Reviewerly/exHarmony huggingface dataset:
An Open-Ended Continual Learning for Food Recognition Using Class Incremental Extreme Learning Machines

State-of-the-art deep learning models for food recognition do not allow data incremental learning and often suffer from catastrophic interference problems during the class incremental learning. This is an important issue in food recognition since real-world food datasets are open-ended and dynamic, involving a continuous increase in food samples and food classes. Model retraining is often carried out to cope with the dynamic nature of the data, but this demands high-end computational resources and significant time. This paper proposes a new open-ended continual learning framework by employing transfer learning on deep models for feature extraction, Relief F for feature selection, and a novel adaptive reduced class incremental kernel extreme learning machine (ARCIKELM) for classification. Transfer learning is beneficial due to the high generalization ability of deep learning features. Relief F reduces computational complexity by ranking and selecting the extracted features. The novel ARCIKELM classifier dynamically adjusts network architecture to reduce catastrophic forgetting. It addresses domain adaptation problems when new samples of the existing class arrive. To conduct comprehensive experiments, we evaluated the model against four standard food benchmarks and a recently collected Pakistani food dataset. Experimental results show that the proposed framework learns new classes incrementally with less catastrophic inference and adapts domain changes while having competitive classification performance.
DoQA - Accessing Domain-Specific FAQs via Conversational QA

The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites.We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs.The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing.Compared to previous work, DoQA comprises well-defined information needs, leading to more coherent and natural conversations with less factoid questions and is multi-domain.In addition, we introduce a more realistic information retrieval (IR) scenario where the system needs to find the answer in any of the FAQ documents.The results of an existing, strong, system show that, thanks to transfer learning from a Wikipedia QA dataset and fine tuning on a single FAQ domain, it is possible to build high quality conversational QA systems for FAQs without indomain training data.The good results carry over into the more challenging IR scenario.In both cases, there is still ample room for improvement, as indicated by the higher human upperbound.
Effective white-box testing of deep neural networks with adaptive neuron-selection strategy

We present Adapt, a new white-box testing technique for deep neural networks. As deep neural networks are increasingly used in safety-first applications, testing their behavior systematically has become a critical problem. Accordingly, various testing techniques for deep neural networks have been proposed in recent years. However, neural network testing is still at an early stage and existing techniques are not yet sufficiently effective. In this paper, we aim to advance this field, in particular white-box testing approaches for neural networks, by identifying and addressing a key limitation of existing state-of-the-arts. We observe that the so-called neuron-selection strategy is a critical component of white-box testing and propose a new technique that effectively employs the strategy by continuously adapting it to the ongoing testing process. Experiments with real-world network models and datasets show that Adapt is remarkably more effective than existing testing techniques in terms of coverage and adversarial inputs found.
Simultaneous multisurface measurement of freeform refractive optics based on computer-aided deflectometry

Freeform optics, due to the more general surface geometry that offers high degrees of design freedom to control light propagation, has already been widely used in both nonimaging optics and imaging optics. With the recent advances in design and fabrication of freeform optics, one of the remaining challenges is how to accurately measure freeform optical surfaces, especially those included in freeform refractive optics. To meet this imperative need, for the first time, we believe, present an effective simultaneous multisurface measurement method for freeform refractive optics. Instead of using a reflected optical field to reconstruct tested optical surfaces, we develop a surface reconstruction method based on a transmitted field to tackle the challenges caused by the low reflectivity and compound effect of multiple reflection of refractive surfaces. The transmitted fields from refractive elements are measured by computer-aided deflectometry in order to achieve a large measurement dynamic range and high accuracy. Using the transmitted fields, a multisurface reconstruction model based on iterative optimization is then employed to achieve the accurate multisurface measurement simultaneously. The proposed method is demonstrated to be very effective and robust in testing freeform refractive optics, with a future potential for in situ metrology.
Bayesian bootstrap quantile regression for probabilistic photovoltaic power forecasting

Abstract Photovoltaic (PV) systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing. Because of the uncertain nature of the solar energy resource, PV power forecasting models are crucial in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies. This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model, in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model. A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients, raising the predictive ability of the final forecasts. Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2% when compared to relevant benchmarks.
DataHop: Spatial Data Exploration in Virtual Reality

Virtual reality has recently been adopted for use within the domain of visual analytics because it can provide users with an endless workspace within which they can be actively engaged and use their spatial reasoning skills for data analysis. However, virtual worlds need to utilize layouts and organizational schemes that are meaningful to the user and beneficial for data analysis. This paper presents DataHop, a novel visualization system that enables users to lay out their data analysis steps in a virtual environment. With a Filter, a user can specify the modification they wish to perform on one or more input data panels (i.e., containers of points), along with where output data panels should be placed in the virtual environment. Using this simple tool, highly intricate and useful visualizations may be generated and traversed by harnessing a user's spatial abilities. An exploratory study conducted with six virtual reality users evaluated the usability, affordances, and performance of DataHop for data analysis tasks, and found that spatially mapping one's workflow can be beneficial when exploring multidimensional datasets.
Interpretation of NLP models through input marginalization

To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input.Since existing methods replace each token with a predefined value (i.e., zero), the resulting sentence lies out of the training data distribution, yielding misleading interpretations.In this study, we raise the out-of-distribution problem induced by the existing interpretation methods and present a remedy; we propose to marginalize each token out.We interpret various NLP models trained for sentiment analysis and natural language inference using the proposed method.
Improving Multilingual Models with Language-Clustered Vocabularies

State-of-the-art multilingual models depend on vocabularies that cover all of the languages the model will expect to see at inference time, but the standard methods for generating those vocabularies are not ideal for massively multilingual applications. In this work, we introduce a novel procedure for multilingual vocabulary generation that combines the separately trained vocabularies of several automatically derived language clusters, thus balancing the trade-off between cross-lingual subword sharing and language-specific vocabularies. Our experiments show improvements across languages on key multilingual benchmark tasks TyDi QA (+2.9 F1), XNLI (+2.1%), and WikiAnn NER (+2.8 F1) and factor of 8 reduction in out-of-vocabulary rate, all without increasing the size of the model or data.
Tight Consistency Bounds for Bitcoin

We establish the optimal security threshold for the Bitcoin protocol in terms of adversarial hashing power, honest hashing power, and network delays. Specifically, we prove that the protocol is secure if [ra < 1/Δ0 + 1/rh,,] where rh is the expected number of honest proof-of-work successes in unit time, ra is the expected number of adversarial successes, and no message is delayed by more than Δ0 time units. In this regime, the protocol guarantees consistency and liveness with exponentially decaying failure probabilities. Outside this region, the simple private chain attack prevents consensus. Our analysis immediately applies to any Nakamoto-style proof-of-work protocol; in the full version of this paper we also present the adaptations needed to apply it in the proof-of-stake setting, establishing a similar threshold there.
An Integrated Impact of Blockchain on Supply Chain Applications

Market conditions have become increasingly dynamic and demanding, resulting in a challenging competitive environment. To adapt to this dynamic environment, supply chains have become increasingly reliant on collaboration, integration, flexibility, and trust between stakeholders. Therefore, the importance of new technological applications in supply chains has grown even more. To improve supply chain flow control, modern specialized applications must be implemented. Blockchain technology has established itself as a necessary component of today’s competitive environment. Companies should invest in blockchain technology to respond quickly to changing market conditions and demands in today’s dynamic business environment. In this regard, this study investigates the effect of blockchain technology utilization on supply chain transparency, supply chain flexibility, and trust in suppliers. For this purpose, first, data were collected from the first 1000 exporting firms declared in 2019 by the Turkey Exporters Assembly using a survey method. Secondly, factor analyses and regression analyses were performed with the dataset obtained from 84 companies. As a result of the analysis, it was determined that blockchain technology utilization increases transparency, flexibility, and trust in supply chains. According to these results, it is suggested that companies should invest in blockchain technology to make their supply chain more transparent, flexible, and secure. Moreover, blockchain plays a crucial role in building mutual trust with supply chain stakeholders. Finally, the research includes some considerations on the positive implications and potential of blockchain in collaboration and integration.
Selective oversampling approach for strongly imbalanced data

Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling. In this paper, we propose the new selective oversampling approach (SOA) that first isolates the most representative samples from minority classes by using an outlier detection technique and then utilizes these samples for synthetic oversampling. We show that the proposed approach improves the performance of two state-of-the-art oversampling methods, namely, the synthetic minority oversampling technique and adaptive synthetic sampling. The prediction performance is evaluated on four synthetic datasets and four real-world datasets, and the proposed SOA methods always achieved the same or better performance than other considered existing oversampling methods.
Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation

To generate "accurate" scene graphs, almost all existing methods predict pairwise relationships in a deterministic manner. However, we argue that visual relationships are often semantically ambiguous. Specifically, inspired by linguistic knowledge, we classify the ambiguity into three types: Synonymy Ambiguity, Hyponymy Ambiguity, and Multi-view Ambiguity. The ambiguity naturally leads to the issue of implicit multi-label, motivating the need for diverse predictions. In this work, we propose a novel plug-and-play Probabilistic Uncertainty Modeling (PUM) module. It models each union region as a Gaussian distribution, whose variance measures the uncertainty of the corresponding visual content. Compared to the conventional deterministic methods, such uncertainty modeling brings stochasticity of feature representation, which naturally enables diverse predictions. As a byproduct, PUM also manages to cover more fine-grained relationships and thus alleviates the issue of bias towards frequent relationships. Extensive experiments on the large-scale Visual Genome benchmark show that combining PUM with newly proposed ResCAGCN can achieve state-of-the-art performances, especially under the mean recall metric. Furthermore, we show the universal effectiveness of PUM by plugging it into some existing models and provide insightful analysis of its ability to generate diverse yet plausible visual relationships.
We Need to Consider Disagreement in Evaluation

Evaluation is of paramount importance in datadriven research fields such as Natural Language Processing (NLP) and Computer Vision (CV).But current evaluation practice in NLP, except for end-to-end tasks such as machine translation, spoken dialogue systems, or NLG, largely hinges on the existence of a single "ground truth" against which we can meaningfully compare the prediction of a model.However, this assumption is flawed for two reasons.1) In many cases, more than one answer is correct.2) Even where there is a single answer, disagreement among annotators is ubiquitous, making it difficult to decide on a gold standard.We discuss three sources of disagreement: from the annotator, the data, and the context, and show how this affects even seemingly objective tasks.Current methods of adjudication, agreement, and evaluation ought to be reconsidered at the light of this evidence.Some researchers now propose to address this issue by minimizing disagreement, creating cleaner datasets.We argue that such a simplification is likely to result in oversimplified models just as much as it would do for end-to-end tasks such as machine translation.Instead, we suggest that we need to improve today's evaluation practice to better capture such disagreement.Datasets with multiple annotations are becoming more common, as are methods to integrate disagreement into modeling.The logical next step is to extend this to evaluation.
A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme

In recent years, the importance of catching humans' emotions grows larger as the artificial intelligence (AI) field is being developed. Facial expression recognition (FER) is a part of understanding the emotion of humans through facial expressions. We proposed a robust multi-depth network that can efficiently classify the facial expression through feeding various and reinforced features. We designed the inputs for the multi-depth network as minimum overlapped frames so as to provide more spatio-temporal information to the designed multi-depth network. To utilize a structure of a multi-depth network, a multirate-based 3D convolutional neural network (CNN) based on a multirate signal processing scheme was suggested. In addition, we made the input images to be normalized adaptively based on the intensity of the given image and reinforced the output features from all depth networks by the self-attention module. Then, we concatenated the reinforced features and classified the expression by a joint fusion classifier. Through the proposed algorithm, for the CK+ database, the result of the proposed scheme showed a comparable accuracy of 96.23%. For the MMI and the GEMEP-FERA databases, it outperformed other state-of-the-art models with accuracies of 96.69% and 99.79%. For the AFEW database, which is known as one in a very wild environment, the proposed algorithm achieved an accuracy of 31.02%.
Encoding-dependent generalization bounds for parametrized quantum circuits

A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical optimization. In particular, theoretical guarantees on the out-of-sample performance of such models, in terms of generalization bounds, have emerged. However, none of these generalization bounds depend explicitly on how the classical input data is encoded into the PQC. We derive generalization bounds for PQC-based models that depend explicitly on the strategy used for data-encoding. These imply bounds on the performance of trained PQC-based models on unseen data. Moreover, our results facilitate the selection of optimal data-encoding strategies via structural risk minimization, a mathematically rigorous framework for model selection. We obtain our generalization bounds by bounding the complexity of PQC-based models as measured by the Rademacher complexity and the metric entropy, two complexity measures from statistical learning theory. To achieve this, we rely on a representation of PQC-based models via trigonometric functions. Our generalization bounds emphasize the importance of well-considered data-encoding strategies for PQC-based models.
Evaluation of criteria for site selection of solar photovoltaic (PV) projects using fuzzy logarithmic additive estimation of weight coefficients

The aim of this study is to determine the degree of importance of criteria affecting site selection of solar photovoltaic (PV) projects using a decision-making model. This study consists of four consecutive stages, as follows: criteria identification, questionnaire (survey), statistical analyses, and degree of importance of criteria. In the first stage, the criteria are determined by reviewing the scientific literature on solar PV projects. Secondly, we conduct a questionnaire to identify the importance of the criteria for solar PV project site selection. We received responses from 33 internationally renowned experts from 22 countries, including academia and industry, using an international evaluation method. Thirdly, statistical analysis is performed in SPSS regarding each criterion, comparing the averages between the groups who filled out the questionnaire. Finally, a novel logarithmic additive estimation of weight coefficients (LAAW) under fuzzy environment is proposed to determine the degree of importance of each criterion for solar PV site selection. The results show that the most important criteria for solar PV site selection are solar radiation, economic performance indicators (net present value (NPV), internal rate of return (IRR), and return on investment (ROI)), carbon emission savings, and policy support.
Bit level color image encryption using Logistic-Sine-Tent-Chebyshev (LSTC) map

The paper presents a bit-level chaos based color image encryption technique using Logistic-Sine-Tent-Chebyshev (LSTC) map. The color image is first decomposed into red, blue, and green color image components. Each color image component is transformed to an 8-bit binary plane. Mutual diffusion of two sequences is used to scramble two binary elements using the LSTC map, cyclic shifts, and the XOR operation. The binary element confusion is performed using a chaos map and transformed into binary bit planes. With the help of the chaos map, the binary element is swapped and transformed into another binary bit plane. Individual cipher text image components are obtained by combining the binary bit planes. Finally, the encrypted image component is combined to generate the color cipher text image. The proposed scheme experimental results show excellent resistance to statistical attack, differential attack, and brute-force attack with a single round of encryption. As a result, the proposed scheme is secure and reliable for transmitting color images over the internet.
Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

The publication of a patient’s dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multiple sensitive attributes do not concentrate on the correlation among the attributes, which in turn leads to much utility loss. An efficient model Heap Bucketization-anonymity (HBA) has been proposed to balance privacy and utility with multiple sensitive attributes. The Heap Bucketization-anonymity model used anatomization to vertically partition the dataset into 1. Quasi-identifier table and 2. Sensitive attribute table. The quasi-identifier is anonymized by implementing k-anonymity and slicing and the sensitive attributes are anonymized by applying slicing and Heap Bucketization. The metrics Normalized Certainty Penalty and KL-divergence have been used to compute the utility loss in the patient dataset. The experimental results show that the HB-anonymity can significantly achieve high privacy with less utility loss than other existing models. The HB-anonymity model not only balances the utility and privacy also eradicates the i) background knowledge attack, ii) quasi-identifier attack iii) membership attack, iv) non-membership attack and v) fingerprint correlation attack.
Identifying Elements Essential for BERT’s Multilinguality

It has been shown that multilingual BERT (mBERT) yields high quality multilingual representations and enables effective zero-shot transfer.This is surprising given that mBERT does not use any crosslingual signal during training.While recent literature has studied this phenomenon, the reasons for the multilinguality are still somewhat obscure.We aim to identify architectural properties of BERT and linguistic properties of languages that are necessary for BERT to become multilingual.To allow for fast experimentation we propose an efficient setup with small BERT models trained on a mix of synthetic and natural data.Overall, we identify four architectural and two linguistic elements that influence multilinguality.Based on our insights, we experiment with a multilingual pretraining setup that modifies the masking strategy using VecMap, i.e., unsupervised embedding alignment.Experiments on XNLI with three languages indicate that our findings transfer from our small setup to larger scale settings.