Reviewerly/exHarmony huggingface dataset:
Data Oblivious Algorithms for Multicores

A data-oblivious algorithm is an algorithm whose memory access pattern is independent of the input values. We initiate the study of parallel data oblivious algorithms on realistic multicores, best captured by the binary fork-join model of computation. We present a data-oblivious CREW binary fork-join sorting algorithm with optimal total work and optimal (cache-oblivious) cache complexity, and in O(łog n łog łog n) span (i.e., parallel time); these bounds match the best-known bounds for binary fork-join cache-efficient insecure algorithms. Using our sorting algorithm as a core primitive, we show how to data-obliviously simulate general PRAM algorithms in the binary fork-join model with non-trivial efficiency, and we present data-oblivious algorithms for several applications including list ranking, Euler tour, tree contraction, connected components, and minimum spanning forest. All of our data oblivious algorithms have bounds that either match or improve over the best known bounds for insecure algorithms.
Nuka: A Generic Engine with Millisecond Initialization for Serverless Computing

Serverless computing is becoming one of the mainstream trends in cloud computing due to its advantages of simplified programming and cost saving. However, existing serverless platforms still adopt Docker container as its execution engine, which has the cold start problem and causes high common-case invocation latency. In this work, we analyze the lifecycle of common-case serverless invocation on existing serverless platforms and find that current container startup and pulling remote images are the two main reasons causing cold start so slow. Based on the study, we implement Nuka, a generic engine with millisecond initialization for serverless computing. Nuka is fully compatible with Docker interface and can smoothly re-place Docker as the execution engine of existing serverless plat-forms. Through the isolation pool that reuses Linux's isolation configurations, Nuka avoids the high cost of container startup's scalability bottleneck, which reduces container's startup time with high concurrency scale. Nuka also avoids pulling remote images through dynamically resolving and importing required software packages from local package caching. A self-adaptive container reuse strategy dynamically controls container's pause time and replica numbers, which effectively reduces the frequency of cold start. Compared with Docker, Nuka can get a millisecond initialization with high concurrency and significantly reduces average time cost of cold startup by 6× on existing serverless platforms.
3D U-Net Brain Tumor Segmentation Using VAE Skip Connection

In clinical practice, the determination of the location, shape, and size of brain tumor can greatly assist the diagnosis, monitoring, and treatment of brain tumor. Therefore, accurate and reliable automatic brain tumor segmentation algorithm is of great significance for clinical diagnosis and treatment. With the rapid development of deep learning technology, more and more efficient image segmentation algorithms have also been applied in this field. It has been proven that U-Net model combined with variational auto-encoder can help to effectively regularize the shared encoder and thereby improve the performance. Based on the VAE-U-Net model, this paper proposes a structure called VAE skip connection. By fusing the position information contained in VAE branch into U-Net decoding stage, the network can retain more high-resolution detail information. In addition, we integrate ShakeDrop regularization into the networks to further alleviate the overfitting problem. The experimental results show that the networks after adding VAE skip connection and ShakeDrop can achieve competitive results on the BraTS 2018 dataset.
An Infrared Array Sensor-Based Method for Localizing and Counting People for Health Care and Monitoring

To build a system for monitoring elderly people living alone, an important step needs to be done: identifying the presence/absence of the person being monitored and his location. Such task has several applications that we discuss in this paper, and remains very important. Several techniques were proposed in the literature. However, most of them suffer from issues related to privacy, coverage or convenience. In the current paper, we propose an infrared array sensor-based approach to detect the presence/absence of a person in a room. We used a wide angle low resolution sensor (i.e., 32×24 pixels) to collect heat-related information from the area monitored, and used Deep Learning (DL) to identify the presence of up to 3 people with an accuracy reaching 97%. Our approach also detects of the presence or absence of a person with a 100% accuracy. Nevertheless, it allows identifying the location of the detected people within a room of dimensions 4×7.4 m with a margin of 0.3 m.
Pathology without microscope: From a projection screen to a virtual slide

We have witnessed successive stages since the Seventies in the advancements towards digital pathology. We agree with Dr Pallua et al on the tremendous changes that are taking place in pathology, all leading toward greater role of digitalization in the field of pathology, both in terms of consultation and teaching. In particular, distance teaching using digital pathology will grow into a mainstream mode of pathology teaching, something that has been reinforced by COVID-19.
TransNet

The accuracy of neural network can be improved by training over multi-participants' pooled dataset, but privacy problem of sharing sensitive data obstructs this collaborative learning. To solve this contradiction, we propose TransNet, a novel solution for privacy-preserving collaborative neural network, whose main idea is to add a transformed layer to the neural network. It has the advantage of lower computation and communication complexity than previous secure multi-party computation based and homomorphic encryption based schemes, and has the superiority of supporting arbitrarily partitioned dataset compared to previous differential privacy based and stochastic gradient descent based schemes, which support horizontally partitioned dataset only. TransNet is trained by a server which pools the transformed data, but has no special security requirement on the training server. We evaluate TransNet's performance over four datasets using different neural network algorithms. Experimental results demonstrate that TransNet is not affected by the number of participants, and trains as quickly as the original neural network does. With proper variables, TransNet gets close accuracy to the baseline which trains over pooled original dataset.
Deep network compression with teacher latent subspace learning and LASSO

Deep neural networks have been shown to excel in understanding multimedia by using latent representations to learn complex and useful abstractions. However, they remain unpractical for embedded devices due to memory constraints, high latency, and considerable power consumption at runtime. In this paper, we propose the compression of deep models based on learning lower dimensional subspaces from their latent representations while maintaining a minimal loss of performance. We leverage on the premise that deep convolutional neural networks extract many redundant features to learn new subspaces for feature representation. We construct a compressed model by reconstruction from representations captured by an already trained large model. As compared to state-of-the-art, the proposed approach does not rely on labeled data. Moreover, it allows the use of sparsity inducing LASSO parameter penalty to achieve better compression results than when used to train models from scratch. We perform extensive experiments using VGG-16 and wide ResNet models on CIFAR-10, CIFAR-100, MNIST and SVHN datasets. For instance, VGG-16 with 8.96M parameters trained on CIFAR-10 was pruned by 81.03 % with only 0.26 % generalization performance loss. Correspondingly, the size of the VGG-16 model is reduced from 35MB to 6.72MB to facilitate compact storage. Furthermore, the associated inference time for the same VGG-16 model is reduced from 1.1 secs to 0.6 secs so that inference is accelerated. Particularly, the proposed student models outperform state-of-the-art approaches and the same models trained from scratch.
Stepped generalized predictive control of test tank temperature based on backpropagation neural network

It is significant to ensure the temperature stability of the test tank, which has a direct impact on reducing production energy consumption, improving tank quality and ensuring experimental results. However, the high nonlinearity and long delay of the temperature control process in the test tank make it difficult to satisfactorily control the temperature by traditional control methods. To solve the problem, this paper designs a temperature prediction model for the test tank based on backpropagation neural network (BPNN), which has a good fitting ability for nonlinear systems. The proposed model was coupled with improved generalized predictive control (GPC) into a new test tank temperature control method, namely, BPNN-based stepped GPC. Simulation results show that the new control method could reduce the prediction error of the BPNN and effectively control the temperature of the test tank.
Improved detection method for traffic signs in real scenes applied in intelligent and connected vehicles

Detecting traffic signs is an essential task for intelligent and connected vehicles. In this study, a modified model based on the method of You Only Look Once (YOLO) is proposed for detecting different types of Chinese signs, including mandatory, prohibitory, danger warning, guide, and tourist signs. Images of Chinese traffic signs are collected in real scenes and a new dataset is established. The modified model combines the DenseNet method with the YOLOv3 network. Dense blocks are used to strengthen feature propagation and promote feature reuse in those feature layers with low resolution in the YOLOv3 network. Experimental results on the test dataset reveal that the average precision of the modified model, the original YOLOv3, and the YOLOv2 networks are 95.92, 94.59, and 89.39%, respectively. Further comparative analyses that give more detailed experimental evaluation results are conducted on the designed model, including (i) the performance of the designed model based on five categories; (ii) the influence of training set size on the designed model; (iii) the performance of the designed model on occlusion and no object conditions in real scenes. The experimental results show that the modified model is effective at fast and accurate Chinese traffic sign detection in real scenes.
Learning to Rank Entities for Set Expansion from Unstructured Data

We propose using learning-to-rank for entity set expansion (ESE) from unstructured data, the task of finding "sibling" entities within a corpus that are from the set characterized by a small set of seed entities. We present a two-channel neural re-ranking model, NESE, that jointly learns exact and semantic matching of entity contexts through entity interaction features. Although entity set expansion has drawn increasing attention in the IR and NLP communities for its various applications, the lack of massive annotated entity sets has hindered the development of neural approaches. We describe DBpedia-Sets, a toolkit that automatically extracts entity sets from a plain text collection, thus providing a large amount of distant supervision data for neural model training. Experiments on real datasets of different scales from different domains show that NESE outperforms state-of-the-art approaches in terms of precision and MAP. Furthermore, evaluation through human annotations shows that the knowledge learned from the training data is generalizable.
Size-Invariant Visual Cryptography With Improved Perceptual Quality for Grayscale Image

The reconstructed image of the size-invariant visual cryptography (VC) is inevitably accompanied by the loss of secret image information and the degradation of perceptual quality. Here, the halftone technique comes to the forefront since it can realistically simulate the grayscale image from a discrete binary image. Thus, by combining VC sharing with grayscale image halftone technique, this paper proposes a size-invariant VC scheme for grayscale image underpinned by the efficient direct binary search (EDBS) algorithm, in which the multi-pixel encryption VC sharing is adopted into the EDBS halftone process. Through local optimizations and global iterations, the optimal reconstructed image is obtained. To further enhance the contrast of the reconstructed image with limited computational power, the image information is probabilistically extracted according to the inverse mapping in the codebook. It is theoretically proved that the proposed scheme is as secure as the traditional VC, while its effectiveness is validated through experiments and comparative analyses.
Embedding 3D models in offline physical environments

Abstract This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space‐efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user‐friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low‐cost autonomous robots, and 3D printing.
Research directions in color movie restoration

Abstract Film restoration is a complex and multidisciplinary field that requires the collaboration of many professionals. Here, we present a series of ongoing unresolved problems, focusing on digitisation, digital restoration and conservation.
Visual Encodings for Networks with Multiple Edge Types

This paper reports on a formal user study on visual encodings of networks with multiple edge types in adjacency matrices. Our tasks and conditions were inspired by real problems in computational biology. We focus on encodings in adjacency matrices, selecting four designs from a potentially huge design space of visual encodings. We then settle on three visual variables to evaluate in a crowdsourcing study with 159 participants: orientation, position and colour. The best encodings were integrated into a visual analytics tool for inferring dynamic Bayesian networks and evaluated by computational biologists for additional evidence. We found that the encodings performed differently depending on the task, however, colour was found to help in all tasks except when trying to find the edge with the largest number of edge types. Orientation generally outperformed position in all of our tasks.
Non-Gaussian Estimation and Dynamic Output Feedback Using the Gaussian Mixture Kalman Filter

This paper considers the problem of non-Gaussian estimation and dynamic output feedback in both linear and nonlinear settings. Estimation with non-Gaussian process noise, although important in fields such as environmental sampling, is typically problem specific and suboptimal. The approach described here uses the Gaussian mixture model to approximate an unknown non-Gaussian distribution and to employ the Kalman filter and its nonlinear variants: the extended and unscented Kalman filters. The estimation error is bounded analytically and illustrated numerically for linear and nonlinear systems. The estimate is used in an output feedback control to guarantee stability of a closed-loop system.
DenseDisp: Resource-Aware Disparity Map Estimation by Compressing Siamese Neural Architecture

Stereo vision cameras are flexible sensors due to providing heterogeneous information such as color, luminance, disparity map (depth), and shape of the objects. Today, Convolutional Neural Networks (CNNs) present the highest accuracy for the disparity map estimation [1]. However, CNNs require considerable computing capacity to process billions of floating-point operations in a real-time fashion. Besides, commercial stereo cameras produce huge size images (e.g., 10 Megapixels [2]), which impose a new computational cost to the system. The problem will be pronounced if we target resource-limited hardware for the implementation. In this paper, we propose DenseDisp, an automatic framework that designs a Siamese neural architecture for disparity map estimation in a reasonable time. DenseDisp leverages a meta-heuristic multi-objective exploration to discover hardware-friendly architectures by considering accuracy and network FLOPS as the optimization objectives. We explore the design space with four different fitness functions to improve the accuracy-FLOPS trade-off and convergency time of the DenseDisp. According to the experimental results, DenseDisp provides up to 39. 1x compression rate while losing around 5% accuracy compared to the state-of-the-art results.
Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg-Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.
A survey of Konkani NLP resources

The first comprehensive survey is presented of natural language processing (NLP) research in Konkani, a low-resource regional Indian language with a small population of native speakers. The combined challenges of complex linguistic phenomena, paucity of documented resources and the presence of neighboring dominant languages are elaborated. The possibilities of crowdsourcing as a means for creating linguistic resources are explored.
An intelligent and personalized course advising model for higher educational institutes

In this study, we present an intelligent personalized course advising model by analyzing, selecting, and modifying effective course advising technologies. It overcomes the existing course advising systems by using common design for all Higher Educational Institutes and making the rule, regulations, curriculum, and grading system more flexible to change without affecting persistent academic data. The proposed model helps students by analyzing and suggesting appropriate courses according to their academic history and special interest via harmonized knowledge of different experts. To develop this model, analyze various requirements were gathered and explicitly analyzed from the course advisory systems and other related technologies, that were used in seven institutes. The proposed model contains seven components, in which they use association rule, rule-based expert system, and fundamental recommendation algorithms according to their suitability for collected requirements. We made more modifications and enhancements on those algorithms to achieve the purpose of each component. We used ISO-25010 quality model with the institutional rules and regulations of three departments to evaluate the proposed model. The obtained results showed that the proposed methodology of the model is an effective way to improve the efficiency of students course advisory systems. It also indicates that advisors and students can save more time through effective and efficient course suggestions during the course registration period.
Approximation of two-variable functions using high-order Takagi–Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization

Abstract This paper proposes a new hybrid method for training high-order Takagi–Sugeno fuzzy systems using sparse regressions and metaheuristic optimization. The fuzzy system is considered with Gaussian fuzzy sets in the antecedents and high-order polynomials in the consequents of fuzzy rules. The fuzzy sets can be chosen manually or determined by a metaheuristic optimization method (particle swarm optimization, genetic algorithm or simulated annealing), while the polynomials are obtained using ordinary least squares, ridge regression or sparse regressions (forward selection, least angle regression, least absolute shrinkage and selection operator, and elastic net regression). A quality criterion is proposed that expresses a compromise between the prediction ability of the fuzzy model and its sparsity. The conducted experiments showed that: (a) the use of sparse regressions and/or metaheuristic optimization can reduce the validation error compared with the reference method, and (b) the use of sparse regressions may simplify the fuzzy model by zeroing some of the coefficients.
Evolutionary Approach to Collectible Arena Deckbuilding using Active Card Game Genes

In this paper, we evolve a card-choice strategy for the arena mode of Legends of Code and Magic, a programming game inspired by popular collectible card games like Hearthstone or TES: Legends. In the arena game mode, before each match, a player has to construct his deck choosing cards one by one from the previously unknown options. Such a scenario is difficult from the optimization point of view, as not only the fitness function is non-deterministic, but its value, even for a given problem instance, is impossible to be calculated directly and can only be estimated with simulation-based approaches. We propose a variant of the evolutionary algorithm that uses a concept of an active gene to reduce the range of the operators only to generation-specific subsequences of the genotype. Thus, we batched learning process and constrained evolutionary updates only to the cards relevant for the particular draft, without forgetting the knowledge from the previous tests.We developed and tested various implementations of this idea, investigating their performance by taking into account the computational cost of each variant. Performed experiments show that some of the introduced active-genes algorithms tend to learn faster and produce statistically better draft policies than the compared methods.