Reviewerly/exHarmony huggingface dataset:
Detection of decorative phase defects on a polymeric non-planar object

This paper presents an optical noncontact method making the phase defects of transparent polymeric or glass objects visible, and image processing techniques detecting these defects. Phase defects affect the phase of the light passing through them, not its amplitude. Therefore, the presented method is based on Fresnel diffraction of a spatially coherent and temporally incoherent light. The method is suitable for both non-planar and planar objects. It is verified on polymeric non-planar objects with different phase defects. The tested objects are made from transparent polycarbonate with a special surface coating showing the orange peel effect. The image processing techniques allow to detect defects in a diffraction image with uneven brightness and nonuniform variance caused by the orange peel of the surface coating.
Adaptive Multi-strategy Learning Particle Swarm Optimization with Evolutionary State Estimation

Particle swarm optimizer (PSO) is a random search algorithm based on group cooperation applied to a wide range of practical problems. But its learning mode is relatively fixed, and it is easy to be trapped in the local optimum. Most variants of PSO use only one learning strategy in the entire evolution process, which may affect their performance when different learning strategies are required under different evolution states. In order to deal with search situations in various complex evolutionary environments, an adaptive multi-strategy learning PSO based on the evolutionary state evaluation called AMSLPSO is proposed. In AMSLPSO, a novel evolutionary state estimation (ESE) method is proposed. In order to meet various complex situations under different search stages, the algorithm judges the current evolution state according to the novel ESE method, and then adaptively selects the learning strategy, three state called exploration state, balance state and exploitation state are corresponding to three learning strategies respectively. The experimental results show that AMSLPSO performs better than or at least comparable with some other advanced PSO variants, on the 30 benchmark functions from CEC2017 real-parameter single-objective test suites.
Weighting the factors affecting attention guidance during free viewing and visual search: The unexpected role of object recognition uncertainty

The factors determining how attention is allocated during visual tasks have been studied for decades, but few studies have attempted to model the weighting of several of these factors within and across tasks to better understand their relative contributions. Here we consider the roles of saliency, center bias, target features, and object recognition uncertainty in predicting the first nine changes in fixation made during free viewing and visual search tasks in the OSIE and COCO-Search18 datasets, respectively. We focus on the latter-most and least familiar of these factors by proposing a new method of quantifying uncertainty in an image, one based on object recognition. We hypothesize that the greater the number of object categories competing for an object proposal, the greater the uncertainty of how that object should be recognized and, hence, the greater the need for attention to resolve this uncertainty. As expected, we found that target features best predicted target-present search, with their dominance obscuring the use of other features. Unexpectedly, we found that target features were only weakly used during target-absent search. We also found that object recognition uncertainty outperformed an unsupervised saliency model in predicting free-viewing fixations, although saliency was slightly more predictive of search. We conclude that uncertainty in object recognition, a measure that is image computable and highly interpretable, is better than bottom-up saliency in predicting attention during free viewing.
Flexible optical interconnects for efficient resource utilization and distributed machine learning training in disaggregated architectures

The explosive growth in data analytics is driving an intensely growing need for compute performance. We will review our ARPA-E PINE ENLITENED experimental testbed and simulation results to motivate performance advantages of disaggregation through the use of flexible photonic interconnect networks for distributed deep learning applications.
A Classical and Machine Learning-Based Reliability Analysis on Catalan Object Encryption Scheme

Designing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lightweight</i> secure cryptographic schemes for Internet of Things (IoT) and radio frequency identification (RFID)-based devices is challenging as a designer needs to address resource-constraints along with physical and classical security notions. Even though plethora of such lightweight encryption schemes have been proposed in the literature, computationally efficient attacks on some has also been published, which stresses the fact that robust and reliable design paradigms to consider during design process. In this article, using classical methods, we launched the full-key attack on a Catalan-object-based encryption scheme proposed by Saracevic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2020) in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> IEEE Transactions on Reliability <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> . Their proposed encryption scheme is efficient and lightweight specifically meant for IoT applications and is based upon combinatorial structure of Catalan key objects. We proved data privacy violation and recover the full encryption-key with computationally efficient algorithm. We also proposed the machine-learning-based regression model to efficiently predict all bits of ciphertext using just a single plaintext without any key. A high correlation between plaintext and ciphertext and full-key recovery poses the encryption scheme to be unreliable to be used for IoT-based applications. To the best of authors’ knowledge, this is the first work on reliability analysis on Catalan-based encryption schemes.
Usability Evaluation of an Interactive Educational Mobile Application

Many educational mobile applications are available to help students achieving their goals in the learning process. For example, an interactive Educational (mobile) Application is an educational application that emphasises the interaction among students and between students and teachers. This type of educational application is also available for students, for example, to help them have direct interaction with their teachers, such as outside the class hours to discuss a particular subject further. In the development of such applications, one of the challenging parts is designing an intuitive user interface to help ensure the usability of the application.In this paper, we present our work on usability evaluation of an interactive educational application to seek feedback and recommendations to improve the developed design. The application is designed to facilitate direct interaction among students and between students and teachers as a form of an online learning activity. The usability evaluation process was conducted remotely (i.e., online) and adopted the Unmoderated Remote Usability Testing (URUT) approach, which involved 25 participants. In this case, an online user interface design and usability tool called Maze was used as a medium to carry out the evaluation. The result shows that the application’s design is in medium grade with a score of 78 (out of 100). Furthermore, the result of the measurement of the user satisfaction level using the SUS questionnaire obtained a score of 80, which indicates that the design of this interactive learning application is acceptable. Other than the score of the usability testing, we collected valuable feedback from the participants that will be used to improve the current design of the application.
A Design of CGK-Based Granular Model Using Hierarchical Structure

In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performance decreases when a cluster is created from data with strong nonlinearity. To improve this problem, GK clustering is used. GK clustering creates clusters using Mahalanobis distance. In this paper, we propose context-based GK (CGK) clustering, which adds a method that considers the output space in the existing GK clustering, to create a cluster that considers not only the input space but also the output space. there is. Based on the proposed CGK clustering, a CGK-based granular model and a hierarchical CGK-based granular model were designed. Since the output of the CGK-based granular model is in the form of a context, it has the advantage of verbally expressing the prediction result, and the CGK-based granular model with a hierarchical structure can generate high-dimensional information granules, so meaningful information with high abstraction value granules can be created. In order to verify the validity of the method proposed in this paper, as a result of conducting an experiment using the concrete compressive strength database, it was confirmed that the proposed methods showed superior performance than the existing granular models.
Selection of optimal models for predicting growth stress in Artemisia desertorum by comparison of linear regression and multiple neural networks: Take the construction of a green mine in the Bayan Obo mine as an example

In recent years, more and more countries are focusing on the control of mining sites and the surrounding ecological environment, and the new environmental concept of green mines has been proposed. By investigating the ecological background of a mine site, pollution and ecological imbalances in the mine can be predicted, managed or transformed. This study investigated the effects of rare earth elements on plant growth in the Baotou Bayan Obo Rare Earth Mine and evaluated soil contamination and subsequent remediation through the measured plant height. Using linear regression, BP(Back Propagation) neural networks, GA-BP(Genetic Algorithm- Back Propagation) neural networks, ELM(Extreme Learning Machine) and GA-ELM(Genetic Algorithm- Extreme Learning Machine) model prediction instruments, the different rare earth solution concentrations were set as input values and the heights of Artemisia desertorum, which as the model plant, were set as output values in the prediction. The results showed that the linear regression predicted the standard error of single La(III), Ce(III) solution and compound La(III) + Ce(III) solution for Artemisia desertorum growth stress was on the high side, 7.02%- 8.92%; the efficiency range of each group of models under BP neural network, GA-BP neural network and ELM neural network were 1.15%- 2.53%, 0.85%- 1.28%, 1.76%- 3.53%; while the efficiency range under GA-ELM neural network was 0.59%- 0.68%, with average error values and predicted values close to the true values. Among them, the MAPE of GA-ELM neural network are significantly lower than other models, and the error decreases with increasing concentration of the compound solution. So GA-ELM neural network can be used as an efficient, fast and reasonable optimal model for predicting the growth stress of Artemisia desertorum in Bayan Obo mining area. The experimental results can provide a theoretical basis for assessing the risk of soil rare earth contamination in the area, evaluating the expectation of later remediation, and provide a degree of new ideas for the construction of green mines.
Trusted quantum remote sensing based on self-testing of entangled states

In a typical scenario of quantum remote sensing, a server at the remote site sends the sensing information to the local site, so that the client there can make a diagnosis about the sensed field. In practice, the reliability of the sensing information may be undermined by kinds of errors, e.g., the imperfections of measurement devices or the attacks from a malicious eavesdropper. An alternative way to circumvent this problem is to encode the sensing information into entangled states, of which the form can be inferred in a device-independent manner, namely, self-testing. In this paper, we propose a protocol to make secure sensing of a parameter at the remote side. Upon receiving the remote sensing data, the local client can figure out the parameter range by inspecting the joint probabilities. In stark contrast, a malicious eavesdropper who is allowed to access all the remote data cannot either acquire the information of the parameter or cheat the client by replacing returned data with fake ones. We apply this protocol to a magnetic sensing scenario, and we show that the client can reliably estimate the strength range of the magnetic field, which could be intimately related to various mineral resources. Consequently, the client should not overslip any mineral resources by identifying the upper bound of magnetic strength. We also show that our protocol is valid even if the entangled states are mildly noisy.
Effective Transfer Learning with Label-Based Discriminative Feature Learning

The performance of natural language processing with a transfer learning methodology has improved by applying pre-training language models to downstream tasks with a large number of general data. However, because the data used in pre-training are irrelevant to the downstream tasks, a problem occurs in that it learns general features rather than those features specific to the downstream tasks. In this paper, a novel learning method is proposed for embedding pre-trained models to learn specific features of such tasks. The proposed method learns the label features of downstream tasks through contrast learning using label embedding and sampled data pairs. To demonstrate the performance of the proposed method, we conducted experiments on sentence classification datasets and evaluated whether the features of the downstream tasks have been learned through a PCA and a clustering of the embeddings.
Shadoks Approach to Low-Makespan Coordinated Motion Planning

This paper describes the heuristics used by the Shadoks team for the CG:SHOP 2021 challenge. This year's problem is to coordinate the motion of multiple robots in order to reach their targets without collisions and minimizing the makespan. It is a classical multi agent path finding problem with the specificity that the instances are highly dense in an unbounded grid. Using the heuristics outlined in this paper, our team won first place with the best solution to 202 out of 203 instances and optimal solutions to at least 105 of them. The main ingredients include several different strategies to compute initial solutions coupled with a heuristic called Conflict Optimizer to reduce the makespan of existing solutions.
dHash-based Anti-Counterfeiting Scheme: Against Tag Re-application Attacks on Batik Tulis

Batik Tulis is a cultural heritage that has historical and economic value. Batik Tulis has its own uniqueness, where Batik Tulis is different from each other even though it has the same motif and is made by the same producer, because Batik Tulis is made manually (handmade) using a tool called "canting". This uniqueness makes Batik Tulis has a fairly high economic value. However, this uniqueness is often exploited by irresponsible parties, by counterfeiting Batik Tulis. In order to address this issue, we developed an anti-counterfeiting scheme that focuses on previously developed tag re-application attacks detection. This approach utilizes of NFC tags, public key cryptography and Perceptual Hash Function (PHF), i.e., difference hash (dHash). dHash provides the relationship between the product and the data stored by the NFC tag. The scheme we have developed is proven to be able to detect tag re- application attack scenarios that the previous scheme could not detect.
A New Key Exchange Protocol Based on Infinite Non-Abelian Groups

In order to resist quantum attacks, a key exchange protocol based on infinite non-abelian groups is proposed in this paper. For the purpose, by the composition of twice the operation of a semidirect product, we construct a shared secret key which contains two hard problems of equivalent decomposition problem (EDP) and discrete logarithm problem (DLP). Then, two methods，algebra attack and brute force attack, were employed to verify the antiattack for the proposed protocol. By a sound mathematical inference, it demonstrates that the proposed protocol possesses security positively. Finally, we analyzed the computational complexity and bit complexity when the protocol being implemented on braid groups, and furthermore, the complexity data confirm the feasibility of establishing the key exchange protocol there. Thus, in any case, security or complexity, the actual use of the proposed protocol means achievable in practice.
A Study on the Effectiveness of Detection of Unbalanced Datasets Based on Faster R-CNN

In the Faster R-CNN target detection task, to address the problem of how the unbalanced distribution of different kinds of datasets affects the recognition effect of the model, a research scheme is proposed to train the model using two different ratios of dataset combination, the two ratios are 1:1 and 1:2 respectively.In this paper, two kinds of objects in electrical equipment, anti-vibration hammer and insulator, are taken as the research objects, and each type of dataset After data processing, the data sets are combined according to the two different ratios set, and then put into the model for training to get the recognition effect. The experimental results show that at Epoch=40,000, the AP(the explanation is shown in TABLE I) of the trained model is much higher for the categories with a small proportion of data than for the categories with a large proportion of data when the ratio of the data sets of anti-vibration hammer and insulator is 1:2 or 2:1, and the AP of the model trained by unbalanced data distribution is unstable compared with the AP of the model trained by the ratio of the two categories of data sets of 1:1. In the actual detection task, it is easy to cause "false detection" or "missed detection". Based on the data unbalance problem, we propose data processing methods such as undersampling and oversampling of the dataset.
Special issue on High Performance Computing Conference ( <scp>BASARIM</scp> ‐2020)

Concurrency and Computation: Practice and ExperienceVolume 34, Issue 9 e6972 EDITORIAL Special issue on High Performance Computing Conference (BASARIM-2020) Ahmet E. Topcu, Corresponding Author Ahmet E. Topcu ahmet.topcu@aum.edu.kw College of Engineering and Technology, American University of the Middle East, Eqaila, Kuwait Correspondence Ahmet E. Topcu, College of Engineering and Technology, American University of the Middle East, Eqaila, Kuwait. Email: ahmet.topcu@aum.edu.kwSearch for more papers by this authorAli Osman Cibikdiken, Ali Osman Cibikdiken KTO Karatay University, Konya, TurkeySearch for more papers by this author Ahmet E. Topcu, Corresponding Author Ahmet E. Topcu ahmet.topcu@aum.edu.kw College of Engineering and Technology, American University of the Middle East, Eqaila, Kuwait Correspondence Ahmet E. Topcu, College of Engineering and Technology, American University of the Middle East, Eqaila, Kuwait. Email: ahmet.topcu@aum.edu.kwSearch for more papers by this authorAli Osman Cibikdiken, Ali Osman Cibikdiken KTO Karatay University, Konya, TurkeySearch for more papers by this author First published: 23 March 2022 https://doi.org/10.1002/cpe.6972Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume34, Issue9Special Issue: 6th High Performance Computing Conference (BASARIM 2020). IOTITBDE201925 April 2022e6972 RelatedInformation
Data breach Analysis for Financial Smart Contracts

In the same way that personal data provide irrefutable information about individuals, they sometimes reveal the legal identity of contracts as well as other specific information. The data a contract generates could represent a competitive advantage. This study's focus was to develop a template of the factors associated with smart contracts data breaches. This model was based on the following variables: smart contracts, exposure level, security level, and structural inputs. The outcome variable was the binary value for a data breach/no data breach. This study covers more than 6000 smart contracts, operating on Etheurm and imported as tables into the study database, the contracts have been selected randomly from the databases of various safety tools such as OYENTE, MAIAN, GASPER, Securify, Solcove, and SmartCheck, These tools are designed to find code vulnerabilities in the smart contracts. 50% of all samples were selected from one source (Securify). Binary logistic regression was employed to examine the data breach model. The findings demonstrate more than 500 cases of actual breaches, with several factors being significantly associated with smart contract data breaches.
Advanced Techniques for Preventing Thermal Imaging Attacks

Thermal cameras can be used to detect user input on interfaces, such as touchscreens, keyboards, and PIN pads, by recording the heat traces left by the users’ fingers after interaction (e.g., typing a message or entering a PIN) and using them to reconstruct the input. While previous work mitigated the thermal attacks by complicating input or distorting heat traces, our research is the first to propose preventing thermal attack using deep learning (DL) techniques to prevent malicious use of thermal cameras. Our DL models detect interfaces in the thermal camera feed and then obfuscate heat traces on them. Our preliminary findings show that the proposed framework can detect interfaces and eliminate authentication information from thermal images. At the same time, our methods still reveal if an interface has been interacted with. Thus, our approach improves security without impacting the utility of the thermal camera.
Don Lindberg’s influence on future generations: The U.S. National Library of Medicine’s biomedical informatics research training programs

Through his visionary leadership as Director of the U.S. National Library of Medicine (NLM), Donald A. B. Lindberg M.D. influenced future generations of informatics professionals and the field of biomedical informatics itself. This chapter describes Dr. Lindberg's role in sponsoring and shaping the NLM's Institutional T15 training programs.