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Academic networks express the academic relationships among authors, papers and conferences, which offer perspectives and tools to rethink and solve traditional problems in the academic field. Among them, conference ranking is a problem which normally produced by collecting information from different sources and considering many factors. Not like most of the existed ranking methods that based on subjective evaluation, in this paper, we develop a Conference Ranking method based on Academic Network Embedding (CRANE) algorithm to simultaneously rank authors and conferences which improving the accuracy of ranking by embedding the Academic Network. The CRANE uses both conference features like internationality, citations and co-authors, and structure in Academic Network to evaluate the ranking of certain conferences. The theoretical analysis and convergence analysis were provided in this paper. Experiments on datasets of the Digital Bibliography & Library Project (DBLP) and AMiner academic platforms also be conducted. The experimental results show that the CRANE algorithm is superior to other algorithms by comparing with the public academic ranking like the China Computer Federation (CCF) ranking. [ABSTRACT FROM AUTHOR]

One important aspect of human behavior understanding is the recognition and monitoring of daily activities. An accurate activity recognition system can improve the quality of life in many key areas. The multi-metric feature extraction and DeepForest classifier designed in this paper effectively had solved the problems of incomprehensive feature extraction and insufficient classifier accuracy. First, this paper extracts a total of 450 dimensional feature vectors using mean, variance, maximum, skewness, minimum, kurtosis, regression of independent variables and sample entropy as feature indicators, so that the features of the original vectors can be presented comprehensively. The dimensionality of the feature set is too large, so PCA is used to reduce the dimensionality of the high-dimensional feature vector. After studying the relationship between the dimensionality reduction and the cumulative contribution rate, it is concluded that the cumulative contribution rate reaches 90% when the dimensionality is 15, which can retain the original features better. Then DeepForest was used as the classifier, and the reduced-dimensional feature set was used as the sample set to divide the training set and test set by 3:1. The model was tested on the test set after training, and the classification accuracy of the test set was 98.202%. Six classifiers, GaussianNB, SVM, K-NN, XGBoost, RandomForest and DecisionTree, were selected as the control experimental group, of which only the RandomForest model reached 97%, while the rest of the control models did not achieve 95% effect, indicating that DeepForest was more accurate in classifying human activities. Next, the generalisation ability of the DeepForest classifier was assessed using Monte Carlo Cross Validation (MCCV), K-fold and its confusion matrix. The MCCV validation used 50% of the data as the training set and 30% of the data as the test set, and set the number of splits to 10, resulting in a mean accuracy of 98.227%. The size of K in the K-fold validation was determined to be 8 based on the number of people conducting the human activity experiment, and the final mean accuracy value obtained was 97.790%. The combined confusion matrix from the K-fold validation (which aggregates the confusion matrix for each classification result) was calculated, and the results showed that the highest accuracy reached 100% for A9 and A14 classifications, where A5 and A10, A15 and A10, A12 and A5 were more likely to be confused, with the highest error rate for A5 classification, which was 4.167%, and the rest of the activity classifications were better. [ABSTRACT FROM AUTHOR]

Machine learning is a popular tool for solving problems, however, incorporating it into a use case with additional business logic poses many challenges. Training, managing and storing many different models is not an easy task, requiring the use of multiple frameworks and languages. To take full advantage of existing frameworks it is necessary to facilitate communication between different programming languages. This paper presents an approach to integrating machine learning in a real-world use case which involves predicting demand for a diverse set of products and combining it with business rules and other components to establish a system that improves and automates the ordering process. Machine learning models are trained on real-world data from a retailer in Austria and the predictions are incorporated into a heuristic that controls and manages stock levels. This work focuses on the challenges that emerge from the integration of machine learning and presents a message bus based architecture to address them. [ABSTRACT FROM AUTHOR]

In Product-Service System (PSS) offerings is crucial to deliver services in the shortest time to maximize customer satisfaction. One of the first contacts that customers have with the provider is usually through remote assistance delivered via telephone or email. Thus, the development of a structured troubleshooting procedure is fundamental for fast problem identification and resolution. While customers exchange with help desk technicians are saved in a company database, they are often lacking proper structure and, thus, are rarely analyzed. This poses a challenge since aggregated data can provide valuable insights for knowledge extraction and reuse, benefiting PSS lifecycle management and improvement (e.g., enhancing troubleshooting, maintenance service, or PSS design). The paper presents a case study where the textual data collected from the customer ticket database have been analyzed to extract the most recurrent problems and the frequently suggested solutions and improve remote troubleshooting. [ABSTRACT FROM AUTHOR]

For the model of a multi-node energy market, the problem of maximizing social welfare is considered. Social welfare refers to the difference between the total utility of consumption and total production and transportation costs. Algorithms for solving this problem are proposed in our previous papers. These algorithms can be used to assess the prospects for gasification of arbitrary non-gasified regions of the country, if estimates of the initial parameters of the model are known - the functions of production costs and demand in nodes and the functions of transmission costs in lines. In this paper, methods for obtaining such estimates are proposed, as well as the results of the analysis of the prospects for gasification of Irkutsk oblast obtained using the described approaches are presented. [ABSTRACT FROM AUTHOR]

Deep AI and big data have reshaped the IT field. Since AI refers to the simulation of human intelligence processes by computer systems for problem solving, the current landscape of IT provides an excellent opportunity for us to re-examine how human cognition has responded to new developments in IT. In this paper, we discuss several issues related to human cognition in IT at AI-enriched big data era. First, we note that deep AI reflects human beings' needs for causality and explanations, and review a couple of related aspects. We then examine human cognition in IT from two perspectives: On the theoretical side, recent developments in quantum computing has the potential to reveal complex nature of human cognition in the form of quantum cognition, and on the practical side, cognitive bias may play a negative role, as demonstrated in the way of dealing with fake news. The complexity of human cognition in IT leads to a proposed multi-layer concept description which is based on our view that a complete concept definition is impossible without incorporating human cognitive feedback in the loop. We conclude this paper by emphasizing the need for integrated study of human cognition in IT. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

The task of modeling a subject whose cognitive activity is directed under the influence of the information environment continues to remain relevant. The difficulties in solving this problem are due to (1) the dependence of the truth of information on time, as a result of which the subject can make decisions based on outdated information, and (2) the heterogeneous nature of the information itself, which, in particular, can be intentionally false (fake). The paper proposes a model of interaction of subjects, including the exchange of information marked both by the time of its creation (receipt) and the quality of information. The model is based on a system of interacting processes and is presented as a set of information graphs describing the stages of interaction. A system of typing graphs and their constituent elements is proposed. The composition tools that allow one to assemble complex graphs from simpler ones are presented. The paper also presents a set of tools corresponding to the proposed model. The tools include a process language interpreter that provides computation of model constructs, as well as an information graph editor, type control tools, debugging tools, and configuration management tools for the computing environment. The proposed tools have been experimentally tested in the development of application systems from the field of implementing the best available technologies. [ABSTRACT FROM AUTHOR]

The paper presents a method that allows the thickness of a compact bone layer and longitudinal wave velocity in the bone to be determined simultaneously with the use of reflected waves, with particular emphasis on the case of layers when the layer thickness is of the same order as the ultrasonic wavelength. This paper presents the use of optimization methods for estimation the thickness of a layer using inverse method. The studied structure is a multilayer structure composed of two layers solid and porous material. To solve the inverse problem, a multi-layer model and ultrasonic signals obtained from cortical and cancellous bone phantoms by the reflection method was used. The genetic algorithm implemented in the Matlab® package Optimization Toolbox applied to find solution the optimization problem. The results of simulation and experiments demonstrate that the thickness of layer was obtained with good accuracy. [ABSTRACT FROM AUTHOR]

he role of target tracking in target tracking is crucial, but there is a problem with low accuracy. Online monitoring methods cannot solve the problem of accurate locking in target tracking, and the recognition rate is low. Therefore, this paper proposes a visual adaptive tracking method for online target tracking. Firstly, the adaptation theory is used to accurately lock the tracking target and divide it according to the target tracking requirements to reduce the interference factors in the proper locking. Then, the adaptation theory is applied to online target tracking, forming an accurate locking sample and continuously and accurately locking the target tracking requirements. MATLAB simulation shows that the accurate locking accuracy and locking time of the visual adaptation tracking method are better than those of the online monitoring method under the condition that the exact locking requirement is particular. [ABSTRACT FROM AUTHOR]

The role of cost, time and labor intensity in the construction process is very important, but there is a problem of poor optimization effect. The standard genetic algorithm cannot solve the problem of index optimization in the construction process, and the optimization rationality is low. Therefore, this paper proposes an improved genetic algorithm to study the objectives of construction projects. Firstly, the k-value clustering theory is used to analyze the characteristic values of the index and divide them according to the changing trend to reduce the disturbance factors in optimization. Then, the k-value clustering theory is used to search for the child-parent node of the indicator to form the final optimization result and complete the multi-objective optimization. MATLAB simulation shows that under certain optimization criteria, improving the genetic algorithm can improve the rationality of multi-objective optimization, and the optimization time is shorter than that standard improves genetic algorithms. [ABSTRACT FROM AUTHOR]

The role of target coordination problem in robot control is very important, but there is a problem of low collaborative control. The goal coordination problem cannot solve the problem of pairing teachers and classrooms in robot control, and it is reasonably Lower. Therefore, this paper proposes a complex algorithm to construct an optimization model for target collaboration problems. First, the management standard is used to divide the robot control data, and the robot control data is carried out according to the management requirements to realize robot control Preprocessing. Then, the management standards are divided according to the teaching standards, forming an optimized set of robot control and digging deeper into the Japanese time. MATLAB simulation shows that complex algorithms' optimization degree and stability are better than those of online control methods because the teaching standards are consistent. [ABSTRACT FROM AUTHOR]

In recent years, along with the rapid development of economy and society, the quality of life of people has been greatly improved, the total amount of electricity consumption has increased rapidly, and the scale of power system has been expanding. With the rapid increase of China's electricity supply, the corresponding infrastructure construction cannot keep up with the development of electricity consumption, resulting in a huge electricity consumption gap. Especially in recent years, some regions of China are facing industrial and industrial transformation, urban re-planning and renovation, leading to the complication of the structure and operation of the power grid, as well as the problem of heavy load and overload of power-using equipment. This paper describes the concepts and principles of the data algorithm and the peak-limit method, and makes a comparison of the economic losses of eight regions before and after the optimization of the data mining (DM) algorithm through experimental analysis. The results verify the effectiveness of the algorithm in solving the peak-limit problem and prove that the algorithm is of great research significance. [ABSTRACT FROM AUTHOR]

Traditional machine translation based on RNN has two major defects: (1)it can only process word-by-word and result in slow training speed; (2)when the sentence is too long, gradient disappearance and explosion reduce the accuracy of translation. To solve this problem, this paper designs a Transformer based machine translation implemented by PyTorch. Compared with traditional machine translation, Transformer uses the attention mechanism to redesign the defects of RNN and effectively solves the problems of efficiency and forgetting. The French-English machine translation based on Transformer designed in this paper achieves a translation accuracy rate of 80% from French to English after training and practical application. [ABSTRACT FROM AUTHOR]

In the article, the analysis of researches was carried out concerning consideration of a question of forecasting of strains of COVID where based on indicators of patients it would be possible to predict with a specific error existence at them of COVID. Therefore, this work is focused on refuting COVID forecasting, as well as the disclosure of new approaches to the application of traditional methods to solve the problem of forecasting. The paper presents the results of forecasting. The preprocessing of medical data was performed, emissions were taken from them, categorical data were converted with the help of one-hot encoder. This paper proposes a system for automatic detection of COVID in a patient during his treatment. To solve this problem, classifiers were used, and the results of using the principles of their work were obtained. [ABSTRACT FROM AUTHOR]

Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. However, it has been proven that developing efficient methods for solving CSP, especially for large problems, is very difficult and challenging. Existing complete methods for problem-solving are in most cases unsuitable. Therefore, proposing hybrid CSP-based methods for problem-solving has been of increasing interest in the last decades. This paper aims at proposing a novel approach that combines incomplete and complete CSP methods for problem-solving. The proposed approach takes advantage of the group search algorithm (GSO) and the constraint propagation (CP) methods to solve problems related to the remote sensing field. To the best of our knowledge, this paper represents the first study that proposes a hybridization between an improved version of GSO and CP in the resolution of complex constraint-based problems. Experiments have been conducted for the resolution of object recognition problems in satellite images. Results show good performances in terms of convergence and running time of the proposed CSP-based method compared to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to improve understanding of the differences and similarities between Information and Communication Technologies (ICTs) that are offered to consumers to gain their knowledge and ICTs that are expected by consumers in terms of their engagement in knowledge sharing. Thus, this paper aims to solve this problem by answering the main research question: Are the ICT offered to consumers to engage them in knowledge sharing in line with their expectations? The presented outcomes result from two sets of data, (1) a survey questionnaire on consumers and (2) a case study online observation of enterprises and public sector organizations (PSOs). Popular and well-known ICTs were taken into consideration. The findings show that both enterprises and PSOs most often offer consumers their own websites. That indicates that they choose an easily accessible and convenient tool for them to facilitate consumer engagement. The comparison of the results taken from two sets of data has revealed that consumer expectations as to ICT usage for knowledge sharing are much higher than the possibilities offered by enterprises and PSOs. This is especially true for Internet forums, industry-specialized portals, and specialized organization websites. The results also show that females would like to use ICTs in greater scope than males. It is especially true for popular information websites and Facebook portal. What is more, the results show statistically significant differences between age generations for two ICTs only, i.e., mobile applications and Facebook portal. Detailed analyses indicated that the elder generations, i.e., Baby-Boomers and Builders, would like to use these ICTs in greater scope than the younger ones. In particular, the results reveal that consumers expect to use ICTs in greater scope than those offered by enterprises and PSOs. However, the results must be interpreted with caution because of their limitations that pertain to the survey sample, which embraced consumers from Poland only, as well as to ICTs selection limited to nine items. The findings presented could be relevant to any enterprise and PSO that would like to engage consumers in providing feedback about their product and service offerings with the ICT usage. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making methods aim to solve complex problems under many factors. These methods each day are more frequently applied and improved. Many researchers try to find a reliable solution for a specific problem or group of problems. More and more often, new methods are created that certain target requirements. That is why solving the specific problem can be executed on multiple methods. In this paper, we provide three new solutions for the problem presented in the referenced paper. For this purpose, the Characteristic Object's Method (COMET), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method were used. The results were then compared using selected similarity coefficients to emphasize the correlation of the resulting rankings. The research showed that choosing a method will have an impact on results and should be considered with caution. [ABSTRACT FROM AUTHOR]

Open-Domain Question Answering (ODQA) is a technique for finding an answer to a given query from a large set of documents. In this paper, we present an experimentation study to compare ODQA candidate solutions in the context of troubleshooting documents. We mainly focus on a well known open-source framework which is called Haystack. This framework comprises two key components which are the Retriever and the Reader. The Haystack Framework comes with several Retriever-Reader combinations and the choice of the best one is still unanswered till now. In this paper, we conduct an experimentation study to compare different Retriever-Reader combinations. Our aim is to come up with the best combination of components in regard to the speed and the processing power within the context of troubleshooting queries. [ABSTRACT FROM AUTHOR]

AI problems which can be solved using a state-based model are a significant portion, covering most NP problems. Although the strategies required to solve such problems are well documented and researched, the model itself is seldom analysed, being considered either secondary or too abstract to tackle in a systematic analysis. Early languages such as PDDL and the many later variants, allow users to describe a state-based model and use a type of exhaustive strategy (usually BFS) to check if that model can solve a defined problem. More recently, SMT-bounded model checking solutions have been employed, a new, generalized approach is described in this paper. Another new contribution described in this paper uses a state classifier to group generated states and thus simplify an exhaustive search for a solution. This allows us to build a complete problem-space having each state within labelled as belonging or not to any path to the goal of the problem. [ABSTRACT FROM AUTHOR]

The paper considers the disadvantages of existing approaches to testing software and hardware data security tools in order to confirm compliance of the implemented functionality with the declared characteristics. It demonstrates the necessity of ensuring completeness and optimality of testing. The paper describes some testing approaches based on the development of mathematical models using the automata theory and ensuring completeness of testing, but leaving the question of its optimality open. It describes the approach proposed earlier by the authors, which ensures both completeness and optimality of testing using the graph theory. In accordance with this approach, the software and hardware data security tool is represented as a directed graph without loops or multiple edges. The graph vertices correspond to the states of the software or hardware component, and the directed edges correspond to the transitions of the data security tool from one state to another when performing non-target functions or security functions. On the basis of this approach, the authors propose an algorithm for solving the problem of testing software and hardware data security tools using a number of well-known algorithms on graphs. In the paper, it is substantiated that a solution to the problem of ensuring completeness and optimality of testing exists if and only if any vertex of the derived graph obtained by removing all unused vertices and edges either belongs to a directed chain or lies in a strongly connected component. Application of the proposed algorithm for solving the problem of testing one of the software and hardware security tools is considered, and the possibility of its application in practice is confirmed. [ABSTRACT FROM AUTHOR]

Recent focus on sustainability and its three pillars (economic, environmental, and social) brought new issues, especially for manufacturing companies, to be addressed. The article uses the methodology of a case study based on observation, panel expert, interviews with employees and the documentation analysis. The research had been carried out in steel processing enterprise in West Pomeranian region in Poland. The purpose of the article is twofold. Firstly, the aim is to present the company in the context of the production scheme and information flow, as well as the scheme for solving a decision problem that may increase the company's sustainable development potential. Secondly, the aim of the paper is to present the methodology of the process of selecting and assessing a range of criteria and fields of operation based on the identified sustainability criteria, which should be taken into account when deciding what type of the product to choose to improve the potential of sustainability of the company. The described methodology of this process is a scientific contribution based on literature research, observation of the company under study, interviews with employees, analysis of documents and work with experts inside the company, which allowed the creation of a tool for determining operational and decision-making areas that are directly related to the company's sustainable development potential. [ABSTRACT FROM AUTHOR]

Domain adaptation is a practicable tool in real world application where there exists data scarcity. Multi-source domain adaptation attracts increasing attention due to its ability to enrich transfer knowledge by combining information from multiple domains. However, knowledge transfer can trigger privacy concerns by accessing source data. In addition, multiple domains can have label heterogeneity problem. In this paper, to solve the mentioned problems, we conduct an incomplete multi-source domain adaptation (IMSDA) method which can address transfer learning with and without the access to source data. As far as we are aware, this is the first work handling source-free incomplete domain adaptation. To take the benefits of multiple sources, multi-task learning is adopted to learn a general source model which can perform on multiple domains. A data matching strategy with and without source data forcing target sample to source latent feature space is developed to combine with self-supervision to adapt source model to the target domain. Experiments on real-world datasets indicate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of modern communication technology, more and more attention has been paid to the security of communication. In recent years, the physical layer security technology provides a new way to solve the problem of secure transmission in wireless communication. Physical layer security has become a hot research topic, but there is no special physical layer security simulation platform to provide great help for technical research. In view of this situation, this paper develops a physical layer security simulation platform based on the cross platform development framework QT, which provides visual demonstration and performance evaluation functions for specific technologies. The platform is developed with C plus plus language, and different simulation scenarios are customized for different technical schemes to simulate the simulation process. Starting from the architecture of the simulation platform, this paper presents the simulation process and evaluation results of several selected physical layer security directions, and describes the simulation platform in detail. [ABSTRACT FROM AUTHOR]

In present paper we investigate an approach to intelligent support of the software white box testing process based on evolutionary paradigm. As part of this approach we solve the urgent problem of finding the optimal set of test data that provides maximum coverage of the code when it is used in the testing process. Traditionally, to solve this problem, an approach is used when the genetic algorithm is used to find the most adapted chromosome, which is a set of test data that ensures passage along the most complex (long) path in the code graph. Many data sets that provide maximum code coverage can be found by repeating this procedure multiple times with preliminary zeroing of the code operation weights corresponding to the chromosomes found earlier. Thus, the fitness function of the genetic algorithm has a simple form, but the process of finding all the data sets is quite long and non-optimal. In this paper, we study a different approach, when an additional component is initially included in the fitness function, which is responsible for the greatest possible difference in the paths of the population, along with a component responsible for the complexity of each path. The basic version of the algorithm is examined and it's more effective modifications have been proposed to achieve the final goal of obtaining the optimal set of test data covering the flow graph in the least expensive way. [ABSTRACT FROM AUTHOR]

In both developed and emerging countries, like Bangladesh, uterine cancer is one of the most common cancers for women. It is also the sixth most common cancer among females in the world and the fourteenth most common cancer in total. In recent years, the high prevalence of uterine cancer in women has risen dramatically. This covers several aspects that are linked to the signs and symptoms of this disease that are to be assessed and tested. Typically, these dimensions are expressed in quantitative and qualitative forms and also exist different kinds of uncertainty. Therefore, by using suitable methods to resolve the problem of uncertainty; otherwise, the detecting procedure will become unreliable. There are several systems proposed in this paper to tackle the problem. None of them, however, is worthy of solving the problem of ambiguity. This paper explains the development of a Belief Rule Based Expert System using the RIMER method, which by taking into account signs and symptoms, is able to detect the involvement of malignant tumors and risk factors for this cancer under uncertainty. Specifically, the system will expect a huge employment in diminishing the cost of lab assessments. The structure will support patients in making judicious strides well early. Moreover, this research has been done on MATLAB environment shown that the yields produced using the made system are more reliable than from manual structure, artificial neural network (ANN) and support vector machine (SVM). [ABSTRACT FROM AUTHOR]

Before our paper, some papers have approached symmetry detection in various attacks of lines. Deep learning has taken off in recent years in many fields in computer graphics, and we are thinking of using big data to solve the symmetry detection problem. Our work aims to solve a niche problem: using deep learning to detect symmetry in objects of the occluded point cloud. As far as we know, we are the first piece of work to deal with such a problem in deep learning settings. We employ points on the symmetry plane and normal vectors as double supervision to help us pinpoint the symmetry plane. Experiments conducted on the YCB-video dataset prove the effectiveness of the work and our method. To see our implementation, please visit https://github.com/Allen--Wu/dense_symmetry. [ABSTRACT FROM AUTHOR]

In the era of industrial big data, the demand for high-speed data transmission is higher and higher. The traditional Modbus protocol uses fixed data frame length transmission, which can not make full use of link resources, resulting in very low transmission rate. In the early stage, in order to solve the problem of traditional ModBus transmission protocol, an algorithm was proposed to adjust the frame length by calculating the optimal frame length through the frame error rate. However, because the frame length of each data frame is adjusted in this algorithm, the link stability becomes worse and the efficiency is reduced. In order to solve this problem, this paper proposes an improved adaptive frame length adjustment algorithm, which adjusts the frame length according to the retransmission failure rate per unit time, and gives different adjustment schemes according to different situations, which improves the link stability. Finally, this paper takes the servo drive data acquisition system as the carrier, and verifies the effectiveness of the algorithm through experiments. [ABSTRACT FROM AUTHOR]

With the fast-emerging trend of deploying Internet-of-things (IoT) in smart homes and cities, next-generation wireless systems (i.e., beyond fifth-generation (5G) of wireless mobile communications) are expected to connect a plurality of devices, including machines, sensors, tablets, cameras, etc. To this end, the radio frequency (RF) spectrum would fail to fulfill the demands of such enormous data-heavy wireless applications, as operating at the RF spectrum would largely suffer from data congestion and interference. Based on the potential of Visible Light Communications (VLC) to solve the problem of spectrum scarcity and satisfy the high data-rate requirements of beyond 5G systems, this paper focuses on utilizing VLC for IoT applications in indoor environments by means of adequately routing data among VLCbased nodes. Operating at the unlicensed optical band in the visible light region utilizes simple Light-Emitting-Diodes (LEDs), which is a health-friendly communication method. VLC has the advantages of providing ultra-high bandwidth, robustness to electromagnetic interference, and inherent physical security. VLC, however, suffers from severe short communication ranges and line-of-sight (LoS) constraints. This paper proposes overcoming such challenges by means of adopting a transmit diversity scheme that transmits messages over several paths. In order to reduce the receiver outage probability, the paper proposes multiple combination schemes at the receiver side, namely selection combining, maximal-ratio combining, and threshold combining. The simulation results assess the effectiveness of the proposed diversity schemes in terms of signal-to-noise-ratio and outage probability and illustrate the suitability of deploying VLC for next-generation indoor networks. The results show that that maximal-ratio combining outperforms selection combining and threshold combining by approximately 10% and 40%, respectively. [ABSTRACT FROM AUTHOR]

Students face problems that might hinder their academic pursuit toward success, problems ranging from trivial matters such as class condition, feeling of the student to severe matters such as family breakdown, economic reasons, and many more. This is a major problem because students shape the future of a nation – which will affect many things in the future. Teachers are looking for an effective way to find what might generally be the best solution for solving certain problems, as each student may face different problems, solving one at a time is not possible with the number of students each year. In this paper, we will try to find factors that might hinder or improve student performance using Pearson correlation between each feature toward the student G3 result. Based on the result, past failures will negatively impact student grades with -0.360415 correlation, and then Mother's Education will positively impact student grades with 0.217147. After finding out which factor affects student grade, we try to predict student grade using ML models to prove whether that factor actually affects student grade. Our MLP 12-Neuron model performs the best with RMSE value of 4.32, followed by Random Forest with RMSE value of 4.52, and finally Decision Tree with RMSE value of 5.69. [ABSTRACT FROM AUTHOR]

In order to obtain an ultrasound image of the human head, it is necessary to solve the corresponding inverse problem. In this case, acoustic approximation is usually considered in order to save computational resources. The purpose of this paper is to figure out which wave phenomena are neglected and whether it is advisable to use the acoustic approximation when studying human head using ultrasound. For this, we performed a comparison of elastic and acoustic wave phenomena using the method Wave Logica of studying solutions of the boundary-value problem of wave equation. This method is based on a combination of classical analytical approaches and analysis of results of computational experiments. To conduct computational experiments, we used the grid-characteristic numerical method on structured curvilinear computational grids in order to take into account the complex multilayer structure of the human head. Also accurate transformation of elastic and acoustic wave equations from three-dimensional case to two-dimensional case and accurate formula for pressure in elastic media in two-dimensional case are discussed in the paper. [ABSTRACT FROM AUTHOR]

In the environment with training data is adequate, a variety of AI algorithms and techniques have emerged and been applied. However, when facing the contradictory problems with uncertainty and only less data, the existing AI still cannot solve them well intelligently. On the cross study of Extenics and artificial intelligence, Extension intelligence technology incorporates the transformation ideas of information expansion and Extension sets into algorithms for artificial intelligence, and put forward a new approach for solving contradictory problems with uncertainty or the presence of conflicts and paradoxes. The paper illustrates the information expansion mechanism of Extension intelligence and its direction of information dimension expansion and enhancement through Basic-element base construction and information expansion service. Taking the problem of "Self-Contradictory" in Chinese fables as an example, we verified the feasibility and intelligence of our method in solving contradiction problems. [ABSTRACT FROM AUTHOR]

The paper proposes an approach to the formal representation of natural language elements based on multi-agent recursive architectures for solving problems of speech recognition, understanding and synthesis. This approach, based on intelligent software agents with a developed cognitive architecture, makes it possible to represent linguistic information at all levels of the language: from specific morphological to abstract semantic. [ABSTRACT FROM AUTHOR]

Learning from our own mistakes is one of the best ways of impactful learning. This comes into play when we practically apply and experience theoretical knowledge. This is where the concept of project based learning becomes the most powerful method. Having the theory knowledge is essential but having only the theory knowledge without knowing its application may not be useful in the long run. Project based learning (PBL) helps students to know the proper application of theoretical principles taken from the books. This paper describes how project based learning helps students in acquiring practical and technical knowledge. Project based learning is a model that helps student learn through projects. This involves the students in designing of new models and acquiring problem solving skills. Now-a-days industries demand graduates with multidisciplinary education. This paper discusses the benefits which are being achieved through development of project "Quad Bike" by our team of students in our college: Hyderabad Institute of Technology and Management (HITAM). [ABSTRACT FROM AUTHOR]

Bloxorz is a block sliding puzzle game that can be categorized as a pathfinding problem. Pathfinding problems are well known problems in Artificial Intelligence field. In this paper, we proposed a single agent implementation to solve level-1 of Bloxorz game using Informed and Uninformed searching algorithms: Breadth-First Search (BFS), Depth-First Search (DFS), and A-star (A*) searching algorithms. The agent solves the problem using the three algorithms to compare their performance and conduct a conclusion that may help in improving the use of searching algorithms in this area. In this paper, A* and breadth-first search algorithms are founded to be more convenient to solve this problem. [ABSTRACT FROM AUTHOR]

Although the network dissemination of Core Socialist Values is a new channel for everyone, the dissemination effect of Core Socialist Values in the network has always fallen short of expectation. In view of the current situation of the network dissemination of Core Socialist Values , this paper analyses the key contradictions existing in it, draws lessons from the thought and process of Extenics in dealing with contradiction resolving, establishes the elements set of the network dissemination of Core Socialist Values and the process of solving the key problems, and forms the dissemination strategies for Core Socialist Values under the network environment based on Extenics. This paper provides a new idea for the network dissemination of Core Socialist Values. [ABSTRACT FROM AUTHOR]

The existing mainstream report system in the market can meet the daily report generation. However, it is difficult to maintain the system later due to the poor satisfaction of its report personalization and the high front-end and backend dependencies. In order to solve the above problems, this paper studies the development idea of Vue components. The technical characteristics of Vue and the source code of the Axios framework are analyzed, and a visual component model is proposed in combination with the application scenarios and development process. The data transmission method of the components is clarified in the actual application layer, which enhances the control of the page in the front-end component development process. The ability to convert complex reports into the construction of visual components and the dynamic data components to obtain data effectively improves the reporting system's development speed and meets the needs of personalized reports. [ABSTRACT FROM AUTHOR]

Using fuzzy logic in multi-criteria problems allows real-world problems to be modeled with greater accuracy. In addition, it is possible to solve problems in which all data are incomplete. The difficulty may be the choice of an appropriate technique leading to a ranking calculation. Methods fully integrated with fuzzy logic can be used. It is also possible to convert fuzzy data to crisp values and then apply traditional MCDA methods. In this paper, the performance of the TOPSIS method in a crisp and fuzzy environment was analyzed. The approaches of operating fully on uncertain data when calculating preference values were compared with converting data to crisp values using selected membership functions. The research has shown that significant differences and discrepancies in how the alternatives are classified are noticeable in the rankings. [ABSTRACT FROM AUTHOR]

The studies on complex business issues in management described by multiple characteristics (indicators), use a variety of methodological approaches. Diverse decision support methods are used to solve problems in this area (depending on the situation considered). These methods include TOPSIS and VMCM. There are methodological similarities between them, but there are also several differences. VMCM eliminates some limitations of TOPSIS regarding, inter alia, the inclusion of non-typical objects, real-world patterns, and time-varying dynamics. Methodological considerations supplemented with an exemplary application are presented in this paper. [ABSTRACT FROM AUTHOR]

Several speech emotion recognition systems have been proposed, mostly targeting a single language; however, the recognition performance is significantly degraded for languages other than the target language. To solve this problem, a method of normalizing features using synthesized speech was proposed by Okabe et al.; however, this method requires a search for the optimal normalization operation. In this paper, we propose a multilingual emotion recognition method that automatically normalizes acoustic features and recognizes emotions using the "recognition rate maximization" criterion. Synthesized speech with the same content as emotional speech was generated and input to a convolutional recurrent neural network with time axis aligned. Feature normalization by comparison with synthesized speech and emotion recognition were performed in a single network, and efficient training was expected. In speech emotion recognition experiments with four languages, normalization improved the recognition accuracy from 47.5% to 49.0%. Because normalization was not effective for utterances in which the speaking rate of the synthesized and emotional speeches differed significantly, we proposed a method to adjust the speaking rate of the synthesized speech and further improved the accuracy by up to 50.4%. [ABSTRACT FROM AUTHOR]

This paper explores how the robotic process automation (RPA) can benefit financial applications. To fully exploit RPA technologies potential will empower higher education and finance, which makes a better future together. The mechanism of RPA to mimic the process of human thinking in solving financial problems was discussed. Important technologies, challenges from cooperativeness, responsiveness and interconnectedness were explored. Exploration of automation technologies for COVID-19 prevention will reduce virus transmission, which empowers society governance, higher education and finance. [ABSTRACT FROM AUTHOR]

In order to solve the problems in the traditional ensemble learning model, selective ensemble algorithms are often used to optimize the ensemble learning model. To settle the shortcomings of the current selective ensemble algorithm existing in the selection efficiency and predict outcomes, this paper proposes selective ensemble algorithm basing on stacking ensemble framework. The algorithm mainly uses the agglomerated hierarchical clustering (AHC) algorithm and the metropolis criterion of simulated annealing to select the type and number of base learners. In terms of empirical analysis, Lending-Club data is used to build a multi-classification model. The experimental results show that compared with the single learner model, the AHC-Metropolis selective ensemble algorithm has better performance and stability in predicting multi-classification problems, and can provide a basis for improving the financial risk control system and ensuring national security. [ABSTRACT FROM AUTHOR]

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. [ABSTRACT FROM AUTHOR]

Digital university audit platform has become an important part of digital campus construction. How to combine big data analysis technology with university audit research, so as to promote the in-depth development of university audit work, is an urgent problem to be solved. Audit work in Colleges and universities is an important work at present. This paper aims to study the construction of university audit platform under the background of big data, explain the current situation of the construction of audit platform, the framework structure of audit platform and the problems and reasons of audit platform, accelerate the construction of information audit technology system, improve governance ability, and assist universities and education authorities in making major decisions. [ABSTRACT FROM AUTHOR]

The paper is concerned with the decision-making problem under fuzzy uncertainty. A decision maker selects one alternative focusing on the impact of an uncontrolled factor. In this paper the case when the information about an uncontrolled factor is fuzzy is considered. Three concepts for solving this problem are offered: a weak solution, a strong solution and a solution on the average. The relationship between solutions is investigated. As an example, the problem of treating soil with pesticides is considered, with a strong solution and a solution on the average being found for this problem. [ABSTRACT FROM AUTHOR]

Abstract Under the policy of "Made in China 2025", industrial design has attracted much attention. More and more industrial design competitions come into our view. But many college students back away from the competitions because of the lack of creative ideas. This paper aims to present an Extenics based method to help students create ideas in formalized and modular ways. We use the method described in this paper to design an outdoor umbrella system to participate in a domestic design competition and get through to the finals. The application has proved that the Extenics can provide a practical method guidance for expanding college students' innovative thinking of industrial design. [ABSTRACT FROM AUTHOR]

Starts from summarizing the important psychological schools and therapies, this paper present a new school of psychological counseling and therapy called Extension Psychology (EP), which generates systematic innovative ideas to solve mental problems by three core steps including modeling, expansion, and transformation based on Extenics. EP believes that more than 80% of psychological problems are due to mental conflicts. By analysis on multi-dimensions of Extenics, most psychological disorder among them could be systematically dispersed and expanded by enlarging individual's thinking dimensions such as from the existing conditions, the goals in terms of elements, principle and discourse domain with the support of information technology. Finally, our case study shows that the systematic innovation solutions will be generated to solve the problems followed by discussion with individuals and their relatives or related people through three core steps in extension psychological counseling. Our research may inspire the development of psychology in the era of big data. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been extended to a series of classical methods in dimensionality reduction for unsu¬pervised learning. However, the differences among the projected data samples may not be sufficient by PCA, causing problems both in data visualization and recognition. Though some nonlinear dimensionality reduction methods have been proposed to preserve the samples' differences, their nonlinear mappings hide the relationships among the data features, e.g., Isometric mapping (Isomap) and Locally Linear Embedding (LLE). In this paper, we propose a linear dimensionality reduction method, called Divergent Projection Analysis (DPA), to preserve the largest differences among the samples. Our DPA projects the sam¬ples into a low-dimensional space such that any two projected samples have a certain distance, resulting in solving a nonconvex optimization problem. The nonconvex problem is solved by the subgradient descent algorithm and particle swarm optimiza¬tion algorithm, and their solution qualities are compared in the experiments. Experiments on a synthetic dataset and three face recognition problems confirm the effectiveness of the proposed method compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes validates an Iterative Feedback Tuning (IFT) algorithm, which is a classical and also popular data-driven algorithm, on tower crane systems. The IFT algorithm is applied to improve the performances of three proportional-integral (PI) controllers by solving an optimization problem. The IFT algorithm is validated using experiments to control the cart position, the arm angular position and the payload position. The experiments are conducted on a tower crane system in authors' laboratory, and the experimental setup is laboratory equipment that has strong nonlinearities. Ten iterations are performed to highlight the control system performance using IFT controllers. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach based on hybrid feature selection that significantly reduces dimensionality of features. In this paper, an efficient method consisting of ReliefF and PCA is proposed which shows remarkable results with different chronic disease datasets. The presented work is suitable for both text and micro-array datasets which determines the optimal value of threshold for the selection of relevant and non-redundant features. To validate the performance of proposed work, ten popular benchmark datasets are used. With the results obtained, it is found that the presented approach reduces more than 50% irrelevant and redundant features from the dataset. Also with the proposed method, the computation time significantly decreases for all considered chronic disease datasets. Moreover, it is experimentally depicted that the threshold value significantly affects the selection of appropriate features. [ABSTRACT FROM AUTHOR]