Reviewerly/exHarmony huggingface dataset:
eTextiles

Electronic textiles, or eTextiles, and connected research and practice communities grew within and across diverse disciplines over the past 20 years. Initially evolving from academic investigations, eTextiles now play a growing role in both industry and education alike. While we are increasingly confronted with resulting eTextile artefacts, we lack a thorough understanding of the underlying making practices, and in particular what role toolkits play in framing, promoting and supporting creation practices. It is timely then to undertake a review of currently available eTextile tools and kits, discussing the technical, cultural and social expectations inscribed in these settings and how their design and technology impacts the emerging field of eTextiles. Here we compile the first overview of both academic research and popular available toolkits, as a basis for an analysis of potential strategies for future directions: how to diversify and professionalise the field and its community of practice.
Single image super-resolution with multi-level feature fusion recursive network

Recursive learning can widen the receptive field of deep convolutional neural network while do not increase model parameters with parameters sharing. And dense skip connections can promote deep feature representation ability by reusing deep features in different receptive fields. Both techniques are very beneficial to improve performance in image restoration tasks. In this paper, we propose a new end to end deep network for single image super-resolution (SISR) by using both recursive residual feature extraction and multi-level features fusion, in which the multi-level deep features are firstly produced from the input low resolution (LR) image by recursive convolution units, and then fused to reconstruct high resolution (HR) image. The proposed scheme could achieve good super-resolution performance with relatively low complexity. Extensive experimental results on the benchmark tests verify the effectiveness of the proposed method.
Growth Enhancement Support Scheme (GESS) and Farmers’ Fertilizer Use in Rural Nigeria

Abstract Fertilizer use in Nigeria is estimated at 13 kg/ha, which is far below the 200 kg/ha recommended by the Food and Agricultural Organization (FAO). The objective of this investigation was to identify the determinant factors of farmers’ participation in Nigeria's Growth Enhancement Support Scheme (GESS). In addition, we determined the impact of the GESS on fertilizer use in rural areas. A total of 1,200 rural farmers were sampled across the six geopolitical zones of Nigeria. Results from the use of a recursive bivariate probit model indicated that GESS significantly impacted on the access and usage of fertilizer among the rural farmers; and that contact with extension agents, ownership of mobile phones, power for charging phone batteries, value output, mobile network coverage, ability to read and write were positive determinants of rural farmers’ participation in the GESS; whereas increased distance to registration and collection centers, and cultural constraints to married women reduced farmers’ tendency to participate in the GESS. The findings suggest that farmers’ participation in the GESS is a critical factor for raising fertilizer use in Nigeria. This implies that food security in sub‐Saharan Africa can be achieved by increasing the participation of rural farmers in the GESS.
Discrete Artificial Potential Field Approach to Mobile Robot Path Planning

The paper introduces a path planning method for an autonomous mobile robot, called the Discrete Artificial Potential Field algorithm (DAPF). The method is different from the currently applied similar path planning approaches, such as the classical APF method, using attractive and repulsive potential field functions or the wave front algorithm. The novelty and originality of the method lies in the construction of the discrete potential field, the method of taking dynamic obstacles into account and achievement of effective solutions in terms of the path length and run time of the algorithm. The DAPF algorithm is capable of finding a collision-free path for a mobile robot in static and dynamic environments. The Path Optimization Algorithm (POA) is also proposed in the paper. Its aim is to modify the collision-free path in order to obtain a smoother and shorter path. The DAPF algorithm runs in near-real time, therefore the method can be used in practical applications. The algorithm is evaluated by simulations in the MATLAB environment and by real experiments with the use of four-wheel differentially driven mobile robots. The results were compared with a heuristic approach based on Ant Colony Optimization and demonstrate the feasibility and effectiveness of the presented approach.
Leveraging post-click feedback for content recommendations

Implicit feedback (e.g., clicks) is widely used in content recommendations. However, clicks only reflect user preferences according to their first impressions. They do not capture the extent to which users continue to engage with the content. Our analysis shows that more than half of the clicks on music and short videos are followed by skips from two real-world datasets. In this paper, we leverage post-click feedback, e.g. skips and completions, to improve the training and evaluation of content recommenders. Specifically, we experiment with existing collaborative filtering algorithms and find that they perform poorly against post-click-aware ranking metrics. Based on these insights, we develop a generic probabilistic framework to fuse click and post-click signals. We show how our framework can be applied to improve pointwise and pairwise recommendation models. Our approach is shown to outperform existing methods by 18.3% and 2.5% respectively in terms of Area Under the Curve (AUC) on the short-video and music dataset. We discuss the effectiveness of our approach across content domains and trade-offs in weighting various user feedback signals.
Investigation of Modeling Units for Mandarin Speech Recognition Using Dfsmn-ctc-smbr

The choice of acoustic modeling units is critical to acoustic modeling in large vocabulary continuous speech recognition (LVCSR) tasks. The recent connectionist temporal classification (CTC) based acoustic models have more options for the choice of modeling units. In this work, we propose a DFSMN-CTC-sMBR acoustic model and investigate various modeling units for Mandarin speech recognition. In addition to the commonly used context-independent Initial/Finals (CI-IF), context-dependent Initial/Finals (CD-IF) and Syllable, we also propose a hybrid Character-Syllable modeling units by mixing high frequency Chinese characters and syllables. Experimental results show that DFSMN-CTC-sMBR models with all these types of modeling units can significantly outperform the well-trained conventional hybrid models. Moreover, we find that the proposed hybrid Character-Syllable modeling units is the best choice for CTC based acoustic modeling for Mandarin speech recognition in our work since it can dramatically reduce substitution errors in recognition results. In a 20,000 hours Mandarin speech recognition task, the DFSMN-CTC-sMBR system with hybrid Character-Syllable achieves a character error rate (CER) of 7.45% while performance of the well-trained DFSMN-CE-sMBR system is 9.49%.
Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN.
Private Coded Matrix Multiplication

In distributed computing system for the master-worker framework, an erasure code is able to mitigate the effects of slow workers, also called stragglers. The distributed computing system combined with coding is referred to as coded computation. For a matrix multiplication, we consider a variation of coded computation that ensures the master's privacy from the workers, which is referred to as private coded matrix multiplication. In the private coded matrix multiplication, the master needs to compute a matrix multiplication on its own matrix and one of the matrices in a library exclusively shared by the external workers. After the master recovers the matrix multiplication through coded matrix multiplication, the workers should not know which matrix in the library was desired by the master, which implies that the master's privacy is ensured. Our problem is a special case of linear private computation, where a linear combination of matrices in the library should be concealed. We propose a private coded matrix multiplication scheme, based on the conventional coded matrix multiplication scheme. In terms of computation time and communication load, we compare our proposed scheme with a conventional robust private information retrieval scheme and private computation schemes.
Does Insurance Have a Future in Governing Cybersecurity?

Cyber insurance could achieve public policy goals for cybersecurity using private-sector means. Insurers assess organizational security postures, prescribe security procedures and controls, and provide postincident services. We evaluate how such mechanisms impact security, identify market dynamics restricting their effectiveness, and sketch out possible futures for cyber insurance as governance.
Wavelet-Domain Low-Rank/Group-Sparse Destriping for Hyperspectral Imagery

Pushbroom acquisition of hyperspectral imagery is prone to striping artifacts in the along-track direction. A hyperspectral destriping algorithm is proposed such that the subbands of a 3-D wavelet transform most affected by pushbroom stripes-namely, those with spatially vertical orientation-are the exclusive focus of destriping. The proposed method features an iterative image decomposition composed of a low-rank model for the stripes coupled with a group-sparse prior on the wavelet coefficients of the subbands in question. While low-rank stripe models have been widely used in the past, they typically have been deployed in conjunction with a total-variation prior on the image that is prone to oversmoothing and residual stripe artifacts. On the other hand, the proposed group-sparse prior not only captures the well-known sparse nature of wavelet coefficients but also capitalizes on their vertical clustering in the subbands in question. In addition, while many prior destriping methods are wavelet-based, they employ 2-D transforms band by band. In contrast, the proposed 3-D wavelet transform provides a greater concentration of stripe information into fewer wavelet coefficients, leading to more effective destriping. Experimental results on both synthetically striped imagery as well as real striped imagery from an actual hyperspectral sensor demonstrate superior image quality for the proposed method as compared with other state-of-the-art methods.
Is there a market for trusted car data?

The used-car trade is characterized by information asymmetries between buyers and sellers leading to uncertainty and distrust, thus causing market inefficiencies. Prior research has shown that blockchain offers a solution: a transparent, trustworthy and verified car history that addresses these issues in the market for 'lemons'. Yet, whether or not there really is a market for trusted car data remains an open question. In particular, it is unclear if trusted car data increases transparency in the market for lemons and how market participants value increased transparency. Hence, through a market game with 50 participants, we explored the effects of trusted car data on the sales price of the cars, and the relative revenue of buyers and sellers. Additionally, we conducted interviews with the participants to elicit the perceived customer value. The results show that blockchain enables an increase in transparency and creates value for both buyers and sellers.
Virtual reality passenger experiences

Our research aims to improve passenger journeys across both public and private transport, in cars, buses, planes and trains by utilizing Mixed Reality head-mounted (e.g. visual/auditory augmented and virtual reality) displays. This paper discusses our initial motivations and formative work in this area, both for in-car VR [33, 32] and for in-flight VR [42], and outlines some of the key challenges we anticipate in enabling mixed reality passenger experiences.
DOPIV: Post-Quantum Secure Identity-Based Data Outsourcing with Public Integrity Verification in Cloud Storage

Public verification enables cloud users to employ a third party auditor (TPA) to check the data integrity. However, recent breakthrough results on quantum computers indicate that applying quantum computers in clouds would be realized. A majority of existing public verification schemes are based on conventional hardness assumptions, which are vulnerable to adversaries equipped with quantum computers in the near future. Moreover, new security issues need to be solved when an original data owner is restricted or cannot access the remote cloud server flexibly. In this paper, we propose an efficient identity-based data outsourcing with public integrity verification scheme (DOPIV) in cloud storage. DOPIV is designed on lattice-based cryptography, which achieves post-quantum security. DOPIV enables an original data owner to delegate a proxy to generate the signatures of data and outsource them to the cloud server. Any TPA can perform data integrity verification efficiently on behalf of the original data owner, without retrieving the entire data set. Additionally, DOPIV possesses the advantages of being identity-based systems, avoiding complex certificate management procedures. We provide security proofs of DOPIV in the random oracle model, and conduct a comprehensive performance evaluation to show that DOPIV is more practical in post-quantum secure cloud storage systems.
On the Robustness of Deep K-Nearest Neighbors

Despite a large amount of attention on adversarial examples, very few works have demonstrated an effective defense against this threat. We examine Deep k-Nearest Neighbor (DkNN), a proposed defense that combines k-Nearest Neighbor (kNN) and deep learning to improve the model's robustness to adversarial examples. It is challenging to evaluate the robustness of this scheme due to a lack of efficient algorithm for attacking kNN classifiers with large k and high-dimensional data. We propose a heuristic attack that allows us to use gradient descent to find adversarial examples for kNN classifiers, and then apply it to attack the DkNN defense as well. Results suggest that our attack is moderately stronger than any naive attack on kNN and significantly outperforms other attacks on DkNN.
VGG16 in Batik Classification based on Random Forest

Batik is one of cultural heritage in Indonesia which has been recognized by UNESCO. It has a unique pattern related to such region in Indonesia. Since Indonesia consist of various culture, a system for preserving culture is highly needed. To accommodate this issue, in this study, we propose a deep learning method which is used to classify Batik types. Our method is based on VGG16 architecture with a Random Forest as the classifier. We test our method using public Batik dataset which is widely used in the previous study. We measure the precision, recall, F-score, and accuracy to evaluate our proposed method performance. The result shows that our proposed method out-performed the existing method which is based on color and texture features. The precision, recall, F-score, and accuracy reach ± 97%.
Effective image super resolution via hierarchical convolutional neural network

An extensive amount of research work has been carried out in image super-resolution using convolutional neural networks. The focus of a significant number of reported approaches is primarily on either increasing the depth or the width of the networks to achieve improvements in performance. This paper proposes a novel hierarchical convolutional neural network (HCNN) for effective image super-resolution by learning features from different levels. More specifically, the proposed framework implements a 3-step hierarchical process, which consists of an edge extraction branch, an edge reinforcement branch, and an image reconstruction branch. Informative edges in an image are extracted and enhanced in the edge extraction and reinforcement branch, which are used as a guidance in the image reconstruction branch. Experimental results on several public datasets demonstrate that the proposed framework can restore high-frequency edges information and achieve superior performances over the state-of-the-art methods. Moreover, through a case study in facial expression recognition, we show that the enhanced images are beneficial in improving the recognition performance, paving the way for more practical applications.
Pseudo-Spectrum Based Speed Square Filter for Track-Before-Detect in Range-Doppler Domain

Track-before-detect (TBD) in range-Doppler plane is of significance for Doppler radars. Existing methods suffer from approximate motion models in range-Doppler plane and inefficiency of energy integration. In this paper, an accurate evolution equation of target range and Doppler with respect to time is presented and a new pseudo-spectrum based TBD method is proposed to effectively integrate target energy and detect weak target in range-Doppler domain. According to common constant velocity motion in Cartesian coordinates, the evolution of range and Doppler is correctly described by a nonlinear function of target range-Doppler and time invariant speed square. Consequently, a novel TBD method, which matches target speed instead of velocity in each direction, is proposed. Each cell in range-Doppler plane is predicted to the last frame according to the time evolution equation. Then, a pseudo-spectrum is constructed around the predicted position according to a point spread function. Multiframe accumulation is achieved by adding the sampling values of the pseudo-spectrum over several cells to the last frame. To match the unknown target speed square, a bank of pseudo-spectrum based speed square filter (PS-SSF) is presented. The target output envelopes in both range-Doppler and speed square domains are derived theoretically and a feasible parameter estimation method is proposed. Numerical results demonstrate that the proposed PS-SSF method can not only achieve effective weak target detection in range-Doppler domain, but also produce precise parameter estimates of range and Doppler, as well as target speed.
Understanding and Visualizing Deep Visual Saliency Models

Recently, data-driven deep saliency models have achieved high performance and have outperformed classical saliency models, as demonstrated by results on datasets such as the MIT300 and SALICON. Yet, there remains a large gap between the performance of these models and the inter-human baseline. Some outstanding questions include what have these models learned, how and where they fail, and how they can be improved. This article attempts to answer these questions by analyzing the representations learned by individual neurons located at the intermediate layers of deep saliency models. To this end, we follow the steps of existing deep saliency models, that is borrowing a pre-trained model of object recognition to encode the visual features and learning a decoder to infer the saliency. We consider two cases when the encoder is used as a fixed feature extractor and when it is fine-tuned, and compare the inner representations of the network. To study how the learned representations depend on the task, we fine-tune the same network using the same image set but for two different tasks: saliency prediction versus scene classification. Our analyses reveal that: 1) some visual regions (e.g. head, text, symbol, vehicle) are already encoded within various layers of the network pre-trained for object recognition, 2) using modern datasets, we find that fine-tuning pre-trained models for saliency prediction makes them favor some categories (e.g. head) over some others (e.g. text), 3) although deep models of saliency outperform classical models on natural images, the converse is true for synthetic stimuli (e.g. pop-out search arrays), an evidence of significant difference between human and data-driven saliency models, and 4) we confirm that, after-fine tuning, the change in inner-representations is mostly due to the task and not the domain shift in the data.
Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach

Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix computations) has recently been investigated from the standpoint of erasure coding in several works. In this work we present a strategy for distributed matrix-vector multiplication based on convolutional coding. Our scheme can be decoded using a lowcomplexity peeling decoder. The recovery process enjoys excellent numerical stability as compared to Reed-Solomon coding based approaches (which exhibit significant problems owing their badly conditioned decoding matrices). Finally, our schemes are better matched to the practically important case of sparse matrix-vector multiplication as compared to many previous schemes. Extensive simulation results corroborate our findings.