Reviewerly/exHarmony huggingface dataset:
Enhancing 21st-century competencies via virtual reality digital content creation

This study aimed to foster development of the 21st-century five core competencies, including Creativity and innovation, Critical thinking and problem solving, Communication, Collaboration, and Computer-information literacy using Student-Centered Active Learning in an instructional design in which student groups created their own virtual reality content. Fifty humanities students participated. Mixed-methods data collection included a questionnaire, VR artifacts grades, student reflective journals, and focus group interviews. The most important findings were that (1) the instructional design of the study led to improvement in all of the five competencies of .01 significance or better (2) additional cycles of VR artifact production led to increases in empirical quality, and (3) the participants also found the hands-on VR creation projects to be distinctive and motivating. This VR content creation project provides detailed implications and guidance for instructors seeking to replicate a hands-on technology-oriented instructional design in order to strengthen the 21st-century core competencies.
Rooftop photovoltaic potential in Istanbul: Calculations based on LiDAR data, measurements and verifications

Usage of solar energy is increasing steadily especially in the rooftop installations of buildings in large cities. This study contains calculations of electrical energy produced by photovoltaic panels placed on roofs of buildings for city of Istanbul using building data and verify calculated results by a mobile measurement system. Three dimensional city model utilizes light detection and ranging data that covers an area of 5400 km2 for the whole city. The main object classes such as ground, building, vegetation were derived, buildings are vectorized and a digital elevation model are used for the generation of a second level of detail. Using the geometric data of the buildings in Istanbul as input to the developed software, the electricity production of panels is calculated on the suitable roofs of the buildings considering the observed climatic conditions and solar radiation for the city. A photovoltaic weather mobile vehicle measurement system was designed and applied for verification of developed model in eight different areas of Istanbul for stationing 50 days at each location. Results of difference between measurements and calculations of solar irradiation and electrical energy production are 2.44% and 14.70%, respectively. Batch processing was performed on 39 districts of Istanbul with 1.3 million buildings and annual electrical energy production from the roofs of all buildings is calculated to be 30.8 TWh at the point of common coupling to the utility. Total rooftop electricity production of Istanbul has a potential to meet 67% of the total electricity consumption for the year 2019.
Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning

We introduce a novel method to teach a robotic agent to interactively explore cluttered yet structured scenes, such as kitchen pantries and grocery shelves, by leveraging the physical plausibility of the scene. We propose a novel learning framework to train an effective scene exploration policy to discover hidden objects with minimal interactions. First, we define a novel scene grammar to represent structured clutter. Then we train a Graph Neural Network (GNN) based Scene Generation agent using deep reinforcement learning (deep RL), to manipulate this Scene Grammar to create a diverse set of stable scenes, each containing multiple hidden objects. Given such cluttered scenes, we then train a Scene Exploration agent, using deep RL, to uncover hidden objects by interactively rearranging the scene. We show that our learned agents hide and discover significantly more objects than the baselines. We present quantitative results that prove the generalization capabilities of our agents. We also demonstrate sim-to-real transfer by successfully deploying the learned policy on a real UR10 robot to explore real-world cluttered scenes. The supplemental video can be found at: https://www.youtube.com/watch?v=T2Jo7wwaXss.
Combining Context Awareness and Planning to Learn Behavior Trees from Demonstration

Fast changing tasks in unpredictable, collaborative environments are typical for medium-small companies, where robotised applications are increasing. Thus, robot programs should be generated in short time with small effort, and the robot able to react dynamically to the environment. To address this we propose a method that combines context awareness and planning to learn Behavior Trees (BTs), a reactive policy representation that is becoming more popular in robotics and has been used successfully in many collaborative scenarios. Context awareness allows for inferring from the demonstration the frames in which actions are executed and to capture relevant aspects of the task, while a planner is used to automatically generate the BT from the sequence of actions from the demonstration. The learned BT is shown to solve non-trivial manipulation tasks where learning the context is fundamental to achieve the goal. Moreover, we collected non-expert demonstrations to study the performances of the algorithm in industrial scenarios.
Entity Alignment of Knowledge Graph by Joint Graph Attention and Translation Representation

Entity alignment is a crucial and challenging research task in the fields of knowledge graph and natural language processing. It aims to integrate knowledge graph information from different languages or different sources and apply it to subsequent knowledge graph construction and other downstream tasks. Currently, most entity alignment solutions are based on knowledge graph embedding, which align entities by mapping entities into low dimensional spaces. But it also face problems such as fail to make full use of the neighbor nodes and relation information of the graph, and the heterogeneity between the knowledge graphs. This paper proposes a method of combining graph attention and translation models. We use graph attention mechanism to convey information about neighboring nodes, and merge relation information into the entity representation, make full use of the internal information of the knowledge graph to learn a better entity representation. Then we combine the graph attention mechanism with knowledge translation representation model to constrain the consistency between different knowledge graphs and guarantee the accuracy of embedding vectors in low-dimensional space. The results of comparative experiments with other entity alignment methods show that the performance of our method is better than others, and the alignment effect is significantly improved.
QSOD: Hybrid Policy Gradient for Deep Multi-agent Reinforcement Learning

When individuals interact with one another to accomplish specific goals, they learn from others’ experiences to achieve the tasks at hand. The same holds for learning in virtual environments, such as video games. Deep multiagent reinforcement learning shows promising results in terms of completing many challenging tasks. To demonstrate its viability, most algorithms use value decomposition for multiple agents. To guide each agent, behavior value decomposition is utilized to decompose the combined Q-value of the agents into individual agent Q-values. A different mixing method can be utilized, using a monotonicity assumption based on value decomposition algorithms such as QMIX and QVMix. However, this method selects individual agent actions through a greedy policy. The agents, which require large numbers of training trials, are not addressed. In this paper, we propose a novel hybrid policy for the action selection of an individual agent known as Q-value Selection using Optimization and DRL (QSOD). A grey wolf optimizer (GWO) is used to determine the choice of individuals’ actions. As in GWO, there is proper attention among the agents facilitated through the agents’ coordination with one another. We used the StarCraft 2 Learning Environment to compare our proposed algorithm with the state-of-the-art algorithms QMIX and QVMix. Experimental results demonstrate that our algorithm outperforms QMIX and QVMix in all scenarios and requires fewer training trials.
Deep Joint Source-Channel Coding for Multi-Task Network

Multi-task learning (MTL) is an efficient way to improve the performance of related tasks by sharing knowledge. However, most existing MTL networks run on a single end and are not suitable for collaborative intelligence (CI) scenarios. In this work, we propose an MTL network with a deep joint source-channel coding (JSCC) framework, which allows operating under CI scenarios. We first propose a feature fusion based MTL network (FFMNet) for joint object detection and semantic segmentation. Compared with other MTL networks, FFMNet gets higher performance with fewer parameters. Then FFMNet is split into two parts, which run on a mobile device and an edge server respectively. The feature generated by the mobile device is transmitted through the wireless channel to the edge server. To reduce the transmission overhead of the intermediate feature, a deep JSCC network is designed. By combining two networks together, the whole model achieves 512 compression for the intermediate feature and a performance loss within 2% on both tasks. At last, by training with noise, the FFMNet with JSCC is robust to various channel conditions and outperforms the separate source and channel coding scheme.
Towards a Conceptual Framework for Understanding the Challenges in Refugee Re-settlement

Upon arrival to a host community, refugees and asylum seekers face immense challenges to rebuild their social capital that is critical in the process of their resettlement. Developing a holistic understanding of these challenges can provide significant opportunities to inform designers and services providers working with this demography. We adopt the lens of social capital coupled with an inquiry into the heterogeneity of refugees and asylum seekers to gain a holistic understanding of various challenges that they with. We accordingly present a conceptual framework that has been iteratively built based on our four years of engagement with refugee communities. The framework highlights three important aspects: cultural backdrops, displacement-related stressors, and social resources in the host community. We offer several implications for technology design, policies, and the theory of social capital that can support members from these communities in their resettlement.
LOTR: Face Landmark Localization Using Localization Transformer

This paper presents a novel Transformer-based facial landmark localization network named Localization Transformer (LOTR). The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial information in the feature map. An LOTR model consists of three main modules: 1) a visual backbone that converts an input image into a feature map, 2) a Transformer module that improves the feature representation from the visual backbone, and 3) a landmark prediction head that directly predicts the landmark coordinates from the Transformer's representation. Given cropped-and-aligned face images, the proposed LOTR can be trained end-to-end without requiring any post-processing steps. This paper also introduces the smooth-Wing loss function, which addresses the gradient discontinuity of the Wing loss, leading to better convergence than standard loss functions such as L1, L2, and Wing loss. Experimental results on the JD landmark dataset provided by the First Grand Challenge of 106-Point Facial Landmark Localization indicate the superiority of LOTR over the existing methods on the leaderboard and two recent heatmap-based approaches. On the WFLW dataset, the proposed LOTR framework demonstrates promising results compared with several state-of-the-art methods. Additionally, we report the improvement in state-of-the-art face recognition performance when using our proposed LOTRs for face alignment.
Publicly Verifiable and Secrecy Preserving Periodic Auctions

In lit markets, all the information about bids and offers in the order book is visible to the public. With this transparency, traders can discover prices and adjust their strategies accordingly. On the other hand, submitting a bulk order by a financial institution will have a significant impact on the market price. Therefore, financial institutions prefer trading on dark pools, which hide order books, to avoid potential losses from negative market impact. However, the lack of transparency hurts price discovery, results in poor execution of trades, and promotes illicit behaviors such as front-running. Hence, several financial regulations have limited trading on dark pools. Subsequently, periodic auctions, which are considered regulation-compliant alternatives to dark pools, have witnessed a surge in trading volumes. Unfortunately, similar to dark pools, there are no guarantees that the operators will neither exploit their exclusive access to the order book nor incorrectly compute the market-clearing price. In this paper, we build a publicly verifiable and secrecy preserving periodic auction protocol to address these challenges. We utilize aggregate Bulletproofs to prove the ordering on a vector of commitments. To alleviate the burden on traders’ computation resources and achieve public verifiability, the protocol delegates the verification of the operator’s work to a smart contract. We evaluate the protocol’s performance, and the results show that it is practical and feasible to deploy.
Enhancing Mid–Low-Resolution Ship Detection With High-Resolution Feature Distillation

To enhance mid-low-resolution ship detection, existing methods generally use image super-resolution (SR) as a preprocessing step and feed the super-resolved images to the detectors. However, these methods only use high-resolution (HR) images as ground-truth labels to supervise the training of their SR module but overlook the rich HR information in the detection stage. Inspired by the recent advances in knowledge distillation, in this letter, we design a feature distillation framework to fully exploit the information in ground-truth HR images to handle mid-low-resolution ship detection. Our framework consists of a student network and a teacher network. The student network first super-resolves input images using an SR module and then feeds the super-resolved images to the detection module. The teacher network whose architecture is the same as the student detection module directly takes HR images as input to generate HR feature representation and then distills these HR features to the student network through a distillation loss. Using our feature distillation framework, HR images are not only used as ground-truth labels to train the SR module but also provide ``ground-truth'' features to train the detection module, which enhances the detection performance of the student network. We apply our framework to several popular detectors, including FCOS, Faster-RCNN, Mask-RCNN, and Cascase-RCNN, and conduct extensive ablation studies to validate its effectiveness and generality. Experimental results on the HRSC2016, DOTA, and NWPU VHR-10 datasets demonstrate that, when applying our framework to Faster-RCNN, our method can outperform several state-of-the-art detection methods in terms of mAP50 and mAP75.
Learning Contrastive Representation for Semantic Correspondence

Dense correspondence across semantically related images has been extensively studied, but still faces two challenges: 1) large variations in appearance, scale and pose exist even for objects from the same category, and 2) labeling pixel-level dense correspondences is labor intensive and infeasible to scale. Most existing methods focus on designing various matching modules using fully-supervised ImageNet pretrained networks. On the other hand, while a variety of self-supervised approaches are proposed to explicitly measure image-level similarities, correspondence matching the pixel level remains under-explored. In this work, we propose a multi-level contrastive learning approach for semantic matching, which does not rely on any ImageNet pretrained model. We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects, while the performance can be further enhanced by regularizing cross-instance cycle-consistency at intermediate feature levels. Experimental results on the PF-PASCAL, PF-WILLOW, and SPair-71k benchmark datasets demonstrate that our method performs favorably against the state-of-the-art approaches.
Commitment over Compound Binary Symmetric Channels

In the commitment problem, two mutually distrustful parties Alice and Bob interact in a two-phase protocol, viz., commit and reveal phase, to achieve commitment over a bit string that Alice possesses. The protocol successfully achieves commitment if, firstly, Alice can commit to sharing a string with Bob, with the guarantee that this string remains hidden from Bob until she chooses to reveal it to him. Secondly, when Alice does reveal a string, Bob is able to detect precisely whether the revealed string is different from the one Alice committed to sharing. Information-theoretically secure commitment is impossible if Alice and Bob communicate only noiselessly; however, communication using a noisy channel can be a resource to realize commitment. Even though a noisy channel may be available, it is possible that the corresponding channel law is imprecisely known or poorly characterized. We define and study a compound-binary symmetric channel (compound-BSC) which models such a scenario. A compound-BSC is a BSC whose transition probability is fixed but unknown to either party; the set of potential values which this transition probability can take, though, is known to both parties a priori. In this work, we completely characterize the maximum commitment throughput or commitment capacity of a compound-BSC. We provide an optimal, computationally-efficient scheme for our achievability, and we derive a converse for general alphabet compound DMCs, which is then specialized for compound-BSCs.
Generalized bias compensated pseudolinear Kalman filter for colored noisy bearings-only measurements

Bias compensated pseudolinear Kalman filter (BC-PLKF) has been shown to solve the bias problem of pseudolinear Kalman filter (PLKF) and outperform Extended Kalman Filter (EKF) and many others in bearings-only target estimation applications with a low computational cost. However, BC-PLKF assumes that measurement noise is white, which is not a valid approximation for some applications such as weather-vane-used guided missiles where wind disturbance appears as a strongly time-correlated measurement noise, estimators performing high-frequency measurement updates, or cascaded Kalman filter-based algorithms. When the well-known noise augmentation method is applied to BC-PLKF, no straightforward solution for the bias compensation is available. First, process noise and observer matrix become coupled leading to unique bias. Second, the measurement autocovariance turns into zero whose inverse is used at the bias compensation step of BC-PLKF. Therefore, a bias analysis is performed for PLKF where the measurement noise is colored. Moreover, the generalized BC-PLKF algorithm (GBC-PLKF) for colored noise-corrupted measurements is derived. Simulations are performed to compare performances of GBC-PLKF, EKF, Cubature Kalman Filter (CKF), BC-PLKF, and colored noise augmented EKF and CKF (C-EKF and C-CKF) with typical air-to-surface missile engagement scenarios. Results verify that GBC-PLKF outperforms all comparison filters with a low computational cost for bearings-only estimation applications.
QuickDSC: Clustering by Quick Density Subgraph Estimation

Density-based clustering is a traditional research topic with the capability of determining clusters of arbitrary shapes. Besides, through the Density Estimator (DE), density-based methods such as MeanShift, and QuickShift can find the local density maximums as Modes that are excellent representatives of the clusters. However, concentrating on the modes only may suffer from the over-segmentation problem. On the other hand, most density-based methods cannot satisfy the scenario requiring partitioning the data samples into exactly K clusters. To overcome these issues, QuickDSC: a novel and efficient clustering algorithm that groups the samples through the Quick Density Subgraph Estimation, is proposed in this work. It firstly identifies the high-density-connected samples as the Density Subgraphs (DSs). And then, the importance of DSs is estimated from two aspects: density and geometric weight. The top-K important DSs are selected as the cluster centers and based on which the cluster memberships of remaining samples are determined. QuickDSC incorporates three crucial clustering attributes: (1) the cluster centroids are modes (as in density-based methods); (2) able to efficiently return results by utilizing the underlying density structure (as in hierarchical clustering methods); and (3) it explicitly returns K clusters (e.g., K-Means, K-Modes). In addition, QuickDSC is theoretically and empirically efficient. It is only slightly slower than classical clustering methods such as K-Means and DBSCAN. Experiments on artificial and real-world datasets demonstrate the advantages of the proposed method, and the clustering quality outperforms the state-of-the-art approaches.
Toward Open Research: A Narrative Review of the Challenges and Opportunities for Open Humanities

Abstract Open research represents a new set of principles and methodologies for greater cooperation, transparent sharing of findings, and access to and re-use of research data, materials or outputs, making knowledge more freely available to wider audiences for societal benefit. Yet, the future success of the international move toward open research will be dependent on key stakeholders addressing current barriers to increase uptake, effectiveness, and sustainability. This article builds on “An Agenda for Open Science in Communication,” raising dialog around the need for a broader view of open research as opposed to open science through a deeper understanding of specific challenges faced by the humanities. It reviews how the multifaceted nature of humanities research outputs make open communication formats more complex and costly. While new avenues are emerging to advance open research, there is a need for more collaborative, coordinated efforts to better connect humanities scholars with the communities they serve.
Condensed Composite Memory Continual Learning

Deep Neural Networks (DNNs) suffer from a rapid decrease in performance when trained on a sequence of tasks where only data of the most recent task is available. This phenomenon, known as catastrophic forgetting, prevents DNNs from accumulating knowledge over time. Overcoming catastrophic forgetting and enabling continual learning is of great interest since it would enable the application of DNNs in settings where unrestricted access to all the training data at any time is not always possible, e.g. due to storage limitations or legal issues. While many recently proposed methods for continual learning use some training examples for rehearsal, their performance strongly depends on the number of stored examples. In order to improve performance of rehearsal for continual learning, especially for a small number of stored examples, we propose a novel way of learning a small set of synthetic examples which capture the essence of a complete dataset. Instead of directly learning these synthetic examples, we learn a weighted combination of shared components for each example that enables a significant increase in memory efficiency. We demonstrate the performance of our method on commonly used datasets and compare it to recently proposed related methods and baselines.