Reviewerly/exHarmony huggingface dataset:
Enhanced Mineral Quantification and Uncertainty Analysis from Downhole Spectroscopy Logs Using Variational Autoencoders

This paper describes an innovative machine-learning application, based on variational autoencoder frameworks, to quantify the concentrations and associated uncertainties of common minerals in sedimentary formations using the measurement of atomic element concentrations from geochemical spectroscopy logs as inputs. The algorithm comprises an input(s), encoder, decoder, output(s), and a novel cost function to optimize the model coefficients during training. The input to the algorithm is a set of dry-weight concentrations of atomic elements with their associated uncertainty. The first output is a set of dry-weight fractions of 14 minerals, and the second output is a set of reconstructed dry-weight concentrations of the original elements. Both sets of outputs include estimates of uncertainty on their predictions. The encoder and decoder are multilayer feed-forward artificial neural networks (ANN), with their coefficients (weights) optimized during calibration (training). The cost function simultaneously minimizes error (accuracy metric) and variance (precision or robustness metric) on the mineral and reconstructed elemental outputs. Training of the weights is done using a set of several-thousand core samples with independent, high-fidelity elemental and mineral (quartz, potassium-feldspar, plagioclase-feldspar, illite, smectite, kaolinite, chlorite, mica, calcite, dolomite, ankerite, siderite, pyrite, and anhydrite) data. The algorithm provides notable advantages over existing methods to estimate formation lithology or mineralogy relying on simple linear, empirical, or nearest-neighbor functions. The ANN numerically capture the multidimensional and nonlinear geochemical relationship (mapping) between elements and minerals that is insufficiently described by prior methods. Training is iterative via backpropagation and samples from Gaussian distributions on each of the elemental inputs, rather than single values, for every sample at each iteration (epoch). These Gaussian distributions are chosen to specifically represent the unique statistical uncertainty of the dry-weight elements in the logging measurements. Sampling from Gaussian distributions during training reduces the potential for overfitting, provides robustness for log interpretations, and further enables a calibrated estimate of uncertainty on the mineral and reconstructed elemental outputs, all of which are lacking in prior methods. The framework of the algorithm is purposefully generalizable so that it can be adapted across geochemical spectroscopy tools. The algorithm reasonably approximates a “global-average” model that requires neither different calibrations nor expert parameterization or intervention for interpreting common oilfield sedimentary formations, although the framework is again purposefully generalizable so it can be optimized for local environments where desirable. The paper showcases a field application of the method for estimating mineral type and abundance in oilfield formations from wellbore-logging measurements.
Research on Malicious TLS Traffic Detection Based on Spatiotemporal Feature Fusion

Aiming at the problem that traditional machine methods rely on expert experience and the effect of malicious traffic identification is not ideal, a deep learning hybrid model is proposed to detect malicious TLS traffic. The model combines one-dimensional convolutional neural network and two-way long-term and short-term memory network to compress and extract network traffic features from two dimensions of space and time series. At the same time, attention score of output information is extracted by attention mechanism, and traffic identification is carried out by using mixed features obtained by fully connected neural network. Based on the open data set, the experimental results show that the accuracy, recall and F1 value of the model on the test set reach 94.67%, 89.66% and 91.08% respectively, which has good recognition effect.
Genomic and Synthetic Biology Digital Biosecurity.

Trends toward automation of synthetic biology and the individualization of biology and medicine raise varied and critical security issues. Digital biosecurity brings together researchers working in secure algorithms, vulnerability assessments, and emerging threat models. The fundamental goal of this digital biosecurity workshop is to identify and present distinct areas of research around making the next generation of biology safer and more secure. The workshop will include a panel overview of the field, including representatives from academia, industry, and non-profits. It will also include novel presentations from the research community. We expect that attendees will leave this workshop with a new appreciation of the research and implementation challenges in maintaining the digital aspects of biosecurity.
Rapid Obstacle Avoidance Planning of Patrol Under Multi-obstacle Constraint

Unmanned Rover plays an important role in detection on the planet surface. As the carrier of scientific investigation, safe and reliable path planning is particularly important for the rover itself. Therefore, it is required that the rover can go to the mission target point through global planning, and it is also required that the rover can avoid local obstacles. In this paper, Dijkstra algorithm and dynamic window approach are combined to make the rover have the global planning ability to get the optimal solution and the local obstacle avoidance ability based on real-time perception. Three groups of physical experiments are carried out to verify the effectiveness of the obstacle avoidance algorithm in multi-obstacle environment.
Developing Automatic English Speaking Skills Testing System Using Speech Recognition

English teachers have been testing students speaking skill through student presentation and other kinds of direct testing.Testing the speaking ability of a big number of students is time consuming.Hence, an automatic model of testing English speaking skill is demanded.A computer assisted testing of English speaking using the technology of speech recognition comes handy.In this paper, we propose a tool to automatically score a student's English-speaking performance.The proposed system applies the speech recognition technology with the string-matching algorithm in PHP language to process the voice input of the test candidate for scoring.The development resulted two main products namely the web-based speaking test app, and the data set for the scoring system.The initial stage focus of the development is the trained data model for the scoring system.The data was tested using Confusion Matrix, and it resulted percentage in accuracy of 80, precision of 84, recall and sensitivity of 94, and F1 Score of 88.This concludes that the app can help the researcher enrich the data and refine the score for a better automatic English-speaking testing system.Hence, once the app and the data model are perfected, it is ready for efficiency in English speaking testing.
Comments on ``Convergence Analysis of Adaptive Exponential Functional Link Network''

This article is to comment on the derivation of the weight-update stability of in-parameter-linear nonlinear learning system with the gradient descent learning rule in the above article. Our comments are not to disqualify the commented article's whole contribution; however, the issues should be pointed out to avoid their proliferation.
Secured cloud data storage with industrial IoT

Cloud computing is the large storage area where resources are available from everywhere to everyone as service. Cloud computing is a type of computing based on shared resources than the local servers to handle various IoT applications such as healthcare, industry, agriculture, smart homes, and so on. Cloud computing is the utilization of different services like software development platforms, storage and software over the internet. Cloud computing is a novel computational paradigm that offers various services to the users due to its performance, high availability, and low cost. Security is an essential barriers for development of the cloud computing. Cloud security includes the set of policy, control and event to preserve the cloud-based system, data and infrastructure. Storage security in cloud is the method of protecting the stored data from unauthorized user access. With development of IoT, devices are linked to gather large information about the users and their activities without any interaction. A combination of IoT and industry is an essential one to promote automation and information of industry. IIoT minimizes the error, costs, increase efficiency and safety in manufacturing and industrial processes. For securing cloud environment data and IIoT, many research works has been introduced in recent times. But, the security attacks and failures cause great trouble against IoT network with lesser data confidentiality rate (DCR) and authentication accuracy (AA). In order to address these problems, different secured cloud storage techniques are reviewed in this study. This paper consists of a comprehensive overview of data storage, various methods of authentication, communication and cryptosystem techniques used to improve the security level during the data storage in cloud computing in cloud. This paper discusses about the better performance authentication, communication and cryptosystem techniques for secured cloud storage.
Pengujian Blackbox pada Aplikasi Penyimpanan berbasis Android menggunakan Teknik Boundary Value Analysis

Mobile app or commonly known as an application on a smartphone is one of the businesses that is currently being developed by many people to meet human needs in many ways. Mobile apps are very widely used because they are easy and almost everyone in Indonesia already has a smartphone. Of the various applications made to facilitate human work, one of which is the application of inventory or storage and recording related to assets or goods that are very important in an organization or company so that in the development of their business they can be analyzed at any time. However, every application creation cannot be separated from testing the application to overcome errors that occur when the application is used. To overcome and look for bugs and errors contained in the application, a test was made on the storage application using the black box testing method with the boundary value analysis (BVA) technique. BVA is one of the tests to find errors in applications from the user's point of view by testing the limit values at the upper and lower limits with the stages of identifying problems, selecting test data, test data, testing using boundary value analysis and also documentation. The results of the study using boundary value analysis as a whole have a success percentage of the application after testing is 5:1 with a success rate of 83.33%.
Linguistic Creativity of the Indonesian for Foreign Speakers (BIPA) Students at the Morphological, Syntactic, and Semantic Levels

The research aimed to describe the forms of linguistic creativity of BIPA students at the morphological, syntactic, and semantic levels. The research method used was descriptive qualitative with content analysis techniques. The research data were 78 sentences written by BIPA students at Bina Nusantara University academic year 2020/2021. The results show that BIPA students’ sentences have linguistic creativity in the form of the morphological, syntactic, and semantic levels sentences. At the morphological level, the process of linguistic creativity in words is carried out by (1) inserting foreign terms into sentences, (2) using non-standard words, and (3) abbreviating words. While in phrases, linguistic creativity is done by (1) reversing the structure of the phrase, (2) forming nominal phrases with less common word pairs, and (3) forming nominal phrases by incorporating elements of student culture. At the syntactic level, linguistic creativity is carried out in the form of compiling compound sentences with the expansion of descriptions, expansion of objects, expansion of subjects, and expansion of predicates. At the semantic level, linguistic creativity is manifested in the form of the figure of speech: personification, simile, hyperbole, repetition, and metaphor.
Program of the Second Debrief Meeting on Education and Research FY2021

Efforts to increase rice productivity by improving rice character's adaptability to dry acid soils are necessary.Hawara Bunar is one of the rice cultivars that has high adaptability in such soils.However, it produces a small number of tillers and has high habitus, which is susceptible to lodging.Plant height character is controlled by MOC1 gene, which is negatively regulated by TAD1 gene.When TAD1 gene is highly expressed, it will cause MOC1 protein degradation, and the plant will become tall.Conversely, when MOC1 protein is abundant, the plant becomes shorter and produces more tillers.To maintain MOC1 protein abundance, we need to knock out the TAD1gene through a gene-editing technique such as CRISPR/Cas9.The research objective was to design sgRNA from TAD1 gene sequence in rice cv.Hawara Bunar and predict mutation in the target gene that will be edited using CRISPR/Cas9.An oligo sequence of sgRNA was designed based on TAD1 gene sequence using chop-chop website.The oligo sgRNA position on the exon1 of the target sequence was used to guide Cas9 to modify the TAD1 gene and became a reference to predict mutations that will likely occur.The designed sgRNA was converted into an oligo duplex, ligated with the expression vector pRGEB32, transformed into E. coli TOP10, and then introduced into Agrobacterium tumefaciens cells.Mutations were predicted to occur in the sgRNA target site and could produce insertion/deletion.We will discuss the details of sgRNA Vector construction and predicted mutations in the presentation.
ORGANIZATION OF WORK WITH STAKEHOLDERS OF THE EDUCATIONAL PROGRAM «PHYSICS» IN NEW CONDITIONS

At the present stage of the development of pedagogical education, the problem of training a physics teacher who is ready to work in the conditions of a new reality, a digital educational environment, instability and multiple non-standard situations is of particular relevance. Educational programs that should provide training for such a teacher are undergoing modernization. This process requires the direct participation of as many stakeholders as possible. Work with stakeholders of the educational program «Physics» should be a holistic process and be based on a fundamentally different methodological basis, which has a complex character. The main methodological approaches to solving the problem of organizing work with stakeholders in the new conditions are the system and cluster approaches. The article analyzes the corrective actions of external stakeholders on the development of the educational program «Physics», which is currently being implemented at the Kazakh National Pedagogical University named after Abai.
An Overview of Facebook’s Journey to Meta - A Case Study

Purpose: Facebook has far more than 2.91 billion users worldwide, as of October 2021 by following its commitment to “Give people the power to build community and bring the world closer together”. Machine learning as well as applied Machine learning helps people to get new contents and related stories. Machine learning (ML) is a technique for recognizing and drawing conclusions from data connections. Speech recognition systems can caption videos on Facebook using machine learning, making them more accessible. The Facebook research team is working with Machine learning technology to give its users the best services. Here an analysis has been done on the history of Facebook, major acquisition of Facebook, applications of AI in Facebook and Financial plans and challenges. The entire Facebook company will now be known as Meta. The Facebook app, on the other hand, will keep its name, and other apps will not be affected. Facebook is now known as 'Meta' because of its new focus which means 'after' or 'beyond' in Greek. However, Instagram, Facebook, WhatsApp, and Messenger are now all part of the 'Meta' company, much like Google's products are all part of the Alphabet company. Artificial intelligence, AI is really essential to Facebook. Approach: For this analysis, a sophisticated survey strategy based on secondary data was applied. Findings: Based on the findings, it is clear that to provide high-quality service, Facebook uses leading edge artificial intelligence/machine learning technologies. Originality: To identify the methodologies employed in the organization's services, a study is conducted based on the existing resources. Paper Type: Descriptive Case Study Research
Analysis of Nominal Pedagogical Experimental Data Using the Example of the Development of Conflict Competence

The paper examines the problem of using mathematical statistics methods in pedagogical research. It identifies and describes the possibilities of using different methods of analysis of nominal data in processing the results of pedagogical experiments. The authors analyzed the frequency of use of statistical criteria in 100 publications in journals included in Web of Science and the Russian Index of Scientific Citation. The criteria for selecting publications were (1) year of issue; (2) reviews of manuscripts; (3) free access to the publication from the publisher; (4) availability of quantitative data on test results before and after pedagogical impact; (5) conclusions about the effectiveness of pedagogical impact. It was found that 45% of publications did not use mathematical statistics methods to test scientific hypotheses. Of the mathematical statistics methods used by the authors, the T-Test and Chi-square Test are more often used. For the study example of developing conflict readiness in future teachers, it presents the possibilities of using: Chi-square Test, Fisher’s Exact Test (Phi test), Cramer 's V Test and Somers’ d Test.
Visual Analysis Of Data Sharing Research Hotspots In China Based On CiteSpace

Taking the CNKI, 2016-2021 by the research literature as data source, data sharing uses CiteSpace information visualization technology, the key words were cited centricity and frequency, clustering and keyword research hot spots are discussed, analysis data sharing the annual identificated found: 2016-2021, the domestic data sharing identificated showed a trend of gradual growth; There are many cooperative groups of authors, but the level of research cooperation among core authors is relatively low. Domestic research on data sharing has rich contents and a wide range of research, but the shortcoming is that there are many repetitive studies and the research is not in-depth. The research hotspots and change trend of data sharing in different years are revealed through keyword citation mutation of literature, which has positive enlightenment significance to the later development of data sharing.
The Construction and Practice of Multimedia Intelligent Classroom in the Information Age

Multimedia classrooms are an important means of teaching technology and an important part of campus informatization. At present, multimedia classrooms mainly have problems such as old equipment, cumbersome management, primitive methods, and a lot of waste of resources. Therefore, using educational information technology integration methods to explore how to build old multimedia classrooms into smart multimedia classrooms, Taking Guangdong Engineering Polytechnic as an example, starting from the background, plan, and purpose of project construction, design the functional structure of smart multimedia classrooms. It focuses on the effect of the integrated construction of multimedia classroom functions, which provides ideas for the construction of a smart campus.
Application of Perceptual Video Hashing for Near-duplicate Video Retrieval

Because of the rapid growth in the VLSI technology, the speed of the Internet and the easy availability of editing software, the number of near-duplicate videos (NDVs) produced, edited or viewed has increased manifold. This has made the effective database management, and the video copyright protection is a challenging problem. To identify the NDVs in the database, a perceptual video hashing scheme for the content-based retrieval of NDVs is proposed in this paper. The perceptual video hashing algorithms used to design the near-duplicate video retrieval (NDVR) application are based on (1) the Achlioptas’s random matrix (ARM), (2) the temporal wavelet transform (TWT) and the ARM, (3) the Tucker decomposition and (4) the parallel factor (PARAFAC) decomposition. The performance of the NDVR application is evaluated using the average precision–recall curves on three video databases created from REEFVID, XIPH, OPEN-VIDEO and TRECVID data sets using various content-preserving operations and malicious modifications. The experimental results on a moderate-size video databases show that the Tucker decomposition-based video hashing algorithm performs better in retrieving NDVs.
Development of Informed Rapidly-Exploring Random Tree Focused on Memory Efficient Path Planning

Based on Rapidly-exploring random trees (RRTs) algorithms, this work demonstrates a new algorithm, iRRT* FN. The algorithm is a modified version of Informed RRT*, using a memory-efficient planning approach. It runs identically as Informed RRT* before the nodes in the tree reach a predetermined limit of nodes. Then the algorithm releases the memory load by pruning nodes in the tree, which are irrelevant to finding the goal. We test the algorithm on 2D models compared to RRT*. Not only do iRRT* FN has a higher convergence rate finding the optimal path, but the algorithm is also proven to have a higher memory efficiency. Especially in the real-world motion planning scenario, which has a large sample space, limiting the number of nodes in the tree can immensely improve the efficiency of RRTs.
Fault tolerant practical quantum digital signature against collective noises

Quantum digital signature (QDS) has been proved to be secure in theory, but will inevitably be interfered by channel noise during the practice transmission of qubits. We propose two practical fault tolerant quantum digital signature protocols for the collective noises. For resisting the collective noises, a decoherence-free subspace (DFS) containing four logical qubits has been constructed, which improves the performance of QDS protocols in terms of communication fidelity. Moreover, we prove that the protocols are secure against forging and repudiation attacks, and further discuss the influence of different verification thresholds on the security and give a quantitative analysis.
Collaborative decision making system in intelligent transportation system using distributed blockchain technology

Intelligent Transportation System (ITS) is one of the promising applications of the Internet of Things (IoT) as the IoT system provides an easy way to collect and monitor the system. One of the critical components to make a city smart is by managing the traffic. The mode of transportation in the city is different, like bike, car, bus, auto, and rickshaw. Most of the vehicles integrated with Information Communication Technology (ICT). As the vehicles share and access the information from the ITS infrastructure, some security issue exists. The security issues are trust management, privacy, data linking, and computational problems. In this paper, the authors first identify the security issue present in the ITS model, then propose a distributed architecture of the ITS system using Blockchain. Then, the Consensus algorithm is used to perform computations in a distributed platform. Ethereum platform used to create a distributed network. The implementation and security analysis are given at the end.