Reviewerly/exHarmony huggingface dataset:
Splay trees on trees

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Splay trees on treesBenjamin Aram Berendsohn and László KozmaBenjamin Aram Berendsohn and László Kozmapp.1875 - 1900Chapter DOI:https://doi.org/10.1137/1.9781611977073.75PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Search trees on trees (STTs) are a far-reaching generalization of binary search trees (BSTs), allowing the efficient exploration of tree-structured domains. (BSTs are the special case in which the underlying domain is a path.) Trees on trees have been extensively studied under various guises in computer science and discrete mathematics. Recently Bose, Cardinal, Iacono, Koumoutsos, and Langerman (SODA 2020) considered adaptive STTs and observed that, apart from notable exceptions, the machinery developed for BSTs in the past decades does not readily transfer to STTs. In particular, they asked whether the optimal STT can be efficiently computed or approximated (by analogy to Knuth's algorithm for optimal BSTs), and whether natural self-adjusting BSTs such as Splay trees (Sleator, Tarjan, 1983) can be extended to this more general setting. We answer both questions affirmatively. First, we show that a -approximation of an optimal size-n STT for a given search distribution can be computed in time (n2t + 1) for all integers t ≥ 1. Second, we identify a broad family of STTs with linear rotation-distance, allowing the generalization of Splay trees to the STT setting. We show that our generalized Splay satisfies a static optimality theorem, asymptotically matching the cost of the optimal STT in an online fashion, i.e. without knowledge of the search distribution. Our results suggest an extension of the dynamic optimality conjecture for Splay trees to the broader setting of trees on trees. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
CM-GCN: A Distributed Framework for Graph Convolutional Networks using Cohesive Mini-batches

Graph convolutional network (GCN) has been shown effective in many applications with graph structures. However, training a large-scale GCN is still challenging due to the high computation cost that grows with the size of the graph. In this paper, we propose CM-GCN, a distributed GCN framework using cohesive mini-batches to accelerate large-scale GCN training. The cohesive mini-batches group nodes that are tightly connected in the graph. As a result, CM-GCN can reduce the computation required to train a GCN. We propose a computation cost function to quantify the computation required for mini-batches. By exploring the submodular property of the computation cost function, we develop an efficient algorithm to partition nodes into tightly coupled mini-batches. Based on the computation cost function, we evenly distribute the workloads of mini-batches to workers. We design asynchronous computations between GCN layers to further eliminating the waiting among workers. We implement a CM-GCN framework and evaluate its performance with graphs that contain millions of nodes. Our evaluation shows that CM-GCN can achieve up to 3X speedup without compromising the training accuracy.
Big Data Analytics Framework for Predictive Analytics using Public Data with Privacy Preserving

There are increasingly leveraging public data with cities increasingly interested in driving both responsiveness to citizen demands and cost savings through data analytics. As public managers seek to augment existing data sources, such as 311 complaints, with existing secondary data, such as US Census products, severe challenges exist. This paper considers the problems inherent in data being collected at divergent geographic levels over different time horizons. An inductive analytical methodology is developed to create units of analysis that are both useful and analytically appropriate for public managers and policy leaders in urban areas. A big data analytics framework for public data, called BDAP, was presented predictive analytics for community need considering data the spatial and temporal location while addressing the data issues such as missing values, privacy-preserving, and predictive modeling. The findings illustrate the power of inductive data curation and privacy-preserving leading to benefits to the big data community. An application for the Open Data Platform was developed using KCMO’s 311 data, crime data and census data.
Future-Proof Web Authentication: Bring Your Own FIDO2 Extensions

The FIDO2 standards for strong authentication on the Internet define an extension interface, which allows them to flexibly adapt to future use cases. The domain of establishing new FIDO2 extensions, however, is currently limited to web browser developers and members of the FIDO alliance. We show how researchers and developers can design and implement their own extensions for using FIDO2 as a well-established and secure foundation to demonstrate innovative authentication concepts or to support custom deployments. Our open-source implementation targets the full FIDO2 stack, such as the Chromium web browser and hardware tokens, to enable tailor-made authentication based on the power of the existing FIDO2 ecosystem. To give an overview of existing extensions, we survey all published FIDO2 extensions by manually inspecting the source code of major web browsers and authenticators. Their current design, however, hinders the implementation of custom extensions, and they only support a limited number of extensions out of the box. We discuss weaknesses of current implementations and identify the lack of extension pass-through as a major limitation in current FIDO2 clients.
Real-time Obstacle Avoidance for Unmanned Aerial Vehicles (UAVs)

Real-time obstacle avoidance is a vital component for unmanned aerial vehicles (UAVs) when autonomously following mobile ground vehicles (MGVs) in unstructured and dynamic environments. The Artificial Potential Field (APF) technique is a widely used method for UAV path planning due to its simplicity, ease of use and its inherent efficiency in obstacle avoidance. However, this technique has many shortcomings with obstacles such as falling in local minima, tendency to produce longer planned paths when avoiding obstacles, high probability of colliding with symmetrical obstacles, and increase in oscillatory movements near obstacles. To overcome these drawbacks, this paper presents a novel two-dimensional path planning technique for obstacle avoidance based on the APF method. The proposed technique produces velocity waypoints for the UAV’s planned path based on GPS data and use of basic distance sensors for obstacle avoidance; therefore, it can be deployed on most types of UAVs utilizing flight controllers with autopilots such as PX4 and Ardupilot. The performance of the proposed technique is validated for different simulation scenarios in ROS and Gazebo supported PX4-SITL. The results show the suitability of the proposed technique for real-time obstacle avoidance for UAVs autonomously following MGVs in dynamic environments with different types of obstacles.
Estimating Focused Pedestrian using Smooth-Pursuits Eye Movements and Point Cloud toward Assistive System for Wheelchair

Intelligent electric wheelchairs have been developed for personal mobility, and eye-gaze measurement is essential for comprehending the user's attention and for assisting operations. In previous studies, the gaze vector (i.e., the visual or optical axis of the eye) was projected onto the environmental map; hence, an eye tracker was installed on the wheelchair. In addition, hardware calibration, which determines the geometric relationship between the eye tracker and other sensors, such as LiDAR, was performed beforehand. Recently, wearable eye-trackers are expected to employ a daily-use device; therefore, the cooperation between sensors is essential without geometric constraints. Accordingly, we propose a method for estimating focused pedestrians using smooth-pursuit eye movements in the real world. Pedestrians are tracked using a point cloud obtained with 3D LiDAR, and the trajectories of the focused pedestrian are recorded on an environmental map constructed with simultaneous localization and mapping. Several experiments were conducted to evaluate the computational methods for the correlation between eye movements and moving objects, and we confirmed the feasibility and the current issues through these experiments.
A Hybrid Shuffled Frog Leaping Algorithm and Its Performance Assessment in Multi-Dimensional Symmetric Function

Ensemble learning of swarm intelligence evolutionary algorithm of artificial neural network (ANN) is one of the core research directions in the field of artificial intelligence (AI). As a representative member of swarm intelligence evolutionary algorithm, shuffled frog leaping algorithm (SFLA) has the advantages of simple structure, easy implementation, short operation time, and strong global optimization ability. However, SFLA is susceptible to fall into local optimas in the face of complex and multi-dimensional symmetric function optimization, which leads to the decline of convergence accuracy. This paper proposes an improved shuffled frog leaping algorithm of threshold oscillation based on simulated annealing (SA-TO-SFLA). In this algorithm, the threshold oscillation strategy and simulated annealing strategy are introduced into the SFLA, which makes the local search behavior more diversified and the ability to escape from the local optimas stronger. By using multi-dimensional symmetric function such as drop-wave function, Schaffer function N.2, Rastrigin function, and Griewank function, two groups (i: SFLA, SA-SFLA, TO-SFLA, and SA-TO-SFLA; ii: SFLA, ISFLA, MSFLA, DSFLA, and SA-TO-SFLA) of comparative experiments are designed to analyze the convergence accuracy and convergence time. The results show that the threshold oscillation strategy has strong robustness. Moreover, compared with SFLA, the convergence accuracy of SA-TO-SFLA algorithm is significantly improved, and the median of convergence time is greatly reduced as a whole. The convergence accuracy of SFLA algorithm on these four test functions are 90%, 100%, 78%, and 92.5%, respectively, and the median of convergence time is 63.67 s, 59.71 s, 12.93 s, and 8.74 s, respectively; The convergence accuracy of SA-TO-SFLA algorithm on these four test functions is 99%, 100%, 100%, and 97.5%, respectively, and the median of convergence time is 48.64 s, 32.07 s, 24.06 s, and 3.04 s, respectively.
A Contrastive Learning-based PPC-UNet for Colorectal Histopathology Whole Slide Image Segmentation

Colorectal cancer (CRC) is the third most common cancer and is usually diagnosed using colonoscopy and biopsy. Diagnosis of pathological biopsy requires professional knowledge and technology. Computer-aided gland and lesion segmentation systems have been proposed to help pathologists in diagnosis of CRC. However, to the best of our knowledge, there has not been a literature work trying to segment different levels of intraepithelial neoplasia in CRC pathological image. To reduce such a research gap, in this paper, we firstly collect a colorectal cancer biopsy histopathology whole slide image (WSI) dataset, named Histo-CRC Biopsy dataset, for algorithm evaluation. We further propose a PPC-UNet network to segment high level, low level intraepithelial neoplasia and normal tissues. The proposed PPC-UNet consists of two modules i.e., a UNet-based network for segmentation, and a pixel-to-propagation consistency (PPC) contrastive learning-based network for UNet encoder pre-training. As the important feature can be learned from the unannotated data during pre-training, our approach can consistently improve the Dice of UNet by around 2% when different ratios of the training data are labeled.
Robust Sample-Based Output-Feedback Path Planning

We propose a novel approach for sampling-based and control-based motion planning. We combine a representation of the environment obtained via a modified version of optimal Rapidly-exploring Random Trees (RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ), with landmark-based output-feedback controllers obtained via Control Lyapunov Functions, Control Barrier Functions, and robust Linear Programming. Our solution inherits many benefits of RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> -like algorithms, such as the ability to implicitly handle arbitrarily complex obstacles. Additionally, it extends planning beyond the discrete nominal paths, as feedback controllers can correct deviations from such paths, and are robust to discrepancies between the planning and real environment maps. We test our algorithms first in simulations and then in experiments, evaluating the robustness of the approach to practical conditions, such as deformations of the environment, mismatches in the dynamical model of the robot, and measurements acquired with a camera with a limited field of view.
A Framework for Eye-Based Human Machine Interface

To improve lifestyle, Human Machine Interface(HMI) plays a significant role. The human eye is one of the most prominent features of the face that shows useful information besides facial expressions, e.g., eye blink, eye motion and pupil motions. These can be used to create an eye-based platform to control different components that can be beneficial for motor disable people. Besides biological activities, eye blinking can also be used for interaction. Most of the existing human machine interface required motion of physical organ, which are not feasible to use by motor disable people. Using eye blink command to interact with machine can give a solution. In this work, we present an eye-based HMI framework using eye blinks and computer vision techniques that can interact between humans and machines. The framework allows defining rules to map blink patterns to perform different activities.We have designed a framework with simple hardware interfacing that can extract blink information and use this information to control computer applications and household appliances. Evaluation results show that the system is highly effective in detecting human eye blink in real-time and can be used to interact with applications and machines easily.
Lung Cancer Types Prediction Using Machine Learning Approach

Lung cancer is the most common type of cancer in India, along with prostate, mouth, breast cancer. Due to smoking, excessive increases in pollution and the inhalation of cancerous elements cause cancer in men and women. Still, the amount of cancer in men is more compared to women. Different imaging modalities are used for diagnoses, such as CT, MRI, PET, and X-Ray. Oncologists and physicians diagnose cancer by seeing gray-scale saturation in the image. It requires experience in both anatomy and physiology of humans with imaging technique knowledge. It takes too much time for diagnosing, and accuracy is less due to a lack of imaging expertise. To avoid time consumption and provide easy diagnosis, the researcher has used an AI-based application technique for diagnosis purposes. In this study, we have used the Ensemble Machine Learning algorithm, i.e., AdaBoost, to predict different lung cancer types. We trained ensemble learners using features extracted from the lung CT images and evaluations done using performance metrics. The Accuracy of Adaboost is 90.74%, with sensitivity is 81.80%, specificity is 93.99%, F1 score 0.8, kappa is 0.753, and AUC is 0.93. The performance of the AdaBoost classifier is compared with the different machine learning algorithms.
An Improved Random Forest Based on Feature Selection and Feature Weighting for Case Retrieval in CBR Systems

: The medical diagnostic process works very similarly to the Case Based Reasoning (CBR) cycle scheme. CBR is a problem solving approach based on the reuse of past experiences called cases. To improve the performance of the retrieval phase, a Random Forest (RF) model is proposed, in this respect we used this algorithm in three different ways (three different algorithms): Classic Random Forest (CRF) algorithm, Random Forest with Feature Selection (RF_FS) algorithm where we selected the most important attributes and deleted the less important ones and Weighted Random Forest (WRF) algorithm where we weighted the most important attributes by giving them more weight. We did this by multiplying the entropy with the weight corresponding to each attribute.We tested our three algorithms CRF, RF_FS and WRF with CBR on data from 11 medical databases and compared the results they produced. We found that WRF and RF_FS give better results than CRF. The experiemental results show the performance and robustess of the proposed approach.
A Novel Image Watermarking Scheme Using LU Decomposition

In recent years, protecting copyright of digital images is an indispensable requirement for owners. To against with rapidly increasing of attacks, many techniques have been proposed in transform domain for ensuring quality of watermarked image, robustness of extracted watermark and execution time. Among these techniques, LU decomposition is considered as an outstanding technique in term of computation. However, it is that not all square matrices have an LU decomposition. Therefore, the suitable blocks need to be chosen before factorizing pixel matrices into lower and upper triangular matrix. In addition, in order to improve the invisibility of watermarked image, watermark should be embedded on one element of L matrix instead of two elements as the previous proposals. In this paper, we propose a novel image watermarking scheme which is based on strategy of LU blocks selection and an improved embedding method. Beside that, the extraction time is significantly sped up by a new solution to get out L(2,1) and L(3,1) elements of L matrix without performing LU decomposition in the extracting stage. According to the experimental results, our proposed method not only has the much better visual quality of watermarked images, but also can effectively extracts the watermark under some attacks.
Copy theory

Abstract In information science, writing, printing, telecommunication, and digital computing have been central concerns because of their ability to distribute information. Overlooked is the obvious fact that these technologies fashion copies, and the theorizing of copies has been neglected. We may think a copy is the same as what it copies, but no two objects can really be the same. “The same” means similar enough as an acceptable substitute for some purpose. The differences between usefully similar things are also often important, in forensic analysis, for example, or inferential processes. Status as a copy is only one form of relationship between objects, but copies are so integral to information science that they demand a theory. Indeed, theorizing copies provides a basis for a more complete and unified view of information science.
MetaHate: A Meta-Model for Hate Speech Detection

We present MetaHate, a NLP meta-model for detecting hatefulness in tweets by combining predictors for hate, emotion, sentiment, and offensiveness. We evaluate this model with the TweetEval benchmark for hate speech detection. MetaHate improves the baseline TweetEval RoBERTa based model on the TweetEval benchmark. Optimizing the decision threshold for the macro-averaged F1-score, MetaHate achieves a F1-score of 0.70, while the TweetEval RoBERTa-Twitter Retrained Hate model achieves a F1-score of 0.63. This improvement on one of the most difficult tasks on the TweetEval benchmark was achieved with no additional training data and negligible computational time and cost. MetaHate demonstrates the utility of leveraging predictions from language models trained for various tasks to improve performance on a single task.
An M-Estimation-Based Improved Interacting Multiple Model for INS/DVL Navigation Method

This paper presents an M-estimation-based Improved Interacting Multiple Model (IIMM) algorithm for inertial navigation system (INS)/Doppler velocity logs (D VL) integrated method to directly apply in complex underwater environment without empirical parameters. Considering the time-varying measurement noise covariance, a variable model structure based on Bayesian estimation law is proposed to adapt to the environment and an adaptive state transition probability matrix is generated to promote the model adaption. Meanwhile, M-estimation-based filter is employed as primary model to save IIMM from the effect of outliers. The performance of the IIMM algorithm is evaluated on simulation, land vehicle, and Yangtze River test, where the state-of-the-art is compared adequately. The robustness simulation test is also performed. It is highlighted that IIMM approach can adapt to actual model rapidly and can resist outliers efficiently. The proposed IIMM method can improve the accuracy and robustness of the navigation system in a severe and unknown environment without increasing the computational load.
JSLIM: Reducing the Known Vulnerabilities of JavaScript Application by Debloating

As the complexity of software projects increases, more and more developers choose to package various external dependency libraries into software projects to simplify software development. Unfortunately, these introduced dependent libraries are likely to introduce many potential security risks. This phenomenon is called software bloat. One way to handle this increased threat is through software debloating, i.e., the removal of dead code and code corresponding to vulnerabilities introduced from external dependency libraries. In our paper, we proposed JSLIM, an effective vulnerability-aware software debloating system. First, JSLIM processes the public vulnerability information through natural language processing technology, obtains the mapping relationship between the vulnerability and the NPM package, and determines which function in the package causes a specific vulnerability. Then, according to the generated function call graph, determine whether the program calls the method that generates the vulnerability in the dependent library. JSLIM removes the code that isn’t called by the program and uses the sandbox to isolate the code that has vulnerabilities but cannot be removed. We conduct experiments on popular open-source JavaScript applications. The experimental results show that our method removes most of the code related to the known vulnerabilities of the application and effectively prevents attackers from exploiting known vulnerabilities in the NPM package to launch attacks on applications.