Reviewerly/exHarmony huggingface dataset:
Feature Space Discriminatively Trained Punjabi Children Speech Recognition System Using Kaldi Toolkit

Despite significant progress has been made in building of ASR system for various adult speech, whereas the children ASR system is still in infant stage for Indian languages. To build Punjabi children speech recognition is one such challenge because of unavailability of zero-speech corpus. In this paper, efforts have been made to build small vocabulary Punjabi continuous children speech corpus. In explored system, four variations of bMMI discriminative techniques have been perform on two context models: Dependent and Independent. Experiment result have shown that system attains Relative Improvement (RI) of 22-26% on fbMMI and fMMI acoustic model as compared to other approaches. Various combination of parameter has been implemented with variation in boosted parameter and iteration values to obtain optimal value of bMMI and fbMMI acoustic models.
Embracing the Fourth Industrial Revolution by Developing a More Relevant Educational Spectrum

The dawn of industrial revolution 4.0 requires the creation of a new educational spectrum that includes teaching and learning content, as well as educational theo-ries that are responsive and relevant to the post-Digital Age. This calls for a modification to seminal theories to enable a successful 21st-century era of teaching and learning. Using Information and Communication Technology devices such as digital projectors, slides, clickers; and smartboards are fairly outdated in the current education sector. The introduction of tablet personal computers and apps, together with learning and classroom management systems, has become a favored pedagogical tool. We have witnessed a global movement towards apps that has resulted in educators being encouraged to use tablets in innovative ways, to enhance the learning experience. The advantages of such innovation can only be realized when students are adept at using digital tools, and when educators integrate the tools meaningfully into their pedagogy. Industrial Revolution 4.0 is emphasizing the significance of computer programming, robotics, and data coding, which has prompted many education departments globally to introduce these fields into the early years of schooling. These fields produce skills that are not only relevant to the time that we live in but influence the future and economic growth of countries. It is crucial that these skills are developed and nurtured at the primary level. This chapter presents an ideal curriculum that spans two years and ignites a desire in students to use their tablet innovatively. It also explores some innovative pedagogic tools and techniques based on literature and personal experience, as well as provides ways to introduce abstract concepts like programming and robotics to novices. The approaches probed herein are from a South African context, with the focus on content characteristics, impact, and importance.
Performance and energy-aware bi-objective tasks scheduling for cloud data centers

Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.
Trajectory optimization for manipulators based on external archives self-searching multi-objective particle swarm optimization

In order to improve the quality of the non-inferior solutions obtained by multi-objective particle swarm optimization (MOPSO), an improved algorithm called external archives self-searching multi-objective particle swarm optimization (EASS-MOPSO) was proposed and applied to a multi-objective trajectory optimization problem for manipulators. The position curves of joints were constructed by using quartic B-splines; the mathematical models of time, energy and jerk optimization objectives for manipulators were established; and the kinematic constraints of joints were transformed into the constraints of the control vertexes of the B-splines. A self-searching strategy of external archives to make non-inferior solutions have the ability to search the surrounding hyperspace was explored, and a diversity maintaining strategy of the external archives was proposed. The results of several test functions by simulation show that the convergence and diversity of the proposed algorithm are better than those of other 4 selected algorithms; the results of the trajectory optimization problem for manipulators by simulation show that the convergence, diversity and time consumption of the proposed algorithm are significantly better than those of non-dominated sorting genetic algorithm.
Cryptanalysis of the GPRS Encryption Algorithms GEA-1 and GEA-2

This paper presents the first publicly available cryptanalytic attacks on the GEA-1 and GEA-2 algorithms. Instead of providing full 64-bit security, we show that the initial state of GEA-1 can be recovered from as little as 65 bits of known keystream (with at least 24 bits coming from one frame) in time $$2^{40}$$ GEA-1 evaluations and using 44.5 GiB of memory. The attack on GEA-1 is based on an exceptional interaction of the deployed LFSRs and the key initialization, which is highly unlikely to occur by chance. This unusual pattern indicates that the weakness is intentionally hidden to limit the security level to 40 bit by design. In contrast, for GEA-2 we did not discover the same intentional weakness. However, using a combination of algebraic techniques and list merging algorithms we are still able to break GEA-2 in time $$2^{45.1}$$ GEA-2 evaluations. The main practical hurdle is the required knowledge of 1600 bytes of keystream.
Blockchain-Based Big Data Integrity Service Framework for IoT Devices Data Processing in Smart Cities

In modern times, smart cities are using advanced technology to control and coordinate business, physical, social and commercial systems to deliver quality services to their residents while ensuring the efficient use of available resources. Many large corporations, as well as government agencies, are involved in building smart cities because smart city activities also require information available from organizations. Blockchain-based technology allows peer-to-peer exchanges as a public logger that records transaction records, smart contracts, agreements, and exports from third party mediators. In the future, smart cities will be equipped with several new technologies, a large amount of data, multiple Internet of Things (IoT) devices, and a unique environment. Data processing among IoT devices in future smart cities is a challenging task. In this paper, the author has proposed the framework of Big Data based blockchain data integrity service to process IoT device data processing in smart cities. The author collected the necessary information from various sources and used three tests to evaluate key performance indicators. The K-mean algorithm was used to separate the data, and a blockchain strategy was used to ensure secure communication between IoT devices. The frame is modelled and tested using up to 100 devices. The proposed framework allows IoT devices to make better use of the information collected from various resources to participate in operations and exchange of information.
Auxiliary Bi-Level Graph Representation for Cross-Modal Image-Text Retrieval

Image-text retrieval is one of the most common tasks in multimodal retrieval. It suffers from the problem of information imbalance between modalities, which is so-called modality gap. It remains challenging because prior methods cannot bridge the gap reasonably. With the help of scene graph, we start by designing an auxiliary bi-level graph representation (ABGR) pipeline that can fully mine the potential information and reduce the information redundancy. By doing so, each modality will be represented by lexical word graph that carries the main content of the information. Specifically, we design a graph feature enhancement (GFE) module to embed the graph-structured information in a common subspace while exploring the relationship between lexical words. As a result, a better representation for both image and text can be obtained, which helps us to evaluate the similarity between images and texts more reasonably. Experimental results conducted on two benchmark datasets Flickr30K and MS-COCO demonstrate the effectiveness of our proposed model for cross-modal retrieval task.
Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking With Unknown Non- Stationary Heavy-Tailed Measurement Noise

A robust generalized labeled multi-Bernoulli (GLMB) filter is presented to perform multitarget tracking (MTT) with unknown non-stationary heavy-tailed measurement noise (HTMN). The HTMN is modeled as a multivariate Student's t-distribution with unknown and time-varying mean. The proposed filter relaxes the restrictive assumption that the mean of HTMN is zero, and can effectively deal with MTT under the condition that the mean of HTMN is unknown and time-varying. The variational Bayesian (VB) approximation is applied in the GLMB filtering framework with the augmented state. The marginal likelihood function is obtained via minimizing the Kullback-Leibler divergence by the variational lower bound. The simulation results demonstrate that the proposed filter can effectively track multiple targets in both linear and nonlinear scenarios when the mean of HTMN is unknown and time-varying.
Principled Ultrasound Data Augmentation for Classification of Standard Planes

Deep learning models with large learning capacities often overfit to medical imaging datasets. This is because training sets are often relatively small due to the significant time and financial costs incurred in medical data acquisition and labelling. Data augmentation is therefore routinely used to expand the availability of training data and to increase generalization. However, augmentation strategies are often chosen on an ad-hoc basis without justification. In this paper, we present an augmentation policy search method with the goal of improving model classification performance. We include in the augmentation policy search additional transformations that are commonly used in medical image analysis and evaluate their performance. In addition, we extend the augmentation policy search to include non-linear mixed-example data augmentation strategies. Using these learned policies, we show that principled data augmentation for medical image model training can lead to significant improvements in ultrasound standard plane detection, with an average F1-score improvement of 7.0% overall over naive data augmentation strategies in ultrasound fetal standard plane classification. We find that the learned representations of ultrasound images are better clustered and defined with optimized data augmentation.
Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs

Computing k-cores on graphs is an important graph mining target as it provides an efficient means of identifying a graph's dense and cohesive regions. Computing k-cores on hypergraphs has seen recent interest, as many datasets naturally produce hypergraphs. Maintaining k-cores as the underlying data changes is important as graphs are large, growing, and continuously modified. In many practical applications, the graph updates are bursty, both with periods of significant activity and periods of relative calm. Existing maintenance algorithms fail to handle large bursts, and prior parallel approaches on both graphs and hypergraphs fail to scale as available cores increase.We address these problems by presenting two parallel and scalable fully-dynamic batch algorithms for maintaining k-cores on both graphs and hypergraphs. Both algorithms take advantage of the connection between k-cores and h-indices. One algorithm is well suited for large batches and the other for small. We provide the first algorithms that experimentally demonstrate scalability as the number of threads increase while sustaining high change rates in graphs and hypergraphs.
Deep Differential Amplifier for Extractive Summarization

For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when optimizing the classification.The imbalanced sentence classification in extractive summarization is inherent, which can't be addressed by data sampling or data augmentation algorithms easily.In order to address this problem, we innovatively consider the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework to enhance the features of summary sentences.Specifically, we calculate and amplify the semantic difference between each sentence and other sentences, and apply the residual unit to deepen the differential amplifier architecture.Furthermore, the corresponding objective loss of the minority class is boosted by a weighted cross-entropy.In this way, our model pays more attention to the pivotal information of one sentence, that is different from previous approaches which model all informative context in the source document.Experimental results on two benchmark datasets show that our summarizer performs competitively against state-of-the-art methods.Our source code will be available on Github.
A Modified Blockchain DPoS Consensus Algorithm Based on Anomaly Detection and Reward-Punishment

The blockchain consensus algorithm can be leveraged to enhance IoT security and promote the efficiency of collaboration among IoT nodes. Delegated Proof of Stake (DPoS) can simultaneously meet the low-cost and high-efficiency requirements and improve the service quality of node collaboration. However, the malicious attacks, selfishness and insufficient enthusiasm of collaborative nodes can affect DPoS consensus process. In view of these challenges, we modify DPoS consensus algorithm, and design a self-selecting abnormal data detection algorithm to identify abnormal data from malicious attacks and selfishness behavior of nodes. Meanwhile, a game theory-based reward and punishment incentive mechanism is proposed to improve the voting enthusiasm of nodes. Simulation results show that the proposed mechanism can effectively reduce the effect of malicious attacks and nodes selfishness in DPoS consensus process.
Roundness Detection of End Face for Shaft Workpiece based on Canny-Zernike Sub Pixel Edge Detection and Improved Hough Transform

In order to improve roundness detection efficiency and accuracy of the shaft end face, a detection method based on improved Canny-Zernike Sub-Pixel Edge Detection and improved Hough Transform is designed and implemented, which overcomes the shortcoming of low accuracy using single Hough Transform Detection. Firstly, the Optimized Canny Operator is used to extract the pixel level edge gradient of the original image, and then relocate the extracted edge at sub-pixel level along the gradient direction with Zernike Moment. Finally, the Improved Hough Roundness Detection Algorithm is used on the sub-pixel edge, and the roundness error is evaluated with the Minimum Region Method. Compared with the traditional pixel level Roundness Detection Algorithm, it has higher positioning accuracy, stronger noise immunity. The accuracy reaches 0.001 pixel level and detection speed reaches millisecond level.
Realizing Active Inference in Variational Message Passing: The Outcome-Blind Certainty Seeker

Active inference is a state-of-the-art framework in neuroscience that offers a unified theory of brain function. It is also proposed as a framework for planning in AI. Unfortunately, the complex mathematics required to create new models can impede application of active inference in neuroscience and AI research. This letter addresses this problem by providing a complete mathematical treatment of the active inference framework in discrete time and state spaces and the derivation of the update equations for any new model. We leverage the theoretical connection between active inference and variational message passing as described by John Winn and Christopher M. Bishop in 2005. Since variational message passing is a well-defined methodology for deriving Bayesian belief update equations, this letter opens the door to advanced generative models for active inference. We show that using a fully factorized variational distribution simplifies the expected free energy, which furnishes priors over policies so that agents seek unambiguous states. Finally, we consider future extensions that support deep tree searches for sequential policy optimization based on structure learning and belief propagation.
Question-aware memory network for multi-hop question answering in human–robot interaction

Abstract Knowledge graph question answering is an important technology in intelligent human–robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different priority for the triples selection in the reasoning steps. Most existing models take the question as a whole and ignore the priority information in it. To solve this problem, we propose question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process. In addition, we incorporate graph context information into knowledge graph embedding model to increase the ability to represent entities and relations. We use it to initialize the QA2MN model and fine-tune it in the training process. We evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets for complex multi-hop question answering. The result demonstrates that QA2MN achieves state-of-the-art Hits @1 accuracy on the two datasets, which validates the effectiveness of our model.
Utility Optimization for Blockchain Empowered Edge Computing with Deep Reinforcement Learning

The combination of mobile edge computing (MEC) and blockchain is transforming the current computing services in Internet of Things networks, by offering task offloading solutions with security enhancement enabled by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme to optimize the system utility in blockchain-empowered MEC. Herein, each edge device (ED) not only handles data tasks but also deals with block mining which makes the system design and optimization highly complex. Therefore, we develop a novel cooperative deep reinforcement learning (DRL) approach which allows EDs to cooperatively offload their data tasks to the MEC server and perform block mining based on a Proof-of-Reputation consensus mechanism. Simulation results demonstrate that the proposed scheme significantly improves offloading utility, reduces blockchain mining latency, and achieves better system utility, compared to other non-cooperative and cooperative schemes.
<scp>QoS</scp>‐aware trust establishment for cloud federation

Abstract Cloud federation enables inter‐layer resource exchanges among multiple, heterogeneous cloud service providers. This article proposes a Quality of Service (QoS) aware trust model for effective resource allocation in response to the various user requests within the Clouds4Coordination (C4C) federation system. This QoS mainly comprises of nine parameters combined into three categories: (i) node profile, (ii) reliability, and (iii) competence. Numerical values for these parameters are computed every ‘ t ’ seconds for each cloud provider. All values measured over an interval Δt are further processed by the proposed model to evaluate the utility associated with a provider (referred to as a discipline in the presented case study). The decision about interacting with a discipline in a collaborative project is based on this utility value. The systems architecture, evaluation methodology, proposed model, and experimental evaluation on a practical test bed is outlined. The proposed QoS‐aware trust evaluation mechanism allows selection of the most useful (based on a utility value) providers. The proposed approach can be used to support federation of cloud services across a number of different application domains.
Rich Prosody Diversity Modelling with Phone-Level Mixture Density Network

Generating natural speech with diverse and smooth prosody pattern is a challenging task. Although random sampling with phone-level prosody distribution has been investigated to generate different prosody patterns, the diversity of the generated speech is still very limited and far from what can be achieved by human. This is largely due to the use of uni-modal distribution, such as single Gaussian, in the prior works of phone-level prosody modelling. In this work, we propose a novel approach that models phone-level prosodies with GMM based mixture density network (GMM-MDN). Experiments on the LJSpeech dataset demonstrate that phone-level prosodies can precisely control the synthetic speech and GMM-MDN can generate more natural and smooth prosody pattern than a single Gaussian. Subjective evaluations further show that the proposed approach not only achieves better naturalness, but also significantly improves the prosody diversity in synthetic speech without the need of manual control.
BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese

We present BARTpho with two versions -- BARTpho_word and BARTpho_syllable -- the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. Our BARTpho uses the large architecture and pre-training scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, our BARTpho outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future research and applications of generative Vietnamese NLP tasks. Our BARTpho models are available at: this https URL
Fringe-width encoded patterns for 3D surface profilometry

This paper presents a new fringe projection method for surface-shape measurement that uses novel fringe-width encoded fringe patterns. Specifically, the projection patterns are adjusted with the width of the fringe as the codeword. The wrapped phase with coding information is obtained by using the conventional wrapped phase calculation method, and the fringe order can be identified from the wrapped phase. After the fringe order is corrected based on the region growing algorithm, the fringe order and the wrapped phase can be directly used to reconstruct the surface. Static and dynamic measurements demonstrated the ability of the method to perform 3D shape measurement with only three projected patterns, single camera and projector in the least case.