Miracle-dz/UniFAR-FTUs huggingface dataset:
Penerapan Bee Colony Optimization Algorithm untuk Penentuan Rute Terpendek (Studi Kasus : Objek Wisata Daerah Istimewa Yogyakarta)

Abstrak Pencarian rute terpendek merupakan suatu permasalahan optimasi yang sering dijadikan studi kasus bagi penelitian. Jarak merupakan faktor yang paling menentukan dalam melakukan penelusuran jalur-jalur yang akan dilalui. Jalur dengan jarak terpendek akan dipilih sebagai jalur pilihan. Algoritma bee colony optimization digunakan dalam penelitian ini untuk menyelesaikan permasalah pencarian rute terpendek. Terdapat dua proses utama pada saat penelusuran jalur yaitu forward dan backward. Algoritma bee colony optimization bekerja pada proses forward. Nilai probabilitas suatu jalur dijadikan dasar pada proses transisi jalur kemudian durasi waggle dance dari tiap lebah yang berhasil menemukan posisi tujuan akan dijadikan rute pilihan. Hasil yang diperoleh dalam penelitian ini adalah algoritma bee colony optimization dapat digunakan untuk menemukan rute terpendek. Jumlah lebah yang dilepas sangat mempengaruhi dalam menemukan rute-rute yang bisa dilalui. Semakin banyak jumlah lebah yang dilepas semakin besar peluang ditemukannya rute terpendek. Kata kunci — Rute Terpendek, Algoritma Bee Colony Optimization. Abstract The shortest path determination is an optimization problem which often used as a case study for research . Distance is the most defining factor in performing the search paths to be passed . Path with the shortest distance would be chosen as a path selection . Bee colony optimization algorithm used in this study to complete problems shortest path determination . There are two main process es during search path that is forward and backward . Bee colony optimization algorithm works on the process forward . The value probability of a path is base intransition process and the duration of waggle dance track of every bee who had found the position of the goal will be a preferred route . The results obtained in this study is the bee colony optimization algorithm can be used to find shortest path . The number of bees are released greatly affects in finding routes that can be passed . The more the number of bees that removed the greater the chances of finding the shortest path. Keyword — Shortest Path , Bee Colony Optimization Algorithm
Approach for Transforming Monolingual Text Corpus into XML Corpus

In this paper, we are presenting the approach to convert the text based monolingual corpus to Part-Of-Speech tagging using an standard tagging tool in tagged file and then convert tagged file in the XML format as per defined DTD (Document Type Definition). The tagged text document is parsed through the logic to generate the corpus in XML and also, it can be further used for Information Retrieval, Text-To-Speech conversion, Word Sense Disambiguation and also useful for preprocessing step of parsing by providing unique tag to each word which reduces the number of parses. General Terms Natural Language Processing
Ghost removal for background subtraction using color similarity comparison

This paper aims to solve the problem of detecting ghost object; which is a common problem in background subtraction algorithm. Ghost object is the false object detected which is not corresponding to any actual object in current image. In this work, we proposed ghost detection and removal method using color similarity comparison. Proposed solution is designed based on the assumption that ghost problem occurs due to the existence of the object in background image instead of in the current image. We are using color similarity between detected foreground area and its surrounding area to first determine whether the object appear in background or current image, consequently identify whether the detected object is a ghost or an actual object. Proposed solution has been tested using various datasets including PETS2001 and own datasets and it is proved that the proposed method is able to solve ghost problem.
Multi-target tracking with stored program adaptive CNN universal machines

This paper shows that the performance of multi-target tracking (MTT) systems can be significantly increased with stored program adaptive cellular array sensors. The primary motivation of the present work is to define a topographic microprocessor architecture for MTT with embedded sensors capable of operating in a process real-time manner. In the ongoing experiments it is assumed that the input data flow is acquired by a single array sensor and the data is processed on an adaptive CNN-UM architecture consisting of both a cellular nonlinear network (CNN) and digital signal processing (DSP) microprocessors. The algorithms designed for this combined hardware platform use adaptive multi-channel CNN solutions for instantaneous position estimation and morphological characterization of all visible targets and the DSP environment for distance calculation, gating, data association, track maintenance and dynamic target motion prediction. A special feature of the architecture is that it allows interactive communication between the sensor and the digital environment. The configuration of functional modules for various real-time applications is discussed. Using real-life video-flows, successful tracking of several maneuvering targets is demonstrated within the proposed adaptive multi-channel framework.
Parallel learning for supervised networks in one dimensional real space

Current supervised learning techniques require a sequential feeding of the training input vectors and their targets. This process is very time consuming and convergence is difficult to control due to the ignorance of the nature of the training vectors. We therefore propose another approach to solving the training problem by analyzing the training vectors and cluster them into groups. Each group then can be learned in parallel. In this paper, we focus our study only on one dimensional, real space input vectors and the class of single-input single-output feedforward neural network. However, the parallel concept developed in this paper can possibly be extended to a higher dimensional space.
Tumor detection in ultrasound B-mode images through motion estimation using a texture detection algorithm

A method is described for the determination of an immobile region of an ultrasound image of the heart. Following a noise reduction operation using an optimal signal-adaptive algorithm, a luminance-based block matching technique is used to determine the motion field. Subsequently, the motion field map as processed and the immobile region is extracted by using a 2-D texture separating algorithm. <<ETX>>
A Roadmap for the Adoption of e-Health

Electronic health (e-health) refers to the delivery of health care with support from various information and communication technologies, such as electronic health records, telemedicine, clinical decision support, and computerized provider order entry. E-health is considered by government, providers, and payers as a primary method of improving quality, safety, and costs associated with the delivery of health care. Although currently on a pedestal, e-health has not been readily adopted by a majority of health care providers in the United States. To achieve greater adoption and use of technology in health care, the health care community needs a roadmap, or model for adoption, that can be used to develop a business case to lower risk for providers who adopt and use technology in clinical practice. This article suggests such a roadmap. The roadmap advocates for (1) greater dissemination of implementation best practices, (2) continued development of a strong e-health workforce, and (3) sustainable resources to help those seeking to adopt and use e-health technologies in clinical practice. The e-health community is invited to advance the roadmap to assist providers in embracing and utilizing information and communication technologies for health care system improvements.
Distributed project scheduling with information sharing in supply chains: part II—theoretical analysis and computational study

The first part of this paper (Lau, J.S.K., Huang, G.Q. and Mak, K.L., Distributed project scheduling with information sharing in supply chains: part I—agent-based negotiation algorithm. Int. J. Prod. Res., 2005, 43, 4813–4838) has outlined the background of this research. The paper proposes and demonstrates a new negotiation-based algorithm (NEG) for solving distributed project scheduling problems (DPSP). This new algorithm not only acknowledges and accommodates the autonomy and independence of individual enterprises in making decisions in the entire supply chain, but also takes advantage of limited information shared among them to improve the quality and efficiency. This second paper will conduct a thorough theoretical analysis and computational study on the proposed algorithm. The theoretical analysis shows that the algorithm converges such that schedules of project and contractor agents do not have conflicts. The computational study reveals that NEG performs better than a centralized heuristic and contract net protocols in terms of the solution quality (e.g. total operating cost). Computational efficiency of NEG is also reasonably comparable and competitive to the extent that good quality solutions can be obtained within an affordable time limit.
Data Aggregation with End-to-End Security for Wireless Sensor Networks

Secure end-to-end data transmission is an important method to protect the data privacy in wireless sensor networks. Authors proposed a data aggregation scheme with end-to-end security for wireless sensor networks. The plaintext of sensor readings only appeared in source node and remote server, the aggregators completed the data integrity verification, sender identity authentication and data aggregation without the plaintext. Analysis and simulation show that our scheme has higher security in terms of resilient against malicious attacks and reduces the communication overhead effectively
Improved Feature Extraction of Hand Movement EEG Signals based on Independent Component Analysis and Spatial Filter

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).
WIRELESS THERMOELECTRIC FLOW SENSOR USING ARM

The main objective of this paper is to design an efficient and enhanced system, which is having the capability to measure flow and temperature of the given any type of material. Here, G.S. M is used as communication media between kit and end user. It makes a case for an adaptive acoustic modem in under sensor networks. We describe the potential benefits of the adaptive modem and describe general digital hardware platform architecture. The sensor system is fixed in the pipe or given any type of material. We are continuously observing the flow rate in the LCD display. The flow rate is monitored by ARM processor. When flow rate is increased our ARM processor automatically sends that information through GSM modem to the monitoring user mobile phone. This System is more secured, and less complex when compare with present methodologies, our proposed system consumes less power.
Ontology‐driven Semantic Search for Requirement Engineering

Requirement elicitation is a challenge in the Subsea Oil extraction industry due to the short project award and execution times that do not allow the complete compilation of a requirement database from the contract documents and appendixes. In this paper, a semantic approach to the automation of relevant inferences involved in the Requirement Analysis (RA) stages is proposed. An ontological model is here proposed to supporting the enrichment of the involved texts along a number of semantic assumptions, aiming to provide a rich explanatory description of the targeted phenomena These general types are used to enable the semantic annotation of texts and sentences within the documentation, i.e. conceptual information useful to characterize design choices (as in specification documents), requirements (design documents) or product descriptions (catalogues). A Machine learning approach is then discussed as a robust and effective solution to annotated texts according to the ontological model.
Clinical significance of serum soluble interleukin-2 receptor-a in extranodal natural killer / T-cell lymphoma ( ENKTL ) : a predictive biomarker for treatment efficacy and valuable prognostic factor

Extranodal natural killer/T-cell lymphoma, nasal type (ENKTL) is an aggressive disease, and no standard treatment and validated prognostic model were established. Serum sIL-2Ra levels were measured in 94 ENKTL patients to evaluate its relationship with clinical features, treatment response, and prognosis. Serum sIL2Ra level was 2964 ± 1613.6 ng/L in ENKTL patients, higher than in normal healthy controls (p \ 0.05). Using median level (2508.5 ng/L) as cutoff, patients were divided into higherand lower-level group (N = 47 for each). The complete remission and overall remission rate were significantly higher in lower-level group (p \ 0.05). After a median follow-up time of 22.0 months, 2-year overall survival and progression-free survival rates were 60.0 and 53.0 %, respectively. Lower sIL-2Ra level significantly correlated with better progression-free survival (PFS) and overall survival (OS) (p = 0.001 and 0.002, respectively). IPI score and treatment responses after 2 cycles of chemotherapy significantly correlated with PFS and OS (p \ 0.05). In a multivariate Cox regression model that included IPI score, treatment responses, and sIL-2Ra level, all three parameters were independent prognostic factors for OS (p = 0.043, 0.001, and 0.025, respectively), and the last two parameters were also independent factors for PFS (p = 0.005 and 0.005, respectively). Elevated serum sIL2Ra level was related to poor responses to treatments and can be used as a valuable biomarker for disease activity. Moreover, serum sIL-2Ra was an independent prognostic factor for both OS and PFS. These results need to be validated in prospective trials and may support the incorporation of anti-CD25 targeted therapy into the treatment realm of ENKTL.
Assessing complex problem-solving skills with multiple complex systems

In this paper we propose the multiple complex systems (MCS) approach for assessing domain-general complex problem-solving (CPS) skills and its processes knowledge acquisition and knowledge application. After defining the construct and the formal frameworks for describing complex problems, we emphasise some of the measurement issues inherent in assessing CPS skills with single tasks (i.e., fixed item difficulty, low or unknown reliability, and a large impact of random errors). With examples of the MicroDYN test and the MicroFIN test (two instances of the MCS approach), we show how to adequately score problem-solving skills by using multiple tasks. We discuss implications for problem-solving research and the assessment of CPS skills in general.
Framework for Content‐Based Image Identification with Standardized Multiview Features

Information identification with image data by means of low‐level visual features has evolved as a challenging research domain. Conventional text‐based mapping of image data has been gradually replaced by content‐based techniques of image identification. Feature extraction from image content plays a crucial role in facilitating content‐based detection processes. In this paper, the authors have proposed four different techniques for multiview feature extraction from images. The efficiency of extracted feature vectors for content‐based image classification and retrieval is evaluated by means of fusion‐based and data standardization–based techniques. It is observed that the latter surpasses the former. The proposed methods outclass state‐of‐the‐art techniques for content‐based image identification and show an average increase in precision of 17.71% and 22.78% for classification and retrieval, respectively. Three public datasets — Wang; Oliva and Torralba (OT‐Scene); and Corel — are used for verification purposes. The research findings are statistically validated by conducting a paired t‐test.
Working with knowledge : how information professionals help organisations manage what they know

In order to manage knowledge, we need to understand the nature of knowledge in organisations. It is helpful to distinguish between three categories of organisational knowledge: tacit knowledge, explicit knowledge, and cultural knowledge. Tacit knowledge is personal knowledge, explicit knowledge is codified knowledge, and cultural knowledge is based on shared beliefs. We use this framework to discuss the role of the information professional with respect to each category of knowledge. Knowledge management initiatives led by information professionals in three organisations are then examined. An analysis of these experiences suggests many opportunities for information professionals to make important contributions in managing an organisation’s knowledge for growth and innovation.
Extracting focused object from low depth-of-field image sequences

The paper proposes a novel unsupervised video object segmentation algorithm for image sequences with low depth-offield (DOF), which is a popular photographic technique enabling to represent the intention of photographer by giving a clear focus only on an object-of-interest (OOI). The proposed algorithm largely consists of two modules. The first module automatically extracts OOIs from the first frame by separating sharply focused OOIs from other out-of-focused foreground or background objects. The second module tracks OOIs for the rest of the video sequence, aimed at running the system in real-time, or at least, semi-real-time. The experimental results indicate that the proposed algorithm provides an effective tool, which can be a basis of applications, such as video analysis for virtual reality, immersive video system, photo-realistic video scene generation and video indexing systems.
Scratchpads Based Optimization for 3D Picture Display on Mobile Devices

Mobile devices such as smartphones have been used widely used nowadays for their portability, flexibility and functionality. Embedded operating systems have provided powerful support to these devices. More complex applications can be developed and used for them. However, mobile devices are still resource limited and this is a problem for many programs such as 3D picture display programs. In this paper, a novel approach is proposed to optimize the performance of 3D picture processing programs. The process of 3D picture processing was analyzed and scratchpads were used as the on-chip buffer for it. The experimental results showed that our approach could achieve its design target.
Network Based Complexity Analysis in Tactile Brain Computer Interface Task

We study the extent to which network based brain complexity analysis methods for EEG feature extraction could result with improved brain state classification accuracy in a tactile Brain Computer Interface (tBCI) paradigm. The state– of–the–art feature extraction and classification methods do not utilize brain network dynamics techniques as a possible way to capture and elucidate with non–invasive, high temporal resolution methods such as EEG. We propose to employ the brain network dynamics and information transfer evaluation methods to create the new EEG feature extraction in the tBCI paradigm. The feasibility of such approach is discussed together with the obtained results and evaluated in comparison with the contemporary methods.
Spatially-Enhanced Auditory Cues and Computer Performance on a Simulated Monitoring Task

This research examines the selection of nonspeech sounds for use in a computer-based application and furthermore assesses the performance of users engaged in a simulated manufacturing task. An empirical study was conducted to evaluate the utility of spatially-enhanced (three-dimensional) auditory cues in the computer interface. The seventy-two participants in this experiment interacted with a simulated interface using one of two spatially-enhanced sound groups (pure and hybrid) as selected from previous studies. Participants used the mouse to interact with the interface simulation and received alert information in the form of auditory cues, textual cues, or both. Performance for the simulated interface was measured in terms of: skill acquisition (learning), perceived workload, task omissions, incorrect responses, and task response time. The results show that spatially-enhanced nonspeech auditory cues can reduce perceived workload and shorten response times.
Latent class linear mixed models - a general approach implemented via SAS(RTM) macro with a tutorial for clinical researchers

STEVEN W. ENCK: Latent Class Linear Mixed Models A General Approach Implemented via SAS R © Macro with a Tutorial for Clinical Researchers (Under the direction of Paul Stewart) Linear mixed models provide a flexible, intuitive method for analyzing repeated-measures data when the population being studied can be thought of as either a single population or as a set of known subpopulations. However, in many cases, the underlying subpopulations are not known. Furthermore, the factors that determine the subpopulations can be extremely complex or unmeasurable. In such cases, a different approach is required in order to more accurately analyze the data. The Latent Class Linear Mixed Model (LCLMM) combines the features of the linear mixed model (LMM) with an additional component, which partitions the population into subpopulations or latent classes. This model has usually been specified with relatively simple, restrictive assumptions. In this dissertation, the methods related to the LCLMM are extended to provide a more general model specification. Fixed-effects may be specified as a combination of class-specific effects and acrossclass effects. Variances may be specified as being class-specific or equal across classes, a general correlation structure for the random effects is permitted, and multiple residual error variances may be fit. The bound proposed by Hathaway [1985] on the variances to ensure consistency is examined in the context of mixtures of linear mixed models. Class membership probabilities may be specified in one of two ways via a logistic regression model or using our proposed method in which class membership is estimated based on the relative fit of the underlying linear mixed models. These methods are implemented in a new SAS R © macro which offers several options for estimation. In addition to an EM algorithm, gradient-based methods, including quasi-Newton, as well as Hessian-based methods, such as Newton-Raphson, are available to the user. Parameter standard errors are estimated, and predictions of the random effects are derived and calculated. Practical issues, including choosing the number of latent classes and estimation method, are discussed and guidelines are provided based on simulation studies. The stability and advantage of the proposed methods are also examined via simulation study. Finally, our methods are applied to several simple simulated datasets as well as to three real-world