Reviewerly/exHarmony huggingface dataset:
Reading comprehension based question answering system in Bangla language with transformer-based learning

Question answering (QA) system in any language is an assortment of mechanisms for obtaining answers to user questions with various data compositions. Reading comprehension (RC) is one type of composition, and the popularity of this type is increasing day by day in Natural Language Processing (NLP) research area. Some works have been done in several languages, mainly in English. In the Bangla language, neither any dataset available for RC nor any work has been done in the past. In this research work, we develop a question-answering system from RC. For doing this, we construct a dataset containing 3636 reading comprehensions along with questions and answers. We apply a transformer-based deep neural network model to obtain convenient answers to questions based on reading comprehensions precisely and swiftly. We exploit some deep neural network architectures such as LSTM (Long Short-Term Memory), Bi-LSTM (Bidirectional LSTM) with attention, RNN (Recurrent Neural Network), ELECTRA, and BERT (Bidirectional Encoder Representations from Transformers) to our dataset for training. The transformer-based pre-training language architectures BERT and ELECTRA perform more prominently than others from those architectures. Finally, the trained model of BERT performs a satisfactory outcome with 87.78% of testing accuracy and 99% training accuracy, and ELECTRA provides training and testing accuracy of 82.5% and 93%, respectively.
Extended logistic map for encryption of digital images

Abstract A novel extended logistic map has been proposed and tested mathematically for security-based applications. Because the designed extended logistic map behaves chaotically across a wide range of logistic control parameters, it is extremely difficult to predict using even the most exhaustive search methods. The map overcomes a significant drawback of simple logistic mapping, which is commonly used in encryption algorithms. The chaotic map designed was also used as a key to shuffle the pixel position of the image for the image shuffling algorithm developed. The algorithm developed produced excellent results and is adequate for providing an encrypted image in resource-constrained systems. Performance results show that this map is highly chaotic and provides high security when applied in image encryption systems.
Stochastic process and tutorial of the African buffalo optimization

This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm's basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms.
Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search

Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product search. However, existing CTR methods still struggle in the product search settings due to the following three challenges including how to more effectively extract users' short-term interests with respect to multiple aspects, how to extract and fuse users' long-term interest with short-term interests, how to address the entangling characteristic of long and short-term interests. To resolve these challenges, in this paper, we propose a new approach named Hierarchical Interests Fusing Network (HIFN), which consists of four basic modules namely Short-term Interests Extractor (SIE), Long-term Interests Extractor (LIE), Interests Fusion Module (IFM) and Interests Disentanglement Module (IDM). Specifically, SIE is proposed to extract user's short-term interests by integrating three fundamental interests encoders within it namely query-dependent, target-dependent and causal-dependent interest encoder, respectively, followed by delivering the resultant representation to the module LIE, where it can effectively capture user long-term interests by devising an attention mechanism with respect to the short-term interests from SIE module. In IFM, the achieved long and short-term interests are further fused in an adaptive manner, followed by concatenating it with original raw context features for the final prediction result. Last but not least, considering the entangling characteristic of long and short-term interests, IDM further devises a self-supervised framework to disentangle long and short-term interests. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of HIFN over state-of-the-art methods.
Explainable Graph-based Fraud Detection via Neural Meta-graph Search

Though graph neural networks (GNNs)-based fraud detectors have received remarkable success in identifying fraudulent activities, few of them pay equal attention to models' performance and explainability. In this paper, we attempt to achieve high performance for graph-based fraud detection while considering model explainability. We propose NGS (Neural meta-Graph Search), in which the message passing process of a GNN is formalized as a meta-graph, and a differentiable neural architecture search is devised to determine the optimized message passing graph structure. We further enhance the model by aggregating multiple searched meta-graphs to make the final prediction. Experimental results on two real-world datasets demonstrate that NGS outperforms state-of-the-art baselines. In addition, the searched meta-graphs concisely describe the information used for prediction and produce reasonable explanations.
Dual exponential coupled cluster theory: Unitary adaptation, implementation in the variational quantum eigensolver framework and pilot applications

In this paper, we have developed a unitary variant of a double exponential coupled cluster theory, which is capable of handling molecular strong correlation with arbitrary electronic complexity. With the Hartree-Fock determinant taken as the reference, we introduce a sequential product of parameterized unitary Ansätze. While the first unitary, containing the excitation operators, acts directly on the reference determinant, the second unitary, containing a set of rank-two, vacuum-annihilating scattering operators, has nontrivial action only on certain entangled states. We demonstrate the theoretical bottleneck of such an implementation in a classical computer, whereas the same is implemented in the hybrid quantum-classical variational quantum eigensolver framework with a reasonably shallow quantum circuit without any additional approximation. We have further introduced a number of variants of the proposed Ansatz with different degrees of sophistication by judiciously approximating the scattering operators. With a number of applications on strongly correlated molecules, we have shown that all our schemes can perform uniformly well throughout the molecular potential energy surface without significant additional implementation cost over the conventional unitary coupled cluster approach with single and double excitations.
3-D Skeleton-Based Human Motion Prediction With Manifold-Aware GAN

In this work we propose a novel solution for 3D skeleton-based human motion prediction. The objective of this task consists in forecasting future human poses based on a prior skeleton pose sequence. This involves solving two main challenges still present in recent literature; (1) discontinuity of the predicted motion which results in unrealistic motions and (2) performance deterioration in long-term horizons resulting from error accumulation across time. We tackle these issues by using a compact manifold-valued representation of 3D human skeleton motion. Specifically, we model the temporal evolution of the 3D poses as trajectory, what allows us to map human motions to single points on a sphere manifold. Using such a compact representation avoids error accumulation and provides robust representation for long-term prediction while ensuring the smoothness and the coherence of the whole motion. To learn these non-Euclidean representations, we build a manifold-aware Wasserstein generative adversarial model that captures the temporal and spatial dependencies of human motion through different losses. Experiments have been conducted on CMU MoCap and Human 3.6M datasets and demonstrate the superiority of our approach over the state-of-the-art both in short and long term horizons. The smoothness of the generated motion is highlighted in the qualitative results.
Regional metal pollution risk assessment based on a long short-term memory model: A case study of the South Altai Mountain mining area, China

The discharge of wastewater and waste rock in mining production activities is a significant hidden cause of soil heavy metal pollution. The accumulation of heavy metals in soil occurs through a variety of processes, and exposure to these metals can permanently damage the human body. Due to multiple factors, such as the formation causes, sources, and distribution trends of heavy metals in mineral resources, reasonably applying different machine learning methods to monitor and evaluate heavy metal pollution remains challenging. In this paper, we choose the copper mining area in the southern Altai Mountains of China as the study area, and 19 different types of spatial data are uniformly managed in a distributed database to improve monitoring efficiency. Furthermore, we propose a heavy metal pollution evaluation framework based on a stacked long short-term memory (LSTM) model, which considers spatial data correlations and extracts spatial clustering features. Information is screened through state updating in the framework, the short-term memory features of long sequences are extracted, and an effective prediction model is established. The results show that 26 of the 31 mining occurrences in the study area are in moderate- and high-pollution-risk grids, suggesting that the spatial distribution of copper mines is consistent with the predicted spatial distribution of pollution risk. Overall, these results show that using the optimized stacked LSTM model to integrate multisource geological features and mine the internal rules of feature information has a positive effect on improving the risk assessment of heavy metal pollution.
Deep learning for Flight Maneuver Recognition: A survey

&lt;abstract&gt; &lt;p&gt;Deep learning for Flight Maneuver Recognition involves flight maneuver detection and recognition tasks in different areas, including pilot training, aviation safety, and autonomous air combat. As a key technology for these applications, deep learning for Flight Maneuver Recognition research is underdeveloped and limited by domain knowledge and data sources. This paper presents a comprehensive survey of all Flight Maneuver Recognition studies since the 1980s to accurately define the research and describe its significance for the first time. In an analogy to the flourishing Human Action Recognition research, we divided deep learning for Flight Maneuver Recognition into vision-based and sensor-based studies, combed through all the literature, and referred to existing reviews of Human Action Recognition to demonstrate the similarities and differences between Flight Maneuver Recognition and Human Action Recognition in terms of problem essentials, research methods, and publicly available datasets. This paper presents the dataset-The Civil Aviation Flight University of China, which was generated from real training of a fixed-wing flight at Civil Aviation Flight University of China. We used this dataset to reproduce and evaluate several important methods of Flight Maneuver Recognition and visualize the results. Based on the evaluation results, the paper discusses the advantages, disadvantages, and overall shortcomings of these methods, as well as the challenges and future directions for deep learning for Flight Maneuver Recognition.&lt;/p&gt; &lt;/abstract&gt;
Virtual Biopsy by Using Artificial Intelligence–based Multimodal Modeling of Binational Mammography Data

Background Computational models based on artificial intelligence (AI) are increasingly used to diagnose malignant breast lesions. However, assessment from radiologic images of the specific pathologic lesion subtypes, as detailed in the results of biopsy procedures, remains a challenge. Purpose To develop an AI-based model to identify breast lesion subtypes with mammograms and linked electronic health records labeled with histopathologic information. Materials and Methods In this retrospective study, 26 569 images were collected in 9234 women who underwent digital mammography to pretrain the algorithms. The training data included individuals who had at least 1 year of clinical and imaging history followed by biopsy-based histopathologic diagnosis from March 2013 to November 2018. A model that combined convolutional neural networks with supervised learning algorithms was independently trained to make breast lesion predictions with data from 2120 women in Israel and 1642 women in the United States. Results were reported using the area under the receiver operating characteristic curve (AUC) with the 95% DeLong approach to estimate CIs. Significance was tested with bootstrapping. Results The Israeli model was validated in 456 women and tested in 441 women (mean age, 51 years ± 11 [SD]). The U.S. model was validated in 350 women and tested in 344 women (mean age, 60 years ± 12). For predicting malignancy in the test sets (consisting of 220 Israeli patient examinations and 126 U.S. patient examinations with ductal carcinoma in situ or invasive cancer), the algorithms obtained an AUC of 0.88 (95% CI: 0.85, 0.91) and 0.80 (95% CI: 0.74, 0.85) for Israeli and U.S. patients, respectively (P = .006). These results may not hold for other cohorts of patients, and generalizability across populations should be further investigated. Conclusion The results offer supporting evidence that artificial intelligence applied to clinical and mammographic images can identify breast lesion subtypes when the data are sufficiently large, which may help assess diagnostic workflow and reduce biopsy sampling errors. Published under a CC BY 4.0 license. Online supplemental material is available for this article.
Achieving the ultimate end-to-end rates of lossy quantum communication networks

The field of quantum communications promises the faithful distribution of quantum information, quantum entanglement, and absolutely secret keys, however, the highest rates of these tasks are fundamentally limited by the transmission distance between quantum repeaters. The ultimate end-to-end rates of quantum communication networks are known to be achievable by an optimal entanglement distillation protocol followed by teleportation. In this work, we give a practical design for this achievability. Our ultimate design is an iterative approach, where each purification step operates on shared entangled states and detects loss errors at the highest rates allowed by physics. As a simpler design, we show that the first round of iteration can purify completely at high rates. We propose an experimental implementation using linear optics and photon-number measurements which is robust to inefficient operations and measurements, showcasing its near-term potential for real-world practical applications.
ConstDet: Control Semantics-Based Detection for GPS Spoofing Attacks on UAVs

UAVs are widely used in agriculture, the military, and industry. However, it is easy to perform GPS spoofing attacks on UAVs, which can lead to catastrophic consequences. In this paper, we propose ConstDet, a control semantics-based detection approach for GPS spoofing attacks of UAVs using machine learning algorithms. Various real experiments are conducted to collect real flight data, on the basis of which ConstDet is designed as a practical detection framework. To train models for the detection of GPS spoofing attacks, specified flight data types are selected as features based on the control semantics, including the altitude control process and the horizontal position control process, since these data are able to represent the dynamic flight and control processes. Multiple machine learning algorithms are used to train and generate the best classifier for GPS spoofing attacks. ConstDet is further implemented and deployed on a real UAV to support onboard detection. Experiments and evaluations validate that ConstDet can effectively detect GPS spoofing attacks and the detection rate can reach 97.70%. The experimental comparison demonstrates that ConstDet has better performance than existing detection approaches.
RULER: discriminative and iterative adversarial training for deep neural network fairness

Deep Neural Networks (DNNs) are becoming an integral part of many real-world applications, such as autonomous driving and financial management. While these models enable autonomy, there are however concerns regarding their ethics in decision making. For instance, fairness is an aspect that requires particular attention. A number of fairness testing techniques have been proposed to address this issue, e.g., by generating test cases called individual discriminatory instances for repairing DNNs. Although they have demonstrated great potential, they tend to generate many test cases that are not directly effective in improving fairness and incur substantial computation overhead. We propose a new model repair technique, RULER, by discriminating sensitive and non-sensitive attributes during test case generation for model repair. The generated cases are then used in training to improve DNN fairness. RULER balances the trade-off between accuracy and fairness by decomposing the training procedure into two phases and introducing a novel iterative adversarial training method for fairness. Compared to the state-of-the-art techniques on four datasets, RULER has 7-28 times more effective repair test cases generated, is 10-15 times faster in test generation, and has 26-43% more fairness improvement on ‍average.
Metaheuristics based long short term memory optimization for sentiment analysis

A tremendous amount of opinionated data is being added to online platforms every day. Social media has become the primary source of collecting user’s opinions about products they have purchased. These online reviews contain important information for the sellers but the information is available in unstructured natural language text. There is a dire need for an automated approach that can extract sentiment from a large number of unstructured reviews. LSTM has shown state-of-the-art results for sentiment analysis in recent years. The performance of LSTM heavily depends on the architectural design. Rather than applying a trial and error approach which can easily mislead the results, an automated optimization technique should be used for architecture related hyperparameter selection. To address this problem, we propose a new framework to optimize the architecture of LSTM using different meta-heuristics. This is the first systematic study of architectural optimization in the context of sentiment analysis using meta heuristics. We have applied Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Firefly, and Cat Swarm Optimization (CSO) for LSTM architecture optimization. The problem of training LSTM has been formulated as an optimization problem and the objective is to maximize F-score. Four benchmark datasets for sentiment analysis have been used in our experiments. PSO and DE show remarkable success in improving the F-score and accuracy. Experimental results demonstrate that the optimal configuration obtained for designing the LSTM architecture using our proposed meta-heuristics significantly improves the accuracy of sentiment analysis.
MSMFN: An Ultrasound Based Multi-Step Modality Fusion Network for Identifying the Histologic Subtypes of Metastatic Cervical Lymphadenopathy

Identifying squamous cell carcinoma and adenocarcinoma subtypes of metastatic cervical lymphadenopathy (CLA) is critical for localizing the primary lesion and initiating timely therapy. B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), ultrasound elastography (UE) and dynamic contrast-enhanced ultrasound provide effective tools for identification but synthesis of modality information is a challenge for clinicians. Therefore, based on deep learning, rationally fusing these modalities with clinical information to personalize the classification of metastatic CLA requires new explorations. In this paper, we propose Multi-step Modality Fusion Network (MSMFN) for multi-modal ultrasound fusion to identify histological subtypes of metastatic CLA. MSMFN can mine the unique features of each modality and fuse them in a hierarchical three-step process. Specifically, first, under the guidance of high-level BUS semantic feature maps, information in CDFI and UE is extracted by modality interaction, and the static imaging feature vector is obtained. Then, a self-supervised feature orthogonalization loss is introduced to help learn modality heterogeneity features while maintaining maximal task-consistent category distinguishability of modalities. Finally, six encoded clinical information are utilized to avoid prediction bias and improve prediction ability further. Our three-fold cross-validation experiments demonstrate that our method surpasses clinicians and other multi-modal fusion methods with an accuracy of 80.06%, a true-positive rate of 81.81%, and a true-negative rate of 80.00%. Our network provides a multi-modal ultrasound fusion framework that considers prior clinical knowledge and modality-specific characteristics. Our code will be available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/RichardSunnyMeng/MSMFN</uri> .