Reviewerly/exHarmony huggingface dataset:
Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks

Statistical and adversarial adaptation are currently two extensive categories of neural network architectures in unsupervised deep domain adaptation. The latter has become the new standard due to its good theoretical foundation and empirical performance. However, there are two shortcomings. First, recent studies show that these approaches focus too much on easily transferable features and thus neglect important discriminative information. Second, adversarial networks are challenging to train. We addressed the first issue by the alignment of transferable spectral properties within an adversarial model to balance the focus between the easily transferable features and the necessary discriminatory features, while at the same time limiting the learning of domain-specific semantics by relevance considerations. Second, we stabilized the discriminator networks training procedure by Spectral Normalization employing the Lipschitz continuous gradients. We provide a theoretical and empirical evaluation of our improved approach and show its effectiveness in a performance study on standard benchmark data sets against various other state of the art methods.
Binary handwriting image enhancement by directional field-guided morphology

This paper proposes a technique for processing handwriting images. The algorithm used in this study is an improvement to the binarisation process. The enhancement focuses on correcting damaged lines that usually arise during the binarisation process, particularly, spurious holes, discontinuities, and eroded boundaries. The presented method uses a morphogical dilation operation in which a structural element is locally adapted using the information from a directional field. The adaptation process involves a new criterion for selecting orientation and shape of a structural element that combines directional field, a coherence measure, and a circular histogram. The field was computed using gradient-based approach, and a method based on a Hessian matrix. During experiments, our method was applied to the output of selected binarisation algorithms. The experiments were conducted on grayscale signature images (from the CEDAR database) and handwriting images (from the DIBCO database). The results of the algorithm were compared to the results of standard morphological operations (dilation, erosion, opening, and closing) and median filtering. The experiments show that the proposed method achieves significant accuracy improvement (8%–12% for Acc, 15%–32% for Acc2 measures), reduces the number of unwanted artefacts, and produces images with less distortion compared to those from standard approaches.
n-Reference Transfer Learning for Saliency Prediction

Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to predict saliency maps on images in new domains that lack sufficient data for data-hungry models. To solve this problem, we propose a few-shot transfer learning paradigm for saliency prediction, which enables efficient transfer of knowledge learned from the existing large-scale saliency datasets to a target domain with limited labeled samples. Specifically, few target domain samples are used as the reference to train a model with a source domain dataset such that the training process can converge to a local minimum in favor of the target domain. Then, the learned model is further fine-tuned with the reference. The proposed framework is gradient-based and model-agnostic. We conduct comprehensive experiments and ablation study on various source domain and target domain pairs. The results show that the proposed framework achieves a significant performance improvement. The code is publicly available at https://github.com/luoyan407/n-reference .
A Multi‐Person Selfie System via Augmented Reality

Abstract The limited length of a selfie stick always poses the problem of distortion in a selfie, in spite of the prevalence of selfie stick in recent years. We propose a technique, based on modifying existing augmented reality technology, to support the selfie of multiple persons, through properly aligning different photographing processes. It can be shown that our technique helps avoiding the common distortion drawback of using a selfie stick, and facilitates the composition process of a group photo. It can also be used to create some special effects, including creating an illusion of having multiple appearances of a person.
Will Quantum Computers Scale Without Inter-Chip Comms? A Structured Design Exploration to the Monolithic vs Distributed Architectures Quest

Being a very promising technology, with impressive advances in the recent years, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Although continued progress in the fabrication and control of qubits is required, quantum computing scalability will depend as well on a comprehensive architectural design considering a distributed multi-core approach as an alternative to the traditional monolithic version, hence including a communications perspective. However, this goes beyond introducing mere interconnects. Rather, it implies consolidating the full communications stack in the quantum computer structure. In this paper, we propose a double full-stack architecture encompassing quantum computation and quantum communications, which we use to address the monolithic versus distributed question with a structured design methodology. For that, we revisit the different quantum computing layers to capture and model their essence by highlighting the open design variables and performance metrics. Using behavioral models and actual measurements from existing quantum computers, the results of simulations suggest that multicore architectures may effectively unleash the full quantum computer potential.
The HHD Dataset

Benchmark datasets are important in document image processing field, as they allow to analyze different approaches and compare their performances in a fair manner. There exist benchmark datasets for several alphabets such as Latin, Arabic and Chinese, but not the Hebrew alphabet. In this paper, a handwritten Hebrew dataset, HHD, is introduced. The HHD dataset is collected from hand-filled forms, and accompanied by their ground truth at character, word and text line levels. Presently, the dataset contains around 1000 document images, and we continue to further enlarge it. To the best of our knowledge, this is the first comprehensive corpus of Hebrew handwritten documents, and we believe it will help leveraging Hebrew documents processing and document processing in general. The dataset can be useful for various research applications, such as word spotting, word recognition, text line alignment, and writer identification. The initial small subset of the HDD for character classification can be downloaded from https://www.cs.bgu.ac.illr-vberatldatalhhd_dataset.zip together with the training and test sets subdivisions. We also provide baseline results for character classification on this initial subset. In the near future, the full HHD dataset will be made freely available to the research community.
Hand Gesture-Based Control of a Front-End Loader

In this paper, we present the design and use of an instrumented glove consisting of a 9-DOF inertial measurement unit (IMU) and resistive flex sensors. The glove is used as a unique human-machine interface to control a Kubota R520s front-end loader, through input gestures, for the excavation of a fragmented rock pile. Raw sensor data from the glove is recorded and transmitted to a computer for gesture recognition. Recognized gestures are then used to command the loader to switch between dig states and control the excavation process. The system allows an operator to observe the entire process from beside the loader, providing them with valuable information about interactions between the loader bucket and rock pile not usually available when seated in the vehicle's cab. Preliminary experiments show that a novice operator was able to improve their performance by using the proposed system, evaluated based on metrics of total and dig completion times, as well as payload.
Demystifying Content-blockers: A Large-scale Study of Actual Performance Gains

With the evolution of the online advertisement and tracking ecosystem, content-filtering has become the reference tool for improving the security, privacy and browsing experience when surfing the Internet. It is also commonly believed that using content-blockers to stop unsolicited content decreases the time needed for loading websites. In this work, we perform a large-scale study with the 100K most popular websites on the actual performance improvements of using content-blockers. We focus our study on two relevant metrics for measuring the browsing performance; page size and loading time. Our results show that using such tools results in small improvements in terms of page size but, contrary to popular belief, it has a negligible impact in terms of loading time. We also find that, in the case of small and lightweight websites, the use of content-blockers can even result in increased loading times.
Multi-face Recognition

When facing the challenge of multi-face recognition, the existing face recognition algorithms have the defects of low accuracy and too slow speed. This paper proposes a multi-face recognition algorithm based on the InsightFace algorithm, which is applied in the scene of large human flow throughput. First, an improved multi-task cascaded convolutional network (Multi-face-MTCNN) algorithm is proposed to accurately realize face detection and feature alignment in a multi-face environment. Moreover, we combine the MobilNetv3 structure and the GhostNet structure to propose a lightweight network structure MobileNetv3-GhostNet that is faster and can extract more facial features. The amount of parameters is greatly reduced, whose size is only 6.7Mb. This algorithm takes into account the accuracy and speed of the multi-face recognition, has good performance, and proposes new ideas for multi-face recognition.
In-Depth Learning of Architectural Heritage with Application of Augmented Reality based on Sequential Scenes

This paper presents a design concept and a working prototype of an augmented reality application to enable the in-depth learning of an architectural design of an existing heritage from a real site. The application uses image recognition to set conditions for displaying different augmented digital contents based on prior location information extracted from the same scene. Consequently, more complex information based on the sequential spatial experience of architectural scenes can be conveyed with the use of the application.
Feature Extraction Method of Radiation Source in Deep Learning Based on Square Integral Bispectrum

Abstract The feature extraction method of radiation source based on deep learning is a hotspot of specific emitter identification research. In the selection of the initial radiation source data for feature extraction, there are mainly two kinds of time series IQ data and frequency domain bispectral data. Both the IQ signal and the signal bispectrum contain the information that can characterize the fingerprint of the radiation source, and the deep learning methods mostly use different deep network structures to obtain better classification performance. This paper proposes a feature extraction method of radiation source based on bispectral data, and designs a deep network structure combining convolution and long short memory network, which has a better classification and recognition rate than a single convolution network and a single LSTM network.
Applications of Machine Learning in Cyber Forensics

Nowadays, individuals and organizations experience an increase in cyber-attacks. Combating such cybercrimes has become the greatest struggle for individual persons and organizations. Furthermore, the battle has heightened as cybercriminals have gone a step ahead, employing the complicated cyber-attack technique. These techniques are minute and unobtrusive in nature and habitually disguised as authentic requests and commands. The cyber-secure professionals and digital forensic investigators enforce by collecting large and complex pools of data to reveal the potential digital evidence (PDE) to combat these attacks and helps investigators to arrive at particular conclusions and/or decisions. In cyber forensics, the challenging issue is hard for the investigators to make conclusions as the big data often comes from multiple sources and in different file formats. The objective is to explore the possible applications of machine learning (ML) in cyber forensics and to discuss the various research issues, the solutions of which will serve out to provide better predictions for cyber forensics.
Stochastic Model Pruning via Weight Dropping Away and Back

Deep neural networks have dramatically achieved great success on a variety of challenging tasks. However, most successful DNNs have an extremely complex structure, leading to extensive research on model compression. As a significant area of progress in model compression, traditional gradual pruning approaches involve an iterative prune-retrain procedure and may suffer from two critical issues: local importance judgment, where the pruned weights are merely unimportant in the current model; and an irretrievable pruning process, where the pruned weights have no chance to come back. Addressing these two issues, this paper proposes the Drop Pruning approach, which leverages stochastic optimization in the pruning process by introducing a drop strategy at each pruning step, namely, drop away, which stochastically deletes some unimportant weights, and drop back, which stochastically recovers some pruned weights. The suitable choice of drop probabilities decreases the model size during the pruning process and helps it flow to the target sparsity. Compared to the Bayesian approaches that stochastically train a compact model for pruning, we directly aim at stochastic gradual pruning. We provide a detailed analysis showing that the drop away and drop back approaches have individual contributions. Moreover, Drop Pruning can achieve competitive compression performance and accuracy on many benchmark tasks compared with state-of-the-art weights pruning and Bayesian training approaches.
PV System Monitoring by IoT in Smart University

The proposed paper is mainly aimed at passing the collected data from smart devices to a server to build a smarter university. The idea is to create the so-called Smart University. The smart university infrastructure includes two main components: IT infrastructure and intelligent engineering systems. Smart engineering systems include power supply systems, water supply, air conditioning, fire protection systems, lighting, access control and video surveillance systems. Part of the proposed systems as elements of smart infrastructure were built at University. Upgrading of the infrastructure with additional elements is forthcoming. The idea is to achieve full synchronization in the work of all university systems using an Internet service.
Design of Blockchain-based Electronic Health Records for Indonesian Context: Narrative Review

Healthcare providers in developing countries manage their medical data in Electronic Health Records (EHRs) system in silos with different structures and data formats that causes the problem of data sharing, interoperability, and data security. This paper aims to propose a concept of blockchain-based EHRs that is relevant to the case of developing countries, especially in Indonesia. The research method used is a narrative review of several literature related to the design of blockchain-based EHRs using Smart Contract from 2016-2020. The design proposed permissioned blockchain with several health care stakeholders as nodes in the network. Different with most previous research that using proof-based, we propose to use vote-based consensus protocol to execute transaction faster. Based on number of national health insurance policy holders, outpatient, and inpatient visits per year, the storage capacity required to keep the transaction is estimated around 2,7 TB per year with 77 transactions per seconds. This design is intended to be a contribution for EHRs platform architecture in the future.
Feature Learning from Image Markers for Object Delineation

Convolutional neural networks (CNNs) have been used in several computer vision applications. However, most well-succeeded models are usually pre-trained on large labeled datasets. The adaptation of such models to new applications (or datasets) with no label information might be an issue, calling for the construction of a suitable model from scratch. In this paper, we introduce an interactive method to estimate CNN filters from image markers with no need for backpropagation and pre-trained models. The method, named FLIM (feature learning from image markers), exploits the user knowledge about image regions that discriminate objects for marker selection. For a given CNN's architecture and user-drawn markers in an input image, FLIM can estimate the CNN filters by clustering marker pixels in a layer-by-layer fashion - i.e., the filters of a current layer are estimated from the output of the previous one. We demonstrate the advantages of FLIM for object delineation over alternatives based on a state-of-the-art pre-trained model and the Lab color space. The results indicate the potential of the method towards the construction of explainable CNN models.
Ship Fault Named Entity Recognition Based on Bilayer Bi-LSTM-CRF

In the named entity recognition task of Chinese electronic ship failure, traditional named entity recognition methods highly rely on manual feature extraction. Therefore, this paper designs a bidirectional long short-term memory (Bi-LSTM) network combined with conditional random field (CRF) network model to optimize the accuracy of ship fault named entity recognition. Firstly, the Chinese ship fault data set is desensitized, and the desensitized text sequence is preprocessed; secondly, the text sequence of ship fault is mapped to the low dimensional vector space by combining the word embedding technology, using the bidirectional long short-term (Bi-LSTM) network model to construct forward and backward semantic features; finally, the input and output of the data are analyzed after entering the conditional random field (CRF) layer, the optimal label of the whole text sequence is obtained through the conditional random field (CRF) layer, and the entity is extracted on this basis. The experimental results show that the model method of combining bilayer bidirectional long short-term memory (Bi-LSTM) network and conditional random field (CRF) can effectively improve the accuracy of named entity recognition of Chinese ship fault.
Stable visualization of connected components in dynamic graphs

One of the primary goals of many systems for the visual analysis of dynamically changing networks is to maintain the stability of the drawing throughout the sequence of graph changes. We investigate the scenario where the changes are determined by a stream of events, each being either an edge addition or an edge removal. The visualization must be updated immediately after each new event is received. Our main goal is to provide the user with an intuitive visualization that highlights the different connected components of the graph while preserving the user’s mental map after each event. The drawing stability is measured in terms of changes in the orthogonal relationships between vertices of two consecutive drawings. We describe two different visualization models, one for the 1-dimensional space and the other for the 2-dimensional space. In both models the connected components are drawn inside rectangular regions. To validate our approach, we report the results of an experimental analysis that compares the drawing stability of the online algorithm with that of an offline algorithm that knows in advance the whole sequence of events. We also present a case study of our online algorithm on a collaboration network.
Image compression-encryption algorithm based on 2D compressive sensing and double random phase encoding

In this paper, an image compression and encryption algorithm based on 2D compressive sensing and double random phase encoding is proposed. Compared with traditional encryption algorithms, 2D compressive sensing can realize image compression and encryption at the same time, with high execution efficiency. Double random phase encoding can not only realize image encryption fast, but also has robustness. Moreover, combining 2D compressive sensing with double random phase encoding can obtain better encryption security. Simulation results show that this algorithm has better image compression performance, and can ensure security and robustness.
A Spatio-Temporal Attention Convolution Block for Action Recognition

Abstract We propose a simple and effective 3D neural network module (STAT) embedded in spatiotemporal attention for action recognition. For a given intermediate feature map, our module sequentially infers the distribution of attention along the two dimensions of space and time, and multiplies it with the current feature map in the form of residual to achieve adaptive generation of the next stage feature map. STAT is a 3D convolution general module combined with attention. It is compatible with any 3D convolution network and can easily replace the 3D convolution kernel. The additional overhead it generates is negligible, and it can be trained end-to-end together with ordinary 3DCNN. By comparing the performance of the currently popular 3D networks on the UCF101 and HMDB51 datasets, experiments show that STAT has certain improvements on most 3D networks, which proves that STAT has a certain universality.
Real-Time Decision Making for Train Carriage Load Prediction via Multi-stream Learning

Real-time traffic planning and scheduling optimization are critical for developing low-cost, reliable, resilient, and efficient transport systems. In this paper, we present a real-time application that uses machine learning techniques to forecast the train carriage load when a train departure from a platform. With the predicted carriage load, crew can efficiently manage passenger flow, improving the efficiency of boarding and alighting, and thereby reducing the time trains spend at stations. We developed the application in collaboration with Sydney Trains. Most data are publicly available on Open Data Hub, which is supported by the Transport for NSW. We investigated the performance of different models, features, and measured their contributions to prediction accuracy. From this we propose a novel learning strategy, called Multi-Stream Learning, which merges streams having similar concept drift patterns to boost the training data size with the aim of achieving lower generalization errors. We have summarized our solutions and hope researchers and industrial users who might be facing similar problems will benefit from our findings.
Some Quantum Error-Correcting Codes with d = 5

Abstract In this paper, we firstly give a computer-support method of searching for Hermitian self-orthogonal linear code. Then two linear codes with dual distance 5 over field F q 2 for q = 2,3, respectively, are chosen as ingredients. By use of this searching method, we totally find 88 Hermitian self-orthogonal linear codes and construct 88 related quantum error-correcting codes with d = 5 . Most of these results are best known quantum codes and a number of them have better parameters than that of previous quantum codes.