Reviewerly/exHarmony huggingface dataset:
Fast and flexible stack‐based inverse tone mapping

Abstract Inverse tone mapping technique is widely used to restore the lost textures from a single low dynamic range image. Recently, many stack‐based deep inverse tone mapping networks have achieved impressive results by estimating a set of multi‐exposure images from a single low dynamic range input. However, there are still some limitations. On the one hand, these methods usually set a fixed length for the estimated multi‐exposure stack, which may introduce computational redundancy or cause inaccurate results. On the other hand, they neglect that the difficulties of estimating each exposure value are different and use the identical model to increase or decrease exposure value. To solve these problems, the authors design an exposure decision network to adaptively determine the number of times the exposure of low dynamic range input should be increased or decreased. Meanwhile, the authors decouple the increasing/decreasing process into two sub‐modules, exposure adjustment and optional detail recovery, based on the characteristics of different variations of exposure values. With these improvements, this method can fast and flexibly estimate the multi‐exposure stack from a single low dynamic range image. Experiments on several datasets demonstrate the advantages of the proposed method compared to state‐of‐the‐art inverse tone mapping methods.
Meta-heuristic and Heuristic Algorithms for Forecasting Workload Placement and Energy Consumption in Cloud Data Centers

The increase of servers in data centers has become a significant problem in recent years that leads to a rise in energy consumption.The problem of high energy consumed by data centers is always related to the active hardware especially the servers that use virtualization to create a cloud workspace for the users.For this reason, workload placement such as virtual machines or containers in servers is an essential operation that requires the adoption of techniques that offer practical and best solutions for the workload placement that guarantees an optimization in the use of material resources and energy consumption in the cloud.In this article, we propose an approach that uses heuristics and meta-heuristics to predict cloud container placement and power consumption in data centers using a Genetic Algorithm (GA) and First Fit Decreasing (FFD).Our algorithms have been tested on CloudSim and the results showed that our methods gave better and more efficient solutions, especially the Genetic Algorithm after comparing them with Ant Colony Optimization (ACO) and Simulated Annealing (SA).
Optimal discrimination of geometrically uniform qubit and qutrit states

We consider discrimination of geometrically uniform states, and analytically solve this problem for qubits. For qutrits, we obtain the exact solution to the optimal measurement when the defining unitary matrix for the geometrically uniform states is degenerate. We also show that if the unitary is nondegenerate then the optimal measurement for discriminating geometrically uniform qubits or qutrits can always be expressed as a rank-1 operator, which converts the original discrimination problem into an optimization of a sum of trigonometric functions with two real variables in the case of qutrits. Additionally, a geometrical interpretation of the optimal measurement for discriminating geometrically uniform qubits is given via the Bloch sphere representation.
Blockchain Based Approach on Gathering Manufacturing Information Focused on Data Integrity

This paper presents a blockchain-based approach to machine data capturing and storage using Hyperledger Fabric with a low-cost, esp32 microcontroller. Our research focuses on the immutability of captured data to ensure that data on the blockchain is valid for use by customers and manufacturers. This requires an embedded implementation to sign collected data before it reaches modern IT environments like IoT gateways or programmable logic controllers (PLC). We discuss the challenges and advantages of using Hyperledger Fabric compared to an already validated implementation based on Ethereum technology. The proposed method demonstrates a solution to overcome the challenges that result from the Hyperledger Fabric communication protocol in an embedded environment. We thus illustrate both the technical design of the implemented logic on the microcontroller and the implementation of the distributed messaging protocol within Hyperledger Fabric. In our study, we validate the implemented method and perform a quantitative comparison with existing solutions from information technology. At last, we discuss the limitations of the proposed method and give an outlook on approaches that can potentially solve them.
Post-Quantum Privacy-Preserving Provable Data Possession Scheme Based on Smart Contracts

Provable data possession (PDP) is a crucial means of protecting the integrity of data in the domain of cloud storage. In the post-quantum era, the PDP scheme that uses lattices relies too heavily on the third-party auditor (TPA), which is not entirely trustworthy and is easily affected by a single point of failure. Moreover, the scheme often leads to the leakage of private user data while attempting to satisfy the demand for public verification. In response to the above problems, this paper designs a protocol for post-quantum privacy-preserving PDP and uses it to develop a scheme based on smart contracts. The proposed scheme has the characteristics of being post quantum and can satisfy the demand for public verification while preserving user privacy. The property of noninteraction of the protocol can reduce transaction fees incurred owing to the frequent operation of the blockchain, and the smart contract with a deposit mechanism can ensure fair payments to all parties. The results of a theoretical analysis and experiments show that the proposed scheme is highly secure and efficient.
Does corporate governance influence firm integrated performance?

The correlation of corporate governance (CG) with accounting and financial performance (AFP) is highly studied (Ismaeel &amp; Soliman, 2022). However, the correlation of CG with marketing performance (MP) and logistic and supply chain performance (LSCP) is rare. Further, the correlation of CG with firm integrated performance (i.e., AFP, MP, and LSCP) is hard to see. Therefore, the present study explores the association and impact of CG with firm integrated performance (FIP). The study followed the deductive approach and adopted correlation and regression analysis techniques using the secondary data from 132 randomly selected cluster samples from 303 listed companies from the Colombo Stock Exchange (CSE). The study found that CG has a positive but weak relationship and a significant positive impact on the FIP. Thus, this study sheds light on the practical implication that managers must not solely depend on financial performance (FP) but concentrate on FIP when they make strategies for organizational performance and development. The study is original since it has proved that the synergy effect of AFP, MP, and LSCP is much higher than standing alone with CG.
Spatial search by continuous-time quantum walk on truncated simplex lattices

Abstract Quantum search via continuous-time quantum walk on truncated simplex lattices is an important example of the quantum search on the structured database. In this letter, when the search target is a set of the marked vertices, the run time of the quantum search and the critical jumping rates are derived. Quantum search with the partial information of the location of the marked vertex is also discussed. Furthermore, with the edge weights suitably adjusted, the three-stage search process can be optimized into a two-stage quantum search algorithm on the second-order truncated simplex lattice.
That’s It! Hyperintensional Total Logic

Abstract Call a truth complete with respect to a subject matter if it entails every truth about that subject matter. One attractive way to formulate a complete truth is to state all the relevant positive truths, and then add: and that’s it . When the subject matters under consideration are non-contingent, a non-trivial conception of completeness must invoke a hyperintensional conception of entailment, and of the completion operation denoted by ‘that’s it’. This paper develops two complementary hyperintensional conceptions of completion using the framework of truthmaker semantics and determines the resulting logics of totality.
Ecological diversity methods improve quantitative examination of student language in short constructed responses in STEM

We novelly applied established ecology methods to quantify and compare language diversity within a corpus of short written student texts. Constructed responses (CRs) are a common form of assessment but are difficult to evaluate using traditional methods of lexical diversity due to text length restrictions. Herein, we examined the utility of ecological diversity measures and ordination techniques to quantify differences in short texts by applying these methods in parallel to traditional text analysis methods to a corpus of previously studied college student CRs. The CRs were collected at two time points (Timing), from three types of higher-ed institutions (Type), and across three levels of student understanding (Thinking). Using previous work, we were able to predict that we would observe the most difference based on Thinking, then Timing and did not expect differences based on Type allowing us to test the utility of these methods for categorical examination of the corpus. We found that the ecological diversity metrics that compare CRs to each other (Whittaker’s beta, species turnover, and Bray–Curtis Dissimilarity) were informative and correlated well with our predicted differences among categories and other text analysis methods. Other ecological measures, including Shannon’s and Simpson’s diversity, measure the diversity of language within a single CR. Additionally, ordination provided meaningful visual representations of the corpus by reducing complex word frequency matrices to two-dimensional graphs. Using the ordination graphs, we were able to observe patterns in the CR corpus that further supported our predictions for the data set. This work establishes novel approaches to measuring language diversity within short texts that can be used to examine differences in student language and possible associations with categorical data.
A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines

With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models.
Data-Driven Anomaly Detection Framework for Complex Degradation Monitoring of Aero-Engine

Data analysis is an important part of aero engine health management. In order to complete accurate condition monitoring, it is necessary to establish more effective analysis tools. Therefore, an integrated algorithm library dedicated for engine anomaly detection is established, which is PyPEFD (Python Package for Engine Fault Detection). Different algorithms for baseline modeling, anomaly detection and trend analysis are presented and compared. In this paper, the simulation data are used to verify the function of the anomaly detection algorithms, successfully completing the detection of multiple faults and comparing the accuracy algorithm under different conditions.
Increasing the Speed of Multiscale Signal Analysis in the Frequency Domain

In the Mallat algorithm, calculations are performed in the time domain. To speed up the signal conversion at each level, the wavelet coefficients are sequentially halved. This paper presents an algorithm for increasing the speed of multiscale signal analysis using fast Fourier transform. In this algorithm, calculations are performed in the frequency domain, which is why the authors call this algorithm multiscale analysis in the frequency domain. For each level of decomposition, the wavelet coefficients are determined from the signal and can be calculated in parallel, which reduces the conversion time. In addition, the zoom factor can be less than two. The Mallat algorithm uses non-symmetric wavelets, and to increase the accuracy of the reconstruction, large-order wavelets are obtained, which increases the transformation time. On the contrary, in our algorithm, depending on the sample length, the wavelets are symmetric and the time of the inverse wavelet transform can be faster by 6–7 orders of magnitude compared to the direct numerical calculation of the convolution. At the same time, the quality of analysis and the accuracy of signal reconstruction increase because the wavelet transform is strictly orthogonal.
Combined Optical Fiber Transmission System Based on QNSC and BER-LM

A quantum noise stream cipher (QNSC) is a physical layer encryption technology based on quantum noise. Bit error rate loopback measurement (BER-LM) is a method to measure the BER of a loopback channel and extract channel characteristics. Then, channel characteristics can be extracted, and consensus keys can be obtained through negotiation. In previous studies, encryption and key distribution were implemented in independent channels. In this paper, we propose a scheme that combines these two technologies in a single fiber channel to achieve encrypted transmission and key distribution. We verified a 20 Gbps QPSK coherent optical transmission system with a PSK/QNSC scheme. The results show that by reasonably setting the negotiation bit position, the consensus key could be obtained through negotiation, and the requirements of transmission performance could be met. When the negotiation bit position was set to seven, the Q-factor of the system was nine, which met the error-free condition of the 7% forward error correction (FEC) limit.
Efficient and Privacy-Preserving Categorization for Encrypted EMR

Electronic Health Records (EHRs) must be encrypted for patient privacy; however, an encrypted EHR is a challenge for the administrator to categorize. In addition, EHRs are predictable and possible to be guessed, although they are in encryption style. In this work, we propose a secure scheme to support the categorization of encrypted EHRs, according to some keywords. In regard to the predictability of EHRs, we focused on guessing attacks from not only the storage server but also the group administrator. The experiment result shows that our scheme is efficient and practical.
Millimeter-Wave Image Deblurring via Cycle-Consistent Adversarial Network

Millimeter-wave (MMW) imaging has a tangible prospect in concealed weapon detection for security checks. Typically, a one-dimensional (1D) linear antenna array with mechanical scanning along a perpendicular direction is employed for MMW imaging. To achieve high-resolution imaging, the target under test needs to keep steady enough during the mechanical scanning process since slight movement can induce large phase variation for MMW systems, which will result in a blurred image. However, in the scenario of imaging of a human body, sometimes it is difficult to meet this requirement, especially for the elderly. Such blurred MMW images would reduce the detection accuracy of the concealed weapons. In this paper, we propose a deblurring method based on cycle-consistent adversarial network (Cycle GAN). Specifically, the Cycle GAN can learn the mapping between the blurred MMW images and the focused ones. To minimize the effect of the shaking blur, we introduce an identity loss. Moreover, a mean squared error loss (MSE loss) is utilized to stabilize the training, so as to obtain more refined deblurred results. The experimental results demonstrate that the proposed method can efficiently suppress the blurring effect in the MMW image.
Dynamic Analysis of Fuzzy Systems

In this work, a new methodology for the dynamic analysis of non-linear systems is developed by applying the Mamdani fuzzy model. With this model, parameters such as settling time, peak time and overshoot will be obtained. The dynamic analysis of non-linear fuzzy systems with triangular membership functions is performed, and linguistic variables describing overly complex or ill-defined phenomena are used to fit the model. Scaling factors will simplify the modification of the variables, making them easier to find the system model. The specifications of second-order characteristics in the time domain, such as overshoot and peak time, will be represented graphically. As a case study, the proposed methods are implemented to analyse the dynamics of a tank and a simple pendulum for first-order and second-order systems, respectively, where it is observed that the proposed methodology offers highly positive results.
Concept evolution detection based on noise reduction soft boundary

Concept evolution detection is an important but difficult task in streaming data analysis, and further the noise may seriously limit the detection performance gains. This paper proposed a concept evolution detection method based on noise reduction soft boundary (NRSB). To reduce the negative effect of noise samples near category boundary, the noise reduction soft boundaries are defined based on the introducing of extension distance, intension distance and corresponding noise reduction process. Then the sample space is divided into in-class, mixed-class and out-of-class areas corresponding to each category. For online new coming samples, three strategies of automatic labeling by model, active labeling by expert and multi-class collaborative labeling will be adopted respectively according to different areas they located in. Automatic model labeling does not need to update the model, which may improve the learning efficiency. Expert active labeling will help with difficult samples in mixed-class area. Multi-class collaborative labeling can change the sample label dynamically during online learning. The experimental results show that the proposed method can reflect the character of dynamic evolution of streaming data well and detect the concept evolution effectively.
A Data Analysis Smart System for the Optimal Deployment of Nanosensors in the Context of an eHealth Application

This paper presents the utilization of the Data Analysis Smart System (DASS) of ARMNANO in a nanotechnology application in electronic health. We made a special approach to the liver situation for patients that have been monitored with respect to two variables concerning their liver status: the Mean Corpuscular Volume (MCV) and the Alkaline phosphotas (ALKPHOS). These variables are analyzed using the autonomous cycle “Conditioning Thinking Mode” (CTM), one of the two autonomic cycles of data analysis tasks that make up the DASS. In this sense, an optimization problem is defined to determine the optimal deployment of nanosensors (NSs) for the proper determination of liver status. The application of genetic algorithms (GA) allows us to find the optimal number of NSs in the system to precisely determine the liver status, avoiding a large data volume. In total, we evaluated its implementation in two case studies and carried out a hyperparameterization process for assuring the definition of the key parameters. The greatest propensity is to place NSs in the regions close to the liver, becoming saturated as the amount of SNs increases (they do not improve the quality of the liver status value).
Bimodal Fusion Network with Multi-Head Attention for Multimodal Sentiment Analysis

The enrichment of social media expression makes multimodal sentiment analysis a research hotspot. However, modality heterogeneity brings great difficulties to effective cross-modal fusion, especially the modality alignment problem and the uncontrolled vector offset during fusion. In this paper, we propose a bimodal multi-head attention network (BMAN) based on text and audio, which adaptively captures the intramodal utterance features and complex intermodal alignment relationships. Specifically, we first set two independent unimodal encoders to extract the semantic features within each modality. Considering that different modalities deserve different weights, we further built a joint decoder to fuse the audio information into the text representation, based on learnable weights to avoid an unreasonable vector offset. The obtained cross-modal representation is used to improve the sentiment prediction performance. Experiments on both the aligned and unaligned CMU-MOSEI datasets show that our model achieves better performance than multiple baselines, and it has outstanding advantages in solving the problem of cross-modal alignment.
Bidirectional RRT*-Based Path Planning for Tight Coordination of Dual Redundant Manipulators

There are closed-chain constraints between the left manipulator and the right manipulator in tight coordination of the dual redundant manipulator. The existing planning algorithms suitable for loose coordination cannot be directly applied to tight coordination, as the planned path cannot satisfy the closed-chain constraints. To solve the above problem, a master-slave planning method based on bidirectional RRT* is proposed for dual redundant manipulators. Bidirectional RRT* is adopted to plan the path of the master manipulator. The path of the slave manipulator is calculated by terminal generalized velocity constraints instead of terminal position and posture constraints. Moreover, a local path replanning strategy is proposed to solve the problem that the planned path is actually not feasible due to the discontinuous joint path of the slave manipulator. The joint self-motion in the null space is utilized to keep the terminal position and posture of the slave manipulator unchanged. The proposed method is verified by simulations and experiments and the results show that it can solve the discontinuity problem, increase the success rate, shorten the planning time and satisfy closed-chain constraints. Therefore, the proposed method can be feasibly and effectively applied to the tight coordination of dual redundant manipulators.