Reviewerly/exHarmony huggingface dataset:
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English

We study training a single end-to-end (E2E) automatic speech recognition (ASR) model for three languages used in Kazakhstan: Kazakh, Russian, and English. We first describe the development of multilingual E2E ASR based on Transformer networks and then perform an extensive assessment on the aforementioned languages. We also compare two variants of output grapheme set construction: combined and independent. Furthermore, we evaluate the impact of LMs and data augmentation techniques on the recognition performance of the multilingual E2E ASR. In addition, we present several datasets for training and evaluation purposes. Experiment results show that the multilingual models achieve comparable performances to the monolingual baselines with a similar number of parameters. Our best monolingual and multilingual models achieved 20.9% and 20.5% average word error rates on the combined test set, respectively. To ensure the reproducibility of our experiments and results, we share our training recipes, datasets, and pre-trained models (https://github.com/IS2AI/MultilingualASR).
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search

Quantization Neural Networks (QNN) have attracted a lot of attention due to their high efficiency. To enhance the quantization accuracy, prior works mainly focus on designing advanced quantization algorithms but still fail to achieve satisfactory results under the extremely low-bit case. In this work, we take an architecture perspective to investigate the potential of high-performance QNN. Therefore, we propose to combine Network Architecture Search methods with quantization to enjoy the merits of the two sides. However, a naive combination inevitably faces unacceptable time consumption or unstable training problem. To alleviate these problems, we first propose the joint training of architecture and quantization with a shared step size to acquire a large number of quantized models. Then a bit-inheritance scheme is introduced to transfer the quantized models to the lower bit, which further reduces the time cost and meanwhile improves the quantization accuracy. Equipped with this overall framework, dubbed as Once Quantization-Aware Training (OQAT), our searched model family, OQATNets, achieves a new state-of-the-art compared with various architectures under different bit-widths. In particular, OQAT-2bit-M achieves 61.6% ImageNet Top-1 accuracy, outperforming 2-bit counterpart MobileNetV3 by a large margin of 9% with 10% less computation cost. A series of quantization-friendly architectures are identified easily and extensive analysis can be made to summarize the interaction between quantization and neural architectures. Codes and models are released at https://github.com/LaVieEnRoseSMZ/OQA
Bayesian Optimization with a Prior for the Optimum

While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts. This causes BO to waste function evaluations on bad design choices (e.g., machine learning hyperparameters) that the expert already knows to work poorly. To address this issue, we introduce Bayesian Optimization with a Prior for the Optimum (BOPrO). BOPrO allows users to inject their knowledge into the optimization process in the form of priors about which parts of the input space will yield the best performance, rather than BO’s standard priors over functions, which are much less intuitive for users. BOPrO then combines these priors with BO’s standard probabilistic model to form a pseudo-posterior used to select which points to evaluate next. We show that BOPrO is around \(6.67\times \) faster than state-of-the-art methods on a common suite of benchmarks, and achieves a new state-of-the-art performance on a real-world hardware design application. We also show that BOPrO converges faster even if the priors for the optimum are not entirely accurate and that it robustly recovers from misleading priors.
SecVKQ: Secure and verifiable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1822" altimg="si869.svg"><mml:mi>k</mml:mi></mml:math>NN queries in sensor–cloud systems

Sensor–cloud has gained increasingly popularity since it bridges the physical world and the cyber world through pervasive computation. This paper focuses on secure and verifiable k nearest neighbor (kNN) queries over large-scale outsourced datasets in sensor–cloud systems. Existing work in this line often incurred high computational and communication overheads, remaining far away from practical and scalable. To this end, we propose SecVKQ, a two-phase search framework, which mainly includes a preliminary screening phase and an exact search phase. SecVKQ purposely takes advantage of secure data separation and adaptive encryption strategy, embracing edge servers into the classic dual-cloud model, so as to optimize query performance. Under SecVKQ, we design a series of secure protocols and develop a succinct verification strategy to derive a unified solution. The experimental results demonstrate the effectiveness of SecVKQ. Compared to the state-of-the-art work, SecVKQ achieves a speed-up of two orders of magnitude in search latency, and a savings of 50% communication cost for verification.
Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)

The recently introduced Embarrasingelly Shallow Autoencoder (EASE) algorithm presents a simple and elegant way to solve the top-N recommendation task. In this paper, we introduce Neural EASE to further improve the performance of this algorithm by incorporating techniques for training modern neural networks. Also, there is a growing interest in the recsys community to utilize variational autoencoders (VAE) for this task. We introduce Focal Loss Variational AutoEncoder (FLVAE), benefiting from multiple non-linear layers without an information bottleneck while not overfitting towards the identity. We show how to learn FLVAE in parallel with Neural EASE and achieve state-of-the-art performance on the MovieLens 20M dataset and competitive results on the Netflix Prize dataset.
A Distributed Method for Fast Mining Frequent Patterns From Big Data

In recent years, knowledge discovery in databases provides a powerful capability to discover meaningful and useful information.For numerous real-life applications, frequent pattern mining and association rule mining have been extensively studied.In traditional mining algorithms, data are centralized and memory-resident.As a result of the large amount of data, bandwidth limitation, and energy limitations when applying these methods to distributed databases, especially in this era of big data, the performance is not effective enough.Hence, data mining on distributed environments has emerged as an important research area.To improve the performance, we propose a set of algorithms based on FP growth that discover FPs that are capable of providing fast and scalable service in distributed computing environments and a brief data structure to store items and counts to minimize the data for transmission on the network.To ensure completeness and execution capability, DistEclat and BigFIM were considered for the experiment comparison.Experiments show that the proposed method has superior cost-effectiveness for processing massive datasets and good capabilities under various experiment conditions.The proposed method on average required only 33% of the execution time and 45% of the transmission cost of DistEclat.Compared to BigFIM, The proposed method on average required 23.3% of the execution time and 14.2% of the transmission cost of BigFIM.
Towards Explainable Semantic Segmentation for Autonomous Driving Systems by Multi-Scale Variational Attention

Explainable autonomous driving systems (EADS) are emerging recently as a combination of explainable artificial intelligence (XAI) and vehicular automation (VA). EADS explains events, ambient environments, and engine operations of an autonomous driving vehicular, and it also delivers explainable results in an orderly manner. Explainable semantic segmentation (ESS) plays an essential role in building EADS, where it offers visual attention that helps the drivers to be aware of the ambient objects irrespective if they are roads, pedestrians, animals, or other objects. In this paper, we propose the first ESS model for EADS based on the variational autoencoder (VAE), and it uses the multiscale second-order derivatives between the latent space and the encoder layers to capture the curvatures of the neurons’ responses. Our model is termed as Mgrad <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> VAE and is bench-marked on the SYNTHIA and A2D2 datasets, where it outperforms the recent models in terms of image segmentation metrics.
From Data to Marine Ecosystem Assessments of the Southern Ocean: Achievements, Challenges, and Lessons for the Future

Southern Ocean ecosystems offer numerous benefits to human society and the global environment, and maintaining them requires well-informed and effective ecosystem-based management. Up to date and accurate information is needed on the status of species, communities, habitats and ecosystems and the impacts of fisheries, tourism and climate change. This information can be used to generate indicators and undertake assessments to advise decision-makers. Currently, most marine assessments are derivative: reliant on the review of published peer-reviewed literature. More timely and accurate information for decision making requires an integrated Marine Biological Observing and Informatics System that combines and distributes data. For such a system to work, data needs to be shared according to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), use transparent and reproducible science, adhere to the principle of action ecology and complement global initiatives. Here we aim to provide an overview of the components of such a system currently in place for the Southern Ocean, the existing gaps and a framework for a way forward.
Meditation Apps and the Promise of Attention by Design

This article demonstrates how meditation apps, such as Headspace and Calm, are imbricated within public discourse about technology addiction, exploring the consequences of this discourse on contemporary mental life. Based on ethnographic research with designers and users of meditation apps, I identify a promise put forth by meditation app companies that I call attention by design: a discursive strategy that frames attention as an antidote to technology addiction, which is ostensibly made possible when design is done right. I argue that attention by design is a promise unfulfilled. Meditation app companies construct attention as socially valuable by endlessly pointing out its purported opposite, technology addiction. Attention by design is promissory in that it keeps promising even when it doesn’t deliver what it promises, compelling the user to return to a practice that represents socially desirable traits that can never be fully acquired—and that often recede further from reach as the person becomes distracted by other obligations and communication mediated through the smartphone. Despite this broken promise, users believe they are becoming more attentive. The promissory attention designed into meditation apps reflects a new form of governmentality, in which users receive a mental nudge to reinterpret similarly designed experiences as different.
A low cost surface strain measurement system using image processing for sheet metal forming applications

Circle and square grid methods are widely used for strain analysis in sheet metal forming operations. In the current paper, images of the deformed grids are captured using a low-cost generic USB microscope and analyzed using an image processing algorithm for automatic strain measurement. The images of the deformed circular grid are analyzed using the segmentation approach, arc support line detection algorithm, and least squared error-ellipse fitting algorithm. In case of the deformed square grids, the corners of the quadrilateral are found using Hough Transform (HT) with clustering and are used to fit the maximum size ellipse inside the quadrilateral by projective collineation method. The effectiveness of different algorithms in measurement has been discussed. Good agreement was found between the results obtained from experiments and simulations in LS-Dyna software. The software is able to measure the strain with a maximum error of 1.933 % and 2.11 % for circular and square grids, respectively.
Hierarchical classification of data with long-tailed distributions via global and local granulation

Automated learning from datasets with a long-tailed distribution has gradually become a research hotspot due to the increasing complexity of large-scale real-world datasets. Existing solutions to long-tailed data classification usually involve re-balancing strategies for global optimization, which can achieve satisfactory results. However, re-balancing strategies tend to alter the original data. In this paper, we propose a knowledge granulation method based on global and local granulation to assist the hierarchical classification of long-tailed data without altering the original data. Firstly, a global classifier is constructed based on the WordNet knowledge organization’s hierarchical structure, which is used to granulate the global data from coarse to fine. Secondly, a local hierarchical classifier adapted to tail data is constructed for tail classes that contain few samples. The hierarchical structure of this local classifier is obtained by granulating the data via spectral clustering rather than by using the semantic hierarchy of classes. Finally, the global classifier is used to preliminarily classify samples, then uncertain samples are further classified by the tail local classifier. Experimental results show that the proposed method outperforms several state-of-the-art models designed for the hierarchical classification of long-tailed data.
Consensus in Blockchain Systems with Low Network Throughput: A Systematic Mapping Study

Blockchain technologies originate from cryptocurrencies. Thus, most blockchain technologies assume an environment with a fast and stable network. However, in some blockchain-based systems, e.g., supply chain management (SCM) systems, some Internet of Things (IoT) nodes can only rely on the low-quality network sometimes to achieve consensus. Thus, it is critical to understand the applicability of existing consensus algorithms in such environments. We performed a systematic mapping study to evaluate and compare existing consensus mechanisms’ capability to provide integrity and security with varying network properties. Our study identified 25 state-of-the-art consensus algorithms from published and preprint literature. We categorized and compared the consensus algorithms qualitatively based on established performance and integrity metrics and well-known blockchain security issues. Results show that consensus algorithms that rely on synchronous network for correctness cannot provide the expected integrity. Such consensus algorithms may also be vulnerable to distributed-denial-of-service (DDOS) and routing attacks, given limited network throughput. Conversely, asynchronous consensus algorithms, e.g., Honey-BadgerBFT, are deemed more robust against many of these attacks and may provide high integrity in asynchronous events.
A Survey of Machine Learning-Based Approaches for Missing Value Imputation

Missing values create issues during the analysis of the dataset. Learning algorithms in an asymmetrical dataset can generate an overrated classification accuracy due to a bias towards the majority class at the expense of the minority class. Missing values in the dataset have a negative impact on the imputation of accuracy; therefore, it could lead to a different output. Some algorithms cannot handle missing values properly, while some techniques give efficient results to estimate the missing values. It is very important to handle missing data because many machine learning algorithm performances reduces due to missing values. It might be possible that the original datasets have some missing data for many factors like data were not kept in a file, data had been corrupted, etc. In this paper, some techniques are discussed which are employed to impute the missing data, and these techniques are compared using their merits and drawbacks.
Goals and challenges in hybrid software development approaches

Abstract The number of companies that use agile methods increases steadily. However, these companies often do not implement a pure agile approach but combine agile and plan‐based methods to so‐called hybrid development approaches. However, the development of these approaches is rather difficult for the companies, since agile and plan‐based approaches often follow opposite concepts. To benefit from agile and plan‐based approaches at the same time, the companies have to identify and address the conflicts between agile and plan‐based methods. The conflicts depend on the goals that are pursued with the implementation of agile and plan‐based methods. However, there is no overview of the exact goals that are pursued in hybrid approaches and which challenges and conflicts arise with them. Therefore, we conducted a systematic mapping study to gather and analyze the goals and challenges in hybrid development approaches. The mapping study is focused on literature that presents the actual needs and goals of companies and projects. Based on our results, we present the influence factors that cause conflicts in hybrid approaches and discuss how these conflicts can be addressed.
Multiview Video-Based 3-D Hand Pose Estimation

Hand pose estimation (HPE) can be used for a variety of human–computer interaction applications, such as gesture-based control for physical or virtual/augmented reality devices. Recent works have shown that videos or multiview images carry rich information regarding the hand, allowing for the development of more robust HPE systems. In this article, we present the multiview video-based three-dimensional (3-D) hand (MuViHand) dataset, consisting of multiview videos of the hand along with ground-truth 3-D pose labels. Our dataset includes more than 402 000 synthetic hand images available in 4560 videos. The videos have been simultaneously captured from six different angles with complex backgrounds and random levels of dynamic lighting. The data has been captured from ten distinct animated subjects using 12 cameras in a semicircle topology where six tracking cameras only focus on the hand and the other six fixed cameras capture the entire body. Next, we implement MuViHandNet, a neural pipeline consisting of image encoders for obtaining visual embeddings of the hand, recurrent learners to learn both temporal and angular sequential information, and graph networks with U-Net architectures to estimate the final 3-D pose information. We perform extensive experiments and show the challenging nature of this new dataset as well as the effectiveness of our proposed method. Ablation studies show the added value of each component in MuViHandNet, as well as the benefit of having temporal and sequential information in the dataset.
A Survey on Consensus Mechanisms for Blockchain Technology

Blockchain technology has been significantly influencing the global economy in recent years. Meanwhile, the inherent consensus problem within blockchain systems turns out to be rather challenging in practice, since malicious parties would try various ways of attacks on the system, which should be carefully addressed by specifically designed consensus mechanisms. This article systematically studies the state-of-the-art consensus mechanisms, and look into their pros and cons, respectively.
QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment

With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, which uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover’s search algorithm in two ways, allowing: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX Simulator framework. Our implementation represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design.
A Versatile Aerial Manipulator Design and Realization of UAV Take-Off from a Rocking Unstable Surface

UAVs are one of the fastest types of robots that can be deployed in a remote environment. Unfortunately, they have a limited flight time and therefore may need to stop occasionally in an unknown, uncontrolled area. However, conventional UAVs require flat and stationary surfaces for a safe landing and take-off. Some studies on adaptive landing approach for UAVs can be found in the past, but adaptive take-off from non-flat surfaces has not been discussed for the most part, yet. In this work, we discuss the problems associated with a conventional UAV take-off from non-flat surfaces and provide a novel approach for UAV take-off from a sloped or rocking surface. We also discuss the design of a novel multitasking three-arm aerial manipulator system with parallel link mechanism and achieve the above-mentioned task. With experiments, we show that the system can provide stability for a UAV landing on a rocking surface that allows for a safe take-off.
A semantic approach to post-retrieval query performance prediction

The importance of query performance prediction has been widely acknowledged in the literature, especially for query expansion, refinement, and interpolating different retrieval approaches. This paper proposes a novel semantics-based query performance prediction approach based on estimating semantic similarities between queries and documents. We introduce three post-retrieval predictors, namely (1) semantic distinction, (2) semantic query drift, and (3) semantic cohesion based on (1) the semantic similarity of a query to the top-ranked documents compared to the whole collection, (2) the estimation of non-query related aspects of the retrieved documents using semantic measures, and (3) the semantic cohesion of the retrieved documents. We assume that queries and documents are modeled as sets of entities from a knowledge graph, e.g., DBPedia concepts, instead of bags of words. With this assumption, semantic similarities between two texts are measured based on the relatedness between entities, which are learned from the contextual information represented in the knowledge graph. We empirically illustrate these predictors' effectiveness, especially when term-based measures fail to quantify query performance prediction hypotheses correctly. We report our findings on the proposed predictors' performance and their interpolation on three standard collections, namely ClueWeb09-B, ClueWeb12-B, and Robust04. We show that the proposed predictors are effective across different datasets in terms of Pearson and Kendall correlation coefficients between the predicted performance and the average precision measured by relevance judgments.