Reviewerly/exHarmony huggingface dataset:
Ecosystemas: Representing Ecosystem Impacts in Design

Personas—distilled representations of particular user groups—are a key part of many design processes. Researchers have created animal personas to represent nonhuman animals as stakeholders in design efforts. However neither human nor animal personas allow for the representation of broader-scale ecological impacts in design decisions. To address this gap, we present an additional conceptual tool for design: the ecosystema. This design construct is analogous to a persona, but at the level of an entire ecosystem rather than of a particular human population or animal species. These constructs could allow ecosystem-wide impacts to influence design processes more effectively, and may provide greater leverage on current environmental crises than existing human-centered techniques.
A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain

With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.
Parity Quantum Optimization: Benchmarks

We present benchmarks of the parity transformation for the Quantum Approximate Optimization Algorithm (QAOA). We analyse the gate resources required to implement a single QAOA cycle for real-world scenarios. In particular, we consider random spin models with higher order terms, as well as the problems of predicting financial crashes and finding the ground states of electronic structure Hamiltonians. For the spin models studied our findings imply a significant advantage of the parity mapping compared to the standard gate model. In combination with full parallelizability of gates this has the potential to boost the race for demonstrating quantum advantage.
Secure Distributed Medical Record Storage using Blockchain and Emergency Sharing Using Multi-Party Computation

Consuming over 10 percent of gross domestic product (GDP) of most developed nations, the healthcare industry forms an enormous part of a country's economy. With this industry catering to such a large section of the population, each diagnosed case may have varying levels of severity. A robust and fluid system of care naturally causes significant privacy concerns involving patient information. We propose a working model for secure storage of historical medical records of a patient and protected sharing between doctors and hospitals. A medical file is encrypted using a random symmetric key and is stored in a decentralized peer-peer storage system. The random symmetric key is encrypted using the patient's public key and is securely stored in a public blockchain. The patient's private key, along with the hospital's public key, is input into a proxy key generation cryptosystem that uses a Yao's Multiparty Computation Circuit, to produce a re-encryption key. This key can be used to re-encrypt the random symmetric key stored in the Blockchain to give a ciphertext that can further be deciphered using the hospital's private key. Thus, with the decoded random symmetric key in hand, the medical record is retrieved from storage and decrypted for access. The medical record is then forwarded to a specialized doctor for specific treatment. This system is secure and provides the doctor with the patient's medical records on demand with a fast and immutable technique.
BackTrack: 2D Back-of-device Interaction Through Front Touchscreen

We present BackTrack, a trackpad placed on the back of a smartphone to track fine-grained finger motions. Our system has a small form factor, with all the circuits encapsulated in a thin layer attached to a phone case. It can be used with any off-the-shelf smartphone, requiring no power supply or modification of the operating systems. BackTrack simply extends the finger tracking area of the front screen, without interrupting the use of the front screen. It also provides a switch to prevent unintentional touch on the trackpad. All these features are enabled by a battery-free capacitive circuit, part of which is a transparent, thin-film conductor coated on a thin glass and attached to the front screen. To ensure accurate and robust tracking, the capacitive circuits are carefully designed. Our design is based on a circuit model of capacitive touchscreens, justified through both physics-based finite-element simulation and controlled laboratory experiments. We conduct user studies to evaluate the performance of using BackTrack. We also demonstrate its use in a number of smartphone applications.
Cloud Computing Curriculum

The accelerating evolution and adoption of cloud computing services is generating increased demand for job skills in this domain. To address this growth, higher education has identified the importance of cloud computing courses that deliver practical and hands-on material relevant to this rapidly evolving field. This is especially relevant as cloud services are quickly becoming the standard computing model for many new computational approaches, including advanced subjects such as machine learning and data science. Since cloud computing influences a number of other traditional computing subjects such as networking and data storage, the ability to incorporate specific components of cloud computing teaching content into a variety of courses has also become important. However, a lack of availability of high-quality teaching material that is easy to integrate, when teaching rapidly evolving cloud-related concepts, continues to be a challenge for instructors. As the volume of teaching material that educators and other providers are willing to share increases, it is important that the content is easily discoverable and accurately catalogued. Using appropriate Learning Objectives (LOs) and tags associated with the correct course material contributes toward a reliable and trustworthy source for faculty. This Working Group (WG) has addressed these challenges by 1) creating a canonical list of LOs, based on the LOs included in the Knowledge Areas (KAs) developed by the ITiCSE working groups in 2018 and 2019; 2) searching for existing teaching material, cataloging the material by LO and then combining the LOs into exemplar modules for some of the more common classes that adopt cloud computing; 3) creating sample syllabi for these exemplars following the ACM Computer Science Curricula 2013 approach; and 4) developing a solution for disseminating the materials and keeping them up to date.
Making Un-Use: When Humans Disengage with Technology

Humans frequently discontinue to use certain technologies, services, or platforms: they deactivate accounts, block content, or find workarounds for un-using functionalities or devices. This is noticeable in contemporary trends, such as digital detox, where an entire market emerged to support users in discontinuing their use. In this essayistic paper, we reflect on a collection of examples where humans disengage with technology by applying Vardouli's concept of 'making use' to the context of discontinued use. We propose making un-use as an epistemological perspective to 'human-artefact engagements'; one that emphasises un-users as enactors of open-ended, temporarily evolving, and creative activities. We depict making un-use as transformational acts, and discuss epistemological and designerly consequences of making un-use to shed light on a not yet explored site of inquiry and a design space that is about to evolve: a design space for making and makers of un-use.
Evaluating Trust Assurance in Indy-Based Identity Networks Using Public Ledger Data

DATA REPORT article Front. Blockchain, 30 April 2021 | https://doi.org/10.3389/fbloc.2021.622090
Human-Aided Saliency Maps Improve Generalization of Deep Learning

Deep learning has driven remarkable accuracy increases in many computer vision problems. One ongoing challenge is how to achieve the greatest accuracy in cases where training data is limited. A second ongoing challenge is that trained models oftentimes do not generalize well even to new data that is subjectively similar to the training set. We address these challenges in a novel way, with the first-ever (to our knowledge) exploration of encoding human judgement about salient regions of images into the training data. We compare the accuracy and generalization of a state-of-the-art deep learning algorithm for a difficult problem in biometric presentation attack detection when trained on (a) original images with typical data augmentations, and (b) the same original images transformed to encode human judgement about salient image regions. The latter approach results in models that achieve higher accuracy and better generalization, decreasing the error of the LivDet-Iris 2020 winner from 29.78% to 16.37%, and achieving impressive generalization in a leave-one-attack-type-out evaluation scenario. This work opens a new area of study for how to embed human intelligence into training strategies for deep learning to achieve high accuracy and generalization in cases of limited training data.
Augmenting features by relative transformation for small data

Small data means that the number of samples in data is small. In such case, their features are often hand-crafted so that they may be insufficient and inconsistent. Currently some feature augmentation methods are proposed to solve the problem. They are mainly for the larger data composed of images and videos, instead of small data. In the case of small data, the human cognition such as cognitive relativity can be more beneficial to machine learning, as humans routinely classify objects according to both their individual features and their environments. The relative transformation is an efficient way to formalize the cognitive relativity and has been validated in improving the performance of the machine learning. Although relationships among categorical central vectors of small data can greatly improve the performance, they have not been considered as features in current methods. This paper uses the relative transformation to model these relationships and then proposes a new feature augmentation method. These relationships as features are learned automatically for each sample through the neural network, instead of determined by the predefined rules. As it only uses the information of small data itself, without requiring any additional knowledge, it can be applied to any data with features. Lots of experimental results on small data validate the proposed method.
Self-actuated Stretchable Partitions for Dynamically Creating Secure Workplaces

The configuration of office environments is related to worker satisfaction and can improve work efficiency. Although open offices promote communication among co-workers, privacy issues surface as well as increased risks from such health concerns as viral infections and pandemics. On the other hand, territorial offices protect worker privacy and reduce infection risks. Unfortunately, such arrangements often hinder communication among co-workers. Although a physical office must satisfy different needs under various scenarios with limited space, the lack of flexibility in furniture designs prevents dynamic space management. In this paper, we propose a self-actuated stretchable partition whose physical height, width, and position can dynamically change to create secure workplaces (e.g., against privacy and infectious risks) without inhibiting group collaboration. To support secure workplace layouts and space reconfigurations, the partitions’ height, length, and position are adapted automatically and dynamically. First, we consider the design space of a self-actuated stretchable partition and implement a proof-of concept prototype with a height-adjustable stand, a roll-up screen, and a mobile robot. We then show some example application scenarios to discuss potential future actuated-territorial offices.
Periodic Orbits of the Logistic Map in Single and Double Precision Implementations

In finite precision implementations of chaotic maps all trajectories are eventually periodic. The goal of this brief is to develop methods for systematic study of effects of finite precision computations on dynamical behaviors of discrete maps and to carry out a study of the logistic map in this context. In particular, we are interested in finding all cycles when the logistic map is implemented in single and double precision and studying properties of these cycles including the size of the basin of attraction, and the maximum and average convergence times.
Complete formalization of taxa with their names, contents and descriptions improves taxonomic databases and access to the taxonomic knowledge they support

As concepts, taxa should be addressed through their name, their description (definition by intension) or their content (definition by extension). Each part of this taxonomic triplet is necessary but none alone sufficient to fully formalize a taxon without ambiguity. However, in practice, taxa are most often only addressed through their name and their extension, circumscribing them by their components (child-taxa) that are added or excluded following the progress of knowledge and discoveries. Here we show that without modifying its content, moving a taxon in the classification affects fundamentally its definition by intension, while the taxon remains considered the same. During taxonomic revisions, a taxonomic concept is not only extended or restricted according to its components, but it can be definitively modified when its contextualization in a classification changes. Because of the recursive property of the classification, defining a taxon by intension is equivalent to addressing its classification, and the taxon is necessarily contextualized. Accordingly, a taxon is therefore only fully defined as a triplet, documenting at the same time its name, its content and its classification, each issue varying independently from each other. If one wants to correctly report or aggregate taxonomic knowledge in taxonomic databases, managing these three issues independently is therefore necessary to assure the relational integrity in the database. Particularly, all aspects managing the taxon contextualization (e.g. its classification) and its eventual changes should be included in taxonomic databases and some recommendations are provided. As discussed, consequences in the taxonomic treatment of the related data that follows the taxa (not the name!) remain underestimated and still need to be better understood and formalized. As complex human concepts, taxa modelization for the digital world remains a real challenge and shows that taxonomy is a much more complex science than usually expected.
Constructing Embodied Algebra by Sketching

Mathematical models and expressions traditionally evolved as symbolic representations, with cognitively arbitrary rules of symbol manipulation. The embodied mathematics philosophy posits that abstract math concepts are layers of metaphors grounded in our intuitive arithmetic capabilities, such as categorizing objects and part-whole analysis. We introduce a design framework that facilitates the construction and exploration of embodied representations for algebraic expressions, using interactions inspired by innate arithmetic capabilities. We instantiated our design in a sketch interface that enables construction of visually interpretable compositions that are directly mappable to algebraic expressions and explorable through a ladder of abstraction [47]. The emphasis is on bottom-up construction, with the user sketching pictures while the system generates corresponding algebra. We present diverse examples created by our prototype system. A coverage of the US Common Core curriculum and playtesting studies with children point to the future direction and potential for a sketch-based design paradigm for mathematics.
Scalable object detection pipeline for traffic cameras: Application to Tfl JamCams

With CCTV systems being installed in the transport infrastructures of many cities, there is an abundance of data to be extracted from the footage. This paper explores the application of the YOLOv3 object detection algorithm, trained on the COCO dataset, to the Transport for London’s (TfL) JamCam feed. The result, open-sourced and publicly available, is a series of easy to deploy Docker pipelines to create, store and serve (through a REST API) data on identified objects on that feed. The pipelines can be deployed to any Linux machine with an NVIDIA GPU to support accelerated computation. We studied how different confidence thresholds affect detections of relevant objects (cars, trucks and pedestrians) in London JamCam scenes. By running the system continuously for three weeks, we built a dataset of more than 2200 detection datapoints for each camera (~6 datapoints an hour). We further visualized the detections on an animated geospatial map, showcasing their effectiveness in identifying traffic patterns typical of an urban city like London, portraying the variation on different object population levels throughout the day.
Vertical Federated Learning for Higher-Order Factorization Machines

In the real world, multiple parties sometimes have different data of common instances, e.g., a customer of a supermarket can be a patient of a hospital. In other words, datasets are sometimes vertically partitioned into multiple parties. In such a situation, it is natural for those parties to collaborate to obtain more accurate prediction models; however, sharing their raw data should be prohibitive from the point of view of privacy-preservation. Federated learning has recently attracted the attention of machine learning researchers as a framework for efficiently collaborative learning of predictive models among multiple parties with privacy-preservation. In this paper, we propose a lossless vertical federated learning (VFL) method for higher-order factorization machines (HOFMs). HOFMs take into feature combinations efficiently and effectively and have succeeded in many tasks, especially recommender systems, link predictions, and natural language processing. Although it is intuitively difficult to evaluate and learn HOFMs without sharing raw feature vectors, our generalized recursion of ANOVA kernels enables us to do it. We also propose a more efficient and robust VFL method for HOFMs based on anonymization by clustering. Experimental results on three real-world datasets show the effectiveness of the proposed method .
Comparing Virtual Constraints and a Physical Stylus for Planar Writing and Drawing in Virtual Reality

Air-drawing, or drawing without the use of a physical surface, is the dominant interaction metaphor for drawing or writing in Virtual Reality (VR). However, we typically use devices that are restricted to two-dimensional planes:mice, pen-and-paper, or the digital equivalent pen-and-tablet devices. We present results from a user study examining differences in performance, user-preference, and handwriting between two implementations of air drawing and passive haptic surface drawing.
eyemR-Vis: A Mixed Reality System to Visualise Bi-Directional Gaze Behavioural Cues Between Remote Collaborators

This demonstration shows eyemR-Vis, a 360 panoramic Mixed Reality collaboration system that translates gaze behavioural cues to bi-directional visualisations between a local host (AR) and a remote collaborator (VR). The system is designed to share dynamic gaze behavioural cues as bi-directional spatial virtual visualisations between a local host and a remote collaborator. This enables richer communication of gaze through four visualisation techniques: browse, focus, mutual-gaze, and fixated circle-map. Additionally, our system supports simple bi-directional avatar interaction as well as panoramic video zoom. This makes interaction in the normally constrained remote task space more flexible and relatively natural. By showing visual communication cues that are physically inaccessible in the remote task space through reallocating and visualising the existing ones, our system aims to provide a more engaging and effective remote collaboration experience.
Understanding the Design Space for Animated Narratives Applied to Illustrations

Animated illustrations are a genre of graphic design that communicate a specific contextualized message using dynamic visuals. While animated illustrations has been gaining popularity across different applications, exploring them through the storytelling lens has received limited attention. In this work, we introduce a design space for animated narratives applied to illustrations. The design space combines a dimension for object types of animation techniques with one for narrative intents served by such animation techniques. We derived our design space from the analysis of 121 high-quality animated illustrations collected from online sources. To evaluate the effectiveness of our design space, we ran a workshop with 18 participants. The results of our workshop indicated that the design space can be used as a tool that supports ideation and increases creativity for designing expressive animated illustrations.
TSGX: Defeating SGX Side Channel Attack with Support of TPM

Intel SGX provides a new method for protecting private data in computer. However, the existence of SGX side channel attacks makes it possible for attackers to steal software internal data. In SGX side channel attacks, it is often necessary to restart the software repeatedly to observe the rule of software. So we give a new SGX defense method, which we call it as TSGX. TSGX combines TPM and SGX to build a reliable software restart counter, thereby preventing the software from being frequently started by SGX side channel attackers. The discussion showed that TSGX is able to effectively limit the number of software restart, making SGX side channel attacks more difficult to steal private data.
Sensor Data Fusion of Intelligent Autonomous Mover for Object Detection and Collision Avoidance in Environments with Surrounding Crowds

In this paper, a sensor data scheme, which combines various types of sensing data for an autonomous mover to achieve object detection & classification, collision avoidance, is presented. The sensing data include distance and DoA information from an ultrasound radar array, point cloud data from a 1-ray LiDAR, and AI-powered image based sensing results from a webcam. Useful environment messages such as the locations (distances and directions), the moving speeds, and the facial directions of the surrounding pedestrians can be extracted after fusion. Since image based sensing does not provide depth information, the fusion tries to find its mapping to the 2-D data from LiDAR and ultrasound radar array. The object distance is used as the major fusion index and data calibration across different sensing devices are performed. A danger model is associated with each detected pedestrian based on the fusion results. This facilitates social aware navigations in indoor people rich environments such as supermarkets. Experimental results are presented in this paper and comparisons with previous work for similar autonomous mover applications are also provided.