Reviewerly/exHarmony huggingface dataset:
Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem

The Bin Packing Problem (BPP) stands out as a paradigmatic combinatorial optimization problem in logistics. Quantum and hybrid quantum-classical algorithms are expected to show an advantage over their classical counterparts in obtaining approximate solutions for optimization problems. We have recently proposed a hybrid approach to the one dimensional BPP in which a quantum annealing subroutine is employed to sample feasible solutions for single containers. From this reduced search space, a classical optimization subroutine can find the solution to the problem. With the aim of going a step further in the evaluation of our subroutine, in this paper we compare the performance of our procedure with other classical approaches. Concretely we test a random sampling and a random-walk-based heuristic. Employing a benchmark comprising 18 instances, we show that the quantum approach lacks the stagnation behaviour that slows down the classical algorithms. Based on this, we conclude that the quantum strategy can be employed jointly with the random walk to obtain a full sample of feasible solutions in fewer iterations. This work improves our intuition about the benefits of employing the scarce quantum resources to improve the results of a diminishingly efficient classical strategy.
A hybrid learning framework for multi-modal facial prediction and recognition using improvised non-linear SVM classifier

Multi-modal classification plays a vital role for the real-time applications since most of the conventional models are independent of homogeneous features with single classifier. In addition, traditional directional gradient descriptors are difficult to find the heterogeneous features on different biometric classification. Feature extraction, segmentation, and multi-modal classification are the essential key factors that improve the true positive rate, error rate, and false positive rate of human-based recognition systems. In this work, a hybrid ensemble-based feature selection ranking measure, hybrid segmentation, and ensemble multi-class multi-modal classification framework on different biometric features is designed. Experimental results show that the proposed multi-class multi-modal ensemble classification framework has better optimization in terms of false positive rate, error rate, and precision than the conventional homogeneous local gradient feature extraction-based classification models on different features.
Investigation of cointegration and causal linkages on Bitcoin volatility during COVID-19 pandemic

In this study, we focus on a prominent feature in Bitcoin: its volatility. This paper aims to examine the volatility action of Bitcoin's price during the COVID-19 pandemic through various angles: COVID-19 fear sentiments, investor fear sentiments, macro-financial factors, and crypto market factors. The study utilises daily data from 11 March 2020 to 31 May 2021. We implemented an ARDL bound testing approach to find cointegration, and the Toda-Yamamoto approach to further examine any existing causal relationships between the variables. The empirical results show that COVID-19 fear increased Bitcoin volatility and a unidirectional causal relation was found between them. Investor fear sentiments revealed that US dollar volatility moved in the same direction as Bitcoin volatility, while VIX was found to be insignificant. Gold, crude oil, and the stock market did not influence the volatility of Bitcoin. Overall, only crypto market factors were cointegrated with Bitcoin volatility in the long run.
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks

Automatic modulation classification (AMC) using the Deep Neural Network (DNN) approach outperforms the traditional classification techniques, even in the presence of challenging wireless channel environments. However, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. In this paper, we propose a novel generative adversarial network (GAN)-based countermeasure approach to safeguard the DNN-based AMC systems against adversarial attack examples. GAN-based aims to eliminate the adversarial attack examples before feeding to the DNN-based classifier. Specifically, we have shown the resiliency of our proposed defense GAN against the Fast-Gradient Sign method (FGSM) algorithm as one of the most potent kinds of attack algorithms to craft the perturbed signals. The existing defense-GAN has been designed for image classification and does not work in our case where the above-mentioned communication system is considered. Thus, our proposed countermeasure approach deploys GANs with a mixture of generators to overcome the mode collapsing problem in a typical GAN facing radio signal classification problem. Simulation results show the effectiveness of our proposed defense GAN so that it could enhance the accuracy of the DNN-based AMC under adversarial attacks to 81%, approximately.
Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library

Diarization is an important task when work with audiodata is executed, as it provides a solution to the problem related to the need of dividing one analyzed call recording into several speech recordings, each of which belongs to one speaker. Diarization systems segment audio recordings by defining the time boundaries of utterances, and typically use unsupervised methods to group utterances belonging to individual speakers, but do not answer the question “who is speaking?” On the other hand, there are biometric systems that identify individuals on the basis of their voices, but such systems are designed with the prerequisite that only one speaker is present in the analyzed audio recording. However, some applications involve the need to identify multiple speakers that interact freely in an audio recording. This paper proposes two architectures of speaker identification systems based on a combination of diarization and identification methods, which operate on the basis of segment-level or group-level classification. The open-source PyAnnote framework was used to develop the system. The performance of the speaker identification system was verified through the application of the AMI Corpus open-source audio database, which contains 100 h of annotated and transcribed audio and video data. The research method consisted of four experiments to select the best-performing supervised diarization algorithms on the basis of PyAnnote. The first experiment was designed to investigate how the selection of the distance function between vector embedding affects the reliability of identification of a speaker’s utterance in a segment-level classification architecture. The second experiment examines the architecture of cluster-centroid (group-level) classification, i.e., the selection of the best clustering and classification methods. The third experiment investigates the impact of different segmentation algorithms on the accuracy of identifying speaker utterances, and the fourth examines embedding window sizes. Experimental results demonstrated that the group-level approach offered better identification results were compared to the segment-level approach, and the latter had the advantage of real-time processing.
Lightweight dense video captioning with cross-modal attention and knowledge-enhanced unbiased scene graph

Dense video captioning (DVC) aims at generating description for each scene in a video. Despite attractive progress for this task, previous works usually only concentrate on exploiting visual features while neglecting audio information in the video, resulting in inaccurate scene event location. In this article, we propose a novel DVC model named CMCR, which is mainly composed of a cross-modal processing (CM) module and a commonsense reasoning (CR) module. CM utilizes a cross-modal attention mechanism to encode data in different modalities. An event refactoring algorithm is proposed to deal with inaccurate event localization caused by overlapping events. Besides, a shared encoder is utilized to reduce model redundancy. CR optimizes the logic of generated captions with both heterogeneous prior knowledge and entities' association reasoning achieved by building a knowledge-enhanced unbiased scene graph. Extensive experiments are conducted on ActivityNet Captions dataset, the results demonstrate that our model achieves better performance than state-of-the-art methods. To better understand the performance achieved by CMCR, we also apply ablation experiments to analyze the contributions of different modules.
Causal mediation analysis using high‐dimensional image mediator bounded in irregular domain with an application to breast cancer

Abstract Mammography is the primary breast cancer screening strategy. Recent methods have been developed using the mammogram image to improve breast cancer risk prediction. However, it is unclear on the extent to which the effect of risk factors on breast cancer risk is mediated through tissue features summarized in mammogram images and the extent to which it is through other pathways. While mediation analysis has been conducted using mammographic density (a summary measure within the image), the mammogram image is not necessarily well described by a single summary measure and, in addition, such a measure provides no spatial information about the relationship between the exposure risk factor and the risk of breast cancer. Thus, to better understand the role of the mammogram images that provide spatial information about the state of the breast tissue that is causally predictive of the future occurrence of breast cancer, we propose a novel method of causal mediation analysis using mammogram image mediator while accommodating the irregular shape of the breast. We apply the proposed method to data from the Joanne Knight Breast Health Cohort and leverage new insights on the decomposition of the total association between risk factor and breast cancer risk that was mediated by the texture of the underlying breast tissue summarized in the mammogram image.
Preparing pathological data to develop an artificial intelligence model in the nonclinical study

Artificial intelligence (AI)-based analysis has recently been adopted in the examination of histological slides via the digitization of glass slides using a digital scanner. In this study, we examined the effect of varying the staining color tone and magnification level of a dataset on the result of AI model prediction in hematoxylin and eosin stained whole slide images (WSIs). The WSIs of liver tissues with fibrosis were used as an example, and three different datasets (N20, B20, and B10) were prepared with different color tones and magnifications. Using these datasets, we built five models trained Mask R-CNN algorithm by a single or mixed dataset of N20, B20, and B10. We evaluated their model performance using the test dataset of three datasets. It was found that the models that were trained with mixed datasets (models B20/N20 and B10/B20), which consist of different color tones or magnifications, performed better than the single dataset trained models. Consequently, superior performance of the mixed models was obtained from the actual prediction results of the test images. We suggest that training the algorithm with various staining color tones and multi-scaled image datasets would be more optimized for consistent remarkable performance in predicting pathological lesions of interest.
Self-supervised Contrastive BERT Fine-tuning for Fusion-Based Reviewed-Item Retrieval

As natural language interfaces enable users to express increasingly complex natural language queries, there is a parallel explosion of user review content that can allow users to better find items such as restaurants, books, or movies that match these expressive queries. While Neural Information Retrieval (IR) methods have provided state-of-the-art results for matching queries to documents, they have not been extended to the task of Reviewed-Item Retrieval (RIR), where query-review scores must be aggregated (or fused) into item-level scores for ranking. In the absence of labeled RIR datasets, we extend Neural IR methodology to RIR by leveraging self-supervised methods for contrastive learning of BERT embeddings for both queries and reviews. Specifically, contrastive learning requires a choice of positive and negative samples, where the unique two-level structure of our item-review data combined with meta-data affords us a rich structure for the selection of these samples. For contrastive learning in a Late Fusion scenario, we investigate the use of positive review samples from the same item and/or with the same rating, selection of hard positive samples by choosing the least similar reviews from the same anchor item, and selection of hard negative samples by choosing the most similar reviews from different items. We also explore anchor sub-sampling and augmenting with meta-data. For a more end-to-end Early Fusion approach, we introduce contrastive item embedding learning to fuse reviews into single item embeddings. Experimental results show that Late Fusion contrastive learning for Neural RIR outperforms all other contrastive IR configurations, Neural IR, and sparse retrieval baselines, thus demonstrating the power of exploiting the two-level structure in Neural RIR approaches as well as the importance of preserving the nuance of individual review content via Late Fusion methods.
Contextualized Graph Embeddings for Adverse Drug Event Detection

Abstract An adverse drug event (ADE) is defined as an adverse reaction resulting from improper drug use, reported in various documents such as biomedical literature, drug reviews, and user posts on social media. The recent advances in natural language processing techniques have facilitated automated ADE detection from documents. However, the contextualized information and relations among text pieces are less explored. This paper investigates contextualized language models and heterogeneous graph representations. It builds a contextualized graph embedding model for adverse drug event detection. We employ different convolutional graph neural networks and pre-trained contextualized embeddings as the building blocks. Experimental results show that our methods can improve the performance by comparing recent ADE detection models, suggesting that a text graph can capture causal relationships and dependency between different entities in a document.
FairRecKit: A Web-based Analysis Software for Recommender Evaluations

FairRecKit is a web-based analysis software that supports researchers in performing, analyzing, and understanding recommendation computations. The idea behind FairRecKit is to facilitate the in-depth analysis of recommendation outcomes considering fairness aspects. With (nested) filters on user or item attributes, metrics can easily be compared across user and item subgroups. Further, (nested) filters can be used on the dataset level; this way, recommendation outcomes can be compared across several sub-datasets to analyze for differences considering fairness aspects. The software currently features five datasets, 11 metrics, and 21 recommendation algorithms to be used in computational experimentation. It is open source and developed in a modular manner to facilitate extension. The analysis software consists of two components: A software package (FairRecKitLib) for running recommendation algorithms on the available datasets and a web-based user interface (FairRecKitApp) to start experiments, retrieve results of previous experiments, and analyze details. The application also comes with extensive documentation and options for result customization, which makes for a flexible tool that supports in-depth analysis.
Cybersecurity and Ethereum Security Vulnerabilities Analysis

As computer technology develops, the popularity of cryptocurrencies and their use will grow, and the newer people enter the industry. It changes the business model between organized businesses out of the need for another trusted party. Blockchain smart contracts can automatically enforce agreed contract between two unknowns. Briefly introduce Ethereum, a cryptocurrency, and focus on the security of its smart contracts in internet transactions. Ethereum was the first platform to support high-level programming languages to implement smart contracts, and the second largest blockchain platform, providing a runtime environment for essentially all Decentralized Finance applications. Bitcoin also supports the development and execution of smart contracts, but it is affected by the nature of the programming language used, and it hardly supports transactions except for verifying signatures. Because smart contracts can support a variety of large transactions, some security vulnerabilities can be extremely costly. In an extensive search and survey, the issue of smart contracts for the Ethereum blockchain was valued. The article will discuss some of the existing or former contract vulnerabilities and their solutions. It concludes with a discussion of the future direction of the smart contract space and provides some suggestions for those researching the field.
Neural invertible variable-degree optical aberrations correction

Optical aberrations of optical systems cause significant degradation of imaging quality. Aberration correction by sophisticated lens designs and special glass materials generally incurs high cost of manufacturing and the increase in the weight of optical systems, thus recent work has shifted to aberration correction with deep learning-based post-processing. Though real-world optical aberrations vary in degree, existing methods cannot eliminate variable-degree aberrations well, especially for the severe degrees of degradation. Also, previous methods use a single feed-forward neural network and suffer from information loss in the output. To address the issues, we propose a novel aberration correction method with an invertible architecture by leveraging its information-lossless property. Within the architecture, we develop conditional invertible blocks to allow the processing of aberrations with variable degrees. Our method is evaluated on both a synthetic dataset from physics-based imaging simulation and a real captured dataset. Quantitative and qualitative experimental results demonstrate that our method outperforms compared methods in correcting variable-degree optical aberrations.
ASAP: Endowing Adaptation Capability to Agent in Human-Agent Interaction

Socially Interactive Agents (SIAs) offer users with interactive face-to-face conversations. They can take the role of a speaker and communicate verbally and nonverbally their intentions and emotional states; but they should also act as active listener and be an interactive partner. In human-human interaction, interlocutors adapt their behaviors reciprocally and dynamically. The endowment of such adaptation capability can allow SIAs to show social and engaging behaviors. In this paper, we focus on modelizing the reciprocal adaptation to generate SIA behaviors for both conversational roles of speaker and listener. We propose the Augmented Self-Attention Pruning (ASAP) neural network model. ASAP incorporates recurrent neural network, attention mechanism of transformers, and pruning technique to learn the reciprocal adaptation via multimodal social signals. We evaluate our work objectively, via several metrics, and subjectively, through a user perception study where the SIA behaviors generated by ASAP is compared with those of other state-of-the-art models. Our results demonstrate that ASAP significantly outperforms the state-of-the-art models and thus shows the importance of reciprocal adaptation modeling.
VISCOUNTH: A Large-scale Multilingual Visual Question Answering Dataset for Cultural Heritage

Visual question answering has recently been settled as a fundamental multi-modal reasoning task of artificial intelligence that allows users to get information about visual content by asking questions in natural language. In the cultural heritage domain, this task can contribute to assisting visitors in museums and cultural sites, thus increasing engagement. However, the development of visual question answering models for cultural heritage is prevented by the lack of suitable large-scale datasets. To meet this demand, we built a large-scale heterogeneous and multilingual (Italian and English) dataset for cultural heritage that comprises approximately 500K Italian cultural assets and 6.5M question-answer pairs. We propose a novel formulation of the task that requires reasoning over both the visual content and an associated natural language description, and present baselines for this task. Results show that the current state of the art is reasonably effective but still far from satisfactory; therefore, further research in this area is recommended. Nonetheless, we also present a holistic baseline to address visual and contextual questions and foster future research on the topic.
Fermionic One-Body Entanglement as a Thermodynamic Resource

There is ongoing controversy about whether a coherent superposition of the occupied states of two fermionic modes should be regarded entangled or not, that is, whether its intrinsic quantum correlations are operationally accessible and useful as a resource. This has been questioned on the basis that such an entanglement cannot be accessed by local operations on individual modes due to the parity superselection rule which constrains the set of physical observables. In other words, one cannot observe violations of Bell's inequality. Here, we show, however, that entanglement of a two-mode fermionic state can be used as a genuine quantum resource in open-system thermodynamic processes, enabling one to perform tasks forbidden for separable states. We thus demonstrate that quantum thermodynamics can shed light on the nature of fermionic entanglement and the operational meaning of the different notions used to define it.
DLBCNet: A Deep Learning Network for Classifying Blood Cells

Background: Blood is responsible for delivering nutrients to various organs, which store important health information about the human body. Therefore, the diagnosis of blood can indirectly help doctors judge a person’s physical state. Recently, researchers have applied deep learning (DL) to the automatic analysis of blood cells. However, there are still some deficiencies in these models. Methods: To cope with these issues, we propose a novel network for the multi-classification of blood cells, which is called DLBCNet. A new specifical model for blood cells (BCGAN) is designed to generate synthetic images. The pre-trained ResNet50 is implemented as the backbone model, which serves as the feature extractor. The extracted features are fed to the proposed ETRN to improve the multi-classification performance of blood cells. Results: The average accuracy, average sensitivity, average precision, average specificity, and average f1-score of the proposed model are 95.05%, 93.25%, 97.75%, 93.72%, and 95.38%, accordingly. Conclusions: The performance of the proposed model surpasses other state-of-the-art methods in reported classification results.