Reviewerly/exHarmony huggingface dataset:
Novel Bayesian Network Incremental Learning Method Based on Particle Swarm Optimization Algorithm

Traditional bayesian network learning mainly uses batch learning method, that is, assuming that the training data can be acquired in one time, the whole learning process will end after learning these samples. But most real-world data is generated sequentially and incrementally over time. Therefore, it is necessary to improve batch learning into incremental learning to adapt to the reality. Many scholars have studied the incremental learning method [, , ] in the bayesian network field. In this paper, the discrete particle swarm optimization (PSO) algorithm is introduced into the structure learning of bayesian network, and a new incremental learning method based on PSO is proposed. Finally, the control variable method is used to compare the parameters of the algorithm, the number of iterations of a single learning and the number of particles, etc., to explore the influence of these factors on the incremental learning effect of bayesian network.
Evaluation of CEISA Application Information System at KPPBC Type Madya Pabean C

Many government agencies use information technology systems to assist in their work. In the direction of a sophisticated service system, high availability, high interoperability, scalable, safe, and easily accessible. One approach is an application that is built on a web basis so that it can be accessed from anywhere if there is an internet and intranet network. The system is not necessarily following the existing and current information system assessment standards, so this study aims to evaluate the maturity level of the application system using the Maturity Model method. The application system studied is CEISA (Customs-Excise Information System Automation), which is used by the Customs and Excise Supervision and Service Office in Indonesia. The location in this research is the Customs and Excise Supervision and Service Office Type Madya Pabean C, located in Pangkalpinang. This study shows the maturity level is at level 1 of the six existing levels. With further development of the system, the maturity level will also increase. As for the results obtained from this study, the maturity level of the CEISA application is at level 1 because someone who is an expert in using the application so that his managerial needs are still lacking. Thus, in the face of the need for a manager's skills shortage, the calculation of the adapted maturity level of COBIT determines the success of a system implemented.
Storing Hypergraph-Based Data Models in Non-Hypergraph Data Storage and Applications for Information Systems

Data structures and especially the relationship among the data entities have changed in the last couple of years. The network-like graph representations of data-model are becoming more and more common nowadays, since they are more suitable to depict these, than the well-established relational data-model. The graphs can describe large and complex networks — like social networks — but also capable of storing rich information about complex data. This was mostly of relational data-model trait before. This also can be achieved with the use of the knowledge representation tool called “hypergraphs”. To utilize the possibilities of this model, we need a practical way to store and process hypergraphs. In this paper, we propose a way by which we can store hypergraphs model in the SAP HANA in-memory database system which has a “Graph Core” engine besides the relational data model. Graph Core has many graph algorithms by default however it is not capable to store or to work with hypergraphs neither are any of these algorithms specifically tailored for hypergraphs either. Hence in this paper, besides the case study of the two information systems, we also propose pseudo-code level algorithms to accommodate hypergraph semantics to process our IS model.
Child Online Safety in Indian Context

Children initiates the usage of Internet during young age and spend more time online. Apart from the benefits like improved education, entertainment, news and gaming, Internet poses severe threats to the children online. Ensuring online safety is a global challenge. The purpose of this paper is to examine online social media responses and awareness posts on children online safety. In this relation, Twitter social media responses after freeing the accusers of children sexual harassment and Facebook pages of some prominent personalities in India for online safety are analyzed. The results reveal that though the people are angry and fearful, they believe judiciary and police system and expecting safety from the same. The analysis of Facebook posts depicts that the concerned authorities are active towards child online safety and providing awareness through their representatives. People demand legal actions against the perpetrators of the crime to punish them. The necessary actions should be taken for cyber-crime awareness information to reach all social media users.
Educational service robotics in the Russian Federation

Abstract Russia has the potential to become a significant player in the educational service robotics market. Unfortunately, this potential remains unrealized. The capacity of the domestic Russian market is still insignificant, and for the sustainable development of domestic developers and manufacturers of robotic systems, it is necessary to focus efforts on foreign markets. The cultural code of the Soviet Union more than twenty years ago formed an attitude towards robots as human assistants, helping people not only explore the world around them, but also help people comprehend themselves. Since the cultural code of the USSR was largely transposed into the culture of modern Russia, a positive attitude towards robotics in general contributes to the popularization of this direction of technology as a type of activity among the population.
Black-box optimization for design of concentrating solar power and photovoltaic hybrid systems with optimal dispatch decisions

The hybridization of concentrating solar power (CSP) and photovoltaics (PV) can enable dispatchable renewable electricity generation at a lower price than current stand-alone CSP systems. However, designing a CSP-PV hybrid system can be challenging because of the many degrees of freedom in design that affect the internal and external system interactions and trade-offs. We develop a methodology to determine optimal designs for CSP-PV hybrids by implementing NLopt's derivative-free, or “black-box,” algorithms around pre-existing CSP-PV hybrid simulation software that utilizes the National Renewable Energy Laboratory’s System Advisor Model (SAM); we then employ a dispatch optimization model to determine operational decisions that maximize a plant’s profits. We present optimal designs for CSP-PV hybrid systems dispatching against four time-of-delivery (ToD) pricing structures. NLopt’s algorithms can improve the base case design’s power purchase agreement (PPA) price by 15% to 21%, depending on the ToD pricing structure. In addition, we present the resulting optimal CSP-PV hybrid design’s annual performance metrics, which tend to have capacity factors between 50% and 62%, but are able to generate electricity during the year’s highest-valued periods about 90% of the time. Lastly, we investigate the trade-offs between capacity factor and PPA price using Pareto fronts and demonstrate that, for some ToD pricing structures, the system capacity factor can increase by 20% but at the expense of a 2% increase in PPA price.
Path Planning for Continuous-curvature Avoidance using Hierarchical Four Parameter Logistic Curves

Robots are widely used as unmanned vehicles in smart factories. In order to operate the robot in a known environment, the robot must be able to plan the path. The planned path must enable the robot to reach a destination from the starting point and the robot must avoid all obstacles during movement. This study uses a four-parameter logic curve path planning method that uses a closed formula solution and a curve with minimal design parameters to quickly generate an ideal path. The characteristics of the curve are used to derive the S and half-S curves as the solution path. The shortest path is used as the selection reference target to select parameters B and C for the four-parameter logic curve. For the S path, the best solution is chosen. The half -S curve is limited by the elastic end-point heading angle, which gives a unique set of parameter solutions. The generated path does not allow the robot to completely avoid obstacles so a hierarchical half-S curve path planning mechanism is used. A via point is generated at the collision point using the gradient vector for the edge of the obstacle. The planner continues to perform half-S path planning until the path planning ends.
Effective Resource Allocation in Cooperative Co-evolutionary Algorithm for Large-Scale Fully-Separable Problems

This paper investigates the effective computational resource allocation for large-scale fully-separable problems under the framework of a cooperative co-evolutionary algorithm called MLSoft. According to different subgroup sizes of the problems, we allocate different numbers of iterations to the subproblems in all the cycles. For high-dimensional subproblems, more iterations are needed during the optimization process; while for low-dimensional subproblems, fewer iterations will be assigned. The experimental results reveal that the proposed resource allocation scheme is simple but effective, which can enhance the performance of MLSoft in solving large-scale fully-separable problems. In addition, we conduct a group of experiments to evaluate the results if a higher weight is assigned to more recent performance in MLSoft. The results show that introducing weight to the latest reward affects very little on the performance of MLSoft.
Modelling crosslinguistic n‑gram correspondence in typologically different languages

Abstract N‑gram analysis (popularized e.g. by Biber et al ., 1999 ) has become a popular method for the identification of recurrent language patterns. Although the extraction of n‑grams from a corpus may seem straightforward, it proves to be very challenging when applied cross-linguistically (cf. e.g. Ebeling and Ebeling, 2013 ; Granger and Lefer, 2013 ; Čermáková and Chlumská, 2017 ). The major issue is that the quantities of n‑grams of a certain length in typologically different languages do not correspond. Consequently, n‑grams of a given length may function differently across languages, rendering a direct comparison inadequate. Our paper introduces a function capable of modelling the relation between the quantities of n‑grams in typologically distant languages, using the example of Czech and English (and some other language pairs). Based on our model, we can suggest what n‑gram lengths should be contrasted to better reflect the size of n‑gram inventories in each language. The correspondence may not be intuitive (e.g. a Czech 2-gram may best correspond to an English 2.5-gram), but it still provides researchers with a general guide as to what might be useful to include in their analysis (e.g. in this case 2-grams in Czech and 2- and 3-grams in English).
Measuring the Effects of Summarization in Cluster-based Information Retrieval

Summarization is an integral part of modern Internet. In social networks, which have become primary information sources, users have grown accustomed to condense their writing. Content providers routinely publish short textual excerpts to these platforms as well. However, with larger quantities of small documents becoming constantly available, search engines now have less data to index, classify and retrieve relevant information. In this regard, more research is needed to show how reliable the current Information Retrieval (IR) algorithms are when confronted to collections of exclusively short documents, such as the ones arising from social media. This paper explores the semantic proximity between human summaries and queries through cluster analysis, and how it relates to IR. Roughly, the k-means algorithm was used to cluster two collections of summaries by their semantic similarity: one in English and one in Spanish. This, to measure how summarization may affect information content in cluster-based IR. Furthermore, the same algorithm was used to measure how documents grouped around a set of artificially generated queries. The results show that, regardless of the language, providing the algorithm with previous category knowledge may contribute to increase the accuracy of cluster-based document classification. Furthermore, some evidences points to the effect of summary quality in retrievability: summaries created by specialized summarizers induced more distinguishable clusters than summaries created by university students. Future work in this area may serve to adapt existing algorithms to big collections of short documents, improving IR performance in cases where machine learning techniques are not available.
Efficient Super Greedy Boosting for Classification

Boosting is a well-known ensemble meta-algorithm, which has been widely used in machine learning. The main idea of boosting is to assemble several weak learners into a stronger one, which significantly improve their performance. However, numerous iterations are usually required for the boosting-type methods to achieve good performance, and thus inevitably bring heavy computational burden. In this paper, we propose a novel boosting method called Super Greedy Boosting (SGBoosting) for binary classification, to reduce the computational cost of the existing boosting-type methods. Instead of one weak learner selected at per iteration of the existing boosting-type methods, the proposed method selects a mini batch of weak learners at per iteration according to the so-called super greedy scheme, and thus needs much fewer iterations to yield sufficient number of weak learners to achieve high accuracy. A series of toy simulations and real data experiments are conducted to demonstrate the efficiency and effectiveness of the proposed Super Greedy Boosting method. The numerical results show that the proposed method can significantly reduce the computational cost with the comparable generalization performance of the state-of-the-art boosting-type methods.
Robust Cryptographical Applications for a Secure Wireless Network Protocol

In this chapter, we aim to discuss three dynamics systems to enhance the data confidentiality and integrity on wireless network security. We start it by a quick, dynamic and random synchronous generator of the unpredictable binary sequences. We combine a large theory concept to produce a lightweight and random stream cipher. This generator combines a linear feedback shift registers LFSRs, the arithmetic of quadratic fields and Boolean functions. Encryption and decryption are done by XRO’ing the output pseudorandom number generator with the plaintext and ciphertext, respectively. It will be used as a symmetric key cipher to avoid serious security problems. To underline the data integrity, we evaluate our system by a dynamic integrity check code DCRCn(M,G) that calculates a checksum of primitive polynomials generator. It proves her robustness by her aptitude to regenerated primitive signals of any generator polynomial. The core of this integrity check process is founded on a dynamic primitive polynomials generator. We aim to prove the data integrity of any transmitted information without broadcast any information about the used generator polynomial. To close, we highlight our cryptographic applications.
Efficient Stitching Algorithm for Stereoscopic VR Images

In this paper, we propose a novel approach for stitching stereoscopic panoramas. When stitching stereoscopic panoramas, the amount of depth retrieved is the most important factor to pay attention to. Also, it is very crucial to deliver the two left and right panoramas with the right depth information to deliver good 3D perception. However, when stitching the two panoramas independently using the state-of-the-art algorithms and methods, we do still have some inconsistencies with the disparity map retrieved from the panoramas. In order to overcome this problem, we propose a method that modifies the latest conventional algorithm by making the two panoramas dependent on one another. This brings two panoramas with a much more consistent disparity map that lets users fully immerse into a comfortable stereoscopic vision.
MI-KGNN: Exploring Multi-dimension Interactions for Recommendation Based on Knowledge Graph Neural Networks

To achieve more accurate recommendations, a consensus of the research community is that not only explicit information (i.e., historical user-item interactions) but also implicit information (i.e., side information) should be utilized. Generally, both explicit and implicit information can be categorized according to the following assumptions: 1) Users with same behaviors are similar; 2) Items related to the same user are similar; 3) Items with same attributes are similar; and 4) Users with same interests are similar. However, none of existing studies has fully explored such information. To this end, we put forward Multi-dimension Interactions based Knowledge Graph Neural Networks (MI-KGNN), i.e., a GNN-based recommendation model that characterizes the similarity between users and items through embedding propagation in the knowledge graph. Specifically, apart from the traditional user-item and item-user interactions, we define another two types of interactions by introducing three different bipartite graphs. On one hand, we explore the interaction between items and the neighborhood during the information aggregation process. On the other hand, we explore the interaction between users and the neighborhood during embedding propagation. These interactions allow information to propagate in the direction indicated by the above four assumptions. In such a way, MI-KGNN effectively extracts both semantic information and structural information in the knowledge graph. Experimental results show that MI-KGNN significantly outperforms state-of-the-art methods in top-K recommendations.
Fuzzy states for dynamic adaptation of the plot of an educational game in relation to the learner’s progress

Educational games are a powerful tutoring tool since they can increase the students' interest, motivation, and engagement. However, they should provide adaptivity to the learners' different needs and characteristics for better educational results. Towards this direction, this paper presents an educational adventure game that adapts its scenario dynamically to the learner's knowledge level and the needs. The adaptation is based on the learner's knowledge level. Due to the fact that modelling the knowledge level is not a straightforward process, but it is characterized by human subjectivity, we use fuzzy logic, which simulates the human thinking way and is ideal for “computing” vague data. In particular, in the presented game, four fuzzy sets are used to describe the learner's knowledge. The defined fuzzy sets are turned to fuzzy states in a state-chart diagram that depicts the adaptation of the game in relation to the learner's progress. More specifically, fuzzy states of the learner's knowledge level determine if the game scenario is going to be dynamically expanded or not concerning her/his progress or nonprogress. The gain of this is that the game adapts its scenario dynamically to each learner's needs and tracks in a state-chart diagram all the stages s/he passed during her/his navigation within the game, and her/his learning results at each interaction with the game. Thus, the learner and teacher receive valuable insight concerning the learner's educational progress on top of maintaining the learner's motivation through the adaptivity of the gaming scenario.
WITHDRAWN: Face smile determination using face and smile detection for perceptual user interfaces (PUIs) for real-time interaction

Face recognition and biometric analytics is a novel domain of research and enormous algorithms going in this dimension. The identification of smile is a two-stage operation. You sense a face first and then wait for a smile and a motion detector breaks the shot in thousands of zones, Criteria for analysis such as white balance and facial level. If a human smile, by specifying various parameters, the camera detects a facial defect, including closing your eyes, clear teeth, bending your lips and raising your lips. In order to improve the sensitivity of the smile automatic function you should change the camera parameters. Authentic smile identification is more successful when participants, particularly their eyes, (with bangs, etc.) do not cover their faces. Hats, masks or sunglasses may also obstruct the work. You should have a big and open-mouthed grin on your subjects. When your teeth are clear, your camera can still detect smiling better. The presented work is focusing on the face smile analytics with the Perceptual User Interfaces for cumulative performance in the biometric analytics.
Use of the Advanced Encryption Standard Algorithm for Encryption Short Message Service on Real Count Applications

Short Message Service (SMS) technology is a popular service among cellular phone users in Indonesia. The development of the SMS facility has been developed in the utilization of Real Count Application. However, the facility in the form of SMS has a vulnerability in the form of wiretapping. The problem that occurs is that there is a gap in knowing the data in the Real Count Application system message delivery so that unauthorized parties can access it. The biggest security gap in SMS communication is when messages are read and stored in the Short Message Service Center (SMSC) for delivery to the database. One solution to overcome this problem is the application of the Advanced Encryption Standard algorithm for encryption of Short Message Service in Real Count Applications. The results obtained with the application of the Advanced Encryption Standard Encryption Algorithm will be able to increase the security of messages sent and ensure their confidentiality against receiving short messages in the real count application system.
Swarms of Mobile Agents: From Discrete to Continuous Movements in Multi-Agent Path Finding

A variant of multi-agent path finding in continuous space and time with geometric agents MAPF is addressed in this paper. The task is to navigate agents that move smoothly between predefined positions to their individual goals so that they do not collide. We introduce a novel solving approach for obtaining makespan optimal solutions called SMT-CBS based on satisfiability modulo theories (SMT). The new algorithm combines collision resolution known from conflict-based search (CBS) with previous generation of incomplete SAT encodings on top of a novel scheme for selecting decision variables in a potentially uncountable search space. We experimentally compare SMTCBS and the previous CCBS algorithm for MAPF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sup> .