Reviewerly/exHarmony huggingface dataset:
Application of a nonparametric technique for testing the hypothesis of independence of random variables in conditions of a large volume of statistical data

The problem of testing the hypothesis about the independence of random variables in conditions of large volumes of statistical data is considered. The results of solving the problem are necessary when estimating probability densities of random variables and synthesizing information processing algorithms. A nonparametric technique is proposed for testing the hypothesis about the independence of random variables in a sample containing a large amount of statistical data. The technique is based on compression of the initial statistical information by decomposition of the range of values of random variables. The generated data array consists of the centers of sampling intervals and the corresponding frequencies of observations from the original sample. The information obtained is used in the construction of a nonparametric pattern recognition algorithm corresponding to the maximum likelihood criterion. The evaluation of the distribution laws in classes is carried out under the assumption of independence and dependence of the compared random variables. When restoring the laws of distribution of random variables in classes, regression estimates of probability densities are used. Under these conditions, estimates of the probabilities of pattern recognition errors in classes are calculated. According to their minimum value, a decision is made on the independence or dependence of random variables. The technique was applied in the analysis of remote sensing data of forest areas, linear and nonlinear dependencies between pairs of spectral characteristics of the objects of study were determined. Keywords:
APPLICATION OF DIGITAL TECHNOLOGIES IN THE FIELD OF ANIMAL HUSBANDRY

In the modern world, the use of digital technologies and automation in agriculture is becoming increasingly widespread. According to the United Nations, by 2050 the world's population may reach 9.7 billion, and in 2080 it will peak at 10.4 billion people, so the demand for food will grow significantly. One of the possible solutions to meet this demand is the development and active implementation of digital technologies in animal husbandry. Modern digital technologies are transforming the industry and providing new opportunities for farmers to increase efficiency and productivity while minimizing their environmental impact. These technologies are also used to optimize farmers' work. One of the industries that benefits most from automation is precision animal husbandry. Precision animal husbandry is agriculture that uses technology to monitor and manage livestock with efficiency and sustainability. Using artificial intelligence-based automation, farmers can collect data about their livestock, such as health and nutritional information, in real time. This data can then be used to make informed decisions about animal care and management. Thanks to digital technology, farmers can improve and optimize their livestock management practices. For example, the health and well-being of animals can be monitored using drones, cameras, and AI sensors that can alert humans to potential problems. In addition, artificial intelligence can be used to make decisions about breeding and feeding strategies by tracking the performance of individual animals. Digital technologies are also being introduced to improve the efficiency of the farm itself. The use of AI-enabled robots can automate certain tasks, such as milking and feeding, allowing farmers to focus on other activities. The use of resources such as water and energy can also be optimized with AI, which can help mitigate the environmental impact of agriculture. Finally, the use of robots on farms helps to improve product traceability. AI-driven sensors can monitor the condition and location (and even identify) of animals to ensure product quality and safety, and increase the sustainability of livestock production.
CAUSeS: An Alternative to CONTU for Assessing Interlibrary Loan Copyright Fees

This article describes the process used at the University of Massachusetts Amherst Libraries to develop and implement a new method for interlibrary loan copyright fee assessment as an alternative to the outdated CONTU (Commission on New Technological Uses of Copyrighted Works) which we call “CAUSeS” (Copyright Assessment for Uses that Substitute for Subscriptions), by reviewing legal standards and considering practical needs.
Exploring the generic fallacy — meta path-dependencies in innovation-practices of ‘drone-making’ (eVTOLs)

Generic technologies are oftentimes heralded as overall beneficial drivers of innovation, especially regarding their flexibility, low cost of adaption (once established) and their inclusiveness toward a variety of actors. This paper adds to literature on innovation-studies by questioning these promises through the lenses of ‘lock in’ and ‘path dependencies’ and asks how generic approaches to innovation may contribute to a fallacy where increased flexibility is assumed yet implicitly, a sort of ‘lock in genericism’ may occur. The paper argues that, for all the advantages that come with the research and adaption of generic technologies, they also bring with them an increased risk of enamourment with innovations that are applicable to a range of potential applications that, in turn, may lead to more specific technological innovations being at the danger of becoming invisible / unwanted altogether. To investigate this phenomenon further, the paper applies the concept of ‘lock in genericism’ to the field of eVTOL-multicopter- / drone-innovation. In this context, the paper analyzes a series of three case-studies to investigate how this ‘lock-in genericism’ emerges from material, temporal and spatial components of drone-making and subsequently seeks to outline a framework for ‘integrating generic technologies’ in this particular field of application (of drones) to overcome the described lock-in in this field while maintaining their advantages. The paper concludes by discussing the relevance of the concept of ‘lock-in genericism’ on a broader level, highlighting the risk of a ‘generic turn’ in contemporary innovation practices that, in turn, requires critical reflection.
Holographic encryption of color video with 4K resolution using phase liquid crystal spatial light modulators

The communications number and information size are significantly increasing. Transmission of various types information (including video information) should be fast and protected from unauthorized access. The use of optical encryption for this purpose makes it possible to encode large data arrays with high cryptographic strength. Most optical information encoding systems use coherent light, which results in an extremely low signal-to-noise ratio in decrypted images. Holographic encryption of color 4K video with digital information input and dynamically changeable encoding holograms by means of the liquid crystal spatial light modulators of high resolution was implemented for the first time. The method is based on optical convolution of the input image with the encoding hologram using spatially-incoherent light. Color encoding is carried out by sequential encryption of different color channels of each video frame. Decryption is carried out by numerical methods of inverse filtering with regularization. Results of this work can be used for creation of high resolution secure video communication systems of new generation.
Context-Aware Clinical Diagnosis Prediction via Hierarchical Ontology Representation

In recent years, the extensive utilization of electronic medical records has led to the preservation of a large amount of historical patient information, culminating in the formation of Electronic Health Record (EHR) dataset. Through technologies such as deep learning and representation learning, EHR dataset has been employed for tasks in the medical field, such as disease prediction. However, the challenges of how to better learn the embeddings of medical codes and how to effectively model historical visit sequences remain significant issues in the field. Therefore, we introduce a novel disease prediction methodology grounded on hierarchical ontology representation and disease evolution. First, we incorporate medical ontology to obtain external prior knowledge and simultaneously utilize the co-occurrence relationships between diseases to construct a disease co-occurrence graph. By integrating information from both sources, we achieve the final embedding of the medical codes. Building on this foundation, we delve deeper into exploring the evolution and changes in diseases, specifying different sequence modeling methods, thereby acquiring the embeddings of the patients. The application of medical ontology is beneficial in mitigating data inadequacy, while the exploration of dynamic disease evolution enhances the effectiveness of predictive models. Experimental results on the MIMIC-III dataset indicate that the proposed method achieved superior performance in disease pre-diction tasks, thus validating the effectiveness of this approach.
Secure and Efficient Federated Statistical Tests for Medical Data based on TEE and Secure Resharing Protocols

The quality of empirical statistical analysis is closely related to the quality and quantity of available data. However, due to privacy concerns, it is often difficult to collect data from different data owners, thus limiting the usefulness of the collected data. Existing solutions mostly focus on evaluating federated statistical analysis using homomorphic encryption (HE) or secure multiparty computation (SMPC). Our solution incorporates trusted execution environment (TEE) for federated statistical tests. Initially, we propose federated statistical tests framework based on single-party TEE. Considering the security of the proposed framework, we then extend single-party TEE into a distributed TEE cluster and combine it with secure resharing protocols to realize model security and confidential computation while preserving data privacy in federated setting. We provide a comprehensive security analysis for the proposed federated statistical tests framework and evaluate the common Welch's t-test on diabetic dataset. Security analysis and experimental evaluation prove that our framework is end-to-end secure and efficient.
A Novel Conditional Medical Image Generation GAN with Latent Space Optimal Transport

Generative adversarial network (GAN) is widely used to augment training set in medical imaging. However, training GANs can be challenging, as they are susceptible to mode collapse. These challenges stem from the fact that deep neural networks can only represent continuous mappings, while in GAN, the generator aims to compute discontinuous transportation maps between the noise distribution and the data distribution. We propose a novel approach that can overcome mode collapse, and in turn, lead to improved capability by GAN to generate specific classes of medical images. Our method starts by mapping the data manifold to the latent space using an autoencoder (AE). Subsequently, we encode the sample labels and integrate them with the latent representations. Next, we employ extended semi-discrete optimal transport (SDOT) mapping, which maps a Gaussian distribution to the empirical latent distribution, thereby generating new latent representations with known labels. Finally, we employ GAN to establish the mapping from the continuous latent distribution induced by the extended SDOT mapping to the real data distribution, generating high-quality images. We conducted extensive experiments on the DermaMNIST and BloodMNIST datasets. The experimental results highlight the exceptional performance of our model in generating images belonging to specified classes.
Periodical publications of the Shevchenko Scientific Society in the reception of the polish press in the late 19th and early 20th centuries

A content analysis of the content of the Polish press of the late 19th and early 20th centuries was carried out. regarding the publication in it of materials about periodicals of the Shevchenko Scientific Society in Lviv. The publications of Polish and Ukrainian authors – Alexander Brückner, Jerzy Dmytrov, Volodymyr Domanytsky, Bohdan Lepky, Jaroslav Leszchynsky, Ostap Lucky, Mychajlo Mochulsky, Aleksander Medynsky, Franciszek Ravita-Gavronsky, Ivan Franko, Stanislaw Zdziarsky – found in the Polish magazines «Kwartalnik Historyczny», «Krytyka», «Lud», «Przegląd Krajowy», «Świat Słowiański» and «Wisła» were researched and characterized. The professional specialization of the press in which these materials were printed, its territorial affiliation, was clarified. A statistical summary of the found publications was made (aspects: publication, authors, territorial component). An attempt was made to identify unknown and pseudonymous authors of the considered publications. Keywords: Shevchenko Scientific Society in Lviv, periodicals of the Shevchenko Scientific Society in Lviv, Polish press, author, article, review.
ShennongGPT: A Tuning Chinese LLM for Medication Guidance

The burgeoning field of large language models (LLMs) holds tremendous promise for healthcare, particularly in the realms of medication guidance and adverse drug reaction prediction. Despite this, extant LLMs grapple with managing intricate polypharmacy scenarios. To address these limitations, we introduce ShennongGPT, a cutting-edge LLM, expressly tailored for robust medication guidance and adverse drug reaction forecasting. Our model employs a novel two-stage training strategy: initial learning from distilled drug databases for foundational knowledge on drug interactions, followed by simulation of human-like decision-making processes through the use of real-world patient data, enhancing the relevance and applicability of its guidance. This two-fold approach empowers ShennongGPT to excel in predicting potential adverse drug reactions and offering personalised medication advice, thereby significantly enhancing medication safety and the overall quality of healthcare services. Rigorous evaluations by healthcare professionals and AI experts highlight the superiority of Shennong GPT, which outperforms existing general and specialty LLMs.
Effectiveness of learner-centered methods in elevating positive attitudes towards English: Meta-analysis study

This paper analyzed the effect of learner-centered methods (cooperative learning, drama and project-based learning) on learners' attitudes towards English lesson. Meta-analysis method was used in the study. 24 studies (25 effect sizes) were discovered through literature review. Analyzes were done using random effects model. Learner-centered methods in foreign language teaching had a large (g=0.925, 95% CI [0.642-1.209], p&amp;lt;.0.000) effect on learners' attitudes towards English lesson. The publication bias was interpreted both visually through Funnel plot and statistically via Rosenthal Classic fail-safe N and Orwin fail-safe N methods. Year of publication, the place of the research, publication type, the method applied, language skill, teaching level, experimental application duration and sample size were identified as moderators. It was concluded that Q-value for the effect sizes for publication year, language skill and teaching level was statistically meaningful and effective on the heterogeneity of effect sizes. One of the remarkable results was that the longer the experimental application period, the more effective it was in increasing positive attitudes towards the lesson.
Implicit Learning of Rotational Decoupling Detection Heads Applied to Remote Sensing Object Detection

Remote sensing image features complicated background, big image size, small object pixels, and variable object direction due to the arbitrary shooting angle. Conventional object detector has poor detection effect on remote sensing image, which is prone to missed detection and false detection. In view of this, an improved YOLOv5 object detection algorithm was proposed for remote sensing image. First, the C3 module was improved. The output feature information of each convolutional module was transmitted to the deep network, so as to preserve more fine-grained feature information and enhance the small object detection capability. The improved downsampling mechanism integrates convolution downsampling with maximum pooling downsampling mechanism in downs amp ling to separate foreground and background information while retaining feature information. A lightweight rotatory decoupling detection head with implicit learning was proposed to address the conflict between regression and classification, thus improving the network detection capability for objects in any direction. In the meantime, an implicit learning matrix was added to guarantee the lightweight detection head and improve the network detection accuracy for multiple tasks. According to experimental results, under the same model size, compared with the original YOLOv5 detection method, the detection accuracy and detection speed of the proposed method were improved by 8% and 15 frames, or 14.7%, respectively on DOTA-v1.5 data set, effectively improving the detection accuracy and speed in remote sensing image detection. The detection accuracy was increased by 3.5% on coco data set in natural scenario. The method herein can still effectively improve the detection accuracy in natural scenario, proving effectiveness of the modified YOLOv5 method.
Learning to Leverage Multi-Scale and Multi-Level Features for Facial Expression Recognition

Recent years, intense research are done with remarkable results on the topic of Facial Expression Recognition (FER). However, extracting meaningful contextual features and aggregating discriminative representations to improve the FER performance are still challenging. To address the above problem, we formulate a simple yet effective multi-scale and multi-level features learning neural network for FER. In our work, the proposed method consists of multiple scale-specific branches and a scale-fusion branch that aims to mine the implicit complementary advantages across different scales and levels. Experiments are conducted on the dataset FER-2013 to verify the effectiveness of the proposed model. In the end, the model produces a 70.0% accurate rate in the experiment which has a better performance than the state of arts.
Reusable Ontology Generation and Matching from Simulation Models

As simulating semiconductor manufacturing grows complex, model reuse becomes appealing since it can reduce the time incurred in developing future models. Also, considering a large network of the semiconductor supply chain, knowledge sharing can enable the efficient development of simulation models in a collaborative organization. Such necessity of reusability and interoperability of simulation models motivates this paper. We will address these challenges through ontological modeling and linking of the simulation components. The first application is generating reusable ontologies from simulation models. Another discussed application is ontology matching for knowledge sharing between simulation components and a meta-model of the semiconductor supply chain. The proposed approach succeeds in automatically transforming simulation into reusable knowledge and identifying interconnection in a semiconductor manufacturing system.
Safeguarding Infrastructure from Cyber Threats with NLP-Based Information Retrieval

Natural disasters disrupt systems, leading to critical infrastructure vulnerabilities prone to cyber-attacks. The MITRE ATT&CK Enterprise Matrix is a knowledge base for threat analyses in the cybersecurity community. Existing processes to derive possible attack methodologies from this Matrix are largely manual and time-consuming. It is essential to automate the information retrieval process to reduce human errors, improve efficiency, and free up resources for identifying unrevealed cyber-attacks. We propose a framework that incorporates Natural Language Processing (NLP) and Text Mining to automatically generate sets of attack paths from the technique descriptions in the Matrix. The framework generates similarity between techniques based on their descriptions and creates an output showing potential pathways an adversary can take to infiltrate a system. The outputs are compared against an annotated approach and attack report. The results of this study provide an approach to more quickly and effectively assess potential cyber-attacks towards protecting critical infrastructure.
Identifying Quality Mersenne Twister Streams for Parallel Stochastic Simulations

The Mersenne Twister (MT) is a pseudo-random number generator (PRNG) widely used in High Performance Computing for parallel stochastic simulations. We aim to assess the quality of common parallelization techniques used to generate large streams of MT pseudo-random numbers. We compare three techniques: sequence splitting, random spacing and MT indexed sequence. The TestU01 Big Crush battery is used to evaluate the quality of 4096 streams for each technique on three different hardware configurations. Surprisingly, all techniques exhibited almost 30% of defects with no technique showing better quality than the others. While all 106 Big Crush tests showed failures, the failure rate was limited to a small number of tests (maximum of 6 tests failed per stream, resulting in over 94% success rate). Thanks to 33 CPU years, high-quality streams identified are given. They can be used for sensitive parallel simulations such as nuclear medicine and precise high-energy physics applications.
FORSA: Exploiting Filter Ordering to Reduce Switching Activity for Low Power CNNs

Switching activity in digital circuits depends on the temporal distribution of the data participating in the operation which directly influences the interconnect, dynamic power and timing of the system. This paper proposes an efficient method for reducing both power and latency of matrix-multiplication operations found in many applications such as convolution neural networks (CNNs). The approach takes advantage of the unique characteristics of CNN with input stationary for efficient multiply-add operations. Since most applications use reduced accuracy for MAC, the proposed work assumed 8-bit fixed-point representation for different quantization levels. As a demonstrator, CIFAR-10 data set has been used for end-to-end analysis of the filters on a 3-ConV with 2-FC CNN model structure. The filters' were re-ordered to reduce the switching behaviour between successive weight fetching. This directly impacts the dynamic power consumption and provides an estimation of 20.62% less classification power with 5-bits for quantization levels and 8-bit fixed-point representation for the MATLAB CNN pipeline. While with the full digital implementation, ~11.43 % reduction is achieved. The difference could be due to the internal power of the cell or the arrival times of the signals. Moreover, reduced switching activity leads to reduced interconnect power which is normally high compared to computing.