Miracle-dz/UniFAR-FTUs huggingface dataset:
SISTEM PENDUKUNG KEPUTUSAN PERANKINGAN CALON SISWA BARU JALUR UNDANGAN MENGGUNAKAN SIMPLE ADDITIVE WEIGHTING (STUDI KASUS : SMK BUMI NUSANTARA WONOSOBO)

Dunia pendidikan masih menghadapi tantangan yang cukup mendasar yaitu masalah mutu dan daya saing pendidikan. Penelitian ini menentukan kriteria-kriteria perankingan penerimaan siswa baru dan bagaimana menerapkan metode Simple Additive Weighting (SAW) ke dalam Sistem Pendukung Keputusan (SPK) untuk penerimaan siswa baru jalur undangan pada SMK Bumi Nusantara Wonosobo yang dapat membantu sekolah dalam memilih siswa baru yang berkualitas. Berdasarkan kriteria-kriteria yang telah ditetakpan ialah kemampuan bahasa inggris, keahlian ekstrakulikuler, tidak buta warna, tamat SMP, nilai SKHU/Ijazah, usia, mengisi formulir, dan sertifikat prestasi. Dari hasil nilai yang diperoleh maka V 1 adalah siswa baru yang berkualitas baik dan memiliki predikat nilai 84 dengan rentan nilai sebagai berikut: 50 – 70 = Cukup, 71 – 82 = Baik, 83 – 100 = Terbaik.
Geometric Transformation of User Queries in Information Retrieval on the Web

In the context of improving the information retrieval on the web, we propose in this paper an original approach based on geometric queries, whose goal is to reduce the problem of the non-selection of relevant documents, this issue is known as silence problem. This approach is based on the transformation of user query into geometric shapes and exploitation of a set of previously executed queries (with relevant responses) to find the closest to the initial query by using a similarity measure. We will focus on the need to involve domain ontology in the construction of geometric queries. Finally this paper describes the implementation performed and draws the first conclusions on various perspectives of this approach.
Function of Sound Devices in the Construction of Metaphoric Meaning in Poetry

This paper presents a study of the relation between the meaning of the words and their phonological features within the poetic context. The article presents a short overview of the existing theories and assumptions in the researched area. Special attention is given to the theory of the arbitrariness of signs and recent studies, suggesting this claim to be incomplete and subject to numerous exceptions. This research is aimed at finding the evidence of metaphorical use of sounds patterns in the poetry of Dylan Thomas. This paper presents a detailed analysis of four poems: “From Love’s First Fever to Her Plague”, “Light Breaks Where No Sun Shines”, “Especially When the October Wind” and “After the Funeral” with a few examples from other poems. The results of the analysis show that sound, being one of the most important elements of poetic texts, is able to obtain a specific semantic meaning of its own but is highly dependent on the context. Interpretation of sound patterns and their expressive potential is crucial for a comprehensive analysis of poetic texts.
Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.
Robust Design of Dynamic System when Ideal Function’s Parameters are Unknown

Robust design is an important method to reduce the effect of noise factors and unit-to-unit variation in order to improve the product quality and which leads to higher customer satisfaction. The robust design of dynamic system is used to find the optimal control factor settings so that the measured response is closest to the ideal function. In this paper, a mathematical formula based on the quality loss function is derived for optimizing the robust design of unlinearized dynamic system. The example of high-precision positioning device is provided to demonstrate the implementation and effectiveness of the proposed method.
Image Co-Segmentation and Efficiency Saliency Detection

: Today there is a massive attempt to exclude the same object from different images.Such problem is not an easy task as it seems, furthermore the algorithm which is presented today is not 100% accurate even though it is efficient. A novel interactive image cosegmentation algorithm using likelihood estimation and higher order energy optimization is proposed for extracting common foreground objects from a group of related images. Our approach introduces the higher order clique’s, energy into the cosegmentation optimization process successfully. A region-based likelihood estimation procedure is first performed to provide the prior knowledge for our higher order energy function.Further extended the work for image saliency detection which is used to automatically locate the content that draws a viewer’s attention in the early stage of visual processing.
Collaborative Location-Based Mobile Game with Error Detection Algorithm

In the recent years, there has been an evolution in game input medium. From just using buttons, players can now interact with games through a wider spectrum of inputs which includes touch screen, camera, light sensor, accelerometer, compass and GPS. This is driven by the availability of these modules and sensors within mobile devices that are omnipresent nowadays. As a result, there has been a creative breakthrough on how games are played today where gaming experience can be made more intuitive and immersive. Localization is one of the input medium where the player’s physical location is used as part of the gameplay. This paper proposes an original gameplay schema that utilizes indoor mobile Wi-Fi localization technique as game input that does not require additional infrastructure. The game takes advantage of the weakness of Wi-Fi localization where environmental influence is significant and makes it part of the gameplay. A simple error detection algorithm is also introduced to maximize the game playability value by balancing game responsiveness and accuracy level.
Meditation Study with Data Analytics

Is Chan (Zen) Still Relevant in 21st Century? What do you get in the studies of Chan ontology, history, Chan’s reflections on Buddhism, Taoism and Confucianism, Chan utterances and debates, Chan and mystics, and Chan poems and drawings? What is your mindset when thinking about Chan in modern societies? Chan is clearly a philosophy that impacts human are view about the essence of science and relates scientific studies to the contemplation of its performer-human. As a matter of fact, transcendental meditation [1], a spiritual development technique, which was popularized by former Hindu ascetic Maharishi Mahesh Yogi has gained popularity in the west during the 1960’s [2]. We envision that by studying Chan with data analytics, we can help clarify the basic concepts of from pragmatic point of view.
A context aware & personalized multiple location trip planner using Facebook check-ins of a user

Travel planning in a new city or a country is tedious and time consuming as the traveller has zero or minimal knowledge on which locations to visit in the new region. To overcome this problem many commercial solutions such as TripAdvisor and Google Trips, as well as research level solutions have been introduced. Even though these solutions are capable of reducing the information overload to the user, they do not perform any personalization in making travel recommendations. Therefore, the need for a recommendation system that is capable of extracting the user's personal travel preferences more accurately is still significant.
Tuning the Program Transformers from CC to PDL

This work proposes an alternative definition of the so-called program transformers used to obtain reduction axioms in the Logic of Communication and Change (LCC). Our proposal uses an elegant matrix treatment of Brzozowski’s equational method instead of Kleene’s translation from finite automata to regular expressions. The two alternatives are shown to be equivalent, with Brzozowski’s method having the advantage of generating smaller expressions for models with average connectivity.
Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

This paper proposes a sensor fusion algorithm by complementary filter technique for attitude estimation of quadrotor UAV using low-cost MEMS IMU. Angular rate from gyroscope tend to drift over a time while accelerometer data is commonly effected with environmental noise. Therefore, high frequency gyroscope signal and low frequency accelerometer signal is fused using complementary filter algorithm. The complementary filter scaling factor K1=0.98 and K2=0.02 are used to merge both gyro and accelerometer. The results show that the smooth roll, pitch and yaw attitude angle can be obtained from the low cost IMU by using proposed sensor fusion algorithm.
Bio-Socially Inspired Strategies in Support of Dynamic Spectrum Access: An Evolutionary Game Theory Perspective

Human and animal societies exhibit complex cognitive and social processes of coordination, cooperation, and competition among their members. Among other functions, these processes can facilitate fairer sharing of resources among community members and enhance individual survival outcomes. In this work, three bio-socially inspired models for secondary users of spectrum in cognitive radio networks are defined and compared to one other within an evolutionary framework. The proposed models reflect successively more sophisticated capabilities of secondary users in distributed spectrum access. The simplest of the three, blind channel access, is shown to be evolutionarily dominant when residual channel capacities are homogeneous. The second more advanced model assumes a capability to sense channel utilization; this model is shown to dominate when the channels have intermediate load and heterogeneous capacities. Finally, the most complex model (additionally) allows for social coalitions and within-group deference; this model is seen to dominate in high load heterogeneous resource settings. We explore the long term evolutionary pressures within societies whose members choose between these three schemes, with natural selection operating via a utility-based fitness function. Our research is based on systematic ns-3 simulation experiments of heterogeneous societies under a range of assumed channel conditions, population sizes, resource demands, and initial user attributes. Our results demonstrate that the secondary user population always evolves to adopt a unique and stable strategy, but that the winning strategy selected depends strongly on channel conditions. Our results further show that this kind of leaderless evolution leads to a significant 12-116% overall improvement in performance compared to
Data classification based on the hybrid intellectual technology

In this paper the data classification technique, implying the consistent application of the SVM and Parzen classifiers, has been suggested. The Parser classifier applies to data which can be both correctly and erroneously classified using the SVM classifier, and are located in the experimentally defined subareas near the hyperplane which separates the classes. A herewith, the SVM classifier is used with the default parameters values, and the optimal parameters values of the Parser classifier are determined using the genetic algorithm. The experimental results confirming the effectiveness of the proposed hybrid intellectual data classification technology have been presented.
A Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions

The prediction of protein–ligand interactions and their corresponding binding free energy is a challenging task in structure-based drug design and related applications. Docking and scoring is broadly used to propose the binding mode and underlying interactions as well as to provide a measure for ligand affinity or differentiate between active and inactive ligands. Various studies have revealed that most docking software packages reliably predict the binding mode, although scoring remains a challenge. Here, a diverse benchmark data set of 99 matched molecular pairs (3D-MMPs) with experimentally determined X-ray structures and corresponding binding affinities is introduced. This data set was used to study the predictive power of 13 commonly used scoring functions to demonstrate the applicability of the 3D-MMP data set as a valuable tool for benchmarking scoring functions.
Application of Multivariate Data Analysis to machine power measurements as a means of tool life Predictive Maintenance for reducing product waste

Modern manufacturing industries are increasingly looking to predictive analytics to gain decision making information from process data. This is driven by high levels of competition and a need to reduce operating costs. The presented work takes data in the form of a power measurement recorded during a medical device manufacturing process and uses multivariate data analysis (MVDA) to extract information leading to the proposal of a predictive maintenance scheduling algorithm. The proposed MVDA model was able to predict with 100 % accuracy the condition of a grinding tool.
APPLICATION OF CONTEXTUAL SCENARIO TO DEFINE AN EMOTION SPACE FOR FACIAL EXPRESSIONS OF HUMANOIDS

: This paper explores what facial expressions humanoids in certain contexts should have and what kinds of emotional spaces should be developed to realize those expressions. Most research regarding the facial expressions of robots have attempted to embody all expressions of human without taking the robot’s use context and function into account. In this study, we focused on educational humanoid robots and elicited 60 kinds of emotion related adjectives by applying contextual scenarios where users were asked to envision possible interactions in over 74 given scenarios in different context. Doing so can overcome the limits of research which does not consider a robot’s use context when it is specialized for particular uses and context. Appropriate facial expressions were derived after frequency analysis, and an emotional dimension was found by MDS (multi-dimensional scaling) on 23 adjectives with high frequency. The result showed that educational humanoid robots should dominantly express emotions related to ‘happiness’, implying that their facial expressions should be designed to express ‘happiness’ more diversely. Finally this study suggests that emotion of ‘happiness’ can be segmented into three intensity groups: high-arousal happiness, mid-arousal happiness, and quiet-arousal happiness; thus, facial expressions should be designed correspondingly in order to enrich human-robot interaction.
Quadrupedal mammals as paragons for walking machines

The idea of building artificial animals is an old dream of manhood, handed down through centuries by mythology and poetry. May it be the Troian horse, Olympia the puppet or L. A. Ryggs "Mechanical horse", always the concepts aimed at an artificial humanoid or at reverse engineering of one of man's pets. Not surprisingly Sony's ® "AIBO" as the first commercially available "animate" is a biomomimetic copy of a pet. This anthropocentric approach ignores the fact that ancestral animals as well as most of the currently living animals are and were small-in the size of a mouse or a rat. We and our pets inherited most of our locomotory capabilities from the ancestral mammals, adapting mechanisms and control by only a a small amount in comparison to what happened 200,000,000 years ago when "modern" mammals were derived from reptile-like forms in a dramatical "reconstruction process". "Biological inspiration" of walking machines using mammals as paragons has to be founded on knowledge about the basic principles of mammalian locomotion, visible in small species. From this starting point, special locomotory adaptations of large cursorial mammals like humans, camels or horses may be identified and seperated from what is our common evolutionary heritage.
Structural Analysis of Source-Code Changes in Large Software through SrcDiff and DiffPath

Comprehension of changes in large software is a major challenge in software development. The srcDiff format supports the analysis of differences in large software systems, however, previous tools had were not complete. A new tool is presented that generates the srcDiff format from two versions of a software system. Furthermore, a new format, diffPath, is presented which is an XPath expression that uniquely identifies a source code change. A tool to generate diffPath expressions has also been created. Both tools are evaluated on releases of the GNU GCC compiler.
This Tweet Brought to You by a Journalist

It is increasingly common for journalists to use reader comments from social media as a quote or source of evidence for news. Past work on the effects of user-generated content has revealed that comments can have a host of negative effects on readers’ psychological processing of news, particularly when reader comments are hostile or uncivil. What happens, however, when online comments are moderated by a journalist? Does comment gatekeeping influence the effects of comments? Informed by the Modality-Agency-Interactivity-Navigability model and warranting theory, an experiment (N = 365) was conducted to answer these questions using a 2 (comment gatekeeping: low vs. high) × 2 (comment valence: positive vs. negative) × 2 (news involvement: low vs. high) design. Results revealed that journalistic gatekeeping of comments decreases article credibility and increases bias through the indirect pathway of source trustworthiness, regardless of news type or comment valence.
A Network-Topology-Based Approach for the Load-Flow Solution of AC–DC Distribution System With Distributed Generations

The ac–dc distribution systems have recently gained huge popularity due to advancements in power converters, high penetration of renewable energy resources, and wide usages of dc loads. However, load flow in such systems is a challenging task due to nonlinear characteristics of power converters. This paper presents a novel load-flow algorithm for ac–dc distribution systems, utilizing the concept of graph theory and matrix algebra. Four developed matrices, loads beyond branch matrix, the path impedance matrix, the path drop matrix, and the slack bus to other buses drop matrix, and simple matrix operations are utilized to obtain load-flow solutions. These matrices reveal the network topology and relevant information about the behavior of ac–dc distribution network during load-flow studies. In contrast with traditional load-flow methods for HVDC systems, the proposed technique does not require any lower–upper decomposition, matrix inversion, and forward–backward substitution of the Jacobian matrix. Because of the aforementioned reasons, the developed technique is computationally efficient. The proposed method has been tested using several case studies of ac–dc distribution network, which includes different operating modes of various power converters. Results show the feasibility and authenticity of the proposed method.
The Design and Implementation of a Cleaning System Prototype

As we all know, data is one of the most valuable assets, however, raw data is often problematic, not conducive to the training of algorithm models. To cope with this, we can process the dirty data with cleaning systems [1] to obtain standard clean data for data statistics, data mininig and other use. Instead of manually modifying data, writing SQLs or other cumbersome methods which are popular present ways to clean data, the article proposes an approach by making use of the Hadoop big data platform to support massive data and support the cleaning of multiple heterogeneous data sources. Moreover, our system prototype supports custom rules and algorithms, can export results to a specified database, greatly simplifying the workload of data cleaning personnel. Based on the system design and theoretical verification presented in this paper, the author implemented a big data cleaning tool based on big data platform. The typical data cleaning process shows that the data cleaning can be achieved and user operations can be simplified on the basis of the theory proposed in this paper.
Recoverable B±trees in centralized database management systems

The B±tree is the most widely used index structure in the current commercial database systems. This paper introduces new B±tree algorithms in which tree-structure modifications such as page splits or merges are executed as atomic actions. A B±tree structure modification, once executed to completion will never be undone no matter if the transaction that triggered such a structure modification commits or aborts later on. In restart recovery from a system crash, the redo pass of the recovery algorithm will always produce a structurally consistent B±tree, on which undo operations by backward-rolling transactions can be performed. A database transaction can contain any number of operations of the form “fetch the first (or next) matching record”, “insert a record”, or “delete a record”, where database records are identified by their primary keys. Repeatable-read-level isolation for transactions is achieved by key-range locking.
Simulating Realistic Synthetic Longitudinal Brain MRIs with known Volume Changes

This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation.