Reviewerly/exHarmony huggingface dataset:
Review of transfer learning in modeling additive manufacturing processes

Modeling plays an important role in the additive manufacturing (AM) process and quality control. In practice, however, only limited data are available for each product due to the relatively high AM cost, which brings challenges in building either a high-quality physics-based or data-based model. Transfer learning (TL) is a new and promising group of approaches where the model of one product (source) may be reused for another product (target) with limited new target data. This paper focuses on reviewing applications of TL in AM modeling to help advance research in this area. First, notations, definitions, and categories of TL methods are introduced along with their application scenarios. Then current applications of TL in AM modeling are summarized along with their limitations. Based on reviewed applications, recommendations are given on how to apply TL for a certain AM problem, from the perspectives of source domain determination, TL method selection, target data generation, and data preprocessing. Finally, future research directions about TL in AM modeling are discussed in the hope to explore more potential of TL in improving the AM model quality with limited data.
Small set of orthogonal product states with nonlocality

A set of orthogonal states in multipartite systems is called to be locally stable if to preserve the orthogonality of the states, only trivial local measurement can be performed for each partite. Locally stable sets of states are always locally indistinguishable yielding a form of nonlocality which is different from the Bell-type nonlocality. In this work, we study the locally stable set of product states with small size. First, we give a lower bound on the size of locally stable sets of product states. It is well known that unextendible product basis (UPB) is locally indistinguishable. But we find that some of them are not locally stable. On the other hand, there exists some small subsets of minimum size UPB that are also locally stable which implies the nonlocality of such UPBs are stronger than other form.
Designing a Virtual Reality Empathy Game framework to create empathic experiences for children

Game Design and Virtual Reality (VR) researchers theorize that VR technologies have great potential to scaffold children’s empathy development. To explore the effectiveness of VR in promoting empathy for children ages 6–9, we developed a framework for a VR Empathy Game based on the results of a participatory design study including 11 children and the Early Childhood Development literature. To evaluate this framework, we developed a game based on it and conducted a qualitative study with 14 children. We explored how children understand the meaning of the game and their approach to playing the game. Using descriptive thematic analysis, we found: (1) VR identity of a player can be used as an indicator of engagement in the game, and (2) a connection between motivation, engagement in the game story, and the game performance. This paper contributes a novel VR Empathy Game framework and insight into its effectiveness in creating empathic experiences for children.
An adaptive transformer model for anomaly detection in wireless sensor networks in real-time

Defense, environmental monitoring, healthcare, home automation, and other fields are just a few of the many that make use of wireless sensor networks. There are sensor nodes in these networks that are sent out into the field to collect data and relay it back to the network's hub. The data streams may be infused with anomalous data due to interferences, malfunctioning nodes, etc. The security of the network depends on the prompt identification of any irregularities. Due to noise, missing data, and the dynamic nature of the network, identifying and detecting abnormal data is difficult.This study introduces a dynamic context-capturing deep learning model for sensor data anomaly detection, constructed as a Transformer with a spatio-temporal attention mechanism. The proposed model is trained and tested with a public dataset built from real-time sensor data captured in a water treatment plant. With an accuracy of 0.09142, F1 of 0.9109, and precision of 0.9191, the STA-Tran model outperforms the best existing methods.
Optimized CPU–GPU collaborative acceleration of zero-knowledge proof for confidential transactions

The potential application scope of blockchain is much broader than its origin of digital currencies, which may include managing smart contract, copyrighted works, and digitization of commercial or organizational registries. Not surprisingly, the confidentiality of transaction information is desired in these various blockchain applications, and is unfortunately not well supported yet. Zero-knowledge proofs such as bulletproofs are good candidate solutions to provide transaction confidentiality in blockchain, due to the ability to verify the truth of information without revealing the information directly. However, zero-knowledge proofs still suffer from high computation overhead and low throughput, and Bulletproofs are still computationally inefficient to be applied in blockchain applications. Edge computing meets confidential transactions' strong need for high performing by providing powerful GPUs and can be a helpful option. In this paper, we first present a CPU–GPU collaborative framework to accelerate the inner-product arguments of Bulletproofs. The experiments show that our implementation achieves an average speedup ratio of 3.7x. On this basis, this paper also proposes to build a high efficient bulletproofs based transaction processing system that supports both confidential and transparent transactions at GPU-enabled edge. Compared with the original bulletproofs version, a GPU accelerated implementation can obtain a speedup ratio up to 4.7x. When multiple bulletproofs are required to execute in bundle, the work focuses on memory optimization and data segmentation, which further increases the speed by 30%. Finally, the overall transaction processing system achieves a 1.5x speedup when both confidential and transparent transactions are parallelized at the edge.
Emergent scale invariance in neural networks

We demonstrate, both analytically and numerically, that learning dynamics of neural networks is generically attracted towards a scale-invariant state. The effect can be modeled with quartic interactions between non-trainable variables (e.g. states of neurons) and trainable variables (e.g. weight matrix). Non-trainable variables are rapidly driven towards stochastic equilibrium and trainable variables are slowly driven towards learning equilibrium described by a scale-invariant distribution on a wide range of scales.
Adaptive smoothness evaluation and multiple asymmetric histogram modification for reversible data hiding

In this paper, an adaptive reversible data hiding (RDH) algorithm based on multiple asymmetric histograms is proposed by making full use of the image content. Different from existing multiple prediction error histogram (PEHs) modification methods that directly cluster all the pixels of a cover image into multiple categories, we firstly utilize a smoothness threshold to exclude as many pixels in complex regions as possible for reducing unnecessary pixel shifting, and then exploit fuzzy C-means with multiple deliberately-designed features to construct multiple sharply-distributed categories, which helps in increasing the subsequent embedding performance. Two asymmetric PEHs for each class are generated using a pair of asymmetric predictors, and the short part of each asymmetric PEH is modified to reduce the number of invalid modifications. The improved discrete particle swarm optimization is used to adaptively select the best bin while reducing computational complexity. The experimental results show that the proposed method outperforms several state-of-the-art RDH methods.
Merging nucleus datasets by correlation-based cross-training

Fine-grained nucleus classification is challenging because of the high inter-class similarity and intra-class variability. Therefore, a large number of labeled data is required for training effective nucleus classification models. However, it is challenging to label a large-scale nucleus classification dataset comparable to ImageNet in natural images, considering that high-quality nucleus labeling requires specific domain knowledge. In addition, the existing publicly available datasets are often inconsistently labeled with divergent labeling criteria. Due to this inconsistency, conventional models have to be trained on each dataset separately and work independently to infer their own classification results, limiting their classification performance. To fully utilize all annotated datasets, we formulate the nucleus classification task as a multi-label problem with missing labels to utilize all datasets in a unified framework. Specifically, we merge all datasets and combine their labels as multiple labels. Thus, each data has one ground-truth label and several missing labels. We devise a base classification module that is trained using all data but sparsely supervised by the ground-truth labels only. We then exploit the correlation among different label sets by a label correlation module. By doing so, we can have two trained basic modules and further cross-train them with both ground-truth labels and pseudo labels for the missing ones. Importantly, data without any ground-truth labels can also be involved in our framework, as we can regard them as data with all labels missing and generate the corresponding pseudo labels. We carefully re-organized multiple publicly available nucleus classification datasets, converted them into a uniform format, and tested the proposed framework on them. Experimental results show substantial improvement compared to the state-of-the-art methods. The code and data are available at https://w-h-zhang.github.io/projects/dataset_merging/dataset_merging.html.
Real-time pedestrian pose estimation, tracking and localization for social distancing

The corona virus pandemic has introduced limitations which were previously not a cause for concern. Chief among them are wearing face masks in public and constraints on the physical distance between people as an effective measure to reduce the virus spread. Visual surveillance systems, which are common in urban environments and initially commissioned for security surveillance, can be re-purposed to help limit the spread of COVID-19 and prevent future pandemics. In this work, we propose a novel integration technique for real-time pose estimation and multiple human tracking in a pedestrian setting, primarily for social distancing, using CCTV camera footage. Our technique promises a sizeable increase in processing speed and improved detection in very low-resolution scenarios. Using existing surveillance systems, pedestrian pose estimation, tracking and localization for social distancing (PETL4SD) is proposed for measuring social distancing, which combines the output of multiple neural networks aided with fundamental 2D/3D vision techniques. We leverage state-of-the-art object and pose estimation algorithms, combining their strengths, for increase in speed and improvement in detections. These detections are then tracked using a bespoke version of the FASTMOT algorithm. Temporal and analogous estimation techniques are used to deal with occlusions when estimating posture. Projective geometry along with the aforementioned posture tracking is then used to localize the pedestrians. Inter-personal distances are calculated and locally inspected to detect possible violations of the social distancing rules. Furthermore, a "smart violations detector" is employed which estimates if people are together based on their current actions and eliminates false social distancing violations within groups. Finally, distances are intuitively visualized with the right perspective. All implementation is in real time and is performed on Python. Experimental results are provided to validate our proposed method quantitatively and qualitatively on public domain datasets using only a single CCTV camera feed as input. Our results show our technique to outperform the baseline in speed and accuracy in low-resolution scenarios. The code of this work will be made publicly available on GitHub at https://github.com/bilalze/PETL4SD.
Feedback attention network for cardiac magnetic resonance imaging super-resolution

Atrial fibrillation (AF) is a common clinical arrhythmia with a high disability and mortality rate. Improving the resolution of atrial structure and its changes in patients with AF is very important for understanding and treating AF.Aiming at the problems of previous deep learning-based image super-resolution (SR) reconstruction methods simply deepening the network, loss of upsampling information, and difficulty in the reconstruction of high-frequency information, we propose the Feedback Attention Network (FBAN) for cardiac magnetic resonance imaging (CMRI) super-resolution. The network comprises a preprocessing module, a multi-scale residual group module, an upsampling module, and a reconstruction module. The preprocessing module uses a convolutional layer to extract shallow features and dilate the number of channels of the feature map. The multi-scale residual group module adds a multi-channel network, a mixed attention mechanism, and a long and short skip connection to expand the receptive field of the feature map, improve the multiplexing of multi-scale features and strengthen the reconstruction of high-frequency information. The upsampling module adopts the sub-pixel method to upsample the feature map to the target image size. The reconstruction module consists of a convolutional layer, which is used to restore the number of channels of the feature map to the original number to obtain the reconstructed high-resolution (HR) image.Furthermore, the test results on the public dataset of CMRI show that the HR images reconstructed by the FBAN method not only have a good improvement in reconstructed edge and texture information but also have a good improvement in the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) objective evaluation indicators.Compared with the local magnified image, the edge information of the FBAN method reconstructed image has been enhanced, more high-frequency information of the CMRI is restored, the texture details are less lost, and the reconstructed image is less blurry. Overall, the reconstructed image has a lighter feeling of smearing, and the visual experience is more apparent and sharper.
Social Networks Privacy Preservation: A Novel Framework

The development of several popular social networks and the publication of social networks’ data have led to the risk of leakage of sensitive and confidential information of individuals. This requires the preservation of privacy before the publication of a user’s data available from his Online Social Network (OSN) presence. Numerous algorithms have been proposed in the area of preserving the privacy of social network users’ information such as K-anonymity and L-diversity. Previous work has shown good results based on the concept of adding edges and noise nodes for achieving K-anonymity and L-diversity. K-anonymization techniques are able to prevent identity disclosure of users but are not sufficient to prevent the disclosure of sensitive information of users. In this direction, a number of techniques for preserving the sensitive information of social network users have been proposed. Although these techniques have shown reasonably good results to achieve anonymity, but they also lead to a substantial change in the original structure of the OSNs. In this article, the problems of preventing sensitive attribute disclosure and reducing the noisy nodes have been addressed by perturbing the sensitive attributes. Existing research uses L-diversity for preventing sensitive attribute disclosure resulting in skewness and similarity attacks. We have addressed the skewness attacks by removing the duplicate noisy nodes from the final dataset to be published for stakeholders by the OSN service providers. All the information of duplicate nodes has been stored in a table named Reference Attribute Table (RAT). This table will be accessible only to the service providers for the purpose of de-anonymizing the data of users. The proposed technique has been extensively evaluated using five metrics viz. APL, ACSPL, RRTI, number of noisy nodes, and information loss using four real-time datasets collected for OSNs namely CORA, ARNET, DBLP, and Twitter. Results of evaluation parameters viz. APL and RRTI show that there is less change in the structure of datasets after anonymization. Results of ACSPL show that our proposed technique is able to preserve sensitive attributes in the datasets. The maximum number of noisy nodes amongst all four datasets is 5.4% and the maximum information loss is 2.2%. Evaluation results make it evident that our proposed technique ensures privacy preservation with less loss of information and thus preserving the utility of published data.
Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction

Accurate workload prediction plays a key role in intelligent scheduling decisions on cloud platforms. There are massive amounts of short-workload sequences in the cloud platform, and the small amount of data and the presence of outliers make accurate workload sequence prediction a challenge. For the above issues, this paper proposes an ensemble learning method based on sample weight transfer and long short-term memory (LSTM), termed as Tr-Predictor. Specifically, a selection method of similar sequences combining time warp edit distance (TWED) and transfer entropy (TE) is proposed to select a source domain dataset with higher similarity for the target workload sequence. Then, we upgrade the basic learner of the ensemble model two-stage TrAdaBoost.R2 to LSTM in the deep model and enhance the ability of the ensemble model to extract sequence features. To optimize the weight adjustment strategy, we adopt a two-stage weight adjustment strategy and select the best weight for the learner according to the sample error and model error. Finally, the above process determines the parameters of the target model and uses the target model to predict the short-task sequences. In the experimental validation, we arbitrarily select nine sets of short-workload data from the Google dataset and three sets of short-workload data from the Alibaba cluster to verify the prediction effectiveness of the proposed algorithm. The experimental results show that compared with the commonly used cloud workload prediction methods Tr-Predictor has higher prediction accuracy on the small-sample workload. The prediction indicators of the ablation experiments show the performance gain of each part in the proposed method.
The Impact of Organizational Training Operations Management on Job Performance: An Empirical Study on Jordanian Government Institutions

The study aimed at identifying the impact of the organizational training operations management with its four elements (identifying training needs, designing training programmes, implementing training programmes and evaluating training programmes) on job performance (impression and learning). The study, which relied on the descriptive-analytical approach, targeted all employees (i.e., 885) in the Jordanian government institutions in the Irbid governorate, which was included in the study, using the intentional sampling method. The questionnaire was adopted as a tool for data collection and a total of 311 questionnaires were retrieved from the total, with 27 excluded due to the incompleteness of the data and their unsuitability for statistical analysis. Therefore, 284 valid questionnaires were used for statistical analysis and the Statistical Package for Social Sciences (SPSS-22) was used to analyse the data. The study found that the process of applying organizational training in its various dimensions achieved a medium level of satisfaction from the employees’ point of view. The study also found that there was a statistically positive impact of the organizational training operations on job performance in all its dimensions. The study recommended the need for senior management to adopt training programmes according to the strategic objectives, and support them continuously, materially and morally. The study stressed the need to evaluate the trainees before enrolling in a training programme to determine his or her level of knowledge.
Grammars for placeholders: The dynamic turn

In verbal communication, when a speaker encounters a word-formulation problem (e.g. memory lapse), she may resort to several linguistic strategies, including the use of a placeholder (PH). A PH is a dummy item with which a speaker fills in the syntactic slot of a target form that she is unable or unwilling to produce. There is a growing body of work investigating PHs in a variety of languages, but the bulk of extant studies provide a descriptive and/or functional analysis and little attention has been paid to formal modelling. In the present article, we offer a wide range of PH examples in Japanese, French, and German, including new data on gender mismatches, and develop a formal account in Dynamic Syntax. We propose a general mechanism to process a PH time-linearly and show how it is implemented in the grammars of several languages. In this analysis, a PH-involving string is parsed incrementally, and a conceptual structure is built up progressively, during which a PH introduces a meta-variable, to be updated based on the parse of a target form on an unfixed node or a LINKed node. The account is further extended to various PH-involving dialogic phenomena in talk-in-interaction.
Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy

The success of human civilization is rooted in our ability to cooperate by communicating and making joint plans. We study how artificial agents may use communication to better cooperate in Diplomacy, a long-standing AI challenge. We propose negotiation algorithms allowing agents to agree on contracts regarding joint plans, and show they outperform agents lacking this ability. For humans, misleading others about our intentions forms a barrier to cooperation. Diplomacy requires reasoning about our opponents' future plans, enabling us to study broken commitments between agents and the conditions for honest cooperation. We find that artificial agents face a similar problem as humans: communities of communicating agents are susceptible to peers who deviate from agreements. To defend against this, we show that the inclination to sanction peers who break contracts dramatically reduces the advantage of such deviators. Hence, sanctioning helps foster mostly truthful communication, despite conditions that initially favor deviations from agreements.