Reviewerly/exHarmony huggingface dataset:
Channel Spatial Collaborative Attention Network for Fine-Grained Classification of Cervical Cells

Accurately classifying cervical cells based on the commonly used TBS (The Bethesda System) standard is critical for building the automatic cytology diagnosing system. However, the existing two publicly available datasets (Herlev and SIPaKMeD) do not provide the TBS labels for the cervical cells, leading to the lack of practicability of the models trained based on them. In addition, most deep learning-based methods for cervical cell classification do not explore the attention spots of the model, resulting in the limit of further focusing on discriminative parts of tiny cells. In this paper, we first build a cervical cell image dataset consisting of 10,294 cell image patches with TBS labels from 238 specimens, named LTCDD (Landing TBS Cervical Cell Dataset). Then we propose a novel visual attention module, channel spatial collaborative attention (CSCA), based on the integration of the channel and spatial attention. Two kinds of comparing experiments on LTCDD and two public datasets show that the CSCA embedded network can achieve the best classification performance. Moreover, the CSCA module performs better than other attention modules when integrated into ResNet. The visualization results illustrate that CSCA embedded networks can pay more attention to the nucleus part of cell images so as to focus on learning discriminative features that are beneficial to the classification of the cells.
Approximate k-Nearest Neighbor Query over Spatial Data Federation

Approximate nearest neighbor query is a fundamental spatial query widely applied in many real-world applications. In the big data era, there is an increasing demand to scale these queries over a spatial data federation, which consists of multiple data owners, each holding a private, disjoint partition of the entire spatial dataset. However, it is non-trivial to enable approximate k-nearest neighbor query over a spatial data federation. This is because stringent security constraints are often imposed to protect the sensitive, privately owned data partitions, whereas naively extending prior secure query processing solutions leads to high inefficiency (e.g., 100 s per query). In this paper, we propose two novel algorithms for efficient and secure approximate k-nearest neighbor query over a spatial data federation. We theoretically analyze their communication cost and time complexity, and further prove their security guarantees and approximation bounds. Extensive experiments show that our algorithms outperform the state-of-the-art solutions with respect to the query efficiency and often yield a higher accuracy.
KEMAMPUAN PENALARAN MATEMATIS: SYSTEMATIC LITERATURE REVIEW

Mathematical reasoning ability is one of the important learning objectives of mathematics to be achieved by students at school.This study aims to analyze qualitative studies on mathematical reasoning abilities conducted in the period 2015-2021.The method used is a Systematic Literature Review (SLR) on all qualitative research articles indexed in Google Scholar, Semantic and ERIC.The search strategy was adapted to the selection criteria and used the PRISMA protocol as a guide for the research instrument.The variables involved in this study are the year of publication, education level, research class, demographics, journal indexer, material analyzed, and type of reasoning ability used.All data obtained are presented in a quantitative descriptive.The results of the SLR research show that studies related to students' mathematical reasoning abilities are widely published in 2020 and 2021.The majority of these studies are carried out at the junior high school level in grade VIII.The study was also dominated in Java & Bali on geometry and algebra materials.Suggestions for future educators or researchers to do more research on mathematical reasoning skills from the elementary school level, in areas outside Java & Bali, on materials other than algebra and geometry as well as research related aspects of proof.
"We are half-doctors": Family Caregivers as Boundary Actors in Chronic Disease Management

Computer-Supported Cooperative Work (CSCW) and Human--Computer Interaction (HCI) research is increasingly investigating the roles of caregivers as ancillary stakeholders in patient-centered care. Our research extends this body of work to identify caregivers as key decision-makers and boundary actors in mobilizing and managing care. We draw on qualitative data collected via 20 semi-structured interviews to examine caregiving responsibilities in physical and remote care interactions within households in urban India. Our findings demonstrate the crucial intermediating roles family caregivers take on while situated along the boundaries separating healthcare professionals, patients and other household members, and online/offline communities. We propose design recommendations for supporting caregivers in intermediating patient-centered care, such as through training content and expert feedback mechanisms for remote care, collaborative tracking mechanisms integrating patient- and caregiver-generated health data, and caregiving-centered online health communities. We conclude by arguing for recognizing caregivers as critical stakeholders in patient-centered care who might constitute technologically assisted pathways to care.
Privileged information learning with weak labels

Privileged information learning is proposed to construct the classifier by incorporating privileged knowledge. At present, most of the privileged information learning methods assume that the instance is accurately labeled. However, in real-world applications, an instance may be weakly labeled. In this paper, we propose a novel privileged information learning method with weak labels (PLWB). The hypothesis of our work is that an instance may be annotated by a number of labelers and different labelers may give different labels to this instance due to distinct professional knowledge and subjective factors. It leads to ambiguous labels of instances, namely weak labels. To solve this problem, our methodology is to give each labeler a weight and incorporate these weights into a privileged information learning model. Our technique is to employ a heuristic framework to optimize the labeler weights and the privileged information learning model jointly. The existing privileged information learning methods do not consider the weak label problem, and assign an equal or random weight to each labeler. Our work is different from these methods. The novelty and theoretical contribution is that this is the first work to deal with the weak label problem in privileged information learning. The merit is that we assign an unknown weight to each labeler and solve the optimal values of these weights in the optimization process, such that the performance of the learning model can be improved with the optimal labeler weights. In the experiments, the tool that we use is MATLAB, in which we implement our algorithm. The experimental datasets include one handwritten categorization dataset, two image classification datasets (i.e., Animals-with-Attributes dataset and Caltech-101 dataset), and one disease diagnosis dataset (i.e., Alzheimer’s Disease Neuroimaging Initiative dataset), in which the number of instances used is 2000, 6180, 8677 and 202, respectively. The obtained results are that: (1) by optimizing the labeler weights, the proposed PLWB method obtains explicitly higher classification accuracy than the existing privileged information learning methods; (2) PLWB has relatively higher training time since it needs to solve the labeler weights in the optimization process.
Improvement of Dynamic Window Approach in Dynamic Obstacle Environment

Abstract The dynamic window approach (DWA) controls the movement of the robot by searching the speed space and is widely used in the path planning of mobile robots. However, in the dynamic obstacle environment, when searching for the optimal speed, it is easy to fall into the local optimal solution, which leads to the failure of path planning. In this paper, we improve DWA. According to the speed of the obstacle, the safe distance of the obstacle is dynamically changed to improve the obstacle distance evaluation item of the DWA. A new evaluation item is introduced into the speed evaluation function to optimize the search rules of the DWA in the speed space. The experimental results show that in the dynamic obstacle environment, this method can ensure the robot reaches the target point smoothly and shorten the time spent in the whole process by improving the speed evaluation function.
Full Tunability and Quantum Coherent Dynamics of a Driven Multilevel System

Tunability of an artificial quantum system is crucial to its capability to process quantum information. However, tunability usually poses significant demand on the design and fabrication of a device. In this work, we demonstrate that Floquet engineering based on longitudinal driving provides distinct possibilities in enhancing the tunability of a quantum system without needing additional resources. In particular, we study a multilevel model based on gate-defined double quantum dots, where coherent interference occurs when the system is driven longitudinally. We develop an effective model to describe the driven dynamics of this multilevel system, and show that it is highly tunable via the driving field. We then illustrate the versatility and rich physics of a driven multilevel system by exploring phenomena such as driving modulation of resonances, adiabatic state transfer, and dark state. In the context of qubit control, we propose noise-resistant quantum gates based on adiabatic passage. The theoretical consideration we present here is rather general, and is in principle valid for other multilevel quantum systems.
Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank

Temporal graph neural networks (T-GNNs) are state-of-the-art methods for learning representations over dynamic graphs. Despite the superior performance, T-GNNs still suffer from high computational complexity caused by the tedious recursive temporal message passing scheme, which hinders their applicability to large dynamic graphs. To address the problem, we build the theoretical connection between the temporal message passing scheme adopted by T-GNNs and the temporal random walk process on dynamic graphs. Our theoretical analysis indicates that it would be possible to select a few influential temporal neighbors to compute a target node's representation without compromising the predictive performance. Based on this finding, we propose to utilize T-PPR, a parameterized metric for estimating the influence score of nodes on evolving graphs. We further develop an efficient single-scan algorithm to answer the top- k T-PPR query with rigorous approximation guarantees. Finally, we present Zebra, a scalable framework that accelerates the computation of T-GNN by directly aggregating the features of the most prominent temporal neighbors returned by the top- k T-PPR query. Extensive experiments have validated that Zebra can be up to two orders of magnitude faster than the state-of-the-art T-GNNs while attaining better performance.
An Empirical Study on Anomaly Detection Using Density-based and Representative-based Clustering Algorithms

In data mining, and statistics, anomaly detection is the process of finding data patterns (outcomes, values, or observations) that deviate from the rest of the other observations or outcomes. Anomaly detection is heavily used in solving real-world problems in many application domains, like medicine, finance , cybersecurity, banking, networking, transportation, and military surveillance for enemy activities, but not limited to only these fields. In this paper, we present an empirical study on unsupervised anomaly detection techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), (DBSCAN++) (with uniform initialization, k-center initialization, uniform with approximate neighbor initialization, and $k$-center with approximate neighbor initialization), and $k$-means$--$ algorithms on six benchmark imbalanced data sets. Findings from our in-depth empirical study show that k-means-- is more robust than DBSCAN, and DBSCAN++, in terms of the different evaluation measures (F1-score, False alarm rate, Adjusted rand index, and Jaccard coefficient), and running time. We also observe that DBSCAN performs very well on data sets with fewer number of data points. Moreover, the results indicate that the choice of clustering algorithm can significantly impact the performance of anomaly detection and that the performance of different algorithms varies depending on the characteristics of the data. Overall, this study provides insights into the strengths and limitations of different clustering algorithms for anomaly detection and can help guide the selection of appropriate algorithms for specific applications.
Shifting from Surveillance-as-Safety to Safety-through-Noticing: A Photovoice Study with Eastside Detroit Residents

Safety has been used to justify the expansion of today's large-scale surveillance infrastructures in American cities. Our work offers empirical and theoretical groundings on why and how the safety-surveillance conflation that reproduces harm toward communities of color must be denaturalized. In a photovoice study conducted in collaboration with a Detroit community organization and a university team, we invited 11 Black mid-aged and senior Detroiters to use photography to capture their lived experiences of navigating personal and community safety. Their photographic narratives unveil acts of "everyday noticing" in negotiating and maintaining their intricate and interdependent relations with human, non-human animals, plants, spaces, and material things, through which a multiplicity of meaning and senses of safety are produced and achieved. Everyday noticing, as simultaneously a survival skill and a more-than-human care act, is situated in residents' lived materialities, while also serving as a site for critiquing the reductive and exclusionary vision embedded in large-scale surveillance infrastructures. By proposing an epistemological shift from surveillance-as-safety to safety-through-noticing, we invite future HCI work to attend to the fluid and relational forms of safety that emerge from local entanglement and sensibilities.
An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.
Ubi Edge: Authoring Edge-Based Opportunistic Tangible User Interfaces in Augmented Reality

Edges are one of the most ubiquitous geometric features of physical objects. They provide accurate haptic feedback and easy-to-track features for camera systems, making them an ideal basis for Tangible User Interfaces (TUI) in Augmented Reality (AR). We introduce Ubi Edge, an AR authoring tool that allows end-users to customize edges on daily objects as TUI inputs to control varied digital functions. We develop an integrated AR-device and an integrated vision-based detection pipeline that can track 3D edges and detect the touch interaction between fingers and edges. Leveraging the spatial-awareness of AR, users can simply select an edge by sliding fingers along it and then make the edge interactive by connecting it to various digital functions. We demonstrate four use cases including multi-function controllers, smart homes, games, and TUI-based tutorials. We also evaluated and proved our system's usability through a two-session user study, where qualitative and quantitative results are positive.
A Comparison between YOLOV7 and YOLOV5-based detection of Combat Targets and Camouflaged Militia

Animals frequently use the camouflage method to disguise themselves. On the battlefield, it is also employed to cover military equipment. Camouflaged objects blend into their environment by adopting colors and textures that match their surroundings. Because of its size and extreme likeness to its surroundings, a camouflaged object cannot be recognized using a general detection approach, making its detection more challenging than that of a general object. This work analyzes the available approaches to address the issue and proposes a camouflage object identification algorithm based on the YOLOv7 algorithm. The self-created military camouflage target data set was used for training and testing, and compared with YOLOv5 with multiple data sets.
Boosting the performance of loss-tolerant measurement-device-independent quantum key distribution

Measurement-device-independent quantum key distribution can remove all possible detector side channels, and is robust against state preparation flaws when further combined with the loss-tolerant method. However, the secure key rate in this scenario is relatively low, thus hindering its practical application. Here, we first present a four-intensity decoy-state protocol where the signal intensity is modulated only in Z basis for key generation while the decoy intensities are modulated in both Z and X bases for parameter estimation. Moreover, we adopt collective constraint and joint-study strategy in statistical fluctuation analysis. We have also experimentally demonstrated this protocol and the result indicates high performance and good security for practical applications.
Federated Multi-Organ Segmentation With Inconsistent Labels

Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners, which helps address the concern of data privacy in medical image analysis. However, the requirement for label consistency across clients by the existing methods largely narrows its application scope. In practice, each clinical site may only annotate certain organs of interest with partial or no overlap with other sites. Incorporating such partially labeled data into a unified federation is an unexplored problem with clinical significance and urgency. This work tackles the challenge by using a novel federated multi-encoding U-Net (Fed-MENU) method for multi-organ segmentation. In our method, a multi-encoding U-Net (MENU-Net) is proposed to extract organ-specific features through different encoding sub-networks. Each sub-network can be seen as an expert of a specific organ and trained for that client. Moreover, to encourage the organ-specific features extracted by different sub-networks to be informative and distinctive, we regularize the training of the MENU-Net by designing an auxiliary generic decoder (AGD). Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods. Source code is publicly available at https://github.com/DIAL-RPI/Fed-MENU.
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification

Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or treat the header and payload equally, failing to mine the potential correlation between bytes. Therefore, in this paper, we propose a byte-level traffic graph construction approach based on point-wise mutual information (PMI), and a model named Temporal Fusion Encoder using Graph Neural Networks (TFE-GNN) for feature extraction. In particular, we design a dual embedding layer, a GNN-based traffic graph encoder as well as a cross-gated feature fusion mechanism, which can first embed the header and payload bytes separately and then fuses them together to obtain a stronger feature representation. The experimental results on two real datasets demonstrate that TFE-GNN outperforms multiple state-of-the-art methods in fine-grained encrypted traffic classification tasks.
Visual-Aware Testing and Debugging for Web Performance Optimization

Web performance optimization services, or web performance optimizers (WPOs), play a critical role in today’s web ecosystem by improving page load speed and saving network traffic. However, WPOs are known for introducing visual distortions that disrupt the users’ web experience. Unfortunately, visual distortions are hard to analyze, test, and debug, due to their subjective measure, dynamic content, and sophisticated WPO implementations.
Autonomous Path Finding and Obstacle Avoidance Method for Unmanned Construction Machinery

The working environment of construction machinery is harsh, and some operations are highly repetitive. The realization of intelligent construction machinery helps to improve economic efficiency and promote industrial development. Construction machinery is different from ordinary passenger vehicles. Aiming at the fact that the existing environmental perception data set cannot be directly applied to construction machinery, this paper establishes the corresponding data set in combination with the specific working conditions of construction machinery and carries out training based on the PointPillars network to realize the environmental perception function applicable to the working conditions of construction machinery. Most construction machinery runs on unstructured roads, and the existing passenger vehicle path planning algorithm is not applicable to construction machinery. Based on this, this paper uses a hybrid A* algorithm to achieve path planning that meets the kinematics of construction machinery and realizes real-time obstacle detection and avoidance. At the same time, this paper combines environmental perception with a path planning algorithm to provide a method of autonomous path finding and obstacle avoidance for construction machinery. Based on the improved pure pursuit algorithm, the high-precision motion control and established trajectory tracking of construction machinery are realized, which lays a certain foundation for the follow-up research and development of related intelligent technologies of construction machinery.