Reviewerly/exHarmony huggingface dataset:
Reproductive method in teaching Russian to foreign students and its innovative aspects

The reproductive method in the system of teaching Russian as a foreign language is aimed at reproducing the ways of activity by foreign students according to the algorithms presented by the teacher, at enriching them with knowledge, skills and abilities, as well as at forming the main mental operations: abstraction, analysis, synthesis, generalization, and others. When learning the «language of the profession», the main mental operations are formed: abstraction, analysis, synthesis, generalization, and others. The reproductive method, having the basis of algorithmization, integrated with the cognitive theory of learning, has reached a new level – the reproductivecognitive method. System analysis in combination with algorithmization becomes the basis of the reproductive and creative method. The methods outlined in the article enrich the knowledge, skills and abilities of students, which are most often carried out at the stages of correction, diagnosis and consolidation of knowledge, but do not guarantee the development of creative abilities, do not allow them to be systematically and purposefully formed. In particular, the concept of the reproductive-creative method is the principle of communicative orientation, which formed the basis of competence-based learning. In competence-based learning, the activity component, unlike other possible approaches, is considered from the viewpoint of competence, i.e., the ability of the student to effectively and independently, but with the help of the teacher, use the language being studied on the basis of the acquired experience in the form of knowledge, skills, and abilities. The knowledge obtained as a result of the use of the explanatory and illustrative method weakly forms skills and abilities. Therefore, thanks to the developed system of tasks, where the algorithmization of the educational process is clearly traced, the teacher organizes the students’ activities by repeatedly reproducing the knowledge communicated to them and the ways of activity shown.
SLADE: A Self-Training Framework For Distance Metric Learning

Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings. We use self-supervised representation learning to initialize the teacher model. To better deal with noisy pseudo labels generated by the teacher network, we design a new feature basis learning component for the student network, which learns basis functions of feature representations for unlabeled data. The learned basis vectors better measure the pairwise similarity and are used to select high-confident samples for training the student network. We evaluate our method on standard retrieval benchmarks: CUB-200, Cars-196 and In-shop. Experimental results demonstrate that with additional unlabeled data, our approach significantly improves the performance over the state-of-the-art methods.
Spectrum and Computing Resource Management for Federated Learning in Distributed Industrial IoT

Federated learning (FL) is a distributed paradigm to support deep neural network (DNN) training while preserving the data owners' privacy. In this paper, we investigate the resource management problem for FL in distributed industrial Internet of Things (IIoT) networks. Specifically, we introduce a three-layer collaborative architecture to support FL. DNNs are trained locally at the selected IIoT devices, and then the DNN model parameters are aggregated by edge servers every FL epoch or by a cloud server every a few FL epochs to update the global DNN model. To enable efficient FL in the resource-limited IIoT networks, judicious computing and spectrum resource allocation is required for training and transmitting the DNN model parameters. Thus, we formulate a joint device selection and resource allocation problem to minimize the FL evaluating loss while satisfying the strict FL epoch delay and devices' energy consumption requirements. Since the decisions of device selection and resource allocation are coupled, we transform the joint optimization problem into a Markov decision process and propose a dynamic resource management scheme based on deep reinforcement learning approaches to efficiently facilitate the FL. Simulation results demonstrate that the proposed scheme can effectively improve the FL performance comparing with benchmarks.
How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice

Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the human-computer interface community. Therefore, organizations that want or need to provide this explainability are confronted with the selection of an appropriate method for their use case. In this paper, we argue there is a need for a methodology to bridge the gap between stakeholder needs and explanation methods. We present our ongoing work on creating this methodology to help data scientists in the process of providing explainability to stakeholders. In particular, our contributions include documents used to characterize XAI methods and user requirements (shown in Appendix), which our methodology builds upon.
The use of careFiji app for contact tracing during the COVID-19 pandemic: Digital gap and challenges faced in Fiji

Globally, digital contact tracing initiatives has been used as a tool to combat the COVID-19 pandemic. The Fijian Government and Ministry of Health are promoting the use of the "careFiji" app to help in contact tracing. This paper will discuss the rollout of the careFiji app which helps in combating COVID-19 in Fiji, and the challenges caused by the digital gap that has surfaced during the pandemic.
An empirical evaluation of text representation schemes to filter the social media stream

Modeling text in a numerical representation is a prime task for any Natural Language Processing downstream task such as text classification. This paper attempts to study the effectiveness of text representation schemes on the text classification task, such as aggressive text detection, a special case of Hate speech from social media. Aggression levels are categorized into three predefined classes, namely: ‘Non-aggressive’ (NAG), ‘Overtly Aggressive’ (OAG), and ‘Covertly Aggressive’ (CAG). Various text representation schemes based on BoW techniques, word embedding, contextual word embedding, sentence embedding on traditional classifiers, and deep neural models are compared on a text classification problem. The weighted F1 score is used as a primary evaluation metric. The results show that text representation using Googles’ universal sentence encoder (USE) performs better than word embedding and BoW techniques on traditional classifiers, such as SVM, while pre-trained word embedding models perform better on classifiers based on the deep neural models on the English dataset. Recent pre-trained transfer learning models like Elmo, ULMFi, and BERT are fine-tuned for the aggression classification task. However, results are not at par with the pre-trained word embedding model. Overall, word embedding using pre-trained fastText vectors produces the best weighted F1-score than Word2Vec and Glove. On the Hindi dataset, BoW techniques perform better than word embeddings on traditional classifiers such as SVM. In contrast, pre-trained word embedding models perform better on classifiers based on the deep neural nets. Statistical significance tests are employed to ensure the significance of the classification results. Deep neural models are more robust against the bias induced by the training dataset. They perform substantially better than traditional classifiers, such as SVM, logistic regression, and Naive Bayes classifiers on the Twitter test dataset.
Public Perception of the German COVID-19 Contact-Tracing App Corona-Warn-App

Several governments introduced or promoted the use of contact-tracing apps during the ongoing COVID-19 pandemic. In Germany, the related app is called Corona-Warn-App, and by end of 2020, it had 22.8 million downloads. Contact tracing is a promising approach for containing the spread of the novel coronavirus. It is only effective if there is a large user base, which brings new challenges like app users unfamiliar with using smartphones or apps. As Corona-Warn-App is voluntary to use, reaching many users and gaining a positive public perception is crucial for its effectiveness. Based on app reviews and tweets, we are analyzing the public perception of Corona-Warn-App. We collected and analyzed all 78,963 app reviews for the Android and iOS versions from release (June 2020) to beginning of February 2021, as well as all original tweets until February 2021 containing #CoronaWarnApp (43,082). For the reviews, the most common words and n-grams point towards technical issues, but it remains unclear, to what extent this is due to the app itself, the used Exposure Notification Framework, system settings on the user's phone, or the user's misinterpretations of app content. For Twitter data, overall, based on tweet content, frequent hashtags, and interactions with tweets, we conclude that the German Twitter-sphere widely reports adopting the app and promotes its use.
A comparison on the use of Perlin-noise and Gaussian noise based augmentation on X-ray classification of lung cancer patient

Abstract The use of deep learning in medical image classification has become an important study in the past few years. The proper use of this method, however, is still hindered by many problems, one of it being the imbalance of dataset available for training which resulted in small-set database. In this study, the effect of noise-based augmentation on the performance of deep learning based classification will be studied. The noises which were used for the augmentation method were Perlin-noise and Gaussian noise. The modality of medical image used in this study is X-ray. 174 X-ray images (87 cancer, 87 normal) were used in this study and will be classified by using transfer learning from previously trained deep learning architecture. The deep learning architecture used was vgg-19. The images were divided into two groups, 138 (69 cancer, 69 normal) images were used for training phase and 36 (18 cancer, 18 normal) were images used for testing phase. Three deep learning models were used for the classification tasks, the first one was retrained to classify the original images, the second one was retrained by using mix of original images and images with Perlin-noise, and the third one was retrained by using mix of original images and images with Gaussian noise. The results showed that the three models returned similar accuracy of 0.8 which indicate that the use of noise-based augmentation can increase the performance of deep learning in classifying medical images with small set training database.
UAV Maneuvering Target Tracking based on Deep Reinforcement Learning

Abstract Maneuvering target tracking means that UAV observes the target through sensors, follows the target and maintains the tracking range with the target. Aiming at the target tracking problem of UAV, we built UAV motion model and UAV tracking model based on reinforcement learning, and verified the feasibility and effectiveness of the method in the simulation environment.
End-to-End Security: When Do We Have It?

Isn't it true that all encryption schemes provide end-to-end security? This is not always the case; end-to-end security can sometimes be difficult to achieve in practice. This article focuses on end-to-end security obstacles in wireless Internet, mobile phone communications, and cloud based e-mail.
Visual analysis of industrial knowledge graph research based on Citespace

This paper selects the papers related to the industrial knowledge map research included in Web Of Science as the data source, and uses the Cite Space software to analyze the current situation, hotspots and trends of the industrial knowledge map research using the bibliometric analysis method. It is found that the research in the field of industrial knowledge graphs is not yet mature, and the research hotspots are knowledge graphs, knowledge representation, and the use of knowledge graphs for modeling and industrial management innovation research. The research trend is based on deep learning, data modeling, artificial intelligence, neural network and other technologies to build knowledge maps.
An experimental comparison of explore/exploit strategies for the learning classifier system XCS

When determining the actions to execute, reinforcement learners are constantly faced with the decision of either exploiting existing knowledge or exploring new options, risking short-term costs but potentially improving performance in the long run. This paper describes and experimentally evaluates four existing explore/exploit strategies for the learning classifier system XCS. The evaluation takes place on three well-known learning problems - two multiplexers and one maze environment. An automized parameter optimization is conducted, showing that different environments require different parametrization of the strategies. Further, our results indicate that none of the strategies is superior to the others. It turns out that multi-step problems with scarce rewards are challenging for the selected strategies, highlighting the need to develop more reliable explore/exploit strategies to tackle such environments.
Implementation of Social Engineering Attack at Institution of Higher Education

The paper shows an investigation utilizing assaults, for example, a phony passage and a phishing page. The past distributions on social building have been checked on, insights of separations are investigated and bearings and component of acknowledgment of assaults having components of social designing are examined. The information from the examination in three better places were gathered and investigated and the substance measurements were given. For examination, three classifications of advanced education organizations were picked: specialized, helpful and blended profiles. Since the exploration was led in instructive organizations during the week, most understudies in the test and graduate understudies partook in the test. For each instructive establishment, an enrollment structure layout was made that emulated the plan of the principle pages. Instances of equipment and programming execution of an average represent assault, information accumulation and investigation are given. So as to develop a test stand, generally accessible segments were picked to show that it is so natural to complete assaults of this sort without critical introductory expenses and uncommon aptitudes. The paper gives measurements on the quantity of associations, consent to utilize the location of the email and secret word, just as authorization to consequently move administration information to the program (cookies). The insights are prepared utilizing uniquely composed calculations. The proposed ways to deal with taking care of the issue of socio-specialized assaults can be utilized and executed for activity on any objects of data action.
VNIR-SWIR infrared (imaging) spectroscopy for geothermal exploration: Current status and future directions

Infrared spectroscopy (IRS) is a method used to identify minerals based on their spectral response to infrared light. Spot-based IRS (also known as 'reflectance spectroscopy') has frequently been used as an analytical method by the geothermal industry, while infrared imaging spectroscopy (IRIS) has only recently been introduced. Research applying IRIS to the geothermal industry is still limited to academic trials, but sufficient progress has been made in algorithm development and application cases to make this technology ready for uptake by the geothermal industry. In contrast, the mineral exploration industry has embraced IRIS for a number of years and is driving its development forward. In this paper, we review the work that has been done in the geothermal industry with spot-based and imaging IRS, as well as review relevant examples from the mineral exploration realm to look for pathfinders for future uses of the method within the geothermal industry. The review focuses on the application of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectroscopy, as the application of long-wave infrared (LWIR) spectroscopy in the geothermal industry is still very limited. To cater for a wide range of audiences, we will explain the background of infrared spectroscopy as well as the commonly used geothermal index minerals and analytical techniques typically employed by the geothermal industry. Our review shows that IRS has a higher sensitivity in identifying kaolinite (along with its degree of crystallinity), ammonium-bearing minerals, and chemical variations of (spectrally-active) minerals compared to other methods. The ability of IRIS to obtain spectra with high spatial resolution enables the method to identify: (1) less common minerals, and (2) the potential to distinguish smectite and illite formed as interlayered minerals from those formed as two different grains. The latter identification typically cannot be done using spot-based IRS. Other parameters that can only be seen in IRIS include spatial relationships amongst minerals and more robust mineral abundance estimates. The mineral exploration industry has demonstrated that applying IRIS can go beyond just identifying minerals. It can also successfully identify host lithologies on intensely altered rocks, as well as identify and extract the position of veins and veinlets. The latter information is important for geothermal exploration, particularly to indicate permeability and cross-cutting relationships amongst alteration minerals. The IRIS applications that have been demonstrated by the mineral exploration community potentially represent the future trend in the geothermal industry. Spot-based IRS has already proven its added value in assisting geothermal exploration and exploitation. With the latest instruments and algorithm developments in place, imaging IRS is now on the brink of demonstrating its value to the geothermal industry.
Operating Room (Re)Scheduling with Bed Management via ASP

Abstract The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms (ORs), taking into account different specialties, lengths, and priority scores of each planned surgery, OR session durations, and the availability of beds for the entire length of stay (LOS) both in the Intensive Care Unit (ICU) and in the wards. A proper solution to the ORS problem is of primary importance for the healthcare service quality and the satisfaction of patients in hospital environments. In this paper we first present a solution to the problem based on Answer Set Programming (ASP). The solution is tested on benchmarks with realistic sizes and parameters, on three scenarios for the target length on 5-day scheduling, common in small–medium-sized hospitals, and results show that ASP is a suitable solving methodology for the ORS problem in such setting. Then, we also performed a scalability analysis on the schedule length up to 15 days, which still shows the suitability of our solution also on longer plan horizons. Moreover, we also present an ASP solution for the rescheduling problem, that is, when the offline schedule cannot be completed for some reason. Finally, we introduce a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real time.
Discriminative visual tracking via spatially smooth and steep correlation filters

Recently, many discriminative correlation filter (DCF)-basedapproaches have been developed to alleviate the undesired boundary effects of traditional DCF-based methods. However, these approaches either focus on the target area by suppressing the background information or exploit a spatial regularisation term to penalise filter coefficients far from the target centre, where real negative samples are exceedingly rare and irrelevant information is integrated into filters. Based on the observation that the tracking model is strongly dependent on the quality of training samples, this paper proposes a novel DCF-based tracker to precisely process the training set through a smooth and steeply decreasing function in two respects. On the one hand, the surrounding information is suitably incorporated into the target. On the other hand, every real background patch is collected as a negative sample. In addition, the target appearance model is adaptively updated by achieving an appropriate trade-off between current and historical models according to their reliability. Experimental results show that our method outperforms other state-of-the-art trackers in terms of accuracy and efficiency. In particular, compared with the baseline tracker, our method achieves gains of 7.4% and 9.9% in terms of the area under the curve (AUC) on the OTB-100 and LaSOT datasets, respectively.
Optimal Design of Hybrid Federated and Centralized Learning in the Mobile Edge Computing Systems

It is a dilemma to balance the tradeoff between the computation efficiency and communication cost of deploying deep learning models in the mobile edge computing (MEC) systems, due to the isolation of collected data and computation capability. To solve this problem, a hybrid federated and centralized learning scheme is first proposed in this paper, where the learning model can be jointly generated based on the centralized learning model and the federated learning model. It can make full use of both the collected data of user terminals and power full computation capability of edge computing servers. Second, to guarantee the model accuracy with communication, computation, and data constraints, an optimization algorithm is designed to keep a sophisticated tradeoff of model accuracy and training cost. Finally, the experiment results base on the image data set are provided, which show that our proposed algorithm can significantly improve the model accuracy with low costs.