Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable Electronic Transfer Fraud Detection with Expert Feature Constructions

Since the magnitude of financial frauds grow rapidly with low clearance rates, detecting and avoiding frauds has been a tremendous challenge for financial institutions. Both the detection performance and interpretability are critical for fraud detection to profile the fraudsters’ modus operandi and to spot vulnerabilities of financial systems/processes. Traditional rule-based approaches yield poor detection performances. Recent machine learning methods basically generate recency, frequency, and temporal features to extract patterns from raw transaction data. On the other hand, this paper generates behavioral and (financial organization’s) segmentation features based on financial expertise and characteristics solely belonging to (non)-fraudulent accounts. While inputting aforementioned features into different models and using accumulated features from past literature generate unstable prediction results, our features generate the best and stable results for the decision-tree-base approach like Extreme Gradient Boosting and Light Gradient Boosting Machine. By using Kolmogorov–Smirnov test, we discover the instable predictive results are caused by vastly different distributions of features that reflects the fast-changing modus operandi in the training/testing sets. Thus, generating training/testing sets by random sampling (compared to chronological separation) is improper for modeling time varying data. Combining XGBoost with our expertise-based features provides clear causal-effect between features and fraudulent labels for further interpretations. The high precision and recall rates allow banks to save screening labor costs and identify frauds without interfering with normal transactions. The quality of our features can be examined by showing that they occupy three out of the five most important features under the ranking procedure in a premium finance publication by Butaru et al. [ Journal of Banking and Finance (72) 218–239 (2016)].
An Ontology-based Approach to Enable Data-Driven Decision-Making in Agile Software Organizations

Software organizations adopt agile practices and data-driven software development aiming at a competitive advantage. This requires changes in the organization’s culture, structure, and practices, which may not be easy . In addition, data items often remain spread in heterogeneous applications, adopting different semantics, which poses a barrier to integrated data usage. Therefore, data-driven decisions in agile development have been uncommon, missing valuable opportunities for informed decision-making. We aim to provide an ontology-based approach that integrates data of software-development support applications using an ontology network to enable data-driven decision making in agile software organizations.
The Use of the Twitter Graphs for Analysing User Emotion for Business

Nowadays, more and more people are using online social media to express their thoughts and opinions on a variety of topics that interest or concern them. Through social networking platforms, people have the ability to communicate directly and share knowledge with people all around the world. Twitter is one of the most popular social media, used by millions of users daily. In particular, people use it to express their opinion directly and freely on whatever concerns them, thereby generating a large amount of data. The abundance of this information and its multifaceted importance, emphasizes how important is to find ways of collecting and analyzing such data in order to extract valuable knowledge. Such data, are a valuable source of information whose extraction can help individuals or even businesses in the decision-making process. The present research focuses on the study of user communication about a brand on Twitter, and in particular on exploiting user feelings about this brand effectively. In more detail, this work promotes the efficient modeling and management of the business-consumer relationship by studying the interactions of users who are discussing a specific brand name. The purpose of this research work is to provide an efficient tool that will enable businesses to use technological and automated tools in order to effectively manage the emotional state of consumers in relation to their brand. Consumer feedback and expressed emotions may be utilized by companies for making decisions regarding marketing research, competitive business intelligence and online reputation management.
Convolutional Neural Network-based a Novel Deep Trend Following Strategy for Stock Market Trading

Extracting or predicting future stock price trends from the current and historical stock trading activity is an open research problem. Convolution Neural Network (CNN) recently shown excellent performance to extract high-level features from raw data in many domains. In this research work, we trained CNN to extract future stock price trends. Trend Following is a trading strategy that does not require prior knowledge of the future stock price trend. In the Trend Following trading strategy, traders buy stock in an uptrend and sell the stock in a downtrend. Trend Following trading strategy is based on the current price trend and the assumption that the current price trend will continue further for some time, but if the current price trend does not proceed then this trading strategy fails to provide the profit. Our objective is to improve the Trend Following trading strategy by combining the advantages of both the CNN-based prediction and the Trend Following trading strategy. In this research work, we proposed a modified CNN-based Trend Following trading strategy named Deep Trend Following (DTF) in which trading decisions are based on the current and predicted future price trend of the stock. Our experimental results are twofold. Firstly our trained CNN-based classifier outperformed the baseline methods. Secondly, the DTF trading strategy outperformed three trading strategies viz the CNN-based, Buy-and-Hold, and the simple Trend Following trading strategies on the American and the Indian index stocks.
Mechanical Diagnosis Based on Similarity Extraction of Time Series

The intelligent diagnosis emphasizes the processing method of knowledge of the historical data. The capability of an intelligent diagnosis system depends on the knowledge possessed by the system, especially by the specific knowledge in application. Currently, most of the important equipment have their own the inspection systems. With the help of these systems, plenty of historical data can be collected in real time. This paper discusses the possibility of the application of similarity extraction and pattern discovery of time series in fault diagnosis by using these historical data, presents the method of time series feature extraction and pattern matching, and advances the possibility of data clustering and pattern discovery based on dimension reduction.
A Privacy Framework for Hierarchical Federated Learning

Federated Learning (FL) enables heterogeneous entities to collaboratively develop an optimized (global) model by sharing data and models in a privacy preserving fashion. We consider a Hierarchical Federated Learning (HFL) environment with data ownership split among the entities representing the edge nodes. Each node can train models on the data they own, as well as request access to data and model(s) owned by their descendant nodes - to optimize their models, perform transfer learning on new data, and develop an ensemble model. Unfortunately, a practical realization of HFL is challenging today due to issues with data/model lineage tracking and providing subsequent privacy guarantees. In this paper, we propose a conceptual framework for HFL by capturing the data/model attributes at each node, including their privacy exposure. The framework enables scenarios where a node output may expose certain attributes of its underlying data, as well as identifying models in the hierarchy that need to be updated once a user whose data was used in their training has opted-out. By designing the computations appropriately and limiting the exposure by the nodes, we show that diﬀerent levels of privacy can be guaranteed.
Multiband Camera System Using Color and Near Infrared Images

Various applications using a camera system have been developed and deployed commercially to improve our daily life. The performance of camera system is mainly dependent on image quality and illumination conditions. Multiband camera has been developed to provide a wealth of information for image acquisition. In this paper, we developed two applications about image segmentation and face detection using a multiband camera, which is available in four bands consisting of a near infrared and three color bands. We proposed a multiband camera system to utilize two different images i.e. color image extracted from Bayer filter and near infrared images. The experimental results showed the effectiveness of the proposed system.
Timing optimization algorithms for sequential circuits

With the advent of deep sub-micron era (DSM), "System-on-chip (SOC)" has become a mainstream of IC industry. Semiconductor devices based on a smaller feature size offer the promise of faster and more highly integrated designs, but also provide a number of new challenges. In SOCs, a large amount of communication time is spent on global multi-clock-period interconnects, which present themselves as the main performance limiting factor. How to handle global interconnects for performance optimization becomes an urgent issue. Another challenge is the increasing coupling effect (also known as crosstalk) between neighboring interconnects. Besides introducing noises on quiet interconnects, crosstalk could potentially change the interconnect delays and cause timing violations in the circuit. In this dissertation, we investigate and propose solutions to a few problems involving global interconnects and crosstalk. To handle global interconnects, we propose techniques at different stages of the design flow. At physical layout stage, we propose to pipeline global interconnects by relocating flip-flops (an operation also known as retiming). Three efficient algorithms are designed to find an optimal retiming with the minimal clock period. We then solve the problem of retiming under both setup and hold constraints more efficiently than the best-known algorithm in the literature. We also consider clock skew scheduling for prescribed skew domains and give an optimal polynomial-time algorithm to minimize the clock period with possible delay padding. At clustering stage, we propose an iterative algorithm that finds an optimal clustering with the minimal maximum-cycle-ratio. At register transfer level (RTL), we use delay relaxation to do interconnect planning. For crosstalk, we propose a circular time representation under which coupling detection is easier and more efficient than state-of-the-art approaches. Using the circular time representation, clock schedule verification with crosstalk is more efficient. We show that the trade-off between a level-sensitive latch and an edge-triggered flop can be leveraged in a sequential circuit design with crosstalk, so that the clock period is minimized by selecting a configuration of mixed latches and flops. We design an effective and efficient algorithm to solve this problem.
A 500 MHz ATE pin driver

A monolithic pin driver with a rise/fall time of 1.75 ns at 5 Vpp, fabricated on a dielectrically isolated, complementary bipolar, polysilicon emitter process, is described. This pin driver circuit consists of a switch, an output buffer, and two differential control elements. A two-stage approach, separating the switch from the output buffer, allows the speed and accuracy requirements of the switch to be decoupled from the load driving capability of the buffer. The circuit performance is discussed. <<ETX>>
PHP Source Code Protection Using Layout Obfuscation and AES-256 Encryption Algorithm

In WhiteSource's report “Total Known Open Source Vulnerabilities Per Language,” PHP is ranked second after C, with 17 percent of all reported vulnerabilities in the last ten years. One of the disadvantages of PHP-based applications is it built without being compiled into machine language instructions. Web servers process the plain source code; consequently, hijackers can easily change, replicate, or redistribute source code without the developer's approval. Obfuscation is one of the solutions to this problem. This paper aims to add layout obfuscation to the former obfuscation approach to improve security. For the first time, a layout obfuscation technique was combined with an AES-256 encryption algorithm. The Design Research Methodology will be used in this paper. In addition, performance tests, functional tests, obfuscated file sizes, and security tests will be performed on the implementation results. The test results imply that the PHP Extension produced in this paper can run according to the designed functional and non-functional requirements.
Can Digital Teaching Portfolios Become Tools for Technology Integration

The purpose of this study was to explore a potential relationship between developing digital teaching portfolios and a propensity to integrate technology in the classroom. Graduate students, who were also practicing K-12 teachers and who were enrolled in the initial educational technology course of an M.Ed. program, created digital teaching portfolios centered on a selfselected growth goal based on the ISTE National Educational Technology Standards for Teachers (NETS-T). The analysis of data from three surveys indicated that there was a positive change in the participants’ perceptions of their technology skill level and their use of digital portfolios as a learning/assessment tool in their K-12 classrooms. Teachers also developed technology skills that they could use in their classrooms. Finally, there was evidence that the digital portfolio process engaged teachers in the process of reflection. A follow-up study is planned for Spring 2005.
Forced Response Analysis of Coupled Cantilever Beams Under Perturbations

OF THE THESIS Forced Response Analysis of Coupled Cantilever Beams Under Perturbations by James Anthony Mullinix Master of Science in Applied Mathematics with a Concentration in Dynamical Systems San Diego State University, 2015 Coupled rotating cantilever beams are ubiquitous in technology. From simple fans to turbine engines and electricity producing windmills, they can be found nearly everywhere. This study aims to characterize possible failure points when considering multiple cantilever beam systems coupled around a central hub which is rotating. The energy equations include effects from gyroscopic as well as Coriolis effects and are fully developed from first physical principles and variational calculus. The study sweeps over industrially practical rotation speeds and tests periodically perturbed rotations for harmonic resonances of natural modes. An application, written in C++, is designed to assemble finite element matrices with consideration of various spatial orientations, node connectivity, external forcing and boundary conditions for generic structures. A second set of programs, written in MatLab, performs multiple analysis routines including static modal analysis as well as forced response analysis via forward time integration. All routines are designed for flexibility and maintainability while keeping efficiency at the core.
Simultaneous Multi-points Measurement in Underwater Acoustic Localization

Underwater acoustic localization is used for detecting three-dimensional positions in underwater vehicles such as remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs). The ultra-short base-line (USBL) system measures a propagation time of sound wave from a transmitter to a receiver, where distance estimation and direction of arrival (DOA) estimation are performed. We investigate simultaneous multiple-point measurement by using multiple sound sources. For highly acoustic reflective environment, we apply the impulse response based generalized cross-correlation function (IR-GCC-PHAT) algorithm that gives higher positioning accuracy than the matched filter (MF) algorithm. The effectiveness of IR-GCC- PHAT has been proven in our experimental evaluation.
LSTM-Based Real-Time SOC Estimation of Lithium-Ion Batteries Using a Vehicle Driving Simulator

Currently, lithium-ion batteries (a type of secondary battery) are used as the primary sources of power in many applications due to their low energy loss as a result of their high energy density and low self-discharge rate, and their ability to store energy for a long time. However, due to the frequent charging and discharging of such batteries, overcharging is inevitable. This can cause system shutdowns, accidents, or property damage due to explosions. Therefore, it is necessary to accurately predict the state of charge (SOC) of batteries for stable and efficient usage. Hence, in this paper, we propose a SOC estimation method using a vehicle driving simulator. After manufacturing the simulator to perform the battery discharge experiment, voltage, current, and discharge-time data were collected. Using the collected data as input parameters for an RNN-based LSTM, we estimated the SOC of the battery and compared the errors to. We then used the developed LSTM surrogate model to conduct discharge experiments and simultaneously estimate the SOC in real-time.
Simulation/optimization Techniques for Multi-Basin Water Resource Planning

A set of simulation and optimization tools capable of analyzing the development and operation of a complex, multi-basin, interconnected water resource are explained. These models provide valuable information regarding the important questions: (1) “When should new projects be build? ” (2) “How big should they be? ” and (3) “How should the system be operated? ” Since these tools were developed by and for practicing engineers, their applicability to real-world problems is mandatory. To assure this, testing was done on an actual proposed project, the Texas Water System.
Loom: Switch-based Cloud Load Balancer with Compressed States

Layer-4 load balancers play a critical role in large-scale data centers. Recently, load balancers implemented on programmable switches have attracted much attention since they overcome the inflexibility of dedicated load balancers and high latency of software load balancers. However, keeping per-connection state easily leads to storage exhaustion, especially under resource exhaustion attacks. Although several stateless load balancers are proposed to address this issue, the state management burden is offloaded to backend servers, causing high deployment and running costs. In this paper, a load balancer called Loom with compressed states is proposed for large-scale data centers. Firstly, we propose a novel classifier-based load balancer idea to avoid directly maintaining per-connection state. Then, a circulating Bloom filter structure is proposed that can efficiently classify connections as well as be implemented on existing programmable switches. Theoretical analysis shows that Loom can maintain 11 ~ 30x more concurrent connections than those directly storing the 5-tuple of connections. Loom is implemented in hardware P4 switches and experimental results indicate that 11 ~ 29x more concurrent connections can be maintained in Loom, which is close to the theoretical results. Besides, Loom is resistant to resource exhaustion attacks and reduces the percentage of broken connections by up to 57% with an SYN flood.
Perspectives of information and communication technologies for engineering education

The information and communication technologies of today and tomorrow are characterized by: the Internet and its related technological networks; mobile telecommunication systems; video-based multimedia application for communication and information; and speech recognition/automated speech output. They are developing into embedded systems with invisible PC-free interfaces and into new breeds of robots and Multi-Agent Systems. These systems have started to change the technical and organizational structure of all human-machine-systems and their control tasks. In this paper, some aspects of these changes are described in view of the future roles of engineers within society. The paper is based on the memorandum which was drafted by an international expert group to be presented at the Congress on Information and Communication during the First World Engineers' Convention, 19-21 June 2000, Hannover, Germany. Subsequently this memorandum was discussed and agreed on by the participants of the Congress workshops. These participants were several hundred engineers from all over the world. Their ideas and concepts have been integrated into this paper.
Iterative methods with mixed-precision preconditioning for ill-conditioned linear systems in multiphase CFD simulations

A new mixed-precision preconditioner based on the iterative refinement (IR) method is developed for the preconditioned conjugate gradient (P-CG) solver and the multigrid preconditioned conjugate gradient (MGCG) solver in the multiphase thermal-hydraulic CFD code JUPITER [1]. In the IR preconditioner, linear systems are approximately solved using a block Jacobi method, which is optimized using fine block decomposition with 2D tiles to facilitate SIMD operations and software pipelining. Mixed-precision computing is designed to avoid influences of the roundoff errors in computing ill-conditioned matrices with extreme contrast of coefficients. Linear systems are normalized so that it can be computed within the dynamic range of FP16. All data is stored in FP16 to reduce memory access, while all computation is performed in FP32. The hybrid FP16/32 implementation keeps the similar convergence property as FP32, while the computational performance is close to FP16. The robustness of the FP16/32 implementation was demonstrated by scanning data formats with different bit lengths of the significand. The developed solvers are optimized on Fugaku (A64FX), and applied to ill-conditioned matrices with 90 billion DOFs in JUPITER. The P-CG and MGCG solvers with the new IR preconditioner show excellent strong scaling up to 8,000 CPUs, where 5.7 × and 3.4 × speedups are respectively achieved from the conventional solvers on Oakforest-PACS (KNL).
Mechanical Harvester for Fresh Market Quality Blueberries

An experimental harvester was developed that utilizes a unique shaker, cane dividing and positioning system, and cushioned catching surfaces to harvest blueberries. Field evaluation demonstrated selectivity and recovery as good as a commercial “rotary” harvester, but with significantly higher fruit quality. The experimental harvester caused minimal damage to the blueberry canes.
Evaluation of the particle size distribution of on-site rockfill using mask R-CNN deep learning model

Particle size distribution (PSD) of the on-site rockfill is one of the most critical factors in evaluating the quality assessment of the rock-filled concrete (RFC). Due to the large quantities and volume, it is difficult to measure the grain size of each rock manually. Image-based methods are widely adopted for the grain segmentation, but the result is not ideal if the rocks are closely connected and overlapped. In this study, a new model Mask R-CNN from the perspective of deep learning is deployed to develop an automatic measurement method of rockfill PSD. The model training was conducted using photos captured from on-site rockfill in Fengguang RFC dam of China. The results of the trained model agree well with the artificial measurements, and it proves Mask R-CNN as an effective technology for the automated estimation of the rockfill PSD in the engineering practice.