Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimisation of supply chain management in an electronic commerce environment

I hereby certify that the work embodied in this thesis is the result of original research and has not been submitted for a higher degree to any other University or Institution. Acknowledgements First of all, I would express my gratitude to Nanyang Technological University and the Information Communication Institute of Singapore (ICIS) for providing me the opportunity to carry on my research work. I would also like to sincerely thank my supervisor, Associate Professor Siew Chee Kheong, David, without whom I could not have fulfilled my task of research, for his guidance and the patience given to my research work and in reviewing my thesis. Especially, I would thank my parents for their dedication, encouragement, and support for me in my work throughout the period. Last but not least, I would thank all the other students and the technicians in ICIS for their kind help in my research work. Abstract A supply chain consists of many entities interacting directly or indirectly to fulfil a customer's request. There are five major drivers within the supply chain that determine its performance in terms of responsiveness and efficiency. These drivers are facilities, production, inventory, transportation and information. Supply chain management is a set of approaches utilised to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that each merchandise is produced and distributed in the right quantities, to the right locations and at the right time, in order to minimize system-wide cost while satisfying service level requirements. Supply chain optimisation is a key to successful supply chain management. Optimisation is a technology for calculating the best possible Electronic commerce (EC) has introduced a new business model and changed the way business is conducted. It involves using information technology for a wide range of value added activities in the supply chain. Information is crucial to supply chain performance because it provides the facts that supply chain managers use to make decisions. EC consists of the hardware and software used throughout a supply chain to gather and analyse information and makes the supply chain visible to a manager. It serves as the eyes and ears of supply chain management, capturing and delivering the information necessary to make a good decision. With EC, managers can make decisions to improve the supply chain's performance based on information rather than educated guesses. Our research work focuses on the supply chain management in an electronic commerce environment, with emphasis on …
Smart Seed Classification System based on MobileNetV2 Architecture

The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniques, particularly CNN's, help to overcome these limitations due to their multi-level architecture. Various deep learning applications in agriculture include crop disease identification, fruit classification, and germination rate monitoring. Seed image analysis is considered a significant task for the preservation of biodiversity and sustainability. This research uses MobileNetV2, a deep learning convolutional neural network (DCNNs) for seed classification. This model has been preferred due to its simple architecture and memory-efficient characteristics. A total of 14 different classes of seeds were used for the experimentation. The results indicate accuracies of 98% and 95% on training and test sets, respectively.
Applying Multi-Objective Genetic Algorithm for Efficient Selection on Program Generation

Automated program generation (APG) is a concept of automatically making a computer program. Toward this goal, transferring automated program repair (APR) to APG can be considered. APR modifies the buggy input source code to pass all test cases. APG regards empty source code as initially failing all test cases, i.e., containing multiple bugs. Search-based APR repeatedly generates program variants and evaluates them. Many traditional APR systems evaluate the fitness of variants based on the number of passing test cases. However, when source code contains multiple bugs, this fitness function lacks the expressive power of variants. In this paper, we propose the application of a multi-objective genetic algorithm to APR in order to improve efficiency. We also propose a new crossover method that combines two variants with complementary test results, taking advantage of the high expressive power of multi-objective genetic algorithms for evaluation. We tested the effectiveness of the proposed method on competitive programming tasks. The obtained results showed significant differences in the number of successful trials and the required generation time.
The effects of screw dislocations on the magnetic properties of magnetite

The values of the hysteresis parameters produced by micromagnetic models are usually lower than those found experimentally, and in the case of grains which have been subjected to stress the simulated values can be as much as an order of magnitude too low. It has been suggested that the presence of crystalline defects within these stressed grains may be responsible for raising the coercivity and saturation remanence values in comparison with unstressed grains. Grains of magnetite containing a regular array of screw dislocations are modelled for different grain sizes and different dislocation densities using a micromagnetic model which considers the exchange, demagnetising, magnetoelastic, magnetocrystalline anisotropy and external field interactions. It is found that the values obtained from the new model for the coercivity and saturation remanence rise with increasing dislocation density in line with experimental results. Models containing dislocations are found to have the magnetic properties of defect-free grains of smaller grain size, again in line with experiment. The unblocking temperature of a ljim grain of magnetite in zero applied field is found to decrease in an approximately linear manner with increasing dislocation density. This thesis shows that by considering the magnetoelastic energy arising from the presence of screw dislocations the discrepancy between the hysteresis parameters of magnetite obtained theoretically and experimentally may be resolved.
Tuning Hyperparameters of Machine Learning Methods for Afan Oromo Hate Speech Text Detection for Social Media

With the rapidly growing penetration of social media networks in linguistically diverse and multicultural developing nations like Ethiopia, the conversations of online users have increasingly become more casual and multilingual. The emergency of hate speech text system. To this end, various automated hate speech detection and classification systems have been developed for resource-rich languages such as English and French even though online users are using many other languages on different social media platforms. Afan Oromo is one natural language used by social media users to express feelings, emotions and share messages. Hence, there is an urgent need for the development of an intelligent system that can automatically detect and classify hate speech, especially for resource-scarce indigenous Ethiopian languages like Afan Oromo. This work is about the identification of hate speech text from comments and posts generated in resource scary poor language Afan Oromo. We prepared first hate speech text detection dataset of Afan Oromo that containing comments and posts from social media. Then, n-gram and TF-IDF feature selection approaches were employed to select features. After the important feature selected Natural language processing tasks applied on the dataset. We applied six machine learning classifiers from default and tuned parameters to detect hate speech text posts and comments. The experiment show that Support Vector Machine outperform 92% values of F-measure than classifiers Afan Oromo hate speech text detection dataset. This Afan Oromo hate speech text dataset publicly available on https://www.naolinfo. info/for further research.
Comparison Corrosion Mapping Solutions using Phased Array and Conventional UT Techniques

There are many kinds of corrosion inspection solutions and choosing the right one may be complex. UT Corrosion mapping is a quantitative way to inspect the thickness of a material affected by corrosion. Oxidation or erosion reduces piece thickness and pulse-echoes distances are directly reduced. While one beam of a mono channel UT device can report one thickness at a time, a multi-beam or linear scan can produce an array of thicknesses. Both techniques are accurate but the inspection requirements may differ and so does the complexity of the setup. Today embedded software provides all the necessary tools to produce charts and spot mapping using a Mono channel UT setup. In addition to that, a PC software application can enhance the workflow by assisting the technician in producing the inspection scan plan, analyzing the data and comparing the information with previous results. For Phased Array techniques, embedded software typically does some corrosion inspection and mapping but with a much higher resolution and provides advanced analysis tools. The paper presented will compare Corrosion Mapping solutions using mono and phased array transducers. In addition, we will explore software solutions that improve inspection jobs. Advantages, benefits, results and limitations in corrosion inspection solutions will also be presented.
A Random Greedy based Design Time Tool for AI Applications Component Placement and Resource Selection in Computing Continua

Artificial Intelligence (AI) and Deep Learning (DL) are pervasive today, with applications spanning from personal assistants to healthcare. Nowadays, the accelerated migration towards mobile computing and Internet of Things, where a huge amount of data is generated by widespread end devices, is determining the rise of the edge computing paradigm, where computing resources are distributed among devices with highly heterogeneous capacities. In this fragmented scenario, efficient component placement and resource allocation algorithms are crucial to orchestrate at best the computing continuum resources. In this paper, we propose a tool to effectively address the component placement problem for AI applications at design time. Through a randomized greedy algorithm, our approach identifies the placement of minimum cost providing performance guar-antees across heterogeneous resources including edge devices, cloud GPU-based Virtual Machines and Function as a Service solutions. Finally, we compare the random greedy method with the HyperOpt framework and demonstrate that our proposed approach converges to a near-optimal solution much faster, especially in large scale systems.
A Simplified Traffic Flow Monitoring System Using Computer Vision Techniques

Millions of Muslims from all over the world visit the holy city of Makkah to perform Umrah throughout the year, but especially in the month of Ramadan the quantity of pilgrims increases significantly. Same conditions can be observed in the month of Dhu al-Hijjah during the Hajj season. To transfer the pilgrims from one place to another various many vehicles are used. It is very important to monitor the traffic flow during these days to avoid any congestion which may lead to any mishap. Therefore, there is a need for a traffic flow monitoring system that can be used to count vehicles, and classify them according to their size to facilitate traffic management. A computer vision based system is proposed in this research that uses MATLAB vision tool to identify and count the vehicles. The novelty of the system is its simplicity that uses a camera as a video source, then suitable algorithms are implemented on it for processing. A portion of a video is used for system training that first obtains a binary foreground from the initial frame, then removes noise and then counts the vehicles present in the frame. The functionality of the proposed system has been verified by a number of experiments with the accuracy of 90%. It can be easily implemented in various environments for vehicles counting that can be used for better traffic management.
Sewerage Stormwater Inflow Prediction by Using System Identification Method with Hammerstein Type of Nonlinear Model

This paper presents a stormwater inflow prediction method based on a rainfall-runoff model. The rainfall-runoff model is derived by using system identification method with a nonlinear model called Hammerstein model, which consists of a static nonlinearity followed by a linear time invariant (LTI) system. It is shown that the Hammerstein model can represent nonlinear phenomena of a rainfall-runoff process effectively, thus the introduction of the Hammerstein model improves the accuracy of inflow prediction results. The effectiveness of the proposed method is illustrated through numerical simulations by using the rainfall-runoff data which is acquired in actual urban catchment and a sewerage treatment plant.
A Novel Range-Free Anchor-Free Localization In WSN Using Sun Flower Optimization Algorithm

One of the foremost complex issues in the application of wireless sensors is locating the sensor. Most standard range-free localization methods ignore the anisotropy factors common in Wireless Sensor Networks (WSNs), resulting in low placement precision. This paper proposes a well-performing Sun Flower Optimization Algorithm (SFO) for implementing the range-free anchor-free localization using Distance Vector-Hop (DV-Hop) in WSN. The localization of unknown nodes in the network is possible using a new objective model under range-free and anchor-free scenarios. The localization accuracy tremendously enhances when differentiated from the conventional methods.
Empirical Study of Finance Distress Prediction Based on Accounting anomalies

This paper uses Logistic regression to establish a financial crisis warning model based on accounting anomalies indicators, financial indicators and non-financial indicators, it makes 224 companies listed on the Shanghai and Shenzhen Stock Exchanges as a research object from 2017 to 2019, including 112 companies in a special trade and that much in normal trade, and it tests the effectiveness of the warning model. The results show that the accounting anomalies indicators can effectively improve the predictive ability of the financial crisis warning model, and it has important reference value for investors to effectively identify financial crisis and government departments to strengthen the supervision of listed companies.
On the Impact of ML use cases on Industrial Data Pipelines

The impact of the Artificial Intelligence revolution is undoubtedly substantial in our society, life, firms, and employment. With data being a critical element, organizations are working towards obtaining high-quality data to train their AI models. Although data, data management, and data pipelines are part of industrial practice even before the introduction of ML models, the significance of data increased further with the advent of ML models, which force data pipeline developers to go beyond the traditional focus on data quality. The objective of this study is to analyze the impact of ML use cases on data pipelines. We assume that the data pipelines that serve ML models are given more importance compared to the conventional data pipelines. We report on a study that we conducted by observing software teams at three companies as they develop both conventional(Non-ML) data pipelines and data pipelines that serve ML-based applications. We study six data pipelines from three companies and categorize them based on their criticality and purpose. Further, we identify the determinants that can be used to compare the development and maintenance of these data pipelines. Finally, we map these factors in a two-dimensional space to illustrate their importance on a scale of low, moderate, and high.
The Role of User Reviews in App Updates: A Preliminary Investigation on App Release Notes*

Release planning for mobile apps has recently become an area of active research. Prior research in this area concentrated on the analysis of release notes and on tracking user reviews to support app evolution with issue trackers. However, little is known about the impact of user reviews on the evolution of mobile apps. Our work explores the role of user reviews in app updates based on release notes. For this purpose, we collected user reviews and release notes of Spotify, the ߢnumber one’ app in the ‘Music’ category in Apple App Store, as the research data. Then, we manually removed non-informative parts of each release note, and manually determined the relevance of the app reviews with respect to the release notes. We did this by using Word2Vec calculation techniques based on the top 80 app release notes with the highest similarities. Our empirical results show that more than 60 % of the matched reviews are actually irrelevant to the corresponding release notes. When zooming in at these relevant user reviews, we found that around half of them were posted before the new release and referred to requests, suggestions, and complaints. Whereas, the other half of the relevant user reviews were posted after updating the apps and concentrated more on bug reports and praise.
Exposing Vulnerable Paths: Enhance Static Analysis with Lightweight Symbolic Execution

Static analysis tools, although widely adopted in industry, suffer from a high false positive rate. This paper aims to refine the results of static analysis tools, by automatically searching for a vulnerable path from given defect report. To realize this goal, we develop SATRACER, a novel tool which integrates symbolic execution techniques with static analysis. SATRACER selectively skips those program parts which can be consistently updated by static analysis, thus drastically improving performance. We have applied SATRACER to a set of 21 real-world applications. Evaluation results show that SATRACER can successfully remove 71.4% false alarms reported by a commercial static analysis tool in 10 hours, and confirmed 29 real use-after-free bugs and 895 real null-pointer-dereference bugs.
A Framework of Value Connection Route for Fresh Agri-product E-commerce: A Grounded Theory Approach in the Context of China

While the traditional business model is difficult to make a real-time response to the market dynamics, fresh agri-products E-commerce (FAP e-commerce) needs a unique operation model. This paper focuses on the value connection route of FAP e-commerce and develops the framework of a sustainable business model which connects suppliers with the customers. Based on grounded theory, the research takes the community lead-warehouse of MeiRiYouXian as the case background to analyze the value connection route from the product and service to the user behavior. Practical implications are drawn from the case study for firms to achieve sustainability through business model innovation in FAP e-commerce field, and for the government to support the F AP e-commerce to achieve sustainable development.
API parameter recommendation based on language model and program analysis

APIs are extensively and frequently used in source code to leverage existing libraries and improve programming productivity. However, correctly and effectively using APIs, especially from unfamiliar libraries, is a non-trivial task. Although various approaches have been proposed for recommending API method calls in code completion, suggesting actual parameters for such APIs still needs further investigating. In this paper, we introduce FLUTE, an efficient and novel approach combining program analysis and language models for recommending API parameters. With FLUTE, the source code of programs is first analyzed to generate syntactically legal and type-valid candidates. Then, these candidates are ranked using language models. Our empirical results on two large real-world projects Netbeans and Eclipse indicate that FLUTE achieves 80% and +90% in Top-1 and Top-5 Precision, which means the tool outperforms the state-of-the-art approach.
Multi-IMU Based Alternate Navigation Frameworks: Performance & Comparison for UAS

On-board state estimation is a persistent challenge to fielding unmanned aerial systems (UAS), particularly when global positioning system (GPS) measurements are not available. The dominant estimation tool remains the extended Kalman filter (EKF) applied to an inertial measurement unit (IMU). The growing availability of low-cost commercial-grade IMUs raises questions about how to best improve sensor readings into an estimate when measurements are available from multiple IMUs. This paper evaluates four different approaches to attitude estimation from multiple IMU measurements and their application in high dynamic motion. The four approaches are fusion of measurements (virtual IMU), fusion of state estimates (Federated KF), feedback fusion state estimate (Feedback Federated KF), and an EKF design incorporating the additional measurements (Augmented KF); these correspond to fusion before, within, or after state estimation. The performance of the approaches are quantified for varying IMU number theoretically and experimentally. The experiments use on-board autopilot hardware implementations of the estimators during maneuvers in a motion capture volume and flights on-board an Unmanned Aerial Systems (UAS) implementation, with the peak and root-mean-square (RMS) errors used to quantify accuracy. The RMS error results indicate that the feedback federated Kalman Filter using five IMUs returns 38% compared to general federated Kalman Filter using 37% accuracy improvement over a single IMU. This improvement compares to a 19% improvement for virtual IMU and 9% improvement for the Augmented KF respectively. These results indicate that both the Federated KF approach achieves the lowest RMS error relative to the virtual IMU and augmented KF approaches and inform the design of multi-IMU UAS pose estimators.
Design of Software Architecture for Neural Network Cooperation: Case of Forgery Detection

Recent technological advances in media tampering has been the cause of many harmful forged images. Tampering detection methods became major research topics to cope with it in the neural network community. The methods almost always aim at detecting a specific forgery. That is, a general detecting method to find any tampering has not been invented so far. This paper concerns about a software architecture for organizing multiple neural networks to detect multiple kinds of forgeries. The key issue here is to construct, from the meta-level, a mechanism for an ensemble of front-end neural networks to select a neural network which makes a decision. Under this architecture, we implemented a prototype for detecting forged images resulted from multiple tampering methods of copy-move and compression. In order to demonstrate that our architecture works well, we examined a case study with a total of 120,000 patches which consist of three classes of copy-move, compression and untampered data, 40,000 patches for each. The result shows our proposed method successfully classified 108,954 out of 120,000 patches with 90.82 % accuracy. We also give discussions on our architectural implication to avoid concept drift. Our architecture is designed to be a context-oriented and meta-level, which has a two-layered structure: meta and base. The neural networks can be categorized into base-level components, whereas a component coordinating the networks is addressed in meta-level. The architecture explains that the concept drift can be handled in the meta-level. Through the discussions on the techniques of transfer learning, online learning, and ensemble learning in terms of the architecture we constructed, it is concluded that we could construct a universal architecture to coordinate machine learning components.