Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodology for the Integration of Optomechanical System Software Models with a Radiative Transfer Image Simulation Model

Stray light, any unwanted radiation that reaches the focal plane of an optical system, reduces image contrast, creates false signals or obscures faint ones, and ultimately degrades radiometric accuracy. These detrimental effects can have a profound impact on the usability of collected Earth-observing remote sensing data, which must be radiometrically calibrated to be useful for scientific applications. Understanding the full impact of stray light on data scientific utility is of particular concern for lower cost, more compact imaging systems, which inherently provide fewer opportunities for stray light control. To address these concerns, this research presents a general methodology for integrating point spread function (PSF) and stray light performance data from optomechanical system models in optical engineering software with a radiative transfer image simulation model. This integration method effectively emulates the PSF and stray light performance of a detailed system model within a high-fidelity scene, thus producing realistic simulated imagery. This novel capability enables system trade studies and sensitivity analyses to be conducted on parameters of interest, particularly those that influence stray light, by analyzing their quantitative impact on user applications when imaging realistic operational scenes. For Earth
Hair - A Good Source of DNA to Solve the Crime

Most often hairs are picked up at the crime scenes and are used as contributing biological evidences in the crime cases. This can be helpful in determining the perpetrators of the crime and providing more information about what happened during the actual incidence. Hairs are commonly encountered in the forensic investigations and can be the good source of DNA. The optimal quantity of template DNA required for PCR based STR Profiling is about 1 ng and sufficient quantity may be present in the root sheath to prove the crime. In the present case, hairs were found in the fist of the deceased and they proved to be an important evidence that linked to the murderer. DNA was extracted from hair roots and profile was generated using PCR based STR Genotyping technology. The DNA profile obtained from the hair root matched with the DNA profile obtained from reference blood of the accused and proved his involvement in the crime.
Feasibility and Reliability of a Developed and Validated Forensic Recording Form for Firearm Injury

Objective: The aim of this study was to develop and validate a forensic recording form for firearm injuries and test the feasibility and reliability of its application. Material and Methods: A cross-sectional study was conducted. The first version was developed using knowledge from the literature search and was checked for validity by 3 forensic physicians using the Content Validity Index (CVI). Feasibility was tested among physicians working at 4 district hospitals. Its reliability was analyzed by 2 forensic physicians using prevalence-adjusted and bias-adjusted kappa. Results: The validity of a developed recording form for firearm injuries was good, with a CVI of 0.8. All items were rated to be feasible, and the format of the recording was rated from good to excellent. The reliabilities ranged from poor agreement to perfect agreement. After considering the validity, feasibility and reliability tests, a final, forensic recording form was established. Conclusion: A systematically constructed forensic recording form for firearm injuries, for any physicians, with less experience in the field of gunshot cases, was developed. This form will be helpful in assisting physicians in the completion of information for any gunshot cases, which may decrease the consequences from incomplete information.
Malware Detection on Server using Distributed Machine Learning

Malware has continued to develop at a disturbing rate despite on-going reduction efforts. This has been considerably more pervasive on web servers, where server computing is an increasingly popular platform for both industry and buyers. As of late, another age of malware families has developed with advanced evasion abilities which make them substantially difficult to identify utilizing ordinary techniques. On one hand, the popularity of worldwide use of Internet absorbs attention of most engineers for delivering their applications. The expanded number of applications, on the other hand, prepares an appropriate prone for a few users to create various types of malware and include them in the market or in outsider markets as sheltered applications. This paper proposes and explores a machine learning based characterization approach for identification of malware and utilizes distributed Support Vector Machine (SVM) algorithm keeping in mind the end goal to detect malicious software(malware) in server computing platform using malicious and benign records.
Finding a suitable threshold value for an iris-based authentication system

Authentication is the first line of defense of any information technology systems. One of the popular methods used today is biometric, and iris authentication is gaining popularity. However, the threshold value is deemed to be secure and appropriate has not been thoroughly studied. Threshold is a value that defines the acceptable amount of the correct bits of the image before securely passing the authentication process. Therefore, the main aim of this research was to find a secure and suitable threshold value used in iris authentication system, where iris localization was done by using Circle Hough Transform technique. Iris image databases v.4 from the Chinese Academy of Sciences Institute of Automatic (CASIA) were used in this research. The way to find the appropriate threshold was to test for the right balance of the GAR, FRR and FAR values when trying to verify the person’s identity. The results of the test revealed that the appropriate threshold had the value of 72.9246 percent of all the available bits of the iris image. Both had a high GAR and very low FAR and FRR values. It can be concluded that the obtained threshold value was suitable and secure.
Automation Control of a Point Merge System at Palma de Mallorca Airport

The main objective of this project is to design an automation control system for a new arrival procedure at Palma de Mallorca airport, which is the Point Merge System (PMS). This new system, suggested by Cercle d’Economia de Mallorca, allows performing Continuous Descent Approach (CDA), reducing fuel consumption, CO2 and acoustic emissions, while safety levels are enhanced and arrival capacity is increased. Additionally, the fact of introducing the automation control would remove the air traffic controllers presence, only needing one person to monitor the system. The PMS concept was developed by Eurocontrol and is based on the merging of two arrival flows in one single entry point, which is afterwards linked to the airport runway. Nowadays, these flows are managed by controllers who give the “direct-to” instruction to pilots to fly towards the merge point, whereas in this project this will be done by the system. In order to achieve the aim of the project, the Network Strategic Tool (NEST) from Eurocontrol has been used, which allows traffic simulations, together with Matlab, in which the NEST files have been read and different time computations have been performed to determine when the aircraft gets the “direct-to” and in this way ensure safety. The obtained results are for two diferent scenarios. On one hand, real traffic data from summer 2018 has been considered, which are used to know the current behavior of the system. On the other hand, with the webpage Demand Data Repository (DDR) of Eurocontrol a traffic forecast for summer 2024 has been generated, which is used to estimate the behaviour and capacity of the system in a future with more traffic. With the results analysis, it is possible to check that nowadays most aircraft would be authorized with a direct entry to the merge point, without waiting. For the 2024 case, most aircraft would have to stay inside the system with a waiting time due to the increase of flights. Lastly, in both scenarios route length increases, while it is possible to perform CDA thanks to the predictability obtained with the system.
Research on Programmable Logic Controller Security

With the convergence of computer technology and industrial networks, attackers are not limited to attacking only individual users’ computers, turning to attack industrial control systems that can cause major infrastructure problems. Programmable Logic Controllers (PLC) are the core components of industrial control systems. Its safety has a profound impact on the safety of the entire industrial system. This paper firstly classifies the security research of PLC according to the structure and function, and expounds the existing security defects of PLC from the aspects of firmware security, operation security and program security. Then it summarizes and analyzes four types of security protection measures: the integrity of verification firmware, protocol security encryption, code formal verification, and program security defence detection. Finally, according to the overall safety of the industrial system and the actual development of the current PLC, we discuss the development trend of safety research.
An Auto-tuner for Quantizing Deep Neural Networks

An Auto-tuner for Quantizing Deep Neural Networks QUAN QUAN Department of Computer Science and Engineering The Graduate School Seoul National University With the proliferation of AI-based applications and services, there are strong demands for efficient processing of deep neural networks (DNNs). DNNs are known to be both computeand memory-intensive as they require a tremendous amount of computation and large memory space. Quantization is a popular technique to boost efficiency of DNNs by representing a number with fewer bits, hence reducing both computational strength and memory footprint. However, it is a difficult task to find an optimal number representation for a DNN due to a combinatorial explosion in feasible number representations with varying bit widths, which is only exacerbated by layer-wise optimization. To address this, an automatic tuner is proposed in this work for DNN quantization. Here, the auto-tuner can efficiently find a compact representation (type, bit width, and bias) for the number that minimizes the user-supplied objective function, while satisfying the accuracy constraint. The evaluation using eleven DNN models on two DNN frameworks targeting an FPGA platform and a bit-serial hardware, demonstrates over 8× (7×) reduction in the parameter size on average when up to 7% (1%) loss of relative accuracy is tolerable, with a maximum reduction of 16×, compared to the baseline using 32-bit floating-point numbers.
Parallel cryptographic hashing: Developments in the last 25 years

Abstract Parallel computing has led to fundamental changes in almost all research areas associated with computer science; cryptography is no exception. Besides improved speed and resource utilization, parallel cryptography is also promising from the security point of view. In particular, cryptographic hash functions (CHFs) – “the swiss knife” of the cryptography toolkit – stand to gain a lot from parallelism. Consequently, there has been an increasing interest in research towards the development of parallel CHFs for faster, more secure and more efficient hashing, particularly since the competition for the SHA-3 standard. This paper attempts to consolidate the research efforts in the field of parallel hashing, exploring various approaches in this direction. Within each approach discussed in the article, CHFs have been classified on the basis of construction and their design, implementation and performance have been explained, with the primary focus on parallelism. Finally, a broad temporal overview of the developments in the field of parallel hashing has been presented.
Advanced Programming of Intelligent Social Robots

Robotics in education is a promising new area: social robots have started to move into schools as part of educational/learning technologies, playing roles in educational settings that range from tutors, teaching assistants and learners, to learning companions and therapeutic assistants. This paper provides an overview of the main computational methods required to program a social robot and equip it with social intelligence. Some applications of social robots in the field of education are reported to show how the use of educational robots may innovate the learning process at different levels and in various contexts.
AdaMD: Adaptive Mapping and DVFS for Energy-Efficient Heterogeneous Multicores

Modern heterogeneous multicore systems, containing various types of cores, are increasingly dealing with concurrent execution of dynamic application workloads. Moreover, the performance constraints of each application vary, and applications enter/exit the system at any time. Existing approaches are not efficient in such dynamic scenarios, especially if applications are unknown, as they require extensive offline application analysis and do not consider the runtime execution scenarios (application arrival/completion, and workload and performance variations) for runtime management. To address this, we present AdaMD, an adaptive mapping and dynamic voltage and frequency scaling (DVFS) approach for improving energy consumption and performance. The key feature of the proposed approach is the elimination of dependency on offline profiled results while making runtime decisions. This is achieved through a performance prediction model having a maximum error of 7.9% lower than the previously reported model and a mapping approach that allocates processing cores to applications while respecting performance constraints. Furthermore, AdaMD adapts to runtime execution scenarios efficiently by monitoring the application status, and performance/workload variations to adjust the previous DVFS settings and thread-to-core mappings. The proposed approach is experimentally validated on the Odroid-XU3, with various combinations of diverse multithreaded applications from PARSEC and SPLASH benchmarks. Results show energy savings of up to 28% compared to the recently proposed approach while meeting performance constraints.
Synthesizing 3D Trump: Predicting and Visualizing the Relationship Between Text, Speech, and Articulatory Movements

The movements of articulators, such as lips, tongue and teeth, play an important role in increasing the language expression capability by unmasking the information hid in text or speech. Hence, it is necessary to deeply mine and visualize the relationship between text, speech and articulatory movements for understanding language in multi-modality and multi-level. As a case study, given text and audio of President Donald John Trump, this paper synthesizes a high quality 3D animation of him speaking with accurate synchronicity between speech and articulators. First, visual co-articulation is modeled by predicting the mapping from text/speech to articulatory movements. Then, based on a reconstructed 3D head model, physiological characteristics and statistical learning are combined to visualize each phoneme. Finally, the visualization results of consecutive phonemes are fused by visual co-articulation model to generate synchronized articulatory animations. Experiments show that the system can not only produce photo-realistic results in front but also distinguish the visual differences among phonemes from unconstrained views.
SMU Data

Network security systems are designed to identify and, if possible, prevent unauthorized access to computer and network resources. Today most network security systems consist of hardware and software components that work in conjunction with one another to present a layered line of defense against unauthorized intrusions. Software provides user interactive layers such as password authentication, and system level layers for monitoring network activity. This paper examines an application monitoring network traffic that attempts to identify Indicators of Compromise (IOC) by extracting patterns in the network traffic which likely corresponds to unauthorized access. Typical network log data and construct indicators are analyzed to predict network intrusion. Based on these indicators, a fitted model was created demonstrating which indicators best predict an intrusion event. In the end we found that XGBoost provided the best accuracy and f-score for our model fit. The IOCs that best predicted an intrusion event were associated with newly recorded events, network traffic, and DNS events.
Implementation of a ConvolutionalNeural Network (CNN) on a FPGA forSign Language’s Alphabet recognition

Since 2012, with the introduction of Convolutional Neural Networks (CNN) for image recognition, great improvements have been made in terms of accuracy, topologies, and understanding of the challenges associated with the image recognition. Several situations where they have already been proven successful include self-driving cars, image tagging and face recognition. Most of the development have been centered in increasing precision while trying to have as little computations as possible. However, most of the topologies and applications still require expensive and power hungry Graphical Processors (GPUs) to be able to deliver fast responses. Therefore, these systems are most of the time located in the very same place where the data is generated or in specially designed data centers. Recently, there has been a growing interest and research towards lowresources architectures for its use in embedded systems, although most of it is still in a theoretical approach. In addition although CNNs have applications different than image recognition, this last one has been proven quite controversial due to the use (or misuse) that some companies and governments have done of them, while most of the research done by universities has been more theoretical. The objective of this bachelor thesis is to use the current theoretical knowledge about CNNs to prove their use in embedded systems while at the same time developing an application that can be beneficial for the society as a whole. According to this objective, the thesis aims to be able to get photos from the Swedish deaf’s people sign language alphabet and identify the letters associated with each of the signs, working on a real time system. For that purpose, big amounts of data have been collected, analyzed and processed and the (embedded systems’ friendly) Zynqnet CNN topology has been modified to fit the application. All together allow more than 85% of the images to be successfully identified using a regular GPU training system. In addition, a custom, high throughput hardware accelerator for that topology has been designed to be placed in an FPGA. Similar precision results than using the GPU have been gotten while reducing space, weight and power consumption. The FPGA accelerator will also reach real-time performance, computing the results for each image in less than 1 second.
On the Volume Conduction Model Validation with Stereo EEG Data

Volume conduction can be defined as the transmission of electric potential and magnetic field generated by a primary current source of brain activation in the surrounding medium, i.e., the human head. Volume conduction simulations are based on sophisticated models whose construction represents a current challenge within the neuroscientific community. Volume conduction models are used in various applications such as electroencephalography (EEG) or magnetoencephalography (MEG) source reconstruction, or in the optimization of the electrode placement in a transcranial electrical stimulation session. Clinical applications based on volume conduction models are, for example, the localization of the epileptogenic zone, i.e., the brain area responsible for the generation of seizures, in the presurgical assessment of focal drug-resistant epilepsy patients, and the antidepressant effects given by transcranial electrical stimulation. Increasing the accuracy of volume conduction simulations is therefore crucial. To the best of our knowledge, the accuracy of volume conduction models have never been validated directly with actual measurements in human patients. The main goal of this thesis is to describe a first attempt to validate volume conduction modeling using electric stimulation stereo-encephalografic (sEEG) data. This work therefore is focused on the research, investigation and test of tools and methods which can be used to describe the accuracy of volume conduction models used in both clinical and basic research. Given a dataset of one pharmaco-resistant epilepsy patient, composed by the anatomical T1 weighted magnetic resonance image (MRI), the electrophysiological signal recorded during electric brain stimulation sessions with sEEG technique and sEEG contact positions extracted by post-implantation CT image, the analysis conducted in this work can be split into three main steps. First, we built volume conduction head models and we simulated the electric potentials during the electric brain stimulations. In this step, we solved the so-called (s)EEG forward problem by means of the finite element method in its classical formulation, and we considered
Human Activity Recognition Using Wearable Inertia Sensor Data adnd Machine Learning

Falling in indoor home setting can be dangerous for elderly population (in USA and globally), causing hospitalization, long term reduced mobility, disability or even death. Prevention of fall by monitoring different human activities or identifying the aftermath of fall has greater significance for elderly population. This is possible due to the availability and emergence of miniaturized sensors with advanced electronics and data analytics tools. This thesis aims at developing machine learning models to classify fall activities and non-fall activities. In this thesis, two types of neural networks with different parameters were tested for their capability in dealing with such tasks. A publicly available dataset was used to conduct the experiments. The two types of neural network models, convolution and recurrent neural network, were developed and evaluated. Convolution neural network achieved an accuracy of over 95% for classifying fall and non-fall activities. Recurrent neural network provided an accuracy of over 97% accuracy in predicting fall, non-fall and a third category activity (defined in this study as “pre/postcondition”). Both neural network models show high potential for being used in fall prevention and management activity. Moreover, two theoretical designs of fall detection systems were proposed in this thesis based on the developed convolution and recurrent neural networks.