Reviewerly/exHarmony huggingface dataset:
Optimal Task Allocation and Coding Design for Secure Edge Computing With Heterogeneous Edge Devices

In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challenges, we propose a unified framework to provide efficiency and confidentiality by coded distributed computing. Within the proposed framework, we use matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task. To minimize resource consumption while achieving information-theoretic security, we investigate two highly-coupled problems, (1) task allocation that assigns data blocks in a computing task to edge devices and (2) linear code design that generates data blocks by encoding the original data with random information. Specifically, we first theoretically analyze the necessary conditions for the optimal solution. Based on the theoretical analysis, we develop an efficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">task allocation</i> algorithm to obtain a set of selected edge devices and the number of coded vectors allocated to them. Using the task allocation results, we then design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secure coded computing</i> schemes, for two cases, (1) with redundant computation and (2) without redundant computation, all of which satisfy the availability and security conditions. Moreover, we also theoretically analyze the optimization of the proposed scheme. Finally, we conduct extensive simulation experiments to demonstrate the effectiveness of the proposed schemes.
Patch-Based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions

Automatic segmentation of tumor eliminates problems associated with manual annotation of region-of-interest (ROI) from medical images, such as significant human efforts and inter-observer variability. Accurate segmentation of head and neck tumor has a tremendous potential for better radiation treatment planning for cancer (such as oropharyngeal cancer) and also for optimized patient care. In recent times, the development in deep learning models has been able to effectively and accurately perform segmentation tasks in semantic segmentation as well as in medical image segmentation. In medical imaging, different modalities focus on different properties and combining the information from them can improve the segmentation task. In this paper we developed a patch-based deep learning model to tackle the memory issue associated with training the network on 3D images. Furthermore, an ensemble of conventional and dilated convolutions was used to take advantage of both methods: the smaller receptive field of conventional convolution allows to capture finer details, whereas the larger receptive field of dilated convolution allows to capture better global information. Using patch-based 3D UNet with an ensemble of conventional and dilated convolution yield promising result, with a final dice score of 0.6911.
Speed Control in Longitudinal Plane of Autonomous Vehicle Using MPC

The speed control of autonomous car is an important aspect of a driver less vehicle. This paper presents a solution for longitudinal motion speed control of autonomous vehicle. The primary objective is to achieve a smooth and safe control of speed for autonomous vehicle. This is accomplished by the design of Model Predictive Controller (MPC) which controls the speed by taking inputs from LIDAR module. The longitudinal motion of vehicle is controlled by taking into consideration the factors such as obstacles, lead vehicles and pedestrians. This leads to smooth acceleration and deceleration in various situations. The proposed system works effectively for both low and high-speed vehicles. The performance of the controller was evaluated for parameters like sample time, prediction horizon using Matlab simulations, and implemented experimentally on autonomous vehicle prototype.
DAMS: Dynamic Association for View Materialization Based on Rule Mining Scheme

In data warehousing, view selection (VS) is an important aspect. Optimal VS needs to be materialized in order to minimize the overall data retrieval time. To support the same, performance metrics like memory constraints to save materialized views, query execution time, and query workloads needs to be addressed to reduce the overall retrieval time. As far as static view materialization (VM) is concerned, pre-computing strategies are required to execute the query workload prior to VM, but the approach is not scalable for small disk sizes. In the current era, the memory requirement is humongous to store pre-computed views in the materialized query table (MQT) that adds an overhead to view maintenance cost and disk sizes. To address the aforementioned issues, the authors propose a novel VM scheme DAMS. DAMS operates in three phases. In the first phase, the scheme chooses a materialized view in a dynamic and on-demand basis to reduce the query processing time. Then, in the second phase, a novel attribute selection algorithm is proposed based on association rule mining (ARM) technique in VS to address historical queries. It selects a candidate view from a pool of such views. As the number of queries is large, the proposed algorithm reduces the computational latency in fetching the view result. Finally, selected views are prioritized by grouping items as clusters set based on support and confidence metrics to speed up VM operations.
User Adoption of Chatbots

Chatbots are being widely used by customers of businesses in many service domains. A chatbot is a computer program that uses machine learning, natural language processing, and artificial intelligence. Because of their support for efficient way of communication, chatbots have the potential of enhancing customer support and therefore customer satisfaction with service delivery. As little is known about adoption of chatbots, the purpose of this research study is to identify the factors that contribute to user adoption of this support tool. In doing so, this paper presents a review of literature and proposes a framework that highlights the factors that contribute to the adoption of chatbots in private service sector.
Text Classification for Azerbaijani Language Using Machine Learning

Text classification systems will help to solve the text clustering problem in the Azerbaijani language. There are some text-classification applications for foreign languages, but we tried to build a newly developed system to solve this problem for the Azerbaijani language. Firstly, we tried to find out potential practice areas. The system will be useful in a lot of areas. It will be mostly used in news feed categorization. News websites can automatically categorize news into classes such as sports, business, education, science, etc. The system is also used in sentiment analysis for product reviews. For example, the company shares a photo of a new product on Facebook and the company receives a thousand comments for new products. The systems classify comments like positive or negative. The system can also be applied in recommended systems, spam filtering, etc. Various machine learning techniques such as Naive Bayes, SVM, Multi-layer Perceptron have been devised to solve the text classification problem in Azerbaijani language.
Progressive Kernel Pruning Based on the Information Mapping Sparse Index for CNN Compression

Network pruning can effectively reduce a model's capacity and computational load, thereby making model deployment in mobile devices less difficult than that without pruning.To improve the pruning rate of the model while maintaining the kernel's feature extraction ability, this paper designs a progressive kernel pruning method for CNN model compression based on the proposed information mapping sparsity index.This method first prunes the kernels in the filter and then prunes the kernels in the convolution layer when the model reaches a certain compression ratio.The whole process is called progressive kernel pruning (PKP).For the kernel pruning process, this paper defines the information mapping sparse index (IMSI), which is used to measure the mapping ability of the convolution kernel related to the amount of information transferred by the convolution operation.When pruning the kernels of filters and layers, according to the IMSI, the kernels with the strongest mapping abilities are retained to transfer as much information as possible with the least number of kernels.Progressive kernel pruning can make use of the characteristic that the model is easy to optimize when kernel pruning in the filter, and it avoids having the model easily fall into local optima when kernel pruning in the layer directly.The experimental results on the CIFAR-10/100 and ImageNet datasets show that compared to the existing CNN model compression methods, the IMSI-based progressive kernel pruning method exhibits better compression performance in processing the model compression tasks that are currently popular.In particular, pruning VGG-16 on CIFAR-10 with our model achieves a compression ratio of 80.8x and an acceleration ratio of 14.8x, which are 5.8x and 4.2x higher than the best results at present, respectively, and the classification accuracy decreases by only 0.59% relative to that of the baseline.
Maximum Likelihood-Based Measurement Noise Covariance Estimation Using Sequential Quadratic Programming for Cubature Kalman Filter Applied in INS/BDS Integration

With the completion of the Beidou-3 system (BDS) in China, INS/BDS integration will become a promising navigation and positioning strategy. However, due to the nonlinear propagation characteristic of INS error and inevitable involvement of inaccurate measurement noise statistics, it is difficult to achieve the optimal solution through the INS/BDS integration. This paper proposes a method of cubature Kalman filter (CKF) with the measurement noise covariance estimation by using the maximum likelihood principle to solve the abovementioned problem. It establishes an estimation model for measurement noise covariance according to the maximum likelihood principle, and then, its estimation is calculated by utilizing the sequential quadratic programming. The estimated measurement noise covariance will be fed back to the procedure of CKF to improve its adaptability. Simulation and comparison analysis verify that the proposed method can accurately estimate measurement noise covariance to effectively restrain its influence on navigation solution, leading to improved navigation performance for the INS/BDS integration.
Smart Induction for Isabelle/HOL (Tool Paper)

Proof assistants offer tactics to facilitate inductive proofs; however, deciding what arguments to pass to these tactics still requires human ingenuity. To automate this process, we present smart_induct for Isabelle/HOL. Given an inductive problem in any problem domain, smart_induct lists promising arguments for the induct tactic without relying on a search. Our in-depth evaluation demonstrate that smart_induct produces valuable recommendations across problem domains. Currently, smart_induct is an interactive tool; however, we expect that smart_induct can be used to narrow the search space of automatic inductive provers.
Slam and navigation of indoor robot based on ROS and lidar

Abstract For indoor service robot, this paper designs an indoor robot based on ROS (robot operating system) operating system. The robot uses two-dimensional lidar as the core sensor to collect the depth information of the environment and uses SLAM algorithm based on particle filter to realize the robot mapping; Based on the framework of ROS, A* algorithm and DWA algorithm are used to realize the navigation function of the robot; The map and navigation are simulated in the gazebo simulation environment to verify the function of path planning, local obstacle avoidance and navigation.
On Covert Quantum Sensing and the Benefits of Entanglement

Motivated by applications to covert quantum radar, we analyze a covert quantum sensing problem, in which a legitimate user aims at estimating an unknown parameter taking finitely many values by probing a quantum channel while remaining undetectable from an adversary receiving the probing signals through another quantum channel. When channels are classical-quantum, we characterize the optimal error exponent under a covertness constraint for sensing strategies in which probing signals do not depend on past observations. When the legitimate user's channel is a unitary depending on the unknown parameter, we provide achievability and converse results that show how one can significantly improve covertness using an entangled input state.
Cosine Meets Softmax: A Tough-to-beat Baseline for Visual Grounding

In this paper, we present a simple baseline for visual grounding for autonomous driving which outperforms the state of the art methods, while retaining minimal design choices. Our framework minimizes the cross-entropy loss over the cosine distance between multiple image ROI features with a text embedding (representing the given sentence/phrase). We use pre-trained networks for obtaining the initial embeddings and learn a transformation layer on top of the text embedding. We perform experiments on the Talk2Car dataset and achieve 68.7% AP50 accuracy, improving upon the previous state of the art by 8.6%. Our investigation suggests reconsideration towards more approaches employing sophisticated attention mechanisms or multi-stage reasoning or complex metric learning loss functions by showing promise in simpler alternatives.
Efficient Lattice-Based Inner-Product Functional Encryption

In the recent years, many research lines on Functional Encryption (FE) have been suggested and studied regarding the functionality, security, or efficiency. Nevertheless, an open problem on a basic functionality, the single-input inner-product (IPFE), remains: can IPFE be instantiated based on the Ring Learning With Errors (RLWE) assumption? The RLWE assumption provides quantum-resistance security while in comparison with LWE assumption gives significant performance and compactness gains. In this paper we present the first RLWE-based IPFE scheme. We carefully choose strategies in the security proofs to optimize the size of parameters. More precisely, we develop two new results on ideal lattices. The first result is a variant of Ring-LWE, that we call multi-hint extended Ring-LWE, where some hints on the secret and the noise are given. We present a reduction from RLWE problem to this variant. The second tool is a special form of Leftover Hash Lemma (LHL) over rings, known as Ring-LHL. To demonstrate the efficiency of our scheme we provide an optimized implementation of RLWE-based IPFE scheme and show its performance on a practical use case. We further present new compilers that, combined with some existing ones, can transfer a single-input FE to its (identity-based, decentralized) multi-client variant with linear size of the ciphertext (w.r.t the number of clients).
Geological Surveys Unite to Improve Critical Mineral Security

A three-nation consortium is pooling geological expertise and resources to address vulnerabilities in supplies of these crucial natural resources.
Model-Based Visual Planning with Self-Supervised Functional Distances

A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-reaching policies with reinforcement learning remains a challenging problem, particularly when rewards are not provided and l2 distances in pixel space are not meaningful. Learned dynamics models are a promising approach for learning about the environment without rewards or task-directed data, but planning to reach goals with such a model requires a notion of functional similarity between observations and goal states. We present a self-supervised method for model-based visual goal reaching, which uses both a visual dynamics model as well as a dynamical distance function learned using model-free reinforcement learning. This approach trains entirely using offline, unlabeled data, making it practical to scale to large and diverse datasets. On several challenging robotic manipulation tasks with only offline, unlabeled data, we find that our algorithm compares favorably to prior model-based and model-free reinforcement learning methods. In ablation experiments, we additionally identify important factors for learning effective distances.
Pedestrian Detection under Parallel Feature Fusion Based on Choquet Integral

Feature-based pedestrian detection method is currently the mainstream direction to solve the problem of pedestrian detection. In this kind of method, whether the appropriate feature can be extracted is the key to the comprehensive performance of the whole pedestrian detection system. It is believed that the appearance of a pedestrian can be better captured by the combination of edge/local shape feature and texture feature. In this field, the current method is to simply concatenate HOG (histogram of oriented gradient) features and LBP (local binary pattern) features extracted from an image to produce a new feature with large dimension. This kind of method achieves better performance at the cost of increasing the number of features. In this paper, Choquet integral based on the signed fuzzy measure is introduced to fuse HOG and LBP descriptors in parallel that is expected to improve accuracy without increasing feature dimensions. The parameters needed in the whole fusion process are optimized by a training algorithm based on genetic algorithm. This architecture has three advantages. Firstly, because the fusion of HOG and LBP features is parallel, the dimensions of the new features are not increased. Secondly, the speed of feature fusion is fast, thus reducing the time of pedestrian detection. Thirdly, the new features after fusion have the advantages of HOG and LBP features, which is helpful to improve the detection accuracy. The series of experimentation with the architecture proposed in this paper reaches promising and satisfactory results.
Colorization Transformer

We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use a conditional autoregressive transformer to produce a low resolution coarse coloring of the grayscale image. Our architecture adopts conditional transformer layers to effectively condition grayscale input. Two subsequent fully parallel networks upsample the coarse colored low resolution image into a finely colored high resolution image. Sampling from the Colorization Transformer produces diverse colorings whose fidelity outperforms the previous state-of-the-art on colorising ImageNet based on FID results and based on a human evaluation in a Mechanical Turk test. Remarkably, in more than 60\% of cases human evaluators prefer the highest rated among three generated colorings over the ground truth. The code and pre-trained checkpoints for Colorization Transformer are publicly available at https://github.com/google-research/google-research/tree/master/coltran
A Practical Federated Learning Framework for Small Number of Stakeholders

Federated Learning (FL) allows to collaboratively build machine learning models between different entities without the need for sharing or gathering the data. In FL, typically there is a global server and a set of clients (stakeholders) to build shared machine learning models. In contrast to distributed machine learning, the controller of the training process (here the global server) never sees the data of the stakeholders participating in FL. Every stakeholder owns his own data and doesn't share it. During the training and learning process, only the model updates (e.g. gradients) are shared. To our best of knowledge, we did not find a publicly available practical federated learning framework for stakeholders. We have built a framework that enables FL for a small number of stakeholders. In the paper, we describe the framework architecture, communication protocol, and algorithms. Our framework is open-sourced and it is easy to set up for stakeholders and ensures that no private information is leaked during the training process.
Trends in Expert System Development: A Practicum Content Analysis in Vocational Education for Over Grow Pandemic Learning Problems

The impact of the COVID-19 has emerged as a varied issue, ranging from the economy, society's social order, and edu­cation, especially when social and physical distancing have been introduced in various areas of community activities as one of the prevention. It has also affected the tertiary edu­cation institution where learning activities should be con­ducted from and at home, forcing the higher education communities to shift learning from conventional learning to online learning. Needless to say, learning that does not re­quire practicum is easier compared to learning that needs it. Consequently, it is a new task for lecturers to ensure the meaningful practicum learning implementation is con­ducted as it should be. Therefore, the creation of expert systems for practicum learning in vocational education is urgently required in online learning. The aim of this litera­ture review was to examine the development of an expert system used at practicum learning in tertiary institutions and how the design of learning systems could be integrated into vocational education in facing the COVID-19 pandemic. The method used was the literature study, through research stages starting from the finding and selection of articles and journals with relevant topics, and then analysing the data. The findings of this study was to identify the development of an expert program in an attempt to promote practicum learning using e-learning in the midst of the COVID-19 pan­demic outbreak.
Multi-position calibration method for laser beam based on cyclicity of harmonic turntable

Laser beam used as a visualizing measuring axis has emerged as a major candidate technology for 3D shape measurement. Until now, a prime limitation has been the highly-accurate positioning of laser beam in the whole measurement space. A multi-position calibration method for laser beam based on the cyclical error of harmonic turntable is proposed in this paper. A flexible calibration field constructed by CMM and PSD-based laser receiving board is presented. The coordinate of laser spot on PSD is obtained and transformed into CMM coordinate system. The direction vector of laser beam is obtained by linear fitting from a set of calibrated laser spots. Then the laser beams are calibrated in multiple positions. Based on the cyclicity of harmonic turntable, the error model of rotation angle is established by Fourier series function. The rotation angles are compensated in whole-space and the positioning of laser beam is obtained with highly-accurate. The experimental results demonstrate that the RMSE of linear fitting of laser beams is no more than 0.006 mm, the RMSE of the compensated rotation angles is no more than 0.003°, the RMSE of the spatial points is 0.089 mm and the RMSE of the reconstructed distances is 0.060 mm.