Reviewerly/exHarmony huggingface dataset:
The RoCS Framework to Support the Development of Autonomous Robots

With the expansion of autonomous robotics and its applications (e.g. medical, competition, military), the biggest hurdle in developing mobile robots lies in endowing them with the ability to interact with the environment and to make correct decisions so that their tasks can be executed successfully. However, as the complexity of robotic systems grows, the need to organize and modularize software for their correct functioning also becomes a challenge, making the development of software for controlling robots a complex and intricate task. In the robotics domain, there is a lack of reference software architectures and, although most robot architectures available in the literature facilitate the creation process with their modularity, existing solutions do not provide development guidance on reusing existing modules. Based on the well-&#x0D; known IBM Autonomic Computing reference architecture (known as MAPE-K), this work defines a refined architecture following the Robotics perspective. To explore the capabilities of the proposed refinement, we implemented the RoCS (Robotics and Cognitive Systems) framework for autonomous robots. We successfully tested the framework under simulated robotics scenarios that mimic typical robotics tasks and evidence the framework reuse capability. Finally, we understand the proposed framework needs further experimental evaluation, particularly, assessments on real-world scenarios.
The IMS–CUBoulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion

In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS-CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2020).The task consists of generating the morphological paradigms of a set of lemmas, given only the lemmas themselves and unlabeled text.Our proposed system is a modified version of the baseline introduced together with the task.In particular, we experiment with substituting the inflection generation component with an LSTM sequenceto-sequence model and an LSTM pointergenerator network.Our pointer-generator system obtains the best score of all seven submitted systems on average over all languages, and outperforms the official baseline, which was best overall, on Bulgarian and Kannada.
A Distributed Neural Network Training Method Based on Hybrid Gradient Computing

The application of deep learning in industry often needs to train large-scale neural networks and use large-scale data sets. However, larger networks and larger data sets lead to longer training time, which hinders the research of algorithms and the progress of actual engineering development. Data-parallel distributed training is a commonly used solution, but it is still in the stage of technical exploration. In this paper, we study how to improve the training accuracy and speed of distributed training, and propose a distributed training strategy based on hybrid gradient computing. Specifically, in the gradient descent stage, we propose a hybrid method, which combines a new warmup scheme with the linear-scaling stochastic gradient descent (SGD) algorithm to effectively improve the training accuracy and convergence rate. At the same time, we adopt the mixed precision gradient computing. In the single-GPU gradient computing and inter-GPU gradient synchronization, we use the mixed numerical precision of single precision (FP32) and half precision (FP16), which not only improves the training speed of single-GPU, but also improves the speed of inter-GPU communication. Through the integration of various training strategies and system engineering implementation, we finished ResNet-50 training in 20 minutes on a cluster of 24 V100 GPUs, with 75.6% Top-1 accuracy, and 97.5% GPU scaling efficiency. In addition, this paper proposes a new criterion for the evaluation of the distributed training efficiency, that is, the actual average single-GPU training time, which can evaluate the improvement of training methods in a more reasonable manner than just the improved performance due to the increased number of GPUs. In terms of this criterion, our method outperforms those existing methods.
Learning Neural Search Policies for Classical Planning

Heuristic forward search is currently the dominant paradigm in classical planning. Forward search algorithms typically rely on a single, relatively simple variation of best-first search and remain fixed throughout the process of solving a planning problem. Existing work combining multiple search techniques usually aims at supporting best-first search with an additional exploratory mechanism, triggered using a handcrafted criterion. A notable exception is very recent work which combines various search techniques using a trainable policy. That approach, however, is confined to a discrete action space comprising several fixed subroutines.In this paper, we introduce a parametrized search algorithm template which combines various search techniques within a single routine. The template's parameter space defines an infinite space of search algorithms, including, among others, BFS, local and random search. We then propose a neural architecture for designating the values of the search parameters given the state of the search. This enables expressing neural search policies that change the values of the parameters as the search progresses. The policies can be learned automatically, with the objective of maximizing the planner's performance on a given distribution of planning problems. We consider a training setting based on a stochastic optimization algorithm known as the cross-entropy method (CEM). Experimental evaluation of our approach shows that it is capable of finding effective distribution-specific search policies, outperforming the relevant baselines.
Causal inference in degenerate systems: An impossibility result

Causal relationships among variables are commonly represented via directed acyclic graphs. There are many methods in the literature to quantify the strength of arrows in a causal acyclic graph. These methods, however, have undesirable properties when the causal system represented by a directed acyclic graph is degenerate. In this paper, we characterize a degenerate causal system using multiplicity of Markov boundaries. We show that in this case, it is impossible to find an identifiable quantitative measure of causal effects that satisfy a set of natural criteria. To supplement the impossibility result, we also develop algorithms to identify degenerate causal systems from observed data. Performance of our algorithms is investigated through synthetic data analysis.
PARAMETER REDUNDANCY AND THE EXISTENCE OF MAXIMUM LIKELIHOOD ESTIMATES IN LOG-LINEAR MODELS

Log-linear models are typically fitted to contingency table data to describe and identify the relationship between different categorical variables. However, the data may include observed zero cell entries. The presence of zero cell entries can have an adverse effect on the estimability of parameters, due to parameter redundancy. We describe a general approach for determining whether a given log-linear model is parameter redundant for a pattern of observed zeros in the table, prior to fitting the model to the data. We derive the estimable parameters or functions of parameters and also explain how to reduce the unidentifiable model to an identifiable one. Parameter redundant models have a flat ridge in their likelihood function. We further explain when this ridge imposes some additional parameter constraints on the model, which can lead to obtaining unique maximum likelihood estimates for parameters that otherwise would not have been estimable. In contrast to other frameworks, the proposed novel approach informs on those constraints, elucidating the model that is actually being fitted.
On the essence of systems in general and social systems

The concept of system has been the basis for deepening the analysis of many natural, mechanical and social phenomena. However, its applications and developments have been based on a very simple definition: complex of interacting components; on which free and imaginative interpretations of the meaning and nature of the components and interaction have been used. The exaggerated and lax use of the concept of system has not only led to many false conclusions, but it has deteriorated its true value and usefulness. In this paper a stricter definition arises and accurate picture of what is a system, in order to limit its application and constrain its use to those phenomena that contain the elements that make up a system and its application is analyzed in the study of systems created by human beings, social systems and natural systems.
Strengthening Potential Heuristics with Mutexes and Disambiguations

Potential heuristics assign a numerical value (potential) to each fact and compute the heuristic value for a given state as the sum of these potentials. A mutex is an invariant stating that a certain combination of facts cannot be part of any reachable state. In this paper, we use mutexes to improve potential heuristics in two ways. First, we show that the mutex-based disambiguations of the goal and preconditions of operators leads to a less constrained linear program yielding stronger heuristics. Second, we utilize mutexes in a construction of new optimization functions based on counting of the number of states containing certain sets of facts. The experimental evaluation shows a significant increase in the number of solved tasks.
Online nonparametric Bayesian analysis of parsimonious Gaussian mixture models and scenes clustering

The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.
Embedding-based Scientific Literature Discovery in a Text Editor Application

Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited.However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load.To define comprehensive search queries requires strong motivation from authors, irrespective of their familiarity with the research field.Moreover, switching between independent applications for literature discovery, bibliography management, reading papers, and writing text burdens authors further and interrupts their creative process.Here, we present a web application that combines text editing and literature discovery in an interactive user interface.The application is equipped with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings, providing a discovery experience tuned to an author's manuscript and his interests.Our application aims to take a step towards more enjoyable and effortless academic writing.The demo of the application 1 and a short video tutorial 2 are available online.
HGMAN: Multi-Hop and Multi-Answer Question Answering Based on Heterogeneous Knowledge Graph (Student Abstract)

Multi-hop question answering models based on knowledge graph have been extensively studied. Most existing models predict a single answer with the highest probability by ranking candidate answers. However, they are stuck in predicting all the right answers caused by the ranking method. In this paper, we propose a novel model that converts the ranking of candidate answers into individual predictions for each candidate, named heterogeneous knowledge graph based multi-hop and multi-answer model (HGMAN). HGMAN is capable of capturing more informative representations for relations assisted by our heterogeneous graph, which consists of multiple entity nodes and relation nodes. We rely on graph convolutional network for multi-hop reasoning and then binary classification for each node to get multiple answers. Experimental results on MetaQA dataset show the performance of our proposed model over all baselines.
Question Quality Improvement: Deep Question Understanding for Incident Management in Technical Support Domain

Technical support domain involves solving problems from user queries through various channels: voice, web and chat, and is both time-consuming and labour intensive. The textual queries in web or chat mode are unstructured and often incomplete. This affects information retrieval and increases the difficulty level for agents to solve it. Such cases require multiple rounds of interaction between user and agent/chatbot in order to better understand the user query. This paper presents a deployed system called Question Quality Improvement (QQI), that aims to improve the quality of user utterance by understanding and extracting important parts of an utterance and gamifying the user interface, prompting them to enter the remaining relevant information. QQI is guided by an ontology designed for the technical support domain and uses co-reference resolution and deep parsing to understand the sentences. Using the syntactics and semantics in the deep parse tree structure various attributes in the ontology are extracted. The system has been in production for over two years supporting around 800 products resulting in a reduction in the time-to-resolve cases by around 29%, leading to huge cost savings. QQI being a core natural language understanding and metadata extraction technology, directly affects more than 8K tickets everyday. These cases are submitted after 50K edits done on the case based on QQI feedback. QQI outputs are used by other technologies such as search and retrieval, case routing for automated dispatch, case-difficulty-prediction, and by the chatbots supported in each product page.
Integrating Web-Based Collaborative Live Editing and Wireframing into a Model-Driven Web Engineering Process

Abstract Today’s Model-Driven Web Engineering (MDWE) approaches automatically generate Web applications from conceptual, domain-specific models. This enhances productivity by simplifying the design process through a higher degree of abstraction. Due to this raised level of abstraction, the collaboration on conceptual models also opens up new use cases, such as the tighter involvement of non-technical stakeholders into Web development. However, especially in the early design stages of Web applications, common practices for requirement elicitation mostly rely on wireframes instead of MDWE, created usually in analog settings. Additionally, state-of-the-art MDWE should integrate established and emerging Web development features, such as Near Real-Time (NRT) collaborative modeling and shared editing on the generated code. The combination of collaborative modeling, coding and wireframing, all in NRT, bears a lot of potential for improving MDWE practices. The challenge when covering these requirements lies with synchronizing source code, wireframes and models, an essential need to cope with regular changes in the software architecture to provide the flexibility needed for agile MDWE. In this contribution, we present a MDWE approach with live code editing and wireframing capabilities. We present the conceptual considerations of our approach, the realization of it and the integration into an overarching development methodology. Following a design science approach, we present the cyclic iterations of developing and evaluating our artifacts, which show promising results for collaborative Web development tasks that could open the gate towards novel, collaborative and agile MDWE techniques.
An N-List-Based Approach for Mining Frequent Inter-Transaction Patterns

Mining frequent inter-transaction patterns (ITPs) from large databases is both useful and of interest. Since frequent inter-transaction patterns (FITPs) are discovered across transactions in a transaction database (TD), the number of patterns is very large. Therefore, the mining time and memory usage are very high. Although several algorithms have been proposed for mining FITPs, they still require long runtime and high memory usage. Recent research shows that N-list-based approaches are very efficient for mining frequent patterns (FPs). Therefore, in this paper, we propose an N-list-based algorithm, called NL-ITP-Miner, to mine FITPs. In the proposed algorithm, we adopt the advantages of the N-list structure to build up the IT-PPC-tree. During the process of building the IT-PPC-tree, NL-ITP-Miner applies our proposed theorems to eliminate infrequent inter-transaction 1-patterns to reduce the search space. NL-ITP-Miner scans the database once to find frequent inter-transaction (FIT) 1-patterns for constructing the IT-PPC-tree, after that, the NL-ITP-Miner algorithm traverses this tree to generate frequent 1-patterns, FIT 1-patterns with their respective N-lists. Besides, we also propose effective pruning strategies that help NL-ITP-Miner to reduce the search space significantly and generate FITPs more quickly. Experiments show that NL-ITP-Miner outperforms the state-of-the-art algorithms for mining FITPs in terms of runtime and memory usage.
Fitting a biomechanical model of the folds to high-speed video data through bayesian estimation

High-speed video recording of the vocal folds during sustained phonation has become a widespread diagnostic tool, and the development of imaging techniques able to perform automated tracking and analysis of relevant glottal cues, such as folds edge position or glottal area, is an active research field. In this paper, a vocal folds vibration analysis method based on the processing of visual data through a biomechanical model of the layngeal dynamics is proposed. The procedure relies on a Bayesian non-stationary estimation of the biomechanical model parameters and state, to fit the folds edge position extracted from the high-speed video endoscopic data. This finely tuned dynamical model is then used as a state transition model in a Bayesian setting, and it allows to obtain a physiologically motivated estimation of upper and lower vocal folds edge position. Based on model prediction, an hypothesis on the lower fold position can be made even in complete fold occlusion conditions occurring during the end of the closed phase and the beginning of the open phase of the glottal cycle. To demonstrate the suitability of the procedure, the method is assessed on a set of audiovisual recordings featuring high-speed video endoscopic data from healthy subjects producing sustained voiced phonation with different laryngeal settings.
Opportunities to reframe moderation practices in the wake of the COVID-19 pandemic

The COVID-19 pandemic has disrupted some traditional assessment practices and drawn attention to the range of evidence students produce in their day-to-day learning.In this edition of Assessment News, Charles Darr and Rosemary Hipkins argue that using this evidence to make sound summative judgements requires a robust understanding of what it means to make a valid judgement, along with a clear commitment to professional learning through social moderation.
Complementary Systems for Off-Topic Spoken Response Detection

Increased demand to learn English for business and education has led to growing interest in automatic spoken language assessment and teaching systems.With this shift to automated approaches it is important that systems reliably assess all aspects of a candidate's responses.This paper examines one form of spoken language assessment; whether the response from the candidate is relevant to the prompt provided.This will be referred to as off-topic spoken response detection.Two forms of previously proposed approaches are examined in this work: the hierarchical attention-based topic model (HATM); and the similarity grid model (SGM).The work focuses on the scenario when the prompt, and associated responses, have not been seen in the training data, enabling the system to be applied to new test scripts without the need to collect data or retrain the model.To improve the performance of the systems for unseen prompts, data augmentation based on easy data augmentation (EDA) and translation based approaches are applied.Additionally for the HATM, a form of prompt dropout is described.The systems were evaluated on both seen and unseen prompts from Linguaskill Business and General English tests.For unseen data the performance of the HATM was improved using data augmentation, in contrast to the SGM where no gains were obtained.The two approaches were found to be complementary to one another, yielding a combined F 0.5 score of 0.814 for off-topic response detection where the prompts have not been seen in training.
PresentationTrainer: Oral Presentation Support System for Impression-Related Feedback

In order to support the pratice of oral presentation, we developed PresentationTrainer which includes (1) a presentation impression prediction system and (2) a presentation slide analysis system. For the presentation impression prediction system, we proposed two methods, using Support Vector Machine and Markov Random Field, or using multimodal neural network, to predict audiences' impressions for speech videos. For the slide analysis system, we used Convolutional Neural Network and Global Average Pooling to evaluate the design of slides. We then used Class Activation Mapping to provide visual feedback for showing which areas should be modified.
ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)

Motivated by the mandate to design and deploy a practical, real-world educational tool for grading, we extensively explore linguistic patterns for Short Answer Scoring (SAS) as well as authorship feedback. We approach the SAS task via a multipronged approach that employs linguistic context features for capturing domain-specific knowledge while emphasizing on domain agnostic grading and detailed feedback via an ensemble of explainable statistical models. Our methodology quantitatively supersedes multiple automatic short answer scoring systems.
The Role of Edmodo Model for Professional Development: The Uses of Blockchain in School Management

Schools are social agents to diffuse merits of technology. The uses of technology foster professional development. Schools have started to make tendencies towards digitalization. Especially learning and teaching contexts become technology supported. In this respect, school infrastructure and learning resources have changed. Teachers play a great role to adopt new changes in learning teaching process to the new generation. This makes innovation in schools to use new technologies and applications for better education practices. This research study aims to create awareness on Edmodo model to teachers and develop their skills on new learning contents for establishing more digitalized school at the secondary education level and analyze the uses of blockchain in school management. The research study was conducted with 21 volunteer teachers working at the secondary level that they get training on the use of Edmodo Content Management System. Case study through action research was done in this research study that self-report was used to gain experiences of teachers. By this way, educational online environment was created for teachers for the uses of learning-teaching process. Research results revealed that teachers enhance their skills and knowledge about the use of the Edmodo content management system increased compared to the pre-seminar. In addition, they gained online collaboration and research skills. This practice created the culture of technology based innovation for their schools.