Reviewerly/exHarmony huggingface dataset:
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation

Monocular 3D human pose and shape estimation is an ill-posed problem since multiple 3D solutions can explain a 2D image of a subject. Recent approaches predict a probability distribution over plausible 3D pose and shape parameters conditioned on the image. We show that these approaches exhibit a trade-off between three key properties: (i) accuracy - the likelihood of the ground-truth 3D solution under the predicted distribution, (ii) sample-input consistency - the extent to which 3D samples from the predicted distribution match the visible 2D image evidence, and (iii) sample diversity - the range of plausible 3D solutions modelled by the predicted distribution. Our method, HuManiFlow, predicts simultaneously accurate, consistent and diverse distributions. We use the human kinematic tree to factorise full body pose into ancestor-conditioned per-body-part pose distributions in an autoregressive manner. Per-body-part distributions are implemented using normalising flows that respect the manifold structure of SO(3), the Lie group of per-body-part poses. We show that ill-posed, but ubiquitous, 3D point estimate losses reduce sample diversity, and employ only probabilistic training losses. Code is available at: https://github.com/akashsengupta1997/HuManiFlow.
Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory

The efficient computation of observables beyond the total energy is a key challenge and opportunity for fault-tolerant quantum computing approaches in quantum chemistry. Here we consider the symmetry-adapted perturbation theory (SAPT) components of the interaction energy as a prototypical example of such an observable. We provide a guide for calculating this observable on a fault-tolerant quantum computer while optimizing the required computational resources. Specifically, we present a quantum algorithm that estimates interaction energies at the first-order SAPT level with a Heisenberg-limited scaling. To this end, we exploit a high-order tensor factorization and block encoding technique that efficiently represents each SAPT observable. To quantify the computational cost of our methodology, we provide resource estimates in terms of the required number of logical qubits and Toffoli gates to execute our algorithm for a range of benchmark molecules, also taking into account the cost of the eigenstate preparation and the cost of block encoding the SAPT observables. Finally, we perform the resource estimation for a heme and artemisinin complex as a representative large-scale system encountered in drug design, highlighting our algorithm's performance in this new benchmark study and discussing possible bottlenecks that may be improved in future work.
Exploiting Diffusion Prior for Real-World Image Super-Resolution

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR). Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we employ a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches. Code and models are available at https://github.com/IceClear/StableSR.
Simple Token-Level Confidence Improves Caption Correctness

The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding. However, state-of-the-art models often misinterpret the correctness of fine-grained details, leading to errors in outputs such as hallucinating objects in generated captions or poor compositional reasoning. In this work, we explore Token-Level Confidence, or TLC, as a simple yet surprisingly effective method to assess caption correctness. Specifically, we fine-tune a vision-language model on image captioning, input an image and proposed caption to the model, and aggregate either algebraic or learned token confidences over words or sequences to estimate image-caption consistency. Compared to sequence-level scores from pretrained models, TLC with algebraic confidence measures achieves a relative improvement in accuracy by 10% on verb understanding in SVO-Probes and outperforms prior state-of-the-art in image and group scores for compositional reasoning in Winoground by a relative 37% and 9%, respectively. When training data are available, a learned confidence estimator provides further improved performance, reducing object hallucination rates in MS COCO Captions by a relative 30% over the original model and setting a new state-of-the-art.
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effective recommendation models. Basically speaking, these models mainly learn the underlying user preference from historical behavior data, and then estimate the user-item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs. The key idea is that the preferences or needs of a user can be expressed in natural language descriptions (called instructions), so that LLMs can understand and further execute the instruction for fulfilling the recommendation task. Instead of using public APIs of LLMs, we instruction tune an open-source LLM (3B Flan-T5-XL), in order to better adapt LLMs to recommender systems. For this purpose, we first design a general instruction format for describing the preference, intention, task form and context of a user in natural language. Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data (252K instructions) with varying types of preferences and intentions. To demonstrate the effectiveness of our approach, we instantiate the instruction templates into several widely-studied recommendation (or search) tasks, and conduct extensive experiments on these tasks with real-world datasets. Experiment results show that the proposed approach can outperform several competitive baselines, including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds light on developing more user-friendly recommender systems, in which users can freely communicate with the system and obtain more accurate recommendations via natural language instructions.
The Cardinality Bound on the Information Bottleneck Representations is Tight

The information bottleneck (IB) method aims to find compressed representations of a variable $X$ that retain the most relevant information about a target variable $Y$. We show that for a wide family of distributions -- namely, when $Y$ is generated by $X$ through a Hamming channel, under mild conditions -- the optimal IB representations require an alphabet strictly larger than that of $X$. This implies that, despite several recent works, the cardinality bound first identified by Witsenhausen and Wyner in 1975 is tight. At the core of our finding is the observation that the IB function in this setting is not strictly concave, similar to the deterministic case, even though the joint distribution of $X$ and $Y$ is of full support. Finally, we provide a complete characterization of the IB function, as well as of the optimal representations for the Hamming case.
Peculiarities of inclusive education of children with psychological development disorders in music schools and art schools

These graphic materials are a poster presentation for the III Congress with international participation "PSYCHOSOMATIC MEDICINE OF THE XXI CENTURY: REALITIES AND PERSPECTIVES". Which took place on November 4-6, 2022 in Kyiv on the Mediamed portal.
DESIGN OF IOT AND ONION AGRICULTURE DATABASE USING BPR LIFE CYCLE

One of the food commodities produced by the agricultural sector with high economic value is red onion. As the population of Indonesia increases, the need for red oniom has also increased. The level of red onion production from year to year is also increasing. Especially the central Java area as the largest red onion producing center in 2021. Therefore, the amount of red onion production needs to be maintained and increased by monitoring overall land conditions. Such as weather conditions, air, temperature, and humidity. A sensor to detect these factors is already available but there is no database to accommodate the data from the sensor. The purpose of this research is to produce a Business Process Model and Notation (BPMN) of red onion surveillance system on Internet of Things (IoT) based farmland. The stages carried out are by collecting data related to the research and analyzing business processes using the Business Process Reengineering Life Cycle (BPR) method. This method improves business processes to become more efficient and renewable. This research produces a database design to accommodate incoming data from Internet of Things sensors. Things (IoT) on red onion farming.
Deep Neural Network-Based Dynamical Object Recognition and Robust Multiobject Tracking Technique for Onboard Unmanned Aerial Vehicle’s Computer Vision-Based Systems

Computer vision-based systems seem highly perspective for semantic analysis of the dynamical objects. However, considering dynamical object recognition and tracking from the unmanned aerial vehicle (UAV) the task to design a robust model for data association is highly challenging due to additional issues, e.g., image degradation, nonfixed object camera distance and shooting focus, and real-time issues. Thus, we propose an accurate deep neural network-based dynamical object recognition and robust multiobject tracking technique based on bidirectional LSTM with the optimized motion and appearance gates as a multiobject tracking backbone, supported by an advanced single-shot detector network improved with residual prediction model and implemented a DenseNet network as well as a YOLOv4eff network as feature extraction. The technique has been trained on VisDrone 2022 and UAVDT datasets with the side-shoot dynamical objects at a height of up to 50 m. The performance analysis on the test stage performed on seven metrics demonstrate that the proposed technique surpasses, by accuracy and robustness ability, other state-of-the-art techniques based on two cumulative MOTA and MOTP, as well as MT and IDsw. In particular, we have dramatically decreased the number of IDsw which implies a better capability to handle several occlusions, which is a desirable property in real-time multiple object tracking. We have pointed out the sensitivity of the tracking performance of our technique on the number of utilizing different sequence lengths and have defined an optimum. Finally, the applicability and reliability of the proposed technique for onboard UAV computer-based systems have been discussed.
Resource trading and miner competition in wireless blockchain networks with edge computing

To promote the application of edge computing in wireless blockchain networks, this paper presents a business ecosystem, where edge computing is introduced to assist blockchain users in implementing the mining process. This paper exploits resource trading and miner competition to enable secure and efficient transactions in the presented business ecosystem. The resource trading problem is formulated as a Stackelberg game between miner candidates and edge computing servers, where computing, caching, and communication resources are jointly optimized to maximize the potential profit. Partial offloading is introduced to further enhance the system performance when compared with the existing work. We analyze the existence and uniqueness of the Nash equilibrium and Stackelberg equilibrium. Based on the optimization result, winners are selected from the set of miner candidates by bidding and constitute the mining network. Simulation results demonstrate that the proposal is able to improve the social welfare of blockchain miners, thus stimulating more blockchain users to join the mining network.
A STUDY OF ELECTRONIC SIGNATURE HANDWRITING UNDER FORCE FEEDBACK PERCEPTION

This paper investigates the dynamic characteristics related to thewriting duration, pressure, and velocity of handwriting using ahaptic device with force feedback for electronic signature and dataacquisition.
Blind image quality assessment by pairwise ranking image series

Image quality assessment (IQA) is constantly innovating, but there are still three types of stickers that have not been resolved: the "content sticker" — limitation of training set, the "annotation sticker" — subjective instability in opinion scores and the "distortion sticker" — disordered distortion settings. In this paper, a No-Reference Image Quality Assessment (NR IQA) approach is proposed to deal with the problems. For "content sticker", we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network; For "annotation sticker", the absolute noise-containing subjective scores are transformed into ranking comparison results, and we design an indirect unsupervised regression based on Eigenvalue Decomposition (EVD); For "distortion sticker", we propose a perception-based distortion classification method, which makes the distortion types clear and refined. Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability. Furthermore, the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.
TECHNOLOGIES OF SYNCHRONOUS AND ASYNCHRONOUS LEARNING IN THE PRACTICE OF DISTANCE TEACHING OF GEOGRAPHICAL DISCIPLINES

Modern global challenges caused by the Covid-19 pandemic, which have been exacerbated by the martial law imposed in Ukraine for almost six months, have determined distance learning technologies with the help of information and communication technologies to be the only alternative to traditional education. The article is devoted to the analysis of synchronous and asynchronous learning technologies in the practice of distance teaching of geographical disciplines in higher education institutions. The article substantiates the expediency of combining both modes of online education organizing, the main goal of which is the formation of nationwide access to educational resources for all students. The application of specialized programs, principles, and trends of global teaching practices for the distribution of materials for teaching geographic disciplines on a digital platform is considered. The effectiveness and risks of synchronous and asynchronous combination in the practice of distance teaching of geographical disciplines are analyzed, based on the main principles of cooperation methods. As a result, it is claimed that the combination of synchronous and asynchronous modes during the implementation of the educational process of studying geographic disciplines remotely allows: 1) to receive quickly available materials; 2) to form a persistent interest in finding and solving problematic issues in the chosen specialty in combination with the study of geographical disciplines; 3) to develop soft skills and motivate students to informal education, as well as support the student community through the presence of cognitive, social and educational elements.
PHYSICAL EDUCATION CLASSES POTENTIAL FOR DEVELOPING STUDENTS� RESPONSIBILITY

Developing personal responsibility is crucial in all spheres of life. Since young people represent the future of the world and are the most active part of population it is topical to develop students� responsibility and Physical Education classes have great potential for it. So, the article is dedicated to the problem of developing university students� responsibility in Physical Education classes. The purpose of the article is to verify experimentally effectiveness of the methodological instruments of partnership interaction in Physical Education classes in order to develop college students� responsibility. The methods of the investigation were general research methods of theoretical level: analysis and synthesis of related researches; empirical level methods: testing, interview, ranking, method of expert assessments, pedagogical experiment, numerous diagnostic techniques. The material of the research involved 215 students of Kharkiv Cooperative Trade and Economics Special College (Ukraine): students of the experimental group (n=100) and the control group (n=115), who attend Physical Education classes as part of the main group. The results of the research have shown the change in the level of developing students� responsibility in the experimental group due to methodological instruments of partnership interaction in Physical Education classes. In the control group there was no mentioned purposeful activity. Overall increase in the average coefficient of responsibility development was 0.20 in the experimental group and 0.05 in the control group.
The Deep Learning Approach For American Sign Language Detection

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of Computer Science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.
THEME AND RHEME IN PETERPAN NOVEL

This study deals with the Theme and Rheme in Peterpan novel. The objective of this study was to find out the kinds and the dominant kind of Theme and Rheme in Peterpan novel. This study was conducted by applying descriptive qualitative research design. The data of this study were taken from Peterpan novel chapter I until chapter XVII. It was found that there were 6 kinds of Theme and Rheme in this study, namely: Simple Theme was 11 clauses or 16.41%, Multiple Theme was 9 clauses or 13.43%, Unmarked Simple Theme was 17 clauses or 25.37%, Unmarked Multiple Theme was 18 clauses or 26.86%, Marked Simple Theme was 2 clauses or 2.98%, and Marked Multiple Theme was 10 clauses or 14.92%. It meant that Unmarked Multiple Theme was the dominant kind in this study because Unmarked Multiple Theme was easier to search in each clause and can be understood by the readers.
IMPROVING STUDENTS’ ACHIEVEMENT IN WRITING DESCRIPTIVE THROUGH COLLABORATIVE WRITING STRATEGY

The objective of this research is to improve students’ achievement in writing descriptive through collaborative writing. This research was conducted at SMP Negeri 28 Medan academic year 2018-2019 as the subject of researcher. The subject of this research consisted of 35 students of the eighth grade. The research was conducted with classroom action research. The research was conducted by dividing into two cycles with 6 meeting for each. The sources of data were qualitative and quantitative. The qualitative data were collected by observation sheets, questionnaires and diary notes. Quantitative data were taken from tests, carried out at the end of every cycle. Based on the result of the data analysis there was an improvement of the result of the students’ achievement in writing descriptive text from each cycle. After doing test on cycle I, there was an improvement of the result of the students’ mean was 72.8 with 24 students who got successful score criteria above 70 or it was 69 % and 11 students who got unsuccessful score criteria above 70 or it was 31 %. Then, an improvement was done in second cycle after reflection in the first cycle, there was an improvement of students’ mean was 77.9 with 31 students who got successful score criteria above 70 or it was 89 % and 4 students who got unsuccessful score criteria above 70 or it was 11 %. In other words, the students’ achievement at writing descriptive text was improved. And based on questionnaire sheets, observation sheets, diary notes, and documentation, it showed that the expression and excitement of the students were also improved.
THE STUDENTS’ ABILITY TO IDENTIFY NOMINAL AND VERBAL SENTENCES IN ENGLISH OF GRADE VIII

This study was conducted by using descriptive qualitative design. The objective of this study was to find out the students’ ability in identifying nominal and verbal sentences. The subject of the research was of grade VIII at SMP Teladan Sei Rampah academic year 2018-2019. The total number the subject was 20 students. To obtain data, a grammar test was administered to the students. After the data had been analyzed, it was found that the students were able to identify nominal and verbal sentences in English. Thus, it was concluded that the students’ of grade VIII at SMP Teladan Sei Rampah academic year 2018-2019 were able to identify nominal and verbal sentences in English.
THE CORRELATION BETWEEN STUDENTS MOTIVATION AND THEIR ACHIEVEMENT SCORE IN ENGLISH LEARNING

This journal discussed the correlation between students’ motivation and their achievement score in English learning for grade VIII students at SMP Teladan Sei Rampah. This study was conducted by using descriptive quantitative research design. The instrument for the data collection was questioner. The population of this study was 20 students. The result of data processing the correlation coefficient number was 0.723 and it was included in the strong category. Thus, it can be concluded that there was a correlation between students’ motivation and learning outcome in English grammar of the grade VIII students and this correlation was positive.
GRAMMAR ERROR BY STUDENTS IN TRANSLATING INDONESIAN INTO ENGLISH

This research was to analyze the students’ grammar errors on the use of present tense and past tense in translating Indonesian sentence into English. The aims were to find out what errors were made by the students and what error was the dominant one. The method used was descriptive qualitative. It was conducted at SMP Swasta Dwitunggal Tanjung Morawa in academic year 2018 – 2019. The population was the students of the eighth grade consisted of 3 classes with the total number of population was 138. The sample was the class VIII-3 consisted of 41 students which was obtained through cluster random sampling with lottery technique. The instrument used was a translation test which consisted of 20 items of Indonesian sentence. Through the data analysis, it was obtained that there were four types of errors made by the eighth grade students, namely error of omission (17.22%), error of addition (32.05%), error of miss-formation (34.44%), and error of miss-ordering (12.91%). The most dominant error was error of miss-formation.