Reviewerly/exHarmony huggingface dataset:
PageRank and HodgeRank on Ethereum Transactions

Mathematical ranking plays a critical role in the era of the internet and bigdata. Google's PageRank is well-known as a trillion-dollar algorithm. Definitely, algorithmic ranking frameworks are found on every search engine. In this paper, the article shall investigate how PageRank can be applied in the blockchain space to build up reliable and verifiable social credit and reputation systems. It is expected to provide a measure of credibility complementary and parallel with FICO, which is not applicable for individuals lacking credit information in financial institutions. Moreover, the approach proposes an unbiased method of interpreting and measuring real social interaction and reputation ranking on a blockchain network. The authors envision a future of payment based on cryptocurrencies (especially stable coins) and digital fiats; thus the proposed credit scoring framework shall be helpful for P2P credit and lending networks, possibly for decentralized finance (Defi) applications.
Design and Implementation of Identity Verification Software Based on Deep Learning

Identity verification, a noncontact biometric identification technology, has important scientific significance in theoretical research and shows great practical value in national security, public safety, and finance. In view of this situation, this paper designs an identity verification software based on deep learning, which has been successfully applied to real-world applications. The central idea of the software can be summarized as follows: First, the lightweight multi-task cascaded convolutional network (MTCNN), which can learn correlations between face detection and alignment, is employed for face detection. The software then conducts face recognition with MobileFaceNet which is an efficient and lightweight neural network, reducing the hardware cost. The test results show that the software meets the design requirements and can complete the corresponding identity confirmation function.
Decolonization through Technology and Decolonization of Technology

Through colonialism, external forces can alter and shift social structures and practices, thus, profoundly marginalizing the collective identities of local and indigenous populations. Decolonization is the undoing of the impacts of colonial domination. While sociotechnical systems (e.g., online platforms) can support the identity work of such marginalized communities, scholars have also discussed how these systems impose values and exhibit colonial impulses. By employing a mixed method approach, I am interested in understanding how the previously colonized Bengali people decolonize their identities on online platforms; how the designs of these platforms support and impede their identity work and expression–to develop decolonial designs for postcolonial cultural contexts.
Virtual Community building for supporting women in developing and sustaining their professional identities

The establishment of one’s professional identity can be essential to one’s sense of worth. For women, such challenges can be further magnified owing to the need to perform traditional roles along with sustaining a professional career. Thus, having virtual spaces to connect with others facing similar travails can help to build community and strategies for endurance. In keeping with this goal, through a three part mixed methods investigation that employs quantitative and qualitative analysis, my dissertation work seeks to understand how virtual communities are helping or can be designed to help support women as they pursue their professional aspirations.
Measuring Gendered Communication Patterns on Enterprise Communication Platforms

Much of the communication in organizations is now taking place on enterprise communication platforms like Slack and Microsoft Teams. These platforms enable modern teamwork, but may also exacerbate discriminatory practices. As discrimination can harm team outcomes, it is essential to study the impact that these communication methods can have. As a case study, we investigate gender discrimination in Mechanical Engineering – one of the least diverse subfields. We investigate whether traditional gendered communication patterns can be found on these platforms, as these patterns can communicate gendered differences that can lead to discrimination. Studying the Slack messages sent in mechanical design teams, we find that being a minority gender (identifying as not a man) is associated with an increase in some measures of emotional and agreeable communication, although not all, and there is no significant association with assertive communication. Future work will relate these patterns to discriminatory practices.
Design and Evaluation of a Virtual Assistant for improving Awareness in Mobile Messaging

Mobile messaging suffers from the lack of effective communication cues that help people infer the availability of others, like in face-to-face communication. My research focuses on designing, implementing, and evaluating an agent that can communicate unavailability to message senders. The design encompasses three phases (1) user modeling – understanding the recipient’s state; (2) preference modeling – understanding both recipient and sender preferences; (3) learning and adaptability – adjusting self-behavior or appropriating the agent to accomplish desired behavior. My dissertation research explores what factors are important in this design and how we can empower control back to users through user-agent interaction.
"We all have skin, we all have blood, we are all human": creating discourse among different conceptualizations of Europe with an interactive art installation

This work studies the creation process of an interactive art installation. Set in a mid-sized city in Germany, it deployed crafting and making as techniques to explore and different conceptualizations of Europe through art form. Findings indicate that the interplay of haptic crafting elements and digital techniques can serve as an entry-point to otherwise abstract topics. The creation process as well as the display of the artwork as part of an exhibition in a museum was able to foster discourse about different political ideas, experiences of exclusion and discrimination, but also of hope and opportunities.
Black Innovation: Creating and Appropriating Technology to Survive and Thrive

Taking tech to task means more than pointing out the problems and injustices. My research will seek solutions and innovative ways to create and reimagine technology that will better serve historically disenfranchised communities. In addition to learning about how technology has fallen short for those communities, I also want to learn how such communities create and appropriate technology for themselves. This requires engaging with equitable design practices. I also want to incorporate Afrofuturism as a design probe into the participatory design speculation that I aspire to facilitate in order to bring out rich responses and visceral contributions from participants.
Before the Tech Bit: Observations on the Workings of a Scottish Community Fridge

In the last few years, we have seen an increased interest by communities in developing technologies, including telecommunication infrastructures. In HCI, we have seen work around food sharing and the role of technology in supporting sustainability efforts to reduce food waste. We bring these two debates together, reflecting on our experience of working with a Community Fridge to explore possibilities of technologies to support ongoing efforts. In finding our definition for community building, our contributions are twofold: (1) we must meaningfully consider community priorities in digital developments; and (2) introduce locally relevant technologies that relate to genuine priorities of grassroots organisations.
Opt-CoInfer: Optimal collaborative inference across IoT and cloud for fast and accurate CNN inference

For fast and accurate Convolutional Neural Network (CNN) inference of massive Internet of Things (IoT) data, Collaborative Inference (CI) based on partition and compression techniques needs to carefully select the collaboration scheme considering both application scenario and inference requirement. However, with emerging partition and compression techniques, it is hard to select a proper collaboration scheme due to the exponential growth of the scheme space and the time-consuming evaluation of a single scheme. Therefore, we present Opt-CoInfer to search the optimal collaboration scheme for collaborative inference (i.e., the fastest/most accurate scheme satisfying the latency/accuracy requirement in any specific scenario). Generally, Opt-CoInfer achieves the optimum by updating the local optimum and reducing the feasible set to empty in an iterative method. Specifically, in each iteration, Opt-CoInfer selects the promising scheme and updates the local optimum according to a statistical model built with the evaluated schemes, then reduces the feasible set by the local optimum and some key observations of collaboration schemes. Experiments show that Opt-CoInfer outperforms single-end and state-of-the-art CI approaches with the same inference requirements in various application scenarios (e.g., up to 3.49× faster with the same accuracy requirement and as low as 37.41% accuracy loss with the same latency requirement).
An Improved Multi-model Particle Filter Track-Before-Detect Algorithm

In the field of weak target detection and tracking, when the target maneuvers, the detection capability of the traditional Multiple-Model (MM) Particle Filter (PF) Track-Before-Detect (TBD) algorithm decreases and the calculation amount of the algorithm increases. In this paper, an improved MM-PF-TBD algorithm is proposed. By adding the Constant Acceleration (CA) model, the Coordinate Turn (CT) model with variable turning rate and the Quasi-Monte Carlo (QMC) method, the improved algorithm could achieve the detection and tracking of weak maneuvering targets. Simulation results show that the detection capacity of the improved algorithm is 15% higher than that of the traditional algorithm, and the improved algorithm has more stable performance in low Signal-to-Noise Ratio (SNR) environment.
Comparison of sparse Gabor-based methods for detection of microseismic events

Event detection and selection is a challenging and time-consuming step in microseismic data processing because signals often are embedded in noisy recordings. Automated triggering-based algorithms can detect many potential events in the data. However, their precision rates often are low, thus requiring substantial manual labor to select desired events of interest. This study investigates two time-frequency methods which combine signal enhancement with automated event detection and selection, namely, (1) the sparse Gabor transform and (2) the neighboring block thresholding. Both methods use thresholding in the time-frequency domain to increase signal enhancement, followed by an energy detection criterion, leading to improved event detection with higher precision rates. However, the neighboring block thresholding causes amplitude fidelity issues and we observe changes in the relative and maximum amplitudes of the waveforms reconstructed from the thresholded coefficients. Conversely, the sparse Gabor transform attenuates the noise significantly while preserving the signals. Thus, this time-frequency method is suitable for enhanced event detection and subsequent processing, including magnitude estimation and moment tensor inversion.
Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality

The pancreas is one of the vital organs in the human body. Early diagnosis of a disease in the pancreas is critical. In this way, the effects of pancreas diseases, especially pancreatic cancer on the person are decreased. With this purpose, artificial intelligence-assisted pancreatic cancer segmentation was performed for early diagnosis in this paper. For this aim, several state-of-the-art segmentation networks, UNet, LinkNet, SegNet, SQ-Net, DABNet, EDANet, and ESNet were used in this study. In the comparative analysis, the best segmentation performance has been achieved by SQ-Net. SQ-Net has achieved a 0.917 dice score, 0.847 IoU score, 0.920 sensitivity, 1.000 specificity, 0.914 precision, and 0.999 accuracy. Considering these results, an artificial intelligence-based decision support system was created in the study.
Image Virtual Viewpoint Generation Method under Hole Pixel Information Update

A virtual viewpoint generation method is proposed to address the problem of low fidelity in the generation of virtual viewpoints for images with overlapping pixel points. Virtual viewpoint generation factors such as overlaps, holes, cracks, and artifacts are analyzed and preprocessed. When the background of the hole is a simple texture, pheromone information around the hole is used as the support, a pixel at the edge of the hole is detected, and the hole is predicted at the same time, so that the hole area is filled in blocks. When the hole background has a relatively complex texture, the depth information of the hole pixels is updated with the inverse 3D transformation method, and the updated area pheromone is projected onto the auxiliary plane and compared with the known plane pixel auxiliary parameters. The hole filling is performed according to the symmetry of the pixel position of the auxiliary reference viewpoint plane to obtain the virtual viewpoint after optimization. The proposed method was validated using image quality metrics and objective evaluation metrics such as PSNR. The experimental results show that the proposed method could generate virtual viewpoints with high fidelity, excellent quality, and a short image-processing time, which effectively enhanced the virtual viewpoint generation performance.
Using AI Motion Capture Systems to Capture Race Walking Technology at a Race Scene: A Comparative Experiment

Background: This study tested the reliability of the 3D coordinates of human joint points obtained by using an AI motion capture system at a race walking scene. Methods: Using a direct linear transformation (DLT) 3D video recording method, 15 race walking athletes were photographed. We compared the average values, standard deviations, and 95% confidence intervals of the multiple correlation coefficients and differences in the 3D coordinate–time curve of the human joint points that were automatically parsed by the AI motion capture system with those that were manually parsed. Results: Except for the left shoulder y coordinates, left hip y and z coordinates, and left toe tip z coordinates, the multiple correlation coefficients between the curve obtained via the automatic analysis and the average curve obtained via the manual analysis of the other coordinates were greater than 0.90, while the difference between the curve obtained via the automatic analysis and the curve obtained via the manual analysis of the left hand, the left wrist, the left hip, and the left toe was less than 0.025 m. Conclusion: The 3D coordinates of the human joint points obtained via the AI motion capture system were highly similar to the average value of the 3D coordinates obtained via the manual analysis, supporting the use of the AI motion capture system as a highly reliable means to capture the technical motion of race walking in the race walking competition context.
Problems and Prospects for the Art Education Development in Higher Educational Institutions Based on Big Data Technologies and Digital Platforms

In order to build an effective system of providing educational services in the art direction, it is necessary to possess an assessment of the results of Big Data initial implementation. Previously conducted studies on the specifics of implementing digital platforms in the educational art space are incomplete and insufficient. Most universities of different countries introduce these systems independently, and, therefore, there are no methods and principles for implementing such measures in the educational process. The purpose of the present research is to assess the role of Big Data and digital platforms in improving the quality and efficiency of art education. An analysis of assessing the implementation and functioning of digital systems in the leading higher educational institutions of the art direction in Poland, the Czech Republic, and Ukraine was carried out. This made it possible to generalize the experience gained and identify the main trends of this process. This made it possible to generalize the experience gained and identify the main trends of this process. In particular, 70% of students have a positive attitude towards using digital platforms that allow them to expand their awareness and informativeness, and 73% note these platforms as a source of obtaining available information. This made it possible to generalize the experience gained and identify the main tendencies of this process. As a result, modelling of the concept of using Big Data in higher education in the art direction has been presented. The main methods and examples of using Big Data in art education have been defined and characterized. Prospective directions for further application and implementation of innovative digital systems have been indicated. The use of research results creates opportunities for more flexible expansion of existing digital systems and the formation of new directions for subsequent implementation in the educational process. The research pointed to the most significant problem of comprehensive using Big Data by students due to ignorance and lack of awareness of the potential of Big Data in terms of planning, forecasting behavioral actions both in the process of learning, and in future professional activities. Further development of the research topic should focus on the quantitative and qualitative assessment of existing systems and the formation of a detailed methodology for the introduction of the latest educational services in the art education system.
FAIME: A Framework for AI-Assisted Musical Devices

In this paper, we present a novel framework for the study and design of AI-assisted musical devices (AIMEs). Initially, we present taxonomy of these devices and illustrate it with a set of scenarios and personas. Later, we propose a generic architecture for the implementation of AIMEs and present some examples from the scenarios. We show that the proposed framework and architecture are a valid tool for the study of intelligent musical devices.
Remote Sensing Image Colorization Based on Joint Stream Deep Convolutional Generative Adversarial Networks

With the development of deep neural networks, especially generation networks, gray image coloring technology has made great progress. As one of the fields, remote sensing image colorization needs to be solved urgently. This is because remote sensing images cannot obtain clear color images due to the limitations of shooting equipment and transmission equipment. Compared with ordinary images, remote sensing images are characterized by the uneven spatial distribution of objects, therefore, it is a great challenge to ensure the spatial consistency of coloring. To embrace this challenge, we propose a new joint stream DCGAN including a micro stream and a macro stream, in which the latter is set as a prior to constrain the former for colorization. In addition, the Low-level Correlation Feature Extraction (LCFE) module is proposed to obtain the salient shallow detail feature with global correlation, which is used to enhance the global constraints as well as supplement the low-level information to the micro stream. What's more, we propose the Gated Selection (GSM) module by selecting useful information using a gated scheme to fuse features from two streams appropriately. Comprehensive comparison and ablation experiments are implemented and verify the proposed method performs surpasses other methods in both qualitative and quantitative metrics.
Action Detection System Based on Pose Information

This paper introduces an action detection system based on pose information. The system utilizes view-invariant pose feature not relying on machine learning techniques, and it can detect human actions regardless of camera settings thus it is easy to apply for any target actions. System users only need to register sample images including target actions beforehand. In detection phase, the system receives an image from live camera at short intervals and computes the similarity between captured image and each pre-registered sample image. If the similarity is higher than the specified threshold, the system judges the target action is detected. We evaluated the detection performance of a common action "phone-call" using cellphone and confirmed its effectiveness.
CMR3D

Existing deep learning-based 3D object detectors typically rely on the appearance of individual objects and do not explicitly pay attention to the rich contextual information of the scene. In this work, we propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework, which takes a 3D scene as an input and strives to explicitly integrate useful contextual information of the scene at multiple levels to predict a set of object bounding-boxes along with their corresponding semantic labels. To this end, we propose to utilize a context enhancement network that captures the contextual information at different levels of granularity followed by a multi-stage refinement module to progressively refine the box positions and class predictions. Extensive experiments on the large-scale ScanNetV2 benchmark reveals the benefits of our proposed method, leading to an absolute improvement of 2.0% over the baseline. In addition to 3D object detection, we investigate the effectiveness of our CMR3D framework for the problem of 3D object counting. Our source code is available at https://github.com/Dhanalaxmi17/CMR3D.
Possibilistic simulation of quantum circuits by classical circuits

In breakthrough work, Bravyi, Gosset, and K\"{o}nig (BGK) [Science, 2018] unconditionally proved that constant depth quantum circuits are more powerful than their classical counterparts. Their result is equivalent to saying that a particular family of constant depth quantum circuits takes classical circuits at least $\Omega(\log n)$ depth to "simulate", in a certain sense. In our paper, we formalise their sense of simulation, which we call "possibilistic simulation" or "p-simulation", and construct explicit classical circuits that can p-simulate any depth-$d$ quantum circuit with Clifford and $t$ $T$-gates in depth $O(d+t)$. Our classical circuits use $\{\text{NOT, AND, OR}\}$ gates of fan-in $\leq 2$.