Reviewerly/exHarmony huggingface dataset:
Curriculum Learning for Goal-Oriented Semantic Communications With a Common Language

Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the various communication costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms classical RL and CL without inference scheme in terms of convergence time, task execution cost and time, reliability, and belief efficiency.
An access control model based on blockchain master-sidechain collaboration

Abstract The centralized storage and centralized authorization approach in medical information systems can lead to data tampering and private information privacy leakage, while the traditional access control model has an overly simple authentication approach, relies excessively on trusted third-party organizations for the enforcement of access control policies, and has low efficiency in processing access requests. To address these problems, this paper proposes an access control model based on the collaboration of blockchain main and side chains, AC-BMS. Firstly, a password-based authentication scheme is designed based on doctors’ identity information; then Polygon side chain is designed to enhance the storage scalability of the blockchain; finally, the access node information on the main Ethereum chain is located on the side chain, and resources are obtained by executing Roll-up contracts deployed on the side chain. It is confirmed by simulation experiments in Hyperledger Fabric that the access efficiency and throughput of the blockchain access model proposed in this paper are improved when the number of accesses is multiplied, the average access time is saved by 2–3 s, the latency time is floating and stable, and the security, scalability, and availability are enhanced.
Daily solar radiation estimation in Belleville station, Illinois, using ensemble artificial intelligence approaches

Accurate estimating the solar radiation (SR) in a region is an essential issue in hydrological studies, energy engineering, architecture, and agricultural planning. In the current study, the conventional multiple linear regression (MLR), multilayer perceptron neural network (MLPNN), and adaptive neuro-fuzzy inference system (ANFIS) improved using evolutionary algorithms, the particle swarm optimization algorithm (PSO), differential evolution (DE), ant colony optimization for continuous domains (ACOR), and genetic algorithm (GA), as well as three different ensemble techniques, including simple average ensemble (SAE), weighted average ensemble (WAE), and neural network ensemble (NNE) were applied to model daily solar radiation (DSR) in Belleville station in Illinois, USA. For this purpose, the DSR data for eleven stations collected from 2015 to 2019 were obtained from ISWS (the Institute of Illinois State Water Survey). Seven models were then run using three distinct input datasets to estimate the DSR in the study area. The findings indicated that the evolutionary algorithms are significantly effective in boosting the performance of the ANFIS model when the number of inputs increases. It was also found that ensemble methods can improve the performance of the single models in estimating daily solar radiation. Overall, the results indicated that the ANFIS-GA had the best performance for the selected inputs (R2=0.947, RMSE = 2.010 (MJ m−2 day −1), MAPE = 15.61, NSE = 0.947). The results of the complexity analysis showed that although the ANFIS-GA algorithm had the highest accuracy in estimating daily solar radiation in the study area, it has more complexity than ANFIS-PSO and ANFIS-DE algorithms.
Survey on performance optimization for database systems

The performance optimization of database systems has been widely studied for years. From the perspective of the operation and maintenance personnel, it mainly includes three topics: prediction, diagnosis, and tuning. The prediction of future performance can guide the adjustment of configurations and resources. The diagnosis of anomalies can determine the root cause of performance regression. Tuning operations improve performance by adjusting influencing factors, e.g., knobs, indexes, views, resources, and structured query language (SQL) design. In this review, we focus on the performance optimization of database systems and review notable research work on the topics of prediction, diagnosis, and tuning. For prediction, we summarize the techniques, strengths, and limitations of several proposed systems for single and concurrent queries. For diagnosis, we categorize the techniques by the input data, i.e., monitoring metrics, logs, or time metrics, and analyze their abilities. For tuning, we focus on the approaches commonly adopted by the operation and maintenance personnel, i.e., knob tuning, index selection, view materialization, elastic resource, storage management, and SQL antipattern detection. Finally, we discuss some challenges and future work.
A FIR filter assisted with the predictive model and ELM integrated for UWB-based quadrotor aircraft localization

Abstract To improve the accuracy of the Ultra-Wide Band (UWB) based quadrotor aircraft localization, a Finite Impulse Response (FIR) filter aided with an integration of the predictive model and Extreme Learning Machine (ELM) is proposed in this work. The FIR filter estimates the quad-rotor aircraft ’s position by fusing the positions measured with the UWB and Inertial Navigation System respectively. When the UWB dada are unavailable, both the ELM and the predictive model are used to provide the measurements, replacing those unavailable UWB data, for the FIR filter. The ELM estimates the measurement via the mapping between the one step prediction of state vector and the measurement built when the UWB data are available. For the predictive model, we mathematically describe the missing UWB data. Then, both the measurements estimated with the ELM and predictive model are employed to estimate the observations via Mahalanobis distance. The test results show that the FIR filter aided by the predictive model/ELM integrated can reduce the Cumulative Distribution Function and position Root Mean Square Error effectively when the UWB is unavailable. Compared with the ELM assisted FIR filter, the proposed FIR filter can reduce the localization error by about 48.59 %, meanwhile, the integrated method is close to the method with a better solution. Graphical Abstract
A Pseudorandom Number Generator Based on the Chaotic Map and Quantum Random Walks

In this paper, a surjective mapping that satisfies the Li–Yorke chaos in the unit area is constructed and a perturbation algorithm (disturbing its parameters and inputs through another high-dimensional chaos) is proposed to enhance the randomness of the constructed chaotic system and expand its key space. An algorithm for the composition of two systems (combining sequence based on quantum random walks with chaotic system’s outputs) is designed to improve the distribution of the system outputs and a compound chaotic system is ultimately obtained. The new compound chaotic system is evaluated using some test methods such as time series complexity, autocorrelation and distribution of output frequency. The test results showed that the new system has complex dynamic behavior such as high randomicity, unpredictability and uniform output distribution. Then, a new scheme for generating pseudorandom numbers is presented utilizing the composite chaotic system. The proposed pseudorandom number generator (PRNG) is evaluated using a series test suites such as NIST sp 800-22 soft and other tools or methods. The results of tests are promising, as the proposed PRNG passed all these tests. Thus, the proposed PRNG can be used in the information security field.
Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals

Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of −12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect.
A Survey on High-Dimensional Subspace Clustering

With the rapid development of science and technology, high-dimensional data have been widely used in various fields. Due to the complex characteristics of high-dimensional data, it is usually distributed in the union of several low-dimensional subspaces. In the past several decades, subspace clustering (SC) methods have been widely studied as they can restore the underlying subspace of high-dimensional data and perform fast clustering with the help of the data self-expressiveness property. The SC methods aim to construct an affinity matrix by the self-representation coefficient of high-dimensional data and then obtain the clustering results using the spectral clustering method. The key is how to design a self-expressiveness model that can reveal the real subspace structure of data. In this survey, we focus on the development of SC methods in the past two decades and present a new classification criterion to divide them into three categories based on the purpose of clustering, i.e., low-rank sparse SC, local structure preserving SC, and kernel SC. We further divide them into subcategories according to the strategy of constructing the representation coefficient. In addition, the applications of SC methods in face recognition, motion segmentation, handwritten digits recognition, and speech emotion recognition are introduced. Finally, we have discussed several interesting and meaningful future research directions.
Agile Storyboard and Software Development Leveraging Smart Contract Technology in Order to Increase Stakeholder Confidence

We present a solution based on blockchain technology and smart contracts for agile project management in light of the continuing transition in the software development industry. Due to the fact that these technologies are self-executing, customized, and impervious to tampering, they are considered to be crucial for the transition to a more efficient, transparent, and transactive payment gateway between major stakeholders. These major stakeholders will be able to communicate through smart contracts, which will act as a bridge between them. As part of their responsibility, they will make sure that all of the terms of the contract are met and acknowledged by all members of the team. As a result of our research, we propose a model in which payouts could be automatically enabled and penalties or grants could be introduced based on performance. If any changes were to be made to the contract in the future, all parties involved would be automatically notified. To maintain the development cycle, they should accept these changes as soon as possible. Because of this, the product owner and client are able to concentrate their resources on more profitable and productive tasks, without the need to monitor this aspect of agile project management. Our proposed model brings together different partners with the objective of successfully developing different IT projects by leveraging software engineering solutions such as smart contracts.
Investigating the User Interface Design Frameworks of Current Mobile Learning Applications: A Systematic Review

Online learning has replaced traditional face-to-face classroom instruction in the educational system. Learning via mobile, or mobile learning, is one of the solutions that most learners use since it is less expensive and easier to adopt on the go. However, in addition to hindering information transfer, issues such as small screen size and bad interface design can also make learning more cognitively demanding. This paper presents a systematic literature review on the user interface design of mobile learning applications based on the preferred reporting items for systematic reviews and meta-analyses approach. Articles selected for this study were published after the COVID-19 outbreak, between 2020 and 2022. The goal of this research was to outline the current user interface design criteria and guidelines applied when designing a mobile learning application and explore how these factors affect the learner’s cognitive load. It also aimed to identify potential research gaps and future opportunities in the creation of a UID guideline/framework for mobile learning. The findings of this study may be used as a guideline for designers, developers, educators, instructors, and others who are interested in creating a mobile learning application that provides learners with an effective knowledge and mobile learning experience.
Adverse Geology Identification Through Mineral Anomaly Analysis During Tunneling: Methodology and Case Study

Accurate and effective identification of adverse geology is crucial for safe and efficient tunnel construction. Current methods of identifying adverse geology depend on the experience of geologists and are prone to misjudgment and omissions. Here, we propose a method for adverse geology identification in tunnels based on mineral anomaly analysis. The method is based on the theory of geoanomaly, and the mineral anomalies are geological markers of the presence of adverse geology. The method uses exploration data analysis (EDA) to calculate mineral anomaly thresholds, then evaluates the mineral anomalies based on the thresholds and identifies adverse geology based on the characteristics of the mineral anomalies. We have established a dynamic expansion process for background samples to achieve the dynamic evaluation of mineral anomalies by adjusting anomaly thresholds. This method has been validated and applied in a tunnel excavated in granite. As shown herein, in the tunnel range of 142 + 800–142 + 860, the fault F37 was successfully identified based on an anomalous decrease in the diagenetic minerals plagioclase and hornblende, as well as an anomalous increase in the content of the alteration minerals chlorite, laumonite, and epidote. The proposed method provides a timely warning when a tunnel enters areas affected by adverse geology and identifies whether the tunnel is gradually approaching or moving away from the fault. In addition, the applicability, accuracy, and further improvement of the method are discussed. This method improves our ability to identify adverse geology, from qualitative to quantitative, and can provide reference and guidance for the identification of adverse geology in mining and underground engineering.
Efficient decoding up to a constant fraction of the code length for asymptotically good quantum codes

We introduce and analyse an efficient decoder for quantum Tanner codes that can correct adversarial errors of linear weight. Previous decoders for quantum low-density parity-check codes could only handle adversarial errors of weight . We also work on the link between quantum Tanner codes and the Lifted Product codes of Panteleev and Kalachev, and show that our decoder can be adapted to the latter. The decoding algorithm alternates between sequential and parallel procedures and converges in linear time.
Applications of natural language processing in software traceability: A systematic mapping study

A key part of software evolution and maintenance is the continuous integration from collaborative efforts, often resulting in complex traceability challenges between software artifacts: features and modules remain scattered in the source code, and traceability links become harder to recover. In this paper, we perform a systematic mapping study dealing with recent research recovering these links through information retrieval, with a particular focus on natural language processing (NLP). Our search strategy gathered a total of 96 papers in focus of our study, covering a period from 2013 to 2021. We conducted trend analysis on NLP techniques and tools involved, and traceability efforts (applying NLP) across the software development life cycle (SDLC). Based on our study, we have identified the following key issues, barriers, and setbacks: syntax convention, configuration, translation, explainability, properties representation, tacit knowledge dependency, scalability, and data availability. Based on these, we consolidated the following open challenges: representation similarity across artifacts, the effectiveness of NLP for traceability, and achieving scalable, adaptive, and explainable models. To address these challenges, we recommend a holistic framework for NLP solutions to achieve effective traceability and efforts in achieving interoperability and explainability in NLP models for traceability.
Measurement of Similarity between Use Case Description and Sequence Diagram in Software Requirement Specification using Text Analysis for Dtrain Application

Dtrain application is built by implementing software documents, one of which is the Software Requirement Specification (SRS). In compiling software documents, there is a mismatch between one document element with other elements. Therefore, this study will measure the similarity between the Use Case Description (UCD) on the SRS case study Dtrain with Sequence Diagrams (SD) to determine how similar or compatible the two SRS elements are. This study aims to measure the similarity of UML artifacts between UCD and SD contained in a software development document. The suitability of the two artifacts can be done by analyzing the text in the description (UCD) and object (SD) sections. Based on the results and discussion, this study resulted in the following. First, a study that finds level of similarity between UCD and the SD by comparing the step results performed an extraction on the UCD and the extraction of messages and actors on the SD. Second, there are five use cases in the Dtrain application, so the UCD and SD are each 5. Third, the highest similarity value results are found in SD 1 and SD 5, with a similarity value of 0.712. Fourth, the results of the validation value using algorithm code to get a kappa score of 0.331. Fifth, the results of the validation obtained from results of the expert questionnaire got a value of 0.8905.
Average Intensity Sign (AIS) Feature based Offline Signature Verification for Forgery Detection using Machine Learning

The human offline signs are the utmost extensively approved biometric verification procedures in colleges, industries, banking and various domain due to its easiness and exclusivity. Numerous computerized technologies have been established to predict the human offline signature for forgery detection. Because, the offline biometric identification system has expanded a histrionic movement. This paper Suggests a technique for offline sign identification system using image processing techniques. The proposed offline sign identification system has four stages. In the first stage, the pre-processing of human sig can be done. The pre-processing stage can be done by means of image processing concepts which includes color conversion and next smoothening technique can be done using Gaussian filter. And followed by noise removal method can be done using morphological operation. The second stage of proposed system is sign detection using various edge detection algorithm. In the third stage, extract the Average Intensity Sign (AIS) featurefrom detected sign image. Finally, signature verification system can be utilized for forgery detection using machine learning SVM techniques. The proposed sign identification method demonstrations that the real time experimental output has extreme achievement ratio. This proposed algorithm yields average accuracyof 98.91% in SVM (RBF) in 36 AIS features when associated to an SVM(polynomial) classifier.
Evolutionary Optimization with Deep Transfer Learning for Content based Image Retrieval in Cloud Environment

Recently, image model in cloud computing (CC) grabbed great deal of interest to guarantee confidentiality of data and secure data communication among clients cloud storage and cloud server, and end-users. Conventional secure image retrieval (IR) approaches are not suitable to adopt a large-scale IR in cloud environment. In the field of computer vision (CV), an effectual representation of image feature vectors for image retrieval will remain a significant issue. various research was conducted on CBIR by utilizing machine learning (ML) techniques and numerous descriptors. This article designs a Dwarf Mongoose Optimization with Transfer Learning for Content based Image Retrieval in Cloud Environment, named DMOTL-CBIRC. The presented DMOTL-CBIRC technique intends to retrieve similar images in the cloud sever based on QIs. It follows a three stage process namely feature extraction, parameter tuning, and similarity measurement. Initially, the presented DMOTL-CBIRC technique exploits SqueezeNet method for an effectual generation of feature vectors (FVs) for the input image and QIs. Besides, the DMO technique is used for the hyperparameter tuning of the SqueezeNet model. For similarity measurement, Manhattan distance is applied which retrieves the highly similar images. The experimental evaluation of the DMOTL-CBIRC technique is investigated on Corel10K dataset and the outcomes highlighted the improvements of the DMOTL-CBIRC technique over other models.