Reviewerly/exHarmony huggingface dataset:
Parallel Multi-Environment Shaping Algorithm for Complex Multi-step Task

Because of the sparse reward and the sequence of the complex multi-step task, there is a big challenge in reinforcement learning, that is, the agent needs to implement several consecutive sequential steps to complete the whole task without the intermediate reward. Reward Shaping and Curriculum Learning algorithms are always used to solve this challenge, but Reward Shaping is prone to sub-optimal policies and Curriculum Learning easily suffers from the catastrophic forgetting problem. In this paper, we propose a novel algorithm called Parallel Multi-Environment Shaping (PMES), where several sub-environments are built based on human knowledge to make the agent aware of the importance of intermediate steps, each of which corresponds to a key intermediate step. Specifically, the learning agent is trained under these parallel multiple environments including the original environment and several sub-environments by synchronous advantage actor-critic algorithm. And the PMES algorithm has the mechanism of adaptive reward shaping to adjust the reward function. In this way, PMES algorithm effectively incorporates human experience by multiple different environments rather than only shaping the reward function, which combines the benefits of Reward Shaping and Curriculum Learning algorithms while avoiding their drawbacks. Extensive experiments on the mini-game ‘Build Marines’ of StarCraft II environment show that our proposed algorithm is more effective than Reward Shaping, Curriculum Learning, and PLAID algorithms, which is almost close to the level of human Grandmaster. And compared with the existing work, it takes less time and computing resources to reach a good result.
Pseudo-quantile functional data clustering

This paper studies the problem of functional data clustering. Functional data have their own characteristics and contain rich information that cannot be obtained when regarding the data as multivariate data. Functional data are inherently infinite-dimensional, so classical clustering techniques for finite-dimensional data may not be suitable for functional data. There are several clustering methods for functional data based on probabilistic models or basis expansion approaches. However, most of these depend on the symmetric structure of the model or the mean response; hence, these cannot reflect characteristics of the distribution of data beyond the mean, such as behavior at the extremes. In this paper, we propose a new approach for functional data clustering based on the concept of an asymmetric norm. For this purpose, we consider pseudo-quantiles, such as M-quantiles and expectiles, and their corresponding curves that can provide rich distributional information about hidden structures in the data at various levels. Moreover, as a theoretical justification for the proposed method, a strong consistency property is investigated. Results from numerical examples, including real data analysis, demonstrate the promising empirical properties of the proposed approach.
Research on Information Recognition of VAT Invoice Based on Computer Vision

With the promotion of the bill exchange system throughout the world, the use of VAT invoices has exploded. In order to solve the problems of low efficiency, high error rate and high labor intensity of manual entry of electronic system of VAT invoice, a method of recognizing VAT invoice information based on computer vision was proposed. Firstly, the invoice image was preprocessed, and the tilt correction was implemented by local adaptive threshold and Hough transform. Then the key information area was segmented and the target object was taken out by projection method. Finally, the characters were recognized by OCR technology. The results show that the method has low error rate and high efficiency, and the average character recognition accuracy can reach 96.21%.
Strategy of Artificial Intelligence Assisted Health Tourism in the Perspective of Global Region Based on Markov Chain Model

Strategy of the artificial intelligence assisted health tourism in the perspective of global region based on the Markov chain model is studied in this paper. In the process of developing the core big data visualization platform of the scenic spots, the application of visualization technology can display the collected business data efficiently, concisely and clearly. To effectively then analyze the data, the paper has listed novelties. (1) The model assumes that the covariance matrix of factor variables is the identity matrix and that the transition probabilities are also sometimes homogeneous. (2) The Internet of things is roughly recognized as having three levels, the first layer, also the bottom layer, refers to the perception layer used to perceive. The core and comprehensive analysis are done for the efficient analysis.
Perturb more, trap more: Understanding behaviors of graph neural networks

While graph neural networks (GNNs) have shown great potential in various graph-related tasks, their lack of transparency has hindered our understanding of how they arrive at their predictions. The fidelity to the local decision boundary of the original model, indicating how well the explainer fits the original model around the instance to be explained, is neglected by existing GNN explainers. In this paper, we first propose a novel post hoc framework based on local fidelity for any trained GNNs, called TraP2 , which can generate a high-fidelity explanation. Considering that both the relevant graph structure and important features inside each node must be highlighted, a three-layer architecture in TraP2 is designed: i) the interpretation domain is defined by the Tra nslation layer in advance; ii) the local predictive behaviors of the GNNs being explained are probed and monitored by the P erturbation layer, in which multiple perturbations for graph structure and feature level are conducted in the interpretation domain; and iii) highly faithful explanations are generated by fitting the local decision boundary of GNNs being explained through the P araphrase layer. We evaluated TraP2 on several benchmark datasets under the four metrics of accuracy, area under receiver operating characteristic curve, fidelity, and contrastivity, and the results prove that it significantly outperforms state-of-the-art methods.
Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance

The uncertain disturbance in the system signals can lead to biased state estimates and, in turn, can lead to deterioration in the performance of state estimation for a nonlinear dynamic system. In order to address these issues, this paper develops an adaptive fitting H-infinity filter (AFHF) based moving-window by combining the novel noise estimator with fitting H-infinity filtering. Specifically speaking, the novel noise estimator is designed to estimate the process and measurement noise characteristics during a fixed window epoch on the basic of the moving-window technique. Subsequently, the noise characteristics at each window epoch is regarded as the input noise means and covariances of fitting H-infinity filtering at next epoch. Further, the attenuation level is adaptively calculated at each time step to change the structure of AFHF. The Monte-Carlo simulations and INS/GPS integrated navigation experiments are set up for the sake of verifying the superior performance of the proposed filtering with uncertain disturbances.
Variable Block Size Hybrid Fractal Technique for Image Compression

This paper presents a hybrid fractal method for color and grey scale image compression based on varying block size. The designed and developed fractal technique improves compression parameters like compression ratio (C.R), Peak Signal to Noise Ratio (P.S.N.R), Mean Square Error (M.S.E), coding redundancy and entropy of a reconstructed image. The RGB/Grey image is divided to blocks of varying ranges. The maximum and minimum window size of image range blocks, when applied to fractal compression and hybridized with proposed algorithm on iterative transforms improves compression parameters. Results are compared with the latest methods and implementation is done with the various categories of images. The results show that the proposed method works effectively for different categories of images and improves the C.R and P.S.N.R parameters.
Analysis of Vulnerabilities That Can Occur When Generating One-Time Password

A one-time password (OTP) is a password that is valid for only one login session or transaction, in IT systems or digital devices. This is one of the human-centered security services and is commonly used for multi-factor authentication. This is very similar to generating pseudo-random bit streams in cryptography. However, it is only part of what is used as OTP in the bit stream. Therefore, the OTP mechanism requires an algorithm to extract portions. It is also necessary to convert hexadecimal to decimal so that the values of the bit strings are familiar to human. In this paper, we classify three algorithms for extracting the final data from the pseudo random bit sequence. We also analyze the fact that a vulnerability occurs during the extraction process, resulting in a high frequency of certain numbers; even if cryptographically secure generation algorithms are used.
Image Encryption:Traditional Methods vs Alternative Methods

Nowadays, securing images is essential because transmissions of images are suffering from various security attacks. Images like medical images, remote-sensing images, etc. have great value both in money and information. Therefore, there are several sophisticated methods for encrypting images. This paper mainly focuses on a survey of such methods and comparative evaluation of them. Here, the reviewers have used 13 papers for the survey and comparative evaluation. Various aspects of image encryption are surveyed and a comparative evaluation of the papers using different parameters such as security attacks, information loss, speed, overhead, etc. is performed. The conclusions are stated with the help of suitable tables. We hope this paper gives an insight into Image Encryption, Encryption Methods, Properties of Images, Evaluation Criteria, Analysis, Attacks, Resistance, Noise, and Information Loss.
HyOASAM: A Hybrid Open API Selection Approach for Mashup Development

At present, Mashup development has attracted much attention in the field of software engineering. It is the focus of this article to use existing open APIs to meet the needs of Mashup developers. Therefore, how to select the most appropriate open API for a specific user requirement is a crucial problem to be solved. We propose a Hybrid Open API Selection Approach for Mashup development (HyOASAM), which consists of two basic approaches: one is a user-story-driven open API discovery approach, and the other is multidimensional-information-matrix- (MIM-) based open API recommendation approach. The open API discovery approach introduces user stories in agile development to capture Mashup requirements. First, it extracts three components from user stories, and then, it extracts three corresponding properties from open API descriptions. Next, the similarity calculation is performed on two sets of data. The open API recommendation approach first uses MIM to store open APIs, Mashups, and the invoking relationship between them. Second, it enters the matrix obtained in the previous step into a factorization machine model to calculate the association scores between the Mashups and the open APIs, and TOP-N open API lists for creating the Mashup are obtained. Finally, experimental comparison and analysis are carried out on the PWeb dataset. The experimental results show that our approach has improved significantly.
Framework for Analysis and Detection of Fraud in Health Insurance

The health insurance industry generates a wide range of data from patients' information to provider payment and claims report. The impact of fraud, waste, and abuse (FWA) in medical management is on the rise and contributes significantly to the increase in cost. Traditional methods of handling fraud include human inspection and heuristic rules. They are time-consuming, impractical and insufficient. Data Mining and Machine Learning play a dominant role in detecting and preventing fraud. We explore the use of statistical methods to create a rule-based heuristic engine that works with self-learning Decision Trees. This paper introduces a hybrid framework that combines domain expertise (Rule Engine), supervised learning (Decision Trees & Averaged Perceptron) and unsupervised learning (outlier analysis, k-means Clustering) techniques to identify fraudulent claims from a given set of outstanding claims. The investigation team is intimated with a weighted priority queue of outstanding claims listing the most-likely fraudulent claims with remarks for proactive and retrospective analysis. Our initial case study with one insurer demonstrates an increase in hit-rate by 209.4%.
Study of Recent Web Service Recommendation Methods

With the expansion in service-oriented computing (SOC), Web services are effectively accessible over the internet; we can without much of an effort to offer our data and software with the assistance of web services. Utilizing web services a client can ready to do the complicated task in simple manners. Web service recommendations can help the users to choose the appropriate services and service-oriented software systems. The best strategy to suggest web services with better QoS regard gets a huge amount of thought. Web services are mostly used for building service-oriented applications. The amount of openly accessible Web services is constantly developing the Internet. In any case, this development makes it hard for a client to pick a proper Web service amongst a colossal number of service contenders. An ignoble service assurance may cause various issues for the resulting applications. The investigation and near the analysis of recent studies related to Web Service Recommendation is considered in this review. The result of this paper guarantees the different research gaps recognized by the literature review.
Digit eyes: Learning-related changes in information access in a computer game parallel those of oculomotor attention in laboratory studies

Active sensing theory is founded upon the dynamic relationship between information sampling and an observer's evolving goals. Oculomotor activity is a well studied method of sampling; a mouse or a keyboard can also be used to access information past the current screen. We examine information access patterns of StarCraft 2 players at multiple skill levels. The first measures are analogous to existing eye-movement studies: fixation frequency, fixation targets, and fixation duration all change as a function of skill, and are commensurate with known properties of eye movements in learning. Actions that require visual attention at moderate skill levels are eventually performed with little visual attention at all. This (a) confirms the generalizability of laboratory studies of attention and learning using eye movements to digital interface use, and (b) suggests that a wide variety of information access behaviors may be considered as a unified set of phenomena.
A green, energy, and trust-aware multi-objective cloud coalition formation approach

The workload of cloud providers is often uncertain due to the dynamic and varied client demands, thus presenting a significant challenge. If cloud providers fail to meet changing demands during peak hours, they may experience service requests’ delays and rejections, damaging both their reputation and their profit margin. The idea of cloud federations is contemplated as a solution to this challenge by allowing a group of cloud providers to combine their resources, as and when required, to satisfy client requests dynamically. Despite the merits of the existing cloud federation approaches, they suffer from several limitations, such as performance issues, the inability to converge to a solution in complex scenarios, the lack of consideration of SLA requirements during the federation formation phase, and the lack of flexibility offered to clients to prioritize specific objectives during the formation of the cloud federation. This work addresses these deficiencies by presenting a green, practical, and customizable SLA-based approach to cloud federation formation. Inspired by social gaming, the proposed approach can rapidly form a stable coalition of cloud providers able to fulfill client requests, while ensuring the satisfaction of SLA requirements in terms of performance, energy consumption, CO2 emissions, and providers’ trustfulness. At the heart of this approach lies a new coalition formation algorithm, dubbed the “multi-criterion coalition formation algorithm (MCCF).” Unlike existing collation formation algorithms, the MCCF algorithm offers clients the flexibility to prioritize the objectives used for cloud federation formation. Based on those preferences, the MCCF algorithm generates stable SLA-aware groups of cloud providers that dynamically adapt to network changes using a self-healing process. The MCCF algorithm has been implemented and extensively tested using a wide range of scenarios, and its performance was compared with that of two state of the art approaches: The optimal approach and the split-and-merge approach. The results show that the MCCF algorithm outperforms the optimal and the split-and-merge approaches in terms of coalition size, individual providers’ payoff, and mistrust cost. Moreover, the proposed approach yields a zero % request rejection rate and an execution time that is 8 to 16 times faster than the split-and-merge and optimal approaches, while enabling a reduction of the Carbon footprint of the cloud coalition operations. Owing to these advantages and its ability to offer a flexible and customizable solution to clients, the MCCF algorithm represents a promising and environmentally conscious cloud collation formation approach that is ideal for real-time applications and practical cloud deployments.
A Holistic Abstraction to Ensure Trusted Scaling and Memory Speed Trusted Analytics

In this study, a trusted holistic abstraction is proposed and analytically discussed using universal scalability law and Markovian chain Monte-Carlo method. Moreover, a feedback mechanism is modeled to explain the elasticity performance of the proposed distributed system. The system extends the data locality to the edges in a trusted manner and ensures trust while scaling the whole system and increasing the number of nodes. By the help of such a trusted solution, lineage information of the data at the edges enable fault-recovery from an available checkpoint, while maximizing the trustworthiness of the overall system. Innovative distributed data structures, make databases fresh for all scaled nodes by unifying the memory resources; minimize the need to trusted third parties via trusted distributed data structures, which uses checksums of the datum periodically. Hence, multi-layer neural networks and hierarchical tree structures, has confidential data, can be updated and trained dynamically. Searching speed and performance of an object or set of objects in massive systems is maximized while keeping the trustworthiness of the total system. Initial results indicate that the trust cost worth to pay to scale and to keep the performance of the whole system. The System also shows good elasticity in the case of sudden provisioning/de-provisioning of control nodes. The proposed system also has satisfactory resource-allocation capability with efficient clustering thanks to the introduction of distributed ledger-based transaction management and lineage data recording for dynamic management of DAG structures, has sub-modular and disjoint cluster sets. Initial results of micro-blog analytics indicate promising performance of unified batch/interactive/ad-hoc querying with the holistic abstraction.
Towards a Generic Approach for Memory Forensics

The era of information technology has, unfortunately, contributed to the tremendous rise in the number of criminal activities. However, digital artifacts can be utilized in convicting cybercriminal and exposing their activities. The digital forensics science concerns about all aspects related to cybercrimes. It seeks digital evidence by following standard methodologies to be admitted in court rooms. This paper concerns about memory forensics for the unique artifacts it holds. Memory contains information about the current state of systems and applications. Moreover, an application's data explains how a criminal has been interacting the application just before the memory is acquired. Memory forensics at the application level is currently random and cumbersome. Targeting specific applications is what forensic researchers and practitioner are currently striving to provide. This paper suggests a general solution to investigate any application. Our solution aims to utilize an application's data structures and variables' information in the investigation process. This is because an application's data has to be stored and retrieved in the means of variables. Data structures and variables' information can be generated by compilers for debugging purposes. We show that an application's information is a valuable resource to the investigator.
Classified Counting and Tracking of Local Vehicles in Manila Using Computer Vision

Many countries have improved their traffic surveillance system by using computer vision to classify and track different types of vehicles. Having this data can lessen management cost and help improve rules and regulations in route planning. In Manila, using the common vehicle type dataset for traffic management is inefficient. The city has at least 9 types of vehicles present in its main roads, and at least 21 types of vehicles in secondary and tertiary roads. By using digital image processing, an algorithm for classified counting and tracking was created. The algorithm utilizes machine learning methods to create a local dataset with 16 types of vehicles for the city. After successfully creating the dataset, the system can detect all the present vehicles on the selected footage accurately. In a recorded video containing 11 types of vehicles, 92.96% were correctly classified and 95% were counted. The location of the Region of Interest (ROI) for counting must be strategically placed to avoid misclassified counting. The local dataset can also be improved by collecting data from other roads, and by adding the other local classes such as tricycles and pedicabs.