Reviewerly/exHarmony huggingface dataset:
Top-Down System for Multi-Person 3D Absolute Pose Estimation from Monocular Videos

Two-dimensional (2D) multi-person pose estimation and three-dimensional (3D) root-relative pose estimation from a monocular RGB camera have made significant progress recently. Yet, real-world applications require depth estimations and the ability to determine the distances between people in a scene. Therefore, it is necessary to recover the 3D absolute poses of several people. However, this is still a challenge when using cameras from single points of view. Furthermore, the previously proposed systems typically required a significant amount of resources and memory. To overcome these restrictions, we herein propose a real-time framework for multi-person 3D absolute pose estimation from a monocular camera, which integrates a human detector, a 2D pose estimator, a 3D root-relative pose reconstructor, and a root depth estimator in a top-down manner. The proposed system, called Root-GAST-Net, is based on modified versions of GAST-Net and RootNet networks. The efficiency of the proposed Root-GAST-Net system is demonstrated through quantitative and qualitative evaluations on two benchmark datasets, Human3.6M and MuPoTS-3D. On all evaluated metrics, our experimental results on the MuPoTS-3D dataset outperform the current state-of-the-art by a significant margin, and can run in real-time at 15 fps on the Nvidia GeForce GTX 1080.
Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm

The Jump Point Search (JPS) algorithm ignores the possibility of any-angle walking, so the paths found by the JPS algorithm under the discrete grid map still have a gap with the real paths. To address the above problems, this paper improves the path optimization strategy of the JPS algorithm by combining the viewable angle of the Angle-Propagation Theta* (AP Theta*) algorithm, and it proposes the AP-JPS algorithm based on an any-angle pathfinding strategy. First, based on the JPS algorithm, this paper proposes a vision triangle judgment method to optimize the generated path by selecting the successor search point. Secondly, the idea of the node viewable angle in the AP Theta* algorithm is introduced to modify the line of sight (LOS) reachability detection between two nodes. Finally, the paths are optimized using a seventh-order polynomial based on minimum snap, so that the AP-JPS algorithm generates paths that better match the actual robot motion. The feasibility and effectiveness of this method are proved by simulation experiments and comparison with other algorithms. The results show that the path planning algorithm in this paper obtains paths with good smoothness in environments with different obstacle densities and different map sizes. In the algorithm comparison experiments, it can be seen that the AP-JPS algorithm reduces the path by 1.61–4.68% and the total turning angle of the path by 58.71–84.67% compared with the JPS algorithm. The AP-JPS algorithm reduces the computing time by 98.59–99.22% compared with the AP-Theta* algorithm.
The Effect of Foreign Direct Investment on Poverty Alleviation in East Africa Countries

Foreign Direct Investment (FDI) is crucial to support economic development for developed and developing countries. The aim of this paper is to examine the effect of Foreign Direct Investment (FDI) on poverty alleviation in East Africa Countries. The study adopted a time series data research design where by secondary data were used. The population applied on the financial records from 1987/88 - 2017/18 financial years (Annual Data). The sample size of the study was 31 annual observations. Tanzania was purposively sampled to be used as research location of this study. The data collected from various reliable sources which included the National Bureau of Statistics (NBS), Bank of Tanzania (BOT), Tanzania Investment Centre (TIC), World Bank (WB), International Monetary Fund (IMF) and United Nation Conference on Trade Development (UNCTAD). The results of the study revealed that FDI have statistical significant effects on poverty alleviation on East African countries with P- Value 0.008. The study recommends that policy makers in collaboration with the government have to pertain favorable and investment’s friendly strategies as well as commenced essential strategies transformation so as to solve the problems associated with investment sector in East Africa Countries which usually hinder the development and growth of the FDI
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading off model quality to reduce the compute complexity, but often do not achieve wall-clock speedup. We argue that a missing principle is making attention algorithms IO-aware -- accounting for reads and writes between levels of GPU memory. We propose FlashAttention, an IO-aware exact attention algorithm that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM. We analyze the IO complexity of FlashAttention, showing that it requires fewer HBM accesses than standard attention, and is optimal for a range of SRAM sizes. We also extend FlashAttention to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method. FlashAttention trains Transformers faster than existing baselines: 15% end-to-end wall-clock speedup on BERT-large (seq. length 512) compared to the MLPerf 1.1 training speed record, 3$\times$ speedup on GPT-2 (seq. length 1K), and 2.4$\times$ speedup on long-range arena (seq. length 1K-4K). FlashAttention and block-sparse FlashAttention enable longer context in Transformers, yielding higher quality models (0.7 better perplexity on GPT-2 and 6.4 points of lift on long-document classification) and entirely new capabilities: the first Transformers to achieve better-than-chance performance on the Path-X challenge (seq. length 16K, 61.4% accuracy) and Path-256 (seq. length 64K, 63.1% accuracy).
A Novel Cascade Model for End-to-End Aspect-Based Social Comment Sentiment Analysis

The end-to-end aspect-based social comment sentiment analysis (E2E-ABSA) task aims to discover human’s fine-grained sentimental polarity, which can be refined to determine the attitude in response to an object revealed in a social user’s textual description. The E2E-ABSA problem includes two sub-tasks, i.e., opinion target extraction and target sentiment identification. However, most previous methods always tend to model these two tasks independently, which inevitably hinders the overall practical performance. This paper investigates the critical collaborative signals between these two sub-tasks and thus proposes a novel cascade social comment sentiment analysis model for jointly tackling the E2E-ABSA problem, namely CasNSA. Instead of treating the opinion target extraction and target sentiment identification as discrete procedures in previous works, our new framework takes the contextualized target semantic encoding into consideration to yield better sentimental polarity judgment. Additionally, extensive empirical results show that the proposed approach effectively achieves a 68.13% F1-score on SemEval-2014, 62.34% F1-Score on SemEval-2015, 56.40% F1-Score on SemEval-2016, and 50.05% F1-score on a Twitter dataset, which is higher than the existing approaches. Ablated experiments demonstrate that the CasNSA model substantially outperforms state-of-the-art methods, even when using fixed words embedding rather than pre-trained BERT fine tuning. Moreover, in-depth performance analysis on the social comment datasets further validates that our work gains superior performance and reliability effectively and efficiently in realistic scenarios.
The Defuzzification Methods Comparison of Mamdani Fuzzy Inference System in Predicting Tofu Production

One of the tofu-producing companies in Kupang City is Bintang Oesapa. With the Covid-19 pandemic,the factory needs to reconsider the amount of production by taking into account the unpredictability ofdemand and resources to minimize losses due to excessive accumulation or shortages of supplies. Indetermining the amount of production, Mamdani’s Fuzzy Inference System (FIS) can be used, whichis a method for the analysis of an uncertain system. This method has three stages in the process ofdecision making, namely fuzzification, inferencing and defuzzification. In the defuzzification stage,the FIS Mamdani has five methods, namely Centroid, Bisector, Mean of Maximum (MOM), Smallestof Maximum (SOM), and Largest of Maximum (LOM). This study discusses an application of FISMamdani with five defuzzification methods for determining daily tofu production. The purpose of thisstudy is to offer a solution by first comparing the five defuzzification methods in assessing the amount oftofu production at the Bintang Oesapa factory and then determining that which is most appropriate. Theinput variables used in this research are the amount of demand and the amount of available stock, whilethe amount of production is our variable of interest. The results showed that the best defuzzificationmethod was the MOM method with an accuracy level of 94.73% and a small error value, 5.27%. TheMOM defuzzification is expected to aid decision makers in determining the best amount of productionduring the pandemic.
Integral Cryptanalysis of WARP based on Monomial Prediction

WARP is a 128-bit block cipher published by Banik et al. at SAC 2020 as a lightweight alternative to AES. It is based on a generalized Feistel network and achieves the smallest area footprint among 128-bit block ciphers in many settings. Previous analysis results include integral key-recovery attacks on 21 out of 41 rounds. In this paper, we propose integral key-recovery attacks on up to 32 rounds by improving both the integral distinguisher and the key-recovery approach substantially. For the distinguisher, we show how to model the monomial prediction technique proposed by Hu et al. at ASIACRYPT 2020 as a SAT problem and thus create a bit-oriented model of WARP taking the key schedule into account. Together with two additional observations on the properties of WARP’s construction, we extend the best previous distinguisher by 2 rounds (as a classical integral distinguisher) or 4 rounds (for a generalized integral distinguisher). For the key recovery, we create a graph-based model of the round function and demonstrate how to manipulate the graph to obtain a cipher representation amenable to FFT-based key recovery.
Inshore Ship Detection in Large-Scale SAR Images Based on Saliency Enhancement and Bhattacharyya-like Distance

While the detection of offshore ships in synthetic aperture radar (SAR) images has been widely studied, inshore ship detection remains a challenging task. Due to the influence of speckle noise and the high similarity between onshore buildings and inshore ships, the traditional methods are unable to achieve effective detection for inshore ships. To improve the detection performance of inshore ships, we propose a novel saliency enhancement algorithm based on the difference of anisotropic pyramid (DoAP). Considering the limitations of IoU in small-target detection, we design a detection framework based on the proposed Bhattacharyya-like distance (BLD). First, the anisotropic pyramid of the SAR image is constructed by a bilateral filter (BF). Then, the differences between the finest two scales and the coarsest two scales are used to generate the saliency map, which can be used to enhance ship pixels and suppress background clutter. Finally, the BLD is used to replace IoU in label assignment and non-maximum suppression to overcome the limitations of IoU for small-target detection. We embed the DoAP into the BLD-based detection framework to detect inshore ships in large-scale SAR images. The experimental results on the LS-SSDD-v1.0 dataset indicate that the proposed method outperforms the basic state-of-the-art detection methods.
Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events

Arrival-time picking is a critical step in microseismic data processing, and thus the quality control of arrival results is necessary. Conventional picking methods may be inaccurate or inconsistent due to varied signal-to-noise ratios (SNR) and waveform patterns of the events recorded in different time sections. To address this issue, we propose a quality assessment method based on waveform similarity coefficients to evaluate arrival results and also a global optimization algorithm based on iterative cross-correlation to refine arrival times. The recordings after moveout correction are applied to calculate the intra-event and inter-event waveform coefficients for the quality assessment of arrival results. The residual time differences of intra-event and inter-event traces are calculated sequentially using an enhanced iterative cross-correlation method. In addition, the stacked waveform of each event after the intra-event residual time correction is introduced for global optimization to obtain the inter-event residual time discrepancies. We use both synthetic data and field data to validate the proposed method. The results indicate that the proposed method yields more robust and reliable results. The quality assessment of the optimized arrivals is greatly enhanced compared to the adjusted picks obtained from single event-based processing methods.
Over-Generation Cannot Be Rewarded: Length-Adaptive Average Lagging for Simultaneous Speech Translation

Simultaneous speech translation (SimulST) systems aim at generating their output with the lowest possible latency, which is normally computed in terms of Average Lagging (AL). In this paper we highlight that, despite its widespread adoption, AL provides underestimated scores for systems that generate longer predictions compared to the corresponding references. We also show that this problem has practical relevance, as recent SimulST systems have indeed a tendency to over-generate. As a solution, we propose LAAL (Length-Adaptive Average Lagging), a modified version of the metric that takes into account the over-generation phenomenon and allows for unbiased evaluation of both under-/over-generating systems.
Optimized Seam-Driven Image Stitching Method Based on Scene Depth Information

It is quite challenging to stitch images with continuous depth changes and complex textures. To solve this problem, we propose an optimized seam-driven image stitching method considering depth, color, and texture information of the scene. Specifically, we design a new energy function to reduce the structural distortion near the seam and improve the invisibility of the seam. By additionally introducing depth information into the smoothing term of energy function, the seam is guided to pass through the continuous regions of the image with high similarity. The experimental results show that benefiting from the new defined energy function, the proposed method can find the seam that adapts to the depth of the scene, and effectively avoid the seam from passing through the salient objects, so that high-quality stitching results can be achieved. The comparison with the representative image stitching methods proves the effectiveness and generalization of the proposed method.
Digital Art and Identity Merging Human and Artificial Intelligence: Enter the Metaverse

This Symposium explores themes of digital art and identity with respect to Artificial Intelligence and the developing ideas of the "metaverse".The paper provides some background to the concept of the metaverse in the context of digital culture.Then each participant provides a statement from their viewpoint and experience.These cover diverse issues based on the varied backgrounds and expertise of the contributors.
Analysis of quantum correlations within the ground state of a three-level Lipkin model

The performance of beyond mean-field methods in solving the quantum many-body problem for fermions is usually characterized by the correlation energy measured with respect to the underlying mean-field value. In this paper we address the issue of characterizing the amount of correlations associated to different approximations from a quantum information perspective. With this goal in mind, we analyze the traditional Hartree-Fock (HF) method with spontaneous symmetry breaking, the HF with symmetry restoration and the generator coordinate method in an exactly solvable fermion model known as the three-level Lipkin model. To characterize correlations including entanglement and beyond we use the quantum discord between different partition orbitals. We find that for physically motivated partitions, the quantum discord of the exact ground state is reasonably well reproduced by the different approximations. However, other partitions create ``fake quantum correlations'' in order to capture quantum correlations corresponding to partitions for which the Hartree-Fock solution fails. Those are removed and redistributed through a symmetry restoration process.
Visualization in Motion: A Research Agenda and Two Evaluations

We contribute a research agenda for visualization in motion and two experiments to understand how well viewers can read data from moving visualizations. We define visualizations in motion as visual data representations that are used in contexts that exhibit relative motion between a viewer and an entire visualization. Sports analytics, video games, wearable devices, or data physicalizations are example contexts that involve different types of relative motion between a viewer and a visualization. To analyze the opportunities and challenges for designing visualization in motion, we show example scenarios and outline a first research agenda. Motivated primarily by the prevalence of and opportunities for visualizations in sports and video games we started to investigate a small aspect of our research agenda: the impact of two important characteristics of motion-speed and trajectory on a stationary viewer's ability to read data from moving donut and bar charts. We found that increasing speed and trajectory complexity did negatively affect the accuracy of reading values from the charts and that bar charts were more negatively impacted. In practice, however, this impact was small: both charts were still read fairly accurately.
Teachers and Participatory Action Research for Developing Learning Environments

This research aims to develop learning environments through Participatory Action Research (PAR) methodology in a specific context of the Department of Electronics, Nong Han Industrial and Community Education College. Three development outcomes are expected: (1) changes in expected and non-expected outcomes of action; (2) action-based learning in the researchers, research participants and the institute and (3) knowledge from the grounded theory. The results are as the followings: 1) there are positive changes in both expected and unanticipated outcomes consisting of the implementation of the co-designated development approaches and the nature of the expected learning environments, 2) the researchers, research participants, and the institute learn about the efficiency of working together, which makes everyone realize how inefficient working alone was in the past, and 3) a body of knowledge gained from practice describes the relationship between the expected changes, and the driving force and resistances to change as well as ways to overcome the resistances.
A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

The growing need for access to safer food items is increasing, and hence, there is a need for a better supply chain management system in the food industry is increasing. The increased complexity of the existing systems tends to introduce more issues to the stakeholders, and also, the cost of product traceability is quite high. Hence, the industry is looking for effective solutions in relation to drug traceability, and the application of Blockchain technology enables the stakeholders in the food and beverage (F&amp;B) sector to track the movement of goods, supported in gathering the required details so that the contaminated products can be identified and recalled without much delay and lesser recall costs to protect the lives of the individuals. The tampered food items are increasing and are impacting the supply chain process, brand name of the companies, and claim assurance. They create an adverse impact on the health of the individuals and cause higher economic loss to the health-care industry. The existing studies tend to focus on laying emphasis of the need for an enhanced, effective, and end tracking systems in the industry. The emergence of Blockchain technology enables centralized tracking of information support in enhancing the data privacy and increasing transparency and support in eradicating the tampered food products in the supply chain system. These approaches leverage the usage of smart contracts and decentralize the storage of information in a secure manner for enhanced product traceability in the F&amp;B industry. The implementation of smart contracts generates better data governance, which tends to meet the needs and requirements of the stakeholders, and applies effective measures of food traceability. The primary objective of the study is to perform an analysis of Blockchain in enhancing drug traceability in the food sector. The researcher uses quantitative analysis for the study as it helps in understanding the critical determinants influencing drug traceability in food effectively, the survey method is used to gather the information, and past reviews are also used to possess a better understanding of the subject area effectively.
Multimodal registration across 3D point clouds and CT-volumes

Multimodal registration is a challenging problem in visual computing, commonly faced during medical image-guided interventions, data fusion and 3D object retrieval. The main challenge of multimodal registration is finding accurate correspondence between modalities, since different modalities do not exhibit the same characteristics. This paper explores how the coherence of different modalities can be utilized for the challenging task of 3D multimodal registration. A novel deep learning multimodal registration framework is proposed by introducing a siamese deep learning architecture, especially designed for aligning and fusing modalities of different structural and physical principles. The cross-modal attention blocks lead the network to establish correspondences between features of different modalities. The proposed framework focuses on the alignment of 3D point clouds and the micro-CT 3D volumes of the same object. A multimodal dataset consisting of real micro-CT scans and their synthetically generated 3D models (point clouds) is presented and utilized for evaluating our methodology.