Reviewerly/exHarmony huggingface dataset:
Optimal Multi-port-based Teleportation Schemes

In this paper, we introduce optimal versions of a multi-port based teleportation scheme allowing to send a large amount of quantum information. We fully characterise probabilistic and deterministic case by presenting expressions for the average probability of success and entanglement fidelity. In the probabilistic case, the final expression depends only on global parameters describing the problem, such as the number of ports $N$, the number of teleported systems $k$, and local dimension $d$. It allows us to show square improvement in the number of ports with respect to the non-optimal case. We also show that the number of teleported systems can grow when the number $N$ of ports increases as $o(N)$ still giving high efficiency. In the deterministic case, we connect entanglement fidelity with the maximal eigenvalue of a generalised teleportation matrix. In both cases the optimal set of measurements and the optimal state shared between sender and receiver is presented. All the results are obtained by formulating and solving primal and dual SDP problems, which due to existing symmetries can be solved analytically. We use extensively tools from representation theory and formulate new results that could be of the separate interest for the potential readers.
Truth or Error? Towards systematic analysis of factual errors in abstractive summaries

This paper presents a typology of errors produced by automatic summarization systems.The typology was created by manually analyzing the output of four recent neural summarization systems.Our work is motivated by the growing awareness of the need for better summary evaluation methods that go beyond conventional overlap-based metrics.Our typology is structured into two dimensions.First, the Mapping Dimension describes surface-level errors and provides insight into word-sequence transformation issues.Second, the Meaning Dimension describes issues related to interpretation and provides insight into breakdowns in truth, i.e., factual faithfulness to the original text.Comparative analysis revealed that two neural summarization systems leveraging pretrained models have an advantage in decreasing grammaticality errors, but not necessarily factual errors.We also discuss the importance of ensuring that summary length and abstractiveness do not interfere with evaluating summary quality.
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction

Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets.However, there has been little focus on verifying and ensuring the quality of the datasets, and on how lower-quality data might affect GEC performance.We indeed found that there is a non-negligible amount of "noise" where errors were inappropriately edited or left uncorrected.To address this, we designed a self-refinement method where the key idea is to denoise these datasets by leveraging the prediction consistency of existing models, and outperformed strong denoising baseline methods.We further applied task-specific techniques and achieved state-of-the-art performance on the CoNLL-2014, JFLEG, and BEA-2019 benchmarks.We then analyzed the effect of the proposed denoising method, and found that our approach leads to improved coverage of corrections and facilitated fluency edits which are reflected in higher recall and overall performance.
A Unified Framework of Epidemic Spreading Prediction by Empirical Mode Decomposition- Based Ensemble Learning Techniques

In this paper, a unified susceptible-exposed-infected-susceptible-aware (SEIS-A) framework is proposed to combine the epidemic spreading process with individuals' self-query behaviors on the Internet. An epidemic spreading prediction model that contains two phases is established based on the SEIS-A framework. To deal with the nonstationary complex characteristic of the time series data of disease density, it is decomposed through the empirical mode decomposition (EMD) method to obtain the intrinsic mode functions (IMFs) in phase I. To enhance the prediction performance, the ensemble learning techniques that use the self-query data as an external input are applied to these IMFs in phase II. Finally, an empirical study on the prediction of weekly consultation rates of hand-foot-and-mouth disease (HFMD) in Hong Kong is conducted to validate the effectiveness of the proposed method. The main advantage of this method is that it outperforms other learning methods on fluctuating complex epidemic spreading data.
COVID-19: A Novel Framework to Globally Track Coronavirus Infected Patients using Blockchain

An outbreak of coronavirus caused by a novel virus called SARS-CoV-2 occurred at the end of 2019.The unexpected outbreak and unchecked global spread of COVID-19 indicate that the current global healthcare networks have limitations in addressing the crises for public safety.The detection of infected or tested patients globally is challenging when patients travel abroad.As such, innovative technology such as blockchain has emerged as a potential approach for addressing coronavirus patient tracking.Blockchain technology can tackle pandemics by allowing early detection of outbreaks, protecting individual privacy while maintaining data security by using smart contracts.Motivated by these facts, we propose a novel blockchain-based framework that integrates intercountry for COVID-19 to track infected or tested patients globally using the methodology of design science research.The proposed framework could help governments, aviation industries, health authorities, and residents make essential decisions on infection identification, infection prediction, and infection prevention.
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training

Pre-training large language models has become a standard in the natural language processing community.Such models are pretrained on generic data (e.g.BookCorpus and English Wikipedia) and often fine-tuned on tasks in the same domain.However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training is required.In practice, staged multi-domain pre-training presents performance deterioration in the form of catastrophic forgetting (CF) when evaluated on a generic benchmark such as GLUE.In this paper we conduct an empirical investigation into known methods to mitigate CF.We find that elastic weight consolidation provides best overall scores yielding only a 0.33% drop in performance across seven generic tasks while remaining competitive in bio-medical tasks.Furthermore, we explore gradient and latent clustering based data selection techniques to improve coverage when using elastic weight consolidation and experience replay methods.
The potential of quantum annealing for rapid solution structure identification

The recent emergence of novel computational devices, such as quantum computers, coherent Ising machines, and digital annealers presents new opportunities for hardware-accelerated hybrid optimization algorithms. Unfortunately, demonstrations of unquestionable performance gains leveraging novel hardware platforms have faced significant obstacles. One key challenge is understanding the algorithmic properties that distinguish such devices from established optimization approaches. Through the careful design of contrived optimization tasks, this work provides new insights into the computation properties of quantum annealing and suggests that this model has the potential to quickly identify the structure of high-quality solutions. A meticulous comparison to a variety of algorithms spanning both complete and local search suggests that quantum annealing’s performance on the proposed optimization tasks is distinct. This result provides new insights into the time scales and types of optimization problems where quantum annealing has the potential to provide notable performance gains over established optimization algorithms and suggests the development of hybrid algorithms that combine the best features of quantum annealing and state-of-the-art classical approaches.
Safe and Efficient Lane Change Maneuver for Obstacle Avoidance Inspired From Human Driving Pattern

One of the most important and fundamental topics in autonomous navigated vehicle research is the lane change maneuver for obstacle avoidance or overtaking maneuver. In the literature, the lane change maneuver path for car-like vehicles has widely been generated with geometrically smooth segments by solving boundary conditions under given constraints. This paper proposes a new method of continuous curvature path generation for the issue of lane change maneuver for obstacle avoidance while solving the clothoids composition problem using an efficient algorithmic procedure. Conventional approaches resorting to mathematical or engineering optimization without considering human-side activity and response may fail to deliver driving performance that is favorable to humans. The novelty of the proposed method lies in its adoption of a human driving pattern, which is non-symmetric and composed of two different modes of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">avoidance</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recovery</i> during the maneuver, and utilizes the property given by an appropriate iterative algorithm which takes into account all the constraints in order to solve the problem. As compared to conventional methods, the proposed method not only provides overall safety for obstacle avoidance, but also exhibits efficiency of increased comfort and human like steering motion during the lane change maneuver. The proposed path planning method is compared to other methods in order to validate its efficiency for safe and smooth obstacle avoidance maneuver.
A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering

The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area.In this paper, we show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon and also use it to evaluate robustness of different answer selection approaches.We introduce a simple framework that enables an automated analysis of the conversational question answering (QA) performance using question rewrites, and present the results of this analysis on the TREC CAsT and QuAC (CANARD) datasets.Our experiments uncover sensitivity to question formulation of the popular state-of-the-art models for reading comprehension and passage ranking.Our results demonstrate that the reading comprehension model is insensitive to question formulation, while the passage ranking changes dramatically with a little variation in the input question.The benefit of QR is that it allows us to pinpoint and group such cases automatically.We show how to use this methodology to verify whether QA models are really learning the task or just finding shortcuts in the dataset, and better understand the frequent types of error they make.
An Overview of Advances in Image Colorization Using Computer Vision and Deep Learning Techniques

Automatic image colorization as a process has been studied extensively over the past 10 years with importance given to its many applications in grayscale image colorization, aged/degraded image restoration etc. In this study, we attempt to trace and consolidate developments made in Image colorization using various computer vision techniques and methodologies, focusing on the emergence and performance of Generative Adversarial Networks (GANs). We talk in depth about GANs and CNNs, namely their structure, functionality and extent of research. Additionally, we explore the advances made in image colorization using other Deep Learning frameworks ranging from LeNets to MobileNets in order of their evolution in detail. We also compare existing published works showcasing new advancements and possibilities, and predominantly emphasize the importance of continuing research in image colorization. We further analyze and discuss potential applications and challenges of GANs to tackle in the future.
Immersive Interactive Quantum Mechanics for Teaching and Learning Chemistry

The impossibility of experiencing the molecular world with our senses hampers teaching and understanding chemistry because very abstract concepts (such as atoms, chemical bonds, molecular structure, reactivity) are required for this process. Virtual reality, especially when based on explicit physical modeling (potentially in real time), offers a solution to this dilemma. Chemistry teaching can make use of advanced technologies such as virtual-reality frameworks and haptic devices. We show how an immersive learning setting could be applied to help students understand the core concepts of typical chemical reactions by offering a much more intuitive approach than traditional learning settings. Our setting relies on an interactive exploration and manipulation of a chemical system; this system is simulated in real-time with quantum chemical methods, and therefore, behaves in a physically meaningful way.
Education in the Era of COVID-19: Innovative Solutions to Real Challenges

The COVID-19 outbreak has caused serious concerns to global education systems.Efforts to contain COVID-19 prompted unscheduled closure of schools in over 190 countries worldwide, leaving nearly 1.6 billion learners without concrete alternative schooling options.The study investigates the impact of COVID-19 on education from the lenses of teachers, students and parents.Data was collected through surveys and virtual focus group sessions administered to 150 respondents, from Trinidad & Tobago, and Venezuelan and Cuban migrants.The results of the qualitative research show that COVID-19 has had adverse effects on education including, learning disruptions, and decreased access to education facilities and teaching and learning resources.The findings also show that the education world has become heavily reliant on technology to ensure continued learning online during the pandemic.However, online education is shown to be hindered by poor infrastructures including, network, power, inaccessibility and unavailability issues, compounded by poor digital skills.The study underscores the damaging effects of COVID-19 on education sector and the need for all educational institutions, educators, and learners to adopt technology, and improve their digital skills in line with the emerging global trends and realities in education.
Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction

The goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form subject, predicate, object .For example, given the sentence »Beethoven composed the Ode to Joy.«, we are expected to extract the triple Beethoven, composed, Ode to Joy .In this work, we systematically compare different neural network architectures and training approaches, and improve the performance of the currently best models on the OIE16 benchmark (Stanovsky and Dagan, 2016) by 0.421 F 1 score and 0.420 AUC-PR, respectively, in our experiments (i.e., by more than 200% in both cases).Furthermore, we show that appropriate problem and loss formulations often affect the performance more than the network architecture.
Understanding the Characteristics, Benefits and Challenges of Agile it Project Management: A Literature Based Perspective

The objectives of this study was to bring out the understanding of the concept of agile IT project management; what it is and what it is not.It was also aimed at comparing the pros and cons of both agile and traditional methods of IT project management in a typical industry setting; the challenges of going purely agile, and so on.It is purely a review of literature of peer reviewed papers sourced mainly from Google Scholar.It was revealed that agile outweigh the traditional methods in terms of benefits, but its implementation poses a lot of challenges due to a number of issues, paramount among them being organizational culture and empowerment of the project team.This has resulted in a number of industries sticking to the traditional methods despite the overwhelming benefits of agile.In another school of thought, the combination of the two paradigms is the way forward.
Qualitative Numeric Planning: Reductions and Complexity

&#x0D; &#x0D; &#x0D; Qualitative numerical planning is classical planning extended with non-negative real variables that can be increased or decreased "qualitatively", i.e., by positive indeterminate amounts. While deterministic planning with numerical variables is undecidable in general, qualitative numerical planning is decidable and provides a convenient abstract model for generalized planning. The solutions to qualitative numerical problems (QNPs) were shown to correspond to the strong cyclic solutions of an associated fully observable non-deterministic (FOND) problem that terminate. This leads to a generate-and-test algorithm for solving QNPs where solutions to a FOND problem are generated one by one and tested for termination. The computational shortcomings of this approach for solving QNPs, however, are that it is not simple to amend FOND planners to generate all solutions, and that the number of solutions to check can be doubly exponential in the number of variables. In this work we address these limitations while providing additional insights on QNPs. More precisely, we introduce two polynomial-time reductions, one from QNPs to FOND problems and the other from FOND problems to QNPs both of which do not involve termination tests. A result of these reductions is that QNPs are shown to have the same expressive power and the same complexity as FOND problems.&#x0D; &#x0D; &#x0D;
Learning Explainable Decision Rules via Maximum Satisfiability

Decision trees are a popular choice for providing explainable machine learning, since they make explicit how different features contribute towards the prediction. We apply tools from constraint satisfaction to learn optimal decision trees in the form of sparse k-CNF (Conjunctive Normal Form) rules. We develop two methods offering different trade-offs between accuracy and computational complexity: one offline method that learns decision trees using the entire training dataset and one online method that learns decision trees over a local subset of the training dataset. This subset is obtained from training examples near a query point. The developed methods are applied on a number of datasets both in an online and an offline setting. We found that our methods learn decision trees which are significantly more accurate than those learned by existing heuristic approaches. However, the global decision tree model tends to be computationally more expensive compared to heuristic approaches. The online method is faster to train and finds smaller decision trees with an accuracy comparable to that of the k-nearest-neighbour method.
Image-Based Visual Servoing Control of Robot Manipulators Using Hybrid Algorithm With Feature Constraints

The challenge in addressing uncalibrated visual servoing (VS) control of robot manipulators with unstructured environments is to obtain appropriate interaction matrix and keep the image features in the field of view (FOV), especially when the non-Gaussian noise disturbance exists in the VS process. In this article, a hybrid control algorithm which combines bidirectional extreme learning machine (B-ELM) with smooth variable structure filter (SVSF) is proposed to estimate interaction matrix and tackle visibility constraints. For VS, the nonlinear mapping between image features and interaction matrix is approximated using the B-ELM learning. To increase the capability of anti-interference, the SVSF is employed to re-estimate interaction matrix. A constraint function presenting feature coordinates and region boundaries is given and added to the velocity controller, which drags image features away from the restricted region and ensures the smoothness of the velocities. Since the camera and robot model parameters are not required in developing the control strategy, the servoing task can be fulfilled flexibly and simply. Simulation and experimental results on a conventional 6-degree-of-freedom manipulator verify the effectiveness of the proposed method.
A Wizard of Oz Study Simulating API Usage Dialogues With a Virtual Assistant

Virtual Assistant technology is rapidly proliferating to improve productivity in a variety of tasks. While several virtual assistants for everyday tasks are well-known (e.g., Siri, Cortana, Alexa), assistants for specialty tasks such as software engineering are rarer. One key reason software engineering assistants are rare is that very few experimental datasets are available and suitable for training the AI that is the bedrock of current virtual assistants. In this paper, we present a set of Wizard of Oz experiments that we designed to build a dataset for creating a virtual assistant. Our target is a hypothetical virtual assistant for helping programmers use APIs. In our experiments, we recruited 30 professional programmers to complete programming tasks using two APIs. The programmers interacted with a simulated virtual assistant for help - the programmers were not aware that the assistant was actually operated by human experts. We then annotated the dialogue acts in the corpus along four dimensions: illocutionary intent, API information type(s), backward-facing function, and traceability to specific API components. We observed a diverse range of interactions that will facilitate the development of dialogue strategies for virtual assistants for API usage.
Monitoring Blockchain Cryptocurrency Transactions to Improve the Trustworthiness of the Fourth Industrial Revolution (Industry 4.0)

A completely new economic system is required for the era of Industry 4.0. Blockchain technology and blockchain cryptocurrencies are the best means to confront this new trustless economy. Millions of smart devices are able to complete transparent financial transactions via blockchain technology and its related cryptocurrencies. However, via blockchain technology, internet-connected devices may be hacked to mine cryptocurrencies. In this regard, monitoring the network of these blockchain-based transactions can be very useful to detect the abnormal behavior of users of these cryptocurrencies. Therefore, the trustworthiness of the transactions can be assured. In this paper, a novel procedure is proposed to monitor the network of blockchain cryptocurrency transactions. To do so, a hidden Markov multi-linear tensor model (HMTM) is utilized to model the transactions among nodes of the blockchain network. Then, a multivariate exponentially weighted moving average (MEWMA) control chart is applied to the monitoring of the latent effects. Average run length (ARL) is used to evaluate the performance of the MEWMA control chart in detecting blockchain network anomalies. The proposed procedure is applied to a real dataset of Bitcoin transactions.