Reviewerly/exHarmony huggingface dataset:
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling

We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-literal babbling (GLIB), a simple and general method for exploration in such problems. GLIB samples relational conjunctive goals that can be understood as specific, targeted effects that the agent would like to achieve in the world, and plans to achieve these goals using the transition model being learned. We provide theoretical guarantees showing that exploration with GLIB will converge almost surely to the ground truth model. Experimentally, we find GLIB to strongly outperform existing methods in both prediction and planning on a range of tasks, encompassing standard PDDL and PPDDL planning benchmarks and a robotic manipulation task implemented in the PyBullet physics simulator. Video: https://youtu.be/F6lmrPT6TOY Code: https://git.io/JIsTB
The Pheromone-Based Harmony Search Algorithm for the Asymmetric Traveling Salesman Problem

This paper presents a modification of the Harmony Search algorithm (HS) adjusted to an effective solving of instances of the Asymmetric Traveling Salesman Problem. The improvement of the technique spans the application of a pheromone, which, by serving the role of long-term memory, enables the improvement of the quality of determined results, especially for tasks characterized by a significant number of vertices. The publication includes the results of tests that suggest the achievement of effectiveness improvement through the modification of the HS and recommendations concerning the proper configuration of the algorithm.
Towards compositional automated planning

The development of efficient propositional satisfiability problem solving algorithms (SAT solvers) in the past two decades has made automated planning using SAT-solvers an established AI planning approach. Modern SAT solvers can accommodate a wide variety of planning problems with a large number of variables. However, fast computing of reasonably long plans proves challenging for planning as satisfiability. In order to address this challenge, we present a compositional approach based on abstraction refinement that iteratively generates, solves and composes partial solutions from a parameterized planning problem. We show that this approach decomposes the monolithic planning problem into smaller problems and thus significantly speeds up plan calculation, at least for a class of tested planning problems.
Pruning Based Deep Network is Used for Text Detection of Natural Scene Images

The text in natural scene images contains a wealth of content, marking a large amount of information about this scene, which is of great value for image applications. Because of the blurring, distortion, warping and other problems of the text in natural scene, it is extremely difficult to detect it. The best tool for images processing is CNN, but the number of channel layers in recent CNN is getting deeper and deeper, most of them have hundreds of layers, which consumes a lot and requires high equipment. In this paper, the channel level is screened, and then the useless channel level is deleted. Combined with the latest Yolo v3 model, which has a good effect on small target detection, the text in natural scene is detected after pruning, and finally the performance is reflected on the ICDAR2015 data set. Experimental results show that the pruned model has the slightly lower precision than the original model, but the training time is greatly reduced.
Recognizing Credit Card Fraud Transaction using Spending Behavior-based Transaction Features

Flexibility and easiness for making financial transaction has continued to make credit card transaction a popular alternative financial payment especially during Corona-19 Pandemic that hamper almost worldwide.However, recognizing credit card fraudulent transaction is still an open problem despite many methods have been proposed.The main problems, among others, are imbalanced nature of the credit card transaction data and no agreed upon features to represent the transaction.This paper presents a novel approach to address such problem by exploiting card user spending behavior as the main feature to represent the transaction.in our experiment with Random forest we put max features parameter value 18 and 75 as number of trees in Random forest.While our experiment with OCSVM is by using 4 different kernels which are RBF, Polynomial, Linear and Sigmoid, and, we tuned nu and gamma parameter to get the optimal precision, recall and AUC for our Classifier.The experiment results found that One-Class SVM model achieve 0.984 AUC for training and 0.985 AUC for testing; whilst, Random Forest model achieves 0.991 AUC for training and 0.943 AUC for testing.
The Change Management Strategy in the Private Sector Insurance Companies after Covid-19

This study would discuss the concept of change management and organization development as well as discusses the strategies for the insurance industry private organizations to incorporate change within it.Moreover, the study would identify the changes that were incorporated by the organization during the different pandemic situations of the past and after COVID-19 pandemic.This research followed quantitative research method by collecting responses from 163 participant's non-random sample, who answered the questionnaire survey which was distributed to specific employees working in private insurance companies in Dubai from different level.The survey conducted by sending the link through email and via WhatsApp.The results showed that the private insurance companies applied different change strategy during COVID-19 and planning to implement more plans after pandemic to reach their goals and achieve the customer satisfactions in order to survive and compete others.The researcher conclude that the insurance private companies got increase in revenues in some services but in the other hand they struggle from some issues like others, however, some recommendations provided at the end of the report.
Role of Financial Development in Economic Globalization: Evidence from Six Developing Countries

Among different dimensions of globalization, economic globalization is one of the dimensions which involve the increased independence of different economies of the world that comes from the enhanced trade across the borders, transfer of international capital across the countries and through the increases exchange of technologies. This concept is especially important for the developing countries as they are in the process of establishment and globalization. The current study associates this concept with the financial development (FD) and the objective of the study is to investigate the impact of FD on the economic globalization (EG) in the developing countries. In this regard, the data has collected from the developing countries which include Romania, Jordan, Mauritius, Oman, Poland and Somalia. The data has been collected from the website of World Development Indicators by the World Bank for the time period of 27 years. After data collection, the researcher used different techniques and tools such as unit root test cross sectional dependence and correlation test, Granger Casualty test etc. and the desired results were obtained. The results of the current study have indicated that the both dimensions of financial development i.e. gross domestic savings and private credit has favourable impact on EG. In addition, the influence of the two control variables i.e. GDP and FDI has also been found as significant and positive. This study has the implications for the developing countries as they can get assistance in many aspects with the objective to increase globalization.
Intention and Goal-Conciliation

I analyze in this study potentialities of modeling communicative interactions assuming one can conceive processes of ostensive communication as converting practical intentions into informative and communicative intentions. So, one can only achieve collaboratively a practical intention through the recognition the speaker intends to make mutually manifest or more manifest certain set of assumptions by overtly or communicative ostensible stimuli. Firstly, I present the goal-conciliation abductive-deductive architecture in the case of self-conciliation, then I discuss a case of collaborative heteroconciliation, and finally, I draw some considerations about intention in the light of an approach to communication as a proactive agency.
Extending Graph Pattern Matching with Regular Expressions

Graph pattern matching, which is to compute the set M(Q, G) of matches of Q in G, for the given pattern graph Q and data graph G, has been increasingly used in emerging applications e.g., social network analysis. As the matching semantic is typically defined in terms of subgraph isomorphism, two key issues are hence raised: the semantic is often too rigid to identify meaningful matches, and the problem is intractable, which calls for efficient matching methods. Motivated by these, this paper extends matching semantic with regular expressions, and investigates the top-k graph pattern matching problem. (1) We introduce regular patterns, which revise traditional pattern graphs by incorporating regular expressions; extend traditional matching semantic by allowing edge to regular path mapping. With the extension, more meaningful matches could be captured. (2) We propose a relevance function, that is defined in terms of tightness of connectivity, for ranking matches. Based on the ranking function, we introduce the top-k graph pattern matching problem, denoted by $$\mathsf {TopK}$$ . (3) We show that $$\mathsf {TopK}$$ is intractable. Despite hardness, we develop an algorithm with early termination property, i.e., it finds top-k matches without identifying entire match set. (4) Using real-life and synthetic data, we experimentally verify that our top-k matching algorithms are effective, and outperform traditional counterparts.
Development of Digital Intelligence Among Participants of Inclusive Educational Process

The article analyzes the problem of the development of digital intelligence among participants of inclusive educational process in the context of the global digitalization of modern society. The level of development of this problem is described. A brief analytical review of scientific research of digital educational environments and digitalization of education is presented. The features and advantages of this innovative approach are demonstrated. The risks for the mental and personal development of students in the transition of modern education to digital format are listed. The results of a pilot research of the development of the main components of digital intelligence among participants in an inclusive educational process (teachers, students and their parents) are presented. A model of the targeted development of digital intelligence in the practice of an inclusive educational organization is proposed.
A Paradigm Shift towards On-Premise Modern Data Center Infrastructure for Agility and Scalability in Resource Provisioning

In this Modern Era, Datacenters are the backbone of today's enterprise businesses.To expand their business growth, an organization should first identify whether they are going to stay with on-premises or cloud infrastructure.In this paper we are going to explore the evolution towards on-premises Modern Data Centre (DC) from the basic classical, "siloed" traditional infrastructure.The old traditional data centre relies on more hardware and physical servers, needs more individual team to maintain, consumes more electricity, incurs more cost on buying machines for extra workloads when company grows.Maintaining the data securely is also a crucial task in siloed infrastructure because it needs more configuration and administration task which may lead to error-prone.The Next level of converged infrastructure in which the configurations are of pre-defined bundled templates which cannot be scaled further.The one more level-up the existing on-premise hyper-converged infrastructure in which CPU and storage will be coupled in one plane and network is in another plane and here when scalability is needed extra nodes to be created completely again is an additional overhead.In this paper the proposed Composable Infrastructure which supports all kinds of traditional and modern workloads with fluid pool of independent resource provisioning can be done intelligently through predictive unified API with more scalability, High availability and agility by means of the technologies like virtualization and containerization like the power of cloud infrastructure and services.Moreover, the software industry is highly fluid in this present decade to adapt the old software model to fit with evolving containerized Micro-services based applications which requires greater scalability and fast deployment.Hence the evolved on-premises composable modern infrastructure which is more dynamic in provisioning the resources with unlimited scaling is compared with other existing infrastructures with various parameters like Energy efficiency, High Availability, Cost and Agility.
Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering

We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MG-MAPF not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized.
A Novel Hybrid Machine Learning Approach to Classify the Sentiment Value of Natural Language Processing in Big Data

Natural Language Processing (NLP) is a computer program utilized in big data applications to specify the customer review also it's a major part of AI.Thus NLP strategy is processed with several languages because the language is differing from state to state.Furthermore, sentiment analysis in NLP is advanced in many applications and various languages to evaluate the sentiment value.But part of speech specification for different language is too difficult.To overcome this problem the current research aimed to develop a novel Evolving C4.5 machine learning with Spider Monkey Optimization (EC4.5-ML-SMO) to classify the sentiment analysis in Telugu language efficiently.Moreover, the fitness function of SMO enhanced the accuracy of sentiment classification in Telugu dataset.Finally, the effectiveness of proposed module is evaluated with recent existing works and attained better result by achieving better classification accuracy rate.
Hotel Employee Acceptance Assessment System With Android-Based Moora Method

Hotel is a company engaged in the field of services, which requires employees who have competencies in accordance with their fields. At present the existing employees are deemed to lack competency in accordance with their field of work. In order for employees to have competence according to their field of work, the hotel must carry out an acceptance assessment that requires certain criteria, such as education, foreign language acquisition, discipline, experience and others. One of the appropriate decision making methods for evaluating acceptance is the MOORA (Multi Objective Optimization on the basis of Ratio Analysis) method which is a multi-objective system that optimizes two or more conflicting criteria. The MOORA method has a degree of flexibility and ease of understanding in separating the subjective parts of an evaluation process into criteria, by determining the weights and attributes as decision making, the form of decision matrices and ranking. The criteria used in this study are height, experience, discipline, interviews, written tests, foreign languages, friendly, computer, software and medical checkups. To facilitate the assessment of acceptance, this system is applied based on Android, so that in addition to the hotel, prospective employees can also find out the results of the assessment. Based on the implementation of the system application, the use of the MOORA method for the hotel employee acceptance evaluation system can produce the right decision system, in accordance with the predetermined ranking, criteria and weight of the assessment
Performance Analysis of Arithmetic Logic Unit with Reversible Logic

With advent of quantum computing the urge of using energy efficient digital circuits have become need of hour for many power constrained applications.Our aim is to develop energy efficient combinational and sequential circuits to implement energy efficient applications for large networks in a standalone manner.Clearly quantum computing principle backs the aim and provides digital circuits with better operational and computing methods to save energy which can further be used to enhance lifetime of networks to work for longer time.This paper presents application of reversible logic in arithmetic logic unit.This unit is integral part for almost all digital computational circuit.The results can be viewed in wider purview of its induction in many industrial applications.
Affinity Propagation SMOTE approach for Imbalanced dataset used in Predicting Student at Risk of Low Performance

Imbalanced datasets affect the performance of classification algorithms in predicting student performance.There are several techniques in combatting class imbalance and one of the most known is the Synthetic Minority Oversampling Technique (SMOTE).It is an oversampling technique that generates synthetic data along the line of the minority instances and its neighbors.However, it has a drawback on the distribution and generation of noisy samples which is the main reason of its many variations.In the cluster approaches for SMOTE, Affinity Propagation (AP) SMOTE is one of them.This approach uses affinity propagation to automatically produce clusters and cluster exemplars used to select the clusters to be oversampled.This way, the sparsity and generation of noisy samples will be avoided.The data used for the study is the student performance of freshman students of Davao Oriental State College of Science and Technology (DOSCST) as well as their enrolment data.The dataset comprises 10 features and 2112 instances, the imbalance ratio between majority and minority is 17.85.SMOTE and AP SMOTE are applied to the imbalanced dataset.The output is used in the J48 and Naïve Bayes classifiers to predict the student at risk of getting low performance in their freshman years in the college.The classifiers' performance is evaluated using f-measure, g-mean, and Areas Under the Curve (AUC).Results showed that AP SMOTE outperforms the original SMOTE with a percentage lead of .60%,.88%,1.2% using the J48 classifier.The percentage lead for Naïve Bayes is 3.2%, 6.58%, 3.30%, respectively.
A New Synthesis: Research Resources to Research Experiences

Libraries should develop a new model for providing information resources and analytical tools for the use of scholars working in the current multidisciplinary research environment. This model, A New Synthesis, based on today’s research experience should replace the present concept of the “collection budget.” Sources are proliferating and traditional scholarly resources are no longer at the core. Research itself has changed. Previously, finding information was primary, but now information is plentiful and today’s challenges are to understand, analyze, and extract insight from these vast resources. To address this challenge, newly designed libraries are appearing that are radically different, reconceptualizing learning spaces, technological infrastructure, and research labs for scholars and students. Yet, the concept of the collection budget is little changed. We must embrace a paradigm that allows us to envision holistically the development and investment necessary to support current research. To enable expanded capacity for supporting today’s Grand Challenge research and to ensure the critical relevancy of academic libraries in this endeavor, we must employ a new synthesis. Sources can no longer be viewed independently from the tools needed to analyze them. Critical elements include: redeploying funds to an array of open platforms; shifting the focus from access to knowledge creation; and investing in spaces, technology, and people that will help researchers solve problems in new ways. We are at a moment when building these services and placing them at the heart of libraries requires fundamental organizational and financial change. Reconceiving current spending on collections is essential to this change.
Cloud Data Pipeline Automation Framework with Integrated Budget Management

Today is the era of Big Data and Cloud Computing.Organizations, Universities and Governments are investing heavily in big data analysis which provide strategically important insights.The cloud platforms, with virtually infinite amount of on demand and scalable resources, are used to execute the required data transformations and pipelines.As the scope of the data analysis activities widen, organizations need a framework to schedule the data pipelines and achieve the objectives of Budget management, Data pipeline prioritization and Reuse encouragement.This paper presents a "Cloud Data Pipeline Automation Framework" with several checks and controls.The framework was evaluated on Google Cloud Platform and the results establish the effectiveness of the framework.By using this automation approach, organizations would realize the true benefits offered by the "pay-as-you-use" cloud platforms.
Tools &amp; Techniques used in the Language of Advertisements: A Linguistic Analysis of Indian TV Commercial Ads

The present paper aims to highlight the linguistic tools and techniques used in the language of advertisements.The study becomes significant as the language used in the advertisements is purposely and deliberately created.The deliberate use of language makes the advertisements eye-catching and gets the attention of its viewers.This study investigates 75 different Indian TV advertisements and does linguistic analysis at phonological, morphological, and stylistic levels.At these three levels, the study reveals the use of phonological devices; such as rhyme, alliteration, and assonance; morphological devices; code-mixing; the degree of comparison, hybridization, and reduplication; and stylistic devices; antithesis, apostrophe, hyperbole, metaphor, onomatopoeia, and personification.Besides these devices, the study also focuses on graphological and national aspects that play an essential role in the advertising language.
A Light Recommendation Algorithm of We-Media Articles Based on Content

Since the explosive growth of we-medias today, personalized recommendation is playing an increasingly important role to help users to find their target articles in vast amounts of data. Deep learning, on the other hand, has shown good results in image processing, computer vision, natural language processing, and other fields. But it's a relative blank in the application of we-media articles recommendation. Combining the new features of we-media articles, this paper puts forward a recommendation algorithm of we-media articles based on topic model, Latent Dirichlet Allocation (LDA), and deep learning algorithm, Recurrent Neural Networks (RNNs). Experiments on the real datasets show that the combined method outperforms the traditional collaborative filtering recommendation and non-personalized recommendation method.