Reviewerly/exHarmony huggingface dataset:
HYBRID MODEL FOR CRYPTOCURRENCY INVESTMENT STRATEGIES

Cryptocurrencies are digital assets based on the blockchain, they use cryptography to guarantee their ownership and ensure the integrity of transactions, in addition to allowing control over the creation of additional units. Since its creation in 2009, with Bitcoin as the first cryptocurrency, the market and the number of cryptocurrencies have grown rapidly, as has the interest of those seeking high returns due to its rapid appreciation, whether these investors are individuals or financial institutions. However, this market has characteristics of high volatility and uncertainty, causing prices to vary at very high levels and also at low levels, all of which makes investment decisions in cryptocurrencies very difficult for investment managers. This article proposes a decision-making support methodology for managing investments in the cryptocurrency market, which considers a conservative investment risk profile and seeks to reduce risk and maximize investment return. The methodology aims to establish return levels and estimate the transition probabilities of returns for each level, this is done based on the historical price of cryptocurrencies and using the analysis of Markov chains, which are integrated into the multiple decision trees to identify the cryptocurrency that projects the highest return when it is sold, in one or two periods after acquisition. The results are compared with real data, and the efficiency of the methodology is verified.
Analisis ungkapan sarkasme dalam Bahasa Bugis: Kajian leksikologi

Bugis language is one of the regional languages in Indonesia, especially in South Sulawesi. Along with the development of the era of globalization which is increasing rapidly making language also experience development, thus affecting the style of language. Language style consists of several kinds, one of which is sarcasm. This study aims to describe the form, meaning, and factors that influence the occurrence of sarcasm in Bugis language. The type of research used is qualitative. Sources of data were obtained from oral data, Bugis-Indonesian dictionaries, books, and relevant previous articles. Data collection was carried out using editing techniques (collecting and checking data), organizing (recording and presenting facts), and founding (analyzing data findings), while data analysis was carried out using Miles and Huberman's data analysis techniques, namely data reduction, data display, and withdrawal. conclusion. This study found the results that the 21 data that had been obtained and analyzed by researchers contained a form of expression of Bugis language sarcasm that was often used as a form of designation sarcasm. Then, the factor that most often influences is the joke factor because it is to liven up the atmosphere. Keywords: Expression, Sarcasm, Bugis Language
Enabling in-car location-based experiential learning with Presentria GO

The COVID-19 pandemic has changed how millions around the globe are educated. The 2nd or 3rd waves of the disease have made learning in classrooms unsafe once again. Many schools are forced to send their students home to take online classes under their government's lock-down protocols. For many young learners, engaging with school is a significant part of their well-being, which has been compromised by the extended period of remote learning and low social interaction levels during the pandemic. New and innovative solutions to address learners' needs have been called during this pandemic. The Presentria GO system is an innovative solution that enables students from K-12 to higher education to learn experientially from their cars during a city excursion. Through a survey with 74 educators and a series of expert interviews and focus group discussions, insights into the feasibility of this active learning mode are explored. This paper proposes the concept of 'in-car location-based experiential learning' as one of the methods to engage students during the pandemic and beyond.
Determining Work-Rest Schedules for Visual Tasks That Use Optical Head-Mounted Displays Based on Visual Fatigue and Visually Induced Motion Sickness Recovery

This study aimed to determine work-rest schedules for visual tasks of different lengths by evaluating visual fatigue and visually induced motion sickness (VIMS) using an optical head-mounted display (OHMD). Thirty participants were recruited to perform 15 and 30 min visual tasks using an OHMD. After completing each visual task, participants executed six levels of rest time. Critical flicker fusion frequency (CFF) values, relative electroencephalography indices, and Simulator Sickness Questionnaire (SSQ) scores were collected and analyzed. Results indicated that after completing the 15 and 30 min visual tasks, participants experienced visual fatigue and VIMS. There was no significant difference between baseline CFF values, four electroencephalography relative power index values, and SSQ scores when participants completed a 15 min visual task followed by a 20 min rest and a 30 min visual task followed by a 30 min rest. Based on our results, a 20 min rest for visual fatigue and VIMS recovery after a 15 min visual task on an OHMD and a 25 min rest for visual fatigue and VIMS recovery after a 30 min visual task on an OHMD are recommended. This study suggests a work-rest schedule for OHMDs that can be used as a reference for OHMD user guidelines to reduce visual fatigue and visually induced motion sickness.
Loss Function Dynamics and Landscape for Deep Neural Networks Trained with Quadratic Loss

Abstract Knowledge of the loss landscape geometry makes it possible to successfully explain the behavior of neural networks, the dynamics of their training, and the relationship between resulting solutions and hyperparameters, such as the regularization method, neural network architecture, or learning rate schedule. In this paper, the dynamics of learning and the surface of the standard cross-entropy loss function and the currently popular mean squared error (MSE) loss function for scale-invariant networks with normalization are studied. Symmetries are eliminated via the transition to optimization on a sphere. As a result, three learning phases with fundamentally different properties are revealed depending on the learning step on the sphere, namely, convergence phase, phase of chaotic equilibrium, and phase of destabilized learning. These phases are observed for both loss functions, but larger networks and longer learning for the transition to the convergence phase are required in the case of MSE loss.
Semiotic and Cultural Analysis on Local Product Brand Name

To create strategically relevant and significant brand names, factors such as culture is one of the most powerful and immediate ways to communicate an identity-image and linkage to the consumer.Thus, enables potential buyers to relate by delivering brand communication value of the brand by inspiring or persuading them by certain images and associations.However, interpretation of each brand name can be tricky as manipulation of social interaction as well as multiple types of relationships with the brands; leading to varying brand images (noise).The aims of this research were to explore cultural aspects and their meaning as brand communication value of 50 local brand names of various products as a purposeful communicative act in branding strategy.The brand names were sampled limited on a simple random sampling selected from billboard advertisement.Content analysis method through Charles Sanders Peirce's semiotic model was used in this analysis to describe and analyse the connotation, denotation, concepts, and relationships in making inferences about the cultural messages imprinted on the brand names.The findings indicated culture still can be a source of influence in brand naming strategies.Malay language and terms by the usage of local names giving local value expression that need to be taken into account in basic strategy of brand-building as future reference.This is the consumer self-image in branding, while behind cultural expression usually lies religious motivation to influence formation of a consumer behavior such as stressing out the use of Arabic terms and Jawi script.Therefore, the study gives more insight on the brand name patterns and culture as symbolic meaning which created emotional ties of brand preference.And, the decision-making process can be much easier for producers or marketers in strategizing brand names, especially for local markets that need to be tailored to target the local community.
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss

An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastive learning. However, image-to point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the contrastive losses pushing away semantically similar point and image regions and thus disturbing the local semantic structure of the learned representations, and 2) severe class imbalance as pretraining gets dominated by over-represented classes. We propose to alleviate the self-similarity problem through a novel semantically tolerant image-to-point contrastive loss that takes into consideration the semantic distance between positive and negative image regions to minimize contrasting semantically similar point and image regions. Additionally, we address class imbalance by designing a class-agnostic balanced loss that approximates the degree of class imbalance through an aggregate sample-to-samples semantic similarity measure. We demonstrate that our semantically-tolerant contrastive loss with class balancing improves state-of-the art 2D-to-3D representation learning in all evaluation settings on 3D semantic segmentation. Our method consistently outperforms state-of-the-art 2D-to-3D representation learning frameworks across a wide range of 2D self-supervised pretrained models.
Uncertainty Quantification for Local Model Explanations Without Model Access

We present a model-agnostic algorithm for generating post-hoc explanations and uncertainty intervals for a machine learning model when only a static sample of inputs and outputs from the model is available, rather than direct access to the model itself. This situation may arise when model evaluations are expensive; when privacy, security and bandwidth constraints are imposed; or when there is a need for real-time, on-device explanations. Our algorithm uses a bootstrapping approach to quantify the uncertainty that inevitably arises when generating explanations from a finite sample of model queries. Through a simulation study, we show that the uncertainty intervals generated by our algorithm exhibit a favorable trade-off between interval width and coverage probability compared to the naive confidence intervals from classical regression analysis as well as current Bayesian approaches for quantifying explanation uncertainty. We further demonstrate the capabilities of our method by applying it to black-box models, including a deep neural network, trained on three real-world datasets.
Infusing Commonsense World Models with Graph Knowledge

While language models have become more capable of producing compelling language, we find there are still gaps in maintaining consistency, especially when describing events in a dynamically changing world. We study the setting of generating narratives in an open world text adventure game, where a graph representation of the underlying game state can be used to train models that consume and output both grounded graph representations and natural language descriptions and actions. We build a large set of tasks by combining crowdsourced and simulated gameplays with a novel dataset of complex actions in order to to construct such models. We find it is possible to improve the consistency of action narration models by training on graph contexts and targets, even if graphs are not present at test time. This is shown both in automatic metrics and human evaluations. We plan to release our code, the new set of tasks, and best performing models.
State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise

Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed Kalman filters for wireless sensor networks in the presence of non-Gaussian impulsive noise. However, these algorithms currently fail to take account of data packet drops. The present work addresses this issue by proposing a distributed maximum correntropy Kalman filter that accounts for data packet drops (i.e., the DMCKF-DPD algorithm). The effectiveness and feasibility of the algorithm are verified by simulations conducted in a wireless sensor network with intermittent observations due to data packet drops under a non-Gaussian noise environment. Moreover, the computational complexity of the DMCKF-DPD algorithm is demonstrated to be moderate compared with that of a conventional distributed Kalman filter, and we provide a sufficient condition to ensure the convergence of the proposed algorithm.
Gated Self-supervised Learning For Improving Supervised Learning

In past research on self-supervised learning for image classification, the use of rotation as an augmentation has been common. However, relying solely on rotation as a self-supervised transformation can limit the ability of the model to learn rich features from the data. In this paper, we propose a novel approach to self-supervised learning for image classification using several localizable augmentations with the combination of the gating method. Our approach uses flip and shuffle channel augmentations in addition to the rotation, allowing the model to learn rich features from the data. Furthermore, the gated mixture network is used to weigh the effects of each self-supervised learning on the loss function, allowing the model to focus on the most relevant transformations for classification.
Compress Then Test: Powerful Kernel Testing in Near-linear Time

Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on $n$ sample points. However, existing kernel tests either run in $n^2$ time or sacrifice undue power to improve runtime. To address these shortcomings, we introduce Compress Then Test (CTT), a new framework for high-powered kernel testing based on sample compression. CTT cheaply approximates an expensive test by compressing each $n$ point sample into a small but provably high-fidelity coreset. For standard kernels and subexponential distributions, CTT inherits the statistical behavior of a quadratic-time test -- recovering the same optimal detection boundary -- while running in near-linear time. We couple these advances with cheaper permutation testing, justified by new power analyses; improved time-vs.-quality guarantees for low-rank approximation; and a fast aggregation procedure for identifying especially discriminating kernels. In our experiments with real and simulated data, CTT and its extensions provide 20--200x speed-ups over state-of-the-art approximate MMD tests with no loss of power.
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these applications is to reach a destination without colliding with obstacles or other robot arms. Commonly used sampling-based path planning approaches such as RRT require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing the approaches in new environments, a tedious engineering effort in setting hyperparameters needs to be conducted, which is time- and cost-intensive. On the other hand, Deep Reinforcement Learning has shown remarkable results in dealing with unknown environments, generalizing new problem instances, and solving motion planning problems efficiently. On that account, this paper proposes a Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We evaluated our model against state-of-the-art sampling-based planners in several experiments. The results show the superiority of our planner in terms of path length and execution time.
Delving Deep into Pixel Alignment Feature for Accurate Multi-view Human Mesh Recovery

Regression-based methods have shown high efficiency and effectiveness for multi-view human mesh recovery. The key components of a typical regressor lie in the feature extraction of input views and the fusion of multi-view features. In this paper, we present Pixel-aligned Feedback Fusion (PaFF) for accurate yet efficient human mesh recovery from multi-view images. PaFF is an iterative regression framework that performs feature extraction and fusion alternately. At each iteration, PaFF extracts pixel-aligned feedback features from each input view according to the reprojection of the current estimation and fuses them together with respect to each vertex of the downsampled mesh. In this way, our regressor can not only perceive the misalignment status of each view from the feedback features but also correct the mesh parameters more effectively based on the feature fusion on mesh vertices. Additionally, our regressor disentangles the global orientation and translation of the body mesh from the estimation of mesh parameters such that the camera parameters of input views can be better utilized in the regression process. The efficacy of our method is validated in the Human3.6M dataset via comprehensive ablation experiments, where PaFF achieves 33.02 MPJPE and brings significant improvements over the previous best solutions by more than 29%. The project page with code and video results can be found at https://kairobo.github.io/PaFF/.
A Survey on Human Action Recognition

Human Action Recognition (HAR), one of the most important tasks in computer vision, has developed rapidly in the past decade and has a wide range of applications in health monitoring, intelligent surveillance, virtual reality, human computer interaction and so on. Human actions can be represented by a wide variety of modalities, such as RGB-D cameras, audio, inertial sensors,etc. Consequently, in addition to the mainstream single modality based HAR approaches, more and more research is devoted to the multimodal domain due to the complementary properties between multimodal data. In this paper, we present a survey of HAR methods in recent years according to the different input modalities. Meanwhile, considering that most of the recent surveys on HAR focus on the third perspective, while this survey aims to provide a more comprehensive introduction to HAR novices and researchers, we therefore also investigate the actions recognition methods from the first perspective in recent years. Finally, we give a brief introduction about the benchmark HAR datasets and show the performance comparison of different methods on these datasets.
Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis

During the past decade, the Unmanned-Aerial-Vehicles (UAVs) have attracted increasing attention due to their flexible, extensive, and dynamic space-sensing capabilities. The volume of video captured by UAVs is exponentially growing along with the increased bitrate generated by the advancement of the sensors mounted on UAVs, bringing new challenges for on-device UAV storage and air-ground data transmission. Most existing video compression schemes were designed for natural scenes without consideration of specific texture and view characteristics of UAV videos. In this work, we first contribute a detailed analysis of the current state of the field of UAV video coding. Then we propose to establish a novel task for learned UAV video coding and construct a comprehensive and systematic benchmark for such a task, present a thorough review of high quality UAV video datasets and benchmarks, and contribute extensive rate-distortion efficiency comparison of learned and conventional codecs after. Finally, we discuss the challenges of encoding UAV videos. It is expected that the benchmark will accelerate the research and development in video coding on drone platforms.
CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation

The goal of image ordinal estimation is to estimate the ordinal label of a given image with a convolutional neural network. Existing methods are mainly based on ordinal regression and particularly focus on modeling the ordinal mapping from the feature representation of the input to the ordinal label space. However, the manifold of the resultant feature representations does not maintain the intrinsic ordinal relations of interest, which hinders the effectiveness of the image ordinal estimation. Therefore, this paper proposes learning intrinsic Consistent Ordinal REpresentations (CORE) from ordinal relations residing in groundtruth labels while encouraging the feature representations to embody the ordinal low-dimensional manifold. First, we develop an ordinal totally ordered set (toset) distribution (OTD), which can (i) model the label embeddings to inherit ordinal information and measure distances between ordered labels of samples in a neighborhood, and (ii) model the feature embeddings to infer numerical magnitude with unknown ordinal information among the features of different samples. Second, through OTD, we convert the feature representations and labels into the same embedding space for better alignment, and then compute the Kullback Leibler (KL) divergence between the ordinal labels and feature representations to endow the latent space with consistent ordinal relations. Third, we optimize the KL divergence through ordinal prototype-constrained convex programming with dual decomposition; our theoretical analysis shows that we can obtain the optimal solutions via gradient backpropagation. Extensive experimental results demonstrate that the proposed CORE can accurately construct an ordinal latent space and significantly enhance existing deep ordinal regression methods to achieve better results.
A Coreset Learning Reality Check

Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.