techniques on medical images. To obtain better quality images many denoising approaches have been introduced. Gaussian filter is a spatial domain filter, which is proper to blur and to remove noise from images. Since the Gaussian filter modifies the input signal by convolution with a Gaussian function it is a computationally intensive algorithm. Hence to enhance the performance of the algorithm, it is better to perform two 1-D convolution operations instead of one 2-D convolution operation and then parallelize it. In this paper in order to increase the performance of 1-D convolution operation, we exploit both Data- and Thread-Level Parallelism using parallel programming models such as Intrinsic Programming Model, Compiler's Automatic Vectorization and Open Multi-Processing. The experimental results were shown that the performance of our implementations is much higher than other approaches Performance improvements of Multi-threaded version of all implementations are significantly improved compared to single-core implementations, and a speedup of 52.33x obtained over the optimal scalar implementation.
Optimizing Ontology Alignment through Improved NSGA-II

Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. This work investigates the ontology matching problem, which is a challenge in the semantic web (SW) domain. Due to the complex heterogeneity between two different ontologies, it is arduous to get an excellent alignment that meets all DMs’ demands. To this end, a popular MOEA, i.e., nondominated sorting genetic algorithm (NSGA-II), is investigated to address the ontology matching problem, which outputs the knee solutions in the PF to meet diverse DMs’ requirements. In this study, for further enhancing the performance of NSGA-II, we propose to incorporate into NSGA-II’s evolutionary process the monkey king evolution algorithm (MKE) as the local search algorithm. The improved NSGA-II (iNSGA-II) is able to better converge to the real Pareto optimum region and ameliorate the quality of the solution. The experiment uses the famous benchmark given by the ontology alignment evaluation initiative (OAEI) to assess the performance of iNSGA-II, and the experiment results present that iNSGA-II is able to seek out preferable alignments than OAEI’s participators and NSGA-II-based ontology matching technique.
Social-Aware Privacy-Preserving Mechanism for Correlated Data

We study a privacy-preserving data collection problem by considering individuals' data correlation and social relationship. A data collector gathers data from some data reporters to perform certain analysis with a privacy-preserving mechanism. Due to the data correlation, the analysis will cause privacy leakage not only to the data reporters but also to those individuals who do not report data. Owing to the social relationship among them, the data reporters would consider the possibility of adding some random noise to the reported data to reduce the privacy leakage. The privacy loss of the individuals (both data reporters and non-reporters) depend on all the data reporters' strategies, which naturally leads to a game theoretical analysis. A key result shows that the data reporters can be ordered based on their levels of joint considerations of social relationship and data correlation, and at the Nash Equilibrium of the game at most one data reporter with the most significant consideration may add noise to the reported data. We design an efficient algorithm for the data collector to construct the data reporter set, and derive the optimal privacy-preserving mechanism to ensure all the data reporters' truthful reporting. We conduct extensive simulations with the Facebook social data to demonstrate some insights: It is optimal for the data collector to adopt a more conservative mechanism when the data correlation or the social relationship is stronger. Compared with the data correlation information, the social network information plays a more critical role in the data collector's utility maximization problem.
Prototypes as Objects of Desire

We set out to explore the role of prototypes as instruments of knowledge for HCI research. We pursue an epistemological inquiry on 'how' prototypes can be used when users, designers and researchers work in common on the development of future technological objects. First, we offer critical commentary on prototypes as instruments of knowledge by engaging with existing literature. Under-explored themes are developed to argue for approaching prototypes as objects of desire. Desires are different from needs and requirements and ought to be considered more directly in research prototyping processes. We identify five roles for prototypes as objects of desire for research and articulate four seeding dynamics that govern exploration of future use. These are exemplified through analysis of two cases where groups of people were working in common around designing interactive objects. We conclude that prototypes as objects of desire allow exploration of 'yet-to-be-known' shared technology beyond needs and requirements.
Squeezing and slowed quantum decoherence in the double-slit experiment

We study the slowing of the decoherence effect in the double-slit experiment by considering an initially correlated Gaussian state. The effects of the decoherence are included in a specific propagator which we use to obtain the density matrix at the detection screen. We calculate the uncertainties in the position and momentum for the density matrix as a function of the decoherence at the detection screen. We show that for a contractive initial state and specific times of propagation the state at the detection screen is squeezed in position in comparison with the standard Gaussian superposition. For this squeezed Gaussian superposition state, we observe that the fringe visibility is more robust to the decoherence in comparison with the standard Gaussian superposition. Then, we calculate the negativity of the Wigner function and study its behavior as a function of the decoherence and correlation parameters at the detection screen. We observe that the negativity decreases more slowly for the squeezed Gaussian superposition.
Techno-moder: A proposal for a new morpho-functional humus form developing on Technosols revealed by micromorphology

Human impact on soil systems is now widely acknowledged. “Technosols” appeared in soil classification only a decade ago and the notion of “techno humus system” has emerged even more recently. Yet the variety of humus forms that could develop on Technosol remains largely unknown. To contribute to filling this knowledge gap, we studied the humus form developing on two Technosols resulting from the activity of a former coking plant and compared it with one developed on a Cambisol as a reference for the area. We used field description and micromorphology on thin sections coupled with a point-and-count method to precisely describe the surface of the humipedons of the area studied. A total of 13,841 counts and identifications were performed on 11 thin sections and spread into 17 fabric units. Micromorphology on thin soil sections revealed that epi-saprophagous organisms transformed most of the neo-formed organic matter on the Technosols studied, leading to an accumulation of feces on the surface of the humipedon. Our results suggested that the humus form developing on the Technosols was similar to a moder and exhibited the same ectorganic horizons (OL, OF, zOH), whilst overlaying a technogenic one composed mostly of coal fragments. We would like to propose the term “techno-moder” for this new humus form, developing in the absence of an A horizon. On the other hand, micromorphology allowed us to classify the humus form developed on the Cambisol as a eumull containing anthropic coal by-products. Specification of the humus form developing on such soils could represent a promising way to specify pedological processes such as organic matter decomposition, soil aggregation and also to enrich the humus form classification on Technosols.
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning

Resolution of the complex problem of image retrieval for diagram images has yet to be reached. Deep learning methods continue to excel in the fields of object detection and image classification applied to natural imagery. However, the application of such methodologies applied to binary imagery remains limited due to lack of crucial features such as textures,color and intensity information. This paper presents a deep learning based method for image-based search for binary patent images by taking advantage of existing large natural image repositories for image search and sketch-based methods (Sketches are not identical to diagrams, but they do share some characteristics; for example, both imagery types are gray scale (binary), composed of contours, and are lacking in texture). We begin by using deep learning to generate sketches from natural images for image retrieval and then train a second deep learning model on the sketches. We then use our small set of manually labeled patent diagram images via transfer learning to adapt the image search from sketches of natural images to diagrams. Our experiment results show the effectiveness of deep learning with transfer learning for detecting near-identical copies in patent images and querying similar images based on content.
Development of a Common Framework for Outcome Based Accreditation and Rankings

The Outcome Based Accreditation and Assessment includes NBA, NAAC, NIRF have been assisting the stakeholders in engineering education for SWOC analysis. They also help them to meet the standards and Quality in Higher Education. The NBA, NAAC and NIRF uses their own framework to measure the quality of the Engineering education for Accreditation and Rankings respectively. The criteria, key aspects and indicators of the three are different but overlapping. These differences in assessment are making the institutions to work for three different models throughout the academic year giving them less space to concentrate in the core academics. This paper brings out with ample clarity on a common framework, based on the analysis of the key indicators, to meet the standards of NBA, NACC and NIRF. This paper also discusses the design of Data Capturing System for the proposed common framework.
Robust Parking Path Planning with Error-Adaptive Sampling under Perception Uncertainty

In automated parking systems, a path planner generates a path to reach the vacant parking space detected by a perception system. To generate a safe parking path, accurate detection performance is required. However, the perception system always includes perception uncertainty, such as detection errors due to sensor noise and imperfect algorithms. If the parking path planner generates the parking path under uncertainty, problems may arise that cause the vehicle to collide due to the automated parking system. To avoid these problems, it is a challenging problem to generate the parking path from the erroneous parking space. To solve this conundrum, it is important to estimate the perception uncertainty and adapt the detection error in the planning process. This paper proposes a robust parking path planning that combines an error-adaptive sampling of generating possible path candidates with a utility-based method of making an optimal decision under uncertainty.By integrating the sampling-based method and the utility-based method, the proposed algorithm continuously generates an adaptable path considering the detection errors. As a result, the proposed algorithm ensures that the vehicle is safely located in the true position and orientation of the parking space under perception uncertainty.
Analyzing Recent Research Trends of Computer Science from Academic Open-access Digital Library

The wider utilization of information and web technologies, database technologies, development of internet infrastructure has led to the evolution of digital libraries. In particular, digital libraries serve enormous number of various users and play an essential role as repositories and source of investigation and intelligence. With the emergence of IoT (Internet of Things) and different academic open-access digital libraries, the automatic extraction of advantageous knowledge from text data has been more and more a significant subject of research in data mining. In this paper, we perform web scraping system, statistical analyses from the arXiv repository and discuss the results of analyzing recent research trends in this academic open-access digital library.
Low-latency hand gesture recognition with a low resolution thermal imager

Using hand gestures to answer a call or to control the radio while driving a car, is nowadays an established feature in more expensive cars. High resolution time-of-flight cameras and powerful embedded processors usually form the heart of these gesture recognition systems. This however comes with a price tag. We therefore investigate the possibility to design an algorithm that predicts hand gestures using a cheap low-resolution thermal camera with only 32×24 pixels, which is light-weight enough to run on a low-cost processor. We recorded a new dataset of over 1300 video clips for training and evaluation and propose a lightweight low-latency prediction algorithm. Our best model achieves 95.9% classification accuracy and 83% mAP detection accuracy while its processing pipeline has a latency of only one frame.
Privacy-Preserving Anomaly Detection Using Synthetic Data

With ever increasing capacity for collecting, storing, and processing of data, there is also a high demand for intelligent knowledge discovery and data analysis methods. While there have been impressive advances in machine learning and similar domains in recent years, this also gives rise to concerns regarding the protection of personal and otherwise sensitive data, especially if it is to be analysed by third parties, e.g. in collaborative settings, where it shall be exchanged for the benefit of training more powerful models. One scenario is anomaly detection, which aims at identifying rare items, events or observations, differing from the majority of the data. Such anomalous items, also referred to as outliers, often correspond to problematic cases, e.g. bank fraud, rare medical diseases, or intrusions, e.g. attacks on IT systems. Besides anonymisation, which becomes difficult to achieve especially with high dimensional data, one approach for privacy-preserving data mining lies in the usage of synthetic data. Synthetic data comes with the promise of protecting the users' data and producing analysis results close to those achieved by using real data. However, since most synthetisation methods aim at preserving rather global properties and not characteristics of individual records to protect sensitive data, this form of data might be inadequate due to a lack of realistic outliers. In this paper, we therefore analyse a number of different approaches for creating synthetic data. We study the utility of the created datasets for anomaly detection in supervised, semi-supervised and unsupervised settings, and compare it to the baseline of the original data.