Reviewerly/exHarmony huggingface dataset:
Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling

This research addresses the problem of acoustic modeling of low-resource languages for which transcribed training data is absent. The goal is to learn robust frame-level feature representations that can be used to identify and distinguish subword-level speech units. The proposed feature representations comprise various types of multilingual bottleneck features (BNFs) that are obtained via multi-task learning of deep neural networks (MTL-DNN). One of the key problems is how to acquire high-quality frame labels for untranscribed training data to facilitate supervised DNN training. It is shown that learning of robust BNF representations can be achieved by effectively leveraging transcribed speech data and well-trained automatic speech recognition (ASR) systems from one or more out-of-domain (resource-rich) languages. Out-of-domain ASR systems can be applied to perform speaker adaptation with untranscribed training data of the target language, and to decode the training speech into frame-level labels for DNN training. It is also found that better frame labels can be generated by considering temporal dependency in speech when performing frame clustering. The proposed methods of feature learning are evaluated on the standard task of unsupervised subword modeling in Track 1 of the ZeroSpeech 2017 Challenge. The best performance achieved by our system is $9.7\%$ in terms of across-speaker triphone minimal-pair ABX error rate, which is comparable to the best systems reported recently. Lastly, our investigation reveals that the closeness between target languages and out-of-domain languages and the amount of available training data for individual target languages could have significant impact on the goodness of learned features.
Magic state distillation with the ternary Golay code

The ternary Golay code -- one of the first and most beautiful classical error-correcting codes discovered -- naturally gives rise to an 11-qutrit quantum error correcting code. We apply this code to magic state distillation, a leading approach to fault-tolerant quantum computing. We find that the 11-qutrit Golay code can distill the "most magic" qutrit state -- an eigenstate of the qutrit Fourier transform known as the strange state -- with cubic error-suppression and a remarkably high threshold. It also distills the "second-most magic" qutrit state, the Norell state, with quadratic error-suppression and an equally high threshold to depolarizing noise.
Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models

This paper presents the second version of the dialogue system named Alquist competing in Amazon Alexa Prize 2018. We introduce a system leveraging ontology-based topic structure called topic nodes. Each of the nodes consists of several sub-dialogues, and each sub-dialogue has its own LSTM-based model for dialogue management. The sub-dialogues can be triggered according to the topic hierarchy or a user intent which allows the bot to create a unique experience during each session.
Improved quantum state tomography for systems with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mi>X</mml:mi><mml:mo>+</mml:mo><mml:mi>Y</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:math> couplings and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math> readouts

Quantum device characterization via state tomography plays an important role in both validating quantum hardware and processing quantum information, but it needs the exponential number of the measurements. For the systems with XX+YY-type couplings and Z readouts, such as superconducting quantum computing (SQC) systems, traditional quantum state tomography (QST) using single-qubit readout operations at least requires $3^n$ measurement settings in reconstructing an $n$-qubit state. In this work, I proposed an improved QST by adding 2-qubit evolutions as the readout operations and obtained an optimal tomographic scheme using the integer programming optimization. I respectively apply the new scheme on SQC systems with the Nearest-Neighbor, 2-Dimensional, and All-to-All connectivities on qubits. It shows that this method can reduce the number of measurements by over 60% compared with the traditional QST. Besides, comparison with the traditional scheme in the experimental feasibility and robustness against errors were made by numerical simulation. It is found that, the new scheme has good implementability and it can achieve comparable or even better accuracy than the traditional scheme. It is expected that the experimentalist from the related fields can directly utilize the ready-made results for reconstructing quantum states involved in their research.
Successive Over-Relaxation ${Q}$ -Learning

In a discounted reward Markov decision process (MDP), the objective is to find the optimal value function, i.e., the value function corresponding to an optimal policy. This problem reduces to solving a functional equation known as the Bellman equation and a fixed point iteration scheme known as the value iteration is utilized to obtain the solution. In literature, a successive over-relaxation (SOR)-based value iteration scheme is proposed to speed-up the computation of the optimal value function. The speed-up is achieved by constructing a modified Bellman equation that ensures faster convergence to the optimal value function. However, in many practical applications, the model information is not known and we resort to reinforcement learning (RL) algorithms to obtain optimal policy and value function. One such popular algorithm is Q -learning. In this letter, we propose SOR Q -learning. We first derive a modified fixed point iteration for SOR Q -values and utilize stochastic approximation to derive a learning algorithm to compute the optimal value function and an optimal policy. We then prove the almost sure convergence of the SOR Q -learning to SOR Q -values. Finally, through numerical experiments, we show that SOR Q -learning is faster compared to the standard Q -learning algorithm.
An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues

The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the distribution of vocabulary and utterance types. Using ConversationShape as a lens, we take a closer look at several conversational search datasets and compare them with other dialogue datasets to better understand the types of dialogue interaction they represent, either driven by the information seeker or the assistant. We discover that deviations from the ConversationShape of a human-human dialogue of the same type is predictive of the quality of a human-machine dialogue.
Cloud-Based Privacy-Preserving Collaborative Consumption for Sharing Economy

Cloud computing has been a dominant paradigm for a variety of information processing platforms, particularly for enabling various popular applications of sharing economy. However, there is a major concern regarding data privacy on these cloud-based platforms. This work presents novel cloud-based privacy-preserving solutions to support collaborative consumption applications for sharing economy. In typical collaborative consumption, information processing platforms need to enable fair cost-sharing among multiple users for utilizing certain shared facilities and communal services. Our cloud-based privacy-preserving protocols, based on homomorphic Paillier cryptosystems, can ensure that the cloud-based operator can only obtain an aggregate schedule of all users in facility sharing, or a service schedule conforming to service provision rule in communal service sharing, but is unable to track the personal schedules or demands of individual users. More importantly, the participating users are still able to settle cost-sharing among themselves in a fair manner for the incurred costs, without knowing each other's private schedules or demands. Our privacy-preserving protocols involve no other third party who may compromise privacy. We also provide an extensive evaluation study and a proof-of-concept system prototype of our protocols.
Spin squeezing in symmetric multiqubit states with two non-orthogonal Majorana spinors

Majorana geometric representation of pure N-qubit states obeying exchange symmetry is em- ployed to explore spin squeezing properties in the family of states with two distinct spinors. Dicke states are characterized by two orthogonal spinors and belong to this family - but they are not spin squeezed. On the otherhand, those constituted by two non-orthogonal spinors exhibit spin squeezing.
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents. Our proposed algorithm, called Shared Experience Actor-Critic (SEAC), applies experience sharing in an actor-critic framework. We evaluate SEAC in a collection of sparse-reward multi-agent environments and find that it consistently outperforms two baselines and two state-of-the-art algorithms by learning in fewer steps and converging to higher returns. In some harder environments, experience sharing makes the difference between learning to solve the task and not learning at all.
The Memento Tracer Framework: Balancing Quality and Scalability for Web Archiving

Web archiving frameworks are commonly assessed by the quality of their archival records and by their ability to operate at scale. The ubiquity of dynamic web content poses a significant challenge for crawler-based solutions such as the Internet Archive that are optimized for scale. Human-driven services such as the Webrecorder tool provide high-quality archival captures but are not optimized to operate at scale. We introduce the Memento Tracer framework that aims to balance archival quality and scalability. We outline its concept and architecture and evaluate its archival quality and operation at scale. Our findings indicate quality is on par or better compared against established archiving frameworks and operation at scale comes with a manageable overhead.
On Secret Sharing with Newton’s Polynomial for Multi-Factor Authentication

Security and access control aspects are becoming more and more essential to consider during the design of various systems and the tremendous growth of digitization. One of the related key building blocks in this regard is, essentially, the authentication process. Conventional schemes based on one or two authenticating factors can no longer provide the required levels of flexibility and pro-activity of the access procedures, thus, the concept of threshold-based multi-factor authentication (MFA) was introduced, in which some of the factors may be missing, but the access can still be granted. In turn, secret sharing is a crucial component of the MFA systems, with Shamir’s schema being the most widely known one historically and based on Lagrange interpolation polynomial. Interestingly, the older Newtonian approach to the same problem is almost left without attention. At the same time, it means that the coefficients of the existing secret polynomial do not need to be re-calculated while adding a new factor. Therefore, this paper investigates this known property of Newton’s interpolation formula, illustrating that, in specific MFA cases, the whole system may become more flexible and scalable, which is essential for future authentication systems.
Automated Reuse Recommendation of Product Line Assets Based on Natural Language Requirements

Software product lines (SPLs) are based on reuse rationale to aid quick and quality delivery of complex products at scale. Deriving a new product from a product line requires reuse analysis to avoid redundancy and support a high degree of assets reuse. In this paper, we propose and evaluate automated support for recommending SPL assets that can be reused to realize new customer requirements. Using the existing customer requirements as input, the approach applies natural language processing and clustering to generate reuse recommendations for unseen customer requirements in new projects. The approach is evaluated both quantitatively and qualitatively in the railway industry. Results show that our approach can recommend reuse with 74% accuracy and 57.4% exact match. The evaluation further indicates that the recommendations are relevant to engineers and can support the product derivation and feasibility analysis phase of the projects. The results encourage further study on automated reuse analysis on other levels of abstractions.
Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning

Temporal observations such as videos contain essential information about the dynamics of the underlying scene, but they are often interleaved with inessential, predictable details. One way of dealing with this problem is by focusing on the most informative moments in a sequence. We propose a model that learns to discover these important events and the times when they occur and uses them to represent the full sequence. We do so using a hierarchical Keyframe-Inpainter (KeyIn) model that first generates a video's keyframes and then inpaints the rest by generating the frames at the intervening times. We propose a fully differentiable formulation to efficiently learn this procedure. We show that KeyIn finds informative keyframes in several datasets with different dynamics and visual properties. KeyIn outperforms other recent hierarchical predictive models for planning. For more details, please see the project website at \url{this https URL}.
How to Integrate Security Compliance Requirements with Agile Software Engineering at Scale?

Integrating security into agile software development is an open issue for research and practice. Especially in strongly regulated industries, complexity increases not only when scaling agile practices but also when aiming for compliance with security standards. To achieve security compliance in a large-scale agile context, we developed S2C-SAFe: An extension of the Scaled Agile Framework that is compliant to the security standard IEC 62443-4-1 for secure product development. In this paper, we present the framework and its evaluation by agile and security experts within Siemens' large-scale project ecosystem. We discuss benefits and limitations as well as challenges from a practitioners' perspective. Our results indicate that S2C-SAFe contributes to successfully integrating security compliance with lean and agile development in regulated environments. We also hope to raise awareness for the importance and challenges of integrating security in the scope of Continuous Software Engineering.
Analysis, Design and Practical Validation of an Augmented Reality Teaching System Based on Microsoft HoloLens 2 and Edge Computing

In recent years, the education sector has incorporated the use of new technologies and computing devices into classrooms, which allowed for implementing new ways for enhancing teaching and learning. One of these new technologies is augmented reality (AR), which enables creating experiences that mix reality and virtual elements in an attractive and visual way, thus helping teachers to foster student interest in learning certain subjects and abstract concepts in novel visual ways. This paper proposes to harness the potential of the latest AR devices in order to enable giving AR-enabled lectures and hands-on labs. Specifically, it proposes an architecture for providing low-latency AR education services in a classroom or a laboratory. Such a low latency is achieved thanks to the use of edge computing devices, which offload the cloud from the traditional tasks that are required by dynamic AR applications (e.g., near real-time data processing, communications among AR devices). Depending on the specific AR application and the number of users, the wireless link (usually WiFi) could be overloaded if the network has not been properly designed, and the overall performance of the application can be compromised, leading to high latency and even to wireless communication failure. In order to tackle this issue, radio channel measurements and simulation results have been performed by means of an in-house developed 3D ray-launching tool, which is able to model and simulate the behaviour of an AR-enabled classroom/laboratory in terms of radio propagation and quality of service. To corroborate the obtained theoretical results, a Microsoft HoloLens 2 teaching application was devised and tested, thus demonstrating the feasibility of the proposed approach.
Grey-Box Learning of Register Automata

Model learning (a.k.a. active automata learning) is a highly effective technique for obtaining black-box finite state models of software components. We show how one can boost the performance of model learning techniques for register automata by extracting the constraints on input and output parameters from a run, and making this grey-box information available to the learner. More specifically, we provide new implementations of the tree oracle and equivalence oracle from the RALib tool, which use the derived constraints. We extract the constraints from runs of Python programs using an existing tainting library for Python, and compare our grey-box version of RALib with the existing black-box version on several benchmarks, including some data structures from Python’s standard library. Our proof-of-principle implementation results in almost two orders of magnitude improvement in terms of numbers of inputs sent to the software system. Our approach, which can be generalized to richer model classes, also enables RALib to learn models that are out of reach of black-box techniques, such as combination locks.
Explaining AI-Based Decision Support Systems Using Concept Localization Maps

Human-centric explainability of AI-based Decision Support Systems (DSS) using visual input modalities is directly related to reliability and practicality of such algorithms. An otherwise accurate and robust DSS might not enjoy trust of domain experts in critical application areas if it is not able to provide reasonable justifications for its predictions. This paper introduces Concept Localization Maps (CLMs), which is a novel approach towards explainable image classifiers employed as DSS. CLMs extend Concept Activation Vectors (CAVs) by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier. They provide qualitative and quantitative assurance of a classifier’s ability to learn and focus on similar concepts important for human experts during image recognition. To better understand the effectiveness of the proposed method, we generated a new synthetic dataset called Simple Concept DataBase (SCDB) that includes annotations for 10 distinguishable concepts, and made it publicly available. We evaluated our proposed method on SCDB as well as a real-world dataset called CelebA. We achieved localization recall of above 80% for most relevant concepts and average recall above 60% for all concepts using SE-ResNeXt-50 on SCDB. Our results on both datasets show great promise of CLMs for easing acceptance of DSS in clinical practice.
An immersive performative architectural design tool with daylighting simulations: a building information modeling-based approach

Purpose In performative architectural design, daylighting is a crucial design consideration; however, the evaluation of daylighting in the design process can be challenging. Immersive environments (IEs) can create a dynamic, multi-sensory, first-person view in computer-generated environments, and can improve designers' visual perception and awareness during performative design processes. This research addresses the need for interactive and integrated design tools for IEs toward better-performing architectural solutions in terms of daylighting illumination. In this context, building information modeling and performance simulations are identified as critical technologies to be integrated into performative architectural design. Design/methodology/approach This research adopts a design science research (DSR) methodology involving an iterative process of development, validation and improvement of a novel and immersive tool, HoloArch, that supports design development during daylighting-informed design processes. HoloArch was implemented in a game engine during a spiral software development process. HoloArch allows users to interact with, visualize, modify and explore architectural models. The evaluation is performed in two workshops and a user study. A hybrid approach that combines qualitative and quantitative data collection was adopted for evaluation. Qualitative data analyses involve interviews, while quantitative data analyses involve both daylighting simulations and questionnaires (e.g. technology acceptance model (TAM), presence and system usability scale (SUS)). Findings According to the questionnaire results, HoloArch had 92/100 for SUS, a mean value of 120.4 for presence questionnaire (PQ) and 9.4/10 for TAM. According to the simulation results, all participants improved the given building's daylighting performance using HoloArch. The interviews also indicated that HoloArch is an effective design tool in terms of augmented perception, continuous design processes, performative daylighting design and model interaction. However, challenges still remain regarding the complete integration of tools and simultaneous simulation visualization. The study concludes that IEs hold promising potentials where performative design actions at conceptual, spatial and architectural domains can take place interactively and simultaneously with immediate feedback. Originality/value The research integrates building information modeling (BIM), performative daylighting simulations and IEs in an interactive environment for the identification of potentials and limitations in performative architectural design. Different from existing immersive tools for architecture, HoloArch offers a continuous bidirectional workflow between BIM tools and IEs.
Bite-Sized Virtual Reality Learning Applications: A Pattern-Based Immersive Authoring Environment

Bite-sized learning is a current educational trend in which educators divide content into relatively small, easily comprehensible chunks, called nuggets. In this paper, we introduce an authoring toolkit that relies on VR implementation of nuggets and show that a nugget-based approach is also facilitating the authoring of VR learning content. In particular, we present Immersive Nugget Tiles (IN-Tiles), a novel authoring toolkit aimed at authors who are not experts in VR. With IN-Tiles, manipulating VR nuggets and authoring VR learning content can be directly accomplished within a virtual environment allowing authors to immediately experience the results of their authoring efforts in VR. We discuss the underlying concepts of IN-Tiles, specifically how to visualize VR nuggets in a virtual environment and how to present affordances that support authoring and manipulating VR nuggets. We report the results of a user study where we evaluated the IN-Tiles toolkit and compared it to a conventional 2D authoring environment that also relies on component-based VR. The results support the hypothesis that nugget-based immersive authoring tools are suitable to create bitesized VR applications successfully and that authoring directly in VR has an added value particularly for authors who are no IT specialists.