Reviewerly/exHarmony huggingface dataset:
Card Mapper

We explore how usage data captured from ideation cards can enable reflection on design. We deployed a deck of ideation cards on a Masters level module over two years, developing the means to capture the students' designs into a digital repository. We created two visualisations to reveal the relative co-occurrences of the cards as concept space and the relative proximity of designs (through cards used in common) as design space. We used these to elicit reflections from the perspectives of students, teachers and card designers. Our findings inspire ideas for extending the data-driven use of ideation cards throughout the design process; informing the redesign of cards, the rules for using them and their live connection to supporting materials and enabling stakeholders to reflect and recognise challenges and opportunities. We also identified the need, and potential ways, to capture a richer design rationale, including annotations, discarded cards and varying card interpretations.
Improving Human-Machine Interaction for a Powered Wheelchair Driver by Using Variable-Switches and Sensors that Reduce Wheelchair-Veer

The integration of proportional switches for human-computer interaction and sensors with veer correction systems are presented. The transducers and sensors improve control, assist wheelchair drivers and reduced wheelchair veer, especially on slopes. The systems also reduce effort. The proportional switches are particularly useful for disabled people who do not have enough skill to use a joystick, or who lack sufficient hand-grasp and release ability, or who have movement disorders. The new systems were tested using laboratory test rigs. The test rigs were reused later to teach human users. A rolling road was then built to test the systems before user trials were undertaken. The angle of the wheelchair casters provided feedback and that feedback was used to reduce drift. A new electronic system matched the caster angles to the driver input. A case study is described. Results are presented, and they suggest there are advantages to using variable rather than digital or binary switches. The veer correction system can assist when a user is traversing a slope. The transducers and systems have been tested at Chailey heritage and are proved to be useful in assisting powered-wheelchair users. The proportional switches isolate the gross motor functions and filter out uncontrolled movement. The sensor system helps users to steer on uneven or sloping ground. The transducers also provide more control during turning and can reduce the turn radius as well as can lower frustration and conserving energy.
PedJointNet: Joint Head-Shoulder and Full Body Deep Network for Pedestrian Detection

Pedestrian detection when occlusions exist represents a great challenge in real-world applications, including urban autonomous driving and surveillance systems.However, the head-shoulder feature of pedestrians, which is more stable and less likely to be occluded than other areas of the body, can be used as a complement to full body prediction to boost pedestrian detection accuracy.In this paper, we investigate the unique features of the head-shoulder and full body features belonging to pedestrians.Then, instead of using a popular general object detection framework like R-CNN series, SSD, or YOLO, we propose a novel pedestrian detection network, called PedJointNet, that simultaneously regresses two bounding boxes to localize the head-shoulder and full body regions based on a feasible object detection backbone.Moreover, unlike the traditional strategy of keeping the weights fixed for each attribute, we design an inbuilt mechanism to dynamically and adaptively adjust the relationships of the head-shoulder and full body predictions for more accurate pedestrian localization.We validate the effectiveness of the proposed method using the CUHK-SYSU, TownCentre, and CityPersons datasets.Overall, our two-pronged prediction approach achieves excellent performance in detecting both non-occluded and occluded pedestrians, especially under circumstances involving occlusion, as compared to other state-of-the-art methods.
SmPFT: Social media based profile fusion technique for data enrichment

People use different social networking platforms for distinct purposes. The information across each micro-blogging site is often partial. A better profile of an individual can be built, if we amalgamate the complementary information from various sites. This data enrich profile can be useful in a number of online services i.e. marketing of any product across sites, friend recommendation, etc. To integrate profile information, it is essential to identify individuals in distinct social networking platforms. This study aims to identify identical users across different social media platforms. Existing works on user profile matching frameworks are restricted to certain social networks as some of the previously available streaming APIs are not available now. In this work, there are no such dependencies over the streaming APIs as it is based on the uniqueness of usernames, which are identical among various social networking sites. We also efficiently exploit the information redundancies, due to individual similar behavioral patterns which can be used during mapping. We have tested our system over 500 users in the real-time scenario, considering only those profiles which generate their content predominately in the English language. The total dataset comprises of over 1.1 Million tweets and 0.63 Million URLs, in which 35.6% URLs contained the geotagged information. Our model is able to identify 6.3% more identical users than the traditional approaches. There are several application areas such as friends recommendation, future place prediction, leaders identification, and information diffusion across social media sites that can benefit from the outcoming of this work.
Deep Player Behavior Models

Finding and maintaining the right level of challenge with respect to the individual abilities of players has long been in the focus of game user research (GUR) and game development (GD). The right difficulty balance is usually considered a prerequisite for motivation and a good player experience. Dynamic difficulty adjustment (DDA) aims to tailor difficulty balance to individual players, but most deployments are limited to heuristically adjusting a small number of high-level difficulty parameters and require manual tuning over iterative development steps. Informing both GUR and GD, we compare an approach based on deep player behavior models which are trained automatically to match a given player and can encode complex behaviors to more traditional strategies for determining non-player character actions. Our findings indicate that deep learning has great potential in DDA.
BILU-NEMH: A BILU neural-encoded mention hypergraph for mention extraction

The natural language processing (NLP) denotes a technique used to process data such as text and speech. Some of the fundamental research in NLP includes the named entity recognition, which recognizes the named entities (i.e., persons and companies) from texts, the semantic parsing, which converts a natural language utterance to a logical form, and the co-reference resolution, which extracts the nouns (including pronouns and noun phrases) pointing to the same reference body. In this paper, we focus on the mention extraction and classification, proposing a neural-encoded mention-hypergraph model named the BILU-NEMH to extract the mention entities from a content. The proposed BILU-NEMH model combines a mention hypergraph model with the encoding schema and neural network. The proposed model can effectively capture the overlapping mention entities of an unbounded length. The proposed model was verified by the experiments, and the obtained experimental results showed that the proposed model achieved better performance and greater effectiveness than the existing related models on most standard datasets.
Lightweight True Random Bit Generators in PLDs: Figures of Merit and Performance Comparison

We investigate and compare three low complexity circuit topologies to be implemented in programmable logic devices, for the design of True Random Bit Generators for Lightweight Cryptography. The architectures, based on the general idea of Digital Nonlinear Oscillators (DNOs), have been compared on the basis of measurement campaigns, carried out referring to figures of merit specifically introduced to assess the quality and reliability of the oscillators under test.
Privacy-preserving delegable authentication in the internet of things

The expanding Internet of Things (IoT) technology offers the ease of communication with and access to multiple services for companies and individuals. However, because of the limited trustworthiness set on smart devices, as well as the ever-increasing amount of them, challenges for security and privacy protection have been growing. In this paper, we propose a new authentication solution that enables a smart device to securely connect to services, based on attribute-based credentials. Our solution allows IoT devices to authenticate to various services in an efficient way, without compromising their privacy. Indeed, during the authentication of an IoT device to a particular service, a new credential is generated such that only relevant attributes are disclosed to the actual service. Moreover, this operation is delegated to a gateway in order to relieve the workload at devices' side.
Doing Things with Research through Design

This workshop provides a venue within CHI for research through design (RtD) practitioners to present their work and discuss how, with whom, and why it is used. Building on the success of prior RtD and design research workshops at CHI, this workshop will focus on how RtD artifacts are used, with the goal of connecting diverse works with broader methodologies in HCI and Design.
Enhancing continuous variable quantum key distribution with a heralded hybrid linear amplifier

We propose a method to improve transmission distance of continuous-variable quantum key distribution via a heralded hybrid linear amplifier (HLA), involving an ideal deterministic linear amplifier and a probabilistic noiseless linear amplifier. The phase-insensitive amplification of quantum light states may experience a degradation of signal-to-noise ratio (SNR) of 3dB which can be surmounted by the probabilistic amplification process. The new hybrid amplifier allows one to tune between the regimes of high gain or high noise reduction and control the trade-off between the amplification coefficient and a finite heralding probability flexibly. Our equipment could clone unknown coherent states with fidelity surpassing the no-cloning limit by abandoning determinism and using probabilistic methods. The setup has a better compatibility with continuous variable protocols as it only uses Gaussian elements and a post-selection algorithm. The hybrid linear amplifier enhances the SNR and hence the transmission distance of the CVQKD protocol is extended. Both the asymptotic secret key rate and the secret key rate in a finite-size scenario are analyzed in this paper.
The logics of technology decentralization – the case of distributed ledger technologies

Decentralization is heralded as the most important technological design aspect of distributed ledger technologies (DLTs).In this chapter we'll analyze the concept of decentralization, with the goal to understand the social, legal, economic forces that produce more or less decentralized techno-social systems.We first give an overview of decentralization as a political ideology, and as an ideal and natural end-point in the development of digital technologies.We then move beyond this discourse and treat decentralization, its extent, its mode, the systems which it can refer to as the products of particular economic, political, social dynamics around and within these techno-social systems.We then point at the concrete forces that shape the actual degree of (de)centralization.Through this, we show that the extent to which a techno-social system is (de)centralized at any given moment should not be measured by its distance from an ideological ideal of total decentralization, but should be seen as the sum of all the social, economic, political, legal forces that impact a techno-social system.
Parasite worm egg automatic detection in microscopy stool image based on Faster R-CNN

This paper proposed a method based on Faster R-CNN for detection of human parasite eggs in stool images. The shapes, and patterns of parasite worm in egg micro images are very diversity, therefore proposing and choosing the good model to detect them is necessary to help the doctors discover the potential disease by worm in human. To be sure for the proposal, we executed many various experiments, and retrieved dataset from two independent resources. The training set is retrieved in standard biology image library, meanwhile the evaluation image set is retrieved from real patients. The precision, recall and other values evaluated in the experiments represented the effectiveness of the method. The various experiments with the outstanding results proved the correctness of the proposal.
Weighted Secret Image Sharing for a $(k,n)$ Threshold Based on the Chinese Remainder Theorem

In general, in a secret image sharing (SIS) scheme with a (k, n) threshold, the participants have equal weights, and their shares have the same average light transmission.No share can reveal any information about the secret.Only when the number of participants involved in restoration is greater than or equal to k can the secret image be revealed.However, on some occasions, the participants' weights need to be set differently, and their shares have different effects on the restoration of the secret image.Therefore, there are many studies of weighted SIS, including schemes based on visual secret sharing using random grids (VSSRG).However, they can only share binary images, not grayscale images.Therefore, we propose a scheme based on the Chinese remainder theorem (CRT) for sharing grayscale images.The shares are generated with different weights.When the threshold is reached and shares with higher weights are involved in restoration, the quality of the restored image is higher.As the number of participating shares increases, the quality of the recovered secret image increases, and if all the shares are involved, lossless restoration can be achieved.INDEX TERMS (k, n) threshold, secret image sharing, the Chinese remainder theorem, weighted secret image sharing.
Align My Curriculum: A Framework to Bridge the Gap between Acquired University Curriculum and Required Market Skills

With the advancement of technology, academics and curriculum developers are always under pressure to provide students with skills that match the market’s requirements. A systematic and continuous examination of the market is needed, to stay up to date with the required skills, and then to update the curriculum to train the students with required market skills. In this article, we present a framework referred to as Align My Curriculum (AMC). The AMC framework aims to facilitate alignment between acquired university curriculum outcomes and required market skills. It can be used to classify, compare and visualize the data of a university curriculum and job vacancies in the market. The presented framework benefits academics and curriculum developers by improving the courses and therefore bridging the skills gap. Stakeholders from both academia and industry can gain insights into the predominant required and acquired skills. In addition, it may be useful for analysts, students, and job applicants. This article describes the architecture, implementation and experimental results, with visual analysis to help decision and policy-makers.
Anatomical Structure Segmentation in Ultrasound Volumes Using Cross Frame Belief Propagating Iterative Random Walks

Ultrasound (US) is widely used as a low-cost alternative to computed tomography or magnetic resonance and primarily for preliminary imaging. Since speckle intensity in US images is inherently stochastic, readers are often challenged in their ability to identify the pathological regions in a volume of a large number of images. This paper introduces a generalized approach for volumetric segmentation of structures in US images and volumes. We employ an iterative random walks (IRW) solver, a random forest learning model, and a gradient vector flow (GVF) based interframe belief propagation technique for achieving cross-frame volumetric segmentation. At the start, a weak estimate of the tissue structure is obtained using estimates of parameters of a statistical mechanics model of US tissue interaction. Ensemble learning of these parameters further using a random forest is used to initialize the segmentation pipeline. IRW is used for correcting the contour in various steps of the algorithm. Subsequently, a GVF-based interframe belief propagation is applied to adjacent frames based on the initialization of contour using information in the current frame to segment the complete volume by frame-wise processing. We have experimentally evaluated our approach using two different datasets. Intravascular ultrasound (IVUS) segmentation was evaluated using 10 pullbacks acquired at 20 MHz and thyroid US segmentation is evaluated on 16 volumes acquired at [Formula: see text] MHz. Our approach obtains a Jaccard score of [Formula: see text] for IVUS segmentation and [Formula: see text] for thyroid segmentation while processing each frame in [Formula: see text] for the IVUS and in [Formula: see text] for thyroid segmentation without the need of any computing accelerators such as GPUs.
Using acceptance tests to predict files changed by programming tasks

In a collaborative development context, conflicting code changes might compromise software quality and developers productivity. To reduce conflicts, one could avoid the parallel execution of potentially conflicting tasks. Although hopeful, this strategy is challenging because it relies on the prediction of the required file changes to complete a task. As predicting such file changes is hard, we investigate its feasibility for BDD (Behaviour-Driven Development) projects, which write automated acceptance tests before implementing features. We develop a tool that, for a given task, statically analyzes Cucumber tests and infers test-based interfaces (files that could be executed by the tests), approximating files that would be changed by the task. To assess the accuracy of this approximation, we measure precision and recall of test-based interfaces of 513 tasks from 18 Rails projects on GitHub. We also compare such interfaces with randomly defined interfaces, interfaces obtained by textual similarity of test specifications with past tasks, and interfaces computed by executing tests. Our results give evidence that, in the specific context of BDD, Cucumber tests might help to predict files changed by tasks. We find that the better the test coverage, the better the predictive power. A hybrid approach for computing test-based interfaces is promising.
Hand Pointing Gestures Based Digital Menu Board Implementation Using IR-UWB Transceivers

Digital menu boards (DMB) are convenient for customers as well as sellers.In this paper, we have implemented a DMB using IR-UWB transceivers.Unlike the traditional touch-based interfaces for menu selection, in our proposed system, users can select items from the menu without touching the screen.The screen is used to display the menu, and the users point to the specific menu item to select it.Multiple radar transceivers are used to create a virtual space divided into different grid blocks in front of the digital display.Patterns in the radar data are analyzed using a multiclass support vector machine (SVM) classifier and a histogram of oriented gradient descriptor.The system is trained at two different distances from the radar sensors in order to make it robust against distance changes.The proposed hand pointing-based DMB system was verified through different experiments, with different grid sizes, to investigate accuracy dependence on grid size.The results showed high accuracy; therefore, the system can be used in real-life scenarios.
Visual Tracking via Auto-Encoder Pair Correlation Filter

Robust visual tracking is one of the most challenging problems in computer vision applications. However, the limited training data and the computational complexity have severely affected tracking performance. In this paper, we propose an auto-encoder pair model for visual tracking which is composed of source domain network and target domain network to help a more accurate localization. We adopt the dense circular samples of the object state to increase the number of training samples and prevent model overfitting. Meanwhile, a difference regularization term is also introduced into our framework to penalize the large appearance variations of the object in two domains. The alternating optimization is used to solve the optimization problems. Furthermore, our method alleviates the model update problem and improves the tracking speed by using long-term and short-term updating scheme. In addition, the target domain filter is updated by introducing the updated source domain filter to avoid the object drift. Comprehensive experiments on some challenging benchmarks demonstrate that our approach concurrently improves both tracking accuracy and speed.
Evaluating neural word embeddings created from online course reviews for sentiment analysis

Social media are providing the humus for the sharing of knowledge and experiences and the growth of community activities (e.g., debating about different topics). The analysis of the user-generated content in this area usually relies on Sentiment Analysis. Word embeddings and Deep Learning have attracted extensive attention in various sentiment detection tasks. In parallel, the literature exposed the drawbacks of traditional approaches when content belonging to specific contexts is processed with general techniques. Thus, ad-hoc solutions are needed to improve the effectiveness of such systems. In this paper, we focus on user-generated content coming from the e-learning context to demonstrate how distributional semantic approaches trained on smaller context-specific textual resources are more effective with respect to approaches trained on bigger general-purpose ones. To this end, we build context-trained embeddings from online course reviews using state-of-the-art generators. Then, those embeddings are integrated in a deep neural network we designed to solve a polarity detection task on reviews in the e-learning context, modeled as a regression. By applying our approach on embeddings trained using background corpora from different contexts, we show that the performance is better when the background context is aligned with the regression context.
Application of Evolutionary Deep Neural Netwok to Photovoltaic Generation Forecasting

This paper proposes a new method for photovoltaic generation forecasting in power systems. The proposed method is based on Restricted Boltzmann Machine (RBM) of Feature Detector. In recent years, the use of photovoltaic generation is widely spread due to the suppression of carbon dioxide emissions, but they have a drawback that the generation output is affected by weather conditions. As a result, it is necessary to predict photovoltaic generation output and maintain the balance between generation and loads efficiently. This paper presents a Deep-Neural Network-based method that makes use of RBM of Deep Neural Network, Multi-layer perceptron (MLP) and Predator-Prey Brain Storm Optimization of evolutionary computation to evaluate better predicted values. The proposed method is tested for real data of photovoltaic generation unit in Japan.