Reviewerly/exHarmony huggingface dataset:
Partially Reliable Transport Layer for QUICker Interactive Immersive Media Delivery

requirements such as high bandwidth (i.e., several Gbps) and low latency (i.e., five milliseconds). Today, most video-streaming applications leverage the transmission control protocol (TCP) for reliable end-to-end transmission. However, the reliability of TCP comes at the cost of additional delay due to factors such as connection establishment, head-of-line (HOL) blocking, and retransmissions under sub-optimal network conditions. Such behavior can lead to stalling events or freezes, which are highly detrimental to the user's Quality of Experience (QoE). Recently, QUIC has gained traction in the research community, as it promises to overcome the shortcomings of TCP without compromising on reliability. However, while QUIC vastly reduces the connection establishment time and HOL blocking, thus increasing interactivity, it still underperforms while delivering multimedia due to retransmissions under lossy conditions. To cope with these, QUIC offers the possibility to support unreliable delivery, like that of the user datagram protocol (UDP). While live-video streaming applications usually opt for completely unreliable protocols, such an approach is not optimal for immersive media delivery since it is not affordable to lose certain data that might affect the end user's QoE. In this paper, we propose a partially reliable QUIC-based data delivery mechanism that supports both reliable (streams) and unreliable (datagrams) delivery. To evaluate its performance, we have considered two immersive-video delivery use cases, namely tiled 360-degree video and volumetric point clouds. Our approach outperforms state-of-the-art protocols, especially in the presence of network losses and delay. Even at a packet loss ratio as high as 5%, the number of freezing events for a 120-second video is almost zero as against 120 for TCP.
Personal Attribute Prediction from Conversations

Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can be regarded as a main source to populate the PKB. Given a user, a user attribute, and user utterances from a conversational system, we aim to predict the personal attribute value for the user, which is helpful for the enrichment of PKBs. However, there are three issues existing in previous studies: (1) manually labeled utterances are required for model training; (2) personal attribute knowledge embedded in both utterances and external resources is underutilized; (3) the performance on predicting some difficult personal attributes is unsatisfactory. In this paper, we propose a framework DSCGN based on the pre-trained language model with a noise-robust loss function to predict personal attributes from conversations without requiring any labeled utterances. We yield two categories of supervision, i.e., document-level supervision via a distant supervision strategy and contextualized word-level supervision via a label guessing method, by mining the personal attribute knowledge embedded in both unlabeled utterances and external resources to fine-tune the language model. Extensive experiments over two real-world data sets (i.e., a profession data set and a hobby data set) show our framework obtains the best performance compared with all the twelve baselines in terms of nDCG and MRR.
Towards an Error-Detecting Code

In this paper we consider an error-detecting code that we have previously defined using quasigroups. The code is defined in the following way: Each input block $$a_0a_1\dots a_{n-1}$$ of symbols from the quasigroup used for coding is extended into block $$a_0a_1\dots a_{n-1}d_0d_1\dots d_{n-1}$$ , where the redundant symbols $$d_i, i\in \{0, 1, \dots , n-1\}$$ , are calculated using the quasigroup operation $$*$$ , i.e., $$d_i=a_i*a_{i+1 \; (mod \; n)}$$ . In our previous work we classified the quasigroups of order 4 according to their probability of undetected errors. Since in our previous work we showed that the quasigroups in the first two classes give almost equal probabilities of undetected errors, now we will compare the quasigroups from these two classes according to the number of errors that the code surely detects when they are used for coding. In order to do this, first using simultions we will obtain the number of errors that the code surely detects when for coding is used a quasigroup from the second best class of quasigroups of order 4 for coding. We will show that from the aspect of the number of errors that the code surely detects, these quasigroups are equally good for coding as the quasigroups from the best class of quasigroups when the length of the input blocks is at least three characters from the quasigroup of order 4.
Personalised meta-path generation for heterogeneous graph neural networks

Abstract Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous information networks (HINs) into low-dimensional node representations. To date, most HGRL models rely on hand-crafted meta-paths. However, the dependency on manually-defined meta-paths requires domain knowledge, which is difficult to obtain for complex HINs. More importantly, the pre-defined or generated meta-paths of all existing HGRL methods attached to each node type or node pair cannot be personalised to each individual node. To fully unleash the power of HGRL, we present a novel framework, Personalised Meta-path based Heterogeneous Graph Neural Networks (PM-HGNN), to jointly generate meta-paths that are personalised for each individual node in a HIN and learn node representations for the target downstream task like node classification. Precisely, PM-HGNN treats the meta-path generation as a Markov Decision Process and utilises a policy network to adaptively generate a meta-path for each individual node and simultaneously learn effective node representations. The policy network is trained with deep reinforcement learning by exploiting the performance improvement on a downstream task. We further propose an extension, PM-HGNN ++ , to better encode relational structure and accelerate the training during the meta-path generation. Experimental results reveal that both PM-HGNN and PM-HGNN ++ can significantly and consistently outperform 16 competing baselines and state-of-the-art methods in various settings of node classification. Qualitative analysis also shows that PM-HGNN ++ can identify meaningful meta-paths overlooked by human knowledge.
Voronouskaya Univariate and Multivariate Asymptotic Expansions for Sigmoid Functions Induced Quasi-interpolation Neural Network Operators Revisited

Here we study further the quasi-interpolation arctangent-algebraic-Gudermannian-generalized symmetrical activation functions relied neural network operators of one hidden layer. Based on fractional calculus theory we derive fractional Voronovskaya type asymptotic expansions for the approximation of these operators to the unit operator, as we are studying the univariate case. We treat also analogously the multivariate case by using Fréchet derivatives. The functions under approximation are Banach space valued. It follows [17].
Improving Spoofing Capability for End-to-end Any-to-many Voice Conversion

Audio deep synthesis techniques have been able to generate high-quality speech whose authenticity is difficult for humans to recognize. Meanwhile, many anti-spoofing systems have been developed to capture artifacts in the synthesized speech that are imperceptible to human hearing, thus a continuous escalating race of 'attacking and defending' in voice deepfake has started. Hence, to further improve the probability of successfully cheating anti-spoofing systems, we propose a fully end-to-end, any-to-many voice conversion method based on a non-autoregressive structure with the addition of two light but strong post-processing strategies namely silence replacement and global noise perturbation. Experimental results show that the proposed method performs better than current baselines in fooling several state-of-the-art anti-spoofing systems. Better naturalness and speaker similarity are also achieved, resulting in our proposed method showing high deception performance against humans.
Finding the Right Balance: Technical and Political Decentralization in the Token Economy

Abstract Token economy instances can be built on a variety of protocols, such as distributed ledger technology (DLT) protocols. DLT protocols are used to operate DLT systems that perform, in a decentralized way, several tasks (e.g., the prevention of double-spending) that are usually handled by central actors, such as banks. Using DLT can be subject to challenges in terms of interoperability between DLT systems (e.g., due to incompatible interfaces), limiting the dynamics in token economy instances, for example, regarding collaboration between organizations operating on separate distributed ledgers. Cross-ledger interoperability (CLI) systems can connect DLT systems. However, it is unclear how the degree of decentralization (i.e., technical and political) of CLI systems affects token economy instances that comprise multiple DLT systems. To better understand such effects, we describe patterns for the implementation of CLI systems and present our notion of political decentralization in token economy instances using CLI. We describe potential implications of CLI patterns and governance mechanisms on token economy instances. Drawing from those implications, we discuss balancing centralization and decentralization in token economy instances that comprise multiple DLT and CLI systems.
PPDS: Privacy Preserving Data Sharing for AI applications Based on Smart Contracts

With the development of artificial intelligence, the need for data sharing is becoming more and more urgent. However, the existing data sharing methods can no longer fully meet the data sharing needs. Privacy breaches, lack of motivation and mutual distrust have become obstacles to data sharing. We design a privacy-preserving, decentralized data sharing method based on blockchain smart contracts, named PPDS. To protect data privacy, we transform the data sharing problem into a model sharing problem. This means that the data owner does not need to directly share the raw data, but the AI model trained with such data. The data requester and the data owner interact on the blockchain through a smart contract. The data owner trains the model with local data according to the requester's requirements. To fairly assess model quality, we set up several model evaluators to assess the validity of the model through voting. After the model is verified, the data owner who trained the model will receive reward in return through a smart contract. The sharing of the model avoids direct exposure of the raw data, and the reasonable incentive provides a motivation for the data owner to share the data. We describe the design and workflow of our PPDS, and analyze the security using formal verification technology, that is, we use Coloured Petri Nets (CPN) to build a formal model for our approach, proving its security through simulation execution and model checking. Finally, we demonstrate effectiveness of PPDS by developing a prototype with its corresponding case application.
Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty

Efficient and effective learning is one of the ultimate goals of deep reinforcement learning (DRL), although the compromise has been made most of the time, especially for the application of robot manipulations. Learning is always expensive for robot manipulation tasks and the learning effectiveness could be affected by the system uncertainty. In order to solve the above challenges, in this study, we proposed a simple but powerful reward shaping method, namely Dense2Sparse. It combines the fast convergence advantage of the dense reward and the noise/uncertainty isolation merit of the sparse reward, to achieve a balance between the learning efficiency and the effectiveness, which makes it suitable for robot manipulation tasks. We evaluated our Dense2Sparse method with a series of ablation experiments using the deep learning-based state estimator with system uncertainty. The testing results show that, in both simulation and real robot implementation, the proposed Dense2Sparse method is capable of getting a higher expected reward and success rate compared with the ones using standalone dense or sparse reward, and it also has a superior tolerance for system uncertainty.
Structured Pruning for Deep Convolutional Neural Networks via Adaptive Sparsity Regularization

Structured pruning is a promising method to reduce the computational cost and memory load, and then accelerate the inference process of deep neural networks. Therefore, it facilitates the deep convolutional model application on resource-constrained devices, such as IoT devices and embedded systems. To make a compact deep convolutional neural network model, many network pruning methods enforce the sparsity by imposing sparse constraints on weight parameters during training and pruning some insignificant weights. However, those methods usually impose sparse constraints without any guidance, while simply limiting the compression rate. This paper proposes a dynamic scheme to impose the sparse constraints according to the filter weights, which can guide sparsity-induced training to choose import channels in a deep convolutional neural network. The extensive experiments present that our adaptive sparsity-induced training is more efficient than a static training scheme. Compared to the existing techniques, the proposed method achieved a superior pruning performance on CIFAR10 with 91.5% parameters reduction and 61.6% floating point operations (FLOPs). On the object detection task, our method achieved 91.2% parameter reductions and 65.8% FLOPs reductions on YOLOV5s. Furthermore, we observed a significant acceleration on the inference process of the pruned models on real resource-constrained devices.
Online versus Offline Optimization Methods for Raman Amplifier Optimization

In this work, we evaluate machine learning (offline) and evolutionary strategy (online) techniques for the Raman pump power optimization. Experimental results show that, although reusable and accurate, online tools may be time-consuming for reconfigurable amplifiers.
A Novel Physical Layer Key Generation Method Based on WGAN-GP Adversarial Autoencoder

The reciprocity of wireless channel between legitimate nodes is the premise of physical layer security key generation. Deep neural networks provide the non-linear transformation method to extract symmetric keys from reciprocal channel responses and show better freedom and performance than traditional methods. However, the hidden layer output in neural networks is unpredictable and the extracted high-dimensional features can not be estimated in advance, which is difficult to be applied to the key generation in the physical layer. In this paper, the key generation method based on WGAN-GP adversarial autoencoder is proposed to extract features efficiently between legitimate nodes, and the features can be fitted to the Gaussian distribution. Meanwhile, the Wasserstein distance and gradient penalty are applied in the game training to solve the gradient explosion. Compared with the PCA method, the proposed method has higher security key capacity, lower key error rate, and no interaction in the key generation process, which avoids key leakage.
Reentrancy Vulnerability Detection of Smart Contract Based on Bidirectional Sequential Neural Network with Hierarchical Attention Mechanism

In recent years, with the advent of the blockchain 2.0 era, the security problems of smart contracts have gradually emerged. The detection of contract vulnerabilities is currently a research hotspot in blockchain security [1]. Current research methods are mainly based on traditional software defect analysis methods for vulnerabilities detection, but the detection accuracy and false alarm rate are not satisfactory. In this paper, we propose a bidirectional long short-term memory neural network model (HAM-BiLSTM for short) with hierarchical attention mechanism, which takes the code segment and account information of a smart contract as input. It divides the input samples into three levels as documents: word level, sentence level and document level, and introduces attention mechanism in different levels. The aim is to detect reentrancy vulnerability more accurately and reduce the false alarm rate of the model as much as possible. The neural network model classifies smart contracts through softmax layers by learning feature information from training samples to determine the presence of reentrancy vulnerability. The experiments demonstrate that our proposed solution and model increase detection accuracy and reduce false alarm rate.
Formal Verification of Neural Networks: a Case Study about Adaptive Cruise Control

Formal verification of neural networks is a promising technique to improve their dependability for safety critical applications. Autonomous driving is one such application where the controllers supervising different functions in a car should undergo a rigorous certification process. In this paper we present an example about learning and verification of an adaptive cruise control function on an autonomous car. We detail the learning process as well as the attempts to verify various safety properties using the tool NeVer2 a new framework that integrates learning and verification in a single easy-to-use package intended for practictioners rather than experts in formal methods and/or machine learning.
3D Skill Trees in Mixed Reality for Creating and Visualizing Learning Plans

Curricula at university and learning paths in self-regulated learning span a complex planning space that is difficult to visualize. Students need to plan their decisions ahead to take a valid order of courses that align with their learning goals. Previous approaches used 2D skill tree visualizations to map the dependencies which, however, cannot convey mixtures of long-term goals and short-term action plans well. We present an approach that realizes learning plans as 3D skill tree structures in mixed reality. With the extra dimension, clearer layouts can be found which combine views on long-term goals and specific actions. By bringing the 3D learning plans to mixed reality, depth perception can help the students to understand the 3D structure of the tree. Moreover, nodes of the tree can be anchored at places in the real world which aids the students’ spatial memory to help them remember the plan. The application is evaluated in two user studies with 17 and 53 participants, respectively. Although the amount of text and typing should be minimized, the results show that 3D learning plans are understood by students. As the hardware becomes more available, the 3D learning plans can help students to structure their studies.
Discriminative Region Transfer Network for Cross-Database Micro-Expression Recognition

Compared to conventional micro-expression analysis, cross-database micro-expression recognition (CDMER) considers a more practical scenario, where the training and testing samples come from different databases. Under this problem setting, most previous micro-expression recognition methods may suffer severe performance degradation due to dataset bias or domain shift. This makes CDMER a challenging but interesting problem. Most existing CDMER methods transfer global micro-expression features without considering the different contributions of facial regions to micro-expression. To tackle this problem, we first confirm the following argument: for samples of the same category from different datasets, their discriminative facial regions are similar and relevant. Therefore, we can transfer the knowledge about discriminative facial regions learned from the source domain to the target domain directly. Based on this argument, we design a novel deep domain adaption method called discriminative region transfer network (DRTN). The DRTN uses an adversarial-based adaptation method to align the source and the target distribution in the learned feature space. Then during transfer, we focus on the discriminative facial regions by the feat of attention mechanism. We conducted extensive experiments on CASME II and SMIC datasets and achieves very competitive results. These evaluations convincingly demonstrate the effectiveness of our proposed method.
Immersive Virtual Reality Environments: a proposal to enhance preservice teacher’s communicative competences

This paper addresses a real need for an interactive tool to gather evidence on relevant emotions involved in classroom conflict management while providing learners with an experiential opportunity, feedback, and feedforward to improve classroom management and communicative skills. Hence, the classroom conflict transformation occurs into a learning opportunity. This research plan proposes implementing an experiential and experimental initial training program to train preservice teachers as a module to complement the Secondary School Teaching, Vocational Training, and Language Teaching master’s Degree’s current curriculum. Virtual Reality Learning Environment (VRLE) allows users to embody learning experiences to produce intense and real emotional sensations. At the same time, a VRLE recreates a real Secondary School classroom gathered from school disciplinary reports and preservice teachers’ observations during their field practicum. Exposure to these VRLE scenarios will allow instant learners’ feedback and feedforward, so they start noticing the most crucial aspects of the classroom environment: immediacy, simultaneity, and unpredictability. Finally, it discusses the research question, the proposed methodology, solutions, and contributions to initial teacher training to enhance preservice teacher’s communicative competences
A Token‐based Compilation Error Categorization and Its Applications

Abstract Compilation errors are unavoidable during the debugging process of novice students. Compiler error messages can help novices to localize and remove errors, but these messages are difficult to understand for students. Previous studies have investigated the compilation error categorization by analyzing compiler error messages, but the categorization cannot cover all kinds of errors, which limits the evaluation of compilation error studies. Therefore, a comprehensive categorization for compilation errors is needed for evaluating the performance of models or tools related to compilation errors. In this study, we first propose a new compilation error categorization, which is based on the smallest unit of the program, tokens. The experiments on 29,573 programs from three datasets show that our proposed compilation error categorization can cover more types of errors and the distribution of the error categorization are significantly different between the datasets. Then, based on our proposed categorization, we develop a neural network model CLACER (CLAssification of Compilation ERrors) for predicting the compilation errors. The results indicate that CLACER can improve the compiler's error localization accuracy and predicts the compilation error effectively. Moreover, based on the proposed categorization, we conduct empirical studies to evaluate the performance of three repairing tools (i.e., DeepFix, RLAssist, and MACER). The comparison results illustrate that DeepFix and RLAssist can fix more errors in the category of delimiter than errors in other categories. Furthermore, MACER performs better than DeepFix and RLAssist because it has a sufficient repairing pattern set for the errors. We also provide some suggestions for improving the repairing tools in the future.