Reviewerly/exHarmony huggingface dataset:
Motions in Microseconds via Vectorized Sampling-Based Planning

Modern sampling-based motion planning algorithms typically take between hundreds of milliseconds to dozens of seconds to find collision-free motions for high degree-of-freedom problems. This paper presents performance improvements of more than 500x over the state-of-the-art, bringing planning times into the range of microseconds and solution rates into the range of kilohertz, without specialized hardware. Our key insight is how to exploit fine-grained parallelism within sampling-based planners, providing generality-preserving algorithmic improvements to any such planner and significantly accelerating critical subroutines, such as forward kinematics and collision checking. We demonstrate our approach over a diverse set of challenging, realistic problems for complex robots ranging from 7 to 14 degrees-of-freedom. Moreover, we show that our approach does not require high-power hardware by also evaluating on a low-power single-board computer. The planning speeds demonstrated are fast enough to reside in the range of control frequencies and open up new avenues of motion planning research.
Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles

This paper addresses the advancements in on-road trajectory planning for Autonomous Passenger Vehicles (APV). Trajectory planning aims to produce a globally optimal route for APVs, considering various factors such as vehicle dynamics, constraints, and detected obstacles. Traditional techniques involve a combination of sampling methods followed by optimization algorithms, where the former ensures global awareness and the latter refines for local optima. Notably, the Constrained Iterative Linear Quadratic Regulator (CILQR) optimization algorithm has recently emerged, adapted for APV systems, emphasizing improved safety and comfort. However, existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by incorporating higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk. This inclusion facilitates a richer representation in our cost and constraint design. We also address roadway compliance, emphasizing adherence to lane boundaries and directions, which past work often overlooked. Lastly, we adopt a relaxed logarithmic barrier function to address the CILQR's dependency on feasible initial trajectories. The proposed methodology is then validated through simulation and real-world experiment driving scenes in real time.
Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew

We present DictaLM, a large-scale language model tailored for Modern Hebrew. Boasting 7B parameters, this model is predominantly trained on Hebrew-centric data. As a commitment to promoting research and development in the Hebrew language, we release both the foundation model and the instruct-tuned model under a Creative Commons license. Concurrently, we introduce DictaLM-Rab, another foundation model geared towards Rabbinic/Historical Hebrew. These foundation models serve as ideal starting points for fine-tuning various Hebrew-specific tasks, such as instruction, Q&A, sentiment analysis, and more. This release represents a preliminary step, offering an initial Hebrew LLM model for the Hebrew NLP community to experiment with.
The Metaverse – the Next Stage of the Internet

The article aims to investigate the development of a little studied in our country, but at the same time considered as one of the most promising, significant and rapidly developing technologies of the Fourth Industrial Revolution – the metaverse. Therefore, the object of the study is precisely the metaverse, and its subject – the appearance, essence, development, and perspectives of the metaverse. In order to achieve this goal, a descriptive approach is used to describe what the metaverse is, an explanatory approach is used to explain how the metaverse works, and then a prognostic approach is used to see the prospects of the metaverse. Following this logic, the article begins with the emergence of the idea of the metaverse and the first steps towards its realization. Then it goes with an explanation of what the metaverse is, examining the basic elements that are necessary for it to function, continues with the different realities through which the metaverse can reveal itself, with the digital twins and avatars through which the physical world is duplicated in the metaverse, and ends with the different types of metaverses in the context of the increasing competition between companies and the emergence of multiple metaverse projects. The article ends with the prospects for the development of the metaverse and ultimately with the conclusion that it implies many new perspectives and contradictions that humanity will somehow have to deal with.
Speckle-based optical encryption with complex-amplitude coding and deep learning

We propose a speckle-based optical encryption scheme by using complex-amplitude coding and deep learning, which enables the encryption and decryption of complex-amplitude plaintext containing both amplitude and phase images. During encryption, the amplitude and phase images are modulated using a superpixel-based coding technique and feded into a digital micromirror device. After passing through a 4f system, the information undergoes disturbance modulation by a scattering medium, resulting in a diffracted speckle pattern serving as the ciphertext. A Y-shaped convolutional network (Y-Net) model is constructed to establish the mapping relationship between the complex-amplitude plaintext and ciphertext through training. During decryption, the Y-Net model is utilized to quickly extract high-quality amplitude and phase images from the ciphertext. Experimental results verify the feasibility and effectiveness of our proposed method, demonstrating that the potential of integrating speckle encryption and deep learning for optical complex-amplitude encryption.
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data

Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples from 34 high-fidelity direct numerical simulations, which addresses the current limited availability of 3D high-fidelity reacting and non-reacting compressible turbulent flow simulation data. With this data, we benchmark a total of 49 variations of five deep learning approaches for 3D super-resolution - which can be applied for improving scientific imaging, simulations, turbulence models, as well as in computer vision applications. We perform neural scaling analysis on these models to examine the performance of different machine learning (ML) approaches, including two scientific ML techniques. We demonstrate that (i) predictive performance can scale with model size and cost, (ii) architecture matters significantly, especially for smaller models, and (iii) the benefits of physics-based losses can persist with increasing model size. The outcomes of this benchmark study are anticipated to offer insights that can aid the design of 3D super-resolution models, especially for turbulence models, while this data is expected to foster ML methods for a broad range of flow physics applications. This data is publicly available with download links and browsing tools consolidated at https://blastnet.github.io.
Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction

Reconstructing 3D clothed human avatars from single images is a challenging task, especially when encountering complex poses and loose clothing. Current methods exhibit limitations in performance, largely attributable to their dependence on insufficient 2D image features and inconsistent query methods. Owing to this, we present the Global-correlated 3D-decoupling Transformer for clothed Avatar reconstruction (GTA), a novel transformer-based architecture that reconstructs clothed human avatars from monocular images. Our approach leverages transformer architectures by utilizing a Vision Transformer model as an encoder for capturing global-correlated image features. Subsequently, our innovative 3D-decoupling decoder employs cross-attention to decouple tri-plane features, using learnable embeddings as queries for cross-plane generation. To effectively enhance feature fusion with the tri-plane 3D feature and human body prior, we propose a hybrid prior fusion strategy combining spatial and prior-enhanced queries, leveraging the benefits of spatial localization and human body prior knowledge. Comprehensive experiments on CAPE and THuman2.0 datasets illustrate that our method outperforms state-of-the-art approaches in both geometry and texture reconstruction, exhibiting high robustness to challenging poses and loose clothing, and producing higher-resolution textures. Codes will be available at https://github.com/River-Zhang/GTA.
The Price of Minkowski Lattice Gauge Theories

in High-Energy Physics over the coming decade. This projection is based on abundant successful results that have emerged using lattice gauge theory over the years: on continued improvement in theoretical frameworks and algorithmic suits; on the forthcoming transition into the exascale era of high-performance computing; and on a skillful, dedicated, and organized community of lattice gauge theorists in the U.S. and worldwide. The prospects of this effort in pushing the frontiers of research in High-Energy Physics have recently been studied within the U.S. decadal Particle Physics Planning Exercise (Snowmass 2021), and the conclusions are summarized in this Topical Report.
PERANCANGAN MEDIA BELAJAR BERHITUNG UNTUK ANAK TUNANETRA USIA DINI 4-6 TAHUN

Blind students need different support and treatment from normal children during the learning process. Numeracy type learning media specifically made for early blind children, is still not widely found. Blind numeracy learning media that have been circulating such as abacus and abacus have a goal that focuses more on mathematical skills in children at a more mature age. The design is implemented to help blind children to recognize numbers by using learning media adapted to their physical conditions and abilities. The research method used is qualitative descriptive and design methods using User Centered Design which departs from user problems in the problem of using existing learning media. The implementation of learning media was applied at SLB Roudhotul Zannah, from 10 respondents of blind students conducting pre-tests and postests produced as much as 22% pre-test and 33% post-test, then with learning media that has been designed to be very effective to help achieve learning, especially for blind students.
PENGEMBANGAN MEDIA VIDEO ANIMASI BERBASIS POWTOON MATERI KONVERSI SATUAN PANJANG MUATAN MATEMATIKA KELAS III SD

The rapid development of science and technology affects all aspects of human life. Aspects in the field of education are one of the recipients of the impact of science and technology development. Developments in the world of education are very prominent in the digital world, especially aspects of learning media. One of the mathematics learning media that can be developed is powtoon-based animated video media. This study aims to produce products that meet valid, practical and effective criteria. The development model used is the ADDIE model with 5 stages. In this study the researchers took 17 students in class III SD Negeri 04 Sungai Rumbai as test subjects. Data was collected using validation sheets, practicality and effectiveness sheets. The validation sheet is filled in by the validator, namely a lecturer at Dharmas Indonesia University. The results of the material validity sheet obtained a percentage of 94.3%, categorized as very valid. The results of the construct/media validity sheet obtained 80% results categorized as valid, the media validity sheet obtained 95% results categorized as very valid. The lesson plan validity sheet obtained 94% results which were categorized as very valid and the question validity sheet obtained 97% results which were categorized as very valid. The results of the teacher's response practicality sheet obtained 88.9% results categorized as very practical, student response practicality sheets obtained 95% results categorized as very practical. Results Analysis of effectiveness data obtained results of 88% for students who completed and 12% for students who did not complete, so that powtoon-based animated video media was categorized as valid, practical and effective to use.
Human Action Detection Based on Multimodal Feature Fusion for Human-Robot Collaborative Assembly

With the development of human-robot collaboration technology, action detection, which can detect human actions during the assembly process and improve the fluency of collaboration, has a significant value in industrial assembly tasks. However, in practical application scenarios, the effect of action detection is affected by the low action difference of human-robot collaborative assembly tasks, single data modality, etc. In order to effectively solve this problem, a human-robot collaborative assembly framework based on action detection is proposed, in which we propose an action detection method Multi-Ad proposed with higher accuracy and generalization capability. Multi-Ad adopts the method of fusing multimodal features of RGB, optical flow, and skeleton sequences to enhance the extracted data information, which can improve the accuracy of action detection. Experimental results on the thumos14 dataset show that the proposed method is better than previous methods in terms of action detection accuracy.
Single Underwater Image Enhancement via Contrast Stretching and Fusion

In this paper, a novel single underwater image enhancement method is proposed to address color distortion, poor contrast and detail blur in underwater degraded images. First, the two attenuation channels of underwater degraded images are color compensated in the RGB color space. Then, the improved White Patch hypothesis is used to implement color correction. Moreover, contrast stretching and detail preservation of color-corrected images are carried out in the Lab color space. Finally, the two images and the weights defined are fused to obtain the final image at multiple scales. The experiment shows that the proposed method can effectively improve color distortion, enhance contrast and reduce blur in multiple underwater scenes, which is superior to existing methods.
Wave-for-Safe: Multisensor-based Mutual Authentication for Unmanned Delivery Vehicle Services

In recent years, the deployment of unmanned vehicle delivery services has increased unprecedentedly, leading to a need for enhanced security due to the risk of leaving high-value packages to an unauthorized third party during pickup or delivery. Existing authentication methods such as QR code and one-time password are inadequate, as they are susceptible to attacks and provide only one-way authentication. This paper, for the first time to our best knowledge, proposes Wave-for-Safe (W4S) --- a novel mutual authentication system that utilizes multi-modal sensors on both the user's smartphone and the unmanned vehicle. W4S uses random hand-waving by the legitimate user to achieve robust authentication by obtaining highly correlated sensory data measured by the Inertial Measurement Unit (IMU) in the smartphone and sensors in the unmanned vehicle (e.g., mmWave radar and camera). We propose several novel methods to overcome challenges such as heterogeneous data processing, asynchronization, and imitating attacks. The prototype is implemented on an unmanned vehicle and various smartphones, and evaluation in different real-world scenarios shows that W4S achieves an equal error rate below 0.013 against various attacks.
IMDAC: A robust intelligent software defect prediction model via multi‐objective optimization and end‐to‐end hybrid deep learning networks

Abstract Software defect prediction (SDP) aims to build an effective prediction model for historical defect data from software repositories by some specialized techniques or algorithms, and predict the defect proneness of new software modules. Nevertheless, the complex internal intrinsic structure hidden behind the defect data makes it challenging for the built prediction model to capture the most expressive defect feature representations, and largely limits the SDP performance. Fortunately, artificial intelligence is interacting closely with humans and provides powerful intelligent technical support for addressing these SDP issues. In this article, we propose a robust intelligent SDP model called IMDAC based on deep learning and soft computing techniques. This model has three main advantages: (1) an effective deep generative network—InfoGAN (information maximizing GANs) is employed to conduct data augmentation, namely generating sufficient defect instances and achieving defect class balance simultaneously. (2) Select the fewest representative feature subset for the minimum error via an advanced multi‐objective optimization approach—MSEA (multi‐stage evolutionary algorithm). (3) Build a powerful end‐to‐end deep defect predictor by hybrid deep learning techniques—DAE (Denoising AutoEncoder) and CNN (convolutional neural network), which can not only reconstruct a clean “repaired” input with strong robustness and generalization capabilities via DAE, but also learn the abstract deep semantic features with strong discriminating capability via CNN. Experimental results verify the superiority and robustness of the IMDAC model across 15 software projects.
Correlational Study of Students’ Moodle Log for Assessments and Feedback on Assessments on Moodle for a Mandatory Pre-Degree English Course

This study explored the correlation between students’ Moodle logs on assessments and feedback on assessments on LLFXX Moodle page. Nine forms of assessments and four forms of feedback on assessments on LLFXX Moodle page were studied. Four of these assessments are part of students’ continuous assessments which add up to 50% of their Course work. Three of the feedbacks on assessments are for these continuous assessed assignments and tests. 80 Blended mode students from the Laucala campus of the university participated in this research. LLFXX, which is a mandatory pre-degree English course was used for the purpose of this research. A quantitative research method was employed to analyse data. Data was extracted on Moodle logs of LLFXX Blended mode Laucala students and were analyzed using two statistical tests. Pearson’s chi-square test in SPSS was used to find the correlation between Moodle logs for assessments and feedback for assessments on LLFXX Moodle page. Pearson’s correlational coefficient test from SPSS was used to find the degree of association between Moodle logs for each of the assessments and their feedbacks in LLFXX. There was no correlation (statistically insignificant) found between the Moodle logs for test 2 for LLFXX and Moodle logs for feedback on tests. However, there was a statistically significant correlation found between the Moodle logs of other assessments and their feedback for LLFXX.
A Deep Learning Approach for Arabic Manuscripts Classification

For centuries, libraries worldwide have preserved ancient manuscripts due to their immense historical and cultural value. However, over time, both natural and human-made factors have led to the degradation of many ancient Arabic manuscripts, causing the loss of significant information, such as authorship, titles, or subjects, rendering them as unknown manuscripts. Although catalog cards attached to these manuscripts might contain some of the missing details, these cards have degraded significantly in quality over the decades within libraries. This paper presents a framework for identifying these unknown ancient Arabic manuscripts by processing the catalog cards associated with them. Given the challenges posed by the degradation of these cards, simple optical character recognition (OCR) is often insufficient. The proposed framework uses deep learning architecture to identify unknown manuscripts within a collection of ancient Arabic documents. This involves locating, extracting, and classifying the text from these catalog cards, along with implementing processes for region-of-interest identification, rotation correction, feature extraction, and classification. The results demonstrate the effectiveness of the proposed method, achieving an accuracy rate of 92.5%, compared to 83.5% with classical image classification and 81.5% with OCR alone.
US CIRCIA law will improve cybersecurity long-term

Significance It empowers the Cybersecurity and Infrastructure Security Agency (CISA) to develop detailed regulations about when and how cybersecurity incidents must be reported to the government. CIRCIA’s intention is to help collect more accurate data on breaches, identify trends and share threat information with the public and private sectors. Impacts CIRCIA will apply more broadly than any previous federal data breach rules, encompassing nearly all critical infrastructure sectors. The government will need to incentivise firms to submit detailed information about their security and breach following the initial filing. Cybersecurity compliance is a rising cost centre for US firms.
Research on the Design of Financial Management Model Based on SOM-PNN Driven by Digital Economy

This study proposes a novel financial risk prediction methodology by harnessing the power of self-organizing mapping (SOM) neural network and probabilistic neural network (PNN). The amalgamation of SOM and PNN's advantageous characteristics is seamlessly integrated into the algorithm posited within this paper. In order to collate and prognosticate data, the SOM network employs a two-dimensional topological framework comprising of two layers of neurons. Subsequently, the PNN model expeditiously furnishes the final classification outcomes by processing the output results obtained from the SOM model. The technique developed atop this composite model offers accelerated computation, effectively mitigates the impact of noisy samples, and significantly augments model accuracy. Finally, the effectiveness of the proposed method was demonstrated through a comprehensive financial risk analysis of listed companies from 2016 to 2020. The experimental results show that the SOM-PNN method has achieved high accuracy in predicting the financial difficulties experienced by traditional companies in the selected company samples, exceeding 85%. Especially when the sample data is insufficient, its accuracy reaches 80%, surpassing other algorithms. Statement: In the modern era, financial institutions use big data to perform background analysis and review, continuously optimize, and adjust, in order to introduce quantitative analysis methods into every link of risk management as far as possible. This allows financial institutions to quickly achieve balance in the game process of risk and income, and achieve Profit maximization in local or even more space.
Solving 2048-a detailed comparison of strategies

2048 is a mathematical puzzle game which although looks relatively simple with primitive and easy to understand rules. It has been proved that 2048 is Pspace Hard and also computationally np-hard in determining the end tile from a given starting position. Although there have been several attempts in solving 2048 using machine learning algorithms , none of them are easily human understand- able and some of them are also compromised in their consistency. We have thus created our own algorithm that is easier to understand and comprehend by human players. We have also performed a detailed comparison in the pattern strategies explored by most human players.We have concluded on the impor- tance of flexibility in solving the mathematical game. These strategies could be combined in further research to solve the np-hard game.