Reviewerly/exHarmony huggingface dataset:
A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems

This article presents two new hybrid swarm-human based meta-heuristic optimization algorithms benefitting from the synergy of whale optimization algorithm (WOA) and social group optimization (SGO) known as Hybrid Social Whale Optimization Algorithm (HS-WOA and HS-WOA+). HS-WOA and HS-WOA+ are hybridized combining the exploratory capabilities of WOA and convergence capabilities of SGO with a perfect balance between exploration and exploitation. A comparative analysis of the new proposed hybrid algorithm is performed through various benchmark functions. Various test cases to analyze the algorithm's performance like influence of population size, effect of dimensionality, effect of iterative count is performed and compared. The proposed algorithms are compared with modern-meta-heuristics and variants of WOA and SGO to justify its performance. The performance is evaluated statistically through the Wilcoxon's rank-sum test and Friedman's non-parametric test while the convergence curves and acceleration rates are provided to demonstrate the convergence capabilities of the proposed hybrid algorithms and the computational times are recorded to showcase the computational speeds of all the algorithms used in comparison. Composite benchmarking functions are considered to analyze the exploratory prowess and the algorithms' capability to avoid local entrapment. To assess and evaluate the performance of the proposed algorithms with real world optimization tasks, four standard engineering problems with penalty constraints are added to the test bench. Further, a multi-unit production planning problem with correction constraints is deployed through the proposed algorithms. The benchmarking results prove that HS-WOA and HS-WOA+ s' performance is competitive and better than the various algorithms tested against and had a statistically significant performance with lower computational times. The algorithms performed well for both standard engineering problems and the multi-unit production planning problem outperforming the various algorithms in the literature.
Multi-label classification based ensemble learning for human activity recognition in smart home

In recent times with advancements in wireless sensor technologies, human activity recognition in smart home environments have gained significant interest amongst the researchers. Numerous literatures have been focusing only on single occupant-based activity recognition because of escalating difficulties with multiple occupants within the same conditions. But having multiple occupants is more general. Therefore, in this study, the application of the Classifier Chain method of Multi-Label Classification is described to address the complicated problem of multi-resident activity recognition. Four different classifiers namely Bernoulli Naïve Bayes, Decision Tree, Logistic Regression and K-Nearest Neighbor as base classifiers for this multi-label classification approach are implemented and a comparative study of these model's performance is presented. Furthermore, the Majority Voting Ensemble Classifier method based on the ensemble learning is developed for the activity recognition problem. All these models are evaluated using several evaluation metrics on publicly available ARAS datasets. Through the results obtained from experiments, it can be deduced that the Classifier Chain method not only handles the challenges of this complex problem competently but also exhibits the importance of multi-label classification approach towards the field of activity recognition.
Extremal quantum states

The striking differences between quantum and classical systems predicate disruptive quantum technologies. We peruse quantumness from a variety of viewpoints, concentrating on phase-space formulations because they can be applied beyond particular symmetry groups. The symmetry-transcending properties of the Husimi Q function make it our basic tool. In terms of the latter, we examine quantities such as the Wehrl entropy, inverse participation ratio, cumulative multipolar distribution, and metrological power, which are linked to the intrinsic properties of any quantum state. We use these quantities to formulate extremal principles and determine in this way which states are the most and least “quantum”; the corresponding properties and potential usefulness of each extremal principle are explored in detail. While the extrema largely coincide for continuous-variable systems, our analysis of spin systems shows that care must be taken when applying an extremal principle to new contexts.
Pre-service teacher’s self-perception of digital literacy: The case of Israel

Teachers provide society with literacy needs. They instruct students to acquire the essential skills and competencies required for a successful social integration. Thus, the need to identify digital readiness in teachers. The purpose of this study is to assess the level of digital literacies and digital readiness of students majoring in education. The research method includes a questionnaire comprising 54 items. The sample consists of 1265 students. The results show that more than half of the participants report an overall high level of literacy in all areas. Their sense of readiness for teamwork and their ethical readiness is high. Nonetheless, a low sense of readiness is found in a first and advanced order of readiness. The practical implications of these findings are crucial, as they can assist faculty and educational policymakers identify the strengths and weaknesses of students' digital literacies.
Infrared Image Detection of Substation Insulators Using an Improved Fusion Single Shot Multibox Detector

The thermographic diagnostic of substation insulators can facility to detect their insulation status in time. However, the detection effect of infrared insulator images is usually poor due to the complex background of substation scene images. In order to solve this problem and improve the automatic detection capability of substation equipment, an infrared insulator image detection model based on the improved feature fusion single shot multibox detector is proposed in this paper. The model combines multi-scale feature maps to generate a new feature pyramid, and designs a new feature enhancement module in the shallow network of the model to improve its ability to extract features of infrared images of substation insulators. Meanwhile, the clustering algorithm is used to calculate the target aspect ratio information of the dataset to realize the adaptive change of the aspect ratio of the default box. Furthermore, the transfer learning is employed to further improve the learning efficiency of the model. Before the formal experiment, the design scheme of feature enhancement module is confirmed by ablation studies. Experiments are performed on the collected infrared insulator images, instrument transformer images and Pascal VOC 2007 dataset. It is demonstrated experimentally that this model achieves excellent detection results.
Computational universality of symmetry-protected topologically ordered cluster phases on 2D Archimedean lattices

What kinds of symmetry-protected topologically ordered (SPTO) ground states can be used for universal measurement-based quantum computation in a similar fashion to the 2D cluster state? 2D SPTO states are classified not only by global on-site symmetries but also by subsystem symmetries, which are fine-grained symmetries dependent on the lattice geometry. Recently, all states within so-called SPTO cluster phases on the square and hexagonal lattices have been shown to be universal, based on the presence of subsystem symmetries and associated structures of quantum cellular automata. Motivated by this observation, we analyze the computational capability of SPTO cluster phases on all vertex-translative 2D Archimedean lattices. There are four subsystem symmetries here called ribbon, cone, fractal, and 1-form symmetries, and the former three are fundamentally in one-to-one correspondence with three classes of Clifford quantum cellular automata. We conclude that nine out of the eleven Archimedean lattices support universal cluster phases protected by one of the former three symmetries, while the remaining lattices possess 1-form symmetries and have a different capability related to error correction.
Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis

Objectives: To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis. Design: Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin. Setting: Medical ICU of a large quaternary- care academic medical center in the United States. Patients or Subjects: Adult (&gt; 18 year yr old), medical ICU patients were included in the study. Interventions: No additional interventions were made beyond the standard of care for this study. Measurements and Main Results: During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54–66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0–14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%). Conclusions: We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients.
Detecting multipartite entanglement structure with minimal resources

Recently, there are tremendous developments on the number of controllable qubits in several quantum computing systems. For these implementations, it is crucial to determine the entanglement structure of the prepared multipartite quantum state as a basis for further information processing tasks. In reality, evaluation of a multipartite state is in general a very challenging task owing to the exponential increase of the Hilbert space with respect to the number of system components. In this work, we propose a systematic method using very few local measurements to detect multipartite entanglement structures based on the graph state --- one of the most important classes of quantum states for quantum information processing. Thanks to the close connection between the Schmidt coefficient and quantum entropy in graph states, we develop a family of efficient witness operators to detect the entanglement between subsystems under any partitions and hence the entanglement intactness. We show that the number of local measurements equals to the chromatic number of the underlying graph, which is a constant number, independent of the number of qubits. In reality, the optimization problem involved in the witnesses can be challenging with large system size. For several widely-used graph states, such as 1-D and 2-D cluster states and the Greenberger-Horne-Zeilinger state, by taking advantage of the area law of entanglement entropy, we derive analytical solutions for the witnesses, which only employ two local measurements. Our method offers a standard tool for entanglement structure detection to benchmark multipartite quantum systems.
Deep Sparse Representation-Based Classification

We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoencoder along with a fully-connected layer. The role of the autoencoder network is to learn robust deep features for classification. On the other hand, the fully-connected layer, which is placed in between the encoder and the decoder networks, is responsible for finding the sparse representation. The estimated sparse codes are then used for classification. Various experiments on three different datasets show that the proposed network leads to sparse representations that give better classification results than state-of-the-art SRC methods. The source code is available at: github.com/mahdiabavisani/DSRC.
Evaluation of multislice inputs to convolutional neural networks for medical image segmentation

When using convolutional neural networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice [two-dimensional (2D)] or whole volumes [three-dimensional (3D)]. One common alternative, in this study denoted as pseudo-3D, is to use a stack of adjacent slices as input and produce a prediction for at least the central slice. This approach gives the network the possibility to capture 3D spatial information, with only a minor additional computational cost.In this study, we systematically evaluate the segmentation performance and computational costs of this pseudo-3D approach as a function of the number of input slices, and compare the results to conventional end-to-end 2D and 3D CNNs, and to triplanar orthogonal 2D CNNs. The standard pseudo-3D method regards the neighboring slices as multiple input image channels. We additionally design and evaluate a novel, simple approach where the input stack is a volumetric input that is repeatably convolved in 3D to obtain a 2D feature map. This 2D map is in turn fed into a standard 2D network. We conducted experiments using two different CNN backbone architectures and on eight diverse data sets covering different anatomical regions, imaging modalities, and segmentation tasks.We found that while both pseudo-3D methods can process a large number of slices at once and still be computationally much more efficient than fully 3D CNNs, a significant improvement over a regular 2D CNN was only observed with two of the eight data sets. triplanar networks had the poorest performance of all the evaluated models. An analysis of the structural properties of the segmentation masks revealed no relations to the segmentation performance with respect to the number of input slices. A post hoc rank sum test which combined all metrics and data sets yielded that only our newly proposed pseudo-3D method with an input size of 13 slices outperformed almost all methods.In the general case, multislice inputs appear not to improve segmentation results over using 2D or 3D CNNs. For the particular case of 13 input slices, the proposed novel pseudo-3D method does appear to have a slight advantage across all data sets compared to all other methods evaluated in this work.
Faster Person Re-identification

Fast person re-identification (ReID) aims to search person images quickly and accurately. The main idea of recent fast ReID methods is the hashing algorithm, which learns compact binary codes and performs fast Hamming distance and counting sort. However, a very long code is needed for high accuracy (e.g. 2048), which compromises search speed. In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy. It uses shorter codes to coarsely rank broad matching similarities and longer codes to refine only a few top candidates for more accurate instance ReID. Specifically, we design an All-in-One (AiO) framework together with a Distance Threshold Optimization (DTO) algorithm. In AiO, we simultaneously learn and enhance multiple codes of different lengths in a single model. It learns multiple codes in a pyramid structure, and encourage shorter codes to mimic longer codes by self-distillation. DTO solves a complex threshold search problem by a simple optimization process, and the balance between accuracy and speed is easily controlled by a single parameter. It formulates the optimization target as a $$F_{\beta }$$ score that can be optimised by Gaussian cumulative distribution functions. Experimental results on 2 datasets show that our proposed method (CtF) is not only $$8\%$$ more accurate but also $$5\times $$ faster than contemporary hashing ReID methods. Compared with non-hashing ReID methods, CtF is $$50\times $$ faster with comparable accuracy. Code is available at https://github.com/wangguanan/light-reid .
Neural text similarity of user reviews for improving collaborative filtering recommender systems

According to the advent of technology and the expansion of using the World Wide Web, there has been an enormous increase in the number of Internet retailers and their customers. The amount of data is exploding due to the numerous users and the variety of products. In such a condition, recommender systems play an important role in helping users and providing suggestions based on their taste. Using recommender systems' suggestions, users can save their time on finding their personalized, useful, and favorite items without being overwhelmed by a large set of items. User-based collaborative filtering recommender systems for each user find similar users based on their ratings and suggest their favorite products to the intended user. In this paper, we present a model to improve recommender systems by finding similar users based on their reviews in addition to their ratings. To this end, we compute users' reviews similarity utilizing seven different approaches, out of which two techniques are lexical-based, two techniques benefit from the neural representation of words, and three techniques are based on the neural representation of texts. Our experiments on two different datasets from Amazon show that the proposed model for capturing review's similarity significantly improved the performance of the recommender system. Moreover, the model based on Long Short Term Memory (LSTM) network achieves the best results.
VLANet: Video-Language Alignment Network for Weakly-Supervised Video Moment Retrieval

Video Moment Retrieval (VMR) is a task to localize the temporal moment in untrimmed video specified by natural language query. For VMR, several methods that require full supervision for training have been proposed. Unfortunately, acquiring a large number of training videos with labeled temporal boundaries for each query is a labor-intensive process. This paper explores a method for performing VMR in a weakly-supervised manner (wVMR): training is performed without temporal moment labels but only with the text query that describes a segment of the video. Existing methods on wVMR generate multi-scale proposals and apply query-guided attention mechanism to highlight the most relevant proposal. To leverage the weak supervision, contrastive learning is used which predicts higher scores for the correct video-query pairs than for the incorrect pairs. It has been observed that a large number of candidate proposals, coarse query representation, and one-way attention mechanism lead to blurry attention map which limits the localization performance. To address this issue, Video-Language Alignment Network (VLANet) is proposed that learns a sharper attention by pruning out spurious candidate proposals and applying a multi-directional attention mechanism with fine-grained query representation. The Surrogate Proposal Selection module selects a proposal based on the proximity to the query in the joint embedding space, and thus substantially reduces candidate proposals which leads to lower computation load and sharper attention. Next, the Cascaded Cross-modal Attention module considers dense feature interactions and multi-directional attention flows to learn the multi-modal alignment. VLANet is trained end-to-end using contrastive loss which enforces semantically similar videos and queries to cluster. The experiments show that the method achieves state-of-the-art performance on Charades-STA and DiDeMo datasets.
CFPS: Collaborative filtering based source projects selection for cross-project defect prediction

Software defect prediction aims at helping developers allocate existing resources by predicting defect-prone modules prior to the testing phase. In the past decade, cross-project defect prediction (CPDP) have gained more attention than within-project defect prediction (WPDP) as WPDP is usually inefficient with the scarcity of training data due to the absence of historical defect data. Currently most CPDP studies focus on selecting appropriate training instances for improving the performance of defect prediction while few studies pay attention to the selection of appropriate source projects. However, in practice, source projects selection is the basis and prerequisite of training instances selection as an increasing number of open source software defect data are now available. In present study, we propose a Collaborative Filtering based source Projects Selection (CFPS) method for cross-project defect prediction. For a given new project, the similarity between it and each historical project is firstly calculated and thus the corresponding similarity repository could be obtained. Then CFPS mines the applicability among historical projects for constructing an applicability repository. Finally, with the aforementioned applicability and similarity repository, the popular user-based collaborative filtering algorithm is employed to recommend the appropriate source projects for the given new project. In the experiment, we have empirically validated the importance and necessity of selecting appropriate source projects. Furthermore, the experimental results also demonstrate that the proposed CFPS method is feasible and effective.
Sentiment Analysis of IMDb Movie Reviews Using Long Short-Term Memory

The sentiment analysis is an emerging research area where vast amount of data are being analyzed, to generate useful insights in regards to a specific topic. It is an effective tool which can serve governments, corporations and even consumers. Text emotion recognizing lays a key role in this framework. Researchers in the fields of natural language processing (NLP) and machine learning (ML) have explored a variety of methods to implement the process with highest accuracy possible. In this paper the Long Short-Term Memory (LSTM) classifier is used for analyzing sentiments of the IMDb movie reviews. It is based on the Recurrent Neural Network (RNN) algorithm. The data is effectively preprocessed and partitioned to enhance the post classification performance. The classification performance is studied in terms of accuracy. Results show a best classification accuracy of 89.9%. It confirms the potential of integrating the designed solution in modern text based sentiments analyzers.