Reviewerly/exHarmony huggingface dataset:
SRLGRN: Semantic Role Labeling Graph Reasoning Network

This work deals with the challenge of learning and reasoning over multi-hop question answering (QA).We propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find the supporting facts and the answer jointly.The proposed graph is a heterogeneous document-level graph that contains nodes of type sentence (question, title, and other sentences), and semantic role labeling sub-graphs per sentence that contain arguments as nodes and predicates as edges.Incorporating the argument types, the argument phrases, and the semantics of the edges originated from SRL predicates into the graph encoder helps in finding and also the explainability of the reasoning paths.Our proposed approach shows competitive performance on the HotpotQA distractor setting benchmark compared to the recent state-of-the-art models.
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation

Large-scale training datasets lie at the core of the recent success of neural machine translation (NMT) models.However, the complex patterns and potential noises in the large-scale data make training NMT models difficult.In this work, we explore to identify the inactive training examples which contribute less to the model performance, and show that the existence of inactive examples depends on the data distribution.We further introduce data rejuvenation to improve the training of NMT models on large-scale datasets by exploiting inactive examples.The proposed framework consists of three phases.First, we train an identification model on the original training data, and use it to distinguish inactive examples and active examples by their sentence-level output probabilities.Then, we train a rejuvenation model on the active examples, which is used to re-label the inactive examples with forwardtranslation.Finally, the rejuvenated examples and the active examples are combined to train the final NMT model.Experimental results on WMT14 English-German and English-French datasets show that the proposed data rejuvenation consistently and significantly improves performance for several strong NMT models.Extensive analyses reveal that our approach stabilizes and accelerates the training process of NMT models, resulting in final models with better generalization capability. 1
Revisiting Modularized Multilingual NMT to Meet Industrial Demands

The complete sharing of parameters for multilingual translation (1-1) has been the mainstream approach in current research.However, degraded performance due to the capacity bottleneck and low maintainability hinders its extensive adoption in industries.In this study, we revisit the multilingual neural machine translation model that only share modules among the same languages (M2) as a practical alternative to 1-1 to satisfy industrial requirements.Through comprehensive experiments, we identify the benefits of multi-way training and demonstrate that the M2 can enjoy these benefits without suffering from the capacity bottleneck.Furthermore, the interlingual space of the M2 allows convenient modification of the model.By leveraging trained modules, we find that incrementally added modules exhibit better performance than singly trained models.The zero-shot performance of the added modules is even comparable to supervised models.Our findings suggest that the M2 can be a competent candidate for multilingual translation in industries. En En Ko EnEs Ko ⋯
Multivariate discrimination in quantum target detection

We describe a simple multivariate technique of likelihood ratios for improved discrimination of signal and background in multi-dimensional quantum target detection. The technique combines two independent variables, time difference and summed energy, of a photon pair from the spontaneous parametric down-conversion source into an optimal discriminant. The discriminant performance was studied in experimental data and in Monte-Carlo modelling with clear improvement shown compared to previous techniques. As novel detectors become available, we expect this type of multivariate analysis to become increasingly important in multi-dimensional quantum optics.
Discrete Graphical Models — An Optimization Perspective

This monograph is about discrete energy minimization for discrete graphical models. It considers graphical models, or, more precisely, maximum a posteriori inference for graphical models, purely as a combinatorial optimization problem. Modeling, applications, probabilistic interpretations and many other aspects are either ignored here or find their place in examples and remarks only. It covers the integer linear programming formulation of the problem as well as its linear programming, Lagrange and Lagrange decomposition-based relaxations. In particular, it provides a detailed analysis of the polynomially solvable acyclic and submodular problems, along with the corresponding exact optimization methods. Major approximate methods, such as message passing and graph cut techniques are also described and analyzed comprehensively. The monograph can be useful for undergraduate and graduate students studying optimization or graphical models, as well as for experts in optimization who want to have a look into graphical models. To make the monograph suitable for both categories of readers we explicitly separate the mathematical optimization background chapters from those specific to graphical models.
Tackling the Low-resource Challenge for Canonical Segmentation

Canonical morphological segmentation consists of dividing words into their standardized morphemes.Here, we are interested in approaches for the task when training data is limited.We compare model performance in a simulated low-resource setting for the highresource languages German, English, and Indonesian to experiments on new datasets for the truly low-resource languages Popoluca and Tepehua.We explore two new models for the task, borrowing from the closely related area of morphological generation: an LSTM pointer-generator and a sequence-to-sequence model with hard monotonic attention trained with imitation learning.We find that, in the low-resource setting, the novel approaches outperform existing ones on all languages by up to 11.4% accuracy.However, while accuracy in emulated low-resource scenarios is over 50% for all languages, for the truly lowresource languages Popoluca and Tepehua, our best model only obtains 37.4% and 28.4% accuracy, respectively.Thus, we conclude that canonical segmentation is still a challenging task for low-resource languages.
Exploring Virtual Reality in the Higher Education Classroom: Using VR to Build Knowledge and Understanding

irtual reality (VR), as an informative medium, possesses the potential to engage students with immersive, interactive, and informative experiences. When presented in VR, immersive virtual environments (IVEs) can provide three-dimensional visual simulations that can be used to inform students about concepts in specific contexts that would be near impossible to achieve with more traditional teaching methodologies. It is proposed that existing learning frameworks can benefit from exploring the modalities of interaction that are presently afforded via VR from the experiential perspectives of the students. An evaluation is presented that focused on the appraisal of student experiences of immersive technologies as applied in a higher education context, specifically in the use of VR for the exploration of geomorphology theory by physical geography students. This research supports further development of the immersive learning discipline from three different perspectives. First, an empathy mapping method was applied to visualize student experiences and externalize our observed knowledge of student users for creating a shared understanding of their needs and to aid in lesson planning decision making when using VR in the classroom. Second, student experiences were captured using a technology-focused user experience questionnaire to obtain student attitudes immediately post-task. Finally, to assist teachers with the creation of a student-centered lesson plans that incorporate VR in the classroom, eight heuristic guidelines (focus, provocation, stimulation, collaboration, control, digital life, learner skills, multimodal experience) were developed. It is proposed that these findings can be used to provide support for the use of mixed reality and immersive virtual environments in learning that encompass the challenges faced by students and the interdisciplinary education community at large.
Classification of Negative Information on Socially Significant Topics in Mass Media

Mass media not only reflect the activities of state bodies but also shape the informational context, sentiment, depth, and significance level attributed to certain state initiatives and social events. Multilateral and quantitative (to the practicable extent) assessment of media activity is important for understanding their objectivity, role, focus, and, ultimately, the quality of the society’s “fourth power”. The paper proposes a method for evaluating the media in several modalities (topics, evaluation criteria/properties, classes), combining topic modeling of the text corpora and multiple-criteria decision making. The evaluation is based on an analysis of the corpora as follows: the conditional probability distribution of media by topics, properties, and classes is calculated after the formation of the topic model of the corpora. Several approaches are used to obtain weights that describe how each topic relates to each evaluation criterion/property and to each class described in the paper, including manual high-level labeling, a multi-corpora approach, and an automatic approach. The proposed multi-corpora approach suggests assessment of corpora topical asymmetry to obtain the weights describing each topic’s relationship to a certain criterion/property. These weights, combined with the topic model, can be applied to evaluate each document in the corpora according to each of the considered criteria and classes. The proposed method was applied to a corpus of 804,829 news publications from 40 Kazakhstani sources published from 01 January 2018 to 31 December 2019, to classify negative information on socially significant topics. A BigARTM model was derived (200 topics) and the proposed model was applied, including to fill a table of the analytical hierarchical process (AHP) and all of the necessary high-level labeling procedures. Experiments confirm the general possibility of evaluating the media using the topic model of the text corpora, because an area under receiver operating characteristics curve (ROC AUC) score of 0.81 was achieved in the classification task, which is comparable with results obtained for the same task by applying the BERT (Bidirectional Encoder Representations from Transformers) model.
CUSTOMHyS: Customising Optimisation Metaheuristics via Hyper-heuristic Search

There is a colourful palette of metaheuristics for solving continuous optimisation problems in the literature. Unfortunately, it is not easy to pick a suitable one for a specific practical scenario. Moreover, oftentimes the selected metaheuristic must be tuned until finding adequate parameter settings. Therefore, this work presents a framework based on a hyper-heuristic powered by Simulated Annealing for tailoring population-based metaheuristics. To do so, we recognise search operators from well-known techniques as building blocks for new ones. The presented framework comprises six main modules coded in Python, which can be used independently, and which help explore new metaheuristics.
A Systematic Appraisal of Side Channel Evaluation Strategies

In this paper we examine the central question that is how well do side channel evaluation regimes capture the true security level of a product. Concretely, answering this question requires considering the optimality of the attack/evaluation strategy selected by the evaluator, and the various steps to instantiate it. We draw on a number of published works and discuss whether state-of-the-art solutions for the different steps of a side-channel security evaluation offer bounds or guarantees of optimality, or if they are inherently heuristic. We use this discussion to provide an informal rating of the steps' optimality and to put forward where risks of overstated security levels remain.
Collecting Verified COVID-19 Question Answer Pairs

We release a dataset of over 2,100 COVID-19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites.We include an additional 24, 000 questions pulled from online sources that have been aligned by experts with existing answered questions from our dataset.This paper describes our efforts in collecting the dataset and summarizes the resulting data.Our dataset is automatically updated daily and available at https://github.com/JHU-COVID-QA/scraping-qas.So far, this data has been used to develop a chatbot providing users information about COVID-19.We encourage others to build analytics and tools upon this dataset as well.
A Lightweight CNN Model for Refining Moving Vehicle Detection From Satellite Videos

With the recent development of earth observation technology, the satellites have obtained the ability to capture city-scale videos, which enable potential applications in intelligent traffic management.Because of the broad field-of-view, the moving vehicles in satellite videos are usually composed of only tens of pixels, making it difficult to differentiate true objects from noise and other distractors.In addition, the edges of tall building tops are often mistakenly detected as moving vehicles because of the effects of motion parallax.This paper proposed a terse framework that can effectively suppress false targets, achieving high precision and recall.The study involves three parts: 1) An adaptive filtering method is proposed to reduce noise, thus making the detection algorithm more reliable; 2) Several background subtraction models are tested, and the best one is chosen to produce the preliminary detection results at high recall but low accuracy; 3) A lightweight convolutional neural network (LCNN) is designed and trained on a small collection of samples, and then used to eliminate false targets.The experiments and evaluations demonstrate that our method can largely improve the precision at the expense of a slight reduction of recall.
Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP

Iran, along with China, South Korea, and Italy was among the countries that were hit hard in the first wave of the COVID-19 spread.Twitter is one of the widely-used online platforms by Iranians (Statcounter, 2020) inside and abroad for sharing their opinion, thoughts, and feelings about a wide range of issues.In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time.We applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics.We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point.We additionally categorized the broader content of tweets that shows satire, followed by news, is the dominant tweet type among Iranian users.While this framework and methodology can be used to track public response to ongoing developments related to COVID-19, a generalization of this framework can become a useful framework to gauge Iranian public reaction to ongoing policy measures or events locally and internationally.
An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos

Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.
Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.
The Governance in the Corporate Excellence Model – The 4th generation model

The objective of the study is to identify the impact of corporate excellence model on corporate governance in the official sector of the United Arab Emirates (UAE). The study employs a survey method composed of 30 questions. Following the United Nations (UN) Convention against Corruption, the questionnaire was divided into 6 components: contribution, transparency, accountability, rule of law, efficiency, and effectiveness. The sample consists of 691 employees which represents 30% of total population. Data is analyzed using SPSS. Results indicate that implementing the corporate excellence model positively affects UAE corporate governance. Relatively, the participation, rule of law, justice, efficiency, and effectiveness are implemented at the level of managers and deputy managers, while accountability is depicted at the level of employees. Important recommendations are derived. To ensure excellence and empower leaders and managers, companies should incentivize them. Training opportunities with international organizations and awards system will contribute to motivate employees, elevate their sense of loyalty and integrity while broadening their knowledge of successful governance practices.
Stability of discrete-time feed-forward neural networks in NARX configuration

The idea of using Feed-Forward Neural Networks (FFNNs) as regression functions for Nonlinear AutoRegressive eXogenous (NARX) models, leading to models herein named Neural NARXs (NNARXs), has been quite popular in the early days of machine learning applied to nonlinear system identification, owing to their simple structure and ease of application to control design. Nonetheless, few theoretical results are available concerning the stability properties of these models. In this paper we address this problem, providing a sufficient condition under which NNARX models are guaranteed to enjoy the Input-to-State Stability (ISS) and the Incremental Input-to-State Stability (δISS) properties. This condition, which is an inequality on the weights of the underlying FFNN, can be enforced during the training procedure to ensure the stability of the model. The proposed model, along with this stability condition, are tested on the pH neutralization process benchmark, showing satisfactory results.
Distance E-Learning in the System of Professional Development of Corporation Managers: Challenges of COVID-19

The corporate education system is implemented through sequential and step-by-step in-company e-training based on integrity of goals, objectives, functions, learning technologies, and content structure of the training material, which add flexibility and vitality to the entire learning process with an advanced reflection of reality. The definition of a corporate training center in terms of a systematic approach is formulated as a system in which training and development of personnel are initiated and managed by the company itself, carried out within the company and involving internal resources. Thus, training and professional development of managers is not just an element of organizational practice or type of activity of certain employees of the organization, but a fully fledged part of the development strategy of the organization and the entire existing organizational system. To implement this process, modern transnational corporations (TNCs) create corporate training centers in which training and personnel development is initiated and managed by the company itself, carried out within the company and involve internal resources. The algorithm of creating a corporate training center covers a number of stages, in particular: support by the management; legal and financial support; creation of organizational and pedagogical conditions; program and content implementation support and practical application of the personnel training system; and a control and reporting system.
Automated and Intelligent System for Monitoring Swimming Pool Safety Based on the IoT and Transfer Learning

Recently, integrating the Internet of Things (IoT) and computer vision has been utilized in swimming pool automated surveillance systems. Several studies have been proposed to overcome off-time surveillance drowning incidents based on using a sequence of videos to track human motion and position. This paper proposes an efficient and reliable detection system that utilizes a single image to detect and classify drowning objects, to prevent drowning incidents. The proposed system utilizes the IoT and transfer learning to provide an intelligent and automated solution for off-time monitoring swimming pool safety. In addition, a specialized transfer-learning-based model utilizing a model pretrained on “ImageNet”, which can extract the most useful and complex features of the captured image to differentiate between humans, animals, and other objects, has been proposed. The proposed system aims to reduce human intervention by processing and sending the classification results to the owner’s mobile device. The performance of the specialized model is evaluated by using a prototype experiment that achieves higher accuracy, sensitivity, and precision, as compared to other deep learning algorithms.