Reviewerly/exHarmony huggingface dataset:
Neural Pruning Search for Real-Time Object Detection of Autonomous Vehicles

Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to difficulties for object detection. To facilitate this, we propose a compiler-aware neural pruning search framework to achieve high-speed inference on autonomous vehicles for 2D and 3D object detection. The framework automatically searches the pruning scheme and rate for each layer to find a best-suited pruning for optimizing detection accuracy and speed performance under compiler optimization. Our experiments demonstrate that for the first time, the proposed method achieves (close-to) real-time, 55ms and 97ms inference times for YOLOv4 based 2D object detection and PointPillars based 3D detection, respectively, on an off-the-shelf mobile phone with minor (or no) accuracy loss.
Blended Learning Space for Primary and Secondary Education

Emergency remote education during the COVID-19 pandemic has opened the door for everyone not only to see the prospect of blended teaching and learning but also to understand how critical it is to incorporate technological development to enhance learning and access to education. While face-to-face in-classroom teaching and learning methods help children and young people in developing vital social, communicative, developmental, and digital competencies, it is also critical to embrace the digital transformation that is happening around everyday life and throughout society. Based on secondary data, this chapter explores the key challenges and opportunities for policy and practice in resource-constrained contexts. This chapter utilizes Bangladesh as a case study to elicit the transition and transformation process of teaching and learning at the primary and secondary education levels in the global south during the emergency.
One Hot Garbling

Garbled Circuit (GC) is the main practical 2PC technique, yet despite great interest in its performance, GC notoriously resists improvement. Essentially, we only know how to evaluate GC functions gate-by-gate using encrypted truth tables; given input labels, the GC evaluator decrypts the corresponding output label. Interactive protocols enjoy more sophisticated techniques. For example, we can expose to a party a (masked) private value. The party can then perform useful local computation and feed the resulting cleartext value back into the MPC. Such techniques are not known to work for GC. We show that it is, in fact, possible to improve GC efficiency, while keeping its round complexity, by exposing masked private values to the evaluator. %without introducing rounds of communication. Our improvements use garbled one-hot encodings of values. By using this encoding we improve a number of interesting functions, e.g., matrix multiplication, integer multiplication, field element multiplication, field inverses and AES S-Boxes, integer exponents, and more. We systematize our approach by providing a framework for designing such GC modules. Our constructions are concretely efficient. E.g., we improve binary matrix multiplication inside GC by more than 6x in terms of communication and by more than 4x in terms of WAN wall-clock time. Our improvement circumvents an important GC lower bound and may open GC to further improvement.
Skynet: Performance-driven Resource Management for Dynamic Workloads

A primary concern for cloud operators is to increase resource utilization while maintaining good performance for applications. This is particularly difficult to achieve for three reasons: users tend to overprovision applications, applications are diverse and dynamic, and their performance depends on multiple resources. In this paper, we present Skynet, an automated and adaptive cloud resource management approach that addresses all three concerns. Skynet uses performance level objectives (PLOs) to capture user intentions about required performance more accurately to remove the user from the resource allocation loop. Then, Skynet estimates the resources required to achieve the target PLO. For this purpose, we employ a Proportional Integral Derivative (PID) controller per application and adjust its parameters on the fly. Finally, to capture the dependence of applications on different or multiple resources, Skynet extends the traditional one-dimensional PID controller to estimate CPU, memory, I/O throughput, and network throughput. Essentially, Skynet builds a model on-the-fly to map target PLOs to resources for each application, taking into account multiple resources and changing input load. We implement Skynet as an end-to-end, custom scheduler in Kubernetes and evaluate it using real workloads on both a private cluster and AWS. Skynet decreases PLO violations by more than 7.4x and increases resource utilization by more than 2x, compared to Kubernetes. Essentially, Skynet builds a model on-the-fly to map target PLOs to resources for each application, taking into account multiple resources and changing input load. We implement Skynet as an end-to-end, custom scheduler in Kubernetes and evaluate it using real workloads on both a private cluster and AWS. Skynet decreases PLO violations by more than 7.4x and increases resource utilization by more than 2x, compared to Kubernetes.
Self-Attention-Based Multiscale Feature Learning Optical Flow With Occlusion Feature Map Prediction

Even though optical flow approaches based on convolutional neural networks have achieved remarkable performance with respect to both accuracy and efficiency, large displacements and motion occlusions remain challenges for most existing learning-based models. To address the abovementioned issues, we propose in this paper a self-attention-based multiscale feature learning optical flow computation method with occlusion feature map prediction. First, we exploit a self-attention mechanism-based multiscale feature learning module to compensate for large displacement optical flows, and the presented module is able to capture long-range dependencies from the input frames. Second, we design a simple but effective self-learning module to acquire an occlusion feature map, in which the predicted occlusion map is utilized to correct the optical flow estimation in occluded areas. Third, we explore a hybrid loss function that integrates the photometric and smoothness losses into the classical endpoint error (EPE)-based loss to ensure the accuracy and robustness of the presented network. Finally, we compare the proposed method with some state-of-the-art approaches using the MPI-Sintel and KITTI test databases. The experimental results demonstrate that the proposed method achieved competitive performance with respect to both accuracy and robustness, and it produced the better results compared to other methods under large displacements and motion occlusions.
A deep neural network based framework for restoring the damaged persian pottery via digital inpainting

Conservation of the historical artworks involves carefully planning interventions to prolong the material, historical, and design integrity of humanity's-built heritage. Among which, restoration of the destroyed parts of the artworks via inpainting is one of the most critical stages in the restorative conservation. Indeed, one of the significant assets of any art-historical work is its aesthetic aspects. However, usually due to the visual complexity and the diversity of the motifs, the number of patterns that a restorer artist can have in mind when repairing them, are limited. Also, the inpainting process of such artworks might be very slow and prone to errors; a slight error can cause severe mistakes for the aesthetic aspects of the artifact. Therefore, the need to develop an intelligent system to provide an image of the refurbished artifact before to the restoration operation, is undeniable. Motivated by the idea that the computer-vision-based image inpainting process could be an efficient solution for this purpose, in this paper, for the first time, we propose a framework that employs a deep artificial neural network-based approach to inpaint the images of Persian pottery for restoring their damaged parts. The framework can repair irregular structures that usually exist in Persian pottery and inpaints relatively large missing parts. Besides, the restored parts were seamlessly integrated with the rest of the image without the need for any further postprocessing. To evaluate the proposed framework, we have collected 677 images from different Kubachi ware (known as Persian pottery) to study the inpainting results. The provided dataset is available free for the research community. Quantitative and qualitative evaluations of the results show that the proposed framework performs well, where the inpainted parts are visually and semantically plausible, and the details of the predicted colors in the damaged areas are very satisfying.
Few-Shot Learning Text Classification in Federated Environments

With increasing privacy restrictions on personal devices, data is often prevented from leaving these devices and reaching the central servers where critical operations are carried out to research and train models better. It is crucial to improve such data and fine-tune the performance of machine learning models in use today. Here, few-shot learning is applicable with federated datasets, where each device holds a limited amount of data only. In this paper, we carry out experiments using an Induction Network (using meta-learning for classification tasks) or a pre-trained BART model by customising it for each node in our dataset. The former helps with a solution devoid of recurrent communication among the node and also slashes computational costs significantly. The latter is bias-free and works efficiently with sparse data. Furthermore, upon reviewing the literature, it was observed that there is a lack of research work entailing the application of few-shot learning algorithms coupled on natural language processing tasks in a federated setting. We attempt to address this gap with this research.
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes

We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research. The new dataset contains the largest number of rainy images (58,542) to date, 5,000 of which provide semantic segmentations and 3,658 provide object instance segmentations. The RaidaR images cover a wide range of realistic rain-induced artifacts, including fog, droplets, and road reflections, which can effectively augment existing street scene datasets to improve data-driven machine perception during rainy weather. To facilitate efficient annotation of a large volume of images, we develop a semiautomatic scheme combining manual segmentation and an automated processing akin to cross validation, resulting in 10-20 fold reduction on annotation time. We demonstrate the utility of our new dataset by showing how data augmentation with RaidaR can elevate the accuracy of existing segmentation algorithms. We also present a novel unpaired image-to-image translation algorithm for adding/removing rain artifacts, which directly benefits from RaidaR.
Privacy-Enhanced Federated Learning with Weighted Aggregation

The pervasive adoption of machine learning (ML) techniques by social network operators has led to a growing concern in the personal data privacy of their customers. ML inevitably accesses and processes users' personal data, which could potentially breach the relevant privacy protection regulations if not performed carefully. In this backdrop, Federated Learning (FL) is an emerging area that allows ML on distributed data without the data leaving their stored location. Typically, FL starts with an initial global model, with each datastore uses its local data to compute the gradient based on the global model, and uploads their gradients (instead of the data) to an aggregation server, at which the global model is updated and then distributed to the local datastores iteratively. However, depending on the nature of the services operated by social networks, data captured at different locations may carry different significance to the business operation, hence a weighted aggregation will be highly desirable for enhancing the quality of the FL model. Furthermore, to prevent the data leakage from aggregated gradients, cryptographic mechanisms are needed to allow secure aggregation of FL. As such, this paper proposes a privacy-enhanced FL scheme, based on cryptographic mechanisms that allow both the data significance evaluation and weighted aggregation of local models in a privacy-preserving manner. Experimental results show that our scheme is practical and secure.
Breast Cancer Detection using Machine Learning Algorithms

Breast cancer is diagnosed in over 2 million people worldwide each year., according to cancer research. accounting for the majority of all cancer diagnoses and deaths., rendering it a major public health issue. However, in its early stages, it is still a curable cancer. Early detection of breast cancer, combined with prompt and effective treatment, improves patients' prognosis and recovery. There is a high risk of confusion and incorrect detection when classifying tumors, which must be addressed. Unnecessary therapies can be avoided if patients are correctly classified. This research is focused on machine learning (ML) algorithms, with the aim of reviewing a python methodology and its use in cancer diagnosis and prognosis through the development of a basic machine learning model. Machine learning has a distinct benefit, It is capable of distinguishing important characteristics in large datasets of breast cancer. The method is commonly used in pattern recognition and prediction modelling.
Stream-based active learning for sliding windows under the influence of verification latency

Abstract Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier’s performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label’s utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label’s arrival date a priori and the classifier’s training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.
Modeling the Context of the Problem Domain of Time Series with Type-2 Fuzzy Sets

Data analysis in the context of the features of the problem domain and the dynamics of processes are significant in various industries. Uncertainty modeling based on fuzzy logic allows building approximators for solving a large class of problems. In some cases, type-2 fuzzy sets in the model are used. The article describes constructing fuzzy time series models of the analyzed processes within the context of the problem domain. An algorithm for fuzzy modeling of the time series was developed. A new time series forecasting scheme is proposed. An illustrative example of the time series modeling is presented. The benefits of contextual modeling are demonstrated.
Detection of Acute Lymphoblastic Leukemia by Utilizing Deep Learning Methods

Leukemia is a deadly illness that compromises the lives of numerous patients throughout the world. Leukemia does not shape strong tumors, yet structures a huge number of anomalous white platelets which group out the ordinary platelets. Deep learning algorithms are largely employed in the treatment of leukemia, for detecting if leukemia is present in a patient. This paper proposes two different classification models for detection of Acute Lymphoblastic Leukemia (ALL) utilizing ALL-IDB2 dataset which consists of microscopic images of blood. Hybrid AlexNet and Machine Learning based ALL detection model comprises three-stages which are image pre-processing, feature extraction using AlexNet, a pre-trained convolutional neural network, and classification, employing SVM, kNN, XGBoost, and Decision Trees. Subsequently, the AlexNet based detection model employs image pre-processing after which both feature extraction and classification are performed using AlexNet. Results show that the second detection model performs better than the first with 100% accuracy in classification.
Constraint-Based Unsupervised Domain Adaptation Network for Multi-Modality Cardiac Image Segmentation

The cardiac CT and MRI images depict the various structures of the heart, which are very valuable for analyzing heart function. However, due to the difference in the shape of the cardiac images and imaging techniques, automatic segmentation is challenging. To solve this challenge, in this paper, we propose a new constraint-based unsupervised domain adaptation network. This network first performs mutual translation of images between different domains, it can provide training data for the segmentation model, and ensure domain invariance at the image level. Then, we input the target domain into the source domain segmentation model to obtain pseudo-labels and introduce cross-domain self-supervised learning between the two segmentation models. Here, a new loss function is designed to ensure the accuracy of the pseudo-labels. In addition, a cross-domain consistency loss is also introduced. Finally, we construct a multi-level aggregation segmentation network to obtain more refined target domain information. We validate our method on the public whole heart image segmentation challenge dataset and obtain experimental results of 82.9% and 5.5 on dice and average symmetric surface distance (ASSD), respectively. These experimental results prove that our method can provide important assistance in the clinical evaluation of unannotated cardiac datasets.
Burst Photography for Learning to Enhance Extremely Dark Images

Capturing images under extremely low-light conditions poses significant challenges for the standard camera pipeline. Images become too dark and too noisy, which makes traditional enhancement techniques almost impossible to apply. Recently, learning-based approaches have shown very promising results for this task since they have substantially more expressive capabilities to allow for improved quality. Motivated by these studies, in this paper, we aim to leverage burst photography to boost the performance and obtain much sharper and more accurate RGB images from extremely dark raw images. The backbone of our proposed framework is a novel coarse-to-fine network architecture that generates high-quality outputs progressively. The coarse network predicts a low-resolution, denoised raw image, which is then fed to the fine network to recover fine-scale details and realistic textures. To further reduce the noise level and improve the color accuracy, we extend this network to a permutation invariant structure so that it takes a burst of low-light images as input and merges information from multiple images at the feature-level. Our experiments demonstrate that our approach leads to perceptually more pleasing results than the state-of-the-art methods by producing more detailed and considerably higher quality images.
A Semantic Segmentation based POI Coordinates Generating Framework for On-demand Food Delivery Service

Nowadays, on-demand food delivery service has become fashionable in China. The efficiency of food delivery relies heavily on accurate coordinates of destination Points of Interest (POI). However, the coordinates of the destination POIs from the existing geospatial data warehouses still have many problems that perplex couriers severely. The major problems can be concluded in two categories: 1) the deviation of POI coordinates; 2) the lack of POI coordinates. To address these problems, we propose a POI-coordinate-generating framework based on couriers' and users' behavioral data of historical waybills. In particular, we start with a combinatorial strategy to assign waybills to Areas of Interest (AOI). Second, we generate a destination POI name by processing the user address for each waybill, and all waybills are grouped by the corresponding POI name. Then, a data density image of the behavioral data is generated for each group, with the ground-truth location of the POI labeled. Finally, a U-Net is trained by using the images generated in the previous step to infer locations of the POIs. We evaluated this framework by launching experiments and case studies on large-scale datasets, and the result shows our framework can predict coordinates of POIs accurately. These predicted coordinates can be used to calibrate deviated coordinates of many POIs and complement the geospatial data warehouse.
Knowledge-Driven Machine Learning and Applications in Wireless Communications

The power of big data and machine learning has been drastically demonstrated in many fields during the past twenty years which somehow leads to the vague even false understanding that the huge amount of precious human knowledge accumulated to date seems to no longer matter. In this paper, we are pioneering to propose the knowledge-driven machine learning (KDML) model to exhibit that knowledge can play an important role in machine learning tasks. Compared with conventional machine learning, KDML contains a unique knowledge module based on specific domain knowledge, which is able to simplify the machine learning network structures, reduce the training overhead and improve interpretability. Channel estimation problem of wireless communication is taken as a case verification because such machine learning-based solutions face huge challenges in terms of accuracy, complexity, and reliability. We integrate the classical wireless channel estimation algorithms into different machine learning neural networks and propose KDML-based channel estimators in Orthogonal Frequency Division Multiplexing (OFDM) and Massive Multiple Input Multiple Output (MIMO) system. The experimental results in both communication systems validate the effectiveness of the proposed KDML-based channel estimators.
Non-malleable Time-Lock Puzzles and Applications

Time-lock puzzles are a mechanism for sending messages “to the future”, by allowing a sender to quickly generate a puzzle with an underlying message that remains hidden until a receiver spends a moderately large amount of time solving it. We introduce and construct a variant of a time-lock puzzle which is non-malleable, which roughly guarantees that it is impossible to “maul” a puzzle into one for a related message without solving it.Using non-malleable time-lock puzzles, we achieve the following applications: The first fair non-interactive multi-party protocols for coin flipping and auctions in the plain model without setup. Practically efficient fair multi-party protocols for coin flipping and auctions proven secure in the (auxiliary-input) random oracle model. As a key step towards proving the security of our protocols, we introduce the notion of functional non-malleability, which protects against tampering attacks that affect a specific function of the related messages. To support an unbounded number of participants in our protocols, our time-lock puzzles satisfy functional non-malleability in the fully concurrent setting. We additionally show that standard (non-functional) non-malleability is impossible to achieve in the concurrent setting (even in the random oracle model).