Reviewerly/exHarmony huggingface dataset:
Application of Attention Mechanism with Prior Information in Natural Language Processing

When using deep learning methods to model natural language, a recurrent neural network that can map input sequences to output sequences is usually used. Considering that natural language contains more complicated syntactic structures, and the performance of cyclic neural networks in long sentence processing will decrease, scholars have introduced an attention mechanism into the model, which has improved the above problems to a certain extent. The existing attention mechanism still has some shortcomings, such as the inability to explicitly obtain the known syntactic structure information in the sentence, and the poor interpretability of the output probability. In response to the above problems, this article will improve the attention mechanism in the recurrent neural network model. Firstly, the prior information in the natural language sequence is constructed as a graph model through syntactic analysis and other means, and then the graph structure regularization term is introduced into the sparse mapping. A new function netmax is constructed to replace the softmax function in the traditional attention mechanism, thereby improving the performance of the model and making the degree of association. The input values corresponding to larger input samples are closer, making the output of the attention mechanism easier to understand. The innovation of this paper mainly lies in that the weight calculation method which can be widely used in the attention mechanism is proposed by combining the deep learning model with statistical knowledge, which opens a channel to introduce the prior information for the deep learning model in natural language processing tasks.
QUasi-Affine TRansformation Evolutionary Algorithm for Feature Selection

Du, Zhi-Gang Pan, Tien-Szu Pan, Jeng-Shyang Chu, Shu-ChuanQUasi-Affine TRansformation Evolutionary Algorithm (QUATRE) is a currently emerging meta-heuristic evolutionary algorithm. QUATRE has the ability to balance exploitation and exploration in the optimization process, and the algorithm optimization uses matrix operations to greatly reduce the time complexity for solving the same problem. This series of advantages makes this algorithm adopted by a large number of researchers. In this paper, QUATRE is used to optimize the Feature Selection (FS) of the wrapper method. K-Fold Cross-Validation (KFCV) method is also used to divide the test set and training set of the sample, and then use the K Nearest Neighbor (KNN) algorithm for feature classification. In the optimization process, we use a threshold (choice) for feature identification to select useful features. Finally, the 9 standard test data sets in UCI are used to verify the effectiveness of the QUATRE algorithm.
Sensitivity of Chaotic Dynamics Prediction to Observation Noise

Recent advances in nonlinear time-series prediction demonstrated the ability of recurrent neural network to forecast chaotic time series on a multi-step horizon, outperforming previous approaches. Researches considered chaotic systems with different degree of complexity, but the analysis was mainly limited to the noise-free case. In this work, we extend the analysis to a noisy environment, in order to fill the gap between deterministic and real-world time series. We consider four archetypal deterministic chaotic systems each with different levels of additive noise, representing the observation uncertainty always affecting practical applications. A time series of solar irradiance is also taken into account as a real-world case study. Various neural architectures, including feed-forward and recurrent networks, are adopted as predictors. LSTM cells are used as recurrent neurons, with a special focus on the training approach. As in the noise-free case, LSTM trained without the traditional teacher forcing, i.e., with a training that replicates the forecasting conditions, proved to be the best architecture. The experiments on the archetypal systems also shows that the error due to the model identification is negligible if compared to the one caused by a small observation noise. In other words, system identification and predictions are well distinct tasks.
Blockchain Technology in Renewable Energy Certificates in Brazil

Several renewable energy certificate (RECs) applications point out that the blockchain technology can be useful in ensuring the traceability and transparency of transactions, despite some barriers to its implementation, such as the legal and market development. However, it is not clear how the organizational positioning, in relation to its given market, influences the artifact developed. In this study, through design science research (DSR) and case study methodology, we structure the problem space of two different positioned organizations in the sustainability field, with blockchain-based applications to produce and trade RECs. We find out that: (a) the position of the organization in relation to other stakeholders changes the behavior of the technology adoption; (b) the technological solution preceded the perception of the problem; (c) organizations create different representations of the artifact for each stakeholder. We suggest other studies to deepen these findings in order to better develop theories that explain how organizations see their problem when developing technological solutions while using DSR.
A Nested Bi-level Optimization Framework for Robust Few Shot Learning

Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal.Hence, it fails to address the domain shift between base and novel classes in few-shot learning. In this work, we propose a novel robust meta-learning algorithm, NESTEDMAML, which learns to assign weights to training tasks or instances. We con-sider weights as hyper-parameters and iteratively optimize them using a small set of validation tasks set in a nested bi-level optimization approach (in contrast to the standard bi-level optimization in MAML). We then applyNESTED-MAMLin the meta-training stage, which involves (1) several tasks sampled from a distribution different from the meta-test task distribution, or (2) some data samples with noisy labels.Extensive experiments on synthetic and real-world datasets demonstrate that NESTEDMAML efficiently mitigates the effects of ”unwanted” tasks or instances, leading to significant improvement over the state-of-the-art robust meta-learning methods.
A Hybrid Multi-group Co-evolution Intelligent Optimization Algorithm: PSO-GWO

A single swarm intelligence algorithm will be limited by its own search ability, and when dealing with some more complex matters, there will be problems such as low accuracy of solution and easy to fall into local optimum. However, the hybrid optimization method can achieve the purpose of improving the performance of the algorithm through information exchange between different populations. Therefore, in this paper, the particle swarm algorithm has the characteristics of individual memory, and the particle position update is used to replace the gray wolf individual position update, so that the gray wolf algorithm has memorability in the optimization. It can be seen from the optimization results of eight benchmark test functions that the "Multiply Species Cooperative Evolution" method combines the advantages of different algorithms, and it has obvious global convergence and strong competitiveness.
Research on Insulator Defect Detection and Recognition in Overhead Transmission Line Based on Convolutional Neural Network

In the overhead transmission line, the insulator plays a very important role in the operation of the power system. Because the insulator is exposed to the field for a long time, the insulator is often damaged, iced and hung with foreign matters. This paper studies the defects of the insulator in the overhead transmission line. In overhead transmission lines, human eyes cannot directly observe the defects of insulators. Insulators in overhead transmission lines are often photographed with the help of unmanned drone, helicopter and other auxiliary equipment, and then identified by different methods. In order to improve the accuracy of insulator defect identification in overhead transmission lines, this paper uses hierarchical detection method to detect and identify insulators. Firstly, fast r-cnn algorithm is used to locate insulators and cut insulators, so as to increase the proportion of insulators in the image. Secondly, image enhancement method is used to expand the data set, finally, yolo-v4 algorithm is used to detect the defects of insulators. In order to improve the speed of insulator detection in overhead transmission lines, resnet50 is used as fast r-cnn feature extraction network in the first level detection; In order to improve the identification accuracy of insulator defects in overhead transmission lines, mobilenetv2 is used as the feature extraction network of yolo-v4 in the second level detection. Experimental results show that this method can effectively and accurately identify and detect insulator defects in overhead transmission lines.
An objective measure for assessing the quality of contrast enhancement on magnetic resonance images

Post-processing algorithms like histogram equalization and its variants have been widely employed to enhance the contrast of Magnetic Resonance (MR) images. Objective metrics that can collectively reflect the improvement in contrast and inadvertent distortions are necessary to rate the quality of enhanced images. The objective of this paper is to formulate an objective statistic for rating the quality of contrast enhancement on MR images and to test the agreement of the proposed metric with the subjective fidelity ratings. An objective metric named Cumulative Quality of Contrast-Enhanced Images (CQCEI), for assessing the quality of contrast enhancement, especially the performance of the histogram equalization and its variants, on MR images is proposed. The CQCEI is formulated such that it collectively accounts for various aspects of image quality pertaining to the contrast enhancement, namely improvement in contrast, shift in mean brightness, saturation and noise-amplification. The CQCEI has shown good agreement with subjective fidelity ratings on contrast-enhanced MR images.
RoundPIN: Shoulder Surfing Resistance for PIN Entry with Randomize Keypad

The security of a PIN is largely supported by the authentication process in ATM. Most authentication methods like traditional are based on using PIN as direct entry and this technique has been shown lots of drawbacks such as vulnerability to password space, and shoulder-surfing. In this paper, a new approach is proposed called RoundPIN depends on the appearance of the numerical password through one of the buttons after selecting it by the user and it is done through a number of rounds, the numbers are arranged randomly on the keypad. Due to the variable aspect of the chosen button and the random appearance of the numbers in each connection session and also the selection process will take place through three buttons three auxiliary, the proposed approach can maintain high secure session to enter the PIN to resist shoulder surfing, which is difficult for attackers to observe a user's PIN. The performance evaluation of the proposed approach is achieved in two parts, the first one is based on security analysis. Then a pilot study of thirty users is conducted to evaluate the useability of the proposed approach. It is noticed that the proposed approach can maintain a high level of security as well as acceptable level of useability and user satisfaction compared the conventional keypad system.
A Novel Fuzzy Logic-Based Improved Cuckoo Search Algorithm

Cuckoo Search (CS) algorithm is a nature-inspired optimization algorithm (NIOA) with less control parameters that is stable, versatile, and easy to implement. CS has good global search capabilities, but it is prone to local optima problems. As a result, it may be possible to improve the classic CS algorithm's optimization capability. Centered on fuzzy set theory, this paper introduces an improved CS version. The population of solutions has been divided into two fuzzy sets, and each solution is assigned to one of the sets based on its fitness. The fuzzy collection centroids, global best solution advice, and Lévy distribution dependent mutation are all used to boost the population's solutions. With well-accepted objective functions such as Otsu inter class variance and Kapur's entropy, the experimental analysis has been conducted on the CEC-2014 test suite and image multi-level thresholding domain. The proposed fuzzy cuckoo search (FCS) algorithm is compared to the classical CS, PSO, FA, SMA, and BA algorithm and provides satisfactory results.
An improved forensic-by-design framework for cloud computing with systems engineering standard compliance

“Forensic-by-design” is an emergent and ambitious paradigm that extends the Digital Forensic Readiness (DFR) perspective. Similar to Security-by-design, this new vision advocates the integration of Forensic requirements into the system's design and development stages to get “Forensic-ready” systems. While it seems promising, we hypothesize that: (a) this new alternative is not effective for some open boundaries systems, and (b) this strategy is not fully aligned with the Systems and software Engineering (SE) standards. A six phases research methodology based on systematic literature review, mapping, and analysis was adopted. Our results confirm indeed the stated hypothesis, identify missing key factors, and point out potential omissions. A new System and software Engineering driven Forensic-by-design framework, with an emphasis on Cloud computing systems, is therefore proposed.
List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System

Edge computing needs the close cooperation of cloud computing to better meet various needs. Therefore, ensuring the efficient implementation of applications in cloud computing is not only related to the development of cloud computing itself but also affects the promotion of edge computing. However, resource management and task scheduling strategy are important factors affecting the efficient implementation of applications. Therefore, aiming at the task scheduling problem in cloud computing environment, this paper proposes a new list scheduling algorithm, namely, based on a virtual scheduling length (BVSL) table algorithm. The algorithm first constructs the predicted remaining length table based on the prescheduling results, then constructs a virtual scheduling length table based on the predicted remaining length table, the current task execution cost, and the actual start time of the task, and calculates the task priority based on the virtual scheduling length table to make the overall path the longest task is scheduled first, thus effectively shorten the scheduling length. Finally, the processor is selected for the task based on the predicted remaining length table. The selected processor may not be the earliest for the current task, but it can shorten the finish time of the task in the next phase and reduce the scheduling length. To verify the effectiveness of the scheduling method, experiments were carried out from two aspects: randomly generated graphs and real-world application graphs. Experimental results show that the BVSL algorithm outperforms the latest Improved Predict Priority Task Scheduling (IPPTS) and RE-18 scheduling methods in terms of makespan, scheduling length ratio, speedup, and the number of occurrences of better quality of schedules while maintaining the same time complexity.
Development of a Machine Learning Model for Image-based Email Spam Detection

Combatting email spam has remained a very daunting task. Despite the over 99% accuracy in most non-image-based spam email detection, studies on image-based spam hardly attain such a high level of accuracy as new email spamming techniques that defeat existing spam filters emerges from time to time. The number of email spams sent out daily has remained a key factor in the continued use of spam. In this paper, a simple convolutional neural network model, 123DNet was developed and trained with 28,929 images drawn from 2 public datasets and a Personally Generated dataset. The model was optimized to the least set of layers to have 1 input layer, 2 embedded Convolutional layers as a hidden layer, and 3 neural network layers. The model was tested with a total of 4,339 images of the three dataset samples and then with a separate set of 1,200 images to test performance on never-seen-before images. A Classification Performance analysis was carried out using the confusion matrix. Performance metrics including Accuracy, Precision, True Negative Accuracy, Sensitivity, Specificity, and F1 Measure were computed to ascertain the model’s performance. The Model returned an F1 Score of 97% on a public dataset’s test sample and 88% on Never-seen-before test samples outperforming some pre-existing models while performing significantly well on the newly generated image test samples. It is recommended that a model that performed so well with new never-seen-before spam images be integrated into spam filtering systems. Keywords- Convolutional Neural Network, Deep Learning, Image-based Spam Detection
Audience-Dependent Explanations for AI-Based Risk Management Tools: A Survey

Artificial Intelligence (AI) is one of the most sought-after innovations in the financial industry. However, with its growing popularity, there also is the call for AI-based models to be understandable and transparent. However, understandably explaining the inner mechanism of the algorithms and their interpretation is entirely audience-dependent. The established literature fails to match the increasing number of explainable AI (XAI) methods with the different stakeholders' explainability needs. This study addresses this gap by exploring how various stakeholders within the Swiss financial industry view explainability in their respective contexts. Based on a series of interviews with practitioners within the financial industry, we provide an in-depth review and discussion of their view on the potential and limitation of current XAI techniques needed to address the different requirements for explanations.
Enhanced Seagull Optimization Algorithm Adapted for Artificial Neural Network Training

The learning process is one of the most difficult problems in the artificial neural networks. This task refers to the challenge of finding the appropriate values for connection weights and biases and directly influences the neural network classification accuracy. Since the search space is enormous, traditional optimization techniques are not suitable as they are prone to slow converging speed and getting trapped to the local optima. In this paper, an enhanced seagull optimization algorithm was proposed to address the task of neural networks training. Conducted experiments included ten binary classification benchmark datasets to evaluate the performance of proposed enhanced seagull optimization algorithm. The obtained results indicate that the proposed method has promising performance, as it was able to achieve better overall results than other state-of-the-art algorithms that were included in the comparative analysis, in terms of classification accuracy and converging speed.Bacanin, Nebojsa Antonijevic, Milos Vukobrat, Nikola Bezdan, Timea Zivkovic, Miodrag
Delivering Superior Customer Experience through New-Age Technologies

The unique power of modern technologies and virtual consumers are indicators of a new marketing paradigm. Customer Experience (CX) is at the heart of a successful product or service offering. With an aim to deliver an enhanced experience, today, brands have incorporated several smart technologies like radio frequency identification systems, smart shopping carts, augmented reality (AR), and virtual reality, among others. AR’s incorporation of immersive real-time virtual content into the consumer’s view of the physical world makes for a superior CX. Artificial intelligence-based technologies help consumers better understand their own expectations. The AI-empowered and natural language processing stimulated chatbots are designed to comprehend human language and address the consumer’s appetite for immediate responses to queries. The article confers how these new-age technologies are transforming the CX. In addition, the article attests industry examples who are benefitting with these new-age technologies like AR, AI, and chatbots. It shall also explain the challenges faced by the organizations during the digital journey by employing multiple new-age technologies adopted in 2021.
Platonic entanglement

We present a construction of highly entangled states defined on the topology of a platonic solid using tensor networks based on ancillary Absolute Maximally Entangled (AME) states. We illustrate the idea using the example of a quantum state based on AME(5,2) over a dodecahedron. We analyze the entropy of such states on many different partitions, and observe that they come on integer numbers and are almost maximal. We also observe that all platonic solids accept the construction of AME states based on Reed-Solomon codes since their number of facets, vertices and edges are always a prime number plus one.
Spectroscopy framework and review of characterization of energy resource sites

Site characterization and rapid reliable identification of energy resources play a key role for future efficient energy production. Over the last several decades, many laboratory and in-situ techniques were developed to qualitatively and quantitatively characterize the sites and minerals. Despite the advancements, there are still many challenges associated with exploration, rapid detection, and spatial interpolation of the energy resources within a site. Spectroscopy techniques offer solutions to the current on-going efforts on site characterization, exploration and collection of resources, quality control monitoring during production, and reclamation of the production sites due to environmental contamination. Spectral analysis has shown great promise in providing in-situ measurements that are comparable to arduous laboratory physio-chemical analysis. Spectroscopy is a fairly new technology in some sectors and has seen limited use but has shown great potential in exceeding the minimum standards implemented. This paper presents review of the current spectroscopy techniques that have been used in the agriculture, landfill, nuclear power, mining, and ground contamination industries with respect to the production of energy. A general overview of how spectral analysis techniques are being used to benefit each of these sectors along with some of the drawbacks associated with each is presented. Three frameworks including basic process, operation flowchart, minimum number of tests to be performed, and information on spatial interpolation analysis are presented. These frameworks along with the basic processes can be implemented for characterization of energy resource sites.
Gravitational acceleration estimation with a nonlinear Mach–Zehnder interferometer

We investigate the use of a nonlinear optical interferometer to estimate local gravitational acceleration. We study a Mach–Zehnder interferometer (MZI) in an optical fountain configuration where a Kerr nonlinear medium is placed in the arms of the interferometer. This configuration allows the parameter estimation to exceed the standard Heisenberg limit of a linear interferometer. Compared to previous studies, we argue that this interferometer configuration is better posed and achieves improved precision for similar scale and pump powers.