Reviewerly/exHarmony huggingface dataset:
Research on task scheduling scheme for quantum computing cloud platform

Quantum computing devices are usually demanding on the environment, with high operating conditions and maintenance costs. The supporting quantum computing operation software also has a high professional threshold. These make it more difficult to deploy and use locally. Cloud based quantum computing services will play an important role in computing power in the quantum era. At present, high-quality quantum computing QPU is scarce, which brings access needs of a large number of users. The computing tasks of a single user are too large, resulting in a large number of resources being occupied by a single user for a period of time, affecting the use of other users or tasks. A new quantum computing cloud platform task scheduling scheme is proposed to classify users and tasks and provide differentiated service opportunities. This scheme will enable high priority users to reduce waiting time and get a better experience.
How does Personality Matter? Investigating The Effect of Big-Five Personality Traits on Virtual Reality Acceptance Behavior

Virtual reality (VR) is one of the recent technological innovations that will shape the future of the tourism industry. Experiencing travel in a virtual world is a practice that will become increasingly popular in the coming years. Using the Big Five Model (BFM) as a theoretical framework, the study examines the role of personality in influencing behavioral intention (BI) to use virtual tourism. The study also examines the role of personal innovativeness (PIV) in determining tourists' behavioral intentions to use virtual tourism. Involving 374 participants and based on Artificial Neural Network (ANN) method, this study highlights the impact of psychographic traits, specifically openness to experience (OPN), agreeableness (AGR), conscientiousness (CONS), extraversion (EXTRA), neuroticism (NR), and personal innovation (PIV), on behavioral intention to use virtual tourism. The results of the study revealed that OPN followed by CONS and EXTRA are the most significant traits characterizing the user's personality that accepts VR-tourism.
Document-Level Event Temporal Relation Extraction on Global and Local Cues

Most previous work focused on extracting event temporal relations that the events appear in the same sentence or in two adjacent sentences, failing to address those nonadjacent-sentence event relations, which limits the development of tem-poral relation extraction at document-level and its real-world application. In this paper, we propose a novel Document-level event Temporal Relation Extraction (DTRE) model which can incorporate effective global cues with local cues. In particular, we select both the contextual sentences strongly related to the events and the temporal words in the context as global cues, which can provide additional semantic cues to extract those nonadjacent-sentence event temporal relations. Moreover, we further encode the events and their neighbor words as local cues to extract those intra-sentence relations and enhance the event representation. Experimental results on the English dataset show that our proposed DTRE outperforms several state-of-the-art baselines, especially for handling those nonadjacent-sentence temporal relations.
Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization

We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum annealers (QA). The computational premise of quantum computers has cultivated the re-design of various existing vision problems into quantum-friendly forms. Experimental QA realisations can solve a particular non-convex problem known as the quadratic unconstrained binary optimization (QUBO). Yet a naive-QUBO cannot take into account the restrictions on the parameters. To introduce additional structure in the parameter space, researchers have crafted ad-hoc solutions incorporating (linear) constraints in the form of regularizers. However, this comes at the expense of a hyper-parameter, balancing the impact of regularization. To date, a true constrained solver of quadratic binary optimization (QBO) problems has lacked. Q-FW first reformulates constrained-QBO as a copositive program (CP), then employs Frank-Wolfe iterations to solve CP while satisfying linear (in)equality constraints. This procedure unrolls the original constrained-QBO into a set of unconstrained QUBOs all of which are solved, in a sequel, on a QA. We use D-Wave Advantage QA to conduct synthetic and real experiments on two important computer vision problems, graph matching and permutation synchronization, which demonstrate that our approach is effective in alleviating the need for an explicit regularization coefficient.
Optical-Enhanced Oil Tank Detection in High-Resolution SAR Images

In recent years, object detection in high-resolution SAR images has made significant progress, especially after the introduction of deep learning. However, objects like dense oil tanks, which are compactly arranged in SAR images, are still challenging to recognize due to the unique imaging mechanism of SAR. Inspired by human learning from comparison, we propose a multi-stage framework for oil tank detection in SAR images using optical image enhancement. Specifically, in the training stage, we build a teacher-student network to align the semantic information between the two modalities, where the optical features are used to guide the corresponding SAR feature learning. While in the inference stage, the learned network detects oil tanks using only SAR images as input. Besides, a pre-training stage before training is applied to further improve the network’s ability for SAR feature extraction, which is realized by the proposed paired optical-SAR self-supervised learning. To verify the effectiveness of the proposed method, we perform experiments on our newly built SpaceNet6-OTD dataset. Extensive experiments demonstrate that the proposed method can effectively improve the accuracy of detecting oil tanks in SAR images. Datasets, codes, and more results will be released at: https://EIS-VIPG.github.io/SpaceNet6-OTD/.
ART-SS: An Adaptive Rejection Technique for Semi-supervised Restoration for Adverse Weather-Affected Images

In recent years, convolutional neural network-based single image adverse weather removal methods have achieved significant performance improvements on many benchmark datasets. However, these methods require large amounts of clean-weather degraded image pairs for training, which is often difficult to obtain in practice. Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weatherdegraded images due to the domain gap between synthetic and real world images. To deal with this problem, various semi-supervised restoration (SSR) methods have been proposed for deraining or dehazing which learn to restore clean image using synthetically generated datasets while generalizing better using unlabeled real-world images. The performance of a semi-supervised method is essentially based on the quality of the unlabeled data. In particular, if the unlabeled data characteristics are very different from that of the labeled data, then the performance of a semi-supervised method degrades significantly. We theoretically study the effect of unlabeled data on the performance of an SSR method and develop a technique that rejects the unlabeled images that degrade the performance. Extensive experiments and ablation study show that the proposed sample rejection method increases the performance of existing SSR deraining and dehazing methods significantly. Code is available at: https://github.com/rajeevyasarla/ART-SS .
LILA-BOTI : Leveraging Isolated Letter Accumulations By Ordering Teacher Insights for Bangla Handwriting Recognition

Word-level handwritten optical character recognition (OCR) remains a challenge for morphologically rich languages like Bangla. The complexity arises from the existence of a large number of alphabets, the presence of several diacritic forms, and the appearance of complex conjuncts. The difficulty is exacerbated by the fact that some graphemes occur infrequently but remain indispensable, so addressing the class imbalance is required for satisfactory results. This paper addresses this issue by introducing two knowledge distillation methods: Leveraging Isolated Letter Accumulations By Ordering Teacher Insights (LILA-BOTI) and Super Teacher LILA-BOTI. In both cases, a Convolutional Recurrent Neural Network (CRNN) student model is trained with the dark knowledge gained from a printed isolated character recognition teacher model. We conducted inter-dataset testing on BN-HTRd and BanglaWriting as our evaluation protocol, thus setting up a challenging problem where the results would better reflect the performance on unseen data. Our evaluations achieved up to a 3.5% increase in the F1-Macro score for the minor classes and up to 4.5% increase in our overall word recognition rate when compared with the base model (No KD) and conventional KD.
How much does input data type impact final face model accuracy?

Face models are widely used in image processing and other domains. The input data to create a 3D face model ranges from accurate laser scans to simple 2D RGB photographs. These input data types are typically deficient either due to missing regions, or because they are underconstrained. As a result, reconstruction methods include embedded priors encoding the valid domain of faces. System designers must choose a source of input data and then choose a reconstruction method to obtain a usable 3D face. If a particular application domain requires accuracy X, which kinds of input data are suitable? Does the input data need to be 3D, or will 2D data suffice? This paper takes a step toward answering these questions using synthetic data. A ground truth dataset is used to analyze accuracy obtainable from 2D landmarks, 3D landmarks, low quality 3D, high quality 3D, texture color, normals, dense 2D image data, and when regions of the face are missing. Since the data is synthetic it can be analyzed both with and without measurement error. This idealized synthetic analysis is then compared to real results from several methods for constructing 3D faces from 2D photographs. The experimental results suggest that accuracy is severely limited when only 2D raw input data exists.
On Detecting Semantic Clones in Constraint Logic Programs

Deciding whether two code fragments are semantic clones, or type-4 clones, is a problem with many ramifications. Current research often focuses on the problem in an imperative or object-oriented setting and most existing work uses abstract syntax trees, program dependency graphs, program metrics or text-based, token-based and machine learning-based approaches to identify semantic clones. In this work, we adopt a fundamentally different point of view and express clone detection as a search problem in a logic programming setting. Due to their restricted syntax and semantics, (constraint) logic programs are by nature simple and elegant candidates for automated analysis. After having formalized the clone detection problem at the level of predicates, we develop a study of the different parameters that come into play in the resulting framework. We try and identify the complexity issues involved in a general semantic clone detection procedure that essentially computes so-called most specific generalizations for predicates written in constraint logic programming (CLP). Even though well-known for basic structures such as literals and terms, generalization (or anti-unification) of more complex structures such as clauses and predicates has received very little attention. We show that the anti-unification allows both to control the search and guide the detection of cloned predicates. We pinpoint where efficient approximations are needed in order to be able to identify semantic code clones in a manageable time frame.
Anomaly detection in complex data: a practical application when outliers are few

Ocean Networks Canada (ONC) runs a cabled ocean observatory that, among a wide variety of oceanographic instruments, has more than 30 hydrophones deployed. These hydrophones continuously send audio data back from the ocean floor, resulting in greater than 7 TB of data per hydrophone per year. To quickly visualize these acoustic data, power spectral density plots (spectrograms) are generated and stored in the ONC archive. While hydrophones generally perform well in the field, losses in sensitivity or ‘dropouts’ occasionally occur at isolated frequencies or across all bands for short periods of time. While such dropouts can be spotted on a spectrogram via visual inspection, the manual review of thousands of images is costly and impractical. With the current number of ONC’s hydrophones, it takes a data specialist over eight hours to inspect a day’s worth of spectrograms. We used a total of 2185 spectrogram images to train an autoencoder model. These images were split into three data sets: regular spectrograms without anomalies (1024 images that were separated in training and validation data sets), test data set (1000 unclassified images) and spectrograms containing anomalous signals (161 images). We used mean squared error (MSE) as a metric for the image reconstructions provided by the model and to establish the threshold of 0.006 to identify anomalous spectrograms. The trained model was capable of identifying most anomalous images from regular ones and correctly labelled approximately 93% of the images with anomalous features in the anomalous data sets.
Efficient Multi-Authority Attribute-Based Encryption with Policy Hiding and Updating

Attribute-based encryption (ABE) is a popular cryptographic technology to guarantee data security in the Internet of things (IoTs). However, the single attribute authorization in traditional ABE scheme is tasked with costly calculations, which will become a bottleneck in IoTs. Besides, access policy in a plaintext form may reveal personal information. In addition, data owners need to have flexibility to change access policy. In this paper, we propose a multi-authorization attribute encryption scheme that supports policy hiding, policy updating and ciphertext validation. In our scheme, the computational cost of ciphertext decryption is constant, which reduces computational overhead and storage usage. Security analyses prove that our scheme realizes the security in static security model and anti-collusion attack model. Finally, we demonstrate that the performance of the scheme is more efficient than previous ABE schemes.
KITPose: Keypoint-Interactive Transformer for Animal Pose Estimation

Animal pose estimation has received increasing attention in recent years. The main challenge for this task is the diversity of animal species compared to their human counterpart. To address this issue, we design a keypoint-interactive Transformer model for high-resolution animal pose estimation, namely KITPose. Since a high-resolution network maintains local perception and the self-attention module in Transformer is an expert in connecting long-range dependencies, we equip the high-resolution network with a Transformer to enhance the model capacity, achieving keypoints interaction in the decision stage. Besides, to smoothly fit the pose estimation task, we simultaneously train the model parameters and joint weights, which can automatically adjust the loss weight for each specific keypoint. The experimental results obtained on the AP10K and ATRW datasets demonstrate the merits of KITPose, as well as its superior performance over the state-of-the-art approaches.
Improvement of Final Exponentiation for a Pairing on FK12 Curve and its Implementation

Pairings on elliptic curves are used for innovative protocols such as ID-based encryption and zk-SNARKs. To make the pairings secure, it is important to consider the STNFS which is the special number field sieve algorithm for discrete logarithms in the finite field. The Fotiadis-Konstantinou curve with embedding degree 12(FK12), is known as one of the STNFS secure curves. To an efficient pairing on the FK12 curve, there are several previous works that focus on final exponentiation. The one is based on lattice-based method to decompose the hard part of final exponentiation and addition chain. However, there is a possibility to construct a more efficient calculation algorithm by using the relations appeared in the decomposition calculation algorithm than that of the previous work. In this manuscript, the authors propose a relation of the decomposition and verify the effectiveness of the proposed method from the execution time.
VBlock—Blockchain-Based Traceability in Medical Products Supply Chain Management: A Case Study in VietNam

The COVID-19 is putting tremendous pressure on the medical facilities supply, as the demand for facilities has significantly outweighed the production capability. Several rogue traders have taken advantage of this issue to distribute counterfeit products. Moreover, some sellers advertise genuine products with unreasonably high prices. Our team believes that fake or overpriced facilities will significantly complicate the battle against COVID, thereby posing millions of lives to risk. That is why our team is developing V-Block. V-Block is a supply chain management system that harnesses the power of Blockchain. Its primary goals are to assist the government in tracking the product’s distribution process and help customers avoid questionable deals.
Urdu Handwritten Ligature Generation Using Generative Adversarial Networks (GANs)

Deep learning has significantly improved handwriting text recognition, esp. for Latin scripts. Arabic scripts including Urdu is a family of complex scripts and they pose difficult challenges for deep learning architectures. Data availability is a significant obstacle in developing Urdu handwriting recognition systems. Since gathering data is a costly and challenging task, there is a need to increase training data using novel approaches. One possible solution is to make a model that can generate similar yet different samples from the existing data samples. In this paper, we propose such models based on Generative Adversarial Networks (GANs) that have the ability to synthesize realistic samples similar to the original dataset. Our generator is class conditioned to produce Urdu samples of varying characters that differ in style. Visual and quantitative analysis convey that generated samples are of realistic nature and can be used to increase datasets. Synthesized samples integrated with the existing training set is shown to increase the performance of a handwriting recognition system.
How do Programmers Use the Internet? Discovering Domain Knowledge from Browsing and Coding Behaviors

The Internet is an effective tool for learners to gain new knowledge. Often, people use search engines (e.g., Google) rather than accessing websites directly. People have their search techniques to find specific information. In particular, people with domain knowledge tend to search more efficiently than novices. By understanding the gap between people with domain knowledge and novices, the novice can understand the path to becoming an expert. Therefore, in this study, we wanted to know what differences exist in search and programming behavior with and without domain knowledge. In this experiment, we asked a group with and without domain knowledge to solve ten programming problems and collected search logs (input knowledge) and compilation logs (output knowledge). Specifically, the first dataset consisted of 13 participants who had taken a university programming class. The second dataset consisted of 20 participants who had not taken a programming class and had no domain knowledge. We examined differences in search and compilation behavior based on participants’ domain knowledge from this data. Since we observed a difference between each group when referring to the correlation coefficient, we performed a binary classification of novice and experienced participants using Random Forest, and achieved an average precision of 0.95, indicating that there were different trends in behavior with and without domain knowledge.
Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution

Estimating the pose and shape of hands and objects under interaction finds numerous applications including aug-mented and virtual reality. Existing approaches for hand and object reconstruction require explicitly defined physical constraints and known objects, which limits its application domains. Our algorithm is agnostic to object models, and it learns the physical rules governing hand-object interaction. This requires automatically inferring the shapes and physi-cal interaction of hands and (potentially unknown) objects. We seek to approach this challenging problem by proposing a collaborative learning strategy where two-branches of deep networks are learning from each other. Specifically, we transfer hand mesh information to the object branch and vice versa for the hand branch. The resulting optimi-sation (training) problem can be unstable, and we address this via two strategies: (i) attention-guided graph convo-lution which helps identify and focus on mutual occlusion and (ii) unsupervised associative loss which facilitates the transfer of information between the branches. Experiments using four widely-used benchmarks show that our frame-work achieves beyond state-of-the-art accuracy in 3D pose estimation, as well as recovers dense 3D hand and object shapes. Each technical component above contributes meaningfully in the ablation study.
Remote Sensing Cross-Modal Retrieval by Deep Image-Voice Hashing

Remote sensing image retrieval aims at searching remote sensing images of interest among immense volumes of remote sensing data, which is an enormous challenge. Direct use of voice for human-computer interaction is more convenient and intelligent. In this paper, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Image-Voice Hashing</i> (DIVH) method is proposed for remote sensing image-voice retrieval. First, the whole framework is composed of the image and the voice feature learning subnetwork. Then, the hash code learning procedure will be leveraged in remote sensing image-voice retrieval to further improve retrieval efficiency and reduce memory footprint. Hash code learning maps the deep features of images and voices into a common Hamming space. Finally, image-voice pairwise loss is proposed, which considers the similarity preservation and balance of hash codes. The similarity preserving and the balance controlling term of the loss function improve the similarity preservation from original data space to Hamming space and the discrimination of binary code, respectively. This unified cross-modal feature and hash code learning framework significantly reduce the semantic gap between the two modal data. Experiments demonstrate that the proposed DIVH method can achieve superior retrieval performance than other state-of-the-art remote sensing image-voice retrieval methods.