Reviewerly/exHarmony huggingface dataset:
Digital Society, Algorithmic Harm, and the Pandemic Response

This chapter examines how policy responses to the Covid-19 pandemic have deepened the harms and injustice embedded in the exploitative designs of many technological platforms and digital infrastructures. The rapid surge in the number of people and frequency of interaction with technologies using data-driven innovations – from big-data analytics, automated decision-making, artificial intelligence/machine learning (AI/ML), to deep learning – is not only unprecedented but also deeply problematic. The policy failure to address the impact of this shift, particularly the algorithmic harms caused to people and communities who are most marginalised, has the potential to lead to irrevocable consequences for social justice and equity. The long-term human cost of the pandemic cannot be truly assessed if these harms are not fully accounted for and, more importantly, mitigated through inclusive and equitable innovation and policies.
Research on Information Education Platform based on Structured Big Data Assisted Cloud Computing

The degree of educational informatization has become the weather vane of the national educational modernization degree. This paper takes the "cloud computing" technology as the starting point, starting from the process of educational work, and designs two platforms of educational resources and educational management. This paper focuses on the education informatization platform of cloud computing, scientific research and education system, etc. These informatization software use different system architectures, data organization, and development languages, which inevitably lead to loopholes when integrated. There are many educational courses stored on the educational informatization platform, the query is very complicated, which is not conducive to improving the user's learning efficiency. At the same time, the advantages and service methods of providing a cloud-based teaching resource platform are discussed and researched, and the problems and difficulties encountered in the implementation process are put forward.
Fault simulation and identification of capacitive casings based on fault tree theory

The safe and stable operation of the power system is related to the quality and efficiency of national production. With the mature development of the detection technology of large power equipment, online monitoring has gradually become an important means of detecting the insulation status of equipment. Casings, as the core component of power transformer, bears the electromagnetic environment of high voltage and strong electric field in operation, and has multiple interfaces (oil-air interface). The complex working environment of casing leads to the occurrence of faults, so it has high engineering value to study the on-line monitoring system of Casing. Firstly, this article designed casing shrinkage ratio model, and make tail damp and metal particle discharge fault artificially. Secondly, we extract five kinds of characteristic value in the laboratory including the capacitance variation, the dielectric loss tangent, the maximum discharge amplitude, the maximum amplitude and the average discharge pulse number. At last, we used the analytic hierarchy process (AHP) to build the fault tree model for fault diagnosis and classification of two kinds of discharge.
Parachute control system of sounding rocket based on multi-sensor fusion

In this paper, a method for controlling the parachute opening device of a small sounding rocket through a multi-sensor fusion algorithm is proposed. This method uses IMU, airspeed tube and GNSS to obtain acceleration, velocity and position information respectively. After processing the three sets of data, the corresponding speed data is obtained, and the more accurate speed data is obtained through the Kalman filter algorithm. The vertical speed is used as the basis for the parachute-opening judgment condition and the timing of parachute-opening. Through simulation and experimental results, this method of parachute control of the sounding rocket proved to be very effective. This method can significantly reduce the accuracy requirements for the sounding rocket sensors, and obtain relatively high-precision results through the fusion processing of data collected by multiple low-precision sensors. This method can be used in the parachute control system of low-cost sounding rockets, which is of great significance for reducing the cost of small sounding rockets and promoting commercial applications.
Identification method of the feeder-transformer connection relationship in distribution network based on fault outage information

In order to solve the problem of identification and verification of topological relationship between feeder and distribution transformer in distribution network, an identification method of the feeder-transformer connection relationship in distribution network based on fault outage information inversion is proposed. Firstly, the identification model of feeder-transformer connection relationship is established through the single fault outage information set. Then, through the identification model of feeder-transformer connection relationship, the feeder fault standard outage information set and feeder fault real-time outage information set are established. Finally, the distribution transformer fault connection information set and distribution transformer missing connection information set are determined by the difference set between the feeder fault standard outage information set and feeder fault real-time outage information set. The example analysis shows that this method can identify the feeder-transformer relationship of distribution network based on the topology information and real-time fault alarm information of DMS. The method is simple and reliable, can better meet the actual needs of the site, and has good promotion value.
Improved Harris hawks optimization algorithm based on collaborative quantization

Harris hawk optimization algorithm (HHO) is one of the population-based algorithms proposed by in 2019. It has received great attention from researchers. However, HHO algorithm still has some problems, such as the exploitation ability is too large compared with the exploration ability, which leads to low optimization accuracy, slow convergence speed and so on. Therefore, the collaborative strategy and quantization strategy are introduced. With the support of the two strategies, the algorithm can avoid falling into local extremum in the early phase of iteration and improve its optimization accuracy at the late phase of iteration. Through the test of four representative functions, compared with the other three basic algorithms, the proposed algorithm greatly improves the optimization accuracy and convergence speed of the optimal solution, and the ability to find global extremum is greatly improved.
Artificial Intelligence with CBC Based Morphometric Parameters Aimed Toward Effective Diagnostic Practices for Dysplasia Associated Hematological Malignancies

Abstract Introduction/Objective The diagnosis and classification of dysplasia associated hematological neoplasms is dominated by morphology. Current round of study made use of potential ‘fingerprints’ among routinely generated diagnostic data particularly morphological and immature fraction-related (morphometric) parameters produced during routine complete blood count (CBC) testing in hemat-oncology department through artificial intelligence predictive modeling. Methods/Case Report Along conventional statistics, neural network models were trained on anonymized demographical, clinical and diagnostic data of total 1624 individuals with common hematological neoplasms. In addition, validation conducted on independent dataset. The frameworks were trained to differentiate hematological malignancies cases with various sub-entities of Dysplasia against Non-dysplastic group as a control cohort. Results (if a Case Study enter NA) Our predictive model attained greater precisions; percent incorrect prediction were remained 10.3 and 4.6 for training and testing phases, respectively along with a 95.4% negative predictive value (NPV). Moreover, higher accuracy (93.1%) was obtained during prospective validation in challenge of independent dataset. Model’s performance related metrics: the gain and lift chart, predictive-pseudo probability chart, and receiver operative curve (ROC) curve were noted as persuasive. Considering the sensitivity and specificity, area under the curve (AUC) values were also noted quite convincing; 0.954 for Non-dysplasia group while 0.994 for acute myeloid leukemia with dysplasia (AML-Dys) followed by 0.992, 0.988, 0.986, 0.984, 0.973, and 0.962 for chronic myelo- monocytic leukemia (CMML), refractory anemia with excess blast-I (RAEB-I), refractory anemia with excess blast-II (RAEB-II), refractory anemia with multi-lineage dysplasia (RCMD), Hypoplastic myelodysplastic syndrome (H-MDS), and refractory anemia with uni-lineage dysplasia (RCUD) respectively. Conclusion The negative predictive efficiency of our framework advocates its utility as a screening tool for the rapid expulsion of Dysplasia associated hematological neoplasms in hemat-oncology sections, aiding prompt care decisions.
A Survey on Machine Learning-Based Approaches for Leukaemia Detection

Leukaemia is one of the blood malignancies because of the strange expansion of white platelets in the bone marrow of the human body. A haematologist utilises microscopic investigation of the human blood, which encourages the need for techniques that are infinitesimal image shading, division, arrangement, and bunching, which permits the identification of a patient's experience with leukaemia. The manual infinitesimal assessment of bone marrow is less precise, tedious, and vulnerable to mistakes, which causes it to be hard for laboratory labourers to precisely perceive the qualities of impact cells. The additionally contrasted informational indexes and diverse shading models allow you to look at the presentation's changed shading pictures. Experts and professionals who work with stained photographs of leukaemia patients will benefit from this method's discovery of a crucial component. Algorithms for detecting leukaemia have been studied in this research. The methodology and efficiency are compared, which can be used for different analyses by the researchers.
APD: Learning Diverse Behaviors for Reinforcement Learning Through Unsupervised Active Pre-Training

Unsupervised pre-training in reinforcement learning enables the agent to gain prior environmental knowledge, which is then fine-tuned in the supervised stage to quickly adapt to various downstream tasks. In the absence of task-related rewards, pre-training aims to acquire policies (i.e., behaviors) that generate different trajectories to explore and master the environment. Previous research categorizes states into their associated behaviors by learning a supervised discriminator. However, an underlying problem persists: such discriminator is trained in lack of relevant data, leading to an underestimation of reward for new states and inadequate exploration. To this end, we introduce an unsupervised active pre-training algorithm for diverse behavior induction (APD). We explicitly characterize the behavior variables with a state-dependent sampling method, and the agent can decompose the entire state space into parts for fine-grained and diverse behavior learning. Specifically, a particle-based entropy estimator is applied to optimize a combination of behavioral entropy and mutual information objective. Moreover, we develop behavior-based representation learning to compress states into the latent space. Experiments show that our method can improve exploration efficiency and outperforms most state-of-the-art unsupervised algorithms on a number of continuous control tasks in the DeepMind Control Suite.
Developing an Intelligent Tool for Breast Cancer Prognosis Using Artificial Neural Network

Today, there is ample scientific evidence that Breast Cancer (BC) is a global health challenge given its prevalence and invasive nature. Therefore, early detection of BC can help minimize the devastating effects of the disease. This study aimed to design a Clinical Decision Support System (CDSS) based on the best Artificial Neural Network (ANN) configuration to identify patients quickly. Using a single-center registry, we retrospectively reviewed the records of 3380 suspected BC cases. The independence test of Chi-Square at P&lt;0.01 was utilized to select the most important criteria. Then the different ANN configuration was implemented in the Matlab R2013 environment and compared using some evaluation criteria. Finally, the best ANN configuration was obtained. After implementing feature selection, 20 variables were determined as the most relevant factors. The experimental results indicate that the best performance was obtained by the 20-25-1 configuration with PPV=90.9%, NPV=99.7%, Sensitivity=98.9%, Specificity=97.9%, Accuracy=98.1%, and AUC=0.958. The proposed software can identify cases of BC from healthy individuals with optimal diagnostic accuracy. Additionally, it might be integrated as a practical and helpful tool in natural clinical settings for easy and effective disease screening.
Deep Learning Based Frame Interpolation for Stop-motion Animation

This study focuses on deep-learning-based frame interpolation facilitating stop-motion animation production. Through deep learning of the movements of stop-motion animation, frame interpolation works systematically toward its own production. Based on an understanding of the exaggeration or deletion of motions, the proposed frame interpolation technique differs from other linear frame interpolation techniques. This project used the stop motion animation Teenie Scouts BigFive produced by Taktoon Enterprise, a subsidiary of Chung-Ang University Technology Holdings, which was created using 4K UHD high-resolution images at a frame rate of 15 fps.
Cyber Socio-Technological Problems of the Networked Society and Their Analysis

The article analyzes the history of the evolution of society and the internet socio-technological problems of the networked society are investigated.
The Study on the Potential Efficiency of Enhanced Learning within the Educational Environment

Student performance can be hindered by large class sizes, a lack of participation, and a lack of a sense of identity, all of which can be detrimental to student success. eLearning solutions can give chances to solve these issues by students to participate in self-confidently in their learning activities. Lessons can run as smoothly as possible no matter how many participants are in them and how far they are from their in-person peers thanks to various techniques such as video conferences, eLearning, and collaborative software. These tools can be used by academics who teach a fully remote class or a partially remote class. Educators and students should be more adaptable in these systems since nowadays are increasingly reliant on technology. The purpose of this study is to explain how technology may be utilized to find the efficiency of Technology-Enhanced Learning and how it can be sustained over time. It suggests that when technology is used to scale up access to high quality material, encourage customized instruction, increase opportunities for practice, or raise learning engagement, it may accelerate student learning by as much as possible.
Extraction of Event-Related Information from Text for the Representation of Cultural Heritage

In knowledge representation systems for Cultural Heritage (CH) there is a vast amount of curated textual information for CH objects and sites. However, the large-scale study of the accumulated knowledge is difficult as long as it is provided in the form of free text. By extracting the most significant pieces of information from textual descriptions of CH objects and sites and compiling them in a single comprehensive knowledge graph, conforming to a standard would facilitate its exploitation from multiple perspectives including study, presentation and narratives. The method proposed by this research work was to employ Natural Language Processing, and reinforcement learning for semantic knowledge extraction, and a knowledge representation standard of the CH domain for the knowledge graph thus making the extracted knowledge directly compatible with linked open data platforms and CH representation systems.
Dataverse.no

The service for open research datasets Dataverse.no was established in 2017. Five years later, it holds some 1,300 datasets created by researchers at fourteen partner institutions. All submitted datasets are curated (checked) before they are published by curators at the various institutions. In addition, curators have established courses and webinars helping researchers make their datasets as FAIR as possible (FAIR = Findable, Accessible, Interoperable, Reusable). In this episode, Leif Longva and Philipp Conzett tell about how it has expanded, from a subject-specific archive called TROLLing (Tromsø Repository of Language and Linguistics) to the generic, CoreTrustSeal-certified service that we see today.
Development of biology interactive multimedia based on vegetation diversity research results for high school students

The research objective was to determine the characteristics, feasibility, and practicality of biology interactive multimedia based on the results of research on the vegetation diversity on one of the beaches in Indonesia for grade X high school students. The focus of this research is to develop interactive multimedia for grade X high school ecology material. The research method used is research and development from Borg and Gall (1983). The product being developed is interactive multimedia biology that is packaged in a PowerPoint show so that it can be used with a laptop or device. The results of the validation assessment by multimedia experts and ecological materials show that the product is very feasible, while the assessment by the teacher shows that the product is very good so that it can be used for grade X high school students. Biology Interactive Multimedia (BIM) integrated with the research result of vegetation diversity can be used as an alternative media for biology in high school to improve the process and learning outcomes.
An Object Detection Framework with Modified YOLOv3 for Camera Based Perception Systems

An object detection framework based on modified YOLOv3 architecture is proposed for perception systems. In order to test the proposed object detector, a novel dataset consisting of road images for Indian road scenarios has been developed with various environmental conditions. The seven classes are considered in the object detection, in which five are vehicles frequently encountered during the Indian driving scenarios along with pedestrians and riders. The modified YOLOv3 promises the mean average precision of 84% for the detection of specified classes in the dataset. The F1 score and average IoU for the modified YOLOv3 are 81% and 71.78% respectively. The detection kernel is modified to train on the proposed dataset. The object detection results are compared with Faster Region-based Convolutional neural network (R-CNN), cascade R-CNN, single sort detector (SSD), baseline YOLOv3, and modified YOLOv3-tiny. The proposed object detector based on modified YOLOv3 yields improved mAP and hence it is more suitable for camera-based perception systems used in autonomous driving.
Risk Management Survey in the Context of Flood Events and Rob in Campus I Lambung Mangkurat University

North Banjarmasin District, such as Campus I Lambung Mangkurat University (ULM), which is on the street of Brigjen H Hasan Basry, is at risk of flooding and water inundation. The height of inundation that occurred in front of the main entrance of the campus ranged from 40 to 60 cm. Campus I ULM was inundated, and many buildings and classrooms were difficult to reach by motorcycle and car. The Regional Disaster Management Agency (BPBD) of Banjarmasin City says tidal flooding (rob) could occur in November 2021 because of the sea tides. The occurrence of tidal flooding (rob) was caused by the activity of sea tides and phases of the new moon, a spike in precipitation, and the possibility of high winds. A field survey has been conducted to collect this research data in October 2022. Interviews, questionnaires, observations, and reviews of flood-affected areas served as sources of information. Data collection via questionnaires was administered to respondents, including staff and lecturers of Lambung Mangkurat University. When filling out this questionnaire, people can look at what has happened in two ways: the probability and the impact of risk. The outcomes are flood risk vulnerability evaluations for the development of mitigation plans.
E-Voting System Using U-Net Architecture with Blockchain Technology

In order to give power to the right person, any democracy must have a fair voting system that meets the needs of the people. In India, there are two types of voting system: paper ballots and electronic voting machines (EVMs), both of which have similar drawbacks. The provision of security in a digital voting system has long been a major concern. In the proposed work, implemented an interface. Firstly, the user has to register after which they will be authenticated and validated through many phases such as OTP and IRIS recognition to establish whether they are valid or fraudulent voters. The election process will be done under blockchain technology. The blockchain technology is to provide transparency, security, reliability. Therefore, there will be an increase in the voting percentage because each and every vote counts; there will be no delay in calculating ballots.
Location Tracking via Bluetooth

The inclination to forget and misplace things gradually increases with aging, and this creates a difficult situation for elderly people to remember and locate the objects without assistance. In this novel era, this phenomenon is quite common with all groups to misplace the objects which are used frequently, for instance, vehicle keys, glasses, wallets, and so forth. Finding misplaced objects is usually time taking and makes an individual stressed out; if this situation is prolonged, it makes the individual miss deadlines, difficult to manage the activities, and eventually stressed out. To reduce the work of finding the misplaced objects and to support elderly people to manage the activities, the proposed application comes into play. Our application titled I AM HERE works using Bluetooth technology and iTag, which can be used to track the location of the objects using just a mobile phone. I AM HERE application mainly focuses on identifying the location of misplaced objects. An iTag is attached to the objects used regularly everyday and is commonly misplaced. The iTags are identified by the I AM HERE application using Bluetooth technology individually. The mobile application is developed in the Kodular platform which is an open-source tool. The application mainly targets the elderly people who rely on others for everyday activities to identify the location of their objects by using a smart mobile phone.
Resonant and non-resonant optimizations by multi-constraint quantum control theory in molecular rotational states

It is a promising research for optimization of quantum gate in the field of quantum computation. We investigate the feasibility of implementing the single-qubit gate (Hadamard) in molecular rotational system. By applying the Multi-constraint quantum optimal control method, the excepted final states can be achieved based on the molecular rotational states both in resonant and non-resonant cases with the control pulses. The permanent electric dipole moment is ignored in non-resonance. Besides, the zero-pulse area constraint and the constant fluence constraint are employed to optimize shapes of control pulses. Finally, we show that the Hadamard gate can be realized with the high fidelity (0.9999) and also examine the dependence of the fidelity on pulse fluence as well as the control pulse.