Reviewerly/exHarmony huggingface dataset:
Representation Learning and NLP

Abstract Natural languages are typical unstructured information. Conventional Natural Language Processing (NLP) heavily relies on feature engineering, which requires careful design and considerable expertise. Representation learning aims to learn representations of raw data as useful information for further classification or prediction. This chapter presents a brief introduction to representation learning, including its motivation and basic idea, and also reviews its history and recent advances in both machine learning and NLP.
Low‐cost and two‐cycle hardware structures of PRINCE lightweight block cipher

Summary In this paper, low‐cost and two‐cycle hardware structures of the PRINCE lightweight block cipher are presented. In the first structure, we proposed an area‐constrained structure, and in the second structure, a high‐speed implementation of the PRINCE cipher is presented. The substitution box (S‐box) and the inverse of S‐box (S‐box −1 ) blocks are the most complex blocks in the PRINCE cipher. These blocks are designed by an efficient structure with low critical path delay. In the low‐cost structure, the S‐boxes and S‐boxes −1 are shared between the round computations and the intermediate step of PRINCE cipher. Therefore, the proposed architecture is implemented based on the lowest number of computation resources. The two‐cycle implementation of PRINCE cipher is designed by a processing element (PE), which is a general and reconfigurable element. This structure has a regular form with the minimum number of the control signal. Implementation results of the proposed structures in 180‐nm CMOS technology and Virtex‐4 and Virtex‐6 FPGA families are achieved. The proposed structures, based on the results, have better critical path delay and throughput compared with other's related works.
Targeting Development Aid with Machine Learning and Mobile Phone Data

Recent papers demonstrate that non-traditional data, from mobile phones and other digital sensors, can be used to roughly estimate the wealth of individual subscribers. This paper asks a question more directly relevant to development policy: Can non-traditional data be used to more efficiently target development aid? By combining rich survey data from a "big push" anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from other households deemed ineligible. We show that supervised learning methods leveraging mobile phone data can identify ultra-poor households as accurately as standard survey-based measures of poverty, including consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source. We discuss the implications and limitations of these methods for targeting extreme poverty in marginalized populations.
Magic Machines for Refugees

This paper presents findings from a set of 'magic machines' workshops with newly arrived Iraqi refugees in Australia. The aim was to allow a broad range of response in designing innovative and creative technologies that can help refugees deal with specific challenges. To bring the 'future' into the present and to understand their needs and experiences, we asked 12 participants to create low-fi objects from different materials and to enact them in different scenarios. The magic machines workshops helped access refugees' voices and provided future contexts for them to deal with their challenges. The data analysis of the two workshops revealed three broad themes: information provision barriers, security and ethical challenges, and mistrust and cultural aspects. Our findings show that adopting a speculative design approach has encouraged refugees to have a strong voice- creatively articulated in the form of a set of magic machines. The study offers insights into refugees' perceptions of the future and current technology. It also informs policymakers of the issues around current policy hurdles newcomer refugees face in their settlement in the host community.
Robust Visual Tracking With Occlusion Judgment and Re-Detection

A detection algorithm is often used to remedy tracking failures in a typical single-target visual tracking algorithm.In practice, when a target is occluded for a long time, neither the tracking module nor the detection module can accurately predict the position of the target.To accurately locate the target, we first introduce the 1 2 loss function to reduce the sensitivity of correlation filter-based method to local occlusion.To solve the instability of algorithms based on the single feature in complex scene, we use the histogram of oriented gradient (HOG) features and color names (CN) features to train a filter respectively, and the fusion weights are calculated according to the difference between the response value of each filter and the expected response value.At the same time, we adaptively update the model online by calculating the sensitivity of different filters.We follow the re-detection idea in long-term tracking, the peak to sidelobe ratio (PSR) is used to judge the serious occlusion, and we use support vector machine (SVM) for re-detection after severe occlusion or target out-of-view.In this paper, 34 sets of sequences are selected to evaluate the proposed algorithm.The sufficient experimental results demonstrate that our algorithm has strong anti-occlusion ability and robustness performance.We compare our proposal with several state-of-the-art algorithms under all the sequences of OTB100, and our algorithm yields highly competitive performance for tracking.
RGCF: Refined graph convolution collaborative filtering with concise and expressive embedding

Graph Convolution Networks (GCNs) have attracted significant attention and have become the most popular method for learning graph representations. In recent years, many efforts have focused on integrating GCNs into recommender tasks and have made remarkable progress. At its core is to explicitly capture the high-order connectivities between nodes in the user-item bipartite graph. However, we found some potential drawbacks existed in the traditional GCN-based recommendation models are that the excessive information redundancy yield by the nonlinear graph convolution operation reduces the expressiveness of the resultant embeddings, and the important popularity features that are effective in sparse recommendation scenarios are not encoded in the embedding generation process. In this work, we develop a novel GCN-based recommendation model, named Refined Graph convolution Collaborative Filtering (RGCF), where a refined graph convolution structure is designed to match non-semantic ID inputs. In addition, a new fine-tuned symmetric normalization is proposed to mine node popularity characteristics and further incorporate the popularity features into the embedding learning process. Extensive experiments were conducted on three public million-size datasets, and the RGCF improved by an average of 13.45% over the state-of-the-art baseline. Further comparative experiments validated the effectiveness and rationality of each part of our proposed RGCF. We released our code at https://github.com/hfutmars/RGCF.
Introduction to digital image steganography

The Internet and cloud services are widely used in transmission of large amounts of data over open networks and insecure channels, exposing private and secret data to serious situations. Thus ensuring that information transmission over these mediums is safe and secure has become one of the most important issues in the data security field. To keep unauthorized persons away from the transmitted information, a variety of techniques have been introduced, and steganography is one of them. As a technique for covert communication, steganography aims to hide secret messages in a normal message achieving the least possible statistical detectability and without drawing suspicion. Steganography techniques can be applied on a various types of data such as text, image, video, and audio data. This chapter presents a general and brief introduction to exemplify the tremendous progress achieved recently in the topic of image steganography to help readers and researchers in discovering new research gaps in the theoretical foundations and practical implementations of steganography techniques.
A Pointer Meter Recognition Algorithm Based on Deep Learning

In order to build an intelligent, unmanned and managed substation, the substation gradually adopts inspection robots instead of manual work. However, during the inspection process, the automatic identification of the pointer type meter always has the problem that the recognition accuracy is not high and is susceptible to illumination changes. In this paper, the problem of pointer instrument identification is studied. The method of instrument detection and localization algorithm, pointer and instrument scale fitting in complex environment is studied systematically. A pointer meter identification algorithm suitable for intelligent substation inspection robot is proposed. Using the positioning function of deep learning, the instrument classification algorithm based on Faster R-CNN is used to classify three types of tables: voltmeter, ammeter and digital table. Image processing of the positioned pointer meter image, such as tilt correction, extraction of scale segments, pointer line segments, line fitting, repairing the default tick marks, etc. is operated to calculate a more accurate pointer reading.
Dynamic Multiple-Replica Provable Data Possession in Cloud Storage System

In cloud storage scenarios, data security has received considerably more attention than before.To ensure the reliability and availability of outsourced data and improve disaster resilience and data recovery ability, important data files possessed by users must be stored on multiple cloud service providers (CSPs).However, we know that CSP is invariably not reliable.In this situation, to verify the integrity of replica files stored by users on multiple CSPs simultaneously, a new dynamic multiple-replica provable data possession (DMR-PDP) scheme is proposed.In addition, due to the importance of the tag set, we utilize vector dot products instead of using the modular power calculation in the traditional PDP scheme, which greatly reduces the calculation time and storage space usage.Moreover, a novel dynamic data structure, the divided addressversion mapping table (DAVMT), is presented and used to solve the problem of data dynamic operation.A practical experiment validates the effectiveness of our proposed scheme in the end.
A Proposal for an Explainable Fuzzy-based Deep Learning System for Skin Cancer Prediction

Explainable deep learning (XDL) is a research field that aims to make deep learning predictions or classifications more understandable for humans. Literature shows that deep learning (DL) algorithms are more precise in terms of their prediction than traditional machine learning (ML) algorithms. Nevertheless, they lack the interpretability and explainability that less complex algorithms are more fitted. Nowadays, one of the main goals of XDL research is to make these algorithms highly precise and explainable. There exist different systems and techniques, but only limited research on explaining deep neural networks using soft computing approaches. The purpose of this research is to propose the theoretical fundamentals of an explainable fuzzy-based deep learning (EFBDL) system that is both precise and explainable. The system comprises two main parts. First, the deep network part composed of a convolutional neural network (CNN) based on Inception V4 for image classification, a transfer learning mechanism, and a feature extraction algorithm based on neuron perturbation. Second, a soft computing part comprised of a fuzzy rule-based system (FRBS), a hierarchical network for natural language generation named granular linguistic model of a phenomenon (GLMP), and a human-machine integration methodology for linguistic rules named highly interpretable linguistic knowledge (HILK). The output of the overall system is an explanation of the neural network decision using natural language. This system focuses on preventing skin cancer rather than healing it. Thus, governments could use this kind of system for implementing policies focused on prevention and save in overall treatment costs of the disease.
vtGraphNet: Learning weakly-supervised scene graph for complex visual grounding

As a challenging cross-modal task, current visual grounding is usually addressed by directly analyzing the unstructured scene and matching the query text with all region proposals, which is prone to errors, especially when the scene and/or query text are complex. In this paper, we study such complex visual grounding problem and propose to build a query dependent visual-textual (VT) scene graph to jointly understand the image and query text. To avoid the difficulty of obtaining ground-truth scene graphs, we propose vtGraphNet to effectively learn the bi-modal scene graph in a weakly-supervised way, where the only supervision is the manually annotated grounding region. Specifically, we first use an ARU Tagging model to sequentially tag every query word as either an attribute, a relationship or an auxiliary. If a word is tagged as attribute, we develop an attribute-assigning model to associate it to a region proposal. If a word is tagged as relationship, we develop a relationship-referring model to associate it to a pair of region proposals. A simple yet effective graph consistency loss function is constructed to constrain the above associations to form a feasible compact VT scene graph, from which discriminative region features can be extracted and used to locate the grounding object by classification. Extensive experiments on benchmark datasets validate the superiority of our approach in handling both simple and complex visual grounding tasks.
Guided Depth Map Super-Resolution Using Recumbent Y Network

Low spatial resolution is a well-known problem for depth maps captured by low-cost consumer depth cameras. Depth map super-resolution (SR) can be used to enhance the resolution and improve the quality of depth maps. In this paper, we propose a recumbent Y network (RYNet) to integrate the depth information and intensity information for depth map SR. Specifically, we introduce two weight-shared encoders to respectively learn multi-scale depth and intensity features, and a single decoder to gradually fuse depth information and intensity information for reconstruction. We also design a residual channel attention based atrous spatial pyramid pooling structure to further enrich the feature's scale diversity and exploit the correlations between multi-scale feature channels. Furthermore, the violations of co-occurrence assumption between depth discontinuities and intensity edges will generate texture-transfer and depth-bleeding artifacts. Thus, we propose a spatial attention mechanism to mitigate the artifacts by adaptively learning the spatial relevance between intensity features and depth features and reweighting the intensity features before fusion. Experimental results demonstrate the superiority of the proposed RYNet over several state-of-the-art depth map SR methods.
A dataset describing data discovery and reuse practices in research

Abstract This paper presents a dataset produced from the largest known survey examining how researchers and support professionals discover, make sense of and reuse secondary research data. 1677 respondents in 105 countries representing a variety of disciplinary domains, professional roles and stages in their academic careers completed the survey. The results represent the data needs, sources and strategies used to locate data, and the criteria employed in data evaluation of these respondents. The data detailed in this paper have the potential to be reused to inform the development of data discovery systems, data repositories, training activities and policies for a variety of general and specific user communities.
Data Mining Methodology in Support of a Systematic Review of Human Aspects of Cybersecurity

Cybersecurity is an evolving field in the area of human-computer interactions (HCI), but human factors is a relevant area to consider when approaching cybersecurity. This report illustrates the findings of a systematic literature review of current publications on the emerging trends of human factors in cybersecurity. Analyses of content and bibliometrics were accomplished by using tools such as VOS Viewer, MAXQDA, Harzing, and AuthorMapper to establish the findings of emerging trends in the field. This report includes a step-by-step procedure for conducting the content analyses in each tool. The areas of human factors and cybersecurity are examined based on the data of the content analyses. A key finding is that human factors theory emerged from content analysis, and can be a basis for future research.
Generating Visible Spectrum Images from Thermal Infrared using Conditional Generative Adversarial Networks

Thermal Infrared cameras have a unique set of capabilities that enables them to see and capture images in situations where vision is severely obscured, including dark, shade, and foggy conditions. Conversion of TIR image to visible spectrum makes them more interpretable, but automating transformation is a challenging task as it requires estimation of both chrominance and luminance for each pixel, posing the need for a more complex model that is capable of capturing higher-level semantics of a TIR image to generate a perceptually realistic RGB image. The use of Conditional Generative Adversarial Networks (cGANs) is proposed with a combination of content and adversarial losses to tackle this problem. The proposed cGAN uses a U-Net auto-encoder network as generator, and a patch based discriminator for adversarial training. The cGAN learns the most effective loss function during training, producing state-of-the-art perceptually realistic outputs. Qualitative and quantitative analyses show that our approach outperforms existing methods.
Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is a fulminant inflammatory lung injury that develops in patients with critical illnesses, affecting 200,000 patients in the United States annually. However, a recent study suggests that most patients with ARDS are diagnosed late or missed completely and fail to receive life-saving treatments. This is primarily due to the dependency of current diagnosis criteria on chest x-ray, which is not necessarily available at the time of diagnosis. In machine learning, such an information is known as Privileged Information - information that is available at training but not at testing. However, in diagnosing ARDS, privileged information (chest x-rays) are sometimes only available for a portion of the training data. To address this issue, the Learning Using Partially Available Privileged Information (LUPAPI) paradigm is proposed. As there are multiple ways to incorporate partially available privileged information, three models built on classical SVM are described. Another complexity of diagnosing ARDS is the uncertainty in clinical interpretation of chest x-rays. To address this, the LUPAPI framework is then extended to incorporate label uncertainty, resulting in a novel and comprehensive machine learning paradigm - Learning Using Label Uncertainty and Partially Available Privileged Information (LULUPAPI). The proposed frameworks use Electronic Health Record (EHR) data as regular information, chest x-rays as partially available privileged information, and clinicians' confidence levels in ARDS diagnosis as a measure of label uncertainty. Experiments on an ARDS dataset demonstrate that both the LUPAPI and LULUPAPI models outperform SVM, with LULUPAPI performing better than LUPAPI.
A SME Credit Evaluation System Based on Blockchain

As the development of the national economy, SMEs(Small and Medium-sized Enterprises) have played an important role in most aspects of economic growth and commercial credit has become one essential factor in enterprises' development. Thus, developing one excellent credit evaluation system which reflects the credit level of SMEs well has become one hotspot in corresponding research field. This paper has explored one distributed credit evaluation system based on blockchain technology which has intelligent protocols and distributed features to provide credit records through unchangeable timestamps and distributed ledger and improve the defects in current centralized systems.
Implementation and analysis of image steganography using Least Significant Bit and Discrete Wavelet Transform techniques

Steganography is classified among the foremost methods employed in data security to conceal and safeguard confidential messages in the data transmitted. Security, especially data security, is an important requisite in today's world hence Steganography has great significance. The paper deals with understanding and implementation of steganography on different images using two different techniques: Least Significant Bit method(secret image is hidden using the bits at least significant level of the cover image) and Discrete Wavelet Transform method(secret image is hidden by modification of the wavelet coefficients of cover image). The image to be transmitted secretly is both encoded and decoded using these methods and a detailed analysis of the resultant images is performed using various image parameters. These experimentally obtained and compared efficiency parameters, thus, demonstrate the efficiency of the methodology proposed in the paper.
Ensemble topic modeling using weighted term co-associations

Topic modeling is a popular unsupervised technique that is used to discover the latent thematic structure in text corpora. The evaluation of topic models typically involves measuring the semantic coherence of the terms describing each topic, where a single value is used to summarize the quality of an overall model. However, this can create difficulties when one seeks to interpret the strengths and weaknesses of a given topic model. With this in mind, we propose a new ensemble topic modeling approach that incorporates both stability information, in the form of term co-associations, and semantic similarity information, as derived from a word embedding constructed on a background corpus. Our evaluations show that this approach can simultaneously yield higher quality models when considering the produced topic descriptors and document-topic assignments, while also facilitating the comparison and evaluation of solutions through the visualization of the discovered topical structure, the ordering of the topic descriptors, and the ranking of term pairs which appear in topic descriptors.