Reviewerly/exHarmony huggingface dataset:
Moog Minitaur: the editor and exclusive USB lock

I love my Moog Minitaur synth. It’s a great little bass station that packs a punch. However, I have been facing some issues using its full functionality. Namely, there’s a software plugin that enables you to tweak all the settings from the DAW. It controls the synth over midi via USB.
How to fix a broken Crumar Bit99 synthesizer

A friend chucked me an old Crumar Bit99 synthesizer from the 1980s. It’s a beast! Lovely bass sounds. Totally unusable interface. See figure A. However, when I received it, the unit was in a bad state. Terrible fuzzy white noise sound along with every note. It sounded as though it was totally wrecked. It’s actually, though, very easy to restore.
How to install Ubuntu on the HP Dragonfly Elite (hint: disable Optane)

There’s a prominent post at Ars Technica called Linux on laptops: Ubuntu 19.10 on the HP Dragonfly Elite G1 that implies that it is easy and straightforward to install Ubuntu on the HP Dragonfly Elite laptop. The post is correct that releases later than 19.10 have full kernel support for nearly all of the laptop’s hardware, but there are some very important caveats to the install.
On participatory design in library architecture

This week, our COPIM WP2/WP3 reading group discussed Meunier, Benjamin, and Olaf Eigenbrodt, ‘More Than Bricks and Mortar: Building a Community of Users Through Library Design’, &lt;em&gt;Journal of Library Administration&lt;/em&gt;, 54.3 (2014), 217–32 &amp;lt;https://doi.org/10.1080/01930826.2014.915166&amp;gt;. We were interested to consider the implications of participatory design in library architecture for new digital infrastructures.
The Edited Collection and its Discontents

I have just finished editing a book collection in which Robin de Mourat, Donato Ricci, and Bruno Latour ask: how does a format make a public?
Is ‘the money in the system’?

One of the oft-repeated adages in the scholarly communications world is that ‘the money is in the system’, it’s just badly distributed. This is one of the core problems with APCs; they don’t distribute funds in a similar way to subscriptions, so even if we could afford it, we still have a problematic distribution. What if this isn’t true, though, that the level of funding will remain the same?
Open access monographs and the REF-after-REF 2021

Since yesterday’s post on The UKRI Open Access Review Consultation Document my inbox has been swamped by journalists, librarians, and publishers asking what the policy means for REF. The short answer is that, at the moment, it means nothing. ##What the review says Work funded just by REF (i.e. that acknowledges only unhypothecated QR awarded from the last REF) is not in scope for the OA policy.
Zotero and auto-downloading open access books

This bank holiday, I wanted to spend some time playing around with Zotero’s automatic ingest of open access books. There are some problems with this. For recap, Zotero offers users a way easily to ingest items using built-in metadata on a page. It supports Dublin Core, various RDF implementations, and COinS. Here’s the problem, though: if you want automatic lookup by ISBN, you have to use the COinS translator/provide COinS metadata.
When infrastructural support clashes with DORA and good research assessment practices

Here’s an interesting one for me. The article processing charge (APC) model for open access is attracting a lot of flack. It’s being called the “scourge” of the scholarly communications world and is criticized for perpetuating global epistemic inequality. I think this is right in many ways. It’s why I co-founded the Open Library of Humanities, to show that other models are possible and more equitable.
Academic author rights of presentation on mutable digital platforms

An interesting discussion today with one of my senior publishing technology developers, Mauro Sanchez, led me to thinking about the rights of presentation and author rights to object to derogatory treatment of work published in scholarly journals. Namely: in the digital age, if one has a publication in a journal, what rights do the publishers have to change that platform and the underlying objects of publication?
One-shot domain adaptive real-time 3D obstacle detection in farmland based on semantic-geometry-intensity fusion strategy

By introducing deep learning, LiDAR-based solutions have achieved impressive accuracy in 3D obstacle detection. However, gathering and labeling sufficient samples is the precondition for the effectiveness of existing solutions. This precondition is difficult to satisfy in actual farmland due to the scarcity of distinctive obstacle samples as well as the time-consuming and specialized labeling process. In practice, detection models trained on specific datasets may fail to generalize well to real farmland, as they lack adaptability to different categories and scenes. To address this limitation, this paper proposes a novel one-shot domain adaptive real-time 3D obstacle detection method based on semantic-geometry-intensity fusion strategy. By introducing the concept of one-shot domain adaptation, the proposed method enables fine-grained 3D obstacle detection with just one sample per category. Specifically, a semantic-geometry-intensity space generator is designed to bridge the category gap between training and test samples. The integration of semantic-geometry-intensity space-based classifier and centerpoint-based anchor-free locator is designed to keep a decent balance between accuracy and efficiency. The switching between object sample enhancer and fusion point cloud generator is designed to handle distribution differences of both points and categories. The obstacle detection system designed based on the proposed method has been tested in real farmland, achieving an overall F1 score of 89.54% and a Frame Rate of 21.32 Frames Per Second (FPS). These experimental results demonstrate the high accuracy and efficiency of the proposed method in performing obstacle detection.
Mutual information-based feature disentangled network for anomaly detection under variable working conditions

Anomaly detection is of great significance to ensure operational safety of advanced equipment. However, existing anomaly detection algorithms presume that the working conditions are invariable, which cannot adapt to actual industrial scenarios and may cause false or missing alarms when the working conditions change. To address this issue, we expect to learn a condition-independent feature space, from which anomaly indicators can be derived to enable condition-agnostic anomaly detection. The objective to find such space is then deduced intuitively and justified by information theory. Correspondingly, a Mutual Information-based Feature Disentangled Network (MIFD-Net) is proposed for anomaly detection under variable working conditions. Specifically, to ensure feature independence from working conditions, the mutual information between latent representations and conditions is minimized by reducing its variational upper bound. Meanwhile, a self-supervised contrastive loss is deduced to maximize the mutual information between latent features and observed signals, which improves the fault discriminability of latent features. Finally, both feature constraints are integrated via dynamic weights in model training. Reconstruction errors derived from latent features are defined as anomaly scores for condition-agnostic anomaly detection. Experiments on part-level and component-level fault datasets demonstrate the superiority of the proposed method under both multiple and varying working condition scenarios.
UBR: User-Centric QoE-Based Rate Adaptation for Dynamic Network Conditions

The prevalence of video streaming applications has led to an escalation in users' demands for high-quality services. Numerous endeavors have been undertaken in the realm of quality-of-experience (QoE) models and adaptive bitrate (ABR) algorithms to fulfill this demand. Nevertheless, the existing QoE models exhibit a significant gap with users' actual experience. ABR algorithms are vulnerable in dynamic network environments. We present an integrated system with an accurate QoE model and an environment-robust adaptation algorithm to ensure high user satisfaction in dynamic network conditions. We define a QoE model that accurately estimates the user's QoE by considering the viewing environment and video content. We then design a meta-reinforcement learning-based adaptation algorithm that adapts to dynamic network conditions. We systematically integrate them, allowing it to update its policy with QoE feedback within a few shots.
Otter: Simplifying Embedded Sensor Data Collection and Analysis using Large Language Models

Wireless embedded systems assist us in collecting data from the physical world, through sensor data analysis, such systems allow us to understand our environment. However, deploying wireless embedded systems and analyzing the collected data remains significantly challenging. This is due to the steep learning curve required to implement custom machine-learning models and other algorithms for data analysis. Furthermore, it is also challenging to program individual embedded devices. The diversity of the available platforms and their capabilities further compounds this problem. In response, we introduce an end-to-end system, called the Otter. It facilitates simple sensor data collection using commodity-embedded platforms. Moreover, it employs a large language model to design a natural language interface for the analysis and extraction of useful information from the sensor data. We present our preliminary work on prototyping this system, applying it to a specific use case of hand gesture detection. Otter represents one of the first systems to leverage the enhanced capabilities of large language models for simplifying wireless embedded system deployments.
Practically Adopting Human Activity Recognition

Existing inertial measurement unit (IMU) based human activity recognition (HAR) approaches still face a major challenge when adopted across users in practice. The severe heterogeneity in IMU data significantly undermines model generalizability in wild adoption. This paper presents UniHAR, a universal HAR framework for mobile devices. To address the challenge of data heterogeneity, we thoroughly study augmenting data with the physics of the IMU sensing process and present a novel adoption of data augmentations for exploiting both unlabeled and labeled data. We consider two application scenarios of UniHAR, which can further integrate federated learning and adversarial training for improved generalization. UniHAR is fully prototyped on the mobile platform and introduces low overhead to mobile devices. Extensive experiments demonstrate its superior performance in adapting HAR models across four open datasets.
Catch Me If You Can: Demonstrating Laser Tethering with Highly Mobile Targets

Conventional wisdom holds that laser-based systems cannot handle mobility due to the strong directionality of laser light. We challenge this belief by presenting Lasertag, a generic system framework that tightly integrates laser steering with optical tracking to maintain laser connectivity with high-velocity targets. Lasertag creates a constantly connected, laser-based tether between the Lasertag core unit and a remote target, irrespective of the target's movement. Key elements of Lasertag include (1) a novel optical design that superimposes the optical paths of a steerable laser beam and an image sensor, (2) a lightweight optical tracking mechanism for passive retroreflective markers, (3) an automated mapping method to translate scene points to laser steering commands, and (4) a predictive steering algorithm that overcomes limited image sensor frame rates and laser steering delays to quadruple the steering rate up to 151 Hz. We demonstrate Lasertag's tethering capabilty with various mobile targets, such as a VR headset worn during active game play, a remotely-controlled moving robot, and more. Lasertag paves the way for laser applications in highly mobile settings.
Breaking Mobile Notification-based Authentication with Concurrent Attacks Outside of Mobile Devices

Notification-based authentication is an emerging Two-Factor Authentication (2FA) and passwordless solution that leverages interactive notifications on mobile devices to establish an additional layer of security beyond passwords. This method has gained popularity due to its convenience and ease of deployment in organizational settings. In this work, we aim to evaluate the effectiveness of notification-based authentication systems when a malicious entity is present on the user's computer, such as a keylogger or malicious extension, without compromising the mobile devices or communication channels. Furthermore, we investigate how the lack of information provided to users during the authentication workflow can lead to the approval of malicious authentication requests. Notably, we highlight the vulnerability of cross-service attacks, where an attacker authenticates to Service B while the user is attempting to authenticate to Service A. Our proof-of-concept attack program demonstrates the susceptibility of various notification-based authentication systems, and our user study reveals an alarming 82.2% cross-service attack success rate. These findings suggest a potential vulnerability in notification-based authentication systems, where the attacker compromise user account without compromising possession-factor device, such as smartphones.
THE RELATIONSHIP BETWEEN PROSPECTIVE TEACHERS’ PERCEIVED IMPORTANCE OF ONLINE TEACHING COMPETENCIES AND THEIR SELF-EFFICACY BELIEFS

Online teaching caught in-service teachers off-guard with emergency distance education and sparked interest to teacher education programs. Purpose of this study is to explore self-efficacy beliefs of prospective teachers in teaching online (SETO) and to determine the relationship between pre-service teachers’ importance of online teaching competencies (IOTC) perceptions and their SETO beliefs. 101 pre-service teachers were asked to fill faculty readiness to teach online scale and the responses were analyzed through ANOVA and Pearson Correlation. The results showed a significant difference between pre-service teachers’ majors, exposure to ICT-related experiences, and their SETO beliefs. In addition, there is a significant relationship between pre-service teachers’ IOCT perceptions and SETO beliefs. Understanding the existing SETO beliefs of pre-service teachers is critical because it provides evidence to reassess how pre-service teachers are supported to build their online teaching competencies. The results are expected to make a significant contribution to research on establishing online teaching competencies in Turkiye and assisting teachers in understanding the value of those competencies; as a result, potential implementers may have stronger online teaching self-efficacy in their distance classrooms. The study suggests incorporating technology-based resources into teacher education courses within a digital pedagogy competencies framework to increase preservice teachers’ self-efficacy.
Vehicle Detection and Tracking for the Gas Station Digital Twin System

Abstract Objectives/Scope A digital twin is a virtual representation of a real-world system used to digitally model performance, identify inefficiencies, and design solutions to improve its physical counterpart. With the success of digital twin systems in various scenarios, the digital twin system of gas station is expected to effectively improve the management efficiency and intelligence level of gas stations. Some typical applications include data analysis, traffic prediction, and route planning in gas station, etc. Methods, Procedures, Process The key part of the digital twin system of gas station is how to achieve accurate and efficient vehicle detection and tracking under various environmental conditions. Aiming at this goal, this paper proposes a practical vehicle detection and tracking method for gas station scenarios. We assume the digital twin system consists of a number of digital cameras and precisious 3D models of scene and facilities in gas station. We then present the proposed vehicle detection and tracking pipeline which includes the following three steps: 1) camera internal and external parameters calibration with known scene; 2) vehicle detection and real-time multi-target tracking, and 3) cross-camera re-identification and occlusion handling. Results, Observations, Conclusions We have conducted extensive experiments on several datasets. Experimental results show that the proposed method has good robustness to lighting and occlusion, and can effectively solve the problem of vehicle detection and tracking for the digital twin system of gas station. Novel/Additive Information In a real gas station scenario, real-time tracking of multiple vehicles can be achieved with an accuracy over 90%.
Business Agility: Enabling PDO to Thrive in a Rapidly Changing World

Abstract Agile methodology is one of the important and popular approaches used in project management. It is widely implemented in software development, and it shows success results in the implementation. Unlike waterfall approach, Agile drives the team to work simultaneously on different phases of the project, that's why it's known to be faster and more adoptive. It also emphasizes flexibility, collaboration, and enable continuous improvement. The waterfall approach, is a linear system of working that manages projects step by step, where project phase does not advance until the earlier phase completed and receives final approval (Figure 1) With the internet taking the world to another era in mid 1990s, the traditional project management approaches found to be outdated. These approaches didn't allow the developers to be responsive enough to the dynamic changes happening in the market. Therefore, looking for alternative for theses outdated approaches became an urgent need to survive in the market.
Characterizing the Stimulated Reservoir Volume Using Manifold Learning on 3d Motions of Microseismic Sources

Abstract Uniform manifold approximation and projection (UMAP) finds a low-dimensional representation of a complex dataset while preserving local structure and global distances present in high-dimensional data. Using UMAP, a high-dimensional microseismic data is transformed into a lower-dimensional, graph-based representation. In this paper, UMAP is applied on accelerometer signals recorded during an intermediate field-scale fracture propagation experiment at the Sanford Underground Research Facility in South Dakota. Our analysis shows that the short-time Fourier Transform of signals recorded by a single channel of the 3D accelerometer is the best feature extraction technique for the desired representation learning. For the first time, we have successfully identified the distinct fracture planes corresponding to each micro-earthquake location using accelerometer data from an intermediate-scale hydraulic stimulation experiment. Due to the scatter of microseismic events and associated uncertainty of microseismic event locations, the assignment of reliable fracture-plane label to a microseismic event is possible only for a small percentage of events that have sufficiently high signal to noise ratio, and/or are favorably located for unambiguous assignment. In this paper, we also present a semi-supervised learning method based on UMAP for label propagation to assign fracture-plane labels to many unlabeled microseismic events, utilizing a limited amount of labeled data, which have pre-assigned fracture-plane labels. The proposed semi-supervised label propagation on a low-dimensional manifold achieves exceptional precision and recall (&amp;gt;92%) while employing just a small percentage (~20%) of microseismic events with fracture-plane labels. With just a small set of labeled microseismic events, it is possible to assign fracture-plane labels to four times as many microseismic events.
Creating Digital Watermarks in Bitmap Images Using Lagrange Interpolation and Bezier Curves

The article is devoted to the introduction of digital watermarks, which formthe basis for copyright protection systems. Methods in this area are aimed at embedding hidden markers that are resistant to various container transformations. This paper proposes a method for embedding a digital watermark into bitmap images using Lagrange interpolation and the Bezier curve formula for five points, called Lagrange interpolation along the Bezier curve 5 (LIBC5). As a means of steganalysis, the RS method was used, which uses a sensitive method of double statistics obtained on the basis of spatial correlations in images. The output value of the RS analysis is the estimated length of the message in the image under study. The stability of the developed LIBC5 method to the detection of message transmission by the RS method has been experimentally determined. The developed method proved to be resistant to RS analysis. A study of the LIBC5 method showed an improvement in quilting resistance compared to that of the INMI image embedding method, which also uses Lagrange interpolation. Thus, the LIBC5 stegosystem can be successfully used to protect confidential data and copyrights.
Accepting the Digital Challenge: Public Perceptions and Attitudes toward Interactivity in Data Journalism

In the context of digitization, traditional journalism is facing transformation and innovation. Among them, the interactivity of visual graphics in data journalism is crucial for attracting and retaining online users, but few studies have examined public perceptions and attitudes toward it. In this study, we proposed a model to validate the relationship between users’ perceived interactivity and their attitudes toward data journalism, and we included user affective and cognitive factors (enjoyment and engagement) related to this as possible mediating variables in the model for validation. We conducted experiments (n = 75) using data journalism containing map visualizations with three levels of interactivity (low, medium, and high) in China. Furthermore, an exploratory evaluation of the experimental group provided further insights into the differences in interactions between groups, and the emerging five key concepts of data journalism design. Overall, all our hypotheses are supported, with enjoyment and engagement mediating the relationship between perceived interactivity and users’ attitudes toward the news. In addition, the experimental group with higher interaction potential also reported more positive attitudes toward journalism. Therefore, if data journalism and visualization designers want to attract and retain users in the future, enhancing user interaction on news pages will be a proven method.