Reviewerly/exHarmony huggingface dataset:
Model of advanced training of physics teachers at the present stage

This article discusses a model for training future physics teachers. The conceptual model of training future physics teachers shows the complete structure of training, methodological features of training for professional activity, future physics teachers at different stages of training: bachelor's, master's, postgraduate and professional retraining. The methodological features of the training consist in the examples of introduction of physical concepts. The article presents the evidence base for the effectiveness of the methodological system of training future physics teachers for secondary schools, lyceums and gymnasiums in the Moscow region. The article presents an approbation of the results of objective assessment of the quality of training of physics teachers, based on the results of research activities of students, achievements in all-Russian educational competitions and participation in the International competition "WorldSkills". Interaction of the Department and faculty is carried out with secondary schools, lyceums, gymnasiums, higher educational institutions in the framework of scientific and practical, scientific and methodological conferences for schoolchildren, students, teachers and teaching staff. The article highlights the factors that affect the success of training physics teachers, examples of which are: fruitful interaction with the external environment, both directly with the personal participation of representatives of educational institutions, and through networking; continuous monitoring to assess the success of teaching activities by graduates of this field of training; improving the quality of student recruitment, through the annual holding of physics Olympiads for students of secondary schools in the Moscow region.
EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation

As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment. Despite its tremendous popularity, the security of LDA has rarely been studied. This poses severe risks to security-critical tasks such as sentiment analysis and peer-reviewer assignment that are based on LDA. In this paper, we are interested in knowing whether LDA models are vulnerable to adversarial perturbations of benign document examples during inference time. We formalize the evasion attack to LDA models as an optimization problem and prove it to be NP-hard. We then propose a novel and efficient algorithm, EvaLDA to solve it. We show the effectiveness of EvaLDA via extensive empirical evaluations. For instance, in the NIPS dataset, EvaLDA can averagely promote the rank of a target topic from 10 to around 7 by only replacing 1% of the words with similar words in a victim document. Our work provides significant insights into the power and limitations of evasion attacks to LDA models.
KOSMOS: Knowledge-graph Oriented Social media and Mainstream media Overview System

We introduce KOSMOS, a knowledge retrieval system based on the constructed knowledge graph of social media and mainstream media documents. The system first identifies key events from the documents at each time frame through clustering, extracting a document to represent each cluster, then describing the document in terms of 5W1H (Who, What, When, Where, Why, How). The event centric knowledge graph is enhanced by relation triplets and entity disambiguation from the representative document. This knowledge retrieval is supported by a web interface that presents a graph visualisation of related nodes and relevant articles based on a user query. The interface facilitates understanding relationships between events reported in mainstream and social media journalism through the KOSMOS information extraction pipeline, which is valuable to understand media slant and public opinions. Finally, we explore a use case in extracting events and relations from documents to understand the media and community's view to the 2020 COVID19 pandemic.
The secondary dispersion halos of platinum group elements and rare elements in rocks of the Vysotsky ore occurrence, Svetloborsky massif, Middle Urals

Geological searches for secondary dispersion halos have important practical search value in areas with lack of exposure.In weakly exposed areas with a loose cover thick ness of 0.2-10 m, testing is carried out using secondary dispersion halos -i.e. on the surface of eluvium, deluvium or the bottoms of a loose incision.The Svetloborsky dunite clinopyroxenite zonal massif, like other massifs of the Ural Platinum belt, is characterized by a rather closed inaccessible territory, partially covered by a cover of quaternary sediments with a thickness of up to 10 m, in some areas -up to 25 m.Also, almost complete absence of chromite schlieres is characteristic of the Svetloborsky massif, which are usually a clear visual sign of the presence of platinum-metal mineralization.Therefore, when conducting search operations within the massif, in addition to direct ore testing of bedrock and weathering crust, a lithogeochemical survey was carried out and the contents of platinum, palladium, gold and rare elements in secondary dispersion halos were determined.
Processing of Semantic Relationships in a Monolingual Dictionary for Creation of Machine-Readable Resources

Dictionaries are a useful and accessible resource which exists practically in all languages around the world. Their content is structured following very well-known rules, which interconnect their entries in different kind of semantic relationships. In this paper, different methods are applied to process verb definitions to identify semantic relationships among the entries of a monolingual dictionary. Synonyms, hyponymies/ hypernymies and simple collocations are the semantic content we could process for creating machine-readable semantic resources. We think that this kind of processing could be useful for less-resourced languages.
Variable-Shot Adaptation for Online Meta-Learning

Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks. However, in many real world settings, it is more natural to view the problem as one of minimizing the total amount of supervision --- both the number of examples needed to learn a new task and the amount of data needed for meta-learning. Such a formulation can be studied in a sequential learning setting, where tasks are presented in sequence. When studying meta-learning in this online setting, a critical question arises: can meta-learning improve over the sample complexity and regret of standard empirical risk minimization methods, when considering both meta-training and adaptation together? The answer is particularly non-obvious for meta-learning algorithms with complex bi-level optimizations that may demand large amounts of meta-training data. To answer this question, we extend previous meta-learning algorithms to handle the variable-shot settings that naturally arise in sequential learning: from many-shot learning at the start, to zero-shot learning towards the end. On sequential learning problems, we find that meta-learning solves the full task set with fewer overall labels and achieves greater cumulative performance, compared to standard supervised methods. These results suggest that meta-learning is an important ingredient for building learning systems that continuously learn and improve over a sequence of problems.
NEW TUTORS’ PERSPECTIVE ON MOODLE PERFORMANCE: A CASE STUDY IN UNIVERSITAS TERBUKA

The purpose of this study was to investigate the perspective of new tutors using Moodle at the Universitas Terbutka (UT).This study uses a qualitative method with a case study design. The research informants were seven new UT lecturers with different educational backgrounds. Researchers used a questionnaire to investigate new tutor perspectives on Moodle's performance. The questionnaire is closed which consists of 8 questions. In the questionnaire, the researcher modified the questions by classifying the range of responses with 5 scales: strongly agree, agree, doubt, disagree, strongly disagree. In applying the questionnaire, the researcher took two steps. First, researchers distributed questionnaires to study participants. Second, researchers began to extract, analyze, and conclude the data that had been collected using a questionnaire. In extracting data from the questionnaire, the researcher developed an analytical method based on Dey's theory which consists of describing phenomena, classifying them, and seeing how our concepts are related.The results reveal that there are three main things in relation to the new tutor's perspective: friendly to use, simple modification features, and convenient assessment tools. The results of this study are a major concern for tutors because this aspect is an important part that can support teaching and learning activities when using Moodle.
Sensor‐based activity and state recognition in dementia patients in stationary care as basis for situation‐aware assistive devices

Abstract Background Wearable sensors to monitor activity are easily available nowadays. Intelligent assistive technologies bear potential to offer innovative solutions that increase safety and facilitate independent activities in persons with dementia (PwD). It is important to incorporate knowledge about user‘s current state (e.g. cognitive and physical abilities) and situation to generate suitable interventions and provide meaningful assistance for PwD. Method Within the “SAMi”‐project, we set up an (ongoing) field study with PwD living in a nursing home. Participants wear smartwatches to measure activity (accelerometry) and enable indoor positioning via Bluetooth. In parallel, subjects' behavior is annotated in real time by trained observers using an elaborated scheme that contains activity, posture and place of the person, as well as disorientation, falls or request for help. The annotation scheme and possible intervention domains were derived from interviews with different groups of stakeholders. With the field study, we aim to identify in real time if a patient is at need of support and to select the most promising intervention in a given situation. Therefore, we plan to train algorithms on the synchronized sensor‐data and examine the accuracy of situation‐classification. Result To date, we included nine PwD in the age of 76‐96 years in moderate to severe stages of dementia (MMSE 5‐18). We gained &gt;183 hours of observational data, accompanied by sensor data. We observed 134 events of disorientation, 95 events of caregiver support but only 3 requests for help. Ca. 79% of the annotated time patients wore the smartwatch. The majority of patients accepted the device (despite advanced cognitive decline). Setting up the technical infrastructure for the indoor positioning was challenging, for example due to restricted access to power points. Conclusion Analysis of data from wearable and ambient sensors bears potential to build a situation‐aware assistive device and is feasible to acquire in the setting of dementia patients living in nursing homes. A large number of situations where need for help became obvious was matched only by very few instances of active help seeking by the patients. This emphasizes the necessity for automated sensor‐based recognition of such situations.
Optimasi Sistem Irigasi Lahan Tada Hujan menggunakan Algoritma Ant Colony Optimization berbasis Tenaga Surya

Indonesia is a country that has vast agriculture and has a majority source of income as farmer. The agriculture area still cannot be optimized considering that still a lot of dry agriculture land and have not got good irrigation system, several problems that cause are still a lot of dry agriculture which higher position of irrigation system available and there are limited land so that if irrigation system built or DAM then operational coast outweigh of impact on the land, another problem occurs is limited ability of community for built independent and modern irrigation system so that assistance is needed and technology that can be utilized as resource for built independent and modern irrigation system. One of potential resource in Indonesia is energy of sun which can be converted to electric power, this is energy very much in Indonesia, so that can be using as energy to pump water from springs to be distributed to dry agriculture land. To be able to reduce human power and monitoring this paper using Ant Colony Optimization as computation system. This algorithm used to optimized water distribution evenly on dry agriculture land which is adapted to the soil conditions.
New Distance Measurements for Image SimilarityNew Distance Measurements for Image Similarity

New measures have been proposed for assessing the similarity of gray-level images. The famous structuralsimilarity index measurement (SSIM) has been designed using statistical approach that fails with highnoise (lowPSNR). The two proposed measures have been suggested, the first one depend on Manhattandistance and standard division, this measure combined from two parts: the first part depend on theManhattan distance which is used in geometric features the second part is based on statistical feature. Thesecond measure utilized the modification of Euclidian distant. The two proposed similarity measures areoutcome for human face. The new measures outperform the classical SSIM in detecting image similarityat low PSNR, with significant difference in performance. The results were (95.3% for the firstmeasurement) and (99.2% for the second measurement). We used database Face94
Proactive Interaction Framework for Intelligent Social Receptionist Robots

Proactive human-robot interaction (HRI) allows the receptionist robots to actively greet people and offer services based on vision, which has been found to improve acceptability and customer satisfaction. Existing approaches are either based on multi-stage decision processes or based on end-to-end decision models. However, the rule-based approaches require sedulous expert efforts and only handle minimal pre-defined scenarios. On the other hand, existing works with end-to-end models are limited to very general greetings or few behavior patterns (typically less than 10). To address those challenges, we propose a new end-to-end framework, the TransFormer with Visual Tokens for Human-Robot Interaction (TFVT-HRI). The proposed framework extracts visual tokens of relative objects from an RGB camera first. To ensure the correct interpretation of the scenario, a transformer decision model is then employed to process the visual tokens, which is augmented with the temporal and spatial information. It predicts the appropriate action to take in each scenario and identifies the right target. Our data is collected from an in-service receptionist robot in an office building, which is then annotated by experts for appropriate proactive behavior. The action set includes 1000+ diverse patterns by combining language, emoji expression, and body motions. We compare our model with other SOTA end-to-end models on both offline test sets and online user experiments in realistic office building environments to validate this framework. It is demonstrated that the decision model achieves SOTA performance in action triggering and selection, resulting in more humanness and intelligence when compared with the previous reactive reception policies.
Sistem Diagnosis Penyakit Hipokalemia menggunakan Gabungan Metode Forward Chaining dan Certainty Factor

Hypokalemia can be a serious problem in humans which results in paralysis and causes death. This is caused by sufferers who are late in analyzing the symptoms and diseases. To facilitate. To speed up the process of diagnosis of these symptoms, an expert system can be done. This study aims for an initial diagnosis based on the symptoms experienced. This expert system can be used as a tool in making decisions. This study uses the method Forward Chaining and Certainty Factor (CF) to overcome the uncertainty of hypokalemia. The results of this expert system can help early diagnose hypokalemia, so that the disease can be treated as early as possible so as not to cause the disease to get worse in the future.Keywords:Forward Chaining, Diagnosis, Hypokalemia, Certainty Factor, Expert System.
Invariant Points on the Screening Plane: a Geometric Definition of the Likelihood Ratio (LR+)

From the fundamental theorem of screening we obtain the following mathematical relationship relaying the positive predictive value ($\rho(\phi)$) of a screening test to the prevalence ($\phi$) of disease: $\displaystyle\lim_{\varepsilon \to 2}{\displaystyle \int_{0}^{1}}{\rho(\phi)d\phi} = 1$ where $\varepsilon$ is equal to the sum of the sensitivity ($a$) and specificity ($b$) parameters of the test in question. However, identical values of $\varepsilon$ may yield different shapes of the screening curve since $\varepsilon$ does not respect traditional commutative properties given the invariant points of the screening plane. In order to distinguish between two screening curves with identical $\varepsilon$ values, we make use of the angle $\beta$ created by the line between the origin invariant and the prevalence threshold $\phi_e$ to make a right-angle triangle. In this work, we provide derivation of this angle $\beta$ and show its value to be: $\beta = arctan(\Psi) = arctan\left(\sqrt{\frac{1-b}{a}}\right)$ From the above relationship, we derive the positive likelihood ratio (LR+), defined as the likelihood of a test result in patients with the disease divided by the likelihood of the test result in patients without the disease, as follows: $LR+ = \frac{a}{1-b} = cot^2{(\beta)}$ Using the concepts of the prevalence threshold and the invariant points on the screening plane, the work herein presented provides a new geometric definition of the positive likelihood ratio (LR+) throughout the prevalence spectrum and describes a formal measure to compare the performance of two screening tests whose $\varepsilon$ are equal.
Paragraph and Its Development Pattern On The Essay Writing Of Elementary School Students

This study aimed to describe the quality of the paragraph and its development pattern in the essay writing of elementary school (Sekolah Dasar – SD) students in Jambi City (Kota Jambi – KJ) (SD-KJ). This study utilized a "quantitative descriptive" design. The population was all essays written by the fifth-grade students of SD-KJ. The sample of this research was 35 paragraphs from the essay writing of the fifth-grade students of SD-KJ. The data analysis steps were searching for frequency distribution of paragraph quality and development pattern; grouping the paragraph frequency distribution according to their quality; counting the percentage of each paragraph quality and type of development pattern; and presenting the results. Regarding to paragraph quality, the results are: (1) paragraph completeness, good (5.71%), poor (68.57%), and bad (25.70%); (2) paragraph unity, good (34.58%); poor (51.42%); and bad (14.28%); (3) paragraph order, good (28.57%); poor (51.42%); and bad (20%); and (4) paragraph coherence, good (54.28%); poor (34.28%); and bad (11.42%). Reffering to the implementation of the paragraph development pattern, the results are paragraph development pattern of comparison and contradiction: 20%, analogy: 5.71%, giving examples: 8.57%, cause and effect: 48.57%; general-specific or specific-general: 11.42%, and classification: 5.71%.
Critical Analysis of Strategies Towards Creating an Adequate Level of Awareness on Cybercrime among the Youth in Gauteng Province

This study aims to determine any measures taken by the South African Police Service (SAPS) to create awareness about cybercrime among the youth in the selected policing areas in the Gauteng province. A qualitative research method was applied using semi-structured interviews to find the views of participants, of measures if any, to create youths’ awareness in the area of cybercrime. A total of 37 participants comprised of 29 youths aged between 19 and 35 years, including an additional eight participants from the SAPS Crime Intelligence Unit who agreed to participate. Among these participants, there were 18 females and 19 males. The findings highlighted that there was a lack of awareness on the measures taken by the SAPS in educating the youth about the risks associated with cybercrime. The other challenges highlighted by the SAPS were a lack of capacity, resources, and training to increase the technical skills amongst the SAPS members to work effectively on cybercrime-related challenges, lack of collaboration among role players to respond adequately to cybercrime, and ineffective implementation of cybercrime policies, therefore, there was a lack of cybercrime-related campaigns. Based on the findings, five themes were explored in this study, including a lack of capacity, resources, and training to increase the technical skills amongst the SAPS members to work effectively on cybercrime-related challenges, lack of collaboration among role players to respond adequately to cybercrime and ineffective implementation of cybercrime policies. The recommendations are provided as a potential step towards tailoring education packages and awareness programs to ensure at-risk groups are equipped with actionable mechanisms to protect themselves against cybercrimes.
Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets.

In this paper, we present an experiment on using deep learning and transfer learning techniques for emotion analysis in tweets and suggest a method to interpret our deep learning models. The proposed approach for emotion analysis combines a Long Short Term Memory (LSTM) network with a Convolutional Neural Network (CNN). Then we extend this approach for emotion intensity prediction using transfer learning technique. Furthermore, we propose a technique to visualize the importance of each word in a tweet to get a better understanding of the model. Experimentally, we show in our analysis that the proposed models outperform the state-of-the-art in emotion classification while maintaining competitive results in predicting emotion intensity.
Using Interdependencies for the Prioritization and Reprioritization of Requirements in Incremental Development

There is a growing trend to develop and deliver the software in an incremental manner; to achieve greater consistency in the developed software and better customer satisfaction during the requirement engineering process.Some of the developed increments in the incremental model will be delivered to consumers and run in their environments, so a set of these requirements are evaluated, introduced, and delivered as the first increment.Other requirements are delivered as the next step and so on for the next increment.The priority of requirements plays an important role in each increment, but it is precluded by the interdependences between the requirements and resources constraints.Therefore, this paper introduces a model for requirements prioritization and a reprioritization based on these important factors.The first one is the requirement interdependencies which are described as a hybrid approach of tractability list and directed acyclic graph, and the second factor is the constraints of the requirements resources that are used based on the queuing theory for requirements reprioritization.In order to achieve this, two algorithms namely; Priority Dependency Graph (PDG) and Resources Constraints Reprioritization (RCR), were proposed with a linear time complexity and implemented via a case study.