Search

Containerisation demonstrates its efficiency in application deployment in Cloud Computing. Containers can encapsulate complex programs with their dependencies in isolated environments making applications more portable, hence are being adopted in High Performance Computing (HPC) clusters. Singularity, initially designed for HPC systems, has become their de facto standard container runtime. Nevertheless, conventional HPC workload managers lack micro-service support and deeply-integrated container management, as opposed to container orchestrators. We introduce a Torque-Operator which serves as a bridge between HPC workload manager (TORQUE) and container orchestrator (Kubernetes). We propose a hybrid architecture that integrates HPC and Cloud clusters seamlessly with little interference to HPC systems where container orchestration is performed on two levels., European Union’s Horizon 2020 research and innovation programme, Projekt DEAL

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

A dynamic stiffness element for flexural vibration analysis of delaminated multilayer beams is developed and subsequently used to investigate the natural frequencies and modes of two-layer beam configurations. Using the Euler-Bernoulli bending beam theory, the governing differential equations are exploited and representative, frequency-dependent, field variables are chosen based on the closed form solution to these equations. The boundary conditions are then imposed to formulate the dynamic stiffness matrix (DSM), which relates harmonically varying loads to harmonically varying displacements at the beam ends. The bending vibration of an illustrative example problem, characterized by delamination zone of variable length, is investigated. Two computer codes, based on the conventional Finite Element Method (FEM) and the analytical solutions reported in the literature, are also developed and used for comparison. The intact and defective beam natural frequencies and modes obtained from the proposed DSM method are presented along with the FEM and analytical results and those available in the literature.

Let S be a complete star-omega semiring and Sigma be an alphabet. For a weighted omega-pushdown automaton P with stateset 1...n, n greater or equal to 1, we show that there exists a mixed algebraic system over a complete semiring-semimodule pair ((S<>)^nxn, (S<>)^n) such that the behavior ||P|| of P is a component of a solution of this system. In case the basic semiring is the Boolean semiring or the semiring of natural numbers (augmented with infinity), we show that there exists a mixed context-free grammar that generates ||P||. The construction of the mixed context-free grammar from P is a generalization of the well known triple construction and is called now triple-pair construction for omega-pushdown automata., Comment: In Proceedings AFL 2017, arXiv:1708.06226. The article was prepared as a joint work with the late Zolt\'an \'Esik (1951-2016) whose definite intention was to publish the results

The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.

A waveguide with a constant, simply connected section S is considered under the condition that the substance filling the waveguide is characterized by permittivity and permeability that vary smoothly over the section S, but are constant along the waveguide axis. Ideal conductivity conditions are assumed on the walls of the waveguide. On the basis of the previously found representation of the electromagnetic field in such a waveguide using 4 scalar functions, namely, two electric and two magnetic potentials, Maxwells equations are rewritten with respect to the potentials and longitudinal components of the field. It appears possible to exclude potentials from this system and arrive at a pair of integro-differential equations for longitudinal components alone that split into two uncoupled wave equations in the optically homogeneous case. In an optically inhomogeneous case, this approach reduces the problem of finding the normal modes of a waveguide to studying the spectrum of a quadratic self-adjoint operator pencil.

Efficient allocation of radio access network (RAN) resources remains an important challenge with the introduction of 5G networks. RAN virtualization and division into logical subnetworks - slices - puts this task into a new perspective. In the paper we present a software tool based on the OMNeT++ platform and developed for performance analysis of a network slicing policy with SLA-based slice performance isolation. The tool is designed using the object-oriented approach, which provides flexibility and extensibility of the simulation model. The paper briefly presents the slicing policy under study and focuses on the simulators architecture and design. Numerical results are provided for illustration.

In this work, a numerical study of the solutions of the parabolic and hyperbolic equations of heat conduction with the same physical parameters is carried out and a comparative analysis of the results obtained is carried out. The mathematical formulation of the problem is discussed. The action of the laser is taken into account through the source function, which was chosen as a double femtosecond laser pulse. In the hyperbolic equation, in contrast to the parabolic one, there is an additional parameter that characterizes the relaxation time of the heat flux. In addition, the source of the hyperbolic equation contains an additional term - the derivative of the power density of the source of the parabolic equation. This means that the temperature of the sample is influenced not only by the power density of the source, but also by the rate of its change. The profiles of the sample temperature at different times and its dynamics at different target depths are shown. The calculations were carried out for different time delays between pulses and for different relaxation parameters.

Time Series Forecasting has always been a very important area of research in many domains because many different types of data are stored as time series. Given the growing availability of data and computing power in the recent years, Deep Learning has become a fundamental part of the new generation of Time Series Forecasting models, obtaining excellent results.As different time series problems are studied in many different fields, a large number of new architectures have been developed in recent years. This has also been simplified by the growing availability of open source frameworks, which make the development of new custom network components easier and faster.In this paper three different Deep Learning Architecture for Time Series Forecasting are presented: Recurrent Neural Networks (RNNs), that are the most classical and used architecture for Time Series Forecasting problems; Long Short-Term Memory (LSTM), that are an evolution of RNNs developed in order to overcome the vanishing gradient problem; Gated Recurrent Unit (GRU), that are another evolution of RNNs, similar to LSTM.The article is devoted to modeling and forecasting the cost of international air transportation in a pandemic using deep learning methods. The author builds time series models of the American Airlines (AAL) stock prices for a selected period using LSTM, GRU, RNN recurrent neural networks models and compare the accuracy forecast results.

The preservation of quadratic integrals on approximate solutions of autonomous systems of ordinary differential equations x ˙ = f ( x ) , found by the trapezoidal scheme, is investigated. For this purpose, a relation has been established between the trapezoidal scheme and the midpoint scheme, which preserves all quadratic integrals of motion by virtue of Cooper’s theorem. This relation allows considering the trapezoidal scheme as dual to the midpoint scheme and to find a dual analogue for Cooper’s theorem by analogy with the duality principle in projective geometry. It is proved that on the approximate solution found by the trapezoidal scheme, not the quadratic integral itself is preserved, but a more complicated expression, which turns into an integral in the limit as ∆ t → 0 . Thus the concept of conjugate difference schemes is investigated in pure algebraic way. The results are illustrated by examples of linear and elliptic oscillators. In both cases, expressions preserved by the trapezoidal scheme are presented explicitly.

We document frequent occurrences of negative conversion premium (NCP) events in the Chinese convertible bond market, when the bond is convertible and the underlying stock can be freely sold. This implies that when an NCP event occurs, existing stock holders can earn a riskless profit through a long-short strategy which sells the underlying stock and buys the convertible bond at the same time, then converts the bond into stocks. Facing short sale constraints, traders not holding any position in the underlying stock can still profit from an overnight trading strategy which buys the convertible bond at the NCP event day t, then sells the converted stock on day t + 1. We also find that the next-day opening prices following NCP events are significantly lower, which is evidence for the stock selling from the overnight trading strategy. Overall, our findings show that investors in China are aware of the NCP events and they earn abnormal returns through active trading. However, it remains as a puzzle why existing stock holders such as institutional investors do not trade away the negative conversion premium through the riskless long-short strategy.

Unexpected acquisitions can occur in the marketplace, which can significantly impact the market environment. Thus, predicting future pairs of the acquirer and acquiree companies is important for acquisition or investment strategy. This prediction is a challenging problem due to the following requirements: to incorporate various non-financial factors and to address the lack of negative samples. Concerning the former, we proposed including a network feature that represented the importance of an acquirer and an acquiree in the investment and category networks, as well as a company relation feature associated with their similarity and closeness. Considering the latter requirement, as negative examples, we set the pairs of acquirers and acquirees with the features that were similar to those of positive examples. This allowed learning minor differences between the companies selected for acquisition and the candidate ones. We evaluated our proposed prediction model using 2000-2018 acquisition logs collected from CrunchBase. Based on the analysis of the high SHapley additive explanation (SHAP) value features, we found that the newly considered network and company relation features had high significance (10 out of 22 top key features). We also clarified how these novel features contributed to the prediction of acquisition occurrence by interpreting the SHAP value.

Recent discoveries of distinct molecular subtypes have led to remarkable advances in treatment for a variety of diseases. While subtyping via unsupervised clustering has received a great deal of interest, most methods rely on basic statistical or machine learning methods. At the same time, techniques based on graph clustering, particularly clique-based strategies, have been successfully used to identify disease biomarkers and gene networks. A graph theoretical approach based on the paraclique algorithm is described that can easily be employed to identify putative disease subtypes and serve as an aid in outlier detection as well. The feasibility and potential effectiveness of this method is demonstrated on publicly available gene co-expression data derived from patient samples covering twelve different disease families.

Nowadays, the Internet of Things (IoT) is a common word for the people because of its increasing number of users. Statistical results show that the users of IoT devices are dramatically increasing, and in the future, it will be to an ever-increasing extent. Because of the increasing number of users, security experts are now concerned about its security. In this research, we would like to improve the security system of IoT devices, particularly in IoT botnet, by applying various machine learning (ML) techniques. In this paper, we have set up an approach to detect botnet of IoT devices using three one-class classifier ML algorithms. The algorithms are: one-class support vector machine (OCSVM), elliptic envelope (EE), and local outlier factor (LOF). Our method is a network flow-based botnet detection technique, and we use the input packet, protocol, source port, destination port, and time as features of our algorithms. After a number of preprocessing steps, we feed the preprocessed data to our algorithms that can achieve a good precision score that is approximately 77–99%. The one-class SVM achieves the best accuracy score, approximately 99% in every dataset, and EE’s accuracy score varies from 91% to 98%; however, the LOF factor achieves lowest accuracy score that is from 77% to 99%. Our algorithms are cost-effective and provide good accuracy in short execution time.

It is difficult to predict to do a random sampling technique with the consideration that the existing population is very large, so it is not possible to study the existing population. Thus, in the form of a population representative, this research is part of the total customers who stay at Graha Buana Hotel Medan. The decisions taken must consider well based on the data that is held, especially those that are closely related to the hotel service system. The stages of research carried out in research are to conduct interviews by direct questioning to the section related to research, direct observation of events that occur at the research site, analyzing and designing and making applications and testing applications. The Rough Set method is one of the methods above that allows us to make decisions in hotel services because in this method there are formulations or stages of problem mechanics and there is a result (decision) from a possible combination of the above criteria. From the results (decisions) that come from the processed data mining, it can be used as a reference for decision making. The results obtained in this study are finding a rule with the rough set method used to obtain the results of each criterion for service satisfaction results. The results obtained in this study are finding a rule with the rough set method used to obtain the results of each criterion for service satisfaction results.

Disease is very susceptible to occur in children under five because the immune system in children under five has not been fully developed. Lack of knowledge about the diseases of children under five and the symptoms they experience makes parents fearful. The lack of knowledge of children's diseases from experts can result in delayed treatment. Problems that occur can be overcome by utilizing artificial intelligence technology. One of the artificial intelligence technologies is an expert system. Information needs very quickly from an expert to deal with problems or diseases of children under five that are expected by parents or society. So that is what drives the development of a software application, namely an expert system for the identification of diseases of children under five. An expert system for the identification of toddlers' diseases is made as a tool to diagnose diseases experienced by toddlers by using the symptoms experienced by toddlers as a tool to detect diseases experienced by children under five. The system can identify 5 types of disease with 23 symptoms of disease. This expert system uses the development method of problem identification, system design, implementation and testing. Inference in this expert system uses the Bayes theorem method. This system is built with Visual Basic and Microsoft Access as the database. The results of consulting tests with this system show that the system is able to determine the disease along with the initial treatment and treatment solutions that must be carried out, based on the symptoms previously selected by the user.

In modern smart cities, there is a quest for the highest level of integration and automation service. In the surveillance sector, one of the main challenges is to automate the analysis of videos in real-time to identify critical situations. This paper presents intelligent models based on Convolutional Neural Networks (in which the MobileNet, InceptionV3 and VGG16 networks had used), LSTM networks and feedforward networks for the task of classifying videos under the classes "Violence" and "Non-Violence", using for this the RLVS database. Different data representations held used according to the Temporal Fusion techniques. The best outcome achieved was Accuracy and F1-Score of 0.91, a higher result compared to those found in similar researches for works conducted on the same database.

Wireless internet service in educational buildings plays a crucial role in telecommunications for the knowledge sharing process. Therefore, various factors that may limit internet services coverage in the building should be eliminated or reduced. One such factor is path losses. Path losses are caused by multiple obstacles between the transmitting and receiving antennas. The problem of path losses in the education building can be solved by providing signal booster devices or Wireless Fidelity (Wi-Fi). But not all college buildings have such tools. Besides, WiFi devices also have limitations on bandwidth and the number of users. Thus, mobile communication devices or smartphones located inside the education buildings still need internet service coverage from the transmitter antenna outside the building. An accurate propagation model is required so that the transmitter antenna outside the building can provide internet service coverage to the inside of the building. This paper had been analyzed the selection of propagation models using three validation formulas, namely Mean Error (ME), Root Mean Square Error (RMSE), and Standard Deviation Error (SDE). This paper used several propagation models, namely the 3GPP Model, Winner+ Model, and COST231 Model. Based on the analysis of calculation and measurement data, it is known that the COST231 model is the most accurate because it has the lowest ME, RMSE, and SDE values.

With so many hotels and other accommodation facilities in the city of Medan. More and more people are coming and staying and using the facilities at each hotel. Whether it's rooms, meeting room facilities, facilities to hold events, and much more are available in every hotel. Every hotel wants to give satisfaction to guests who come to use the hotel facilities and services. Likewise with SwissbeliNN hotels which have a 3 (three) star classification. Therefore, the opinions of guests and complaints given are very useful to improve the quality of hotel services. To find out, we will look at the satisfaction level of each guest. In this case, the writer applies the servqual method to see the level of guest satisfaction that has been obtained. To find out what is still lacking in the services provided. The purpose of this research is to find out the shortcomings of the hotel for the services provided to hotel visitors, as well as to fulfill the complaints of each visitor to the hotel from the results of the research carried out, with the objectives described later to be able to provide solutions to hotel leaders to fulfill the research results. obtained. The research methods carried out include collecting data directly from the hotel related to the problems that arise to be researched, literature studies, analyzing the problems to be studied based on the data collection that has been obtained, designing applications to overcome problems that arise and making it easier to find out the level of satisfaction of visitors. against managed hotels, as well as testing applications that are made to test the level of success obtained from the application being built. Based on the research conducted, the average perceived service quality score was 3.54. This score is in the somewhat unsatisfactory category when compared with the average score of service quality score which is perceived to be lower than expected. This indicates a gap. The highest average score is given to responsiveness with a value of 3.62 while the lowest score is given to the dimension of empathy with a value of 3.44.

Students' interest in a field is usually seen from the grades resulting from students' learning in the classroom. This is a common thing for parents and teachers to do. In the research conducted by researchers this time is in the field of design studied by students in the classroom in one semester, namely the subjects Photoshop and CorelDraw. The grades taken are grades that include the value of theory, practical grades, assignments, and quiz assignments obtained by students in the classroom. The four grades will be calculated until they get a provision on whether or not the student graduates in the subject. These values will be studied by researchers using the Naïve Bayes method, so that it can be known how much can be said to pass in this design lesson. Researchers conducted research using Rapid Miner program, where the data will be divided into 2 parts, namely by using some existing data as learning samples and the rest as test data. The results obtained from the experiment were 146 students graduated in the field of design, and 119 students failed. This suggests the experiment using the Naïve Bayes method was successful if the experiment data was entered a lot

The global telecommunications industry has witnessed tremendous challenge in the last two decades. Telecommunications companies in Nigeria have formulated policies that could enable them to cope with the global challenge. However, most of them have not really employed strategic options that could enable them to cope with the changing condition. The main objective of this study is to examine the effect of Miles and Snows strategies which is considered the major approach that could promote competitiveness in the industry to enable them manage and survive other present and future challenges. The study adopted survey research design and population composed of 7,567 top and middle level management staff of four major service providers MTN, Airtel, Globacom and 9Mobile with sample size of 380 determined scientifically through Yamane’s formula. Combinations of judgemental and stratified random sampling techniques were used to select participants for the study from the selected Telecommunication Companies. Out of the 380 content validated questionnaire subjected to cronbach’s alpha measure of reliability distributed, 369 were retrieved representing a response rate of 97.1%. The study reaffirmed the efficacy of Mile and Snow’s competitive strategy. It was recommended that Telecom Company should adopt prospector, analyzer, and defender strategies to improve their performances.

We present an algorithm and a software architecture for a cloud-based system that executes cyclic scientific workflows whose structure may change during run time. Existing approaches either rely on workflow definitions based on directed acyclic graphs (DAGs) or require workarounds to implement cyclic structures. In contrast, our system supports cycles natively, avoids workarounds, and as such reduces the complexity of workflow modelling and maintenance. Our algorithm traverses workflow graphs and transforms them iteratively into linear sequences of executable actions. We call these sequences process chains. Our software architecture distributes the process chains to multiple compute nodes in the cloud and oversees their execution. We evaluate our approach by applying it to two practical use cases from the domains of astronomy and engineering. We also compare it with two existing workflow management systems. The evaluation demonstrates that our algorithm is able to execute dynamically changing workflows with cycles and that design and maintenance of complex workflows is easier than with existing solutions. It also shows that our software architecture can run process chains on multiple compute nodes in parallel to significantly speed up the workflow execution. An implementation of our algorithm and the software architecture is available with the Steep Workflow Management System that we released under an open-source license. The resources for the first practical use case are also available as open source for reproduction.

Sudoku is known as a NP-complete combinatorial number-placement puzzle. In this study, we propose the first cP~system solution to generalised Sudoku puzzles with $m \times m$ cells grouped in $m$ blocks. By using a fixed constant number of rules, our cP~system can solve all Sudoku puzzles in sublinear steps. We evaluate the cP~system and discuss its formal verification.

In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming (MIP) branch-and-bound tree by using a learned policy. Previous work using imitation learning indicates the feasibility of acquiring a node selection policy, by learning an adaptive node searching order. In contrast, our imitation learning policy is focused solely on learning which of a node's children to select. We present an offline method to learn such a policy in two settings: one that comprises a heuristic by committing to pruning of nodes; one that is exact and backtracks from a leaf to guarantee finding the optimal integer solution. The former setting corresponds to a child selector during plunging, while the latter is akin to a diving heuristic. We apply the policy within the popular open-source solver SCIP, in both heuristic and exact settings. Empirical results on five MIP datasets indicate that our node selection policy leads to solutions significantly more quickly than the state-of-the-art precedent in the literature. While we do not beat the highly-optimised SCIP state-of-practice baseline node selector in terms of solving time on exact solutions, our heuristic policies have a consistently better optimality gap than all baselines, if the accuracy of the predictive model is sufficient. Further, the results also indicate that, when a time limit is applied, our heuristic method finds better solutions than all baselines in the majority of problems tested. We explain the results by showing that the learned policies have imitated the SCIP baseline, but without the latter's early plunge abort. Our recommendation is that, despite the clear improvements over the literature, this kind of MIP child selector is better seen in a broader approach using learning in MIP branch-and-bound tree decisions., Authors' version, not publisher's final version which is available at DOI

The energy of a sidigraph is defined as the sum of absolute values of real parts of its eigenvalues. The iota energy of a sidigraph is defined as the sum of absolute values of imaginary parts of its eigenvalues. Recently a new notion of energy of digraphs is introduced which is called the total energy of digraphs. In this paper, we extend this concept of total energy to sidigraphs. We compute total energy formulas for negative directed cycles and show that the total energy of negative directed cycles with fixed order increases monotonically. We introduce complex adjacency matrix to give the integral representation for total energy of sidigraphs. We discuss the increasing property of total energy over some particular subfamilies of $S_{n,h}$, where $S_{n,h}$ contains $n$-vertex sidigraphs with each cycle having length $h$. Using the Cauchy-Schwarz inequality, we find upper bound for the total energy of sidigraphs. Finally, we find the class of noncospectral equienergetic sidigraphs.

Nowadays this concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. In this paper, to increase the air traffic management accuracy and legitimacy we used the bidirectional long short-term memory (Bi-LSTMs) and extreme learning machines (ELM) to design the structure of a deep learning network method. The Kaggle data set and different performance parameters and statistical criteria have been used in MATLAB to validate the proposed method. Using the proposed method has improved the criteria factors of this study. The proposed method has had a % increase in air traffic management in comparison to other papers. Therefore, it can be said that the proposed method has a much higher air traffic management capacity in comparison to the previous methods.

The classic M/M/$m$/$n$ queueing model is modified by allowing a given task to require up to $m$ servers to be performed. Moreover, the maximum time that a task can wait in the queue before being executed is a random variable having an exponential distribution. Both FIFO (First In, First Out) and priority disciplines are considered. The case when $m=2$ is treated: the state space needed to fully describe the system is given, its size is calculated and the balance equations are presented when $n=1,2$ and 3. The queueing process can be used to model cluster-type multiprocessor computing systems.

Recent developments in both optical wireless communication (OWC) systems and implanted medical devices (IMDs) have introduced transdermal optical wireless (TOW) technology as a viable candidate for extremely high-speed in-body to out-of-body wireless data transmissions, which are growing in demand for many vital biomedical applications, including telemetry with medical implants, health monitoring, neural recording and prostheses. Nevertheless, this emerging communication modality is primarily hindered by skin-induced attenuation of the propagating signal bit carrier along with its stochastic misalignment-induced fading. Thus, by considering a typical modulated retroreflective (MRR) TOW system with spatial diversity and optimal combining (OC) for signal reception in this work, we focus, for the first time in the MRR TOW literature, on the stochastic nature of generalized pointing errors with non-zero boresight (NZB). Specifically, under these circumstances, novel analytical mathematical expressions were derived for the total average bit error rate (BER) of various system configurations. Their results revealed significant outage performance enhancements when spatial diversity was utilized. Moreover, taking into consideration the total transdermal pathloss along with the effects of stochastic NZB pointing errors, the critical average signal-to-noise ratio (SNR) metric was evaluated for typical power spectral-density values.

The more rapid growth of information technology in the world, so that more and more people are taking advantage of this advancement in information technology in various activities of life or to solve various problems. One of them is filling positions that have not been filled for promotion processes often experience problems because the submission of prospective candidates who can occupy the position by matching employee profiles and job profiles is not resolved properly. To minimize these constraints, a decision support system is needed that can analyze employee performance in accordance with existing job profiles. The Profile Matching process is carried out to determine career planning recommendations by calculating sub-criteria from each aspect (work attitude and intellectual capacity) which consists of 4 stages including the calculation of the GAP value, the calculation of the core and secondary factor values ​​and the calculation of the total value and ranking. The results of this study will produce a form in the form of employee recommendations with the highest score of 5.4163 on behalf of Ariz and Widi which has the same numerical value as the number from the calculation method used., Semakin pesatnya perkembangan teknologi informasi di dunia, maka semakin banyak pula orang yang memanfaatkan kemajuan teknologi informasi ini dalam berbagai kegiatan kehidupan atau untuk menyelesaikan berbagai permasalahan. Salah satunya adalah pengisian posisi yang belum diisi untuk proses promosi sering mengalami kendala karena pengajuan calon yang dapat menempati posisi tersebut dengan cara mencocokkan profil karyawan dan profil pekerjaan tidak terselesaikan dengan baik. Untuk meminimalisir kendala tersebut maka diperlukan suatu sistem pendukung keputusan yang dapat menganalisis kinerja karyawan sesuai dengan profil pekerjaan yang ada. Proses Profile Matching dilakukan untuk menentukan rekomendasi perencanaan karir dengan menghitung sub kriteria dari masing-masing aspek (sikap kerja dan kemampuan intelektual) yang terdiri dari 4 tahapan yaitu penghitungan nilai GAP, penghitungan nilai core dan secondary factor dan perhitungan nilai total dan peringkat. Hasil penelitian ini akan menghasilkan formulir berupa rekomendasi pegawai dengan skor tertinggi sebesar 5,4163 atas nama Ariz dan Widi yang memiliki nilai numerik yang sama dengan angka dari metode perhitungan yang digunakan.

The IEEE 1588 precision time protocol (PTP) is very important for many industrial sectors and applications that require time synchronization accuracy between computers down to microsecond and even nanosecond levels. Nevertheless, PTP and its underlying network infrastructure are vulnerable to cyber-attacks, which can stealthily reduce the time synchronization accuracy to unacceptable and even damage-causing levels for individual clocks or an entire network, leading to financial loss or even physical destruction. Existing security protocol extensions only partially address this problem. This paper provides a comprehensive analysis of strategies for advanced persistent threats to PTP infrastructure, possible attacker locations, and the impact on clock and network synchronization in the presence of security protocol extensions, infrastructure redundancy, and protocol redundancy. It distinguishes between attack strategies and attacker types as described in RFC7384, but further distinguishes between the spoofing and time source attack, the simple internal attack, and the advanced internal attack. Some experiments were conducted to demonstrate the impact of PTP attacks. Our analysis shows that a sophisticated attacker has a range of methodologies to compromise a PTP network. Moreover, all PTP infrastructure components can host an attacker, making the comprehensive protection of a PTP network against a malware infiltration, as for example exercised by Stuxnet, a very tedious task.

Adding a connected dominating set of vertices to a graph $G$ increases its number of Hadwiger $h(G)$. Based on this obvious property in [2] we introduced a new invariant $\eta(G)$ for which $\eta(G)\leq h(G)$. We continue to study its property. For a graph $G$ with independence number three without induced chordless cycles $C_7$ and with $n(G)$ vertices, $\eta(G)\geq n(G)/4$.

The theory of finitely supported algebraic structures represents a reformulation of Zermelo-Fraenkel set theory in which every classical structure is replaced by a finitely supported structure according to the action of a group of permutations of some basic elements named atoms. It provides a way of representing infinite structures in a discrete manner, by employing only finitely many characteristics. In this paper we present some (finiteness and fixed point) properties of finitely supported self-mappings defined on the finite power set of atoms.

Let $G =(V(G),E(G))$ be a simple undirected graph of order $n$, and let $S \subseteq V(G)$. If every vertex in $V(G)-S$ is adjacent to at least one vertex in $S$, then the set $S$ is called a \textit{dominating set}. The \textit{domination number} of $G$ is the minimum cardinality taken over all sets of $S$, and it is denoted by $\gamma(G)$. Recently, the effect of one or more edges deletion on the domination number has been examined in many papers. Let $F\subseteq E(G)$. The \textit{bondage number} $b(G)$ of $G$ is the minimum cardinality taken over all sets of $F$ such that $\gamma(G-F) > \gamma(G)$. In the literature, a lot of domination and bondage parameters have been defined depending on different properties. In this paper, we investigate the \emph{bondage, strong and weak bondage numbers} of complementary prism graphs of some well-known graph families.

This paper is dedicated to generating process of the Romanian Cyrillic lexicon used between 1967 and 1989. The rules for transliteration of words from the modern Romanian lexicon to their equivalents written in Cyrillic were established and argued. A backtracking algorithm has been developed and implemented that generates the Cyrillic lexicon using the transliteration rules. This algorithm actually is a tool to facilitate the work of the expert. The work of the expert is reduced to checking the transliterated variants and changing the transliteration rules.

The concept of maximum deficiency matrix $M_{df}(G)$ of a simple graph $G$ is introduced in this paper. Let $G=(V,E)$ be a simple graph of order $n$ and let $df(v_{i})$ be the deficiency of a vertex $v_{i}$, $i=1,2, \ldots, n$, then the maximum deficiency matrix $M_{df}(G) = [f_{ij}]_{n \times n}$ is defined as: \[ f_{ij}= \begin{cases} max\{df(v_{i}), df(v_{j})\}, & \text{if $v_{i}v_{j} \in E(G)$} \\ 0~~~~~~~~~~~~~~~~~~~~~~~~, & \text{otherwise.} \end{cases} \] Further, some coefficients of the characteristic polynomial $\phi(G; \gamma)$ of the maximum deficiency matrix of $G$ are obtained. The maximum deficiency energy $E{M_{df}}(G)$ of a graph $G$ is also introduced. The bounds for $E{M_{df}}(G)$ are established. Moreover, maximum deficiency energy of some standard graphs is shown, and if the maximum deficiency energy of a graph is rational, then it must be an even integer.

The area is the center of problems in the administrative record management of Kebayoran District, because of its dense condition and it is difficult to determine land measurements due to the density of residential areas. The problem in Indonesia to this day is that the administrative boundaries of the kelurahan already exist, but the administrative boundaries for the Rukun Warga / Rukun Tetangga (RW / RT) do not yet exist. The local government of DKI already has a large scale map (1: 1,000) to map RW administrative boundaries. Large-scale mapping (Batas RW) is useful for accurate information on incidence of dengue fever or other diseases, thereby eliminating information bias due to the use of village boundary maps. Another benefit is the accuracy of address management for customers, for example PDAM customers, to facilitate verification of customer data with large-scale maps, especially those that only include RT / RW addresses, without mentioning street names and household numbers. The method used is data mining K-Means Clustering. By using this method, the data that has been obtained can be grouped into several clusters, where the application of the KMeans Clustering process uses Excel calculations. The processed data is divided into 3 clusters, namely: high cluster (C1), medium cluster (C2) and low cluster (C3). The iteration process of this research occurs 2 times so that an assessment is obtained in classifying the household / neighborhood unit based on the Kelurahan. The results obtained are that there is 1 neighborhood unit with the highest cluster (C1), there are 4 neighborhood units with 4 medium clusters (C2), and 5 neighborhood units with the lowest cluster (C3). This data can be input to the sub-district to disseminate information about dengue fever, health education, and for the accuracy of PDAM customer address management and others., Kawasan merupakan pusat permasalahan dalam pengelolaan pencatatan administrasi Kabupaten Kebayoran Baru, karena kondisinya yang padat dan sulit untuk menentukan pengukuran lahan akibat padatnya kawasan pemukiman. Persoalan di Indonesia hingga saat ini adalah batas administratif kelurahan sudah ada, namun batas administratif untuk Rukun Warga / Rukun Tetangga (RW / RT) belum ada. Pemerintah Daerah DKI sudah memiliki peta skala besar (1: 1.000) untuk memetakan batas administrasi RW. Pemetaan skala besar (Batas RW) berguna untuk mendapatkan informasi yang akurat tentang kejadian demam berdarah atau penyakit lain, sehingga menghilangkan bias informasi akibat penggunaan peta batas desa. Manfaat lainnya adalah keakuratan pengelolaan alamat untuk pelanggan, misalnya pelanggan PDAM, untuk memudahkan verifikasi data pelanggan dengan peta skala besar, terutama yang hanya mencantumkan alamat RT / RW, tanpa menyebutkan nama jalan dan nomor rumah tangga. Metode yang digunakan adalah data mining K-Means Clustering. Dengan menggunakan metode ini maka data yang telah diperoleh dapat dikelompokkan menjadi beberapa cluster, dimana penerapan proses KMeans Clustering menggunakan perhitungan Excel. Data yang diolah dibagi menjadi 3 cluster, yaitu: cluster tinggi (C1), cluster sedang (C2) dan cluster rendah (C3). Proses iterasi penelitian ini dilakukan sebanyak 2 kali sehingga diperoleh penilaian dalam mengelompokkan ruta / rukun tetangga berdasarkan kelurahan. Hasil yang diperoleh adalah terdapat 1 RT dengan klaster tertinggi (C1), ada 4 RT dengan 4 klaster sedang (C2), dan 5 RT dengan klaster paling rendah (C3). Data ini dapat menjadi masukan bagi kelurahan untuk menyebarluaskan informasi tentang demam berdarah, penyuluhan kesehatan, ketepatan pengelolaan alamat pelanggan PDAM dan lain-lain

Cardiotocography is a series of inspections to determine the health of the fetus in pregnancy. The inspection process is carried out by recording the baby's heart rate information whether in a healthy condition or contrarily. In addition, uterine contractions are also used to determine the health condition of the fetus. Fetal health is classified into 3 conditions namely normal, suspect, and pathological. This paper was performed to compare a classification algorithm for diagnosing the result of the cardiotocographic inspection. An experimental scheme is performed using feature selection and not using it. CFS Subset Evaluation, Info Gain, and Chi-Square are used to select the best feature which correlated to each other. The data set was obtained from the UCI Machine Learning repository available freely. To find out the performance of the classification algorithm, this study uses an evaluation matrix of precision, Recall, F-Measure, MCC, ROC, PRC, and Accuracy. The results showed that all algorithms can provide fairly good classification. However, the combination of the Random Forest algorithm and the Info Gain Feature Selection gives the best results with an accuracy of 93.74%.

This paper provides analysis of WiMAX systems from its background to their architectures highlighting merits and demerits, topologies, structures and looks deep into transceiver systems architecture, then specializes on the radio frequency (RF) front-end part of the WiMAX transceiver system in which a model is designed. Thereafter, the model is implemented in MATLAB Simulink and the results are investigated and analyzed.

This work shows the possibility of using spectral analysis in order to detect characteristic points in recorded images. The specific point is a marker in the form of a diode that flashes at a certain frequency. Main assumptions of the processing algorithm are the recording of a sequence of images and treatment change of level of brightness for each pixel as a time signal. The amplitude spectrum is determined for each time signal. The result of data processing is an amplitude image whose pixels brightness corresponding to the intensity of source of pulsating light emitting specific frequency. This new data representation is used to detect position of markers. The algorithm was re¬searched in order to select optimal marker colors and pulsation frequency. The results are described in a summary.

The Search Engine has a critical role in presenting the correct pages to the user because of the availability of a huge number of websites, Search Engines such as Google use the Page Ranking Algorithm to rate web pages according to the nature of their content and their existence on the world wide web. SEO can be characterized as methodology used to elevate site keeping in mind the end goal to have a high rank i.e., top outcome. In this paper the authors present the most search engine optimization like (Google, Bing, MSN, Yahoo, etc.), and compare by the performance of the search engine optimization. The authors also present the benefits, limitation, challenges, and the search engine optimization application in business.

Information technologies allow for improving production systems functioning especially thanks to a possibility of solving complex production problems in a very short time. The production system designing is increasingly based on virtual reality, and more specifically on the concept of a digital factory. It enables to create virtual models of real objects and use them for visualization of products and manufacturing processes. The presented examples of new information technologies, which are used in production practice, are the main object of this paper.

This article presents a proportional relationship between Shutter and the value of the resolution scanning system that allows decision making for modeling 3D parts used in reverse engineering and additive manufacturing. As a first step, the object of interest is treated to dim excessive brightness, then the object is scanned (by point cloud or mesh) with the use of a Handyscan 700 scanner. The point cloud is processed with the Geomagic software Desing X to generate a CAD image and a “.stl” file for 3D printing.

In this paper the method of improving production control in engineer to order [ETO] small and medium sized enterprises is presented. Briefly, the strategy of Mass Customization [MC] and a concept of the hybrid MC-ETO production system are demonstrated. Thereafter, a method of choosing components for small batch manu-facturing in advance, under conditions of single unit ETO production system, with application of fuzzy logic is described. This approach can be used in ETO companies during their transition into the hybrid MC-ETO production systems. The research was done in a collaboration with experts from the real ETO pro-duction system, in Polish SME, which manufactures mechanical parts.

There has been a rapid development of Information and communication Technologies (ICTs) globally, and this includes the Geographic Information Systems (GISs) which has evolved over the years and aimed at solving many geographic problems as fast and efficiently as possible. The importance of GIS to students and lecturers in higher educational institutions is it’s ability to easily analyse locations together with the conventional database systems. This paper assessed the correlation between the availability and use of GIS in Higher Educational Institutions (HEIs). The study is exploratory in nature and employed the use of questionnaires as instrument to collect data. Simple random sampling technique was used to select one hundred and ten (110) lecturers one thousand five hundred and seventy-nine (1579) students from a university. Findings revealed that GIS for analysis is still at its lowest ebb and hence calls for an action to motivate or encourage lecturers and students to use it. Though there are indications that GIS will be of great value in schools, students and lecturers they will be happy to use it for their teaching and learning. The study also revealed availability of GIS was not tied to its usage.

The Indonesian Navy organization has the duties and responsibilities of state sovereignty. In carrying out its duties, very large and large data and real time data include data on ships in all Indonesian waters from satellite and terrestrial AIS, from radar, data from long range cameras, data from other agencies such as weather data, marine accident data, natural disasters data and fishing areas data. The managed data consists of structured, semi-structured and unstructured data. Thus, big data is needed within the Indonesian Navy. Therefore, this study analyzes the strategies that need to be taken to be able to develop a big data in the Indonesian Navy. The method used is qualitative analytic research using SWOT and AHP. The research subjects were Section Heads at command and control center, Head of Section at Information and Data Processing Services. Data With this combined method, the resulting main strategy is to implement Big Data with existing and adequate system resources, manageable costs, set up network infrastructure and sufficient bandwidth using available big data software by optimizing government regulations regarding single data for communication and data sharing from other agencies and optimizing advanced API technology.

Kebutuhan informasi menjadi sangat penting terutam dalam proses pengambilan keputusan. Salah satu cara yang dapat digunakanyaitu menggunakan teknologi informasi dan komunikasi seperti sistem informasi. sistem informasi sendiri memiliki berbagai macam diantaranya yaitu sistem informasi eksekutif yang banyak digunakan untuk menyajikan informasi kepada pihak eksekutif dalam mengambil keputusan atau kebijakan. Seperti halnya yang terjadi diberbagai kecamatan saat ini kebutuhan informasi penduduk menjadi sangat penting mengingat dibutuhkan dalam pengambilan kebijakan bagi pemerintah. untuk itu di dalam penelitian ini dibuat sebuah sistem informasi eksekutif penduduk guna membantu memetakan keadaan penduduk berdasarkan data kependudukan itu sendiri. untuk menghasilkan sistem informasi eksekutif sesuai dengan kebutuhan eksekutif maka proess pengembangan digunakan waterfall model dengan lima tahapan pengembangan communication, planning, modeling, construction, dan deployment. Hasil dari pengembangan berupa sistem informasi eksekutif yang dapat memberikan informasi secara visual kepada pihak terkait. Sistem informasi juga berjalan dengan baik yang dibutkikan dari hasil pengujian yang dilakukan.

Ikatan Anggar Seluruh Indonesia atau disingkat IKASI merupakan induk organisasi cabang anggar diseluruh Indonesia. IKASI Purbalingga sekarang ada sekitar 73 anggota dari tahun 2010 sampai sekarang dan selalu bertambah setiap tahunnya. Namun dengan banyaknya anggota tidak diimbangi dengan sistem pencatatan dan pemantauan atlet yang baik. Hal ini membuat IKASI Purbalingga seringkali terhambat dalam pendataan dan melihat perkembangan atlet saat akan digunakan sebagai bahan acuan untuk memilih atlet yang akan diikutkan perlombaan tingkat provinsi, nasional maupun internasional karena masih menggunakan cara manual. Tujuan dari penelitian ini adalah membangun sebuah aplikasi pengolahan data dan statistik atlet IKASI berbasis website menggunakan framework Laravel. Penelitian ini membahas tentang pembuatan aplikasi pengelolaan data dan cara melihat perkembangan atlet sehingga dapat dipantau dengan baik. Metode pengembangan sistem yang digunakan dalam penelitian ini adalah metode waterfall, dimana proses pembuatannya bertahap hingga diujikan Hasil dari penelitian ini menunjukkan 84,76% pengguna menyatakan aplikasi ini setuju untuk digunakan memantau perkembangan atlet, agar memudah dalam setiap latihan dan pertandingan.

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with the overall survival and other clinicopathological variables, and included a combination of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) artificial neural networks, gene-set enrichment analysis (GSEA), Cox regression and other machine learning and predictive analytics modeling [C5.0 algorithm, logistic regression, Bayesian Network, discriminant analysis, random trees, tree-AS, Chi-squared Automatic Interaction Detection CHAID tree, Quest, classification and regression (C&amp, R) tree and neural net)]. From an initial 54,613 gene-probes, a set of 488 genes and a final set of 16 genes were defined. Secondly, two identified markers of the immune checkpoint, PD-L1 (CD274) and IKAROS (IKZF4), were validated in an independent series from Tokai University, and the immunohistochemical expression was quantified, using a machine-learning-based Weka segmentation. High PD-L1 associated with poor overall and progression-free survival, non-GCB phenotype, Epstein–Barr virus infection (EBER+), high RGS1 expression and several clinicopathological variables, such as high IPI and absence of clinical response. Conversely, high expression of IKAROS was associated with a good overall and progression-free survival, GCB phenotype and a positive clinical response to treatment. Finally, the set of 16 genes (PAF1, USP28, SORT1, MAP7D3, FITM2, CENPO, PRCC, ALDH6A1, CSNK2A1, TOR1AIP1, NUP98, UBE2H, UBXN7, SLC44A2, NR2C2AP and LETM1), in combination with PD-L1, IKAROS, BCL2, MYC, CD163 and TNFAIP8, predicted the survival outcome of DLBCL with an overall accuracy of 82.1%. In conclusion, building predictive models of DLBCL is a feasible analytical strategy.

Bina Darma foundation is a non-profit organization established by SWCU and GMKI. The main activities of the Bina Darma foundation are a leadership training program, journal publishing, and guesthouse rental. Problems that occur in the foundation include the management of human resources related to IT is not appropriate. To solve the problem, there needs to be control and evaluation to see how the condition of IT governance in Bina Darma Foundation uses the framework COBIT 5 domain DSS (Delivery, Service, and Support). The result of this research is capability level is currently at level 1 (Performed process), while the capability level is expected to be at level 2 (Managed process). Therefore, recommendations are given so that the IT governance in Bina Darma Foundation can work optimally to support the achievement of the organization's business objectives efficiently.