Reviewerly/exHarmony huggingface dataset:
Completing the Fifth PN Precision Frontier via the EFT of Spinning Gravitating Objects

We put forward a broader picture of the effective theory of a spinning particle within the EFT of spinning gravitating objects, through which we derive and establish the new precision frontier at the fifth PN (5PN) order. This frontier includes higher-spin sectors, quadratic and quartic in the spin, which both display novel physical features, due to the extension of the effective theory beyond linear order in the curvature. The quadratic-in-spin sectors give rise to a new tidal effect, and the quartic-in-spin sectors exhibit a new multipolar deformation. We then generalize the concept of tidal operators and of spin-induced multipolar operators, and make conjectures on the numerical values of their Wilson coefficients, and on the effective point-particle action of Kerr black holes. We confirm the generalized actions for generic compact binaries of the NLO quartic-in-spin sectors which were derived via the extension of the EFT of gravitating spinning objects. We first present the corresponding interaction potentials and general Hamiltonians, which consist of 12 distinct sectors, with a new one due to the new multipolar deformation. These Hamiltonians give the full physical information on the binary system, which mostly gets lost in higher-spin sectors, when going to the aligned-spins configuration. Moreover these general Hamiltonians uniquely allow us to find the complete Poincaré algebra at the 5PN order with spins, including the third subleading quadratic-in-spin sectors. We derive consequent observables for GW applications. Finally, to make contact with the scattering problem, we also derive the extrapolated scattering angles for aligned spins. Our completion of the Poincaré algebra provides the strongest validation of our most comprehensive new results, and thus that the 5PN order has now been established as the new precision frontier.
Strain measurement by contour analysis

Background: The determination of yield stress curves for ductile metals from uniaxial material tests is complicated by the presence of tri-axial stress states due to necking. A need exists for a straightforward solution to this problem. Objective: This work presents a simple solution for this problem specific to axis-symmetric specimens. Equivalent uniaxial true strain and true stress, corrected for triaxiality effects, are calculated without resorting to inverse analysis methods. Methods: A computer program is presented which takes shadow images from tensile tests, obtained in a backlight configuration. A single camera is sufficient as no stereoscopic effects need to be addressed. The specimen's contours are digitally extracted, and strain is calculated from the contour change. At the same time, stress triaxiality is computed using a novel curvature fitting algorithm. Results: The method is accurate as comparison with manufactured solutions obtained from Finite Element simulations show. Application to 303 stainless steel specimens at different levels of stress triaxiality show that equivalent uniaxial true stress -- true strain relations are accurately recovered. Conclusions: The here presented computer program solves a long-standing challenge in a straightforward manner. It is expected to be a useful tool for experimental strain analysis.
A Performance Study of Variational Quantum Algorithms for Solving the Poisson Equation on a Quantum Computer

Recent advances in quantum computing and their increased availability has led to a growing interest in possible applications. Among those is the solution of partial differential equations (PDEs) for, e.g., material or flow simulation. Currently, the most promising route to useful deployment of quantum processors in the short to near term are so-called hybrid variational quantum algorithms (VQAs). Thus, variational methods for PDEs have been proposed as a candidate for quantum advantage in the noisy intermediate scale quantum (NISQ) era. In this work, we conduct an extensive study of utilizing VQAs on real quantum devices to solve the simplest prototype of a PDE -- the Poisson equation. Although results on noiseless simulators for small problem sizes may seem deceivingly promising, the performance on quantum computers is very poor. We argue that direct resolution of PDEs via an amplitude encoding of the solution is not a good use case within reach of today's quantum devices -- especially when considering large system sizes and more complicated non-linear PDEs that are required in order to be competitive with classical high-end solvers.
Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning

We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, and numerically, via Monte Carlo simulations. In particular, we obtain a phase diagram summarizing their performance as a function of the control parameters such as the quality and quantity of the training dataset and the network storage, valid in the limit of large network size and structureless datasets. Moreover, we establish a bridge between macroscopic observables standardly used in statistical mechanics and loss functions typically used in the machine learning. As technical remarks, from the analytic side, we implement large deviations and stability analysis within Guerra's interpolation to tackle the not-Gaussian distributions involved in the post-synaptic potentials while, from the computational counterpart, we insert Plefka approximation in the Monte Carlo scheme, to speed up the evaluation of the synaptic tensors, overall obtaining a novel and broad approach to investigate neural networks in general.
Interaction Region Visual Transformer for Egocentric Action Anticipation

Human-object interaction is one of the most important visual cues and we propose a novel way to represent human-object interactions for egocentric action anticipation. We propose a novel transformer variant to model interactions by computing the change in the appearance of objects and human hands due to the execution of the actions and use those changes to refine the video representation. Specifically, we model interactions between hands and objects using Spatial Cross-Attention (SCA) and further infuse contextual information using Trajectory Cross-Attention to obtain environment-refined interaction tokens. Using these tokens, we construct an interaction-centric video representation for action anticipation. We term our model InAViT which achieves state-of-the-art action anticipation performance on large-scale egocentric datasets EPICKTICHENS100 (EK100) and EGTEA Gaze+. InAViT outperforms other visual transformer-based methods including object-centric video representation. On the EK100 evaluation server, InAViT is the top-performing method on the public leaderboard (at the time of submission) where it outperforms the second-best model by 3.3% on mean-top5 recall.
Spin-augmented observables for efficient photonic quantum error correction

We demonstrate that the spin state of solid-state emitters inside micropillar cavities can serve as measure qubits in syndrome measurements. The photons, acting as data qubits, interact with the spin state in the microcavity and the total state of the system evolves conditionally due to the resulting circular birefringence. By performing a quantum non-demolition measurement on the spin state, the syndrome of the optical state can be obtained. Furthermore, due to the symmetry of the interaction, we can alternatively choose to employ the optical states as measure qubits. This protocol can be adapted to various resource requirements, including spectral discrepancies between the data qubits and codes with modified connectivities, by considering entangled measure qubits. Finally, we show that spin-systems with dissimilar characteristic energies can still be entangled with high levels of fidelity and tolerance to cavity losses in the strong coupling regime.
Group Buying Recommendation Model Based on Multi-task Learning

In recent years, group buying has become one popular kind of online shopping activity, thanks to its larger sales and lower unit price. Unfortunately, research seldom focuses on recommendations specifically for group buying by now. Although some recommendation models have been proposed for group recommendation, they can not be directly used to achieve real-world group buying recommendation, due to the essential difference between group recommendation and group buying recommendation. In this paper, we first formalize the task of group buying recommendations into two sub-tasks. Then, based on our insights into the correlations and interactions between the two sub-tasks, we propose a novel recommendation model for group buying, MGBR, built mainly with a multi-task learning module. To improve recommendation performance further, we devise some collaborative expert networks and adjusted gates in the multi-task learning module, to promote the information interaction between the two sub-tasks. Furthermore, we propose two auxiliary losses corresponding to the two sub-tasks, to refine the representation learning in our model. Our extensive experiments not only demonstrate that the augmented representations in our model result in better performance than previous recommendation models, but also justify the impacts of the specially designed components in our model.
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding

The 3D visual grounding task has been explored with visual and language streams comprehending referential language to identify target objects in 3D scenes. However, most existing methods devote the visual stream to capturing the 3D visual clues using off-the-shelf point clouds encoders. The main question we address in this paper is "can we consolidate the 3D visual stream by 2D clues synthesized from point clouds and efficiently utilize them in training and testing?". The main idea is to assist the 3D encoder by incorporating rich 2D object representations without requiring extra 2D inputs. To this end, we leverage 2D clues, synthetically generated from 3D point clouds, and empirically show their aptitude to boost the quality of the learned visual representations. We validate our approach through comprehensive experiments on Nr3D, Sr3D, and ScanRefer datasets and show consistent performance gains compared to existing methods. Our proposed module, dubbed as Look Around and Refer (LAR), significantly outperforms the state-of-the-art 3D visual grounding techniques on three benchmarks, i.e., Nr3D, Sr3D, and ScanRefer. The code is available at https://eslambakr.github.io/LAR.github.io/.
FutureHuman3D: Forecasting Complex Long-Term 3D Human Behavior from Video Observations

We present a generative approach to forecast long-term future human behavior in 3D, requiring only weak supervision from readily available 2D human action data. This is a fundamental task enabling many downstream applications. The required ground-truth data is hard to capture in 3D (mocap suits, expensive setups) but easy to acquire in 2D (simple RGB cameras). Thus, we design our method to only require 2D RGB data while being able to generate 3D human motion sequences. We use a differentiable 2D projection scheme in an autoregressive manner for weak supervision, and an adversarial loss for 3D regularization. Our method predicts long and complex behavior sequences (e.g. cooking, assembly) consisting of multiple sub-actions. We tackle this in a semantically hierarchical manner, jointly predicting high-level coarse action labels together with their low-level fine-grained realizations as characteristic 3D human poses. We observe that these two action representations are coupled in nature, and joint prediction benefits both action and pose forecasting. Our experiments demonstrate the complementary nature of joint action and 3D pose prediction: our joint approach outperforms each task treated individually, enables robust longer-term sequence prediction, and outperforms alternative approaches to forecast actions and characteristic 3D poses.
Digital Image Cryptography using Lookup Table

Protecting digital images is an important task, because images may be secret or may be contain valuable secret information. In this paper research a simple, efficient and high secure method of digital images protection will be introduced. The method will use a chaotic logistic map model with a selected chaotic parameter to generate a lookup table, which will be used in the encryption and decryption phases. The proposed method can use one lookup table to encrypt-decrypt color images, or it can use three lookup tables to encrypt-decrypt each color channel. The method will secure the encrypted image by using a complicated private key, which will provide a suitable key space capable to resist hacking attacks, the method outputs will be very sensitive to the selected private key contents, any minor changes in these contents in the decryption phase will be considered as a hacking attempt by producing a damaged decrypted image. It will be shown how using lookup table simplifies the process of image cryptography using the indexes and the indexes contents of the lookup table. The proposed method will be implemented and the obtained results will be analyzed to prove the quality of encryption and decryption phases based on the calculated MSE, PSNR, CC and NSCR. The speed of the proposed method will be tested, to show how the proposed method will increase the throughput of data cryptography by decreasing both the encryption and decryption times.
Webs of Proximity and Just-in-Time Information

Disciplinary webs of proximity frequently overlap at the periphery of a topic where interests intersect for problem-solving. Failure to account for disciplinary differences can result in dis-ease – tension that interferes with meaning-making. This can be especially problematic in just-in-time information settings. An unexpected social media case study involving severe weather reporting and algorithm-driven system censorship makes evident the role of a constellation of pragmatic factors that can enhance or hinder just-in-time information delivery. Employing webs of proximity, we probe the severe weather censorship event with complementary bodies of knowledge and disciplinary perspectives. Intersectionalities are discussed through lenses of proximity and epidata. Entanglements of commonality between differing web plots are represented in a negotiation vestibule. The possibility of the communication channel itself being noise is presented. The vestibule highlights opportunities for negotiation points to attempt functional meaning-making.
“Arsip Kacau”: How Messy Records Are Perceived by Indonesians in the Context of Document Disease

This paper examines how the Indonesian people perceive and construct arsip kacau (messy records). Messy records as documents seem to have the power to make humans psychologically ill by becoming uneasy, discomfort, stressed, burnout, and uncomfortable, all of which are diseases. This is similar to documentality introduced by Frohmann that documents can enable, produce, encourage, permit, and influence a person to experience document disease. This paper uses a qualitative approach with data collection in the form of literature and surveys from Indonesian people. The perception of archivists and the general public in Indonesia concerning the cause of the “messy records” is that archivists emphasize the absence of a system. In contrast, the general public emphasizes that human resources with minimal knowledge cause messy records and the absence of a system and standard operating procedures. In addition, they also mentioned that it would impact organizational performance and concentration at work, stress, lost and mixed files, unorganized rooms, fatigue, anxious thoughts, decision-making, and dirty organizing records. This finding can be developed further to explore the impact of the causes of the emergence of messy records in archival work which causes various feelings of records/archives users ranging from messy, disordered or even trauma-informed practice in archival work.
Technology Report: Optical Bar Code for Internet Access Application based on Optical camera communication and Bluetooth Control

&lt;p&gt;We demonstrate an internet access application based on optical camera communication and bluetooth. The app will access the website while the camera in the phone receives the optical signal. © 2022 The Author(s)&lt;/p&gt;
Data Hiding Using Image Processing

The data hiding using image processing can be done by steganography. The word “steganography” means covered in hidden writing. Using steganography information can be hidden in carriers such as images, audio files, text files, videos etc. In this study, we proposed a new framework of an image steganography system to hide a text in an image.
H-Onto: An Efficient Ontology Information Retrieval Method using Web Protege

This article presents an efficient and unique ontology information retrieval using web protegeby taking into account the modelling, processing, and conversion of ontological knowledge into database search request components of ontology. It mainly focuses on the existing methodologies of ontology retrieval approaches in terms of loss of data and semantics, structural mapping and applicable to domain integrity. The main objective of this article is to apply the ontological approach on the dataset for retrieval of information to produce more efficient information retrieval from knowledge graph which is termed as H-Onto.
Sentiment Analysis using SVM-based SSO Intelligence Algorithm

Facebook and Twitter, as two known social media become popular sources of big data that give the right to people to share and express their feedback about products, services, politicians, events, and every aspect of life in the form of short texts. The classification of sentiments could be automated through machine learning and enhanced using appropriate feature extraction methods. In this work, we collected the most recent tweets about (Biden, Benzema, Apple, and NASA) using Twitter-API and assigned sentiment scores using a rule-based lexicon approach; after pre-processing stage, each dataset is divided into 80% as a training set, and rest 20% as testing set. After that, the Distributed bag of words, Distributed memory mean, Distributed Memory Concatenation, and Term Frequency-Inverse Document Frequency models are used for feature extraction from pre-processed tweets. Depending on the Shark smell optimizer algorithm, the SVM technique was used to classify the extracted features. The SSO was used to tune and select the best value for SVM parameters to optimize the overall model performance. The results display that these optimizers have an essential impact on increasing the model accuracy. After optimization, the model accuracy reached 92.12%, while the highest accuracy without optimization was 88.69% for various feature extraction methods.
Mark-Virtual Friend using Python

In the present era life has become smarter and more advanced. We are already familiar with some voice services like Google assistant, Siri and Alexa etc. Now in our voice support system, it can work like automatic chrome, opens youtube from a web browser, and sign into the gmail account and helps in locking the window. Mark works by entering voice and rendering voice output and displaying text on the screen. Our project's main agenda is to make work easier by delivering faster results with a computer. It compares voice input with our microphones and processes the given commands and provides necessary solutions and answers that users ask the microphone and processes the given commands and provides necessary solutions and answers that users ask. Speech Recognition translates inputted voice into text enables communication with the computer.
Understanding Distributed Leadership and Insights for Chinese Educational Institutions in the Context of Digital Transformation: A Literature Review

When education across all levels, is no exception for meeting the needs of industry 4.0 and the new demand of the digital economy and society, distributed leadership is an effective reform strategy for organization's transition to digital transformation. 174 articles related to distributed leadership were selected from eight core-international journals in the field of educational leadership and management with an average h-index of 45, and 64 articles with the keywords of distributed leadership published in the CSSCI and core journals were found. The 248 articles in total were reviewed for analysis with three aspects (research themes and theories; research methodology and analytical methods; discovery and revelation) which were synthesized from the systematic conceptual framework of literature review by Hallinger (2013,2014), the research conclusion frameworks by Bennett et al. (2003) and Tian et al. (2016). The literature review was conducted on four aspects (who, why, what and how) for knowing which most scholars are concerned and for informing educational institutions with insights on distributed leadership for future development.
A Low-rank Approximation for MDPs via Moment Coupling

We introduce a framework to approximate a Markov Decision Process that stands on two pillars: state aggregation -- as the algorithmic infrastructure; and central-limit-theorem-type approximations -- as the mathematical underpinning of optimality guarantees. The theory is grounded in recent work Braverman et al (2020} that relates the solution of the Bellman equation to that of a PDE where, in the spirit of the central limit theorem, the transition matrix is reduced to its local first and second moments. Solving the PDE is $\textit{not}$ required by our method. Instead, we construct a "sister" (controlled) Markov chain whose two local transition moments are approximately identical with those of the focal chain. Because of this $\textit{moment matching}$, the original chain and its "sister" are coupled through the PDE, a coupling that facilitates optimality guarantees. Embedded into standard soft aggregation algorithms, moment matching provided a disciplined mechanism to tune the aggregation and disaggregation probabilities. The computational gains arise from the reduction of the effective state space from $N$ to $N^{\frac{1}{2}+\epsilon}$ is as one might intuitively expect from approximations grounded in the central limit theorem.
PELATIHAN KIAT MENJADI PEMBELAJAR BAHASA INGGRIS YANG LEBIH BERHASIL BAGI SISWA SMA/SMK DI KOTA PALEMBANG

English is an international language in Indonesia. It is important for students to master English because of its very central role in every aspect of this country. SMA and SMK students in Palembang City should prepare more seriously for their English language skills to face a better future. The English they study will have a very significant effect when they continue their education to tertiary level or enter the world of work. In this activity, the Darussalam Polytechnic lecturers, especially the lecturers for the English course, provided very important information for students in Palembang City to be successful in mastering English. This activity was carried out through a Zoom meeting with 40 participants consisting of high school and vocational students in Palembang City. This activity was carried out for 1 day on September 4 2022