Reviewerly/exHarmony huggingface dataset:
A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

A hybrid multi-modal ensemble learning model is proposed for short-term solar irradiance forecasting based on historical observations and sky images in this paper. In the proposed model, the historical time series of solar irradiance is utilized to extract temporal characteristics. Meanwhile, the sky camera images are used as an exogenous input to offer the cloud cover information. A powerful ensemble learning model, Extreme Gradient Boosting (XGBoost), is employed to capture the function relationships between input features and future observations. Since the mean squared error loss is sensitive to the extreme large or small historical irradiance, a novel loss function is proposed to improve the robustness of XGBoost. In order to find out the best controlling parameters, Rao-1 algorithm is employed due to its fast search capability. To validate the performance of the proposed method, a solar irradiance dataset containing three-year historical observations and ground-based sky camera images collected from the Folsom is employed. Meanwhile, four commonly applied methods, Lasso, Ridge regression, support vector regression and boosted regression trees, are considered as benchmarking methods. The forecasting horizons from 5 to 30 min are considered for all compared methods while two metrics, mean absolute error and root mean squared error, are computed. Experimental results prove that the proposed hybrid model has better forecasting performance compared with benchmarking methods over all forecasting horizons.
Lightweight RFID Ownership Transfer Protocol Based on Blockchain

With regard to current RFID tags, as their owners change during the circulation process, the ownership of the tags is transferred, and the data containing the tag information is also transferred. Data is stored in the blockchain, which is a distributed ledger system maintained by a decentralized collective. A complete ownership transfer requires a download, decryption, and encrypted upload of the label content information from the blockchain server. This mode will bring tedious and repetitive data processing problems when carrying out multiple consecutive ownership transfers. Seriously affect the efficiency of the entire process, and will bring safety issues. For this reason, this paper proposes a lightweight RFID ownership transfer protocol that uses a proxy re-encryption algorithm to process tag content information. That is, the blockchain server uses proxy re-encryption to perform ciphertext conversion of the encrypted information, so that the ciphertext encrypted by the user Alice's public key is converted into the ciphertext encrypted with the user Bob's public key, and the user Bob can directly decrypt it with his private key. This paper uses a lightweight hash function calculation to ensure the security of ownership transfer. The experimental security analysis of the protocol show that the proposed protocol meets the security requirements and improves the efficiency of ownership transfer.
Path Planning of Manipulator Based on Improved RRT-Connect Algorithm

Aiming at the problems of randomicity, blindness and low efficiency of RRT-Connect Algorithm, an improved RRT-Connect Algorithm is proposed in this paper. First, the idea of artificial potential field is introduced to guide the random tree to grow in the direction of the target, to reduce the blindness of path searching, to increase the guiding force and to accelerate the planning speed, finally, the b-spline curve is used to smooth the planned path, to eliminate the non-smooth points in the path, and to obtain the continuous trajectory of the manipulator, it can avoid the problems such as the quick change of direction when the manipulator performs the grasping task, improve the execution precision and prolong the service life of the manipulator. Matlab simulation results show that this algorithm has some advantages in the path planning of complex environment. Compared with the traditional RRT-Connect, the improved RRT-Connect can complete the path planning task accurately and efficiently.
Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs

Tangled Program Graphs (TPG) represents a genetic programming framework in which emergent modularity incrementally composes programs into teams of programs into graphs of teams of programs. To date, the framework has been demonstrated on reinforcement learning tasks with stochastic partially observablePartially observable state spaces or time series prediction. However, evolving solutions to reinforcement tasks often requires agents to demonstrate/ juggle multiple properties simultaneously. Hence, we are interesting in maintaining a population of diverse agents. Specifically, agent performance on a reinforcement learning task controls how much of the task they are exposed to. Premature convergence might therefore preclude solving aspects of a task that the agent only later encounters. Moreover, ‘pointless complexity’ may also result in which graphs largely consist of hitchhikers. In this research we benchmark the utilization of rampant mutation (multiple mutations applied simultaneously for offspring creation) and action programs (multiple actions per state). Several parameterizations are also introduced that potentially penalize the introduction of hitchhikers. Benchmarking over five VizDoom tasks demonstrates that rampant mutation reduces the likelihood of encountering pathologically bad offspring while action programs appears to improve performance in four out of five tasks. Finally, use of TPG parameterizations that actively limit the complexity of solutions appears to result in very efficient low dimensional solutions that generalize best across all combinations of 3, 4 and 5 VizDoom tasks.
An Efficient Ensemble Architecture for Privacy and Security of Electronic Medical Records

Electronic medical records, one of the sensitive data, are stored in public or private cloud service providers. Cloud systems provide security with firewall and intrusion detection systems, and these systems ensure privacy with access control and end-to-end encryption. However, while sending data to the cloud system, attackers can capture the data with the help of Man in the Middle attacks and vulnerabilities of the storage systems. In the middleware architecture proposed in this study, access control protocol, key distributor and end-to-end hybrid encryption which are based on user roles were innovatively used to overcome security issues in data transmission. In this system, writing and updating requests are encrypted asymmetrically, and reading requests were encrypted symmetrically. This solution distinguishes the proposed method from previous studies. According to this solution the operating performance of the system is increased. In addition, the attacker cannot see the actual data in a cyber-attacks because the sensitive data is distributed to the users with their private keys. This result shows that the access, write and update of electronic medical records are performed with the principles of security and privacy.
Multi Expression Programming for solving classification problems

Abstract Multi Expression Programming (MEP) is a Genetic Programming variant that encodes multiple solutions in a single chromosome. This paper introduces and deeply describes several strategies for solving binary and multi-class classification problems within the multi solutions per chromosome paradigm of MEP. Extensive experiments on various classification problems are performed. MEP shows similar or better performances than other methods used for comparison (namely Artificial Neural Networks and Linear Genetic Programming).
Exploring digital cultural heritage through browsing

Digitisation of our cultural heritage has created vast digital archives, many of which are publicly accessible via the web. However, publicly accessible does not necessarily mean that the content of the collection is truly accessible to the wider public. The reason for this is that the standard interface for accessing the collections is the search box. This is very effective for experienced users who have the information seeking skills and domain knowledge to formulate appropriate queries, but for the less knowledgeable users the white search box represents a significant hurdle. This group of users require a generous interface that provides them with an overview over the collection and the ability to explore it, without having to explicitly enter a search keyword. In this chapter we discuss a range of existing approaches that have been used to provide less knowledgeable users with such interfaces and then present the Digital Museum Map, an algorithm and interface for automatically generating a virtual museum interface for an unstructured collection, that can then be explored by browsing through the virtual museum.
Experimental Realization of a Measurement-Induced Entanglement Phase Transition on a Superconducting Quantum Processor

Ergodic quantum many-body systems undergoing unitary dynamics evolve towards increasingly entangled states characterized by an extensive scaling of entanglement entropy with system volume. At the other extreme, quantum systems repeatedly measured may be stabilized in a measurement eigenstate, a phenomenon known as the quantum Zeno effect. Recently, the intermediate regime in which unitary evolution is interspersed with quantum measurements has become of interest. Numerical studies have reported the existence of distinct phases characterized by volume- and area-law entanglement entropy scaling for infrequent and frequent measurement rates, respectively, separated by a critical measurement rate. The experimental investigation of these dynamic quantum phases of matter on near-term quantum hardware is challenging due to the need for repeated high-fidelity mid-circuit measurements and fine control over the evolving unitaries. Here, we report the realization of a measurement-induced entanglement transition on superconducting quantum processors with mid-circuit readout capability. We directly observe extensive and sub-extensive scaling of entanglement entropy in the volume- and area-law phases, respectively, by varying the rate of projective measurements. We further demonstrate phenomenological critical behavior of the transition by performing a data collapse for different system sizes. Our work paves the way for the use of mid-circuit measurement as an effective resource for quantum simulation on near-term quantum computers, for instance by facilitating the study of dynamic and long-range entangled quantum phases.
Anomaly Detection in Power Grid IoT System based on Isolated Forest

research-article Anomaly Detection in Power Grid IoT System based on Isolated Forest Authors: Ning Li State Grid Shandong Electric Power Company, China State Grid Shandong Electric Power Company, ChinaView Profile , Xue Liu State Grid Shandong Electric Power Company, China State Grid Shandong Electric Power Company, ChinaView Profile , Ziyan Liu State Grid Shandong Electric Power Company, China State Grid Shandong Electric Power Company, ChinaView Profile , Lin Mao State Grid Shandong Electric Power Company, China State Grid Shandong Electric Power Company, ChinaView Profile , Lina Zhao State Grid Shandong Electric Power Company, China State Grid Shandong Electric Power Company, ChinaView Profile , Xie Wang Nanjing University of Finance and Economics, China Nanjing University of Finance and Economics, ChinaView Profile Authors Info & Claims WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent TechnologyDecember 2021 Pages 9–12https://doi.org/10.1145/3498851.3498922Published:11 April 2022Publication History 2citation52DownloadsMetricsTotal Citations2Total Downloads52Last 12 Months52Last 6 weeks11 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Seamless Integration and Testing for MAS Engineering

Testing undeniably plays a central role in the daily practice of software engineering, and this explains why better and more efficient libraries and services are continuously made available to developers and designers. Could the MAS developers community similarly benefit from utilizing state-of-the-art testing approaches? The paper investigates the possibility of bringing modern software testing tools as those used in mainstream software engineering into multi-agent systems engineering. Our contribution explores and illustrates, by means of a concrete example, the possible interactions between the agent-based programming framework ASC2 (AgentScript Cross-Compiler) and various testing approaches (unit/agent testing, integration/system testing, continuous integration) and elaborate on how the design choices of ASC2 enable these interactions.
Research on Post Evaluation of Power Grid’s Technological Transformation Project Based on Combined Weighting and Matter-Element Extension Met hod

Characterized by large number, large transformation scope, different transformation purposes and various transformation contents, it’s difficult to evaluate the implementation effect of technological transformation project of power grid and analyze its transformation benefit. Based on the post evaluation theory, this paper puts forward the evaluation index system and evaluation method suitable for the technological transformation project of power grid, and sets up a post evaluation model based on the whole life cycle. 28 indicators are constructed for in-depth study from seven aspects: preliminary work, implementation preparation, implementation management, settlement and final account management, archives management, investment control and operational performance. In order to avoid the problem that the previous evaluation process was greatly influenced by the subjectivity of experts, the analytic hierarchy process and entropy weight method were combined to determine the index weight, and the matter-element extension model was used to carry out the post evaluation. Finally, comprehensive evaluation and analysis of the precision load cutting technological transformation project in Jiangsu province are carried out to verify the practicality and effectiveness of the model. The research results are helpful to guide the post evaluation work and improve the lean management level of the power grid’s technological transformation project.
Gated Character-aware Convolutional Neural Network for Effective Automated Essay Scoring

Automated Essay Scoring (AES) is a challenging topic in Natural Language Processing. Many current state-of-the-art approaches are based on deep learning models. However, most AES models overlook the importance of character-level information, which is important to both the performance and the fairness. The character-level information is able to provide orthographic knowledge (e.g., spelling) and help the learning of infrequent and ⟨UNK⟩ tokens. In this paper, we propose a Gated Character-aware Convolutional Neural Network (GCCNN) model for the AES task. The proposed GCCNN model incorporates character-level information by a character-level encoder and a gated fusion mechanism. First, the character-level encoder learns word embeddings from sequences of characters by a hierarchical convolutional neural network. Next, the gated fusion mechanism adaptively controls the amount of word-level and character-level information to be fused using vector gating. Then, the essay-level encoder learns an essay representation based on the fused word embeddings. Finally, the fully connected layer maps the essay representation into its corresponding score. The experimental results show that our GCCNN model outperforms the baseline deep learning models. In addition, our qualitative analysis also demonstrates the importance of character-level information for tackling the out-of-vocabulary problem in grading essays.
Real-Time FPGA-Based Binocular Stereo Vision System with Semi-Global Matching Algorithm

Binocular stereo vision, which is an attractive topic in the computer vision area, mainly focuses on extracting depth information through a stereo matching algorithm. The semi-global matching algorithm (SGM) is well-studied but is hard to be applied in real-time because of the high computational cost. In this work, we report an FPGA-based stereo vision system for the SGM. The system contains a camera controller, a module for image rectification, and the SGM accelerator. The SGM accelerator module can extract the depth information with very high speed by a parallel pixel-level pipeline architecture. The left-right check module further ensures the quality of the disparity map. We implement the system on the Stratix-V platform with a 14.9% error rate and it consumes about 58K LUTs, 59K registers, and 5.2M bits of on-chip memory. The maximum working frequency can reach up to 135.96 MHz for the XGA (1024×768) video and 128 disparity range with the power dissipation of 4.302 W and the processing speed of 173 XGA-frames per second.
LIBRARY AND LIBRARY SCIENCE IN THE INFRASTRUCTURE OF KNOWLEDGE ECONOMICS

Modern scientific disciplines engaged in the system book (information) - library - reader (user) are experiencing considerable influence on the part of conceptions and theoretical elaborations being worked out within the frames of different sciences, perfecting existing ontological (for example, library science - bibliology) and establishing new symmetric and asymmetric connections. Library science interrelations development -as a scientific branch complexly studying processes of socializing knowledge fixed in the document stream - with economic sciences is conditioned by diversification of its aspects, expansion of research fields as well as new directions coming into being in economic sciences themselves.
The Contributions of Reading Strategies and Reading Frequencies Toward Students’ Reading Comprehension Skill in Higher Education

This study was conducted because of the low level of students’ reading skill. The reading strategies and the reading frequencies were assumed as the factors affecting students’ reading skill. The aim of this study was to describe and to analyze the contribution of reading strategies and reading frequencies toward students’ reading comprehension skill. This study used the quantitative method with the descriptive correlational design. The population was 3000 students who attended Bahasa Indonesia subject in Universitas Negeri Padang, and 353 students were taken as samples by using proportional stratified random sampling. The instruments of this study were reading strategies scale, reading frequencies scale and a reading comprehension test which has been valid and reliable. Based on research findings and discussion, the following can be concluded: (1) reading strategies contributed significance and positively to the students' comprehension reading skill which is 19.6% (R = 0.443 R2 = 0.196 and p = 0.000), i.e. reading strategies contributed directly toward the skills of reading comprehension. Therefore, the higher the reading strategies, the better the reading comprehension skills of the students; (2) reading strategies contributed significance toward the students' reading comprehension skills of 33.1% (R = 0.575 R2 = 0.331 and p = 0.000), i.e. reading frequencies contributed directly to reading comprehension skills. Therefore, the better the reading frequencies, the better the reading comprehension skills of the students; (3) reading strategies and the reading frequencies together contributed significance toward the students' reading comprehension skills of 33.2% (R = 0.575, R2 = 0.332 and p = 0.000), i.e. reading strategies and reading frequencies together contributed directly to the comprehension reading skill. Therefore, the higher the reading strategies and reading frequencies will be the better the reading comprehension skill of the students.
A Continuous-Variable Quantum Repeater based on Quantum Scissors

Using the quantum scissors operation for entanglement distillation and a single-rail Bell state projection for non-Gaussian entanglement swapping, we show a multiplexed quantum repeater scheme for continuous-variable entanglement distribution over a pure loss communication channel.
Multi-objective Prediction based Task Scheduling Method in Cloud Computing

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.
Recognition of Off-line Kannada Handwritten Characters by Deep Learning using Capsule Network

Handwritten character recognition is an important subfield of Computer Vision which has the potential to bridge the gap between humans and machines. Machine learning and Deep learning approaches to the problem have yielded acceptable results throughout, yet there is still room for improvement. off-line Kannada handwritten character recognition is another problem statement in which many authors have shown interest, but the obtained results being acceptable. The initial efforts have used Gabor wavelets and moments functions for the characters. With the introduction of Machine Learning, SVMs and feature vectors have been tried to obtain acceptable accuracies. Deep Belief Networks, ANNs have also been used claiming a con- siderable increase in results. Further advanced techniques such as CNN have been reported to be used to recognize Kannada numerals only. In this work, we budge towards solving the problem statement with Capsule Networks which is now the state of the art technology in the field of Computer Vision. We also carefully consider the drawbacks of CNN and its impact on the problem statement, which are solved with the usage of Capsule Networks. Excellent results have been obtained in terms of accuracies. We take a step further to evaluate the technique in terms of specificity, precision and f1-score. The approach has performed extremely well in terms of these measures also.