Reviewerly/exHarmony huggingface dataset:
MMPE: A Multi-Modal Interface for Post-Editing Machine Translation

Nico Herbig, Tim Düwel, Santanu Pal, Kalliopi Meladaki, Mahsa Monshizadeh, Antonio Krüger, Josef van Genabith. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.
Evaluation of Quantum Approximate Optimization Algorithm based on the approximation ratio of single samples.

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm to solve binary-variable optimization problems. Due to its expected robustness to systematic errors and the short circuit depth, it is one of the promising candidates likely to run on near-term quantum devices. We project the performance of QAOA applied to the Max-Cut problem and compare it with some of the best classical alternatives, both for exact or approximate solution. When comparing approximate solvers, their performance is characterized by the computational time taken to achieve a given quality of solution. Since QAOA is based on sampling, we introduce performance metrics based on the probability of observing a sample above a certain quality. In addition, we show that the QAOA performance varies significantly with the graph type. By selecting a suitable optimizer for the variational parameters and reducing the number of function evaluations, QAOA performance improves by up to 2 orders of magnitude compared to previous estimates. Especially for 3-regular random graphs, this setting decreases the performance gap with classical alternatives.
Learning from Mistakes: Combining Ontologies via Self-Training for Dialogue Generation.

Natural language generators (NLGs) for task-oriented dialogue typically take a meaning representation (MR) as input. They are trained end-to-end with a corpus of MR/utterance pairs, where the MRs cover a specific set of dialogue acts and domain attributes. Creation of such datasets is labor-intensive and time-consuming. Therefore, dialogue systems for new domain ontologies would benefit from using data for pre-existing ontologies. Here we explore, for the first time, whether it is possible to train an NLG for a new larger ontology using existing training sets for the restaurant domain, where each set is based on a different ontology. We create a new, larger combined ontology, and then train an NLG to produce utterances covering it. For example, if one dataset has attributes for family-friendly and rating information, and the other has attributes for decor and service, our aim is an NLG for the combined ontology that can produce utterances that realize values for family-friendly, rating, decor and service. Initial experiments with a baseline neural sequence-to-sequence model show that this task is surprisingly challenging. We then develop a novel self-training method that identifies (errorful) model outputs, automatically constructs a corrected MR input to form a new (MR, utterance) training pair, and then repeatedly adds these new instances back into the training data. We then test the resulting model on a new test set. The result is a self-trained model whose performance is an absolute 75.4% improvement over the baseline model. We also report a human qualitative evaluation of the final model showing that it achieves high naturalness, semantic coherence and grammaticality
A semi-supervised convolutional neural network based on subspace representation for image classification

Abstract This work presents a shallow network based on subspaces with applications in image classification. Recently, shallow networks based on PCA filter banks have been employed to solve many computer vision-related problems including texture classification, face recognition, and scene understanding. These approaches are robust, with a straightforward implementation that enables fast prototyping of practical applications. However, these architectures employ either unsupervised or supervised learning. As a result, they may not achieve highly discriminative features in more complicated computer vision problems containing variations in camera motion, object’s appearance, pose, scale, and texture, due to drawbacks related to each learning paradigm. To cope with this disadvantage, we propose a semi-supervised shallow network equipped with both unsupervised and supervised filter banks, presenting representative and discriminative abilities. Besides, the introduced architecture is flexible, performing favorably on different applications whose amount of supervised data is an issue, making it an attractive choice in practice. The proposed network is evaluated on five datasets. The results show improvement in terms of prediction rate, comparing to current shallow networks.
Feature Noise Induces Loss Discrepancy Across Groups

The performance of standard learning procedures has been observed to differ widely across groups. Recent studies usually attribute this loss discrepancy to an information deficiency for one group (e.g., one group has less data). In this work, we point to a more subtle source of loss discrepancy---feature noise. Our main result is that even when there is no information deficiency specific to one group (e.g., both groups have infinite data), adding the same amount of feature noise to all individuals leads to loss discrepancy. For linear regression, we thoroughly characterize the effect of feature noise on loss discrepancy in terms of the amount of noise, the difference between moments of the two groups, and whether group information is used or not. We then show this loss discrepancy does not vanish immediately if a shift in distribution causes the groups to have similar moments. On three real-world datasets, we show feature noise increases the loss discrepancy if groups have different distributions, while it does not affect the loss discrepancy on datasets where groups have similar distributions.
Mapping Manuscript Migrations Knowledge Graph: Data for Tracing the History and Provenance of Medieval and Renaissance Manuscripts

The Mapping Manuscript Migrations (MMM) project transformed three separate datasets relating to the history and provenance of medieval and Renaissance manuscripts into a unified knowledge graph. The source databases are: Schoenberg Database of Manuscripts, from the Schoenberg Institute for Manuscript Studies, University of Pennsylvania; Bibale, from the Institut de recherche et d’histoire des textes (IRHT-CNRS, Paris); and Medieval Manuscripts in Oxford Libraries, from the Bodleian Libraries, University of Oxford. The data consist of more than 20 million RDF triples which have been mapped to the MMM Data Model. The model combines classes and properties from CIDOC-CRM and FRBR, together with some specific MMM elements. The Knowledge Graph was created using the MMM data transformation pipeline. The MMM dataset is available from the Zenodo repository, and can be directly deployed on a SPARQL endpoint using a docker recipe. To test and demonstrate its usefulness, the MMM Knowledge Graph is in use in the MMM Semantic Portal: https://mappingmanuscriptmigrations.org.
HOURLY GLOBAL SOLAR RADIATION ESTIMATION BASED ON MACHINE LEARNING METHODS IN ESKISEHIR

Due to the increasing importance of knowing the global solar radiation (GSR) amount incident on solar panels; short term data, such as hourly global solar radiation (HGSR), is essentially required to obtain more accurate and reliable power generation prediction. Nowadays, Machine Learning (ML) methods are becoming a huge trend for data forecasting. Therefore, in this paper, a comparison between Collares-Pereira &amp;amp; Rabl empirical model modified by Gueymard (CPRG) and ML methods for HGSR estimation in Eskişehir city in Turkey is conducted. Artificial Neural Network (ANN), Regression Tree (RT) and Support Vector Regression (SVR) are ML methods that are used to predict HGSR. Besides, hourly metrological and geographical parameters for the year 2014 are taken as inputs in the training models. The inputs are solar time, solar hour angle, Julian day number, daily GSR, altitude, longitude, latitude, hourly average humidity, hourly temperature and hourly pressure. To demonstrate these techniques, a comparison is implemented using MATLAB software with the help of existing toolboxes. Finally, this study proved that ML methods outperform the CPRG model and not to mention that they have far more accurate results. Although almost all ML models gave similar results, SVR was the best among them with a correlation coefficient of 0.979532 for the training set and 0.978244 for the testing set. In a nutshell, ML are successful methods that should be to be taken into consideration to perfectly estimate HGSR now and in the future in the field of solar renewable energy estimation.
UnrealText: Synthesizing Realistic Scene Text Images From the Unreal World

Synthetic data has been a critical tool for training scene text detection and recognition models. On the one hand, synthetic word images have proven to be a successful substitute for real images in training scene text recognizers. On the other hand, however, scene text detectors still heavily rely on a large amount of manually annotated real-world images, which are expensive. In this paper, we introduce UnrealText, an efficient image synthesis method that renders realistic images via a 3D graphics engine. 3D synthetic engine provides realistic appearance by rendering scene and text as a whole, and allows for better text region proposals with access to precise scene information, e.g. normal and even object meshes. The comprehensive experiments verify its effectiveness on both scene text detection and recognition. We also generate a multilingual version for future research into multilingual scene text detection and recognition. The code and the generated datasets are released at: https://github.com/Jyouhou/UnrealText/.
Optimization of quantum computing models inspired by D-wave quantum annealing

With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important. The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations. This study constructs a new Quantum-Inspired Annealing (QIA) framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one. Through various configurations of the 1D Ising model, the new framework can achieve ground state, corresponding to the optimum of classical problems, with higher probability up to 28% versus classical counterpart (22% in case). This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian, but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.
Query Classification with Multi-objective Backoff Optimization

E-commerce platforms greatly benefit from high-quality search that retrieves relevant search results in response to search terms. For the sake of search relevance, Query Classification (QC) has been widely adopted to make search engines robust against low text quality and complex category hierarchy. Generally, QC solutions categorize search queries and direct users to the suggested categories whereby the search results are then retrieved. In this way, the search scope is contextually constrained to increase search relevance. However, such operations might risk deteriorating e-commerce metrics when irrelevant categories are suggested. Thus, QC solutions are expected to demonstrate high accuracy. Unfortunately, existing QC methods mainly focus on the intrinsic performance of classifiers whereas fail to consider post-inference optimization that could further improve reliability. To fill up the research gap, we propose the Query Classification with Multi-objective Backoff (QCMB). The proposed solution consists of two steps: 1) hierarchical text classification that classifies search queries into multi-level categories; and 2) multi-objective backoff that substitutes potentially misclassified leaf categories with appropriate ancestors that optimize the trade-off between accuracy and depth. The proposed QCMB is evaluated using the real-world search data of Trade Me that is the largest e-commerce platform in New Zealand. Compared with the benchmarks, QCMB delivers superior solutions with flexible tuning to satisfy different users' demands. To the best of our knowledge, this work is the first attempt to enhance QC with multi-objective optimization.
Beyond Domain APIs: Task-oriented conversational modeling with unstructured knowledge access

Most prior work on task-oriented dialogue systems are restricted to a limited coverage of domain APIs, while users oftentimes have domain related requests that are not covered by the APIs. In this paper, we propose to expand coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources. We define three sub-tasks: knowledge-seeking turn detection, knowledge selection, and knowledge-grounded response generation, which can be modeled individually or jointly. We introduce an augmented version of MultiWOZ 2.1, which includes new out-of-API-coverage turns and responses grounded on external knowledge sources. We present baselines for each sub-task using both conventional and neural approaches. Our experimental results demonstrate the need for further research in this direction to enable more informative conversational systems.
Accelerated Computer Vision Inference with AI on the Edge

Computer vision is not just about breaking down images or videos into constituent pixels, but also about making sense of those pixels and comprehending what they represent. Researchers have developed some brilliant neural networks and algorithms for modern computer vision. Tremendous developments have been observed in deep learning as computational power is getting cheaper. But data-driven deep learning and cloud computing based systems face some serious limitations at edge devices in real-world scenarios. Since we cannot bring edge devices to the data-centers, so we bring AI to the edge devices with AI on the Edge. OpenVINO toolkit is a powerful tool that facilitates deployment of high-performance computer vision applications to the edge devices. It converts existing applications into hardwarefriendly and inference-optimized deployable runtime packages that operate seamlessly at the edge. The goals of this paper are to describe an in-depth survey of problems faced in existing computer vision applications and to present AI on the Edge along with OpenVINO toolkit as the solution to those problems. We redefine the workflow for deploying computer vision systems and provide an efficient approach for development and deployment of edge applications. Furthermore, we summarize the possible works and applications of AI on the Edge in future in regard to security and privacy.
A Mini Comparative Study of Requirements Modelling Diagrams towards Swimlane: Evidence of Enterprise Resource Planning System

Abstract Model-based requirements engineering (MBRE) is an approach that empowers requirements engineers with adequate models to perform requirements engineering (RE) activities. This approach is increasingly becoming an essential part of system engineering projects as it reduces development time, enhances analysis capability and increases the potential for reuse. Use case diagram, activity diagram, business process management and notation diagram (BPMN) and swimlane diagram are among the diagram used in representing the requirements. However, the swimlane diagram has received little attention in the literature and lack of empirical support, particularly in the requirements engineering field. In this study, a multi-criteria comparative work is performed where three other diagrams are identified and examined against the swimlane diagram. Walkthrough sessions are conducted using enterprise application documents with real users to validate the model-based requirements exemplified using the swimlane diagram. These activities are carried out to highlight the efficacy of the swimlane diagram in representing requirements in the RE field. The results confirmed that the swimlane diagram outperforms NL in overall completion time for the requirement validation process. Furthermore, it gives users a better understanding as they did not need to read textual requirements which are typically ambiguous, confusing, and lengthy.
Configuration and Control of KMR iiwa Mobile Robots using ROS2

In this paper, we present a novel system for controlling a KMR iiwa mobile robot using ROS2. The KMR iiwa is a mobile robot with a manipulator mounted on the base that is developed by the robot manufacturer KUKA. The developed system integrates with the Sunrise.OS operating system of the mobile robot and exposes sensor and control interfaces over UDP and TCP sockets. The controllability of the mobile robot from ROS2 is verified using the Cartographer and Navigation2 projects.
PCANN: Distributed ANN Architecture for Image Recognition in Resource-Constrained IoT Devices

As deployment of Internet of Things (IoT) devices gain momentum, there is an increased interest in implementing machine learning (ML) algorithms on IoT devices. Most of the existing ML solutions, however, rely on a central server to execute data-intensive ML models, because most devices in IoT systems do not have sufficient storage and computing resources. This paper presents a distributed Artificial Neural Networks (ANN) architecture, called PCANN, which allows execution of a complex image recognition task on a collection of resource-constrained IoT devices. Our solution separates a single ML model into multiple small modules that are executed by the distributed IoT devices. The solution effectively reduces storage and computing requirements for individual devices to store and process ML model. We design multiple PCANN models and utilize the models for human posture recognition as the case study. The experimental results show that the distributed PCANN architecture achieves comparable accuracy as the classical ANN model, while the average size of each PCANN module is largely reduced.
Expectations of Artificial Intelligence for Pathology

Within the last ten years, essential steps have been made to bring artificial intelligence (AI) successfully into the field of pathology. However, most pathologists are still far away from using AI in daily pathology practice. If one leaves the pathology annihilation model, this paper focuses on tasks, which could be solved, and which could be done better by AI, or image-based algorithms, compared to a human expert. In particular, this paper focuses on the needs and demands of surgical pathologists; examples include: Finding small tumour deposits within lymph nodes, detection and grading of cancer, quantification of positive tumour cells in immunohistochemistry, pre-check of Papanicolaou-stained gynaecological cytology in cervical cancer screening, text feature extraction, text interpretation for tumour-coding error prevention and AI in the next-generation virtual autopsy. However, in order to make substantial progress in both fields it is important to intensify the cooperation between medical AI experts and pathologists.
Person Re-identification in Videos by Analyzing Spatio-temporal Tubes

Abstract Typical person re-identification frameworks search for k best matches in a gallery of images that are often collected in varying conditions. The gallery usually contains image sequences for video re-identification applications. However, such a process is time consuming as video re-identification involves carrying out the matching process multiple times. In this paper, we propose a new method that extracts spatio-temporal frame sequences or tubes of moving persons and performs the re-identification in quick time. Initially, we apply a binary classifier to remove noisy images from the input query tube. In the next step, we use a key-pose detection-based query minimization technique. Finally, a hierarchical re-identification framework is proposed and used to rank the output tubes. Experiments with publicly available video re-identification datasets reveal that our framework is better than existing methods. It ranks the tubes with an average increase in the CMC accuracy of 6-8% across multiple datasets. Also, our method significantly reduces the number of false positives. A new video re-identification dataset, named Tube-based Re-identification Video Dataset (TRiViD), has been prepared with an aim to help the re-identification research community.
The Effect of Exergame Education on Balance in Children

The purpose of this study was to investigate the effects of exergame training which can be conducted on stable floor; at school with smart board and at home with TV screen, on students’ balance improvement. 53 students with age average of 10,25±0.504 were voluntarily participated to this research. In this experimental research, pretest-posttest with control group model was used as a testing model. Participant students were divided into exergame group and control group by using random sampling method. While balance training with ‘nintendo wii’ game console was applied to exergame group three days a week during eight weeks, control group didn’t take any application. For both of the groups, pretest-posttest was evaluated through static balance measurement with ‘balance error scoring system (BESS)’ and dynamic balance with ‘techno-body measuring device’. Furthermore, development level between these two groups was observed. Pretest results comparison was conducted with unpaired t-test. In the analysis of development level between groups, for pretest-posttest values repetitive measurement, two-way anova analysis was used. In pretest measurements, It was not observed any difference between control and exargames applied groups dynamic and static balance ability (p>,05).By the way, in static and dynamic posttest measurement, It was observed significant differences between groups in favor of exergame group (p<,05). In this study, It was considered that balance training with exergame dramatically improves both static and dynamic balance abilities. As a result, It can be suggested that exergame should be used for improving the balance in children as an alternative training method both in the schools and houses.