Reviewerly/exHarmony huggingface dataset:
FedGS: A federated group synchronization framework for heterogeneous data

<h2>Abstract</h2> FedGS is a federated group synchronization framework implemented on LEAF-MX. It provides a novel client selection strategy and an innovative group collaborative training protocol based on compound step synchronization to make federated learning excellent on non-independent and identically distributed (non-i.i.d.) data, achieving higher accuracy, lower loss, and faster convergence. FedGS is an open-source, readable, and easy-to-use framework.
Blockchain for Education: Verification and Management of Lifelong Learning Data

In recent years, blockchain technology has been applied in the educational domain because of its salient advantages, i.e., transparency, decentralization, and immutability.Available systems typically use public blockchain networks such as Ethereum and Bitcoin to store learning results.However, the cost of writing data on these networks is significant, making educational institutions limit data sent to the target network, typically containing only hash codes of the issued certificates.In this paper, we present a system based on a private blockchain network for lifelong learning data authentication and management named B4E (Blockchain For Education).B4E stores not only certificates but also learners' training data such as transcripts and educational programs in order to create a complete record of the lifelong education of each user and verify certificates that they have obtained.As a result, B4E can address two types of fake certificates, i.e., certificates printed by unlawful organizations and certificates issued by educational institutions for learners who have not met the training requirements.In addition, B4E is designed to allow all participants to easily deploy software packages to manage, share, and check stored information without depending on a single point of access.As such, the system enhances the transparency and reliability of the stored data.Our experiments show that B4E meets expectations for deployment in reality.
Analysis on Tracking Performance of a Radar with Uncoded and Coded Transponder

This paper discusses angular noise of a tracking radar for on-board uncoded and coded transponder of Satellite Launch Vehicles during transonic regime due to the formation of condensation around launch vehicle nose cone. In transonic regime, uncoded transponder tracking radars observe a large number of extra echo pulses in tracking gate for a short time, excess interrogation is experienced by on board Transponders which cause large signal dips in radar receiver systems, thereby, disturbing the angles of the tracking radars. This paper analyses angular noise suppression with coded transponder tracking radars during transonic regime.
Skeleton-based Human Action Recognition by the Integration of Euclidean distance

With the growing popularity of somatosensory interaction devices, human action recognition becomes more and more attractive in many application scenarios. Nowadays, it takes a long time for some behavior recognition methods to train a model. To solve the problem, this paper proposes a human skeleton model for human action recognition. The joint coordinates are extracted using OpenPose and the thermodynamic diagram, and then we extract partial features and overall features, as well as Euclidean distances between joints. All the data collected above makes up the motion features of independent video frames. What's more, we leverage Multilayer Perceptron (MLP) classifier to classify all the motion features. On the KTH and ICPR datasets, we test the accuracy of the mode verified by changing several parameters. When the image resolution is 320*240, weight is 12 and stride is 1, the highest accuracy rate of single-person behavior recognition is 0.821. When weight is 10 and stride is 1, the highest accuracy rate of multi-person behavior recognition is 0.812, and the high running speed enables the mode to be real-time.
Solar thermal energy conversion

Energy is currently a critical demand for both national and global growth. Furthermore, the environmental consequences of using fossil fuels for power generation are concerning, and a fantastic alternative method is to employ renewable energy resources. However, effective power generation from renewable resources can be the most pressing issue in the next decades. In comparison to other renewable energy sources, solar energy is the most prevalent. The concepts of energy conversion from solar irradiation to electrical or thermal energy using photovoltaic panels and solar thermal collectors are the leading technologies. Because of the relative movement of the earth and the sun, the solar irradiation is converted into energy by the panel or collector. The sun tracking technology integrated into panels or collectors is the most effective way to improve output power. This chapter describes the geometry of the sun and the earth, the computation of solar radiation incident on the panel or collector, and various tracking technologies.
Brain Programming and Its Resilience Using a Real-World Database of a Snowy Plover Shorebird

Even when deep convolutional neural networks have proven to be effective at saliency detection, they have a vulnerability that should not be ignored: they are susceptible to adversarial attacks, making them highly unreliable. Reliability is an important aspect to consider when it comes to salient object detection; without it, an attacker can render the algorithm useless. Brain programming–an evolutionary methodology for visual problems–is highly resilient and can withstand even the most intense perturbations. In this work, we perform for the first time a study that compares the resilience against adversarial attacks and noise perturbations using a real-world database of a shorebird called the Snowy Plover in a visual attention task. Database images were taken on the field and even posed a detection challenge due to the nature of the environment and the bird's physical characteristics. By attacking three different deep convolutional neural networks using adversarial examples from this database, we prove that they are no match for the brain programming algorithm when it comes to resilience, suffering significant losses in their performance. On the other hand, brain programming stands its ground and sees its performance unaffected. Also, by using images of the Snowy Plover, we refer to the importance of resilience in real-world issues where conservation is present. Brain programming is the first highly resilient evolutionary algorithm used for saliency detection tasks.
Computing the Slant Degree of Digital Ink Chinese Characters Handwritten by CFL Beginners Based on Elliptical Enclosing Shape

research-article Share on Computing the Slant Degree of Digital Ink Chinese Characters Handwritten by CFL Beginners Based on Elliptical Enclosing Shape Authors: Yun Lai College of Information Science, Beijing Language and Culture University, China College of Information Science, Beijing Language and Culture University, ChinaView Profile , Xi-wen Zhang College of Information Science, Beijing Language and Culture University, China College of Information Science, Beijing Language and Culture University, ChinaView Profile Authors Info & Claims CSAI 2021: 2021 5th International Conference on Computer Science and Artificial IntelligenceDecember 2021 Pages 91–98https://doi.org/10.1145/3507548.3507562Online:09 March 2022Publication History 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on Anomaly Detection of Smart Meter Based on Big Data Mining

With the development of smart grids and the widespread application of advanced measurement systems, power companies have obtained super-large power data, and the use of these data will bring huge benefits to the development of the power grid. Therefore, this paper analyzes the development status of electric power big data and the processing methods of data analysis, proposes a smart meter anomaly detection framework, and explains the advantages of the detection algorithm to provide a data mining solution for the development of electric power big data.
Distributed Training from Multi-sourced Data

A distributed system is a set of logical or physical units capable of performing calculations and communicating with each other. Nowadays, these systems are at the heart of technologies such as the Internet of Things IoT, the Internet of Vehicles IoV, etc. These systems collect data, perform calculations and make decisions. On the other hand, deep learning (DL) has led to enormous progress in the field of artificial intelligence. Since the precision of DL to form a set of reference data on a single machine is known, it becomes more interesting to form several models and distribute the intelligence over the different nodes of the system by different calculation strategies. In this paper we propose a method using deep neural networks on several machines by distributing the dataset before starting the training, ensuring communication between them in order to improve the calculation time and accuracy.
RevASIDE: Assignment of Suitable Reviewer Sets for Publications from Fixed Candidate Pools

Scientific publishing heavily relies on the assessment of quality of submitted manuscripts by peer reviewers. Assigning a set of matching reviewers to a submission is a highly complex task which can be performed only by domain experts. We introduce RevASIDE, a reviewer recommendation system that assigns suitable sets of complementing reviewers from a predefined candidate pool without requiring manually defined reviewer profiles. Here, suitability includes not only reviewers’ expertise, but also their authority in the target domain, their diversity in their areas of expertise and experience, and their interest in the topics of the manuscript. We present three new data sets for the expert search and reviewer set assignment tasks and compare the usefulness of simple text similarity methods to document embeddings for expert search. Furthermore, an quantitative evaluation demonstrates significantly better results in reviewer set assignment compared to baselines. A qualitative evaluation also shows their superior perceived quality.
Financial Big Data Security and Privacy in X-Accounting. A Step Further to Implement the Triple-Entry Accounting

The introduction of a Triple-entry blockchain-based accounting system has been suggested in different forms by some authors in various parts of the world. Multiple challenges are currently hindering its introduction. Among many others, this article focuses on two main challenges for adopting blockchain accounting: security and privacy. The primary assumption is that a generalised accounting framework should be developed in a permissioned platform. Accounting data are periodically and mandatorily disclosed in financial statements that summarise the companies’ financial performances. However, accounting books are not expected to be made available to the public, including detailed and sensitive data. Indeed, disclosing all the accounting transactions might compromise the confidentiality of the company know-how, including a list of suppliers, mark-up, a list of customers and so forth. The introduction of a shared ledger might be considered a threat that could jeopardise business big data confidentiality, data security and privacy. This article assesses these perspectives to provide valuable solutions.
Design of Tank Inspection Robot Navigation System Based on Virtual Reality

Facing the demand for safety inspection of large oil storage tanks, there is an urgent need for in-oil corrosion inspection robot technology that does not open the tank. As an excellent human-computer interaction technology, virtual reality technology can be applied to in-oil tank inspection robots to achieve robot control and monitoring in invisible environment. This paper breaks through the virtual environment construction, inspection path planning algorithm design and virtual-real scene matching technologies, and develops the inspection robot human-computer interaction system based on the virtual reality technology. In the virtual 3D environment, the system can achieve inspection robot operational planning, online registration, remote control and other functions in invisible environment.
RSAC: A Robust Deep Reinforcement Learning Strategy for Dimensionality Perturbation

Artificial agents are used in autonomous systems such as autonomous vehicles, autonomous robotics, and autonomous drones to make predictions based on data generated by fusing the values from many sources such as different sensors. Malfunctioning of sensors was noticed in the robotics domain. The correct observation from sensors corresponds to the true estimate of the dimension value of the state vector in deep reinforcement learning (DRL). Hence, noisy estimates from these sensors lead to dimensionality impairment in the state. DRL policies have shown to stagger its decision by the wrong choice of action in case of adversarial attack or modeling error. Hence, it is necessary to examine the effect of dimensionality perturbation on neural policy. In this regard, we analyze whether subtle dimensionality perturbation that occurs due to the noise in the source of input at the testing time distracts agent decisions. Also, we propose an RSAC (robust soft actor-critic) approach that uses a noisy state for prediction, and estimates target from nominal observation. We find that the injection of such noisy input during training will not hamper learning. We have done our simulation in the OpenAI gym MuJoCo (Walker2d-V2) environment and our empirical results demonstrate that the proposed approach competes for SAC’s performance and makes it robust to test time dimensionality perturbation.
Toward Delicate Anomaly Detection of Energy Consumption for Buildings: Enhance the Performance From Two Levels

Buildings are highly energy-consuming and therefore are largely accountable for environmental degradation.Detecting anomalous energy consumption is one of the effective ways to reduce energy consumption.Besides, it can contribute to the safety and robustness of building systems since anomalies in the energy data are usually the reflection of malfunctions in building systems.As the most flexible and applicable type of anomaly detection approach, unsupervised anomaly detection has been implemented in several studies for building energy data.However, no studies have investigated the joint influence of data structures and algorithms' mechanisms on the performance of unsupervised anomaly detection for building energy data.Thus, we put forward a novel workflow based on two levels, data structure level and algorithm mechanism level, to effectively detect the imperceptible anomalies in the energy consumption profiles of buildings.The proposed workflow was implemented in a case study for identifying the anomalies in three real-world energy consumption datasets from two types of commercial buildings.Two aims were achieved through the case study.First, it precisely detected the contextual anomalies concealed beneath the time variation of the energy consumption profiles of the three buildings.The performance in terms of areas under the precision-recall curves (AUC_PR) for the three given datasets were 0.989, 0.941, and 0.957, respectively.Second, more broadly, the joint effect of the two levels was examined.On the data level, all four detectors on the contextualized data were superior to their counterparts on the original data.On the algorithm level, there was a consistent ranking of detectors regarding their detecting performances on the contextualized data.The consistent ranking suggests that local approaches outperform global approaches in the scenarios where the goal is to detect the instances deviating from their contextual neighbors rather than the rest of the entire data.
Dynamic Butterfly ACM for Risk Optimization on the Real-Time Unix Operating System

The system access control mechanism is the process of communicating, mediating and transforming each and every request to system resources, application and data maintained by the real time operating system and determining whether the request should be approve, created, granted, valid or denied as per system security policy. The access control mechanism, assessment and decision is enforced by implementing regulations established by a security policy. The different access control policies can be applied, corresponding to different level (development, deployment &amp; production) of criteria for defining what should, and what, when and how should be or not be allowed, and, in some sense, to different definitions of what ensuring security means. We have to investigate the basic concepts behind access control design and enforcement, and point out different security requirements that may need to be taken into consideration.We have to apply the attributes of UFS (Read, Write &amp; Execute) to integrate, communicate, synchronizing on real time operating system for all the time and every time.
SCAA—Sensitivity Context Aware Anonymization—An Automated Hybrid PPUDP Technique for Big Data

In today's world, Big Data privacy has become one of the major and challenging aspects of business organizations, where they use Big Data for analytical operations. Big Data is described by its volume, variety, and velocity. Big Data constitutes various types of data, and they are structured, semi-structured, and unstructured. The world's major portion of data is of type unstructured i.e., it does not have a definite model and specific to a domain. The major types of unstructured data are text, audio, and video data. When these data are subjected to data analytics operation without a prior application of domain-specific privacy-preserving data publishing techniques, then there will be a chance of privacy breach. The existing privacy-preserving approaches mainly operate on structured and semi-structured data. There is a significant need for an automated approach to protect the privacy of the unstructured data which usually exists in high volumes and also to retain unstructured data utility to perform efficient analytics operations. In this paper, an automated hybrid anonymization approach for unstructured data has been proposed with backpropagation-based utility enhancement which preserves privacy of unstructured text data and also contributes to utility retainment of text data.
Improving Software Design by Mitigating Code Smells

Improving design is a key research challenge during software maintenance. It aims at identifying and mitigating structural characteristics that may cause future problems (code smells). The existing approaches lack in considering the developer’s expertise and penalty consideration during inheritance usage. Therefore, this paper targets identifying three key code smells viz refused bequest, shotgun surgery, and duplicated code. The refused bequest detection approach is based on probabilistic penalized inheritance usage and domain closeness among methods. The shotgun surgery is detected using combined use of co-change and usage pattern based functional relations. Finally, the duplicated code is based on the abstract tree representation and identification of longest common subsequence between the inorder/ postorder traversals. The experimental results uphold the feasibility of the proposed approach.
Relational dynamic Bayesian network modeling for uncertainty quantification and propagation in airline disruption management

Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, reactive airline disruption management during schedule execution, and proactive airline disruption management after schedule execution to enable the construction of quantitative tools that can allow an intelligent agent to rationalize complex interactions and procedures for robust airline disruption management. Specifically, we use historical scheduling and operations data from a major U.S. airline to facilitate the development and assessment of the UTFM, defined by hidden Markov models (a special class of probabilistic graphical models) that can efficiently perform pattern learning and inference on portions of large data sets. We employ the UTFM to assess two independent and separately disrupted flight legs from the airline route network. Assessment of a flight leg from Dallas to Houston, disrupted by air traffic control hold for bad weather at Dallas, revealed that proactive disruption management for turnaround in Dallas before schedule execution is impractical because of zero transition probability between turnaround and taxi-out. Assessment of another flight leg from Chicago to Boston, disrupted by air traffic control hold for bad weather at Boston, showed that proactive disruption management before schedule execution is possible because of non-zero state transition probabilities at all phases of flight operation.
Smart Hotel Management Under the Background of Big Data

Traditional hotel management and service quality are easily affected by service personnel, and various procedures are cumbersome. Smart hotel management under the control of big data information can effectively improve the quality of hotel stays. The installation of smart guest room control systems has become a major trend. Based on this, this paper designs an intelligent hotel management system, which realizes the informatization and intelligence of hotel management. The smart home system developed in this paper has been promoted to many hotels, showing good market value. At the same time, this paper also proposes a collaborative filtering algorithm based on the comprehensive similarity of users, and builds a personalized movie recommendation system based on this, and realizes the display of the movie recommendation system to make smart hotel management more personalized. Experimental results show that our smart hotel management system and personalized movie recommendation system have the characteristics of high stability, and intelligence. They are easy to maintain and manage, and can effectively reduce operating costs and improve service quality.