Reviewerly/exHarmony huggingface dataset:
CLOUD NETWORK / COMPUTING IMPACT ON INTER-ORGANIZATIONAL TRUST WHICH EFFECTS PERFORMANCE OF SUPPLY CHAIN MANAGEMENT: A STUDY OF PAKISTAN’S FIRMS

This paper analyzes Cloud Computing Impact on Inter-organizational Trust which affects the performance of Supply Chain Management of different Retail, Manufacturing, Financial, IT and Services firms in Karachi, city of Pakistan. A theoretical framework has been constructed that illuminates the role of Inter-organizational Trust in Cloud Computing (CC) and how this can reproduce the superior performance of Supply Chain (SCP) by using arguments of the Social Capital Theory (SCT). A questionnaire was run on 200 individuals having a different position like Senior Manager, Middle Manager and Officer of different Retail, Manufacturing, Financial, IT and Services firms in Karachi, state of Pakistan, and statistically analyze the hypothesis by step by step regression test. The result suggests that Cloud Computing (CC) would be controlled to support Inter-organizational Trust (IOT) to have a strong and significant influence on the association of Supply Chain. To improve complete Inter-organizational Trust (IOT) terms and conditions in organizations managers have to implement Cloud Computing base which creates radical improvement in Supply Chain Performance and the results, therefore, reinforce the across the board to explanation augured by the useful theoretical framework as Social Capital Theory (SCT).&#x0D;
Interferometric Coupling-based Modulator for Large-Scale Integrated Photonic Systems

Integrated photonics, an emerging branch of photonics in micron-scale proximity, has drawn tremendous attention due to its capability for the generation, manipulation and detection of optical signals on a single chip. Microring modulators (MRMs) and Mach-Zehnder modulators (MZMs) are two frequently used photonic devices in modern electro-optic communications that also play an important role in the development of photonic computing systems [1] , [2] . However, their modulation technique which is based on operational wavelength-peak drift may cause optical crosstalk when one of the peaks moves towards an adjacent peak, especially in a large-scale optical system. Consequently, the optical modulation amplitude (OMA) of the adjacent peak reduces. This limits the implementation scale of an integrated system based on multiple cascaded modulators. It has been reported that an optimized MRM design could support up to 108 wavelength division multiplexing (WDM) channels [3] .
Cross-verification of independent quantum devices

Today’s intermediate-scale quantum devices are on the brink of outperforming their classical counterparts. Yet these devices remain noisy, and with no trusted classical computers to check their results, we are left with a dire need to verify that they indeed perform as intended. The standard approach to this problem is to benchmark the performance of the individual building blocks using techniques such as quantum process tomography or randomized benchmarking and then extrapolate to the full system. In practice, however, such an extrapolation can be unreliable due to non-Markovian effects, unmodelled stray noise or temporally and spatially correlated noise. Hence, there is a need for full-system verification techniques able to capture every source of noise and imperfection.
Micro-level dynamics in hidden action situations with limited information

The hidden-action model provides an optimal sharing rule for situations in which a principal assigns a task to an agent who makes an effort to carry out the task assigned to him. However, the principal can only observe the task outcome but not the agent's actual action. The hidden-action model builds on somewhat idealized assumptions about the principal's and the agent's capabilities related to information access. We propose an agent-based model that relaxes some of these assumptions. Our analysis lays particular focus on the micro-level dynamics triggered by limited information access. For the principal's sphere, we identify the so-called Sisyphus effect that explains why the optimal sharing rule can generally not be achieved if the information is limited, and we identify factors that moderate this effect. In addition, we analyze the behavioral dynamics in the agent's sphere. We show that the agent might make even more of an effort than optimal under unlimited information, which we refer to as excess effort. Interestingly, the principal can control the probability of making an excess effort via the incentive mechanism. However, how much excess effort the agent finally makes is out of the principal's direct control.
Binarization for low-quality ESPI fringe patterns based on preprocessing and clustering

Massive inherent speckle noise and extremely low contrast make it difficult to binarize electronic speckle pattern interferometry (ESPI) fringe patterns. In this paper, we present a binarization based on preprocessing and fuzzy C-means (FCM) clustering for low-quality ESPI fringe patterns. First, we use the multiscale retinex (MSR) algorithm to enhance the original fringe pattern to improve the contrast between the bright and dark fringes. Then, the local entropy of the enhanced fringe pattern is calculated and the second-order oriented partial differential equation algorithm is introduced to filter the local entropy map. Finally, the FCM is applied to cluster the local entropy filtering map, and the pixels of the fringe pattern are classified into two categories: bright fringes and dark fringes. To verify the reliability and universality of the proposed method, we provide a qualitative evaluation of six experimental ESPI subtraction fringe patterns and two computer-simulated ESPI addition fringe patterns. Experimental results exhibit that the proposed method can provide good binarization performances.
Motor Bearing Fault Diagnosis based on Multi-domain Feature Extraction and Improved Optimized Decision Tree

In view of the large noise of the collected signals in the current fault diagnosis methods of motor bearings, the incomplete feature extraction, and the low efficiency and accuracy of fault diagnosis, this paper proposes a method based on multi-domain signal feature extraction and Bayesian Optimized decision tree fault diagnosis method for motor bearings.The method mainly includes signal feature extraction and optimized decision tree fault diagnosis.First, the bearing vibration signal data set is used to extract feature values in the time domain, frequency domain, and time-frequency domain respectively; then Bayesian optimization is used to supervise the decision tree.The parameters are optimized, and the training data is used to train and predict the optimized decision tree classifier.At the same time, the same data set is used to train the decision tree model without feature value extraction, the optimized decision tree model without feature value extraction, and the decision tree model with feature value extraction.The diagnosis results of the four models are compared, and the results show that the method can effectively reduce the dimensionality of the signal data, and can greatly improve the fault diagnosis rate and the fault diagnosis accuracy rate.
DIGITAL CULTURE AS AN OBJECT OF CULTURAL STUDIES: THE PROBLEM OF METHODOLOGICAL ALTERNATIVES

Modern domestic research of digital culture, according to the author, is mainly based on scientific approaches developed by the Euro-American philosophical and scientific thought. This position seems counterproductive, as it condemns Russian scientists to eternal lag. The article offers a number of alternative approaches to the study of digital culture. The author presents some subject areas and methods of studying digital culture, which are on the periphery of scientific interests. It offers a number of steps away from established research traditions. First, the author shares phenomenal and noumenal aspects of digital culture. The emphasis on the nominal aspect opens up the possibility of analyzing the value-semantic content of digital culture, identifying the specifics of “digital creativity” as a semantic and syntactic process. Secondly, the author expands the ideas about the genealogy of digital culture. In particular, the role of philosophy in the formation of a new digital method of culture coding is explicated, the way some ideas of structuralism, axiology, phenomenology legitimized the formation of this method is demonstrated. Third, the author falsifies (in the sense of Popper) the tradition of identifying postmodern culture with digital culture. Based on comparative analysis it is proved that the value-semantic content of these cultures do not coincide, that digital culture is a synthesis of the ideals of modernism and postmodernism. Fourth, the author attempts to determine the ontological status of digital culture. He argues that the inherent ability of this culture to reproduce itself makes a person from the subject of cultural production to its object. This non-anthropocentric turn generates an unprecedented alienation of culture. Digital culture is turning into a force that dominates man, turns man into a being not just controlled, manipulated, but also devoid of authenticity. At the same time, non-anthropocentric turn creates unprecedented participation person to the culture. The growing dependence of man on artificial technologies puts culture at the epicenter of human existence. Changing the ontological status of culture entails the need for a radical revision of the conceptual apparatus of its research. The concept of “culture” is spontaneously replaced by the concept of “postculture”. In conclusion, the article emphasizes the vital importance of studying digital culture, the need for theoretical study of ideas about digital culture as a post-culture.
The Effect of X-ray Energy Overlaps on the Microanalysis of Chevkinite (Ce, La, Ca, Th)4(Fe2+, Mg)2(Ti, Fe3+)3Si4O22 Using SEM EDS-WDS

A light REE (LREE)-bearing mineral called chevkinite (Ce, La, Ca, Th)4(Fe2+, Mg)2(Ti, Fe3+)3Si4O22, originating from a heavy metal placer deposit Aksu Diamas in Turkey, previously assessed for potential REE extraction as a by-product of magnetite production, was studied using scanning electron microscopy with energy and wavelength-dispersive spectrometers (SEM EDS-WDS). This mineral exhibits analytical challenges associated with severe X-ray energy overlaps between the REE, titanium, and barium. Here, we present an iterative process, showing that SEM EDS-WDS is a viable technique for obtaining good quality quantitative data. SEM EDS-WDS is an in situ, non-destructive, and relatively non-expensive technique, but operator’s experience is essential to obtain good quality data. In cases where the peak fitting remains challenging, in particular, and where the constituents have large differences in abundance, an assessment of the X-ray spectrum to qualitatively assign all peaks is essential prior to quantitative analysis.
The Effect of Within-Bag Sampling on End-to-End Multiple Instance Learning

End-to-end multiple instance learning (MIL) is an important concept with a wide range of applications. It is gaining increased popularity in the (bio)medical imaging community since it may provide a possibility to, while relying only on weak labels assigned to large regions, obtain more fine-grained information. However, processing very large bags in end-to-end MIL is problematic due to computer memory constraints. We propose within-bag sampling as one way of applying end-to-end MIL methods on very large data. We explore how different levels of sampling affect the performance of a well-known high-performing end-to-end attention-based MIL method, to understand the conditions when sampling can be utilized. We compose two new datasets tailored for the purpose of the study, and propose a strategy for sampling during MIL inference to arrive at reliable bag labels as well as instance level attention weights. We perform experiments without and with different levels of sampling, on the two publicly available datasets, and for a range of learning settings. We observe that in most situations the proposed bag-level sampling can be applied to end-to-end MIL without performance loss, supporting its confident usage to enable end-to-end MIL also in scenarios with very large bags. We share the code as open source at https://github.com/MIDA-group/SampledABMIL
Best practices in the use of mobile learning by university teachers of Didactics Language-Literature

The use of mobile learning is becoming increasingly widespread among university teachers, thanks to its many benefits. Moreover, its application is associated with good teaching practices. The aim of this study is to analyse the development of good teaching practices in the use of m-learning by university teachers from the Department of Didactics of Language and Literature, as well as to identify the factors that influence the development of best practices. The research method developed is based on a transversal design. 110 teachers from the Department of Language and Literature Didactics participated in the study. The instrument used is the scale of Analysis of M-learning practices at the University (APMU). The results show that more than half of the teachers use the teaching method based on mobile learning. It can be concluded that in the development of best practices, gender is not influential, but age and teaching experience are. In addition, personal concern and experience in the use of mobile devices by teachers is a key factor in the appropriate development of the m-learning method.
On Generating Transferable Targeted Perturbations

While the untargeted black-box transferability of adversarial perturbations has been extensively studied before, changing an unseen model's decisions to a specific `targeted' class remains a challenging feat. In this paper, we propose a new generative approach for highly transferable targeted perturbations (\ours). We note that the existing methods are less suitable for this task due to their reliance on class-boundary information that changes from one model to another, thus reducing transferability. In contrast, our approach matches the perturbed image `distribution' with that of the target class, leading to high targeted transferability rates. To this end, we propose a new objective function that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains. Based on the proposed objective, we train a generator function that can adaptively synthesize perturbations specific to a given input. Our generative approach is independent of the source or target domain labels, while consistently performs well against state-of-the-art methods on a wide range of attack settings. As an example, we achieve $32.63\%$ target transferability from (an adversarially weak) VGG19$_{BN}$ to (a strong) WideResNet on ImageNet val. set, which is 4$\times$ higher than the previous best generative attack and 16$\times$ better than instance-specific iterative attack. Code is available at: {\small\url{this https URL}}.
Stock Price Prediction using Support Vector Machine in the Second Wave of Covid-19 Pandemic

The aim of the research is to predict The Jakarta Composite Index (JKSE) when the second wave of the Covid-19 occurred in Indonesia. The method used in this research is Support Vector Machine (SVM). The data used in this study is JKSE for period April 2021 to August 2021. The data is divided into two parts, the training data and the testing data. The training data starts from April 1st to July 30th 2021. The data used as ground truth is the data from August 2nd to August 31st 2021. To get the best hyperparameter, the research uses Grid Search with RBF kernel. The results show the best hyperparameters using RBF kernel are C=100, =0.01, and = 3. The prediction result obtained tends to be stable and has a slight decrease. This result if we compared with the cumulative cases and the total recovery cases of the Covid-19, the investor has a positive sentiment towards the Indonesian government in solving the Covid-19 pandemic.
Impulse Noise Removal Based on Hybrid Genetic Algorithm

In this paper, we introduce a new method, impulse noise removal based on hybrid genetic algorithm (INRHGA) to remove impulse noise at different noise densities of noise while preserving the main features of the image. The proposed approach merges the genetic algorithm and methods for filtering images that are combined into the population as essential solutions to create a developed and improved population. A set of individuals is developed into a number of iterations using factors of crossover and mutation. Our method develops a group of images instead of a set of parameters from the filters. We then introduced some of the concepts and steps of it. The proposed algorithm is compared with some image denoising algorithm. By using Peak Signal to Noise Ratio (PSNR), structural similarity (SSIM). For example, for Lenna image with 60% salt and pepper noise density, PSNR, SSIM results of AMF, MDBUTMFG and NAFSM methods are 20,39/ 28.74/ 29.85 and 0.5679/ 0.8312/ 0.8818 respectively, while PSNR, SSIM results of the proposed algorithm are 29.92 and 0.8838, respectively. Experimental results indicate that INRHGA is very effective and visually comparable with the above-mentioned methods at different levels of noise.
How To Train Neural Networks for Flare Removal

When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color bleeding, haze, etc.) and this diversity in appearance makes flare removal challenging. Existing analytical solutions make strong assumptions about the artifact's geometry or brightness, and therefore only work well on a small subset of flares. Machine learning techniques have shown success in removing other types of artifacts, like reflections, but have not been widely applied to flare removal due to the lack of training data. To solve this problem, we explicitly model the optical causes of flare either empirically or using wave optics, and generate semi-synthetic pairs of flare-corrupted and clean images. This enables us to train neural networks to remove lens flare for the first time. Experiments show our data synthesis approach is critical for accurate flare removal, and that models trained with our technique generalize well to real lens flares across different scenes, lighting conditions, and cameras.
Organization of Educational Environment at Pedagogical University: Concepts and Scenario of Changes

The subjects of education satisfaction analysis with the teacher training results at the University was carried out. The training deficiencies and new challenges were identified, which made it possible to determine the guidelines for the development of pedagogical university. A variant to transform the organizational scheme of communications between university departments was presented to increase the efficiency of educational environment organization for professional training of future teachers.
A Questionnaire Survey of the Earthquake Network App's Users in Peru Following an M8 Earthquake in 2019

The 'Earthquake Network’ (EQN) is an app which detects earthquakes by creating an ad-hoc network of smartphone's accelerometer sensors and provides early warnings of earthquakes via the same smartphone app.The EMSC (Euro-Mediterranean Seismological Centre) and the University of Bergamo conducted an online survey, following an earthquake of magnitude M8 on 2019-05-26 07:41:13.6 UTC in Northern Peru with epicentre (5.81S, 75.27W). This survey targeted EQN users in the felt area of the earthquakes and was conducted from 2019-07-23 to 2019-08-18. It aimed at assessing users’ understanding and reaction to the EQN early warning for this specific earthquake.The questionnaire was written in Spanish since it is the most commonly spoken language in the studied area. Individuals who use the app in Spanish were invited to complete the survey via an advertisement on the Earthquake Network app. A PDF containing the questionnaire and the relationship between the questions is included in this archive. 3805 respondents took the survey, including 2 719 that were actually in the area at the time.The analysis: Results derived from this dataset will be included as part of a submitted Science article (Bossu et al. '“Shaking in 5 seconds!” A Voluntary Smartphone-based Earthquake Early Warning System', 2021) to show that respondents received notifications from the Earthquake Network App before feeling the shaking but also that many did not immediately “drop, hold and cover' and were too intent on warning those close to them of the impending danger. All respondents consented that their data could be used for research purposes. The EMSC and University of Bergamo made sure not to collect or diffuse personal data.The dataset is a zip-file that contains the questionnaire responses as a comma-separated text file (csv) and a pdf containing a representation of the questionnaire that was presented to respondents.
Existence and stability of periodic solutions in a neural field equation

We study the existence and linear stability of stationary periodic solutions to a neural field model, an intergo-differential equation of the Hammerstein type. Under the assumption that the activation function is a discontinuous step function and the kernel is decaying sufficiently fast, we formulate necessary and sufficient conditions for the existence of a special class of solutions that we call 1-bump periodic solutions. We then analyze the stability of these solutions by studying the spectrum of the Frechet derivative of the corresponding Hammerstein operator. We prove that the spectrum of this operator agrees up to zero with the spectrum of a block Laurent operator. We show that the non-zero spectrum consists of only eigenvalues and obtain an analytical expression for the eigenvalues and the eigenfunctions. The results are illustrated by multiple examples.
Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping

Bottom-up approaches for image-based multi-person pose estimation consist of two stages: (1) keypoint detection and (2) grouping of the detected keypoints to form person instances. Current grouping approaches rely on learned embedding from only visual features that completely ignore the spatial configuration of human poses. In this work, we formulate the grouping task as a graph partitioning problem, where we learn the affinity matrix with a Graph Neural Network (GNN). More specifically, we design a Geometry-aware Association GNN that utilizes spatial information of the keypoints and learns local affinity from the global context. The learned geometry-based affinity is further fused with appearance-based affinity to achieve robust keypoint association. Spectral clustering is used to partition the graph for the formation of the pose instances. Experimental results on two benchmark datasets show that our proposed method outperforms existing appearance-only grouping frameworks, which shows the effectiveness of utilizing spatial context for robust grouping. Source code is available at: https://github.com/jiahaoLjh/PoseGrouping.