Reviewerly/exHarmony huggingface dataset:
On the Differences and Similarities of fMM and GBFHS

fMM and GBFSH are two prominent bidirectional heuristic search algorithms. Over the past few years, there has been a great deal of theoretical and empirical work on both of these algorithms. As part of the research conducted on these algorithms, some interesting theoretical properties were proven for fMM and not for GBFSH and vice versa. In addition, both of them are used as benchmarks for evaluation bidirectional heuristic search algorithms. In this paper we show that fMM infused by a lower-bound propagation and GBFSH are equivalent. In essence, every instance of fMM can be mapped to an instance of GBFSH that expands the exact sequence of nodes and vice versa. This equivalence indicates that all theoretical properties proven for one algorithm hold for both algorithm, and that future analyses and benchmarks can consider only one of these algorithms.
KU-CST at the SIGMORPHON 2020 Task 2 on Unsupervised Morphological Paradigm Completion

We present a model for the unsupervised discovery of morphological paradigms.The goal of this model is to induce morphological paradigms from the bible (raw text) and a list of lemmas.We have created a model that splits each lemma in a stem and a suffix, and then we try to create a plausible suffix list by considering lemma pairs.Our model was not able to outperform the official baseline, and there is still room for improvement, but we believe that the ideas presented here are worth considering.
ONLINE COURSE AS A TOOL TO IMPLEMENT THE PRINCIPLE OF PERSONALIZATION IN MASTER’S DEGREE

The article deals with the implementation of the personalization principle in a Master’s degree program based on blended learning approach. The contradiction between the requirements for personalization declared in state educational standards and the real situation at University encourages the authors to search for the ways to apply the principle of personalization in Master’s degree educational process. The problem is analyzed in terms of the humanistic system-synergetic methodology, which justifies the use of personalization through humanistic approach to education based upon harmonious interaction of University teachers with students. Questionnaires, documentation and literature analysis prove the feasibility of personalization to be implemented via blended learning approach has it meets the modern requirements in education and corresponds to the Master stydents’ learning needs. The paper presents the key features of personalization in blended learning settings, which are implemented in the author’s online course on the German language for Masters.
Validasi bahan ajar menulis teks nonfiksi berbasis pendekatan genre

This research aims to describe the validity of teaching materials in writing nonfiction texts based on genre approach. The genre approach is an approach that emphasizes text types. The type of research is descriptive qualitative. The data obtained comes from the assessment of experts (lecturers), teachers, and students. The subjects in this study were three experts and five Indonesian language teachers, and 44 eighth grade students as respondents. The technique of collecting data is done by using a questionnaire. Data collection instruments include expert validation sheets, teacher assessment sheets, and student response sheets. The results of this study indicate nonfikdi text writing materials based on a genre approach based on expert velidation, teacher ratings, and student responses get a percentage score of 82.36% with good category so it can be concluded that the higher the score obtained, the better the validity results of the product.
Breakdown Detection in Negotiation Dialogues (Student Abstract)

In human-human negotiation, reaching a rational agreement can be difficult, and unfortunately, the negotiations sometimes break down because of conflicts of interests. If artificial intelligence can play a role in assisting with human-human negotiation, it can assist in avoiding negotiation breakdown, leading to a rational agreement. Therefore, this study focuses on end-to-end tasks for predicting the outcome of a negotiation dialogue in natural language. Our task is modeled using a gated recurrent unit and a pre-trained language model: BERT as the baseline. Experimental results demonstrate that the proposed tasks are feasible on two negotiation dialogue datasets, and that signs of a breakdown can be detected in the early stages using the baselines even if the models are used in a partial dialogue history.
Determining the Possibility of Transfer Learning in Deep Reinforcement Learning Using Grad-CAM (Student Abstract)

Humans are usually good at guessing whether the two games are similar to each other and easily estimate how much time to master new games based on the similarity. Although Deep Reinforcement Learning (DRL) has been successful in various domains, it takes much training time to get a successful controller for a single game. Therefore, there has been much demand for the use of transfer learning to speed up reinforcement learning across multiple tasks. If we can automatically determine the possibility of transfer learning in DRL domain before training, it could efficiently transfer knowledge across multiple games. In this work, we propose a simple testing method, Determining the Possibility of Transfer Learning (DPTL), to determine the transferability of models based on Grad-CAM visualization of the CNN layer from the source model. Experimental results on Atari games show that the transferability measure is successfully suggesting the possibility of transfer learning.
Survey on Blockchain: Backbone of Cryptocurrency

Technology like Blockchain is fast growing among other technologies that are now explored into various fields including services in finance, services in land holdings, department of health, academic organizations and many more. Crypto currency is a biggest shift towards assets online which is tradable. It is built with Blockchain as the backbone technology where trading takes place only online. Digital Wallets on computers or smartphones are used to store crypto currency. Using these digital wallets crypto currency transactions can be made. Every transaction is stored in a block that are chained together to formBlockchain.This paper, "Survey on Blockchain: backbone of Cryptocurrency", is a detailed survey on Blockchain and its applications.
Towards a Term Clustering Framework for Modular Ontology Learning

This paper aims to analyze and adopt the term clustering method for building a modular ontology according to its core ontology. The acquisition of semantic knowledge focuses on noun phrase appearing with the same syntactic roles in relation to a verb or its preposition combination in a sentence. The construction of this co-occurrence matrix from context helps to build feature space of noun phrases, which is then transformed to several encoding representations including feature selection and dimensionality reduction. In addition, word embedding techniques are also presented as feature representation. These representations are clustered respectively with K-Means, K-Medoids, Affinity Propagation, DBscan and co-clustering algorithms. The feature representation and clustering methods constitute the major sections of term clustering frameworks. Due to the randomness of clustering approaches, iteration efforts are adopted to find the optimal parameter and provide convinced value for evaluation. The DBscan and affinity propagation show their outstanding effectiveness for term clustering and NMF encoding technique and word embedding representation are salient by its promising facilities in feature compression.
Towards End-to-End Natural Language Story Generation Systems

Storytelling and story generation systems usually require knowledge about the story world to be encoded in some form of knowledge representation formalism, a notoriously time-consuming task requiring expertise in storytelling and knowledge engineering. In order to alleviate this authorial bottleneck, in this paper we propose an end-to-end computational narrative system that automatically extracts the necessary domain knowledge from corpus of stories written in natural language and then uses such domain knowledge to generate new stories. Specifically, we employ narrative information extraction techniques that can automatically extract structured representations from stories and feed those representations to an analogy-based story generation system. We present the structures we used to connect two existing computational narrative systems and report our experiments using a dataset of Russian fairy tales. Specifically we look at the perceived quality of the final natural language being generated and how errors in the pipeline affect the output.
Deep Bayesian Quadrature Policy Optimization

We study the problem of obtaining accurate policy gradient estimates using a finite number of samples. Monte-Carlo methods have been the default choice for policy gradient estimation, despite suffering from high variance in the gradient estimates. On the other hand, more sample efficient alternatives like Bayesian quadrature methods have received little attention due to their high computational complexity. In this work, we propose deep Bayesian quadrature policy gradient (DBQPG), a computationally efficient high-dimensional generalization of Bayesian quadrature, for policy gradient estimation. We show that DBQPG can substitute Monte-Carlo estimation in policy gradient methods, and demonstrate its effectiveness on a set of continuous control benchmarks. In comparison to Monte-Carlo estimation, DBQPG provides (i) more accurate gradient estimates with a significantly lower variance, (ii) a consistent improvement in the sample complexity and average return for several deep policy gradient algorithms, and, (iii) the uncertainty in gradient estimation that can be incorporated to further improve the performance.
A Fast Detection Algorithm of Small Targets Based on YOLOv3

Robots, endowed with computer vision function by on-board cameras and embedded systems, have become popular in the modem power industry. In order to realize automatic image analysis at the data acquisition part, a fast detection algorithm especially for small target based on YOLOv3 was proposed. It is proposed to up sample by 2 X feature map which is down sampled by 8 X of the previous network, and concatenate it with the output of the second Res block unit, and the last Res block unit of the previous network was abandoned. To obtain more features of the small target, add two Resnet units in the second Res block unit of Darknet53 in YOLO V3 network structure. Aiming at deploying the algorithm to the hardware, model pruning was applied to decrease model size and improve detection speed. The experimental results demonstrate that the mAP (Mean Average Precision) reaches 0.85, and the proposed algorithm is capable of real-time detection as faster than 15 frames per second (fps).
Heptapod B and whorfianism. Language extrapolation in science fiction

The Sapir-Whorf hypothesis states that the language someone speaks shapes their thoughts. Although this view may have fallen into disrepute in the field of linguistics, its influence, the Whorfianism, has been the number one showcase in science fiction works that somehow approach language, and more specifically, invented languages. This paper uses Ted Chiang’s award-winning novella Story of your life (1998) and its filmic adaptation Arrival (2016) directed by Denis Villeneuve and written by Eric Heisserer as a case study to investigate this literary phenomenon. The considerations of Guy Deutscher (2010), Stockwell (2006) and Ria Cheyne (2008), as well as the authors’ own viewpoints, are vitally important for that. The result is a speculative and comparative analysis that contributes to a better understanding of the frequent connexion of science fiction, glossopoesis and Whorfianism.
Continuous variable teleportation protocol for split-squeezed Bose Einstein condensates

In the present paper, a quantum teleportation protocol for split-squeezed Bose-Einstein Condensates is proposed that transfers a spin coherent state between two locations using entanglement introduced by squeezing interactions. One axis squeezing is used for this purpose, two axis anti squeezing further reduces the noise. The protocol uses continuous variable quadrature measurements that works for short interaction times in the Holstein-Primakoff approximation. The performance of the protocol is also analyzed by calculating the fidelity to show that it exceeds the classical bound.
Solving Travelling Salesman Problem (TSP) by Hybrid Genetic Algorithm (HGA)

The Traveling Salesman Problem (TSP) is easy to qualify and describe but difficult and very hard to be solved.There is known algorithm that can solve it and find the ideal outcome in polynomial time, so it is NP-Complete problem.The Traveling Salesman Problem (TSP) is related to many others problems because the techniques used to solve it can be easily used to solve other hard Optimization problems, which allows of circulating it results on many other optimization problems.Many techniques were proposed and developed to solve such problems, including Genetic Algorithms.The aim of the paper is to improve and enhance the performance of genetic algorithms to solve the Traveling Salesman Problem (TSP) by proposing and developing a new Crossover mechanism and a local search algorithm called the Search for Neighboring Solution Algorithm, with the goal of producing a better solution in a shorter period of time and fewer generations.The results of this study for a number of different size standard benchmarks of TSP show that the proposed algorithms that use Crossover proposed mechanism can find the optimum solution for many of these TSP benchmarks by (100%) ,and within the rate (96%-99%) of the optimal solution to some for others.The comparison between the proposed Crossover mechanism and other known Crossover mechanisms show that it improves the quality of the solutions.The proposed Local Search algorithm and Crossover mechanism produce superior results compared to previously propose local search algorithms and Crossover mechanisms.They produce near optimum solutions in less time and fewer generations.
A Poor College Students Identified Model Based on Decision Tree

In this paper, we propose a Poor College Students identified model based on decision tree. The model improves the accuracy of the poor students' identification and reduces the workload. According to the results, we propose a way to supplement the students with high consumption frequency and low consumption money. This is a good way to supplement the incomplete and inaccurate identification of poor students in colleges. First, we process campus card consumption data through data cleaning. Then, we introduce a list of poor students from the academic office, and select different attributes in the consumption data to form multiple training samples. Finally, the J48 algorithm in the Weka platform is used to analyze the sample data, and the college poor student identification model is obtained. Through the comparison and practice with the list of accredited laboratories, the accuracy of our model can reach about 70%, and the way of meal replenishment was also well received by teachers and students.
SUBJECTNESS DEVELOPMENT WITH INSTRUMENTS OF DISTANCE GROUP EDUCATION IN VIRTUAL SPACE

Application of distance education in the format of internet trainings in a virtual educational environment has psychological potential of students subjectness development. Article's purpose is revealing of trainings' efficiency in Internet as a modern method of education, carried out remotedly, for subjectness development. According to the results of conducted analysis, subjectness is determined through possibility to be a subject i.e. initiator, creator and producer of activity. Group educational-psychological work carried out in network space is active social-psychological education in the conditions of group with involving of some forms of internet communication. One of leading and most widespread technologies of group educational-psychological works are internet trainings. There are discovered factors of productive distance group education and psychological group work, based on integration with modern information technologies and aimed at development of communicative competence and subjectness as a goal achievement of educational system. Results, got during the leadthrough of distance educational courses (internet trainings) of communicative competence and subjectness development, and also influence of application of multimedia and informative-communicative technologies and technologies of work in social networks, have been analyzed. There are selected prospects of future researches of forming and development with distance group education instruments in a format of educational-psychological internet trainings. Keywords: subjectness, communicative competence, distance education. Accepted: 04.06.2020 Reviewed: 19.06.2020 Published: 29.06.2020 DOI: h ttp://doi.org/10.31108/3.2020.4.2. 6
UPAYA PENERAPAN MEDIA YOUTUBE DALAM PENINGKATAN KETERAMPILAN MENYIMAK UNSUR CERITA LISAN

It is motivated by a corona pandemic that makes students study at home and their low ability to listen to elementary students. This study attempts to describe the improvement of listening skills in identifying verbal stories through YouTube. The research method used is descriptive qualitative. The study was conducted at Muhammadiyah Karangharjo Elementary School with a sample of grade III students totaling 27 students. The results of this study, an increase in students' listening skills using Youtube media.
Plaintext-Verifiably-Checkable Encryption and Its Extension in Dual-Server Setting

Plaintext-checkable encryption (PCE), first introduced by Canard et al., allows users searching on encrypted data through plaintexts. It provides a useful primitive in the cloud computing security. However, existing PCE schemes could not guarantee verifiability to prevent from malicious adversaries. To concentrate with, we provide the notion of plaintext-verifiably-checkable encryption (PVCE). Our PVCE scheme could check that if a ciphertext is valid, even though it could pass the check procedure, to avoid malicious ciphertexts. Furthermore, we extend the work in dual-server setting, called dual-server plaintext-verifiably-checkable encryption (DS-PVCE), to resist offline message recover attack. We also give the security definition IND-CCA-FS, IND-CCA-BS and IND-CCA-BS-II in standard model for DS-PVCE and prove that DS-PVCE is secure under these security notions. Then we give constructions of PVCE and DS-PVCE from pairing-friendly smooth projective hash function (PF-SPHF) and their instantiations based on k-MDDH assumption. At the end of this paper, we provide implementation to compare existing PCE schemes and our schemes, which shows that DS-PVCE has very high check efficiency compared with other PCE schemes.
PROVOCATIVE TECHNIQUES IN TEACHING STUDENTS AND CONDITIONS OF THEIR APPLICATION

Statement of the problem and the purpose of the article. The article analyses didactic potential and conditions of application of provocative techniques in training. Their typology and examples of such tasks are given. Provocative techniques allow expanding the methodological repertoire of teachers, contribute to solving a number of topical tasks in education: formation of necessary knowledge, stimulation of cognitive and speech activity of students, development of thinking, formation of the need for self-determination and self-development. The methodology of the study is the analysis and synthesis of Russian and foreign scientific literature, experience in application of provocative techniques in training. Research results. On the basis of the analysis of scientific sources, the peculiarities of the use of provocation in the educational process have been determined, and a typology of provocative techniques has been developed. The main features of provocation in training are noted, such as the challenge of emotional reaction, game nature of educational activity, stimulation of students’ activity, reflexive discussion after completion of a task; novel, non-standard tasks. Conclusion. As separate types the following provocative techniques are proposed: “from the opposite” (analysis of a negative model, preparation of a negative program, etc.), “choice” (in a situation where there is no unambiguously correct answer or the answer is already set and no choice is provided), “search” (find a mistake or some fact, including its absence), “provocation through interaction” (built on the involvement of other students in a provocative action). The conditions for the use of provocative techniques are also formulated. The conclusion is that provocative techniques as a way of organizing educational activities have significant didactic potential and can be included in university educational process.
Hybrid Machine Learning: A Tool to Detect Phishing Attacks in Communication Networks

Phishing is a cyber-attack that uses disguised email as a weapon and has been on the rise in recent times.Innocent Internet users if peradventure clicking on a fraudulent link may cause him to fall victim to divulging his personal information such as credit card PIN, login credentials, banking information, and other sensitive information.There are many ways in which attackers can trick victims to reveal their personal information.In this article, we select important phishing URLs features that can be used by an attacker to trick Internet users into taking the attacker's desired action.We use two machine learning techniques to accurately classify our data sets.We compare the performance of other related techniques with our scheme.The results of the experiments show that the approach is highly effective in detecting phishing URLs and attained an accuracy of 97.8% with 1.06% false-positive rate, 0.5% false-negative rate, and an error rate of 0.3%.The proposed scheme performs better compared to other selected related work.This shows that our approach can be used for real-time applications in detecting phishing URLs.
Using Ontology-Based Approach to Improved Information Retrieval Semantically for Historical Domain

Searching and retrieving documents from large historical archives prove to be challenging for the information retrieval (IR) field as historians typically employ their knowledge, experience, and intuition. There are several works done on the application of IR in historical documents. As such, the conventional IR model is mostly used a simple Bag-of-Word (BOW) approach and usually unable to support precise document retrieval for the domain of history. We proposed an ontology-based approach to semantically index and ranked rich historical documents. The historical documents relating to the Vietnam War were chosen for this study. Several existing ontologies have been reviewed to identify the most suitable concepts and properties which contain rich information pertaining to relevant entities such as an event, time, and people. The domain ontology was developed by utilizing the existing Simple News and Press (SNaP) ontology and extended with concepts related to the Vietnam War. The ontology was then semantically mapped with concepts found in a collection of 133 documents relating to the Vietnam war. In this paper, we also proposed a simple ontology-based weighting mechanism derived from the classic tf-idf scoring scheme. Finally, 20 SPARQL queries are implemented to do the evaluation. The evaluation shows that the proposed ontological-based approach achieved better results as compared to the base-line BM-25 probabilistic retrieval model in terms of precision and recall metrics. The use of the ontology-based approach in document retrieval can compete with the keyword-based approach.