Reviewerly/exHarmony huggingface dataset:
Investigating Technology Concepts to Support Rohingya Refugees in Malaysia

Abstract The Rohingya ethnic group is one of the most persecuted minorities, whose specific circumstances pose unique and interesting challenges. The digital revolution is transforming the world and is leaving refugees behind (UNHCR). In this paper, we present a multiphase user centred approach we used to explore how Rohingya refugees could access assistance by utilizing technology they already have or share; both early on and long after they arrived in a new country. The findings we present from phase 1, which was an early participatory design phase, were confirmed and endorsed by the Rohingya community, UNHCR and local charities during phase 3. These findings, suggest that concepts aiming to assist Rohingya refugees should primarily focus on: safety, social capital, upskilling, and language translation. Finally, we present the iterative user centred design, implementation, and evaluation of one of these concepts focusing on safety, before being deployed within the Rohingya community in Malaysia.
Structured Theorem for Quantum Programs and its Applications

This article proves a structured program theorem for flowchart quantum programs. The theorem states that any flowchart quantum program is equivalent to a single quantum program that repeatedly executes a quantum measurement and a subprogram, so long as the measurement outcome is true. Moreover, their expected runtime, variance, and general moments are the same. This theorem simplifies the quantum program’s verification significantly. – We derive an analytical characterization of the termination problem for quantum programs in polynomial time. Our procedure is more efficient and accurate with much simpler techniques than the analysis of this problem, as described in [ 29 ]. – We compute the expected runtime analytically and exactly for quantum programs in polynomial time. This result improves the methods based on the weakest precondition calculus for the question recently developed in [ 31 , 34 ]. – We show that a single loop rule is a relatively complete Hoare logic for quantum programs after applying our structured theorem. Although using fewer rules, our method verifies a broader class of quantum programs, compared with the results in [ 45 ] and [ 56 ].
Description of The Experiment: The Effectiveness of the Use of an Interactive Pedagogical Website by a Teacher of Russian as a Foreign Language

The purpose of the study . To describe a pedagogical experiment for the research work “Pedagogical conditions for the formation of communicative competencies in the digital educational environment of a Russian university (in the preparation of students from foreign countries)” on the example of the introduction of an interactive pedagogical website into the digital educational environment. The intended goal defined the tasks: 1) define the concept of pedagogical experiment; 2) state the essence of the subject of the experiment; 3) make a scientific description of the experiment. In pedagogical science, it is definitely not possible to conduct an experiment for a number of psychological and physiological reasons. To get a more accurate result, you need to use different methods. This research article presents two types of pedagogical experiment. Materials and methods . To define the concept of pedagogical experiment, we studied the works of A. Leontiev on active mind, O. Borovkova on the problem of experience in historical science, V. Zagvyazinsky and V. Slastenin on methodology, methods and the importance of didactic research. They turned to the philosophical encyclopedia and the vision of the modern philosopher E. Loone about the futility of conducting an experiment in the presence of a documentary source, although F. Bacon considers the pilot experiment to be extremely important and fundamental, and his thought served as an epigraph to the article. They turned to the psychological dictionary and noted that the experiment is defined as cognition. Having proved the effectiveness of teaching the introduction of information technologies in pedagogical conditions, we found out, analyzing the work of V. Krutetsky, what abilities a modern teacher should have. We stopped at the criteria of A. Sidenko’s pedagogical experiment. Using the methods of observation, description, analysis and survey, we described in the article a search and verification experiment using the method of concomitant changes. In addition, an experiment was conducted with two groups: experimental and control. Results . A pedagogical experiment cannot be as accurate as in fundamental science, so the value of the article lies in a detailed description of the pedagogical experiment. Different methods were used: concomitant changes, where the principle is causation, and a parallel method, where the effectiveness of the interactive pedagogical website was tested by comparing the results of the two groups. Conclusion . The author of the article proved that the pedagogical experiment must be carried out in different ways in order to bring its results closer to the most accurate ones. Our experiments, the results of which are reliable, have proved the effectiveness of using an interactive pedagogical website in preparing foreign students for studying at Russian universities.
Geology of Winu-Ngapakarra, Great Sandy Desert of Western Australia, a Recently Discovered Intrusion-Related Cu-Au Deposit

Abstract The Winu-Ngapakarra Cu-Au deposit (Winu) was discovered in late 2017. Winu is hosted in metamorphosed massive sandstones, siltstones, and lesser mafic rocks that are possible distal and deep-water correlatives of the Malu Formation of the Yeneena basin, also host to the large Telfer Au-Cu deposit. Structure at Winu is dominated by an inclined dome formed through interference between NNW- and WNW-trending folds. Copper-gold mineralization occurs in en echelon left-stepping lodes with strike lengths between 350 and 750 m and vertical depths exceeding 750 m, northerly trends, and moderate easterly dips. These higher-grade lodes are surrounded by an extensive halo of low-grade mineralization. Two gold-rich lodes in the southeast part of the Winu deposit strike roughly easterly, toward a gold-rich satellite deposit (Ngapakarra) approximately 2 km east of Winu. Mineralization at Winu is hosted by numerous thin, brittle veins and breccias. At least four distinct vein sets associated with Cu-Au mineralization are recognized: V1—early, weakly mineralized K-feldspar-white mica stockworks; V2—transitional magmatic-hydrothermal K-feldspar–rich veins; V3—quartz-sulfide–rich veins, breccias, and fractures and quartz-bismuth-gold veins with similar timing; and V4—poorly mineralized quartz veins. Barren carbonate-, chlorite-, white mica-, and pyrite-rich fractures (V5), breccias, and faults as well as thin dolerite dikes cut mineralization at Winu. Re-Os dating of molybdenite intergrown with chalcopyrite yielded ages of 658 to 655 Ma for the mineralized veins at Winu, and Ar-Ar dating of biotite yielded 619 Ma for an unmineralized post-ore quartz-biotite vein. A well-developed supergene chalcocite blanket overlies the primary mineralization. Element associations and vein and alteration textures and mineralogy classify Winu as an intrusion-related Cu-Au deposit of Neoproterozoic age. Although it shares features with the reduced porphyry copper-gold group, other features, such as the dominance of pyrite over pyrrhotite and the presence of trace amounts of sulfate minerals in the mineralization, are more in accordance with oxidized, intrusion-related systems. Mineralization at Winu was most likely triggered by rapidly changing stress fields during the Paterson orogeny and cooling of multiple fluid pulses above an intruding granite pluton that progressively decreased in temperature over time. Winu is unique in this region in that it preserves evidence for an early, reduced intrusion-related gold system overprinted by a more oxidized intrusion-related copper system.
Preparing Future Teachers to Implement Distance Learning for Schoolchildren

The purpose of the study . The article deals with the issue of graduates’ (future teachers) preparation for the solution of professional tasks related to the organization of distance learning for schoolchildren. The analysis of current approaches to the organization of professional training of students of pedagogical universities for the implementation of distance learning is carried out. The results of students’ survey in order to identify professional deficits of graduates in the context of the implementation of distance learning technologies are presented. The main directions of improving the graduates’ professional skills in the framework of the discipline “Distance learning technologies” and work program structure are given. Materials and methods . Analysis of scientific and methodological literature on the preparation of graduates of pedagogical universities for the implementation of distance learning. The students’ survey to identify competencies and professional deficiencies in the field of application of distance learning technologies. Research results . The main competencies based on the analysis of scientific and methodological literature are identified and characterized. The formation of these competencies is aimed at further successful, effective and safe implementation by future teachers of activities in the field of schoolchildren’s distance learning. A number of competencies such as personal, professional and pedagogical, psychological, technical and extreme (ability and readiness to work in sudden complicated conditions) that are particularly in demand from the perspective problem under this study are highlighted. The students’ survey revealed professional deficits in the field of application of distance learning technologies. Methodological aspects of the organization of the distance learning process prevailed. Meanwhile, a significant number of students noted the lack of the formation of specific digital skills, which are necessary for the implementation of distance learning. It is important to highlight such professional deficits as a group work organization in a remote format (both in methodological and technical aspects); the organization of work with parents; the teacher’s time management.All these aspects determine the importance of purposeful work on the formation of appropriate skills of future teachers within the discipline “Distance learning technologies”. The content of this discipline is formed according to the results of the study.The results of the study can be used to improve the system of training of future teachers and the adjustments to the content of academic discipline programs.
Near-optimal task-driven sensor network configuration

A coupled path-planning and sensor configuration method is proposed. The path-planning objective is to minimize exposure to an unknown spatially-varying scalar field, called the threat field, measured by a network of sensors. Gaussian Process regression is used to estimate the threat field from these measurements. Crucially, the sensors are configurable, i.e., parameters such as location and size of field of view can be changed. A main innovation of this work is that sensor configuration is performed by maximizing a so-called task-driven information gain (TDIG) metric, which quantifies uncertainty reduction in the cost of the planned path. For computational efficiency, a surrogate metric called the self-adaptive mutual information (SAMI) is introduced and shown to be submodular. The proposed method is shown to vastly outperform traditionally decoupled information-driven sensor configuration in terms of the number of measurements required to find near-optimal plans.
A Comparative Analysis of Sampling Techniques for Click-Through Rate Prediction in Native Advertising

Native advertising is a popular form of online advertisements that has similar styles and functions with the native content displayed on online platforms, such as news, sports and social websites.It can better capture users' attention, and they have gained increasing popularity in many online platforms and among advertisers.In advertising, Click Trough Rate (CTR) prediction is essential but challenging due to data sparsity: the non-clicks constitute most of the data, whereas clicks form a significantly smaller portion.The performance of 19 class imbalance approaches is compared in this study with the use of four traditional classifiers, to determine the most effective imbalance methods for our native ads dataset.The data used is real traffic data from Finland over the course of seven days provided by the native advertising platform ReadPeak.The resampling methods used include seven undersampling techniques, four oversampling techniques, four hybrid sampling techniques, and four ensemble systems.The findings demonstrate that class imbalance learning can enhance the model's capacity for classification by as much as 20%.In general, oversampling is more stable comparatively.But, undersampling performed the best with Random Forest.Our study also demonstrates that the imbalance ratio plays an important role in the performance of the model and the features importance.
Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model

The resource usage behaviors of application workloads are currently the primary concern of cloud providers offering hosting services.These services should be able to adapt to workload changes by automatically provisioning and de-provisioning resources so that, at all times, the existing resources in a system match the current service demand.Such behavior can be achieved manually by hiring a DevOps team to manage the application's resources.Another option would be automating the resource provisioning processing using automated rules.Once such rules are met, the hosting environment will scale the resources accordingly.However, managing a DevOps team or creating flaky rules can lead to overscaling application resources.This work proposes a new approach: a proactive auto-scaling framework built on an ensemble model.Such a model utilizes several machine learning techniques to scale application resources to match resource demand before the need arises.We evaluated our solution against three real production applications hosted on Cegedim Cloud Hosting Environment, an industrial environment serving several cloud applications from various domains, and against other machine learning models used in similar proactive auto-scaling experiments mentioned in past work.The experimentation results show that predicting application resources like CPU or RAM is feasible.Moreover, even in production environments, our ensemble model performs optimally in the CPU case and is near the optimal model when predicting RAM resources.
Physical Passive Patch Adversarial Attacks on Visual Odometry Systems

Deep neural networks are known to be susceptible to adversarial perturbations – small perturbations that alter the output of the network and exist under strict norm limitations. While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the model’s output on a set of inputs. Universal perturbations present a more realistic case of adversarial attacks, as awareness of the model’s exact input is not required. In addition, the universal attack setting raises the subject of generalization to unseen data, where given a set of inputs, the universal perturbations aim to alter the model’s output on out-of-sample data. In this work, we study physical passive patch adversarial attacks on visual odometry-based autonomous navigation systems. A visual odometry system aims to infer the relative camera motion between two corresponding viewpoints, and is frequently used by vision-based autonomous navigation systems to estimate their state. For such navigation systems, a patch adversarial perturbation poses a severe security issue, as it can be used to mislead a system onto some collision course. To the best of our knowledge, we show for the first time that the error margin of a visual odometry model can be significantly increased by deploying patch adversarial attacks in the scene. We provide evaluation on synthetic closed-loop drone navigation data and demonstrate that a comparable vulnerability exists in real data. A reference implementation of the proposed method and the reported experiments is provided at https://github.com/patchadversarialattacks/patchadversarialattacks .
Diffusion Models for Counterfactual Explanations

Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable. In this paper, we propose DiME, a method allowing the generation of counterfactual images using the recent diffusion models. By leveraging the guided generative diffusion process, our proposed methodology shows how to use the gradients of the target classifier to generate counterfactual explanations of input instances. Further, we analyze current approaches to evaluate spurious correlations and extend the evaluation measurements by proposing a new metric: Correlation Difference. Our experimental validations show that the proposed algorithm surpasses previous state-of-the-art results on 5 out of 6 metrics on CelebA.
ADEL: Adaptive Distribution Effective-Matching Method for Guiding Generators of GANs

Research on creating high-quality, realistic fake images has engendered immense improvement in GANs. However, GAN training is still subject to mode collapse or vanishing gradient problems. To address these issues, we propose an adaptive distribution effective-matching method (ADEL) that sustains the stability of training and enables high performance by ensuring that the training abilities of the generator and discriminator are maintained in balance without bias in either direction. ADEL can help the generator’s training by matching the difference between the distribution of real and fake images. As training is ideal when the discriminator and generator are in a balanced state, ADEL works when it is out of a certain optimal range based on the loss value. Through this, ADEL plays an important role in guiding the generator to create images similar to real images in the early stage when training is difficult. As training progresses, it naturally decays and gives model more freedom to generate a variety of images. ADEL can be applied to a variety of loss functions such as Kullback-Liebler divergence loss, Wasserstein loss, and Least-squares loss. Through extensive experiments, we show that ADEL improves the performance of diverse models such as DCGAN, WGAN, WGAN-GP, LSGAN, and StyleGANv2 upon five datasets, including low-resolution (CIFAR-10 and STL-10) as well as high-resolution (LSUN-Bedroom, Church, and ImageNet) datasets. Our proposed method is very simple and has a low computational burden, so it is expandable and can be used for diverse models.
Control of direct current motor by using artificial neural networks in Internal model control scheme

In this research, control of the Direct Current motor is accomplished using a neuro controller in the Internal Model Control scheme. Two Feed Forward Neural Networks are trained using historical input-output data. The first neural network is trained to identify the object's dynamic behavior, and that model is used as an internal model in the control scheme. The second neural network is trained to obtain an inverse model of the object, which is applied as a neuro controller. Experiment is conducted on the real direct current motor in laboratory conditions. Obtained results are compared to those achieved by implementing the Direct Inverse Control method with the same neuro controller. It was demonstrated that the proposed control method is simple to implement and the system robustness is achieved, which is a great benefit, aside from the fact that no mathematical model of the system is necessary to synthesize the controller of the real object.
Business process automation: New challenges to increasing the efficiency and competitiveness of companies

Background: Scientific and technical progress are unstoppable. Nowadays, information technology affects every sphere of social and economic life. The automation of business processes is a common part of business and accounting practice. Moreover, the COVID-19 pandemic has accelerated the automation in companies. Regularly occurring transactions, which were performed manually before, are gradually being replaced by information technology and automatic robotic systems. The companies fully use cloud storages, artificial intelligence, or blockchain technologies. Purpose: The purpose of the paper is to identify areas and possibilities for improvement of business and accounting processes with use of information technologies, digitalization, and automation. Methodology: For the purpose of the paper, a standard methodology review of literature sources was performed. Then we conducted a brief questionnaire survey in order to find out the level of automation and digitization of business processes in companies in the Slovak Republic. Findings and conclusions: It is expected that, as a result of robotization and automation of business processes, some professions will gradually disappear within a few decades. Even the crisis related to COVID19 pandemic had an impact on the acceleration of using information technologies in business processes related to their digitalization and automation. The paper describes possibilities of automation with focus on e-shop, which make the processes safer, faster, more effective, eco-friendly, with fewer errors. On the other hand, there are some challenges and threats the accounting entities have to consider when implementing artificial intelligence, information technology in their business processes, such as cyber security, the Internet and electricity outages. We have observed that companies in Slovakia have their business processes more or less automated. However, the automation is the domain of bigger and multinational companies. The higher initial costs related to automation are limitation for smaller companies that need to consider benefits and costs the automation requires. Limitations and future research: Our paper is based on the theoretical background with a brief research in the area of implementing information technologies in companies in the Slovak Republic. The number of returned questionnaire surveys that were the base for our brief and initial research was very low. More detailed analysis of level of automation in the Slovak companies in comparison with companies abroad, their readiness for changes related to automation, as well as threats, challenges, pros and cons of automation may be the topic for the future research.
TeCM-CLIP: Text-Based Controllable Multi-attribute Face Image Manipulation

In recent years, various studies have demonstrated that utilizing the prior information of StyleGAN can effectively manipulate and generate realistic images. However, the latent code of StyleGAN is designed to control global styles, and it is arduous to precisely manipulate the property to achieve fine-grained control over synthesized images. In this work, we leverage a recently proposed Contrastive Language Image Pretraining (CLIP) model to manipulate latent code with text to control image generation. We encode image and text prompts in shared embedding space, leveraging powerful image-text representation capabilities pretrained on contrastive language images to manipulate partial style codes in the latent code. For multiple fine-grained attribute manipulations, we propose multiple attribute manipulation frameworks. Compared with previous CLIP-driven methods, our method can perform high-quality attribute editing much faster with less coupling between attributes. Extensive experimental illustrate the effectiveness of our approach. Code is available at https://github.com/lxd941213/TeCM-CLIP.