Reviewerly/exHarmony huggingface dataset:
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning

Federated Learning has shown great potentials for the distributed data utilization and privacy protection. Most existing federated learning approaches focus on the supervised setting, which means all the data stored in each client has labels. However, in real-world applications, the client data are impossible to be fully labeled. Thus, how to exploit the unlabeled data should be a new challenge for federated learning. Although a few studies are attempting to overcome this challenge, they may suffer from information leakage or misleading information usage problems. To tackle these issues, in this paper, we propose a novel federated semi-supervised learning method named FedTriNet, which consists of two learning phases. In the first phase, we pre-train FedTriNet using labeled data with FedAvg. In the second phase, we aim to make most of the unlabeled data to help model learning. In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set. Finally, FedTriNet uses the new training set to retrain the model. Experimental results on three publicly available datasets show that the proposed FedTriNet outperforms state-of-the-art baselines under both IID and Non-IID settings.
VIAProMa: An Agile Project Management Framework for Mixed Reality

With the COVID-19 pandemic, distributed and remote working became a necessity but in agile project management, social interactions like daily standup meetings in Scrum are vital for the project success. Mixed reality can provide a new way of combining remote collaboration with innovative 3D visualizations to analyze the project status. In this paper, we present a visual immersive analytics framework for project management (VIAProMa). It imports data from project management tools like the GitHub issue tracker for open-source projects. With these task data as the basis, it can generate three-dimensional visualizations, e.g. about the overall progress or the competences of individual developers. Developers, stakeholders and end users can meet in the collaborative virtual environment as avatars and establish a spatial structure with the task cards and visualizations. Therefore, VIAProMa with its adapted and customized mixed reality project management features supports both the shared meetings and the information flow in the project. The shared environment makes it a suitable tool for DevOpsUseXR, an extension to the DevOps workflow, where end users are able to participate in the development process in mixed reality. The resulting implementation is available as an open-source project with cross-platform capabilities targeting the Microsoft HoloLens, HTC VIVE and Android smartphones, as well as tablets. The framework is applied in university teaching classes to convey agile methodology in mixed reality programming practices.
Attribute-driven image captioning via soft-switch pointer

Visual attributes detection provides rich semantic concepts for image captioning. Some previous methods attempt to directly encode the attributes into vectors and generate the corresponding captions, which ignore the correlations between the image regions and attributes. In this paper, we consider to bridge the gap between visual features and detected attributes: first to look at a specific region of the image and second to decide which attribute to attend to. We propose an attribute-driven image captioning approach consisting of two parts: the visual positioning part and the attribute selection part. Specifically, we introduce the pointer-generator network into the second part of our model as a soft-switch, which determines whether to generate a word through the hidden state or point to a detected attribute at each decoding step. Qualitative and Quantitative experiments show that our model can improve the coverage of key visual attributes and significantly boost the overall performance.
A Requirement-based Regression Test Selection Technique in Behavior-Driven Development

Regression testing is an essential software maintenance activity before the release of a new version implementing a bug fix or a new feature. A regression test selection (RTS) technique chooses a subset of existing test cases to ensure that the system will not be adversely affected by the latest modifications. With the rise of DevOps, behavior-driven development (BDD) is growing in popularity as it is in close alignment with agile practices, for example, continuous integration. Hence, it is necessary to propose a novel and effective RTS technique for BDD specifically to accelerate the development process while ensuring software quality. Since most existing techniques for RTS are code-based and thus subject to some limitations, we present a requirement-based technique which uses the requirements in BDD to select test cases in both high-level (acceptance testing) and low-level (unit testing). Our technique firstly illustrates the new requirement with a scenario, and subsequently computes the semantic similarity of the new scenario and all existing scenarios with the vector space model. According to the results, the modification-traversing regression test cases can be selected in a semi-automated way. We also conduct an experimental study to evaluate our technique in terms of inclusiveness, precision, efficiency and generality. The study shows that our technique is applicable for BDD and effective in practice.
Constrained Incomplete Argumentation Frameworks

Operations like belief change or merging have been adapted to the context of abstract argumentation. However, these operations may require to express some uncertainty or some disjunction in the result, which is not representable in classical AFs. For this reason, some of these works require a set of AFs or a set of extensions as the outcome of the operation, somehow to represent a disjunction of AFs or extensions. In parallel, the notion of Incomplete AFs (IAFs) has been developed recently. It corresponds to AFs where the existence of some arguments or attacks may be uncertain. Each IAF can be associated with a set of classical AFs called completions, that correspond to different ways of "resolving the uncertainty". While these IAFs could be good candidates for a compact representation of a "disjunction" of AFs, we prove that this model is not expressive enough. Then we introduce Constrained IAFs, that include a propositional formula allowing to select the set of completions used for reasoning. We prove that this model is expressive enough for representing any set of AFs, or any set of extensions. Moreover, we show that the complexity of credulous and skeptical reasoning is the same as in the case of IAFs. Finally, we show that CIAFs can be used to model a new form of extension enforcement.
Scene Text Detection with Scribble Line

Scene text detection, which is one of the most popular topics in both academia and industry, can achieve remarkable performance with sufficient training data. However, the annotation costs of scene text detection are huge with traditional labeling methods due to the various shapes of texts. Thus, it is practical and insightful to study simpler labeling methods without harming the detection performance. In this paper, we propose to annotate the texts with scribble lines instead of polygons for text detection. It is a general labeling method for texts with various shapes and requires low labeling costs. Furthermore, a weakly-supervised scene text detection framework is proposed to use the scribble lines for text detection. The experiments on several benchmarks show that the proposed method bridges the performance gap between the weakly labeling method and the original polygon-based labeling methods, with even better performance. We will release the weak annotations of the benchmarks in our experiments and hope it will benefit the field of scene text detection to achieve better performance with simpler annotations.
Recurrent Neural Network Transducer for Japanese and Chinese Offline Handwritten Text Recognition

In this paper, we propose an RNN-Transducer model for recognizing Japanese and Chinese offline handwritten text line images. As far as we know, it is the first approach that adopts the RNN-Transducer model for offline handwritten text recognition. The proposed model consists of three main components: a visual feature encoder that extracts visual features from an input image by CNN and then encodes the visual features by BLSTM; a linguistic context encoder that extracts and encodes linguistic features from the input image by embedded layers and LSTM; and a joint decoder that combines and then decodes the visual features and the linguistic features into the final label sequence by fully connected and softmax layers. The proposed model takes advantage of both visual and linguistic information from the input image. In the experiments, we evaluated the performance of the proposed model on the two datasets: Kuzushiji and SCUT-EPT. Experimental results show that the proposed model achieves state-of-the-art performance on all datasets.
Evolutionary Generative Adversarial Networks with Crossover Based Knowledge Distillation

Generative Adversarial Networks (GAN) is an adversarial model, and it has been demonstrated to be effective for various generative tasks. However, GAN and its variants also suffer from many training problems, such as mode collapse and gradient vanish. In this paper, we firstly propose a general crossover operator, which can be widely applied to GANs using evolutionary strategies. Then we design an evolutionary GAN framework named C-GAN based on it. And we combine the crossover operator with evolutionary generative adversarial networks (E-GAN) to implement the evolutionary generative adversarial networks with crossover (CE-GAN). Under the premise that a variety of loss functions are used as mutation operators to generate mutation individuals, we evaluate the generated samples and allow the mutation individuals to learn experiences from the output in a knowledge distillation manner, imitating the best output outcome, resulting in better offspring. Then, we greedily select the best offspring as parents for subsequent training using discriminator as an evaluator. Experiments on real datasets demonstrate the effectiveness of CE-GAN and show that our method is competitive in terms of generated images quality and time efficiency.
Virtual Reality in Italian Museums: A Brief Discussion

This paper discusses the current use of Virtual Reality technology within the Italian museum field. It intends to provide a brief list of the main cases of VR applications developed for Italian museums, by focusing the discussion above the last year scenario. Indeed, during the lockdown period, due to the covid-19 pandemic, the use of technology in museums have been greater than in the previous years. The aim is to provide a brief description of this changed scenario in relation to the use of VR technology, attesting to best practices on the one hand and raising critical issues on the other.
Examining theory use in design research on fantasy play

Play is an essential activity in children’s lives. In the Child Computer Interaction (CCI) field, many authors refer to play and play theories when they describe their work. Play theories can come from many different disciplines, such as psychology, sociology and learning sciences. Theories from different disciplines can provide interpretations and inspiration sources when designing for play. In this study, we explore what theory clusters authors use and how they are used when design researchers report on design work for fantasy play. Based on 19 artefact-centred papers from the ACM digital library from the period 1999–2018, we analyse four components of reported theory use: design intention, design argumentation, design decisions and design evaluation. This paper provides a list of theory clusters that designers report on, showing that different clusters also indicate different conceptualisations of play. Furthermore, it describes three common strategies of theory cluster use: for contextualising the value of play, for highlighting the outcome of play and using design cases as ‘theory’ for supporting making design decisions. The paper concludes by providing reflective questions about how to report on the use of theory in designing for fantasy play. The questions can be used in order for future work in the Child Computer Interaction community to be precise and transparent about theory use in order to make it easier to build upon each other’s work.
An Empirical Study on Using CNNs for Fast Radio Signal Prediction

Accurate radio frequency power prediction in a geographic region is a computationally expensive part of finding the optimal transmitter location using a ray tracing software. We empirically analyze the viability of deep learning models to speed up this process. Specifically, deep learning methods including CNNs and UNET are typically used for segmentation, and can also be employed in power prediction tasks. We consider a dataset that consists of radio frequency power values for five different regions with four different frame dimensions. We compare deep learning-based prediction models including RadioUNET and four different variations of the UNET model for the power prediction task. More complex UNET variations improve the model on higher resolution frames such as $$256\times 256$$ . However, using the same models on lower resolutions results in overfitting and simpler models perform better. Our detailed numerical analysis shows that the deep learning models are effective in power prediction and they are able to generalize well to the new regions.
Role based access control using identity and broadcast based encryption for securing cloud data

Since integrity of data on cloud cannot be assured, several clients and users hesitate to upload their crucial data to the cloud, which eventually hinders cloud storage development. One of the biggest challenges with respect to cloud security is to ensure data confidentiality and one of the solutions to this issue can be by restricting unauthorized access to user data stored on cloud. In due course of time much research has been proposed using cryptographic techniques along with access control model(s) to deal with security issues pertaining to untrusted cloud environments. This work illustrates Role-Based access control policies over user data and permits the owner of the data to store it in an encrypted pattern to the cloud, thereby, allowing only permissible roles to access the data. Hence, the proposed work is an amalgamation of Role-Based Encryption (RBE) scheme using Identity and Broadcast based Encryption scheme to ensure data integrity in public clouds. In this paper we discuss the usage of several algorithmic modules that demonstrates how roles are governed by the membership rights, user revocation, encryption and decryption processes. Finally, the proposed model is compared with its peers on the basis of encryption and decryption time.
Multi-view self-supervised learning for 3D facial texture reconstruction from single image

Recent years witnessed that deep learning based methods have achieved significant progresses in recovering 3D face shape from single image. However, reconstructing realistic 3D facial texture from single image is still a challenging task due to the unavailability of large-scale training datasets and the low expression ability of previous statistical texture models (e.g. 3DMM). In this paper, we introduce a novel deep architecture trained by self-supervision with multi-view setup, to reconstruct 3D facial texture. Specifically, we first obtain incomplete UV texture map from input facial image, and then introduce a Texture Completion Network (TC-Net) to inpaint missing areas. To train TC-Net, firstly, we collect 50,000 triplets of facial images from in-the-wild videos, each triplet consists of a nearly frontal, a left-side, and a right-side facial images. With this dataset, we propose a novel multi-view consistency loss that ensures consistent photometric, face identity, 3DMM identity, and UV texture among multi-view facial images. This loss allows to optimize TC-Net in a self-supervision way without using ground-truth texture map as supervision. In addition, multi-view input images are only required in training to provide self-supervision, and our method only needs single input image in inference. Extensive experiments show that our method achieves state-of-the-art performance in both qualitative and quantitative comparisons.
G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency

Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA) method for graph-based learning tasks. However, it still remains prohibitively challenging to inference GNNs over large graph datasets, limiting their application to large-scale real-world tasks. While end-to-end jointly optimizing GNNs and their accelerators is promising in boosting GNNs' inference efficiency and expediting the design process, it is still underexplored due to the vast and distinct design spaces of GNNs and their accelerators. In this work, we propose G-CoS, a GNN and accelerator co-search framework that can automatically search for matched GNN structures and accelerators to maximize both task accuracy and acceleration efficiency. Specifically, G-CoS integrates two major enabling components: (1) a generic GNN accelerator search space which is applicable to various GNN structures and (2) a one-shot GNN and accelerator co-search algorithm that enables simultaneous and efficient search for optimal GNN structures and their matched accelerators. To the best of our knowledge, G-CoS is the first co-search framework for GNNs and their accelerators. Extensive experiments and ablation studies show that the GNNs and accelerators generated by G-CoS consistently outperform SOTA GNNs and GNN accelerators in terms of both task accuracy and hardware efficiency, while only requiring a few hours for the end-to-end generation of the best matched GNNs and their accelerators.
Locally-Constrained de Bruijn Codes: Properties, Enumeration, Code Constructions, and Applications

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de Bruijn graph</i> , its sequences, and their various generalizations, have found many applications in information theory, including many new ones in the last decade. In this paper, motivated by a coding problem for emerging memory technologies, a set of sequences which generalize the window property of de Bruijn sequences, on its shorter subsequences, are defined. These sequences can be also defined and viewed as constrained sequences. Hence, they will be called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">locally-constrained de Bruijn sequences</i> and a set of such sequences will be called a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">locally-constrained de Bruijn code</i> . Several properties and alternative definitions for such codes are examined and they are analyzed as generalized sequences in the de Bruijn graph (and its generalization) and as constrained sequences. Various enumeration techniques are used to compute the total number of sequences for any given set of parameters. A construction method of such codes from the theory of shift-register sequences is proposed. Finally, we show how these locally-constrained de Bruijn sequences and codes can be applied in constructions of codes for correcting synchronization errors in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> -symbol read channel and in the racetrack memory channel. For this purpose, these codes are superior in their size to previously known codes.
Deep Q-Learning: Theoretical Insights From an Asymptotic Analysis

Deep Q-learning is an important reinforcement learning algorithm, which involves training a deep neural network, called deep Q-network, to approximate the well-known Q-function. Although wildly successful under laboratory conditions, serious gaps between theory and practice as well as a lack of formal guarantees prevent its use in the real world. Adopting a dynamical systems perspective, we provide a theoretical analysis of a popular version of deep Q-learning under realistic and verifiable assumptions. More specifically, we prove an important result on the convergence of the algorithm, characterizing the asymptotic behavior of the learning process. Our result sheds light on hitherto unexplained properties of the algorithm and helps understand empirical observations, such as performance inconsistencies even after training. Unlike previous theories, our analysis accommodates state Markov processes with multiple stationary distributions. In spite of the focus on deep Q-learning, we believe that our theory may be applied to understand other deep learning algorithms.
Trajectory Optimization with Optimization-Based Dynamics

We present a framework for bi-level trajectory optimization in which a system’s dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this lower-level problem are passed to an upper-level trajectory optimizer. This optimization-based dynamics representation enables constraint handling, additional variables, and non-smooth behavior to be abstracted away from the upper-level optimizer, and allows classical unconstrained optimizers to synthesize trajectories for more complex systems. We provide a path-following method for efficient evaluation of constrained dynamics and utilize the implicit-function theorem to compute smooth gradients of this representation. We demonstrate the framework by modeling systems from locomotion, aerospace, and manipulation domains including: acrobot with joint limits, cart-pole subject to Coulomb friction, Raibert hopper, rocket landing with thrust limits, and planar-push task with optimization-based dynamics and then optimize trajectories using iterative LQR.
Knowledge-enhanced recommendation using item embedding and path attention

Recommender systems have attracted widespread attention in various online applications. To effectively recommend the needed items of users, knowledge graphs have been introduced to provide rich and complementary information to infer user preferences in recommender systems. Existing efforts have explored user preferences through specific paths and item embedding in knowledge graphs. However, user preferences have hardly been fully captured because users and items are always separately modeled. To address this problem, we propose a model to represent items from a user’s perspective that provides effective supplementary information. User preferences encoded in historically clicked items are propagated along links in the knowledge graph. We propose a gated attention unit to capture user preferences from specific types of paths. Based on the captured preference information through the knowledge graph and supplementary item information, we generate effective reasoning paths to infer the underlying rationale of user–item interactions using the sequential model. Through extensive experiments on real-world datasets, we demonstrate that the proposed model achieves significant improvements over the state-of-the-art solutions.