Reviewerly/exHarmony huggingface dataset:
Teaching Innovation in Higher Education Through Virtual Reality

In the knowledge society in which we find ourselves, traditional expository classes in which students played a passive role have been shown not to meet students' expectations and do not respond to educational demands. Technology has gradually been incorporated into all areas of life to the point where it has become the central axis of life. The education sector has also been strongly influenced by this growing trend in recent years. E-learning and immersive technologies, such as au-mentary and virtual reality, offer a series of benefits and potentialities in training practice at any educational stage and in any area of knowledge, making learning meaningful and contextualised, facilitating the understanding of abstract content, enriching teaching materials, avoiding risks, overcoming spatial and temporal obstacles and arousing the interest of students due to the motivating and innovative content they present, as the main features. Therefore, its inclusion in university classrooms of any discipline should be encouraged, as it represents a significant increase in the quality of teaching.
Ten Years of Active Learning Techniques and Object Detection: A Systematic Review

Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy in the field of computer vision, harnessing the capabilities of machine learning (ML) to automatically identify and perform image-based objects localisation while actively engaging human expertise to iteratively enhance model performance and foster machine-based knowledge expansion. Their prior success, demonstrated in a wide range of fields (e.g., industry and medicine), motivated this work, in which a comprehensive and systematic review of OD and AL techniques was carried out, considering reputed technical/scientific publication databases—such as ScienceDirect, IEEE, PubMed, and arXiv—and a temporal range between 2010 and December 2022. The primary inclusion criterion for papers in this review was the application of AL techniques for OD tasks, regardless of the field of application. A total of 852 articles were analysed, and 60 articles were included after full screening. Among the remaining ones, relevant topics such as AL sampling strategies used for OD tasks and groups categorisation can be found, along with details regarding the deep neural network architectures employed, application domains, and approaches used to blend learning techniques with those sampling strategies. Furthermore, an analysis of the geographical distribution of OD researchers across the globe and their affiliated organisations was conducted, providing a comprehensive overview of the research landscape in this field. Finally, promising research opportunities to enhance the AL process were identified, including the development of novel sampling strategies and their integration with different learning techniques.
Moving from ISAD(G) to a CIDOC CRM-based Linked Data Model in the Portuguese Archives

Archives are facing numerous challenges. On the one hand, archival assets are evolving to encompass digitized documents and increasing quantities of born-digital information in diverse formats. On the other hand, the audience is changing along with how it wishes to access archival material. Moreover, the interoperability requirements of cultural heritage repositories are growing. In this context, the Portuguese Archives started an ambitious program aiming to evolve its data model, migrate existing records, and build a new archival management system appropriate to both archival tasks and public access. The overall goal is to have a fine-grained and flexible description, more machine-actionable than the current one. This work describes ArchOnto, a linked open data model for archives, and rules for its automatic population from existing records. ArchOnto adopts a semantic web approach and encompasses the CIDOC Conceptual Reference Model and additional ontologies, envisioning interoperability with datasets curated by multiple communities of practice. Existing ISAD(G)-conforming descriptions are being migrated to the new model using the direct mappings provided here. We used a sample of 25 records associated with different description levels to validate the completeness and conformity of ArchOnto to existing data. This work is in progress and is original in several respects: (1) it is one of the first approaches to use CIDOC CRM in the context of archives, identifying problems and questions that emerged during the process and pinpointing possible solutions; (2) it addresses the balance in the model between the migration of existing records and the construction of new ones by archive professionals; and (3) it adopts an open world view on linking archival data to global information sources.
Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data

The prediction of prognostic outcome is critical for the development of efficient cancer therapeutics and potential personalized medicine. However, due to the heterogeneity and diversity of multimodal data of cancer, data integration and feature selection remain a challenge for prognostic outcome prediction. We proposed a deep learning method with generative adversarial network based on sequential channel-spatial attention modules (CSAM-GAN), a multimodal data integration and feature selection approach, for accomplishing prognostic stratification tasks in cancer. Sequential channel-spatial attention modules equipped with an encoder-decoder are applied for the input features of multimodal data to accurately refine selected features. A discriminator network was proposed to make the generator and discriminator learning in an adversarial way to accurately describe the complex heterogeneous information of multiple modal data. We conducted extensive experiments with various feature selection and classification methods and confirmed that the CSAM-GAN via the multilayer deep neural network (DNN) classifier outperformed these baseline methods on two different multimodal data sets with miRNA expression, mRNA expression and histopathological image data: lower-grade glioma and kidney renal clear cell carcinoma. The CSAM-GAN via the multilayer DNN classifier bridges the gap between heterogenous multimodal data and prognostic outcome prediction.
Randomized adaptive quantum state preparation

We develop an adaptive method for quantum state preparation that utilizes randomness as an essential component and that does not require classical optimization. Instead, a cost function is minimized to prepare a desired quantum state through an adaptively constructed quantum circuit, where each adaptive step is informed by feedback from gradient measurements in which the associated tangent space directions are randomized. We provide theoretical arguments and numerical evidence that convergence to the target state can be achieved for almost all initial states. We investigate different randomization procedures and develop lower bounds on the expected cost function change, which allows for drawing connections to barren plateaus and for assessing the applicability of the algorithm to large-scale problems.
A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers

Reinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a framework that uses Transformers to enhance the training of online off-policy RL agents and address the challenges described above through self-attention. The proposal introduces a hybrid agent with a mixed policy that combines an online off-policy agent with an offline Transformer agent using the Decision Transformer architecture. By sequentially exchanging the experience replay buffer between the agents, the agent’s learning training efficiency is improved in the first iterations and so is the training of Transformer-based RL agents in situations with limited data availability or unknown environments.
NEHATE: Large-Scale Annotated Data Shedding Light on Hate Speech in Nepali Local Election Discourse

The use of social media during election campaigns has become increasingly popular. However, the unbridled nature of online discourse can lead to the propagation of hate speech, which has far-reaching implications for the democratic process. Natural Language Processing (NLP) techniques are being used to counteract the spread of hate speech and promote healthy online discourse. Despite the increasing need for NLP techniques to combat hate speech, research on low-resource languages such as Nepali is limited, posing a challenge to the realization of the United Nations’ Leave No One Behind principle, which calls for inclusive development that benefits all individuals and communities, regardless of their backgrounds or circumstances. To bridge this gap, we introduce NEHATE, a large-scale manually annotated dataset of hate speech and its targets in Nepali local election discourse. The dataset comprises 13,505 tweets, annotated for hate speech with further sub-categorization of hate speech into targets such as community, individual, and organization. Benchmarking of the dataset with various algorithms has shown potential for performance improvement. We have made the dataset publicly available at https://github.com/shucoll/NEHate to promote further research and development, while also contributing to the UN SDGs aimed at fostering peaceful, inclusive societies, and justice and strong institutions.
SlideGCN: Slightly Deep Graph Convolutional Network for Multilingual Sentiment Analysis

Multilingual sentiment analysis refers to the process of sentiment scoring while gathering insights from data in different languages. Many research studies have been conducted to perform multilingual sentiment analysis. However, most of these studies focus on the short-distance semantics which consists in modeling local consecutive word sequences. In this work, we consider the global word co-occurrence in the whole corpus, which capture both short- and long-distance semantics, to convey more meaningful insights for the analysis. We propose an approach called MSA-GCN (Multilingual Sentiment Analysis based on Graph Convolutional Network) while supporting both short- and long-distance semantics. We build a single heterogeneous text graph for a multilingual corpus based on sequential, semantic, and statistical information. Then, a slightly deep graph convolutional network learns embeddings for all nodes in a semi-supervised manner. Extensive experiments are carried out on various datasets, and the results demonstrate the effectiveness of the proposed approach.
Innovation and Entrepreneurship Education: A New Curriculum Paradigm in the Gambia

This paper discusses on the new curriculum approach being pursued in The Gambia, which is introducing innovation and entrepreneurship courses in all educational programmes at tertiary and higher education levels. The Government of The Gambia through the Ministry of Higher Education, Research, Science and Technology (MoHERST) has in 2021 taken up a policy decision to introduce through and embedded approach innovation and entrepreneurship modules in all educational courses at tertiary and higher education levels (MoHERST, 2022). The paper relying on existing literature and policy documents discusses on the education structure of the Gambia and the qualifications frameworks in place to guide the deployment of this new curriculum approach, identifying the collaboration pathways with industry actors and in alignment with international best practices (NAQAA, 2022). The paper further elaborates on the role of each of the actors of the innovation ecosystem and how they coordinate in promoting the development and implementation of the new curriculum in The Gambia. It has detailed how the academic institutions, industry actors, regulators, students, as well as communities interrelate in nurturing and pursuing innovation and entrepreneurship skills towards strengthening the innovation ecosystem in The Gambia. This paper further discusses on the potential benefits of the new curriculum approach and the impact it will create in supporting and ensuring a sustained socio-economic development is achieved in The Gambia. Finally, the paper concluded by proposing policy recommendations for adoption in enriching and upgrading the new curriculum approach in a bid to improving its impact in the overall national development drive in the Gambia.
Analisis Bibliometrik Publikasi Isu Kebocoran Data Menggunakan VOSviewer

Data leakage can harm individuals who experience it, such as identity theft, financial fraud, or even physical security threats. This study aims to analyze scientific publications and research activities in data leakage using bibliometric techniques by involving quantitative analysis using a set of journals as reference sources. The literature for this study was obtained using Publish or Perish software with the keyword "data leakage" and a search result limit of 500. Furthermore, from these results, 85 journals relevant to the research topic were selected for visualization using VOSviewer software. The results showed that the highest number of data leakage publications occurred in 2021, namely 30 journals published that year. Based on the results of the bibliometric analysis conducted, it is known that there are 9 clusters based on keywords and 19 clusters based on author. The most frequently researched keywords include personal data, privacy, data leakage, legal protection, security, cryptography, encryption, and description. On the other hand, some keywords are rarely discussed in publications, namely, air transportation, electronics, and Caesar.
Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation

The precision in evaluating global ambient light profoundly impacts the performance of image-dehazing technologies. Many approaches for quantifying atmospheric light intensity suffer from inaccuracies, leading to a decrease in dehazing effectiveness. To address this challenge, we introduce an approach for estimating atmospheric light based on the polarization contrast between the sky and the scene. By employing this method, we enhance the precision of atmospheric light estimation, enabling the more accurate identification of sky regions within the image. We adapt the original dark channel dehazing algorithm using this innovative technique, resulting in the development of a polarization-based dehazing imaging system employed in practical engineering applications. Experimental results reveal a significant enhancement in the accuracy of atmospheric light estimation within the dark channel dehazing algorithm. Consequently, this method enhances the overall perceptual quality of dehazed images. The proposed approach demonstrates a 28 percent improvement in SSIM and a contrast increase of over 20 percent when compared to the previous method. Additionally, the created dehazing system exhibits real-time processing capabilities.
Mitigation of frequency collisions in superconducting quantum processors

The reproducibility of qubit parameters is a challenge for scaling up superconducting quantum processors. Signal cross talk imposes constraints on the frequency separation between neighboring qubits. The frequency uncertainty of transmon qubits arising from the fabrication process is attributed to deviations in the Josephson junction area, tunnel barrier thickness, and the qubit shunt capacitor. We decrease the sensitivity to these variations by fabricating larger Josephson junctions and reduce the wafer-level standard deviation in resistance down to 2%. We characterize 32 identical transmon qubits and demonstrate the reproducibility of the qubit frequencies with a 40 MHz standard deviation (i.e., 1%) with qubit quality factors exceeding 2 million. We perform two-level-system (TLS) spectroscopy and observe no significant increase in the number of TLSs causing qubit relaxation. We further show by simulation that for our parametric-gate architecture, and accounting only for errors caused by the uncertainty of the qubit frequency, we can scale up to 100 qubits with an average of only three collisions between quantum-gate transition frequencies, assuming $2%$ cross talk and $99.9%$ target gate fidelity.
Deep Learning Short Text Sentiment Analysis Based on Improved Particle Swarm Optimization

Manually tuning the hyperparameters of a deep learning model is not only a time-consuming and labor-intensive process, but it can also easily lead to issues like overfitting or underfitting, hindering the model’s full convergence. To address this challenge, we present a BiLSTM-TCSA model (BiLSTM combine TextCNN and Self-Attention) for deep learning-based sentiment analysis of short texts, utilizing an improved particle swarm optimization (IPSO). This approach mimics the global random search behavior observed in bird foraging, allowing for adaptive optimization of model hyperparameters. In this methodology, an initial step involves employing a Generative Adversarial Network (GAN) mechanism to generate a substantial corpus of perturbed text, augmenting the model’s resilience to disturbances. Subsequently, global semantic insights are extracted through Bidirectional Long Short Term Memory networks (BiLSTM) processing. Leveraging Convolutional Neural Networks for Text (TextCNN) with diverse convolution kernel sizes enables the extraction of localized features, which are then concatenated to construct multi-scale feature vectors. Concluding the process, feature vector refinement and the classification task are accomplished through the integration of Self-Attention and Softmax layers. Empirical results underscore the effectiveness of the proposed approach in sentiment analysis tasks involving succinct texts containing limited information. Across four distinct datasets, our method attains impressive accuracy rates of 91.38%, 91.74%, 85.49%, and 94.59%, respectively. This performance constitutes a notable advancement when compared against conventional deep learning models and baseline approaches.
Decoherence-induced strongly sub-Poissonian nonlocality

Quantum decoherence tends to bring about the emergence of the classical from the quantum world through the interaction of a quantum system with the environment, which is the main impediment to the realization of quantum information processing. Counterintuitively, we propose that the decoherence enables the creation of a bright source of strongly sub-Poissonian nonlocality. The absence of the decoherence makes such a source impossible. Decoherence is devised to be adjustable and manipulable on the basis of the dressed-atom approach and combination mode technique. The strong sub-Poissonianity and robust Bell violation are originated from the large squeezing parameter of the reservoir pertaining to the controllable decoherence within the framework of the reservoir theory.
Time series numerical association rule mining variants in smart agriculture

Abstract Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method’s potential and opened the door for further extension.
Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation

Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code at https://github.com/SUSTechBruce/RESTC-Source-code .
Occupancy-MAE: Self-Supervised Pre-Training Large-Scale LiDAR Point Clouds With Masked Occupancy Autoencoders

Current perception models in autonomous driving heavily rely on large-scale labelled 3D data, which is both costly and time-consuming to annotate. This work proposes a solution to reduce the dependence on labelled 3D training data by leveraging pre-training on large-scale unlabeled outdoor LiDAR point clouds using masked autoencoders (MAE). While existing masked point autoencoding methods mainly focus on small-scale indoor point clouds or pillar-based large-scale outdoor LiDAR data, our approach introduces a new self-supervised masked occupancy pre-training method called Occupancy-MAE, specifically designed for voxel-based large-scale outdoor LiDAR point clouds. Occupancy-MAE takes advantage of the gradually sparse voxel occupancy structure of outdoor LiDAR point clouds and incorporates a range-aware random masking strategy and a pretext task of occupancy prediction. By randomly masking voxels based on their distance to the LiDAR and predicting the masked occupancy structure of the entire 3D surrounding scene, Occupancy-MAE encourages the extraction of high-level semantic information to reconstruct the masked voxel using only a small number of visible voxels. Extensive experiments demonstrate the effectiveness of Occupancy-MAE across several downstream tasks. For 3D object detection, Occupancy-MAE reduces the labelled data required for car detection on the KITTI dataset by half and improves small object detection by approximately 2% in AP on the Waymo dataset. For 3D semantic segmentation, Occupancy-MAE outperforms training from scratch by around 2% in mIoU. For multi-object tracking, Occupancy-MAE enhances training from scratch by approximately 1% in terms of AMOTA and AMOTP. Codes are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chaytonmin/Occupancy-MAE</uri> .