Reviewerly/exHarmony huggingface dataset:
Deep Neural Network Ensembles

Current deep neural networks suffer from two problems; first, they are hard to interpret, and second, they suffer from overfitting. There have been many attempts to define interpretability in neural networks, but they typically lack causality or generality. A myriad of regularization techniques have been developed to prevent overfitting, and this has driven deep learning to become the hot topic it is today; however, while most regularization techniques are justified empirically and even intuitively, there is not much underlying theory. This paper argues that to extract the features used in neural networks to make decisions, it's important to look at the paths between clusters existing in the hidden spaces of neural networks. These features are of particular interest because they reflect the true decision-making process of the neural network. This analysis is then furthered to present an ensemble algorithm for arbitrary neural networks which has guarantees for test accuracy. Finally, a discussion detailing the aforementioned guarantees is introduced and the implications to neural networks, including an intuitive explanation for all current regularization methods, are presented. The ensemble algorithm has generated state-of-the-art results for Wide-ResNets on CIFAR-10 (top 5 for all models) and has improved test accuracy for all models it has been applied to.
Cost-Constrained feature selection in binary classification: adaptations for greedy forward selection and genetic algorithms

With modern methods in biotechnology, the search for biomarkers has advanced to a challenging statistical task exploring high dimensional data sets. Feature selection is a widely researched preprocessing step to handle huge numbers of biomarker candidates and has special importance for the analysis of biomedical data. Such data sets often include many input features not related to the diagnostic or therapeutic target variable. A less researched, but also relevant aspect for medical applications are costs of different biomarker candidates. These costs are often financial costs, but can also refer to other aspects, for example the decision between a painful biopsy marker and a simple urine test. In this paper, we propose extensions to two feature selection methods to control the total amount of such costs: greedy forward selection and genetic algorithms. In comprehensive simulation studies of binary classification tasks, we compare the predictive performance, the run-time and the detection rate of relevant features for the new proposed methods and five baseline alternatives to handle budget constraints.In simulations with a predefined budget constraint, our proposed methods outperform the baseline alternatives, with just minor differences between them. Only in the scenario without an actual budget constraint, our adapted greedy forward selection approach showed a clear drop in performance compared to the other methods. However, introducing a hyperparameter to adapt the benefit-cost trade-off in this method could overcome this weakness.In feature cost scenarios, where a total budget has to be met, common feature selection algorithms are often not suitable to identify well performing subsets for a modelling task. Adaptations of these algorithms such as the ones proposed in this paper can help to tackle this problem.
Towards efficient deep neural network training by FPGA-based batch-level parallelism

Abstract Training deep neural networks (DNNs) requires a significant amount of time and resources to obtain acceptable results, which severely limits its deployment in resource-limited platforms. This paper proposes DarkFPGA, a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA platform. First, we explore batch-level parallelism to enable efficient FPGA-based DNN training. Second, we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit parallelism. Moreover, an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource constraints. Our results show that the accelerator is able to perform about 10 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.
Using evolutionary computation to infer the decision maker’s preference model in presence of imperfect knowledge: A case study in portfolio optimization

It is usually very difficult to elicit the parameter values of models representing decision makers' preferences. Consequently, some imprecision, ill-determination and arbitrariness are unavoidable. Moreover, such elicitation cannot be performed by traditional optimization techniques in a reasonable time. Therefore, we present here a novel elicitation method guided by a genetic algorithm whose main contribution is coping with imperfect knowledge. The latter is done by using interval numbers representing all the possible values that the parameters can attain. The assessment of the method showed its high ability to reproduce the decision maker's preferences. Finally, as the method proposed in this paper is the complement of the authors' previous work regarding the optimization of stock portfolios, we provide a case study in such a field. We use differential evolution to obtain the most satisfactory portfolio. The results reported here show that the best portfolio returns are obtained when the elicitation method is exploited, and we conclude that the new overall approach might be an interesting alternative to the already-existing methods.
Multivariate Time Series Missing Data Imputation Using Recurrent Denoising Autoencoder

This paper presents a novel method for imputing missing data of multivariate time series by adapting the Long Short Term-Memory(LSTM) and Denoising Autoencoder(DAE). Missing data are ubiquitous in many domains; proper imputation methods can improve performance on many tasks. Our method focus on multivariate time series, applying bidirectional LSTM to learn temporal information and DAE to learn correlation between variables, and we combine these two models by using LSTM as the encoder component of DAE. Several real-world datasets, including electroencephalogram(EEG), electromyogram(EMG) and electronic health records(EHRs), are extracted to test the performance of our method. Through simulation studies, we compare the proposed recurrent denoising autoencoder with several baseline imputation methods and demonstrate its effectiveness in both missing data estimation and label prediction after imputation.
Towards Experiencing Eating as Play

There is an increasing trend in HCI to combine eating and technology. We highlight the potential of interactive technology to support an experiential perspective on eating, in particular, how interactive technology can support experiencing eating as play. To understanding this, we reflect on four playful interactive eating systems we designed and two other works to articulate five strategies: make eating challenging, break cultural norms, design across eating stages, reduce eating autonomy, and playfully extend the social aspect. For each, we also include practical implementation options to provide designers with initial guidance on how they can begin to support experiencing eating as play. Ultimately, with our work, we aim to facilitate a future where eating is more playful.
An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons

An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers.
Semi-Automatic Annotation of Objects in Visual-Thermal Video

Deep learning requires large amounts of annotated data. Manual annotation of objects in video is, regardless of annotation type, a tedious and time-consuming process. In particular, for scarcely used image modalities human annotation is hard to justify. In such cases, semi-automatic annotation provides an acceptable option. In this work, a recursive, semi-automatic annotation method for video is presented. The proposed method utilizes a state-of-the-art video object segmentation method to propose initial annotations for all frames in a video based on only a few manual object segmentations. In the case of a multi-modal dataset, the multi-modality is exploited to refine the proposed annotations even further. The final tentative annotations are presented to the user for manual correction. The method is evaluated on a subset of the RGBT-234 visual-thermal dataset reducing the workload for a human annotator with approximately 78% compared to full manual annotation. Utilizing the proposed pipeline, sequences are annotated for the VOT-RGBT 2019 challenge.
Data for glomeruli characterization in histopathological images

The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH This data is also related to the research paper entitle "Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation", published in Computer Methods and Programs in Biomedicine Journal [1]. In that article, different methods based on deep learning for glomeruli segmentation and their classification into normal and sclerotic glomerulous are presented and discussed. The raw data used is described and provided here. In addition, the detected glomeruli are also provided as individual image files. These data will encourage research on artificial intelligence (AI) methods, create and compare fresh algorithms, and measure their usability in quantitative nephropathology.
A Palette of Deepened Emotions: Exploring Emotional Challenge in Virtual Reality Games

Recent work introduced the notion of 'emotional challenge' promising for understanding more unique and diverse player experiences (PX). Although emotional challenge has immediately attracted HCI researchers' attention, the concept has not been experimentally explored, especially in virtual reality (VR), one of the latest gaming environments. We conducted two experiments to investigate how emotional challenge affects PX when separately from or jointly with conventional challenge in VR and PC conditions. We found that relatively exclusive emotional challenge induced a wider range of different emotions in both conditions, while the adding of emotional challenge broadened emotional responses only in VR. In both experiments, VR significantly enhanced the measured PX of emotional responses, appreciation, immersion and presence. Our findings indicate that VR may be an ideal medium to present emotional challenge and also extend the understanding of emotional (and conventional) challenge in video games.
A proposed framework for improved software requirements elicitation process in SCRUM: Implementation by a real‐life Norway‐based IT project

Abstract Software requirement engineering (SRE) is the initial stage of the software engineering process and entails several challenges relating to the identification, implementation, evolution, and management of requirements for the development of software systems that meet the desired functionality quality standards. Agile software development (ASD) is a well‐known software development process that follows a lightweight and adaptive methodology to support the incremental development of software systems. Since the ASD follows an adaptive procedure (ie, a flexible, nanostructured, and dynamic approach) of software development, it becomes challenging to perform SRE that follows a structured approach for managing software requirements throughout the life cycle of software development. The primary challenges for SRE, while pursuing an ASD, include, but are not limited to, identification of incomplete requirements, implementation of vague requirements, prioritization of secondary functional requirements, and compromise on users' involvement that negatively impacts the software system's functionality and quality. To overcome these challenges, some of the recent research efforts have focused on managing different phases of SRE during software development using the ASD. However, there is a lack of solutions that provide software engineers/practitioners with a solution. Besides, frameworks to effectively manage all the phases of SRE as per the needs of the ASD are needed. We propose a framework that unifies joint requirements document (JRD) and SCRUM to incrementally (a) identify, (b) implement, (c) evolve, and (d) manage system requirements throughout the software development cycle. We have used the action research method to qualitatively validate the proposed framework based on data and user requirements collected from a product‐focused (multinational) Norway‐based software company. The results of the validation are based on statistical analysis that reflects the usefulness of the proposed framework for SRE in ASD‐based software engineering and development. Future research is the possible application of a machine learning approach (es) in the intended context for agile requirements elicitations.
Presumptive Detection of Cyberbullying on Twitter through Natural Language Processing and Machine Learning in the Spanish Language

Nowadays, the constant development of information and communication technologies (ICTs) has changed the inter-personal interaction, allowing to transfer real experiences to a virtualized medium such as Internet. In this sense, although the space-time barriers of traditional communication are broken and social relationships are strengthened, problems related to adverse behaviors may arise. Bullying, defined as an act that threatens a person's holistic well-being, becomes cyberbullying when it is done over Internet, causing anxiety problems, depression and even suicide attempts. For this reason, it is essential to detect this type of behaviour in time. This research deploys a Spanish cyberbullying prevention system (SPC), which relies on Natural Language Processing (NLP) methods and different machine learning techniques (Naive Bayes, Support Vector Machine and Logistic Regression), using Twitter as the basis for the extraction of knowledge bases or corpus. Several precision metrics and variable corpus sizes are used for the training. The learning results reach a maximum accuracy of 93%, verified through the application of three study cases.
The Self-Organizing Restricted Boltzmann Machine for Deep Representation with the Application on Classification Problems

Recently, deep learning is proliferating in the field of representation learning. A deep belief network (DBN) consists of a deep network architecture that can generate multiple features of input patterns, using restricted Boltzmann machines (RBMs) as a building block of DBN. A deep learning model can achieve extremely high accuracy in many applications that depend on the model structure. However, specifying various parameters of deep network architecture like the number of hidden layers and neurons is a difficult task even for expert designers. Besides, the number of hidden layers and neurons is typically set manually, while this method is costly in terms of time and computational cost, especially in big data. In this paper, we introduce an approach to determine the number of hidden layers and neurons of the deep network automatically during the learning process. To this end, the input vector is transformed from the feature space with a low dimension into the new feature space with a high dimension in a hidden layer of RBM. In the following, new features are ranked according to their discrimination power between classes in the new space, using the Separability-correlation measure for feature importance ranking algorithm. The algorithm uses the mean of weights as a threshold, so the neurons whose weights exceed the threshold are retained, and the others are removed in the hidden layer. The number of retained neurons is presented as a reasonable number of neurons. The number of layers is also determined in the deep model, using the validation data. The proposed approach acts as a regularization method since the neurons whose weights are lower than the threshold are removed; thus, RBM learns to copy input merely approximate. It also prevents over-fitting with a suitable number of hidden layers and neurons. Eventually, DBN can determine its structure according to the input data and is the self-organizing model. The experimental results on benchmark datasets confirm the proposed method.
Adversarial Attacks for Image Segmentation on Multiple Lightweight Models

Due to the powerful ability of data fitting, deep neural networks have been applied in a wide range of applications in many key areas.However, in recent years, it was found that some adversarial samples easily fool the deep neural networks.These input samples are generated by adding a few small perturbations based on the original sample, making a very significant influence on the decision of the target model in the case of not being perceived.Image segmentation is one of the most important technologies in the medical image and automatic driving field.This paper mainly explores the security of deep neural network models based on the image segmentation tasks.Two lightweight image segmentation models on the embedded device suffered from the white-box attack by using local perturbations and universal perturbations.The perturbations are generated indirectly by a noise function and an intermediate variable so that the gradient of pixels can be propagated unlimitedly.Through experiments, we find that different models have different blind spots, and the adversarial samples trained for a single model have no transferability.In the end, multiple models are attacked by our joint learning.Finally, under the constraint of low perturbation, most of the pixels in the attacked area have been misclassified by both lightweight models.The experimental result shows that the proposed adversary is more likely to affect the performance of the segmentation model compared with the FGSM.
Overdrive2k: Efficient Secure MPC over $$\mathbb {Z}_{2^k}$$ from Somewhat Homomorphic Encryption

Recently, Cramer et al. (CRYPTO 2018) presented a protocol, $$\mathsf {SPDZ2k}$$, for actively secure multiparty computation for dishonest majority in the pre-processing model over the ring $$\mathbb {Z}_{2^k}$$, instead of over a prime field $$\mathbb {F}_p$$. Their technique used oblivious transfer for the pre-processing phase, more specifically the MASCOT protocol (Keller et al. CCS 2016). In this paper we describe a more efficient technique for secure multiparty computation over $$\mathbb {Z}_{2^k}$$ based on somewhat homomorphic encryption. In particular we adapt the Overdrive approach (Keller et al. EUROCRYPT 2018) to obtain a protocol which is more like the original SPDZ protocol (Damgård et al. CRYPTO 2012). To accomplish this we introduce a special packing technique for the BGV encryption scheme operating on the plaintext space defined by the $$\mathsf {SPDZ2k}$$ protocol, extending the ciphertext packing method used in SPDZ to the case of $$\mathbb {Z}_{2^k}$$. We also present a more complete pre-processing phase for secure computation modulo $$2^k$$ by adding a new technique to produce shared random bits.
A Novel Saliency Detection Algorithm Based on Adversarial Learning Model

The traditional salient object detection models can be divided into several classes based on the low-level features of images and contrast between the pixels. This paper proposes an adversarial learning model (ALM) that includes the generative model and discriminative model. The ALM uses the original image as an input of the generative model to extract the high-level features and forms an initial salient map. Then, the discriminative model is utilized to compare differences in the features between the initial salient map and the ground truth, and the obtained differences are sent to the convolutional layers of the generative model to adjust the parameters for the generative model updating. Due to the serial-iterative adjustment, the salient map of the generative model becomes more similar to the ground truth. Lastly, the ALM forms the salient map fused with the super-pixels by enhancing the color and texture features, so the final salient map is obtained. The ALM is not limited to the color and texture features; on the contrary, it fuses multiple features and achieves good results in the salient target extraction. The experimental results show that ALM performs better than the other ten state-of-the-art models on three different datasets. Thus, the proposed ALM is widely applicable to the salient target extraction.
Counting crowds with varying densities via adaptive scenario discovery framework

The task of crowd counting is to estimate the number of pedestrian in crowd images. Due to camera perspective and physical barriers among dense crowds, how to construct a robust counting model for varying densities and various scenarios has become a challenging problem. In this paper, we propose an adaptive scenario discovery framework for counting crowds with varying densities. The framework is structured with two parallel pathways that are trained to represent different crowd densities and present in the proper geometric configuration using different sizes of the receptive field. A third adaption branch is designed to adaptively recalibrate the pathway-wise responses by discovering and modeling the dynamic scenarios implicitly. We conduct experiments using the adaptive scenario discovery framework on five challenging crowd counting datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.
Handling Gaussian blur without deconvolution

The paper presents a new theory of invariants to Gaussian blur. Unlike earlier methods, the blur kernel may be arbitrary oriented, scaled and elongated. Such blurring is a semi-group action in the image space, where the orbits are classes of blur-equivalent images. We propose a non-linear projection operator which extracts blur-insensitive component of the image. The invariants are then formally defined as moments of this component but can be computed directly from the blurred image without an explicit construction of the projections. Image description by the new invariants does not require any prior knowledge of the blur kernel parameters and does not include any deconvolution. The invariance property could be extended also to linear transformation of the image coordinates and combined affine-blur invariants can be constructed. Experimental comparison to three other blur-invariant methods is given. Potential applications of the new invariants are in blur/position invariant image recognition and in robust template matching.