Reviewerly/exHarmony huggingface dataset:
View Based Review Exploration

In an e-commerce site, reviews help augment basic product descriptions with detailed user experiences and opinions on the product. These reviews greatly influence the purchase decisions of oncoming customers. Since these reviews are haphazardly presented, they call for befitting review exploration techniques. We propose a novel view based review exploration system that can be used to control the depth and content of the retrieved reviews. We propose three views depending on the depth personified by them: the Bird-eye view, the Unidirectional view, and the Microscopic view. The views present a ranked list of relevant reviews that meet the view criteria. The proposed view based exploration recorded precision as high as 0.85.
The True Online Continuous Learning Automation (TOCLA) in a continuous control benchmarking of actor-critic algorithms

Reinforcement learning problems are often discretised, use linear function approximation, or perform batch updates. However, many applications that can benefit from reinforcement learning contain continuous variables and are inherently non-linear, for example, the control of aerospace or maritime robotic vehicles. Recent work has brought focus onto online temporal difference methods, specifically for using non-linear function approximation. In this paper, we evaluate the Forward Actor-Critic against the regular Actor-Critic, and Continuous Actor-Critic Learning Automation. We also propose and evaluate a new algorithm called True Online Continuous Learning Automation (TOCLA) which combines these two approaches. The chosen benchmark problem was the Mountain Car Continuous-vO environment from OpenAI Gym, which represents a further step in complexity over the benchmark used to test the Forward Actor Critic in previous works. Our results demonstrate the superiority of TOCLA in terms of its sensitivity to hyper-parameter selection compared with the Forward Actor Critic, Continuous Actor Critic Learning Automation, and Actor Critic algorithms.
Default Modal Systems as Algebraic Updates

Default Logic refers to a family of formalisms designed to carry out non-monotonic reasoning over a monotonic logic (in general, Classical First-Order or Propositional Logic). Traditionally, default logics have been defined and dealt with via syntactic consequence relations. Here, we introduce a family of default logics defined over modal logics. First, we present these default logics syntactically. Then, we elaborate on an algebraic counterpart. We do the latter by extending the notion of a modal algebra to acommodate for the main elements of default logics: defaults and extensions. Our algebraic treatment of default logics concludes with an algebraic completeness result. To our knowledge, our approach is novel, and it lays the groundwork for studying default logics from a dynamic logic perspective.
Fault Impact Estimation for Lightweight Fault Detection in Image Filtering

Classical redundancy-based fault detection techniques, such as Duplication with Comparison (DWC), rely on replicating the computation and comparing the replicas&#x2019; output at a bit-wise granularity. In many application environments these costs are prohibitive, especially when applications are characterized by an intrinsic level of tolerance. This article presents a novel fault-detection approach for the specific context of image filtering. Peculiarity of the proposed approach is that it estimates the impact of the fault on the processed output, in order to determine whether the image is usable or should be re-processed. To limit overheads, the proposed solution exploits Approximate Computing (AC), allowing the definition of disciplined AC strategies to trade-off between accuracy and costs. Core of our solution is the successful combination of Image Quality Assessment metrics and Machine Learning models to assess the visual impact of the fault in a lightweight manner. Extensive experimental campaigns demonstrate the effectiveness of the solution, achieving achieving a reduction in terms of execution time up to 44 percent with respect to the classical DWC, with a fault detection precision ranging from 94.58 to 96.70 percent, and recall ranging from 88.2 to 97.8 percent, depending on the adopted level of approximation.
Application of fuzzy soft sets to analyze the statistical strength of S-boxes

For the evaluation of the substitution boxes, the majority logic criterion is used to analyze the statistical strength of the existing substitution boxes. The main objective of this paper is to make a decision on the analysis and selection of the most appropriate S-box based on a fuzzy soft-aggregation operator. Instead of the usual practice in which a single parameter is considered, we are considering several parameters that will definitely give us a comprehensive analysis of the S-boxes.
Use Information You Have Never Observed Together: Data Fusion as a Major Step Towards Realistic Test Scenarios

Scenario-based testing is a major pillar in the development and effectiveness assessment of automated driving systems. Thereby, test scenarios address different information layers and situations (normal driving, critical situations and accidents) by using different databases. However, the systematic combination of accident and / or normal driving databases into new synthetic databases can help to obtain scenarios that are as realistic as possible. This paper shows how statistical matching (SM) can be applied to fuse different categorial accident and traffic observation databases. Hereby, the fusion is demonstrated in two use cases, each featuring several fusion methods. In use case 1, a synthetic database was generated out of two accident data samples, whereby 78.7% of the original values could be estimated correctly by a random forest classifier. The same fusion using distance-hot-deck reproduced only 67% of the original values, but better preserved the marginal distributions. A real-world application is illustrated in use case 2, where accident data was fused with over 23,000 car trajectories at one intersection in Germany. We could show that SM is applicable to fuse categorial traffic databases. In future research, the combination of hot-deck-methods and machine learning classifiers needs to be further investigated.
Characterization of Objects in Indoor Spaces of Human Occupation Using Knowledge Graphs

This paper proposes a knowledge graph to describe the semantic relationships of objects present in an indoor space, such as a bedroom in a house. We base our work in the representation model Object1-Predicate-Object2. In the predicate, we consider spatial relationships such as above, below, under, on top, next to, in, has, in front, and behind. To fulfill our purpose, we used the Grakn NoSQL database, which allows defining a knowledge graph schema of type entity-relation. The knowledge graph obtained needs a previously identified object in order to start looking for the correct relationships. That object is considered as input to the algorithm. The information on the semantic relationship has proved effectiveness in characterizing objects using Grakn, through which it was possible to charac-terize objects and their relationships based on the principles of knowledge representation.
Resource and Job Execution Context-Aware Hadoop Configuration Tuning

MapReduce is a programming model, which is widely-used in parallel processing of big data. Its' performance is significantly affected by configuration parameters. However, the huge parameter space and interact of parameters make it impossible to explore all the parameter combinations manually. In this paper, we propose RJHCT, a novel approach to automatically tune the configuration parameters for MapReduce applications. We use random forest regression to predict the execution time of single task, and then we predict the job execution time by packing algorithm. Leveraging the prediction model, we use genetic algorithm to search the optimal configuration parameters automatically for a given MapReduce application. Experimental results demonstrated that RJHCT improves the performance of MapReduce applications by factors of 0.28x compared with the default configuration.
Implementation of Coaching Approach to Formation, Maintenance and Development of Safety Culture at the «Rosatom» State Corporation Enterprises

The paper considers the possibility of using coaching as an innovative and effective technology for managerial interaction with personnel in the daily activities of the nuclear power plant leaders. The results of diagnostics of the development of management skills of managers at the Rostov nuclear power plant are presented. The obtained results of the study can be used as the basis for the strategy of forming and increasing leadership in matters of safety culture at the enterprises of the nuclear industry
POPO: Pessimistic Offline Policy Optimization

Offline reinforcement learning (RL) aims to optimize policy from large pre-recorded datasets without interaction with the environment. This setting offers the promise of utilizing diverse and static datasets to obtain policies without costly, risky, active exploration. However, commonly used off-policy deep RL methods perform poorly when facing arbitrary off-policy datasets. In this work, we show that there exists an estimation gap of value-based deep RL algorithms in the offline setting. To eliminate the estimation gap, we propose a novel offline RL algorithm that we term Pessimistic Offline Policy Optimization (POPO), which learns a pessimistic value function. To demonstrate the effectiveness of POPO, we perform experiments on various quality datasets. And we find that POPO performs surprisingly well and scales to tasks with high-dimensional state and action space, comparing or outperforming tested state-of-the-art offline RL algorithms on benchmark tasks.
KRNNT: Polish Recurrent Neural Network Tagger Extended

The article presents a state-of-the-art complete part-of-speech tagger for Polish which uses recurrent neural networks. The networks allow accessing the full left and right context of a sentence in comparison to a context window. The tagger uses an external morphological analyzer. In comparison to the best Polish taggers, it does not use word form as a feature for the classifier, there is no separate classifier for unknown words, and predictions are not limited to tags provided by a morphological analyzer. The accuracy is higher—it achieves 28% error reduction and 7% points higher accuracy for unknown words. The tagger also might work faster than others by utilizing GPU. The tagger participated in PolEval 2017 POS Tagging competition and won task B and task C. Additionally, results for PolEval 2020 Morphosyntactic tagging of Middle, New and Modern Polish are reported. The paper is an extension of the Language & Technology Conference paper [25].
Structuring Multi-Layered Musical Feedback for Digital Bodily Interaction: Two Approaches to Multi-layered Interactive Musical Feedback Systems

This paper describes to approaches to develop simple systems for expressive bodily interaction with music, without prior musical knowledge on the user's part. It discusses two almost oppositional models: 1. Modifying a preexisting recording through spatial articulation, and 2. Rule based ad-hoc composition of a musical piece of indefinite length, based on precomposed chord progression(s). The approaches differ both in interaction models as well as in musical feedback.
Using Simulations in Teaching Research Academic Writing Course

The article focuses on teaching research academic writing to undergraduates taking a Russian-American dual-degree program offered by Baikal International Business School and its partner University of Maryland Global Campus. As the American program is online, writing courses are mandatory. Being the final course in the writing module, research academic writing is challenging not only for students but also for the instructor. This makes the teacher look for new ways to improve the quality of teaching and to motivate undergraduates. The paper describes action research aiming at introducing simulation games in research academic writing course to engage students in classroom activities, vary practical assignments, and make classes more interactive. Using observation, student questionnaires and interviews, the author compares her class atmosphere and student involvement before and after the intervention. As a result of her research, the author comes to the conclusion that simulation activities, besides other advantages, can be successfully used for teaching academic writing since they increase motivation and student engagement creating a positive learning environment. Moreover, pair or group work can minimize difficulties, which helps to better understand the material. Furthermore, simulation activities have a practical value enabling students to develop skills and competences vital for future managers. Also the paper offers simulation activities developed for the course which are given in the appendices
Trees in the Real Field

This paper proposes anBetti, Alessandro algebraic view of trees which opens the doors toGori, Marco an alternative computational scheme with respect to classic algorithms. In particular, it is shown that this view is very well-suited for machine learning and computational linguistics.
Mapping fMRI voxel activations to CNN feature space for ease of categorization

We observe the impact of using category averaged feature vectors as intermediaries in predicting object categories from fMRI(Functional Magnetic Resonance Imaging) voxel activations. The validation accuracy of state-of-art prediction methods falls drastically when multiple classes are used at the same time, pointing towards the overlapping nature of representations in the voxel activations. To overcome this disadvantage, we map these overlapping representation to a more separable representation. The equivalent of these representations in the field of Computer Vision is a Convolutional Neural Network(CNN) feature vector. After taking into consideration the structural trade-offs the Ventral Temporal Cortex possesses to achieve efficient categorization, we designed a model whose architecture tries to mimic these functional nuances. There are two parts to the implementation - Estimation of feature vectors and efficient category prediction from the estimated feature vectors. We inspected the perceptual similarity of the estimated feature vectors by the use of Annoy tree. We found that Deep ReLU-MLP(Rectified Linear Unit-Multilayer Perceptron) performs better at decoding fMRI voxel activations compared to Sparse Linear Regressor. While inspecting the perceptual neighborhood of the decoded feature vector, we found a significantly higher percentage of the feature vectors predicted from visual perception experiments mapped to the correct neighborhood than in the case of visual imagery experiment.
STEAM Powered K-12 Cybersecurity Education

The importance of incorporating cybersecurity education in K-12 to develop and strengthen the pipeline of students who pursue a cybersecurity major in college along with teaching cyber-awareness to all students cannot be overstated. Through efforts, such as the National Institute of Science and Technology (NIST) National Initiative for Cybersecurity Education (NICE) K-12 cybersecurity conferences and the NICE K-12 working groups this message is being spread to K-12 educators across the country. In Virginia, like many other states, there is a disparity among student and teacher preparation in cybersecurity between urban and rural areas. Schools lack two key resources: teachers with the required competencies and access to isolated computing networks – required for hands on exercises in security. Currently, efforts to introduce security are usually focused only at the high school level where students have already self-selected into relatively small interest groups. This paper describes the result of year-long, NSA funded project (PICSAR) designed to increase the number of teachers with competency in cybersecurity, while increasing the pipeline of students interested in cybersecurity. The project accomplished the first goal by providing graduate instruction in cybersecurity education and workshops to K-12 teachers. These same teachers then helped to accomplish the second goal through the development of age appropriate, integrated, STEAM lesson plans from Kindergarten through the 12th grade. For each topic in cybersecurity (e.g. Cryptography), a skills progression plan was developed and then lesson plans developed and piloted to appropriately introduce the topic at each grade level.
Probabilistic Synthesis for Program with Non-API Operations

The current approaches to program synthesis focus more on works that contain only API methods. However, our survey shows that the ratio of non-API operations to API calls is very close in Java programs. Although non-API operations are difficult to obtain specific information from user intent, those operations in such as mathematics and logic are frequent and important, and that is an indispensable key in practical development tasks. We present Cana (Capsulate Non-API), an improved approach of probabilistic synthesis for non-API operations. Cana synthesizes programs with non-API operations through two main ideas. One is to encapsulate non-API operations into API calls, and the user also can provide related information when describing incomplete specifications. The other is heuristic strategies to solve the difficult problems that select variables with the same type. Experiments show that Cana can synthesize programs contains combinations of non-API operations in seconds.
Prediction of Customer Purchase Intention Using Linear Support Vector Machine in Digital Marketing

Abstract Digital marketing is taken into account the well-liked method comparing to traditional marketing. It can be used by both researchers and academicians for social media marketing and to predict the customers purchase intention. The Proposed work revolves around some valuable information and processes in accordance to the behavior of customer during the online purchase. Business owners, scientists, researchers all post their ads, details on the Web so that they can be linked to owners quickly and easily by web scrap searching on searchable product websites to gain a lot of data from websites. Details on websites are stored in an unstructured manner. To avoid this issue, Web Scraping helps to gather unstructured content and turn it into a structured type that can be used for further study. Hence, customer price and rating of product evaluation and prediction has become an important research area. The analysis is done by Support Vector Machine (SVM-Linear) to gather several information and provide variation analysis. The major goal remains to investigate and analyze the extracted dataset using ML oriented algorithms with best accuracy possible. The analysis has a proper path to sentimental analysis of parameters in accordance to the ratings and price of the product to find proper accurate calculations.
WITHDRAWN: A novel joint data-hiding and compression scheme based on SMVQ and image inpainting

For the advanced digital pictures an innovative joint data concealing and compression methodology is proposed in our system. This is achieved by utilizing side match vector quantization (SMVQ) and image inpainting. Information stowing away and compression are the two parts which can be integrated into a single module cleanly. Apart from the squares within the furthest left and the top of the image, all the remaining squares in the raster-examining request can be inserted with mystery data and compacted all the while by SMVQ or image encoding adaptively as per the present inserting bit on the sender facet. To regulate the visual twisting and fault dispersion vector quantization is additionally used for mind boggling squares. These two are brought by the liberal compression. The receiver can accomplish the extraction of mystery pieces and picture decompression effectively as per the index estimates in the segmented areas. This is subsequent to dividing the image packed codes into a progression of segments by the marker bits.
Kimia-5MAG – A Dataset for Learning the Magnification in Histopathology Images

Recent advances in medical imaging have created many possibilities for the exploitation of both microscopic images in digital form and the whole slide images (WSIs) for multiple tasks such as classification, prediction, and retrieval. This is mainly due to annotated datasets available through various research organizations. Magnification level is an important factor as pathologist views the biopsy samples at various magnifications to reach a diagnosis. Whereas WSIs generally do contain the magnification information, microscopic snapshots are often captured without attaching the magnification information. In this paper, we introduce a new dataset, Kimia-5MAG, consisting of 33,345 patches at 5 different magnification classes created from WSIs made publicly available by The Cancer Genome Atlas (TCGA). There exists a large number of microscopic snapshots captured from camera-mounted microscopes but are of little use for automatic processing due to lack of magnification information. One direction to make use of these datasets is learning the magnification level from high resolutions captured WSIs and transferring the knowledge to microscopic snapshots. We investigate combinations of several deep networks and classifiers to predict different magnification levels. The proposed framework achieves 93% classification accuracy. We also analyze the effect of rotation on magnification prediction.
Towards Better Text Processing Tools for the Ainu Language

In this paper we present our research devoted to the development of Natural Language Processing technologies for the Ainu language, a critically endangered language isolate spoken by the Ainu people, the native inhabitants of northern parts of the Japanese archipelago. In particular, we focused on improving the existing tools for transcription normalization, word segmentation (tokenization) and part-of-speech tagging. In the experiments we applied two Ainu language dictionaries from different domains (literary and colloquial) and created a new data set by combining them. The experiments confirmed the positive effect of these modifications on the overall performance of the tools, especially with objective samples unrelated to the training data. We also discuss further improvements obtained by applying corpus-driven language models to the problem of word segmentation and using a state-of-the-art tool for training part-of-speech taggers.
Innovative language teaching (on the example of pre-school education)

The subject of the research in this article is the innovative methods of teaching a foreign language. The author considers innovative activity as a process of mutual influence, the strength of which depends on both the ability to use various innovative methods and techniques in teaching, and the spiritual wealth of the personality of the teacher. This article deals with the problem of finding new methods and ways to increase motivation for teaching foreign languages. One of these forms is new pedagogical technologies, the introduction of which facilitates effective language learning. The article defines the concepts of "Pedagogical technology" and "Smart education", which can provide a high level of education that meets the challenges and opportunities of today's world. The author emphasizes that it is not enough for a new generation of teachers to be competent in their field of knowledge, it is necessary for the educational process to use methodological innovations that are currently associated with the use of interactive teaching methods. The author considers several interactive methods, the implementation of which will contribute to the achievement of the goal - improving the efficiency of the educational process, achieving high efficiency for all students.