Reviewerly/exHarmony huggingface dataset:
Presence-Only Geographical Priors for Fine-Grained Image Classification

Appearance information alone is often not sufficient to accurately differentiate between fine-grained visual categories. Human experts make use of additional cues such as where, and when, a given image was taken in order to inform their final decision. This contextual information is readily available in many online image collections but has been underutilized by existing image classifiers that focus solely on making predictions based on the image contents. We propose an efficient spatio-temporal prior, that when conditioned on a geographical location and time, estimates the probability that a given object category occurs at that location. Our prior is trained from presence-only observation data and jointly models object categories, their spatio-temporal distributions, and photographer biases. Experiments performed on multiple challenging image classification datasets show that combining our prior with the predictions from image classifiers results in a large improvement in final classification performance.
Group-Wise Deep Object Co-Segmentation With Co-Attention Recurrent Neural Network

Effective feature representations which should not only express the images individual properties, but also reflect the interaction among group images are essentially crucial for real-world co-segmentation. This paper proposes a novel end-to-end deep learning approach for group-wise object co-segmentation with a recurrent network architecture. Specifically, the semantic features extracted from a pre-trained CNN of each image are first processed by single image representation branch to learn the unique properties. Meanwhile, a specially designed Co-Attention Recurrent Unit (CARU) recurrently explores all images to generate the final group representation by using the co-attention between images, and simultaneously suppresses noisy information. The group feature which contains synergetic information is broadcasted to each individual image and fused with multi-scale fine-resolution features to facilitate the inferring of co-segmentation. Moreover, we propose a groupwise training objective to utilize the co-object similarity and figure-ground distinctness as the additional supervision. The whole modules are collaboratively optimized in an end-to-end manner, further improving the robustness of the approach. Comprehensive experiments on three benchmarks can demonstrate the superiority of our approach in comparison with the state-of-the-art methods.
Multiple steps ahead solar photovoltaic power forecasting based on univariate machine learning models and data re-sampling

Accurate predictions of solar Photovoltaic (PV) power generation at different time scales are essential for reliable operations of energy management systems. Contemporary methods can accurately predict solar PV power a few steps ahead but fail to maintain high level of prediction accuracy as the number of steps increases. In this paper, we present a simple but effective univariate approach to predict solar PV power output multiple steps ahead. The objective is to maintain high level accuracy of the machine learning algorithms for multiple steps ahead prediction. The novelty of the proposed approach lies in the integration of a data re-sampling technique with machine learning algorithms. The data re-sampling technique enables machine learning algorithms to compute multiple steps ahead predictions via simple single-step ahead prediction on the re-sampled time-series. At each prediction step, the proposed approach first obtains a new representation of the original time series based on the re-sampling process. A separate prediction model is then implemented for single-step prediction on the re-sampled time series. The single-step ahead prediction of re-sampled time series corresponds to m-th step ahead prediction of original time series as determined by the re-sampling factor m. Since the proposed approach computes multiple steps ahead predictions by combining a set of models designed for single-step ahead prediction, it significantly reduces the error at higher prediction steps. We have evaluated the effectiveness of the proposed approach for 5-min to 3-h ahead predictions using 2 years of data from a 1.22 MW PV system in Australia.
MIC: Mining Interclass Characteristics for Improved Metric Learning

Metric learning seeks to embed images of objects such that class-defined relations are captured by the embedding space. However, variability in images is not just due to different depicted object classes, but also depends on other latent characteristics such as viewpoint or illumination. In addition to these structured properties, random noise further obstructs the visual relations of interest. The common approach to metric learning is to enforce a representation that is invariant under all factors but the ones of interest. In contrast, we propose to explicitly learn the latent characteristics that are shared by and go across object classes. We can then directly explain away structured visual variability, rather than assuming it to be unknown random noise. We propose a novel surrogate task to learn visual characteristics shared across classes with a separate encoder. This encoder is trained jointly with the encoder for class information by reducing their mutual information. On five standard image retrieval benchmarks the approach significantly improves upon the state-of-the-art. Code is available at https://github.com/Confusezius/metric-learning-mining-interclass-characteristics.
Wavelet-based residual attention network for image super-resolution

Image super-resolution (SR) is a fundamental technique in the field of image processing and computer vision. Recently, deep learning has witnessed remarkable progress in many super-resolution approaches. However, we observe that most studies focus on designing deeper and wider architectures to improve the quality of image SR at the cost of computational burden and speed. Few researches adopt lightweight but effective modules to improve the efficiency of SR without compromising its performance. In this paper, we propose the Wavelet-based residual attention network (WRAN) for image SR. Specifically, the input and label of our network are four coefficients generated by the two-dimensional (2D) Wavelet transform, which reduces the training difficulty of our network by explicitly separating low-frequency and high-frequency details into four channels. We propose the multi-kernel convolutional layers as basic modules in our network, which can adaptively aggregate features from various sized receptive fields. We adopt the residual attention block (RAB) that contains channel attention and spatial attention modules. Thus, our method can focus on more crucial underlying patterns in both channel and spatial dimensions in a lightweight manner. Extensive experiments validate that our WRAN is computationally efficient and demonstrate competitive results against state-of-the-art SR methods.
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features

Recently, several techniques to learn the embedding for a given graph dataset have been proposed. Among them, Graph2vec is significant in that it unsupervisedly learns the embedding of entire graphs which is useful for graph classification. This paper develops an algorithm which improves Graph2vec. First, we point out two limitations of Graph2vec: (1) Edge labels cannot be handled and (2) Graph2vec does not always preserve structural information enough to evaluate the structural similarity, because it bundles the node label information and the structural information in extracting subgraphs. Our algorithm overcomes these limitations by exploiting the line graphs (edge-to-vertex dual graphs) of given graphs. Specifically, it complements either the edge label information or the structural information which Graph2vec misses with the embeddings of the line graphs. Our method is named as GL2vec (Graph and Line graph to vector) because it concatenates the embedding of an original graph to that of the corresponding line graph. Experimentally, GL2vec achieves significant improvements in graph classification task over Graph2vec for many benchmark datasets.
Probabilistic Kalman filter for moving object tracking

Kalman filter has been successfully applied to tracking moving objects in real-time situations. However, the filter cannot take into account the existing prior knowledge to improve its predictions. In the moving object tracking, the trajectories of multiple targets in the same environment could be available, which can be viewed as the prior knowledge for the tracking procedure. This paper presents the probabilistic Kalman filter (PKF) that is able to take into account the stored trajectories to improve tracking estimation. The PKF has an extra stage after two steps of the Kalman filter to refine the estimated position of the targets. The refinement is obtained by applying the Viterbi algorithm to a probabilistic graph, that is constructed based on the observed trajectories. The graph is built in the offline situation and could be adapted in the online tracking. The proposed tracker has higher accuracy compared to the standard Kalman filter and could handle widespread problems such as occlusion. Another significant achievement of the proposed tracker is to track an object with anomalous behaviors by drawing an inference based on the constructed probabilistic graph. The PKF was applied to several manually-built videos and several other video-bases containing severe occlusions, which demonstrates a significant performance in comparison with other state-of-the-art trackers.
Blockchain and business ethics

Abstract This paper provides, from a business ethics perspective, a basic clustering of the morally (a) favorable, (b) unfavorable, and (c) ambivalent dimensions of blockchain technology and its various emergent applications. Instead of proffering specific assessments on particular aspects of blockchain‐based business models, we aim to offer an initial overview that charters the territory so that future research can bring about such moral assessments in an informed and orderly fashion. The main contribution of this paper lies in identifying several morally ambivalent dimensions of blockchain technology, which we finally link to two strands of business ethics research: ethical and legal aspects of legislation as well as a link to Habermasian corporate social responsibility theory arguing for transparent data production and consumption on the blockchain. We conclude that future research is necessary for moral assessment of the ambivalent cases, since their ethical evaluation changes depending on whether one analyzes them through the lenses of utilitarianism, contractarianism, deontology, and virtue ethics, respectively.
F-measure curves: A tool to visualize classifier performance under imbalance

Learning from imbalanced data is a challenging problem in many real-world machine learning applications due in part to the bias of performance in most classification systems. This bias may exist due to three reasons: (1) Classification systems are often optimized and compared using performance measurements that are unsuitable for imbalance problems; (2) most learning algorithms are designed and tested on a fixed imbalance level of data, which may differ from operational scenarios; (3) the preference of correct classification of classes is different from one application to another. This paper investigates specialized performance evaluation metrics and tools for imbalance problem, including scalar metrics that assume a given operating condition (skew level and relative preference of classes), and global evaluation curves or metrics that consider a range of operating conditions. We focus on the case in which the scalar metric F-measure is preferred over other scalar metrics, and propose a new global evaluation space for the F-measure that is analogous to the cost curves for expected cost. In this space, a classifier is represented as a curve that shows its performance over all of its decision thresholds and a range of possible imbalance levels for the desired preference of true positive rate to precision. Curves obtained in the F-measure space are compared to those of existing spaces (ROC, precision-recall and cost) and analogously to cost curves. The proposed F-measure space allows to visualize and compare classifiers' performance under different operating conditions more easily than in ROC and precision-recall spaces. This space allows us to set the optimal decision threshold of a soft classifier and to select the best classifier among a group. This space also allows to empirically improve the performance obtained with ensemble learning methods specialized for class imbalance, by selecting and combining the base classifiers for ensembles using a modified version of the iterative Boolean combination algorithm that is optimized using the F-measure instead of AUC. Experiments on a real-world dataset for video face recognition show the advantages of evaluating and comparing different classifiers in the F-measure space versus ROC, precision-recall, and cost spaces. In addition, it is shown that the performance evaluated using the F-measure of Bagging ensemble method can improve considerably by using the modified iterative Boolean combination algorithm.
SMS Spam Message Detection using Term Frequency-Inverse Document Frequency and Random Forest Algorithm

The daily traffic of Short Message Service (SMS) keeps increasing. As a result, it leads to dramatic increase in mobile attacks such as spammers who plague the service with spam messages sent to the groups of recipients. Mobile spams are a growing problem as the number of spams keep increasing day by day even with the filtering systems. Spams are defined as unsolicited bulk messages in various forms such as unwanted advertisements, credit opportunities or fake lottery winner notifications. Spam classification has become more challenging due to complexities of the messages imposed by spammers. Hence, various methods have been developed in order to filter spams. In this study, methods of term frequency-inverse document frequency (TF-IDF) and Random Forest Algorithm will be applied on SMS spam message data collection. Based on the experiment, Random Forest algorithm outperforms other algorithms with an accuracy of 97.50%.
Symmetry-adapted variational quantum eigensolver

We propose a scheme to restore spatial symmetry of Hamiltonian in the variational-quantum-eigensolver (VQE) algorithm for which the quantum circuit structures used usually break the Hamiltonian symmetry. The symmetry-adapted VQE scheme introduced here simply applies the projection operator, which is Hermitian but not unitary, to restore the spatial symmetry in a desired irreducible representation of the spatial group. The entanglement of a quantum state is still represented in a quantum circuit but the nonunitarity of the projection operator is treated classically as postprocessing in the VQE framework. By numerical simulations for a spin-$1/2$ Heisenberg model on a one-dimensional ring, we demonstrate that the symmetry-adapted VQE scheme with a shallower quantum circuit can achieve significant improvement in terms of the fidelity of the ground state and has a great advantage in terms of the ground-state energy with decent accuracy, as compared to the non-symmetry-adapted VQE scheme. We also demonstrate that the present scheme can approximate low-lying excited states that can be specified by symmetry sectors, using the same circuit structure for the ground-state calculation.
Record and Reward Federated Learning Contributions with Blockchain

Although Federated Learning allows for participants to contribute their local data without it being revealed, it faces issues in data security and in accurately paying participants for quality data contributions. In this paper, we propose an EOS Blockchain design and workflow to establish data security, a novel validation error based metric upon which we qualify gradient uploads for payment, and implement a small example of our blockchain Federated Learning model to analyze its performance.
Perspectives on risks and standards that affect the requirements engineering of blockchain technology

Blockchain aims to transform businesses and other forms of transactions from a centralized, human-based to a shared, algorithm-based trust model, which enables a new risk management paradigm. Misaligned incentives in different principal – agent scenarios are important risk factors from governance point of view. With blockchain, these misalignments are accounted for algorithmically, therefore novel governance models are possible. What role do risks play in terms of deciding for, or against the adoption of blockchain? How to best define requirements to achieve it? This paper explores standards and risk as factors, which can support or hinder the sustained application of blockchain in a broad scope of environments. We conducted a systematic literature review that outlines a current understanding of perceived risk surrounding the adoption and use of blockchain technology in the context of requirements engineering. Furthermore, selected models for managing risks are presented. Finally, areas where deeper research is required are identified. We conclude that a gap exists in normative frameworks that affect the adoption and sustainable use of blockchain technology. Closing this gap can support the sustainable use of blockchain technology.
ML-LOO: Detecting Adversarial Examples with Feature Attribution

Deep neural networks obtain state-of-the-art performance on a series of tasks. However, they are easily fooled by adding a small adversarial perturbation to the input. The perturbation is often imperceptible to humans on image data. We observe a significant difference in feature attributions between adversarially crafted examples and original examples. Based on this observation, we introduce a new framework to detect adversarial examples through thresholding a scale estimate of feature attribution scores. Furthermore, we extend our method to include multi-layer feature attributions in order to tackle attacks that have mixed confidence levels. As demonstrated in extensive experiments, our method achieves superior performances in distinguishing adversarial examples from popular attack methods on a variety of real data sets compared to state-of-the-art detection methods. In particular, our method is able to detect adversarial examples of mixed confidence levels, and transfer between different attacking methods. We also show that our method achieves competitive performance even when the attacker has complete access to the detector.
A double decomposition-based modelling approach to forecast weekly solar radiation

To meet the future energy demand and avert any looming crises, efforts are being carried out to utilize sustainable and renewable energy resources. In this paper, the naturally occurring non-linearity and non-stationarity deficiencies within the climatological predictors to forecast solar radiation (Rdn) are resolved via a multivariate empirical mode decomposition method (MEMD). First, a set of antecedent weekly lags at timescale (t-1) of input datasets were collated and then were divided into training and testing subsets. The MEMD method is restricted to dissolve the training and testing climatic data independently into intrinsic modes functions (IMFs). As the numbers of total IMFs were very large, the singular value decomposition (SVD) algorithm is accustomed for dimensionality reduction simultaneously capturing the most relevant oscillatory features embedded within the IMFs. Finally, the random forest (RF) model is applied to forecast Rdn at selected solar-rich regions in Australia. The resulting hybrid MEMD-SVD-RF model was established as a consequence of the aforementioned modelling strategy. The results are benchmarked with other comparative models. The hybrid MEMD-SVD-RF model generates better and reliable forecasts having significant implications for renewable and sustainable energy applications and resources management.
CNN with Phonetic Attention for Text-Independent Speaker Verification

Text-independent speaker verification imposes no constraints on the spoken content and usually needs long observations to make reliable prediction. In this paper, we propose two speaker embedding approaches by integrating the phonetic information into the attention-based residual convolutional neural network (CNN). Phonetic features are extracted from the bottleneck layer of a pretrained acoustic model. In implicit phonetic attention (IPA), the phonetic features are projected by a transformation network into multi-channel feature maps, and then combined with the raw acoustic features as the input of the CNN network. In explicit phonetic attention (EPA), the phonetic features are directly connected to the attentive pooling layer through a separate 1-dim CNN to generate the attention weights. With the incorporation of spoken content and attention mechanism, the system can not only distill the speaker-discriminant frames but also actively normalize the phonetic variations. Multi-head attention and discriminative objectives are further studied to improve the system. Experiments on the VoxCeleb corpus show our proposed system could outperform the state-of-the-art by around 43% relative.
CHAT-Bot: A cultural heritage aware teller-bot for supporting touristic experiences

Cultural heritage is an important resource that allows us to know and promote a territory. In this respect, it is important to experiment with the enhancement of cultural heritage by adopting approaches that meet the dynamic needs of various types of users. The aim of this paper is to introduce a recommender system capable of developing adaptive tourist routes. In fact, the proposed system suggests points of interest and related services according to both the profile of the tourist and contextual aspects. In particular, the interaction of the user with the system occurs through a chatbot that allows to build a real dialog. In order to show the potential of the proposed approach, a prototype was developed to support the user in building a customized tourist route related to some of the most important cultural sites in Campania (a region in Southern Italy): Herculaneum, Paestum and Pompeii.
Property Inference for Deep Neural Networks

We present techniques for automatically inferring formal properties of feed-forward neural networks. We observe that a significant part (if not all) of the logic of feed forward networks is captured in the activation status (on or off) of its neurons. We propose to extract patterns based on neuron decisions as preconditions that imply certain desirable output property e.g., the prediction being a certain class. We present techniques to extract input properties, encoding convex predicates on the input space that imply given output properties and layer properties, representing network properties captured in the hidden layers that imply the desired output behavior. We apply our techniques on networks for the MNIST and ACASXU applications. Our experiments highlight the use of the inferred properties in a variety of tasks, such as explaining predictions, providing robustness guarantees, simplifying proofs, and network distillation.