Reviewerly/exHarmony huggingface dataset:
Aerobics Movement Decomposition Action Teaching System Based on Intelligent Vision Sensor

With the development of the times, teaching has not only stayed between people, but also gradually developed into the teaching interaction between man and machine. In the past, the teaching form was relatively single and old. Based on the intelligent visual sensor, this paper develops an auxiliary teaching system for the decomposition of aerobics action and reasonably uses the Internet and algorithms to catalog a series of aerobics action systems into the system. The DTW dynamic motion matching algorithm of the system will recognize human actions more accurately. The system will feed back human actions to the system in real time based on human feature recognition. Then, after comparison, the system will display the standard posture of this action and the aerobics posture in the next step. Therefore, this system develops teaching not only in class, but everywhere. The system not only improves the teaching quality of aerobics, but also strengthens the physical quality of teenagers. It has a new understanding of the standardization of aerobics teaching. After the function of the system is complete, the system will be distributed to aerobics learners. In many feedback information, the average use satisfaction has reached about 80%, which is a good performance index for the performance of the system itself.
An Efficient Algorithm for Web Log Data Preprocessing

At present, the Internet has become the main source of information. Users can get a vast amount of information from the Internet. Web usage mining which is an application of data mining can be utilized to extract useful information from the weblog. In this paper, an efficient algorithm for preprocessing of weblog data is proposed. A total of 82.08% data deduction has been observed by utilizing the proposed algorithm.
The Multi-Risk Analysis of Various Disasters in Karanganyar Regency

Abstract Karanganyar Regency is one of the districts in Central Java which prone of the natural disasters. Based on data Posdes 2018, it is known that the types of disasters that often occurred in Karanganyar Regency during the last three years, from 2015 to 2017 were landslides, floods, and drought. Therefore, it is important to know the multi-risk level of the three types of disasters. The method used in this research is scoring and weighting, as well as overlay between constituent parameters. Based on the results of research using the VCA matrix multiplication method, it is known that the high risk level covers 3,589.67 Ha or 4.46% areas that spread in 15 districts (except Jatipuro and Jumapolo); moderate risk level covers 34,624.94 Ha or 43.04% areas that spread in 16 districts (except Tasikmadu); The low risk level covers 42,254.98 Ha or 52.53% areas that spread in 7 districts (Colomadu, Gondangrejo, Tasikmadu, Matesih, Tawangmangu, Jumapolo, Jatiyoso).
Vulnerability Mapping of Toddler Pneumonia in East Java, Indonesia, Using the K-medoids Clustering Algorithms

In Indonesia, pneumonia is the second leading cause of death in toddler after diarrhea. East Java Province is one of the provinces with a high rate of toddler pneumonia. Pneumonia cases in East Java are the second highest in Indonesia and still a big health problem that must be solved. Mapping of toddler pneumonia susceptibility can provide an overview of the relationship between regions and the level of susceptibility. Spatial analysis can determine the pattern of disease distribution and possible causes to plan more effective community-based pneumonia efforts. This paper proposes a new approach to map the susceptibility level of toddler pneumonia in East Java using the K-medoids clustering algorithm. The attributes used as factors influencing vulnerability consisted of the number of children under five, the number of patients, the number of patients treated, healthy homes, sanitation, clean water sources, TUPM, weighed toddlers, malnutrition and poor nutrition. These criteria have a strong correlation with each other to determine the level of susceptibility of toddler pneumonia. The susceptibility level of toddler pneumonia is divided into low, medium and high levels. The mapping is visualized spatially-temporal from 2015-2019. The results of average silhouette of the K-medoids clustering 2015-2019 data generated are 0.33 better than K-means.
RT-Sniper: A Low-Overhead Defense Mechanism Pinpointing Cache Side-Channel Attacks

Since cache side-channel attacks have been serious security threats to multi-tenant systems, there have been several studies to protect systems against the attacks. However, the prior studies have limitations in determining only the existence of the attack and/or occupying too many computing resources in runtime. We propose a low-overhead pinpointing solution, called RT-Sniper, to overcome such limitations. RT-Sniper employs a two-level filtering mechanism to minimize performance overhead. It first monitors hardware events per core and isolates a suspected core to run a malicious process. Then among the processes running on the selected core, RT-Sniper pinpoints a malicious process through a per-process monitoring approach. With the core-level filtering, RT-Sniper has an advantage in overhead compared to the previous works. We evaluate RT-Sniper against Flush+Reload and Prime+Probe attacks running SPEC2017, LMBench, and PARSEC benchmarks on multi-core systems. Our evaluation demonstrates that the performance overhead by RT-Sniper is negligible (0.3% for single-threaded applications and 2.05% for multi-threaded applications). Compared to the previous defense solutions against cache side-channel attacks, RT-Sniper exhibits better detection performance with lower performance overhead.
Real Time Personal Data Protection Using Image Processing

The use of personal mobile devices is increasing at a rapid rate due to digitalization of the world. These devices are portable and can be used at various locations governing access to these devices is a challenging task. To solve this problem, an application has been developed that helps in implementing the authorization mechanism on a device using image processing and machine learning techniques. Face recognition is one of the best methods to govern access on a device. Authorization mechanism has implemented using Local Binary Pattern Histograms (LBPH) along with Haar-cascade for face detection. The algorithm has trained with the users' database that has face data of all authorized users. The algorithm generates a configuration model as output which is used to recognize the current user of a device. When unauthorized access or spoof attack is detected the application will automatically implement the alerting mechanism.
Towards Automating Social Distance Violations using AIoT

The pandemic caused by the COVID 19 virus had a tremendous impact on the retail and tourism industry resulting in a loss of around two trillion dollars per year. Recovering completely from the pandemic may take several years and safety guidelines like social distancing will remain a norm for many years to come. Many governments have levied heavy fines for businesses that fail to implement social distancing guidelines. Implementing manual social distancing policies can be expensive and error prone. This paper proposes a smart system that uses cameras along with Artificial Intelligence Internet of Things (AIoT) edge devices like the Nvidia Nano GPU to automatically detect social distance violations on the edge. The system achieved an accuracy of 92.2% and an F1-Score of 0.851 using a single camera only. On an Nvidia Nano, the system has a very acceptable rate of 1.7 frames per second on the edge obviating the need for any expensive back-end transmission and processing. The system uses the MQTT protocol to instantly notify the manager or owner of the business via a mobile App and a Web application for necessary action.
Assisting university faculty in emergency teaching in the face of Covid-19 : A mentoring experience in digital competence at the University of Málaga

The tactical virtualisation of university teaching in the face of the Covid-19 pandemic has required institutions to respond to the training demands of the teaching staff with flexible initiatives that go beyond the established educational curricula and programmes. The results of one of these initiatives is presented in this paper with regard to participation by the professors and the evaluation of the project. We refer to the mentoring programme for digital competencies of the Faculty of Communication Sciences at the University of Malaga, in which the professors themselves have trained and guided other professors at the university in order to help them adapt their teaching. The results show a high level of participation and satisfaction on the part of the faculty, as well as a positive impact on teaching according to the students themselves. The article concludes by sharing guidelines for action that can be transferred to other centres and universities, in addition to reflecting on practices and ways of learning that can be applied beyond the pandemic.
Knowledge Distillation via Entropy Map for Scene Text Detection

In recent years, state-of-the-art scene text detectors use cumbersome segmentation models as their detection frameworks, but this type of detection model is difficult to deploy to mobile devices with limited computing resources. An effective method to achieve a good compromise between detection accuracy and model complexity is knowledge distillation. The existing knowledge distillation methods cannot make full use of the output characteristics of the segmentation-based text detection, that is, each pixel on the segmentation map represents the probability that the pixel belongs to the text. The results of the entropy map corresponding to the visualized segmentation map show that the degree of confusion between the information entropy map of the student network and the teacher network reflects the gap in generalization ability. In this work, we propose a novel knowledge distillation via entropy map(KDEM). Specifically, the entropy map of the teacher network segmentation map is used as knowledge to guide student network learning. In order to eliminate the possible adverse effects of entropy in non-target regions, we multiply the information entropy map of the teacher network with the mask of the text region to extract the knowledge related to the target. Experiments on three benchmark datasets: MSRA-TD500, ICDAR 2015 and Total-Text, show that our proposed knowledge distillation via entropy map consistently improve the F1-score of the student network and is better than the other three mainstream knowledge distillation methods.
Blockchain-Oriented Query Capability Optimization

Aiming at the problems of limited query types and low processing performance of traditional full-node-oriented queries, this paper adds relational semantics to block data to support complex queries composed of basic relational operators such as selection, projection, and connection. The system only stores one block of data, so no additional storage costs are added. In addition, transactions belonging to the same table are distributed to multiple blocks according to the timeline of their occurrence, which significantly increases I/O overhead. This paper designs table-level bitmap indexes and hierarchical indexes to speed up data access and optimize query processing. And ultimately significantly reduce the response time of the query.
A Joint Detection and State Estimation Algorithm for Pulse Doppler Radar

In order to improve the detection sensitivity of pulse Doppler (PD) radar, a joint detection and state estimation algorithm based on random finite set is proposed. In the framework of formal Bernoulli filter, the state model and observation model of the algorithm are designed at the range-Doppler (RD) echo signal level when there is only a single target, which makes full use of the dynamic difference of phase and amplitude change between target and background. Since the state vector can be divided into linear and non-linear parts, Rao-Blackwellized particle filter (RB-PF) is used to realize the prediction and update of the state vector. The simulation results show that the algorithm can effectively improve the state estimation accuracy and detection sensitivity.
Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuoustime series analysis and forward prediction applications.The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information.Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream.The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window.The DQO model is identified from the samples of the real-time flow of data over the sliding time window.A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps.The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine.The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.
Implementation of Tactical Decision Aids Based on Event Knowledge Graph

Abstract This paper proposes a new tactical decision aids method based on event knowledge graph (EventKG). In the warfare domain, EventKG can be constructed through event types design, event network construction and transition probability computation between events. Initially, four event classes are introduced in accordance with the OODA loop, and eighteen subclasses are further decomposed. With the aids of a common event template, all the events taking place in the battle field can be described. Event networks are built by adopting the hierarchical task network (HTN) and described through Bayesian network, to exhibit various relations between battle events. Transition probability, namely the occurrence probability of next possible event, is computed by using the prior probability and conditional probability of event occurring. On the basis of structured EventKG, entity knowledge graph (EKG) and entity relation knowledge graph (ERKG), tactical decision aid instructions can be generated by combining with the battlefield situation information.
TFCluster: An Efficient Algorithm to Mine Maximal Differential Function-resource Biclusters for Single Pilot Operations Safety Analysis

Single Pilot Operations (SPO) is an important direction in the development of modern aviation technology. In SPO mode, there needs to construct air-ground cooperating mode among onboard pilot, ground station and air traffic control, which is accompanied by a deeper level of task synthesis. But the high level of task synthesis also introduces safety issues that cannot be underestimated. In this paper, we propose an efficient differential bicluster mining algorithm: TFCluster, to discover potential hazardous elements or propagation mechanism by mining the real-valued resource-function matrixes for task synthesis safety analysis. In order to mine efficiently, several pruning techniques are designed for generating maximal biclusters without candidate maintenance. Taking the typical operating scenario—flight conflict during cruising phase in SPO mode as an example, we show how to use our proposed algorithm for SPO system safety analysis.
Structure of the journal citation network Math-Net.Ru

According to the portal Math-Net.Ru a graph of journal citation is constructed. To increase the reliability of the model, the citation time interval was chosen from 2010 to 2021, when the distribution of citation articles stabilized at the level of 3500-4500 citations per year. The structure of link aging is studied and it is shown that their half-life is equal to 8 years. Therefore, the publication date of the cited articles was limited to 2002. For the constructed citation graph, the main properties, such as a small diameter and a high density, are obtained, indicating a high level of scientific communication in the Math-Net.Ru. Adequacy of the journal citation graph Math-Net.Ru as a model of scientific communication confirmed by comparing the ranking of journals in the citation graph with their SCIENCE INDEX rating in eLIBRARY.RU. A direct moderate relationship between the two rankings is shown. A number of meaningful conclusions are drawn from the analysis of the citation graph.
Automated Essay Scoring

This book discusses the state of the art of automated essay scoring, its challenges and its potential. One of the earliest applications of artificial intelligence to language data (along with machine translation and speech recognition), automated essay scoring has evolved to become both a revenue-generating industry and a vast field of research, with many subfields and connections to other NLP tasks. In this book, we review the developments in this field against the backdrop of Elias Page's seminal 1966 paper titled "The Imminence of Grading Essays by Computer."
Mitigating Preconception in Machine Learning Classifiers

Modern Machine Learning (ML) approaches are aimed at enhancing model performance (behaviors and accuracy) through historical data available for the specific model. Continued use of machine learning has been witnessed in the real-world business including self-driving cars, health diagnosis systems, fraud detection, and customer churn predictions among other areas demanding intense safety and testing. Amid the intense application of the technology, however lies a growing user concern and interest in insights and decisions made by a classifier due to the increasingly complex models known for hiding crucial information regarding their outcome generation. Overreliance in model performance therefore can no longer be considered a sure metric to model performance due to the underlying trust issues and therefore the need for model interpretation or explanation to extend and make machine models potential sources of knowledge. This research paper delves into the understanding of the various approaches to successful model interpretation, further proposing enhancement of Local Interpretable Model-agnostic Explainer (LIME) via ‘Calculus Local Interpretable Model-agnostic Explainer’ (C-LIME) as an intuitive approach to black-box ML model interpretation. We further evaluate the framework performance demonstrating significant enhancement in black-box model interpretations without compromising prediction accuracy.
An Improved Cross-Domain Sentiment Analysis Based on a Semi-Supervised Convolutional Neural Network

The dependence on Internet in our daily life is ever-growing, which provides opportunity to discover valuable and subjective information using advanced techniques such as natural language processing and artificial intelligence. In this chapter, the research focus is a convolutional neural network for three-class (positive, neutral, and negative) cross-domain sentiment analysis. The model is enhanced in two-fold. First, a similarity label method facilitates the management between the source and target domains to generate more labelled data. Second, term frequency-inverse document frequency (TF-IDF) and latent semantic indexing (LSI) are employed to compute the similarity between source and target domains. Performance evaluation is conducted using three datasets, beauty reviews, toys reviews, and phone reviews. The proposed method enhances the accuracy by 4.3-7.6% and reduces the training time by 50%. The limitations of the research work have been discussed, which serve as the rationales of future research directions.
Practical Attacks on Reduced-Round 3D and Saturnin

Abstract 3D, an advanced encryption standard-like cipher employed three-dimensional structure, was proposed in 2008. Its recommended number of rounds is 22. Although the longest key recovery attack can currently reach 13 rounds, the complexity of existing attacks for &amp;gt;6 rounds seems to exceed the practically feasible complexity. Thus, a practical attack for 7-round 3D has yet to be developed. Recently, a lightweight block cipher called Saturnin has been selected as a second-round candidate in the National Institute of Standards and Technology standardization for lightweight cryptography. Saturnin also employs a three-dimensional structure and provides high security against quantum and classic attacks. In this paper, we investigate the yoyo attack on these two ciphers. Combined with the meet-in-the-middle technique, we apply the yoyo trick to 7-round 3D and recover the whole 512-bit secret key with $2^{15}$ plaintexts and adaptively chosen ciphertexts and $2^{16.5}$ complexity of full encryptions. To our best knowledge, it is the first practical key recovery attack for 7-round 3D to date. For Saturnin, we found a minor typo in its design report. The designers intended to make a super round containing two S-layers, but one was inadvertently omitted in the algorithm description. We propose a 5-super-round key recovery attack, which is suitable for both one-S-layer version and two-S-layer version. Since the round function of Saturnin has better diffusion, which leads that the meet-in-the-middle technique cannot be applied to this cipher directly. For the one-S-layer version, we address this problem by proposing a new technique called reducing key sets. This technique will fail on the other version, which proves the necessity of containing two S-layers in one-super-round. Finally, our attack requires $2^{39.1}$ plaintext pairs and adaptively chosen ciphertext pairs and $2^{46}$ one-round encryptions.
Research on Multi-dimensional Time Series Anomaly Detection Method Based on Temporal Deep Residual Shrinkage Networks

Multi-dimensional time series are a series of data recorded by multiple variables in the order of time. Anomaly detection refers to the detection of data that do not conform to normal expectations. Existing multi dimensional time series anomaly detection methods do not well distinguish the anomalies in the data, either in the effectiveness of the algorithm, or in the time cost. Based on the research at home and abroad, an algorithm with both algorithm effectiveness and reducing time cost advantages: Temporal Deep Residual Shrinkage Networks, TDRSN. Combining Time Convolutional Network (TCN) with Deep Residual Shrinkage Network (DRSN), adopt dilated causal convolution to expand the receptive field, introduce the soft threshold of self attention, enabling it to not only remember historical data, reduce the impact of gradient vanishing and gradient exploding, but also automatically set thresholds without expert experience, eliminate redundant features and reduce the influence of unimportant features. The experimental results show that TDRSN has a better anomaly detection effect than TCN, DRSN and other classical time series anomaly detection models.