Reviewerly/exHarmony huggingface dataset:
The Wheel of Wikipedia Collaborating at Boston University Libraries

Librarians at Boston University Libraries cultivated their curiosity and enthusiasm for Wikipedia into a community for campus-wide collaboration at Boston University (BU). The librarians' first experiences learning to edit Wikipedia at local events inspired them to later host their own events at BU Libraries. These events led to the libraries' instrumental support for implementing the Wikipedia Student Program at BU through a collaborative undertaking between faculty and librarians. As a result of building these new collaborations, the libraries became a point of contact for faculty and students interested in Wikipedia, found a new medium with which to explore shared pedagogical goals between the libraries and different departments, and gained visibility by being formally acknowledged in the publications and awards of colleagues at BU who are leading Wikipedia initiatives.
Levels of Representation and Data Infrastructures in Entomo-3D: An applied research approach to addressing metadata curation issues to support preservation and access of 3D data

This paper employs an action-based research approach to address the question of how to create a sustainable workflow to support long term access, interoperability, and reuse of 3D data. This is applied research, stemming from the Entomo-3D collaboration between Virginia Tech University Libraries and the Virginia Tech Department of Entomology to digitize a university insect pollinator collection. The paper will describe infrastructure to support data management and transformation, as well as new challenges that have emerged from this effort.
Industrial Use-Case : AR for Manual Assembly in Industry

Augmented Reality (AR), part of Industry 4.0 concepts, is an emerging technology with a great potential in assisting humans in a wide range of industrial processes. Among various use-cases, AR has started to be used as a training tool in manual assembly, by enabling workers to access contextualized digital information overlaid in the physical world. However, very few AR solutions have been adopted so far in industrial sectors, mainly because of technical and acceptability issues, as well as the effort to create AR contents. In this paper we present an AR training system designed for and within the framework of manual assembly production. The proposed approach aims to find the right balance between usability, effectiveness, user acceptance and authoring efforts, to address significant industrial challenges and provide an AR training tool adapted to the shop floor context. We demonstrated the usability and effectiveness of the proposed AR system in multiple experiments and comparatively evaluated authoring efforts with the state-of-the-art. Overall, our proposal reported excellent usability scores and was almost unanimously preferred by the participants to the experiments. Authoring was almost twice as fast as the state-of-the-art while the error rate during training was zero, validating therefore the effectiveness of the system.
A Framework for Safe Execution of User-Uploaded Algorithms

In recent years, a trend has existed for an open benchmark aiming for reproducible and comparable benchmarking results. The best reproducibility can be achieved when performing the benchmarks in the same hard- and software environment. This can be offered as a web service. One challenge of such a web service is the integration of new algorithms into the existing benchmarking tool due to security concerns. In this paper, we present a framework that allows the safe execution of user-uploaded algorithms in such a benchmark-as-a-service web tool. To guarantee security as well as reproducibility and comparability of the service, we extend an existing system architecture to allow the execution of user-uploaded algorithms in a virtualization environment. Our results show that although the results from the virtualization environment are slightly slower by around 3.7% to 4.7% compared with the native environment, the results are consistent across all scenarios with different algorithms, object shapes, and object complexity. Moreover, we have automated the entire process from turning on/off a virtual machine, starting benchmark with intended parameters to communicating with the backend server when the benchmark has finished. Our implementation is based on Microsoft Hyper-V that allows us to benchmark algorithms that use Single Instruction, Multiple Data (SIMD) instruction sets as well as access to the Graphics Processing Unit (GPU).
Phrase-Gesture Typing on Smartphones

We study phrase-gesture typing, a gesture typing method that allows users to type short phrases by swiping through all the letters of the words in a phrase using a single, continuous gesture. Unlike word-gesture typing, where text needs to be entered word by word, phrase-gesture typing enters text phrase by phrase. To demonstrate the usability of phrase-gesture typing, we implemented a prototype called PhraseSwipe. Our system is composed of a frontend interface designed specifically for typing through phrases and a backend phrase-level gesture decoder developed based on a transformer-based neural language model. Our decoder was trained using five million phrases of varying lengths of up to five words, chosen randomly from the Yelp Review Dataset. Through a user study with 12 participants, we demonstrate that participants could type using PhraseSwipe at an average speed of 34.5 WPM with a Word Error Rate of 1.1%.
PPTPS: Building privacy-preserving auditable service with traceable timeliness for public cloud storage

Many works are designed to improve efficiency or enhance security and privacy of publicly-auditable cloud storage. However, building timeliness for cloud storage has not been well studied. Few works presented time-sensitive cloud storage and only focused on specific issues, such as the earliest creation time of files or resistance against a procrastinating auditor. Therefore, there leaves an absence of building traceable timeliness for publicly-auditable cloud storage. In this paper, we propose a solution PPTPS to build Privacy-Preserving auditable service with Traceable timeliness for Public cloud Storage. First, we use the security properties of the blockchain to provide a time-stamp for each phase. It enables the timeliness of cloud storage. Second, we construct efficient publicly-verifiable cloud storage. Third, a customized random mask solution is proposed to prevent privacy leakage during the auditing phase. Moreover, we formally proved the security of PPTPS. At last, experimental results demonstrate that PPTPS is economically sound and technically viable.
ARDW: An Augmented Reality Workbench for Printed Circuit Board Debugging

Debugging printed circuit boards (PCBs) can be a time-consuming process, requiring frequent context switching between PCB design files (schematic and layout) and the physical PCB. To assist electrical engineers in debugging PCBs, we present ARDW, an augmented reality workbench consisting of a monitor interface featuring PCB design files, a projector-augmented workspace for PCBs, tracked test probes for selection and measurement, and a connected test instrument. The system supports common debugging workflows for augmented visualization on the physical PCB as well as augmented interaction with the tracked probes. We quantitatively and qualitatively evaluate the system with 10 electrical engineers from industry and academia, finding that ARDW speeds up board navigation and provides engineers with greater confidence in debugging. We discuss practical design considerations and paths for improvement to future systems. A video demo of the system may be accessed here: https://youtu.be/RbENbf5WIfc .
Pattern Labelling of Business Communication Data

Abstract Systematic pattern recognition as well as the corresponding description of determined patterns entail numerous challenges in the application context of high-dimensional communication data. These can cause increased effort, especially with regard to machine-based processing concerning the determination of regularities in underlying datasets. Due to the increased expansion of dimensions in multidimensional data spaces, determined patterns are no longer interpretable by humans. Taking these challenges into account, this paper investigates to what extent pre-defined communication patterns can be interpreted for the application area of high-dimensional business communication data. An analytical perspective is considered by taking into account a holistic research approach and by subsequently applying selected Machine Learning methods from Association Rule Discovery, Topic Modelling and Decision Trees with regard to the overall goal of semi-automated pattern labelling. The results show that meaningful descriptions can be derived for the interpretation of pre-defined patterns.
Search with Space: Find and Visualize Furniture in Your Space

Online shopping in the home category enables quick and convenient access to large catalog of products. In particular, users can simultaneously browse for functional requirements, such as size and material, while evaluating aesthetic fit, such as color and style, across hundreds of product offerings. However, the typical user flow requires navigating to an e-commerce retailer’s website first, setting the search/filter parameters that may be generic, and then landing on product pages, one at a time, to make a decision. Upon purchase, ”does not fit” is among the top reasons for returning a product. Amalgamating the above information, we present Search with Space, a novel interactive approach that a) inputs the user’s space as a search parameter to b) filter for product matches that will physically fit, and c) visualize these matches in the user’s space at true scale and in a format that facilitates simultaneous comparison. Briefly, the user leverages augmented reality (AR) to set a proxy 3d product in the desired location, updates the proxy’s dimensions, and takes photos from preset angles. Using spatial information captured with AR, a web-based gallery page is curated with all the product matches that will physically fit and products are shown at true scale in their original photos. The user may now browse products visualized in the context of their space and evaluate based on their shopping criteria, share the gallery page with designers or partners for asynchronous feedback, re-use the photos for a different product class, or re-capture their space with different criteria altogether. Search with Space inverts the typical user journey by starting with the user’s space and maintaining that context across all touch points with the catalog.
Bandwidth constrained cooperative object detection in images

Cooperative autonomous systems, such as swarms, multi-camera systems, or fleets of self-driving vehicles, can better understand a given scene by sharing multiple modalities and varying viewpoints. This superposition of data adds robustness and redundancy to a system typically burdened with obstructions and unrecognizable, distant objects. Collaborative perception is a key component of cooperative autonomous systems where modalities can include camera sensors, LiDAR, RADAR, and depth images. Meanwhile, the amount of useful information that can be shared between agents in a cooperative system is constrained by current communication technologies (e.g. bandwidth limitations). Recent developments in learned compression can enable the training of end-to-end cooperative systems using deep learning with compressed communication in the pipeline. We explore the use of a deep learning object detector in a cooperative setting with a learned compression model facilitating communication between agents. To test our algorithm, this research will focus on object detection in the image domain as a proxy for one of the modalities used by collaborative systems.
Using non-linear activation functions to increase robustness of AI models to adversarial attacks

Image classification tasks leverage CNN to yield accurate results that supersede their predecessor human-crafted algorithms. Applicable use cases include Autonomous, Face, Medical Imaging, and more. Along with the growing use of AI image classification applications, we see emerging research on the robustness of such models to adversarial attacks, which take advantage of the unique vulnerabilities of the Artificial Intelligence (AI) models to skew their classification results. While not visible to the Human Visual System (HVS), these attacks mislead the algorithms and yield wrong classification results. To be incorporated securely enough in real-world applications, AI-based image classification algorithms require protection that will increase their robustness to adversarial attacks. We propose replacing the commonly used Rectifier Linear Unit (ReLU) Activation Function (AF), which is piecewise linear, with non-linear AF to increase their robustness to adversarial attacks. This approach has been considered in recent research and is motivated by the observation that non-linear AF tends to diminish the effect of adversarial perturbations in the DNN layers. To gain credibility of the approach, we have applied Fast Sign Gradient Method (FGSM), and Hop-Skip- Jump (HSJ) attacks to a trained classification model of the MNIST dataset. We then replaced the AF of the model with non-linear AF (Sigmoid, GeLU, ELU, SeLU, and Tanh). We concluded that while attacks on the original model have a 100% success rate, the attack success rate is dropped by an average of 10% when non-linear AF is used.
Performance metrics with operational context for video object detection algorithms

Performance metrics that are used by the academia to evaluate the performance of video object detection algorithms are usually not informative for assessing whether or not the algorithms of interest are suitable (mature enough) for real-world deployment. We propose an approach to define the performance metrics that are suitable for various operational scenarios. In particular, we define four operational modes: Surveillance (alarm), situational awareness, detection and tracking. We then describe the performance metrics for the needs of each operational scenario, and explain the underlying reasoning. The metrics are compatible with the common practices for constructing in-house video datasets. We believe that these metrics provide useful insight for the usability of the algorithms. We also demonstrate that an algorithm which (at first glance) seem to have insufficient performance for deployment can be used in a real-world system (with simple post-processing) if its parameters are configured to provide high performance scores for scenario-specific metrics. We also show that the same underlying algorithm can be used for different operational scenarios if its parameters (and/or post-processing steps) are adjusted to meet the criteria based on relevant scenario-based performance metrics.
Centralized tracking and bidirectional long short-term memory for abnormal behaviour recognition

The contribution of this paper is an evaluation of the chain of calculations for a behaviour recognition method. That is, from person detection, centralized person tracking to a bi-directional long short-term memory (BiLSTM). The centralized person tracking fuses detections from distributed and multimodal sensors. The BiLSTM learns long-time dependencies in the tracking data sequences. We use experimental sensor data from visual and thermal infrared sensors. The sensor data describe five scenarios with people performing normal and abnormal behaviours. The results indicate that the mean recognition accuracy is rather high. However, with position as the only input data, the robustness of the method is rather low. The robustness increases by adding velocity to the dataset. Velocity adds important information, even though velocity appears very messy when visualized in diagrams. Furthermore, the BiLSTM is compared with the unidirectional long short-term memory (LSTM) and the gated recurrent unit (GRU).
Evaluating active learning methods for synthetic aperture radar maritime object detection

Maritime object detection in synthetic aperture radar (SAR) imagery has seen a resurgence of interest due to the introduction of deep object detection models and large-scale datasets from competitions such as xView3. However, as novel examples are seen in the wild and as new SAR sensors emerge, existing models will need to be retrained with relevant new data. Active learning (AL) aims to automate and optimize this data curation process. In this work, we evaluate state-of-the-art AL algorithms for the task of SAR maritime object detection. Through analyzing and identifying gaps in current AL solutions we seek to motivate efforts to improve their utility in practical settings.
Dynamic-automatic pipelines for finding topic-specific information clusters using NLP methods in connection with a model-driven approach

Finding and extracting topic-specific information from free-text sources is an important task for classifying and distinguishing content of information systems. Such a compression process of information, in which non-relevant text parts can also be ignored, is also advantageous with regard to the further machine processing and evaluation of topic-specific documents. State-of-the-art approaches normally use well-trained modern Natural Language Processing (NLP) methods to solve such tasks. However, use cases can arise where no suitable training data sets are available to adequately prepare or fine-tune the NLP methods used. In this paper, we want to detail a model-driven approach, applying an XML data model to an application-specific scenario, combining different NLP methods into a dynamic automated NLP pipeline. The goal of this pipeline is the automatic extraction of specific information (related to certain domains or topics) from text documents allowing a structured further processing of this information. Specifically, a scenario is considered where such information has to be aligned to a given information model, defining e.g. the terms relevant for the further processing. The solution approaches described here deal with a scenario in which information clusters on a specific topic can be obtained from a given data set, even without domain-specific model training. The basis is the use of a dynamic (i.e., using different NLP methods and models) and fully automatic (i.e., using different topics at the same time) pipeline architecture combined with an XML data model. The presented approach details and extends our earlier work and gives new qualitative and first quantitative results.
Combatting fraud on travel, identity, and breeder documents

It is important to combat fraud on travel documents (e.g., passports), identity documents (e.g., ID-card) and breeder documents (e.g., birth certificates) to facilitate the travel of bona-fide travelers and to prevent criminal cross-border activities, such as terrorism, illegal migration, smuggling, and human trafficking. However, it is challenging and time consuming to verify all document security features manually. New technologies can assist in the automated fraud detection in these documents, which may result in faster and more consistent checks. This paper presents and evaluates four new technologies in automated document analysis. The first recognizes printing techniques. The second assists in the recognition of fraud in details. The third extracts information from the document, which can be used to detect anomalies at a tactical level. The fourth category concerns the analysis of travel patterns, using information from the visa pages in passports. The performance is assessed for each element with quantitative performance metrics.
Federated tool for anonymization and annotation in image data

The increasing complexity of security challenges requires Law Enforcement Agencies (LEAs) to have improved analysis capabilities, e.g., with the use of Artificial Intelligence (AI). However, it is challenging to make large enough high-quality training and testing datasets available to the community that is developing AI tools to support LEAs in their daily work. Due to legal and ethical issues, it is often undesirable to share raw data with personal information. These issues can lead to a chicken-egg problem, where annotation/anonymization and development of an AI tool depend on each other. This paper presents a federated tool for semi-automatic anonymization and annotation that facilitates the sharing of AI models and anonymized data without sharing raw data with personal information. The tool uses federated learning to jointly train object detection models to reach higher performance by combining the annotation efforts of multiple organizations. These models are used to assist a person to anonymize or annotate image data more efficiently with human oversight. The results show that our privacy-enhancing federated approach – where only models are shared – is almost as good as a centralized approach with access to all data.
APMD: Adversarial Pixel Masking Derivative for multispectral object detectors

Recently, adversarial patches have been used successfully, to fool object detectors by hiding a specific or suppress almost all relevant detections in an image. Although there are various ways to harden against or identify those attacks in the visual spectrum, there is only a small fraction that actually evaluates these mechanisms on thermal infrared input data. Thermal infrared object detectors and classifiers cannot be fooled with pixel optimized adversarial patches, but they are still prone to Gaussian function patches. This paper (I) investigates two methods for hardening real-time infrared object detectors against adversarial patches. One of these methods is our novel (II) APMD, an extension of an already existing adversarial robustness mechanism, that relies on (unsupervised) adversarial training, to clear adversarial patches for deep learning object detectors in the infrared spectrum. We therefore (III) generate adversarial patches, that fool object detectors in the infrared spectrum in three different ways, and evaluate them with real-world data, recorded with the experimental platform MODISSA. Our results show, that the hardened system is fast enough to be used in a real-time environment and successfully detects and inhibits adversarial attacks.