Miracle-dz/UniFAR-FTUs huggingface dataset:
Context-Based Fake News Detection Model Relying on Deep Learning Models

Nowadays, due to the great accessibility to the internet, people seek out and consume news via social media due to its low cost, ease of access, and quick transmission of information. The tremendous leverage of social media applications in daily life makes them significant information sources. Users can post and share different types of information in all their forms with a single click. However, the cost becomes expensive and dangerous when non-experts say anything about anything. Fake news are rapidly dominating the dissemination of disinformation by distorting people’s views or knowledge to influence their awareness and decision-making. Therefore, we have to identify and prevent the problematic effects of falsified information as soon as possible. In this paper, we conducted three experiments with machine learning classifiers, deep learning models, and transformers. In all experiments, we relied on word embedding to extract contextual features from articles. Our experimental results showed that deep learning models outperformed machine learning classifiers and the BERT transformer in terms of accuracy. Moreover, results showed almost the same accuracy between the LSTM and GRU models. We showed that by combining an augmented linguistic feature set with machine or deep learning models, we can, with high accuracy, identify fake news.
A smartphone application that informs weight shifting behavior to promote tissue health

ACKNOWLEDGEMENTS This thesis could not have been possible without the help of so many people. The following groups helped in one way or another. Thank you to my advisor, Dr. Stephen Sprigle, who pushed me and corrected me. His feedback was straightforward and honest at times, but as he has reiterated to me, his goal was to make sure I could be proud to earn my degree from Georgia Tech. He once said that he was not a cheerleader, but a coach who motivates his students to learn skills that will later be useful. Thank you to Sharon Sonenblum for helping me with understanding the hardware and data associated with this project. She took time away from her busy schedule to explain how the data logger collects information and what the numbers it collects mean. Thank you to Minna Hong of Shepherd Center who made the recruitment of subjects so much easier. Her humor and drive to help me made her much more approachable in what was already a stressful situation for me. She was always willing to send out announcements for me and scrambled to help grab her co-workers to help me with my study. Thank you to Brian Jones of Aware Home and Wireless RERC for helping to fund the compensation portion of the user studies. Your contributions made it a lot easier to find willing participants. believing in me and praying for me; Ryan for helping me stay sane and being there to help me get my mind off research; my awesome girlfriend Bomi who stayed up with me, took care of me, had faith in me through challenging times, and helped me keep pushing toward the finish line. Thanks be to God. May His name be honored and glorified through this paper and in my actions afterwards.
In Silico Identification of Novel Inhibitors Targeting the Homodimeric Interface of Superoxide Dismutase from the Dental Pathogen Streptococcus mutans

The microaerophile Streptococcus mutans, the main microaerophile responsible for the development of dental plaque, has a single cambialistic superoxide dismutase (SmSOD) for its protection against reactive oxygen species. In order to discover novel inhibitors of SmSOD, possibly interfering with the biofilm formation by this pathogen, a virtual screening study was realised using the available 3D-structure of SmSOD. Among the selected molecules, compound ALS-31 was capable of inhibiting SmSOD with an IC50 value of 159 µM. Its inhibition power was affected by the Fe/Mn ratio in the active site of SmSOD. Furthermore, ALS-31 also inhibited the activity of other SODs. Gel-filtration of SmSOD in the presence of ALS-31 showed that the compound provoked the dissociation of the SmSOD homodimer in two monomers, thus compromising the catalytic activity of the enzyme. A docking model, showing the binding mode of ALS-31 at the dimer interface of SmSOD, is presented. Cell viability of the fibroblast cell line BJ5-ta was not affected up to 100 µM ALS-31. A preliminary lead optimization program allowed the identification of one derivative, ALS-31-9, endowed with a 2.5-fold improved inhibition power. Interestingly, below this concentration, planktonic growth and biofilm formation of S. mutans cultures were inhibited by ALS-31, and even more by its derivative, thus opening the perspective of future drug design studies to fight against dental caries.
DESAIN KEY PERFORMANCE INDICATOR UNTUK PENGUKURAN KINERJA INDUSTRI JASA BERDASARKAN BUDAYA ORGANISASI DENGAN MENGGUNAKAN METODE BSC-AHP (Studi Kasus Hotel Di Semarang)

In 2014 tourist arrivals in Semarang decreased when compared to 2013, which caused the occupancy rate also decreased, this led to increasingly fierce competition among businessmen hospitality. Performance measurement is one way companies look at how big the achievement of the company today. Performance measurement method used is balanced scorecard. This method is able to assess the company not only from the financial aspect, but also from non-financial aspects with reference to the four perspectives that exist. Determination key performance indicator (KPI) in this study consider the vision, mission and values of organizational culture within the company. By using four perspectives in the BSC, there are 12 key performance indicator (KPI) with different interests for each perspective and its KPI based on the weighted analitycal hierarchy process (AHP). The measurement results with the company BSC BSC showed a score of 51.979 included in the final score BSC value of 0, which indicates the value of the company's performance in the Semarang Hotel Grasia sufficient criteria, which means it is still the same as the performance targets set by the company.
Enabling Technologies for Broadband In-Band On-Channel Digital Radio

Enabling Technologies for Broadband In-Band On-Channel Digital Radio Zixia Hu Chair of the Supervisory Committee: Professor Hui Liu Department of Chair Due to inadequate spectrum resources and increasing demands for high quality multimedia services, traditional analog broadcasting is being migrated into digital radio around the world. This dissertation focuses on the development of several innovative broadcasting technologies and the design of so-called Broadband Digital Radio (BDR) system, which is suitable for digital audio and data broadcasting in FM and AM channels. The new broadcasting system synergistically integrates frequency hopping, LDPC coding, hierarchical modulation and band aggregation techniques to increase the data rate, improve spectrum efficiency, and at the same time, provide the spectrum flexibility necessary in high quality audio and multimedia services. One of the key features in the BDR is the powerful hierarchical modulation scheme that offers significant performance enhancement over existing schemes (e.g., DVB-T). In this dissertation, we present a novel decoding algorithm for the hierarchical modulation. Unlike the traditional hierarchical demodulation approaches which suffer from serious interlayer interference (ILI), the proposed method exploits the structure information of the secondary layer to mitigate the ILI impairment, thereby offers significant gains in reception performance. For the digital network, frequency planning is needed as in other digital broadcasting signals. Most importantly, the single frequency network (SFN) architecture will be used in the BDR deployment. The hierarchical modulation technique is also adopted in the SFN to provide both global and local information. Accordingly we develop a low-complexity successive interference cancellation (SIC) algorithm to ILI and inter-cell interference (ICI) in the HM based SFN. The performance evaluation and decoding complexity comparisons indicate that the proposed structured SIC approach offers a good performance-complexity trade-off, especially for the HM-based SFN scenarios. Finally, a BDR prototype has been developed on a personal computer (PC) based software defined radio (SDR) platform. Numerical results and initial laboratory tests demonstrate significant performance advantages of the new design over existing systems such as the Digital Radio Mondiale (DRM) and Hybrid Digital (HD) radio.
Feature extraction and classification based crop disease detection using deep neural network and ensemble convolutional neural network architecture

Crop disease identification is crucial for avoiding production losses and lowering the amount of agricultural items produced. Machine learning-based approaches can be utilized to solve these types of problems. Deep Learning Techniques, which are commonly employed in image processing, have recently been involved in a number of agricultural applications. This research suggests detecting agricultural diseases using feature extraction and classifying those using deep learning algorithms. For the extraction of the segmented feature, we employ a pre-training model-based Deep Neural Network. The segmented feature will then be categorized using an ensemble classifier composed of Visual Geometry Group (19) and Inception V3++ architecture. This Ensemble Convolution Neural Networks (EN-CNN) classification will increase illness detection accuracy while minimizing time. Pre-trained Deep Convolution Neural Network (PDCNN) and EN-CNN are used to classify damaged and healthy leaves in a dataset of healthy and diseased leaves. The suggested model's resilience is empirically shown for crop disease detection in tomatoes and grapes. The suggested approach evaluated parameters with a 97 percent accuracy rate, a 98 percent precision rate, a 98 percent recall rate, and a 98 percent F-1 score rate.
Anti-Inflammatory Effects of Gynura procumbens on RAW264.7 Cells via Regulation of the PI3K/Akt and MAPK Signaling Pathways

Gynura procumbens is a traditional herb and food extensively cultivated in China and Southeast Asian countries. In this work, the crude extract (CE) of G. procumbens was purified with macroporous resin to obtain the refined fraction, and its anti-inflammatory activity was compared with that of CE. Moreover, the detailed mechanisms of anti-inflammatory activity were also investigated for the first time. The results indicated that CE was more effective in anti-inflammatory activity and it could reduce the secretion of NO, TNF-α, and PGE2 via decreasing the iNOS, TNF-α, and COX-2 genes transcription and related proteins translation, which were associated with the inhibition of AP-1 and NF-κB nuclear translocation and downregulation of PI3K/Akt and MAPK signaling pathways. In conclusion, the extract of G. procumbens has a promising potential in inflammation-related disorders alleviation, and these findings could provide the basis for the comprehensive utilization of G. procumbens and the new functional food development.
Microstructure and Properties of Hydroxyapatite Coatings Made by Aerosol Cold Spraying–Sintering Technology

Hydroxyapatite is a widely used material used for the bioactivation of an implant’s surface. A promising hydroxyapatite coating approach is the kinetic deposition of powder particles. The possibility of solid-state deposition improvement through the merging of Aerosol Deposition and Low Pressure Cold Spraying techniques is a promising prospect for improving the deposition efficiency and the quality of coatings. The objective of the paper is to study the possibilities of hydroxyapatite coating structure modification through changes in the coating process and post-heat treatment. The novel Aerosol Cold Spraying system joining Low Pressure Cold Spraying and Aerosol Deposition was used for the deposition of coatings. The coating’s post-processing was conducted using two techniques: Spark Plasma Sintering and Pressureless Sintering. The coating’s structure was examined using scanning, transmission, and light microscopy, and X-ray diffraction. Substrate–coating bond strength was assessed using a tensile test. Homogenous buildup using Aerosol Cold Spraying of hydroxyapatite was achieved. Various pores and microcracks were visible in the sprayed coatings. The deposition process and the thermal post-processing did not lead to significant degradation of the hydroxyapatite phase. As a result of the Spark Plasma Sintering and Pressureless Sintering at 800 °C, an increase in tensile adhesion bond strength and crystal size was obtained.
Interpretable Federated Transformer Log Learning for Cloud Threat Forensics

—Threat detection and forensics have become an imperative objective for any digital forensic triage. Supervised approaches have been proposed for inferring system and network anomalies; including anomaly detection contributions using syslogs. Nevertheless, most works downplay the importance of the interpretability of a model’s decision-making process. In this research, we are among the ﬁrst to propose an interpretable federated transformer log learning model for threat detection supporting explainable cyber forensics. The proposed model is generated by training a local transformer-based threat detection model at each client in an organizational unit. Local models learn the system’s normal behavior from the syslogs which keep records of execution ﬂows. Subsequently, a federated learning server aggregates the learned model parameters from local models to generate a global federated learning model. Log time-series capturing normal behavior are expected to differ from those possessing cyber threat activity. We demonstrate this difference through a goodness of ﬁt test based on Karl-Pearson’s Chi-square statistic. To provide insights on actions triggering this difference, we integrate an attention-based interpretability module. We implement and evaluate our proposed model using HDFS, a publicly available log dataset, and an in-house collected and publicly-released dataset named CTDD, which consists of more than 8 million syslogs representing cloud collaboration services and systems compromised by different classes of cyber threats. Moreover, through different experiments, we demonstrate the log agnostic capability and applicability of our approach on real-world operational settings such as edge computing systems. Our interpretability module manifests signiﬁcant attention difference between normal and abnormal logs which provide insightful interpretability of the model’s decision-making process. Finally, we deem the obtained results as a validation for the appropriate adoption of our approach in achieving threat forensics in the real world.
Sturge–Weber Syndrome with Bilateral Port-Wine Stain

Sturge–Weber syndrome is a rare congenital neurocutaneous disorder characterized by dermatological, ophthalmological, and neurological manifestations. It occurs due to abnormal persistence of embryonic vascular plexus. Here, we describe a case of four years seven months female with seizures, developmental delay, intellectual disability, and bilateral port-wine stain diagnosed as type I (classical) Sturge–Weber syndrome. The ophthalmological evaluation was unremarkable. Electroencephalogram showed abnormalities suggestive of a structural lesion in the right cerebral hemisphere. CT scan of the head revealed volume loss of right brain parenchyma with linear, cortical, as well as subcortical calcifications more evident in the right hemisphere. The child should be followed up regularly until adulthood for ophthalmological evaluation, recurrence of seizures, and other manifestations of this disorder.
Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

Forecasting groundwater contaminant plume development is critical for determining risks to downgradient receptors and predicting the time to site closure. However, accurate forecasts are a formidable challenge due to the complexities of a heterogeneous subsurface. While historically groundwater well data in combination with numerical flow models have been used for this task, the advent of machine learning offers new data‐driven opportunities for improving contaminant fate and transport predictions. In this study, we interrogate the viability of two forecasting models—Prophet and damped Holt's exponential smoothing model—for predicting groundwater contaminant plume development. The impacts of spatial and temporal data density on the accuracy of the forecasts are evaluated. For wells with declining contaminant concentrations, the damped Holt's method achieves more accurate forecasts. However, only Prophet allows for the inclusion of exogenous regressors, enabling predictions of future declining trends in wells with still increasing contaminant concentrations. Application of these models does not only require robust training data, but also an understanding of model biases. Overall, powerful data‐driven models are already available for contaminant plume prediction, but groundwater sampling approaches will have to improve, for instance, through the collection of real‐time spatial and temporal high‐resolution data, to take full advantage of their capabilities.
UV-Activated NO2 Gas Sensing by Nanocrystalline ZnO: Mechanistic Insights from Mass Spectrometry Investigations

In this work, the photostimulated processes of O2 and NO2 molecules with the surface of ZnO under UV radiation were studied by in situ mass spectrometry in the temperature range of 30–100 ∘C. Nanocrystalline needle-like ZnO was synthesized by decomposition of basic zinc carbonate at 300 ∘C, and the surface concentration of oxygen vacancies in it were controlled by reductive post-annealing in an inert gas at 170 ∘C. The synthesized materials were characterized by XRD, SEM, low-temperature nitrogen adsorption (BET), XPS, Raman spectroscopy, and PL spectroscopy. Irradiation of samples with UV light causes the photoabsorption of both O2 and NO2. The photoadsorption properties of ZnO are compared with its defective structure and gas-sensitive properties to NO2. A model of the sensor response of ZnO to NO2 under UV photoactivation is proposed.
Post-COVID-19 Tourists’ Preferences, Attitudes and Travel Expectations: A Study in Guayaquil, Ecuador

Expectations about a destination influence the tourist experience during the travel process stages. In the post-COVID-19 normalcy, people are adjusting their priorities and social values. Therefore, it becomes crucial to identify tourists’ expectations before traveling. The objectives of this research were: (a) identify the preferences of tourists; (b) establish the attitudes of tourists; and (c) determine the expectations of tourists for post-COVID-19 destination selection. The study analyzed a sample of 491 people during pandemic lockdowns in Guayaquil, Ecuador. Statistical techniques such as exploratory and confirmatory factor analysis were used in data analysis. The results show that after the pandemic, tourists prefer urban tourism, followed by cultural tourism and traveling with relatives. It also shows a more responsible and supportive attitude when traveling. Likewise, the results support the dimensional structure that explains a set of post-pandemic tourist expectations. Five factors were identified: Smart Care, pricing strategy, safety, comfort, and social distancing. Finally, the theoretical and managerial implications of the results that will guide for tourism destination managers were discussed.
The Effectiveness of Islamic Religious Education Teaching Management in the Development of Characteristics in Students

The phenomenon of the decline in the morals of the nation's generation, including the nation's elite, often becomes an apology for some people to give scathing criticism of educational institutions. This is very reasonable because education actually has a very basic mission, namely to form a complete human being with morals as one of the main indicators, the nation's generation with noble character is one of the profiles expected from the practice of national education. The purpose of this study was to obtain a clear picture and an in-depth analysis of the effectiveness of the teaching management of Islamic Religious Education in moral development at SDN Cibabat Mandiri 3 and SDN Cibabat 4 . In this study, researchers generally use descriptive qualitative research methods through observation, interviews, document review, questionnaires and other media. In analyzing the data, the researcher uses Miles and Hubermen's theory which consists of three activity lines, namely data reduction, data presentation, and conclusion drawing or verification. Based on results research at SDN Cibabat Mandiri 3 and SDN Cibabat 4 Cimahi City could drawn conclusion that effectiveness management PAI teaching in construction morals in students has apply principles and goals management education, start with apply planning, implementation, and evaluation in every activity as well as the programs presented. This thing impact positive on every activity classroom teaching and outside class moment PAI teaching is carried out nor in construction morality in students.
Waste-to-energy Plant Operation Planning Based on Stochastic Simulation

In many cases, waste-to-energy (WtE) plants are combined heat and power producers. They are often integrated into a central heating system and they also export electricity to a grid. Therefore, they have to plan their operation for the next day, next month, etc. However, it may be a challenging task due to stochastic nature of some input variables such as varying lower heating value of waste, boiler performance resulting in fluctuating steam parameters, irregular internal steam consumption or its export. This paper presents a novel tool for a WtE plant operation planning under uncertainty. The crucial part of the tool is a stochastic model of a technology. The stochastic model was developed using operational data from an existing plant. The model was then implemented into simulation tool and well-known Monte Carlo simulation method was applied. Utilization of simulation results for operation planning is described on an example and the contribution of stochastic approach is evaluated using historical data.