Reviewerly/exHarmony huggingface dataset:
Penman: An Open-Source Library and Tool for AMR Graphs

Meaning Representation (AMR) (Banarescu et al., 2013) is a framework for semantic dependencies that encodes its rooted and directed acyclic graphs in a format called PENMAN notation.The format is simple enough that users of AMR data often write small scripts or libraries for parsing it into an internal graph representation, but there is enough complexity that these users could benefit from a more sophisticated and well-tested solution.The open-source Python library Penman provides a robust parser, functions for graph inspection and manipulation, and functions for formatting graphs into PENMAN notation.Many functions are also available in a command-line tool, thus extending its utility to non-Python setups.
Toward <scp>cross‐platform</scp> electronic health record<scp>‐driven</scp> phenotyping using Clinical Quality Language

Abstract Introduction Electronic health record (EHR)‐driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time‐consuming, error‐prone, and platform‐specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high‐throughput, cross‐platform phenotyping. Methods We selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results. Results CQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross‐platform execution resulted in identical patient cohorts generated by both data platforms. Conclusions CQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross‐platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR‐driven phenotyping and scale in learning health systems.
Dealing with Pure New User Cold-Start Problem in Recommendation System Based on Linked Open Data and Social Network Features

Preferring accuracy over computation time or vice versa is very challenging in the context of recommendation systems, which encourages many researchers to opt for hybrid recommendation systems. Currently, researchers are trying hard to produce correct and accurate recommendations by suggesting the use of ontology, but the lack of techniques renders to take its full advantage. One of the major issues in recommender systems bothering many researchers is pure new user cold-start problem which arises due to the absence of information in the system about the new user. Linked Open Data (LOD) initiative sets standards for interoperability among cross domains and has gathered enormous amount of data over the past years, which provides various ways by which recommender system’s performance can be improved by enriching user’s profile with relevant features. This research work focuses on solving pure new user cold-start problem by building user’s profile based on LOD, collaborative features, and social network-based features. Here, a new approach is devised to compute item similarity based on ontology, thus predicting the rating of nonrated item. A modified method to calculate user’s similarity based on collaborative features to deal with other issues such as accuracy and computation time is also proposed. The empirical results and comparative analysis of the proposed hybrid recommendation system dictate its better performance specifically for providing solution to pure new user cold-start problem.
AI for Explaining Decisions in Multi-Agent Environments

Explanation is necessary for humans to understand and accept decisions made by an AI system when the system's goal is known. It is even more important when the AI system makes decisions in multi-agent environments where the human does not know the systems' goals since they may depend on other agents' preferences. In such situations, explanations should aim to increase user satisfaction, taking into account the system's decision, the user's and the other agents' preferences, the environment settings and properties such as fairness, envy and privacy. Generating explanations that will increase user satisfaction is very challenging; to this end, we propose a new research direction: xMASE. We then review the state of the art and discuss research directions towards efficient methodologies and algorithms for generating explanations that will increase users' satisfaction from AI system's decisions in multi-agent environments.
Quantifying the Value of 5G and Edge Cloud on QoE for AR/VR

Augmented and Virtual Reality promises have yet to be met, it remains inhibited by an average user experience. Limits in local processing (headset/handset), form factors and display/optical limitations have kept the technology from reaching its full potential. 5G and Cloud-based processing offers the potential to improve performance, comfort/ergonomics, mobility and QoE barriers. Our research demonstrates a methodology for assessing AR/VR QoE performance on mobile networks and how 5G and cloud-based processing create a better user experience, accelerating the growth of this nascent market. QoE models are proposed for AR/VR which define the key quality indicators (KQIs) for each AR/VR application and we correlate the KQIs performance to network KPIs. The study demonstrates how typical LTE KPIs are insufficient to deliver an immersive QoE for AR/VR. Finally, we quantify the increase in QoE with 5G and edge cloud based on the expected KPI performance improvements. The proposed AR/VR QOE models are evaluated in the context of the enhanced network performance with 5G and Edge Cloud deployment.
Learning to Slide Unknown Objects with Differentiable Physics Simulations

We propose a new technique for pushing an unknown object from an initial configuration to a goal configuration with stability constraints.The proposed method leverages recent progress in differentiable physics models to learn unknown mechanical properties of pushed objects, such as their distributions of mass and coefficients of friction.The proposed learning technique computes the gradient of the distance between predicted poses of objects and their actual observed poses, and utilizes that gradient to search for values of the mechanical properties that reduce the reality gap.The proposed approach is also utilized to optimize a policy to efficiently push an object toward the desired goal configuration.Experiments with real objects using a real robot to gather data show that the proposed approach can identify mechanical properties of heterogeneous objects from a small number of pushing actions.
Meru as a Hindu Sacred Building Architecture with a High Roof and Resistant to Earthquakes in Bali, Indonesia

Meru has meaning as a symbol of Mount Mahameru, symbol of God and the universe that serves as a place of worship of the gods and ancestors.Meru is found in large temples in Bali, Indonesia with its trademark characteristic that the roof has a high overlapping reaching 10 meters or more with the number of roofs always odd so that Meru becomes a landmark in every temple in Bali.Meru is a very beautiful sacred building that was built based on the accuracy of proportions, the logic of construction techniques and the beauty of decoration, which holds fast to local wisdom based on Traditional Balinese Architecture.This study aims to explore the meaning and philosophy of Meru and why the construction of Meru as an earthquake resistant building.With a qualitative explorative study of primary data collection such as: surveys, observations and interviews and combined with secondary data in the form of old legacy writing, study shows that Meru not only has beauty as a sacred building but also is an earthquake-resistant building construction that has proven its reliability compared to other Hindu sacred buildings.
Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events

Abstract Objective The goal of this study is to develop a robust Time Event Ontology (TEO), which can formally represent and reason both structured and unstructured temporal information. Materials and Methods Using our previous Clinical Narrative Temporal Relation Ontology 1.0 and 2.0 as a starting point, we redesigned concept primitives (clinical events and temporal expressions) and enriched temporal relations. Specifically, 2 sets of temporal relations (Allen’s interval algebra and a novel suite of basic time relations) were used to specify qualitative temporal order relations, and a Temporal Relation Statement was designed to formalize quantitative temporal relations. Moreover, a variety of data properties were defined to represent diversified temporal expressions in clinical narratives. Results TEO has a rich set of classes and properties (object, data, and annotation). When evaluated with real electronic health record data from the Mayo Clinic, it could faithfully represent more than 95% of the temporal expressions. Its reasoning ability was further demonstrated on a sample drug adverse event report annotated with respect to TEO. The results showed that our Java-based TEO reasoner could answer a set of frequently asked time-related queries, demonstrating that TEO has a strong capability of reasoning complex temporal relations. Conclusion TEO can support flexible temporal relation representation and reasoning. Our next step will be to apply TEO to the natural language processing field to facilitate automated temporal information annotation, extraction, and timeline reasoning to better support time-based clinical decision-making.
RETRACTED: Research on the innovation of college English teaching based on curriculum thinking

In order to realize the quantitative evaluation and analysis of the effect of college English teaching innovation reform, this paper puts forward the evaluation model of college English teaching innovation reform based on curriculum thinking, establishes the big data analysis model of college English teaching innovation reform effect, analyzes the constraint parameters of college English teaching innovation reform effect evaluation by means of statistical quantitative analysis, and abstracts the entropy characteristic quantity of big data, which is the effect of college English teaching innovation reform. The methods of spatial game equilibrium control and fuzzy decision are used to optimize and evaluate the effect evaluation of college English teaching innovation reform, the expert system analysis model of college English teaching innovation reform effect evaluation is established, and the hierarchical grey relational analysis method is used to carry out adaptive optimization and decision control for the evaluation of college English teaching innovation reform effect. The curriculum ideological and political model is used to optimize the reform of college English teaching innovation, and the quantitative analysis of the effect of college English teaching innovation reform is realized. The test results show that this method can effectively realize the evaluation decision of college English teaching innovation reform, with high reliability and strong innovative learning ability.
Material Food Probe

Interactions with food are complex, integrating rich multisensory experiences within emotionally meaningful social contexts. Yet, the opportunities for food as material resource for emotional communication have been less explored. We describe a two-month project with 5 couples centered on the co-design of personalized flavors for emotional communication. These were experienced through a three-day preliminary study involving a 3D food printer in participants' homes. We discuss the value of our findings indicating preferences for both remembered and imagined positive flavors and their integration in focal intimacy practices to support emotional coregulation. We also discuss material food probes and their value for exploring and inspiring both design-with, and design-around food.
LADV: Deep Learning Assisted Authoring of Dashboard Visualizations From Images and Sketches

Dashboard visualizations are widely used in data-intensive applications such as business intelligence, operation monitoring, and urban planning. However, existing visualization authoring tools are inefficient in the rapid prototyping of dashboards because visualization expertise and user intention need to be integrated. We propose a novel approach to rapid conceptualization that can construct dashboard templates from exemplars to mitigate the burden of designing, implementing, and evaluating dashboard visualizations. The kernel of our approach is a novel deep learning-based model that can identify and locate charts of various categories and extract colors from an input image or sketch. We design and implement a web-based authoring tool for learning, composing, and customizing dashboard visualizations in a cloud computing environment. Examples, user studies, and user feedback from real scenarios in Alibaba Cloud verify the usability and efficiency of the proposed approach.
Accuracy of neural networks for the simulation of chaotic dynamics: Precision of training data vs precision of the algorithm

We explore the influence of precision of the data and the algorithm for the simulation of chaotic dynamics by neural network techniques. For this purpose, we simulate the Lorenz system with different precisions using three different neural network techniques adapted to time series, namely, reservoir computing [using Echo State Network (ESN)], long short-term memory, and temporal convolutional network, for both short- and long-time predictions, and assess their efficiency and accuracy. Our results show that the ESN network is better at predicting accurately the dynamics of the system, and that in all cases, the precision of the algorithm is more important than the precision of the training data for the accuracy of the predictions. This result gives support to the idea that neural networks can perform time-series predictions in many practical applications for which data are necessarily of limited precision, in line with recent results. It also suggests that for a given set of data, the reliability of the predictions can be significantly improved by using a network with higher precision than the one of the data.
Unmanned Aerial Vehicle for Search and Rescue Mission

Catastrophic events such as earthquakes, landslides, floods, etc bring about a significant loss of human life. In such threatening conditions, manual search and rescue operations for assisting victims trapped underneath the rubble may not be beneficial as expected as well as timely rescue may not be possible at the earliest. To get over this adversity this paper proposes an autonomous drone technology system named `unmanned aerial vehicle (UAV) for search and rescue mission'. In case any victims have detected, this system assists in the rescue process by monitoring the affected areas and reverting a signal to the rescue hub. The drone is mounted with human detection sensors, Object collision detection sensor and communication modules. The major feature that makes Unmanned Aerial Vehicle unique from all other existing systems is that it is fully autonomous. Besides, existing systems may get damaged while unmanned aerial vehicle [UAV] is damage-free during detection.
BSV-PAGS: Blockchain-based special vehicles priority access guarantee scheme

Traffic jams often occur, with the increasing number of private cars. Special vehicles (e.g. police cars, ambulances and fire trucks) often encounter traffic jams when carrying out emergency tasks, which may lead to unexpected consequences (e.g. causing serious damage to our routine and property). In order to ensure that special vehicles can quickly pass through crowded road and arrive at the accident scene in time, this paper proposes a blockchain-based special vehicles priority access guarantee scheme (BSV-PAGS). In BSV-PAGS, we introduce blockchain into the Internet of Things (IoT), and construct a decentralized distributed edge node network based on blockchian, which guarantees the information sharing among the edge nodes as well as processing the data efficiently. The intelligent terminal device will upload the special vehicle information to the edge node in the blockchain, and then the uploaded information will sync to each edge node through the information sharing smart contract, realizing the information sharing among the edge nodes. In addition, we use machine learning method (e.g. Bag of Visual Words and Support Vector Machine) to design and train special vehicle detectors to improve the recognition accuracy of special vehicles. We design a special vehicle priority access smart contract to achieve real-time scheduling of vehicles and ultimately ensure the priority of special vehicles. Then, we carried out a security analysis of BSV-PAGS. Finally, we test a large number of simulation experiments based on Ethereum clients. The experimental results show that our scheme is feasible and effective.
McDRAM v2: In-Dynamic Random Access Memory Systolic Array Accelerator to Address the Large Model Problem in Deep Neural Networks on the Edge

The energy efficiency of accelerating hundreds of MB-large deep neural networks (DNNs) in a mobile environment is less than that of a server-class big chip accelerator because of the limited power budget, silicon area, and smaller buffer size of static random access memory associated with mobile systems. To address this challenge and provide powerful computing capability for processing large DNN models in power/resource-limited mobile systems, we propose McDRAM v2, which is a novel in-dynamic random access memory (DRAM) systolic array accelerator architecture. McDRAM v2 makes the best use of large in-DRAM bandwidths for accelerating various DNN applications. It can handle large DNN models without off-chip memory accesses, in a fast and efficient manner, by exposing the large DRAM capacity and large in-DRAM bandwidth directly to an input systolic array of a processing element matrix. Additionally, it maximizes data reuse using a systolic multiply-accumulate (MAC) structure. The proposed architecture maximizes the utilization of large-scale MAC units by judiciously exploiting the DRAM's internal bus and buffer structure. An evaluation of large DNN models in the fields of image classification, natural language processing, and recommendation systems shows that it achieves 1.7 times tera operations per second (TOPS), 3.7 times TOPS/watt, and 8.6 times TOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> improvements over a state-of-the-art mobile graphics processing unit accelerator, and 4.1 times better energy efficiency over a state-of-the-art server-class accelerator. Moreover, it incurs a minimal overhead, i.e., a 9.7% increase in area, and uses less than 4.4 W of peak operating power.
DLGNet: A Transformer-based Model for Dialogue Response Generation

Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses.On the other hand, transformer-based models such as GPT-2 have demonstrated an excellent ability to capture long-range structures in language modeling tasks.In this paper, we present DLGNet, a transformer-based model for dialogue modeling.We specifically examine the use of DL-GNet for multi-turn dialogue response generation.In our experiments, we evaluate DLGNet on the open-domain Movie Triples dataset and the closed-domain Ubuntu Dialogue dataset.DLGNet models, although trained with only the maximum likelihood objective, achieve significant improvements over state-of-the-art multi-turn dialogue models.They also produce best performance to date on the two datasets based on several metrics, including BLEU, ROUGE, and distinct n-gram.Our analysis shows that the performance improvement is mostly due to the combination of (1) the long-range transformer architecture with (2) the injection of random informative paddings.Other contributing factors include the joint modeling of dialogue context and response, and the 100% tokenization coverage from the byte pair encoding (BPE).
Recognizing Euphemisms and Dysphemisms Using Sentiment Analysis

This paper presents the first research aimed at recognizing euphemistic and dysphemistic phrases with natural language processing.Euphemisms soften references to topics that are sensitive, disagreeable, or taboo.Conversely, dysphemisms refer to sensitive topics in a harsh or rude way.For example, "passed away" and "departed" are euphemisms for death, while "croaked" and "six feet under" are dysphemisms for death.Our work explores the use of sentiment analysis to recognize euphemistic and dysphemistic language.First, we identify near-synonym phrases for three topics (FIRING, LYING, and STEALING) using a bootstrapping algorithm for semantic lexicon induction.Next, we classify phrases as euphemistic, dysphemistic, or neutral using lexical sentiment cues and contextual sentiment analysis.We introduce a new gold standard data set and present our experimental results for this task.
Knowledge-Enhanced Graph Neural Networks for Sequential Recommendation

With the rapid increase in the popularity of big data and internet technology, sequential recommendation has become an important method to help people find items they are potentially interested in. Traditional recommendation methods use only recurrent neural networks (RNNs) to process sequential data. Although effective, the results may be unable to capture both the semantic-based preference and the complex transitions between items adequately. In this paper, we model separated session sequences into session graphs and capture complex transitions using graph neural networks (GNNs). We further link items in interaction sequences with existing external knowledge base (KB) entities and integrate the GNN-based recommender with key-value memory networks (KV-MNs) to incorporate KB knowledge. Specifically, we set a key matrix to many relation embeddings that learned from KB, corresponding to many entity attributes, and set up a set of value matrices storing the semantic-based preferences of different users for the corresponding attribute. By using a hybrid of a GNN and KV-MN, each session is represented as the combination of the current interest (i.e., sequential preference) and the global preference (i.e., semantic-based preference) of that session. Extensive experiments on three public real-world datasets show that our method performs better than baseline algorithms consistently.