Reviewerly/exHarmony huggingface dataset:
Sensor data modeling with Bayesian networks

This research aims to extract knowledge of sensors behavior resorting to Bayesian networks (BNs) and dynamic Bayesian networks (DBNs), a time-based BN version. These two types of models belong to the group of probabilistic graphical models (PGMs). These graphical models can be very useful to get insights from data in order to improve sensor capabilities in the industry of fire detection systems, since it can provide the conditional dependence structure among various sensor variables. Relevant sensors with fire alerts were selected and studied at device level. We conduct a data fusion analysis since we deal with heterogeneous data sources, Remote Alert (RA) with sensor states and Condition Monitoring (CM) with numerical data. To achieve an accurate fusion of the data, a pipeline was designed to align both sources of data in a regular time interval. Furthermore, a change point detection (CPD) method was used to discretize the numerical variables. In addition, one-hot encoding was used to create binarized datasets and combine all data (RA+CM). Our modeling helps understanding the dependencies among the sensor variables, highlighting that individual devices of the same type can have a very different probabilistic behavior along the time, probably due to be installed in distinct regions. Moreover, the models helped capturing strange probabilistic sensor behavior such as a low probability of a NORMAL state happening given that states FIRE, WARNING and TROUBLE did not happen.
“Alone in Space” - Escape Room Game for VR

Virtual reality (VR) technology has undergone a huge breakthrough in development and distribution over the last few years. Now it is becoming an increasingly popular tool not only in the field of creating computer games but also in industry and educational process. From the beginning, VR devices were big, uncomfortable, and very expensive. For this reason, they were used mainly in research laboratories, high-tech enterprises, and military installations. Currently, leading companies in the field of new technologies, such as HTC, Google, Sony, Samsung, have filled the market with compact and accessible devices for nesting in the VR. They managed to create a completely new category of consumer goods. The paper is devoted to the study of VR technologies and their use for the development of our own free-of-charge game. The created game combines several genres, escape room with elements of the genre of Shooter (FPS) and Logic. Our game could be used to practice logic and observation.
An Inclusive Task-Aware Framework for Radiology Report Generation

To avoid the tedious and laborious radiology report writing, the automatic generation of radiology reports has drawn great attention recently. Previous studies attempted to directly transfer the image captioning method to radiology report generation given the apparent similarity between these two tasks. Although these methods can generate fluent descriptions, their accuracy for abnormal structure identification is limited due to the neglecting of the highly structured property and extreme data imbalance of the radiology report generation task. Therefore, we propose a novel task-aware framework to address the above two issues, composed of a task distillation module turning the image-level report to structure-level description, a task-aware report generation module for the generation of structure-specific descriptions, along with a classification token to identify and emphasize the abnormality of each structure, and an auto-balance mask loss to alleviate the serious data imbalance between normal/abnormal descriptions as well as the imbalance among different structures. Comprehensive experiments conducted on two public datasets demonstrate that the proposed method outperforms the state-of-the-art methods by a large margin (3.5% BLEU-1 improvement on MIMIC-CXR dataset) and can effectively improve the accuracy regarding the abnormal structures. The code is available at https://github.com/Reremee/ITA .
Efficient Question Answering in Chatbot Using TF-IDF and Cosine Similarity

Chatbots have gained more popularity in recent years with new technologies. Chatbots are available on all the platforms like WhatsApp, Web browsers, social media, mobile phones. Most of the available chatbots are not able to answer all user queries. They could not figure out the correct answer from the stored records. Chatbots give replies to some fixed questions. This paper proposes an effective method to search for a response to a user query. For this purpose, we used cosign similarity along with term frequency-inverse document frequency (TF-IDF). The proposed solution help to chatbot finds the correct answer to the user query from the available data. This paper collected information about kidney diseases from various resources and stored it in a file. We developed an interactive chatbot that will search datasets and figure out the answer to a question asked by a user. This paper explores and evaluates how the current chatbot technologies assist users questioner. The proposed chatbot will respond if it is available in the datasets. Cosine similarity is the fundamental formula for answering, and TF-IDF will help search for the best in all available documents. Our result shows 85% accuracy in figuring out the correct answer that is most suitable for NLP research. Experiment and questionnaire evaluation results show that chatbots could help solutions and reduce human intervention. Our chatbot also performed better than the counseling service in the healthcare platform on which the chatbot is based.
Rancangan Storyboard Aplikasi Pengenalan Isen-Isen Batik Berbasis Multimedia Interaktif

Culture is a wealth that must be maintained and preserved by the people of Indonesia. One of the Indonesian cultures that is still growing and developing today is the art of batik. Batik is a cultural heritage that has been designated by UNESCO since 2009 as a human heritage for oral and non-material culture (Masterpieces of the Oral and Intangible Heritage of Humanity). Batik has its own beauty, a variety of color patterns and curves of lines strung together into a beautiful unit that is written on the cloth. Adding aesthetic value in the form of isen is a very appropriate thing to beautify batik cloth. In every design, patterns and motifs contained in the fabric, of course it has its own meaning and philosophy. The aesthetic value in batik isen is an addition to the soul of batik, this is because isen is only used to add decoration to enrich the motif as well as to build a certain meaning that the creator wants to convey. Designing technology in the form of interactive multimedia applications as outlined in a storyboard is one of the important steps that must be taken in developing applications for introducing batik isen-isen. The results of the research carried out are, a storyboard design that has been completed has been implemented into an interactive multimedia application to introduce isen-isen to the public. The results of tests carried out using a black box test, it was found that the application was running according to the function and design of the storyboard that was made.
‘Time Capsule’ Archiving through Strong Dark Archives (SDA): Designing Trustable Distributed Archives for Sensitive Materials

Archivists regularly contend with a wide range of security threats, including data breaches, inadvertent loss, and legal action by those hoping to make sealed records public. These threats are particularly salient when sensitive materials are donated with delayed-release conditions. Trust in archivists’ ability to enforce such conditions gives donors the confidence to enter into the historical record materials that they might otherwise destroy. But as these materials are increasingly born-digital (and therefore hackable, convenient to exfiltrate en masse, and more easily corrupted), and as governments and private parties become ever more aggressive in their efforts to secure early releases, we must innovate in order to stand still. To compensate for these new dynamics, we propose Strong Dark Archives (SDA), a blended legal and technical protocol for securing delayed-released archival materials among a network of libraries. SDA leverages modern cryptography and institutional agreements to coordinate access-control across multiple accredited archival organizations, providing broad resilience to data breaches, technical failures, and legal process. Through this distributed approach to security, SDA imposes meaningful friction on efforts to force the early disclosure of archival records.
Blockchain-based raw material shipping with PoC in Hyperledger Composer

In today?s world, a lot of various kinds of raw materials are shipped from one place to another as per the requirement of industries. This shipping process involved many multiple levels with multiple personalities or authorities. The intermediates may be influenced by some illegal external factors and there may be some theft or modification in the raw material which is in the shipping process. This generates a significant loss if the material is of high cost. Presently, the advancements in information technology precede a method to restrict this loss and it is blockchain. Blockchain technology is an essential feature in enabling a comprehensive view of events back to origination. The shipping chain of raw materials that provides integrity and tamper resistance for raw materials in the shipping process is proposed in the manuscript. we have also provided proof of concept (PoC) in Hyperledger Composer with performance evaluation.
Blocked chains of governance: Using blockchain technology for carbon offset markets?

Information and communication technology (ICT) plays a critical role in environmental governance; however, research into power in governance has not focused on the impact of ICT. In this study, we analyze the use of blockchain in a voluntary carbon offset market using the “Four Faces of Power” (compulsory, institutional, structural, and productive) conceptual framework to determine how ICT can change the power dynamics within a network of stakeholders. Proponents have proposed that blockchain technology can solve several issues that carbon marketplaces and offsets face, such as cybersecurity, traceability, and financial liquidity. Despite these proposals, there is little scholarship on existing cases using blockchain in carbon offsets. We found that the use of blockchain technology by the company Veridium changed the compulsory and institutional power dynamics within the network of stakeholders it was a member of. Veridium’s choice to use blockchain technology was likely the result of structural and productive power dynamics surrounding the technology at the time. The power dynamics changed because the use of blockchain for Veridium’s carbon offset market caused additional stakeholders to join the network of stakeholders. The new stakeholders held greater compulsory and institutional power than Veridium. This research contributes to the limited scholarship focused on ICT and power in environmental governance. Empirically it contributes to the ongoing discussion around the possibilities of blockchain technology for climate policy.
A Minimal Disclosure Signature Authentication Scheme Based on Consortium Blockchain

The traditional identity authentication method not only brings single point of failure, but also easily leads to privacy leakage. In order to solve the problem of information leakage caused by identity authentication, this paper proposes a consortium blockchain-based minimal signature authentication scheme MDSA. We use the structure of Merkle Tree to generate digital credentials for users. In particular, this scheme combining zero-knowledge proofs to provide compliance proofs for user identity attribute values to further achieve minimal authentication. We propose an attribute-based authentication model. In general, our scheme allows for self-sovereign identity, secure creation, and distributed storage. We use FISCO BCOS-based blockchain prototype system as our testbed, and the evaluations show that MDSA is feasible and secure, with linear increases in credential generation and authentication overhead.
SOCIAL DISTANCE DETECTION

COVID-19 is an illness caused by the SARSCoV-2 virus. The majority of COVID-19 patients will have low to normal symptoms and will recover without requiring extra care. Otherwise, some patients will become extremely sick and require medical support. A Deep Learning model is made to track people and measure the distance between them. To prevent the transmission of the infection, making people maintain the minimum distance is the key objective. We apply YOLOv3 and other convolutional neural network-based methods, as well as a distance measurement method, to recognise individuals. This Deep Learning model can determine whether members of a large group maintain their distance from one another. If they maintain a 6 feet distance, it will be indicated in green; if not, it will be highlighted in red. This can help identify social disparities and lessen the spread of COVID-19
ALL-018 Application of Machine Learning in the Diagnosis of Acute Lymphoblastic Leukemia

The ordinary morphologic diagnosis of acute lymphoblastic leukemia (ALL) by a pathologist depends on examining the patient's peripheral blood (PB) together with the bone marrow (BM) blood films. However, this manual aspect of diagnosis is susceptible to discrepancies. We now have a newly introduced technology that allows us to overcome individual variation in the diagnosis of ALL, so-named machine learning, which depends on a complex, preprogrammed convolutional networks matrix.Challenging machine-aided systems that utilize microscopic blood film images to recognize and diagnose ALL based on a preprogrammed deep convolutional neural network (CNN), i.e., machine learning algorithms.We collected a dataset of images composed of PB and BM smear images of two classes: ALL and normal control blood. We analyzed 192 samples of digital images: 96 images of patients with ALL and 96 images of healthy normal controls. For each smear sample, we collected the results of clinical data (clinical history and examination) and laboratory data (morphological, cytochemical, and immunophenotyping assessment). We challenged seven types of CNN models to diagnose ALL: AlexNet, VGG16, VGG19, GoogLeNet, ResNet50, ResNet101, and Inception-v3.Comparing the ability of seven models to diagnose ALL revealed that the AlexNet had the lowest accuracy of 95.51%, followed by VGG16 (92.13%) and VGG19 (93.83%). Inception-v3 had the highest accuracy (99.98%) and was able to detect almost all ALL cases.The statistical measures of the Inception-v3 performance revealed promising results in detecting ALL cases: the sensitivity, specificity, and accuracy of Inception-v3 reached 99.98% for detection of ALL.
A Novel, Energy-Efficient Smart Speed Adaptation Based on the Gini Coefficient in Autonomous Mobile Robots

Using energy efficiently is an important parameter in mobile robots. The majority of the energy consumption takes place in the motors. As such, past studies have investigated how to reduce the usage time of motors. Although the relationship between task energy and speed energy is considered in these studies, the qualification of the task, the amount of energy used, and the speed relation have not been taken into account as a whole. Parameters that affect each other in determining the speed profile, such as the criteria by which energy saving is determined, the maximum speed limit, acceleration, the load, and the ground relation, have not been taken into account holistically. Another research focus concerns the need to distribute energy in a balanced manner, in accordance with the qualification of the task, and to ensure the movement occurs at the optimum speed. In this study, a new dynamic (online) intelligent speed and acceleration adaptation method, based on the task structure and energy balance, was developed for a specific path that overcomes the shortcomings of existing models. The Gini coefficient was used for the balanced distribution of energy. Sharp speed changes were prevented with the remaining path and the balanced distribution of the remaining energy. The current model is compared with the trapezoidal speed profile structure.
VMAPD: Generate Diverse Solutions for Multi-Agent Games with Recurrent Trajectory Discriminators

Recent algorithms designed for multi-agent tasks focus on finding a single optimal solution for all the agents. However, in many tasks (e.g., matrix games and transportation dispatching), there may exist more than one optimal solution, while previous algorithms can only converge to one of them. In many practical applications, it is important to develop reasonable agents with diverse behaviors. In this paper, we propose ”variational multi-agent policy diversification” (VMAPD), an on-policy framework for discovering diverse policies for coordination patterns of multiple agents. By taking advantage of latent variables and exploiting the connection between variational inference and multi-agent reinforcement learning, we derive a tractable evidence lower bound (ELBO) on the trajectories of all agents. Our algorithm uses policy iteration to maximize the derived lower bound and can be simply implemented by adding a pseudo reward during centralized learning. And the trained agents do not need to access the pseudo reward during decentralized execution. We demonstrate the effectiveness of our algorithm on several popular multi-agent testbeds. Experimental results show that VMAPD finds more solutions with similar sample complexity compared with other baselines.
Hybrid Cloud Scaleout: Orchestrating Workloads with GitLab

The project The FAIR Data Spaces project aims to create a common cloud-based data space for industry and research by connecting services already created in Gaia-X, IDS, NFDI and EOSC, and demonstrates this in its own demonstrators. The demonstrator FAIR Data Quality Analysis and Workflows is giving researchers a platform to define and run workflows for FAIR data, the demonstrator aims to serve as a showcase for a hybrid cloud scale-out scenario. While running user defined workflows on research data stored in git repositories, the essence of the demonstrator is hiding the technical complexity of the hybrid cloud scale-out that is needed to supply the computational power for running the workflow steps. In order to achieve this the demonstrator uses state-of- the-art cloud technologies combined with the most recent developments from the Gaia- X frameworks.
Parallel Implementation of PIPO and Its Application for Format Preserving Encryption

The PIPO block cipher, a domestic lightweight block cipher, was announced at ICISC’20. In particular, the bitslicing technique is implemented in the S-Layer for the PIPO block cipher. Because this is a part that can be operated in parallel, we implemented the PIPO block cipher efficiently in a parallel approach through AVX2 instructions, and provide implementations for ECB and CTR modes. Compared to the existing PIPO implementation, we achieved a performance improvement by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.345\times $ </tex-math></inline-formula> . In addition, we applied the AVX2-PIPO implementation to the round function of format-preserving encryption. When repeatedly encrypting 128-byte plaintext, we achieved performance similar to that of the existing FF1-AES implementation. The FF1-AVX2-PIPO implementation successfully encrypted the database and enabled efficient database management in terms of memory space and speed factor. Finally, AVX2-PIPO-CTR and FF1-AVX2-PIPO were applied to image processing. In the case of CTR mode, the encryption performance was better than that of ECB mode. Partial encryption with object detection and FF1-AVX2-PIPO was successfully performed, and it is expected that privacy protection in CCTV or image processing can be improved.
Design and Implementation of a Medical Question and Answer System Based on Deep Learning

Medical services play a pivotal role in people’s lives and in the national economy. Although the number of healthcare facilities is currently growing every year, there are still major problems in terms of access and pressure on the flow of people. Therefore, there is an urgent need for complementary medical services to alleviate the flow of patients and their psychological burden and to enable them to receive timely medical advice. This article designs and implements a medical Q&amp;A system based on deep learning. We took a retrieval-based approach, using crawler technology that has been manually reviewed to build the Q&amp;A database, and the Seq2Seq algorithm and the TF-IDF model to build the answer generation model. The medical question and answer system developed enable effective Q&amp;A and relevant medical advice to be given. The algorithm proposed in this paper can quickly provide users with accurate answers compared to conventional search methods in real datasets.
A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset

The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling Technique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' performance. The SMOTE filter is examined before and after applying feature selection approaches to measure the model's accuracy with supervised and unsupervised algorithms. Three supervised/unsupervised algorithms are examined based on feature selection approaches to predict the students' performance. The findings show that supervised algorithms (LMT, Simple Logistic, and Random Forest) got high accuracy after applying SMOTE without feature selection. The prediction accuracies of unsupervised algorithms (Canopy, EM, and Farthest First) are enhanced after applying feature selection approaches and SMOTE filter.
The Young Researcher’s Guide to Creating Data Mastersheets

Researchers who have completed a study need to transfer their data from individual patient research files to a mastersheet. This article is a beginner's guide to creating data spreadsheets that can be shared with others and imported into statistical programs. The article discusses choice of spreadsheet software, use of separate worksheets for main data and granular data, use of columns for variables and rows for patient data, coding data, entry of data into cells in the worksheets, and other matters.