Reviewerly/exHarmony huggingface dataset:
Fusion of Sensor Measurements and Target-Provided Information in Multitarget Tracking

Tracking multiple time-varying states based on heterogeneous observations is a key problem in many applications. Here, we develop a statistical model and algorithm for tracking an unknown number of targets based on the probabilistic fusion of observations from two classes of data sources. The first class, referred to as target-independent perception systems (TIPSs), consists of sensors that periodically produce noisy measurements of targets without requiring target cooperation. The second class, referred to as target-dependent reporting systems (TDRSs), relies on cooperative targets that report noisy measurements of their state and their identity. We present a joint TIPS–TDRS observation model that accounts for observation-origin uncertainty, missed detections, false alarms, and asynchronicity. We then establish a factor graph that represents this observation model along with a state evolution model including target identities. Finally, by executing the sum-product algorithm on that factor graph, we obtain a scalable multitarget tracking algorithm with inherent TIPS–TDRS fusion. The performance of the proposed algorithm is evaluated using simulated data as well as real data from a maritime surveillance experiment.
The coronavirus pandemic: Accessible technology for education, employment, and livelihoods

During the 2020 coronavirus pandemic, the use of technology to continue societal functions, maintain daily living, and gather information became near ubiquitous overnight. In the United States, universities moved to remote online education, non-essential personnel were asked to work from home, many businesses were forced to rapidly embrace online purchasing and even entertainers held concerts on social media platforms. Professional medical personnel, as well as state and local officials, held daily press briefings about the virus on television, radio, and social media. Disaster research has shown that often disasters exacerbate preexisting inequalities during all-hazards and therefore there are several populations likely to be more at risk, such as people with disabilities, and older adults. While complications from the coronavirus were more severe for older adults and those with preexisting health conditions, the preparedness and response efforts to contain the virus lessened the resiliency of other populations during the US outbreaks, potentially hampering their ability to recover at the same rate. For example, several press conferences from officials failed to include or display ASL interpreters. This would put some Americans at a disadvantage since they may have been unable to receive all of the pertinent information on how to keep safe. Furthermore, with nearly 22% of households without broadband internet access (per Pew Research Center), several individuals will not be able to take advantage of the rapid remote learning and work-from-home recommendations. Even when individuals have access to the Internet, older adults have been found to be less knowledgeable about privacy and security tools than younger adults.
Unsupervised thermal-to-visible domain adaptation method for pedestrian detection

Pedestrian detection is a common task in the research area of video analysis and its results lay the foundations of a wide range of applications. It is commonly known that under challenging illumination and weather conditions, conventional visible cameras perform poorly and this limitation could be catered using thermal imagery. But, due to the fact that annotated thermal datasets are less available compared to the visible ones, in this paper we emphasis the need for leveraging information from the visible domain to perform detection in the thermal domain at no additional annotation cost. Precisely, we propose a domain adaptation method by incorporating feature distribution alignments into Faster R-CNN architecture at different levels and at two phases of the network. The resulting proposed adaptive detector has the advantage of covering different aspects of the domain shift in order to improve the overall performance. The proposed detector is evaluated on KAIST multispectral dataset and the obtained results demonstrate its effectiveness by improving the adaptability in the thermal domain. Also, by means of comparisons to other existing works, better results are obtained. Additional experiments are conducted on other datasets to further justify the obtained results.
Global research trends in augmented reality: scientometric mapping based on Scopus database

Purpose This study aims to present a scientometric analysis of publications related to “Augmented Reality.” In today’s Information Technology-driven era, augmented reality (A.R.) has evolved as a new immersive data source for developing knowledge combining authentic and digital images. Consequently, extensive research is going on “Augmented Reality” to discover its potential in knowledge development. Methodology The paper analyses and emphasizes the bibliographic data of Scopus articles with a suitable search query. The study was done concerning the chronological growth of research publications, highly cited publications, productive countries, prominent journals, prolific authors and institutions, author and country co-authorship network analysis and keywords analysis. The analysis was conducted by using open-source tools such as VOSViewer, Biblioshiny and Gephi. Findings The study reveals that a maximum number of publications on research in “Augmented Reality” are in the form of conference proceedings and articles. A.R., Virtual reality and A.R. application are the keywords with maximum number of occurrences. A significant number of publications are done in the USA, followed by Germany in the year 2020. Originality/value This study provides a precise idea of work done in different countries, a network of co-authorship between authors and countries, publication and citation impact of authors, journals, institutions and countries, year-wise progression and trending “augmented reality” topics research. This investigation will be advantageous for researchers and stakeholders to obtain rigorous bibliographic knowledge on literature related to the topic and work accordingly for R&amp;D activities.
What Students Learn With Personal Data Physicalization

I describe the results of implementing a personal data physicalization assignment in an information visualization course for senior undergraduate and graduate students in computer science and software engineering. By collecting data about themselves and representing this data in physical forms, students were able to 1) learn about data visualization, 2) design creatively, and 3) learn about themselves. While data physicalization in education has been explored with nontechnical and novice audiences, the experience I report in this article provides evidence that data physicalization also has benefits for technical, visualization-savvy students.
How AI chatbots have reshaped the frontline interface in China: examining the role of sales–service ambidexterity and the personalization–privacy paradox

Purpose This study serves two purposes: (1) to evaluate the effects of organizational ambidexterity by examining how the balanced and the combined sales–service configurations of chatbots differ in their abilities to enhance customer experience and patronage and (2) to apply information boundary theory to assess the contingent role that chatbot sales–service ambidexterity can play in adapting to customers' personalization–privacy paradox. Design/methodology/approach An online survey of artificial intelligence chatbots users was conducted, and a mixed-methods research design involving response surface analysis and polynomial regression was adopted to address the research aim. Findings The results of polynomial regressions on survey data from 507 online customers indicated that as the benefits of personalization decreased and the risk to privacy increased, the inherently negative (positive) effects of imbalanced (combined) chatbots' sales–service ambidexterity had an increasing (decreasing) influence on customer experience. Furthermore, customer experience fully mediated the association of chatbots' sales–service ambidexterity with customer patronage. Originality/value First, this study enriches the literature on frontline ambidexterity and extends it to the setting of human–machine interaction. Second, the study contributes to the literature on the personalization–privacy paradox by demonstrating the importance of frontline ambidexterity for adapting to customer concerns. Third, the study examines the conduit between artificial intelligence (AI) chatbots' ambidexterity and sales performance, thereby helping to reconcile the previously inconsistent evidence regarding this relationship.
Online Next-Best-View Planner for 3D-Exploration and Inspection With a Mobile Manipulator Robot

Robotic systems performing end-user oriented autonomous exploration can be deployed in different scenarios which not only require mapping but also simultaneous inspection of regions of interest for the end-user. In this work, we propose a novel Next-Best-View (NBV) planner which can perform full exploration and user-oriented exploration with inspection of the regions of interest using a mobile manipulator robot. We address the exploration-inspection problem as an instance of Multi-Objective Optimization (MOO) and propose a weighted-sum-based information gain function for computing NBVs for the RGB-D camera mounted on the arm. For both types of exploration tasks, we compare our approach with an existing state-of-the-art exploration method as the baseline and demonstrate our improvements in terms of total volume mapped and lower computational requirements. The real experiments with a mobile manipulator robot demonstrate the practicability and effectiveness of our approach outdoors.
Adoption of open innovation in the COVID-19 emergency: developing a process-based information coordination system

Purpose The COVID-19 pandemic has stimulated a global movement of open innovation initiatives aimed to provide knowledge and tools to support policy decisions and actions in the emergency scenario. The authors describe an open innovation process aimed to build an information coordination system to reduce the infection diffusion within the population. Design/methodology/approach The authors use coordination theory principles to elaborate a framework of activities, resources and dependencies among information resources and producers in the COVID-19 emergency. The framework was applied to develop a case study aimed at describing a health emergency system implemented by Dyrecta Lab (a research laboratory on computer science) and CITEL (a medical research center). Findings The authors describe the existence of relevant “flow,” “fit” and “share” dependencies within the activities of infection containment and medical treatment. The authors identify eight key resources and a number of actors involved in those activities, and describe a platform able to gather a multitude of epidemic-related metrics with the purpose to address dependencies and support decision making. Research limitations/implications The authors provide insights for advancing the academic discussion on process coordination principles in time-constrained, volatile and highly demanding scenarios. Practical implications The value of the authors’ research can be identified for practitioners engaged to develop innovative development projects for public utility. The authors provide a contribution also for first responders and health operators involved in management of the current and future emergencies. Originality/value The adoption of process coordination principles is a relatively new and powerful approach to analyze and optimize the processes that characterize the management of emergency scenarios. Besides, the study and application of open innovation in healthcare are partially limited.
A survey on business process view integration: past, present and future applications to blockchain

Purpose The complexity of business environments often causes organizations to produce several inconsistent views of the same business process (BP), leading to fragmentation. BP view integration attempts to produce an integrated view from different views of the same model, facilitating the management of BP models. Design/methodology/approach To study the trends of BP view integration, the authors conduct an extensive and systematic literature review to summarize findings since the 1970s. With a starting corpus of 918 documents, this survey draws up a systematic inventory of solutions used in academia and industry. By narrowing it down to 71 articles, the authors discuss in-depth 17 BP integration techniques papers, classifying each solution according to 9 criteria. Findings The authors' study shows that most view-integration methods (11) utilize annotation-based matching, based on formal merging rules. While most solutions are formalized, only approximately half are validated with a real-world use case scenario. View integration can be applied to areas other than database schema integration and BP view integration. Practical implications By summarizing existing knowledge up to June 2021, the authors explore possible future research directions. The authors highlight the application of view integration to the blockchain research area, where stakeholders can have different views on the same blockchain. The authors expect that this study contributes to interdisciplinary research across view integration, namely to the context of blockchain. Originality/value This survey serves to pave the way for future trends, where the authors highlight the application of view integration to blockchain research.
Greedy-layer pruning: Speeding up transformer models for natural language processing

Fine-tuning transformer models after unsupervised pre-training reaches a very high performance on many different natural language processing tasks. Unfortunately, transformers suffer from long inference times which greatly increases costs in production. One possible solution is to use knowledge distillation, which solves this problem by transferring information from large teacher models to smaller student models. Knowledge distillation maintains high performance and reaches high compression rates, nevertheless, the size of the student model is fixed after pre-training and can not be changed individually for a given downstream task and use-case to reach a desired performance/speedup ratio. Another solution to reduce the size of models in a much more fine-grained and computationally cheaper fashion is to prune layers after the pre-training. The price to pay is that the performance of layer-wise pruning algorithms is not on par with state-of-the-art knowledge distillation methods. In this paper, Greedy-layer pruning is introduced to (1) outperform current state-of-the-art for layer-wise pruning, (2) close the performance gap when compared to knowledge distillation, while (3) providing a method to adapt the model size dynamically to reach a desired performance/speedup tradeoff without the need of additional pre-training phases. Our source code is available on https://github.com/deepopinion/greedy-layer-pruning.
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation

In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms. In this paper, we present a new recommendation problem, Trigger-Induced Recommendation (TIR), where users’ instant interest can be explicitly induced with a trigger item and follow-up related target items are recommended accordingly. TIR has become ubiquitous and popular in e-commerce platforms. In this paper, we figure out that although existing recommendation models are effective in traditional recommendation scenarios by mining users’ interests based on their massive historical behaviors, they are struggling in discovering users’ instant interests in the TIR scenario due to the discrepancy between these scenarios, resulting in inferior performance. To tackle the problem, we propose a novel recommendation method named Deep Interest Highlight Network (DIHN) for Click-Through Rate (CTR) prediction in TIR scenarios. It has three main components including 1) User Intent Network (UIN), which responds to generate a precise probability score to predict user’s intent on the trigger item; 2) Fusion Embedding Module (FEM), which adaptively fuses trigger item and target item embeddings based on the prediction from UIN; and (3) Hybrid Interest Extracting Module (HIEM), which can effectively highlight users’ instant interest from their behaviors based on the result of FEM. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of DIHN over state-of-the-art methods. Our code is available 1.
Learning the noise fingerprint of quantum devices

Noise sources unavoidably affect any quantum technological device. Noise's main features are expected to strictly depend on the physical platform on which the quantum device is realized, in the form of a distinguishable fingerprint. Noise sources are also expected to evolve and change over time. Here, we first identify and then characterize experimentally the noise fingerprint of IBM cloud-available quantum computers, by resorting to machine learning techniques designed to classify noise distributions using time-ordered sequences of measured outcome probabilities.
Data Management Policies and Practices of Digital Archaeological Repositories

This article presents the results of a survey of data management policies and practices of digital archaeological repositories in Europe and beyond. The survey was carried out in 2021 under the auspices of the European project ARIADNEplus and the COST Action SEADDA. Its main purpose was to collect and analyse information about current policies that determine access to and reuse of data held by digital archaeological repositories, and to investigate the guidance and support needed to make these repositories and data FAIR (Findable, Accessible, Interoperable and Reusable). These policies comprise the regulations of heritage and research authorities/agencies, councils and other institutions at different levels (European, national/regional, local) as well as the repository rules governing deposition, access to, and reuse of archaeological data. The repositories are operated both by heritage sector institutions and by the research and higher education sector. The survey represents a bottom-up approach by focusing on the actual policies and practices of digital archaeological repositories, which may reflect higher level regulations. A reality check in this regard can enable heritage and research authorities, councils and other institutions to reinforce or put in place regulations that bring current repository policies and practices closer to the ideal of providing FAIR and open access data. The survey results show that there is room for improvement in this regard and some suggestions are made here for future initiatives.
Analytical Methods for High-Rate Global Quantum Networks

The development of a future global quantum communication network (or quantum Internet) will enable high-rate private communication and entanglement distribution over very long distances. However, the large-scale performance of ground-based quantum networks (which employ photons as information carriers through optical fibers) is fundamentally limited by fiber quality and link length, with the latter being a primary design factor for practical network architectures. While these fundamental limits are well established for arbitrary network topologies, the question of how to best design global architectures remains open. In this work, we introduce a large-scale quantum network model called weakly regular architectures. Such networks are capable of idealizing network connectivity, provide freedom to capture a broad class of spatial topologies, and remain analytically treatable. This allows us to investigate the effectiveness of large-scale networks with consistent connective properties and unveil critical conditions under which end-to-end rates remain optimal. Furthermore, through a strict performance comparison of ideal ground-based quantum networks with that of realistic satellite quantum communication protocols, we establish conditions for which satellites can be used to outperform fiber-based quantum infrastructure, rigorously proving the efficacy of satellite-based technologies for global quantum communications.Received 3 June 2021Revised 18 January 2022Accepted 22 February 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010349Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNetwork optimizationNetwork structureQuantum communicationQuantum networksQuantum InformationNetworks
Quantum gradient descent algorithms for nonequilibrium steady states and linear algebraic systems

The gradient descent approach is the key ingredient in variational quantum algorithms and machine learning tasks, which is an optimization algorithm for finding a local minimum of an objective function. The quantum versions of gradient descent have been investigated and implemented in calculating molecular ground states and optimizing polynomial functions. Based on the quantum gradient descent algorithm and Choi-Jamiolkowski isomorphism, we present approaches to simulate efficiently the nonequilibrium steady states of Markovian open quantum many-body systems. Two strategies are developed to evaluate the expectation values of physical observables on the nonequilibrium steady states. Moreover, we adapt the quantum gradient descent algorithm to solve linear algebra problems including linear systems of equations and matrix-vector multiplications, by converting these algebraic problems into the simulations of closed quantum systems with well-defined Hamiltonians. Detailed examples are given to test numerically the effectiveness of the proposed algorithms for the dissipative quantum transverse Ising models and matrix-vector multiplications.
RescoreBERT: Discriminative Speech Recognition Rescoring With Bert

Second-pass rescoring is an important component in automatic speech recognition (ASR) systems that is used to improve the outputs from a first-pass decoder by implementing a lattice rescoring or n-best re-ranking.While pretraining with a masked language model (MLM) objective has received great success in various natural language understanding (NLU) tasks, it has not gained traction as a rescoring model for ASR.Specifically, training a bidirectional model like BERT on a discriminative objective such as minimum WER (MWER) has not been explored.Here we show how to train a BERT-based rescoring model with MWER loss, to incorporate the improvements of a discriminative loss into fine-tuning of deep bidirectional pretrained models for ASR.Specifically, we propose a fusion strategy that incorporates the MLM into the discriminative training process to effectively distill knowledge from a pretrained model.We further propose an alternative discriminative loss.This approach, which we call RescoreBERT, reduces WER by 6.6%/3.4% relative on the LibriSpeech clean/other test sets over a BERT baseline without discriminative objective.We also evaluate our method on an internal dataset from a conversational agent and find that it reduces both latency and WER (by 3 to 8% relative) over an LSTM rescoring model.