Reviewerly/exHarmony huggingface dataset:
Student’s Perception Of Using Virtual Tour As Learning Media For Elementary Students In Sukodono

Since the COVID-19 pandemic spread in the world especially Indonesia,, people have to adapt with the new condition. When all of the activities must be done in online, including the learning process. There are so many schools and teacher that look for the most suitable media in learning pocess. From then on, researchers make a research, in a collaboration with PKM team, to help teacher and students get a suitable media in learning process. This research discusses about student’s perception on virtual tour as the learning media. The mix method is used in collect the data, by using some questionnaires and interviews, researchers process the data until get a conclusion. From the result data can be conclude that using virtual tour can help teacher serve a new media in teaching their students.
Virtual Reality Skateboard Extending Metaverse

Implementing a virtual reality skateboard which would be further expanded into the Metaverse, a cyberspace where multiple people can come together and interact virtually. Further implementation of this VR experience into the Metaverse will be conceptualized in the research paper published along with the project. The motive behind the project was to let people experience certain activities which are hard to realize in the physical world. Our project consists of 2 parts, hardware, and software. The hardware consists of a skateboard, Arduino, VR headset, jumper wires, mini breadboard, an HC-06 Bluetooth module, an MPU-6050 accelerometer or gyro, and a 9V battery with a battery box that has an on/off switch and a barrel plug (to power the Arduino board). Software is a mobile application that is developed using the Unreal Engine, on which the terrain is built and the user wearing the VR headset can experience the virtual world thus created. The software and hardware will be connected using the plugins which are available in Unreal.
Dynamic 3D phase-shifting profilometry based on a corner optical flow algorithm

Real-time 3D reconstruction has been applied in many fields, calling for many ongoing efforts to improve the speed and accuracy of the used algorithms. Phase shifting profilometry based on the Lucas–Kanade optical flow method is a fast and highly precise method to construct and display the three-dimensional shape of objects. However, in this method, a dense optical flow calculation is required for the modulation image corresponding to the acquired deformed fringe pattern, which consumes a lot of time and affects the real-time performance of 3D reconstruction and display. Therefore, this paper proposes a dynamic 3D phase shifting profilometry based on a corner optical flow algorithm to mitigate this issue. Therein, the Harris corner algorithm is utilized to locate the feature points of the measured object, so that the optical flow needs to calculate for only the feature points which, greatly reduces the amount of calculation time. Both our experiments and simulations show that our method improves the efficiency of pixel matching by four times and 3D reconstruction by two times.
Blockchain with Machine Learning for Financial Portfolio Management

Portfolio management diversifies investment across multiple assets for increased return and reduced risks. In the decentralized world of Web3, financial management companies are increasingly investing in blockchain ledger systems for asset transactions. As a result, new models of portfolio trading are needed. The paper presents a prototype of a portfolio management system with a machine learning-enabled recommend subsystem. The proposed account role mechanism combines user-defined functions and automated asset management operations. The combination of blockchain and artificial intelligence enables smart portfolio management.
Real Time Violence Detection Using Autonomous Intelligent Surveillance Robot

Intelligent surveillance robots dominate the entire world with its efficiency and accuracy helping in performing dangerous task.The surveillance robot helps to survey the area. The violence keeps on growing day by day.This paper presents an intelligent surveillance robot capable of detecting and preventing violence in two modes - motion-controlled and autonomous. The motion-controlled mode allows the user to manually control the robot's movement and camera orientation, while the autonomous mode allows the robot to navigate and monitor the environment independently. In both modes, the robot employs a deep learning algorithm taken from CNN and LSTM for accurately detecting violence and sending a notification through email with the image of the violent activity.On a authenticated dataset, the proposed concept has been examined, the outcomes looks promising in regards to accuracy and speed.The integration of motion control and autonomous operation modes makes the surveillance robot versatile and effective in a range of surveillance scenarios. Overall, this project demonstrates the potential of machine learning and robotics for enhancing security and surveillance measures.
Unsupervised Low-Light Video Enhancement With Spatial-Temporal Co-Attention Transformer

Existing low-light video enhancement methods are dominated by Convolution Neural Networks (CNNs) that are trained in a supervised manner. Due to the difficulty of collecting paired dynamic low/normal-light videos in real-world scenes, they are usually trained on synthetic, static, and uniform motion videos, which undermines their generalization to real-world scenes. Additionally, these methods typically suffer from temporal inconsistency (e.g., flickering artifacts and motion blurs) when handling large-scale motions since the local perception property of CNNs limits them to model long-range dependencies in both spatial and temporal domains. To address these problems, we propose the first unsupervised method for low-light video enhancement to our best knowledge, named LightenFormer, which models long-range intra- and inter-frame dependencies with a spatial-temporal co-attention transformer to enhance brightness while maintaining temporal consistency. Specifically, an effective but lightweight S-curve Estimation Network (SCENet) is first proposed to estimate pixel-wise S-shaped non-linear curves (S-curves) to adaptively adjust the dynamic range of an input video. Next, to model the temporal consistency of the video, we present a Spatial-Temporal Refinement Network (STRNet) to refine the enhanced video. The core module of STRNet is a novel Spatial-Temporal Co-attention Transformer (STCAT), which exploits multi-scale self- and cross-attention interactions to capture long-range correlations in both spatial and temporal domains among frames for implicit motion estimation. To achieve unsupervised training, we further propose two non-reference loss functions based on the invertibility of the S-curve and the noise independence among frames. Extensive experiments on the SDSD and LLIV-Phone datasets demonstrate that our LightenFormer outperforms state-of-the-art methods.
Mathematical Methods for the Shape Analysis and Indexing of Tangible CH Artefacts

Thanks to continuous efforts towards 3D digitisation in the Cultural Heritage (CH) domain, we have experienced increasing interest in mathematical methods and computer graphics tools that can concretely support archaeological research and extend curatorial systems, so that the potential of digital data can be exploited beyond the mere rendering and visualisation of artefacts. This trend calls for robust and supportive methods for the analysis, characterisation and documentation of digital artefacts. Many are the applications that can assist the work of the professionals in the field of artistic/archaeological research, and curation: hypothesis formulation and interpretation, indexing and retrieval of content, and exploration of collections along new and dynamic dimensions. In this work, we present a variety of computational approaches tailored to CH applications, and archaeology especially, tested in real use cases by practitioners of the field, spanning from shape analysis to indexing of 3D assets.
Enhancing User Experience: Exploring the Interactions and Visualisation of Data Through Bullet Points and Typographic Design

This study aims to examine the impact of bullet points on data comprehension and establish a visual hierarchy. Additionally, it includes a comprehensive analysis of prior research on the effectiveness of bullet points in the context of data visualisation. The importance of typography in establishing a visual hierarchy is underscored, alongside an analysis of different typographic styles and their impact on the emotional aspects of user experience. This study examines case studies that illustrate the successful application of typographic design in data visualisation, specifically focusing on using bullet points. Enhancing user engagement through bullet points, exploring interactive elements for visualising data, and examining the effects of different typographic approaches are delved. This study comprehensively examines best practices and practical recommendations for developing emotionally appealing data visualisation bullet points. Moreover, the utilisation of typographic design to convey data insights by employing principles of data-driven typography is examined. This study examines the application of data-driven typography in obtaining intricate information through bullet points and its impact on emotional responses and user comprehension.
Succinct Arguments for RAM Programs via Projection Codes

Motivated by the goal of proving statements that involve small subsets of a big database, we introduce and study the notion of projection codes. A standard error-correcting code allows one to encode a message $$\textbf{x}$$ into a codeword $$\textbf{X}$$ , such that even if a constant fraction of $$\textbf{X}$$ is corrupted, the message $$\textbf{x}$$ can still be recovered. A projection code extends this guarantee to any subset of the bits of $$\textbf{x}$$ . Concretely, for every projection of $$\textbf{x}$$ to a subset $$\textbf{s}$$ of its coordinates, there is a subset $$\textbf{S}$$ of comparable size such that the projected encoding $$\textbf{X}|_\textbf{S}$$ forms a robust encoding of the projected message $$\textbf{x}|_\textbf{s}$$ . Our first main result is a construction of projection codes with a near-optimal increase in the length of $$\textbf{x}$$ and size of $$\textbf{s}$$ . We then apply this to obtain our second main result: succinct arguments for the computation of a RAM program on a (big) committed database, where the communication and the run-time of both the prover and the verifier are close to optimal even when the RAM program run-time is much smaller than the database size. Our solution makes only a black-box use of a collision-resistant hash function, providing the first black-box alternative to previous non-black-box constructions with similar asymptotic efficiency.
Black-Hole Radiation Decoding Is Quantum Cryptography

We propose to study equivalence relations between phenomena in high-energy physics and the existence of standard cryptographic primitives, and show the first example where such an equivalence holds. A small number of prior works showed that high-energy phenomena can be explained by cryptographic hardness. Examples include using the existence of one-way functions to explain the hardness of decoding black-hole Hawking radiation (Harlow and Hayden 2013, Aaronson 2016), and using pseudorandom quantum states to explain the hardness of computing AdS/CFT dictionary (Bouland, Fefferman and Vazirani, 2020). In this work we show, for the former example of black-hole radiation decoding, that it also implies the existence of secure quantum cryptography. In fact, we show an existential equivalence between the hardness of black-hole radiation decoding and a variety of cryptographic primitives, including bit-commitment schemes and oblivious transfer protocols (using quantum communication). This can be viewed (with proper disclaimers, as we discuss) as providing a physical justification for the existence of secure cryptography. We conjecture that such connections may be found in other high-energy physics phenomena.
Learning Category Discriminability for Active Domain Adaptation

Active Domain Adaptation (ADA) attempts to improve the adaptation performance on a target domain by annotating informative target data with a limited budget. Previous ADA methods have significantly advanced by incorporating domain representativeness and predictive uncertainty. However, they only focus on domain-level alignment and ignore the category discriminability of two domains, which may cause classwise mismatching. These mismatched data are overlooked by the above query strategy. To solve this, a Learning Category Discriminability approach is proposed for active domain adaptation. Specifically, it achieves semantic-level alignment and selects the informative target data consistent with the domain adaptation based on task-specific classifiers. To overcome the class imbalance from the small queried data, progressive augmentation of the queried set with confident pseudo labels is designed in our work. In addition, discriminability and diversity learning for unlabeled target samples are performed to improve the reliability of pseudo labels, which makes the classification boundaries more applicable to the target domain. Extensive experiments on two benchmarks, Office-31 and Office-Home, demonstrate the superiority of the proposed method.
Graph Contrastive Learning Method with Sample Disparity Constraint and Feature Structure Graph for Node Classification

Most of the existing graph contrastive learning methods for node classification focus on exploiting topological information of the attributed networks, with little attention to the attribute information of the networks. Furthermore, in most graph contrastive learning methods, both positive and negative sample representations are generated by the same encoder, which makes them somewhat coupled and not conducive to distinguishing between them. But to our knowledge, most of these methods ignore the coupling between positive and negative sample representations, which may negatively affect the performance of the methods. To address these two issues, we propose a new method called DCFSG, in which we generate a feature structure graph from the attribute matrix, and then learn the node representations jointly from the topological structure graph and the feature structure graph. In addition, we impose a disparity constraint on the positive and negative samples and add corresponding loss to the objective function, which can help to lower the coupling between positive and negative samples and improves the performance of our method. The experimental results on real networks validate the effectiveness of our method.
Prototype-Augmented Contrastive Learning for Few-Shot Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to learn a classification model from the source domain with much-supervised information, which is applied to the utterly unsupervised target domain. However, collecting enough labeled source samples is difficult in some scenarios, decreasing the effectiveness of previous approaches substantially. Therefore, a more challenging and applicable problem called few-shot unsupervised domain adaptation is considered in this work, where a classifier trained with only a few source labels needs to show strong generalization on the target domain. The prototype-based self-supervised learning method has presented superior performance improvements in addressing this problem, while the quality of the prototype could be further improved. To mitigate this situation, a novel Prototype-Augmented Contrastive Learning is proposed. A new computation strategy is utilized to rectify the source prototypes, which are then used to improve the target prototypes. To better learn semantic information and align features, both in-domain prototype contrastive learning and cross-domain prototype contrastive learning are performed. Extensive experiments are conducted on three widely used benchmarks: Office, OfficeHome, and DomainNet, achieving accuracy improvement of over 3%, 1%, and 0.5%, respectively, demonstrating the effectiveness of the proposed method.
Style Augmentation and Domain-Aware Parametric Contrastive Learning for Domain Generalization

The distribution shift between training data and test data degrades the performance of deep neural networks (DNNs), and domain generalization (DG) alleviates this problem by extracting domain-invariant features explicitly or implicitly. With limited source domains for training, existing approaches often generate samples of new domains. However, most of these approaches confront the issue of losing class-discriminative information. To this end, we propose a novel domain generalization framework containing style augmentation and Domain-aware Parametric Contrastive Learning (DPCL). Specifically, features are first decomposed into high-frequency and low-frequency components, which contain shape and style information, respectively. Since the shape cues contain class information, the high-frequency components remain unchanged. Then Exact Feature Distribution Mixing (EFDMix) is used for diversifying the low-frequency components, which fully uses each order statistic of the features. Finally, both components are re-merged to generate new features. Additionally, DPCL is proposed, based on supervised contrastive learning, to enhance domain invariance by ignoring negative samples from different domains and introducing a set of parameterized class-learnable centers. The effectiveness of the proposed style augmentation method and DPCL is confirmed by experiments. On the PACS dataset, our method improves the state-of-art average accuracy by 1.74% using ResNet-50 backbone and even achieves excellent performance in the single-source DG task.
Class key feature extraction and fusion for 2D medical image segmentation

The size variation, complex semantic environment and high similarity in medical images often prevent deep learning models from achieving good performance.To overcome these problems and improve the model segmentation performance and generalizability.We propose the key class feature reconstruction module (KCRM), which ranks channel weights and selects key features (KFs) that contribute more to the segmentation results for each class. Meanwhile, KCRM reconstructs all local features to establish the dependence relationship from local features to KFs. In addition, we propose the spatial gating module (SGM), which employs KFs to generate two spatial maps to suppress irrelevant regions, strengthening the ability to locate semantic objects. Finally, we enable the model to adapt to size variations by diversifying the receptive field.We integrate these modules into class key feature extraction and fusion network (CKFFNet) and validate its performance on three public medical datasets: CHAOS, UW-Madison, and ISIC2017. The experimental results show that our method achieves better segmentation results and generalizability than those of mainstream methods.Through quantitative and qualitative research, the proposed module improves the segmentation results and enhances the model generalizability, making it suitable for application and expansion.
Assessing the Photovoltaic Power Generation Potential of Highway Slopes

The solar photovoltaic (PV) power generation system (PGS) is a viable alternative to fossil fuels for the provision of power for infrastructure and vehicles, reducing greenhouse gas emissions and enhancing the sustainability of road transport systems. A highway slope is generally an idle public area with high accessibility, which is the ideal application scenario for a PV PGS. The assessment of PV power generation potential (PGP) is key for the planning and design of PV PGS projects. Previous approaches to potential assessments are mainly based on digital maps and image processing techniques, which do not fully consider the impacts of the highway orientation, the slope geometric characteristics, and the PV panel placement scheme on the evaluation results. Therefore, this study proposes an assessment method for the PV PGP on highway slopes using the design or calculated highway and slope geometric parameters and the solar radiation received by PV panels under the desirable placement scheme. Highway segmentation and geometric parameter calculation methods were established, and the optimal PV array placement schemes for typical slope orientations were determined by simulating the PV power generation in the software PVsyst (version 7.2). Afterwards, the theoretical PGP could be calculated using the received solar radiation and the available slope area. By subtracting the energy loss caused by temperature changes, the operation of inverters, and the PV modules’ performance decay, the actual PV PGP could be obtained. Finally, a case study of the solar PGP assessment of a 1.97 km long highway section is provided, and the feasibility of the proposed method is verified.
An automated detection of confusing variable pairs with highly similar compound names in Java and Python programs

Abstract Variable names represent a significant source of information regarding the source code, and a successful naming of variables is key to producing readable code. Programmers often use a compound variable name by concatenating two or more words to make it more informative and enhance the code readability. While each compound variable name is descriptive, a collection of them sometimes produces “confusing” variable pairs if their names are highly similar, e.g., “shippingHeight,” vs. “shippingWeight.” A confusing variable pair would adversely affect the code readability because it can cause a misreading or mix-up of variables during the programming or code review activities. Toward automated support for enhancing code readability, this paper conducts a large-scale investigation of compound variable names in Java and Python programs. The investigation collects 116,921,127 pairs of compound-named variables from 1,876 open-source Java projects and 106,943,523 pairs of such variables from 2,427 open-source Python projects. Then, this study analyzes those variable pairs from two perspectives of name similarity: string similarity and semantic similarity. Through an evaluation study with 30 human participants, the data analyses show that both string and semantic similarity can help detect confusing variable pairs in Java and Python programs. In order to distill confusing variable pairs automatically, support tools for detecting confusing variable pairs are also developed in this study.
Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups

Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups. Specifically, unequal accuracy rates were obtained for women and dark-skinned people. To mitigate the bias of gender classifiers, the vision community has developed several strategies. However, the efficacy of these mitigation strategies is demonstrated for a limited number of races mostly, Caucasian and African-American. Further, these strategies often offer a trade-off between bias and classification accuracy. To further advance the state-of-the-art, we leverage the power of generative views, structured learning, and evidential learning towards mitigating gender classification bias. We demonstrate the superiority of our bias mitigation strategy in improving classification accuracy and reducing bias across gender-racial groups through extensive experimental validation, resulting in state-of-the-art performance in intra- and cross dataset evaluations.