Reviewerly/exHarmony huggingface dataset:
Towards reciprocity in Participatory Design processes

Albeit achieving reciprocity is considered essential in much Participatory Design (PD) research, there remains a gap in the current literature on how PD practitioners working in community settings can obtain this. Through a literature review, we identified four characteristics of reciprocity that were used to analyse six different projects that all took place in the same neighbourhood and with the same group of designers. Starting from projects that failed in achieving reciprocal relationships, we distilled four defining and interconnected elements or handles for enabling reciprocity in PD processes. To attain reciprocity in a PD project, the project should be embedded in the community setting. This also implies that the involved designers need to have the willingness to be (or become) engaged, invest time and self-disclose personal information to build relationships within the community in order to establish a symbiotic agreement.
Incorporating part-whole hierarchies into fully convolutional network for scene parsing

In this paper, a new approach to scene parsing is proposed which integrates part-whole hierarchies relationship in the last feature map to assign a semantic class label to each pixel. Recently, deep learning-based approaches have had a great impact on scene parsing. However, these methods could not preserve the spatial information about the high-level (or mid-level) features. Hence, Hinton, one of the fathers of deep learning, introduced the capsule concept to encode pose information such as orientation. All of the capsules which have a similar pose matrix value are grouped to form a parent capsule. However, their work has two challenges: 1) the extensive time required to perform dynamic routing agreement to obtain the routing coefficient and 2) the variation of the appearance and the spatial hierarchies between part capsules and their corresponding parent are not encoded. In this study, to consider these challenges, the general Hough transform (GHT) and tensor normal distribution are utilized to propose a novel capsule concept. In this case, each capsule has k offset vectors for each semantic class. The offset vectors are oriented from the capsule to the k other capsules which have an effective role in assigning that capsule to a specific semantic class. The problem formulation is proposed such that evaluating the approach on large datasets is feasable. Also, a new score function is designed to accumulate the vote's strengths for capsule class estimation. To do so, we use tensor normal distribution in which the covariance matrix is defined as the Kronecker product of the capsule feature covariance and the between-capsule covariance. The proposed approach, for the first time, encodes the relations between part capsules to vote to a whole capsule through the between-capsule covariance matrix. To evaluate our proposed approach, it is applied to SiftFlow, NYUD-v2 and PASCAL VOC 2012 datasets. The results show that our approach achieves superior performance.
Silicon Photonic Microring Resonators: Design Optimization Under Fabrication Non-Uniformity

Microring resonators (MRRs) are very often considered as the primary building block in silicon photonic integrated circuits (PICs). Despite many advantages, MRRs are considerably sensitive to fabrication non-uniformity (a.k.a. fabrication process variations), necessitating the use of power-hungry compensation methods (e.g., thermal tuning) to guarantee their reliable operation. Moreover, the design space of MRRs is complicated and includes several highly correlated design parameters, preventing designers from easily exploring and optimizing the design of MRRs against fabrication process variations (FPVs). In this paper, for the first time, we present a comprehensive design space exploration and optimization of MRRs against FPVs. In particular, we indicate how physical design parameters in MRRs can be optimized during design time to enhance their tolerance to FPVs while also improving the insertion loss and quality factor in such devices. Fabrication results obtained by measuring multiple fabricated MRRs designed using our design optimization solution demonstrate a significant 70% improvement on average in MRRs tolerance to different FPVs. Such improvement indicates the efficiency of our novel design optimization solution in reducing the tuning power required for reliable operation of MRRs.
3D microcalcification detection using a color Doppler twinkling artifact with optimized transmit conditions: Preliminary results

Purpose Mammography is the only method that has been proven to detect breast microcalcifications (MCs), but the sensitivity of mammography varies according to breast density. This paper proposes an ultrasound (US) color Doppler twinkling artifact (CDTA) method with optimized transmit conditions to identify breast MCs without ionizing radiation. Methods The transmit conditions for US color Doppler imaging (CDI) were optimized to enhance the sensitivity of the twinkling artifact (TA) that arises from random scattering on rough surfaces of breast MCs. To validate the proposed breast MC detection method, a chicken breast phantom with MC particles (groups of particles &lt;400 and &lt;240 ) was fabricated and scanned by a digital mammography system and an US research platform by an L11‐5v linear array probe with a three‐dimensional (3D) motion tracking system. Results From the phantom experiment, the proposed 3D CDTA imaging method with optimized transmit conditions (i.e., a center frequency of 5.0 MHz, an f‐number of 1.3, and a peak negative pressure of 1.83 MPa) successfully detected all 16 MC particles, comparable to detection with mammography. For a human breast surgical specimen in the ex vivo study, all 10 MC clusters, marked by a radiologist on the mammogram, were identified with the proposed 3D CDTA imaging method. Conclusions In the phantom and ex vivo breast specimen studies, the proposed 3D CDTA imaging method successfully detected MCs, and the spatial localization was highly correlated with the mammogram results. These results indicate that the proposed 3D CDTA imaging method has great potential for the detection of MCs without ionizing radiation.
Conjecture mapping to support vocationally educated adult learners in open-ended tasks

Background This case reports on a teacher education course that aimed to support adult learners with a vocational education background to accomplish open-ended tasks. Conjecture mapping was used to identify the most salient design features, and to test if, how, and why these course features supported learners. Methods: Inspired by ethnographic approaches, sustained engagement and multiple data sources were used to explain the effects of the course design on participants’ behavior and perceptions: student and teacher interviews, observations, and artifacts. Findings: The results reveal that almost all of the proposed design features stimulated the participants toward the intended enactment processes, which in turn yielded the intended learning outcomes. For instance, worked examples (i.e., design feature) not only engendered the production of artifacts that meet high standards (i.e., enactment process) because they clarify the task requirements, but also fostered a safe structure (i.e., enactment process) by providing an overall picture of the task. Contribution: The conjecture map resulting from this study provides a theoretical frame to describe, explain, and predict how specific course design features support vocationally educated adult learners (VEAL) in open-ended tasks, and assists those who aim to implement open-ended tasks in similar contexts.
Response Time Analysis for Explainability of Visual Processing in CNNs

Explainable artificial intelligence (XAI) methods rely on access to model architecture and parameters that is not always feasible for most users, practitioners, and regulators. Inspired by cognitive psychology, we present a case for response times (RTs) as a technique for XAI. RTs are observable without access to the model. Moreover, dynamic inference models performing conditional computation generate variable RTs for visual learning tasks depending on hierarchical representations. We show that MSDNet, a conditional computation model with early-exit architecture, exhibits slower RT for images with more complex features in the ObjectNet test set, as well as the human phenomenon of scene grammar, where object recognition depends on intrascene object-object relationships. These results cast light on MSDNet's feature space without opening the black box and illustrate the promise of RT methods for XAI.
Learning pareto optimal solution of a multi-attribute bilateral negotiation using deep reinforcement

This paper aims to design an intelligent buyer to learn how to decide in an incomplete information multi-attribute bilateral simultaneous negotiation. The buyer does not know the negotiation strategy of the seller and only have access to the historical data of the previous negotiations. Using the historical data and clustering method, the type of seller is identified online during the negotiation. Then, the deep reinforcement learning method is utilized to support the buyer to learn its optimal decision. In the complete information case, we prove that the negotiation admits a unique Nash bargaining solution with possibly asymmetric negotiation powers. In comprehensive simulation studies, the efficiency of the proposed learning agent is evaluated in different scenarios and we show that the learning negotiation with incomplete information is converged to a Pareto optimal solution. Then, using the concept of the Nash bargaining solution, the negotiation power of the buyer is assessed in negotiation.
Illuminating energy

Interventions aiming to shift household energy consumption patterns commonly involve the provision of energy feedback. This feedback typically constitutes detailed numerical data regarding energy generation, consumption and price, and is communicated via energy monitors, apps, and other smart devices. Yet for a number of reasons both the concept of energy feedback and its implementation are rather problematic. Most significantly, interventions commonly depend on the ability of laypeople to interpret often complex and unfamiliar numerical information, as well as use, and regularly engage with, the various devices which communicate the feedback data. Moreover, by emphasising the perceived importance of data expressed in units like kilowatts and kilowatt hours, conventional feedback interventions inadvertently serve to displace the intuitive ways in which householders already engage with, and understand, energy. While the impetus to communicate energy feedback remains, there is nonetheless an evident need to do so in a more accessible manner. Drawing on preliminary findings from an ethnographic research project, this paper considers the opportunities for smart lights to visually communicate energy feedback in an intelligible and meaningful way that builds on, rather than overlooks, laypeople's existing capacities.
Participation otherwise

In response to Arturo Escobar's invitation to design new worlds, it is fundamental to map forms of participation often silenced by hegemonic practices of participatory design (PD). In this full day interactive workshop, we hope to map practices of Participatory Design Otherwise, and reflect about what does it means to participate by/from the Global South, especially Latin America. Our intention is to make visible the nature and territoriality of these ways of doing. Understanding that this event will be held for the first time in Latin America, we seek a theoretical alignment with Latin American thinkers, running this workshop based on the techniques and methods of Theatre of the Oppressed, developed by Augusto Boal. We will also use references inspired by black and indigenous ancestry, as the “Pedagogia das Encruzilhadas (the Pedagogy of the Crossroads) and the “Pedagogia do Parente” (Relative's Pedagogy). As indigenous leader Ailton Krenak says, we need to “postponing the end of the world” by nurturing our subjectivities and expand our horizons to other creativities and narratives. That's what we expect to see in this rich encounter.
Secure-channel free searchable encryption with multiple keywords: A generic construction, an instantiation, and its implementation

In public key encryption with keyword search (PEKS), a secure channel is required in order to send trapdoors to the server, whereas in secure-channel free PEKS (SCF-PEKS), no such secure channel is required. In this paper, we propose a generic construction of SCF-PEKS with multiple keywords (SCF-MPEKS) from hidden vector encryption, tag-based encryption, and a one-time signature. Our generic construction provides adaptive security, where the test queries are allowed in the security model, and does not require random oracles. In addition to providing an instantiation of our generic construction, which is the first adaptive secure SCF-MPEKS scheme in the standard model, we implement the SCF-MPEKS scheme by using the PBC library. Moreover, we extend the Boneh-Waters range search technique, and show that the running time of our encryption algorithm is approximately twice as fast as that of the Boneh-Waters encryption algorithm.
NROWAN-DQN: A stable noisy network with noise reduction and online weight adjustment for exploration

Deep reinforcement learning has been applied more and more widely nowadays, especially in various complex control tasks. Noise is vital to reinforcement learning exploration. The appropriate noise not only avoids local optimum solutions due to the lack of exploration, but also prevents unstable learning caused by excessive perturbations. Noisy networks can bring more efficient exploration to reinforcement learning. They enable agents to take actions more randomly at the start of training and tend to make the agents produce stable outputs in the subsequent learning. However, this tendency is not always enough to find a stable policy for an agent, which decreases the efficiency and stability. To address this issue, this paper proposes NROWAN-DQN, i.e., Noise Reduction and Online Weight Adjustment NoisyNet-DQN. Firstly, we develop a novel noise regularization method for NoisyNet-DQN to reduce the noise of output layers. Secondly, we design an online weight adjustment strategy for noise reduction. Finally, we evaluate this algorithm in four standard domains and analyze properties of hyper-parameters. Our results show that NROWAN-DQN outperforms prior algorithms in all these domains. In addition, NROWAN-DQN also shows better stability. The variance of the NROWAN-DQN score is significantly reduced, especially in some action-sensitive environments. This means that in some environments where high stability is required, NROWAN-DQN will be more appropriate than NoisyNet-DQN.
Very-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network

To address the serious challenges to traditional power systems caused by high variability of solar production, especially the ramp events, this paper is aimed at predicting solar radiation accurately at a very-short-term scale, i.e. several minutes ahead. Quite different from traditional methods, our designed system conducts time series prediction based upon meteorological data and sky images' features with the Long Short-Term Memory (LSTM) network to learn the long-term dependency. Experiments have been conducted based on sky images' feature extracted by Convolutional Neural Network, meteorological data and solar geometric data with a large number of features collected over a seven-summer period. Experimental results show that the proposed forecasting system outperforms other methods in the literature, such as solar forecasting based upon optical flow tracking, Feedforward Neural Network (FNN) and Support Vector Regression (SVR), in terms of accuracy and robustness.
Reciprocal Research

Researchers in various fields have been discussing the ethics of field research, particularly their responsibility to provide concrete benefits to participants. For example, when designing technology for their participants the discussion has centered around how and whether the technology benefits participants. We argue, that design projects can be reoriented towards benefiting participants from the outset, by slightly changing the process, activities, and initial object of design. Focusing instead on how to immediately impact the goals of the participants, designers can then progressively introduce technology to scale their impact. In this paper we present an ongoing instantiation of this process in rural West Virginia, where we have been teaching computer classes at a local library. We have found that our participants and partners are more appreciative of our efforts, and that the data we are gathering is just as, if not more, rich than previous design methodologies that we have used.
Analogy and metareasoning: Cognitive strategies for robot learning

Robots share a conundrum central to all intelligence. Like humans, robots not only must address novel situations but also must start from what they already know: how, then, can any robot deal with novelty? In this chapter, we examine two cognitive strategies for addressing novelty: analogy and metareasoning. Analogy addresses new problems in a manner similar to a familiar problem; the familiar problem is part of the context of addressing the new problem. We show how a cognitive robot may use analogy to learn from a small number of initial demonstrations. However, analogical reasoning and reasoning more generally do not necessarily guarantee success in an open, dynamic world. This brings metareasoning into play. We show how a cognitive robot may use metareasoning to recover and learn from failure; here, failure forms part of the context for metareasoning. In the other direction, we discuss how these experiments in robot learning inform the development of cognitive theories of analogy and metareasoning.
Modality and Depth in Touchless Smartphone Augmented Reality Interactions

Augmented reality (AR) on smartphone devices allows people to interact with virtually placed objects anchored in the real world through the device's viewport. Typically, smartphone AR interactions work with the device's 2D touchscreen disconnected from the modality and depth of the virtual objects. In this paper, we studied 15 participants' preferences, performances, and cognitive loads on a set of common tasks performed on smartphones at two interaction depths (close-range and distant) with two touchless modalities (hand tracking and screen dwell). We find that distant AR interactions, strongly preferred by the participants, were significantly faster and took less cognitive effort. We observed that within both interaction depths, modalities performed equally. When designing touchless modalities on smartphones, we suggest using distant interactions when overall performance is the top priority, otherwise, using hand tracking or screen dwell as back-ups for each other can be equally effective.
Using Machine Learning Methods to Develop a Short Tree-Based Adaptive Classification Test: Case Study With a High-Dimensional Item Pool and Imbalanced Data

This study explores advanced techniques in machine learning to develop a short tree-based adaptive classification test based on an existing lengthy instrument. A case study was carried out for an assessment of risk for juvenile delinquency. Two unique facts of this case are (a) the items in the original instrument measure a large number of distinctive constructs; (b) the target outcomes are of low prevalence, which renders imbalanced training data. Due to the high dimensionality of the items, traditional item response theory (IRT)-based adaptive testing approaches may not work well, whereas decision trees, which are developed in the machine learning discipline, present as a promising alternative solution for adaptive tests. A cross-validation study was carried out to compare eight tree-based adaptive test constructions with five benchmark methods using data from a sample of 3,975 subjects. The findings reveal that the best-performing tree-based adaptive tests yielded better classification accuracy than the benchmark method IRT scoring with optimal cutpoints, and yielded comparable or better classification accuracy than the best benchmark method, random forest with balanced sampling. The competitive classification accuracy of the tree-based adaptive tests also come with an over 30-fold reduction in the length of the instrument, only administering between 3 to 6 items to any individual. This study suggests that tree-based adaptive tests have an enormous potential when used to shorten instruments that measure a large variety of constructs.
An Image Denoising Method Based on Deep Residual GAN

Abstract As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects.
Using a Genetic Algorithm to optimize a stacking ensemble in data streaming scenarios

The requirements of Machine Learning applications are changing rapidly. Machine Learning models need to deal with increasing volumes of data, and need to do so quicker as responses are expected more than ever in real-time. Plus, sources of data are becoming more and more dynamic, with patterns that change more frequently. This calls for new approaches and algorithms, that are able to efficiently deal with these challenges. In this paper we propose the use of a Genetic Algorithm to Optimize a Stacking Ensemble specifically developed for streaming scenarios. A pool of solutions is maintained in which each solution represents a distribution of weights in the ensemble. The Genetic Algorithm continuously optimizes these weights to minimize the cost function. Moreover, new models are added at regular intervals, trained on more recent data. These models eventually replace older and less accurate ones, making the ensemble adapt continuously do changes in the distribution of the data.
A Novel Content Detection Approach for Handwritten English letters

The rise of Artificial Intelligence technology along with machine and deep learning are opening up almost limitless possibilities. There is also an element of fear towards this exponential growth. But it's the humanization of our machines and devices that is seldom discussed or reported. But we deem it acceptable to nominate an unfortunate volunteer to retype notes scribbled on tiny pieces of paper so they can send to other attendees. Once again we need to ask ourselves if this is the most efficient method of managing our workload in this digital age. In order to solve this problem, we are proposing a solution to solve this. Instead of predicting by word, here we will be segregating the cursive English letter to individual characters and predicting it via trained Convolutional Neural Network (CNN) model. By using this methodology, increase in rate of prediction of the individual characters is been increased and it could be implemented to digitalize the forms in companies which will be a greater level of automation.