Reviewerly/exHarmony huggingface dataset:
Nanotechnology based Mega Machine Design for Large Scale Air Cleaning – Prospects and Challenges

Air pollution is a major component of environmental degradation in the 21st century worldwide due to the increased use of fossil fuels in various industries and their contribution to contaminating the natural atmosphere around us. Environmental pollution is posing a challenge to human existence and the continuation of human generation on this planet. Environmental degradation is considered a major affecting factor of climate change which includes both global warming driven by industrial emissions of green gases and large-scale shift in weather patterns. This problem is growing on a large scale throughout the world and poses challenges to survival and sustainability. Nanotechnology being universal technology can be used to solve this problem of environmental degradation by cleaning the degraded air on large scale and can counter the contribution of natural and industrial pollution. In this paper, the proposal of construction and use of nanotechnology based mega machine is analyzed and the prospectus and challenges of such machine production and maintenance for large scale air cleaning are discussed. The advantages, benefits, constraints, and disadvantages of production and use of such mega machines from various stakeholders’ points of view are identified. Finally, the idea of self-sustainable nanotechnology filters based mega machines is proposed using solar/wind energy supported systems for automatic and self-sustainable use.
Bio-SODA UX: enabling natural language question answering over knowledge graphs with user disambiguation

The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Hence, these approaches often cannot be applied directly to complex scientific datasets where no prior training data is available. In this paper, we focus on the challenges of natural language processing over knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA, a natural language processing engine that does not require training data in the form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a generic graph-based approach for translating user questions to a ranked list of SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm that includes node centrality as a measure of relevance for selecting the best SPARQL candidate query. Our experiments with real-world datasets across several scientific domains, including the official bioinformatics Question Answering over Linked Data (QALD) challenge, as well as the CORDIS dataset of European projects, show that Bio-SODA outperforms publicly available KGQA systems by an F1-score of least 20% and by an even higher factor on more complex bioinformatics datasets. Finally, we introduce Bio-SODA UX, a graphical user interface designed to assist users in the exploration of large knowledge graphs and in dynamically disambiguating natural language questions that target the data available in these graphs.
Real-Time Effective Monitoring and Control in Oil and Gas Industry Using SCADA Technology as a Management Tool

Pipeline leakage detection, if not addressed swiftly/promptly, could result to loss of lives and properties. Researches have shown that the best means of curbing or drastically minimizing these losses is through real time monitoring using DCS and CAO-SCADA Technology as the management tool. This tool was adopted in this work and then applied to a particular oil and gas terminal in Nigeria. Research questions were generated and data obtained as well as the research instrument used were validated. A total of one hundred and thirty-five respondents were used for the study. From the formulated testing based on z-test, three hypotheses were investigated termed as HO1, HO2 and HO3. HO1 test hypothesis of pipeline failure as a result of leakages along it and if the leakage detection is because there is no DSC, CAO-SCADA management tool along the pipeline. The result obtained showed that z-calculated is less than the critical value of 1.96 with a significant level of 0.05. From the results obtained, the hypothesis HO1 is accepted, thus failure of this pipeline is because the DSO, CAO-SCADA is not present in the system. HO2 testing determined if there exists any difference significantly between the DSC and CAO-SCADA system. From the result, the z-calculated which is 0.0065 is less than the critical value which is 1. This implies that the null hypothesis (HO2) is accepted. Meaning that there is no significant difference between CAO-SCADA and the DCO for the effective management and monitoring and safety operations in the pipeline. The last hypothesis is to test if there exists significant difference between DCS and CAO-SCADA system for effective industrial system control of leakages in pipeline. Out of the 135(100%) respondents, 106(78.52%) agreed with the recommendations made by the researchers while 12.59% partially agreed. 4(5.18%) have a clear understanding and 5(0.0370%) are indifferent about the recommendations made.
Modeling and Analysis of Multifocus Picture Division Algorithm Based on Deep Learning

As a complex machine learning algorithm, deep learning can extract object shape information and more complex and advanced information in images by using a deep learning model. In order to solve some problems of deep learning in image feature extraction and classification, this paper designs a modeling method of multifocus image segmentation algorithm based on deep learning. The acceleration effect of FPGA (field programmable gate array) on deep learning and weight sharing is analyzed. By introducing deep learning, the trouble of determining the weight coefficient is eliminated, and the energy function is simplified. Therefore, the relevant parameters of multifocus image segmentation can be easily set, and better results can be obtained. The multifocus image segmentation algorithm based on deep learning can not only obtain closed and smooth segmentation curves but also adaptively deal with topology changes due to high segmentation accuracy and stable algorithm. The results show that the model effectively combines the local and global information of the image, so that the model has good robustness. The depth learning algorithm is used to calculate the average value of local inner and outer pixels of an image. Even for complex images, relatively simple contour curves can be obtained.
African researchers do not think differently about Open Data

A key motivation for Open Science is accessibility. For researchers in resource-poor economies, this translates into access to the methods, data and publications that will foster scientific research and discovery in such communities and environments. Attitudes toward Open Science are in flux, and there is a growing awareness of the roles and responsibilities that researchers have to one another in this regard. This paper explores how African researchers approach issues relating to Open Data by reporting on the State of Open Data Report data. Focusing on the attitudes toward Open Data, this paper reports on how African researchers view (i) data sharing, (ii) the use of shared data, and (iii) the Open Data ecosystem. The findings show that, although the attitudes of African researchers have changed over time, they are not very different from those held by their international counterparts. These findings will aid policymakers, as well as academic and research institutions, in highlighting the areas of future growth for Open Data in Africa.
Formation of ICT competencies of a future mathematics teacher when teaching stochastics in the context of digital transformation of education

The inclusion of the author’s course of studying the functionality of the “Electronic Perm Educational System” (EPES) in the university training of a pedagogical sphere specialist contributes to the development of the ability to work in a digital environment in basic and additional education. The teaching of this course is focused on the use of technologies related to the implementation of educational engineering, which has now gone beyond the scope of technical specialties and has spread to the field of training specialists in the field of pedagogical design, design of educational programs and trajectories. Particular attention in this course is paid to the formation of ICT competencies of future teachers in the development of digital products in the process of studying specialized disciplines and elective courses, in particular, such as “Probability theory and mathematical statistics”, “Electronic Perm Educational System”, implemented on the basis of competence oriented, design and engineering approaches. The possibility of accumulation of these approaches in the work of student design workshops in the EPES Library cluster is demonstrated. As a result of the activities of such workshops, a digital educational product (DEP) is supposed to be needed in real learning practice and has prospects for further use by the student already in the role of a teacher after graduation: a lesson script, an electronic test or other educational content. Special tasks for students are characterized, aimed at developing and diagnosing the ICT competence of future mathematics teachers at the Perm Pedagogical University. Usually, the solving of the task includes the following stages: development of annotations, creation of a passport and subject content of the DEP, organization of approbation, presentation, defense and publication of the developed materials in EPES. The description of diagnostics of ICT competence of a future mathematics teacher in three of its components is given: general user, general pedagogical and subject-pedagogical.
Synthetic Biology 2.0: The Dawn of a New Era

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTSynthetic Biology 2.0: The Dawn of a New EraHuimin Zhao*Huimin ZhaoDepartment of Chemical and Biomolecular Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States*Email: [email protected]More by Huimin ZhaoCite this: ACS Synth. Biol. 2022, 11, 7, 2221Publication Date (Web):July 15, 2022Publication History Published online15 July 2022Published inissue 15 July 2022https://doi.org/10.1021/acssynbio.2c00332Copyright © Published 2022 by American Chemical SocietyRIGHTS & PERMISSIONSArticle Views1739Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (541 KB) Get e-AlertsSUBJECTS:Bioengineering and biotechnology,Biology,Biotechnology,Synthetic biology,Systems biology Get e-Alerts
Motion Control Analysis of Tennis Robot Based on Ant Colony Algorithm

Visual recognition and automatic control technology is an important way to realize robot automatic ball picking. Therefore, a tennis robot motion control method based on ant colony algorithm is evaluated. After the camera position was fixed, the motion control system software was designed, and the optimal path was solved by ant colony algorithm. The results show that the two adjacent positioning errors and the current total positioning errors fluctuate around -0.05 mm and -2.29 mm, respectively, and the fluctuation range is less than 3.50 mm. Ant colony algorithm is superior to greedy algorithm in path planning of collecting tennis balls. The number of iterations of ant colony algorithm for optimal path planning of 30 to 50 tennis balls is about 20 to 30 times, and the path length is reduced to reduce the time of collecting tennis balls, which meets the actual work requirements.
Temporal grafter network: Rethinking LSTM for effective video recognition

Long short-term memory (LSTM) networks are widely used to handle temporal or sequential data, and have great potential for video recognition. Existing LSTM-based video recognition methods either insert LSTM modules at the end of 2D convolutional neural networks (CNNs), called global LSTM methods, or build networks solely by stacking multiple LSTM modules. Unfortunately, these LSTM-based video recognition methods are not competitive, compared to state-of-the-art 3D CNNs or two-stream CNNs. In order to fully explore the potential of LSTM, this paper rethinks its role in video recognition network architectures and proposes a novel Temporal Grafter Network (TGN). Specifically, we develop an efficient and effective variant of convolutional LSTM module, which is grafted between different stages of very deep 2D CNNs for temporal modeling and delivery. Our TGN can capture local motion patterns of varying scales inherent in feature maps from high to low resolutions, while attending to the spatial context information and modeling global temporal dependency across the whole video. The proposed TGN can capture and transmit temporal information throughout very deep 2D CNNs, overcoming the downsides of existing LSTM-based methods and being able to make full use of the potential of LSTM for effective video recognition and early action recognition. We perform extensive ablation study to verify the effectiveness of our proposed methods, and experiments on three widely used video benchmarks show that our methods can achieve performance matching or better than the state-of-the-arts.
Improved Security Proof for the Camenisch-Lysyanskaya Signature-Based Synchronized Aggregate Signature Scheme

The Camenisch-Lysyanskaya signature scheme in CRYPTO 2004 is a useful building block to construct privacy-preserving schemes such as anonymous credentials, group signatures or ring signatures. However, the security of this signature scheme relies on the interactive assumption called the LRSW assumption. Even if the interactive assumptions are proven in the generic group model or bilinear group model, the concerns about these assumptions arise in a cryptographic community. This fact caused a barrier to the use of cryptographic schemes whose security relies on these assumptions. Recently, Pointcheval and Sanders proposed the modified Camenisch-Lysyanskaya signature scheme in CT-RSA 2018. This scheme satisfies the EUF-CMA security under the new q-type assumption called the Modified-q-Strong Diffie-Hellman-2 (q-MSDH-2) assumption. However, the size of a q- type assumptions grows dynamically and this fact leads to inefficiency of schemes. In this work, we revisit the Camenisch-Lysyanskaya signature-based synchronized aggregate signature scheme in FC 2013. This scheme is one of the most efficient synchronized aggregate signature schemes with bilinear groups. However, the security of this synchronized aggregate scheme was proven under the one-time LRSW assumption in the random oracle model. We give the new security proof for this synchronized aggregate scheme under the 1-MSDH-2 (static) assumption in the random oracle model with little loss of efficiency.
Students’ perspectives on using virtual reality technology in a university-level agricultural mechanics course

Simulator technologies have become more prominent in many educational contexts in recent years. Simulator technologies such as virtual reality (VR) exist in many forms and can be used for different purposes, including weld process training. University-level course students have previously reported using VR technology can positively contribute to a course experience. Limited data exist regarding students’ perspectives of using VR technology to develop welding-related psychomotor skills in a university-level agricultural mechanics course. Through two focus groups conducted during the Spring 2018 semester, we sought to describe the perspectives nine students had on using a VR technology application throughout their weld process training. Students indicated that while using a VR technology application can be useful, it should not take the place of using actual welding equipment as part of the teaching and learning processes. We recommend faculty who are considering using a VR technology application should carefully analyze current instructional needs and course structures to ensure using VR technology will adequately address students’ educational requirements and will complement live weld process training procedures.
Partnership as a Basic Component of the Communicative Competence of Future Teachers in a Master's Program

The article presents the results of theoretical analysis in the scientific literature of the problem of partnership interaction of applicants for higher pedagogical education. The basic concepts of the research are substantiated. It is revealed: partnership interaction is a humanistically oriented pedagogical process or personal contact of the subjects of the educational process, which results in mutual positive changes in their behavior, activities, qualities, attitudes. The emerging model of partnership interaction of masters is emerging It is revealed: partnership interaction is a humanistically oriented pedagogical process or personal contact of the subjects of the educational process, which results in mutual positive changes in their behavior, activities, qualities, attitudes. A model of partnership interaction of masters has been developed, which appears as an ideal image of pedagogical activity in a certain direction and contains conceptual, goal-motivational, semantic, methodical, effective components. A method of implementing the model was created, which provided for the formation of a creative level of partnership in the context of communicative competence of higher education seekers, which includes content, methodological and evaluation components. The stages and directions of introduction of the author's model by means of pedagogical disciplines (motivational, cognitive, activity) are revealed. The results of the pedagogical experiment are presented. Comparison of the results of the ascertaining and formative stages of the experiment revealed positive changes in the levels of development of partnership interaction of the respondents of the experimental group, in particular the growth of creative and constructive levels. In the control group, no significant changes in the defined parameters occurred. This conclusion confirms the effectiveness of the author's model implemented in the pedagogical experiment.
GIFs in online written interaction: embodied cues and beyond

&#x0D; &#x0D; &#x0D; Recent research has characterized GIFs as a means for speakers to reproduce non-linguistic cues such as bodily actions and facial expressions in online written interaction, which is first and foremost text-based. They make it possible to translate in digital interaction what those non-linguistic cues enable to do in face-to-face conversation: express emotion and affect, and elaborate on what is being said. This article explores further the role and functions of GIFs as embodied cues and goes beyond, where GIF use diverges from body language in face-to-face conversation.&#x0D; &#x0D; &#x0D;
Demo Abstract: Capuchin: A Neural Network Model Generator for 16-bit Microcontrollers

Resource-optimized deep neural networks (DNNs) nowadays run on microcontrollers to perform a wide variety of audio, image and sensor data classification tasks. Despite comprehensive support for deep learning tools for 32-bit microcontrollers, performing deep learning inferences on 16-bit microcontrollers still remains a chal-lenge. Although there are some tools for implementing neural net-works on 16-bit systems, generally, there is a large gap in efficiency between the development tools for 16-bit microcontrollers and 32-bit (or higher) systems. There is also a steep learning curve that discourages beginners inexperienced with microcontrollers and programming in C to develop efficient and effective deep learning models for 16-bit microcontrollers. To fill this gap, we have created a neural network model generator that (1) automatically transfers parameters of a pre-trained DNN or CNN model from commonly used frameworks to a 16-bit microcontroller, and (2) automatically implements the model on the microcontroller to perform on-device inference. The optimization of data transfer saves time and mini-mizes chances of error, and the automatic implementation reduces the complexity to implement DNNs and CNNs on ultra-low-power microcontrollers.
Edge detection using nonlinear structure tensor

Abstract In order to improve the performance of edge detection for noisy images, a new edge detection method based on nonlinear structure tensor is proposed. First, the tensor product of noisy images is calculated. The tensor product is diffused according to the image gradient, which depends on the tensor product itself. Finally, the eigenvalues and eigenvectors of the diffusion tensor product are calculated, and the edges of the image are detected according to the eigenvalues. The method is compared with other methods. The experimental results show that the average number of edge points detected by method 1, method 2, method 3, and this method are 513.7, 530.0, 509.0, and 719.3, respectively. The average detection time of method 1, method 2, method 3, and this method were 65.3, 54.9, 57.3, and 33.6 s, respectively. When the number of edge detection is the largest, the average detection time of this method is significantly smaller than that of the three comparison methods. Therefore, this method is more suitable for edge detection of noisy images, and the performance of this method is better than that of the comparison method and can improve the performance of edge detection of noisy images.