Reviewerly/exHarmony huggingface dataset:
Analysis of Content Production Strategies and Impacts in the Context of Information Fragmentation

With the development of technology and society, fragmented information is flooding people's lives and has a significant impact on users. The purpose of this study is to analyze the impact of fragmented information on users in order to fill the gap in this research field, alert people to the negative effects of fragmented information, and make better use of fragmented information. The entire study is divided into three parts: the causes of fragmented information, content production strategies, and the impact on users. The production strategy of fragmented information is unique in terms of content and in terms of information delivery. It uses technology to constantly analyze the action characteristics, social characteristics and environmental characteristics of users' browsing behavior. In terms of the impact on users, this study concludes that fragmented information does have some positive impacts, but more importantly, the negative consequences, such as information cocoon, fragmented information anxiety, and difficulty in concentration, should be given sufficient attention.
A time-efficient and noise-resistant cryptosystem based on discrete wavelet transform and chaos theory: An application in image encryption

Due to the exponential rise of multimedia technologies, digital information privacy is now more crucial than ever. The process of transmitting data across a channel such as the internet has become a challenging task. In recent years, a variety of techniques for encrypting digital data have been proposed. However, due to shortcomings such as a high computational cost and weak security, it is important to build an encryption scheme that can provide a suitable level of security while also being time-effective. This research introduces a Discrete Wavelet Transform (DWT), chaotic map, and substitution box-based encryption scheme that is both time efficient and provides high levels of security for digital images. DWT is utilized to reduce the computation complexity of the encryption scheme proposed in this research. This is done by transforming the input image into its four frequency sub-bands (ΓΓ, Γħ, ħΓ, and ħħ) and selecting only the low-frequency sub-band (ΓΓ) for encryption, while the substitution boxes (S-boxes) and a chaotic map are used to increase the level of security. Using chaotic maps, random sequences are also generated to introduce confusion and diffusion. The final encrypted image is created by combining all of the non-modified and encrypted frequency bands. Finally, the results show that proposed encryption system nears the optimum values with a key-space of 2265 to avoid a brute-force attack.
FAST: Fast and Accurate Adaptation in Live Video Analytics Using Intermediate Features

Live video analytics need to adapt the optimal configuration to achieve the requirements for various videos of environments. Prior works for adaptation suffer from the accuracy drop due to the large overhead and incorrect estimation. We present FAST, a Feature-based Adaptation SysTem, for immediate and accurate adaptation in live video analytics. To overcome limitations of prior works, we leverage the intermediate feature of the task. Intermediate features enable fine-grained adaptation and ambiguity distinction, which leads to reducing the frame size to be transmitted and increasing the accuracy. We design end-to-end systems for two representative tasks (object detection and heartrate measurement) to verify the benefits of the intermediate features. Evaluations show FAST achieves up to 6.95 x lower average frame size and 2.69 x higher accuracy compared to state-of-the-art adaptation.
VCRS: A Novel Approach to Virtual Reality Cycling for Balance Rehabilitation

This paper presents the hardware and software design of Virtual Cycling Rehab System (VCRS), a novel and affordable system for balance rehabilitation via virtual reality (VR). The system integrates real-time sensor communication with an Oculus Quest-based virtual environment created in Unity. This integration enables precise avatar movement and facilitates the assessment of lateral balance during cycling rehab sessions. The accelerometer data from one side of the pedals is accurately translated into in-game physics to create realistic avatar movement. The system also utilizes gyroscope data to calculate and display a graphic representation of the patient's balancing score to guide the patient's lateral balance. Physicians can monitor the session via live mirroring the VR HMD and graphed balance data on a local device. At the end of each session, the user receives feedback in the form of session time, game score, and a percentage indicating their level of lateral balance.
IMPLEMENTASI METODE LINEAR CONGRUENTIAL GENERATOR PADA GAME PUZZLE BERBASIS ANDROID

Games has become one thing in everyday life. Formerly, the game is only used as a means of entertainment alone, but now the game has become the extent of its function. For example, game can be a mean of learning, business land, and competed as one of the sport by professionals. The development of platform games can also be seen directly by the community, the games only played on computers and consoles in past time, however now it has entered the era of mobile games. Puzzle game is a game that displays pieces of the image where the pieces of the image is scrambled arrangement to provide its own challenge to arrange it. This puzzle game is a game of drawing pieces with the rules of a piece can only be moved by sliding it into empty space. Generally people who play the puzzle, take a long time in completing the game. This is because in the puzzle there is no additional information available to help searching the solution when the process of arranging the pieces into a challenge for those who play it. Images of a puzzle will be scrambled using the Linear Congruential Generator (LCG) method. Linear Congruential Generator using an initial state then with a special formula will generate a pseudo-random number. The initial state used is taken from a variety of sources that are considered quite random. Thus, the Linear Congruential Generator will produce a series of numbers that resemble random numbers. Linear Congruential Generator is widely used today, it eases the implementation and speed as the main factor why Linear Congruential Generator is used.Keywords : Games, Puzzle Games, Linear Congruential Generator (LCG)
Las redes sociales y efecto en las relaciones interpersonales

In the present work we sought to identify the degree to which the use of social networks affects the interpersonal relationships of the 5th semester students of the CECyTE Guanajuato Valle de Santiago campus in the August-December 2017 semester, for which an investigation of mixed cut applying four different data collection instruments: interviews, surveys, focus group and observation.
STEM centre as a factor in the development of formal and non-formal STEM education

Abstract The article is devoted to the study of STEM education. It is one of the important directions of educational reform of the XXI century and can be implemented through the integration of formal and non-formal education. The authors of the paper study the ecosystem of formal and non-formal STEM education. Researchers have identified the main characteristics of this ecosystem such as the harmonious combination of formal and informal components of STEM learning, increasing the motivation of students to STEM sciences, constant updating of educational programs and professional development programs for teachers; use of non-formal education approaches; constant connection with external communities of STEM professionals; building partnerships between schools and non-formal institutions. The authors have developed the main components of the model of functioning of the educational STEM centre as an education ecosystem. In particular, they are educational research, assessment (evaluation), research projects, innovative teaching methods, programs for training, undergraduate education, teacher training, mentoring, services, communications, cooperation, infrastructure, connection, networking. To assess the extent of the impact of various forms of formal and non-formal learning activities on the results of the implementation of STEM education, an expert survey was conducted. The results of the survey are systematized, illustrated with the help of infographics, statistically significant relationships between individual parameters of the proposed model are established. To test this model in practice, an experiment was conducted. It lasted from 2016 to 2020. Researchers have confirmed the hypothesis that STEM education gives the best results due to a combination of formal and non-formal levels. Therefore, their STEM centre promotes STEM education among the general public, training and retraining of educators involved in STEM, development of STEM competencies of pupils and students.
Sparse neural network optimization by Simulated Annealing

The over-parameterization of neural networks and the local optimality of backpropagation algorithm have been two major problems associated with deep-learning. In order to reduce the redundancy of neural network parameters, the conventional approach has been to prune branches with small weights. However, this only solves the problem of parameter redundancy, not providing any global optimality guarantees. In this paper, we overturn back-propagation and combine the sparse network optimization problem and the network weight optimization problem using a non-convex optimization method, namely Simulated Annealing. This method can complete network training under the premise of controlling the amount of parameters. Different from simply updating network parameters using gradient descent, our method simultaneously optimizes the topology of the sparse network. With the guarantee of global optimality of Simulated Annealing solution, the performance of the sparse network optimized by our method has exceeded the one trained by backpropagation only.
FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers’ concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.
So, you want to build a decision-support tool? Assessing successes, barriers, and lessons learned for tool design and development

First posted September 12, 2023 For additional information, contact: Director, Integrated Information Dissemination DivisionWater Resources Mission AreaU.S. Geological Survey12201 Sunrise Valley DriveReston, Virginia 20192Contact Pubs Warehouse The purpose of this study is to increase understanding of how the U.S. Geological Survey (USGS) is developing decision-support tools (DSTs) by documenting successes and barriers across all levels of USGS scientific tool creation and outreach. These findings will help streamline future tool design and development processes. We provide a synthesis of lessons learned and best practices across a spectrum of USGS decision-support efforts to, A, provide guidance to future efforts and, B, identify knowledge gaps and opportunities for knowledge transfer and integration. We present this information as five guiding principles for those striving to create effective DSTs. These principles are: (1) use an adaptive, iterative design process, (2) collaborate across disciplines and organizations, (3) engage with the target users of the tool, (4) develop an empirical understanding of use and usability, and (5) plan for the tool’s full life span. By providing guidance on how effective DSTs are realized at every phase of development (from planning to maintenance), these principles provide a starting point to improve the process of designing DSTs and thus help further the USGS mission of delivering actionable science.
Thermal Imaging Detection System: A Case Study for Indoor Environments

Currently, there is an increasing need for reliable mechanisms for automatically detecting and localizing people-from performing a people-flow analysis in museums and controlling smart homes to guarding hazardous areas like railway platforms. A method for detecting people using FLIR Lepton 3.5 thermal cameras and Raspberry Pi 3B+ computers was developed. The method creates a control and capture library for the Lepton 3.5 and a new person-detection technique that uses the state-of-the-art YOLO (You Only Look Once) real-time object detector based on deep neural networks. A thermal unit with an automated configuration using Ansible encapsulated in a custom 3D-printed enclosure was used. The unit has applications in simple thermal detection based on the modeling of complex scenes with polygonal boundaries and multiple thermal camera monitoring. An easily deployable person-detection and -localization system based on thermal imaging that supports multiple cameras and can serve as an input for other systems that take actions by knowing the positions of people in monitored environments was created. The thermal detection system was tested on a people-flow analysis performed in the Czech National Museum in Prague. The contribution of the presented method is the development of a small and simple detection system that is easily mountable with wide indoor as well as outdoor applications. The novelty of the system is in the utilization of the YOLO model for thermal data.
Seabed in the Andes: Exploring “Splace” in Transnational STS

This commentary engages with the digital materials of STS in/through Turkey, Kenya, Japan, and Ecuador collected between 2018 and 2019, and with the collection of essays inspired by these materials. I use the notion of “splace” as an exploratory tool that challenges the division between space and place, and I also invite thinking about STS formations with geological formations I encountered on the Chilean Andes.
Acquisition, Processing and Visualization of Meteorological Data in Real-Time Using Apache Flink

Today, the issue of data processing has become a topic of vital importance for society and at the business level. Data has been essential for decision-making and business opportunities for companies. Data processing is not a trivial task since large amounts of data are generated in different formats, which makes it difficult to process, store and visualize them. This chapter presents an architecture for real-time data stream processing on Apache Flink. Sensors connected to a prototype of a weather station built for this purpose were used to generate the data. To transmit the data generated by the weather station prototype to Apache Flink, the Apache Kafka streaming tool was used. Finally, the Elasticsearch and Kibana tools were used for data processing and visualization. The tests carried out to verify the operation of all the components of the proposed architecture were satisfactory.
About the generalizability of deep learning based image quality assessment in mammography

Abstract One method of assessing the image quality of a mammography unit is to estimate a contrast-detail-curve (CDC) that is obtained from images of a technical phantom. It has been proposed to estimate this CDC by using an end-to-end neural network (NN) which only needs one image to determine the CDC. That approach, however, has been developed on the basis of images of one single mammography unit. In this work, we train NNs on synthetic images of contrast-detail phantoms for mammography and test the so-trained NNs on images that are obtained from real mammography units. The goal of this paper is to demonstrate that such a deep learning approach is capable to generalize to predict CDCs for various real mammography units. Our experiments cover various manufacturers and the proposed approach is shown to work across different NN architectures and preprocessing methods which highlights its generalizability.
Hyperledger Blockchain-Enabled Cold Chain Application for Flower Logistics

Flower logistics usually encounter difficulties like disrupted temperature control, inadequate packaging or damaged goods, contamination issues, damaged products, massive document exchanges, and many other cold chain challenges. To tackle these difficulties, we developed a blockchain application based on Hyperledger technologies for the flower cold chain. The Fabric under the Hyperledger was used as the underlying architecture of the blockchain network, and then the application scenarios and business processes of the blockchain were constructed by the Hyperledger Composer. Cold chain partners can have a holistic view of transactions through the Composer and retrieve detailed information by querying specific shipments or transactions. The specification of Hyperledger smart contracts can also avoid tampering with data during cold chain shipments and the temperature data can be monitored in real-time via sensors. In addition, smart contracts created by Hyperledger can offer automated functionality and availability of validated data. The developed application can increase trust, security, transparency, and the traceability of flower logistics transactions.
Educational Videos Recommendation System Based on Topic Modeling

Video recommendation systems in e-learning platforms are a specific type of recommendation system that use algorithms to suggest educational videos to students based on their interests and preferences. Student’s written feedback or reviews can provide more detailed about the educational video, including its strengths and weaknesses. In this paper, we build education video recommender system based on learners’ reviews. We use LDA topic model on textual data extracted from educational videos to train language modle as an input to supervised CNN model. Additionally, we used latent factor modle on extracts the educational videos’ features and learner preference from learners’ historical data as an output CNN model. In Our proposed technique, we hybrid user ratings and reviews to tackle sparsity and cold start problem in recommender system. Our recommender use user review to suggest a new recommended videos, but in case there is no review (empty cell in matrix factorization) or unclear comment then we will take user rating on that educational video. We work on real-world big and heterogynous dataset from coursera. Result shows that new production rating from learners reviews can be used to make good new recommended videos to student that not previously seen and reduce cold start and sparsity problem affects.
Differentially Private Copulas, DAG and Hybrid Methods: A Comprehensive Data Utility Study

Differentially Private (DP) synthetic data generation (SDG) algorithms take as input a dataset containing private, confidential information and produce synthetic data with comparable statistical characteristics. The significance of such techniques is rising due to the growing awareness of the extent of data collection and usage in organizational contexts, as well as the implementation of new stricter data privacy regulations. Given the growing academic interest in DP SDG techniques, our study intends to perform a comparative evaluation of the statistical similarities and utility (in terms of machine learning performances) of a specific set of related algorithms in the realistic context of credit-risk and banking. The study compares PrivBayes, Copula-Shirley, and DPCopula algorithms and their variants using a proposed evaluation framework across three different datasets. The purpose of this study is to perform a thorough assessment of the score and to investigate the impact of different values of the privacy budget ( $$\epsilon $$ ) on the quality and usability of synthetic data generated by each method. As a result, we highlight and examine the deficiencies and capabilities of each algorithm in relation to the features’ properties of the original data.
NEREON - An Underwater Dataset for Monocular Depth Estimation

Structures associated with offshore wind energy production require an arduous and cyclical inspection and maintenance (O&M) procedure. Moreover, the harsh challenges introduced by sub-sea phenomena hamper visibility, considerably affecting underwater missions. The lack of quality 3D information within these environments hinders the applicability of autonomous solutions in close-range navigation, fault inspection and intervention tasks since these have a very poor perception of the surrounding space. Deep learning techniques are widely used to solve these challenges in aerial scenarios. The developments in this subject are limited regarding underwater environments due to the lack of publicly disseminated underwater information. This article presents a new underwater dataset: NEREON, containing both 2D and 3D data gathered within real underwater environments at the ATLANTIS Coastal Test Centre. This dataset is adequate for monocular depth estimation tasks, which can provide useful information during O&M missions. With this in mind, a benchmark comparing different deep learning approaches in the literature was conducted and presented along with the NEREON dataset.