Miracle-dz/UniFAR-FTUs huggingface dataset:
Sms Filtering Menggunakan Naive Bayes Classifier Dan Fp-growth Algorithm Frequent Itemset

Abstrak SMS (Short Message Service) masih menjadi pilihan utama sebagai media komunikasi walaupun sekarang ponsel semakin berkembang dengan berbagai media komunikasi aplikasi messenger. Seiring dengan berkembangnya berbagai media komunikasi lain, beberapa operator di beberapa negara menurunkan tarif SMS untuk tetap menarik minat pengguna ponsel. Namun penurunan tarif ini menyebabkan meningkatnya SMS spam, karena dimanfaatkan oleh beberapa pihak sebagai salah satu alternatif untuk iklan hingga penipuan. Hal itu menjadi permasalahan penting karena dapat mengganggu dan merugikan pengguna. Naive Bayes dianggap sebagai salah satu learning algorithm yang sangat efektif dan penting untuk machine learning dalam information retrieval. Naive Bayes terbukti memiliki kinerja yang baik dalam klasifikasi teks dan deteksi SMS spam [2,10] dengan menunjukan akurasi yang tinggi. Dengan dikolaborasikan algoritma yang mampu menentukan frequent itemset dengan baik maka mampu menghasilkan tingkat akurasi yang lebih baik [2]. Karena tidak hanya setiap kata yang dianggap mutually independent, tetapi juga kata yang frequent sebagai kata yang single, independent dan mutually exclusive [2], sehingga mampu meningkatkan nilai peluang dan menyebabkan sistem lebih tepat dalam klasifikasi. Dalam hal ini digunakan FP-Growth untuk mining frequent pattern yang memiliki performansi yang baik dan efisien karena tidak membutuhkan pembangkitan kandidat frequent [4]. Hasil penelitian penggunan kolaborasi antara Naive Bayes dan FP-Growth menghasilkan akurasi rata- rata terbesar sebesar 98, 506% dan lebih unggul 0,025% dari metode tanpa melibatkan FP-Growth untuk dataset SMS Spam Collection v.1, serta meningkatkan nilai precision sehingga hasil klasifikasi lebih akurat. Kata Kunci: ekstraksi kata kunci, KEA, social media
Improvement on bintree multi-class categorization algorithm based on SVM

It's a hotspot to research on support vector machine that extends from two-class issues to multi-class.Among all kinds of methods,bintree multi-class text categorization algorithm based on support vector machine is more effective in training and sorting than others,and it works out the impartibility problem.So it is a good method.The dissertation systematically researches and analyses bintree multi-class text categorization algorithm based on support vector machine,and then has some improvement on it.That is,we assembles firstly,and then sorts them when the size of testing texts is too large.The aim of this improvement is to make the testing text be computed more aimable,but does not begin from the base crunode of bintree at all time.The improvement can enhance the effect of text categorization and make it move accurat when the size of testing texts is too large and the quantity of sorted function is too much.
Possibilistic Games with Incomplete Information

Bayesian games offer a suitable framework for games where the utility degrees are additive in essence. This approach does nevertheless not apply to ordinal games, where the utility degrees do not capture more than a ranking, nor to situations of decision under qualitative uncertainty. This paper proposes a representation framework for ordinal games under possibilistic incomplete information (π-games) and extends the fundamental notion of Nash equilibrium (NE) to this framework. We show that deciding whether a NE exists is a difficult problem (NP-hard) and propose a Mixed Integer Linear Programming (MILP) encoding. Experiments on variants of the GAMUT problems confirm the feasibility of this approach.
Hydroxide Ion Diffusion in Anion-Exchange Membranes at Low Hydration: Insights from Ab Initio Molecular Dynamics

Operation of anion-exchange membrane (AEM) fuel cells (AEMFCs) results in gradients in the cell that can lead to low-hydration conditions within the cell. It is therefore important to investigate hydroxide ion diffusion in AEMs with low water-to-cation ratios (λ ≤ 4, λ≡nH2O/ncation). In this work, ab initio molecular dynamics simulations are presented to explore hydroxide ion solvation complexes and diffusion mechanisms in model AEMs at low hydration. By changing the cation spacing within the AEM and the degree of hydration, six different idealized AEM models are created in which the water distribution is not uniform. It is shown that distinct water distributions impart unique OH– diffusion mechanisms that fall into three regimes. The observed mechanisms, nondiffusive, vehicular, and a mixture of structural and vehicular diffusion, depend on the presence or absence of a second solvation shell of the hydroxide ion and on the local water structure. The results suggest that the water distribution is a better.
Deterministic Stellar BIST for Automotive ICs

As the automotive industry enters a period of rapid evolution changing the way cars are designed and produced, the number of complex safety-critical components deployed in advanced driver assistance systems or autonomous vehicles is progressively rising with high-end models containing around 120 MCUs. These integrated circuits must adhere to stringent requirements for high quality and long-term reliability driven by functional safety standards. This requires test solutions that address challenges posed by automotive electronics. This paper presents Stellar BIST—a next generation compression scheme for in-system automotive test. The proposed solution can work with any sequential test compression. It builds on a finding that certain clusters of test vectors are capable of detecting many random-resistant faults, where a cluster consists of a parent (base) pattern and its transformed derivatives. Stellar BIST involves generating vectors based on simultaneous and multiple complements of scan slices of encodable parent patterns. The multiple complements are also skewed between successive patterns to diversify the resultant tests. The new scheme elevates compression to values unachievable through conventional reseeding-based solutions and provides significant tradeoffs between storage requirements and test application time, critical for in-system automotive applications. The experimental results obtained for industrial designs and different fault models illustrate feasibility of the proposed test scheme and are reported herein.
Students Model in Different Learning Styles of Academic Achievement at the University of Phayao, Thailand

Attitudes and learning styles can affect academic achievement at different levels. While analyzing attitudes and learning styles can not only use basic statistics, using advanced tools to analyze the students' in-depth elements is discussed. Therefore, this research offers an appropriate method for clustering academic achievement (GPA) that support student’s attitudes and learning styles. At the same time, this research is aimed to study the level of attitudes towards learning styles in different academic achievement of students at the University of Phayao. The data collection was conducted from 195 students from 17 schools and colleges at the University of Phayao, Thailand. The results show that there is a variety of cluster in students’ attitudes and learning styles with a significant pattern (types of success) of the students’ model, while the model performance has a very high efficiency to the model. In future work, it will be applied with other universities in Thailand and also used in developing applications for providing a program recommended for appropriate educational programs.
Significant Role of Al in Ternary Layered Double Hydroxides for Enhancing Electrochemical Performance of Flexible Asymmetric Supercapacitor

The Al effect on the electrochemical properties of layered double hydroxides (LDHs) is not properly probed, although it is demonstrated to notably promote the capacitive behavior of LDHs. Herein, ternary NiCo2Alx layered double hydroxides with varying levels of Al stoichiometry are purposely developed, grown directly on mechanically flexible and electrically conducting carbon cloth (CC@NiCo2Alx‐LDH). Al plays a significant role in determining the structure, morphology, and electrochemical behavior of NiCo2Alx‐LDHs. At an increasing level of Al in NiCo2Alx‐LDHs, there is a steady evolution from 1D nanowire to 2D nanosheets. The CC@NiCo2Al‐LDH at an appropriate level of Al and with the nanowire–nanosheet mixed morphology exhibits both significantly enhanced electrochemical performance and excellent structural stability, with about a 2.3‐fold capacitance of NiCo2‐OH. When applied as the anode in a flexible asymmetric supercapacitor (ASC), the CC@NiCo2Al‐LDH gives rise to a remarkable energy density of 44 Wh kg−1 at the power density of 462 W kg−1, together with remarkable cyclic stability with 91.2% capacitance retention over 15 000 charge–discharge cycles. The present study demonstrates a new pathway to significantly improve the electrochemical performance and stability of transition metal LDHs, which are otherwise unstable in structure and poorly performing in both rate and cycling capability.
Place Recognition using Multiple Feature Types

Place recognition has been intensively studied in the context of robot vision. BoW-based approach gains its popularity for its efficiency and robustness using features extracted from images. Many features have been examined in the past for place recognition purpose. However, there is no such feature that can outperform others in all environments. Each feature has its own advantage, thus, they should be carefully chosen depending on the context and environments. In this paper, we propose a modified vocabulary tree with the ability of merging multiple kinds of features such that it allows users to customize different combination of features for better place recognition performance. The system is tested in real-time on real-world datasets and the experiments demonstrates the advantage of our system compared to existing approaches.
Study of two-layer trust model in service grid based on behavior

In open service grid,to establish trust relationships among different domains is a basic problem so as to cooperate between them.In this paper a two-layer trust model based on behavior was proposed to enhance grid security and extensibility.This trust model was used to resolve trust issues between entities in different domains.The upper layer established and maintained recommendation trust relationships between different domains in grid.Lower layer was in charge of how domain manager evaluated trust of entities in his domain.An algorithm was given to adjust trust relationships between domains based on entities interactions,and a practical method to process recommendation trust.At last,the experiment results show that two-layer trust model is effective and extensible.
Emotional eye movement analysis using electrooculography signal

In this study, for recognition of (positive, neutral and negative) emotions using EOG signals, subjects were stimulated with audio-visual stimulus to elicit emotions. Hjorth parameters and Discrete Wavelet Transform (DWT) (Haar mother wavelet) were employed as feature extractor. Support Vector Machine (SVM) and Naive Bayes (NB) were used for classifying the emotions. The results of multiclass classifications in terms of classification accuracy show best performance with the combination DWT+SVM and Hjorth+NB for each of the emotions. The average SVM classifier's accuracy with DWT for horizontal and vertical eye movement are 81%, 76.33%, 78.61% and are 79.85%, 75.63% and 77.67% respectively. The experimental results show the average recognition rate of 78.43%, 74.61%, and 76.34% for horizontal and 77.11%, 74.03%, and 75.84% for vertical eye movement when Naive Bayes group with Hjorth parameter. Above result indicates that it has the potential to be used as real-time EOG-based emotion assessment system.
Leaking supervisory controls and data acquisition commands over unpadded TCP/IP encryption through differential packet size analysis

MODBUS is the most common protocol in industrial control systems for relaying commands and fetching data. Still, the protocol has zero built‐in security. Thus, modern implementations want to control remote units through the internet to maximize utility, they often implement MODBUS over TCP/IP connections. To cope with the inherent vulnerabilities, trending implementations of MODBUS‐TCP utilize encryption on TCP packets to protect the commands and data being transferred. Still, side characteristics can be exploited to break the confidentiality of these command executions. In this article, we present attacks that leak what is being executed on a real‐world Supervisory Controls and Data Acquisition (SCADA) system running MODBUS, even if commands are encrypted over a TCP/IP channel. Presented scenario attacks use network traffic sniffed from a hardware‐in‐the‐loop simulated Power Grid Industrial Control System. We also show that command execution can be leaked no matter when the attacker starts sniffing traffic, provided he has some prior knowledge on how the SCADA system works. Only restriction is that the configuration must use unpadded encryption; something that proved to be more common than expected.
Improving reliability of large-scale multimedia services

Online multimedia communication services such as Skype and Google Hangouts, are used by millions of users every day. They have Service Level Agreements (SLAs) covering various aspects like reliability, response times, and up-times. They provide acceptable quality on average, but users occasionally suffer from reduced audio quality, dropped video streams, and failed sessions. The cost of SLA violation is low customer satisfaction, fines, and even loss of business. Service providers monitor the performance of their services, and take corrective measures when failures are encountered. Current techniques for managing failures and anomalies are reactive, do not adapt to dynamic changes, and require massive amounts of data to create, train, and test the predictors. In addition, the accuracy of the these methods is highly compromised by changes in the service environment and working conditions. Furthermore, multimedia services are composed of complex software components typically implemented as web services. Efficient coordination of web services is challenging and expensive, due to their stateless nature and their constant change. We propose a new approach to creating dynamic failure predictors for multimedia services in real-time and keeping their accuracy high during run-time changes. We use synthetic transactions to generate current data about the service. The data is used in its ephemeral state to create, train, test, and maintain accurate failure predictors. Next, we propose a proactive light-weight approach for estimating the capacity of different components of the multimedia system, and using the estimates in allocating resources to multimedia sessions in real time. Last, we propose a simple and effective optimization to current web service transaction management protocols. We have implemented all the proposed methods for failure prediction, capacity estimation, and web services coordination in a large-scale, commercial, multimedia system that processes millions of sessions every day. Our empirical results show significant performance gains across several metrics, including quality of the multimedia sessions, number of failed sessions, accuracy of failure prediction, and false positive rates of the anomaly detectors.
Nonlinear Control Structure of Grid Connected Modular Multilevel Converters

This paper implements nonlinear control structure based on Adaptive Fuzzy Sliding Mode (AFSM) Current Control and Unscented Kalman Filter (UKF) to estimate the capacitor voltages from the measurement of arm currents of Modular Multilevel Converter (MMC). UKF use nonlinear unscented transforms in the prediction step in order to preserve the stochastic characteristics of a nonlinear system. In order to design adaptive robust control strategy and nonlinear observer, mathematical model of MMC using rotating d-q theory has been used. Digital time-domain simulation studies are carried out in the Matlab/Simulink environment to verify the performance of the overall proposed control structure during different case studies.
Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms.
High Performance Computing on MapReduce Programming Framework

Big Data may be a large quantity of data that can't be managed by the standard data management system. Hadoop may be a technological answer to Big Data. Hadoop Distributed filing system (HDFS) and MapReduce programming model is employed for storage and retrieval of the massive data. The Tera Bytes size file will be simply keep on the HDFS and might be analyzed with MapReduce. This paper provides introduction to Hadoop HDFS and MapReduce for storing huge amount of files and retrieve info from these files. Our experimental work done on Hadoop by applying variety of files as input to the system and so analyzing the performance of the Hadoop system. we've got studied the number of bytes written and read by the system and by the MapReduce. we've got analyzed the behavior of the map technique and therefore the reduce technique with increasing range of files and therefore the quantity of bytes written and read by these tasks.
Constructing flat inputs for two-output systems

In this paper, we study the problem of constructing flat inputs for two-output dynamical systems. The notion of flat inputs has been introduced by Waldherr and Zeitz [2008, 2010] and can be seen as dual to that of flat outputs. In the single-output case, a flat input can be constructed if and only if the original system together with its output is observable. In the multi-output case, the observability is not necessary for the existence of flat inputs. We start by discussing the case when the dynamical system together with the given output is observable and we present a generalization of the results of Waldherr and Zeitz [2010] by relating them with the notion of minimal differential weight. Then, we give our main theorems. We consider the unobservable case for which we study the local and global problems. We completely describe the local case, discuss the issue of the minimal modification of the original system, and propose a solution for the global problem. Finally, we explain how our results can be applied to secure communication.
Single Chinese Character Country Name Recognition

Single Character(shortened form) Country Name(SCCN),is a kind of shorted form of a Country name,which composed of one Chinese Character,such as “英”(ying1,England)and “美”(mei3,America). The SCCN recognition is a part of the task of Chinese Named Entity Recognition(NER). This paper investigates the usage of SCCNs in real text, then builds a modal for recognition based on rules,using temporary wordlist and contextual information as main resources.Using the manual segmented text as input,F-score of our method achieved 96.33% in the close test and 94.96% in the open test.
Study of stereoscopic shape perception for virtual design

Virtual environments are increasingly used to replace real prototypes, but are we sure that shape perception is the same in both environments, i.e. can we affirm that the shapes that are designed and validated in virtual environments will be perceived the same once a physical prototype has been built? This study traces a series of perception tests in which two virtual shapes (a sphere and a shape without symmetry or obvious monoscopic cues) will be compared to their respective actual shape. Projections change throughout the tests to highlight the influential parameters. We compare influence of head tracking, adjusting interpupillar distance, position of the virtual object in relation to the screen and the orientation of the modified shape. We show that if all these parameters seem to have an influence, head tracking appears extremely important for a good perception.
Intelligent Tuning of Augmented $L_{1}$ Adaptive Control for Cerebral Palsy Kids Rehabilitation

Walking ability can be lost due to several neurological diseases. In this paper, we are interested in the rehabilitation of pediatric patients. For this purpose, an extended $L_{1}$ adaptive control has been proposed to control 2 DOF lower limbs exoskele-tons used to rehabilitate kids suffering from Cerebral Palsy (CP). Two methods based on PID controllers have been combined with $L_{1}$ adaptive control in order to ensure the limb movement along a predefined trajectory. Then, a comparison study has been established based on tracking errors. Moreover, to improve the robustness of these controllers against uncertainties, several tests with the variations of children masses and lengths have been carried out, with the presence of torque limits using fuzzy logic.
Efficient algorithms and implementations for signal processing

R eproduced with permission of the copyright owner. Further reproduction prohibited without permission. 3.2 The Solution . 17 3.3 Error E s t im a te . 20 4 O N T H E EX ISTENC E OF W EY L-H E ISE N BE R G A N D A F F IN E FR A M E S IN L2(R) . 21 4.1 The F jblen. 21 4.2 The Weyl-Heisenberg C a s e . 22 4.2. 1 Definitions and Some Known R e s u l ts . 22 4.2. 2 Conditions for VV-H G e n e ra to rs . 23 4.3 The Affine C a s e . 30 4.3. 1 Definitions and Some Known R e s u l ts . 30 4.3.2 Conditions on Affine Mother W a v e le ts . 32 4.4 The Semi-Irregular W-H F r a m e s . 35 5 D E V E L O PM E N T A N D IM PLEM EN TA TIO N OF A PA R A LLEL 2-D FORW ARD FW T A LG O R ITH M . 39 5.1 Wavelets B a s ic s . 39 5.1. 1 Continuous Wavelet T ra n s fo rm . 39 5.1. 2 Discretized Wavelet Transform . 40 5.1. 3 M ultireso lu tion . 41 5.1. 4 Discrete Wavelet Transforms . 43 5.1. 5 How Do Wavelets W o rk ? 45 5.2 Design and Analysis of Parallel 2-D Forward FW T Algorithms . 48 5.2. 1 1-D F W T . 49 5.2. 2 A 2-D Parallel FW T Algorithm and A n a ly s is . 56 5.2. 3 The A lg o r ith m . 56 5.3 E x p e r im e n t. 60