Reviewerly/exHarmony huggingface dataset:
Intimacy in online classrooms

Abstract In this article I describe the transition of a group of university students in Australia into an online learning environment during COVID-19 pandemic disruptions. I reflect upon my intersubjective experiences as the lecturer in an unexpected situation of urgency and physical distancing. Research has acknowledged synchronous virtual learning contexts as less psychologically distancing than previously thought. I argue that these contexts can foster profound intimacy between participants through linguistic and multimodal means. I use an auto-ethnographic narrative inquiry approach to share observations gained retrospectively through multimodal, critically reflexive, social semiotic discourse analysis of audio-visual recordings of synchronous workshops conducted in 2020. I attempt to fill the gap in research on intimacy in online educational settings by suggesting that intimacy can be created by linguaplay, personal testimonies, and contrived chaotic material ecologies . I advocate moving away from an obsession with standardising and generating student knowledge in formal online learning to a stance that values intimacy, connection, and spontaneity.
Automatic Demirci–Selçuk Meet-In-The-Middle Attack On SIMON

Abstract Demirci–Selçuk meet-in-the-middle (DS-MITM) attack is an effective method for cryptanalysis. As far as we know, the published automatic results of DS-MITM attack are all for byte-oriented ciphers. In this article, we first propose the automatic analysis method of DS-MITM attack for bit-oriented ciphers based on constraint programming, which is integrated with key-bridging technique. Based on the automatic modeling method, we propose the first result of DS-MITM attack on SIMON, which is a family of lightweight block ciphers proposed by the National Security Agency (NSA) in 2013.
Object‐meta and MSGAE‐MP: Multi‐dimensional video anomaly detection

Abstract Autoencoders have been widely used in video anomaly detection, and there are many variants. However, since these Autoencoders use frames as the input of the reconstruction network, they can only learn the pixel information in the frame and lack other key information. In addition, complete reconstruction of the frame will lead to memory consumption and waste of resources. In order to learn multi‐dimensional information and reduce memory usage, we propose to use the Object‐meta instead of video frames, and the Memory Search Guided Autoencoder with Memory Pools (MSGAE‐MP) to reconstruct. Every Object‐meta comes from the object and is composed of the type mask, position mask, optical flow, and pixels of the object. In this way, the multi‐dimensional information carried by the input can be strengthened. After generating Object‐meta, the MSGAE‐MP will use the high‐dimensional information to guide search in memory modules during the reconstruction, and it will construct multi‐level memory pools, so as to reconstruct Object‐meta in different dimensions. Experiments show that our method is feasible and has achieved excellent results.
One-shot detection limits of time-alignment two-photon illumination radar

Quantum radar has recently gained increasing importance in a number of military applications. The estimation accuracy of one-shot quantum illumination events is significant in target detection. However, the accuracy is inevitably deteriorated by measurement noises. The traditional one-shot illumination emits a single photon towards a certain area which thermal noise exists in the path to, and the states of the received photons are hard to distinguish in the following processing. Therefore, a new optical probe source is proposed in this work. The independent detecting unit in the enhanced illumination is comprised of two photons aligned in time by using Hong–Ou–Mandel (HOM) interferometer. Further, one-shot detection in a general discrete model is realized and it proves a significant promotion in accuracy. The expansion of useful parts in parameter space and the lower minimal error probability for hypothesis testing have been mathematically demonstrated. The accuracy of one-shot detection can be effectively improved by the proposed scheme implying that it possesses great potential applications in quantum illumination and imaging.
Research on application of dynamic adaptive cache strategy and artificial intelligence technology in power engineering data processing

In order to improve the effect of power engineering data processing, this paper combines dynamic adaptive caching strategy and artificial intelligence technology to study power engineering data processing. This paper proposes a mechanism for the survival time of cached content. The survival time is set according to the frequency of content requests and the distance of the content node, so that the cached content of downstream nodes close to the user has a longer time. At the same time, after the content in the upstream node expires, it can free up cache space for the rollback operation. Finally, this paper tests the system model of this paper through experimental research. The research results show that the power engineering data processing system based on dynamic adaptive caching strategy and artificial intelligence technology proposed in this paper has certain effects.
An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning

Physicists have had a keen interest in the areas of Artificial Intelligence (AI) and Machine Learning (ML) for some time now, with a special inclination towards unravelling the mechanism at the core of the process of learning. In particular, exploring the underlying mathematical structure of a neural net (NN) is expected to not only help us in understanding the epistemological meaning of `Learning' but also has the potential to unravel the secrets behind the workings of the brain. Here, it is worthwhile to establish correspondences and draw parallels between methods developed in core areas of Physics and the techniques developed at the forefront of AI and ML. Although recent explorations indicating a mapping between the Renormalisation Group(RG) and Deep Learning(DL) have shown valuable insights, we intend to investigate the relationship between RG and Autoencoders(AE) in particular. We will use Transfer Learning(TL) to embed the procedure of coarse-graining in a NN and compare it with the underlying mechanism of encoding-decoding through a series of tests.
Variations and interpretations of naturality in call-by-name lambda-calculi with generalized applications

In the context of intuitionistic sequent calculus, "naturality" means permutation-freeness (the terminology is essentialy due to Mints).We study naturality in the context of the lambda-calculus with generalized applications and its multiary extension, to cover, under the Curry-Howard correspondence, proof systems ranging from natural deduction (with and without general elimination rules) to a fragment of sequent calculus with an iterable left-introduction rule, and which can still be recognized as a call-by-name lambda-calculus.In this context, naturality consists of a certain restricted use of generalized applications.We consider the further restriction obtained by the combination of naturality with normality w.r.t. the commutative conversion engendered by generalized applications.This combination sheds light on the interpretation of naturality as a vectorization mechanism, allowing a multitude of different ways of structuring lambda-terms, and the structuring of a multitude of interesting fragments of the systems under study.We also consider a relaxation of naturality, called weak naturality: this not only brings similar structural benefits, but also suggests a new "weak" system of natural deduction with generalized applications which is exempt from commutative conversions.In the end, we use all of this evidence as a stepping stone to propose a computational interpretation of generalized application (whether multiary or not, and without any restriction): it includes, alongside the argument(s) for the function, a general list -a new, very general, vectorization mechanism, that structures the continuation of the computation.
STUDENT’S VOICES ON LANGUAGE LEARNER AUTONOMY IN DISTANCE LEARNING

Learner autonomy is a complex construct which becomes the focus of language learning, consisting of four dimensions which later are summed up into three, such as initiating, monitoring, and evaluating. The study aimed to know students’ perception and behaviors on learner autonomy in distance learning. The data was gathered from 187 students in the population, but only 43 returned the questionnaire. The 61 items questionnaire was analyzed and found that in the initiating category, most students perceive the learner autonomy positively and indicate the behavior of dreaming to be a good English speaker. While the monitoring category shows the item of modifying methods to find which suits them best. The third category is reflected in their being eager to find out their English proficiency by taking TOEFL tests and the like, voluntarily. While the behavior which is related to ICT or online learning is their willingness to use English to communicate with strangers on the Internet.
Detection of price manipulation fraud through rational choice theory: evidence for the retail industry in Taiwan

All enterprises, regardless of industry, are exposed to fraud risk. In the retail industry, the perpetrators of fraud manipulate the sales price of products instead of the quantity sold to avoid inventory discrepancies. Fraud examiners attempt to identify anomalous transactions using data analysis. This study analyzes the causes of fraudulent behavior, conceptualized based on the aspect of rational choice, and proposes an anomalous transaction detection model using variables identified as indicating fraud. A two-phase experiment analyzing a real-world data set from a retailer in Taiwan was designed to evaluate the performance of fraud variables. The findings demonstrate that these variables could slightly improve the results of the analysis and demonstrate that machine learning is applicable to fraud detection. In addition, this study contributes to rational choice theory to validate the applicability in fraud detection. Fraud examiners in the retail industry could reduce fraud losses by adopting the proposed approach, implemented using Weka software, to identify anomalies.
VREd: Virtual Reality in Engineering Education for Immersed and Interactive Learning: A Case Study

Virtual reality (VR) is known for providing immersed user experience of 3D virtual world. In this paper, we have evaluated the learning of the engineering students and have also compared their learning with other the students who have not used VR. To conduct our study, we have developed a 3D model for various engineering fields such as, architecture, automobile and mechanical and so on. Further, we have also included VR interactions such as grabbing various 3D model, changing materials of the model and so on, to provide students an immersed experience of various 3D models of their respective discipline. Through the study, we have observed the students approximately 90% of students had good learning experience and have understood the concept better.
AR/VR-Based Comprehensive Framework for Virtual Convocation

In this pandemic, people have not been able to celebrate their achievements and accomplishments with their friends and family. One of those achievements is the convocation/graduation ceremony of the students who have successfully invested their hard-work and dedication for achieving this prestigious milestone. The number of people that can attend a physical graduation ceremony is limited due to restricted venue space, seating, and food. Students are unable to invite all of their family members and friends to their big day. Students can invite as many family members as they like to a virtual convocation event. Their family may be anywhere in the world, but they would not miss this momentous occasion. No one is left behind, whether it's kids, parents, or their family and friends, thanks to live transmission to remote audiences and on-demand post-event sessions. Students often return to their native country and are unable to attend their graduation ceremonies. This enables them to attend and create memories with their friends, classmates, and family. This is where the project idea comes into play, where a cross-platform app will be made that will be able to provide a real-life environment to the students and their family members where they will be able to enjoy the convocation ceremony without breaking any social distancing norms.
Federated split GANs for collaborative training with heterogeneous devices

<h2>Abstract</h2> Applications based on machine learning (ML) are greatly facilitated by mobile devices and their enormous volume and variety of data. To better safeguard the privacy of user data, traditional ML techniques have transitioned toward new paradigms like federated learning (FL) and split learning (SL). However, existing frameworks have overlooked device heterogeneity, greatly hindering their applicability in practice. In order to address such limitations, we developed a framework based on both FL and SL to share the training load of the discriminative part of a GAN to different client devices. We make our framework available as open-source software<sup>1</sup>1https://github.com/YukariSonz/FSL-GAN..
Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis

The appropriate feature/predictor selection is as significant as building efficient estimation methods for the accurate estimation of power consumption, which is required for self-awareness and autonomous decision systems. Traditional methodologies define predictors by assessing whether there is a relationship between the predictors and the response variable. Contrarily, this study determines predictors based on their individual and group impacts on the estimation accuracy directly. To analyze the impact of predictors on the power-consumption estimation of an IT rack in a data center, estimations were carried out employing each prospective predictor separately using the measured data under the real-world workload. Then, the ratio of CPU usage was set as the default predictor, and the remaining variables were assigned as the second predictor one by one. By utilizing the same approach, the best combination of predictors was determined. As a result, it was discovered that some variables with a low correlation coefficient with power consumption improved the estimation accuracy, whereas some variables with high correlation coefficients worsened the estimation result. The CPU is the most power-consuming component in the server and one of the most used predictors in the literature. However, the estimation accuracy obtained using only the CPU is 10 times worse than the estimation result conducted by utilizing the predictor set determined at the end of the experiments. This study shows that instead of choosing predictors only from one point of view or one method, it is more convenient to select predictors by assessing their influence on estimation results. Examining the trend and characteristics of the estimated variable should also be considered.
identification of lithological characteristics using multispectral landsat 8 oli imagery in the cipatujah area, west java, indonesia

Abstract The Cipatujah area is part of the Southern Mountains of West Java which has diverse and unevenly distributed lithology. The lithology that dominates the Cipatujah and surrounding areas originate from the volcanic activities such as lava, volcanic breccias, tuffs, and intrusions. While the sedimentary rocks that compose them are limestone and sandstone rocks. The lithology that dominates the southern region is carbonate sedimentary rocks, which are represented by sandstone units. In the northern part, the lithologies are dominated by deposition results volcanic activity consists of various materials originating from andesitic lava units that extend to the east of the research area, while the volcanic breccia deposited from north to the west of the research area. There is a tuff unit layer above the volcanic breccia to the south. In the eastern area deposited carbonate rock units that form the karst landscape. Lithology characterization and determination of rock units in the Cipatujah area were carried out using image processing techniques from color composite bands from Landsat-8 (OLI) data. Geological analysis using SWIR-2 (7), SWIR-1 (6), and blue (2) composite bands and lithology using near-infrared (5) composites SWIR-1 (6), and SWIR-2 (7) bands. Then the analysis results are examined with geological data from the mapping that has been done before. Approach to band composite analysis by verifying geological data taken directly to help improve the identification and validation of better and more measured lithological distribution.
Therapeutic and Learning Benefits of Reading During COVID-19 Among Spanish Secondary Students

During the confinement due to COVID-19, the educational centers tried to cope as much as possible with the continuity of the teaching-learning processes. In this research, it was tried to verify if the implementation of a proposal that promoted reading among secondary school students of a science subject, characteristic to improve their learning as well as benefit their mental health. The comparison of the results obtained in a test before and after carrying out the proposal confirmed that there was an improvement in learning and a perception of therapeutic effects of reading in this difficult situation. It is necessary to continue carrying out proposals of this type, even without being subjected to situations of anxiety and stress, in order to improve both learning and the mental health of students.
3D CNN Hardware Circuit for Motion Recognition Based on FPGA

In recent years, three-dimensional convolutional neural network (3D CNN) has been widely used in the fields of action recognition and video analysis. The general purpose processors are difficult to achieve efficient and intensive computing, and the deployment of 3D CNN based on FPGA has the advantages of low power consumption, high energy efficiency, and customizability, and has gradually become a hot choice for deploying convolutional neural networks in many embedded scenarios. This paper designs a small 3D convolutional neural network based on the classic 3D convolutional neural network C3D, and uses the general matrix multiplication (GEMM) to map the 3D convolution calculation to the 2D matrix multiplication calculation. The matrix is divided into blocks and transmitted to the FPGA through the AXI bus, and the multiplication operation of the block matrix is realized through a two-dimensional multiply-accumulate array. A System on Chip (SoC) architecture is built on the PYNQ platform, using ARM Cortex-A9 as the process control core, and the calculation of the entire matrix is completed under reasonable block and scheduling on the ARM. The IP core of matrix calculation is designed using High Level Synthesis (HLS), and the corresponding parallel optimization scheme is given. Experiments have verified that the prototype design of the hardware acceleration circuit achieves low power consumption, high energy efficiency, and high precision motion recognition while using less hardware resources.
Disk anomaly detection for remote maintenance control system of natural gas pipeline based on multi-source domain transfer and incremental learning

Accurate disk anomaly detection is of great significance for maintaining the safe and stable operation of the remote control system of natural gas pipelines. Most disk transfer algorithms use only a single-source domain model for offline transfer learning, resulting in lower classifier performance. This paper proposes a disk online anomaly detection algorithm combining transfer learning and incremental learning. A transfer learning algorithm based on data distribution similarity and dynamic index weighted ensemble is proposed. According to multiple source domain models and target domain model negative data in hyperspace, k neighbour data of each target domain negative data is extracted, and then different test criteria are adopted for each sub-model according to the number of target domain positive data, and index reweighting is performed according to the model detection capability. An incremental learning algorithm based on online automatic annotation is proposed. The data popped up by the sliding window is automatically marked at the current disk state, and the update period of the prediction model is dynamically adjusted according to the false detection rate and the missed detection rate. By setting three different experiments for comparison, the effectiveness and advanced nature of the method proposed in this paper are illustrated.
Target Detection Network for Underwater Image Based on Adaptive Anchor Frame and Re-parameterization

For low target detection rate and inaccurate localization of most target detection networks in special underwater environments, we propose an underwater target detection algorithm with adaptive anchor frames. The adaptive anchor frame selection strategy differs from the traditional methods of manually designing anchor frames and obtaining anchor frames using the K -means clustering algorithm. Our method is based on the K -means clustering algorithm and consists of a grouping strategy and a dynamic K -value strategy. It can also automatically calculate the appropriate numbers of anchor boxes and layers of network output based on the characteristics of label data. And performance of the network is guaranteed while avoiding the computation caused by redundant structures. The simple but powerful RepVGG classification network is used as the backbone network. Therefore, the feature extraction capability of the network is enhanced and the detection performance of the network on fuzzy targets is improved. The experimental results show that the algorithm in this paper achieves better detection results in the underwater target detection task compared to other algorithms.
Study of the Evolution of the Performance Ratio of Photovoltaic Plants Operating in a Utility-Scale Installation Located at a Subtropical Climate Zone Using Mixed-Effects Linear Modeling

This paper assessed the evolution of the performance ratio (PR) of a utility-scale photovoltaic (PV) installation that operates at subtropical climate conditions. The period of study encompassed 8 years, and the PR was calculated according to the ICE 61724 standard with a monthly resolution. A linear mixed effects model (LME) is a suitable tool for analyzing longitudinal data. Three LME models were assessed to provide the degradation rate. The “null model” evaluates the general relationship between PR and time with a monthly declination rate (ΔPR%) of 0.0391%/month. The “typology model” considered the relationship between PR and, as covariates, time, Manufacturer, Technology, and NominalP. Only the ΔPR% related to NominalP was found to be significant, so that, when the nominal power of a type of PV module used for a PV production unit is increased by one unit, the ΔPR% of the corresponding unit increases by 0.000897%/month. Finally, the “location model” took into account the relationship between PR and, as covariates, time, Edge, and LengthSt. These last two factors were significant, resulting in an increase of 0.0132%/month for a PV unit located at the edge of the facility and 0.00117%/month and per PV production unit when considering the length of a street, respectively.