Reviewerly/exHarmony huggingface dataset:
Usability of Cross-Device Interaction Interfaces for Augmented Reality in Physical Tasks

The shortcomings of established input methods for Augmented Reality (AR) head-mounted displays (HMDs) motivate us to investigate the use of AR HMDs with smartphones and smartwatches to improve AR interaction in physical tasks. However, it is unclear whether the cross-device interaction interfaces are efficient for AR systems on physical tasks because the physical tasks can break the interaction flow. In this work, we conducted a user study to explore this. The user study consists of three subtasks, respectively requiring one of the three representative user interface (UI) controls (buttons, sliders, or text input). We implemented them with mid-air gestures, a smartphone, and a smartwatch. We compared the three interfaces and found that the smartphone and the smartwatch interaction interfaces have greater usability, provide a better user experience, and have lower workloads than the mid-air gesture interface. However, there is no significant difference in terms of efficiency for simple interactions. We discuss the limitations of this research and directions for future work.
A YOLO v3-tiny FPGA Architecture using a Reconfigurable Hardware Accelerator for Real-time Region of Interest Detection

With the recent advances in the fields of machine learning, neural networks and deep-learning algorithms have become a prevalent subject of computer vision. Especially for tasks like object classification and detection Convolutional Neu-ronal Networks (CNNs) have surpassed the previous traditional approaches. In addition to these applications, CNNs can recently also be found in other applications. For example the parametrization of video encoding algorithms as used in our example is quite a new application domain. Especially CNN's high recognition rate makes them particularly suitable for finding Regions of Interest (ROIs) in video sequences, which can be used for adapting the data rate of the compressed video stream accordingly. On the downside, these CNN require an immense amount of processing power and memory bandwidth. Object detection networks such as You Only Look Once (YOLO) try to balance processing speed and accuracy but still rely on power-hungry GPUs to meet real-time requirements. Specialized hardware like Field Programmable Gate Array (FPGA) implementations proved to strongly reduce this problem while still providing sufficient computational power. In this paper we propose a flexible architecture for object detection hardware acceleration based on the YOLO v3-tiny model. The reconfigurable accelerator comprises a high throughput convolution engine, custom blocks for all additional CNN operations and a programmable control unit to manage on-chip execution. The model can be deployed without significant changes based on 32-bit floating point values and without further methods that would reduce the model accuracy. Experimental results show a high capability of the design to accelerate the object detection task with a processing time of 27.5 ms per frame. It is thus real-time-capable for 30 FPS applications at frequency of 200 MHz.
Pattern Analysis of Money Flows in the Bitcoin Blockchain

Bitcoin is the first and highest valued cryptocurrency that stores transactions in a publicly distributed ledger called the blockchain. Understanding the activity and behavior of Bitcoin actors is a crucial research topic as they are pseudonymous in the transaction network. In this article, we propose a method based on taint analysis to extract taint flows—dynamic networks representing the sequence of Bitcoins transferred from an initial source to other actors until dissolution. Then, we apply graph embedding methods to characterize taint flows. We evaluate our embedding method with taint flows from top mining pools and show that it can classify mining pools with high accuracy. We also found that taint flows from the same period show high similarity. Our work proves that tracing the money flows can be a promising approach to classifying source actors and characterizing different money flow patterns.
Token-Level Fact Correction in Abstractive Summarization

This paper addresses fact correction for abstractive summarization of which aim is to edit a system-generated summary into a new source-consistent summary. The summaries generated by abstractive summarization models often contain various kinds of factual errors. Thus, fact correction becomes essential to apply abstractive summarization to real-world applications. However, most existing methods for fact correction focus only on entity-level errors, which occasions the error correction methods to miss non-entity errors such as inconsistent tokens or mentions. Therefore, this paper presents a token-level fact correction that resolves inconsistencies of a system-generated summary at the token level. Since a token is the smallest meaning-bearing unit, all kinds of errors can be corrected if the errors are rectified at this level. The proposed fact corrector examines the consistency of a summary at the summary level like existing methods, but corrects the found inconsistencies at the token level. Thus, the proposed corrector consists of three modules of a summary fact checker, a token fact checker, and a fact emender. The summary fact checker inspects if a system-generated summary is factually consistent with a source text, the token fact checker finds out the tokens which cause inconsistency, and the fact emender actually replaces the inconsistency-causing tokens with correct tokens in the source text. Since these modules are closely related and affect one another, they are jointly trained to improve the performance of each module. The effectiveness of the proposed fact corrector is empirically proven from two viewpoints of consistency and summarization performance. For correcting inconsistencies in a summary, it is shown that the summaries by the proposed corrector are more factually consistent than those by its competitors. In addition, the proposed corrector outperforms the current state-of-the-art corrector even in automatic summarization performance.
Modelling of the Internet Computer Protocol Architecture: The Next Generation Blockchain

The Internet Computer Protocol is described as a third-generation blockchain system that aims to provide secure and scalable distributed systems through blockchains and smart contracts. In this position paper, this innovative architecture is introduced and then discussed in view of its modeling and simulation aspects. In fact, a properly defined digital twin of the Internet Computer Protocol could help its design, development, and evaluation in terms of performance and resilience to specific security attacks. To this extent, we propose a multi-level simulation model that follows an agent-based paradigm. The main issues of the modeling and simulation, and the main expected outcomes, are described and discussed.
The applicability of sunshine-based global solar radiation models modified with meteorological factors for different climate zones of China

With the development of renewable energy, the exploitation and utilization of solar energy resources also need continuous progress, but solar radiation data shortage has become a serious concern. A method for estimating global solar radiation has been developed to address this issue. The sunshine-based model is currently the most widely used model due to its high calculation accuracy and few input parameters. This paper will first review 13 subcategories (8 categories in total) of the global solar radiation prediction model based on sunshine. Subsequently, the astronomical factors were introduced to modify empirical coefficients, and 8 new categories of models based on sunshine rate were introduced. The radiation data from 83 meteorological stations in China was used to train and validate the model, and the performance of the model was evaluated by using evaluation indicators, such as coefficient of determination (R 2 ), root mean square error (RMSE), mean absolute bias error (MABE), mean bias error (MBE), and global performance index (GPI). The results show that the R 2 value of the unmodified empirical model is in a range of 0.82–0.99, and the RMSE value is in a range of 0.018–3.09. In contrast, with the introduction of the astronomical factor, the model accuracy improves significantly, and the modified power function model (N3) gains its best performance. The R 2 of model N3 is in a range of 0.86–0.99, and the RMSE value is in a range of 0.018–2.62. The R 2 increases by 0.49%, while the RMSE value 6.44%. Above all, it does not require the input of other meteorological parameters for predicting the value of global solar radiation.
Hybrid Optimization Based Multi-Objective Path Planning Framework for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been touted as a cost-effective way to follow sensitive resources across wide areas. The extensive wireless sensor networks that UAVs travel through have allowed them to acquire exceptional amounts of data. The path planning of UAV operations in low-altitude urban environments is the main emphasis of this research. Regarding two objectives, namely journey distance and safety level, a Multi-Objective Path Planning (MOPP) framework is introduced. A unique hybrid optimization approach is used to optimally find the best flight path between adjacent acquisition stations based on these MOPPs. The conceptual fusion of traditional DHOA with WOA results in the suggested hybrid model known as Deer Hunter Updated Whale Optimization (DHUWO). Ultimately, the projected collision-free optimal path framework referred to Multi-Objective Path Planning with DHUWO (MOPP-DHUWO) model is compared over the existing models in terms of safety level and distance as well.
THE USE OF DIGITAL FEEDBACK ON ELSA SPEAK IN LEARNING PRONUNCITAION FOR SEVENTH GRADE OF JUNIOR HIGH SCHOOL

In learning English, students must be equipped with knowledge of good and correct pronunciation so that they can be a support for students in English pronunciation. One application that can be used to practice pronunciation by Artificial Intelligence is ELSA Speak. In ELSA Speak, students can learn the correct pronunciation generated from native speakers, and can practice their pronunciation and get digital feedback from ELSA Speak, and correct their pronunciation. Two main discussions were the implementation of digital feedback on Elsa Speak for students and the benefits of digital feedback on ELSA Speak in learning English. This research uses qualitative case studies, in which observation and semi-structured interviews were used as instruments in collecting the data. The result of the research shown that ELSA Speak can help students to learn how to pronounce some words, and this application can make the students be more confident when want to talk with English language.
A Trust and Explainable Federated Deep Learning Framework in Zero Touch B5G Networks

The emergent Zero touch Service and Management (ZSM) paradigm aims to automate the orchestration and management of running network slices, in Beyond 5G networks (B5G), with an unprecedented level of scalability. To achieve this vision, ZSM calls for a large usage of advanced deep learning algorithms, in order to dynamically build efficient decisions. In this context, Federated deep Learning (FL) proved their efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Indeed, FL-based solutions give “machine-centric” decisions about running network slices and their performance, which will be then executed/applied by managers, i.e., slice manager staff/module. However, FL-enabled solutions do not provide any details about why and how such decisions were made, and thus such decisions cannot be properly trusted/understood by slice managers. To alleviate this issue, we leverage eXplainable Artificial Intelligence (XAI) paradigm that aims to improve the transparency of black-box FL decision-making process. In particular, XAI helps to explain the FL-based decisions to make them interpretable/trustable by network slices managers. In this paper, we design a novel XAI-powered framework to explain FL-based decisions. We first build a deep learning model in federated way, to predict key performance indicators (KPI) of network slices. Our FL-based KPI prediction is useful for the configuration and the management of network slice lifecycle, especially for the Service Level Agreement (SLA) violation and the network slice re-configuration. Then, we develop several XAI models on the top of our FL-based model, such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), RuleFit, and Partial Dependence Plot (PDP), to enhance the level of trust, credibility (of the local data/model), transparency, and explanation of the FL-based decisions, while adhering the data privacy, to different B5G network stakeholders, such as slice managers. Experiments results show the efficiency of our XAI-powered framework, to explain FL-based decisions related to latency KPI predictions.
Recursive Subtyping for All

Recursive types and bounded quantification are prominent features in many modern programming languages, such as Java, C#, Scala or TypeScript. Unfortunately, the interaction between recursive types, bounded quantification and subtyping has shown to be problematic in the past. Consequently, defining a simple foundational calculus that combines those features and has desirable properties, such as decidability , transitivity of subtyping, conservativity and a sound and complete algorithmic formulation has been a long time challenge. This paper presents an extension of kernel ‍ F ≤ , called F ≤ µ , with iso-recursive types. F ≤ is a well-known polymorphic calculus with bounded quantification. In F ≤ µ we add iso-recursive types, and correspondingly extend the subtyping relation with iso-recursive subtyping using the recently proposed nominal unfolding rules. We also add two smaller extensions to F ≤ . The first one is a generalization of the kernel ‍ F ≤ rule for bounded quantification that accepts equivalent rather than equal bounds. The second extension is the use of so-called structural folding/unfolding rules, inspired by the structural unfolding rule proposed by Abadi, Cardelli, and Viswanathan [1996]. The structural rules add expressive power to the more conventional folding/unfolding rules in the literature, and they enable additional applications. We present several results, including: type soundness; transitivity and decidability of subtyping; the conservativity of F ≤ µ over F ≤ ; and a sound and complete algorithmic formulation of F ≤ µ . Moreover, we study an extension of F ≤ µ , called F ≤≥ µ , which includes lower bounded quantification in addition to the conventional (upper) bounded quantification of F ≤ . All the results in this paper have been formalized in the Coq theorem prover.
Learning multiple gaussian prototypes for open-set recognition

Open-set recognition aims to deal with unknown classes that do not exist in the training phase. The key is to learn effective latent feature representations for classifying the already known classes as well as detecting new emerging ones. In this paper, we learn multiple Gaussian prototypes to better represent the complex classes distribution in both generative and discriminative ways. With the generative constraint, the latent variables of the same class clusters compactly around the corresponding Gaussian prototypes, preserving extra space for the samples of unknown classes. The discriminative constraint separates the Gaussian prototypes of different classes, which further improves the discrimination capability for the known classes. Importantly, the entire framework can be directly derived from the Bayesian inference, thus providing theoretical support for open-set recognition. Experimental results of different datasets verify the reliability and effectiveness of the proposed method. Our code is available at: https://github.com/LiuJMzzZ/MGPL.
TWo-IN-one-SSE: Fast, Scalable and Storage-Efficient Searchable Symmetric Encryption for Conjunctive and Disjunctive Boolean Queries

Searchable Symmetric Encryption (SSE) supports efficient yet secure query processing over outsourced symmetrically encrypted databases without the need for decryption. A longstanding open question has been the following: can we design a fast, scalable, linear storage and low-leakage SSE scheme that efficiently supports arbitrary Boolean queries over encrypted databases? In this paper, we present the design, analysis and prototype implementation of the first SSE scheme that efficiently supports conjunctive, disjunctive and more general Boolean queries (in both the conjunctive and disjunctive normal forms) while scaling smoothly to extremely large encrypted databases, and while incurring linear storage overheads and supporting extremely fast query processing in practice. We quantify the leakage of our proposal via a rigorous cryptographic analysis and argue that it achieves security against a well-known class of leakage-abuse and volume analysis attacks. Finally, we demonstrate the storage-efficiency and scalability of our proposed scheme by presenting experimental results of a prototype implementation of our scheme over large real-world databases.
Lessons Learned from Human Pose Interaction in an Industrial Spatial Augmented Reality Application

This paper is a technical description of a novel Augmented Reality application in the industrial domain of furniture production. In the presented case, workers suffered from high cognitive load in doing end-of-line quality inspection and individual handling of a high variety of products. The proposed solution consists of a Spatial Augmented Reality system, where a projector directly displays information on the product to assist the worker. At the same time results of an in-line quality inspections are shown which can be interactively modified through human gestures. The main contribution of this work is two-fold: (1) in contrast to other industrial augmented reality applications the described one technically builds upon a deep neural net based pose estimation. This allows a seamless interaction with the system and tracking of human actions rather than deriving them from the current state of the work piece, (2) the paper recapitulates experiences and results of the approach with a focus on lessons learned for using human pose estimation in smart production.
Mean estimation when you have the source code; or, quantum Monte Carlo methods

Suppose y is a real random variable, and one is given access to “the code” that generates it (for example, a randomized or quantum circuit whose output is y). We give a quantum procedure that runs the code O(n) times and returns an estimate for μ = E[y] that with high probability satisfies , where σ = stddev[y]. This dependence on n is optimal for quantum algorithms. One may compare with classical algorithms, which can only achieve the quadratically worse . Our method improves upon previous works, which either made additional assumptions about y, and/or assumed the algorithm knew an a priori bound on σ, and/or used additional logarithmic factors beyond O(n). The central subroutine for our result is essentially Grover's algorithm but with complex phases.* The full version of the paper can be accessed at https://arxiv.org/abs/2208.07544
Blockchain Technology Based Crowdfunding Using Smart Contracts

An innovative and modern avenue for startup fundraising is crowdfunding. There are many different forms of crowdfunding, but the fundamental goal is to raise enough money to produce or provide services. The internet and social media are essential for nonprofit organizations and entrepreneurs to raise money. Blockchain is a decentralised, immutable, and secure technology that maintains the transparency of all interactions between parties. The foundation of crowdfunding is the mutual trust between investors and stakeholders. Blockchain based smart contracts has enormous promise for both crowdfunding organisations and individual users. Blockchain-based crowdfunding platforms boost the legitimacy of various initiatives and endeavours, attracting large amounts of funds from donors and investors. The basics of crowdfunding, its existing limits, and the role of blockchain technology in crowdfunding will all be covered in the first section of the paper. Then it talks about other crowdfunding systems built on blockchain that have appeared recently. The explanation of the proposed system and its implementation and outcomes follows.
Automated Exploration and Implementation of Distributed CNN Inference at the Edge

For model inference of convolutional neural networks (CNNs), we nowadays witness a shift from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute- and memory-intensive CNNs on Internet of Things devices at the Edge is challenging as they typically have limited resources. One approach to address this challenge is to leverage all available resources across multiple edge devices to execute a large CNN by properly partitioning it and running each CNN partition on a separate edge device. However, there currently does not exist a design and programming framework that takes a trained CNN model as input and subsequently allows for efficiently exploring and automatically implementing a range of different CNN partitions on multiple edge devices to facilitate distributed CNN inference. Therefore, in this article, we propose a novel framework that automates the splitting of a CNN model into a set of submodels as well as the code generation needed for the distributed and collaborative execution of these submodels on multiple, possibly heterogeneous, edge devices, while supporting the exploitation of parallelism among and within the edge devices. In addition, since the number of different CNN mapping possibilities on multiple edge devices is vast, our framework also features a multistage and hierarchical design space exploration methodology to efficiently search for (near-)optimal distributed CNN inference implementations. Our experimental results demonstrate that our work allows for rapidly finding and realizing distributed CNN inference implementations with reduced energy consumption and memory usage per edge device, and under certain conditions, with improved system throughput as well.
A Brief Survey of Methods for Analytics over RDF Knowledge Graphs

There are several Knowledge Graphs expressed in RDF (Resource Description Framework) that aggregate/integrate data from various sources for providing unified access services and enabling insightful analytics. We observe this trend in almost every domain of our life. However, the provision of effective, efficient, and user-friendly analytic services and systems is quite challenging. In this paper we survey the approaches, systems and tools that enable the formulation of analytic queries over KGs expressed in RDF. We identify the main challenges, we distinguish two main categories of analytic queries (domain specific and quality-related), and five kinds of approaches for analytics over RDF. Then, we describe in brief the works of each category and related aspects, like efficiency and visualization. We hope this collection to be useful for researchers and engineers for advancing the capabilities and user-friendliness of methods for analytics over knowledge graphs.