Reviewerly/exHarmony huggingface dataset:
Data correction and evolution analysis of the ProgrammableWeb service ecosystem

The evolution analysis on Web service ecosystems has become a critical problem as the frequency of service changes on the Internet increases rapidly. Developers need to understand these evolution patterns to assist in their decision-making on service selection. ProgrammableWeb is a popular Web service ecosystem on which several evolution analyses have been conducted in the literature. However, the existing studies have ignored the quality issues of the ProgrammableWeb dataset and the issue of service obsolescence. In this study, we first report the quality issues identified in the ProgrammableWeb dataset from our empirical study. Then, we propose a novel method to correct the relevant evolution analysis data by estimating the life cycle of application programming interfaces (APIs) and mashups. We also reveal how to use three different dynamic network models in the service ecosystem evolution analysis based on the corrected ProgrammableWeb dataset. Our experimental experience iterates the quality issues of the original ProgrammableWeb and highlights several research opportunities.
Models and criteria for monitoring the quality of education

Introduction. The article focuses on monitoring the quality of education, analyzes the shortcomings of modern models and criteria functioning in this area. The purpose of the study is to build models and develop a criterion for their implementation in the field of education, free from these shortcomings. Materials and Methods. The methodology of solving this problem is based on the integration of mathematics and pedagogy (education studies) in the wide range of interactions of educational technologies, mathematical statistics and new results obtained by the author in the field of processing the findings of educational research investigations. Results. Achieving the goal includes three problems: the first is to build models for the study of dependent and independent samples; the second is to develop a single criterion for their implementation for the constructed models (instead of 12), free from known shortcomings; the third is to test and compare with other criteria in the process of solving specific problems in the field of education. The listed problems have been solved. Conclusions. The constructed models and the specified criterion are applicable for the study of dependent and independent samples, determine the direction of change caused by the introduction of a new educational technology, generalize almost all models and criteria known in this area.
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition

Subword units are commonly used for end-to-end automatic speech recognition (ASR), while a fully acoustic-oriented subword modeling approach is somewhat missing.We propose an acoustic data-driven subword modeling (ADSM) approach that adapts the advantages of several text-based and acousticbased subword methods into one pipeline.With a fully acousticoriented label design and learning process, ADSM produces acoustic-structured subword units and acoustic-matched target sequence for further ASR training.The obtained ADSM labels are evaluated with different end-to-end ASR approaches including CTC, RNN-Transducer and attention models.Experiments on the LibriSpeech corpus show that ADSM clearly outperforms both byte pair encoding (BPE) and pronunciationassisted subword modeling (PASM) in all cases.Detailed analysis shows that ADSM achieves acoustically more logical word segmentation and more balanced sequence length, and thus, is suitable for both time-synchronous and label-synchronous models.We also briefly describe how to apply acoustic-based subword regularization and unseen text segmentation using ADSM.
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models

Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning.In this paper, we present ReGen, a bidirectional generation of text and graph leveraging Reinforcement Learning (RL) to improve performance.Graph linearization enables us to re-frame both tasks as a sequence to sequence generation problem regardless of the generative direction, which in turn allows the use of Reinforcement Learning for sequence training where the model itself is employed as its own critic leading to Self-Critical Sequence Training (SCST).We present an extensive investigation demonstrating that the use of RL via SCST benefits graph and text generation on WebNLG+ 2020 and TEKGEN datasets.Our system provides state-of-the-art results on WebNLG+ 2020 by significantly improving upon published results from the WebNLG 2020+ Challenge for both text-to-graph and graph-to-text generation tasks.
Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.
Timing Constraints Imposed by Classical Digital Control Systems on Photonic Implementations of Measurement-Based Quantum Computing

Most of the architectural research on photonic implementations of measurement-based quantum computing (MBQC) has focused on the quantum resources involved in the problem with the implicit assumption that these will provide the main constraints on system scaling. However, the ‘flying-qubit’ architecture of photonic MBQC requires specific timing constraints that need to be met by the classical control system. This classical control includes, for example: the amplification of the signals from single-photon detectors to voltage levels compatible with digital systems; the implementation of a control system which converts measurement outcomes into basis settings for measuring subsequent cluster qubits, in accordance with the quantum algorithm being implemented; and the digital-to-analog converter (DAC) and amplifier systems required to set these measurement bases using a fast phase modulator. In this paper, we analyze the digital system needed to implement arbitrary one-qubit rotations and controlled-NOT (CNOT) gates in discrete-variable photonic MBQC, in the presence of an ideal cluster state generator, with the main aim of understanding the timing constraints imposed by the digital logic on the analog system and quantum hardware. We have verified that the design works using functional simulations, and have used static timing analysis of a Xilinx FPGA (7 series) to provide a practical upper bound on the speed at which the adaptive measurement processing can be performed, in turn constraining the photonic clock rate of the system. The design and testing system is freely available, for use as the basis of analysis of more complex designs, incorporating more recent proposals for photonic quantum computing.} Our work points to the importance of co-designing the classical control system in tandem with the quantum system in order to meet the challenging specifications of a photonic quantum computer.
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering

Visual question answering (VQA) is challenging not only because the model has to handle multi-modal information, but also because it is just so hard to collect sufficient training examples -there are too many questions one can ask about an image.As a result, a VQA model trained solely on human-annotated examples could easily over-fit specific question styles or image contents that are being asked, leaving the model largely ignorant about the sheer diversity of questions.Existing methods address this issue primarily by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing.In this paper, we take a drastically different approach.We found that many of the "unknowns" to the learned VQA model are indeed "known" in the dataset implicitly.For instance, questions asking about the same object in different images are likely paraphrases; the number of detected or annotated objects in an image already provides the answer to the "how many" question, even if the question has not been annotated for that image.Building upon these insights, we present a simple data augmentation pipeline SIMPLEAUG to turn this "known" knowledge into training examples for VQA.We show that these augmented examples can notably improve the learned VQA models' performance, not only on the VQA-CP dataset with language prior shifts but also on the VQA v2 dataset without such shifts.Our method further opens up the door to leverage weakly-labeled or unlabeled images in a principled way to enhance VQA models.Our code and data are publicly available at https://github.com/heendung/simpleAUG.Q: Where are the napkins?Q: What is the oven made of?
Deep Conditional Transformation Models

Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditional transformation models provide a semi-parametric approach that allows to model a large class of conditional CDFs without an explicit parametric distribution assumption and with only a few parameters. Existing estimation approaches within this class are, however, either limited in their complexity and applicability to unstructured data sources such as images or text, lack interpretability, or are restricted to certain types of outcomes. We close this gap by introducing the class of deep conditional transformation models which unifies existing approaches and allows to learn both interpretable (non-)linear model terms and more complex neural network predictors in one holistic framework. To this end we propose a novel network architecture, provide details on different model definitions and derive suitable constraints as well as network regularization terms. We demonstrate the efficacy of our approach through numerical experiments and applications.
Teaching and Learning with Technology During the COVID-19 Pandemic: Highlighting the Need for Micro-Meso-Macro Alignments

All over the world teaching and learning transitioned to forms of online education due to the COVID-19 pandemic. In this contribution, we recognize challenges that this disruptive change brought about for teachers and learners. We reflect on these challenges, based on discussions at EDUsummIT2019 in Quebec about the theme “Learners and learning contexts: New alignments for the digital age”. Informed by theoretical conceptualization and empirical evidence we identify micro-meso-macro alignments that need to be in place to move education into the digital age: alignments for quality learning contexts, alignments in support for teachers, and alignments through partnerships.
Teaching English Online to Students with Autism Spectrum Disorder and Down Syndrome During the COVID-19 Pandemic

The unprecedented reality of the covid-19 pandemic has led students and teachers to adapt to new routines and technological resources so that they can meet the pedagogical requirements generated by this world sanitary emergency. This case study explored a methodological intervention with students from an English school for special needs students in Manizales (Colombia). Our team of two university professors and seven pre-service teachers provided online English lessons to 17 students with autism spectrum disorder (asd) and 13 with Down syndrome (ds). To do it, we used the principles of universal design for learning (udl) and an eclectic method which combines strategies from thre specific approaches: The Presentation, Practice, and Production approach (ppp), the Treatment and Education of Autistic and Related Communication-Handicapped Children (teacch) approach and a behavioral management approach. The purpose of this study was to establish the impact of the implementation of such strategies on the English Language Learning (ell) process of the asd and ds learners. Three data collection techniques, including two surveys to parents and a researcher journal, were used. Findings indi­cated that: (a) the combination of various stimuli and methodological strategies from a variety of approaches enhanced learning, (b) the use of images and pictograms fostered memory, (c) setting clear routines promoted self-regulation skills, and (d) the families and students’ challenges were turned into opportunities. It was concluded that teaching English online to asd and ds students requires knowledge of their conditions and suitable strategies from an eclectic instructional approach.
Office Building’s Occupancy Prediction Using Extreme Learning Machine Model with Different Optimization Algorithms

Abstract Increasing energy efficiency requirements lead to lower energy consumption in buildings, but at the same time occupants’ influence on the energy balance of the building during the use phase becomes more crucial. The randomness of the building’s occupancy often leads to the mismatch of the predicted and measured energy demand, also called Energy Performance Gap. Therefore, prediction of occupancy is important both in the design and use phases of the building. The goal of the study is to apply Extreme Learning Machine (ELM) models with different optimisation algorithms – Genetic (GA-ELM) and Simulated Annealing (SA–ELM) for occupancy prediction in an office building based on measured CO 2 concentrations. Both models show similar and high accuracy of prediction: R 2 – 0.73–0.74 and RMSE – 1.8–1.9 for the whole measured period. Influence of population size, number of neurons, and number of iterations on results accuracy was also analysed and recommendations are given. It was concluded that both methods are suitable for occupancy prediction, but because of different simulation times, SA-ELM is recommended for the Building Management Systems (BMS), where higher speed is required.
Adversarial Examples for Evaluating Math Word Problem Solvers

Standard accuracy metrics have shown that Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets.However, the extent to which existing MWP solvers truly understand language and its relation with numbers is still unclear.In this paper, we generate adversarial attacks to evaluate the robustness of state-of-the-art MWP solvers.We propose two methods Question Reordering and Sentence Paraphrasing to generate adversarial attacks.We conduct experiments across three neural MWP solvers over two benchmark datasets.On average, our attack method is able to reduce the accuracy of MWP solvers by over 40 percentage points on these datasets.Our results demonstrate that existing MWP solvers are sensitive to linguistic variations in the problem text.We verify the validity and quality of generated adversarial examples through human evaluation.
Intelligent spacing selection model under energy-saving constraints for the selection of communication nodes in the Internet of Things

Abstract Current IoT communication node spacing selection process show may potential areas for improvements such as high delay ratio, high total energy consumption ratio, confusion of the optimal communication information band, intelligent spacing node design under the constraints of the energy-saving selection of IoT communication. Based on energy-saving constraints, the link status between nodes is evaluated through link stability and link quality. In order to prevent the generation of serious noisy nodes and frequency hopping data, the interference nodes under the intrusion of the Internet of Things are identified by determining transition amplitude of the noise nodes in the transmission data sequence. Finally, according to the calculation results of the optimal communication node selection, the design of the intelligent spacing selection model for the communication nodes of the Internet of Things is realized. The simulation results show that the established model not only reduces energy consumption of nodes, shortens the average transmission delay of nodes, but also improves anti-interference effect of node spacing selection.
Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis

Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient's preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two questions: can a single checkup predict the need of active treatment?, and which attributes of VS development are important in decision making on active treatment? Using a machine-learning analysis of medical records of 93 patients, the objectives were addressed using two classification tasks: a time-independent case-based reasoning (CBR), where each medical record was treated as independent, and a personalized dynamic analysis (PDA), during which we analyzed the individual development of each patient's state in time. Using the CBR method we found that Koos classification of tumor size, speech reception threshold, and pure tone audiometry, collectively predict the need for active treatment with approximately 90% accuracy; in the PDA task, only the increase of Koos classification and VS size were sufficient. Our results indicate that VS treatment may be reliably predicted using only a small set of basic parameters, even without the knowledge of individual development, which may help to simplify VS treatment strategies, reduce the number of examinations, and increase cause effectiveness.
A hybrid methodology for distribution level photovoltaic power production forecasting verified at the distribution system of Cyprus

Accurate photovoltaic (PV) power forecasting is an essential tool that enables the efficient integration of solar electricity and energy trading. This work proposes a novel methodology for the implementation of a forecasting tool that provides aggregated PV production day-ahead forecasts for the PV systems installed at a distribution level. Specifically, the tool is a cloud-based platform, comprising of a data quality block, a weather forecasting model, a machine learning power prediction step and an up-scaling aggregation stage. In this context and in the absence of a fully observable distribution system, the aggregated PV generation was estimated using a clustering approach and up-scaling the measured generation of reference systems to the aggregated installed capacity. The results demonstrated that the implemented tool exhibited high forecasting accuracies, lower than 10% given by the mean absolute percentage error when applied to the distribution system of Cyprus. Furthermore, the comparative benchmarking of the up-scaling techniques against the estimated aggregated PV generation demonstrated that the best-performing approach was the hybrid model, which provided a normalised root mean square error of 10.29% and mean absolute percentage error of 9.11%. Finally, useful information is provided for establishing a robust day-ahead forecasting methodology that is based on an optimal supervised learning and hybrid up-scaling approach.
MEM-KGC: Masked Entity Model for Knowledge Graph Completion With Pre-Trained Language Model

The knowledge graph completion (KGC) task aims to predict missing links in knowledge graphs. Recently, several KGC models based on translational distance or semantic matching methods have been proposed and have achieved meaningful results. However, existing models have a significant shortcoming–they cannot train entity embedding when an entity does not appear in the training phase. As a result, such models use randomly initialized embeddings for entities that are unseen in the training phase and cause a critical decrease in performance during the test phase. To solve this problem, we propose a new approach that performs the KGC task by utilizing the masked language modeling (MLM) task that is used for a pre-trained language model. Given a triple (head entity, relation, tail entity), we mask the tail entity and consider the head entity and the relation as a context for the tail entity. The model then predicts the masked entity from among all entities. Then, the task is conducted by the same process as an MLM task, which predicts a masked token with a given context of tokens. Our experimental results show that the proposed method achieves significantly improved performances when unseen entities appear during the test phase and achieves state-of-the-art performance on the WN18RR.
Standards-based Streaming Analytics and its Visualization

As OTT (over-the-top) media streaming and underlying technologies have matured, streaming analytics has become more important, especially in a heterogeneous device ecosystem, where new devices or software updates can potentially cause streaming issues. In this paper we consider SAND (Server and Network Assisted DASH), CMCD (Common Media Client Data) and Streaming Quality of Experience Events, Properties and Metrics (CTA-2066) as standards to enable interoperable, standard-based streaming analytics for the predominant streaming formats MPEG-DASH and HLS. We focus on the visualization aspect of streaming metrics in UI (user interface) dashboards.
An observational method for determining daily and regional photovoltaic solar energy statistics

This paper presents a classical estimation problem for calculating the energy generated by photovoltaic solar energy systems, on a daily, annual, regional and national basis. Our methodology relies on two data sources: PVOutput, an online portal with solar energy production measurements, and modelled irradiance data available for large parts of Africa and Europe, from the Royal Netherlands Meteorological Institute. Combining these, we obtain probability functions of observing energy production, given the irradiation. These are applied to a PV systems database, using Monte Carlo sampling, allowing daily and annual solar energy production to be calculated. These are, in turn, used to calculate solar energy production per municipality. As a case study, we apply this methodology to one country in particular, namely the Netherlands. By examining the variation in our estimates as a result of taking different subsets of PVOutput systems with certain specifications such as azimuth, tilt and inverter loading ratio, we obtain specific annual energy yields in the range of 877-946kWh/kWp and 838-899kWh/kWp for 2016 and 2017 respectively. The current method used at Statistics Netherlands assumes this to be 875kWh/kWp, irrespective of irradiation, meaning the yields were underestimated in 2016 and overestimated in 2017. In the case of the Netherlands, this research demonstrates that an irradiation based measure of solar energy generation is necessary. More generally, this research shows that different types of open data sources may be combined to develop models that calculate the energy production of PV system populations.