Reviewerly/exHarmony huggingface dataset:
Tree-Constrained Pointer Generator for End-to-End Contextual Speech Recognition

Contextual knowledge is important for real-world automatic speech recognition (ASR) applications. In this paper, a novel tree-constrained pointer generator (TCPGen) component is proposed that incorpo-rates such knowledge as a list of biasing words into both attention-based encoder-decoder and transducer end-to-end ASR models in a neural-symbolic way. TCPGen structures the biasing words into an efficient prefix tree to serve as its symbolic input and creates a neu-ral shortcut between the tree and the final ASR output distribution to facilitate recognising biasing words during decoding. Systems were trained and evaluated on the Librispeech corpus where biasing words were extracted at the scales of an utterance, a chapter, or a book to simulate different application scenarios. Experimental results showed that TCPGen consistently improved word error rates (WERs) compared to the baselines, and in particular, achieved sig-nificant WER reductions on the biasing words. TCPGen is highly efficient: it can handle 5,000 biasing words and distractors and only add a small overhead to memory use and computation cost.
Plasticity-Stability Preserving Multi-Task Learning for Remote Sensing Image Retrieval

Deep learning-based multi-task learning (MTL) methods have recently attracted attention for content-based image retrieval (CBIR) applications in remote sensing (RS). For a given set of tasks (e.g., scene classification, semantic segmentation, and image reconstruction), existing MTL methods employ a joint optimization algorithm on the direct aggregation of task-specific loss functions. Such an approach may provide limited CBIR performance when: 1) tasks compete or even distract each other; 2) one of the tasks dominates the whole learning procedure; or 3) characterization of each task is underperformed compared to single-task learning. This is mainly due to the lack of: 1) plasticity condition (which is associated with sensitivity to new information) or 2) stability condition (which is associated with protection from radical disruptions by new information) of the whole learning procedure. To avoid this issue, as a first time, we propose a novel plasticity-stability preserving MTL (PLASTA-MTL) approach to ensure the plasticity and the stability conditions of the whole learning procedure independently of the number and type of tasks. This is achieved by defining two novel loss functions. The first loss function is the plasticity preserving loss (PPL) function that aims to enforce the global image representation space to be sensitive to new information learned with each task. This is achieved by minimizing the difference of gradient magnitudes for the global representation and task-specific embedding spaces. The second loss function is the stability preserving loss (SPL) function that aims to protect the global representation space radically disrupted by a new task. This is achieved by minimizing the angular distances between the task gradients over global representation space. To effectively employ the proposed loss functions, we also introduce a novel sequential optimization algorithm. Experimental results show the effectiveness of the proposed approach compared to the state-of-the-art MTL methods in the context of CBIR.
Further Analysis and Improvements of a Lattice-Based Anonymous PAKE Scheme

To improve the security of mobile networks in the postquantum era, Dabra <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> recently proposed a lattice-based anonymous password-authenticated key exchange (LBA-PAKE) protocol for mobile devices. Especially, LBA-PAKE is claimed to support the key reuse. However, we find that LBA-PAKE is still vulnerable to the signal leakage attack when the master key is reused. We propose two strategies to reduce the needed number of queries in our attack. Compared to the method of Bindel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , our method reduces the required queries by more than 75%. Our experiments show that breaking LBA-PAKE needs less than 2 min. Through analysis of why LBA-PAKE fails in their security proof, we further propose an improved protocol without incurring extra computation costs. The formal security analysis shows that our improved scheme supports all features of LBA-PAKE while thwarting the signal leakage attack. Moreover, the implementation of our improved protocol demonstrates its efficiency in mobile networks.
Completely Blind Quality Assessment of User Generated Video Content

In this work, we address the challenging problem of completely blind video quality assessment (BVQA) of user generated content (UGC). The challenge is twofold since the quality prediction model is oblivious of human opinion scores, and there are no well-defined distortion models for UGC content. Our solution is inspired by a recent computational neuroscience model which hypothesizes that the human visual system (HVS) transforms a natural video input to follow a straighter temporal trajectory in the perceptual domain. A bandpass filter based computational model of the lateral geniculate nucleus (LGN) and V1 regions of the HVS was used to validate the perceptual straightening hypothesis. We hypothesize that distortions in natural videos lead to loss in straightness (or increased curvature) in their transformed representations in the HVS. We provide extensive empirical evidence to validate our hypothesis. We quantify the loss in straightness as a measure of temporal quality, and show that this measure delivers acceptable quality prediction performance on its own. Further, the temporal quality measure is combined with a state-of-the-art blind spatial (image) quality metric to design a blind video quality predictor that we call STraightness Evaluation Metric (STEM). STEM is shown to deliver state-of-the-art performance over the class of BVQA algorithms on five UGC VQA datasets including KoNViD-1K, LIVE-Qualcomm, LIVE-VQC, CVD and YouTube-UGC. Importantly, our solution is completely blind i.e., training-free, generalizes very well, is explainable, has few tunable parameters, and is simple and easy to implement.
Capabilities and Practices in DevOps: A Multivocal Literature Review

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context.</i> To meet the demands of customers and market, Information Technology (IT) organizations are seeking to implement DevOps. While many succeed in DevOps adoption, others lack the knowledge on how to incorporate DevOps culture, process, measurements, and techniques in their business. Thus, successful adoption is still inconsistent, highlighting the need to provide management with relevant information to support the development of DevOps Capabilities effectively. But what are these Capabilities? Unfortunately, there is still a lack of clarity about DevOps Capabilities and their relationships to DevOps Practices and Outcomes among researchers and practitioners. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective.</i> This research aims to gather community consensus on the relationship between Capabilities and Practices, so a better DevOps implementation can be mapped. Seeking to define DevOps Capabilities and Practices concepts and to identify, organize and summarize Capabilities as they relate to Practices. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method.</i> A Multivocal Literature Review (MLR) is conducted, with 93 documents gathered and thoroughly examined from throughout the community, including books, scientific articles, white papers, and conferences, among others. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results.</i> This survey contributes a list of 37 organized Capabilities, their mentions in literature, and their definitions. The concepts of Practices and Capabilities were mapped and categorized in an ordered taxonomy. It is concluded that industry research has much outweighed scientific research on this topic, with Capabilities evolving dynamically over time, reinforcing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">team collaboration and communication</i> as the most crucial one. The study's Outcomes will assist researchers and practitioners understand how Capabilities and Practices are related at different levels and how to better implement them.
Remote Production for Live Holographic Teleportation Applications in 5G Networks

Holographic Teleportation is an emerging media application allowing people or objects to be teleported in a real-time and immersive fashion into the virtual space of the audience side. Compared to the traditional video content, the network requirements for supporting such applications will be much more challenging. In this paper, we present a 5G edge computing framework for enabling remote production functions for live holographic Teleportation applications. The key idea is to offload complex holographic content production functions from end user premises to the 5G mobile edge in order to substantially reduce the cost of running such applications on the user side. We comprehensively evaluated how specific network-oriented and application-oriented factors may affect the performances of remote production operations based on 5G systems. Specifically, we tested the application performance from the following four dimensions: (1) different data rate requirements with multiple content resolution levels, (2) different transport-layer mechanisms over 5G uplink radio, (3) different indoor/outdoor location environments with imperfect 5G connections and (4) different object capturing scenarios including the number of teleported objects and the number of sensor cameras required. Based on these evaluations we derive useful guidelines and policies for future remote production operation for holographic Teleportation through 5G systems.
Online Path Planning Framework for UAV in Rural Areas

A motion strategy plays an important role in supporting autonomous Unmanned Aerial Vehicle (UAV) movement. Many studies have been conducted to improve the motion frameworks in terms of its robustness, safety and performance. Most of them worked on the prior known maps scenario where the area information was collected by Global Positioning System (GPS) and satellite cameras. Even though the scheme can provide high quality map, the computation of motion planning remains dependent on the communication signal. In the rural areas such as forests and mountains, where communication signal does not perform well, unclear and noisy terrain maps can be generated and lead to mission failure. Therefore, it is significant that an alternative framework to enhance autonomous UAV motion performance in these certain conditions should be developed. Our work focuses on developing a high performance path planner for autonomous UAV motion when communication signal does not work well in rural areas. The search mission problem in forest terrain has been implemented in 3D simulation as an evaluation. By conducting a simulation process repeatedly with different test cases for positions, time constraints, flight speed (3-11 m/s) and flight range, our path planning framework can achieve completeness between 90-100% and better performance compared to others.
MMAuth: A Continuous Authentication Framework on Smartphones Using Multiple Modalities

With the wide use of smartphones, more private data are collected and saved in the smartphones. This raises higher requirements for secure and effective user authentication scheme. Continuous authentication leverages behavioral biometrics as identity information and shows promising characteristics for user verification in a continuous and passive means. However, most studies require users to operate the smartphones in a specific mobile application or perform user-defined touch operations. This paper studies the continuous authentication on smartphones in the wild, where it is hard to characterize touching behavior accurately due to the complexity of usage context and cross-use of various types of touch gestures. Towards this end, in this paper, we propose a continuous authentication framework using multiple modalities, named as MMAuth, which integrates the heterogeneous information of user identity from multiple modalities (e.g., motion movement pattern, touch dynamics, usage context). A time-extended behavioral feature set (TEB) and a deep learning based one-class classifier (DeSVDD) are developed for performing more accurate authentication. Evaluations are conducted using a novel unconstrained smartphone usage dataset collected from 100 volunteers in real world as well as a public laboratory dataset. Extensive experimental results demonstrate that the state-of-the-art authentication performance of MMAuth in both unconstrained and laboratory environment, and the effectiveness of its two proposed modules (the TEB feature set and the DeSVDD classifier). Additional experiments on system robustness, in terms of usability to different touch gestures, sensitivity to various mobile applications, and scalability to user space, are also provided to examine the applicability of MMAuth.
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction

CTR prediction is essential for modern recommender systems. Ranging from early factorization machines to deep learning based models in recent years, existing CTR methods focus on capturing useful feature interactions or mining important behavior patterns. Despite the effectiveness, we argue that these methods suffer from the risk of label sparsity (i.e., the user-item interactions are highly sparse with respect to the feature space), label noise (i.e., the collected user-item interactions are usually noisy), and the underuse of domain knowledge (i.e., the pairwise correlations between samples). To address these challenging problems, we propose a novel Multi-Interest Self-Supervised learning (MISS) framework which enhances the feature embeddings with interest-level self-supervision signals. With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item). Based on that, contrastive learning losses are further applied to the augmented views of interest representations, which effectively improves the feature representation learning. Furthermore, our proposed MISS frame-work can be used as an “plug-in” component with existing CTR prediction models and further boost their performances. Extensive experiments on three large-scale datasets show that MISS significantly outperforms the state-of-the-art models, by up to 13.55% in AUC, and also enjoys good compatibility with representative deep CTR models.
Replacing pooling functions in Convolutional Neural Networks by linear combinations of increasing functions

Traditionally, Convolutional Neural Networks make use of the maximum or arithmetic mean in order to reduce the features extracted by convolutional layers in a downsampling process known as pooling. However, there is no strong argument to settle upon one of the two functions and, in practice, this selection turns to be problem dependent. Further, both of these options ignore possible dependencies among the data. We believe that a combination of both of these functions, as well as of additional ones which may retain different information, can benefit the feature extraction process. In this work, we replace traditional pooling by several alternative functions. In particular, we consider linear combinations of order statistics and generalizations of the Sugeno integral, extending the latter's domain to the whole real line and setting the theoretical base for their application. We present an alternative pooling layer based on this strategy which we name "CombPool" layer. We replace the pooling layers of three different architectures of increasing complexity by CombPool layers, and empirically prove over multiple datasets that linear combinations outperform traditional pooling functions in most cases. Further, combinations with either the Sugeno integral or one of its generalizations usually yield the best results, proving a strong candidate to apply in most architectures.
Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems

Abstract What explains the dramatic progress from 20th‐century to 21st‐century AI, and how can the remaining limitations of current AI be overcome? The widely accepted narrative attributes this progress to massive increases in the quantity of computational and data resources available to support statistical learning in deep artificial neural networks. We show that an additional crucial factor is the development of a new type of computation. Neurocompositional computing adopts two principles that must be simultaneously respected to enable human‐level cognition: the principles of Compositionality and Continuity. These have seemed irreconcilable until the recent mathematical discovery that compositionality can be realized not only through discrete methods of symbolic computing, but also through novel forms of continuous neural computing. The revolutionary recent progress in AI has resulted from the use of limited forms of neurocompositional computing. New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
User preference mining based on fine-grained sentiment analysis

User preference mining is an application of data mining that attracts increasing attention. Although most of the existing user preference mining methods achieved significant performance improvement, the sentiment tendencies of users were seldom considered. This paper proposes fine-grained sentiment analysis for preference mining. The powerful feature representation capabilities of deep neural networks have significantly improved the performance of fine-grained sentiment analysis. But two main challenges remain when using deep neural network models: incomplete user feature extraction and insufficient interaction. In response, a pre-training language model is employed to encode user features to fully explore potential interests of users, a linguistic knowledge model is introduced to assist the encoding, a multi-scale convolution neural network is adopted to capture text features at different scales and fully utilize the text information, and the fine-grained sentiment analysis task is modeled as a sequence labeling problem to explore the sentiment polarity of user evaluation. Experiments on a user review data set are used to verify the new approach. Experimental results of precision, recall rate and F1-value show that the proposed approach performs better, and is more effective than baseline models. For example, the F1-value is increased by 4.27% compared to the best performing baseline model. Findings have important implications for research and practice.
Real-time semantic segmentation with local spatial pixel adjustment

The research of semantic segmentation networks has achieved a significant breakthrough recently. However, most part of methods have difficulty in utilizing information generated at each stage, which resulting in pixel value dislocation and blurred boundaries for small-scale objects. To overcome these challenges, a local spatial pixel adjustment network (LSPANet) is proposed in this paper, which mainly consists of a dual-branch decoding fusion (DDF) module and a spatial pixel cross-correlation (SPCC) block. Specifically, the DDF module takes the high-level and low-level feature maps with different stages as the input, and gradually eliminates the discrepancy in the information of the feature map to fuse a variety of information extracted in the encoder stage. The SPCC block adopts the horizontal spatial pixel adjustment (HSPA) module and the vertical spatial pixel adjustment (VSPA) module to capture the relationship of each pixel value in the local horizontal and vertical space respectively, and then assign the importance to all values based on this relationship. LSPANet is evaluated on Cityscapes and Camvid datasets. The experimental results show that our network achieves 77.1% mIoU with 2 M parameters on the challenging Cityscapes dataset and the inference speed exceeds 30 FPS in a single GTX 2080 Ti GPU.
A game theoretic approach of optimal adoption time of blockchain: A case of ship operators

• A game-theoretic model is developed considering multiple competing ship operators. • A novel algorithm is proposed to solve the numerical Nash equilibrium. • A mixed pricing model achieves faster and wider blockchain adoption. • Substitution policy only threatens small operators under a fixed pricing model. • Blockchain’s cost-effectiveness is crucial to advance blockchain adoption. With increasing uncertainties in the global economy, companies are facing fierce competition. Blockchain has the potential to enhance companies’ competitiveness by streamlining processes, improving productivity and reducing costs. It is meaningful to study whether and when companies would adopt blockchain and under what conditions. We develop a game theoretic model to analyse the introduction of blockchain to companies from a big customer with bargaining power. In this study, ship operators need to decide their optimal adoption time when facing a request from a big shipper to adopt blockchain with a threat of substitution policy from the shipper. An algorithm is developed to obtain the numerical solutions of ship operators’ optimal adoption time. Our analysis suggests that 1) the substitution policy only matters to small companies and is only necessary under fixed pricing model; 2) a threshold applies for substitution ratio and cut-off time to effectively induce small companies to adopt blockchain early; 3) blockchain developers should consider mixed pricing model instead of fixed pricing model for faster and wider blockchain adoption; 4) blockchain initiators should focus more on improving the technology’s cost-effectiveness rather than rely heavily on externalities like substitution policies to promote blockchain adoption.
Employees’ Innovative Work Behavior and Change Management Phases in Government Institutions: The Mediating Role of Knowledge Sharing

The change management process in government institutions includes many challenges that require identifying the factors that may facilitate such a process. This study aims to examine the mediating impact of knowledge sharing (KS) on the relationship between employees’ innovative work behavior (EIWB) and change management phases in governmental institutions, based on Lewin’s model. The data were collected from 300 employees working in governmental institutions in Saudi Arabia using a structured survey questionnaire method. Structural equation modeling (SEM-PLS) with bootstrap procedures was used to test the research hypotheses. The results of the study demonstrated that KS has a significant mediating impact on the relationship between EIWB and the stages of change management (unfreezing, change process, and refreezing). Additionally, KS had greater mediating effects on the unfreezing and refreezing phases than on the change process phase. These results indicate the importance of promoting EIWB and encouraging a knowledge sharing culture for the successful implementation of change programs in governmental institutions.