Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Explainability via Model Transforms (Student Abstract)

Understanding the emerging behaviors of reinforcement learning agents may be difficult because such agents are often trained using highly complex and expressive models. In recent years, most approaches developed for explaining agent behaviors rely on domain knowledge or on an analysis of the agent’s learned policy. For some domains, relevant knowledge may not be available or may be insufficient for producing meaningful explanations. We suggest using formal model abstractions and transforms, previously used mainly for expediting the search for optimal policies, to automatically explain discrepancies that may arise between the behavior of an agent and the behavior that is anticipated by an observer. We formally define this problem of Reinforcement Learning Policy Explanation(RLPE), suggest a class of transforms which can be used for explaining emergent behaviors, and suggest meth-ods for searching efficiently for an explanation. We demonstrate the approach on standard benchmarks.
Perancangan Multimedia Virtual Tour Sebagai Media Informasi Mahasiswa Universitas Nusa Mandiri

In line with the development of virtual reality technology which is very, very fast, based on the increase in technology, a new innovation application is formed, namely virtual tours. With virtual tours we can explore a location to get information and images about the place using only a laptop or cellphone, information kiosk or other electronic media, without having to bother going to the actual place. Currently, the means from the application of information about the rooms and facilities available at the University of Nusa Mandiri (UNM) which is one of the campuses in the East Jakarta area, namely Jatiwaringin, is still less communicative because it does not describe the place. Based on this problem, by creating a virtual tour application that was developed using a first person controller, it is hoped that it can provide information about the location of the rooms and facilities at the University of Nusa Mandiri (UNM) and provide a clearer picture of the place, without having to walk down every floor there is. on the campus building, by looking at this application, students are expected to understand the position and location of its existence according to their needs.
The Bullets Puzzle: A Paper-and-Pencil Minesweeper

In this paper, we introduce a technique for AI generation of the Bullets puzzle, a paper-and-pencil variant of Minesweeper. Whereas traditional Minesweeper can be lost due to the need to guess mine or non-mine positions, our puzzle is fully deducible from a minimal clue set. Puzzle generation is based on analysis and optimization of solutions from a human-like reasoning engine that classifies types of deductions. Additionally, we provide insights to subjective puzzle quality, minimal clue sampling trade-offs, and optimal bullet density.
AUTOMATED MAMMAL LOCALIZATION AND IDENTIFICATION IN CAMERA TRAP IMAGES FOR THE NORTHEASTERN UNITED STATES

1. Camera traps are popular for monitoring animal populations and communities, primarily because they eliminate physical handling of animals. However, image acquisition typically outpaces information extraction. Most deep-learning based animal classifiers do not localize animals, limiting their applicability. Existing networks that localize animals have relatively high training data and hardware requirements. 2. To reduce the hardware and training data requirements, we extended the the Machine Learning for Wildlife Image Classification network (MLWIC2) to a Faster R-CNN. MLWIC2 is currently the most accurate wildlife classification network, and also the shallowest at 18 layers. We compared our model’s performance at object localization, species identification, and deployment speed to the performance of a generically pre-trained 50-layer Faster R-CNN to determine a) relative importance of task similarity in pre-training vs. backbone depth, b) whether additionally finetuning the backbones during training is advantageous c) whether the Faster R-CNN architecture benefits from incorporating the feature pyramid network (FPN) and cascading pyramid network (CPN) modules, and d) how backbone depth and the additional modules affect deployment speeds. 3. We found that the deeper network provides a slight advantage for classification accuracy, while the shallower network with higher task similarity produces a slight advantage for object localization. The additional modules provided dramatic gains for the 18 layer backbone for both classification and localization. On a NVIDIA 1080-ti gpu, the 18-layer backbone trains ~ 30% faster than the 50-layer backbone. In deployment the 18-layer backbone is 2.5x faster than the 50-layer backbone, and 9.4x faster than Megadetector. These results show that backbone network task similarity, paired with the FPN and CPN modules, can substitute for depth, which improves deployment speeds. Our model is suitable for modest hardware and for integration into more complex pipelines. These are important steps towards the automation of data acquisition from camera trap images.
Bridging the Gap between Expression and Scene Text for Referring Expression Comprehension (Student Abstract)

Referring expression comprehension aims at grounding the object in an image referred to by the expression. Scene text that serves as an identifier has a natural advantage in referring to objects. However, existing methods only consider the text in the expression, but ignore the text in the image, leading to a mismatch. In this paper, we propose a novel model that can recognize the scene text. We assign the extracted scene text to its corresponding visual region and ground the target object guided by expression. Experimental results on two benchmarks demonstrate the effectiveness of our model.
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks

Amongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientific community developed strategies to order the examples according to their estimated complexity, to distil knowledge from larger networks, or to exploit the principles behind adversarial machine learning. A different idea has been recently proposed, named Friendly Training, which consists in altering the input data by adding an automatically estimated perturbation, with the goal of facilitating the learning process of a neural classifier. The transformation progressively fades-out as long as training proceeds, until it completely vanishes. In this work we revisit and extend this idea, introducing a radically different and novel approach inspired by the effectiveness of neural generators in the context of Adversarial Machine Learning. We propose an auxiliary multi-layer network that is responsible of altering the input data to make them easier to be handled by the classifier at the current stage of the training procedure. The auxiliary network is trained jointly with the neural classifier, thus intrinsically increasing the 'depth' of the classifier, and it is expected to spot general regularities in the data alteration process. The effect of the auxiliary network is progressively reduced up to the end of training, when it is fully dropped and the classifier is deployed for applications. We refer to this approach as Neural Friendly Training. An extended experimental procedure involving several datasets and different neural architectures shows that Neural Friendly Training overcomes the originally proposed Friendly Training technique, improving the generalization of the classifier, especially in the case of noisy data.
Color and territory. Empathy as a tool to enable international collaborative design projects.

Empathy, Aesthetic, Collaboration, Participatory design
BLOCKCHAIN TECHNOLOGY AND ITS APPLICATION IN EDUCATIONAL PROCESS//ACTUAL PROBLEMS OF APPLIED SCIENCES JOURNAL WORLD ISSN 2733-3817, no. 1(18) (April 30, 2022)/ Chief Editor D. Sidorkin /Open European Academy of Public Sciences : Tallinn, Estonia, 2022 - P. 33-37.

The article discusses possible of using blockchain technology in education. The authors suggest using the technology not only for storing documents, but also to solve the problem in this way: the direct application of blockchain in various components of the educational process and the addition of disciplines devoted to blockchain to the curriculum. As a basis for the development of educational materials on possible blockchain courses, it is proposed to use the Ethereum platform.
“Online Learning” untuk Meningkatkan Kemampuan Bahasa Inggris Siswa di Masa Pandemi Covid-19

This community service activity is included to the community partnership program. The targets are people who are not economically productive, they are the students of elementary school (SD), junior high school (SMP), and senior high school (SMA) in Pondok Babadan Baru, RT.01, RW. 13, Beji, East Ungaran. The activities carried out in this program are handling problems in learning English for students during the COVID-19 pandemic. According to most students, English subject is a difficult subject. To make it being easy, it needs a practice when we learn a language. In fact, most students do not use English in their daily life. One of the reasons is because of unsupportive environment, such as parents who cannot speak English. Another problem is in the learning system used in the pandemic era that is online learning. Even though students are familiar with technology, the technology used for learning is still foreign to them. Therefore, this community service activity will help students increasing their motivation in learning English during the pandemic so that their ability in reading, listening, speaking and writing using English can improve. The method of service activities applied is in the form of assistance in learning English through Google Meet application. The result of this service activity is increased students’ motivation in learning English during the pandemic which makes students to be confident in using English. The results of this activity are expected to be used as guidelines in dealing with children's problems in learning during the COVID-19 pandemic.
Pemanfaatan Kolam Tampung Sebagai Upaya Mitigasi Banjir Kawasan Pada Sub Sistem Medokan Ayu Kota Surabaya

Medokan Ayu is an area from Surabaya Drainage Master Plan 2018-2038, located in Rayon Jambangan and reported to be inundated. This caused by overcapacity of the existing drainage channel to accommodate the 2-year flood discharge. From 134 channels observed, 27 channels overflowed, and 20 out of 27 canals are located in residential areas so the inundation hampered the local citizens activities. For a discharge period of 10 years, overflow occurs in the upstream and the middle of the main channel due to the slope of the channel is too flat. Since it is not possible to change the base elevation upstream or downstream, a reservoir was created as a solution by utilizing the government's inventory area located near the downstream of the channel. This reservoir will have pump that stream the water back to downstream channel. So, with this flood control system, Medokan Ayu area can be free from inundation.
Filtering Misleading Repair Log Labels to Improve Predictive Maintenance Models

One of the main challenges for predictive maintenance in real applications is the quality of the data, especially the labels. In this paper, we propose a methodology to filter out the misleading labels that harm the performance of Machine Learning models. Ideally, predictive maintenance would be based on the information of when a fault has occurred in a machine and what specific type of fault it was. Then, we could train machine learning models to identify the symptoms of such fault before it leads to a breakdown. However, in many industrial applications, this information is not available. Instead, we approximate it using a log of component replacements, usually coming from the sales or maintenance departments. The repair history provides reliable labels for fault prediction models only if the replaced component was indeed faulty, with symptoms captured by collected data, and it was going to lead to a breakdown. However, very often, at least for complex equipment, this assumption does not hold. Models trained using unreliable labels will then, necessarily, fail. We demonstrate that filtering misleading labels leads to improved results. Our central claim is that the same fault, happening several times, should have similar symptoms in the data; thus, we can train a model to predict them. On the contrary, replacements of the same component that do not exhibit similar symptoms will be confusing and harm the ML models. Therefore, we aim to filter the maintenance operations, keeping only those that can be used to predict each other. Suppose we can train a successful model using the data before a component replacement to predict another component replacement. In that case, those maintenance operations must be motivated by the same, or a very similar, type of fault. We test this approach on a real scenario using data from a fleet of sterilizers deployed in hospitals. The data includes sensor readings from the machines describing their operations and the service logs indicating the replacement of components when the manufacturing company performs the service. Since sterilizers are complex machines consisting of many components and systems interacting with each other, there is the possibility of faults happening simultaneously.
Predicting Economic Activities Trends for Saudi and Non- Saudi Establishments in Saudi Arabia by Using Time Series Forecasting and Deep Learning Models

Data related to economic and stock market information are widely used for representing non- stationary time series. The economy goes through different phases, from recession to growth. This project focused on models predicting total added establishments in each economic activity. It aimed to predict the number of establishments after the impact of covid- 19 and estimate the accuracy of the prediction models. Prediction models used for inexpensive, quick evaluation of the added number of existing establishments leading to business risk mitigation. The research was conducted on data extracted from ministry of human resource and social development database. Therefore, the carried- out analysis highlights four activities under examination to capture the significance changes. These activities are construction, health, accommodation and information technology. In this study, five time series models are selected and applied to the business data describing expansion and recession. After model performance evaluation, deep learning models with respect to sliding window approach to predict short term values are recommended and perform better than traditional models. LSTM outperforms the other models in health with 18.22 RMSE and 90.65 RMSE for the information technology. DNN with two hidden layers got the best RMSE for accommodation and construction activities which is equal to 183.98 and 1387.78 respectively. Such work indicates that predicting overall added establishments may assist investors and companies in making economic choices, such as when to invest, increase, or reduce production.
Teacher new professionalism in the light of the personality polysubjectivity development

Introduction. The relevance of the study is due to the high requirements of the society on the professionalism of a modern teacher who has to perform the activity under flux and ambiguity conditions, to solve new and occasional tasks, to design and implement personal and professional routes in accordance with the changing professional challenges and real-life situations. The research is aimed to perceive the essence of the concept “new teacher professionalism” in the context of the diversity of teacher professional trajectories and in the aspect of personality polysubjectivity. The empirical problem is to diagnose the dominant professional trajectories of future teachers at the final stage of the university training course (in the “Self- real” and “Self-ideal” spheres). Materials and methods. 5th-year students (186 persons) of Belgorod State National Research University took part in the survey. The study is based on analyses of students' reflective judgments quality data concerning new professionalism and poly-subjectivity phenomena, methods of mathematical statistics (Pearson symmetric correlation coefficient). Results. The article presents the features of teacher professionalism associated with the process of a teacher polysubjectivity development as an ability to take an active subjective position in multidimensional professional sphere. The results showed the dominant traditional professional trajectories “subject teacher” (average point – 4.07), and “teacher-educator” (4.00) in the “Self-real” sphere of future teachers, as well as the “digital teacher” (3.86) trajectory due to the transition of the university education process to distance learning format in the pandemic conditions. The trajectories “teacher-mentor (coach)” (average point – 4.65), “teacher-educator” (4.53), “teacher-methodologist” (4.51) and “media-teacher” (4.47) are specific for future teachers in the “Self-ideal” sphere. The professional trajectory “digital teacher” is mainly correlated to the contradiction “Self-real” – “Self-ideal” (r = 0.758, p=0.05). Conclusion. Teacher polysubjectivity integrates all the levels of teacher formation and development process, subject-field-driven features, professional values; it allows the teacher creating personal professional trajectory, which is the basis of new teacher professionalism.
Combating Collusion Rings Is Hard but Possible

A recent report of Littmann published in the Communications of the ACM outlines the existence and the fatal impact of collusion rings in academic peer reviewing. We introduce and analyze the problem Cycle-Free Reviewing that aims at finding a review assignment without the following kind of collusion ring: A sequence of reviewers each reviewing a paper authored by the next reviewer in the sequence (with the last reviewer reviewing a paper of the first), thus creating a review cycle where each reviewer gives favorable reviews. As a result, all papers in that cycle have a high chance of acceptance independent of their respective scientific merit. We observe that review assignments computed using a standard Linear Programming approach typically admit many short review cycles. On the negative side, we show that Cycle-Free Reviewing is NP-hard in various restricted cases (i.e., when every author is qualified to review all papers and one wants to prevent that authors review each other's or their own papers or when every author has only one paper and is only qualified to review few papers). On the positive side, among others, we show that, in some realistic settings, an assignment without any review cycles of small length always exists. This result also gives rise to an efficient heuristic for computing (weighted) cycle-free review assignments, which we show to be of excellent quality in practice.
GENDER DIFFERENCES IN THE STRATEGY INVENTORY FOR LANGUAGE LEARNING (SILL)

ABSTRACTThis research investigates the learning strategies used by English as a foreign language (EFL) students and gender differences in the strategy inventory for language learning (SILL) in Indonesia. To gather the data, this study employed quantitative descriptive methods with a Google form-based questionnaire involving 110 college students, and the data were analysed using SPSS 25. The fifty items of the questionnaire were divided into six strategies: memory strategy, cognitive strategy, metacognitive strategy, compensation strategy, emotion strategy, and social strategy. Findings of the study indicate that the students use all the strategies in learning English, and all these strategies are in a high level of use except for memory strategy which is in a moderate level. Gender differences data show that most male students preferred the social strategy (36%), whereas most female students preferred metacognitive strategy (56.47%) suggesting that male and female students have different learning styles. The results of this research shed light on the importance of raising teachers’ awareness of their students’ diversity in learning strategies. Thus, teachers should improve their repertoire of teaching techniques and strategies to meet students’ different learning styles in the classroom. ABSTRAKPenelitian ini menyelidiki strategi pembelajaran yang digunakan oleh siswa bahasa Inggris sebagai bahasa asing (EFL) dan perbedaan gender dalam inventarisasi strategi untuk pembelajaran bahasa (SILL) di Indonesia. Untuk mengumpulkan data, penelitian ini menggunakan metode kuantitatif dengan kuesioner berbasis Google form yang melibatkan 110 mahasiswa, dan data dianalisis menggunakan SPSS 25. Lima puluh item kuesioner dibagi menjadi enam strategi: strategi memori, strategi kognitif, strategi metakognitif. strategi, strategi kompensasi, strategi emosi, dan strategi sosial. Temuan penelitian menunjukkan bahwa siswa menggunakan semua strategi dalam belajar bahasa Inggris, dan semua jenis strategi berada di tingkat tinggi kecuali strategi memori yang berada di tingkat sedang. Data perbedaan gender menunjukkan bahwa sebagian besar siswa laki-laki lebih menyukai strategi sosial (36%), sedangkan sebagian besar siswa perempuan lebih menyukai strategi metakognitif (56,47%) menunjukkan bahwa siswa laki-laki dan perempuan memiliki gaya belajar yang berbeda secara signifikan. Hasil penelitian ini menyoroti pentingnya meningkatkan kesadaran guru tentang keragaman siswa mereka dalam strategi pembelajaran. Dengan demikian, guru harus meningkatkan repertoar teknik dan strategi pengajaran mereka untuk memenuhi gaya belajar siswa yang berbeda di kelas.
Using Conditional Independence for Belief Revision

We present an approach to incorporating qualitative assertions of conditional irrelevance into belief revision, in order to address the limitations of existing work which considers only unconditional irrelevance. These assertions serve to enforce the requirement of minimal change to existing beliefs, while also suggesting a route to reducing the computational cost of belief revision by excluding irrelevant beliefs from consideration. In our approach, a knowledge engineer specifies a collection of multivalued dependencies that encode domain-dependent assertions of conditional irrelevance in the knowledge base. We consider these as capturing properties of the underlying domain which should be taken into account during belief revision. We introduce two related notions of what it means for a multivalued dependency to be taken into account by a belief revision operator: partial and full compliance. We provide characterisations of partially and fully compliant belief revision operators in terms of semantic conditions on their associated faithful rankings. Using these characterisations, we show that the constraints for partially and fully compliant belief revision operators are compatible with the AGM postulates. Finally, we compare our approach to existing work on unconditional irrelevance in belief revision.
Modeling Abstract Algebra as an OWL Ontology (Student Abstract)

Description logic ontologies serve to model classifications and structural relationships, and to represent and reason about domain knowledge. Modeling the basic classification of abstract algebraic structures as an ontology demonstrates the difficulties presented by their logical semantics, and shed light on the limitations to accurately model further topics in algebra and related mathematical domains.