Reviewerly/exHarmony huggingface dataset:
Analysis of the SAW Model in the Selection of Majors at ISB Atma Luhur

The department is part of the faculty, and is the focus of the field of study chosen by students. The choice of majors is very important to think about beforehand because it will have an impact on the results of these graduates and the job prospects sought later. There are many things that can be considered by high school graduates in choosing a major because if you choose rashly, you will be at risk of failing along the way. For this reason, research was carried out with an emphasis on the criteria for selecting majors at ISB Atma Luhur. The method used in this study is SAW (Simple Additive Weighting) which is a weighted sum method that is weighting criteria and alternative attributes so that normalization can be carried out and calculation of preference values is carried out and the highest and lowest rankings are obtained which are used as decision recommendations. Respondent data was taken based on the sample, namely through a non-probability sampling technique, namely in this study the respondent could only provide one opportunity to be sampled from the population so that one respondent could not be sampled twice. Respondents who were sampled were 100 people who were taken from high school graduates. The criteria presented include department accreditation, number of professionally certified lecturers, study program scholarships, and facilities. Determination of criteria based on the results of interviews with respondents and the results are taken based on the criteria with the most choices. The results of the study are alternative Information Systems with a weight or value obtained from the SAW calculation results, namely 1, Informatics Engineering with a weight of 0.8, while Digital Business is 0.6. From the results of these calculations, it can be concluded that the department that gets the highest score, namely the Information Systems major, can also be used as a recommendation as a major to be chosen by students.
JR2Net: Joint Monocular 3D Face Reconstruction and Reenactment

Face reenactment and reconstruction benefit various applications in self-media, VR, etc. Recent face reenactment methods use 2D facial landmarks to implicitly retarget facial expressions and poses from driving videos to source images, while they suffer from pose and expression preservation issues for cross-identity scenarios, i.e., when the source and the driving subjects are different. Current self-supervised face reconstruction methods also demonstrate impressive results. However, these methods do not handle large expressions well, since their training data lacks samples of large expressions, and 2D facial attributes are inaccurate on such samples. To mitigate the above problems, we propose to explore the inner connection between the two tasks, i.e., using face reconstruction to provide sufficient 3D information for reenactment, and synthesizing videos paired with captured face model parameters through face reenactment to enhance the expression module of face reconstruction. In particular, we propose a novel cascade framework named JR2Net for Joint Face Reconstruction and Reenactment, which begins with the training of a coarse reconstruction network, followed by a 3D-aware face reenactment network based on the coarse reconstruction results. In the end, we train an expression tracking network based on our synthesized videos composed by image-face model parameter pairs. Such an expression tracking network can further enhance the coarse face reconstruction. Extensive experiments show that our JR2Net outperforms the state-of-the-art methods on several face reconstruction and reenactment benchmarks.
ACE: Cooperative Multi-Agent Q-learning with Bidirectional Action-Dependency

Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which is the ever-changing targets at every iteration when multiple agents update their policies at the same time. Starting from first principle, in this paper, we manage to solve the non-stationarity problem by proposing bidirectional action-dependent Q-learning (ACE). Central to the development of ACE is the sequential decision making process wherein only one agent is allowed to take action at one time. Within this process, each agent maximizes its value function given the actions taken by the preceding agents at the inference stage. In the learning phase, each agent minimizes the TD error that is dependent on how the subsequent agents have reacted to their chosen action. Given the design of bidirectional dependency, ACE effectively turns a multi-agent MDP into a single-agent MDP. We implement the ACE framework by identifying the proper network representation to formulate the action dependency, so that the sequential decision process is computed implicitly in one forward pass. To validate ACE, we compare it with strong baselines on two MARL benchmarks. Empirical experiments demonstrate that ACE outperforms the state-of-the-art algorithms on Google Research Football and StarCraft Multi-Agent Challenge by a large margin. In particular, on SMAC tasks, ACE achieves 100% success rate on almost all the hard and super hard maps. We further study extensive research problems regarding ACE, including extension, generalization and practicability.
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predictions made from HGNs are obscure to humans. Existing explainability techniques are mainly proposed for GNNs on homogeneous graphs. They focus on highlighting salient graph objects to the predictions whereas the problem of how these objects affect the predictions remains unsolved. Given heterogeneous graphs with complex structures and rich semantics, it is imperative that salient objects can be accompanied with their influence paths to the predictions, unveiling the reasoning process of HGNs. In this paper, we develop xPath, a new framework that provides fine-grained explanations for black-box HGNs specifying a cause node with its influence path to the target node. In xPath, we differentiate the influence of a node on the prediction w.r.t. every individual influence path, and measure the influence by perturbing graph structure via a novel graph rewiring algorithm. Furthermore, we introduce a greedy search algorithm to find the most influential fine-grained explanations efficiently. Empirical results on various HGNs and heterogeneous graphs show that xPath yields faithful explanations efficiently, outperforming the adaptations of advanced GNN explanation approaches.
CodeStylist: A System for Performing Code Style Transfer Using Neural Networks

Code style refers to attributes of computer programs that affect their readability, maintainability, and performance. Enterprises consider code style as important and enforce style requirements during code commits. Tools that assist in coding style compliance and transformations are highly valuable. However, many key aspects of programming style transfer are difficult to automate, as it can be challenging to specify the patterns required to perform the transfer algorithmically. In this paper, we describe a system called CodeStylist which uses neural methods to perform style transfer on code.
Conflicting Interactions among Protection Mechanisms for Machine Learning Models

Nowadays, systems based on machine learning (ML) are widely used in different domains. Given their popularity, ML models have become targets for various attacks. As a result, research at the intersection of security/privacy and ML has flourished. Typically such work has focused on individual types of security/privacy concerns and mitigations thereof. However, in real-life deployments, an ML model will need to be protected against several concerns simultaneously. A protection mechanism optimal for a specific security or privacy concern may interact negatively with mechanisms intended to address other concerns. Despite its practical relevance, the potential for such conflicts has not been studied adequately. In this work, we first provide a framework for analyzing such conflicting interactions. We then focus on systematically analyzing pairwise interactions between protection mechanisms for one concern, model and data ownership verification, with two other classes of ML protection mechanisms: differentially private training, and robustness against model evasion. We find that several pairwise interactions result in conflicts. We also explore potential approaches for avoiding such conflicts. First, we study the effect of hyperparameter relaxations, finding that there is no sweet spot balancing the performance of both protection mechanisms. Second, we explore whether modifying one type of protection mechanism (ownership verification) so as to decouple it from factors that may be impacted by a conflicting mechanism (differentially private training or robustness to model evasion) can avoid conflict. We show that this approach can indeed avoid the conflict between ownership verification mechanisms when combined with differentially private training, but has no effect on robustness to model evasion. We conclude by identifying the gaps in the landscape of studying interactions between other types of ML protection mechanisms.
Design of a Web-Based Learning Management System for Physics Education FKIP University of Riau

21st-century technology requires everyone to see and continue to advance in learning in various forms. Technology is a reference in the success of the learning process supported by the quality of teaching, teaching staff, and curriculum. The Physics Education Study Program at the University of Riau in its learning still uses conventional methods. Learning is only carried out by students and lecturers in class in a short time so students often do not understand the material provided. Based on these problems, we need a learning management system. The goal is to add a limited amount and short meeting time. The system was built using the PHP and MySQL programming languages with the CodeIgniter framework. This study uses the Research and Development (R&amp;D) method and software development uses the System Development Life Circle (SDLC) method with a variation of the v-model. Based on software testing using a black box, all features run 100% well and the User Experience Questionnaire (UEQ) test found that the Learning Management System has good appeal, clarity, and certainty. While efficiency is categorized as very good, stimulation and novelty are included as above average.
PATRON: Perspective-Aware Multitask Model for Referring Expression Grounding Using Embodied Multimodal Cues

Humans naturally use referring expressions with verbal utterances and nonverbal gestures to refer to objects and events. As these referring expressions can be interpreted differently from the speaker's or the observer's perspective, people effectively decide on the perspective in comprehending the expressions. However, existing models do not explicitly learn perspective grounding, which often causes the models to perform poorly in understanding embodied referring expressions. To make it exacerbate, these models are often trained on datasets collected in non-embodied settings without nonverbal gestures and curated from an exocentric perspective. To address these issues, in this paper, we present a perspective-aware multitask learning model, called PATRON, for relation and object grounding tasks in embodied settings by utilizing verbal utterances and nonverbal cues. In PATRON, we have developed a guided fusion approach, where a perspective grounding task guides the relation and object grounding task. Through this approach, PATRON learns disentangled task-specific and task-guidance representations, where task-guidance representations guide the extraction of salient multimodal features to ground the relation and object accurately. Furthermore, we have curated a synthetic dataset of embodied referring expressions with multimodal cues, called CAESAR-PRO. The experimental results suggest that PATRON outperforms the evaluated state-of-the-art visual-language models. Additionally, the results indicate that learning to ground perspective helps machine learning models to improve the performance of the relation and object grounding task. Furthermore, the insights from the extensive experimental results and the proposed dataset will enable researchers to evaluate visual-language models' effectiveness in understanding referring expressions in other embodied settings.
Geometry-Aware Network for Domain Adaptive Semantic Segmentation

Measuring and alleviating the discrepancies between the synthetic (source) and real scene (target) data is the core issue for domain adaptive semantic segmentation. Though recent works have introduced depth information in the source domain to reinforce the geometric and semantic knowledge transfer, they cannot extract the intrinsic 3D information of objects, including positions and shapes, merely based on 2D estimated depth. In this work, we propose a novel Geometry-Aware Network for Domain Adaptation (GANDA), leveraging more compact 3D geometric point cloud representations to shrink the domain gaps. In particular, we first utilize the auxiliary depth supervision from the source domain to obtain the depth prediction in the target domain to accomplish structure-texture disentanglement. Beyond depth estimation, we explicitly exploit 3D topology on the point clouds generated from RGB-D images for further coordinate-color disentanglement and pseudo-labels refinement in the target domain. Moreover, to improve the 2D classifier in the target domain, we perform domain-invariant geometric adaptation from source to target and unify the 2D semantic and 3D geometric segmentation results in two domains. Note that our GANDA is plug-and-play in any existing UDA framework. Qualitative and quantitative results demonstrate that our model outperforms state-of-the-arts on GTA5-&gt;Cityscapes and SYNTHIA-&gt;Cityscapes.
Self-Supervised Joint Dynamic Scene Reconstruction and Optical Flow Estimation for Spiking Camera

Spiking camera, a novel retina-inspired vision sensor, has shown its great potential for capturing high-speed dynamic scenes with a sampling rate of 40,000 Hz. The spiking camera abandons the concept of exposure window, with each of its photosensitive units continuously capturing photons and firing spikes asynchronously. However, the special sampling mechanism prevents the frame-based algorithm from being used to spiking camera. It remains to be a challenge to reconstruct dynamic scenes and perform common computer vision tasks for spiking camera. In this paper, we propose a self-supervised joint learning framework for optical flow estimation and reconstruction of spiking camera. The framework reconstructs clean frame-based spiking representations in a self-supervised manner, and then uses them to train the optical flow networks. We also propose an optical flow based inverse rendering process to achieve self-supervision by minimizing the difference with respect to the original spiking temporal aggregation image. The experimental results demonstrate that our method bridges the gap between synthetic and real-world scenes and achieves desired results in real-world scenarios. To the best of our knowledge, this is the first attempt to jointly reconstruct dynamic scenes and estimate optical flow for spiking camera from a self-supervised learning perspective.
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection

Out-of-distribution (OOD) detection can be used in deep learning-based applications to reject outlier samples from being unreliably classified by deep neural networks. Learning to classify between OOD and in-distribution samples is difficult because data comprising the former is extremely diverse. It has been observed that an auxiliary OOD dataset is most effective in training a ``rejection'' network when its samples are semantically similar to in-distribution images. We first deduce that OOD images are perceived by a deep neural network to be semantically similar to in-distribution samples when they share a common background, as deep networks are observed to incorrectly classify such images with high confidence. We then propose a simple yet effective Key In-distribution feature Replacement BY inpainting (KIRBY) procedure that constructs a surrogate OOD dataset by replacing class-discriminative features of in-distribution samples with marginal background features. The procedure can be implemented using off-the-shelf vision algorithms, where each step within the algorithm is shown to make the surrogate data increasingly similar to in-distribution data. Design choices in each step are studied extensively, and an exhaustive comparison with state-of-the-art algorithms demonstrates KIRBY's competitiveness on various benchmarks.
Cognition and Ideology in the Irony of the Batak Toba Language

The research discussed the irony of the Batak Toba language that was studied in two domains at once, namely the realization of the process of cognition and the realization of social praxis. The process of cognition was analyzed using a conceptual metaphorical approach, while social praxis used a critical discourse analysis approach of Fairclough's model. The research involved the qualitative research. The data obtained from various sources, such as eight song lyrics, nine folk tales, two sermons, one traditional procession, and two oral conversations. Data collection was done by sorting out which expressions were classified as irony, determining metaphors, mapping the source and target domains, then identifying cognitive processes and social praxis. It is found that irony in the Batak Toba language realizes the ability of cognition, which can be seen from the use of metaphors of animals, plants, body parts, and even inanimate objects in conceptualizing character, identity, attitudes, behavior, and human existence. Irony in the Batak Toba language also realizes social praxis. This is because irony shows a connection with the surrounding nature, describes the reality of social value identity, shows euphemism expressions to avoid social conflict, and describes ideology in maintaining good horizontal relations. Thus, it can be concluded that the irony of the Batak Toba language realizes the ability of cognition and ideology of the Batak Toba people, which can reflect the local wisdom of the community itself.
Optimalisasi Model ARIMA dalam Prakiraan Curah Hujan di Jambi

Process determine predicted monthly rainfall values for 2022, this study models rainfall forecasts. Observations of monthly rainfall over 30 years (1992–2022) were used to collect the data. The data is then analyzed using the Minitab application's ARIMA model to examine historical trends. For each model utilized, the forecast results are connected with the actual observation data in 2022. The model (1,0,1), (1,0,1) is used to calculate the difference to acquire a result of 54%, which is the best correlation value. The most accurate model for predicting monthly rainfall in Jambi is this one.
Chapter 9: The Geometric Random Variable

The geometric random variable is also defined using binomial trials, but without specifying a fixed number of trials. We again have independent trials, each with only two possible outcomes labeled “success” and “failure,” and for each trial the probability of success is p. Now our random variable Y is defined as the number of failures before the first success occurs. The notation we will use isY ∼ Geom(p).The support for the random variable is the nonnegative integers; Y = 0 if the first trial is a success. The support is an infinite set, but since it is only countably infinite, the random variable is discrete.
An End-to-End Multi-Scale Conditional Generative Adversarial Network for Image Deblurring

&lt;p&gt;For image deblurring, multi-scale approaches have been widely used as deep learning methods recently. In this paper, a novel multi-scale conditional generative adversarial network (CGAN) is proposed to make full use of image features, which outperforms most state-of-the-art methods. We define a generator network and a discriminator network. First of all, we use the multi-scale residual modules proposed in this paper as main feature extraction blocks, and add skip connections to extract multi-scale image features at a finer granularity in the generator network. Secondly, we construct PatchGAN as the discriminator network to enhance the local feature extraction capability. In addition, we combine the adversarial loss based on Wasserstein GAN with gradient penalty (WGAN-GP) theory with the content loss defined by perceptual loss as the total loss function, which is conducive to improving the consistency between the generated images and the ground-truth sharp images in content. The experimental results show that the method in this paper outperforms the state-of-the-art methods in visualization and quantitative results.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Design of English video course platform based on machine learning and Sobel algorithm

English video teaching is an indispensable part of English teaching, but there are certain difficulties in the design of English video courses, which need to be guided by artificial intelligence methods. Based on machine learning algorithm and Sobel algorithm, this paper builds an English video classroom design platform, uses image technology to process video, and proposes an improved algorithm based on Sobel operator. Moreover, this paper uses Sobel horizontal and vertical detection operators to define the edge strength of the block to be repaired and introduces a weight factor to enable the improved algorithm to repair images with different texture levels. In addition, this article improves the original SSD by introducing STD based on the image standard deviation and uses the degree of gray value dispersion between image blocks as a criterion for comparing whether two image blocks are similar, which effectively improves the accuracy of the search strategy. The experimental results show that the algorithm constructed in this paper can play a certain effect in teaching.
From Data Transparency and Security to Interfirm Collaboration-A Blockchain Technology Perspective

In recent years, blockchain technology has gained significant attention and recognition in both academic and practical contexts, due to its remarkable attributes of scalability, security, and sustainability. However, despite the growing interest, there is still a lack of exploration regarding the potential of blockchain to improve data transparency, enhance information security, and facilitate knowledge sharing. To address this gap, this study conducts a focused review of recent studies to examine precisely these aspects of blockchain technology. Various paradigms that highlight how the utilization of blockchain can enhance data transparency, bolster information security, and enable seamless knowledge sharing among organizations, are identified and proposed. These advancements surpass the capabilities of traditional methods of storing and sharing information.