Reviewerly/exHarmony huggingface dataset:
Improved Chinese Sentence Semantic Similarity Calculation Method Based on Multi-Feature Fusion

In this paper, an improved long short-term memory (LSTM)-based deep neural network structure is proposed for learning variable-length Chinese sentence semantic similarities. Siamese LSTM, a sequence-insensitive deep neural network model, has a limited ability to capture the semantics of natural language because it has difficulty explaining semantic differences based on the differences in syntactic structures or word order in a sentence. Therefore, the proposed model integrates the syntactic component features of the words in the sentence into a word vector representation layer to express the syntactic structure information of the sentence and the interdependence between words. Moreover, a relative position embedding layer is introduced into the model, and the relative position of the words in the sentence is mapped to a high-dimensional space to capture the local position information of the words. With this model, a parallel structure is used to map two sentences into the same high-dimensional space to obtain a fixed-length sentence vector representation. After aggregation, the sentence similarity is computed in the output layer. Experiments with Chinese sentences show that the model can achieve good results in the calculation of the semantic similarity.
Fine-Grained Fairness Analysis of Abusive Language Detection Systems with CheckList

Current abusive language detection systems have demonstrated unintended bias towards sensitive features such as nationality or gender.This is a crucial issue, which may harm minorities and underrepresented groups if such systems were integrated in real-world applications.In this paper, we create ad hoc tests through the CheckList tool (Ribeiro et al., 2020) to detect biases within abusive language classifiers for English.We compare the behaviour of two BERT-based models, one trained on a generic abusive language dataset and the other on a dataset for misogyny detection.Our evaluation shows that, although BERT-based classifiers achieve high accuracy levels on a variety of natural language processing tasks, they perform very poorly as regards fairness and bias, in particular on samples involving implicit stereotypes, expressions of hate towards minorities and protected attributes such as race or sexual orientation.We release both the notebooks implemented to extend the Fairness tests and the synthetic datasets usable to evaluate systems bias independently of CheckList.
Alpha at SemEval-2021 Task 6: Transformer Based Propaganda Classification

This paper describes our system participated in Task 6 of SemEval-2021: this task focuses on multimodal propaganda technique classification and it aims to classify given image and text into 22 classes.In this paper, we propose to use transformer-based (Vaswani et al., 2017) architecture to fuse the clues from both image and text.We explore two branches of techniques including fine-tuning the text pre-trained transformer with extended visual features and fine-tuning the multimodal pre-trained transformers.For the visual features, we experiment with both grid features extracted from ResNet(He et al., 2016) network and salient region features from a pretrained object detector.Among the pre-trained multimodal transformers, we choose ERNIE-ViL (Yu et al., 2020), a two-steam crossattended transformers model pre-trained on large-scale image-caption aligned data.Finetuning ERNIE-ViL for our task produces a better performance due to general joint multimodal representation for text and image learned by ERNIE-ViL.Besides, as the distribution of the classification labels is extremely unbalanced, we also make a further attempt on the loss function and the experiment results show that focal loss would perform better than cross-entropy loss.Lastly, we ranked first place at sub-task C in the final competition.
Adaptive Synapse Arrangement in Cortical Learning Algorithm

The cortical learning algorithm (CLA) is a time-series data prediction method that is designed based on the human neocortex. The CLA has multiple columns that are associated with the input data bits by synapses. The input data is then converted into an internal column representation based on the synapse relation. Because the synapse relation between the columns and input data bits is fixed during the entire prediction process in the conventional CLA, it cannot adapt to input data biases. Consequently, columns not used for internal representations arise, resulting in a low prediction accuracy in the conventional CLA. To improve the prediction accuracy of the CLA, we propose a CLA that self-adaptively arranges the column synapses according to the input data tendencies and verify its effectiveness with several artificial time-series data and real-world electricity load prediction data from New York City. Experimental results show that the proposed CLA achieves higher prediction accuracy than the conventional CLA and LSTMs with different network optimization algorithms by arranging column synapses according to the input data tendency.
Quantum and semi-quantum sealed-bid auction: vulnerabilities and advantages

A family of existing protocols for quantum sealed-bid auction is critically analyzed, and it is shown that they are vulnerable under several attacks (e.g., the participant's and non-participant's attacks as well as the collusion attack of participants) and some of the claims made in these works are not correct. We obtained the bounds on the success probability of an eavesdropper in accessing the sealed-bids. Further, realizing the role of secure sealed-bid auction in the reduction of corruption, a new protocol for sealed-bid auction is proposed which is semi-quantum in nature, where the bidders do not have quantum resources, but they can perform classical operations on the quantum states. The security of the proposed protocol is established against a set of attacks, and thus it is established that the proposed protocol is free from the vulnerabilities reported here in the context of the existing protocols.
On the safety of vulnerable road users by cyclist detection and tracking

In this work, orientation detection using Deep Learning is acknowledged for a particularly vulnerable class of road users,the cyclists. Knowing the cyclists' orientation is of great relevance since it provides a good notion about their future trajectory, which is crucial to avoid accidents in the context of intelligent transportation systems. Using Transfer Learning with pre-trained models and TensorFlow, we present a performance comparison between the main algorithms reported in the literature for object detection,such as SSD, Faster R-CNN and R-FCN along with MobilenetV2, InceptionV2, ResNet50, ResNet101 feature extractors. Moreover, we propose multi-class detection with eight different classes according to orientations. To do so, we introduce a new dataset called "Detect-Bike", containing 20,229 cyclist instances over 11,103 images, which has been labeled based on cyclist's orientation. Then, the same Deep Learning methods used for detection are trained to determine the target's heading. Our experimental results and vast evaluation showed satisfactory performance of all of the studied methods for the cyclists and their orientation detection, especially using Faster R-CNN with ResNet50 proved to be precise but significantly slower. Meanwhile, SSD using InceptionV2 provided good trade-off between precision and execution time, and is to be preferred for real-time embedded applications.
A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.
Accelerating GNN training with locality-aware partial execution

Graph Neural Networks (GNNs) are increasingly popular for various prediction and recommendation tasks. Unfortunately, the graph datasets for practical GNN applications are often too large to fit into the memory of a single GPU, leading to frequent data loading from host memory to GPU. This data transfer overhead is highly detrimental to the performance, severely limiting the training throughput.
Universal quantum computation and quantum error correction using discrete holonomies

Holonomic quantum computation exploits a quantum state's non-trivial, matrix-valued geometric phase (holonomy) to perform fault-tolerant computation. Holonomies arising from systems where the Hamiltonian traces a continuous path through parameter space have been well-researched. Discrete holonomies, on the other hand, where the state jumps from point to point in state space, have had little prior investigation. Using a sequence of incomplete projective measurements of the spin operator, we build an explicit approach to universal quantum computation. We show that quantum error correction codes integrate naturally in our scheme, providing a model for measurement-based quantum computation that combines the passive error resilience of holonomic quantum computation and active error correction techniques. In the limit of dense measurements we recover known continuous-path holonomies.
wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction

Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose wsGAT, an extension of the Graph Attention Network (GAT) layers, meant to address the lack of GNNs that can handle graphs with signed and weighted links, which are ubiquitous, for instance, in trust and correlation networks. We first evaluate the performance of our proposal by comparing against GCNII in the weighed link prediction task, and against SGCN in the link sign prediction task. After that, we combine the two tasks and show their performance on predicting the signed weight of links, and their existence. Our results on real-world networks show that models with wsGAT layers outperform the ones with GCNII and SGCN layers, and that there is no loss in performance when signed weights are predicted.
Estimating the Photon-Number Distribution of Photonic Channels for Realistic Devices and Applications in Photonic Quantum Information Processing

Characterising the input-output photon-number distribution of an unknown optical quantum channel is an important task for many applications in quantum information processing. Ideally, this would require deterministic photon-number sources and photon-number-resolving detectors, but these technologies are still work-in-progress. In this work, we propose a general method to rigorously bound the input-output photon number distribution of an unknown optical channel using standard optical devices such as coherent light sources and non-photon-number-resolving detectors/homodyne detectors. To demonstrate the broad utility of our method, we consider the security analysis of practical quantum key distribution systems based on calibrated single-photon detectors and an experimental proposal to implement time-correlated single photon counting technology using homodyne detectors instead of single-photon detectors.
Learning Constraints and Descriptive Segmentation for Subevent Detection

Event mentions in text correspond to realworld events of varying degrees of granularity.The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes.Since knowing the span of descriptive contexts of event complexes helps infer the membership of events, we propose the task of event-based text segmentation (EVENTSEG) as an auxiliary task to improve the learning for subevent detection.To bridge the two tasks together, we propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EVENTSEG prediction, as well as guiding the model to make globally consistent inference.Specifically, we adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.Experimental results show that the proposed method outperforms baseline methods by 2.3% and 2.5% on benchmark datasets for subevent detection, HiEve and IC, respectively, while achieving a decent performance on EVENTSEG prediction 1 .
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, a wide range of novel AL strategies has been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: (1) They explicitly consider (multiple) human annotators whose performances can be affected by various factors, such as missing expertise. (2) They generalize the interaction with human annotators by considering different query and annotation types, such as asking an annotator for feedback on an inferred classification rule. (3) They take more complex cost schemes regarding annotations and misclassifications into account. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.
Master equations and quantum trajectories for squeezed wave packets

The interaction between matter and squeezed light has mostly been treated within the approximation that the field-correlation time is small. Methods for treating squeezed light with more general correlations currently involve explicitly modeling the systems producing the light. We develop a general purpose input-output theory for a particular form of narrowband squeezed light, a squeezed wave-packet mode, that only concerns the statistics of the squeezed field and the shape of the wave packet. This formalism allows us to derive the input-output relations and the master equation. We also consider detecting the scattered field using photon counting and homodyne measurements, which necessitates the derivation of the stochastic master equation. The non-Markovian nature of the field manifests itself in the master equation as a coupled hierarchy of equations. We illustrate these with consequences for the decay and resonance fluorescence of two-level atoms in the presence of such fields.
Post Triangular Rewiring Method for Shorter RRT Robot Path Planning

This paper proposed the 'Post Triangular Rewiring' method that minimizes the sacrifice of planning time and overcomes the limit of Optimality of sampling-based algorithm such as Rapidly-exploring Random Tree (RRT) algorithm. The proposed 'Post Triangular Rewiring' method creates a closer to the optimal path than RRT algorithm before application through the triangular inequality principle. The experiments were conducted to verify a performance of the proposed method. When the method proposed in this paper are applied to the RRT algorithm, the Optimality efficiency increase compared to the planning time.
A Knowledge Graph Embedding Based Approach for Learning Path Recommendation for Career Goals

Nowadays, many online courses in the Information Technology (IT) field provided by different organizations make it difficult for learners to screen the courses that are best suitable for their career development. Many approaches have been raised to suggest a personalized learning path based on the learner’s career goals; however, most of them use traditional techniques and face the problems of sparse data, cold start, and lack of advisory results’ interpretation. In this paper, we address these problems by using Knowledge Graph Embedding (KGE) which is known as one of approaches of Graph-based models. This approach has emerged as a phenomenon and has not been widely applied in the field of learning path recommendation. We propose a new knowledge graph (KG) architecture for representing entities and their semantic relationships. The main entities identified in the KG are courses, occupations, the units of knowledge, and the relationships are the semantic connections between these entities. In existing KG architectures only exploit the hierarchy of intermediate knowledge units. Whereas, our KG architecture provides a specific classification of these learning objects, and this helps the semantic relationship of the subjects become more unambiguous and connected. Then, we explore and build experiments for our proposed KG architecture by using KGE techniques to prove the effectiveness. The experimental results show that our solution is worth considering and promises to bring a high degree of efficiency in the learning path recommendation system.
Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets

&lt;span style="font-size: 9pt; font-family: 'Times New Roman', serif;"&gt;Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.&lt;/span&gt;
CAAGP: Rethinking channel attention with adaptive global pooling for liver tumor segmentation

Channel attention, a channel-wise method often used in computer vision tasks, including liver tumor segmentation tasks, is able to model the channel relationship to augment the representation ability of feature maps. Channel attention could adaptively generate channel-wise responses using global pooling, which aggregates spatial information roughly. Actually, global pooling may introduce the loss of fine information, which is vital for segmentation tasks. Hence, we rethink the problem and propose the channel attention with adaptive global pooling(short for CAAGP), which preserves spatial and fine-grained information for liver tumor segmentation tasks when channel attention is generated. The model consists of three main parts, including improved self-attention, adaptive global pooling and responses generation modules. Self-attention achieves excellent performance in the computing of the spatial attention, while introducing serious calculation and memory burdens. In order to remedy these burdens, we improve self-attention and consider aggregating spatial information from x and y directions respectively. Extensive experiments have been conducted to verify the effectiveness of our proposed method. Our CAAGP outperforms other attention mechanisms significantly in liver tumor segmentation, especially for tumors with small size.
Enhanced Learning Experiences Based on Regulatory Fit Theory Using Affective State Detection

Predicting learners' affective states through the internet has great impact on their learning experiences. Hence, it is important for an intelligent tutoring system (ITS) to consider the learners' affective state in their learning models. This research work focuses on finding learners' frustration levels during learning. Motivating the learners appropriately can enhance their learning experiences. Therefore, the authors also bring in a strategy to respond to learners' affective states in order to motivate them. This work uses Behavioral theory for goal generation, and frustration index is calculated. Based on the frustration level of the learner, motivational messages are displayed to the learners using Regulatory fit theory. The authors evaluated the model using t-test by collecting learners' data from MoodleCloud. The results of the evaluation demonstrate that 80% of the learners' performance significantly increases statistically as an impact of motivational messages provided in response to the learners' frustration.
Bridging the Gap Between Paired and Unpaired Medical Image Translation

Medical image translation has the potential to reduce the imaging workload, by removing the need to capture some sequences, and to reduce the annotation burden for developing machine learning methods. GANs have been used successfully to translate images from one domain to another, such as MR to CT. At present, paired data (registered MR and CT images) or extra supervision (e.g. segmentation masks) is needed to learn good translation models. Registering multiple modalities or annotating structures within each of them is a tedious and laborious task. Thus, there is a need to develop improved translation methods for unpaired data. Here, we introduce modified pix2pix models for tasks CT$\rightarrow$MR and MR$\rightarrow$CT, trained with unpaired CT and MR data, and MRCAT pairs generated from the MR scans. The proposed modifications utilize the paired MR and MRCAT images to ensure good alignment between input and translated images, and unpaired CT images ensure the MR$\rightarrow$CT model produces realistic-looking CT and CT$\rightarrow$MR model works well with real CT as input. The proposed pix2pix variants outperform baseline pix2pix, pix2pixHD and CycleGAN in terms of FID and KID, and generate more realistic looking CT and MR translations.