Reviewerly/exHarmony huggingface dataset:
Prn-Sorb-Slam

A parallelized region proposal network-based ORB feature extraction technique is proposed in which a significantly smaller number of bounding boxes are produced on the currently rectified stereo image and the next stereo frame for selective extraction of features and then matching. The optical flow field highlights how feature descriptors tend to diverge from the focus of expansion. This chapter uses the term PRN-SORB, which is an abbreviation for parallelized region proposal network-based Swift ORB. The development of the proposed PRN-SORB-SLAM-i.e., parallelized region proposal network-based swift ORB SLAM-which utilizes a stereo-vision setup to project a trajectory for local path planning is elaborated in detail in the chapter. The depth perception and formation of a fused image from a given stereo image pair work on the triangulation principle. A solution, however, is to make use of bundle adjustment.
From global to local MDI variable importances for random forests and when they are Shapley values

Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variables to predict a certain output. On the other hand, methods based on Shapley values have been introduced to refine the analysis of feature relevance in tree-based models to a local (per instance) level. In this context, we first show that the global Mean Decrease of Impurity (MDI) variable importance scores correspond to Shapley values under some conditions. Then, we derive a local MDI importance measure of variable relevance, which has a very natural connection with the global MDI measure and can be related to a new notion of local feature relevance. We further link local MDI importances with Shapley values and discuss them in the light of related measures from the literature. The measures are illustrated through experiments on several classification and regression problems.
Bayesian network-based missing mechanism identification (BN-MMI) method in medical research

Traditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research.The procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research.The simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data.It was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.
A Large-Scale Dataset for Empathetic Response Generation

Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset. This is especially the case for response generation where emotion plays an important role. However, existing empathetic datasets remain small, delaying research efforts in this area, for example, the development of emotion-aware chatbots. One main technical challenge has been the cost of manually annotating dialogues with the right emotion labels. In this paper, we describe a large-scale silver dataset consisting of 1M dialogues annotated with 32 fine-grained emotions, eight empathetic response intents, and the Neutral category. To achieve this goal, we have developed a novel data curation pipeline starting with a small seed of manually annotated data and eventually scaling it to a satisfactory size. We compare its quality against a state-of-the-art gold dataset using both offline experiments and visual validation methods. The resultant procedure can be used to create similar datasets in the same domain as well as in other domains.
Pengembangan Media Pembelajaran PowerPoint Interaktif untuk Keterampilan Menyimak Cerita Fiksi Kelas IV Sekolah Dasar

This research is based on the findings of the difficulties experienced by students in learning fiction listening skills caused by several factors. One of the factors is that teachers tend to only use the speech method and do not use more interactive learning media. The research method used is descriptive qualitative with a D&D model research. Data were collected by interview, document analysis, expert validation of the media, and documentation. The results showed that the media commonly used at SDN Gudang Kopi II was still dominant in conventional visual media so that it was non-flexible. The design of this interactive learning media development with the waterfall development model, is conceptualized with the tutorial type and includes audio-visual interactive multimedia. As for the expert review/validation of the development of interactive powerpoint learning media, the final round by design experts significantly to 95.66%. The final round of material experts significantly to 93,85%. The final round of teacher as a user significantly to 95.55%. This means that interactive powerpoint learning media is very feasible to use based on the conversion of assessment criteria.
Interactive Label Cleaning with Example-based Explanations

We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples. Existing approaches are flawed, in that they only relabel incoming examples that look ``suspicious'' to the model. As a consequence, those mislabeled examples that elude (or don't undergo) this cleaning step end up tainting the training data and the model with no further chance of being cleaned. We propose Cincer, a novel approach that cleans both new and past data by identifying pairs of mutually incompatible examples. Whenever it detects a suspicious example, Cincer identifies a counter-example in the training set that -- according to the model -- is maximally incompatible with the suspicious example, and asks the annotator to relabel either or both examples, resolving this possible inconsistency. The counter-examples are chosen to be maximally incompatible, so to serve as explanations of the model' suspicion, and highly influential, so to convey as much information as possible if relabeled. Cincer achieves this by leveraging an efficient and robust approximation of influence functions based on the Fisher information matrix (FIM). Our extensive empirical evaluation shows that clarifying the reasons behind the model's suspicions by cleaning the counter-examples helps acquiring substantially better data and models, especially when paired with our FIM approximation.
Guaranteeing the Learning of Ethical Behaviour through Multi-Objective Reinforcement Learning

∗Research supported by projects AI4EU (H2020-825619), LOGISTAR (H2020-769142),COREDEM (H2020-785907), Crowd4SDG (H2020-872944), CI-SUSTAIN (PID2019-104156GB-I00), COMRIDI18-1-0010-02, MISMIS PGC2018-096212B-C33, TAILOR(H2020-952215), 2017 SGR 172 and 2017 SGR 341. Manel Rodriguez-Soto was fundedby the Spanish Government with an FPU grant (ref. FPU18/03387).
PRELIMINARY FINDINGS OF A VIRTUAL REALITY APP FOR CHILDREN WITH SPECIAL NEEDS

Immersive Virtual Reality (VR) is slowly finding its way into classrooms as an alternative form of experience and action-oriented learning.Generally VR shows a value-added for topics that either are too far away to visit, too small to touch, to dangerous or expensive to manipulate or simply virtual.This range of application is potentially extended for children with special needs or complex disorders.One reason may be that our world is simply too complex for them and hence jeopardizes their health.But often people simply are too stressed to take some extra time to take care in the daily routine.Therefore, a value added for an immersive VR learning unit may simply be a realistic model of a public place like a train station where these children can exercise and practice their skills, e.g., finding places.Together with a foundation for children with special needs, their therapists and teachers, and five children a learning unit playing at a train station was evaluated as a pilot regarding usability and acceptance amongst all concerned stakeholders.Although only a very limited number of participants were involved the pilot application showed potential for further development in this very special context.
Reversible Gate Logic Adder with Parity Preserving Design

Reversible logic circuits have drawn attention from a variety of fields, including nanotechnology, optical computing, quantum computing, and low-power CMOS design. Low-power and high-speed adder cells (like the BCD adder) are used in binary operation-based electronics. The most fundamental digital circuit activity is binary addition. It serves as a foundation for all subsequent mathematical operations. The main challenge today is to reduce the power consumption of adder circuits while maintaining excellent performance over a wide range of circuit layouts. Error detection in digital systems is aided by parity preservation. This article proposes a concept for a fault-tolerant parity- preserving BCD adder. To reduce power consumption and circuit quantum cost, the proposed method makes use of reversible logic gates like IG, FRG, and F2G. Comparing the proposed circuit to the current counterpart, it has fewer constant inputs and garbage outputting devices and is faster.
ИСПОЛЬЗОВАНИЕ ГЕЙМИФИКАЦИИ В ОБРАЗОВАНИИ, ЕЕ ВЛИЯНИЕ НА КОНКУРЕНТОСПОСОБНОСТЬ РАБОТНИКОВ НА РЫНКЕ ТРУДА

Gamification is the use of methods and tools similar to those used in computer games, in non-gaming processes to increase engagement and interest in the performance of employees' duties.Gamification can also be defined as a set of actions and processes for solving problems using characteristics of game elements.While the typical gaming elements are by no means new, they are indeed becoming more common in non-gaming contexts such as websites, digital marketing, enterprise apps, and even virtual to-do lists and productivity tools.However, there is another huge area in which gamification is widespread and that is education.Studies show that for many people, studying is not the most interesting activity. However, if the material is presented in an interesting and playful way, the ability to memorize increases several times. Thus, gamification in education can increase and maintain student engagement. They also stop being afraid of mistakes. Making a mistake, the gamer understands that you need to learn how to make the right decisions. This helps him get closer to his goal. In this sense, gamification in learning helps to combat psychological inertia. As a result, students learn the necessary material much faster and practice skills that will later be used in real life.Thus, the quality of education increases, and as a result, the labor market receives a highly qualified specialist who meets the needs of the economy and modern society.
Groundwater anomaly related to CCS-CO<sub>2</sub> injection and the 2018 Hokkaido Eastern Iburi earthquake in Japan

Carbon capture and storage (CCS) is considered a key technology for reducing CO 2 emissions into the atmosphere.Nonetheless, there are concerns that if injected CO 2 migrates in the crust, it may trigger slip of pre-existing faults.In order to test if this is the case, covariations of carbon, hydrogen, and oxygen isotopes of groundwater measured from Uenae well, southern Hokkaido, Japan are reported.This well is located 13 km away from the injection point of the Tomakomai CCS project and 21 km from the epicenter of September 6 th , 2018 Hokkaido Eastern Iburi earthquake (M 6.7).Carbon isotope composition was constant from June 2015 to February 2018, and decreased significantly from April 2018 to November 2019, while total dissolved inorganic carbon (TDIC) content showed a corresponding increase.A decrease in radiocarbon and d 13 C values suggests aquifer contamination by anthropogenic carbon, which could possibly be attributable to CCS-injected CO 2 .If such is the case, the CO 2 enriched fluid may have initially migrated through permeable channels, blocking the fluid flow from the source region, increasing pore pressure in the focal region and triggering the natural earthquake where the brittle crust is already critically stressed.
MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain

We present MobIE, a German-language dataset, which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. To the best of our knowledge, this is the first German-language dataset that combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks. We make MobIE public at this https URL.
Teaching English during Covid-19 pandemic: Using “Busuu” in speaking class

Covid-19 Pandemic forced all educational activities move into online learning to prevent the spread of virus. This new learning style compelled teacher to implement proper learning media. The development of technology makes it possible to bridge between conducting online learning and implementing proper learning media. As a teacher, we should use this internet advantages as an instrument that can help us in improving our teaching learning process. Furthermore, we must also consider and adapt learning activities to students’ needs.This article aims at describing the implementation of Busuu in speaking Class. Furthermore, it Investigated the students’ response on the implementation of Busuu. Thus, descriptive qualitative was carried out. There were 35 participants involved in this research. The instruments used in the research were observation and interview.The findings showed that the lecturer's adoption of Busuu in teaching speaking entails several procedures. Three steps comprise the teaching and learning process or procedures: an introductory session, a core activity, and a concluding session. Second, the lecturer's use of Busuu in teaching speaking elicited both favorable and negative responses from the students. Positive response included the fact that Busuu is enjoyable, inspires students to learn, and provides students with numerous speaking activities. The students considered the Busuu to be interesting, interesting, and reinforcing their learning, and reported that they are an effective medium and source of material for facilitating the retention of new knowledge, boosting peer collaboration, and improving their interest and motivation for studying. Meanwhile, one of the criticisms leveled regarding Busuu's use in teaching and learning to speak is that it requires a strong and stable internet connection. Additionally, students must pay a fee to access premium features.
Research on Inverse Kinematics of Robot Based on Improved Chicken Swarm Algorithm

Aiming at the problems existing in traditional chicken colony algorithm, this article proposes an improved CSO algorithm (ICSO). Firstly, the target optimization strategy is introduced in the process of rooster updating, the mutation factor is introduced in the process of hen updating, and the influence of rooster is increased in the rule of chick updating. Secondly, the validity and excellence of ICSO algorithm are verified by benchmark functions. Finally, the ICSO algorithm is applied to the inverse kinematics of the robot. The results show that compared with CSO and PSO, ICSO algorithm can solve inverse kinematics problems with high precision.
Comparative Study of Experimentally Measured and Calculated Solar Radiations for Two Sites in Algeria

This paper presents a comparison study between the measured solar radiations on site and the calculated solar radiation based on the most theoretical models presented in the literature up to date. Indeed, for such purposes, this paper focusses on the analysis of the data of the measured solar radiation collected on two sites in Algeria such as Tlemcen (34°52′58″ N 01°19′00″ W, elevation 842 m) and Senia (35°39′ N 0°38′ W, elevation: 77 m). In order to check the accuracy of the proposed model, the experimental collected data of the solar radiation obtained from the existing radiometric stations installed at the two locations under investigation, are compared with the estimated or predicted solar radiations obtained from the Capderou and R.Sun models, where four days under clear skies are selected from different seasons to achieve this comparison. Second, the daily averages of the experimental global solar irradiation are compared to those predicted by Mefti model for both the sites. Finally, a validation is carried out based on the obtained experimental monthly global irradiations and with those estimated by Coppolino and Sivkov models. A relative difference is used in this case to judge the reliability and the accuracy of each model for both sites.
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach

We propose a fully differentiable architecture for simultaneous semantic and instance segmentation (a.k.a. panoptic segmentation) consisting of a convolutional neural network and an asymmetric multiway cut problem solver. The latter solves a combinatorial optimization problem that elegantly incorporates semantic and boundary predictions to produce a panoptic labeling. Our formulation allows to directly maximize a smooth surrogate of the panoptic quality metric by backpropagating the gradient through the optimization problem. Experimental evaluation shows improvement by backpropagating through the optimization problem w.r.t. comparable approaches on Cityscapes and COCO datasets. Overall, our approach shows the utility of using combinatorial optimization in tandem with deep learning in a challenging large scale real-world problem and showcases benefits and insights into training such an architecture.
A Study on the Effect of Language Ability on Programming Interests

This study aimed to identify the effects of three kinds of language competence on three kinds of programming interests. This studys participants consisted of 39 college students who had begun to learn C programming. It classified language competence into reading, writing, and grammatical understanding, and programming interests into situational interest, latent interest, and actualized interest, and analyzed the effects of each variable of those three groups. This study used Pandas for analysis, and performed reliability testing, descriptive statistics analysis, correlation analysis, and regression analysis. Pearsons correlation coefficients between the three items on language competence and the three items on programming interests were as follows: .54 ~ .88 in the first survey; .54 ~.95 in the second survey; and .66~.94 in the third survey. All the p values were
Anomaly Detection in Arabic Texts using Ngrams and Self Organizing Maps

Every written text in any language has one author or more authors (authors have their individual sublanguage). An analysis of text if authors are not known could be done using methods of data analysis, data mining, and structural analysis. In this paper, two methods are described for anomaly detections: ngrams method and a system of Self-Organizing Maps working on sequences built from a text. there are analyzed and compared results of usable methods for discrepancies detection based on character n-gram profiles (the set of character n-gram normalized frequencies of a text) for Arabic texts. Arabic texts were analyzed from many statistical characteristics point of view. We applied some heuristics for measurements of text parts dissimilarities. We evaluate some Arabic texts and show its parts they contain discrepancies and they need some following analysis for anomaly detection. The analysis depends on selected parameters prepared in experiments. The system is trained to input sequences after which it determines text parts with anomalies using a cumulative error and winner analysis in the networks. Both methods have been tested on Arabic texts and they have a perspective contribution to text analysis.
Inclusiveness of Online Education: Problems and Prospects

This study attempts to assess the problems and prospects of online education related to its compliance with the requirements of inclusivity. The authors argue that the inclusiveness of online education is poorly understood and requires a comprehensive scientific understanding. The research is based on a cognitive approach that defines a person as a system that knows the world and is influenced by communication that occurs in the learning environment. There are two indicators of the inclusiveness of online education: socio-cultural and technical adaptability. The first is related to the willingness of students and teachers to accept people with disabilities as full-fledged subjects of the educational process, and the second determines the compliance of the technical tools of online education with the requirements of inclusivity. The empirical basis is a sociological study conducted by the authors, during which students and teachers of the Southern Federal University were interviewed. Based on the data obtained from the survey, it was found that students and teachers are tolerant of people with disabilities and are ready to cooperate with them on an equal basis. The most serious problem is considered to be the compliance of online education with the technical requirements of inclusivity. It is concluded that the software used for online education does not meet the requirements of inclusivity and should be replaced with an analog intended for educational purposes.
Research on the Construction of Wisdom Library under the Background of Smart City

With the development of technology, the progress of society and the increasing demand for material culture, the construction and development of “smart city” has become an unstoppable wave. The construction of “smart city” has pointed out a new direction for the development of the library and also provided new ideas.