Reviewerly/exHarmony huggingface dataset:
Deep hierarchical reinforcement learning in a markov game applied to fishery management decision making

In this article, we propose an approach based on deep hierarchical reinforcement learning to help the management of a fishery formalized by a markov game. Two actors, the fishermen seeking to maximize their profit and the decision-makers seeking to manage the exploitation in the best possible way through the introduction of quotas. For each, we test two learning approaches, one based only on observations, the other on pre-processing leading to beliefs. Several learning algorithms are tested and the results are compared. This has allowed us to identify a minimum amount of knowledge to have for optimal quota setting that also leads to significant and sustainable gains without prior knowledge.
Graph Convolutional Network Based Generative Adversarial Networks for the Algorithm Selection Problem in Classification

In this work, we address the algorithm selection problem for classification via meta-learning and generative adversarial networks. We focus on the dataset representation question. The matrix representation of classification dataset is not sensitive to swapping any two rows or any two columns. We suggest a special method to reduce a dataset to a unified form. This allows to apply generative adversarial networks to classification dataset generation. In this setting, a generator generates new classification datasets in a matrix form, while a conditional discriminator is trying to predict for a dataset and an algorithm if the dataset is real and the algorithm would show the best performance on this dataset. We also suggest a graph convolutional network as a discriminator that is capable to work with such forms, which encode a dataset as a weighted graph with nodes representing objects.
People Detection in a Depth Sensor Network via Multi-View CNNs trained on Synthetic Data

In this work an approach for wide-area indoor people detection with a network of depth sensors is presented. We propose an end-to-end multi-view deep learning architecture which takes three foreground segmented overlapping depth images as input and predicts the marginal probability distribution of people present in the scene. In contrast to classical data-driven approaches our method does not make use of any real image data for training but uses a randomized generative scene model to generate synthetic depth images which are used to train our proposed deep learning architecture. The evaluation shows promising results on a publicly available data set.
Cryptanalysis of Cramer-Shoup Like Cryptosystems Based on Index Exchangeable Family

The Cramer-Shoup (CS) like cryptosystem based on index exchangeable family (IEF) construction is a novel scheme introduced in Asiaccs 2016 by Li et al. Its versatility was illustrated by building two public key encryption (PKE) schemes, a cramer-shoup encryption scheme based on IEFs, as well as an outsourcing technique based on non-abelian analog. However, the two schemes are not secure over the recommended linear group of Li et al. For them, we provide a new key-recovery attack by solving a linear equation respectively. Furthermore, we peel off complex encryption and decryption processes and propose more than three different attack methods. Finally, we give a corresponding example to illustrate the correctness of our attack methods. Our attack methods break an instance of claiming 80 bit security less than one minute under a personal computer.
GRAPHIC ORGANIZERS FOR THE ENHANCEMENT OF EFL READING COMPREHENSION

It is widely known that effective readers are those who have appropriately developed reading skills. If those skills are not developed, the readers will not achieve the levels of perception needed to solve problems or pass academic tests, neither in their mother tongue nor in a foreign language. For this reason, it is imperative to find the right strategies to enhance reading comprehension in English as Foreign Language (EFL) learners. One of those strategies is the use of graphic organizers, and this study aimed to measure the extent to which it influences the development of reading comprehension of Unidad Educativa Joaquin Lalama students, in Ambato-Ecuador. 40 students in the control group and 35 students in the experimental group constituted the sample. The data was collected through the observation technique and the comparison of results from a reading comprehension pre-test and post-test. Finally, this study found the use of graphic organizers to be effective for developing reading comprehension in the subjects observed.
Automated Identification of Individuals in Wildlife Population Using Siamese Neural Networks

Similarity learning coupled with semi-hard pair mining has been applied successfully in human individual identification using images of faces. This approach is coupled with innovative training data sampling techniques, trained to optimise a ranking loss function, aimed at increasing model performance at a minimal additional computational cost. We demonstrate that similarity learning coupled with semi-hard negative pair mining, minimising a triplet loss function, can be applied in the identification of wild animals: Lions, Zebra, Nyalas, and Chimpanzees. There is varying performance depending on the dataset being studied and the network architecture. There is improved performance on models trained using semi-hard triplets on the Chimpanzees hold out test-set data; VGG-19 achieves a 96% accuracy and DenseNet-201 90.1% accuracy. Mean average precision was measured for the different network architectures, varying performances were obtained depending on dataset and network depth.
Machine Learning Models Interpretations: User Demands Exploration

Automated decision making is becoming more and more popular in various domains and demonstrates high performance capabilities. The growing model complexity has limited the opportunities for understanding and justifying the model behaviour. Explainable Artificial Intelligence (XAI) has emerged to make complex models more transparent and provide insights of model behaviour. There are numerous XAI tools for implementing different types of explanations, but the majority of these tools’ outputs are quite complex and can be misused. Therefore, this research aims to make explanations more comprehensible. We plan to review existing approaches to explanation, study user needs for interpretation tools and propose the design of the tool, selecting the appropriate approach and returning explanation in a simple form.
Using a Semi-Evolutionary Algorithm to Optimize Deep Network Hyper-Parameters with an Application to Donor Detection

The performance of deep learning depends on finding an appropriate architecture to fit the model to the data. As deep networks (DNs) have been scaled up to different application areas, network architectures have become difficult to design manually. This paper proposes a new method, SEODP, for optimizing DN architectures and hyper-parameters by using a semi-evolutionary and semi-random approach, which can automatically evolve DN architectures for a given dataset. The experimental results show that, compared with manual search, SEODP is much more effective, and compared with grid search, SEODP can achieve optimal performance much faster. We also apply SEODP to a real-world social behavioral dataset from a charity organization for detection of potential donors. The results show that SEODP is a promising approach for optimizing DN architectures over different types of datasets.
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions

Graph Neural Networks (GNNs) have been drawing significant attention to representation learning on graphs. Recent works developed frameworks to train very deep GNNs and showed impressive results in tasks like point cloud learning and protein interaction prediction. In this work, we study the performance of such deep models in large-scale graphs. In particular, we look at the effect of adequately choosing an aggregation function on deep models. We find that GNNs are very sensitive to the choice of aggregation functions (e.g. mean, max, and sum) when applied to different datasets. We systematically study and propose to alleviate this issue by introducing a novel class of aggregation functions named Generalized Aggregation Functions. The proposed functions extend beyond commonly used aggregation functions to a wide range of new permutation-invariant functions. Generalized Aggregation Functions are fully differentiable, where their parameters can be learned in an end-to-end fashion to yield a suitable aggregation function for each task. We show that equipped with the proposed aggregation functions, deep residual GNNs outperform state-of-the-art in several benchmarks from Open Graph Benchmark (OGB) across tasks and domains.
Resisting newborn attacks via shared Proof-of-Space

In the cryptocurrency literature, Proof-of-Space has been a potential alternative for permissionless distributed consensus protocols not only due to its recyclable nature but also the potential to support multiple chains simultaneously. Namely, the same storage resource can be contributed to the consensus of more than one chain. However, a direct shared proof of the same storage brings about newborn attacks on new chain launching since holders of a substantial amount of resources can easily devastate a new chain with minor underlying storage at almost no cost, deviating from the decentralized principle of cryptocurrencies. To fix this gap, we propose an innovative framework of single-chain Proof-of-Space and further present a novel multi-chain scheme which resists newborn attacks effectively by elaborately combining shared proof and chain-specific proof of storage. Our framework covers both classical Nakamoto consensus (with one leader per round) and hybrid consensus (with multiple leaders per round). Specific protocols for both cases are presented. A committee-based consensus is leveraged to realize the multiple leader case. We show that both consensus schemes have realized our desired functionality without compromising consistency or liveness.
Cloud Advection and Spatial Variability of Solar Irradiance

A model for predicting smoothing of solar irradiance by spatially distributed collectors was analyzed. The model assumed cloud advection dominates the relationship between sites and represented the distributed plant with a transfer function. The transfer function representing the smoothing effect was shown to be the Fourier transform of the plant's spatial distribution, and as such, the plant represents a low-pass filter. Comparison with measured data from the HOPE-Melpitz campaign showed that the model is able to replicate dynamics present in the measured plant transfer function and showed good frequency domain agreement. Generalization of the approach is needed for broader applicability, as the current analysis only validated against one-dimensional, advection dominated conditions. However, the approach warrants further study as it has demonstrated an ability to reveal frequency domain characteristics not currently reflected by state-of-the-art models.
REAP: A Method for Pruning Convolutional Neural Networks with Performance Preservation

This paper presents a pruning method, Reconstruction Error Aware Pruning (REAP), to reduce the redundancy of convolutional neural network models for accelerating their inference. In REAP, we have the following steps: 1) Prune the channels whose outputs are redundant and can be reconstructed from the outputs of other channels in each convolutional layer; 2) Update the weights of the remaining channels by least squares method so as to compensate the error caused by pruning. This is how we compress and accelerate the models that are initially large and slow with little degradation. The ability of REAP to maintain the model performances saves us lots of time and labors for retraining the pruned models. The challenge of REAP is the computational cost for selecting the channels to be pruned. For selecting the channels, we need to solve a huge number of least squares problems. We have developed an efficient algorithm based on biorthogonal system to obtain the solutions of those least squares problems. In the experiments, we show that REAP can conduct pruning with smaller sacrifice of the model performances than several existing methods including the previously state-of-the-art one.
IGSSTRCF: Importance Guided Sparse Spatio-Temporal Regularized Correlation Filters For Tracking

This paper proposes a novel Importance Guided Sparse Spatio-Temporal Regularization based Correlation Filter (IGSSTRCF) tracker. Our formulation explicitly models the variations in the correlation filters and associated spatial weights in successive frames. By imposing a sparsity penalty on these variations, the formulation ensures that only relevant changes are incorporated during updates. This results in more robust filter coefficients that minimize the tracking drift. The IGSSTRCF also includes an adaptive channel importance estimation strategy that assigns an importance weight to each feature channel during training. The proposed formulation is efficiently solved via the alternating direction method of multipliers. A comparative analysis is shown on TC128, UAV123, VOT-2017, and VOT-2019 datasets; and we present an ablation study to demonstrate the contribution of each component of the IGSSTRCF. It is observed that we outperform several state-of-the-art trackers and each component of the proposed IGSSTRCF contributes positively towards tracker performance.
In-house digital image correlation technique for large structures

Monitoring and inspection of large structures such as wind turbines are challenging due to their size scale and the generic capabilities of available methods. The present study is based on performing 3D vibrations studies of large structures for health monitoring and designing purposes. An in-house digital image correlation (DIC) technique is developed to measure 3D displacements. The technique uses two externally-triggerable cameras to record the images simultaneously. The image-correlation method is incorporated with nonlinear optimization and distortion correction algorithms to ensure accuracy and acquire small in-plane and out-of-plane displacements. In a field experiment, the developed in-house DIC technique is used to perform static and dynamic testing of a light pole of 3m height. A large-sized checker-pattern-based calibration board is used for calibrating the designed stereovision system. The in-house DIC method can measure static and dynamic 3D displacements and accurately obtain the natural frequencies of the light pole. The fundamental natural frequency of the pole is 4.95 Hz, which is validated using a three-axis accelerometer.
Tools and Techniques for Privacy-aware, Edge-centric Distributed Deep Learning

Training and inferencing phases of Deep Learning (DL) are compute-intensive that require substantial amount of cloud-hosted resources. However, real-time needs of some edge-based applications as well as the variable and wildly fluctuating edge-cloud latency require new ways to exploit clusters of edge devices in a decentralized and federated manner that perform on-device or edge-based DL training/inferencing. However, edge-based DL is fraught with many challenges including the need to discover the right resources, heterogeneity in resource types leading to non-uniform execution times among cluster members, increased incidences of failures and network disconnectivity leading to consistency issues, preserving privacy of data used in DL tasks, the type of distributed DL algorithm used and its performance on the chosen resources, and many others. To address this plethora of challenges, this paper proposes solutions, which include privacy-preserving, inferencing on heterogeneous edge devices, and CAP-driven trade-offs and Blockchain-based consensus.
Image Inpainting Using Double Discriminator Generative Adversarial Networks

Abstract The image inpainting algorithms based on GAN have problems such as color inconsistencies of the inpainted region with the surrounding part and the mode collapse during the training. This paper proposes an inpainting algorithm based on double discriminator generative adversarial networks (GANs). This algorithm introduces the loss function of WGAN-GP into a global discriminator and local discriminator independently. It combines the Mean-Square Error (MSE) and feature reconstruction loss function to train the inpainting model and repair the missing area. The algorithm uses more dilated convolutions instead of standard convolutions to obtain a larger receptive field and the skip connection to enhance the structural prediction ability of the generator. The experiment results on the labeled faces in the wild dataset show that our algorithm improves the quality of image inpainting and the stability of the training process.
Educational-Methodical Developments Projects Management Based on Fuzzy Logic

The article demonstrates the importance of using risk analysis in the implementation of an educational-methodological project (EMP) and confirms the feasibility of using fuzzy logic for risk assessment. The use of fuzzy models allows to consider both quantitative and qualitative characteristics, as well as to represent fuzzy descriptions using fuzzy sets and linguistic variables. The fuzzy production model described (FPM) contains ten input linguistic variables, that characterize the risk factors, five output linguistic variables, that characterize the risks of EMP, including an integral risk indicator, that characterizes the quality level of the educational program in the as a whole and five rule bases. EMP allows to remove the restrictions on the number of input variables taken into consideration and to integrate both qualitative and quantitative approaches to assess the quality of methodological documents. The process of implementing the fuzzy modeling of the rule base of the specialized Fuzzy Logic Toolbox package of the MATLAB software is described. The mechanism for obtaining estimates based on the Mamdani algorithm allows getting a numerical value of the quality level, its linguistic description, as well as the degree of confidence of the expert in the resulting assessment.
Hyperderivative power sums, Vandermonde matrices, and Carlitz multiplication coefficients

We investigate interconnected aspects of hyperderivatives of polynomials over finite fields, q-th powers of polynomials, and specializations of Vandermonde matrices. We construct formulas for Carlitz multiplication coefficients using hyperderivatives and symmetric polynomials, and we prove identities for hyperderivative power sums in terms of specializations of the inverse of the Vandermonde matrix. As an application of these results we give a new proof of a theorem of Thakur on explicit formulas for Anderson's special polynomials for log-algebraicity on the Carlitz module. Furthermore, by combining results of Pellarin and Perkins with these techniques, we obtain a new proof of Anderson's theorem in the general case.
Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data

The human microbiome plays an important role in human health and disease status. Next-generating sequencing technologies allow for quantifying the composition of the human microbiome. Clustering these microbiome data can provide valuable information by identifying underlying patterns across samples. Recently, Fang and Subedi (2023) proposed a logistic normal multinomial mixture model (LNM-MM) for clustering microbiome data. As microbiome data tends to be high dimensional, here, we develop a family of logistic normal multinomial factor analyzers (LNM-FA) by incorporating a factor analyzer structure in the LNM-MM. This family of models is more suitable for high-dimensional data as the number of free parameters in LNM-FA can be greatly reduced by assuming that the number of latent factors is small. Parameter estimation is done using a computationally efficient variant of the alternating expectation conditional maximization algorithm that utilizes variational Gaussian approximations. The proposed method is illustrated using simulated and real datasets.
Scaling digital screen reading with one-shot learning and re-identification

Using only a mobile phone app, our objective is to cheaply retro-fit digital meters (e.g blood pressure, blood glucose or industrial gauges) with `smart' data transfer capabilities. Using the mobile phone camera we build an app to securely and accurately transcribe information from digital meter screens. Only a single labelled training image of a target meter is required to build a custom screen reading module. Here we show how this can scale to potentially hundreds of different meters by learning to recognising the meter type so that the reading module can be automatically selected. This makes the system very easy for a user who would need to scan multiple different meter types. To this end, we build a CNN based system which runs in real-time on mobile device with very high read accuracy and meter recognition. Our contributions include (i) a method of one-shot training by synthesis through domain shift reduction, (ii) a deep embedding network for scale, translation and rotation invariant re-identification of digital meters, (iii) a highly accurate and efficient mobile phone app for recognising and parsing digital meter screens and (iv) release of a new digital meter re-identification dataset.
On the Risks of Trusting Artificial Intelligence: The Case of Cybersecurity

In this chapter, I draw on my previous work on trust and cybersecurity to offer a definition of trust and trustworthiness to understand to what extent trusting AI for cybersecurity tasks is justified and what measures can be put in place to rely on AI in cases where trust is not justified, but the use of AI is still beneficial.
Structurally Enhanced Interactive Attention Network for Aspect-Level Sentiment Classification

Aspect sentiment analysis main purpose is to identify it in the context of the target of sentiment polarity. Previous approaches have recognized the importance of syntactic dependency tree structure information for sentiment classification. However, the target word associated with the sentiment expression of nodes for the target words is particularly important, while the rest of the edge nodes played an auxiliary role. Therefore, we intend to extract the side nodes associated with the target and then interactively express them with the context. Through this kind of structurally enhanced expression, the context and dependence information of the two are transmitted from opinion words to aspect words, which strengthens the discriminability of supervision. The experimental results on the 2014 datasets verify the effect of our model.
Methodology for content preparation of online courses

At the basis of this article is the goal of increasing the efficiency of the process of building interactive online courses by analyzing and developing specific requirements for the training content provided by the authors so that the subsequent stages of developing effective online courses can be optimized and implemented with as few iterations as possible.
Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent

We propose a fast training procedure for the support vector machines (SVM) algorithm which returns a decision boundary with the same coefficients for any data set, that differs only in the number of support vectors and kernel function values. The modification is based on the recently proposed SVM without a regularization term based on stochastic gradient descent (SGD) with extreme early stopping in the first epoch. We realize two goals during the first epoch: we decrease the objective function value, and we tune the margin hyperparameter M. Experiments show that a training procedure with validation can be speed up substantially without affecting sparsity and generalization performance.