Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Overall Performance of TLC SSD by Exploiting Dissimilarity of Flash Pages

TLC flash has three types of pages to accommodate the three bits in each TLC physical cell exhibiting very different program latencies. This paper proposes PA-SSD to effectively improve the overall performance by exploiting the dissimilarity of TLC pages on program latency throughout the write request handling workflow. The main idea behind PA-SSD is to coordinately allocate the same type of pages for sub-requests of any given user write request, to mitigate the potential program latency imbalance among the sub-requests, and to schedule sub-requests according to their page-types. We achieve the PA-SSD design goal by answering three key research questions: (1) how to properly determine page-type for each user write request? (2) how to actually allocate a physical page for each sub-request with an assigned page-type from (1)? (3) how to effectively schedule the sub-requests in the chips queues when their page-types are judiciously allocated from (2)? To answer the first question, we propose seven page-type specifying schemes to investigate their effects under different workloads. We answer the second question by redesigning the page allocation strategy in TLC SSD to uniformly and sequentially determine physical pages for allocation following the internal programming process of TLC flash. Lastly, a page-type aware scheduling policy is presented to reorder the sub-requests within chips’ queues. Our experiments show that PA-SSD can accelerate both the write and read performance. Particularly, our proposed queue-depth based page-type specifying scheme improves write performance by 2.6 times and read performance by 1.5 times over the conventional TLC SSD.
Modeling and Control of Peak Current Mode Non-ideal Buck Converter

Based on the energy conservation method and the average modeling method of switching elements, considering the non-ideal parameters of the actual circuit, the whole peak current mode control non-ideal Buck converter system model is established. On this basis, for the equivalent power level, there are still some problems such as slow response speed and poor stability. A voltage controller compensation method is proposed and a system model including voltage controller compensation network is established. Through the analysis of the model and the simulation in Matlab, it can be seen that the model is intuitive and clear in physical meaning, which can more accurately reflect the working condition of the actual circuit. The designed voltage controller can improve the performance of the converter and can be DC-DC. The design of the converter system control circuit provides a theoretical basis.
Self-adaptation modeling for service evolution on the Internet of Things (IoT)

One of the most important concepts of the internet of things is related to software services, where the system must be able to provide real-time data collected from various environments. So that software services represent a variety of physical and virtual real-world objects that can grow very fast. This condition is related to the ability of self-adaptation software services. However, the existing model of self-adaptation is generally not paying attention to the requirements for service evolution. The objective of this paper is to introduce self-adaptation modeling techniques which consist of, first, domain modeling of the internet of things to represent real-world context; second, developing an inference engine for context inference. As a form of evaluation, this model is applied to the patient monitoring system that will relate to the concept of self-adaptation of various systems, devices, actors, and environment. The case study results show the system’s ability to anticipate changes in context and growth needs.
DEVELOPMENT OF MOBILE DEVICE FOR GAMMA RADIATION MEASUREMENT UTILIZING LORA AS THE COMMUNICATION MEANS

Public protection is one of important issues when operating nuclear facility. In case of accident occurs, the facility owner and related organizations shall make decision whether to evacuate people or not, based on the level of the accident and radiation dose rate released to the environment. In this study, as part of the decision support system for nuclear emergency response, a prototype of mobile radiation measurement system has been developed. The device consists of Geiger-Muller (GM)-based radiation measurement board, Global Positioning System (GPS) module, microcontroller board, and low power LoRa module for communication. Radiation dose rate along with its geoposition were recorded and sent to base station equipped with LoRa gateway for connecting LoRa network to TCP/IP-based network. The measurement data is then published to storage server using Message Queuing Telemetry Transport (MQTT) protocol. Power consumption, measurement of counter/timer accuracy, communication ranges testing, and radiation dose rate measurement were performed around Puspiptek area to demonstrate the functionality of the system.Keywords: Radiation monitoring, Decision Support System, Mobile, LoRa, GPS
Aplicação de algoritmos de agrupamento para descoberta de padrões de defeito em software JavaScript

Macedo, Charles Mendes de. Application of clustering algorithms for discovering bug patterns in JavaScript software. 2019. 82 p. Dissertation (Master of Science) – School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 2019. Applications developed with JavaScript language are increasing every day, not only for client-side, but also for server-side and for mobile devices. In this context, the existence of tools to identify faults is fundamental in order to assist developers during the evolution of their applications. Most of these tools use a list of predefined faults that are discovered from the observation of the programming best practices and developer intuition. To improve these tools, the automatic discovery of faults and code smells is important because it allows to identify which ones actually occur in practice and frequently. A tool that implements a semiautomatic strategy for discovering bug patterns by grouping the changes made during the project development is the BugAID. The objective of this work is to contribute to the BugAID tool, extending this tool with improvements in the extraction of characteristics to be used by the clustering algorithm. The extended module that extracts the characteristics is called BE+. Additionally, an evaluation of the clustering algorithms used for discovering fault patterns in JavaScript software is performed.
Diffusion probabilistic LMS algorithm.

In this paper, a novel diffusion estimation algorithm is proposed from a probabilistic perspective by combining diffusion strategy and the probabilistic least-mean-squares (PLMS) at all agents. The proposed method diffusion probabilistic LMS (DPLMS) is more robust to input signal and impulsive interference than the DSE-LMS, DRVSSLMS and DLLAD algorithms. Instead of minimizing the estimate error, the DPLMS algorithm is derived from approximating the posterior distribution with an isotropic Gaussian distribution. The stability of mean performance and computational complexity are analyzed theoretically. Results from the simulation indicate that the DPLMS algorithm is more robust to input signal and impulsive interference than the DSE-LMS, DRVSSLMS and DLLAD algorithms. These results suggest that the DPLMS algorithm can perform better in identifying the unknown coefficients under the complex and changeable impulsive interference environments.
On selected issues of Boolean function application in symmetric key cryptography against side channel attacks

Boolean function plays a prominent role in building strong cryptography algorithm especially in symmetric block cipher. However, there may be some issues and conflicting criteria which prevent such a Boolean function to resist the algorithm to all known attacks. Within this work, the nonlinearity and correlation immunity issues of Boolean function is discussed followed by complementary counter measure against side channel attack especially differential power attack (DPA)
Synchronization of Frequency Hopping by LSTM Network for Satellite Communication System

In this letter, a novel method for synchronization of frequency hopping signal is proposed for tactical satellite communication system. Proposed method consists of serial search for coarse acquisition and use of long short-term memory (LSTM) network for fine acquisition. During training, the LSTM network is able to learn trend of energy signal used for fine acquisition. As a result, it can perform quick and robust fine acquisition in the presence of noise. Validation of proposed method is conducted by simulations. Simulation results show that the proposed method leads to decreased time for synchronization and improved timing estimation performance, as compared to other methods.
A Multi-Methodology Approach to Creating a Causal Loop Diagram

Developing causal loop diagrams (CLDs) involves identifying stakeholders and endogenous variables and formulating variable causal relationships. Traditionally, the CLDs are developed mainly using a qualitative approach such as literature review, observations and interviews with stakeholders. However, modellers may question which stakeholders should be approached, whether the relevant variables are selected, and what to do when stakeholders perceive different variable relationships in the CLDs differently. Applying in a case study, this research proposes a multi-method approach by combining both quantitative and qualitative methods to select stakeholders, identify endogenous/exogenous variables, and develop the CLDs. The proposed quantitative method is expected to provide modellers with a justifiable stakeholder and variable selection process. The method also highlights possible hidden variables and relationships, which were further explored with a traditional qualitative approach.
A Wizard-based Approach for Secure Code Generation of Single Sign-On and Access Delegation Solutions for Mobile Native Apps

Many available mobile applications (apps) have poorly implemented Single Sign-On and Access Delegation solutions leading to serious security issues. This could be caused by inexperienced developers who prioritize the implementation of core functionalities and/or misunderstand security critical parts. The situation is even worse in complex API scenarios where the app interacts with several providers. To address these problems, we propose a novel wizard-based approach that guides developers to integrate multiple third-party Identity Management (IdM) providers in their apps, by (i) “enforcing” the usage of best practices for native apps, (ii) avoiding the need to download several SDKs and understanding their online documentations (a list of known IdM providers with their configuration information is embedded within our approach), and (iii) automatically generating the code to enable the communication with the different IdM providers. The effectiveness of the proposed approach has been assessed by implementing an Android Studio plugin and using it to integrate several IdM providers, such as OKTA, Auth0, Microsoft, and Google.
Teachers’ evaluation of MotionAR: An augmented reality-based motion graphing application

This research presents the results of the initial evaluation of MotionAR: an augmented reality (AR) based motion graphing application for physics classes. MotionAR uses AR trackers to measure relative motion between objects, hence producing a motion graph. The subject of the evaluation is an improved version of our application that addresses recommendations from the first prototype. Among these recommendations were: the use of words instead of arrows to identify targets, the use of the Cartesian coordinate system, and previews of the displacement vs. time, velocity vs. time and acceleration vs. time graphs. In-service teachers used a 10-item questionnaire for evaluation. The teacher-evaluators come from diverse educational backgrounds (rural and urban schools, 0 to 24 years in teaching, no e-learning model in school vs 1:1 student-to-tablet PC ratio, science vs. non-science majors) which makes the initial evaluation a good baseline data. This ongoing research will present the lessons learned from the evaluation, including insights in developing AR applications for physics classes, and recommendations for teaching motion graphs utilizing AR.
Differential Privacy meets Verifiable Computation: Achieving Strong Privacy and Integrity Guarantees

Often service providers need to outsource computations on sensitive datasets and subsequently publish statistical results over a population of users. In this setting, service providers want guarantees about the correctness of the computations, while individuals want guarantees that their sensitive information will remain private. Encryption mechanisms are not sufficient to avoid any leakage of information, since querying a database about individuals or requesting summary statistics can lead to leakage of information. Differential privacy addresses the paradox of learning nothing about an individual, while learning useful information about a population. Verifiable computation addresses the challenge of proving the correctness of computations. Although verifiable computation and differential privacy are important tools in this context, their interconnection has received limited attention. In this paper, we address the following question: How can we design a protocol that provides both differential privacy and verifiable computation guarantees for outsourced computations? We formally define the notion of verifiable differentially private computation (VDPC) and what are the minimal requirements needed to achieve VDPC. Furthermore, we propose a protocol that provides verifiable differentially private computation guarantees and discuss its security and privacy properties.
When Deep Reinforcement Learning Meets 5G-Enabled Vehicular Networks: A Distributed Offloading Framework for Traffic Big Data

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.
Hardware Testing of Electric Hot Water Heaters Providing Energy Storage and Demand Response Through Model Predictive Control

This paper presents results from hardware testing which demonstrate that, 1) systems of water heaters under Model Predictive Control can be reliably dispatched to deliver set-point levels of power to within 2% error at very short timescales with minimal sensing requirements, and 2) a classical steady state model commonly used for simulation of electric hot water heaters can be inaccurate vs. results obtained on hardware. These results improve upon the current state of knowledge and show a promising pathway to control hot water heaters as energy storage systems capable of delivering flexible capacity and fast acting ancillary services on a firm basis. These energy products are shown to be deliverable without compromising the availability of hot water at the residence, even in control implementations which do not have sensors to monitor actual water use for the predictive optimization.
Efficient and Robust Methods for Audio and Video Signal Analysis

This thesis presents my research concerning audio and video signal processing and machine learning. Specifically, the topics of my research include computationally efficient classifier compounds, automatic speech recognition (ASR), music dereverberation, video cut point detection and video classification. Computational efficacy of information retrieval based on multiple measurement modalities has been considered in this thesis. Specifically, a cascade processing framework, including a training algorithm to set its parameters has been developed for combining multiple detectors or binary classifiers in computationally efficient way. The developed cascade processing framework has been applied on video information retrieval tasks of video cut point detection and video classification. The results in video classification, compared to others found in the literature, indicate that the developed framework is capable of both accurate and computationally efficient classification. The idea of cascade processing has been additionally adapted for the ASR task. A procedure for combining multiple speech state likelihood estimation methods within an ASR framework in cascaded manner has been developed. The results obtained clearly show that without impairing the transcription accuracy the computational load of ASR can be reduced using the cascaded speech state likelihood estimation process. Additionally, this thesis presents my work on noise robustness of ASR using a nonnegative matrix factorization (NMF) -based approach. Specifically, methods for transformation of sparse NMF-features into speech state likelihoods has been explored. The results reveal that learned transformations from NMF activations to speech state likelihoods provide better ASR transcription accuracy than dictionary label -based transformations. The results, compared to others in a noisy speech recognition -challenge show that NMF-based processing is an efficient strategy for noise robustness in ASR. The thesis also presents my work on audio signal enhancement, specifically, on removing the detrimental effect of reverberation from music audio. In the work, a linear prediction -based dereverberation algorithm, which has originally been developed for speech signal enhancement, was applied for music. The results obtained show that the algorithm performs well in conjunction with music signals and indicate that dynamic compression of music does not impair the dereverberation performance.
Security Framework for IoT End Nodes with Neural Networks

The premise of the Internet of Things (IoT) is to connect not only computers and mobile devices, but also interconnect smart buildings, homes, and cities, as well as electrical and water grids, automobiles, and airplanes just to mention some examples. IoT leads to the development of a wide range of advanced information services that are pervasive, cost-effective, and can be accessed from anywhere and at any time. In this paper we present a multilayer architecture to integrate devices to the IoT, making it available from everywhere at any time. However, with the introduction of IoT we will be experiencing grand challenges to secure and protect its advanced information services due to the significant increase of the attack surface, complexity, heterogeneity, and number of interconnected resources. In order to deal with such challenges, we introduce an IoT Framework to build trustworthy and secure IoT applications and services. The framework enables developers to consider security issues at all IoT layers and integrate security algorithms with the functions and services offered in each layer instead of considering security in an ad-hoc and after thought manner. We show the applicability of our methodology to secure and protect IoT end nodes providing them with the capabilities for self-monitoring and self-recovering after an external event has occurred.
Transformation of Medical Diagnostics with Machine Learning by Considering the Example of Atrial Fibrillation Identification

The paper addresses the problem of detecting one of the most common cardiac arrhythmias atrial fibrillation with artificial intelligence. The arrhythmia increases the risk of suffering from a stroke massively. Because of this, it is essential to detect atrial fibrillation early. As the arrhythmia occurs in short sequences, it is only possible to detect the disease in long-term measurements for example with electrocardiography. All common current detection techniques are calculating the R-R intervals with variations of the root mean square of successive differences. Because this approach is inflexible and expensive, a major hospital in Germany suggests the implementation of an artificial intelligence solution for atrial fibrillation detection. The aim of the paper is to study the feasibility of atrial fibrillation detection with artificial intelligence in the clinical setting
Performance Evaluation of TCP Vegas over TCP Reno and TCP NewReno over TCP Reno

In the Transport layer, there are two types of Internet Protocol are worked, namely- Transmission Control Protocol (TCP) and User datagram protocol (UDP). TCP provides connection oriented service and it can handle congestion control, flow control, and error detection whereas UDP does not provide any of service. TCP has several congestion control mechanisms such as TCP Reno, TCP Vegas, TCP New Reno, TCP Tahoe, etc. In this paper, we have focused on the behavior performance between TCP Reno and TCP Vegas, TCP New Reno over TCP Reno, when they share the same bottleneck link at the router. For instigating this situation, we used drop-tail and RED algorithm at the router and used NS-2 simulator for simulation. From the simulation results, we have observed that the performance of TCP Reno and TCP Vegas is different in two cases. In drop tail algorithm, TCP Reno achieves better Performance and throughput and act more an aggressive than Vegas. In Random Early Detection (RED) algorithm, both of congestion control mechanism provides better fair service when they coexist at the same link. TCP NewReno provides better performance than TCP Reno.