Reviewerly/exHarmony huggingface dataset:
Machine translation systems and quality assessment: a systematic review

Abstract Nowadays, in the globalised context in which we find ourselves, language barriers can still be an obstacle to accessing information. On occasions, it is impossible to satisfy the demand for translation by relying only in human translators, therefore, tools such as Machine Translation (MT) are gaining popularity due to their potential to overcome this problem. Consequently, research in this field is constantly growing and new MT paradigms are emerging. In this paper, a systematic literature review has been carried out in order to identify what MT systems are currently most employed, their architecture, the quality assessment procedures applied to determine how they work, and which of these systems offer the best results. The study is focused on the specialised literature produced by translation experts, linguists, and specialists in related fields that include the English–Spanish language combination. Research findings show that neural MT is the predominant paradigm in the current MT scenario, being Google Translator the most used system. Moreover, most of the analysed works used one type of evaluation—either automatic or human—to assess machine translation and only 22% of the works combined these two types of evaluation. However, more than a half of the works included error classification and analysis, an essential aspect for identifying flaws and improving the performance of MT systems.
From Technology to Society: An Overview of Blockchain-Based DAO

Decentralized Autonomous Organization (DAO) is believed to play a significant role in our future society governed in a decentralized way. In this article, we first explain the definitions and preliminaries of DAO. Then, we conduct a literature review of the existing studies of DAO published in the recent few years. Through the literature review, we find out that a comprehensive survey towards the state-of-the-art studies of DAO is still missing. To fill this gap, we perform such an overview by identifying and classifying the most valuable proposals and perspectives closely related to the combination of DAO and blockchain technologies. We anticipate that this survey can help researchers, engineers, and educators acknowledge the cutting-edge development of blockchain-related DAO technologies.
LINDA-BN: An interpretable probabilistic approach for demystifying black-box predictive models

The use of sophisticated machine learning models for critical decision-making faces the challenge that these models are often applied as a ‘black-box’. This has led to an increased interest in interpretable machine learning, where post-hoc model-agnostic algorithms present a useful mechanism for generating interpretations of complex learning models. This paper proposes a novel approach based on Bayesian Networks to generate local post-hoc model-agnostic interpretations of a black-box predictive model. Consequently, the proposed approach presents features that are conditionally dependent between each other and that are directly influencing the class variable. This enables the decision-maker to better understand how features are related and why a certain prediction was made. Compared to the existing post-hoc interpretation methods, the contribution of our approach is three-fold: (1) as a probabilistic graphical model, the extracted Bayesian network can provide interpretations through conditional dependencies in a graphical structure regarding what input features and how/why they contributed to a prediction; (2) for complex decision problems with many features, a Markov blanket can be generated from the extracted Bayesian network to provide interpretations with a focused view on those input features that directly contributed to a prediction; (3) the extracted Bayesian network enables the identification of four different rules which can inform the decision-maker about the confidence level in a prediction, thus helping the decision-maker assess the reliability of predictions learned by a black-box model. We implemented the proposed approach, applied it in the context of two well-known public datasets and analysed the results, which are made available in an open-source repository: https://github.com/catarina-moreira/LINDA_DSS.
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection

Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to real-world applications, we introduce a large-scale High-Fidelity Mask dataset, namely HiFiMask. Specifically, a total amount of 54,600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors. Along with the dataset, we propose a novel Contrastive Context-aware Learning (CCL) framework. CCL is a new training methodology for supervised PAD tasks, which is able to learn by leveraging rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks. Extensive experimental evaluations on HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of our method. The codes and dataset will be released soon.
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation

Highly skewed long-tail item distribution is very common in recommendation systems. It significantly hurts model performance on tail items. To improve tail-item recommendation, we conduct research to transfer knowledge from head items to tail items, leveraging the rich user feedback in head items and the semantic connections between head and tail items. Specifically, we propose a novel dual transfer learning framework that jointly learns the knowledge transfer from both model-level and item-level: 1. The model-level knowledge transfer builds a generic meta-mapping of model parameters from few-shot to many-shot model. It captures the implicit data augmentation on the model-level to improve the representation learning of tail items. 2. The item-level transfer connects head and tail items through item-level features, to ensure a smooth transfer of meta-mapping from head items to tail items. The two types of transfers are incorporated to ensure the learned knowledge from head items can be well applied for tail item representation learning in the long-tail distribution settings. Through extensive experiments on two benchmark datasets, results show that our proposed dual transfer learning framework significantly outperforms other state-of-the-art methods for tail item recommendation in hit ratio and NDCG. It is also very encouraging that our framework further improves head items and overall performance on top of the gains on tail items.
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). RECON uses a graph neural network to learn representations of both the sentence as well as facts stored in a KG, improving the overall extraction quality. These facts, including entity attributes (label, alias, description, instance-of) and factual triples, have not been collectively used in the state of the art methods. We evaluate the effect of various forms of representing the KG context on the performance of RECON. The empirical evaluation on two standard relation extraction datasets shows that RECON significantly outperforms all state of the art methods on NYT Freebase and Wikidata datasets.
Neural, symbolic and neural-symbolic reasoning on knowledge graphs

Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning have promoted neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic, neural and hybrid reasoning on knowledge graphs. We survey two specific reasoning tasks — knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.
Implementation of Blockchain Consensus Algorithm on Embedded Architecture

The adoption of Internet of Things (IoT) technology across many applications, such as autonomous systems, communication, and healthcare, is driving the market’s growth at a positive rate. The emergence of advanced data analytics techniques such as blockchain for connected IoT devices has the potential to reduce the cost and increase in cloud platform adoption. Blockchain is a key technology for real-time IoT applications providing trust in distributed robotic systems running on embedded hardware without the need for certification authorities. There are many challenges in blockchain IoT applications such as the power consumption and the execution time. These specific constraints have to be carefully considered besides other constraints such as number of nodes and data security. In this paper, a novel approach is discussed based on hybrid HW/SW architecture and designed for Proof of Work (PoW) consensus which is the most used consensus mechanism in blockchain. The proposed architecture is validated using the Ethereum blockchain with the Keccak 256 and the field-programmable gate array (FPGA) ZedBoard development kit. This implementation shows improvement in execution time of 338% and minimizing power consumption of 255% compared to the use of Nvidia Maxwell GPUs.
Maximum Information Measure Policies in Reinforcement Learning with Deep Energy-Based Model

we provided a framework for the acquisition of articulated electricity regulations for consistent states and actions, but it has only been attainable in summarised domains since then. Developers adapt our environment to learning maximum entropy policies, leading to a simple Q-learning service, which communicates the global optimum through a Boltzmann distribution. We could use previously approved amortized Stein perturbation theory logistic regression rather than estimated observations from that distribution form to obtain a stochastic diffusion network. In simulated studies with underwater and walking robots, we confirm that the entire algorithm's cost provides increased exploration or term frequency that allows the transfer of skills between tasks. We also draw a comparison to critical actor methods, which can represent on the accompanying energy-based model conducting approximate inference. Misleading multiplayer uses the recompense power to ensure that the user is further from either the evolutionary algorithms but has now evolved to become a massive task in developing intelligent exploration for deep reinforcement learning. In a misleading game, nearly all cutting-edge research techniques, including those qualify superstition yet, even with self-recompenses, which achieves enhanced outcomes in the sparse re-ward game, often easily collapse into global optimization traps. We are introducing another exploration tactic called Maximum Entropy Expand (MEE) to remedy this shortage (MEE). Based on entropy rewards but the off-actor-critical reinforced learning algorithm, we split the entity adventurer policy into two equal parts, namely, the target rule and the adventure policy. The explorer law is used to interact with the world, and the target rule is used to create trajectories, with the higher precision of the targets to be achieved as the goal of optimization. The optimization goal of the targeted approach is to maximize extrinsic rewards in order to achieve the global result. The ideal experience replay used to remove the catastrophic forgetting issue that leads to the operator's information becoming non-normalized during the off-exploitation period. To prevent the vulnerable, diverging, and generated by the dangerous triad, an on-policy form change is used specifically. Users analyse data likening our strategy with a region technique for deep learning, involving grid world experimentation techniques and deceptively recompense Dota 2 environments. The case illustrates that the MME strategy tends to be productive in escaping the current paper's coercive incentive trap and learning the correct strategic plan.
μNAS

IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage. Designing neural networks for such a platform requires an intricate balance between keeping high predictive performance (accuracy) while achieving low memory and storage usage and inference latency. This is extremely challenging to achieve manually, so in this work, we build a neural architecture search (NAS) system, called μNAS, to automate the design of such small-yet-powerful MCU-level networks. μNAS explicitly targets the three primary aspects of resource scarcity of MCUs: the size of RAM, persistent storage and processor speed. μNAS represents a significant advance in resource-efficient models, especially for "mid-tier" MCUs with memory requirements ranging from 0.5 KB to 64 KB. We show that on a variety of image classification datasets μNAS is able to (a) improve top-1 classification accuracy by up to 4.8%, or (b) reduce memory footprint by 4-13×, or (c) reduce the number of multiply-accumulate operations by at least 2×, compared to existing MCU specialist literature and resource-efficient models. μNAS is freely available for download at https://github.com/eliberis/uNAS
Watching Myself Watching Birds

In recent years, Sustainable HCI researchers have begun to investigate “noticing” as a design research method useful in efforts to decenter the human in design. Through an autoethnographic bird watching practice combining field observation, journaling, and making practices, we examine how noticing affects us and our way of relating to birds. We found that bird watching surfaces a feeling of abjection, or a simultaneous repulsion and fascination with a part of oneself one rejects in pursuit of personal growth. Along the way, we honed a practice of attunement through deep listening and field recording, which enabled immersive "ecological" experiences. We offer (1) an account of our method and process, (2) the framework of abjection as an approach to designing amongst the complexity of human/non-human interaction, and (3) reflections on how to design for ecological thinking in the push towards a posthuman design.
Path Planning Based on Combinaion of Improved A-STAR Algorithm and DWA Algorithm

In the path planning of grid map, the influence of node search strategy makes the traditional A-STAR algorithm shown with the defects of non theoretically optimal path in actual path planning, many infection points and many redundant points. Aiming at the deficiency of path planning by traditional algorithm, this paper proposes to eliminate the limitation of node movement direction in traditional A-STAR algorithm by extending its search neighborhood, design a process to remove the redundant sub nodes in the same direction due to neighborhood extension, and combine with velocity evaluation function of the improved DWA algorithm to avoid temporary obstacles. The simulation results show that relative to the traditional A-STAR algorithm, the improved A-STAR algorithm can have shorter path and fewer inflection points; compared with the traditional DWA algorithm, the dynamic path planning combined with the improved A-STAR algorithm and DWA algorithm has fewer nodes and smoother path, which can improve the working security and efficiency of mobile robot in simulation environment.
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks

Attention-based sequence-to-sequence modeling provides a powerful and elegant solution for applications that need to map one sequence to a different sequence. Its success heavily relies on the availability of large amounts of training data. This presents a challenge for speech applications where labelled speech data is very expensive to obtain, such as automatic speech recognition (ASR) and speech translation (ST). In this study, we propose a general multi-task learning framework to leverage text data for ASR and ST tasks. Two auxiliary tasks, a denoising autoencoder task and machine translation task, are proposed to be co-trained with ASR and ST tasks respectively. We demonstrate that representing text input as phoneme sequences can reduce the difference between speech and text inputs, and enhance the knowledge transfer from text corpora to the speech to text tasks. Our experiments show that the proposed method achieves a relative 10~15% word error rate reduction on the English LIBRISPEECH task compared with our baseline, and improves the speech translation quality on the MUST-C tasks by 3.6~9.2 BLEU.
Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics

A large number of metaheuristics inspired by natural and social phenomena have been proposed in the last few decades, each trying to be more powerful and innovative than others. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of metaheuristics when solving optimisation problems. When the metaphors are stripped away, are these algorithms different in their behaviour? To help to answer this question, we propose a data-driven, graph-based model, search trajectory networks (STNs) in order to analyse, visualise and directly contrast the behaviour of different types of metaheuristics. One strength of our approach is that it does not require any additional sampling or algorithmic methods. Instead, the models are constructed from data gathered while the metaheuristics are solving the optimisation problems. We present our methodology, and consider in detail two case studies covering both continuous and combinatorial optimisation. In terms of metaheuristics, our case studies cover the main current paradigms: evolutionary, swarm, and stochastic local search approaches.
Real-Time Small Drones Detection Based on Pruned YOLOv4

To address the threat of drones intruding into high-security areas, the real-time detection of drones is urgently required to protect these areas. There are two main difficulties in real-time detection of drones. One of them is that the drones move quickly, which leads to requiring faster detectors. Another problem is that small drones are difficult to detect. In this paper, firstly, we achieve high detection accuracy by evaluating three state-of-the-art object detection methods: RetinaNet, FCOS, YOLOv3 and YOLOv4. Then, to address the first problem, we prune the convolutional channel and shortcut layer of YOLOv4 to develop thinner and shallower models. Furthermore, to improve the accuracy of small drone detection, we implement a special augmentation for small object detection by copying and pasting small drones. Experimental results verify that compared to YOLOv4, our pruned-YOLOv4 model, with 0.8 channel prune rate and 24 layers prune, achieves 90.5% mAP and its processing speed is increased by 60.4%. Additionally, after small object augmentation, the precision and recall of the pruned-YOLOv4 almost increases by 22.8% and 12.7%, respectively. Experiment results verify that our pruned-YOLOv4 is an effective and accurate approach for drone detection.
Cascaded Encoders for Unifying Streaming and Non-Streaming ASR

End-to-end (E2E) automatic speech recognition (ASR) models, by now, have shown competitive performance on several benchmarks. These models are structured to either operate in streaming or non-streaming mode. This work presents cascaded encoders for building a single E2E ASR model that can operate in both these modes simultaneously. The proposed model consists of streaming and non-streaming encoders. Input features are first processed by the streaming encoder; the non-streaming encoder operates exclusively on the output of the streaming encoder. A single decoder then learns to decode either using the output of the streaming or the non-streaming encoder. Results show that this model achieves similar word error rates (WER) as a standalone streaming model when operating in streaming mode, and obtains 10% – 27% relative improvement when operating in non-streaming mode. Our results also show that the proposed approach outperforms existing E2E two-pass models, especially on long-form speech.
Deep Learning-Based Artificial Intelligence for Mammography

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.
Comparative study of Twitter Sentiment On COVID - 19 Tweets

Recently, the number of tweets on COVID-19 are increasing at an unprecedented rate by including positive, negative and neutral tweets. This diversified nature of tweets has attracted the researchers to perform sentiment analysis and analyze the varied emotions of a large public towards COVID-19. The traditional sentiment analysis techniques will only find out the polarity and classify it as either positive, negative or neutral tweets. As an advanced step, the proposed research work attempts to find the sentiment of tweets using Logistic Regression sentiment analysis, VADER sentiment analysis and BERT sentiment analysis. The proposed analysis methods are more sensitive to sentiment expressions in social media contexts, while it can be generalized on the basis of the domain. Even though 3 different algorithms are implemented, all the preprocessing and further steps excluding the sentiment analysis algorithm will remain identical. The identical processing steps will help to compare the proposed three different sentiment analysis algorithms. Furthermore, there are many useful applications with this proposed analysis, as this work obtains a public opinion for the government officials or even for the health officials and help them to work on the basis of the obtained results.