Reviewerly/exHarmony huggingface dataset:
Analysis of Emotional Education Infiltration in College Physical Education Based on Emotional Feature Clustering

Integration of emotional education physical education research in colleges and universities is a topic that has both theoretical and practical implications. Teachers in physical education should pay attention not only to the development of students’ physical knowledge and skills but also to their emotional education, emphasizing the positive role of emotional factors. Emotion is a type of internal experience that people have when deciding whether or not objective things can meet their needs. The diversity of emotional needs, the contradiction of emotional experience, the singleness of emotional orientation, and the universality of negative emotions are all new characteristics of contemporary college students’ emotional development. A teaching feedback strategy generation algorithm based on the emotional learning ontology is designed to obtain the students’ perception and understanding of the learning process without the need for question-and-answer interaction with the students. Based on “emotional triggers,” a better understanding of students’ learning status can be provided, as well as better cognitive and emotional support. The main measure and means to improve teaching quality and efficiency in physical education is to combine cognition, emotion, and education. This paper discusses the connotation and scope of emotional education in colleges and universities and proposes ideas for strengthening emotional education in colleges and universities in light of the new characteristics of college students’ emotional development, based on the clustering of emotional features, in terms of emphasizing the emotional influence of teachers, bolstering students’ aesthetic education, and exploring new avenues for college students’ emotional education.
Archives Management in Support Official Administration in Toho Subdistrict Head Office, Mempawah Regency

This study aims to analyze and describe the management of archives in support of administrative order at the Toho sub-district office, Toho district, Mempawah regency. This problem was chosen to look at the issues that exist in the management of archives or archives that exist, especially in the Toho District office of Mempawah Regency, especially those related to the problem of managing letters and arranging archives at the Toho Sub-district office because there are phenomena that indicate where in the management, mail or control. And the arrangement of the archives is still not done well; in other words, it has not been done as desired, or there are still some obstacles, especially those related to archive problems. Therefore, the archive is essential in supporting the orderly administration in the office so that there are no archiving problems that will make the paperwork in the office not recorded neatly or adequately. The results of this study indicate that the management, control, and arrangement of archives or archives at the Toho sub-district office are still not carried out properly, so an understanding of adequate archival equipment procedures is needed so that activities in archives management can be carried out as well as possible. This must be guided by the Governor's Regulation No. 453 of 2005 concerning reasonable and correct archival procedures.
Artificial Neural Networks Oriented Testbed for Multiantenna Wireless Application

This research article provides viable research solutions by using Artificial Neural Networks (ANN) for Multiantenna wireless applications with less computational complexity. Artificial Neural Networks Oriented Testbed (ANNOT) is proposed where intelligence through artificial neurons are exploited for training multiantenna wireless application. A feedforward backpropagation network is trained with the required input parameters using training algorithms and its convergence for iterations for target parameters are simulated and developed. The ANNOT intelligently provides the required outputs from the trained values when validated for the tested output parameter in Multiantenna wireless application such as data transmission. Testbed input target parameters are bandwidth, signal power, channel statistics, noise power and output parameters metrics are capacity, probability of error which are executed in matrix laboratory (MATLAB). Obtained results are analyzed in gradient based algorithms and variants of neural networks for mean square error (MSE) against number of iterations/epochs which provide optimized results from ANNOT with less computational complexity. Validation results are also obtained for capacity and probability of error for data transmission multiantenna wireless application.
Visual servoing of a laser beam through a mirror

Abstract In this study, we present a new approach to improving vocal fold access to perform phonomicrosurgery. It is done by shooting the laser through a mirror to reach the vocal fold hidden parts. A geometrical study of laser shooting path was conducted for vocal fold anatomical constraints, followed by devising a laser-shooting system conceptual design. Control laws were developed and tested by simulation and validated experimentally on a test bench in a monocular and stereoscopic configuration. Simulation and experimental results are provided to demonstrate the effectiveness of the developed approach.
A Lexicogrammar Approach to Analyze Response Strategies to Apology in English Conversations

The acts of apologizing and responding to apology are common activities in communication since using only either of these two crucial speech acts cannot itself establish fully effective communication. This article aims to examine response strategies to apology that are used in English romance and family films and find out the lexico-grammatical realizations of response utterances in English conversations. This article was qualitatively designed with the support of the researchers’ observation of the films' scripts extracted from English films, subsequently the researchers’ description of the data collection into separate categories. The results of this study revealed that English film characters were more likely to accept than reject apologies. Direct positive comments were favoured by the English, with indirect positive responses coming in second.
Analytic Study of Russian Experience in Economic Development and Integration in the Global Economy: Lessons for Iran and Developing Countries

Economic development is one of the main concerns of todays countries, significantly those influenced by the collapse of the communist bloc and the failure of a centralized planning and growth pattern of socialism. As a result, Russias economy in the post-collapse period witnessed chaos in domestic production and the rise in commodity prices. However, Russia is now the 11th largest economy globally, and the countrys exports, which was $ 78 billion in 1995, amounted to more than $ 420 billion. This paper attempts to analyze this experience concerning the role of government in developing and integrating into the global economy. Therefore, the main question is the governments role in developing and integrating into the global economy from 1995 to 2011. This paper attempts to review the Russian experience due to its similarity in post-Soviet period with the Iranian economys current circumstances, analyzing and comparing solutions that a government must consider to be transferred from a government-run to a market economy. The survey shows that the Russian government successfully took a step toward economic liberalization and integration in the global economy.
Video Saliency Detection by using an Enhance Methodology Involving a Combination of 3DCNN with Histograms

When watching pictures or videos, the Human Visual System has the potential to concentrate on important locations. Saliency detection is a tool for detecting the abnormality and randomness of images or videos by replicating the human visual system. Video saliency detection has received a lot of attention in recent decades, but due to challenging temporal abstraction and fusion for spatial saliency, computational modelling of spatial perception for video sequences is still limited.Unlike methods for detection of salient objects in still images, one of the most difficult aspects of video saliency detection is figuring out how to isolate and integrate spatial and temporal features.Saliency detection, which is basically a tool to recognize areas in images and videos that catch the attention of the human visual system, may benefit multimedia applications such as video or image retrieval, copy detection, and so on. As the two crucial steps in trajectory-based video classification methods are feature point identification and local feature extraction. We suggest a new spatio-temporal saliency detection using an enhanced 3D Conventional neural network with an inclusion of histogram for optical and orient gradient in this paper.
Spatial Conflict Risk Measurement and Early Warning Based on Network Information Processing

The contradiction of land use is becoming more and more fierce in China, which poses a serious threat to the structural integrity and quantity of ecological land, the uncontrolled expansion of urban construction land, the imbalance between the spatial structure of agricultural land and ecological land, and the unreasonable spatial structure of various types of land within the city. This study mainly discusses the risk measurement and early warning of spatial conflict based on network information processing. In this study, GIS technology is used to deal with the risk of spatial conflict. Spatial conflict, in essence, is an objective geographical phenomenon arising from the competition for spatial resources in the process of human-land relations and is the result of the interaction between natural geographical factors and human factors, and natural geographical processes and human processes, and is a key factor affecting the sustainable development of the region. The logical network attack path composed of IP sequence captured by the space conflict network early warning and emergency response system is transformed into the attack path composed of a corresponding physical address sequence, which is directly displayed on the electronic map. At the same time, the attributes related to the spatial conflict events in the research area are also displayed in the form of a list. When the macro network early warning and emergency response system detect the occurrence of space conflict events in the study area, its emergency response subsystem queries the strategy database and outputs the emergency strategy in real-time by inputting the warning information analyzed by the early warning information analysis subsystem. Finally, the system realizes the functions of network node information visualization processing, network early warning and emergency response information visualization processing, and electronic layer basic operation. It is found that the industrial added value of the study area of ABC urban agglomeration has reached 283.140 billion yuan in recent three years, accounting for 60.25% of the industrial added value of the three cities and 30.98% of the whole province. The results show that the improper spatial regional combination has an important impact on the spatial conflict, and the improper spatial regional combination will significantly improve the level of spatial conflict.
Evaluation System of English Blended Learning Effect Based on Big Data Technology

In order to improve the effect of English teaching, this paper combines data mining algorithms to construct an intelligent learning system. Moreover, starting from the actual situation, this paper combines the actual needs of English teaching to construct a blended learning system framework. Blended learning provides a new idea for English teaching. As a result, the purpose of this work is to do some empirical research on how to successfully implement blended learning in college English classes. The findings of the study suggest that blended learning may help students improve their English application skills. Furthermore, this article discovered that students’ independent learning capacity, learning passion, and systematic comprehension of maritime equipment were enhanced to some amount using data mining and cloud classroom blended learning resource construction and teaching practise. Finally, this work integrated theoretical and experimental research to demonstrate that the system developed in this paper has a specific impact.
Estimation with Heisenberg-Scaling Sensitivity of a Single Parameter Distributed in an Arbitrary Linear Optical Network

Quantum sensing and quantum metrology propose schemes for the estimation of physical properties, such as lengths, time intervals, and temperatures, achieving enhanced levels of precision beyond the possibilities of classical strategies. However, such an enhanced sensitivity usually comes at a price: the use of probes in highly fragile states, the need to adaptively optimise the estimation schemes to the value of the unknown property we want to estimate, and the limited working range, are some examples of challenges which prevent quantum sensing protocols to be practical for applications. This work reviews two feasible estimation schemes which address these challenges, employing easily realisable resources, i.e., squeezed light, and achieve the desired quantum enhancement of the precision, namely the Heisenberg-scaling sensitivity. In more detail, it is here shown how to overcome, in the estimation of any parameter affecting in a distributed manner multiple components of an arbitrary M-channel linear optical network, the need to iteratively optimise the network. In particular, we show that this is possible with a single-step adaptation of the network based only on a prior knowledge of the parameter achievable through a "classical" shot-noise limited estimation strategy. Furthermore, homodyne measurements with only one detector allow us to achieve Heisenberg-limited estimation of the parameter. We further demonstrate that one can avoid the use of any auxiliary network at the price of simultaneously employing multiple detectors.
POWER LOSS REDUCTION OF DISTRIBUTION NETWORK IN DENSELY INDUSTRIALIZED COASTAL BELT BY DEVELOPMENT OF HYDROPHOBIC COATING APPLYING ACCELERATED AGING FOR CERAMIC INSULATOR

This paper aims to reduce the power losses by improving the hydrophobic strength of ceramic insulators for densely industrialized coastal areas to save them from flashover due to contamination. The hydrophobic strength of ceramic insulators must increase to resolve the contamination occurrence and power losses problem. The system progresses by creating an artificial chamber to provide five different environmental conditions heat, rain, fog, dust, and humidity. The testing part of the system includes taking readings for rain happened, temperature, humidity, and pH for five years using data from reliable sources and creating acidic rain, temperature, dust, and fog artificially to obtain results more accurately. The Coating includes nitrocellulose material dissolved in a solvent mixture of diacetone alcohol, Hexa hydro phenol, ethyl acetate, benzol, and toluene in a spray form to apply on the insulators. The coated insulator was tested in an artificial aging chamber by applying all the environmental effects of the coastal areas for five years using accelerated testing. After performing tests, the results show a 7% decrease in contamination development on the ceramic insulator surface. Hence improving its hydrophobic strength and losses of the power system minimized.
Towards a community-led approach to improve the design of early warning systems and anticipatory action for flood risk preparedness

&amp;lt;p&amp;gt;As the world faces an uncertain future due to climate variability, environmental and climate change, and an increase in extreme hydrometeorological events, investing in early warning early action mechanisms can be an effective way to prepare and adapt to changes and extremes and reduce any impending impacts. Such an investment will require an understanding of the information needs of the users/user-groups, and in particular, the communities at risk, to ensure the design of tailored anticipatory actions, as well as an evaluation of how forecasts perform in detecting these extreme events and their impacts. This helps to ensure that flood-risk preparedness actions are better contextualised and not taken in vain. Community-led approaches for anticipatory action planning are based on the engagement with the communities at risk and can be an effective way of ensuring that: 1) the information needs of the specific user-groups are identified and integrated with the development of preparedness actions and plans; 2) data on loss and damages to lives and livelihoods can be used to demonstrate how reliable the forecasts are in informing early actions at the community level; and 3) gaps and challenges that hinder effective use of early warning information are identified across user-groups to help improve on the design and dissemination of early warning information. In this talk, we bring together information collected at the community and disaster management levels together with a recent evaluation of flood forecasts using impact (loss and damage) reports at a district level, to show how community-led approaches can help towards improving early warning mechanisms. By integrating global hydro-meteorological forecasts with information on crop calendars and impact reports collected from farmers and local communities, an enhanced impact-based flood early warning system focusing on crop impacts, as well as the natural hazard, is developed for a flood-prone district in Uganda (Katakwi).&amp;lt;/p&amp;gt;
Weak Tweak-Keys for the CRAFT Block Cipher

CRAFT is a lightweight tweakable Substitution-Permutation-Network (SPN) block cipher optimized for efficient protection of its implementations against Differential Fault Analysis (DFA) attacks. In this paper, we present an equivalent description of CRAFT up to a simple mapping on the plaintext, ciphertext and round tweakeys. We show that the new representation, for a sub-class of keys, leads to a new structure which is a Feistel network, with non-linear operation and key addition only on half the state. Consequently, it reveals a class of weak keys for which CRAFT is less resistant against differential and linear cryptanalyses. As a result, we present one weak-key single-tweak differential attack on 23 rounds (with time complexity of 294 encryptions and data complexity of 274 chosen plaintext/tweak/ciphertext tuples and works for 2112 weak keys) and one weak-key related-tweak attack on 26 rounds of the cipher (with time complexity of 2105 encryptions and data complexity 273 chosen plaintext/tweak/ciphertext tuples and works for 2108 weak keys). Note that these attacks do not break the security claim of the CRAFT block cipher.
Seasonal Forecasting of Horn of Africa&amp;#8217;s Long Rains Using Physics-Guided Machine Learning

&amp;lt;p&amp;gt;The Horn of Africa is known to be prone to climate impacts; the frequent occurrence of droughts and floods creates vulnerable conditions in the region. Gaining knowledge on (sub-)seasonal weather prediction and generating more reliable long-term forecasts is an important asset in building resilience. Most of the region is characterized by a bimodal precipitation cycle with rainfall seasons in boreal spring (March&amp;amp;#8211;May), termed the long rains, and boreal autumn (October&amp;amp;#8211;November), termed the short rains. Previous studies on seasonal forecasting focused mostly on empirical linear regression methods using information from ocean&amp;amp;#8211;atmosphere modes. To date, the potential of more complex methods, such as machine learning approaches, in improving seasonal precipitation predictability in the Horn of Africa still remains understudied.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, machine learning models targeting precipitation during the long rains are developed. The focus on the long rains is motivated by the fact that it is the main rain season in the region and the sources of predictability have proven to be more difficult to pin down. The long rain season has a weak internal coherence and looking at the months separately has proven to enhance prediction skill. Therefore, machine learning models are constructed for the different months (March, April, and May) separately at lead times of 1&amp;amp;#8211;3 months. Following an extensive survey of literature, the predictors of the long rain precipitation at seasonal timescales selected in this study include coupled oceanic-atmospheric oscillation indices (such as MJO, ENSO and PDO), regions of zonal winds over 200mb and 850mb and sea-surface temperature (SST) regions with strong correlation to long rain precipitation. Further, a selection of additional terrestrial and oceanic predictors is guided by Lagrangian transport modeling, used to identify the regions sourcing moisture during the long rains. This set of predictors include soil moisture, land surface temperature, normalized vegetation index (NDVI), leaf area index (LAI) and SST, which are averaged over the climatological source region of long rain precipitation. Finally, we provide new insights into the predictability of long rain precipitation at seasonal timescales by analyzing the relative importance of the different predictors used for developing the machine learning model.&amp;lt;/p&amp;gt;
Sistem Pakar Deteksi Dini Penyakit Preeklamsia pada Ibu Hamil Menggunakan Metode Certainty Factor

Preeclampsia is a disease in pregnant women characterized by high blood pressure and positive urine protein. The disease has a high risk of maternal and fetal death, so there is a need for early detection of mothers at risk of preeclampsia. Early online detection of preeclampsia is the best solution during the Covid-19 pandemic by analyzing the influencing factors. The purpose of this study is to build an expert system for early detection of preeclampsia in pregnant women using the Certainty Factor method and the waterfall system development model in order to provide the possibility of pregnant women suffering from preeclampsia. Testing the accuracy of 30 medical record data for pregnant women resulted in a system accuracy level of 90%, while usability testing resulted in a user satisfaction level of 55 with the System Usability Testing (SUS) score criteria being "Poor", therefore improvements are needed on expert system in the future.
Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods

Lower-body detection can be useful in many applications, such as the detection of falling and injuries during exercises. However, it can be challenging to detect the lower-body, especially under various lighting and occlusion conditions. This paper presents a novel lower-body detection framework using proposed anthropometric ratios and compares the performance of deep learning (convolutional neural networks and OpenPose) and traditional detection methods. According to the results, the proposed framework helps to successfully detect the accurate boundaries of the lower-body under various illumination and occlusion conditions for lower-limb monitoring. The proposed framework of anthropometric ratios combined with convolutional neural networks (A-CNNs) also achieves high accuracy (90.14%), while the combination of anthropometric ratios and traditional techniques (A-Traditional) for lower-body detection shows satisfactory performance with an averaged accuracy (74.81%). Although the accuracy of OpenPose (95.82%) is higher than the A-CNNs for lower-body detection, the A-CNNs provides lower complexity than the OpenPose, which is advantageous for lower-body detection and implementation on monitoring systems.