Miracle-dz/UniFAR-FTUs huggingface dataset:
Penentuan Arsitektur Jaringan Syaraf Tiruan Backpropagation (Bobot Awal dan Bias Awal) Menggunakan Algoritma Genetika

Abstrak Kelemahan dari jaringan syaraf tiruan backpropagation adalah sangat lama untuk konvergen dan permasalahan lokal mininum yang membuat jaringan syaraf tiruan (JST) sering terjebak pada lokal minimum. Kombinasi parameter arsiktektur, bobot awal dan bias awal yang baik sangat menentukan kemampuan belajar dari JST untuk mengatasi kelemahan dari JST backpropagation. Pada penelitian Ini dikembangkan sebuah metode untuk menentukan kombinasi parameter arsitektur, bobot awal dan bias awal. Selama ini kombinasi ini dilakukan dengan mencoba kemungkinan satu per satu, baik kombinasi hidden layer pada architecture maupun bobot awal, dan bias awal. Bobot awal dan bias awal digunakan sebagai parameter dalam perhitungan nilai fitness. Ukuran setiap individu terbaik dilihat dari besarnya jumlah kuadrat galat (sum of squared error = SSE) masing – masing individu, individu dengan SSE terkecil merupakan individu terbaik. Kombinasi parameter arsiktektur, bobot awal dan bias awal yang terbaik akan digunakan sebagai parameter dalam pelatihan JST backpropagation. Hasil dari penelitian ini adalah sebuah solusi alternatif untuk menyelesaikan permasalahan pada pembelajaran backpropagation yang sering mengalami masalah dalam penentuan parameter pembelajaran. Hasil penelitian ini menunjukan bahwa metode algoritma genetika dapat memberikan solusi bagi pembelajaran backpropagation dan memberikan tingkat akurasi yang lebih baik, serta menurunkan lama pembelajaran jika dibandingkan dengan penentuan parameter yang dilakukan secara manual. Kata kunci Jaringan syaraf tiruan, algoritma genetika, backpropagation, SSE, lokal minimum Abstract The weakness of back propagation neural network is very slow to converge and local minima issues that makes artificial neural networks (ANN) are often being trapped in a local minima. A good combination between architecture, intial weight and bias are so important to overcome the weakness of backpropagation neural network. This study developed a method to determine the combination parameter of architectur, initial weight and bias. So far, trial and error is commonly used to select the combination of hidden layer, intial weight and bias. Initial weight and bias is used as a parameter in order to evaluate fitness value. Sum of squared error(SSE) is used to determine best individual. individual with the smallest SSE is the best individual. Best combination parameter of architecture, initial weight and bias will be used as a paramater in the backpropagation neural network learning. The results of this study is an alternative solution to solve the problems on the backpropagation learning that often have problems in determining the parameters of the learning. The result shows genetic algorithm method can provide a solution for backpropagation learning and can improve the accuracy, also reduce long learning when it compared with the parameters were determined manually. Keywords: Artificial neural network, genetic algorithm, backpropagation, SSE, local minima.
Effect of severe image compression on face recognition algorithms

In today’s information age, people will depend more and more on computers to obtain and make use of information, there is a big gap between the multimedia information after digitization that has large data and the current hardware technology that can provide the computer storage resources and network band width. For example, there is a large amount of image storage and transmission problem. Image compression becomes useful in cases when images need to be transmitted across networks in a less costly way by increasing data volume while reducing transmission time. This paper discusses image compression to effect on face recognition system. For compression purposes, we adopted the JPEG, JPEG2000, JPEG XR coding standard. The face recognition algorithms studied are SIFT. As a form of an extensive research, Experimental results show that it still maintains a high recognition rate under the high compression ratio, and JPEG XR standards is superior to other two kinds in terms of performance and complexity.
Proteomics- an emerging arena in periodontal diagnosis and treatment

Proteins are the main components of the physiological metabolic pathways of cells. The onset, progression and severity of periodontal disease are mainly mediated by various protein molecules. The use of proteins as diagnostic biomarkers in periodontal diseases has increased over the last few years. Proteomics is the branch of molecular biology that involves study of protein properties on a large scale that helps in understanding the disease processes at protein level. Saliva and other oral fluids give clinically relevant information as they contain diagnostic protein biomarkers for periodontal diseases. These proteomic biomarkers incorporate various host and bacteria-derived enzymes, virulence factors and inflammatory mediators that are the main culprits for the key pathogenic processes in periodontal disease – inflammation, collagen degradation and bone resorption. In this review paper an attempt has been made to review the significance of proteomics in the diagnosis and assessment of progression of periodontal diseases. Keywords: Proteins, Proteomics, Periodontitis, Biomarkers, Saliva, Gingival crevicular fluid
Supporting adolescents with type 1 diabetes during the transition from child to adult services: a literature review

This literature review considers the personal and practice implications of type 1 diabetes amongst adolescents. The review is part of a wider Post Graduate study and here 25 largely qualitative articles are critically considered. The selection process and thematic review lead to the identification of the themes: ‘social and emotional transition’ and ‘service related transition’. Social and emotional transition relates to the experiences of adolescents diagnosed with type 1 diabetes. These included increased risk of anxiety, depression and eating disorders which were exasperated during the challenging adolescent period. The service related transition refers to this vulnerable group not fitting well into child or adult services and the added pressures of managing the move from one to another. This included expectation of increased autonomy and problems of poor self-management. There are some examples of positive practice found in this review largely grounded in improved personal and service transition. The review considers problems encountered and informs professionals of gaps in practice, encompassing the distinct difficulties adolescents with type 1 diabetes may experience.
Proceedings of the 17th annual meeting on Association for Computational Linguistics

These are the proceedings of the 17th Annual Meeting of the Association for Computational Linguistics. The field which began as a study of mechanical translation has broadened to include much more. The Program Committee tried to have as many areas represented here as possible, In the conference's sessions are papers on topics as varied as speech perception, the formal definition of knowledge representations, the reality of a cognitive model of spatial knowledge, and computer-assisted dialect adaption. At the same time, we have tried to highlight current trends. So there are several papers on topics like word-driven versus rule-driven parsers, graph representations of knowledge, dialogue models, and natural language access to databases.
An automatic annotation system for audio data containing music

Submitted to the Department of Electrical Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degrees of Bachelor of Science in Electrical Engineering and Computer Science and Master of Engineering in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology May 1, 1999 @ Copyright 1999 Janet Marques. All rights reserved. The author hereby grants to M.I. T. permission to reproduce and distribute publicly paper and electronic copies of this thesis and to grant others the right to do so.
Divide-and-conquer large-scale support vector classification

Several research fields have to deal with very large classification problems, e.g. human-computer interface and bioinformatics. However, the majority of the pattern recognition methods intended for large-scale problems aim to merely adapt standard classification models, without considering if those algorithms are actually appropriated for dealing with large data. Some models specifically developed for problems with large number of samples had been proposed, but few works have been done concerning problems with large number of classes. CombNET-II was one of the first methods proposed for such a kind of task. It consists of a sequential clustering vector-quantization based gating network (stem network) and several multilayer perceptron based expert classifiers (branch networks). With the objectives of increasing the classification accuracy and providing a more flexible solution, this work proposes a new model based on the CombNET-II structure, the CombNET-III. It replaces the branch networks’ algorithm with multiclass support vector machines and introduces a new probabilistic framework that outputs posterior class probabilities, enabling the model to be applied in different scenarios. In order to address the new model’s major drawback, its high classification computational complexity, a new two-layered gating network structure, the SGA-II, is presented. It reduces the compromise between number of clusters and accuracy, increasing the model’s performance. This high accuracy gating network enables the removal the low confidence expert networks from the decoding procedure. This, in addition to a new faster strategy for calculating multiclass support vector machines outputs, results in a computational complexity reduction of more than one order of magnitude. The extended structure also outperforms compared methods when applied to database with a large number of samples, confirming the CombNET-III model’s flexibility. In addition to those structures, several solutions for accuracy improvement and complexity reduction are presented, including methods based on feature subset selection.
Electronic Transport Behavior of the Closed and Open-Shell Forms of Polychlorotrimethylphenyl

The current switching behavior of closed and open-shell forms of Polychlorotrimethylphenyl between gold electrodes are studied by using nonequilibrium Green's functions in combination with density functional theory. The present computational results show that the two forms of polychlorotrimethylphenyl molecule really demonstrate switching behavior, which confirms the experimental observation. It is also found that the switching behavior depends on the electronic properties of the two forms of polychlorotrimethylphenyl but not on the contact modes. The transmission coefficients and current-voltage curves under low-bias voltage are also reported. AMS subject classifications: 81V55
Characteristics of Individuals Who Receive Pharmacotherapy While in Treatment For Opioid Use Disorder in the United States

Supplemental Digital Content is available in the text. Objective: Treatments that include pharmacotherapy are the standard of care for opioid use disorder (OUD), but they are underutilized. This paper uses a large sample of treatment admissions for OUD in the United States to examine characteristics predictive of receiving treatments that include pharmacotherapy. Methods: Data come from the 2014 Treatment Episode Dataset—Admissions. Generalized linear regression models were utilized to assess the association of receiving pharmacotherapy treatment with a number of demographic, treatment, and disorder characteristics. Results: Less than one third of treatment admissions for an OUD involved pharmacotherapy. Among the factors most associated with receiving pharmacotherapy treatment were referring oneself to treatment, as opposed to a medical or nonmedical professional, and receiving treatment in a nonintensive outpatient setting, as opposed to intensive outpatient or residential settings. Conclusion: Encouraging professionals to refer clients to programs that offer treatments that include pharmacotherapy, and incorporating pharmacotherapy into a greater diversity of treatment settings, could help increase access to evidence-based care for OUD.
An Empirical Investigation of Wireless Application Protocol (WAP) Services Usage Determinants

The purpose of this study is to investigate Wireless Application Protocol (WAP) services usage determinants utilising the technology acceptance model (TAM), theory of reasoned action (TRA) and theory of planned behaviour (TPB). Data was collected via a questionnaire survey on a random convenience sample of 150 respondents who owned hand-held devices. Results from multiple regression analysis confirmed that perceived usefulness, perceived behavioural control and attitude are important determinants to explain WAP services usage. In addition, perceived usefulness and perceived ease of use influence the individual's attitude, which in turn affects usage intentions. This study contributes to the body of knowledge by introducing WAP services usage intentions from the Malaysian viewpoint, which was previously limited.
Suppression of quercetin-induced autophagy enhances cytotoxicity through elevating apoptotic cell death in human bladder cancer cells

Objective: Quercetin, a natural dietary compound, has been demonstrated with antitumor activities against several types of cancers by disrupting cell cycle and inducing apoptotic cell death. However, human bladder cancer cells such as 5637 and T24 cells expressing mutant p53 are resistant to a 24 hrs quercetin treatment. In this study, the anticancer effect of quercetin was evaluated in these bladder cancer cells. Materials and Methods: The bladder cancer cells treated with quercetin were subjected to evaluated cell apoptosis by caspase activity, TUNEL assay and cell viability assay. The cell autophagy was assessed by detecting procession of LC3-II autophagic marker protein. Results: After 48 and 72 hrs of incubation, quercetin was found to be significantly effective in inhibiting proliferation of 5637 and T24 cells in a dose-dependent manner. Quercetin treatment increased the caspase 3/7 activities, percentage of subG0/G1 cells, and DNA fragmentation, indicating an induced apoptotic cell death. Pretreatment of a pan-caspase inhibitor, Z-VAD-FMK, attenuated the quercetin-decreased cell viability, suggesting that the cytotoxicity caused by quercetin mainly via apoptotic cell death. We also found that quercetin induced autophagy, as evidenced by the increased processing of LC3-II, a specific marker of autophagy. The disruption of autophagic flux by using bafilomycin A1, an autophagy inhibitor, caused significant accumulation of cellular p62 and LC3-II. In addition, the pretreatment of autophagy inhibitors, Baf A1 and chloroquine, strongly augmented apoptosis in 5637 and T24 cells, indicating the suppression of quercetin-induced autophagy enhanced apoptosis. Furthermore, the decreased cell viability and increased LC3-II processing were attenuated in quercetin-treated cells which pretreated with a reactive oxygen species (ROS) scavenger, N-acetyl cystine (NAC) suggested that quercetin-induced cytotoxicity and autophagy were initiated by the generation of ROS. Conclusion: This study proposes that combined treatment of autophagy inhibitor which sensitizes cells to quercetin treatment may be a better therapeutic approach to reduce bladder cancer cells proliferation.
Quantitative Assessment of Lower-limb Perfusion for Patients with Peripheral Artery Disease

Diagnostic procedures of noninvasive assessment for patients with peripheral artery disease (PAD) have been advancing for decades. Among diagnostic imaging modalities, lower-limb perfusion (LLP) planar scintigraphy and SPECT/CT are exclusively used for diagnosing lower-limb ischemia, therapeutic efficacies, and risk stratification in PAD patients. Of these modalities, LLP SPECT/CT particularly shows more accuracy in providing quantitative assessment of LLP using innovative imaging devices and dedicated software. dividing
The Influence of Gender on Beginning Teachers' Perceptions of Their Technological Pedagogical Content Knowledge (TPACK).

TPACK is emerging as an influential framework for conceptualising teacher knowledge in regards to integrating ICT and is generating considerable international research interest. To date, the question of whether gender plays a role in how teachers self-assess their TPACK knowledge has not figured greatly in this research. This paper seeks to explore this possible role by using an adapted form of the Schmidt et al (2009b) instrument to survey two cohorts of beginning teachers (64 in the first cohort and 142 in the second) from Victoria, Australia. It suggest that while both genders rate their knowledge highly, especially Content Knowledge, there are significant differences in how male and female beginning teachers rated their knowledge, with males rating their knowledge higher in both years of this study.
Parallel Algorithms are Good for Streaming

In this paper we show how PRAM algorithms can be turned into efficient streaming algorithms for several classical combinatorial problems in theW-Stream model. In this model, at each pass one input stream is read and one output stream is written; streams are pipelined in such a way that the output stream produced at pass i is given as input stream at pass i + 1. Our techniques yield near-optimal algorithms (up to polylog factors) for several classical problems in this model including sorting, connectivity, minimum spanning tree, biconnected components, and maximal independent set.
High performance integration of data parallel file systems and computing: optimizing mapreduce

The ongoing data deluge brings parallel and distributed computing into the new data-intensive computing era, where many assumptions made by prior research on grid and High-Performance Computing need to be reviewed to check their validity and explore their performance implication. Data parallel systems, which are different from traditional HPC architecture in that compute nodes and storage nodes are not separated, have been proposed and widely deployed in both industry and academia. Many research issues, which did not exist before or were not under serious consideration, arise in this new architecture and have drastic influence on performance and scalability. MapReduce has been introduced by the information retrieval community, and has quickly demonstrated its usefulness, scalability and applicability. Its adoption of data centered approach yields higher throughput for data-intensive applications. In this thesis, we present our investigation and improvement of MapReduce. We identify the inefficiencies of various aspects of MapReduce such as data locality, task granularity, resource utilization, and fault tolerance, and propose algorithms to mitigate the performance issues. Extensive evaluation is presented to demonstrate the effectiveness of our proposed algorithms and approaches. Besides, I, along with Yuan Luo and Yiming Sun, observe the inability of MapReduce to utilize cross-domain grid resources, and propose a MapReduce extension called Hierarchical MapReduce (HMR). In addition, to speed up the execution of our bioinformatics data visualization pipelines containing both single-pass and iterative MapReduce jobs, a workflow management system Hybrid MapReduce (HyMR) is presented built by Rang and me upon Hadoop and Twister. The thesis also includes a detailed performance evaluation of Hadoop and some storage systems, and provides useful insights to both framework and application developers.
Improving outcomes for brain metastases in EGFR mutated NSCLC.

Tyrosine kinase inhibitors (TKIs) of activating epidermal growth factor receptor (EGFR) mutations have revolutionized the care of patients with advanced non-small cell lung cancer (NSCLC) who harbor a targetable EGFR mutation. Compared to platinum doublet chemotherapy, median overall survival (OS) has improved from a historical 8–12 months with chemotherapy alone (1) to 26 months with first generation EGFR TKIs gefitinib and erlotinib (2).
Length of stay and odds of MRSA acquisition: a dose–response relationship?

Abstract The association between methicillin-resistant Staphylococcus aureus (MRSA) colonisation and/or infection with increased morbidity and mortality among hospital patients has long been recognised. We sought to build on previous studies to identify modifiable risk factors associated with the acquisition of MRSA colonisation and infection by conducting a retrospective cohort study on patients admitted through the Emergency Department of an acute tertiary-care general hospital in Singapore which implemented universal on-admission MRSA screening. Patients were assigned to the acquisition or non-acquisition group depending on whether they acquired MRSA during their admission. We used logistic regression models with a patient being in the acquisition group as the binary outcome to identify factors associated with MRSA acquisition. A total of 1302 acquisition and 37 949 non-acquisition group patients were analysed. Fifteen variables were included in the multivariate model. A dose–response relationship between length of stay and odds of MRSA acquisition was observed, with a length of stay 3 weeks or more (Adj OR 11.78–57.36, all P < 0.001) being the single biggest predictor of MRSA acquisition. Other variables significantly associated with MRSA acquisition were: male gender, age 65 or greater, previous MRSA colonisation or infection, exposure to certain antibiotics and surgery, and history of diabetes.
PRACTICAL NETWORK ANOMALY DETECTION USING DATA MINING TECHNIQUES

Network anomaly detection is an effective way to detect intrusions which defends our computer systems or network from attackers on the Internet. In this paper, we introduce the current research works in network anomaly detection and consider serveral pratical solutions for this issue. Different from signature-based method, data mining techniques can automatically extract normal pattern from a large set of network data and distinguish them from each other. However, those data mining techniques, such as classification, clustering, association rules and feature selection, can not be applied into this problem directly due to the characteristic of network data and technique themseleves. We analyze those unfitness and propose some adaptation to detect anomaly timely and accurately.
Cross Media Video Analysis for News Program Management

In this paper, we present a system for Chinese news program management based on cross media video analysis. Audio, caption text and video frames are all important for a person to understand the meaning of the video. Given these facts, we devised a system integrating continuous Chinese speech recognition (ASR), video caption text recognition (VOCR) and object/scene recognition (OR). The news program is firstly segmented to a serial of segments by anchor person detection. Then the ASR and VOCR recognition results are treated as two paragraphs of text, and we translate them to two bags of words to represent the original recognition results. By analysing the correspondance of the words in ASR result and VOCR result, we can get a trusted set of words to depict the video content of a segment of news program. In the last step, we implement the object/scene classification based on the keyframes analysis aided by the above recognition words. Experiments show that our news management system is efficient.