Reviewerly/exHarmony huggingface dataset:
IMPROVEMENT KNOWLEDGE IN IMPLEMENTATION OF INTERNET OF THINGS'S SUPPORTING SENSORS FOR TEACHERS IN SMK WALISONGO SEMARANG

Internet of Things is a phenomenon of information technology not only playing a role in the economic field, but also in the field of education. Today every teacher, especially at Vocational High Schools (SMK) is required to teach information technology, one of which is the Internet of Things. As such, it meets the needs of the industrial world which requiring skills and competitiveness in the technology from the vocational school graduates. This encourages teachers to always update knowledge about the development of information technology. However, not all information technology can be taught optimally, especially sensors material and its implementation on the Internet of Things. This occurs as the Internet of Things is a fairly new technology. In addition, the time for teachers to learn the latest technology is limited due to unsupported facilities and administrative tasks that need to be completed immediately. This has an impact on the minimum level of teacher mastery for sensors that support Internet of Things technology including teachers at the SMK Walisongo Semarang. Through the Community Service (PkM) program at the Universitas Semarang which was held on Tuesday, November 2, 2021, the PkM team expected an increasing knowledge of the implementation of sensors supporting Internet of Things technology for teachers at SMK Walisongo Semarang. There are 4 types of sensors used in this PkM program, namely proximity sensors, humidity sensors, temperature sensors, and light sensors which will be presented using the practical method. The PkM that has been carried out, it is found that the results of this service is that the knowledge of teachers on Internet of Things sensors has increased up to 75%.. It is hoped that this program will be a will increase teacher knowledge at the SMK Walisongo Semarang and can help them create vocational graduates who have the skills and competitiveness in the world of technology.
Image Encryption Algorithm Based on Combination Chaotic System and DNA Coding

In order to increase the chaotic parameter range, improve the initial sensitivity of the chaotic system and the security of the image encryption algorithm, This paper proposed a new image encryption based on combination chaotic system and DNA coding. Two one-dimensional chaotic maps are nested to obtain a combined chaotic system, and the chaotic sequences generated by the combined chaotic system is amplified and modulated to obtain the DNA coding rule sequences and the scrambling sequences, and the uniformity of the coding rules are improved through rule replacement operation. Using DNA encoding, DNA exclusive-or(XOR),and DNA decoding operations to achieve plain image diffusion, bit-level permutation is performed on the diffused image through the permutation sequences to obtain encrypted image. Theoretical and experimental simulation analysis shows that the chaotic interval of the combined chaotic system is increased and the initial value sensitivity is improved. The encryption algorithm based on the combined chaotic system and DNA coding can effectively resist statistical attacks, differential attacks and exhaustive attacks, the security of the encryption algorithm is also significantly improved compared to other algorithms.
On the Security of a Verifiable Data Aggregation Scheme for Internet of Things

Liu et al. proposed a verifiable data aggregation scheme for Internet of Things based on a certificateless aggregate signature algorithm. (DOI: 10.1109/ICC.2017.7996720, 2017 IEEE International Conference on Communications). Under the random oracle model, Liu et al.'s scheme was proved that there is unforgeability under the adaptive chosen message attack. However, we analyze the security of Liu et al.'s scheme and show that it cannot resist type of II attacks, coalition attacks and forge attacks without any secret authorization. To overcome the above security flaws, we propose a new certificateless aggregate signature scheme based on Liu et al.'s scheme. According to the security and performance analysis, the enhanced scheme improves the security and maintains the performance of the original scheme.
Design and Implementation of Image Signal Processor Based on CUDA

With the rapid development of image signal processor (ISP), the implementation of ISP based on CPU or FPGA has become a major focus of current research. However, the implementation of CPU-based ISP has a long runtime and the implementation of FPGA-based ISP is inflexible, difficult to program and debug, and unable to display image in real time. To solve these problems, this paper designs and implements an ISP based on CUDA with the advantages of short runtime, easy debugging, and real-time image display. The designed ISP includes pipeline design, vivid color manipulation, CPU and GPU co-processing, and the selection of output and display image at any stage. The experimental results show that for images with resolution of 2000 x 3008, the speedup ratio can reach 63 without considering the data transfer time and more than 53 with considering the data transfer time.
Enhancing replica management in a cloud environment using data mining based dynamic replication algorithm

Cloud computing has recently gained a lot of popularity and attention from the research community. One of the many on-demand services that large-scale applications provide to cloud customers is storage, which accumulates more generated data and subsequently leads to the need for storage. Despite the fact that users can use the cloud to store and provide the type of storage that desire, it still takes a significant amount of time to store and retrieve data due to the large accumulation of data. Due to the need to improve data availability, response time, reliability, and migration costs, the current storage engine needs to be replicated across multiple sites. When copies are properly distributed, data replication speeds up execution. The biggest challenges in data replication are choosing which data to replicate, where to put it, how to manage replication, and how many replicas it needs. Therefore, various studies have been carried out on some data mining-based data replication systems to evaluate replication issues and manage cloud storage. In most cases, data replication in a data mining environment is done using data mining along with a replication algorithm and a data grid policy. In addition, this paper addresses replica management issues and proposes affordable data replication in the cloud that satisfies all Quality of Service (QoS) requirements.
Assessment of capital, investments and trade flows in West Africa: directions for economic efficiency

The Economic Community of West African States (ECOWAS) was established to actualise the harmonisation, coordination of national policies, and the promotion of integration programmes within the union. Thus, given the 1986 policy-shifts for liberal economic system and integration, this study assesses the extent to which there has been pay-offs or otherwise using the macroeconomic indicators of trade, investment and capital flows within the union. This is with a view to ascertaining if there has been any direction for economic efficiency as well as establishing directions for future sustainability of ECOWAS as a union. Using a data set of 1980-2018, productivity growth ascertains the rate of efficiency within the sub-region; while the Vector Error Correction Model (VECM) determines the direction of causality, the extent of (in) economic efficiency vis-à-vis selected macroeconomic indicators. The findings show that while productivity is still low in the region, exogenous factors stimulate efficiency more than endogenous factors. Therefore, we recommend further strengthening of the internal processes of growth through increases in domestic investment and export base diversification within the ECOWAS sub-region.
Research on the Design Strategy for Rural Learning and Labor Base — A Case Study of the Learning and Labor Base in Bayi Village of Yaoji Town

Taking the design of learning and labor base in Bayi Village of Yaoji Town, Xuzhou City as an example, the research aims to study the influences of users, curriculum system and spatial classification on the design of learning and labor base. In accordance with the design principles of safety, involvement, perceptibility and local feature, based on the space planning for regions with different functions such as perception, practice and exploration, the research analyzes and concludes the design strategy for rural learning and labor base, in order to better conduct related design.
Virtual Tourism as a Pedagogical Practice

Post-pandemic anxiety and potential new outbreaks of this and other diseases could be a push, started long ago with the explosion of low-cost mobile devices, for immersive technologies, such as augmented reality, virtual reality, and mixed reality, to assert themselves as credible solutions in contrast to traditional ways of knowing the world. Effectively, these technologies already offer safe alternative ways to “travel” through continuous and uninterrupted interactive experiences for travelers who do not want to give up their own private space, safety, and health. However, this possibility brings with it new questions and challenges, mainly in terms of the development of these experiences, more specifically in the academic and pedagogical field. The authors analyze a project that articulates, in a transdisciplinary way, contents of several curricular units attended by Erasmus students around a VR experience of one of the most emblematic places in the region, the famous Castro of Monte Mozinho.
Digital Transformation in Educational Institutions: Scrutinizing the Debate and Highlighting Success Factors: A qualitative study on the current use of learning management systems and learning analytics in Germany

In German education, digitalization is evolving, and the COVID-19 pandemic has accelerated this process due to closed schools and universities. While these external conditions were almost the same for all institutions, they have dealt with remote teaching in different ways and developed various new learning concepts. We conducted 27 interviews with headmasters, e-learning managers, and teachers from Germany to gain insights into varying attitudes towards learning management systems, as well as the motivations and concerns about future teaching with digital support. Using an inductive method, we identify different obstacles, including lack of digital competencies, necessity of technical equipment, concerns about replacing traditional lessons, but also enthusiasm for new opportunities. We argue that a meaningful use of learning management systems and learning analytics depends on an open and impartial debate on data protection and equal opportunities. Overall, this paper contributes to current research with proposition development about privacy rights and inequalities.
Unsupervised Feature Selection via Metric Fusion and Novel Low-Rank Approximation

Unsupervised feature selection aims to derive a compact set of features with desired generalization ability via removing the irrelevant and redundant features, yet challenging due to the unavailability of labels. Works about unsupervised feature selection always need to construct the similarity matrix, which makes the selected features highly depend on the accuracy of similarity measurement. However, existing works usually leverage a single fixed metric to build similarity matrix, which cannot fit various feature types very well and even damage the local manifold structure. To address this problem, we propose an adaptive multi-metric fusion by automatically integrating similarity across different metrics according to the specific data. Besides, to capture the global structure more precisely, a novel low-rank approximation method is proposed, which is relatively insensitive to the rank-norm parameter. Via the proposed novel low-rank approximation method, better tradeoff between the performance and robustness can be provided. Experimental results show that the accuracy performance of the proposed method can be boosted by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\%-11\%$ </tex-math></inline-formula> , compared with previous methods.
Sentiment Analysis of Weibo Short Text Based on Attention Mechanism and BERT Model

With the gradual development of deep learning techniques, scholars have made great progress in using well-performing neural networks for sentiment analysis of text. However, this technique then became difficult to break the upper limit, and previous techniques were unable to handle texts with complex sentiments, and more fine-grained sentiment analysis was the key to solve the current problem. As a result, multi-categorization text analysis at the comment text level is shifted to aspect-level judgment of statements in comments. In this project, aspect-level sentiment analysis is improved for the short text of weibo comments based on the attention mechanism. The model proposed in this paper uses the attention mechanism to analyze the weights of short texts and precisely weight the summation, and finally the category output of short texts can be obtained. For the characteristics of weibo comments such as few words, short sentences, frequent network phrases and double entendres, this paper achieves good results by improving the lexical model, and the algorithm in this paper conducts comparative experiments on the Chinese data set of weibo users' comments, and the experimental The results show that the algorithm can achieve higher classification accuracy and F1 value in aspect-level sentiment classification task than other algorithms.
Big Data Based Design Framework for Comprehensive Evaluation System of College Students

The development of cloud computing, the Internet of Things, mobile Internet, and other associated technologies are related to big data. Based on the big data technology, the design framework for college students' comprehensive evaluation system is developed to provide flexible, holistic, and easy-of-use intelligent services for teachers and students. The design of college students' comprehensive evaluation system using big data is one of the core contents of smart campus construction. However, the current evaluation system for college students has not incorporated big data, which needs to be further improved. In this context, we analyzed the background and advantages of big data in the evaluation system for college students and then presented the architecture of the evaluation system including data storage layer design, implementation process design, and data demand design. The evaluation model provides a theoretical basis for the development of a smart campus.
Safe Production Mode of Tunnel Construction Under Complex Address Conditions based on Ant Colony Algorithm and Night Vision Imaging System

This paper designs the basic architecture of the tunnel construction safety risk control system based on the ant colony algorithm, and designs an ant colony optimization algorithm embedded with local search technology, and guides the ants to perform a global search through the indicators in the heuristic information synthesis change, using lasers and polarization The integration of multiple technologies such as light, optical filtering and modulation has successfully developed a high-performance active infrared polarized laser night vision imaging system with strong anti-interference ability and image clarity. It has also evolved from a safety management system based on vulnerability evaluation. Research on the specific content of this framework in terms of classification, identification, sharing and transformation of risk management and control information, and informatization of tunnel construction safety risk management and control based on modern information technology.
Tingkat Pengetahuan Masyarakat tentang Obat Tradisional setelah Pemberian E-Booklet di Kabupaten Karimun

Knowledge of traditional medicine is important for the formation of a person's activity in using traditional medicine. Preliminary studies conducted show the level of public knowledge about traditional medicine in the less category. This can be caused by a lack of desire to seek information and lack of education from health workers. For this reason, educational media such as e-booklets are needed. This study aims to see the level of public knowledge about traditional medicine after being given an e-booklet. This study uses a preexperimental method with a one group pretest-posttest design. The population of this research is the residents of Karimun Regency who have used traditional medicine. The sample of this study was 150 respondents who were determined using purposive sampling method. The inclusion criteria for the study were that the respondent was at least 18 years old, had used traditional medicine with the logo and was registered with BPOM for at least the last 3 months, could use a smartphone and was willing to be a respondent. The research instrument is e-booklet media and knowledge questionnaire. The knowledge questionnaire used has been tested for validity and reliability (obtained 20 valid questions with an r value of 0.881). Uni-variate data analysis to describe the level of knowledge and bi-variate analysis was carried out for the analysis of changes in pretest and post-test knowledge scores using the Wilcoxon test. The results obtained before giving the e-booklet are good knowledge as much as 6.6%, sufficient as much as 55.4% and less as much as 38% while after being given the e-booklet the knowledge of respondents with good category is 55.4%, enough is 38% and less as much as 6.6%. Bi-variate analysis obtained p value 0.000. It can be concluded that the given media has a significant effect on respondents knowledge.
14 Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador

Solar energy plays a crucial role in helping cities to decentralize energy production and thus decarbonize the energy mix. Reliable resource assessments are needed to support the deployment of solar power systems, especially in cities of developing countries where large solar potential remains untapped. The aimof this work is to assess the potential of rooftop solar photovoltaic (PV) in three populated cities in Ecuador’s mainland (Quito, Guayaquil and Cuenca) and in the Galapagos Islands. The assessment involves (i) the estimation of the available rooftop area based on geographic information system data, (ii) the calculation of energy yield based on hourly satellite-derived irradiance andmeteorological data, and (iii) the economic feasibility assessment in terms of levelized cost of electricity (LCOE) compared to representative electricity tariffs. In addition, a sensitivity analysis is carried out to assess the variability of the estimated technical and economic potential with respect to changes in the input parameters. The results reveal a total available rooftop area of about 144 km2, mainly concentrated in urban parishes. The estimated energy yield is 16.94 ± 3.38 TWh/a, which could cover almost twice the annual energy consumption in 2019 of the study areas. The economic assessment shows that the LCOE ranges between 7.65 and 21.12 USD cents/kWh. However, the comparison of LCOE against representative residential tariff suggests that rooftop PV technology is not cost-competitive undermost of the financial scenarios. The findings fromthis study will be of interest for local authorities and other decisionmakers to design policies and financing strategies to increase the penetration of rooftop PV and thus exploiting the large potential assessed in the study areas. The described methodology can be used for assessing the potential in other regions of Ecuador and thereby support the diversification and decarbonization of the energy mix in the country
Topic based quality indexes assessment through sentiment

Abstract This paper proposes a new methodology called TOpic modeling Based Index Assessment through Sentiment (TOBIAS). This method aims at modeling the effects of the topics, moods, and sentiments of the comments describing a phenomenon upon its overall rating. TOBIAS is built combining different techniques and methodologies. Firstly, Sentiment Analysis identifies sentiments, emotions, and moods, and Topic Modeling finds the main relevant topics inside comments. Then, Partial Least Square Path Modeling estimates how they affect an overall rating that summarizes the performance of the analyzed phenomenon. We carried out TOBIAS on a real case study on the university courses’ quality evaluated by the University of Cagliari (Italy) students. We found TOBIAS able to provide interpretable results on the impact of discussed topics by students with their expressed sentiments, emotions, and moods and with the overall rating.
S-LDA: Documents Classification Enrichment for Information Retrieval

In recent years, the research on topic modeling techniques has become a hot topic among researchers thanks to their ability to classify and understand a large text corpora which has a beneficial effect on information retrieval performance, but recently user queries are more complicated because they need to know not only which documents are most helpful to them, but also which parts of documents are more or less related to their request. Also, they need to search by topic or document, not merely by keywords. In this context, we propose a new approach of automated text classification based on LDA topic modeling algorithm and the rich semantic document structure which helps to semantically enrich the generated classes by indexing them in the documents sections according to their probabilities distribution and visualize them through a hyper-graph. Experiments have been conducted to measure the effectiveness of our solution compared to topic modeling classification approaches based on text content only. The results show the superiority of our approach.
Apriori Bio's AI Platform Provides Variant-Proof Protection from Viral Threats

GEN EdgeVol. 4, No. 1 Artificial IntelligenceApriori Bio's AI Platform Provides Variant-Proof Protection from Viral ThreatsFollowing more than two years of development, Flagship Pioneering unveils Apriori Bio with $50 million of committed capital.Jonathan D GrinsteinJonathan D GrinsteinSearch for more papers by this authorPublished Online:8 Aug 2022https://doi.org/10.1089/genedge.4.1.97AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 4Issue 1Jan 2022 Information© 2022 by GEN PublishingTo cite this article:Jonathan D Grinstein.Apriori Bio's AI Platform Provides Variant-Proof Protection from Viral Threats.GEN Edge.Jan 2022.593-597.http://doi.org/10.1089/genedge.4.1.97Published in Volume: 4 Issue 1: August 8, 2022PDF download