Miracle-dz/UniFAR-FTUs huggingface dataset:
Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network

This research aims to develop an analysis model for diseases of the water buffalo with the application of the feature selection technique along with the Multi-Layer Perceptron Neural Network (MLP-NN). The data used for analysis were collected from books and documents related to water buffalo diseases and the official website of the Department of Livestock Development. The data consist of the characteristics of 6 water buffalo diseases, including anthrax, hemorrhagic septicemia, brucellosis, foot and mouth diseases, parasitic diseases, and mastitis. Since the amount of the collected data was limited, the synthetic minority over-sampling technique was also employed to adjust the imbalance dataset. The adjusted dataset was used to select the disease characteristics towards the application of two feature selection techniques, correlation-based feature selection and information gain. Subsequently, the selected features were then used for developing the analysis model for water buffalo diseases towards the use of the MLP-NN. The evaluation results given by 10-fold cross-validation, showed that the analysis model for water buffalo diseases developed by correlation-based feature selection and MLP-NN provided the highest level of effectiveness with an accuracy of 99.71%, precision of 99.70%, and recall of 99.72%, implying that the analysis model is effectively applicable. Keywords-water buffalo diseases; feature selection; multi-layer perceptron; neural network; synthetic minority over-sampling
(Hijaiiyah Pattern Recognition Using Grey Level Co-Occurrence Matrices (GLCM) Method With 4 Oriented Angle and Backpropagation Artificial Neural Network)

JTIKA, Vol. 3, No. 1, Maret 2021 ISSN:2657-0327 http://jtika.if. unram.ac. id/index.php/JTIKA/ 146 Pada proses ekstraksi fitur GLCM, parameter ciri yang digunakan yaitu tekstur dari citra huruf hijaiah. Ekstraksi GLCM menghasilkan fitur antara lain energi, kontras, homogenitas, entropi dan korelasi dalam 4 orientasi sudut (0° , 45° , 90° dan 135° ) [2]. Metode Jaringan Syaraf Tiruan (JST) Backpropagation merupakan metode yang sangat baik dalam menangani masalah pengenalan pola yang kompleks. seperti pengoperasian data, peramalan (prediksi) dan pengenalan pola [3]. Berdasarkan uraian di atas penulis mengajukan sebuah penelitian untuk merancang sebuah model pembelajaran mesin (machine learning) untuk mengklasifikasikan pola huruf hijaiah menggunakan metode GLCM dan JST Backpropagation sebagai media pengenalan huruf Arab (hijaiah). Tingkat akurasi dan presisi dari hasil klasifikasi adalah parameter hasil uji atas metode JST Backpropagation.
HERMEUNEUTICS PARADIGM: AN INSIGHT IN THE APPLICATION OF A MEANINGFUL FUNCTIONAL ENGLISH GRAMMAR

This article portrays the fundamental paradigm of language, English in this case, which has transformed from a traditional point of view to a functional one. Dealing with the mandate of the curriculum, both of primary education and tertiary education curriculum, that language learning does not rely simply on patterns, pronunciation, and vocabulary. However, language learning is a learning process by involving the effort in enabling students to strengthen four English skills, listening speaking reading and writing. Though, in this era, communicative competence has been the ultimate goal of language teaching orientation (Savignon, 2017). Changing the mindset of language is not just learning the rules, but also how to change with the mindset that learning language is to be able to actualize students and enable them to use language communicatively and contextually. This view is in line with the hermeneutics philosophical view. It sees language learning as being able to understand meaning and interpret meaning based on context and situation. Language teachers are expected to understand those differences through managing teaching learning process in traditional view and functional view.
Application classification techniques ’ design for interference mitigation in multiprocessor systems

Multiprocessors are the basic building block of all modern computing systems. Despite the benefits yielded by the ability to execute applications concurrently, the rivalry between applications for the chip’s shared resources, such the Last Level Cache and the memory bandwidth, can be detrimental to performance. Especially in commercial cloud environments, the provider is obliged to abide by strict performance guarantees required by certain applications (Quality of Service goals), leading to the isolated execution of the latter in dedicated servers to avoid interference, and consequently to the system’s underutilization. As a result, extensive research has been conducted on the problem of application interference. This diploma thesis focuses on predicting cases where interference might be present by utilizing exclusively data by low-level hardware performance counters gathered during isolated application execution. The main characteristic of our approach is that it does not require executing an application with co-runners to decide whether it will suffer from or create contention, making it ideal for cloud environments, where subjecting an application to artificial interference is prohibitive. Our final mechanisms consists of two machine learning base multiclass classifiers. Each classifier receives a s input a specific set of hardware performance counter values and classifies the application in regards to its ability to cause interference (noise) and its sensitivity to it. We also showcase how the labels we have assigned each application can then be utilized by an application scheduler in a datacenter, in order to maximize the performance of high-priority applications.
Enhancement of antimicrobial effect of some spices extract by using biosynthesized silver nanoparticles

Spices and silver nanoparticles are well recognized for its antimicrobial effect against food borne pathogens. The target of this research was to estimate the antimicrobial activity of four spices (clove, rosemary, thyme and black pepper) and the biosynthesized silver nanoparticles alone and in the combination of each spice with the biosynthesized silver nanoparticles. Broth microdilution method and subsequent checkerboard assay were used for evaluation of the antimicrobial activity. The antimicrobial activity was assessed against bacterial strains (Pseudomonas aeruginosa, Salmonella typhimurium, Escherichia coli ATCC 8739, Bacillus subtilis ATCC 6633) and yeasts (Candida parapsilosis AUMC 8909 and Trichosporon domesticum AUMC 8918). Silver nanoparticles showed good antimicrobial effect and clove exhibited the best antimicrobial effect within the examined spices. The combination demonstrated 8 synergistic and 8 additive effects for inhibition of growth of the tested microorganisms. The microbicidal effects were 7 synergistic and 10 additive effects. The results recommended the addition of spice in combination with silver nanoparticles for enhancement of antimicrobial activity, even for meat packaging in nontoxic concentrations.
Detection and Categorization of Liver Diseases Using Level Set Based Back Propagation Neural Network Technique

To evaluate and analyze liver cancer accurately in a short time Computer-Aided Diagnosis (CAD) has been widely used, as manual detection of the cancer tissue is time consuming and complex task. In this work, the main objective is to detect the liver cancer automatically using CAD based method. We proposed a new approach called Level Set based Back Propagation Neural Network (LS-BPNN) for the detection and categorization of liver cancer automatically. The abdominal image taken from the Computed Tomography (CT) scan is used for the detection of affected liver. From this abdominal image, liver is segmented using Level Set method and tumor is segmented using Practical Swarm Optimization (PSO) method, then the features has been extracted. Next the features from the segmented liver were given to BPNN for classifying the liver cancer, it is generally classified as primary and secondary liver cancer. Primary liver cancer i.e. Hepatocellular carcinoma (HCC) or hepatoma, cholangiocarcinoma and angiosarcoma (hemangiosarcoma). Comparing with Feed Forward Neural Network (FFNN), our method achieved excellent accuracy. We have achieved excellent accuracy when compared with FFNN, BPNN classifier has 97.98% accuracy during the classification process where FFNN has 89% accuracy. The system we developed can be tested with enormous dataset and the radiologist using CT images the liver cancer is detected.
DOUBLE-LAYER SILICENE-BASED CARCERANDS: MOLECULAR CONTAINERS FOR UNSTABLE COMPOUNDS

We have studied the formation of a “host-guest” type сarcerand with a double-layer silicene (DL-S) as a host and an unstable antiaromatic cyclobutadiene as a guest. By employing the methods of MM+, РМ3 and Monte-Carlo, there has been studied the positioning of molecules of cyclobutadiene in a DL-S depending on intercalate concentration and intercalation temperature. At that the deformation vibrations of the DL-S crystal grate do not exceed 0.017 nm, and the vibrations of the intercalate molecules do not exceed 0.025 nm which provides for configuration and conformation stability of the system. The silicene planes do not move relatively each other and the order of the silicon atoms between the planes remains the same – AB. (similar to the silicene single crystal). When initially heated from 0 to ∼273 K, the systems energy grows gradually, then rises sharply between 273–300 K and 350–400 K, then, with the temperature growth, it reaches a plateau which proves its high stability up to ~420 K. In the temperature range 0–273 K there appears physical sorption while chemisorption is observed at higher temperature (∼300 K) which is peculiar of π-π interactions of classical aromatic and quasiaromatic cyclic and heterocyclic systems. There have been calculated the UV-spectra of the DL-S depending on the intercalate concentration in terms of the modified Benes-Hilderbrand method. There has been shown that the association constant of the system studied is 380 l·mol–1, with computation accuracy ≥ 0.977.
BRAND IMPACT ON INCREASING SALES: CASE STUDY “FORTESA HOME”

A company’s brand image or name is what identifies and characterizes it. As the brand awareness increases it is often proved by many well known brands that also the percentage of sales would increase. The purpose of this thesis is to investigate the direct impact of brands on increasing sales, with a special attention to “Fortesa Home” a retail company which sells sanitary products, which will be used as a case study. A questionnaire was prepared for Fortesa’s client and potential clients in order to give the whole analysis a practical touch. The paper work will show in detail how companies can benefit from creating a strong and reliable brand, and how that brand can have an impact on increasing their sales. This all will be explained and performed by taking as an example the company ‘Fortesa Home’. This paper will be dealing with the following questions: what is more significant: The values of the brand or the product itself? What is the most important part of brand equity in order to increase sales? And how to measure brand equity? The literature has been reviewed to understand how to measure brand equity, what is the core dimension of the brand equity. How companies can benefit from creating strong brands and how that is connected with the increasing of sales, this is the main purpose of this thesis. In order to achieve this a questionnaire was handed out to 15 existing clients of “Fortesa Home” and 15 other potential clients, consisting of 27 questions all concerning the research questions as mentioned above. From this paper it can be concluded that the linkage between customers and noncustomers is slightly different due to the effect of the information that they have towards the brand. Existing customers had the chance to create a loyalty to the brand which is the core dimension of the brand equity whereas non-customers are rather neutral but with no negative opinions for the brand and as they stated they would consider being a client in the future, they think that Fortesa is has high quality but offers rather expensive products. Since, brand awareness is closely related to increasing sales, Fortesa by creating a strong and reliable brand has benefited from this through increasing its sales, by offering to its customers exactly what it brand stand for, strong, modern, beautiful designed and high quality products with an excellent after sales service, all these that identify its company name. Brand awareness, perceived quality, and brand associations can strengthen brand loyalty by increasing customer satisfaction and providing reasons to buy the product, and that is what happened to “Fortesa Home” which became also one of its competitive advantage its own name and brand. That is what research study and literature tells us and Fortesa is an example of that and it is the brand that has helped the company grows and increase sales yearly. Finally, brand equity assets provide a firm with a significant advantage: a barrier that may prevent customers from switching to a competitor. Content ABSTRACT ACKNOWLEDGEMENT
Evaluation of agropolitan policy: dependency theory perspective

This study aims to determine the evaluation of agropolitan policy from the perspective of dependency theory that specifically discusses the influence of corn-based agropolitan programs on the area of corn production land, farmer income, educational, and health conditions. This research uses a quantitative approach. The results showed that in the implementation of agropolitan corn-based land area programs, farmers' net income increased.
WATER-POLYPEPTIDE INTERACTION IN CLASSICAL MODELS OF HELIX-COIL TRANSITION

Zimm-Bragg model is the simplest to describe the conformational transitions in biopolymers and is regularly used for preocessing the experimental data. We review the model and its Hamiltonian definition with the goal to introduce the interaction with water into the picture. We show how modified ZB model with the account of water-polypeptide interactions allows to describe both cold denaturation and helix-coil transition and derive such the formula explicitly. The obtained theoretical expression for the helicity degree contains two independent parameters that can be fitted with the experimental data to determine the parameters of cold denaturation and helix-coil transition from a single fit and for a single set of experimental data.
Relationship between autoimmune thyroid dysfunction and diabetes mellitus type 1 in pediatric population

Type 1 diabetes (T1D) and autoimmune thyroid diseases (AITD) frequently occur together within families and in the same individual in most parts of the world. Type 1 diabetes is the most prevalent chronic disease in the population under 18 years of age although there are no reliable data available from many countries. The co-occurrence of T1D and AITD in the same patient is one of the variants of the autoimmune diseases. The two major autoimmune thyroid diseases (ATDs) include Graves’ disease (GD) and autoimmune thyroiditis (AT); both of which are characterized by infiltration of the thyroid by T and B cells reactive to thyroid antigens, by the production of thyroid autoantibodies and by abnormal thyroid function (hyperthyroidism in GD and hypothyroidism in AT). While the exact etiology of thyroid autoimmunity is not known, it is believed to develop when a combination of genetic susceptibility and environmental encounters leads to breakdown of tolerance. It is important to recognize thyroid dysfunction at an early stage by maintaining an appropriate index of suspicion. The presence of both thyroid dysfunction and diabetes mellitus is increasing in prevalence and is seen among many patients. Many different studies have been performed globally to ascertain this relationship. Our aim in this article is to access the correlation between thyroid dysfunction and Type 1 diabetes mellitus and to assess the prevalence of T1DM-associated autoimmune diseases in children and adolescents and their impact on the course of T1DM. We also present suggestions concerning screening tests. Methods: Using internet search, a comprehensive literature review was done and words such as diabetes mellitus, autoimmune thyroid, hypothyroidism, hyperthyroidism, thyroid antibodies, and thyroid problems were searched. The references of the relevant articles on this subject were also searched for further information. Results: Analyses of results of various studies from various parts of the world were considered and their prevalence was noted to access the correlation between thyroid dysfunction and diabetes mellitus. Subclinical hypothyroidism is seen as the commonest thyroid problem among female type 1 diabetics. Conclusion: There is a strong relationship between thyroid dysfunction and Type 1 diabetes mellitus.
Intensified paraglacial slope failures due to accelerating downwasting of a temperate glacier in Mt. Gongga, Southeastern Tibet Plateau

Abstract. Topographic development via paraglacial slope failure (PSF) represents a complex interplay between geological structure, climate, and glacial denudation. Where debris generated by PSFs is deposited on the surface of a glacier, this debris can increase the extent or thickness of a supraglacial debris-cover, in turn modifying glacier ablation and affecting meltwater generation. To date, little attention has been paid to intensity and frequency of PSFs in glacierised, monsoon temperate regions of Southeast Tibet. We mapped PSFs along the 5 km-long, west-east trending ice tongue of Hailuogou Glacier (HLG), Mt. Gongga, using repeat satellite- and UAV-derived imagery between 1990 and 2020. Three types of PSF were identified: (A) rock fall, (B) sediment-mantled slopes slide and collapse, and (C) gully headwards erosion. We analyzed the formation, evolution and current state of these PSFs and discuss these aspects with relation to glacier dynamics and paraglacial geomorphological history. South-facing slopes (true left of HLG) showed more destabilization and higher PSF activity than north-facing slopes. We observed annual average rates of downslope sliding for type B PSFs of 1.6–2.6 ± 0.04 cm d−1, whereas the average upward denudation rate for type C PSFs was 0.7–3.39 cm d−1. We show that type A PSFs are non-ice-contact rock collapses that occur as a long-term paraglacial response following glacier downwasting and the exposure of steep rocky cliffs and which could also be influenced by precipitation, freeze-thaw cycling, earthquakes or other factors. In contrast, type B and C PSFs are a more immediate response to recent glacier downwasting. We further argue that the accelerating downwasting of glacier are used as a preparatory or triggering factor, which could directly or indirectly cause the PSFs.
Fault Tolerant Radiation Monitoring System Using Wireless Sensor and Actor Network in a Nuclear Facility

—In nuclear facilities, the reading of the sensors is very important in the assessments of the system state. The existence of an abnormal state could be caused by a failure in the sensor itself instead of a failure in the system. So, being unable to identify the main cause of the “abnormal state” and take proper actions may end in unnecessary shutdown for the nuclear facility that may have expensive economic consequences. That is why, it is extremely important for a supervision and control system to identify the case where the failure in the sensor is the main cause for the existence of an abnormal state. In this paper, a system based on a wireless sensor network is proposed to monitor the radiation levels around and inside a nuclear facility. A new approach for validating the sensor readings is proposed and investigated using the Castalia simulator.
A GPU-Based Framework for Parallel Spatial Indexing and Query Processing

Support for efficient spatial data storage and retrieval have become a vital component in almost all spatial database systems. Previous work has shown the importance of using spatial indexing and parallel computing to speed up such tasks. While GPUs have become a mainstream platform for high-throughput data processing in recent years, exploiting the massively parallel processing power of GPUs is non-trivial. Current approaches that parallelize one query at a time have low work efficiency and cannot make good use of GPU resources. On the other hand, many spatial database applications are busy systems in which a large number of queries arrive simultaneously. In this research, we present a comprehensive framework named G-PICS for parallel processing of a large number of spatial queries on GPUs. G-PICS encapsulates efficient parallel algorithms for constructing a variety of spatial trees with different space partitioning methods. G-PICS also provides highly optimized programs for processing major spatial query types, and such programs can be accessed via an API that could be further extended to implement user-defined algorithms. While support for dynamic data inputs is missing in existing work, G-PICS implements efficient parallel algorithms for bulk updates of data. Furthermore, G-PICS is designed to work in MultiGPU environments to support datasets beyond the size of a single GPU’s global memory. Empirical evaluation of G-PICS shows significant performance improvement over the stateof-the-art GPU and parallel CPU-based spatial query processing systems. In particular, G-PICS achieves double-digit speedup over such systems in tree construction (up to 53X), and query processing (up to 80X). Moreover, tree update procedure outperforms the tree construction from scratch under different levels of data movement (up to 16X).
Identifying and Resolving Annotation Changes for Natural Language Understanding

Annotation conflict resolution is crucial towards building machine learning models with acceptable performance. Past work on annotation conflict resolution had assumed that data is collected at once, with a fixed set of annotators and fixed annotation guidelines. Moreover, previous work dealt with atomic labeling tasks. In this paper, we address annotation conflict resolution for Natural Language Understanding (NLU), a structured prediction task, in a real-world setting of commercial voice-controlled personal assistants, where (1) regular data collections are needed to support new and existing functionalities, (2) annotation guidelines evolve over time, and (3) the pool of annotators change across data collections. We devise an approach combining information-theoretic measures and a supervised neural model to resolve conflicts in data annotation. We evaluate our approach both intrinsically and extrinsically on a real-world dataset with 3.5M utterances of a commercial dialog system in German. Our approach leads to dramatic improvements over a majority baseline especially in contentious cases. On the NLU task, our approach achieves 2.75% error reduction over a no-resolution baseline.