Reviewerly/exHarmony huggingface dataset:
Investigating transformers in the decomposition of polygonal shapes as point collections

Transformers can generate predictions in two approaches: 1. auto-regressively by conditioning each sequence element on the previous ones, or 2. directly produce an output sequences in parallel. While research has mostly explored upon this difference on sequential tasks in NLP, we study the difference between auto-regressive and parallel prediction on visual set prediction tasks, and in particular on polygonal shapes in images because polygons are representative of numerous types of objects, such as buildings or obstacles for aerial vehicles. This is challenging for deep learning architectures as a polygon can consist of a varying carnality of points. We provide evidence on the importance of natural orders for Transformers, and show the benefit of decomposing complex polygons into collections of points in an auto-regressive manner.
Development of an Algorithmic Approach for Hiding Sensitive Data and Recovery of Data based on Fingerprint Identification for Secure Cloud Storage

Information Security is a unified piece of information technology that has emerged as vibrant technology in the last two decades. To manage security, authentication assumes a significant part. Biometric is the physical unique identification as well as authentication for the third party. We have proposed the security model for preventing many attacks so we are used the innermost layer as a 3DES (Triple Encryption standard) cryptography algorithm that is providing 3- key protection as 64-bit and the outermost layer used the MD5 (Message Digest) algorithm. i. e. providing 128-bit protection as well as we is using fingerprint identification as physical security that is used in third-party remote integrity auditing. Remote data integrity auditing is proposed to ensure the uprightness of the information put away in the cloud. Data Storage of cloud services has expanded paces of acknowledgment because of their adaptability and the worry of the security and privacy levels. The large number of integrity and security issues that arise depends on the difference between the customer and the service provider in the sense of an external auditor. The remote data integrity auditing is at this point prepared to be viably executed. In the meantime, the proposed scheme is depending on identity-based cryptography, which works on the convoluted testament of the executives. The safety investigation and the exhibition assessment show that the planned property is safe and productive.
On the Transfer Learning of Genetic Programming Classification Algorithms

Data classification is a real-world problem that is encountered daily in various problem domains. Genetic programming (GP) has proved to be one of the most versatile algorithms leading to its popularity as a classification algorithm. However, due to its large number of parameters, the manual design process of GP is considered to be a time consuming tedious task. As a result, there have been initiatives by the machine learning community to automate the design of GP classification algorithms. In this paper, we propose the transfer of the design knowledge gained from the automated design of GP classification algorithms from a specific source domain and apply it to design GP classification algorithms for a target domain. The results of the experiments demonstrate that the proposed approach is capable of evolving classifiers that achieve results that are competitive when compared to automated designed classifiers and better than manually tuned parameter classifiers. To the best of our knowledge, this is the first study that examines transfer learning in automated design. The proposed approach is shown to achieve positive transfer.
Finding 3D Dubins Paths with Pitch Angle Constraint Using Non-linear Optimization

This paper presents a novel non-linear programming formulation to find the shortest 3D Dubins path with a limited pitch angle. Such a path is suitable for fix-wing aircraft because it satisfies both the minimum turning radius and pitch angle constraints, and thus it is a feasible and smooth path in the 3D space. The proposed method utilizes the existing decoupled approach as an initial solution and improves its quality by dividing the path into small segments with constant curvature. The proposed formulation encodes the path using the direction vectors that significantly reduce the needed optimization variables. Therefore, a path with 100 segments can be optimized in about one second using conventional computational resources. Although the decoupled paths are usually within 2 % from the lower bound, the proposed approach further reduces the gap by about 30 %.
Micro-Expression Recognition Using VGG19 Convolutional Neural Network Architecture And Random Forest

Micro-expression is a hidden expression of human. This expression occurs in a very short time and very difficult to see or detect in a daily conversation. A system that developed in this research is a system that capable of analyzing micro-expression and show the result performance. The method used is by using Convolutional Neural Network (CNN) with Visual Geometry Group - 19 (VGG19) architecture as feature extractor and random forest as classifier. Architecture CNN VGG19 selected because many case in image classification, CNN always have good performance. The research use SMIC-Cropped database with the distribution of percentage between training:validation of 50:50, 55:45, 60:40, 65:35, 70:30, 75:25, 80:20, 85:15, and 90:10. This research obtained accuracy in the range of 98% - 100%. The distribution percentage of the database which have best performance is obtained when the split is 90:10 with result 99% - 100% accuracy, 99%-100% precision, 100% recall, and 100% f1-score.
Driver-Skeleton: A Dataset for Driver Action Recognition

At present, driver's dangerous driving behavior usually leads to negative outcomes of driving safety and driver action recognition based on skeleton is a current research hotspot. However, there are no large-scale public skeleton datasets for driver action recognition. We present a 3D skeleton information dataset Driver-Skeleton for driver action recognition. This dataset has the advantages of strong pertinence, wide coverage and good scalability. Several experimental subjects are invited to simulate the driver's operation in the cab, and the driver's behavior is divided into 10 classes, which basically covers the driver's common actions in the process of driving. Driver-Skeleton dataset refers to common vehicle models, simulates different vehicle models with different shooting heights, and takes pictures of the experimental objects from different shooting heights. Driver-Skeleton dataset constructed by us used Microsoft Kinect V2 sensor to collect 1423 effective RGB videos from 30 experimental subjects and extract the 3D skeleton information of the driver using these videos. We proposed a two-stream spatial temporal graph convolutional network based on attention mechanism, and experimented on the Driver-Skeleton dataset together with other action recognition methods, and the experimental results confirmed the effectiveness of the dataset. The dataset is freely available at https://github.com/JaxferZ/Driver-Skeleton.git.
Risk and cost benchmarking of solar energy technology based on celestial phenomenon for a power market integration

• Qualitative understanding of the renewable capacities for solar power harvesting. • Deciding the extent to which renewable penetration will be economically viable. • Cognise the influence of astral events for renewable integration in power market. • LCOE as benchmarking criterion for deciding minimum tariff for a solar plant. • CVaR as a secod benchmark for understanding the risk in investment. Intermittency associated with the renewable energy institutes various uncertainty with respect to power market perspective. To overcome the uncertainties associated with the renewable energy source a novel benchmarking approach is proposed. The benchmarking is done in order to compare the economical perspective of various energy sources. The celestial events considered for benchmarking are Aphelion, Perihelion, and solstice (directly affects solar irradiance). The proposed method can determine the harvest quality of solar farming based on profit returns. The solar energy output data provided by the National Institute of Wind Energy (NIWE) has is used to find out the solar energy output during Aphelion, Perihelion, and solstices. For benchmarking of an area, Levelized Cost of Electricity (LCOE), Conditional Value at Risk (CVaR) and carbon pricing index, have been considered. Levelized Cost of Electricity (LCOE) is used to determine the cost of solar energy produced during its lifetime, and CVaR is used to assess the tail-end risk for the profit return. Carbon pricing is benchmarked to understand the environmental impact of the energy sources. The duration chosen is 10.00–14.00 h which is the time of the peak solar irradiances considering a solar thermal and PV system. IEEE 5 and 14 bus systems are chosen for the case study. Based on the results obtained, market condition influences the LCOE. The LCOE is lower for stable market returns and higher for an unstable market. On comparing the benchmark scores considering the best case scenario, solar thermal is having 59% higher LCOE, 5.2 times higher carbon price index and 42% higher CVaR than PV system. Thus the proposed approach will help market participant in decision making related to the profit and risk associated with the renewable energy.
Signals Recognition of Underwater Acoustic Communication based on Artificial Neural Network and Signal Feature Extraction

Modulation pattern recognition is an important part of underwater acoustic communication. Due to the complexity of underwater acoustic media (propagation loss, ocean noise, multipath effect and Doppler effect), underwater acoustic channel is considered to be one of the most challenging wireless communication channels. This paper proposed an intelligent underwater acoustic signal processing and recognition method based on artificial neural network (ANN) and signal feature extraction. Firstly, the real part and imaginary part of the signal are extracted by fast Fourier transform (FFT), the variance, mean and other eigenvalues of the real part and imaginary part are calculated, respectively. Secondly, the extracted signal features are used to train ANN classifier to realize the classification and recognition of different signals. In this way, the intelligent recognition of underwater acoustic signal by data-driven method is realized. Finally, the effectiveness of the proposed method is verified by simulation, and the good recognition effect is achieved.
When Hardness Makes a Difference

Knowledge graph (KG) reasoning is a significant method for KG completion. To enhance the explainability of KG reasoning, some studies adopt reinforcement learning (RL) to complete the multi-hop reasoning. However, RL-based reasoning methods are severely limited by few-shot relations (only contain few triplets). To tackle the problem, recent studies introduce meta-learning into RL-based methods to improve reasoning performance. However, the generalization abilities of their models are limited due to the problem of low reasoning accuracies over hard relations (e.g., language and title). To overcome this problem, we propose a novel model called THML (Two-level Hardness-aware Meta-reinforcement Learning). Specifically, the model contains the following two components: (1) A hardness-aware meta-reinforcement learning method is proposed to predict the missing element by training hardness-aware batches. (2) A two-level hardness-aware sampling is proposed to effectively generate new hardness-aware batches from relation level and relation-cluster level. The generalization ability of our model is significantly improved by repeating the process of these two components in an alternate way. The experimental results demonstrate that THML notably outperforms the state-of-the-art approaches in few-shot scenarios.
Whole slide cervical image classification based on convolutional neural network and random forest

Abstract Cervical cancer is a kind of common female malignancy ranking fourth for mortality worldwide. Traditional histopathological examination, an important diagnosis method of cervical cancer, is still manually performed by pathologists under the microscope, which is labor intensive and error‐prone. In this article, deep learning is used for whole slide cervical image (WSCI) analysis to explore an automatic and effective method for the diagnosis of cervical cancer. We combine convolutional neural network (CNN) and random forest (RF) classifier for whole slide cervical image classification. A new multilevel feature fusion strategy named Ensemble is used for features extraction from the features extracted by CNN. Ensemble that fuses features extracted by convolution layers with different depths in CNN together is capable of capturing fusional features which can describe patches at different levels from different convolutional layers. Principal component analysis (PCA) algorithm is introduced for feature reduction of the multilevel features. Our experiments are carried out on WSCI dataset which consists of a total of 163 WSCIs from 27 patients. We combine CNN + PCA + RF model and CNN + RF model, respectively, with four feature extraction strategies to conduct eight comparative experiments on the 163 WSCIs. Experimental results demonstrate that when using multilevel feature fusion strategy, the classification accuracy of the CNN + PCA + RF model reaches the highest to 99.39%. In addition, the CNN + PCA + RF model conducting the multilevel feature fusion strategy performs better than that conducting single‐level feature extraction strategies.
Secure Namespaced Kernel Audit for Containers

Despite the wide usage of container-based cloud computing, container auditing for security analysis relies mostly on built-in host audit systems, which often lack the ability to capture high-fidelity container logs. State-of-the-art reference-monitor-based audit techniques greatly improve the quality of audit logs, but their system-wide architecture is too costly to be adapted for individual containers. Moreover, these techniques typically require extensive kernel modifications, making it difficult to deploy in practical settings.
Analysis of the Effectiveness of Online Teaching Based on Big Data

Abstract Online teaching is a new teaching method that has emerged with the development of the Internet. Online teaching can realize remote teaching with the help of a network platform, which can not only meet the learning needs of students anytime and anywhere, but also achieve the purpose of teacher-student interaction and personalized teaching. Applying big data to online teaching can undoubtedly improve teaching efficiency to the greatest extent. This article will analyze the effectiveness of online teaching based on big data, and point out that the use of big data analysis can improve the pertinence of teaching and improve the effectiveness of online teaching. Effectiveness improves the quality of teaching.
A Survey of Encrypted Malicious Traffic Detection

With more and more encrypted traffic such as HTTPS, encrypted traffic protects not only normal traffic, but also malicious traffic. Identification of encrypted malicious traffic without decryption has become a research hotspot. Combined with deep learning, an important branch of machine learning, encrypted malicious traffic detection has achieved good results. This paper reviews the detection of encrypted malicious traffic in recent years. Firstly, we classify encrypted malicious traffic. Secondly, we sorts out the extraction characteristics of encrypted malicious traffic, the key and difficult problems we are facing at present. Then, with encrypted malicious traffic detection technology as the main line, we summarized the current detection model from the four core aspects of data collection, data processing, model training and evaluation improvement. Finally, we analyze the problems and point out future research directions.
Smart Rebalancing for Bike Sharing Systems using Quantum Approximate Optimization Algorithm

Smart Mobility is the key component of Smart City initiative that are being explored throughout the world. The bike-sharing system (BSS) aims to provide an alternative mode of Smart Mobility transportation system, and it is being widely adopted in urban areas. The use of bikes for short-distance travel helps to reduce traffic congestion, reduce carbon emissions, and decrease the risk of overcrowding. Effective bike sharing system operations requires rebalancing analysis, which corresponds to transferal of bikes across various bike stations to ensure the supply meets expected demand. In this work, we present Quantum Approximate Optimization Algorithm(QAOA), a variational hybrid quantum-classical algorithm that has shown significant computational advantages in solving combinatorial optimization problems such as bike sharing system rebalancing problem (BSS-RBP). Here, we minimize the overall distance travelled by the transport vehicle across various bike station. In this preliminary work, we demonstrate the application of QAOA using the IBM-Qiskit quantum computing simulator for rebalancing analysis across three bike locations.
Study of Text-to-Speech (TTS) Conversion for Indic Languages

This paper discusses the text-to-speech conversion for Indic languages. Text-to-speech (TTS) systems convert the natural Indic languages like Hindi, Gujarati, Marathi and Punjabi and others into a matching spoken waveform or speech. There have been ample achievements today in the area of speech synthesis that suggests that TTS technology will continue to be a key area of research and development in creating smart TTS applications. In this paper, we aim to deliver a study of various techniques for TTS conversion used in Indic languages. The authors have given comparative analysis of the techniques like concatenation synthesis, pure synthesis and neural network-based synthesis.
The Origin of Patterns

The question is discussed from where the patterns arise that are recognized in the world. Are they elements of the outside world, or do they originate from the concepts that live in the mind of the observer? It is argued that they are created during observation, due to the knowledge on which the observation ability is based. For an experienced observer this may result in a direct recognition of an object or phenomenon without any reasoning. Afterwards and using conscious effort he may be able to supply features or arguments that he might have used for his recognition. The discussion is phrased in the philosophical debate between monism, in which the observer is an element of the observed world, and dualism, in which these two are fully separated. Direct recognition can be understood from a monistic point of view. After the definition of features and the formulation of a reasoning, dualism may arise. An artificial pattern recognition system based on these specifications thereby creates a clear dualistic situation. It fully separates the two worlds by physical sensors and mechanical reasoning. This dualistic position can be solved by a responsible integration of artificially intelligent systems in human controlled applications. A set of simple experiments based on the classification of histopathological slides is presented to illustrate the discussion.
Multivariate Time Series Anomaly Detection On Improved HTM Model

In recent years, industrial big data has attracted much attention as the key technical support of “Intelligent Manufacturing” and “Industrial Internet”. And as the dependence of intelligent manufacturing on digitalization continues to increase, data quality problems caused by device and system failures, harsh environment, improper scheduling and management, duplication or missing of data fields, etc., have more significant impacts on industrial processes. Therefore, the anomaly detection of industrial big data is particularly important. Among the methods onto time series data for anomaly detection, HTM(Hierarchical Temporal Memory) algorithm performs well in the unsupervised univariate time series data anomaly detection, but the capability of original HTM model for detecting multivariate time series anomaly data is insufficient. However, the multivariate data anomaly detection is common in industry and the performance requirements for data anomaly detection are relatively high. Thus, this paper proposes an improved HTM algorithm model - MSP-HTM(Multiple Spatial Poolers HTM) model. The MSP-HTM model respectively encode the value of different dimensions at the same time, and then put the result from encoder into spatial pooler respectively, finally the temporal memory layer merge result from spatial poolers, and predict future data. Experiments show that the MSP-HTM model can improve performance by processing the multivariate time series data in parallel and improve the effect of data anomaly detection.
A machine learning-based approach for picture acquisition timeslot prediction using defective pixels

Estimating the acquisition time of digital photographs is a challenging task in temporal image forensics, but the application is highly demanded for establishing temporal order among individual pieces of evidence and deduce the causal relationship of events in a court case.The forensic investigator needs to identify the timeline of events and look for some patterns to gain a clear overview of activities associated with a crime.This paper aims to explore the presence of defective pixels over time for estimating the acquisition date of digital pictures.We propose a technique to predict the acquisition timeslots of digital pictures using a set of candidate defective pixels in non-overlapping image blocks.First, potential candidate defective pixels are determined through related pixel neighbourhood and two proposed features, called the local variation features to best fit in a machine learning model.The machine learning approach is used to model the temporal behaviour of camera sensor defects in each block using the scores obtained from individually trained pixel defect locations and fused in a majority voting method.Interestingly, timeslot estimation using individual blocks has been shown to be more accurate when virtual sub-classes corresponding to halved timeslots are first considered prior to the reconstruction step.Finally, the last stage of the system consists of the combination of block scores in a second majority voting operation to further enhance performance.Assessed on the NTIF image dataset, the proposed system has been shown to reach very promising results with an estimated accuracy between 88% and 93% and clear superiority over a related state-of-the-art system.
Sustainable Eco-friendly Reward Bin Using RVM by Detecting Recyclable waste and Generating points according to the weight of the Materials

Literally, the whole world is floating on the sea of waste. With the booming rate of population, the production of waste is also boosting at an astonishing rate. As a consequent, waste management has emerged as an important issue in front of the globe. Vivid measures are adopted by different countries to manage junk efficiently. Approaches to waste management differ from country to country based on the culture and perspective of people. Our country, Bangladesh, is a developing as well as third world country. A reward-based RVM machine, for waste management, has been proposed in this paper which will going to reward people according to the material of their deposited waste. The RVM generates reward points when it finds three types of waste, namely metal, glass and plastic. Mechanism of this machine is fully based on Arduino UNO, cheap but productive sensors and some inexpensive elements. The production cost of this RVM is low, and it can attract people by the rewarding system. Reward points can be converted into cash eventually. Though the proposed RVM exhibits some minor flaws, it is the most competent approach of efficacious waste management to Bangladesh.