Reviewerly/exHarmony huggingface dataset:
MPFSIR: An Effective Multi-Person Pose Forecasting Model With Social Interaction Recognition

In recent years, multi-person pose forecasting has gained significant attention due to its potential applications in various fields such as computer vision, robotics, sports analysis, and human-robot interaction. In this paper, we propose a novel deep learning model for multi-person pose forecasting called MPFSIR (multi-person pose forecasting and social interaction recognition) that achieves comparable results with state-of-the-art models, but with up to 30 times fewer parameters. In addition, the model includes a social interaction prediction component to model and predict interactions between individuals. We evaluate our model on three benchmark datasets: 3DPW, CMU-Mocap, and MuPoTS-3D, compare it with state-of-the-art methods, and provide an ablation study to analyze the impact of the different model components. Experimental results show the effectiveness of MPFSIR in accurately predicting future poses and capturing social interactions. Furthermore, we introduce the metric MW-MPJPE to evaluate the performance of pose forecasting, which focuses on motion dynamics. Overall, our results highlight the potential of MPFSIR for predicting the poses of multiple people and understanding social dynamics in complex scenes and in various practical applications, especially where computational resources are limited. The code is available at https://github.com/RomeoSajina/MPFSIR.
Extracting Phonetic Posterior-Based Features for Detecting Multiple Sclerosis From Speech

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system which, in addition to affecting motor and cognitive functions, may also lead to specific changes in the speech of patients. Speech production, comprehension, repetition and naming tasks, as well as structural and content changes in narratives, might indicate a limitation of executive functions. In this study we present a speech-based machine learning technique to distinguish speakers with relapsing-remitting subtype MS and healthy controls (HC). We exploit the fact that MS might cause a motor speech disorder similar to dysarthria, which, with our hypothesis, might affect the phonetic posterior estimates supplied by a Deep Neural Network acoustic model. From our experimental results, the proposed posterior posteriorgram-based feature extraction approach is useful for detecting MS: depending on the actual speech task, we obtained Equal Error Rate values as low as 13.3%, and AUC scores up to 0.891, indicating a competitive and more consistent classification performance compared to both the x-vector and the openSMILE 'ComParE functionals' attributes. Besides this discrimination performance, the interpretable nature of the phonetic posterior features might also make our method suitable for automatic MS screening or monitoring the progression of the disease. Furthermore, by examining which specific phonetic groups are the most useful for this feature extraction process, the potential utility of the proposed phonetic features could also be utilized in the speech therapy of MS patients.
Robust Graph Filter Identification and Graph Denoising From Signal Observations

When facing graph signal processing tasks, it is typically assumed that the graph describing the support of the signals is known. However, in many relevant applications <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the available graph suffers from observational errors and perturbations</i> . As a result, any method that relies on the graph topology and ignores the presence of perturbations may yield suboptimal results. Motivated by this, we propose a novel approach for handling perturbations on the links of the graph and apply it to the problem of robust graph filter (GF) identification from input-output observations. Different from existing works, we formulate a non-convex optimization problem that operates in the vertex domain and jointly performs GF identification and graph denoising. As a result, on top of learning the desired GF, an estimate of the graph is obtained as a byproduct. To handle the resulting bi-convex problem, we design an algorithm that blends techniques from alternating optimization and majorization minimization, showing its convergence to a stationary point. The second part of the paper i) generalizes the design to a robust setup where several GFs are jointly estimated, and ii) introduces an alternative algorithmic implementation that reduces the computational complexity. Finally, the detrimental influence of the perturbations and the benefits resulting from the robust approach are numerically analyzed over synthetic and real-world datasets, comparing them with other state-of-the-art alternatives.
GRD: An Ultra-Lightweight SAR Ship Detector Based on Global Relationship Distillation

Most existing works on lightweight SAR ship detectors sacrifice a lot of detection accuracy to reduce model size. In this letter, we propose an ultra-lightweight detector based on distillation technology, which can reduce the parameter quantity of the model while minimizing the damage to the model’s detection accuracy. Due to the scattering interference and speckle noise in SAR images, directly applying the existing ultra-lightweight detectors cannot achieve satisfactory performance for ship detection. As a result, we design a global relationship distillation (GRD) algorithm for the ultra-lightweight SAR ship detector. This algorithm can preserve more global relationships from the teacher and mitigate the accuracy degradation caused by the noise and interference, especially in complex inshore scenarios. Besides, the features learned by this algorithm are robust, and the pruned model is more stable. The superiority of the GRD method over several state-of-the-art distillation methods has been evaluated on the HRSID dataset.
Period and Some Distribution Properties of a Nonlinear Filter Generator with Dynamic Mapping

This paper focuses on a pseudorandom number generator called an NTU sequence for use in cryptography. The generator is defined with an m-sequence and Legendre symbol over an odd characteristic field. Since the previous researches have shown that the generator has maximum complexity; however, its bit distribution property is not balanced. To address this drawback, the author introduces dynamic mapping for the generation process and evaluates the period and some distribution properties in this paper.
Secure Multi-Party Private Set Intersection with Semi-Honest Nodes

In this paper we consider a multi-party private set intersection system consisting of N source nodes, each has access to a private set, and a master who has access to a public set $\mathcal{A}$. In this system we assume that up to T of the sources are semi-honest, i.e., they follow the protocol but they may share their private data with each other to gain some information about the private set of the other nodes. The master is interested in finding the elements of $\mathcal{A}$ which appeared in at least K of the private set. First, the master sends the public set to the source nodes, and then each source sends a randomized function of its private set and the public set to each of the other sources, called its share. After that, the nodes start communicating with each other and processing their shares, and finally send some results to the master. In this paper, we propose a secure private set intersection scheme that orderwise reduces the communication and computation complexity compared to the conventional information-theoretic private set intersection schemes.
A Factual Sentiment Analysis on Instagram Data – A Comparative Study Using Machine Learning Algorithms

Social media is one of the most significant parts of our daily life. Our social media profiles are a reflection of our emotions. Instagram is the world's most popular photo-based social networking platform, with a reasonably high number of users ranging from regular people to artists, public figures, and top authorities. Users on Instagram may add captions to their images to make them more interesting. In this study, we are focusing on conducting sentiment analysis on Instagram captions by applying three different algorithms. We are concluding that the Logistic Regression algorithm is outperforming along with SMOTE and VADER compared to XG Boost and Random Forest algorithms. We started by acquiring data and dividing it down into little tokens, then we remove connection words and give clean data via the stop word removal mechanism. The cleaned data is then passed via the NLTK (Natural Language Toolkit) passer, which uses the VADER sentiment unit to produce sentiment based on the data. Then applying different algorithms XGBoost, Logistic Regression, and Random Forest on the produced sentiment. The accuracy of algorithms such as XGBoost, Logistic Regression, and Random Forest on sentiment data was also analyzed and tested and can be concluded that Logistic Regression performed well on these kinds of data with more accuracy. Through this work, the accuracy is lifted to a better level and thereby getting a truthful idea of the Instagram captions.
A survey paper on the latest techniques for implicit feature extraction using CCC method

Sentiment Analysis is gathering a lot of attention nowadays as a lot of online data is gathered through blogs, ecommerce websites, product reviews, etc. which people are expressing online. This data is extracted by companies to judge if their products are having a positive outlook or a negative outlook. However, when people express their opinions, they mention not only about the entity but also about the aspects of the entity. A lot of research has gone ahead on gathering opinions on aspects, especially explicit aspects. But little work is done on gathering implicit aspects. This paper provides a survey on different techniques used by researchers to gather implicit aspects. At the end, we propose a methodology to extract implicit aspects from reviews. We propose co-occurrence matrix for all opinions and aspects followed by clustering technique to gather all aspects which are similar in one cluster followed by classification using machine learning techniques. The proposed framework will give suggestions to different researchers in the domain on extracting implicit aspects.
Performance Evaluation of Classic McEliece Post Quantum Cryptography

Classic cryptography algorithms have become more fragile and vulnerable to modern attacks. The advancement of quantum computers and its attacks are now a big threat to the classic cryptography which uses hard mathematical calculation for security, that leads to the commence of the post quantum cryptography which are resistant to the quantum attacks. National Institute of Standardization and Technology is in process of standardizing the post quantum cryptography algorithm based on different factors like encapsulation, decapsulation time, keypair generation time, speed etc. In this paper we will discuss about the performance evaluation of post quantum cryptography algorithms especially the power consumption of Classic McEliece algorithm and its different variation which comes under code-based cryptography. The evaluated results are tabulated and categorized based on the different principles used and the factors leading to major power consumption is identified.
Secure Data Sharing Using Revocable-Storage Identity-Based Encryption

Distributed computing provides flexible and informative information exchange and brings many benefits to both the general public and individuals. However, because the information often contains the most important data, there is a common lock that allows users to easily reuse cloud host information. Therefore, it is important for users to provide cryptographically enhanced access control to shared resources. Personality-based encryption is, for example, a promising encryption approach for building smart data sharing systems. Anyway, access to management is not a versatile solution. In such cases, if your permission is revoked from the database, it must be completely revoked from the cloud server, and you will not be able to retrieve your information once it has been revoked. It has many features of realizability, usefulness, and effectiveness and thus shows the correlation that shows the proposed method diagram used to build a practical and intelligent data provider structure.
Drinfeld Modules in SageMath

We present the first implementation of Drinfeld modules fully integrated in the SageMath ecosystem. First features were released with SageMath 10.0.
Blockchain-based multi-diagnosis deep learning application for various diseases classification

Misdiagnosis is a critical issue in healthcare, which can lead to severe consequences for patients, including delayed or inappropriate treatment, unnecessary procedures, psychological distress, financial burden, and legal implications. To mitigate this issue, we propose using deep learning algorithms to improve diagnostic accuracy. However, building accurate deep learning models for medical diagnosis requires substantial amounts of high-quality data, which can be challenging for individual healthcare sectors or organizations to acquire. Therefore, combining data from multiple sources to create a diverse dataset for efficient training is needed. However, sharing medical data between different healthcare sectors can be problematic from a security standpoint due to sensitive information and privacy laws. To address these challenges, we propose using blockchain technology to provide a secure, decentralized, and privacy-respecting way to share locally trained deep learning models instead of the data itself. Our proposed method of model ensembling, which combines the weights of several local deep learning models to build a single global model, that enables accurate diagnosis of complex medical conditions across multiple locations while preserving patient privacy and data security. Our research demonstrates the effectiveness of this approach in accurately diagnosing three diseases (breast cancer, lung cancer, and diabetes) with high accuracy rates, surpassing the accuracy of local models and building a multi-diagnosis application.
Statistical analysis and forecasting of cotton yield dynamics in Bukhara region

Observations on some phenomena, the nature of which changes in time, are ordered sequences, which is called the time series. In the article, by the method of statistical analysis of time series, the statistical regularity of the series of dynamics of the average yield of cotton in the Bukhara region, the Republic of Uzbekistan (based on the materials of the CSO of the Republic of Uzbekistan for 2001-2019) was studied. Point and interval estimates for the average cotton yield were built with a 95% guarantee, explicit types of trends were determined, and yields in the region were predicted for subsequent years. With the help of Durbin-Watson statistical criteria, it was found that the average cotton yield in the region has an autocorrelation dependence.
Distribution of copper in intermediate rocks of Shaugaz-Kandyrsay interfluve (Eastern Almalyk)

The chemical composition of metasomatites and igneous rocks of the Almalyk ore area (Basin of Shaugaz-Kandir Rivers) and general statistical parameters of the distribution of chemical elements in the ore zones and upper rocks are given. High and low Clark concentrations of the elements in the study area were calculated. Discussed the future of using GIS in different spheres. GIS gives possibilities to collect the data, renew it or use new information in the analysis. It requires a quick change of GIS information about Earth because procedures on the Earth are dynamically changeable. Periodically changing information in GIS allows us to get new information and analyze it. GIS technologies and techniques started using widely in all spheres of humanity. It is important to know its properties.
Minerals of bismuth and antimony in original deposits of zarmitan gold zone, located in granitoid intrusion (Uzbekistan)

Modern methods of nanomineralogy (electron microscopy, electron probe microanalysis) were used to study the ores of one of the largest industrial facilities of Uzbekistan - the Zarmitan gold zone, which includes the Zarmitan, Urtalik, Guzhumsay deposits, which are located in the Koshrabadgranosyenite massif. The development of / Au-W / Au-Bi-Te / Au-As / Au-Ag-Te / Au-Ag-Se / Au-Sb-Ag / Au-Hg / types of ores. Productive mineral-geochemical types of ores are Au-Bi-Te gold-bismuth-telluride, represented by maldonite, tellurides, and sulfosalts of bismuth: hedleyite, joseite, tsumite, tetradymite, matildite, treasure, and also Au-Sb-Ag gold-silver-sulfoantimonide type represented by aurostibite, sulfoantimonidesPb, Fe, Ag: plagionite, jamsonite, boulangerite, goodmundite, ovichiite and gold-pyrite-arsenopyrite with nanogold, lellingite, gersdorfite. The main industrial resource of gold is provided by Au-Bi-Te, Au-Sb-Ag, and partially Au-As types. The objects of the Zarmitan zone belong to the orogenic gold deposits associated with the intrusion. The established mineral and geochemical features of ores are direct signs of prospecting, typification, and assessment of hidden gold mineralization of orogenic belts.
Modeling release and distribution of harmful substances in river

The development and analysis of a computational algorithm for mathematical models of the release and spread of harmful substances is an important area of research that can help mitigate the impact of pollution on the environment and human health. Mathematical models of the distribution of harmful substances in the river are useful for predicting the spread of pollutants and identifying potential environmental and public health risks. The computational algorithm for the problem of diffusion of harmful substances can help researchers and policymakers to understand the mechanisms of pollutant dispersion and identify effective strategies for mitigating the impact of pollution. The algorithm can simulate the release and spread of harmful substances in various scenarios, including accidental spills, industrial discharges, and natural disasters. The results of a computational experiment can provide valuable insights into the behavior of pollutants in different environmental conditions and help identify potential risks and areas of concern. By analyzing the results of a computational experiment, researchers can develop effective strategies for managing and mitigating the impact of pollution on the environment and public health.
Algorithms for calculating limits in water management in irrigation systems

In this article, the algorithms for calculating the limits in water resources management in irrigation systems are carried out, and the water management problems of the basin management of irrigation systems are studied distances from each other.
Uniaxial attitude control of uncrewed aerial vehicle with thrust vectoring under model variations by deep reinforcement learning and domain randomization

Abstract The application of neural networks for nonlinear control has been actively studied in the field of aeronautics. Successful techniques have been demonstrated to achieve improved control performance in simulation using deep-reinforcement learning. To transfer the controller learnt in the simulation of real systems, domain randomization is an approach that encourages the adaptiveness of neural networks to changing environments through training with randomized parameters in environments. This approach applies to an extended context, with changing working environments, including model configurations. In previous studies, the adaptive performance of the domain-randomization-based controllers was studied in a comparative fashion over the model variations. To understand the practical applicability of this feature, further studies are necessary to quantitatively evaluate the learnt adaptiveness with respect to the training conditions. This study evaluates deep-reinforcement-learning and the domain-randomization-based controller, with a focus on its adaptive performance over the model variations. The model variations were designed to allow quantitative comparisons. The control performances were examined, with a specific highlight of whether the model variation ranges fell within or exceeded the randomization range in training.
Quantum coherence and non-Markovianity in a noisy quantum tunneling problem

We investigate the coherence and non-Markovianity of a quantum tunneling system whose barrier is fluctuated by a telegraph noise, and its energy gap is modulated by Gaussian noise. With the help of averaging method, the system dynamics are analytically derived, and the analytical expression for coherence measure and non-Markovianity, focusing on a narrow range of parameter regimes for both initially coherent and non-coherent states are obtained. We observe non-Markovian dynamics in a situation where the Kubo number is high. It is also found that there is no strong relation between the coherence of the system and non-Markovian dynamics except a region in which these two tend to change their behavior at the intermediate noise color for two initial states.
Unmasking online fake job group financial scams: a thematic examination of victim exploitation from perspective of financial behavior

Purpose The purpose of this study is to critically examine a prevalent online scam mechanism, with the aim of understanding its exploitation of behavioral finance principles and group dynamics and propose effective countermeasures. Design/methodology/approach This study uses a blend of case study and thematic analysis, drawing from behavioral finance, social psychology and criminology, using primary source testimonies of victims to provide a detailed exploration of the scam’s operations. Findings This research uncovers the strategic use of four key principles: loss aversion, overconfidence, scarcity bias and social proof, within the scam operation. These tendencies are manipulated to induce victims to progressively invest into the fraudulent scheme, even amid growing suspicions. Research limitations/implications While the research elucidates on the workings of one specific online scam, it is necessary to explore if and how these principles are used in various other online fraudulent schemes, to develop comprehensive countermeasures. Practical implications The findings underscore the urgent need for enhanced public awareness, stronger corporate responsibility and robust regulatory oversight. There is a call for concerted efforts encompassing public education campaigns, fortified security protocols and strong legal frameworks for preventing such scams. Social implications The research emphasizes the need for collective action in ensuring a safe online space. This involves the individual user’s prudence, businesses’ proactive education initiatives and stringent legal actions against fraudulent activities. Originality/value This research offers novel insights into the nuanced manipulation of behavioral finance principles within online scams, based on empirical data from victim testimonials. These findings contribute to the understanding of the psychological mechanisms at play in online scams and are instrumental in formulating effective preventive strategies.