Reviewerly/exHarmony huggingface dataset:
A Deep Learning Approach for Diagnosis and Detection of Acute lymphocytic leukemia

ALL is a fast-growing condition that can impact the bone marrow, blood, and crucial organs within the body. It is a rapidly progressive disorder that requires prompt medical attention and treatment. Timely and accurate diagnosis is crucial to determine appropriate treatment. However, the manual diagnosis by hematologists can be time-consuming and susceptible to errors and inconsistencies. Deep learning, particularly convolutional neural network (CNN/ConvNet) technology, is used to address various challenges in detecting and diagnosing ALL. This advanced technology is capable of accurately analyzing large amounts of data and identifying subtle differences in the genetic and cellular makeup of leukemia cells, has shown promise in medical diagnosis, particularly in visual image analysis. The fast and accurate feature extraction function and trainable network architecture of CNNs make them suitable for this application. In this study, we propose an effective deep framework for CNN networks that enables the rapid and early detection and classification of leukemia cells from microscopic blood smear images. In order to classify cells as either blast cells (ALL) or healthy cells (HEM), the problem was framed as a binary classification challenge. Each individual cell was assessed and classified using this approach. In this study, various CNN-based architectures were evaluated using transfer learning. Specifically, we investigated the performance of VGG16, VGG19, ResNet60, ResNet101, and DensNet169 models. The results indicate that the DensNet169 model outperformed the other models with an accuracy score of 97.62% . These findings demonstrate the potential of transfer learning and the superiority of the DensNet169 model for the accurate and efficient classification of leukemia cells in microscopic blood smear images.
Multiple Moving Objects Using Deep Learning for Trajectory Extraction and Clustering

Multiple Object Tracking (MOT) is a key challenge in computer vision. It is simply the process of tracking multiple objects within the frames of an input video. This technique has applications in many fields like medical research and video surveillance [1], as it can be used to test drug effects in medical research and to identify potentially dangerous behavior through video surveillance. This paper evaluates the tracking ability of You Only Look Once (YOLO) model in two case studies. It also compares the performance of k-means and density-based clustering algorithms (DBSCAN) in these studies. We extract the trajectories of the subjects and analyze their behavior based on their common characteristics of movement in two case studies. The first case study focused on the Cell Tracking Challenge dataset, while the second used the Multiple Subject Tracking (MOT) dataset. We used a deep learning model You Only Look Once (YOLO) for object tracking in two case studies. YOLO uses a fully convolutional neural network (CNN) to process images efficiently. The input to YOLO model passes on its network only once making it a fast model, unlike two-stage models like RCNN, which need the input images to pass the network two times for determining the regions of interest and object detection in different stages. For understanding the behavior of the moving objects, we clustered the tracked objects based on their common behavior. The clustering algorithms were evaluated using silhouette score and Davies-Bouldin Index (DBI) in two case studies. In the first case, k-means achieved a silhouette score of 0.590236 with four clusters and a DBI score of 0.538835 with nine clusters. In the second case, we obtained a silhouette score of 0.658 with two clusters and a DBI score of 0.458 with two clusters. As for DBSCAN algorithm, it got a silhouette score of -0.179 in the first case and -0.208 in the second case. These results indicate that k-means outperformed DBSCAN in these case studies.
A Framework for Human Activity Recognition Application for Therapeutic Purposes

The study of Human Activity Recognition (HAR) involves identifying and categorizing human activities through movements detected by wearable devices, sensors, or videos. HAR has been adopted in various domains, including physical therapy, where patients’ biomechanical abilities are monitored. This study proposes a framework for a human activity recognition-based solution to automate parts of the biomechanical assessment and monitor patients’ activities to assist physiotherapists and provides insight into metrics describing the progression of treatment. The proposed framework uses a visual programming interface to enable physical therapists to describe their specific data set and intended outputs using an intuitive graphical interface. An experiment was conducted to evaluate the performance of common recurrent deep learning architectures used in HAR, in addition to the investigation into the impact of a convolutional layer on their performance. Results indicate that CNN-LSTM is capable of 99.2% accuracy on the MHealth data set, indicating that it is an effective architecture for accurately classifying human activities.
Breaking classification Algorithms: A Black-Box Adversarial Attack for Data Poisoning

Poisoning attacks interfere with machine learning classifiers' performance by introducing malicious data into the learning dataset. In this paper, we propose a black-box adversarial attack for data poisoning in machine learning, where the attacker has no knowledge of the classifier's internal parameters or training data. Our attack utilizes a novel poison threat algorithm that leverages the gradient descent method to generate adversarial examples that deceive the classifier. We evaluate our attack on two popular classification algorithms, decision trees and support vector machines, using the random circles dataset. According to our results, the proposed attack has the ability to compromise the performance of both classifiers, highlighting the need for robust countermeasures against poisoning attacks.
Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines

Recent advances in Graph Neural Networks (GNNs) have changed the landscape of modern graph analytics. The complexity of GNN training and the scalability challenges have also sparked interest from the systems community, with efforts to build systems that provide higher efficiency and schemes to reduce costs. However, we observe that many such systems basically "reinvent the wheel" of much work done in the database world on scalable graph analytics engines. Further, they often tightly couple the scalability treatments of graph data processing with that of GNN training, resulting in entangled complex problems and systems that often do not scale well on one of those axes. In this paper, we ask a fundamental question: How far can we push existing systems for scalable graph analytics and deep learning (DL) instead of building custom GNN systems? Are compromises inevitable on scalability and/or runtimes? We propose Lotan, the first scalable and optimized data system for full-batch GNN training with decoupled scaling that bridges the hitherto siloed worlds of graph analytics systems and DL systems. Lotan offers a series of technical innovations, including re-imagining GNN training as query plan-like dataflows, execution plan rewriting, optimized data movement between systems, a GNN-centric graph partitioning scheme, and the first known GNN model batching scheme. We prototyped Lotan on top of GraphX and PyTorch. An empirical evaluation using several real-world benchmark GNN workloads reveals a promising nuanced picture: Lotan significantly surpasses the scalability of state-of-the-art custom GNN systems, while often matching or being only slightly behind on time-to-accuracy metrics in some cases. We also show the impact of our system optimizations. Overall, our work shows that the GNN world can indeed benefit from building on top of scalable graph analytics engines. Lotan's new level of scalability can also empower new ML-oriented research on ever-larger graphs and GNNs.
POEM: Pattern-Oriented Explanations of Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are commonly used in computer vision. However, their predictions are difficult to explain, as is the case with many deep learning models. To address this problem, we present POEM, a modular framework that produces patterns of semantic concepts such as shapes and colours to explain image classifier CNNs. POEM identifies patterns such as "if sofa then living room", meaning that if an image contains a sofa and the model pays attention to the sofa, then the model classifies the image as a living room. We illustrate the advantages of POEM over existing work using quantitative and qualitative experiments.
REmatch: A Novel Regex Engine for Finding All Matches

In this paper, we present the REmatch system for information extraction. REmatch is based on a recently proposed enumeration algorithm for evaluating regular expressions with capture variables supporting the all-match semantics. It tells a story of what it takes to make a theoretically optimal algorithm work in practice. As we show here, a naive implementation of the original algorithm would have a hard time dealing with realistic workloads. We thus develop a new algorithm and a series of optimizations that make REmatch as fast or faster than many popular RegEx engines while at the same time being able to return all the outputs: a task that most other engines tend to struggle with.
Tigger: A Database Proxy That Bounces with User-Bypass

Developers often deploy database-specific network proxies whereby applications connect transparently to the proxy instead of directly connecting to the database management system (DBMS). This indirection improves system performance through connection pooling, load balancing, and other DBMS-specific optimizations. Instead of simply forwarding packets, these proxies implement DBMS protocol logic (i.e., at the application layer) to achieve this behavior. Consequently, existing proxies are user-space applications that process requests as they arrive on network sockets and forward them to the appropriate destinations. This approach incurs inefficiencies as the kernel repeatedly copies buffers between user-space and kernel-space, and the associated system calls add CPU overhead. This paper presents user-bypass, a technique to eliminate these overheads by leveraging modern operating system features that support custom code execution. User-bypass pushes application logic into kernel-space via Linux's eBPF infrastructure. To demonstrate its benefits, we implemented Tigger, a PostgreSQL-compatible DBMS proxy using user-bypass to eliminate the overheads of traditional proxy design. We compare Tigger's performance against other state-of-the-art proxies widely used in real-world deployments. Our experiments show that Tigger outperforms other proxies --- in one scenario achieving both the lowest transaction latencies (up to 29% reduction) and lowest CPU utilization (up to 42% reduction). The results show that user-bypass implementations like Tigger are well-suited to DBMS proxies' unique requirements.
Federated Calibration and Evaluation of Binary Classifiers

We address two major obstacles to practical deployment of AI-based models on distributed private data. Whether a model was trained by a federation of cooperating clients or trained centrally, (1) the output scores must be calibrated, and (2) performance metrics must be evaluated --- all without assembling labels in one place. In particular, we show how to perform calibration and compute the standard metrics of precision, recall, accuracy and ROC-AUC in the federated setting under three privacy models ( i ) secure aggregation, ( ii ) distributed differential privacy, ( iii ) local differential privacy. Our theorems and experiments clarify tradeoffs between privacy, accuracy, and data efficiency. They also help decide if a given application has sufficient data to support federated calibration and evaluation.
Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy

Differential privacy (DP) data synthesizers are increasingly proposed to afford public release of sensitive information, offering theoretical guarantees for privacy (and, in some cases, utility), but limited empirical evidence of utility in practical settings. Utility is typically measured as the error on representative proxy tasks, such as descriptive statistics, multivariate correlations, the accuracy of trained classifiers, or performance over a query workload. The ability for these results to generalize to practitioners' experience has been questioned in a number of settings, including the U.S. Census. In this paper, we propose an evaluation methodology for synthetic data that avoids assumptions about the representativeness of proxy tasks, instead measuring the likelihood that published conclusions would change had the authors used synthetic data, a condition we call epistemic parity. Our methodology consists of reproducing empirical conclusions of peer-reviewed papers on real, publicly available data, then re-running these experiments a second time on DP synthetic data and comparing the results. We instantiate our methodology over a benchmark of recent peer-reviewed papers that analyze public datasets in the ICPSR social science repository. We model quantitative claims computationally to automate the experimental workflow, and model qualitative claims by reproducing visualizations and comparing the results manually. We then generate DP synthetic datasets using multiple state-of-the-art mechanisms, and estimate the likelihood that these conclusions will hold. We find that, for reasonable privacy regimes, state-of-the-art DP synthesizers are able to achieve high epistemic parity for several papers in our benchmark. However, some papers, and particularly some specific findings, are difficult to reproduce for any of the synthesizers. Given these results, we advocate for a new class of mechanisms that can reorder the priorities for DP data synthesis: favor stronger guarantees for utility (as measured by epistemic parity) and offer privacy protection with a focus on application-specific threat models and risk-assessment.
Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets

The increasing demand for data-driven machine learning (ML) models has led to the emergence of model markets, where a broker collects personal data from data owners to produce high-usability ML models. To incentivize data owners to share their data, the broker needs to price data appropriately while protecting their privacy. For equitable data valuation , which is crucial in data pricing, Shapley value has become the most prevalent technique because it satisfies all four desirable properties in fairness: balance, symmetry, zero element, and additivity. For the right to be forgotten , which is stipulated by many data privacy protection laws to allow data owners to unlearn their data from trained models, the sharded structure in ML model training has become a de facto standard to reduce the cost of future unlearning by avoiding retraining the entire model from scratch. In this paper, we explore how the sharded structure for the right to be forgotten affects Shapley value for equitable data valuation in model markets. To adapt Shapley value for the sharded structure, we propose S-Shapley value, a sharded structure-based Shapley value, which satisfies four desirable properties for data valuation. Since we prove that computing S-Shapley value is #P-complete, two sampling-based methods are developed to approximate S-Shapley value. Furthermore, to efficiently update valuation results after data owners unlearn their data, we present two delta-based algorithms that estimate the change of data value instead of the data value itself. Experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
Consistent Range Approximation for Fair Predictive Modeling

This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.
Mode Division Multiplexing in Silicon Photonics for High-Speed Communication, Computation, and Quantum Information

In this presentation, we discuss challenges of calibrating and programming MZI-based programmable optical processors. We propose a novel architecture that tackles these issues, delivering faster and more energy-efficient programming while maintaining key performance metrics such as minimum insertion loss and resilience to fabrication imperfections.
A Case Study Using Augmented Reality-based Scratch Games in English Learning for Preschool Children

To address the need for more engaging and effective English education, this study compared the effects of three English learning approaches for preschool children: (1) traditional English education, (2) an Augmented Reality (AR) tool built using Scratch, and (3) an iPad-based approach. Teachers created innovative and interactive scenarios under three conditions to teach vocabulary and pronunciation. Thirty preschool children were randomly assigned to the traditional, AR, and iPad groups. Our analysis showed that using the AR tool and iPad approach can improve both students' learning motivation and knowledge acquisition. At the same time, the visualized content is easily remembered, which made learning materials easily accepted by preschool children. Furthermore, using an Augmented Reality approach can provide a more relaxed learning environment, which has the potential to stimulate enthusiasm in pre-school children's English learning.
Research on the Logical Path and Promotion Strategy of Digital Governance in Chinese Higher Education

The modernization of higher education governance is an essential component of the modernization of the national governance system and governance capabilities. Digitalization, as a significant engine driving the modernization of national governance, raises questions on how to achieve integration with higher education governance, construct governance models and organizational forms based on information flow and exchange, improve traditional experience-driven higher education governance methods, enhance governance efficiency, and maximize the driving value of digital technology for the modernization of higher education governance. This study explores the logical path of digital governance in Chinese higher education from the perspectives of historical evolution, developmental drivers, logical premises, and value commitments. It identifies the various elements involved in higher education digital governance and categorizes them into macro, meso, and micro levels, integrating them into the TOE framework and discussing the current predicament and strategies for digital governance in Chinese higher education.
Collaborative TV Control: Towards Co-experience and Social Connectedness

Consuming media together is enjoyable and lets people connect with one another. However, the control of a TV, for instance, is still restricted to one user. This limits individual participation in group decision-making, particularly in the home context which can cause frustration and impact social interaction negatively. In this paper, we explore how we can support users in the living room to collaboratively interact with the TV in order to enhance social interaction and support the selection of a movie everyone is up for watching. Therefore, we investigate three collaborative approaches to movie selection: Consensual, Hierarchical, and Autocratic. We empirically validated the approaches’ effect on perceived co-experience, social connectedness, and team performance as indicators for social interaction. Therefore, we conducted a mixed-subject experiment (N = 30, 10 groups) inside a living room in groups of three choosing a movie together. Results show that the collaborative approach to movie selection has an influence on collaboration and social interaction. Promoting consensual decision-making or enabling users to provide movie recommendations under the hierarchical approach empowers individuals, supports involvement, and is perceived as fair while also enabling a higher chance of picking a movie everyone enjoys watching. Letting only one user interact with the TV, stimulates communication, yet, it enhances the chance of excluding group members. Founded on these insights, we discuss suggestions for the future design of collaborative, interactive media systems toward co-experience and social connectedness.
Point- and Volume-Based Multi-object Acquisition in VR

Multi-object acquisition is indispensable for many VR applications. Commonly, users select a group of objects of interest to perform further transformation or analysis. In this paper, we present three multi-object selection techniques that were derived based on a two-dimensional design space. The primary design dimension concerns whether a technique acquires targets through point-based methods (selecting one object at a time) or volume-based methods (selecting a set of objects within a selection volume). The secondary design dimension examines the mechanisms of selection and deselection (cancel the selection of unwanted objects). We compared these techniques through a user study, emphasizing on scenarios with more randomly distributed objects. We discovered, for example, that the point-based technique was more efficient and robust than the volume-based techniques in environments where the targets did not follow a specific layout. We also found that users applied the deselection mechanism mostly for error correction. We provide an in-depth discussion of our findings and further distill design implications for future applications that leverage multi-object acquisition techniques in VR.
Spatial Augmented Reality in the Factory: Can In-Situ Projections Be Used to Communicate Dangers and Health Risks?

In the context of industrial settings, extensive research of in-situ projections has proven their benefits for task performance. However, to date, these projections have not explicitly addressed policies designed to mitigate the dangers and health risks that are just as important, if not more than task performance considerations in such settings. We developed in-situ projections for three different use cases: (1) assembly support at a workbench, (2) ergonomic lifting, (3) restricted areas, which we studied with 15 representative target users. We found the expected benefits of the task-supporting projection (use case 1), increasing task performance and causing minimal cognitive load. However, our data also suggest that the other projections (use case 2 and 3) did not improve policy compliance. Our findings indicate that in-situ projections are not the most suitable solution to nudge workers to policy compliance in an industrial assembly setting, as most participants ignored the policy after evaluating the dangers themselves. Furthermore, based on our limitations and findings, we reflect on how current study practices can be improved for ubiquitous systems, especially when aiding policy compliance.