Reviewerly/exHarmony huggingface dataset:
Design of a 100 MW solar power plant on wetland in Bangladesh

World-wide a small-scale solar photovoltaic (PV) system is increasingly becoming a popular power source for domestic application. In contrast, large-scale solar power plants are of growing interest for commercial, industrial and community users as an alternative to fossil fuel-driven power plants. Despite the growing interest, scant information on large-scale solar power generation especially in rural and inaccessible locations is available in the public domain. Hence, the primary objective of this study is to design a large-scale (100 MW) solar power plant for wetland areas in Bangladesh. For the 100 MW power plant, a total of 166,670 solar modules (each of which is 2,070mm long, 1,390 mm wide and 45mm thick with 600 W power capacity) have been used. To generate 100 MW electricity (power), around 303 acres (approximately 123 hectares) of the wetland is required keeping the distance of 2.35 m between every two adjacent solar panel mounting rows. A total of six hundred and seventy (670) three-phase grid-tie inverters (GTI) and 40 transformers have been connected to the solar panel. Along with the renewable electricity production, the required wetland (123 hectares) located under the solar panels has been considered for permanent sanctuary to conserve the indigenous fish species. The challenges and risk factors of the 100 MW plant have also been elaborated.
Pars-OFF: A Benchmark for Offensive Language Detection on Farsi Social Media

With the increasing use of social media with its ability for users to share comments immediately, the extent of a system to identify offensive content has become a necessity in all languages. Due to the lack of publicly available resources on offensive language identification for Farsi, which has more than 110 million speakers, we present Pars-OFF, a three-layered annotated corpus for offensive language detection in Farsi to fill the existing gap. The introduced corpus contains 10,563 data samples. The tweets have been collected with a combination of similarity-based and keyword-based data selection techniques to avoid severe unbalancedness. Additionally, as a baseline, this article reports the performance of the traditional machine learning approaches and Transformer based models over the Pars-OFF dataset. The best performance was obtained by the BERT+fastText model, yielding the F1-Macro score of 89.57.
Quantum Machine Learning with Quantum Image Representations

Image classification is an important task in many practical applications. Recently, there have been various works on classification problems using quantum neural network (QNN). Different data encoders have been investigated, but efficient quantum encoding schemes for high-dimensional data such as images are still an open question. In this study, we investigate popular representation formats of quantum images, namely FRQI and NEQR, as data encoders for quantum neural networks. It is found that the FRQI format not only provides the best classification accuracy compared to NEQR and other popular encoding methods but also is consistent across different circuit depths of QNN. It should be noted that FRQI is notorious for its probabilistic representation in quantum image processing. The finding of this study is interesting because FRQI can play an important role in the field of quantum machine learning.
CGIR: Conditional Generative Instance Reconstruction Attacks against Federated Learning

Data reconstruction attack has become an emerging privacy threat to Federal Learning (FL), inspiring a rethinking of FL's ability to protect privacy. While existing data reconstruction attacks have shown some effective performance, prior arts rely on different strong assumptions to guide the reconstruction process. In this work, we propose a novel Conditional Generative Instance Reconstruction Attack (CGIR attack) that drops all these assumptions. Specifically, we propose a batch label inference attack in non-IID FL scenarios, where multiple images can share the same labels. Based on the inferred labels, we conduct a “coarse-to-fine” image reconstruction process that provides a stable and effective data reconstruction. In addition, we equip the generator with a label condition restriction so that the contents and the labels of the reconstructed images are consistent. Our extensive evaluation results on two model architectures and five image datasets show that without the auxiliary assumptions, the CGIR attack outperforms the prior arts, even for complex datasets, deep models, and large batch sizes. Furthermore, we evaluate several existing defense methods. The experimental results suggest that pruning gradients can be used as a strategy to mitigate privacy risks in FL if a model tolerates a slight accuracy loss.
Instance Motion Tendency Learning for Video Panoptic Segmentation

Video panoptic segmentation is an important but challenging task in computer vision. It not only performs panoptic segmentation of each frame, but also associates the same instance across adjacent frames. Due to the lack of temporal coherence modeling, most existing approaches often generate identity switches during instance association, and they cannot handle ambiguous segmentation boundaries caused by motion blur. To address these difficult issues, we introduce a simple yet effective Instance Motion Tendency Network (IMTNet) for video panoptic segmentation. It learns a global motion tendency map for instance association, and a hierarchical classifier for motion boundary refinement. Specifically, a Global Motion Tendency Module (GMTM) is designed to learn robust motion features from optical flows, which can directly associate each instance in the previous frame to the corresponding instance in the current frame. In addition, we propose a Motion Boundary Refinement Module (MBRM) to learn a hierarchical classifier to handle the boundary pixels of moving targets, which can effectively revise the inaccurate segmentation predictions. Experimental results on both Cityscapes and Cityscapes-VPS datasets show that our IMTNet outperforms most state-of-the-art approaches.
Two-Stream Fused Fuzzy Deep Neural Network for Multiagent Learning

In multiagent reinforcement learning (RL), multilayer fully connected neural network is used for value function approximation, which solves large-scale or continuous space problems. However, it is easy to fall into a local optimal and overfitting under partially observed environments. Because each agent lacks the information that plays a key role in decision making beyond the observation field. Even if communication is allowed, the received informations in communication channel have large noise due to the observations of other agents and strong uncertainty if the agent's policy is used as the communication information. To tackle this problem, two-stream fused fuzzy deep neural network (2s-FDNN) was proposed to reduce the uncertainty and noise of information in the communication channel. It is a parallel structure in which the fuzzy inference module reduces the uncertainty of information and the deep neural module reduces the noise of information. Then, we presented Fuzzy MA2C which integrates 2s-FDNN into multiagent deep RL to deal with uncertain communication informations for improving the robustness and generalization under partially observed environments. We empirically evaluate our methods in two large-scale traffic signal control environments using simulation of urban mobility (SUMO) simulator. Results demonstrate that our methods can achieve superior performance against existing RL algorithms.
Panoramic Vision Transformer for Saliency Detection in 360$$^\circ $$ Videos

360 $$^\circ $$ video saliency detection is one of the challenging benchmarks for 360 $$^\circ $$ video understanding since non-negligible distortion and discontinuity occur in the projection of any format of 360 $$^\circ $$ videos, and capture-worthy viewpoint in the omnidirectional sphere is ambiguous by nature. We present a new framework named Panoramic Vision Transformer (PAVER). We design the encoder using Vision Transformer with deformable convolution, which enables us not only to plug pretrained models from normal videos into our architecture without additional modules or finetuning but also to perform geometric approximation only once, unlike previous deep CNN-based approaches. Thanks to its powerful encoder, PAVER can learn the saliency from three simple relative relations among local patch features, outperforming state-of-the-art models for the Wild360 benchmark by large margins without supervision or auxiliary information like class activation. We demonstrate the utility of our saliency prediction model with the omnidirectional video quality assessment task in VQA-ODV, where we consistently improve performance without any form of supervision, including head movement.
A Behavioural Strategy to Nudge Young People to Adopt In-Person Counselling: Gamification

This research is based on the master thesis of Shengen Piao, directed by Jaewoo Joo. Purpose- The purposes of this research are to investigate whether: (1) gamification increases the adoption of in-person counselling, (2) gamification increases usability of in-person counselling, (3) usability of in-person counselling increases adoption of in-person counselling, and (4) vividness of presentation increases the effect of gamification on the usability of in-person counselling. Methods- Eight young people participated in our experiment. In the experiment, a 2 (Gamification: no vs. yes) x 2 (Vividness: low vs. high) between-subjects design was employed to test hypotheses. We were systematically varying the condition in gamification on the different conditions of presentation of vividness. Results- The results of the study showed that (1) gamification increased adoption of in-person counselling, (2) gamification increased usability of in-person counselling, (3) subjective usability increased adoption of in-person counselling. Conclusions- This research firstly examines gamification's effect on the adoption of in-person counselling using carefully designed experiments. Our study demonstrated that gamification nudges young people to adopt in-person counselling, verified usability mediates the relationship, and vividness moderates the relationship. Our findings provide fresh insights into counsellors and the basis for future research.
Fast Top-k Similar Sequence Search on DNA Databases

Top-k similar sequence search is an essential tool for DNA data management. Given a DNA database, it is a problem to extract k similar DNA sequence pairs in the database, which yield the highest similarity among all possible pairs. Although this is a fundamental problem used in the bioinformatics field, it suffers from an expensive computational cost. To overcome these limitations, we propose a novel fast top-k similarity search algorithm for DNA databases. We conducted experiments using real-world DNA sequence datasets, and experimentally confirmed that the proposed method achieves a faster top-k search than baseline algorithms while keeping high accuracy.
Hash Bit Selection With Reinforcement Learning for Image Retrieval

In recent years, binary hashing methods have been widely used in large-scale multimedia retrieval because of the low computational complexity and memory cost. Generally, better retrieval accuracy can be achieved with a longer hash code, which, however, may suffer redundancy. In this paper, we propose a novel hash bit selection method, called Hash Bit Selection with Reinforcement Learning (HBS-RL), which aims to adaptively select the most informative bits from the database binary codes. In our approach, the hash bit selection problem is firstly modeled as a Markov Decision Process (MDP), which is solved with reinforcement learning. HBS-RL learns a policy for bit selection, which effectively identifies the most informative bits by directly maximizing mean Average Precision (mAP) during training. Specially, given a generated bit pool, our HBS-RL can sequentially select bits with different code lengths with a very lightweight fully-connected policy network. The proposed method is evaluated on the MNIST, CIFAR-10, ImageNet and NUS-WIDE datasets, and the results show that it significantly improves the retrieval performance of the existing unsupervised and deep supervised hashing methods. It also outperforms the state-of-the-art bit selection methods. For convenience of repeating our results, we release our source code at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xyez/HBS-RL</uri> .
Partial Multi-Label Feature Selection

Multi-label feature selection is an effective approach to alleviate the high dimensionality of multi-label learning tasks. Most of the existing multi-label feature selection methods are based on the assumption that the relevant labels of each training sample are precisely annotated. However, in real-world applications, this assumption is not always held, each instance may be labeled with a set of candidate labels that contains all the ground-truth labels adulterated with noisy labels, which is called partial multi-label problem. Previous multi-label feature selection methods can not select the optimal feature set in the presence of partial multi-label. Therefore, to tackle this problem we propose a novel partial multi-label feature selection method, called PMLFS. To be specific, ground-truth labels and noisy labels are firstly distinguished in terms of label correlations and the sparsity of noisy labels. Secondly, manifold regularization is incorporated to explore the local structure. Based on the above, we design an objective framework involving <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2,1}$</tex> -norm regularization to achieve partial multi-label feature selection. Finally, extensive experiments on synthetic and real-world partial multi-label data sets demonstrate that the proposed method outperforms the state-of-the-art multi-label feature selection methods.
Vision, status, and topics of X Reality in Education

The maturity of 5G and artificial intelligence has promoted the XRED (X Reality in Education)'s application and implementation. XRED involves the application of X Reality (i.e., augmented reality, virtual reality, or mixed reality) technologies in the process of instruction and learning. Learning assisted by XR technologies can facilitate students' understanding of spatial structure and function, support their learning of language associations, contribute to long-term memory retention, improve physical task performance, enhance motivation, engagement, and learning outcomes, and promote the development of problem-solving abilities. In this study, we provide a general understanding of XRED by illustrating its development concerning funding support, publication venues, software tools, and research topics with the expectation of promoting its future advance and application. We also highlight the importance and necessity of launching the XRED-focused Elsevier journal: Computers & Education: X Reality.
Towards Understanding Metaverse Engagement via Social Patterns and Reward Mechanism: A Case Study of <i>Nova Empire</i>

With the constant fusion of virtual and reality, a new vision of human beings has emerged—Metaverse. At present, the development of metaverse is still in its infancy, and although the industry has put feverish investment into it, there are still many problems that need to be discussed in academia. The first thing is user engagement. Metaverse relies heavily on massive online users to realize its social value. In other words, user engagement is the foundation of the metaverse ecosystem. Therefore, our research uses Nova Empire as a case study, aiming at understanding metaverse engagement via complexity analysis of social patterns and reward mechanisms. Specifically, the behavioral data of 46 954 players in September 2021 are used for analysis. Our observations show that social behavior is not the main factor for user engagement. Then, we perform a correlation analysis of trigger times for different gaming behaviors to verify the above observations. The results prove that the main factors affecting user engagement are game tasks and reward mechanisms in the early stage and social gaming behaviors environment with alliances in the middle and late stages. Finally, we discuss the implications of our findings for the design of future games and propose operation strategies on user engagement to improve the metaverse ecosystem and other online social space.
Preserving the Privacy of Medical Data using Homomorphic Encryption and Prediction of Heart Disease using K-Nearest Neighbor

Data is extremely important in today’s world. Data is used in many aspects and hence protecting the data is more important. With a heavier reliance on computers, there are many potential threats to the data stored. Nowadays organizations tend to store and process required computation on the data on the cloud itself without having to maintain it themselves. These cloud services are affordable and easy to use. But to ensure compliance and maintain privacy, the data must be stored in an encrypted format. To ensure privacy of data in the cloud, Homomorphic Encryption can be efficiently used because it allows processing to take place while data is encrypted. This paper presents the technique and design to perform Homomorphic Encryption on the medical dataset for heart disease and applying KNN machine learning algorithm on the encrypted dataset. To provide a more detailed view, we used different algorithms such as Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree and Random Forest.
Using the Projected Belief Network at High Dimensions

The projected belief network (PBN) is a layered generative network (LGN) with tractable likelihood function, and is based on a feed-forward neural network (FFNN). There are two versions of the PBN: stochastic and deterministic (D-PBN), and each has theoretical advantages over other LGNs. However, implementation of the PBN requires an iterative algorithm that includes the inversion of a symmetric matrix of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M\times M$</tex> in each layer, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> is the layer output dimension. This, and the fact that the network must be always dimension-reducing in each layer, can limit the types of problems where the PBN can be applied. In this paper, we describe techniques to avoid or mitigate these restrictions and use the PBN effectively at high dimension. We apply the discriminatively aligned PBN (PBN-DA) to classifying and auto-encoding high-dimensional spectrograms of acoustic events. We also present the discriminatively aligned D-PBN for the first time.
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction

Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe driving behaviors. While many prior works aim to achieve higher prediction accuracy, few study the adversarial robustness of their methods. To bridge this gap, we propose to study the adversarial robustness of data-driven trajectory prediction systems. We devise an optimization-based adversarial attack framework that leverages a carefully-designed differentiable dynamic model to generate realistic adversarial trajectories. Empirically, we benchmark the adversarial robustness of state-of-the-art prediction models and show that our attack increases the prediction error for both general metrics and planning-aware metrics by more than 50% and 37%. We also show that our attack can lead an AV to drive off road or collide into other vehicles in simulation. Finally, we demonstrate how to mitigate the adversarial attacks using an adversarial training scheme (Our project website is at https://robustav.github.io/RobustPred ).
TransWS: Transformer-Based Weakly Supervised Histology Image Segmentation

Recently, weakly supervised histology image segmentation has received increasingly more attentions. Most solutions utilize a convolutional neural network (CNN) as a classifier and treat the generated class activation map (CAM) as a pseudo annotation, based on which a segmentation network is trained in a supervised manner. This pipeline suffers from two disadvantages. First, the CNN classifier may fail to generate the high-quality CAM that highlights the exact and integral target, resulting in incomplete activation and blurred boundaries. Second, it splits the original problem into two, leading to a sub-optimal solution and low efficiency. To address both issues, we propose a Transformer-based weakly supervised segmentation (TransWS) method for histology images. TransWS is composed of a classification branch and a segmentation branch. The former learns semantic information from image-level annotations and uses CAM to generate pseudo pixel-level annotations. The latter performs the class-agnostic segmentation (CAS), i.e., binary segmentation, under the supervision of pseudo annotations. The semantic information and foreground region are combined to generate the final segmentation result. Comparing to CNN, Transformer is superior in modeling long-term dependencies and can generate more integral and accurate CAMs. More important, both branches in our TransWS can be jointly optimized in an end-to-end manner. We evaluated TransWS on the benchmark GlaS and Camelyon16-P512 datasets. Our results suggest that TransWS outperforms other weakly supervised segmentation competitors, setting a new state of the art.
Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning

Voice Conversion (VC) for unseen speakers, also known as zero-shot VC, is an attractive research topic as it enables a range of applications like voice customizing, animation production, and others. Recent work in this area made progress with disentanglement methods that separate utterance content and speaker characteristics from speech audio recordings. However, many of these methods are subject to the leakage of prosody (e.g., pitch, volume), causing the speaker voice in the synthesized speech to be different from the desired target speakers. To prevent this issue, we propose a novel self-supervised approach that effectively learns disentangled pitch and volume representations that can represent the prosody styles of different speakers. We then use the learned prosodic representations as conditional information to train and enhance our VC model for zero-shot conversion. In our experiments, we show that our prosody representations are disentangled and rich in prosody information. Moreover, we demonstrate that the addition of our prosody representations improves our VC performance and surpasses state-of-the-art zero-shot VC performances.