Reviewerly/exHarmony huggingface dataset:
Hierarchical Query Graph Generation for Complex Question Answering over Knowledge Graph

Knowledge Graph Question Answering aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge graphs. When faced with a complex question with compositional semantics, query graph generation is a practical semantic parsing-based method. But existing works rely on heuristic rules with limited coverage, making them impractical on more complex questions. This paper proposes a Director-Actor-Critic framework to overcome these challenges. Through options over a Markov Decision Process, query graph generation is formulated as a hierarchical decision problem. The Director determines which types of triples the query graph needs, the Actor generates corresponding triples by choosing nodes and edges, and the Critic calculates the semantic similarity between the generated triples and the given questions. Moreover, to train from weak supervision, we base the framework on hierarchical Reinforcement Learning with intrinsic motivation. To accelerate the training process, we pre-train the Critic with high-reward trajectories generated by hand-crafted rules, and leverage curriculum learning to gradually increase the complexity of questions during query graph generation. Extensive experiments conducted over widely-used benchmark datasets demonstrate the effectiveness of the proposed framework.
FlowMatic: An Immersive Authoring Tool for Creating Interactive Scenes in Virtual Reality

Immersive authoring is a paradigm that makes Virtual Reality (VR) application development easier by allowing programmers to create VR content while immersed in the virtual environment. In this paradigm, programmers manipulate programming primitives through direct manipulation and get immediate feedback on their program's state and output. However, existing immersive authoring tools have a low ceiling; their programming primitives are intuitive but can only express a limited set of static relationships between elements in a scene. In this paper, we introduce FlowMatic, an immersive authoring tool that raises the ceiling of expressiveness by allowing programmers to specify reactive behaviors---behaviors that react to discrete events such as user actions, system timers, or collisions. FlowMatic also introduces primitives for programmatically creating and destroying new objects, for abstracting and re-using functionality, and for importing 3D models. Importantly, FlowMatic uses novel visual representations to allow these primitives to be represented directly in VR. We also describe the results of a user study that illustrates the usability advantages of FlowMatic relative to a 2D authoring tool and we demonstrate its expressiveness through several example applications that would be impossible to implement with existing immersive authoring tools. By combining a visual program representation with expressive programming primitives and a natural User Interface (UI) for authoring programs, FlowMatic shows how programmers can build fully interactive virtual experiences with immersive authoring.
CC-News-En

We describe a static, open-access news corpus using data from the Common Crawl Foundation, who provide free, publicly available web archives, including a continuous crawl of international news articles published in multiple languages. Our derived corpus, CC-News-En, contains 44 million English documents collected between September 2016 and March 2018. The collection is comparable in size with the number of documents typically found in a single shard of a large-scale, distributed search engine, and is four times larger than the news collections previously used in offline information retrieval experiments. To complement the corpus, 173 topics were curated using titles from Reddit threads, forming a temporally representative sampling of relevant news topics over the 583 day collection window. Information needs were then generated using automatic summarization tools to produce textual and audio representations, and used to elicit query variations from crowdworkers, with a total of 10,437 queries collected against the 173 topics. Of these, 10,089 include key-stroke level instrumentation that captures the timings of character insertions and deletions made by the workers while typing their queries. These new resources support a wide variety of experiments, including large-scale efficiency exercises and query auto-completion synthesis, with scope for future addition of relevance judgments to support offline effectiveness experiments and hence batch evaluation campaigns.
Exploring the potentials of blockchain application in construction industry: a systematic review

Construction industry are facing trust and transparency issue that has caused loose collaboration among stakeholders. Blockchain technology is found as one of the disrupting digital technology that can response to the problems faced. Recent paper reviews on blockchain application in construction industry are still lacking concrete and not up to state-of-the-art applications. The aim of the paper was therefore to discover how blockchain technology can disrupt construction industry. Two research objectives were established to identify the potential areas blockchain could disrupt construction industry and to identify future research direction of blockchain in construction industry. This study presented systematic mapping study and content analysis of 57 papers from three high quality scientific databases and google scholar. The findings identified six potential areas for blockchain application in construction industry, where supply chain management, building information modelling and contract management are the 3 major aspects highly focussed. Future research may concentrate on developing simulation models, conduct case studies on the real life blockchain applications in construction project, and focus on the blockchain application on the existing systems that are currently in use in construction industry. Other recommendations can be found at discussion of the study.
A product ranking method combining the features–opinion pairs mining and interval-valued Pythagorean fuzzy sets

Mining online reviews has become an important means of identifying consumer behavior and the innovation direction of products. However, it is difficult for both producers and consumers to effectively analyze and extract relevant opinions from a vast number of online reviews. To overcome this problem, a product ranking method that combines feature–opinion pairs mining and interval-valued Pythagorean fuzzy (IVPF) sets was proposed in this study. First, three types of important feature–opinion pairs were clearly defined based on the diversity and complexity of opinion expression forms in Chinese ecommerce reviews. Two deep learning models were then designed to automatically extract the feature–opinion terms and match them into pairs. Afterwards, sentiment analysis techniques were applied to identify sentiment orientation, and the feature–opinion pairs were clustered into groups using K-means clustering algorithm. Meanwhile, considering the confidence level based on the number of online reviews on different products, sentiment value was transformed into interval-value from, including interval membership and non-membership. As the sum of the converted interval membership and non-membership was greater than 1 and their quadratic sum was less than 1, IVPF set was introduced to represent the interval-valued sentiment. Furthermore, based on the interrelationship between product attributes, we proposed an IVPF weighted Heronian mean operator to aggregate the attribute information. Product ranking was then achieved based on the operator and operations under the IVPF information. Finally, a case study was used to verify the feasibility of the proposed method, and comparisons and sensitivity analysis were performed to demonstrate the superiority of our method.
Multi-task Curriculum Framework for Open-Set Semi-supervised Learning

Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available. While existing SSL methods assume that samples in the labeled and unlabeled data share the classes of their samples, we address a more complex novel scenario named open-set SSL, where out-of-distribution (OOD) samples are contained in unlabeled data. Instead of training an OOD detector and SSL separately, we propose a multi-task curriculum learning framework. First, to detect the OOD samples in unlabeled data, we estimate the probability of the sample belonging to OOD. We use a joint optimization framework, which updates the network parameters and the OOD score alternately. Simultaneously, to achieve high performance on the classification of in-distribution (ID) data, we select ID samples in unlabeled data having small OOD scores, and use these data with labeled data for training the deep neural networks to classify ID samples in a semi-supervised manner. We conduct several experiments, and our method achieves state-of-the-art results by successfully eliminating the effect of OOD samples.
Gemini: A Grammar and Recommender System for Animated Transitions in Statistical Graphics

Animated transitions help viewers follow changes between related visualizations. Specifying effective animations demands significant effort: authors must select the elements and properties to animate, provide transition parameters, and coordinate the timing of stages. To facilitate this process, we present Gemini, a declarative grammar and recommendation system for animated transitions between single-view statistical graphics. Gemini specifications define transition “steps” in terms of high-level visual components (marks, axes, legends) and composition rules to synchronize and concatenate steps. With this grammar, Gemini can recommend animation designs to augment and accelerate designers' work. Gemini enumerates staged animation designs for given start and end states, and ranks those designs using a cost function informed by prior perceptual studies. To evaluate Gemini, we conduct both a formative study on Mechanical Turk to assess and tune our ranking function, and a summative study in which 8 experienced visualization developers implement animations in D3 that we then compare to Gemini's suggestions. We find that most designs (9/11) are exactly replicable in Gemini, with many (8/11) achievable via edits to suggestions, and that Gemini suggestions avoid multiple participant errors.
Student perceptions on the benefits of Flipgrid in a HyFlex learning environment

Various course delivery methods exist within higher education ranging from face-to-face to HyFlex (hybrid flexible) methods. This research is a mixed method, exploratory study designed to investigate the perceived effectiveness of Flipgrid, an online video discussion board learning tool that can be utilized on a smart phone, in HyFlex delivery modality business courses. A questionnaire containing both Likert-style and open-ended questions was administered to ten unique undergraduate and graduate business courses over a 2-year period. Results indicate that Flipgrid is a beneficial learning tool and further timely research should be conducted with HyFlex courses becoming more prevalent due to COVID-19.
MIMICS

Search clarification has recently attracted much attention due to its applications in search engines. It has also been recognized as a major component in conversational information seeking systems. Despite its importance, the research community still feels the lack of a large-scale dataset for studying different aspects of search clarification. In this paper, we introduce MIMICS, a collection of search clarification datasets for real web search queries sampled from the Bing query logs. Each clarification in MIMICS is generated by a Bing production algorithm and consists of a clarifying question and up to five candidate answers. MIMICS contains three datasets: (1) MIMICS-Click includes over 400k unique queries, their associated clarification panes, and the corresponding aggregated user interaction signals (i.e., clicks). (2) MIMICS-ClickExplore is an exploration data that includes aggregated user interaction signals for over 60k unique queries, each with multiple clarification panes. (3) MIMICS-Manual includes over 2k unique real search queries. Each query-clarification pair in this dataset has been manually labeled by at least three trained annotators. It contains graded quality labels for the clarifying question, the candidate answer set, and the landing result page for each candidate answer.
A framework for designing and evaluating realistic blockchain-based local energy markets

A growing customer base for solar-plus-storage at the grid edge has resulted in stronger interest at the regulatory level towards energy markets at the distribution level. Local energy markets (LEMs) running on blockchains are being studied as a possible direction, but the relevant literature treats the blockchain component as a black box. We make the case that this approach is flawed because the choices in this layer affect the market’s performance significantly. We explicitly identify the design space that the blockchain layer introduces, and analyze how the design choices made therein affect the performance, governance, and degree of decentralization of these markets. As an exercise, we consider three distinct configurations for a next-generation LEM, and compare their performance on both the blockchain and the market layer via a case study. We demonstrate that simple changes in the data model can decrease the market efficiency by up to 90%. We also show that changes in the way bids get encrypted may result in economic improvements, but they do so at the risk of subverting the proper operation and resilience of the market. The simulations for our case study are conducted via a framework that we developed and open-sourced as part of this work.
Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance

Artificial potential fields (APFs) and their variants have been a staple for collision avoidance of mobile robots and manipulators for almost 40 years. Its model-independent nature, ease of implementation, and real-time performance have played a large role in its continued success over the years. Control barrier functions (CBFs), on the other hand, are a more recent development, commonly used to guarantee safety for nonlinear systems in real-time in the form of a filter on a nominal controller. In this paper, we address the connections between APFs and CBFs. At a theoretic level, we show that given a broad class of APFs, one can construct a CBF that guarantees safety. Additionally, we prove that CBFs obtained from these APFs have additional beneficial properties and can be applied to nonlinear systems. Practically, we compare the performance of APFs and CBFs in the context of obstacle avoidance on simple illustrative examples and for a quadrotor with unknown dynamics, both in simulation and on hardware using onboard sensing.
Explainable Security

In 2017, the Defense Advanced Research Projects Agency (DARPA) launched the Explainable Artificial Intelligence (XAI) program that aims to create a suite of new AI techniques that enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems. In this paper, inspired by DARPA's XAI program, we propose a new paradigm in security research: Explainable Security (XSec). We discuss the "Six Ws" of XSec (Who? What? Where? When? Why? and How?) and argue that XSec has unique and complex characteristics: XSec involves several different stakeholders (i.e., the system's developers, analysts, users and attackers) and is multi-faceted by nature (as it requires reasoning about system model, threat model and properties of security, privacy and trust as well as concrete attacks, vulnerabilities and countermeasures). We define a roadmap for XSec that identifies several possible research directions.
Multi-scale traffic vehicle detection based on faster R–CNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection. However, the environment of vehicle detection is particularly sophisticated in practical processes. It is comparatively difficult to detect vehicles of various scales in traffic scene images, because the vehicles partially obscured by green belts, roadblocks or other vehicles, as well as influence of some low illumination weather. In this paper, we present a model based on Faster R–CNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes. First, we proposed a Retinex-based image adaptive correction algorithm (RIAC) to enhance the traffic images in the dataset to reduce the influence of shadow and illumination, and improve the image quality. Second, in order to improve the feature expression of the backbone network, we conducted Neural Architecture Search (NAS) on the backbone network used for feature extraction of Faster R–CNN to generate the optimal cross-layer connection to extract multi-layer features more effectively. Third, we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets, and improve the robustness of the model for challenging targets such as small scale and severe occlusion. In the implementation of the model, K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model. Our model has been trained and tested on the UN-DETRAC dataset, and the obtained results indicate that our method has art-of-state detection performance.
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild

With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks. Like the manually designed counterparts, we desire the automatically searched architectures to have the ability of being freely transferred to different scenarios. This paper formally puts forward this problem, referred to as NAS in the wild, which explores the possibility of finding the optimal architecture in a proxy dataset and then deploying it to mostly unseen scenarios. We instantiate this setting using a currently popular algorithm named differentiable architecture search (DARTS), which often suffers unsatisfying performance while being transferred across different tasks. We argue that the accuracy drop originates from the formulation that uses a super-network for search but a sub-network for re-training. The different properties of these stages have resulted in a significant optimization gap, and consequently, the architectural parameters “over-fit” the super-network. To alleviate the gap, we present a progressive method that gradually increases the network depth during the search stage, which leads to the Progressive DARTS (P-DARTS) algorithm. With a reduced search cost (7 hours on a single GPU), P-DARTS achieves improved performance on both the proxy dataset (CIFAR10) and a few target problems (ImageNet classification, COCO detection and three ReID benchmarks). Our code is available at https://github.com/chenxin061/pdarts.
Flood &amp; Loot

The Lightning Network promises to alleviate Bitcoin's known scalability problems. The operation of such second layer approaches relies on the ability of participants to turn to the blockchain to claim funds at any time, which is assumed to happen rarely.
A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests

Understanding the digital jump of bank customers is key to design strategies to bring on board and keep online users, as well as to explain the increasing competition from new providers of financial services (such as BigTech and FinTech). This paper employs a machine learning approach to examine the digitalization process of bank customers using a comprehensive consumer finance survey. By employing a set of algorithms (random forests, conditional inference trees and causal forests) this paper identities the features predicting bank customers’ digitalization process, illustrates the sequence of consumers’ decision-making actions and explores the existence of causal relationships in the digitalization process. Random forests are found to provide the highest performance–they accurately predict 88.41% of bank customers’ online banking adoption and usage decisions. We find that the adoption of digital banking services begins with information-based services (e.g., checking account balance), conditional on the awareness of the range of online services by customers, and then is followed by transactional services (e.g., online/mobile money transfer). The diversification of the use of online channels is explained by the consciousness about the range of services available and the safety perception. A certain degree of complementarity between bank and non-bank digital channels is also found. The treatment effect estimations of the causal forest algorithms confirm causality of the identified explanatory factors. These results suggest that banks should address the digital transformation of their customers by segmenting them according to their revealed preferences and offering them personalized digital services. Additionally, policymakers should promote financial digitalization, designing policies oriented towards making consumers aware of the range of online services available.
Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison

A major line of work in graph signal processing [2] during the past 10 years has been to design new transform methods that account for the underlying graph structure to identify and exploit structure in data residing on a connected, weighted, undirected graph. The most common approach is to construct a dictionary of atoms (building block signals) and represent the graph signal of interest as a linear combination of these atoms. Such representations enable visual analysis of data, statistical analysis of data, and data compression, and they can also be leveraged as regularizers in machine learning and ill-posed inverse problems, such as in painting, denoising, and classification.