Reviewerly/exHarmony huggingface dataset:
Robust Light Field Depth Estimation With Occlusion Based On Spatial And Spectral Entropies Data Costs

This paper proposes a novel data cost that combines spatial and spectral entropies to handle the occlusion problem in the light field depth estimation. In previous works, the spatial entropy data cost has been demonstrated to reduce the effect of occluded pixels in an angular patch (i.e., micro-lens pixel) and to yield an accurate depth value in the presence of occlusion. However, our observation notes that the spatial entropy data cost metric is less reliable when the angular resolution becomes smaller as in light field images. In this paper, we propose a new data cost which integrates a proposed spectral entropy data cost with the spatial entropy data cost. An initial depth map which is estimated using the proposed new data cost is further optimized by the standard graph-cut algorithm and filtered by using an edge-preserving filter. Experimental results have confirmed the effectiveness of the proposed method which achieves more accurate depth values even when the angular resolution becomes smaller.
A Platform for Full-Stack Functional Programming

Traditional CPU design shows signs of fatigue, expressed as overwhelming security vulnerabilities. As we investigate functional programming as an alternative to the insecure, traditional imperative programming model, the inexistence of a stable functional programming system infrastructure and code base acts as the classic chicken-and-egg problem. There is no functional programming basic software because there are no functional programming machines, and vice-versa. In this paper we attempt to break this cycle by designing a baseline platform that enables the research on the practical security properties of architectures under a discipline of full-stack functional programming.
Fired Neuron Rate Based Decision Tree for Detection of Adversarial Examples in DNNs

Deep neural network (DNN) is a prevalent machine learning solution to computer vision problems. The most criticized vulnerability of deep learning is its susceptibility towards adversarial images crafted by maliciously adding infinitesimal distortions to the benign inputs. Such negatives can fool a classifier. Existing countermeasures against these adversarial attacks are mainly developed based on software model of DNNs by using modified training during learning or modified input during testing, modifying networks or changing loss/activation functions, or relying on add-on models for classifying unseen examples. These approaches do not consider the optimization for hardware implementation of the learning models. In this paper, a new thresholding method is proposed based on comparators integrated into the most discriminative layers of the DNN determined by their layer-wise fired neuron rates between adversarial and normal inputs. Effectiveness of the method is validated on the ImageNet dataset with 8-bit truncated models for the state-of-the-art DNN architectures. A high detection rate of up to 98% with only 4.5% of false positive rate is achieved. The results show a significant improvement on both detection rate and false positive rate compared with previous countermeasures against the most practical non-invasive universal perturbation attack on deep learning based AI chip.
Cascading Top-Down Attention for Visual Question Answering

For solving Visual Question Answering (VQA), we commonly employ images and questions simultaneously to predict answers. Some attention mechanisms should be used to focus on the most valuable information, because there are too much information extracted from images and questions. Top-Down Attention (TDA) is one of the famous attention mechanisms. For standard TDA, only important regions of the image associated with the question are highlighted. In this work, we propose a Cascading Top-Down Attention (CTDA) model. CTDA highlights the most important information collected from images and questions by a cascading attention process. First, the key words of the question, associated with the image, are highlighted by using a Question Top-Down Attention (QTDA). Then, important regions of the image, associated with the question, are highlighted by using of Image Top-Down Attention (ITDA), useless information of the images and questions can be ignored effectively. We evaluate our model on two popular VQA data sets. CTDA obtains better results than standard TDA and the other state of the art models.
Aircraft Segmentation from Remote Sensing Images using Modified Deeply Supervised Salient Object Detection with Short Connections

Image segmentation is a process of clustering the pixels into some groups. It is essential and useful in image processing and computer vision task. The segmentation result may affect the outcome of many types of work, for example, image recognition and image detection. One of the most exciting works of image segmentation is aircraft segmentation from remote sensing images. This work is very challenging due to many factors, such as the complexity of the background, the shadow, and the various type of aircraft. To handle these problems, in this paper we implement the deeply supervised salient object detection with short connections (DSS) with our dataset which is divided into two categories: simple case and dificult case, and we propose some idea of post-process which can increase some accuracy by using pre-trained and adding L2- normalization in some layer of DSS architecture to create the mask that can reduce the noise from background effectively. From the experimental result, using L2- normalization with our post-processing can increase the performance by 0.61% and 2.14% in the simple case and difficult case respectively, compared with using only the DSS method. So our method performs well when the background is complicated, which always occurs in reality.
Pentadent-Net: Pedestrian Attribute Recognition With Distance Refinement And Correlation Mining

Pedestrian attribute recognition aims to accurately identify attributes from pedestrian images. Visually similar pedestrian images tend to share similar attributes, and the upper and lower clothing attributes normally match in style. However, how to effectively capture these relations remains a challenging topic. In this paper, we propose a novel network Pentadent-Net (PD-Net). The network consists of a “Representation Refining Branch (RRB)” and an “Attribute Fusion Branch (AFB)”. The RRB reduces the feature distances among visually similar images, and the AFB generates a joint-representation for each attribute based on its correlation with other attributes. The polished features from the two branches are then applied to capture the inter-sample relations and attribute dependencies respectively. We also further associate features from the two branches to enrich feature representations. Experimental results on two benchmark datasets (PETA and PA-100K) by our proposed approach demonstrate its advantages over state-of-the-art methods.
Generating High-Fidelity Images with Disentangled Adversarial VAEs and Structure-Aware Loss

While variational autoencoders (VAE) provide the theoretical basis for deep generative models, they often produce "blurry" images which is linked to their training objective. In this paper, we propose the "Sharpened Adversarial Variational Auto-Encoder" (AVAE-S) which uses an adversarial training mechanism to fine-tune the learned latent code vector of the VAE with a specialized objective function. The loss function is designed to uncover global structure as well as the local and high frequency features in VAE and leading to the smaller variance in the aggregated posterior and hence, reducing the blurriness of their generated samples. AVAE-S leverages the learned representations to the meaningful latent features by enforcing feature consistency between the model distribution and the target distribution leading to the sharpened output with better perceptual quality. Then, AVAE-S starts training a GAN network, which generator has been collapsed on the VAE's decoder, upon that learned latent code vector. Moreover, we augment the standard VAE's evidence lower bound objective function with other element-wise similarity measures. Our experiments show that AVAE-S achieves the state-of-the-art sample quality in the common MNIST and CelebA datasets. AVAE-S shares many of the good properties of the VAE (stable training, encoder-decoder architecture, nice latent manifold structure) while generating more realistic images, as measured by the sharpness score.
HLS Implementation of Linear Discriminant Analysis Classifier

Data classification has improved significantly over time and nowadays is used in a variety of purposes and applications. This paper demonstrates the design and implementation of multivariate classifier linear discriminant algorithm on a Field Programable Gate Array (FPGA) as System on Chip (SoC). The classifier is optimized using High Level Synthesis (HLS) techniques. The optimized design is placed on the programmable logic part of the chip while its controller is built on the embedded processor of the same chip. The paper details the process of the classifier design and optimization and reports on the power consumption, resource utilization, latency, and algorithm accuracy before and after optimization.
Kripke-style Semantics and Completeness for Full Simply Typed Lambda Calculus

Abstract Full simply typed lambda calculus is the simply typed lambda calculus extended with product types and sum types. We propose a Kripke-style semantics for full simply typed lambda calculus. We then prove soundness and completeness of type assignment in full simply typed lambda calculus with respect to the proposed semantics. The key point in the proof of completeness is the notion of a canonical model.
Systems and Software Interface Survey

Abstract INCOSE formed the Systems and Software Interface Working Group (SaSIWG) in 2017, in response to Corporate Advisory Board interest in software, and problems identified in the systems and software interface (physical, logical, data and human). This third SaSIWG paper presents the results of a survey of systems engineers, software engineers, and project managers as to what are best practices and what are the priority challenges related to the interface between systems and software. The best practices mentioned by the 31 interviewees are grouped and summarized, followed by information on priority challenges and problems. Systems engineering must be done well, and that includes an ever‐increasing amount of Model‐Based Systems Engineering. It also includes developing and holding to a vision, addressing data, ensuring interdisciplinary work, planning systematic verification, and ensuring modularity. Systems Engineering must evolve to meet new challenges and most important, the expertise of systems engineers must include software engineering.
Fuzzy Conceptualization Model for Document Representation

The Fuzzy Conceptualization Model (FCM) performs a fuzzy mapping of query set to the given word corpus to obtain fuzzy membership degree for a document based on semantic correlation among words quantified by cosine similarity measures between word embeddings of queries and word corpus. A fuzzy membership function is defined for the given search query with word attributes of the given corpus. The generated numerical vector representation is the measure of similarity between word attributes of document and base terms set. The ground truth fuzzy set obtained for the popular terms in the given data corpus is used for creating semantic table for the base term cluster and hence formulates a matrix representation for both accurate and semantically related terms to be getting processed and counted together with out much over head to typical Fuzzy Bag of Words (FBoW) models and its variants. The proposed FCM outperforms other comparative models with high confidence level under paired t-test. FCM performs well when the mapping bound λ is zero since the increased λ values increases the sparsity of the vector representation of the documents.
Evaluating Menu Techniques for Handheld AR with a Smartphone &amp; Mid-Air Pen

Adding a mid-air pen to Handheld Augmented Reality creates a new kind of bimanual interaction for which many fundamental interaction design questions have not been answered yet. In particular, menus are an essential component in most visual interfaces, but it is unclear how to best interact with them in this setting: using the pen in mid-air or on a surface, using the touchscreen, or by moving the smartphone itself. We compared basic menus for these methods by analyzing success rates, selection times, device movement, and subjective ratings. Our results indicate that interacting with a mid-air menu using the pen, and operating a menu with the hand holding the smartphone, are sufficiently competitive to the current standard of two-handed touchscreen interaction, so that interaction designers can freely choose among them based on the interaction context of their application.
Gender Effects in Mobile Application Development

Growing interest in the impact of gender on technology includes concerns with regard to gender bias and its implication in the user experience. Past explorations of gender and design revealed significant differences between female and male users with regard to the perception and the acceptance of dating apps. A novel study was designed to investigate whether gender differences in the user experience could be caused by gender bias in mobile app development. Significant differences in the definition of design priorities emerged with regard to the participants' gender and the persona's perceived gender.
On the Specific Role of Electronic Document and Record Management Systems in Enterprise Integration

The paper aims at providing practical insight into the specific role of electronic document and record management systems (EDRMSs) in the process of enterprise integration (EI) to provide organizational data flow. Based on a case study with the ERDMS Amphora, we identify enabling factors for enterprise integration in order to provide interoperability, where we consider both technological and organizational factors. An EDRMS is not a standalone system, and it must interact with many systems used in organization. In this paper, we discuss some certain information flows based on the EDRMS Amphora and related system.
End-to-End JPEG Decoding and Artifacts Suppression Using Heterogeneous Residual Convolutional Neural Network

Existing deep learning models separate JPEG arti-facts suppression from the decoding protocol as independent task. In this work, we take one step forward to design a true end-to-end heterogeneous residual convolutional neural network (HR-CNN) with spectrum decomposition and heterogeneous reconstruction mechanism. Benefitting from the full CNN architecture and GPU acceleration, the proposed model considerably improves the reconstruction efficiency. Numerical experiments show that the overall reconstruction speed reaches to the same magnitude of the standard CPU JPEG decoding protocol, while both decoding and artifacts suppression are completed together. We formulate the JPEG artifacts suppression task as an interactive process of decoding and image detail reconstructions. A heterogeneous, fully convolutional, mechanism is proposed to particularly address the uncorrelated nature of different spectral channels. Directly starting from the JPEG code in k-space, the network first extracts the spectral samples channel by channel, and restores the spectral snapshots with expanded throughput. These intermediate snapshots are then heterogeneously decoded and merged into the pixel space image. A cascaded residual learning segment is designed to further enhance the image details. Experiments verify that the model achieves outstanding performance in JPEG artifacts suppression, while its full convolutional operations and elegant network structure offers higher computational efficiency for practical online usage compared with other deep learning models on this topic.
A Voice User Interface for football event tagging applications

Manual event tagging may be a very long and stressful activity, due the monotonous operations involved. This is particularly true when dealing with online video tagging, as for football matches, in which the burden of events to tag can consist of many thousands of actions, according to the desired level of granularity. In this work we describe an actual solution, developed for an existing football match tagging application, in which the GUI has been enhanced and integrated with a Voice User Interface, aiming at reducing tagging time and error rate. Empirical tests have revealed the efficiency and the benefits brought by the developed solution.
Effective Processing of Convolutional Neural Networks for Computer Vision: A Tutorial and Survey

Over the past few years, Convolutional neural networks (ConvNets) is emerging as computer vision discipline within deep learning. ConvNets is a key strategy for addressing computer vision problems, yet the theories behind their effectiveness in the processing are not yet fully understood. ConvNets have attained a state of the art-performances on various datasets for computer vision tasks such as remote sensing images scene classification, face recognition, and object detection. This is attributed to their effectiveness in image feature processing. This work reviews the major advances on ConvNets for effective processing in computer vision from some dimensions which include, convolutional layer design configurations, pooling layer strategies, network activation functions, loss functions, network regularization techniques, and ConvNet optimization methods. Further, this works surveys the application of ConvNets on three computer vision tasks, i.e. remote sensing images scene recognition, face recognition, and object detection to demonstrate the effectiveness of convNets in image feature processing. Additionally, datasets for evaluation and benchmarking purposes with the aforementioned computer vision tasks are briefly discussed.
Deep Transfer Collaborative Filtering with Geometric Structure Preservation for Cross-Domain Recommendation

Collaborative filtering (CF) is one of the most effective approaches for recommender systems by exploiting user-item behavior interactions. However, in real applications, the rating matrix is usually sparse, causing a poor performance. Numerous CF methods tend to incorporate the side information for the enrichment of priors. Cross-domain recommendation is an alternative to alleviate these two problems by referring to the knowledge of relevant domains. Due to the sparsity of the ratings and side information, the resulting latent factors might not be effective as expected. In this paper, we incorporate both the side information and deep knowledge transfer in CF models. A general architecture of deep transfer collaborative filtering with geometry preservation (DTCFGP) is proposed by integrating cross-domain collective matrix factorization, deep feature learning and the graph modeling. We exhibit a instantiations of our architecture by employing non-negative matrix tri-factorization and stacked denoising autoencoder (SDAE) in both source and target domains, where the common latent factors are taken as a bridge between domains due to its across-domain stability and data geometric structure is evaluated by using a nearest neighbor graph modeling. Extensive experiments on various real-world datasets demonstrate the effectiveness of our proposed approach in comparison to state-of-the-art approaches.
Towards better technical debt detection with NLP and machine learning methods

Technical debt (TD) is an economical term used to depict non-optimal choices made in the software development process. It occurs usually when developers take shortcuts instead of following agreed upon development practices, and unchecked growth of technical debt can start to incur negative effects for software development processes.
NASABN: A Neural Architecture Search Framework for Attention-Based Networks

Recently, neural architecture search (NAS) has emerged as a technique of growing concern in automatic machine learning (AutoML). Meanwhile, attention-based models, such as attention-based recurrent neural network, transformer-based model, etc., have been widely used in deep learning applications. However, there is no efficient NAS method that can search the architecture of attention-based model so far. To solve this problem, we propose a framework named neural architecture search for attention-based networks (NASABN) by abstracting attention-based models and extracting undefined parts of the model, including the attention layers and cells. NASABN is flexible and general enough to fit different NAS methods, which can also be transferred across different datasets. We conduct extensive experiments with NASABN using gradient descent-based methods like DARTS on Penn Treebank (PTB) and WikiText-2 (WT2) datasets respectively, and achieve competitive performance compared with the state-of-the-art methods.
Difficulty Translation in Histopathology Images

The unique nature of histopathology images opens the door to domain-specific formulations of image translation models. We propose a difficulty translation model that modifies colorectal histopathology images to be more challenging to classify. Our model comprises a scorer, which provides an output confidence to measure the difficulty of images, and an image translator, which learns to translate images from easy-to-classify to hard-to-classify using a training set defined by the scorer. We present three findings. First, generated images were indeed harder to classify for both human pathologists and machine learning classifiers than their corresponding source images. Second, image classifiers trained with generated images as augmented data performed better on both easy and hard images from an independent test set. Finally, human annotator agreement and our model’s measure of difficulty correlated strongly, implying that for future work requiring human annotator agreement, the confidence score of a machine learning classifier could be used as a proxy.
Multi-scaled Non-local Means Parallel Filters for Medical Image Denoising

In recent years, there has been an increased interest in denoising techniques that are applicable in various medical imaging fields. The extraordinary development of the denoising area is no doubt due to the ever expanding and successful computing technology, but also to the emergence of the multi-resolution analysis (MRA) on both mathematical and algorithmic levels. However, many denoising techniques still remain ineffective in dealing with certain types of noise. Other methods can be too expensive, given their nested and complicated structure. Therefore, in this paper, A new multi-scale parallel denoising paradigm is defined and tested. A comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). The comparison is carried out in this framework of multi-scaled parallel denoising, where a Non-Local Means (NLM) filter is implemented and adjusted scale-by-scale to a sample of X-ray benchmark images. Some state-of-the-art denoising methods were also used in the evaluation. The numerical results proved the effectiveness of the multi-scaled parallel denoising in terms of accuracy and speed of convergence, especially when the NLM filtering is coupled with the EMD. This shows a bright future for their medical use in the next few years.