Reviewerly/exHarmony huggingface dataset:
Sapling Similarity: A performing and interpretable memory-based tool for recommendation

Many bipartite networks describe systems where an edge represents a relation between a user and an item. Measuring the similarity between either users or items is the basis of memory-based collaborative filtering, a widely used method to build a recommender system with the purpose of proposing items to users. When the edges of the network are unweighted, the popular common neighbors-based approaches, allowing only positive similarity values, neglect the possibility and the effect of two users (or two items) being very dissimilar. Moreover, they underperform with respect to model-based (machine learning) approaches, although providing a higher interpretability. Inspired by the functioning of Decision Trees, we propose a method to compute similarity that allows also negative values, the Sapling Similarity. The key idea is to look at how the information that a user is connected to an item influences our prior estimation of the probability that another user is connected to the same item: if it is reduced, then the similarity between the two users will be negative, otherwise it will be positive. We show that, when used to build memory-based collaborative filtering, Sapling Similarity provides better recommendations than existing similarity metrics. Then we compare the Sapling Similarity Collaborative Filtering (SSCF, an hybrid of the item-based and the user-based) with state-of-the-art models using standard datasets. Even if SSCF depends on only one straightforward hyperparameter, it has comparable or higher recommending accuracy, and outperforms all other models on the Amazon-Book dataset, while retaining the high explainability of memory-based approaches.
AC-E Network: Attentive Context-Enhanced Network for Liver Segmentation

Segmentation of liver from CT scans is essential in computer-aided liver disease diagnosis and treatment. However, the 2DCNN ignores the 3D context, and the 3DCNN suffers from numerous learnable parameters and high computational cost. In order to overcome this limitation, we propose an Attentive Context-Enhanced Network (AC-E Network) consisting of 1) an attentive context encoding module (ACEM) that can be integrated into the 2D backbone to extract 3D context without a sharp increase in the number of learnable parameters; 2) a dual segmentation branch including complemental loss making the network attend to both the liver region and boundary so that getting the segmented liver surface with high accuracy. Extensive experiments on the LiTS and the 3D-IRCADb datasets demonstrate that our method outperforms existing approaches and is competitive to the state-of-the-art 2D-3D hybrid method on the equilibrium of the segmentation precision and the number of model parameters.
Quantum-safe three-party lattice based authenticated key agreement protocol for mobile devices

Mobile-based web apps have gained popularity among phone devices because of the rapid advancement of mobile communication and equipment capabilities. Users may utilize their handsets to obtain different types of online services through the Internet at any time and from anywhere. The last three decades have seen widespread use of the three-party AKA protocol, built on asymmetric-key cryptography to produce a private communication channel via any cellular connection between two mobile users. The server can help two mobile users with the assistance of a three-party authentication protocol so that they may create a high-entropy shared private session key. According to several recent studies, all the conventional public-key cryptosystem-based three-party AKA schemes could not withstand attacks due to quantum computing. To get resistance to quantum attacks, Islam and Basu (2021) proposed a new password-based three-party authentication protocol for mobile users using ideal lattices. We improve the efficiency of lattice-based authenticated key exchange protocol and the security of this protocol by making it resistant to password-guessing attacks. A comparative study along with performance and security evaluation is also presented, which demonstrates the applicability of the proposed design.
Model "Triple Helix" in Optics of Expert Estimates of Scientific and Pedagogical Community of the Russian Higher Education Institutions

Relevance . The model of " triple helix" is more than 10 years a subject of theoretical judgment and development of applied techniques of her assessment and realization. However, philosophical and sociocultural aspects of her functioning, and also reflexive features among institutional agents remain on the periphery of the scientific analysis. The purpose an objective of this research is the analysis of expert positions of representatives of scientific and pedagogical community of higher education institutions about separate aspects of functioning of model of " triple helix". Objectives: To reveal expert estimates of representatives of scientific and pedagogical community of higher education institutions about separate aspects of functioning of model of "triple helix". Methodology . Research is executed on the basis of existing techniques of an assessment of innovative development of subjects of " triple helix " and empirical methods of poll. Results . Among barriers and contradictions in "spiral" integration of institutes of the power, business and science / education on the basis of expert positions are defined: 1) for the power ‒ vertical isolation and low density of horizontal cross-contacts; 2) for business ‒ scientific researches are focused on short-term prospect, values of a private property; 3) for science / education ‒ scientific researches are focused on long-term prospect, the principles of universality of scientific knowledge, objects of intellectual property. Conclusions . As a result of a situation of civilization and cultural antagonism of social codes "traditions and innovations" the functional mimicry of institutes can act, is actually realized in morphology of values traditionally Russian bureaucracies – a paternalism – corruption. Such imitations can be characterized as the institutional mutations arising in the conditions of an anomy when there is a deconstruction of traditional values, and imperatives of innovative development exists it is only declarative.
Resource Capacity of Academic Staff of Ural Federal District Universities: Qualitative Research

The article presents an analysis of the resource capacity of academic community of Ural Federal District universities based on qualitative research data. The main method was conducting 40 semi-structured interviews with Ural universities academic staff. The study used a quota sample, in which informants were differentiated into groups by age (under and over 40) and by the field of scientific knowledge in which they work. The study included an analysis of statistical data reflecting the main indicators of the development of the RPC of the universities of Ural Federal District in the period 2014–2023. As sources of statistical data, databases of monitoring the effectiveness of higher education organizations, data of the Ministry of Science and Higher Education of the Russian Federation, and «Indicators of Education» statistical collection were used. The main results covered five resource blocks of the RPC: socio-demographic, research, pedagogical, the resource of interaction of the RPS and research teams, moral and symbolic. In aggregate and interrelation, 11 types of RPS resources were disclosed. It was concluded that the results of a comprehensive study of the resourcing of the RPC can serve as a basis for management decisions on staffing strategies of universities in new conditions. The strengths and resource deficits of the RPC were shown. The study allowed us to draw conclusions about those resources that have a cross-cutting character (temporal, moral and ideological) and are necessary as predictors of the actualization of other resources (research, publication, pedagogical).
On Shannon entropy and its applications

A brief and intuitive introduction to Shannon entropy is presented, including some of its properties. The application of this measure is exemplified in two different contexts from what was in its genesis: biological diversity and an original study on student migration.
The Impact of Blockchain Technology on Integrated Green Supply Chain Management in China: A Conceptual Study

Blockchain technology has gained significant attention for its potential to transform various industries, including supply chain management. In the context of integrated green supply chain management in China, this conceptual study explores the implications and potential benefits of blockchain technology. The study focuses on key aspects such as traceability and transparency, carbon footprint reduction and emissions tracking, smart contracts and automated compliance, and stakeholder collaboration and trust-building. Additionally, the study discusses the challenges and barriers to the implementation of blockchain in green supply chains, including technological challenges, legal and regulatory considerations, and adoption barriers. Future research directions, including empirical studies, comparative analysis, and policy implications, are also highlighted. The findings of this study provide valuable insights into the potential impact of blockchain technology on integrated green supply chain management in China.
Enhancement of Kernel Clustering Based on Pigeon Optimization Algorithm

Clustering is one of the essential branches of data mining, which has numerous practical uses in real-time applications.The Kernel K-means method (KK-means) is an extended operative clustering algorithm. However, this algorithm entirely dependent on the kernel function’s hyper-parameter. Techniques that adequately explore the search spaces are needed for real optimization problems and to get optimal answers. This paper proposes an enhanced kernel K-means clustering by employing a pigeon optimization algorithm in clustering. The suggested algorithm finds the best solution by tuning the kernel function’s hyper-parameter and alters the number of clusters simultaneously. Based on five biological and chemical datasets, the results acquired the potential result from the suggested algorithm that is compared to other approaches based on intra-cluster distances and the Rand index. Moreover, findings confirm that the suggested KK-means algorithm achieves the best computation time. The proposed algorithm achieves the necessary support for data clustering.
Learning, inference, and prediction on probability density functions with constrained Gaussian processes

Probability density functions (PDFs) play an important role in machine learning, statistics, and in many real-world applications. In this paper, we introduce a new framework to learn, infer and predict nonparametric PDFs with a Gaussian process prior. This is a challenging problem since there is no explicit formulas for conditional expectations and covariances. The proposed methods have two main advantages: Analyzing PDFs with an intrinsic manifold structure and establishing the connection with the Hilbert sphere when endowed with an appropriate metric. Therefore, all solutions are valid PDFs with non-negative values and integrate to one. We formulate the problem as constrained spherical Gaussian processes leading to an efficient solution with a spherical Hamiltonian Monte Carlo (HMC) sampling. We test and evaluate different strategies with extensive experiments on both simulations and real dataset.
Aethra-net: Single image and video dehazing using autoencoder

A fast and efficient video dehazing system with low computational complexity has a huge demand among drivers during hazy winter nights. There are only a few video dehazing models that exist in literature. Video dehazing requires the sequential extraction and processing of frames. The processed frames must be restored in the same sequence as the original video. However, the existing video dehazing algorithms suffer from color distortion due to the continuous processing of frames. They are not suitable for videos with dense haze. Furthermore, some dehazing systems require hardware, whereas the proposed model is completely software-based to reduce the computational costs. In this paper, an image and video dehazing system called Aethra-Net is developed. A gush enhancer-based autoencoder is modified to obtain the transmission map. The structure of gush enhancement module resembles the processing of light entering the human eye from different paths. The multiple blocks of Resnet-101 layers are employed to overcome vanishing gradient problem. The vessel enhancement filter is also incorporated to enhance the performance of the proposed system. The proposed model has a susceptibility to compute the dehazed images effectively. The proposed model is evaluated on various benchmark datasets and compared with the existing dehazing techniques. Experimental results reveal that the performance of Aethra-Net is found superior as compared to the existing dehazing models.
A Multi-Modal Transformer network for action detection

This paper proposes a novel multi-modal transformer network for detecting actions in untrimmed videos. To enrich the action features, our transformer network utilizes a new multi-modal attention mechanism that computes the correlations between different spatial and motion modalities combinations. Exploring such correlations for actions has not been attempted previously. To use the motion and spatial modality more effectively, we suggest an algorithm that corrects the motion distortion caused by camera movement. Such motion distortion, common in untrimmed videos, severely reduces the expressive power of motion features such as optical flow fields. Our proposed algorithm outperforms the state-of-the-art methods on two public benchmarks, THUMOS14 and ActivityNet. We also conducted comparative experiments on our new instructional activity dataset, including a large set of challenging classroom videos captured from elementary schools.
Machine Learning in Wearable Healthcare Devices

The major characteristic of wearable Technology is it is possible to wear on, in, and much closer to the body. Because of said characteristics, it is termed wearable. Their power is driven by microprocessors and improved with the capability to send and receive data through the Internet. In this COVID pandemic era, we realized the necessity for handy wearable devices for the regular monitoring of patients continuously. Fitness activity tracker was the first wearable that attracted the world to divert toward these technologies. The development of devices like wearable ECG and BP monitoring devices and biosensors followed the fitness activity tracker. This technology has transformed into a full-fledged wearable IoT in a very short span. It is very important to collect and analyze the data generated by the devices in such a way that the diagnosis becomes very easy. But, due to the enormous size of the data generation, it becomes difficult to analyze the data effectively. The inventions in machine learning can help us to cope up with the above problem. We can apply an effective machine learning algorithm to the collected data which can perform preprocessing, feature selection, training, and testing and produce an effective prediction of the condition of the patients. This chapter mainly discusses the design, implementation, and effectiveness of the application of ML on data generated by said devices.
A Multitask Benchmark Dataset for Satellite Video: Object Detection, Tracking, and Segmentation

Video satellites can continuously image large areas and provide dynamic, real-time monitoring of hotspots and objects. The intelligent processing and analysis of satellite video have become a research hotspot in the field of remote sensing. However, the lack of high-quality satellite video datasets limits the development of relevant object detection, object tracking, and object segmentation. In this paper, we build the largest scale satellite video dataset with the most task types supported and object categories, named Satellite Video Multi-Mission Benchmark (SAT-MTB). First, multi-task annotation of aircraft, ships, cars, trains, and their corresponding 14 categories of fine-grained objects in 249 satellite videos is performed based on horizontal bounding boxes (HBB), oriented bounding boxes (OBB), masks, which cover more than 50,000 frames and 1,033,511 annotated object instances. Then, we review the tasks of object detection, object tracking, and object segmentation based on satellite videos, providing a comprehensive overview of progress in related datasets and algorithm research. Finally, we establish the first public benchmark of multi-task algorithms for satellite video object detection, object tracking, and object segmentation, evaluating and analyzing the performance of a total of 47 representative algorithms under different tasks on the constructed dataset. The proposed SAT-MTB will significantly advance research in intelligent processing and analysis of satellite video and related applications.
Multi-lingual opinion mining for social media discourses: an approach using deep learning based hybrid fine-tuned smith algorithm with adam optimizer

Multi-lingual opinion mining has become an important research area in natural language processing, particularly in the context of social media, as it provides valuable insights from user-generated content. In this paper, a deep learning approach based on a hybrid fine-tuned Smith algorithm with Adam optimizer (HFS-AO) is proposed for multi-lingual opinion mining. The approach can process three different languages, namely Marathi, Hindi, and English, which are collected from various web resources using a web scraping algorithm. The collected data is annotated using the Zero-shot instance-weighting technique and pre-processed to remove unnecessary noises and symbols, including data transformation, data cleaning and reduction, stop-word removal, tokenization, lemmatization, stemming, and Parts of Speech Tagging (POS). Naive Bayes vectorization with Laplace smoothing is used for text vectorization, followed by feature selection and classification using the hybrid fine-tuned Smith algorithm with Adam optimizer for polarity classification. The proposed method of multi-lingual opinion mining using a deep learning approach based on a hybrid fine-tuned Smith algorithm with Adam optimizer is effective in extracting valuable insights from social media data in different languages. The approach has demonstrated high accuracy, precision, recall, and F1-Score values, making it a useful tool for improving performance and customer satisfaction by identifying patterns and trends in public opinion. Additionally, the proposed method outperforms other existing methods, including PGM, MCM, CNN, and NBi-LSTM, in terms of computational time and performance making it a valuable contribution to the field of information management.
ESRTMDet: An End-to-End Super-Resolution Enhanced Real-Time Rotated Object Detector for Degraded Aerial Images

The degradation of image resolution reduces the detection performance in aerial imagery because it generates a large number of small objects, and accurately detecting these small objects remains a challenge.Existing methods mostly use a superresolution (SR) model to first obtain the SR image of the low-resolution degraded image (I LR ) and then use this image as the input of the object detection (OD) network to solve this problem.However, this architecture that involves executing a complex SR network before the detector is time-consuming and makes it hard to achieve real-time model inference.To address this challenge, we propose a simple and effective rotated small OD method, named end-to-end superresolution enhanced real-time rotated object detector (ES-RTMDet).First, we design a lightweight embedded feature map superresolution module (ESRM) embedded in the detection model to enhance and amplify the backbone output features, making the detection heads detect small objects more easily.Furthermore, we train a parallel SR network branch (PSRB) simultaneously that uses the backbone feature to restore a high-resolution image.Through our proposed feature alignment loss and feature affinity layer, our PSRB effectively guides the feature map enhancement of ESRM.Finally, through end-to-end joint optimization of the detector and PSRB, the detection performance on I LR is significantly improved.Extensive experiments over DOTA and UCAS-AOD demonstrate that our method can achieve state-of-the-art results.In addition, we discard our PSRB and use I LR as the input during inference, reducing the inference time-consuming of our model.Therefore, our ESRTMDet-X not only achieves 77.11% mean of average precision on the degraded DOTA dataset, but also achieves an amazing inference speed of 337 FPS, thus obtaining the best speed-accuracy tradeoff.
Product Verification using Blockchain Technology: A Systematic Review

Diverse products have flooded so many markets today through unscrupulous vendors all in a bid to make outrageous profits. Hence, the need for products to be verified by extant bodies. There are several means for products to be verified, however, some of those means have also been circumvented. The use of blockchain technology to prevent product replication in supply chain management is a rapidly growing area of research and development. With its features of immutability and decentralization, blockchain technology has the potential to provide secure and tamper-proof tracking and verification of products. This systematic review aims to examine the existing research on the use of blockchain technology in preventing product replication and to identify the effectiveness and challenges of its implementation in industry. The review show that while blockchain technology holds significant potential in preventing product replication, its effectiveness compared to traditional methods and implementation challenges must be addressed.
A Robust Deep Learning-based Approach for Network Traffic Classification using CNNs and RNNs

The application of deep learning has become prevalent in the area of network traffic classification. Deep learning has acquired widespread use in network traffic classification, providing a powerful and flexible approach to identify different types of traffic in a network. This research provides a novel deep learning-based technique for network traffic classification. The proposed method leverages both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify network traffic. This study aims to demonstrate the efficiency of deep learning in classifying network traffic and emphasize the potential of using a combination of CNNs and RNNs for this task. The proposed approach first uses CNNs to extract features from the raw network traffic data, and then uses RNNs to classify the extracted features. The proposed approach was evaluated using a recent dataset of network traffic from different sources, such as the CIC-IDS 2022 dataset. The findings suggest that the proposed technique can attain a high classification accuracy rate (98%), and surpasses traditional machine learning-based methods. Additionally, the study demonstrates the ability of the proposed approach to manage extensive and intricate datasets, making it suitable for real-world network traffic classification tasks. Overall, this paper demonstrates the effectiveness of deep learning-based approaches for network traffic classification, and highlights the potential of using a combination of CNNs and RNNs for this task. In future work, the proposed approach could be further improved by incorporating additional deep learning architectures, such as auto encoders and generative models, to extract more meaningful features from the raw network traffic data.