Reviewerly/exHarmony huggingface dataset:
Automatic Accuracy Prediction for AMR Parsing

Meaning Representation (AMR) represents sentences as directed, acyclic and rooted graphs, aiming at capturing their meaning in a machine readable format. AMR parsing converts natural language sentences into such graphs. However, evaluating a parser on new data by means of comparison to manually created AMR graphs is very costly. Also, we would like to be able to detect parses of questionable quality, or preferring results of alternative systems by selecting the ones for which we can assess good quality. We propose AMR accuracy prediction as the task of predicting several metrics of correctness for an automatically generated AMR parse – in absence of the corresponding gold parse. We develop a neural end-to-end multi-output regression model and perform three case studies: firstly, we evaluate the model’s capacity of predicting AMR parse accuracies and test whether it can reliably assign high scores to gold parses. Secondly, we perform parse selection based on predicted parse accuracies of candidate parses from alternative systems, with the aim of improving overall results. Finally, we predict system ranks for submissions from two AMR shared tasks on the basis of their predicted parse accuracy averages. All experiments are carried out across two different domains and show that our method is effective.
Enhanced embedded zerotree wavelet algorithm for lossy image coding

Embedded zerotree wavelet (EZW) algorithm is the well-known effective coding technique for low-bit-rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (NE-EZW), allowing to achieve a high compression performance in terms of peak-signal-to-noise ratio and bitrate for lossy image compression. To distribute probabilities in a more efficient way, the proposed approach is based on increasing the number of coefficients not to be encoded by the use of new symbols. Furthermore, the proposed method optimises the binary coding by the use of the compressor cell operator. Experimental results demonstrated the effectiveness of the proposed scheme over the conventional EZW and other improved EZW schemes for both natural and medical image coding applications. They have also shown that the proposed approach outperforms the most well-known algorithms, namely set partitioning in hierarchical trees (SPIHT) and JPEG2000.
Object Detection Based on the Improved Single Shot MultiBox Detector

Aiming at the poor effect of deep learning algorithm on small objects detection, the SSD object detection method based on feature fusion is proposed. The reasons for low detection rate and poor robustness of classical SSD object detection methods are analysed; and through the theoretical analysis and comparative experiments, the characteristic fusion layer was proposed. The shallow layers with high resolution and deep layers with strong semantics are fused with the feature fusion structure; finally, a complete feature fusion structure is designed with the residual block to increase the width and depth of the network. The contrast experiment on the PASCAL VOC dataset was conducted for detection capability and detection accuracy, and experimental result indicates that when the confidence is set to 0.5, the mAP of the SSD method based on feature fusion is 78.04%, which is 0.8% higher than the classical SSD algorithm and 4.8% higher than the Faster RCNN algorithm. Obviously, the proposed algorithm improves the ability of small objects, and verifies the effectiveness of the proposed algorithm.
Improved Proxy Re-Encryption With Delegatable Verifiability

Proxy re-encryption cryptosystem enables proxy to re-encrypt the ciphertext and protects the privacy of the corresponding plaintext. Hence, this type of cryptosystem has found tremendous applications in data sharing in cloud computing, email forwarding, securing file systems, and so on. In practice, proxy re-encryption has to support verification of ciphertext to reduce the users' computational burden. The ciphertext verification can be achieved through a public, private, or delegatable manner. Delegatable verification is more generic in that it can be easily converted into the other two verification methods. However, almost all existing schemes with delegatable verifiability only achieve replayable chosen ciphertext security. Hence, in this paper we propose a more secure proxy re-encryption scheme with delegatable verifiability. Specifically, we utilize a short signature scheme to prevent an attacker from forging a valid ciphertext, and prevent an attacker from forging a signature. As a result, our scheme is secure against chosen ciphertext attacks under the standard model.
Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization

We consider the problem of training one-versus-all (OVA) linear classifiers for multiclass or multilabel classification when the number of labels is large. A naive extension of OVA to this problem, even with hundreds of cores, usually requires hours for training on large real world datasets. We propose a novel algorithm called OVA-Primal++ that speeds up the training of OVA by using a tree-structured training order, where each classifier is trained using its parent's classifier as initialization. OVA-Primal++ is both theoretically and empirically faster than the naive OVA algorithm, and yet still enjoys the same highly parallelizability and small memory footprint. Extensive experiments on multiclass and multilabel classification datasets validate the effectiveness of our method.MSC codesOne-versus-All Classification
Possibilistic Logic: From Certainty-Qualified Statements to Two-Tiered Logics – A Prospective Survey

Possibilistic logic (PL) is more than thirty years old. The paper proposes a survey of its main developments and applications in artificial intelligence, together with a short presentation of works in progress. PL amounts to a classical logic handling of certainty-qualified statements. Certainty is estimated in the setting of possibility theory as a lower bound of a necessity set-function. An elementary possibilistic formula is a pair made of a classical logic formula, and a certainty level belonging to a bounded scale. Basic PL handles only conjunctions of such formulas, and PL bases can be viewed as classical logic bases layered in terms of certainty. Semantics is in terms of epistemic states represented by fuzzy sets of interpretations. A PL base is associated with an inconsistency level above which formulas are safe from inconsistency. Applications include reasoning with default rules, belief revision, Bayesian possibilistic networks, information fusion, and preference modeling (in this latter case, certainty is turned into priority). Different extensions of basic PL are briefly reviewed, where levels take values in lattices, are replaced by vectors of levels, or are handled in a purely symbolic manner (without being instantiated). This latter extension may be of interest for explanation purposes. A paraconsistent treatment of inconsistency is also discussed. Still another extension allows for associating possibilistic formulas with sets of agents or sources that support them. In generalized possibilistic logic (GPL), negation and disjunction can be applied as well as conjunction, to possibilistic formulas. It may be viewed as a fragment of modal logic (such as KD45) where modalities cannot be nested. GPL can be still extended to a logic involving both objective and non-nested multimodal formulas. Applications of GPL to the modeling of ignorance, to the representation of answer set programs, to reasoning about other agents’ beliefs, and to a logic of argumentation are outlined. Generally speaking, the interest and the strength of PL relies on a sound alliance between classical logic and possibility theory which offers a rich representation setting allowing an accurate modeling of partial ignorance. The paper focuses more on ideas than on technicalities and provides references for details (Invited talk presented by the second author).
Efficient and Secure Outsourcing of DFT, IDFT, and Circular Convolution

Discrete Fourier transform (DFT), inverse DFT (IDFT), and circular convolution are important tools for analyzing and designing discrete signals and systems, and are widely used in various industries. In order to pursue faster operational efficiencies or more accurate operational results, engineering calculations are often required to be quick and easy. Therefore, it is necessary to reduce the local computational overhead required to perform DFT, IDFT, and circular convolution. In addition to improving the algorithms themselves, cloud computing outsourcing is also an optional method. In this paper, in response to the new challenges brought by cloud computing outsourcing, we design and propose an efficient and secure cloud computing outsourcing protocol for DFT, IDFT, and circular convolution. Through a theoretical explanation and simulation experiment, we show the efficiency, security, and verifiability of the proposed protocol.
Imputing Missing Events in Continuous-Time Event Streams

Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable family of proposal distributions based on a type of bidirectional continuous-time LSTM: Bidirectionality lets the proposals condition on future observations, not just on the past as in particle filtering. Our method can sample an ensemble of possible complete sequences (particles), from which we form a single consensus prediction that has low Bayes risk under our chosen loss metric. We experiment in multiple synthetic and real domains, using different missingness mechanisms, and modeling the complete sequences in each domain with a neural Hawkes process (Mei & Eisner 2017). On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.
How to build a winning business case

Over the years, I've developed several business cases. These cases have taken a number of different forms, depending on the undertaking at hand. However, I've found that a good business case needs to have certain elements to be successful. To my surprise, I’ve also found that many people have different ideas of what a business case entails, and its purpose. Occasionally, I've had debates with colleagues and clients over this very issue. What follows is a two-part series on business case development, starting with defining the concept and the elements of what makes a winning business case. The second part of the series will put these concepts into action with a real-life example from one of my clients, the topic being arguably one of the most difficult cases to make: the adoption of technical standards.
Predicting emotional intensity in social networks

Emotions, which are now commonly portrayed in social media, play a fundamental role in decision making. Having this into account, this work proposes a model to predict (forecast) emotions in social networks. This model specifically predicts, for a user, the proportion of comments that will be publi shed with a particular emotion; this proportion is defined as an emotional intensity of the user in a particular time period. On the contrary of other models, which are focused on a single emotion, the proposed model considers a basic scheme of four emotions and employs these in an interdependent manner. The model, moreover, utilizes three types of features: (1) user-related, (2) contact-related, and (3) environment-related. Prediction is performed using linear regression. Nearly 20 models, including ARIMA, are outperformed by the proposed model (with statistically significant results) when evaluated over a dataset extracted from Twitter. Some potential applications include massive opinion monitoring and recommendations to improve the emotional wellness of social media users (for example, the recommendation of joyful memories).
Collaborative Problem Solving in Local and Remote VR Situations

Virtual Reality supports collaboration among partners across departments and fields, independent of physical boundaries. Virtual reality applications can solve the time and cost consuming logistic problem that companies encounter when sending experts to remote locations. However, it is not yet clear how effective partners collaborate when in remote locations. In one experiment, we examined whether partners who are physically in the same room and interact with each other before they start collaborating affects performance compared to collaborators who meet and interact only within the virtual space. Participants had to solve a Rubik's cube type three-dimensional puzzle by arranging cubes that varied in color within a solution space in such a way, so that each side of the solution space showed a single color. Participants were immersed within a virtual environment and in one condition participants were collocated in the same room (local condition), while in the other one they were located in different rooms (remote condition). Results showed that collaborators in both conditions successfully completed the task but performance was better during the local compared to the remote condition.
Text extraction from natural scene images using Renyi entropy

Nowadays, development in machine vision incorporated with artificial intelligence surpasses the ability of human intelligence and its application expands exponentially with the increasing number of electronic gadgets in our day-to-day life. The explosive revolution in multimedia research leads to the need for expanding the utility of texts in a machine vision environment to promote web search operation. Hence, extracting text from images forms the core aspect of information retrieval-based intelligent system. This article is aimed towards extracting text from unconstrained environments. Here, the significance of the CIE-Lab colour space is analysed over text localisation assisted through Renyi entropy-based thresholding. The proposed algorithm is tested on the MSRA Text Detection 500 dataset (MSRA-TD500) and Street View Text (SVT) datasets, which are challenging datasets. Authors’ proposed Renyi entropy-based text localisation algorithm is successful in identifying blurred texts, texts with different font characteristics and multi-lingual texts with manifold orientations from complex background natural scenes.
Reliability Assessment Model for Industrial Control System Based on Belief Rule Base

This paper establishes a novel reliability assessment method for industrial control system (ICS). Firstly, the qualitative and quantitative information were integrated by evidential reasoning(ER) rule. Then, an ICS reliability assessment model was constructed based on belief rule base (BRB). In this way, both expert experience and historical data were fully utilized in the assessment. The model consists of two parts, a fault assessment model and a security assessment model. In addition, the initial parameters were optimized by covariance matrix adaptation evolution strategy (CMA-ES) algorithm, making the proposed model in line with the actual situation. Finally, the proposed model was compared with two other popular prediction methods through case study. The results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex ICSs.
Smart Waste Management Using Deep Learning with IoT

The emerging rise in the population and urbanisation alarms the nation for its environment safety.The incorrect handling and waste disposal will cause immense threat to these growing cities.Therefore, it has raised the need for proper waste collection and classification of wastes with proper disposal.The current waste disposal system in India consists of unclassified and unorganized wastes collected and then segregated at different stations [7].This segregation done by manual labour forces can bring health related issues to the waste sorters and also being less efficient, time consuming and not completely feasible due to their large amount.This paper proposes a solution that can identify and classify the waste and organize it into the particular waste bin (recyclable, organic and harmful wastes) without any human hand.The system uses deep learning algorithms to identify and classify the wastes into particular category; the categorized recycled and organic wastes can be used for future better purposes.This process will help the environment in making more valuable and ecologically safe and help us to make rich green ecosystem and a promising better future.
Implications of Updating Digital Literacy – A Case Study in an Optometric Curriculum

Aim/Purpose: The aim of this project was to explore a method to enable an updated under-standing of digital literacy to be implemented in curricula in an environment of an existing, but outdated, understanding of digital literacy. . Background: The changing healthcare environment increasingly emphasizes the importance of digital literacy skills; therefore academics in the optometry discipline at Deakin University sought to better understand where digital literacy skills were taught in their program, and whether delivery was implicit or explicit. Methodology: This case study describes a systematic review of the optometric curriculum to first identify where and what digital literacy skills are currently being addressed in the curriculum, identify the gaps, and develop a strategy to address the gaps. Contribution: The main outcome of this work is the development of a spiraling curriculum to support the development of digital literacy skills required in later units of the program and for clinical practice post-graduation. Findings: Although the definition of digital literacy may be outdated, the digital literacy capabilities being addressed in the curriculum had grown as digital technology use by staff and students had expanded. This, together with the realization that students were not as digitally capable as expected, indicated that teaching digital literacy skills needed to be made overt throughout the curriculum. Recommendations for Practitioners: The process developed through this case study provides a strong foundation for course teams, curriculum developers and educational designers to efficiently analyze digital literacy expectations in existing, accredited health-related curricula and improve the curricula by more overtly embedding digital literacy teaching into it. Impact on Society: Graduates of the amended program of study are expected to be better prepared to undertake their future careers in a digitally enhanced and disrupted environment. Future Research: The framework will be used to explore digital literacy teaching practices in other disciplines. A systematic evaluation will be undertaken to identify the benefits and short comings of using the framework. The elements that make up the new definition of digital literacy need to be better articulated to allow curriculum developers to be better informed as to how to interpret the framework in their context.
Using SMT to accelerate nested virtualization

IaaS datacenters offer virtual machines (VMs) to their clients, who in turn sometimes deploy their own virtualized environments, thereby running a VM inside a VM. This is known as nested virtualization.
Emotion Recognition Based Preference Modelling in Argumentative Dialogue Systems

Within this work, we present an approach to model the opinion of a human towards a specific topic in a fine-grained way by using weighted bipolar argumentation graphs. In addition, we discuss how the therefore required rating of related aspects can be collected by means of emotion recognition techniques and discuss an application scenario based on the state-of-the-art Argumentative Dialogue System EVA in which the proposed techniques can be applied.
A New Method for Satellite Control System Fault Pattern Recognition combining Multi-Classification SVM with Kernel Principal Component Analysis

A new method for satellite control system fault pattern recognition is put forward, the most important components of which is feature extraction. Kernel principal component analysis is adopted to work out the most effective feature for fault detection by building a accurate principal component model, and this model makes the feature extraction simple and declines the dimensions of feature space. Furthermore in order to recognize the fault pattern a multi classification support vector machine (SVM) is worked out, and the particle swarm optimization algorithm is used to optimize the parameters of it. The results of simulation indicate that this method can not only detect the fault but also recognize the fault pattern.
The Diaspora and economic development in Africa

While the dominant collective belief asserts that brain drain is detrimental to the development of small economies, new studies hold the reverse view. This paper aims at studying the role of the Diaspora in the economic development of low-income countries with particular focus on African countries. It analyzes both the overall effect and the specific effect of emigration according to the level of education of emigrants. While the empirical results for all developing countries fail to establish an unambiguous relationship between the Diaspora and economic development, those concerning African countries establish a clear and unambiguous relationship. The African Diaspora, especially the high-skilled Diaspora, contributes positively, significantly and robustly to the improvement of income in Africa. These findings challenge the dominant collective belief. Improvements in human capital, total factor productivity and democracy are effective transmission channels of this impact. In addition, while high-skilled emigrants have an overall greater impact on economic development and democracy, those with a low level of education contribute more to remittances to Africa.
Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture

Kishaloy Halder, Min-Yen Kan, Kazunari Sugiyama. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Parallel Traversal of Large Ensembles of Decision Trees

Machine-learnt models based on additive ensembles of regression trees are currently deemed the best solution to address complex classification, regression, and ranking tasks.The deployment of such models is computationally demanding: to compute the final prediction, the whole ensemble must be traversed by accumulating the contributions of all its trees.In particular, traversal cost impacts applications where the number of candidate items is large, the time budget available to apply the learnt model to them is limited, and the users' expectations in terms of quality-of-service is high.Document ranking in web search, where sub-optimal ranking models are deployed to find a proper trade-off between efficiency and effectiveness of query answering, is probably the most typical example of this challenging issue.This paper investigates multi/many-core parallelization strategies for speeding up the traversal of large ensembles of regression trees thus obtaining machine-learnt models that are, at the same time, effective, fast, and scalable.Our best results are obtained by the GPU-based parallelization of the state-of-the-art algorithm, with speedups of up to 102.6x.