Reviewerly/exHarmony huggingface dataset:
Conceptualizing the “Corporate Nervous Net”: Decentralized Strategic Communication Based on a Digital Reporting Indicator Framework

The digital revolution challenges strategic communication. Artificial Intelligence (AI), Big Data, and the Internet of Things (IoT) create a rapidly changing environment for organizations as well as system complexities. To fulfill its task in ensuring the long-lasting success of organizations’ strategic communication needs to continuously adopt to this revolution. This article approaches the question of how strategic communication can adapt to the digitalization. In order to do so, the article conceptualizes the corporate nervous net and a predictive reporting indicator module with real-time feedback loops. As a result, the strategic communication body of knowledge is extended to include digital, assisted, decentralized communication. Decentralized strategic communication proposes a self-organizing, bottom-up approach of strategic communication under the principle of subsidiarity. It keeps complexity at a manageable level and enables the usage of local knowledge and quick adaptation to rapid changes. The proposed resilient approach to strategic communication uses the driving forces of the digital revolution of big data, AI and IoT in its favor instead of trying to control them.
Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals

Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We formalize this method as a dynamic treatment regime which comprises a sequence of decisions, one per stage of intervention, that follow a decision rule which maps current patient information to a recommendation for their next visit time. The dynamics of periodontal health, visit frequency, and patient compliance are complex, yet the estimated optimal regime must be interpretable to domain experts if it is to be integrated into clinical practice. We combine nonparametric Bayesian dynamics modeling with policy-search algorithms to estimate the optimal dynamic treatment regime within an interpretable class of regimes. Both simulation experiments and application to a rich database of electronic dental records from the HealthPartners HMO shows that our proposed method leads to better dental health without increasing the average recommended recall time relative to competing methods. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Automatic generation of fill‐in‐the‐blank question with corpus‐based distractors for e‐assessment to enhance learning

Abstract Knowledge acquisition is the prime objective of a learner from an educational system and evaluating the learner's knowledge is the eventual goal of an examination process. This paper introduces a system which is able to produce fill‐in‐the‐blank questions to test the knowledge of a learner that he or she has accumulated after reading a course material. The question generation task is subdivided into three modules: sentence selection, answer‐key identification, and question formation along with distractor generation. The sentence is selected using a coarse‐grain part‐of‐speech tagset. The answer‐key is extracted by identifying topic‐word in the sentence and question is formed by omitting this topic‐word from the sentence. This paper also highlights an efficient corpus‐based distractor generation technique to produce multiple‐choice fill‐in‐the‐blank test items.
Transfer Learning from Pre-trained BERT for Pronoun Resolution

The paper describes the submission of the team "We used bert!" to the shared task Gendered Pronoun Resolution (Pair pronouns to their correct entities).Our final submission model based on the fine-tuned BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2018) ranks 14th among 838 teams with a multi-class logarithmic loss of 0.208.In this work, contribution of transfer learning technique to pronoun resolution systems is investigated and the gender bias contained in classification models is evaluated.
User-centered evaluation of strategies for recommending sequences of points of interest to groups

Most recommender systems (RSs) predict the preferences of individual users; however, in certain scenarios, recommendations need to be made for a group of users. Tourism is a popular domain for group recommendations because people often travel in groups and look for point of interest (POI) sequences for their visits during a trip. In this study, we present different strategies that can be used to recommend POI sequences for groups. In addition, we introduce novel approaches, including a strategy called Split Group, which allows groups to split into smaller groups during a trip. We compared all strategies in a user study with 40 real groups. Our results proved that there was a significant difference in the quality of recommendations generated by using the different strategies. Most groups were willing to split temporarily during a trip, even when they were traveling with persons close to them. In this case, Split Group generated the best recommendations for different evaluation criteria. We use these findings to propose improvements for group recommendation strategies in the tourism domain.
Quick and accurate attack detection in recommender systems through user attributes

Malicious profiles have been a credible threat to collaborative recommender systems. Attackers provide fake item ratings to systematically manipulate the platform. Attack detection algorithms can identify and remove such users by observing rating distributions. In this study, we aim to use the user attributes as an additional information source to improve the accuracy and speed of attack detection. We propose a probabilistic factorization model which can embed mixed data type user attributes and observed ratings into a latent space to generate anomaly statistics for new users. To identify the persistent outliers in the system, we also propose a sequential attack detection algorithm to enable quick and accurate detection based on the probabilistic model learned from genuine users. The proposed model demonstrates significant improvements in both accuracy and speed when compared to baseline algorithms on a popular benchmark dataset.
Collective embedding for neural context-aware recommender systems

Context-aware recommender systems consider contextual features as additional information to predict user's preferences. For example, the recommendations could be based on time, location, or the company of other people. Among the contextual information, time became an important feature because user preferences tend to change over time or be similar in the near future. Researchers have proposed different models to incorporate time into their recommender system, however, the current models are not able to capture specific temporal patterns. To address the limitation observed in previous works, we propose Collective embedding for Neural Context-Aware Recommender Systems (CoNCARS). The proposed solution jointly model the item, user and time embeddings to capture temporal patterns. Then, CoNCARS use the outer product to model the user-item-time correlations between dimensions of the embedding space. The hidden features feed our Convolutional Neural Networks (CNNs) to learn the non-linearities between the different features. Finally, we combine the output from our CNNs in the fusion layer and then predict the user's preference score. We conduct extensive experiments on real-world datasets, demonstrating CoNCARS improves the top-N item recommendation task and outperform the state-of-the-art recommendation methods.
Quantitative linguistics approach to interlanguage development: a study based on the Guangwai-Lancaster Chinese Learner Corpus

Interlanguage is crucial for the understanding of human language acquisition and development. Based on a collection of L2 Chinese texts from three levels of international students studying in China, this study demonstrates the possibility of adopting quantitative and corpus-based methods to investigate the development of interlanguage. To do so, we used Zipf’s law and related metrics to measure linguistic complexity in terms of different language styles and task types. Results show that (1) quantitative linguistic indices, including H-point and curve length, can differentiate the proficiency levels of L2 Chinese; (2) task type interacts with language style and language level with regard to their effects on language complexity; (3) interlanguage is indeed a self-organizing and self-adapting system; and (4) the Zipfian parameters can differentiate language levels. This study offers a new approach to the study of interlanguage by modeling its dynamic organization system in real language use. It has pedagogical implications for the use of language tasks and language styles with different L2 learner proficiency levels.
Rethinking Education System for the Fourth Industrial Revolution

The use of automation and artificial intelligence in recent times has created two options for stakeholders in the global business environment. The stakeholders are capable of becoming the agents or victims of inevitable transformation. This chapter explores a review of education system across the globe in building human capital to address the challenges and take advantage of the opportunities in the fourth industrial revolution. This chapter combines a literature review approach and personal observation in higher education institutions in advancing education system for the fourth industrial revolution. The use of chatbot as a training needs assessment technique is effective in collecting variety of information about needs, problems, potential problems, perceptions, attitudes, and opinions in the digital age. This chapter holds that teaching contents and techniques should be structured in line with the learners' objectives, students' needs, and skills in high demand by employers in the fourth industrial revolution.
Toward conditionally anonymous Bitcoin transactions: A lightweight-script approach

Bitcoin is being explored for applications in various Internet of Things (IoT) scenarios as a peer-to-peer payment platform. However, security and anonymity problems exist with Bitcoin, which threaten vulnerable IoT facilities. This paper aims to achieve conditional anonymity inside Bitcoin transactions. We first propose an identity-based conditionally anonymous signature (ICAS) algorithm and then design a lightweight Bitcoin script scheme (named pay-to-public-key-hash-with-conditional-anonymity or P2PKHCA), which applies the ICAS algorithm to make conditionally anonymous Bitcoin transactions. P2PKHCA allows the identity manager to trace the real identity of users while preserving users’ anonymity. Furthermore, P2PKHCA is backward compatible in terms of being able to work seamlessly with the existing Bitcoin script scheme pay-to-public-key-hash (P2PKH) in the Bitcoin network. We conduct a security analysis to verify the security features of P2PKHCA and employ a performance evaluation in terms of the cryptographic time and space costs by comparison with P2PKH. The simulation results demonstrate the effectiveness of P2PKHCA in reducing both time cost and data size.
What causes the adoption failure of service robots?

With the emergence of AI-powered products and services, the hospitality industry has started to adopt service robots to transform the guest experience. Despite this growing interest, Henn-na Hotel, the world's first robot hotel, recently announced to abandon half of its robots. This study aims to unveil factors leading to the adoption failure of service robots in the hospitality context using Henn-na Hotel as the case study. Through mining online guest reviews from four different leading online booking sites, we conducted thematic content analysis on a total of 250 negative online reviews. A total of six themes emerged from our data (e.g., human intervention, usefulness, embodiment), illustrating various factors resulting in the adoption failure. Based on this, we come up with six design implications for future researchers and designers to re-think about the interaction process between human and robots, as well as how service robots could be better designed and used in hospitality settings to fulfill guest needs.
Speeding up Deep Learning with Transient Servers

Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such speedups are often desirable-e.g., for rapidly evaluating new model designs-they often come with significantly higher monetary costs due to sublinear scalability. In this paper, we investigate the feasibility of using training clusters composed of cheaper transient GPU servers to get the benefits of distributed training without the high costs. We conduct the first large-scale empirical analysis, launching more than a thousand GPU servers of various capacities, aimed at understanding the characteristics of transient GPU servers and their impact on distributed training performance. Our study demonstrates the potential of transient servers with a speedup of 7.7X with more than 62.9% monetary savings for some cluster configurations. We also identify a number of important challenges and opportunities for redesigning distributed training frameworks to be transient-aware. For example, the dynamic cost and availability characteristics of transient servers suggest the need for frameworks to dynamically change cluster configurations to best take advantage of current conditions.
Scalable nonadiabatic holonomic quantum computation on a superconducting qubit lattice

Geometric phase is an indispensable element for achieving robust and high-fidelity quantum gates due to its built-in noise-resilience feature. However, due to the complexity of manipulation and the intrinsic leakage of the encoded quantum information to non-logical-qubit basis, the experimental realization of universal nonadiabatic holonomic quantum computation is very difficult. Here, we propose to implement scalable nonadiabatic holonomic quantum computation with decoherence-free subspace encoding on a two-dimensional square superconducting transmon-qubit lattice, where only the two-body interaction of neighboring qubits, from the simplest capacitive coupling, is needed. Meanwhile, we introduce qubit-frequency driving to achieve tunable resonant coupling for the neighboring transmon qubits, and thus avoiding the leakage problem. In addition, our presented numerical simulation shows that high-fidelity quantum gates can be obtained, verifying the advantages of the robustness and scalability of our scheme. Therefore, our scheme provides a promising way towards the physical implementation of robust and scalable quantum computation.
Learning Electronic Health Records through Hyperbolic Embedding of Medical Ontologies

Unplanned intensive care units (ICU) readmissions and in-hospital mortality of patients are two important metrics for evaluating the quality of hospital care. Identifying patients with higher risk of readmission to ICU or of mortality can not only protect those patients from potential dangers, but also reduce the high costs of healthcare. In this work, we propose a new method to incorporate information from the Electronic Health Records (EHRs) of patients and utilize hyperbolic embeddings of a medical ontology (i.e., ICD-9) in the prediction model. The results prove the effectiveness of our method and show that hyperbolic embeddings of ontological concepts give promising performance.
Word Embedding Comparison for Indonesian Language Sentiment Analysis

Development of information technology makes the production of data increase dramatically. We can get lots of data from the internet, including data reviews about a product or service. The more data obtained, the system is needed to process it. Sentiment analysis is a text processing of Natural Language Processing (NLP) that can help someone to see the quality of service offered, including hotel services. This paper uses hotel review data to carry out sentiment analysis obtained from the Traveloka website. The data classified using the Long Short-Term Memory (LSTM) algorithm. To get better results, the authors use word embedding to convert words into vectors. This study aims to compare the performance of several word embedding, while word embedding compared is word2vec Continuous Bag of Words CBOW, word2vec skip-gram, doc2vec, and glove. From the experiment conducted, the results show that the glove method has the highest accuracy of 95.52% while the word2vec skip-gram model has the lowest accuracy of 91.81%, so it concluded that the glove method is the best word embedding method for hotel review data.
Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation

Point of interest (POI) recommendation is a significant task in location-based social networks (LBSNs), e.g., Foursquare, Brightkite. It helps users explore the surroundings and help POI owners increase income. While several researches have been proposed for the recommendation services, it lacks integrated analysis on POI recommendation. In this article, the authors propose a unified recommendation framework, which fuses personalized user preference, geographical influence, and social reputation. The TF-IDF method is adopted to measure the interest level and contribution of locations when calculating the similarity between users. Geographical influence includes geographical distance and location popularity. The authors find friends in Brightkite share low common visited POIs. It means friends' interests may vary greatly. Instead of directly getting recommendations from so-called friends in LBSN, the users attain recommendation from others according to their reputation. Finally, experimental results on real-world dataset demonstrate that the proposed method performs much better than other recommendation methods.
Dummy-Based Trajectory Privacy Protection Against Exposure Location Attacks

With the development of positioning technology and location-aware devices, moving objects' location and trajectory information have been collected and published, resulting in serious personal privacy leakage. Existing dummy trajectory privacy preserving method does not consider user's exposure locations, which causes the adversary can easily exclude the dummy trajectories, resulting in a significant reduction in privacy protection. To solve this problem, we propose a dummy-based trajectory privacy protection scheme, which hides the real trajectory by constructing dummy trajectories, considering the spatio-temporal constraints of geographical environment of the user, the exposure locations in trajectory and the distance between dummy trajectories and real trajectory. We design a number of techniques to improve the performance of the scheme. We have conducted an empirical study to evaluate our algorithms and the results show that our method can effectively protect the user's trajectory privacy with high data utility.
Task Assignment and Motion Planning for Bi-Manual Mobile Manipulation

We present a two-layered architecture for task-agent assignment and motion planning of Bi-manual Mobile manipulators for executing complex tasks. We use search trees in temporal windows to determine feasible task assignments of agents using task and spatial constraint-based heuristics. We also introduce a caching scheme for moving between different trees so as to avoid re-planning of those portions of the robot motion that were successful. This greatly reduces the number of calls to the motion planner as compared to direct motion planning after task-agent assignment. We have shown our approach works on a set of complex tasks with significantly lower computation times.
MRI-Based Vocal Tract Representations for the Three-Dimensional Finite Element Synthesis of Diphthongs

The synthesis of diphthongs in three-dimensions (3D) involves the simulation of acoustic waves propagating through a complex 3D vocal tract geometry that deforms over time. Accurate 3D vocal tract geometries can be extracted from Magnetic Resonance Imaging (MRI), but due to long acquisition times, only static sounds can be currently studied with an adequate spatial resolution. In this work, 3D dynamic vocal tract representations are built to generate diphthongs, based on a set of cross-sections extracted from MRI-based vocal tract geometries of static vowel sounds. A diphthong can then be easily generated by interpolating the location, orientation and shape of these cross-sections, thus avoiding the interpolation of full 3D geometries. Two options are explored to extract the cross-sections. The first one is based on an adaptive grid (AG), which extracts the cross-sections perpendicular to the vocal tract midline, whereas the second one resorts to a semi-polar grid (SPG) strategy, which fixes the cross-section orientations. The finite element method (FEM) has been used to solve the mixed wave equation and synthesize diphthongs [Ai] and [Au] in the dynamic 3D vocal tracts. The outputs from a1D acoustic model based on the Transfer Matrix Method have also been included for comparison. The results show that the SPG and AG provide very close solutions in 3D, whereas significant differences are observed when using them in 1D. The SPG dynamic vocal tract representation is recommended for 3D simulations because it helps to prevent the collision of adjacent cross-sections.
Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor

In this work, we address the problem of multi-vehicle detection and tracking for traffic monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection with simultaneous pose estimation. Essentially, we adapt an Extended Kalman Filter (EKF) to work not only with the detections of the vehicles but also with their estimated coarse viewpoints, directly obtained with the vision sensor. We show that enhancing the tracking with observations of the vehicle pose, results in a better estimation of the vehicles trajectories. For the simultaneous object detection and viewpoint estimation task, we present and evaluate two independent solutions. One is based on a fast GPU implementation of a Histogram of Oriented Gradients (HOG) detector with Support Vector Machines (SVMs). For the second, we adequately modify and train the Faster R-CNN deep learning model, in order to recover from it not only the object localization but also an estimation of its pose. Finally, we publicly release a challenging dataset, the GRAM Road Traffic Monitoring (GRAM-RTM), which has been especially designed for evaluating multi-vehicle tracking approaches within the context of traffic monitoring applications. It comprises more than 700 unique vehicles annotated across more than 40.300 frames of three videos. We expect the GRAM-RTM becomes a benchmark in vehicle detection and tracking, providing the computer vision and intelligent transportation systems communities with a standard set of images, annotations and evaluation procedures for multi-vehicle tracking. We present a thorough experimental evaluation of our approaches with the GRAM-RTM, which will be useful for establishing further comparisons. The results obtained confirm that the simultaneous integration of vehicle localizations and pose estimations as observations in an EKF, improves the tracking results.